Project3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research on prepaid plans for Megaline\n",
    "You work as an analyst for the telecom operator Megaline. The company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans is more profitable in order to adjust the advertising budget.\n",
    "You are going to carry out a preliminary analysis of the plans based on a relatively small client selection. You'll have the data on 500 Megaline clients: who the clients are, where they're from, which plan they use, and the number of calls they made and text messages they sent in 2018. Your job is to analyze clients' behavior and determine which prepaid plan is more profitable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries to prepare for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from math import factorial\n",
    "from scipy import stats as st\n",
    "from functools import reduce\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Open the data files and study the general information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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",
      "id           137735 non-null object\n",
      "user_id      137735 non-null int64\n",
      "call_date    137735 non-null object\n",
      "duration     137735 non-null float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 4.2+ MB\n"
     ]
    },
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000_93</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>8.52</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1000_145</td>\n",
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       "      <td>13.66</td>\n",
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       "      <td>2</td>\n",
       "      <td>1000_247</td>\n",
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       "      <td>14.48</td>\n",
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       "      <td>3</td>\n",
       "      <td>1000_309</td>\n",
       "      <td>1000</td>\n",
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       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>4.22</td>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls = pd.read_csv('/datasets/megaline_calls.csv')\n",
    "calls.info()\n",
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     26834\n",
       "8.37       102\n",
       "4.02       102\n",
       "3.91       101\n",
       "7.61       100\n",
       "         ...  \n",
       "25.53        1\n",
       "29.34        1\n",
       "26.05        1\n",
       "36.24        1\n",
       "27.16        1\n",
       "Name: duration, Length: 2802, dtype: int64"
      ]
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     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "calls['duration'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataframe I have 4 columns. There is one float, int, and 2 objects. There are no missing values. There were 137735 calls made during this period of the data. \n",
    "\n",
    "There are 26834 calls with 0.00 duration. Missed calls? People did not pick up? It doesn't matter towards the data because it is included within the price for surf or Ultimate if its just a call but no duration.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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",
      "id              104825 non-null object\n",
      "user_id         104825 non-null int64\n",
      "session_date    104825 non-null object\n",
      "mb_used         104825 non-null float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 3.2+ MB\n"
     ]
    },
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       "      <td>89.86</td>\n",
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      "text/plain": [
       "         id  user_id session_date  mb_used\n",
       "0   1000_13     1000   2018-12-29    89.86\n",
       "1  1000_204     1000   2018-12-31     0.00\n",
       "2  1000_379     1000   2018-12-28   660.40\n",
       "3  1000_413     1000   2018-12-26   270.99\n",
       "4  1000_442     1000   2018-12-27   880.22"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet = pd.read_csv('/datasets/megaline_internet.csv')\n",
    "internet.info()\n",
    "internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      13747\n",
       "414.46        8\n",
       "435.77        8\n",
       "417.46        7\n",
       "313.79        7\n",
       "          ...  \n",
       "638.12        1\n",
       "217.39        1\n",
       "683.90        1\n",
       "243.97        1\n",
       "563.11        1\n",
       "Name: mb_used, Length: 57624, dtype: int64"
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     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "internet['mb_used'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataframe I have similar to data_calls. 1 float, 1 integer, and 2 objects. \n",
    "Actually its similar data from the calls just it is dealing with internet instead of calls. \n",
    "\n",
    "There was 104825 usages of internet during this period. \n",
    "Can probably merge this with calls to see the user usage in general. \n",
    "\n",
    "I will need to calculate mb_used to Gigabyte in order to make sure the data coincides together when we are calculating the usd_per_gb later. \n",
    "\n",
    "Seems like some users did not actually use any mb and a lot of this data is less than a gb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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",
      "id              76051 non-null object\n",
      "user_id         76051 non-null int64\n",
      "message_date    76051 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
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      "text/plain": [
       "         id  user_id message_date\n",
       "0  1000_125     1000   2018-12-27\n",
       "1  1000_160     1000   2018-12-31\n",
       "2  1000_223     1000   2018-12-31\n",
       "3  1000_251     1000   2018-12-27\n",
       "4  1000_255     1000   2018-12-26"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = pd.read_csv('/datasets/megaline_messages.csv')\n",
    "messages.info()\n",
    "messages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataframe we have 1 integer and two objects. \n",
    "\n",
    "There were 76051 messages sent during this data period. Again I will need to merge the user_id to find the general usage all in one frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "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",
      "messages_included        2 non-null int64\n",
      "mb_per_month_included    2 non-null int64\n",
      "minutes_included         2 non-null int64\n",
      "usd_monthly_pay          2 non-null int64\n",
      "usd_per_gb               2 non-null int64\n",
      "usd_per_message          2 non-null float64\n",
      "usd_per_minute           2 non-null float64\n",
      "plan_name                2 non-null object\n",
      "dtypes: float64(2), int64(5), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    },
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       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>ultimate</td>\n",
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      "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plans = pd.read_csv('/datasets/megaline_plans.csv')\n",
    "plans.info()\n",
    "plans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is important information to have since it gives me the information about how the plans work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "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",
      "user_id       500 non-null int64\n",
      "first_name    500 non-null object\n",
      "last_name     500 non-null object\n",
      "age           500 non-null int64\n",
      "city          500 non-null object\n",
      "reg_date      500 non-null object\n",
      "tariff        500 non-null object\n",
      "churn_date    34 non-null object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 31.4+ KB\n"
     ]
    },
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       "      <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",
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       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
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       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
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       "      <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",
       "      <td>4</td>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                   city  \\\n",
       "0     1000   Anamaria      Bauer   45  Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36   Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                          Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "\n",
       "     reg_date    tariff 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  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.read_csv('/datasets/megaline_users.csv')\n",
    "users.info()\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have the users and their information. Will need this later for the city. it also shows what plan they have. I will merge this with the other data to get plan and city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-12-18    3\n",
       "2018-12-19    2\n",
       "2018-12-31    2\n",
       "2018-11-14    2\n",
       "2018-11-29    1\n",
       "2018-12-27    1\n",
       "2018-09-18    1\n",
       "2018-07-31    1\n",
       "2018-11-30    1\n",
       "2018-09-17    1\n",
       "2018-10-22    1\n",
       "2018-10-13    1\n",
       "2018-12-30    1\n",
       "2018-09-07    1\n",
       "2018-11-21    1\n",
       "2018-12-10    1\n",
       "2018-08-16    1\n",
       "2018-12-15    1\n",
       "2018-12-12    1\n",
       "2018-11-11    1\n",
       "2018-09-01    1\n",
       "2018-11-18    1\n",
       "2018-12-26    1\n",
       "2018-12-22    1\n",
       "2018-11-16    1\n",
       "2018-10-07    1\n",
       "2018-11-24    1\n",
       "2018-10-03    1\n",
       "2018-08-19    1\n",
       "Name: churn_date, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['churn_date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>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>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>1491</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name last_name  age                           city  \\\n",
       "491     1491     Terina    Jensen   22  Grand Rapids-Kentwood, MI MSA   \n",
       "\n",
       "       reg_date tariff  churn_date  \n",
       "491  2018-04-27   surf  2018-09-18  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_churn_date_09_18 = users.query('churn_date == \"2018-09-18\"')\n",
    "users_churn_date_09_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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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       "\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",
       "      <td>102568</td>\n",
       "      <td>1491_1</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>55.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102569</td>\n",
       "      <td>1491_2</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102570</td>\n",
       "      <td>1491_3</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-07-24</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102571</td>\n",
       "      <td>1491_4</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>592.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102572</td>\n",
       "      <td>1491_5</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>670.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102762</td>\n",
       "      <td>1491_264</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>288.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102763</td>\n",
       "      <td>1491_266</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>53.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102764</td>\n",
       "      <td>1491_267</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-08-21</td>\n",
       "      <td>692.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102765</td>\n",
       "      <td>1491_268</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102766</td>\n",
       "      <td>1491_269</td>\n",
       "      <td>1491</td>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id session_date  mb_used\n",
       "102568    1491_1     1491   2018-06-19    55.74\n",
       "102569    1491_2     1491   2018-05-05     0.00\n",
       "102570    1491_3     1491   2018-07-24     0.00\n",
       "102571    1491_4     1491   2018-09-30   592.52\n",
       "102572    1491_5     1491   2018-07-11   670.40\n",
       "...          ...      ...          ...      ...\n",
       "102762  1491_264     1491   2018-07-14   288.56\n",
       "102763  1491_266     1491   2018-09-11    53.78\n",
       "102764  1491_267     1491   2018-08-21   692.78\n",
       "102765  1491_268     1491   2018-12-27     0.00\n",
       "102766  1491_269     1491   2018-09-03     0.00\n",
       "\n",
       "[199 rows x 4 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_1491 = internet.query('user_id == \"1491\"')\n",
    "users_1491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\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",
       "      <td>12</td>\n",
       "      <td>1012</td>\n",
       "      <td>Jonelle</td>\n",
       "      <td>Mcbride</td>\n",
       "      <td>59</td>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI MSA</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1022</td>\n",
       "      <td>Bo</td>\n",
       "      <td>Snow</td>\n",
       "      <td>73</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1040</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Santos</td>\n",
       "      <td>72</td>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA</td>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1050</td>\n",
       "      <td>Jone</td>\n",
       "      <td>Owen</td>\n",
       "      <td>23</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-10-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1054</td>\n",
       "      <td>Denis</td>\n",
       "      <td>Frost</td>\n",
       "      <td>50</td>\n",
       "      <td>Phoenix-Mesa-Chandler, AZ MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1067</td>\n",
       "      <td>Robt</td>\n",
       "      <td>Allen</td>\n",
       "      <td>37</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1083</td>\n",
       "      <td>Lyndia</td>\n",
       "      <td>Acevedo</td>\n",
       "      <td>65</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1084</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>Mckinney</td>\n",
       "      <td>21</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1094</td>\n",
       "      <td>Walter</td>\n",
       "      <td>Petersen</td>\n",
       "      <td>54</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1106</td>\n",
       "      <td>Eddy</td>\n",
       "      <td>Garrison</td>\n",
       "      <td>47</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1129</td>\n",
       "      <td>Marin</td>\n",
       "      <td>Bolton</td>\n",
       "      <td>70</td>\n",
       "      <td>Baton Rouge, LA MSA</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>1172</td>\n",
       "      <td>Weldon</td>\n",
       "      <td>Robinson</td>\n",
       "      <td>72</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>1180</td>\n",
       "      <td>Wonda</td>\n",
       "      <td>Hays</td>\n",
       "      <td>24</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>1186</td>\n",
       "      <td>Josiah</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>55</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>1191</td>\n",
       "      <td>Zack</td>\n",
       "      <td>Waters</td>\n",
       "      <td>75</td>\n",
       "      <td>St. Louis, MO-IL MSA</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>1220</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Strong</td>\n",
       "      <td>26</td>\n",
       "      <td>Cincinnati, OH-KY-IN MSA</td>\n",
       "      <td>2018-04-22</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>246</td>\n",
       "      <td>1246</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>Moran</td>\n",
       "      <td>69</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL MSA</td>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>269</td>\n",
       "      <td>1269</td>\n",
       "      <td>Irving</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>39</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX MSA</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281</td>\n",
       "      <td>1281</td>\n",
       "      <td>Zack</td>\n",
       "      <td>Hardy</td>\n",
       "      <td>39</td>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC MSA</td>\n",
       "      <td>2018-07-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>296</td>\n",
       "      <td>1296</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Conley</td>\n",
       "      <td>36</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>1298</td>\n",
       "      <td>Loyce</td>\n",
       "      <td>Cooley</td>\n",
       "      <td>53</td>\n",
       "      <td>Nashville-Davidson–Murfreesboro–Franklin, TN MSA</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1300</td>\n",
       "      <td>Rene</td>\n",
       "      <td>Frost</td>\n",
       "      <td>23</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>315</td>\n",
       "      <td>1315</td>\n",
       "      <td>Mora</td>\n",
       "      <td>Schroeder</td>\n",
       "      <td>45</td>\n",
       "      <td>Raleigh-Cary, NC MSA</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>358</td>\n",
       "      <td>1358</td>\n",
       "      <td>Vaughn</td>\n",
       "      <td>Maldonado</td>\n",
       "      <td>20</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1363</td>\n",
       "      <td>Sandee</td>\n",
       "      <td>Lott</td>\n",
       "      <td>33</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>1402</td>\n",
       "      <td>Jesus</td>\n",
       "      <td>Berry</td>\n",
       "      <td>25</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>414</td>\n",
       "      <td>1414</td>\n",
       "      <td>Georgianne</td>\n",
       "      <td>Herring</td>\n",
       "      <td>30</td>\n",
       "      <td>Urban Honolulu, HI MSA</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>1416</td>\n",
       "      <td>Deja</td>\n",
       "      <td>Moss</td>\n",
       "      <td>25</td>\n",
       "      <td>Minneapolis-St. Paul-Bloomington, MN-WI MSA</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>441</td>\n",
       "      <td>1441</td>\n",
       "      <td>Piedad</td>\n",
       "      <td>Myers</td>\n",
       "      <td>23</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-08-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>1451</td>\n",
       "      <td>Lenard</td>\n",
       "      <td>Harding</td>\n",
       "      <td>53</td>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC MSA</td>\n",
       "      <td>2018-02-15</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>466</td>\n",
       "      <td>1466</td>\n",
       "      <td>Alphonse</td>\n",
       "      <td>Harmon</td>\n",
       "      <td>56</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>467</td>\n",
       "      <td>1467</td>\n",
       "      <td>Nickolas</td>\n",
       "      <td>Deleon</td>\n",
       "      <td>64</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>1491</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  first_name  last_name  age  \\\n",
       "6       1006      Jesusa   Bradford   73   \n",
       "12      1012     Jonelle    Mcbride   59   \n",
       "22      1022          Bo       Snow   73   \n",
       "40      1040       Paris     Santos   72   \n",
       "50      1050        Jone       Owen   23   \n",
       "54      1054       Denis      Frost   50   \n",
       "67      1067        Robt      Allen   37   \n",
       "83      1083      Lyndia    Acevedo   65   \n",
       "84      1084       Wiley   Mckinney   21   \n",
       "94      1094      Walter   Petersen   54   \n",
       "106     1106        Eddy   Garrison   47   \n",
       "129     1129       Marin     Bolton   70   \n",
       "172     1172      Weldon   Robinson   72   \n",
       "180     1180       Wonda       Hays   24   \n",
       "186     1186      Josiah    Douglas   55   \n",
       "191     1191        Zack     Waters   75   \n",
       "220     1220        Jack     Strong   26   \n",
       "246     1246      Stevie      Moran   69   \n",
       "269     1269      Irving   Thompson   39   \n",
       "281     1281        Zack      Hardy   39   \n",
       "296     1296      Weston     Conley   36   \n",
       "298     1298       Loyce     Cooley   53   \n",
       "300     1300        Rene      Frost   23   \n",
       "315     1315        Mora  Schroeder   45   \n",
       "358     1358      Vaughn  Maldonado   20   \n",
       "363     1363      Sandee       Lott   33   \n",
       "402     1402       Jesus      Berry   25   \n",
       "414     1414  Georgianne    Herring   30   \n",
       "416     1416        Deja       Moss   25   \n",
       "441     1441      Piedad      Myers   23   \n",
       "451     1451      Lenard    Harding   53   \n",
       "466     1466    Alphonse     Harmon   56   \n",
       "467     1467    Nickolas     Deleon   64   \n",
       "491     1491      Terina     Jensen   22   \n",
       "\n",
       "                                                 city    reg_date    tariff  \\\n",
       "6              San Francisco-Oakland-Berkeley, CA MSA  2018-11-27  ultimate   \n",
       "12             Chicago-Naperville-Elgin, IL-IN-WI MSA  2018-06-28      surf   \n",
       "22          New York-Newark-Jersey City, NY-NJ-PA MSA  2018-04-20      surf   \n",
       "40    Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA  2018-12-23      surf   \n",
       "50      Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-03-20  ultimate   \n",
       "54                      Phoenix-Mesa-Chandler, AZ MSA  2018-04-21      surf   \n",
       "67                      Grand Rapids-Kentwood, MI MSA  2018-09-24      surf   \n",
       "83             Los Angeles-Long Beach-Anaheim, CA MSA  2018-10-26      surf   \n",
       "84      Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-06-04      surf   \n",
       "94             Los Angeles-Long Beach-Anaheim, CA MSA  2018-10-30      surf   \n",
       "106         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-05-10      surf   \n",
       "129                               Baton Rouge, LA MSA  2018-11-10      surf   \n",
       "172                Boston-Cambridge-Newton, MA-NH MSA  2018-03-30      surf   \n",
       "180         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-12-01      surf   \n",
       "186              Las Vegas-Henderson-Paradise, NV MSA  2018-12-22  ultimate   \n",
       "191                              St. Louis, MO-IL MSA  2018-01-21  ultimate   \n",
       "220                          Cincinnati, OH-KY-IN MSA  2018-04-22      surf   \n",
       "246           Tampa-St. Petersburg-Clearwater, FL MSA  2018-01-09  ultimate   \n",
       "269               Dallas-Fort Worth-Arlington, TX MSA  2018-09-13  ultimate   \n",
       "281             Charlotte-Concord-Gastonia, NC-SC MSA  2018-07-23      surf   \n",
       "296     Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-10-20  ultimate   \n",
       "298  Nashville-Davidson–Murfreesboro–Franklin, TN MSA  2018-09-21      surf   \n",
       "300         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-10-21      surf   \n",
       "315                              Raleigh-Cary, NC MSA  2018-04-26      surf   \n",
       "358         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-04-26      surf   \n",
       "363                   Seattle-Tacoma-Bellevue, WA MSA  2018-01-05  ultimate   \n",
       "402         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-06-11      surf   \n",
       "414                            Urban Honolulu, HI MSA  2018-03-03      surf   \n",
       "416       Minneapolis-St. Paul-Bloomington, MN-WI MSA  2018-10-03      surf   \n",
       "441             Atlanta-Sandy Springs-Roswell, GA MSA  2018-03-08  ultimate   \n",
       "451             Charlotte-Concord-Gastonia, NC-SC MSA  2018-02-15  ultimate   \n",
       "466            Los Angeles-Long Beach-Anaheim, CA MSA  2018-01-26      surf   \n",
       "467             Atlanta-Sandy Springs-Roswell, GA MSA  2018-01-31  ultimate   \n",
       "491                     Grand Rapids-Kentwood, MI MSA  2018-04-27      surf   \n",
       "\n",
       "     churn_date  \n",
       "6    2018-12-18  \n",
       "12   2018-11-16  \n",
       "22   2018-09-07  \n",
       "40   2018-12-30  \n",
       "50   2018-10-07  \n",
       "54   2018-12-31  \n",
       "67   2018-11-24  \n",
       "83   2018-12-18  \n",
       "84   2018-11-11  \n",
       "94   2018-12-12  \n",
       "106  2018-11-14  \n",
       "129  2018-12-27  \n",
       "172  2018-11-29  \n",
       "180  2018-12-22  \n",
       "186  2018-12-31  \n",
       "191  2018-11-30  \n",
       "220  2018-10-13  \n",
       "246  2018-07-31  \n",
       "269  2018-12-15  \n",
       "281  2018-11-14  \n",
       "296  2018-12-18  \n",
       "298  2018-12-19  \n",
       "300  2018-12-19  \n",
       "315  2018-10-03  \n",
       "358  2018-10-22  \n",
       "363  2018-08-16  \n",
       "402  2018-12-26  \n",
       "414  2018-09-01  \n",
       "416  2018-11-21  \n",
       "441  2018-08-19  \n",
       "451  2018-12-10  \n",
       "466  2018-09-17  \n",
       "467  2018-11-18  \n",
       "491  2018-09-18  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_finish = users[users.notnull().all(axis=1)]\n",
    "users_finish"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 34 users that ended their time with Megaline. I will now see if I can find which of them has services after their time was finished with Megaline. These are errors and I will get rid of those users. But first I will change the months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values. The beginning of the year had less amount of use then the end of the year. According to churn_date most of the users are still on their plans because we only have 34 in churn_date meaning only 34 users stopped using the service.\n",
    "\n",
    "A lot of users did not have any duration of calls and a few also never used data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion based on Review\n",
    "\n",
    "No missing values necessarily. Churn Date NaN just means that the person continues having the service. The beginning of the year had less amount of use then the end of the year. According to churn_date most of the users are still on their plans because we only have 34 in churn_date meaning only 34 users stopped using the service. \n",
    "\n",
    "But when checking one of those users 1491 I noticed that even though they ended their service on 2018-09-18 they were still seeming to be using the service on a few different occasions after the termination. This may mean that the whole system of churn_date may not be helpful for analysis. I will be dropping it later anyway because of its missing values\n",
    "\n",
    "A lot of users did not have any duration of calls and a few also never used data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 Converting types, Pivot, Merging. Preparing Data for calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['call_date'] = pd.to_datetime(calls['call_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['month'] = calls['call_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['session_date'] = pd.to_datetime(internet['session_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "users['churn_date'] = pd.to_datetime(users['churn_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['month'] = internet['session_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages['message_date'] = pd.to_datetime(messages['message_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages['month'] = messages['message_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "users['churn_date'] = users['churn_date'].dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "converted the dates that were object type to datetime type so I can pull the month from them.\n",
    "\n",
    "I also did this for churn_date in users data and will now check which users to drop based on if their end date has any data after they ended the service because this will be an error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The number of calls made and minutes used per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>116.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>171.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>49</td>\n",
       "      <td>297.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>374.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>404.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>324.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1499</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>330.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>363.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>288.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>468.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               count     sum\n",
       "user_id month               \n",
       "1000    12        16  116.83\n",
       "1001    8         27  171.14\n",
       "        9         49  297.69\n",
       "        10        65  374.11\n",
       "        11        64  404.59\n",
       "...              ...     ...\n",
       "1498    12        39  324.77\n",
       "1499    9         41  330.37\n",
       "        10        53  363.28\n",
       "        11        45  288.56\n",
       "        12        65  468.10\n",
       "\n",
       "[2258 rows x 2 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_grouped = calls.pivot_table(index = ['user_id', 'month'], values = 'duration', aggfunc = ('count', 'sum'))\n",
    "calls_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_grouped.columns = ['calls', 'minutes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Made a pivot table to find the number of calls made and the minutes used per month for each user. Then changed the columns of count and sum to calls and minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The number of text messages sent per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1001</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1496</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1497</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id\n",
       "user_id month    \n",
       "1000    12     11\n",
       "1001    8      30\n",
       "        9      44\n",
       "        10     53\n",
       "        11     36\n",
       "...            ..\n",
       "1496    9      21\n",
       "        10     18\n",
       "        11     13\n",
       "        12     11\n",
       "1497    12     50\n",
       "\n",
       "[1806 rows x 1 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_grouped = messages.pivot_table(index = ['user_id', 'month'], values = 'id', aggfunc = ('count'))\n",
    "messages_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_grouped.columns = ['messages']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Made a pivot table to get the number of text messages per month per user.\n",
    "Then changed the column name id to messages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The volume of data per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>1901.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1001</td>\n",
       "      <td>8</td>\n",
       "      <td>6919.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13314.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>22330.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>18504.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>23137.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">1499</td>\n",
       "      <td>9</td>\n",
       "      <td>12984.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>19492.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>16813.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>22059.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2277 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mb_used\n",
       "user_id month          \n",
       "1000    12      1901.47\n",
       "1001    8       6919.15\n",
       "        9      13314.82\n",
       "        10     22330.49\n",
       "        11     18504.30\n",
       "...                 ...\n",
       "1498    12     23137.69\n",
       "1499    9      12984.76\n",
       "        10     19492.43\n",
       "        11     16813.83\n",
       "        12     22059.21\n",
       "\n",
       "[2277 rows x 1 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_grouped = internet.pivot_table(index = ['user_id','month'], values = 'mb_used', aggfunc = ('sum'))\n",
    "internet_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Made a pivot table to get the volume of data used per month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging internet usage, calls, and messages per month usage to one table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>16.0</td>\n",
       "      <td>116.83</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>27.0</td>\n",
       "      <td>171.14</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>49.0</td>\n",
       "      <td>297.69</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>65.0</td>\n",
       "      <td>374.11</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>64.0</td>\n",
       "      <td>404.59</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>2288</td>\n",
       "      <td>1470</td>\n",
       "      <td>11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>602.30</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2289</td>\n",
       "      <td>1470</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78.0</td>\n",
       "      <td>560.44</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2290</td>\n",
       "      <td>1484</td>\n",
       "      <td>11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2291</td>\n",
       "      <td>1491</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40.45</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2292</td>\n",
       "      <td>1482</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month   mb_used  calls  minutes  messages\n",
       "0        1000     12   1901.47   16.0   116.83      11.0\n",
       "1        1001      8   6919.15   27.0   171.14      30.0\n",
       "2        1001      9  13314.82   49.0   297.69      44.0\n",
       "3        1001     10  22330.49   65.0   374.11      53.0\n",
       "4        1001     11  18504.30   64.0   404.59      36.0\n",
       "...       ...    ...       ...    ...      ...       ...\n",
       "2288     1470     11      0.00   81.0   602.30     156.0\n",
       "2289     1470     12      0.00   78.0   560.44     153.0\n",
       "2290     1484     11      0.00    2.0    37.50       1.0\n",
       "2291     1491      4      0.00    8.0    40.45       6.0\n",
       "2292     1482     10      0.00    0.0     0.00       2.0\n",
       "\n",
       "[2293 rows x 6 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_calls = pd.merge( left = internet_grouped, right = calls_grouped, how ='outer', on=['user_id', 'month']).fillna(0)\n",
    "\n",
    "usage_per_month =pd.merge( left = internet_calls, right = messages_grouped, how='outer', on=['user_id', 'month']).fillna(0).reset_index()\n",
    "usage_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2293 entries, 0 to 2292\n",
      "Data columns (total 6 columns):\n",
      "user_id     2293 non-null int64\n",
      "month       2293 non-null int64\n",
      "mb_used     2293 non-null float64\n",
      "calls       2293 non-null float64\n",
      "minutes     2293 non-null float64\n",
      "messages    2293 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 107.6 KB\n"
     ]
    }
   ],
   "source": [
    "usage_per_month.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Merged the tables with all the calculations. Some users didn't really take adavantage of the plan it seems and User 1001 has been very active"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_plans = users[['user_id','tariff']]\n",
    "users_plans.columns = ['user_id','plan_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_plans1 = pd.merge(left=users_plans,right= plans,how='outer',on='plan_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>plan_name</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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>ultimate</td>\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>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",
       "      <td>1</td>\n",
       "      <td>1006</td>\n",
       "      <td>ultimate</td>\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>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>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1008</td>\n",
       "      <td>ultimate</td>\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>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",
       "      <td>3</td>\n",
       "      <td>1011</td>\n",
       "      <td>ultimate</td>\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>Halina</td>\n",
       "      <td>Henry</td>\n",
       "      <td>73</td>\n",
       "      <td>Cleveland-Elyria, OH MSA</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1013</td>\n",
       "      <td>ultimate</td>\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>Nicolas</td>\n",
       "      <td>Snider</td>\n",
       "      <td>50</td>\n",
       "      <td>Knoxville, TN MSA</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>495</td>\n",
       "      <td>1494</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Preston</td>\n",
       "      <td>Downs</td>\n",
       "      <td>70</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>1495</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>1496</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>1498</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id plan_name  messages_included  mb_per_month_included  \\\n",
       "0       1000  ultimate               1000                  30720   \n",
       "1       1006  ultimate               1000                  30720   \n",
       "2       1008  ultimate               1000                  30720   \n",
       "3       1011  ultimate               1000                  30720   \n",
       "4       1013  ultimate               1000                  30720   \n",
       "..       ...       ...                ...                    ...   \n",
       "495     1494      surf                 50                  15360   \n",
       "496     1495      surf                 50                  15360   \n",
       "497     1496      surf                 50                  15360   \n",
       "498     1498      surf                 50                  15360   \n",
       "499     1499      surf                 50                  15360   \n",
       "\n",
       "     minutes_included  usd_monthly_pay  usd_per_gb  usd_per_message  \\\n",
       "0                3000               70           7             0.01   \n",
       "1                3000               70           7             0.01   \n",
       "2                3000               70           7             0.01   \n",
       "3                3000               70           7             0.01   \n",
       "4                3000               70           7             0.01   \n",
       "..                ...              ...         ...              ...   \n",
       "495               500               20          10             0.03   \n",
       "496               500               20          10             0.03   \n",
       "497               500               20          10             0.03   \n",
       "498               500               20          10             0.03   \n",
       "499               500               20          10             0.03   \n",
       "\n",
       "     usd_per_minute first_name   last_name  age  \\\n",
       "0              0.01   Anamaria       Bauer   45   \n",
       "1              0.01     Jesusa    Bradford   73   \n",
       "2              0.01      Emely     Hoffman   53   \n",
       "3              0.01     Halina       Henry   73   \n",
       "4              0.01    Nicolas      Snider   50   \n",
       "..              ...        ...         ...  ...   \n",
       "495            0.03    Preston       Downs   70   \n",
       "496            0.03      Fidel      Sharpe   67   \n",
       "497            0.03      Ariel    Shepherd   49   \n",
       "498            0.03       Scot  Williamson   51   \n",
       "499            0.03      Shena     Dickson   37   \n",
       "\n",
       "                                          city    reg_date    tariff  \\\n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA  2018-12-24  ultimate   \n",
       "1       San Francisco-Oakland-Berkeley, CA MSA  2018-11-27  ultimate   \n",
       "2            Orlando-Kissimmee-Sanford, FL MSA  2018-08-03  ultimate   \n",
       "3                     Cleveland-Elyria, OH MSA  2018-01-18  ultimate   \n",
       "4                            Knoxville, TN MSA  2018-12-01  ultimate   \n",
       "..                                         ...         ...       ...   \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-04-19      surf   \n",
       "496  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-09-04      surf   \n",
       "497               New Orleans-Metairie, LA MSA  2018-02-20      surf   \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  \n",
       "0           NaN  \n",
       "1          12.0  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "..          ...  \n",
       "495         NaN  \n",
       "496         NaN  \n",
       "497         NaN  \n",
       "498         NaN  \n",
       "499         NaN  \n",
       "\n",
       "[500 rows x 16 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_plans2 = pd.merge(left=users_plans1, right = users, how='outer', on='user_id')\n",
    "users_plans2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</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>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>16.0</td>\n",
       "      <td>116.83</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</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",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>27.0</td>\n",
       "      <td>171.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>49.0</td>\n",
       "      <td>297.69</td>\n",
       "      <td>44.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>65.0</td>\n",
       "      <td>374.11</td>\n",
       "      <td>53.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>64.0</td>\n",
       "      <td>404.59</td>\n",
       "      <td>36.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2298</td>\n",
       "      <td>1143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Lorina</td>\n",
       "      <td>Stevens</td>\n",
       "      <td>69</td>\n",
       "      <td>Cincinnati, OH-KY-IN MSA</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2299</td>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Kristopher</td>\n",
       "      <td>Lang</td>\n",
       "      <td>28</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2300</td>\n",
       "      <td>1319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Eliseo</td>\n",
       "      <td>Carson</td>\n",
       "      <td>21</td>\n",
       "      <td>Colorado Springs, CO MSA</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2301</td>\n",
       "      <td>1378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Mckinley</td>\n",
       "      <td>Clayton</td>\n",
       "      <td>22</td>\n",
       "      <td>Denver-Aurora-Lakewood, CO MSA</td>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2302</td>\n",
       "      <td>1473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Kirk</td>\n",
       "      <td>Velez</td>\n",
       "      <td>61</td>\n",
       "      <td>Louisville/Jefferson County, KY-IN MSA</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month   mb_used  calls  minutes  messages plan_name  \\\n",
       "0        1000   12.0   1901.47   16.0   116.83      11.0  ultimate   \n",
       "1        1001    8.0   6919.15   27.0   171.14      30.0      surf   \n",
       "2        1001    9.0  13314.82   49.0   297.69      44.0      surf   \n",
       "3        1001   10.0  22330.49   65.0   374.11      53.0      surf   \n",
       "4        1001   11.0  18504.30   64.0   404.59      36.0      surf   \n",
       "...       ...    ...       ...    ...      ...       ...       ...   \n",
       "2298     1143    NaN       NaN    NaN      NaN       NaN      surf   \n",
       "2299     1307    NaN       NaN    NaN      NaN       NaN      surf   \n",
       "2300     1319    NaN       NaN    NaN      NaN       NaN      surf   \n",
       "2301     1378    NaN       NaN    NaN      NaN       NaN      surf   \n",
       "2302     1473    NaN       NaN    NaN      NaN       NaN      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  \\\n",
       "0                  1000                  30720              3000  ...   \n",
       "1                    50                  15360               500  ...   \n",
       "2                    50                  15360               500  ...   \n",
       "3                    50                  15360               500  ...   \n",
       "4                    50                  15360               500  ...   \n",
       "...                 ...                    ...               ...  ...   \n",
       "2298                 50                  15360               500  ...   \n",
       "2299                 50                  15360               500  ...   \n",
       "2300                 50                  15360               500  ...   \n",
       "2301                 50                  15360               500  ...   \n",
       "2302                 50                  15360               500  ...   \n",
       "\n",
       "      usd_per_gb  usd_per_message  usd_per_minute  first_name  last_name age  \\\n",
       "0              7             0.01            0.01    Anamaria      Bauer  45   \n",
       "1             10             0.03            0.03      Mickey  Wilkerson  28   \n",
       "2             10             0.03            0.03      Mickey  Wilkerson  28   \n",
       "3             10             0.03            0.03      Mickey  Wilkerson  28   \n",
       "4             10             0.03            0.03      Mickey  Wilkerson  28   \n",
       "...          ...              ...             ...         ...        ...  ..   \n",
       "2298          10             0.03            0.03      Lorina    Stevens  69   \n",
       "2299          10             0.03            0.03  Kristopher       Lang  28   \n",
       "2300          10             0.03            0.03      Eliseo     Carson  21   \n",
       "2301          10             0.03            0.03    Mckinley    Clayton  22   \n",
       "2302          10             0.03            0.03        Kirk      Velez  61   \n",
       "\n",
       "                                        city    reg_date    tariff churn_date  \n",
       "0      Atlanta-Sandy Springs-Roswell, GA MSA  2018-12-24  ultimate        NaN  \n",
       "1            Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf        NaN  \n",
       "2            Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf        NaN  \n",
       "3            Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf        NaN  \n",
       "4            Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf        NaN  \n",
       "...                                      ...         ...       ...        ...  \n",
       "2298                Cincinnati, OH-KY-IN MSA  2018-10-26      surf        NaN  \n",
       "2299      Boston-Cambridge-Newton, MA-NH MSA  2018-12-31      surf        NaN  \n",
       "2300                Colorado Springs, CO MSA  2018-06-17      surf        NaN  \n",
       "2301          Denver-Aurora-Lakewood, CO MSA  2018-12-17      surf        NaN  \n",
       "2302  Louisville/Jefferson County, KY-IN MSA  2018-12-31      surf        NaN  \n",
       "\n",
       "[2303 rows x 21 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_profit = pd.merge(left=usage_per_month , right= users_plans2, how='outer',on='user_id')\n",
    "data_for_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_for_profit['mb_used'] = np.ceil(data_for_profit['mb_used'])\n",
    "data_for_profit['minutes'] = np.ceil(data_for_profit['minutes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_for_profit['internet'] = data_for_profit['mb_used']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                     0\n",
       "month                      10\n",
       "mb_used                    10\n",
       "calls                      10\n",
       "minutes                    10\n",
       "messages                   10\n",
       "plan_name                   0\n",
       "messages_included           0\n",
       "mb_per_month_included       0\n",
       "minutes_included            0\n",
       "usd_monthly_pay             0\n",
       "usd_per_gb                  0\n",
       "usd_per_message             0\n",
       "usd_per_minute              0\n",
       "first_name                  0\n",
       "last_name                   0\n",
       "age                         0\n",
       "city                        0\n",
       "reg_date                    0\n",
       "tariff                      0\n",
       "churn_date               2143\n",
       "internet                   10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_profit.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Will now work on dropping the users from users that finished their plans but still had data for them. I will also drop the 10 other users from the data because they had no data in the datasets to begin with and that is why they have come up NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
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       "      <th>calls</th>\n",
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       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
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       "      <th>usd_per_minute</th>\n",
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       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>internet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Anamaria</td>\n",
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       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
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       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6920.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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>6920.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>13315.0</td>\n",
       "      <td>49.0</td>\n",
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       "      <td>44.0</td>\n",
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       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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>13315.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22331.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
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       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
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       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22331.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18505.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
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       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
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       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>442.0</td>\n",
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       "      <td>50</td>\n",
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       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2291</td>\n",
       "      <td>1470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2292</td>\n",
       "      <td>1470</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "0        1000   12.0   1902.0   16.0    117.0      11.0  ultimate   \n",
       "1        1001    8.0   6920.0   27.0    172.0      30.0      surf   \n",
       "2        1001    9.0  13315.0   49.0    298.0      44.0      surf   \n",
       "3        1001   10.0  22331.0   65.0    375.0      53.0      surf   \n",
       "4        1001   11.0  18505.0   64.0    405.0      36.0      surf   \n",
       "...       ...    ...      ...    ...      ...       ...       ...   \n",
       "2288     1470    8.0      0.0   84.0    548.0     141.0      surf   \n",
       "2289     1470    9.0      0.0   70.0    442.0     130.0      surf   \n",
       "2290     1470   10.0      0.0   72.0    435.0     145.0      surf   \n",
       "2291     1470   11.0      0.0   81.0    603.0     156.0      surf   \n",
       "2292     1470   12.0      0.0   78.0    561.0     153.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  \\\n",
       "0                  1000                  30720              3000  ...   \n",
       "1                    50                  15360               500  ...   \n",
       "2                    50                  15360               500  ...   \n",
       "3                    50                  15360               500  ...   \n",
       "4                    50                  15360               500  ...   \n",
       "...                 ...                    ...               ...  ...   \n",
       "2288                 50                  15360               500  ...   \n",
       "2289                 50                  15360               500  ...   \n",
       "2290                 50                  15360               500  ...   \n",
       "2291                 50                  15360               500  ...   \n",
       "2292                 50                  15360               500  ...   \n",
       "\n",
       "      usd_per_message  usd_per_minute  first_name  last_name age  \\\n",
       "0                0.01            0.01    Anamaria      Bauer  45   \n",
       "1                0.03            0.03      Mickey  Wilkerson  28   \n",
       "2                0.03            0.03      Mickey  Wilkerson  28   \n",
       "3                0.03            0.03      Mickey  Wilkerson  28   \n",
       "4                0.03            0.03      Mickey  Wilkerson  28   \n",
       "...               ...             ...         ...        ...  ..   \n",
       "2288             0.03            0.03      Gerald      Reese  45   \n",
       "2289             0.03            0.03      Gerald      Reese  45   \n",
       "2290             0.03            0.03      Gerald      Reese  45   \n",
       "2291             0.03            0.03      Gerald      Reese  45   \n",
       "2292             0.03            0.03      Gerald      Reese  45   \n",
       "\n",
       "                                                  city    reg_date    tariff  \\\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                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "3                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "4                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "...                                                ...         ...       ...   \n",
       "2288  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2289  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2290  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2291  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2292  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "\n",
       "     churn_date internet  \n",
       "0           NaN   1902.0  \n",
       "1           NaN   6920.0  \n",
       "2           NaN  13315.0  \n",
       "3           NaN  22331.0  \n",
       "4           NaN  18505.0  \n",
       "...         ...      ...  \n",
       "2288        NaN      0.0  \n",
       "2289        NaN      0.0  \n",
       "2290        NaN      0.0  \n",
       "2291        NaN      0.0  \n",
       "2292        NaN      0.0  \n",
       "\n",
       "[2293 rows x 22 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_profit.dropna(subset = ['month','mb_used','calls', 'minutes', 'messages'], inplace = True) \n",
    "data_for_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\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",
       "      <td>12</td>\n",
       "      <td>1012</td>\n",
       "      <td>Jonelle</td>\n",
       "      <td>Mcbride</td>\n",
       "      <td>59</td>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI MSA</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1022</td>\n",
       "      <td>Bo</td>\n",
       "      <td>Snow</td>\n",
       "      <td>73</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1040</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Santos</td>\n",
       "      <td>72</td>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA</td>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1050</td>\n",
       "      <td>Jone</td>\n",
       "      <td>Owen</td>\n",
       "      <td>23</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-10-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1054</td>\n",
       "      <td>Denis</td>\n",
       "      <td>Frost</td>\n",
       "      <td>50</td>\n",
       "      <td>Phoenix-Mesa-Chandler, AZ MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1067</td>\n",
       "      <td>Robt</td>\n",
       "      <td>Allen</td>\n",
       "      <td>37</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1083</td>\n",
       "      <td>Lyndia</td>\n",
       "      <td>Acevedo</td>\n",
       "      <td>65</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1084</td>\n",
       "      <td>Wiley</td>\n",
       "      <td>Mckinney</td>\n",
       "      <td>21</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1094</td>\n",
       "      <td>Walter</td>\n",
       "      <td>Petersen</td>\n",
       "      <td>54</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1106</td>\n",
       "      <td>Eddy</td>\n",
       "      <td>Garrison</td>\n",
       "      <td>47</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1129</td>\n",
       "      <td>Marin</td>\n",
       "      <td>Bolton</td>\n",
       "      <td>70</td>\n",
       "      <td>Baton Rouge, LA MSA</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>1172</td>\n",
       "      <td>Weldon</td>\n",
       "      <td>Robinson</td>\n",
       "      <td>72</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>1180</td>\n",
       "      <td>Wonda</td>\n",
       "      <td>Hays</td>\n",
       "      <td>24</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>1186</td>\n",
       "      <td>Josiah</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>55</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>1191</td>\n",
       "      <td>Zack</td>\n",
       "      <td>Waters</td>\n",
       "      <td>75</td>\n",
       "      <td>St. Louis, MO-IL MSA</td>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>1220</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Strong</td>\n",
       "      <td>26</td>\n",
       "      <td>Cincinnati, OH-KY-IN MSA</td>\n",
       "      <td>2018-04-22</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>246</td>\n",
       "      <td>1246</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>Moran</td>\n",
       "      <td>69</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL MSA</td>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>269</td>\n",
       "      <td>1269</td>\n",
       "      <td>Irving</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>39</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX MSA</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281</td>\n",
       "      <td>1281</td>\n",
       "      <td>Zack</td>\n",
       "      <td>Hardy</td>\n",
       "      <td>39</td>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC MSA</td>\n",
       "      <td>2018-07-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>296</td>\n",
       "      <td>1296</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Conley</td>\n",
       "      <td>36</td>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>1298</td>\n",
       "      <td>Loyce</td>\n",
       "      <td>Cooley</td>\n",
       "      <td>53</td>\n",
       "      <td>Nashville-Davidson–Murfreesboro–Franklin, TN MSA</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1300</td>\n",
       "      <td>Rene</td>\n",
       "      <td>Frost</td>\n",
       "      <td>23</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>315</td>\n",
       "      <td>1315</td>\n",
       "      <td>Mora</td>\n",
       "      <td>Schroeder</td>\n",
       "      <td>45</td>\n",
       "      <td>Raleigh-Cary, NC MSA</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>358</td>\n",
       "      <td>1358</td>\n",
       "      <td>Vaughn</td>\n",
       "      <td>Maldonado</td>\n",
       "      <td>20</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1363</td>\n",
       "      <td>Sandee</td>\n",
       "      <td>Lott</td>\n",
       "      <td>33</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>1402</td>\n",
       "      <td>Jesus</td>\n",
       "      <td>Berry</td>\n",
       "      <td>25</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>414</td>\n",
       "      <td>1414</td>\n",
       "      <td>Georgianne</td>\n",
       "      <td>Herring</td>\n",
       "      <td>30</td>\n",
       "      <td>Urban Honolulu, HI MSA</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>1416</td>\n",
       "      <td>Deja</td>\n",
       "      <td>Moss</td>\n",
       "      <td>25</td>\n",
       "      <td>Minneapolis-St. Paul-Bloomington, MN-WI MSA</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>441</td>\n",
       "      <td>1441</td>\n",
       "      <td>Piedad</td>\n",
       "      <td>Myers</td>\n",
       "      <td>23</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-08-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>1451</td>\n",
       "      <td>Lenard</td>\n",
       "      <td>Harding</td>\n",
       "      <td>53</td>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC MSA</td>\n",
       "      <td>2018-02-15</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>466</td>\n",
       "      <td>1466</td>\n",
       "      <td>Alphonse</td>\n",
       "      <td>Harmon</td>\n",
       "      <td>56</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>467</td>\n",
       "      <td>1467</td>\n",
       "      <td>Nickolas</td>\n",
       "      <td>Deleon</td>\n",
       "      <td>64</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>1491</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-09-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  first_name  last_name  age  \\\n",
       "6       1006      Jesusa   Bradford   73   \n",
       "12      1012     Jonelle    Mcbride   59   \n",
       "22      1022          Bo       Snow   73   \n",
       "40      1040       Paris     Santos   72   \n",
       "50      1050        Jone       Owen   23   \n",
       "54      1054       Denis      Frost   50   \n",
       "67      1067        Robt      Allen   37   \n",
       "83      1083      Lyndia    Acevedo   65   \n",
       "84      1084       Wiley   Mckinney   21   \n",
       "94      1094      Walter   Petersen   54   \n",
       "106     1106        Eddy   Garrison   47   \n",
       "129     1129       Marin     Bolton   70   \n",
       "172     1172      Weldon   Robinson   72   \n",
       "180     1180       Wonda       Hays   24   \n",
       "186     1186      Josiah    Douglas   55   \n",
       "191     1191        Zack     Waters   75   \n",
       "220     1220        Jack     Strong   26   \n",
       "246     1246      Stevie      Moran   69   \n",
       "269     1269      Irving   Thompson   39   \n",
       "281     1281        Zack      Hardy   39   \n",
       "296     1296      Weston     Conley   36   \n",
       "298     1298       Loyce     Cooley   53   \n",
       "300     1300        Rene      Frost   23   \n",
       "315     1315        Mora  Schroeder   45   \n",
       "358     1358      Vaughn  Maldonado   20   \n",
       "363     1363      Sandee       Lott   33   \n",
       "402     1402       Jesus      Berry   25   \n",
       "414     1414  Georgianne    Herring   30   \n",
       "416     1416        Deja       Moss   25   \n",
       "441     1441      Piedad      Myers   23   \n",
       "451     1451      Lenard    Harding   53   \n",
       "466     1466    Alphonse     Harmon   56   \n",
       "467     1467    Nickolas     Deleon   64   \n",
       "491     1491      Terina     Jensen   22   \n",
       "\n",
       "                                                 city    reg_date    tariff  \\\n",
       "6              San Francisco-Oakland-Berkeley, CA MSA  2018-11-27  ultimate   \n",
       "12             Chicago-Naperville-Elgin, IL-IN-WI MSA  2018-06-28      surf   \n",
       "22          New York-Newark-Jersey City, NY-NJ-PA MSA  2018-04-20      surf   \n",
       "40    Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA  2018-12-23      surf   \n",
       "50      Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-03-20  ultimate   \n",
       "54                      Phoenix-Mesa-Chandler, AZ MSA  2018-04-21      surf   \n",
       "67                      Grand Rapids-Kentwood, MI MSA  2018-09-24      surf   \n",
       "83             Los Angeles-Long Beach-Anaheim, CA MSA  2018-10-26      surf   \n",
       "84      Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-06-04      surf   \n",
       "94             Los Angeles-Long Beach-Anaheim, CA MSA  2018-10-30      surf   \n",
       "106         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-05-10      surf   \n",
       "129                               Baton Rouge, LA MSA  2018-11-10      surf   \n",
       "172                Boston-Cambridge-Newton, MA-NH MSA  2018-03-30      surf   \n",
       "180         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-12-01      surf   \n",
       "186              Las Vegas-Henderson-Paradise, NV MSA  2018-12-22  ultimate   \n",
       "191                              St. Louis, MO-IL MSA  2018-01-21  ultimate   \n",
       "220                          Cincinnati, OH-KY-IN MSA  2018-04-22      surf   \n",
       "246           Tampa-St. Petersburg-Clearwater, FL MSA  2018-01-09  ultimate   \n",
       "269               Dallas-Fort Worth-Arlington, TX MSA  2018-09-13  ultimate   \n",
       "281             Charlotte-Concord-Gastonia, NC-SC MSA  2018-07-23      surf   \n",
       "296     Miami-Fort Lauderdale-West Palm Beach, FL MSA  2018-10-20  ultimate   \n",
       "298  Nashville-Davidson–Murfreesboro–Franklin, TN MSA  2018-09-21      surf   \n",
       "300         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-10-21      surf   \n",
       "315                              Raleigh-Cary, NC MSA  2018-04-26      surf   \n",
       "358         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-04-26      surf   \n",
       "363                   Seattle-Tacoma-Bellevue, WA MSA  2018-01-05  ultimate   \n",
       "402         New York-Newark-Jersey City, NY-NJ-PA MSA  2018-06-11      surf   \n",
       "414                            Urban Honolulu, HI MSA  2018-03-03      surf   \n",
       "416       Minneapolis-St. Paul-Bloomington, MN-WI MSA  2018-10-03      surf   \n",
       "441             Atlanta-Sandy Springs-Roswell, GA MSA  2018-03-08  ultimate   \n",
       "451             Charlotte-Concord-Gastonia, NC-SC MSA  2018-02-15  ultimate   \n",
       "466            Los Angeles-Long Beach-Anaheim, CA MSA  2018-01-26      surf   \n",
       "467             Atlanta-Sandy Springs-Roswell, GA MSA  2018-01-31  ultimate   \n",
       "491                     Grand Rapids-Kentwood, MI MSA  2018-04-27      surf   \n",
       "\n",
       "     churn_date  \n",
       "6    2018-12-18  \n",
       "12   2018-11-16  \n",
       "22   2018-09-07  \n",
       "40   2018-12-30  \n",
       "50   2018-10-07  \n",
       "54   2018-12-31  \n",
       "67   2018-11-24  \n",
       "83   2018-12-18  \n",
       "84   2018-11-11  \n",
       "94   2018-12-12  \n",
       "106  2018-11-14  \n",
       "129  2018-12-27  \n",
       "172  2018-11-29  \n",
       "180  2018-12-22  \n",
       "186  2018-12-31  \n",
       "191  2018-11-30  \n",
       "220  2018-10-13  \n",
       "246  2018-07-31  \n",
       "269  2018-12-15  \n",
       "281  2018-11-14  \n",
       "296  2018-12-18  \n",
       "298  2018-12-19  \n",
       "300  2018-12-19  \n",
       "315  2018-10-03  \n",
       "358  2018-10-22  \n",
       "363  2018-08-16  \n",
       "402  2018-12-26  \n",
       "414  2018-09-01  \n",
       "416  2018-11-21  \n",
       "441  2018-08-19  \n",
       "451  2018-12-10  \n",
       "466  2018-09-17  \n",
       "467  2018-11-18  \n",
       "491  2018-09-18  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_finish"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checked all of the users in users_finish with query. results below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</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>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>internet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2236</td>\n",
       "      <td>1491</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6465.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2237</td>\n",
       "      <td>1491</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11035.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2238</td>\n",
       "      <td>1491</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7834.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2239</td>\n",
       "      <td>1491</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10246.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2240</td>\n",
       "      <td>1491</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7041.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2241</td>\n",
       "      <td>1491</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8481.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2242</td>\n",
       "      <td>1491</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5186.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2243</td>\n",
       "      <td>1491</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6767.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2244</td>\n",
       "      <td>1491</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Terina</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>22</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "2236     1491    5.0   6465.0   48.0    410.0      45.0      surf   \n",
       "2237     1491    6.0  11035.0   64.0    374.0      54.0      surf   \n",
       "2238     1491    7.0   7834.0   60.0    438.0      64.0      surf   \n",
       "2239     1491    8.0  10246.0   54.0    363.0      50.0      surf   \n",
       "2240     1491    9.0   7041.0   52.0    396.0      50.0      surf   \n",
       "2241     1491   10.0   8481.0   75.0    552.0      51.0      surf   \n",
       "2242     1491   11.0   5186.0   46.0    390.0      46.0      surf   \n",
       "2243     1491   12.0   6767.0   55.0    375.0      43.0      surf   \n",
       "2244     1491    4.0      0.0    8.0     41.0       6.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  \\\n",
       "2236                 50                  15360               500  ...   \n",
       "2237                 50                  15360               500  ...   \n",
       "2238                 50                  15360               500  ...   \n",
       "2239                 50                  15360               500  ...   \n",
       "2240                 50                  15360               500  ...   \n",
       "2241                 50                  15360               500  ...   \n",
       "2242                 50                  15360               500  ...   \n",
       "2243                 50                  15360               500  ...   \n",
       "2244                 50                  15360               500  ...   \n",
       "\n",
       "      usd_per_message  usd_per_minute  first_name  last_name age  \\\n",
       "2236             0.03            0.03      Terina     Jensen  22   \n",
       "2237             0.03            0.03      Terina     Jensen  22   \n",
       "2238             0.03            0.03      Terina     Jensen  22   \n",
       "2239             0.03            0.03      Terina     Jensen  22   \n",
       "2240             0.03            0.03      Terina     Jensen  22   \n",
       "2241             0.03            0.03      Terina     Jensen  22   \n",
       "2242             0.03            0.03      Terina     Jensen  22   \n",
       "2243             0.03            0.03      Terina     Jensen  22   \n",
       "2244             0.03            0.03      Terina     Jensen  22   \n",
       "\n",
       "                               city    reg_date tariff churn_date internet  \n",
       "2236  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   6465.0  \n",
       "2237  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0  11035.0  \n",
       "2238  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   7834.0  \n",
       "2239  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0  10246.0  \n",
       "2240  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   7041.0  \n",
       "2241  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   8481.0  \n",
       "2242  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   5186.0  \n",
       "2243  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0   6767.0  \n",
       "2244  Grand Rapids-Kentwood, MI MSA  2018-04-27   surf        9.0      0.0  \n",
       "\n",
       "[9 rows x 22 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_1491_finish = data_for_profit.query('user_id == \"1491\"')\n",
    "users_1491_finish"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User 1012 has an issue at 54 because he finished in month 11. \n",
    "\n",
    "User 1022 has a problem at 80, 81, and 82 because he finished in month 9. \n",
    "\n",
    "User 1050 has a problem at 213 and 214 because he finished in month 10. \n",
    "\n",
    "User 1067 has issue with 312 because he finished in month 11. \n",
    "\n",
    "User 1084 has an issue at 409 because he finished in month 11. \n",
    "\n",
    "User 1106 stopped in month 11 so he has an issue at 504.\n",
    "\n",
    "User 1172 stopped at month 11 so issue with 830. \n",
    "\n",
    "User 1191 stopped in month 11 so issue at 920. \n",
    "\n",
    "User 1220 stopped in month 10 so issue in 1052 and 1053. \n",
    "\n",
    "User 1246 stopped in month 7 so issue in 1157 and 1158. \n",
    "\n",
    "User 1281 finished in month 11 so issue in 1323. \n",
    "\n",
    "User 1315 stopped in month 10 but has data in 11 and 12 month issue is in 1444 and 1445. \n",
    "\n",
    "User 1358 stopped in month 10 so issue in 1654 and 1655. \n",
    "\n",
    "User 1363 stopped in month 8 so issue in all his data which is month 9, 10, 11, 12 or 1688, 1689, 1690, 1691. \n",
    "\n",
    "User 1414 stopped in month 9 so has issue in 1941, 1942, and 1943. \n",
    "\n",
    "User 1416 stopped in month 11 so issue in 1952. \n",
    "\n",
    "User 1441 stopped in month 8 so issue in 2053 and 2054. \n",
    "\n",
    "User 1466 stopped in month 9 so issues in 2145, 2146, and 2147. \n",
    "\n",
    "User 1467 stopped in month 11 so has issue in 2156. \n",
    "\n",
    "User 1491 stopped in month 9 so has an issue in 2241, 2242, 2243.\n",
    "\n",
    "Will now drop the problematic rows from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
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       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</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>1902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6920.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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>6920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13315.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
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       "      <td>0.03</td>\n",
       "      <td>0.03</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>13315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22331.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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>22331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18505.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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>18505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2288</td>\n",
       "      <td>1470</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2289</td>\n",
       "      <td>1470</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2290</td>\n",
       "      <td>1470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2291</td>\n",
       "      <td>1470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2292</td>\n",
       "      <td>1470</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>Reese</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2256 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "0        1000   12.0   1902.0   16.0    117.0      11.0  ultimate   \n",
       "1        1001    8.0   6920.0   27.0    172.0      30.0      surf   \n",
       "2        1001    9.0  13315.0   49.0    298.0      44.0      surf   \n",
       "3        1001   10.0  22331.0   65.0    375.0      53.0      surf   \n",
       "4        1001   11.0  18505.0   64.0    405.0      36.0      surf   \n",
       "...       ...    ...      ...    ...      ...       ...       ...   \n",
       "2288     1470    8.0      0.0   84.0    548.0     141.0      surf   \n",
       "2289     1470    9.0      0.0   70.0    442.0     130.0      surf   \n",
       "2290     1470   10.0      0.0   72.0    435.0     145.0      surf   \n",
       "2291     1470   11.0      0.0   81.0    603.0     156.0      surf   \n",
       "2292     1470   12.0      0.0   78.0    561.0     153.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  \\\n",
       "0                  1000                  30720              3000  ...   \n",
       "1                    50                  15360               500  ...   \n",
       "2                    50                  15360               500  ...   \n",
       "3                    50                  15360               500  ...   \n",
       "4                    50                  15360               500  ...   \n",
       "...                 ...                    ...               ...  ...   \n",
       "2288                 50                  15360               500  ...   \n",
       "2289                 50                  15360               500  ...   \n",
       "2290                 50                  15360               500  ...   \n",
       "2291                 50                  15360               500  ...   \n",
       "2292                 50                  15360               500  ...   \n",
       "\n",
       "      usd_per_message  usd_per_minute  first_name  last_name age  \\\n",
       "0                0.01            0.01    Anamaria      Bauer  45   \n",
       "1                0.03            0.03      Mickey  Wilkerson  28   \n",
       "2                0.03            0.03      Mickey  Wilkerson  28   \n",
       "3                0.03            0.03      Mickey  Wilkerson  28   \n",
       "4                0.03            0.03      Mickey  Wilkerson  28   \n",
       "...               ...             ...         ...        ...  ..   \n",
       "2288             0.03            0.03      Gerald      Reese  45   \n",
       "2289             0.03            0.03      Gerald      Reese  45   \n",
       "2290             0.03            0.03      Gerald      Reese  45   \n",
       "2291             0.03            0.03      Gerald      Reese  45   \n",
       "2292             0.03            0.03      Gerald      Reese  45   \n",
       "\n",
       "                                                  city    reg_date    tariff  \\\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                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "3                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "4                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "...                                                ...         ...       ...   \n",
       "2288  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2289  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2290  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2291  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2292  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "\n",
       "     churn_date internet  \n",
       "0           NaN   1902.0  \n",
       "1           NaN   6920.0  \n",
       "2           NaN  13315.0  \n",
       "3           NaN  22331.0  \n",
       "4           NaN  18505.0  \n",
       "...         ...      ...  \n",
       "2288        NaN      0.0  \n",
       "2289        NaN      0.0  \n",
       "2290        NaN      0.0  \n",
       "2291        NaN      0.0  \n",
       "2292        NaN      0.0  \n",
       "\n",
       "[2256 rows x 22 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_profit_clean = data_for_profit.drop([54, 80, 81, 82, 213, 214, 312, 409, 504, 830, 920, 1052, 1053, 1157, 1158, 1323, 1444, 1445, 1654, 1655, 1688, 1689, 1690, 1691, 1941, 1942, 1943, 1952, 2053, 2054, 2145, 2146, 2147, 2156, 2241, 2242, 2243])\n",
    "data_for_profit_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "In order to make calculations and make things more clear I will round up the megabytes and the minutes when calculating profits. I also changed mb_used to internet to show internet data usage.\n",
    "\n",
    "I merged all the tables together of plans, users, and the rest in order to make sure I can get a good analysis later. I got rid of churn_date because it gave me so many NaNs and is not going to be helpful for analysis. I also decided to get rid of the 10 users who were coming up with NaN in order to clean up for analysis better. They are only 10 users and its 5 surf and 4 ultimate so pretty equal distribution. They did not come with data to begin with and that is why they have NaN.\n",
    "\n",
    "I also dropped the problematic User months from the data that had data after they had already ended their service with Megaline. Why Megaline still had the system getting information from them is probably a glitch in the system. I left those that finished their plan in the 12th month still because they would have still gotten charged at the end of that month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.1  Profit Calculations\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The monthly profit from each user\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def month_prof(row):\n",
    "    #plan = row['plan']\n",
    "    #minutes = row['minutes']\n",
    "    #message = row['messages']\n",
    "    #internet = row['internet']\n",
    "    #monthly_charge_surf = 20\n",
    "    #monthly_charge_ultimate = 70\n",
    "    #calls_profit = 0\n",
    "    #message_profit = 0\n",
    "    #internet_profit = 0\n",
    "    \n",
    " \n",
    "\n",
    "    #if plan == 'surf':\n",
    "        #if minutes > 500:\n",
    "            #calls_profit = math.ceil(minutes - 500)*0.03\n",
    "        #if message > 50:  \n",
    "            #message_profit = (messages - 50)*0.03\n",
    "        #if internet > 15360:\n",
    "            #internet_profit = (math.ceil(internet - 15360)/1024)*10  \n",
    "        \n",
    "    #return monthly_charge_surf + calls_profit + message_profit + internet_profit\n",
    "   \n",
    "    #if plan == 'ultimate':\n",
    "        #if minutes > 500:\n",
    "            #calls_profit = math.ceil(minutes - 3000)*0.01\n",
    "        #if message > 1000:\n",
    "            #message_profit = (message - 1000)*0.01\n",
    "        #if internet > 30720:\n",
    "            #internet_profit = (math.ceil(internet - 30720)/1024)*7\n",
    "        \n",
    "    #return monthly_charge_ultimate + calls_profit + message_profit + internet_profit \n",
    "\n",
    "#data['month_profit'] = data.apply(month_prof, axis = 1)\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Failed attempt at getting profit for each user. Get an error and not sure what it is. So I will try a different way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.1 second attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>internet</th>\n",
       "      <th>messages_extra</th>\n",
       "      <th>internet_extra</th>\n",
       "      <th>minutes_extra</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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>1902.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6920.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>6920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13315.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>13315.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22331.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>22331.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18505.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>18505.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2288</td>\n",
       "      <td>1470</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2289</td>\n",
       "      <td>1470</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2290</td>\n",
       "      <td>1470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2291</td>\n",
       "      <td>1470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>26.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2292</td>\n",
       "      <td>1470</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>24.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2256 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "0        1000   12.0   1902.0   16.0    117.0      11.0  ultimate   \n",
       "1        1001    8.0   6920.0   27.0    172.0      30.0      surf   \n",
       "2        1001    9.0  13315.0   49.0    298.0      44.0      surf   \n",
       "3        1001   10.0  22331.0   65.0    375.0      53.0      surf   \n",
       "4        1001   11.0  18505.0   64.0    405.0      36.0      surf   \n",
       "...       ...    ...      ...    ...      ...       ...       ...   \n",
       "2288     1470    8.0      0.0   84.0    548.0     141.0      surf   \n",
       "2289     1470    9.0      0.0   70.0    442.0     130.0      surf   \n",
       "2290     1470   10.0      0.0   72.0    435.0     145.0      surf   \n",
       "2291     1470   11.0      0.0   81.0    603.0     156.0      surf   \n",
       "2292     1470   12.0      0.0   78.0    561.0     153.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  age  \\\n",
       "0                  1000                  30720              3000  ...   45   \n",
       "1                    50                  15360               500  ...   28   \n",
       "2                    50                  15360               500  ...   28   \n",
       "3                    50                  15360               500  ...   28   \n",
       "4                    50                  15360               500  ...   28   \n",
       "...                 ...                    ...               ...  ...  ...   \n",
       "2288                 50                  15360               500  ...   45   \n",
       "2289                 50                  15360               500  ...   45   \n",
       "2290                 50                  15360               500  ...   45   \n",
       "2291                 50                  15360               500  ...   45   \n",
       "2292                 50                  15360               500  ...   45   \n",
       "\n",
       "                                                  city    reg_date    tariff  \\\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                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "3                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "4                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "...                                                ...         ...       ...   \n",
       "2288  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2289  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2290  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2291  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "2292  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21      surf   \n",
       "\n",
       "     churn_date internet  messages_extra internet_extra minutes_extra profit  \n",
       "0           NaN   1902.0             0.0            0.0           0.0  70.00  \n",
       "1           NaN   6920.0             0.0            0.0           0.0  20.00  \n",
       "2           NaN  13315.0             0.0            0.0           0.0  20.00  \n",
       "3           NaN  22331.0             3.0            7.0           0.0  90.09  \n",
       "4           NaN  18505.0             0.0            4.0           0.0  60.00  \n",
       "...         ...      ...             ...            ...           ...    ...  \n",
       "2288        NaN      0.0            91.0            0.0          48.0  24.17  \n",
       "2289        NaN      0.0            80.0            0.0           0.0  22.40  \n",
       "2290        NaN      0.0            95.0            0.0           0.0  22.85  \n",
       "2291        NaN      0.0           106.0            0.0         103.0  26.27  \n",
       "2292        NaN      0.0           103.0            0.0          61.0  24.92  \n",
       "\n",
       "[2256 rows x 26 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_profit_clean['messages_extra'] = np.maximum((data_for_profit_clean['messages'] - data_for_profit_clean['messages_included']),0)\n",
    "data_for_profit_clean['internet_extra'] = np.maximum((data_for_profit_clean['internet'] - data_for_profit_clean['mb_per_month_included']),0)\n",
    "data_for_profit_clean['minutes_extra'] = np.maximum((data_for_profit_clean['minutes'] - data_for_profit_clean['minutes_included']),0)\n",
    "data_for_profit_clean['internet_extra'] = np.ceil(data_for_profit_clean['internet_extra'] / 1024)\n",
    "data_for_profit_clean['minutes_extra'] = np.ceil(data_for_profit_clean['minutes_extra'])\n",
    "\n",
    "data_for_profit_clean['profit'] = (data_for_profit_clean['usd_monthly_pay'] + data_for_profit_clean['usd_per_gb']*data_for_profit_clean['internet_extra'] +\n",
    "                             data_for_profit_clean['usd_per_message']*data_for_profit_clean['messages_extra'] + data_for_profit_clean['usd_per_minute']\n",
    "                             *data_for_profit_clean['minutes_extra'])\n",
    "data_for_profit_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion \n",
    "\n",
    "Because I ended up cleaning out the NaNs earlier I was able to get profits for the users minus the 10 I cut out of the list because of their NaN values and those problematic User months that were after the User terminated their time with Megaline.\n",
    "\n",
    "2. Changed the variable names...I have had that issue with all of this so far...when looking at some code I can't understand what the person is referring to because all they use is numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data into Surf customers and Ultimate customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>internet</th>\n",
       "      <th>messages_extra</th>\n",
       "      <th>internet_extra</th>\n",
       "      <th>minutes_extra</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6920.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>6920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13315.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>13315.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22331.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>22331.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18505.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>18505.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19370.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</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>19370.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2288</td>\n",
       "      <td>1470</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2289</td>\n",
       "      <td>1470</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2290</td>\n",
       "      <td>1470</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2291</td>\n",
       "      <td>1470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>26.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2292</td>\n",
       "      <td>1470</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>24.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1548 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "1        1001    8.0   6920.0   27.0    172.0      30.0      surf   \n",
       "2        1001    9.0  13315.0   49.0    298.0      44.0      surf   \n",
       "3        1001   10.0  22331.0   65.0    375.0      53.0      surf   \n",
       "4        1001   11.0  18505.0   64.0    405.0      36.0      surf   \n",
       "5        1001   12.0  19370.0   56.0    393.0      44.0      surf   \n",
       "...       ...    ...      ...    ...      ...       ...       ...   \n",
       "2288     1470    8.0      0.0   84.0    548.0     141.0      surf   \n",
       "2289     1470    9.0      0.0   70.0    442.0     130.0      surf   \n",
       "2290     1470   10.0      0.0   72.0    435.0     145.0      surf   \n",
       "2291     1470   11.0      0.0   81.0    603.0     156.0      surf   \n",
       "2292     1470   12.0      0.0   78.0    561.0     153.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  age  \\\n",
       "1                    50                  15360               500  ...   28   \n",
       "2                    50                  15360               500  ...   28   \n",
       "3                    50                  15360               500  ...   28   \n",
       "4                    50                  15360               500  ...   28   \n",
       "5                    50                  15360               500  ...   28   \n",
       "...                 ...                    ...               ...  ...  ...   \n",
       "2288                 50                  15360               500  ...   45   \n",
       "2289                 50                  15360               500  ...   45   \n",
       "2290                 50                  15360               500  ...   45   \n",
       "2291                 50                  15360               500  ...   45   \n",
       "2292                 50                  15360               500  ...   45   \n",
       "\n",
       "                                                  city    reg_date  tariff  \\\n",
       "1                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13    surf   \n",
       "2                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13    surf   \n",
       "3                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13    surf   \n",
       "4                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13    surf   \n",
       "5                      Seattle-Tacoma-Bellevue, WA MSA  2018-08-13    surf   \n",
       "...                                                ...         ...     ...   \n",
       "2288  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21    surf   \n",
       "2289  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21    surf   \n",
       "2290  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21    surf   \n",
       "2291  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21    surf   \n",
       "2292  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA  2018-04-21    surf   \n",
       "\n",
       "     churn_date internet  messages_extra internet_extra minutes_extra profit  \n",
       "1           NaN   6920.0             0.0            0.0           0.0  20.00  \n",
       "2           NaN  13315.0             0.0            0.0           0.0  20.00  \n",
       "3           NaN  22331.0             3.0            7.0           0.0  90.09  \n",
       "4           NaN  18505.0             0.0            4.0           0.0  60.00  \n",
       "5           NaN  19370.0             0.0            4.0           0.0  60.00  \n",
       "...         ...      ...             ...            ...           ...    ...  \n",
       "2288        NaN      0.0            91.0            0.0          48.0  24.17  \n",
       "2289        NaN      0.0            80.0            0.0           0.0  22.40  \n",
       "2290        NaN      0.0            95.0            0.0           0.0  22.85  \n",
       "2291        NaN      0.0           106.0            0.0         103.0  26.27  \n",
       "2292        NaN      0.0           103.0            0.0          61.0  24.92  \n",
       "\n",
       "[1548 rows x 26 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf = data_for_profit_clean.query('plan_name == \"surf\"')\n",
    "data_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1362    12\n",
       "1042    12\n",
       "1408    11\n",
       "1099    11\n",
       "1138    11\n",
       "        ..\n",
       "1271     1\n",
       "1051     1\n",
       "1413     1\n",
       "1266     1\n",
       "1118     1\n",
       "Name: user_id, Length: 332, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf['user_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>internet</th>\n",
       "      <th>messages_extra</th>\n",
       "      <th>internet_extra</th>\n",
       "      <th>minutes_extra</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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>1902.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1006</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2069.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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>12.0</td>\n",
       "      <td>2069.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1006</td>\n",
       "      <td>12.0</td>\n",
       "      <td>32119.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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>12.0</td>\n",
       "      <td>32119.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1008</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17107.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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",
       "      <td>17107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1008</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23677.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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",
       "      <td>23677.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2250</td>\n",
       "      <td>1493</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14992.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-06</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2251</td>\n",
       "      <td>1493</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16330.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-06</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16330.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2252</td>\n",
       "      <td>1493</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15631.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-06</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2253</td>\n",
       "      <td>1493</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15290.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH MSA</td>\n",
       "      <td>2018-03-06</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2269</td>\n",
       "      <td>1497</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11107.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</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>11107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>708 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  mb_used  calls  minutes  messages plan_name  \\\n",
       "0        1000   12.0   1902.0   16.0    117.0      11.0  ultimate   \n",
       "19       1006   11.0   2069.0    2.0     10.0      15.0  ultimate   \n",
       "20       1006   12.0  32119.0    9.0     55.0     139.0  ultimate   \n",
       "26       1008   10.0  17107.0   71.0    451.0      21.0  ultimate   \n",
       "27       1008   11.0  23677.0   63.0    423.0      37.0  ultimate   \n",
       "...       ...    ...      ...    ...      ...       ...       ...   \n",
       "2250     1493    9.0  14992.0   66.0    506.0       0.0  ultimate   \n",
       "2251     1493   10.0  16330.0   66.0    425.0       0.0  ultimate   \n",
       "2252     1493   11.0  15631.0   73.0    473.0       0.0  ultimate   \n",
       "2253     1493   12.0  15290.0   65.0    447.0       0.0  ultimate   \n",
       "2269     1497   12.0  11107.0   54.0    277.0      50.0  ultimate   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  ...  age  \\\n",
       "0                  1000                  30720              3000  ...   45   \n",
       "19                 1000                  30720              3000  ...   73   \n",
       "20                 1000                  30720              3000  ...   73   \n",
       "26                 1000                  30720              3000  ...   53   \n",
       "27                 1000                  30720              3000  ...   53   \n",
       "...                 ...                    ...               ...  ...  ...   \n",
       "2250               1000                  30720              3000  ...   18   \n",
       "2251               1000                  30720              3000  ...   18   \n",
       "2252               1000                  30720              3000  ...   18   \n",
       "2253               1000                  30720              3000  ...   18   \n",
       "2269               1000                  30720              3000  ...   49   \n",
       "\n",
       "                                        city    reg_date    tariff churn_date  \\\n",
       "0      Atlanta-Sandy Springs-Roswell, GA MSA  2018-12-24  ultimate        NaN   \n",
       "19    San Francisco-Oakland-Berkeley, CA MSA  2018-11-27  ultimate       12.0   \n",
       "20    San Francisco-Oakland-Berkeley, CA MSA  2018-11-27  ultimate       12.0   \n",
       "26         Orlando-Kissimmee-Sanford, FL MSA  2018-08-03  ultimate        NaN   \n",
       "27         Orlando-Kissimmee-Sanford, FL MSA  2018-08-03  ultimate        NaN   \n",
       "...                                      ...         ...       ...        ...   \n",
       "2250      Boston-Cambridge-Newton, MA-NH MSA  2018-03-06  ultimate        NaN   \n",
       "2251      Boston-Cambridge-Newton, MA-NH MSA  2018-03-06  ultimate        NaN   \n",
       "2252      Boston-Cambridge-Newton, MA-NH MSA  2018-03-06  ultimate        NaN   \n",
       "2253      Boston-Cambridge-Newton, MA-NH MSA  2018-03-06  ultimate        NaN   \n",
       "2269  Los Angeles-Long Beach-Anaheim, CA MSA  2018-12-10  ultimate        NaN   \n",
       "\n",
       "     internet  messages_extra internet_extra minutes_extra profit  \n",
       "0      1902.0             0.0            0.0           0.0   70.0  \n",
       "19     2069.0             0.0            0.0           0.0   70.0  \n",
       "20    32119.0             0.0            2.0           0.0   84.0  \n",
       "26    17107.0             0.0            0.0           0.0   70.0  \n",
       "27    23677.0             0.0            0.0           0.0   70.0  \n",
       "...       ...             ...            ...           ...    ...  \n",
       "2250  14992.0             0.0            0.0           0.0   70.0  \n",
       "2251  16330.0             0.0            0.0           0.0   70.0  \n",
       "2252  15631.0             0.0            0.0           0.0   70.0  \n",
       "2253  15290.0             0.0            0.0           0.0   70.0  \n",
       "2269  11107.0             0.0            0.0           0.0   70.0  \n",
       "\n",
       "[708 rows x 26 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate = data_for_profit_clean.query('plan_name == \"ultimate\"')\n",
    "data_ultimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1196    12\n",
       "1077    12\n",
       "1214    12\n",
       "1185    12\n",
       "1028    11\n",
       "        ..\n",
       "1190     1\n",
       "1135     1\n",
       "1186     1\n",
       "1184     1\n",
       "1013     1\n",
       "Name: user_id, Length: 155, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate['user_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Analyze the data\n",
    "\n",
    "Describe the customers' behavior. Find the minutes, texts, and volume of data the users of each plan require per month. Calculate the mean, dispersion, and standard deviation. Plot histograms. Describe the distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1548.000000\n",
      "mean      405.304264\n",
      "std       222.416434\n",
      "min         0.000000\n",
      "25%       255.750000\n",
      "50%       402.000000\n",
      "75%       544.250000\n",
      "max      1432.000000\n",
      "Name: minutes, dtype: float64\n",
      "count     708.000000\n",
      "mean      406.432203\n",
      "std       227.720957\n",
      "min         0.000000\n",
      "25%       244.750000\n",
      "50%       399.500000\n",
      "75%       532.500000\n",
      "max      1293.000000\n",
      "Name: minutes, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data_surf['minutes'].describe())\n",
    "print(data_ultimate['minutes'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49437.1134960339"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_surf_minutes = np.var(data_surf['minutes'])\n",
    "variance_surf_minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51783.590036388014"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_ultimate_minutes = np.var(data_ultimate['minutes'])\n",
    "variance_ultimate_minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f83240a26d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_surf.hist('minutes', log=True, density= True)\n",
    "data_ultimate.hist('minutes', log=True, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minutes_per_month = 1200/12\n",
    "minutes_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     193.000000\n",
       "2.0     281.333333\n",
       "3.0     311.434783\n",
       "4.0     332.920000\n",
       "5.0     377.532468\n",
       "6.0     407.690722\n",
       "7.0     425.000000\n",
       "8.0     387.660494\n",
       "9.0     391.453608\n",
       "10.0    405.304721\n",
       "11.0    401.304348\n",
       "12.0    449.684211\n",
       "Name: minutes, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf.groupby('month')['minutes'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     183.750000\n",
       "2.0     380.428571\n",
       "3.0     286.166667\n",
       "4.0     317.095238\n",
       "5.0     384.206897\n",
       "6.0     350.361702\n",
       "7.0     404.271186\n",
       "8.0     394.085714\n",
       "9.0     416.571429\n",
       "10.0    426.295238\n",
       "11.0    423.403226\n",
       "12.0    431.239726\n",
       "Name: minutes, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate.groupby('month')['minutes'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion for minutes\n",
    "\n",
    "There are more calls and minutes in the Surf plan than in the Ultimate plan. Surf has more than double the customers 333 to Surf and 157 to Ultimate in the data. So that makes sense.\n",
    "\n",
    "The data for Surf is skewed right with minutes per user having a mean of 405.25, a dispersion of 49112.75, and a std of 221.68\n",
    "\n",
    "The data for Ultimate is skewed to the right with minutes per user having a mean of 406.70, a disperison of 51579.92, and a std of 227.27 \n",
    "\n",
    "Even though there are more users for Surf the average, dispersion, and std are similar for both plans. Probably because the Ultimate users have more minutes to use in their plan before they get charged more even though there are less customers.\n",
    "\n",
    "I believe that both plans would need about 100 minutes per month for users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Yes I was reffering to total minutes used by all users. Is that not a useful metric?\n",
    "2. I divided by number of months. But now I used a different method and found a difference\n",
    "3. I took the 405 and 407 average from the describe method but I see that I could have found the average a different way for each month.\n",
    "\n",
    "##### So it seems to me from the data above that Surf and Ultimate have very similar averages per month for minutes. The first month starts low and then it rises. The largest amount of minutes per month is for Surf 447 and for Ultimate 431 in the 12th month. The numbers look very similar to me."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1548.000000\n",
      "mean       31.111757\n",
      "std        33.641112\n",
      "min         0.000000\n",
      "25%         3.000000\n",
      "50%        24.000000\n",
      "75%        47.000000\n",
      "max       266.000000\n",
      "Name: messages, dtype: float64\n",
      "count    708.000000\n",
      "mean      37.798023\n",
      "std       34.896049\n",
      "min        0.000000\n",
      "25%        7.000000\n",
      "50%       30.000000\n",
      "75%       61.000000\n",
      "max      166.000000\n",
      "Name: messages, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data_surf['messages'].describe())\n",
    "print(data_ultimate['messages'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1130.9933243027597"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_surf_messages = np.var(data_surf['messages'])\n",
    "variance_surf_messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1216.0142898751956"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_ultimate_messages = np.var(data_ultimate['messages'])\n",
    "variance_ultimate_messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8323e2c350>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_surf.hist('messages', log=True, density= True)\n",
    "data_ultimate.hist('messages', log=True, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.666666666666668"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_per_month = 200/12\n",
    "messages_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     10.500000\n",
       "2.0     12.000000\n",
       "3.0     15.260870\n",
       "4.0     17.400000\n",
       "5.0     24.012987\n",
       "6.0     25.298969\n",
       "7.0     27.033058\n",
       "8.0     28.777778\n",
       "9.0     30.762887\n",
       "10.0    33.682403\n",
       "11.0    32.583333\n",
       "12.0    38.703947\n",
       "Name: messages, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf.groupby('month')['messages'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     15.500000\n",
       "2.0     21.571429\n",
       "3.0     20.250000\n",
       "4.0     22.047619\n",
       "5.0     32.103448\n",
       "6.0     29.340426\n",
       "7.0     32.830508\n",
       "8.0     38.814286\n",
       "9.0     38.380952\n",
       "10.0    39.428571\n",
       "11.0    39.209677\n",
       "12.0    45.561644\n",
       "Name: messages, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate.groupby('month')['messages'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion for messages\n",
    "\n",
    "There are more messages in the Surf plan than in the Ultimate plan. Surf has more than double the customers 333 to Surf and 157 to Ultimate in the data. So that makes sense.\n",
    "\n",
    "The data for Surf is skewed right with messages per user having a mean of 31.15, a dispersion of 1126, and a std of 33.57\n",
    "\n",
    "The data for Ultimate is skewed to the right with messages per user having a mean of 37.55, a disperison of 1207, and a std of 34.77 \n",
    "\n",
    "Here also even though there are more users for Surf the average, dispersion, and std are similar for both plans. Probably because the Ultimate users have more messages to use in their plan before they get charged more even though there are less customers.\n",
    "\n",
    "I believe for both plans the users would need about 16 messages per month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. I was talking about total messages...but I realize that it may not be the best metric since There are more surf customers than Ultimate customers.\n",
    "2. I also divided total by month here but seems it is not the best way.\n",
    "\n",
    "##### So I calculated again by groupby and again found very close call between Surf and Ultimate. The largest amount of messages on average sent in Ultimate was 45 in month 12 and for Surf 38 in month 12. They both started low in January and continued on an upward trend towards December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     1548.000000\n",
      "mean     16560.804910\n",
      "std       8006.517664\n",
      "min          0.000000\n",
      "25%      12041.500000\n",
      "50%      16832.000000\n",
      "75%      20992.750000\n",
      "max      70932.000000\n",
      "Name: internet, dtype: float64\n",
      "count      708.000000\n",
      "mean     17242.920904\n",
      "std       7883.445608\n",
      "min          0.000000\n",
      "25%      12671.750000\n",
      "50%      16859.000000\n",
      "75%      21096.250000\n",
      "max      46596.000000\n",
      "Name: internet, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data_surf['internet'].describe())\n",
    "print(data_ultimate['internet'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64062914.047211036"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_surf_internet = np.var(data_surf['internet'])\n",
    "variance_surf_internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62060933.98526923"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_ultimate_internet = np.var(data_ultimate['internet'])\n",
    "variance_ultimate_internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f83294f7350>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_surf.hist('internet', log=True, density= True)\n",
    "data_ultimate.hist('internet', log=True, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1750.0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_per_month = 21000/12\n",
    "data_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0      4875.000000\n",
       "2.0     12179.444444\n",
       "3.0     13345.956522\n",
       "4.0     11984.660000\n",
       "5.0     13936.857143\n",
       "6.0     15302.000000\n",
       "7.0     16784.090909\n",
       "8.0     16795.820988\n",
       "9.0     16591.917526\n",
       "10.0    17319.798283\n",
       "11.0    16375.706522\n",
       "12.0    18181.911184\n",
       "Name: internet, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf.groupby('month')['internet'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0      6918.500000\n",
       "2.0     17129.142857\n",
       "3.0     18322.083333\n",
       "4.0     16122.190476\n",
       "5.0     16624.965517\n",
       "6.0     15338.425532\n",
       "7.0     16345.288136\n",
       "8.0     17810.042857\n",
       "9.0     17156.226190\n",
       "10.0    17618.971429\n",
       "11.0    17129.604839\n",
       "12.0    18306.082192\n",
       "Name: internet, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate.groupby('month')['internet'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion for Internet\n",
    "\n",
    "There is more internet being used in the Surf plan than in the Ultimate plan. Surf has more than double the customers 333 to Surf and 157 to Ultimate in the data. So that makes sense. But here it seems the usage is pretty much the same except for some outlier at around 70000\n",
    "\n",
    "The data for Surf is skewed right with internet usage per user having a mean of 16558.78, a dispersion of 64175621, and a std of 8013.52\n",
    "\n",
    "The data for Ultimate is almost normal distribution but still skews to the right a bit with internet usage per user having a mean of 17215, a disperison of 61566495.66, and a std of 7851.89 \n",
    "\n",
    "Here again the data is close but not as close because of the outliers in Surf.\n",
    "\n",
    "For both plans the users would need about 1750 volume ofdata per month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. I was talking about total internet use...but I realize that it may not be the best metric since There are more surf customers than Ultimate customers.\n",
    "2. I also divided total by month here but seems it is not the best way.\n",
    "\n",
    "##### So I calculated again by groupby and again found very close call between Surf and Ultimate. The largest amount of internet usage on average used in Ultimate was 18306 in month 12 and for Surf 18181 in month 12. They both started low in January and continued on an upward trend towards December. Surf took some time catching up in the beginning months but eventually they both were averaging about the same data usage towards the later months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Test the hypotheses\n",
    "\n",
    "The average profit from users of Ultimate and Surf calling plans differs.\n",
    "\n",
    "The average profit from users in NY-NJ area is different from that of the users from other regions.\n",
    "\n",
    "You decide what alpha value to use.\n",
    "\n",
    "Explain:\n",
    "How you formulated the null and alternative hypotheses.\n",
    "What criterion you used to test the hypotheses and why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.27624031007752\n",
      "72.35310734463278\n"
     ]
    }
   ],
   "source": [
    "print(data_surf['profit'].mean()) \n",
    "print(data_ultimate['profit'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "ny_nj_users = data_for_profit_clean.query('city == \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_nynj_users = data_for_profit_clean.query('city != \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.621401617250676\n",
      "64.94115649867373\n"
     ]
    }
   ],
   "source": [
    "print(ny_nj_users['profit'].mean()) \n",
    "print(not_nynj_users['profit'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Created two separate tables. One with NY and NJ users and one without NY and NJ users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8323c2da90>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ny_nj_users['profit'].plot(kind='density')\n",
    "not_nynj_users['profit'].plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     20.000000\n",
       "2.0     34.260000\n",
       "3.0     45.792609\n",
       "4.0     40.458600\n",
       "5.0     47.240130\n",
       "6.0     48.935155\n",
       "7.0     62.226529\n",
       "8.0     63.576728\n",
       "9.0     57.952320\n",
       "10.0    65.206652\n",
       "11.0    57.827174\n",
       "12.0    69.979868\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf.groupby('month')['profit'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1.0     70.000000\n",
       "2.0     70.000000\n",
       "3.0     74.666667\n",
       "4.0     73.000000\n",
       "5.0     70.724138\n",
       "6.0     71.638298\n",
       "7.0     71.898305\n",
       "8.0     72.900000\n",
       "9.0     72.083333\n",
       "10.0    72.333333\n",
       "11.0    71.750000\n",
       "12.0    73.404110\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate.groupby('month')['profit'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8323bac190>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_surf.hist('profit', log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8323a04e10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ultimate.hist('profit', log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data_surf['profit'])\n",
    "plt.ylim(0, 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data_ultimate['profit'])\n",
    "plt.ylim(0,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    708.000000\n",
      "mean      72.353107\n",
      "std       11.487385\n",
      "min       70.000000\n",
      "25%       70.000000\n",
      "50%       70.000000\n",
      "75%       70.000000\n",
      "max      182.000000\n",
      "Name: profit, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data_ultimate['profit'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here most of the data falls within 70 dollars. So I will take anything above 70 and make it an outlier for data_ultimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1548.000000\n",
       "mean       60.276240\n",
       "std        55.228362\n",
       "min        20.000000\n",
       "25%        20.000000\n",
       "50%        40.255000\n",
       "75%        80.000000\n",
       "max       588.660000\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf[ 'profit'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here most of the data falls below 200 and there are some huge outliers in the 500s or so. I will make my outliers below 200 then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_surf_new = data_surf[data_surf['profit'] < 180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ultimate_new = data_ultimate[data_ultimate['profit'] <= 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Took away the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    667.0\n",
       "mean      70.0\n",
       "std        0.0\n",
       "min       70.0\n",
       "25%       70.0\n",
       "50%       70.0\n",
       "75%       70.0\n",
       "max       70.0\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate_new['profit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1484.000000\n",
       "mean       52.420849\n",
       "std        38.122761\n",
       "min        20.000000\n",
       "25%        20.000000\n",
       "50%        40.000000\n",
       "75%        80.000000\n",
       "max       179.810000\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf_new['profit'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Ultimate stays the same in its description theres a significant jump in Surf data in the description.\n",
    "\n",
    "Looks like the variance is not the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46690.0"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate_new['profit'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77792.54000000001"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf_new['profit'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surf makes more profit based on number of users but the average profit is better for Ultimate plan\n",
    "\n",
    "Surfs average profit is 52 while Ultimates average profit is 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1453.3448826231886"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_surf_new['profit'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.9600044750238"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ultimate['profit'].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitely have a different variance in the two datasets. Ttest won't be so helpful here. It is normally used for normally distributed data but as we saw Ultimate and Surf are both right skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "There is more profit coming from the not_nynj_users and from the Surf plan in general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  1.0496426908529223e-31\n",
      "We reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Testing hypothesis of: The average profit from users of Ultimate and Surf calling plans differs\n",
    "\n",
    "#data_surf_sample = data_surf['profit'].sample(10)\n",
    "#data_ultimate_sample = data_ultimate['profit'].sample(10)\n",
    "list_surf = data_surf_new['profit']\n",
    "list_ultimate = data_ultimate_new['profit']\n",
    "\n",
    "alpha = 0.05 \n",
    "\n",
    "#results = st.ttest_ind(data_surf_sample, data_ultimate_sample)\n",
    "results = st.ttest_ind(data_surf_new['profit'], data_ultimate_new['profit'])\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"We reject the null hypothesis\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "The average profit from users of Ultimate and Surf calling plans does differ according to the fact that we can't reject the null hypothesis. The alternative hypothesis is that they don't differ. This is most probably the case based on the fact that we have more Surf customers than the plan for Ultimate but if Ultimate had the same number of customers the average profit would look similar. We are using a small sample here and what is interesting is when I used the complete data the null hypothesis was rejected. This may not be the best test for finding out the difference of the plans  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Conclusion\n",
    "\n",
    "The average profit from the users Ultimate and Surf calling plans doesn't differ according to the fact that we reject the null hypothesis. The alternative hypothesis is that they do differ. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.044108814472504344\n",
      "We reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# The average profit from users in NY-NJ area is different from that of the users from other regions.\n",
    "\n",
    "list_nynj = ny_nj_users['profit']\n",
    "list_not_nynj = not_nynj_users['profit']\n",
    "\n",
    "#ny_nj_users_sample = ny_nj_users['profit'].sample(10)\n",
    "#not_nynj_users_sample = not_nynj_users['profit'].sample(10)\n",
    "\n",
    "alpha = .05 \n",
    "\n",
    "#results = st.ttest_ind(ny_nj_users_sample, not_nynj_users_sample)\n",
    "results = st.ttest_ind(list_nynj, list_not_nynj)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"We reject the null hypothesis\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Conclusion\n",
    "\n",
    "The average profit from users in NY-NJ area does not differ from that of the users from other regions based on teh fact that we reject the null hypothesis. The alternative hypothesis is that they will differ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Overall New Conclusion\n",
    "\n",
    "After finding a few errors in the data at the beginning of the project and weening them out and calculating the profits (getting rid of users that had gotten caught with charges after they terminated the plan still kept the data similar)\n",
    "\n",
    "Seems that Surf has better total profit with this data but more customers. The Ultimate plan averages a better profit by 18. We tested the hypothesis even though it is not the best to use. First of all because the variance of the data is different. But in our results we find that we reject the null hypothesis and therefore the Ultimate Plan and Surf Plan average profit are similar and so is the average profit for NYNJ users and Non_NYNJ users. However again this isn't the best test to use but it is the test that we learned so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project completion checklist\n",
    "\n",
    "Mark the completed tasks with 'x'. Then press Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  file opened\n",
    "- [x]  files explored (first rows printed, info() method)\n",
    "- [x]  missing values determined\n",
    "- [x]  missing values filled in\n",
    "- [x]  clarification of the discovered missing values provided\n",
    "- [x]  data types converted\n",
    "- [x]  explanation of which columns had the data types changed and why\n",
    "- [x]  calculated and added to the table: The number of calls made and minutes used per month\n",
    "- [x]  calculated and added to the table: The number of text messages sent per month\n",
    "- [x]  calculated and added to the table: The monthly profit from each user (subtract the free package limit from the total number of calls, text messages, and data; multiply the result by the calling plan value; add the monthly charge depending on the calling plan)\n",
    "- [x]  the following parameters investigated: minutes, texts, volume of data of each plan required per month\n",
    "- [x]  histograms for each parameter created\n",
    "- [x] Described distributions\n",
    "- [x] Test Hypotheses: The average profit from users of Ultimate and Surf calling plans differs\n",
    "- [x] Test Hypotheses: The average profit from users in NY-NJ area is different from that of the users from other regions\n",
    "- [x] Explanation of null and alternative hypotheses\n",
    "- [x]  each stage has a conclusion\n",
    "- [x]  overall conclusion drawn"
   ]
  }
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