project_3_en.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Most Profitable Prepaid Plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Megaline Telecom offers its clients two prepaid plans, Surf and Ultimate. The advertising department wants to know which prepaid plan generates more revenue to adjust the advertising budget."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Introduction](#Introduction)\n",
    "* [Data Pre-Processing](#Data-Pre-Processing)\n",
    "* [Data Analysis](#Data-Analysis)\n",
    "* [Hypothesis Testing](#Hypothesis-Testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project will analyze a dataset containing information on 500 users of Megaline telecommunications services in 2018. The telecommunications services provided include calls, short messages (SMS), and internet which are packaged in two types of prepaid plans, namely surf and ultimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objectives of this project were to:\n",
    "* Study the behavior of prepaid plan users.\n",
    "* Determine which type of plan brings more revenue.\n",
    "* Examine whether the average revenue of Ultimate and Surf phone plan users is different.\n",
    "* Examine whether the average income of users in the NY-NJ area differs from the income of users from other regions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are five datasets used in this project. The dataset containing service usage is found in `calls`, `messages`, and `internet`. The other two datasets are `users` which contains client information, and `plans` which contains information about prepaid packages and tariffs.\n",
    "\n",
    "This project will consist of the following steps:\n",
    "* Data pre-processing\n",
    "* Data analysis\n",
    "* Hypothesis testing\n",
    "\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the required libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math as mt\n",
    "import scipy\n",
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading datasets\n",
    "try:\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",
    "\n",
    "except:\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking `plans` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "source": [
    "# Displaying information from `plans` dataset\n",
    "plans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying plans dataset\n",
    "plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plans` dataset contains the detailed information for each type of plans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking `users` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1005</td>\n",
       "      <td>Livia</td>\n",
       "      <td>Shields</td>\n",
       "      <td>31</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX MSA</td>\n",
       "      <td>2018-11-29</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1006</td>\n",
       "      <td>Jesusa</td>\n",
       "      <td>Bradford</td>\n",
       "      <td>73</td>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA MSA</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007</td>\n",
       "      <td>Eusebio</td>\n",
       "      <td>Welch</td>\n",
       "      <td>42</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1008</td>\n",
       "      <td>Emely</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>53</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1009</td>\n",
       "      <td>Gerry</td>\n",
       "      <td>Little</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA MSA</td>\n",
       "      <td>2018-04-22</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",
       "5     1005      Livia    Shields   31     Dallas-Fort Worth-Arlington, TX MSA   \n",
       "6     1006     Jesusa   Bradford   73  San Francisco-Oakland-Berkeley, CA MSA   \n",
       "7     1007    Eusebio      Welch   42           Grand Rapids-Kentwood, MI MSA   \n",
       "8     1008      Emely    Hoffman   53       Orlando-Kissimmee-Sanford, FL MSA   \n",
       "9     1009      Gerry     Little   19  San Jose-Sunnyvale-Santa Clara, CA 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  \n",
       "5  2018-11-29      surf         NaN  \n",
       "6  2018-11-27  ultimate  2018-12-18  \n",
       "7  2018-07-11      surf         NaN  \n",
       "8  2018-08-03  ultimate         NaN  \n",
       "9  2018-04-22      surf         NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first 10 rows of `users` dataset.\n",
    "users.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "source": [
    "# Displaying information from `users` dataset\n",
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1249.500000</td>\n",
       "      <td>45.486000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>16.972269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1124.750000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1249.500000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1374.250000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id         age\n",
       "count   500.000000  500.000000\n",
       "mean   1249.500000   45.486000\n",
       "std     144.481833   16.972269\n",
       "min    1000.000000   18.000000\n",
       "25%    1124.750000   30.000000\n",
       "50%    1249.500000   46.000000\n",
       "75%    1374.250000   61.000000\n",
       "max    1499.000000   75.000000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the statistical summary of `users` dataset\n",
    "users.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From initial review of the dataset:\n",
    "* There are a total of 500 customers in this dataset, 34 of whom have stopped using the service.\n",
    "* The data type of `reg_date` needs to be changed to datetime.\n",
    "* The youngest customer in the dataset is 18 years old.\n",
    "* The oldest customer in the dataset is 75 years old."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the dataset for editing purpose\n",
    "df = users.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing `reg_date` datatype to datetime.\n",
    "df['reg_date']=pd.to_datetime(df['reg_date'],format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ultimate', 'surf'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values in column `plan` \n",
    "users['plan'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only two prepaid plans, i.e., surf and ultimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Albany-Schenectady-Troy, NY MSA',\n",
       " 'Albuquerque, NM MSA',\n",
       " 'Atlanta-Sandy Springs-Roswell, GA MSA',\n",
       " 'Austin-Round Rock-Georgetown, TX MSA',\n",
       " 'Bakersfield, CA MSA',\n",
       " 'Baltimore-Columbia-Towson, MD MSA',\n",
       " 'Baton Rouge, LA MSA',\n",
       " 'Birmingham-Hoover, AL MSA',\n",
       " 'Boston-Cambridge-Newton, MA-NH MSA',\n",
       " 'Bridgeport-Stamford-Norwalk, CT MSA',\n",
       " 'Buffalo-Cheektowaga, NY MSA',\n",
       " 'Cape Coral-Fort Myers, FL MSA',\n",
       " 'Charleston-North Charleston, SC MSA',\n",
       " 'Charlotte-Concord-Gastonia, NC-SC MSA',\n",
       " 'Chicago-Naperville-Elgin, IL-IN-WI MSA',\n",
       " 'Cincinnati, OH-KY-IN MSA',\n",
       " 'Cleveland-Elyria, OH MSA',\n",
       " 'Colorado Springs, CO MSA',\n",
       " 'Columbia, SC MSA',\n",
       " 'Columbus, OH MSA',\n",
       " 'Dallas-Fort Worth-Arlington, TX MSA',\n",
       " 'Dayton-Kettering, OH MSA',\n",
       " 'Denver-Aurora-Lakewood, CO MSA',\n",
       " 'Detroit-Warren-Dearborn, MI MSA',\n",
       " 'El Paso, TX MSA',\n",
       " 'Fresno, CA MSA',\n",
       " 'Grand Rapids-Kentwood, MI MSA',\n",
       " 'Greensboro-High Point, NC MSA',\n",
       " 'Houston-The Woodlands-Sugar Land, TX MSA',\n",
       " 'Indianapolis-Carmel-Anderson, IN MSA',\n",
       " 'Jacksonville, FL MSA',\n",
       " 'Kansas City, MO-KS MSA',\n",
       " 'Knoxville, TN MSA',\n",
       " 'Las Vegas-Henderson-Paradise, NV MSA',\n",
       " 'Los Angeles-Long Beach-Anaheim, CA MSA',\n",
       " 'Louisville/Jefferson County, KY-IN MSA',\n",
       " 'Memphis, TN-MS-AR MSA',\n",
       " 'Miami-Fort Lauderdale-West Palm Beach, FL MSA',\n",
       " 'Milwaukee-Waukesha, WI MSA',\n",
       " 'Minneapolis-St. Paul-Bloomington, MN-WI MSA',\n",
       " 'Nashville-Davidson–Murfreesboro–Franklin, TN MSA',\n",
       " 'New Haven-Milford, CT MSA',\n",
       " 'New Orleans-Metairie, LA MSA',\n",
       " 'New York-Newark-Jersey City, NY-NJ-PA MSA',\n",
       " 'North Port-Sarasota-Bradenton, FL MSA',\n",
       " 'Oklahoma City, OK MSA',\n",
       " 'Omaha-Council Bluffs, NE-IA MSA',\n",
       " 'Orlando-Kissimmee-Sanford, FL MSA',\n",
       " 'Oxnard-Thousand Oaks-Ventura, CA MSA',\n",
       " 'Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA',\n",
       " 'Phoenix-Mesa-Chandler, AZ MSA',\n",
       " 'Pittsburgh, PA MSA',\n",
       " 'Portland-Vancouver-Hillsboro, OR-WA MSA',\n",
       " 'Providence-Warwick, RI-MA MSA',\n",
       " 'Raleigh-Cary, NC MSA',\n",
       " 'Richmond, VA MSA',\n",
       " 'Riverside-San Bernardino-Ontario, CA MSA',\n",
       " 'Rochester, NY MSA',\n",
       " 'Sacramento–Roseville–Folsom, CA MSA',\n",
       " 'Salt Lake City, UT MSA',\n",
       " 'San Antonio-New Braunfels, TX MSA',\n",
       " 'San Diego-Chula Vista-Carlsbad, CA MSA',\n",
       " 'San Francisco-Oakland-Berkeley, CA MSA',\n",
       " 'San Jose-Sunnyvale-Santa Clara, CA MSA',\n",
       " 'Seattle-Tacoma-Bellevue, WA MSA',\n",
       " 'St. Louis, MO-IL MSA',\n",
       " 'Stockton, CA MSA',\n",
       " 'Tampa-St. Petersburg-Clearwater, FL MSA',\n",
       " 'Tucson, AZ MSA',\n",
       " 'Tulsa, OK MSA',\n",
       " 'Urban Honolulu, HI MSA',\n",
       " 'Virginia Beach-Norfolk-Newport News, VA-NC MSA',\n",
       " 'Washington-Arlington-Alexandria, DC-VA-MD-WV MSA']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values in column `city`\n",
    "sorted(users['city'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicate entries in the column `city`. For simpler grouping, we will only have to create a new column containing only the state names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>state</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",
       "      <td>GA</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",
       "      <td>WA</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",
       "      <td>NV</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",
       "      <td>OK</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",
       "      <td>WA</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>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>NaN</td>\n",
       "      <td>NY-NJ-PA</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>NaN</td>\n",
       "      <td>LA</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>NaN</td>\n",
       "      <td>CA</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>NaN</td>\n",
       "      <td>NY-NJ-PA</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>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 9 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  \\\n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA 2018-12-24  ultimate   \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA 2018-08-13      surf   \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA 2018-10-21      surf   \n",
       "3                                Tulsa, OK MSA 2018-01-28      surf   \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA 2018-05-23      surf   \n",
       "..                                         ...        ...       ...   \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-09-04      surf   \n",
       "496               New Orleans-Metairie, LA MSA 2018-02-20      surf   \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA 2018-12-10  ultimate   \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-02-04      surf   \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA 2018-05-06      surf   \n",
       "\n",
       "    churn_date     state  \n",
       "0          NaN        GA  \n",
       "1          NaN        WA  \n",
       "2          NaN        NV  \n",
       "3          NaN        OK  \n",
       "4          NaN        WA  \n",
       "..         ...       ...  \n",
       "495        NaN  NY-NJ-PA  \n",
       "496        NaN        LA  \n",
       "497        NaN        CA  \n",
       "498        NaN  NY-NJ-PA  \n",
       "499        NaN        FL  \n",
       "\n",
       "[500 rows x 9 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new column `state`\n",
    "df['state'] = df['city'].str.rsplit(', ').str[-1].str[:-4]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking `calls` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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>8.52</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>13.66</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>14.48</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>5.76</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>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_388</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_510</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_521</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_530</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_544</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>4.40</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",
       "3  1000_309     1000  2018-12-28      5.76\n",
       "4  1000_380     1000  2018-12-30      4.22\n",
       "5  1000_388     1000  2018-12-31      2.20\n",
       "6  1000_510     1000  2018-12-27      5.75\n",
       "7  1000_521     1000  2018-12-28     14.18\n",
       "8  1000_530     1000  2018-12-28      5.77\n",
       "9  1000_544     1000  2018-12-26      4.40"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first 10 rows of `calls` dataset\n",
    "calls.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "# Displaying information from `calls` dataset\n",
    "calls.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in this dataset. We will only have to change the data type of the `call_date` column from object to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018-12-27\n",
       "1        2018-12-27\n",
       "2        2018-12-27\n",
       "3        2018-12-28\n",
       "4        2018-12-30\n",
       "            ...    \n",
       "137730   2018-11-21\n",
       "137731   2018-10-20\n",
       "137732   2018-09-21\n",
       "137733   2018-10-10\n",
       "137734   2018-12-29\n",
       "Name: call_date, Length: 137735, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting data type of column `call_date` from object to datetime\n",
    "calls['call_date']=pd.to_datetime(calls['call_date'], format='%Y-%m-%d')\n",
    "calls['call_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>user_id</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>137735.000000</td>\n",
       "      <td>137735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1247.658046</td>\n",
       "      <td>6.745927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>139.416268</td>\n",
       "      <td>5.839241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1128.000000</td>\n",
       "      <td>1.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1247.000000</td>\n",
       "      <td>5.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1365.000000</td>\n",
       "      <td>10.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id       duration\n",
       "count  137735.000000  137735.000000\n",
       "mean     1247.658046       6.745927\n",
       "std       139.416268       5.839241\n",
       "min      1000.000000       0.000000\n",
       "25%      1128.000000       1.290000\n",
       "50%      1247.000000       5.980000\n",
       "75%      1365.000000      10.690000\n",
       "max      1499.000000      37.600000"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying statistical summary of the `calls` dataset \n",
    "calls.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The minimum duration value of 0 might indicate missed or cancelled calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>19</th>\n",
       "      <td>1001_4</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1001_13</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1001_30</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1001_31</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1001_35</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>0.0</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>137714</th>\n",
       "      <td>1499_183</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137715</th>\n",
       "      <td>1499_184</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137720</th>\n",
       "      <td>1499_189</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137722</th>\n",
       "      <td>1499_191</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137725</th>\n",
       "      <td>1499_194</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26834 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id  call_date  duration\n",
       "19        1001_4     1001 2018-12-05       0.0\n",
       "25       1001_13     1001 2018-12-24       0.0\n",
       "31       1001_30     1001 2018-10-17       0.0\n",
       "32       1001_31     1001 2018-11-03       0.0\n",
       "34       1001_35     1001 2018-12-21       0.0\n",
       "...          ...      ...        ...       ...\n",
       "137714  1499_183     1499 2018-11-10       0.0\n",
       "137715  1499_184     1499 2018-09-15       0.0\n",
       "137720  1499_189     1499 2018-12-27       0.0\n",
       "137722  1499_191     1499 2018-12-24       0.0\n",
       "137725  1499_194     1499 2018-10-31       0.0\n",
       "\n",
       "[26834 rows x 4 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking rows with `duration` value of 0\n",
    "calls[calls['duration']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.482339274694162"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating percentage of phonecalls with duration of 0.\n",
    "len(calls[calls['duration']==0])/len(calls)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zero-duration data makes up 19.48% of the entire dataset. These rows will still be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memeriksa jumlah nilai unik pada `user_id`\n",
    "len(calls['user_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 481 customers using the phone call service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "calls.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates were found in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking `messages` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>message_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_125</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_160</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_223</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_251</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_255</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_346</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_386</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_502</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_525</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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\n",
       "5  1000_346     1000   2018-12-29\n",
       "6  1000_386     1000   2018-12-30\n",
       "7  1000_413     1000   2018-12-31\n",
       "8  1000_502     1000   2018-12-27\n",
       "9  1000_525     1000   2018-12-28"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first 10 rows of `messages` dataset\n",
    "messages.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "source": [
    "# Displaying information from `messages` dataset\n",
    "messages.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in this dataset. We only need to change the data type of the column `message_date` from object to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2018-12-27\n",
       "1       2018-12-31\n",
       "2       2018-12-31\n",
       "3       2018-12-27\n",
       "4       2018-12-26\n",
       "           ...    \n",
       "76046   2018-12-24\n",
       "76047   2018-12-24\n",
       "76048   2018-12-31\n",
       "76049   2018-12-24\n",
       "76050   2018-12-23\n",
       "Name: message_date, Length: 76051, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the data type of column `message_date` to datetime\n",
    "messages['message_date'] = pd.to_datetime(messages['message_date'], format='%Y-%m-%d')\n",
    "messages['message_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "402"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of unique values in column `user_id`\n",
    "len(messages['user_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 402 customers using the short message service (SMS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "messages.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates were found in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking `internet` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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",
       "    <tr>\n",
       "      <th>5</th>\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>6</th>\n",
       "      <td>1001_3</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>656.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1001_4</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-04</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001_10</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>135.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1001_15</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>761.92</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\n",
       "5    1001_0     1001   2018-08-24   284.68\n",
       "6    1001_3     1001   2018-12-09   656.04\n",
       "7    1001_4     1001   2018-11-04    16.97\n",
       "8   1001_10     1001   2018-11-27   135.18\n",
       "9   1001_15     1001   2018-12-13   761.92"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first 10 row of internet dataset\n",
    "internet.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>104825.000000</td>\n",
       "      <td>104825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1242.496361</td>\n",
       "      <td>366.713701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>142.053913</td>\n",
       "      <td>277.170542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1122.000000</td>\n",
       "      <td>136.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1236.000000</td>\n",
       "      <td>343.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1367.000000</td>\n",
       "      <td>554.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1693.470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id        mb_used\n",
       "count  104825.000000  104825.000000\n",
       "mean     1242.496361     366.713701\n",
       "std       142.053913     277.170542\n",
       "min      1000.000000       0.000000\n",
       "25%      1122.000000     136.080000\n",
       "50%      1236.000000     343.980000\n",
       "75%      1367.000000     554.610000\n",
       "max      1499.000000    1693.470000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the statistical summary of internet dataset\n",
    "internet.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 0 values in column `mb_used`. We are going to check these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "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>1</th>\n",
       "      <td>1000_204</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1001_26</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09-17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1001_28</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1001_54</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1001_77</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>0.0</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>104797</th>\n",
       "      <td>1499_192</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104804</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104816</th>\n",
       "      <td>1499_211</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104817</th>\n",
       "      <td>1499_212</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104823</th>\n",
       "      <td>1499_218</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13747 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id session_date  mb_used\n",
       "1       1000_204     1000   2018-12-31      0.0\n",
       "14       1001_26     1001   2018-09-17      0.0\n",
       "16       1001_28     1001   2018-10-17      0.0\n",
       "34       1001_54     1001   2018-09-02      0.0\n",
       "43       1001_77     1001   2018-10-31      0.0\n",
       "...          ...      ...          ...      ...\n",
       "104797  1499_192     1499   2018-09-19      0.0\n",
       "104804  1499_199     1499   2018-12-09      0.0\n",
       "104816  1499_211     1499   2018-09-26      0.0\n",
       "104817  1499_212     1499   2018-09-11      0.0\n",
       "104823  1499_218     1499   2018-12-07      0.0\n",
       "\n",
       "[13747 rows x 4 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking rows containing 0 in column`mb_used`.\n",
    "internet[internet['mb_used']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.11423801574052"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate percentage of 0 values in column `mb_used`\n",
    "len(internet[internet['mb_used']==0])/len(internet)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 13.11% of data rows containing 0 values in column `mb_used`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "# Checking information from internet dataset\n",
    "internet.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were no missing value in this dataset. We will only have to change the data type of column `session_date` from object to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018-12-29\n",
       "1        2018-12-31\n",
       "2        2018-12-28\n",
       "3        2018-12-26\n",
       "4        2018-12-27\n",
       "            ...    \n",
       "104820   2018-10-20\n",
       "104821   2018-12-30\n",
       "104822   2018-09-22\n",
       "104823   2018-12-07\n",
       "104824   2018-12-24\n",
       "Name: session_date, Length: 104825, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the data type in column `session_date` to datetime\n",
    "internet['session_date'] = pd.to_datetime(internet['session_date'], format='%Y-%m-%d')\n",
    "internet['session_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "489"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the amount of unique values in column `user_id`\n",
    "len(internet['user_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 489 customers using the internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "internet.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates were found in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting additional columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each user, we are going to find:\n",
    "* Number of calls made\n",
    "* Call minutes used per month\n",
    "* Number of SMS sent per month\n",
    "* Data volume per month\n",
    "* Monthly income of each user (subtract the free package limit from the total number of calls, SMS, and data; multiply the result by the value of the plan; add monthly fees depending on the plan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the duration and number of phonecalls per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "      <th>year_month</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>8.52</td>\n",
       "      <td>2018-12</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>13.66</td>\n",
       "      <td>2018-12</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>14.48</td>\n",
       "      <td>2018-12</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>5.76</td>\n",
       "      <td>2018-12</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>4.22</td>\n",
       "      <td>2018-12</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>137730</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>8.72</td>\n",
       "      <td>2018-11</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>10.89</td>\n",
       "      <td>2018-10</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>8.12</td>\n",
       "      <td>2018-09</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>0.37</td>\n",
       "      <td>2018-10</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>13.86</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137735 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id  call_date  duration year_month\n",
       "0        1000_93     1000 2018-12-27      8.52    2018-12\n",
       "1       1000_145     1000 2018-12-27     13.66    2018-12\n",
       "2       1000_247     1000 2018-12-27     14.48    2018-12\n",
       "3       1000_309     1000 2018-12-28      5.76    2018-12\n",
       "4       1000_380     1000 2018-12-30      4.22    2018-12\n",
       "...          ...      ...        ...       ...        ...\n",
       "137730  1499_199     1499 2018-11-21      8.72    2018-11\n",
       "137731  1499_200     1499 2018-10-20     10.89    2018-10\n",
       "137732  1499_201     1499 2018-09-21      8.12    2018-09\n",
       "137733  1499_202     1499 2018-10-10      0.37    2018-10\n",
       "137734  1499_203     1499 2018-12-29     13.86    2018-12\n",
       "\n",
       "[137735 rows x 5 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column `year_month`\n",
    "calls['year_month']= calls['call_date'].dt.to_period('M')\n",
    "calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>call_count</th>\n",
       "      <th>total_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>16</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64</td>\n",
       "      <td>405</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>2253</th>\n",
       "      <td>1498</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>39</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>41</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>53</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>45</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>65</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration\n",
       "0        1000    2018-12          16             117\n",
       "1        1001    2018-08          27             172\n",
       "2        1001    2018-09          49             298\n",
       "3        1001    2018-10          65             375\n",
       "4        1001    2018-11          64             405\n",
       "...       ...        ...         ...             ...\n",
       "2253     1498    2018-12          39             325\n",
       "2254     1499    2018-09          41             331\n",
       "2255     1499    2018-10          53             364\n",
       "2256     1499    2018-11          45             289\n",
       "2257     1499    2018-12          65             469\n",
       "\n",
       "[2258 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calculating the duration and number of phonecalls made per month\n",
    "call_monthly = calls.pivot_table(index=['user_id','year_month'], values = 'duration', aggfunc=['count', 'sum'])\n",
    "\n",
    "# Changing the column name\n",
    "call_monthly.columns=['call_count', 'total_duration']\n",
    "\n",
    "# Reseting index\n",
    "call_monthly = call_monthly.reset_index()\n",
    "\n",
    "# Rounding up values in `total_duration`\n",
    "call_monthly['total_duration']=call_monthly['total_duration'].apply(np.ceil).astype('int')\n",
    "\n",
    "call_monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the number of SMS per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>message_date</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_125</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_160</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_223</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_251</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_255</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2018-12</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>76046</th>\n",
       "      <td>1497_526</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76047</th>\n",
       "      <td>1497_536</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76048</th>\n",
       "      <td>1497_547</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76049</th>\n",
       "      <td>1497_558</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76050</th>\n",
       "      <td>1497_613</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76051 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  user_id message_date year_month\n",
       "0      1000_125     1000   2018-12-27    2018-12\n",
       "1      1000_160     1000   2018-12-31    2018-12\n",
       "2      1000_223     1000   2018-12-31    2018-12\n",
       "3      1000_251     1000   2018-12-27    2018-12\n",
       "4      1000_255     1000   2018-12-26    2018-12\n",
       "...         ...      ...          ...        ...\n",
       "76046  1497_526     1497   2018-12-24    2018-12\n",
       "76047  1497_536     1497   2018-12-24    2018-12\n",
       "76048  1497_547     1497   2018-12-31    2018-12\n",
       "76049  1497_558     1497   2018-12-24    2018-12\n",
       "76050  1497_613     1497   2018-12-23    2018-12\n",
       "\n",
       "[76051 rows x 4 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column `year_month`\n",
    "messages['year_month']= messages['message_date'].dt.to_period('M')\n",
    "messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>sms_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>1496</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>1496</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>1496</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>1496</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1805</th>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  sms_count\n",
       "0        1000    2018-12         11\n",
       "1        1001    2018-08         30\n",
       "2        1001    2018-09         44\n",
       "3        1001    2018-10         53\n",
       "4        1001    2018-11         36\n",
       "...       ...        ...        ...\n",
       "1801     1496    2018-09         21\n",
       "1802     1496    2018-10         18\n",
       "1803     1496    2018-11         13\n",
       "1804     1496    2018-12         11\n",
       "1805     1497    2018-12         50\n",
       "\n",
       "[1806 rows x 3 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating number of SMS per month\n",
    "message_monthly = messages.pivot_table(index=['user_id','year_month'], values = 'id', aggfunc=['count'])\n",
    "\n",
    "# Changing column name\n",
    "message_monthly.columns=['sms_count']\n",
    "\n",
    "# Reseting index\n",
    "message_monthly = message_monthly.reset_index()\n",
    "\n",
    "message_monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the data volume (MB) per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>year_month</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",
       "      <td>2018-12</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",
       "      <td>2018-12</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",
       "      <td>2018-12</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",
       "      <td>2018-12</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",
       "      <td>2018-12</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>104820</th>\n",
       "      <td>1499_215</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>218.06</td>\n",
       "      <td>2018-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104821</th>\n",
       "      <td>1499_216</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>304.72</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104822</th>\n",
       "      <td>1499_217</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-22</td>\n",
       "      <td>292.75</td>\n",
       "      <td>2018-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104823</th>\n",
       "      <td>1499_218</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104824</th>\n",
       "      <td>1499_219</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>758.31</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id session_date  mb_used year_month\n",
       "0        1000_13     1000   2018-12-29    89.86    2018-12\n",
       "1       1000_204     1000   2018-12-31     0.00    2018-12\n",
       "2       1000_379     1000   2018-12-28   660.40    2018-12\n",
       "3       1000_413     1000   2018-12-26   270.99    2018-12\n",
       "4       1000_442     1000   2018-12-27   880.22    2018-12\n",
       "...          ...      ...          ...      ...        ...\n",
       "104820  1499_215     1499   2018-10-20   218.06    2018-10\n",
       "104821  1499_216     1499   2018-12-30   304.72    2018-12\n",
       "104822  1499_217     1499   2018-09-22   292.75    2018-09\n",
       "104823  1499_218     1499   2018-12-07     0.00    2018-12\n",
       "104824  1499_219     1499   2018-12-24   758.31    2018-12\n",
       "\n",
       "[104825 rows x 5 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new column `year_month`\n",
    "internet['year_month']= internet['session_date'].dt.to_period('M')\n",
    "internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>year_month</th>\n",
       "      <th>total_mb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>1901.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>6919.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>13314.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>22330.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>18504.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>1498</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>23137.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>12984.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>19492.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>16813.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>22059.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2277 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  total_mb\n",
       "0        1000    2018-12   1901.47\n",
       "1        1001    2018-08   6919.15\n",
       "2        1001    2018-09  13314.82\n",
       "3        1001    2018-10  22330.49\n",
       "4        1001    2018-11  18504.30\n",
       "...       ...        ...       ...\n",
       "2272     1498    2018-12  23137.69\n",
       "2273     1499    2018-09  12984.76\n",
       "2274     1499    2018-10  19492.43\n",
       "2275     1499    2018-11  16813.83\n",
       "2276     1499    2018-12  22059.21\n",
       "\n",
       "[2277 rows x 3 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the amount of data volume (MB) used per month\n",
    "inet_monthly = internet.pivot_table(index=['user_id','year_month'], values = 'mb_used', aggfunc=['sum'])\n",
    "\n",
    "# Changing column name\n",
    "inet_monthly.columns=['total_mb']\n",
    "\n",
    "# Reseting index\n",
    "inet_monthly = inet_monthly.reset_index()\n",
    "\n",
    "inet_monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging the tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>call_count</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>sms_count</th>\n",
       "      <th>total_mb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
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       "      <th>2288</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1361</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1519.69</td>\n",
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       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1482</td>\n",
       "      <td>2018-10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1311</td>\n",
       "      <td>2018-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1498.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47\n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15\n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82\n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49\n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30\n",
       "...       ...        ...         ...             ...        ...       ...\n",
       "2288     1349    2018-12         NaN             NaN       61.0  13039.91\n",
       "2289     1361    2018-05         NaN             NaN        2.0   1519.69\n",
       "2290     1482    2018-10         NaN             NaN        2.0       NaN\n",
       "2291     1108    2018-12         NaN             NaN        NaN    233.17\n",
       "2292     1311    2018-06         NaN             NaN        NaN   1498.83\n",
       "\n",
       "[2293 rows x 6 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge dataset call_monthly with message monthly\n",
    "df_merged =  call_monthly.merge(message_monthly, how='outer') \n",
    "\n",
    "# Merge df_merged with internet monthly\n",
    "df_merged = df_merged.merge(inet_monthly, how='outer')\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>surf</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
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       "  </tbody>\n",
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       "<p>2293 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan  \n",
       "0     ultimate  \n",
       "1         surf  \n",
       "2         surf  \n",
       "3         surf  \n",
       "4         surf  \n",
       "...        ...  \n",
       "2288  ultimate  \n",
       "2289      surf  \n",
       "2290      surf  \n",
       "2291      surf  \n",
       "2292  ultimate  \n",
       "\n",
       "[2293 rows x 7 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
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    }
   ],
   "source": [
    "# Create new dataset from df dataset by taking only column `user_id` and `plan`\n",
    "plan = df[['user_id','plan']]\n",
    "\n",
    "# Add a column containing type of plan for each user using merge\n",
    "df_merged = df_merged.merge(plan, on='user_id', how='inner')\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>405.0</td>\n",
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       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
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       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
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       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  \n",
       "0     ultimate        GA  \n",
       "1         surf        WA  \n",
       "2         surf        WA  \n",
       "3         surf        WA  \n",
       "4         surf        WA  \n",
       "...        ...       ...  \n",
       "2288  ultimate  IL-IN-WI  \n",
       "2289      surf     MA-NH  \n",
       "2290      surf     MA-NH  \n",
       "2291      surf     MA-NH  \n",
       "2292  ultimate        UT  \n",
       "\n",
       "[2293 rows x 8 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataset from df dataset by taking only column `user_id` and `state`\n",
    "state = df[['user_id','state']]\n",
    "\n",
    "# Add new column containing state name of each user using merge\n",
    "df_merged = df_merged.merge(state, on='user_id', how='inner')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have just created a dataset containing the usage of all services (call, SMS, and internet), type of plans, and state code for each user."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating duration and additional costs of phone calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "        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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying plans dataset\n",
    "plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>call_count</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>sms_count</th>\n",
       "      <th>total_mb</th>\n",
       "      <th>plan</th>\n",
       "      <th>state</th>\n",
       "      <th>add_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  \n",
       "0     ultimate        GA           0.0  \n",
       "1         surf        WA           0.0  \n",
       "2         surf        WA           0.0  \n",
       "3         surf        WA           0.0  \n",
       "4         surf        WA           0.0  \n",
       "...        ...       ...           ...  \n",
       "2288  ultimate  IL-IN-WI           0.0  \n",
       "2289      surf     MA-NH           0.0  \n",
       "2290      surf     MA-NH           0.0  \n",
       "2291      surf     MA-NH           0.0  \n",
       "2292  ultimate        UT           0.0  \n",
       "\n",
       "[2293 rows x 9 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating additional call duration for surf and ultimate plan users\n",
    "df_merged['add_duration'] =(df_merged['total_duration']-500).where(cond= df_merged['plan']==\"surf\", other = df_merged['total_duration']-3000)\n",
    "\n",
    "# Replacing negative values with 0\n",
    "df_merged.loc[df_merged['add_duration']<0,'add_duration'] = 0 \n",
    "\n",
    "# Replacing null values with 0\n",
    "df_merged.loc[df_merged['add_duration'].isna(),'add_duration'] = 0 \n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
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       "      <th>total_duration</th>\n",
       "      <th>sms_count</th>\n",
       "      <th>total_mb</th>\n",
       "      <th>plan</th>\n",
       "      <th>state</th>\n",
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       "      <td>1001</td>\n",
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       "      <td>surf</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
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       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
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       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1349</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  \n",
       "0     ultimate        GA           0.0            0.0  \n",
       "1         surf        WA           0.0            0.0  \n",
       "2         surf        WA           0.0            0.0  \n",
       "3         surf        WA           0.0            0.0  \n",
       "4         surf        WA           0.0            0.0  \n",
       "...        ...       ...           ...            ...  \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0  \n",
       "2289      surf     MA-NH           0.0            0.0  \n",
       "2290      surf     MA-NH           0.0            0.0  \n",
       "2291      surf     MA-NH           0.0            0.0  \n",
       "2292  ultimate        UT           0.0            0.0  \n",
       "\n",
       "[2293 rows x 10 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating additional call cost based on the type of plans\n",
    "df_merged['add_call_cost'] =(df_merged['add_duration']*0.03).where(cond= df_merged['plan']==\"surf\", other = df_merged['add_duration']*0.01)\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating additional cost from SMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  \n",
       "0     ultimate        GA           0.0            0.0      0.0  \n",
       "1         surf        WA           0.0            0.0      0.0  \n",
       "2         surf        WA           0.0            0.0      0.0  \n",
       "3         surf        WA           0.0            0.0      3.0  \n",
       "4         surf        WA           0.0            0.0      0.0  \n",
       "...        ...       ...           ...            ...      ...  \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0  \n",
       "2289      surf     MA-NH           0.0            0.0     26.0  \n",
       "2290      surf     MA-NH           0.0            0.0     22.0  \n",
       "2291      surf     MA-NH           0.0            0.0     11.0  \n",
       "2292  ultimate        UT           0.0            0.0      0.0  \n",
       "\n",
       "[2293 rows x 11 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating number of additional SMS for each plan\n",
    "df_merged['add_sms'] =(df_merged['sms_count']-50).where(cond= df_merged['plan']==\"surf\", other = df_merged['sms_count']-1000)\n",
    "\n",
    "# Replace negative values with 0\n",
    "df_merged.loc[df_merged['add_sms']<0,'add_sms'] = 0\n",
    "\n",
    "# Replace null values with 0\n",
    "df_merged.loc[df_merged['add_sms'].isna(),'add_sms']=0\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>1001</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00  \n",
       "1         surf        WA           0.0            0.0      0.0          0.00  \n",
       "2         surf        WA           0.0            0.0      0.0          0.00  \n",
       "3         surf        WA           0.0            0.0      3.0          0.09  \n",
       "4         surf        WA           0.0            0.0      0.0          0.00  \n",
       "...        ...       ...           ...            ...      ...           ...  \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00  \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78  \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66  \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33  \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00  \n",
       "\n",
       "[2293 rows x 12 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating additional SMS cost based on type of plans\n",
    "df_merged['add_sms_cost'] =(df_merged['add_sms']*0.03).where(cond= df_merged['plan']==\"surf\", other = df_merged['add_sms']*0.01)\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating additional cost from data volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>6970.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3144.30</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6010.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2290</th>\n",
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       "      <td>2018-11</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1768.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  \n",
       "0        0.00  \n",
       "1        0.00  \n",
       "2        0.00  \n",
       "3     6970.49  \n",
       "4     3144.30  \n",
       "...       ...  \n",
       "2288  6010.05  \n",
       "2289     0.00  \n",
       "2290  1768.26  \n",
       "2291     0.00  \n",
       "2292     0.00  \n",
       "\n",
       "[2293 rows x 13 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcuating additional data volume (GB) based on plan types\n",
    "df_merged['add_mb'] =(df_merged['total_mb']-15360).where(cond= df_merged['plan']==\"surf\", other = df_merged['total_mb']-30720)\n",
    "\n",
    "# Replace negative values with 0\n",
    "df_merged.loc[df_merged['add_mb']<0,'add_mb'] = 0 \n",
    "\n",
    "# Replace null values with 0\n",
    "df_merged.loc[df_merged['add_mb'].isna(),'add_mb']=0\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<p>2293 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
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       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  add_gb  \n",
       "0        0.00     0.0  \n",
       "1        0.00     0.0  \n",
       "2        0.00     0.0  \n",
       "3     6970.49     7.0  \n",
       "4     3144.30     4.0  \n",
       "...       ...     ...  \n",
       "2288  6010.05     6.0  \n",
       "2289     0.00     0.0  \n",
       "2290  1768.26     2.0  \n",
       "2291     0.00     0.0  \n",
       "2292     0.00     0.0  \n",
       "\n",
       "[2293 rows x 14 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert additional data volume from MB to GB and round it up\n",
    "df_merged['add_gb'] = (df_merged['add_mb']/1024).apply(np.ceil)\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>state</th>\n",
       "      <th>add_duration</th>\n",
       "      <th>add_call_cost</th>\n",
       "      <th>add_sms</th>\n",
       "      <th>add_sms_cost</th>\n",
       "      <th>add_mb</th>\n",
       "      <th>add_gb</th>\n",
       "      <th>add_data_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>6970.49</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3144.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</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",
       "      <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>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6010.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1768.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  add_gb  add_data_cost  \n",
       "0        0.00     0.0            0.0  \n",
       "1        0.00     0.0            0.0  \n",
       "2        0.00     0.0            0.0  \n",
       "3     6970.49     7.0           70.0  \n",
       "4     3144.30     4.0           40.0  \n",
       "...       ...     ...            ...  \n",
       "2288  6010.05     6.0           42.0  \n",
       "2289     0.00     0.0            0.0  \n",
       "2290  1768.26     2.0           20.0  \n",
       "2291     0.00     0.0            0.0  \n",
       "2292     0.00     0.0            0.0  \n",
       "\n",
       "[2293 rows x 15 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating additional data cost based on plan types.\n",
    "df_merged['add_data_cost'] =(df_merged['add_gb']*10).where(cond= df_merged['plan']==\"surf\", other = df_merged['add_gb']*7)\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating total additional cost per month for each user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>call_count</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>sms_count</th>\n",
       "      <th>total_mb</th>\n",
       "      <th>plan</th>\n",
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       "      <th>add_duration</th>\n",
       "      <th>add_call_cost</th>\n",
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       "      <th>add_sms_cost</th>\n",
       "      <th>add_mb</th>\n",
       "      <th>add_gb</th>\n",
       "      <th>add_data_cost</th>\n",
       "      <th>total_add_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>6970.49</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3144.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6010.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1768.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  add_gb  add_data_cost  total_add_cost  \n",
       "0        0.00     0.0            0.0            0.00  \n",
       "1        0.00     0.0            0.0            0.00  \n",
       "2        0.00     0.0            0.0            0.00  \n",
       "3     6970.49     7.0           70.0           70.09  \n",
       "4     3144.30     4.0           40.0           40.00  \n",
       "...       ...     ...            ...             ...  \n",
       "2288  6010.05     6.0           42.0           42.00  \n",
       "2289     0.00     0.0            0.0            0.78  \n",
       "2290  1768.26     2.0           20.0           20.66  \n",
       "2291     0.00     0.0            0.0            0.33  \n",
       "2292     0.00     0.0            0.0            0.00  \n",
       "\n",
       "[2293 rows x 16 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding up additional costs per month\n",
    "df_merged['total_add_cost'] = df_merged['add_call_cost']+df_merged['add_sms_cost']+df_merged['add_data_cost']\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the total revenue per month from each user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>year_month</th>\n",
       "      <th>call_count</th>\n",
       "      <th>total_duration</th>\n",
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       "      <th>add_gb</th>\n",
       "      <th>add_data_cost</th>\n",
       "      <th>total_add_cost</th>\n",
       "      <th>monthly_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>1901.47</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>GA</td>\n",
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       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>6970.49</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.09</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3144.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6010.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1768.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  add_gb  add_data_cost  total_add_cost  monthly_price  \n",
       "0        0.00     0.0            0.0            0.00             70  \n",
       "1        0.00     0.0            0.0            0.00             20  \n",
       "2        0.00     0.0            0.0            0.00             20  \n",
       "3     6970.49     7.0           70.0           70.09             20  \n",
       "4     3144.30     4.0           40.0           40.00             20  \n",
       "...       ...     ...            ...             ...            ...  \n",
       "2288  6010.05     6.0           42.0           42.00             70  \n",
       "2289     0.00     0.0            0.0            0.78             20  \n",
       "2290  1768.26     2.0           20.0           20.66             20  \n",
       "2291     0.00     0.0            0.0            0.33             20  \n",
       "2292     0.00     0.0            0.0            0.00             70  \n",
       "\n",
       "[2293 rows x 17 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a column containing the monthly fee according to the plan type.\n",
    "def price(row):\n",
    "    if 'surf' in row:\n",
    "        return 20\n",
    "    else:\n",
    "        return 70\n",
    "\n",
    "df_merged['monthly_price'] = df_merged['plan'].apply(price)\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>6970.49</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.09</td>\n",
       "      <td>20</td>\n",
       "      <td>90.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>surf</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3144.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>20</td>\n",
       "      <td>60.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36730.05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>IL-IN-WI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6010.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>70</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13093.55</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>20</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17128.26</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1768.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>20</td>\n",
       "      <td>40.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13039.91</td>\n",
       "      <td>surf</td>\n",
       "      <td>MA-NH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>20</td>\n",
       "      <td>20.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>UT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>70</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id year_month  call_count  total_duration  sms_count  total_mb  \\\n",
       "0        1000    2018-12        16.0           117.0       11.0   1901.47   \n",
       "1        1001    2018-08        27.0           172.0       30.0   6919.15   \n",
       "2        1001    2018-09        49.0           298.0       44.0  13314.82   \n",
       "3        1001    2018-10        65.0           375.0       53.0  22330.49   \n",
       "4        1001    2018-11        64.0           405.0       36.0  18504.30   \n",
       "...       ...        ...         ...             ...        ...       ...   \n",
       "2288     1204    2018-12         NaN             NaN       78.0  36730.05   \n",
       "2289     1349    2018-10         NaN             NaN       76.0  13093.55   \n",
       "2290     1349    2018-11         NaN             NaN       72.0  17128.26   \n",
       "2291     1349    2018-12         NaN             NaN       61.0  13039.91   \n",
       "2292     1108    2018-12         NaN             NaN        NaN    233.17   \n",
       "\n",
       "          plan     state  add_duration  add_call_cost  add_sms  add_sms_cost  \\\n",
       "0     ultimate        GA           0.0            0.0      0.0          0.00   \n",
       "1         surf        WA           0.0            0.0      0.0          0.00   \n",
       "2         surf        WA           0.0            0.0      0.0          0.00   \n",
       "3         surf        WA           0.0            0.0      3.0          0.09   \n",
       "4         surf        WA           0.0            0.0      0.0          0.00   \n",
       "...        ...       ...           ...            ...      ...           ...   \n",
       "2288  ultimate  IL-IN-WI           0.0            0.0      0.0          0.00   \n",
       "2289      surf     MA-NH           0.0            0.0     26.0          0.78   \n",
       "2290      surf     MA-NH           0.0            0.0     22.0          0.66   \n",
       "2291      surf     MA-NH           0.0            0.0     11.0          0.33   \n",
       "2292  ultimate        UT           0.0            0.0      0.0          0.00   \n",
       "\n",
       "       add_mb  add_gb  add_data_cost  total_add_cost  monthly_price  \\\n",
       "0        0.00     0.0            0.0            0.00             70   \n",
       "1        0.00     0.0            0.0            0.00             20   \n",
       "2        0.00     0.0            0.0            0.00             20   \n",
       "3     6970.49     7.0           70.0           70.09             20   \n",
       "4     3144.30     4.0           40.0           40.00             20   \n",
       "...       ...     ...            ...             ...            ...   \n",
       "2288  6010.05     6.0           42.0           42.00             70   \n",
       "2289     0.00     0.0            0.0            0.78             20   \n",
       "2290  1768.26     2.0           20.0           20.66             20   \n",
       "2291     0.00     0.0            0.0            0.33             20   \n",
       "2292     0.00     0.0            0.0            0.00             70   \n",
       "\n",
       "      total_revenue  \n",
       "0             70.00  \n",
       "1             20.00  \n",
       "2             20.00  \n",
       "3             90.09  \n",
       "4             60.00  \n",
       "...             ...  \n",
       "2288         112.00  \n",
       "2289          20.78  \n",
       "2290          40.66  \n",
       "2291          20.33  \n",
       "2292          70.00  \n",
       "\n",
       "[2293 rows x 18 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total revenue per month\n",
    "df_merged['total_revenue'] = df_merged['total_add_cost'] + df_merged['monthly_price']\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the mean, median, variance, and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to calculate average, median, variance, and standard deviation based on plan type.\n",
    "def calc(data, column):\n",
    "    surf_mean = data.loc[data['plan']=='surf'][column].mean().round(1)\n",
    "    surf_median = data.loc[data['plan']=='surf'][column].median().round(1)\n",
    "    surf_var = np.var(data.loc[data['plan']=='surf'][column]).round(1)\n",
    "    surf_std = np.std(data.loc[data['plan']=='surf'][column]).round(1)\n",
    "    \n",
    "    ulti_mean = data.loc[data['plan']=='ultimate'][column].mean().round(1)\n",
    "    ulti_median = data.loc[data['plan']=='ultimate'][column].median().round(1)\n",
    "    ulti_var = np.var(data.loc[data['plan']=='ultimate'][column]).round(1)\n",
    "    ulti_std = np.std(data.loc[data['plan']=='ultimate'][column]).round(1)\n",
    "\n",
    "    # Store results in a dictionary\n",
    "    result = {'type':['surf', 'ultimate'],\n",
    "           'mean':[surf_mean, ulti_mean],\n",
    "           'median':[surf_median, ulti_median],\n",
    "           'variance':[surf_var, ulti_var],\n",
    "           'stdev': [surf_std, ulti_std]\n",
    "           }\n",
    "    \n",
    "    # Create a results table\n",
    "    result_table = pd.DataFrame(result)\n",
    "    \n",
    "    return result_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>412.6</td>\n",
       "      <td>406.0</td>\n",
       "      <td>46972.6</td>\n",
       "      <td>216.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>410.7</td>\n",
       "      <td>400.0</td>\n",
       "      <td>50446.4</td>\n",
       "      <td>224.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type   mean  median  variance  stdev\n",
       "0      surf  412.6   406.0   46972.6  216.7\n",
       "1  ultimate  410.7   400.0   50446.4  224.6"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for the total call duration per month\n",
    "calc(df_merged,'total_duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call durations on surf and ultimate data packet types have average, median, variance, and stdev that are almost close to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>40.1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1090.5</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>46.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1083.3</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf  40.1    32.0    1090.5   33.0\n",
       "1  ultimate  46.3    41.0    1083.3   32.9"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance and standard deviation for the total SMS per month\n",
    "calc(df_merged,'sms_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of SMS on surf and ultimate data package types has a fairly similar average, median, variance, and stdev. Ultimate users send more SMS, with an average of 46."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>16717.7</td>\n",
       "      <td>16923.8</td>\n",
       "      <td>62128323.2</td>\n",
       "      <td>7882.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>17238.6</td>\n",
       "      <td>16863.4</td>\n",
       "      <td>61239556.1</td>\n",
       "      <td>7825.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type     mean   median    variance   stdev\n",
       "0      surf  16717.7  16923.8  62128323.2  7882.2\n",
       "1  ultimate  17238.6  16863.4  61239556.1  7825.6"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for the total data (MB) per month\n",
    "calc(df_merged,'total_mb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amount of data (MB) in the surf and ultimate data package types has the average, median, variance and stdev which are also quite similar. Ultimate users use more data than surf users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12142.8</td>\n",
       "      <td>110.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf  49.0     0.0   12142.8  110.2\n",
       "1  ultimate   0.0     0.0       0.0    0.0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for the additional call duration per month\n",
    "calc(df_merged,'add_duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no additional call duration for ultimate users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>378.9</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf   7.0     0.0     378.9   19.5\n",
       "1  ultimate   0.0     0.0       0.0    0.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance and standard deviation for the additional SMS per month\n",
    "calc(df_merged,'add_sms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no additional SMS on ultimate users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf   3.9     2.0      29.6    5.4\n",
       "1  ultimate   0.3     0.0       2.6    1.6"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for the additional data (GB) per month\n",
    "calc(df_merged,'add_gb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only a few Ultimate users use additional (GB) data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we can see differences in user behavior for the two types of data plan, where users of the Surf data plan use more additional call duration, SMS, and data/internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>40.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>3052.2</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.7</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf  40.3    20.2    3052.2   55.2\n",
       "1  ultimate   2.3     0.0     129.7   11.4"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for the additional cost per month\n",
    "calc(df_merged,'total_add_cost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher additional income comes from Surf plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>60.3</td>\n",
       "      <td>40.2</td>\n",
       "      <td>3052.2</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>72.3</td>\n",
       "      <td>70.0</td>\n",
       "      <td>129.7</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type  mean  median  variance  stdev\n",
       "0      surf  60.3    40.2    3052.2   55.2\n",
       "1  ultimate  72.3    70.0     129.7   11.4"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the average, median, variance, and standard deviation for total revenue per month\n",
    "calc(df_merged,'total_revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the average and median total revenue for the ultimate package is higher than the surf package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function for plotting the histogram\n",
    "def hist_plot(data, col, bins, upper_range):\n",
    "    data.hist(column = col, by='plan', grid=True, figsize=(20,5), bins = bins, range=(0,upper_range))\n",
    "    return hist_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\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>call_count</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>sms_count</th>\n",
       "      <th>total_mb</th>\n",
       "      <th>add_duration</th>\n",
       "      <th>add_call_cost</th>\n",
       "      <th>add_sms</th>\n",
       "      <th>add_sms_cost</th>\n",
       "      <th>add_mb</th>\n",
       "      <th>add_gb</th>\n",
       "      <th>add_data_cost</th>\n",
       "      <th>total_add_cost</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2293.0</td>\n",
       "      <td>2258.0</td>\n",
       "      <td>2258.0</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>2293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1246.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>16882.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2599.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>28.4</td>\n",
       "      <td>35.7</td>\n",
       "      <td>64.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>143.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>219.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>7869.8</td>\n",
       "      <td>94.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4829.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>48.6</td>\n",
       "      <td>49.5</td>\n",
       "      <td>23.2</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1122.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12347.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1245.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>16873.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1368.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>543.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>21041.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3699.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1432.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>70931.6</td>\n",
       "      <td>932.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>55571.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>568.7</td>\n",
       "      <td>70.0</td>\n",
       "      <td>588.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  call_count  total_duration  sms_count  total_mb  add_duration  \\\n",
       "count   2293.0      2258.0          2258.0     1806.0    2277.0        2293.0   \n",
       "mean    1246.1        61.0           412.0       42.1   16882.2          33.6   \n",
       "std      143.1        31.8           219.3       33.1    7869.8          94.1   \n",
       "min     1000.0         1.0             0.0        1.0      92.3           0.0   \n",
       "25%     1122.0        39.0           259.0       17.0   12347.7           0.0   \n",
       "50%     1245.0        60.0           405.0       34.0   16873.7           0.0   \n",
       "75%     1368.0        80.0           543.8       59.0   21041.5           0.0   \n",
       "max     1499.0       205.0          1432.0      266.0   70931.6         932.0   \n",
       "\n",
       "       add_call_cost  add_sms  add_sms_cost   add_mb  add_gb  add_data_cost  \\\n",
       "count         2293.0   2293.0        2293.0   2293.0  2293.0         2293.0   \n",
       "mean             1.0      4.8           0.1   2599.3     2.8           27.2   \n",
       "std              2.8     16.5           0.5   4829.6     4.9           48.6   \n",
       "min              0.0      0.0           0.0      0.0     0.0            0.0   \n",
       "25%              0.0      0.0           0.0      0.0     0.0            0.0   \n",
       "50%              0.0      0.0           0.0      0.0     0.0            0.0   \n",
       "75%              0.0      0.0           0.0   3699.3     4.0           40.0   \n",
       "max             28.0    216.0           6.5  55571.6    55.0          550.0   \n",
       "\n",
       "       total_add_cost  monthly_price  total_revenue  \n",
       "count          2293.0         2293.0         2293.0  \n",
       "mean             28.4           35.7           64.1  \n",
       "std              49.5           23.2           46.5  \n",
       "min               0.0           20.0           20.0  \n",
       "25%               0.0           20.0           22.5  \n",
       "50%               0.1           20.0           70.0  \n",
       "75%              40.5           70.0           70.0  \n",
       "max             568.7           70.0          588.7  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays descriptive statistics to make it easier to determine the upper bound of the histogram\n",
    "df_merged.describe().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for total call duration per month\n",
    "hist_plot(df_merged, 'total_duration', 80, 1600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call duration histograms for both types of surf and ultimate data packets have a similar distribution and are both right skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for total SMS per month\n",
    "hist_plot(df_merged, 'sms_count', 30, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total SMS histograms for both surf and ultimate data plans are right-skewed. On the surf histogram, the histogram peaks appear to be in the range 0–40, while on the ultimate histogram, the total SMS peak has wider range from 0-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for total data per month\n",
    "hist_plot(df_merged, 'total_mb', 70, 72000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total data histogram (MB) for both types of surf and ultimate data packets is right-skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for additional call duration per month\n",
    "hist_plot(df_merged, 'add_duration', 100, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both types of data packages are dominated by users who do not require additional call duration. The additional call duration is only visible on data-surf packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for additional SMS per month\n",
    "hist_plot(df_merged, 'add_sms', 60, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both plans are dominated by customers who do not use additional SMS. The additional SMS is only seen on Surf data plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for additional data (GB) per month\n",
    "hist_plot(df_merged, 'add_gb', 60, 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both plans are dominated by customers who do not use additional (GB) data. Additional data is more visible in Surf plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.hist_plot(data, col, bins, upper_range)>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for total revenue per month\n",
    "hist_plot(df_merged, 'total_revenue', 60, 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both plans have the highest total revenue from the monthly fees for the main package and are right-skewed. This can be clearly seen, especially in the Ultimate Plan. In addition, the distribution of additional data (GB) and total revenue for Surf plan were quite similar. This indicates that additional revenue is dominated by additional (GB) data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hypothesis to be tested:\n",
    "1. The average revenue from users of Ultimate and Surf plan is different.\n",
    "2. The average revenue from users in the NY-NJ area is different from that of users from other regions.\n",
    "\n",
    "Surf and Ultimate are two distinct and independent groups. Thus, two sample t-test or independent t-test method will be used for the hypothesis test.\n",
    "\n",
    "Usually, the null or H0 hypothesis assumes that there is no significant difference in the means of the two groups. The alternative hypothesis (H1) is the opposite, which assumes that there is a difference in the means of the two groups and that this difference is unlikely to occur due to *sampling error* or *chance*.\n",
    "\n",
    "For the hypothesis to be tested, then:\n",
    "* H0: The average income of the ultimate and surf phone package users is the same.\n",
    "* H1: The average income of users of ultimate and surf packages is different.\n",
    "\n",
    "Similarly:\n",
    "* H0: The average income of users in the NY-NJ area is the same as the income of users from other regions.\n",
    "* H1: The average income of users in the NY-NJ area is different from the income of users from other regions.\n",
    "\n",
    "The alpha value is defined as 0.05 or 5%. With this, we decided that we are willing to take a 5% risk in asserting that the unknown population averages for surf and ultimate package user income are not the same, even though they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value : 9.470478215100305e-09\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# H0: The average income of Ultimate and Surf plan users is the same.\n",
    "# H1: The average income of Ultimate and Surf plan users is different.\n",
    "\n",
    "# Create a variable containing dataset that has been separated by plan type.\n",
    "surf_revenue = df_merged.loc[df_merged['plan']=='surf']['total_revenue']\n",
    "ulti_revenue = df_merged.loc[df_merged['plan']=='ultimate']['total_revenue']\n",
    "\n",
    "# Sets the alpha value\n",
    "alpha = 0.05\n",
    "\n",
    "# Conduct t-test\n",
    "results = st.ttest_ind(surf_revenue, ulti_revenue, equal_var=True)\n",
    "\n",
    "# Calculate and display the p-value\n",
    "print('p_value :', results.pvalue)\n",
    "\n",
    "# Display the results\n",
    "if results.pvalue < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 tidak dapat ditolak')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reject the null hypothesis (H0) which states that the average revenue of Surf and Ultimate plan users is the same and decide we have evidence that the mean income in the population is different for Ultimate and Surf plan users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value : 0.04076784279879048\n",
      "H0 ditolak\n"
     ]
    }
   ],
   "source": [
    "# H0: The average revenue from users in the NY-NJ area is the same as that of users from other regions.\n",
    "# H1: The average revenue of users in the NY-NJ area is different from the revenue of users from other regions.\n",
    "\n",
    "# Create a variable containing dataset that has been separated by region\n",
    "ny_nj_revenue = df_merged[df_merged['state'].isin(['NY-NJ-PA'])]['total_revenue']\n",
    "others_revenue = df_merged[~df_merged['state'].isin(['NY-NJ-PA'])]['total_revenue']\n",
    "\n",
    "# Sets the alpha value\n",
    "alpha = 0.05\n",
    "\n",
    "# Conduct t-test\n",
    "results = st.ttest_ind(ny_nj_revenue, others_revenue)\n",
    "\n",
    "# Calculate and display the p-value\n",
    "print('p_value :', results.pvalue)\n",
    "\n",
    "# Display the results\n",
    "if results.pvalue < alpha:\n",
    "    print('H0 ditolak')\n",
    "else:\n",
    "    print('H0 tidak dapat ditolak')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reject the null hypothesis (H0) which states that the mean revenue of users from NY-NJ and users from other states are the same and decide we have evidence that the mean revenue in the population is different for users from NY-NJ with users from other states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the data exploration, the following were found:\n",
    "* There are a total of 500 customers in the dataset, 34 of them have stopped using the service.\n",
    "* The youngest customer is 18 years old.\n",
    "* The oldest customer is 75 years old.\n",
    "* Data types `reg_date`, `call_data`, `message_date` and `session_date` are not suitable and need to be changed to datetime.\n",
    "* There are zero values on call duration and internet data.\n",
    "\n",
    "After fixing the date data type to datetime, several columns were added to calculate the number of calls, call duration, number of SMS, amount of data (MB), additional call duration, additional SMS, additional data (MB), additional revenue from each service, and total revenue from each user.\n",
    "\n",
    "The analysis was performed by calculating the average, median, variance and standard deviation. Data visualization is done by making a histogram to see the distribution of data. From the analysis obtained:\n",
    "* Average, median, variance and standard deviation values for call duration, number of SMS, amount of data used per month are similar to Surf and Ultimate plan types.\n",
    "* There is an additional call duration on the Surf plan.\n",
    "* There is an additional SMS in the Surf plan.\n",
    "* There is no additional call duration on the Ultimate plan.\n",
    "* There is no additional SMS on the Ultimate plan.\n",
    "* There is additional data (MB) for both plans, but the amount is higher in Surf plan.\n",
    "* The average additional income from the Surf plan is higher than that from the Ultimate plan.\n",
    "* The average total revenue from the Ultimate plan is higher than that from the Surf plan.\n",
    " \n",
    "Hypothesis testing using *two sample t-test* was carried out, and the following conclusions were obtained:\n",
    "* There is a difference in the average user income of the Surf plan and the Ultimate plan.\n",
    "* There are differences in the average income of users from the NY-NJ region and other areas.\n",
    "\n",
    "The plan that brings more revenue is Ultimate. The Ultimate plan can be considered for advertising."
   ]
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