Statistical Data Analysis.ipynb

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   "source": [
    "<div style=\"border:solid green 2px; padding: 20px\"> <h1 style=\"color:green; margin-bottom:20px\">Reviewer's comment v1</h1>\n",
    "\n",
    "Hello Johannah, my name is Dmitrii. Happy to review your project! 🙌\n",
    "\n",
    "As usual you can find my comments under the heading **«Review»**. I will categorize my comments in green, blue or red boxes like this:\n",
    "\n",
    "<div class=\"alert alert-success\">\n",
    "    <b>Success:</b> if everything is done successfully\n",
    "</div>\n",
    "<div class=\"alert alert-warning\">\n",
    "    <b>Remarks:</b> if I can give some recommendations or ways to improve the project\n",
    "</div>\n",
    "<div class=\"alert alert-danger\">\n",
    "    <b>Needs fixing:</b> if the block requires some corrections. Work can't be accepted with the red comments\n",
    "</div>\n",
    "\n",
    "Please don't remove my comments :) If you have any questions don't hesitate to respond to my comments in a different section. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:solid green 2px; padding: 20px\">\n",
    "<b>Reviewer's comment v1</b>\n",
    "    \n",
    "<b>Overall Feedback</b> \n",
    "    \n",
    "Overall well done! I can see that a lot of effort has been made! Your project already looks really good, and you accomplished impressive results.\n",
    "\n",
    "No critical issues are left, so your project has been accepted. Congratulations! However, I want to send it back to you in case you have any questions along the way or you want to make additional changes to the project.\n",
    "  \n",
    "And of course, if you have any questions along the way, remember that you can always reach out to your tutor for any clarification.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:solid blue 2px; padding: 20px\"\n",
    "\n",
    "\n",
    "Hi Dimitri! Thank you very much for your useful feed back I will be implimenting your suggestions for this project as soon as possible. Thanks again"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:solid green 2px; padding: 20px\">\n",
    "<b>Reviewer's comment v2:</b>\n",
    "    \n",
    "<b>Overall Feedback</b> \n",
    "    \n",
    "Hi Johannah! I did not notice any new code or questions, but as I mentioned, there are no critical issues, so your project has been accepted! Wish you cool projects in the next sprints ☘️\n",
    "    \n",
    "PS: As a last thing, I can recommend cool lectures on stat analysis here: https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo  🙌\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which one is a better plan?\n",
    "\n",
    "Megaline company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans brings in more revenue in order to adjust the advertising budget.\n",
    "\n",
    "I will carry out a preliminary analysis of the plans based on a relatively small client selection. I 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.\n",
    "\n",
    "My purpose is to analyze the clients' behavior and determine which prepaid plan brings in more revenue. Preliminarily I will review the datasets and amend the data as needed. Then I will enrich the data with derivitaves from the original which will aid me in my downstream ananlysis. Once the data is complete I will perform statistical and visual analytics to help me draw my conclusions. \n",
    "\n",
    "Thank you for your time reviewing this project!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment v1</b>\n",
    " \n",
    "Great that you added additional information about the project goal and tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls_data=pd.read_csv(r'/datasets/megaline_calls.csv') \n",
    "internet_data=pd.read_csv(r'/datasets/megaline_internet.csv') \n",
    "messages_data=pd.read_csv(r'/datasets/megaline_messages.csv') \n",
    "plans_data=pd.read_csv(r'/datasets/megaline_plans.csv') \n",
    "users_data=pd.read_csv(r'/datasets/megaline_users.csv') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data are separated into several dataframes, I will review each and process any issues I uncover."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   messages_included      2 non-null      int64  \n",
      " 1   mb_per_month_included  2 non-null      int64  \n",
      " 2   minutes_included       2 non-null      int64  \n",
      " 3   usd_monthly_pay        2 non-null      int64  \n",
      " 4   usd_per_gb             2 non-null      int64  \n",
      " 5   usd_per_message        2 non-null      float64\n",
      " 6   usd_per_minute         2 non-null      float64\n",
      " 7   plan_name              2 non-null      object \n",
      "dtypes: float64(2), int64(5), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "plans_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>ultimate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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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  "
      ]
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   "source": [
    "plans_data.head()\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything in this dataframe looks accurate from the information I was given about the plans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   user_id     500 non-null    int64 \n",
      " 1   first_name  500 non-null    object\n",
      " 2   last_name   500 non-null    object\n",
      " 3   age         500 non-null    int64 \n",
      " 4   city        500 non-null    object\n",
      " 5   reg_date    500 non-null    object\n",
      " 6   plan        500 non-null    object\n",
      " 7   churn_date  34 non-null     object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "users_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1005</td>\n",
       "      <td>Livia</td>\n",
       "      <td>Shields</td>\n",
       "      <td>31</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX MSA</td>\n",
       "      <td>2018-11-29</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1006</td>\n",
       "      <td>Jesusa</td>\n",
       "      <td>Bradford</td>\n",
       "      <td>73</td>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA MSA</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007</td>\n",
       "      <td>Eusebio</td>\n",
       "      <td>Welch</td>\n",
       "      <td>42</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1008</td>\n",
       "      <td>Emely</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>53</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1009</td>\n",
       "      <td>Gerry</td>\n",
       "      <td>Little</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA MSA</td>\n",
       "      <td>2018-04-22</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                    city  \\\n",
       "0     1000   Anamaria      Bauer   45   Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28         Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36    Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                           Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40         Seattle-Tacoma-Bellevue, WA MSA   \n",
       "5     1005      Livia    Shields   31     Dallas-Fort Worth-Arlington, TX MSA   \n",
       "6     1006     Jesusa   Bradford   73  San Francisco-Oakland-Berkeley, CA MSA   \n",
       "7     1007    Eusebio      Welch   42           Grand Rapids-Kentwood, MI MSA   \n",
       "8     1008      Emely    Hoffman   53       Orlando-Kissimmee-Sanford, FL MSA   \n",
       "9     1009      Gerry     Little   19  San Jose-Sunnyvale-Santa Clara, CA MSA   \n",
       "\n",
       "     reg_date      plan  churn_date  \n",
       "0  2018-12-24  ultimate         NaN  \n",
       "1  2018-08-13      surf         NaN  \n",
       "2  2018-10-21      surf         NaN  \n",
       "3  2018-01-28      surf         NaN  \n",
       "4  2018-05-23      surf         NaN  \n",
       "5  2018-11-29      surf         NaN  \n",
       "6  2018-11-27  ultimate  2018-12-18  \n",
       "7  2018-07-11      surf         NaN  \n",
       "8  2018-08-03  ultimate         NaN  \n",
       "9  2018-04-22      surf         NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_data.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see that churn date is missing a lot of data.I will fill the NaN with 0 to indicate the user has not cancelled their service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1005</td>\n",
       "      <td>Livia</td>\n",
       "      <td>Shields</td>\n",
       "      <td>31</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX MSA</td>\n",
       "      <td>2018-11-29</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1006</td>\n",
       "      <td>Jesusa</td>\n",
       "      <td>Bradford</td>\n",
       "      <td>73</td>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA MSA</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007</td>\n",
       "      <td>Eusebio</td>\n",
       "      <td>Welch</td>\n",
       "      <td>42</td>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1008</td>\n",
       "      <td>Emely</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>53</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1009</td>\n",
       "      <td>Gerry</td>\n",
       "      <td>Little</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA MSA</td>\n",
       "      <td>2018-04-22</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                    city  \\\n",
       "0     1000   Anamaria      Bauer   45   Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28         Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36    Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                           Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40         Seattle-Tacoma-Bellevue, WA MSA   \n",
       "5     1005      Livia    Shields   31     Dallas-Fort Worth-Arlington, TX MSA   \n",
       "6     1006     Jesusa   Bradford   73  San Francisco-Oakland-Berkeley, CA MSA   \n",
       "7     1007    Eusebio      Welch   42           Grand Rapids-Kentwood, MI MSA   \n",
       "8     1008      Emely    Hoffman   53       Orlando-Kissimmee-Sanford, FL MSA   \n",
       "9     1009      Gerry     Little   19  San Jose-Sunnyvale-Santa Clara, CA MSA   \n",
       "\n",
       "     reg_date      plan  churn_date  \n",
       "0  2018-12-24  ultimate           0  \n",
       "1  2018-08-13      surf           0  \n",
       "2  2018-10-21      surf           0  \n",
       "3  2018-01-28      surf           0  \n",
       "4  2018-05-23      surf           0  \n",
       "5  2018-11-29      surf           0  \n",
       "6  2018-11-27  ultimate  2018-12-18  \n",
       "7  2018-07-11      surf           0  \n",
       "8  2018-08-03  ultimate           0  \n",
       "9  2018-04-22      surf           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_data['churn_date']=users_data['churn_date'].fillna(0)\n",
    "users_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will change the dtype to datetime for the reg_date column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   user_id     500 non-null    int64         \n",
      " 1   first_name  500 non-null    object        \n",
      " 2   last_name   500 non-null    object        \n",
      " 3   age         500 non-null    int64         \n",
      " 4   city        500 non-null    object        \n",
      " 5   reg_date    500 non-null    datetime64[ns]\n",
      " 6   plan        500 non-null    object        \n",
      " 7   churn_date  500 non-null    object        \n",
      "dtypes: datetime64[ns](1), int64(2), object(5)\n",
      "memory usage: 31.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                   city  \\\n",
       "0     1000   Anamaria      Bauer   45  Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36   Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                          Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "\n",
       "    reg_date      plan churn_date  \n",
       "0 2018-12-24  ultimate          0  \n",
       "1 2018-08-13      surf          0  \n",
       "2 2018-10-21      surf          0  \n",
       "3 2018-01-28      surf          0  \n",
       "4 2018-05-23      surf          0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_data['reg_date']=pd.to_datetime(users_data['reg_date'])\n",
    "users_data.info()\n",
    "users_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 137735 entries, 0 to 137734\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   id         137735 non-null  object \n",
      " 1   user_id    137735 non-null  int64  \n",
      " 2   call_date  137735 non-null  object \n",
      " 3   duration   137735 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "calls_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_93</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>8.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_145</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>13.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_247</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_309</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_388</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_510</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_521</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_530</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_544</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1000_693</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1000_705</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>12.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1000_735</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000_778</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1000_826</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1000_842</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1001_0</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>10.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  user_id   call_date  duration\n",
       "0    1000_93     1000  2018-12-27      8.52\n",
       "1   1000_145     1000  2018-12-27     13.66\n",
       "2   1000_247     1000  2018-12-27     14.48\n",
       "3   1000_309     1000  2018-12-28      5.76\n",
       "4   1000_380     1000  2018-12-30      4.22\n",
       "5   1000_388     1000  2018-12-31      2.20\n",
       "6   1000_510     1000  2018-12-27      5.75\n",
       "7   1000_521     1000  2018-12-28     14.18\n",
       "8   1000_530     1000  2018-12-28      5.77\n",
       "9   1000_544     1000  2018-12-26      4.40\n",
       "10  1000_693     1000  2018-12-31      4.31\n",
       "11  1000_705     1000  2018-12-31     12.78\n",
       "12  1000_735     1000  2018-12-29      1.70\n",
       "13  1000_778     1000  2018-12-28      3.29\n",
       "14  1000_826     1000  2018-12-26      9.96\n",
       "15  1000_842     1000  2018-12-27      5.85\n",
       "16    1001_0     1001  2018-09-06     10.06"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data.head(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I will change the call date dtype to datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 137735 entries, 0 to 137734\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype         \n",
      "---  ------     --------------   -----         \n",
      " 0   id         137735 non-null  object        \n",
      " 1   user_id    137735 non-null  int64         \n",
      " 2   call_date  137735 non-null  datetime64[ns]\n",
      " 3   duration   137735 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(1)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "calls_data['call_date']=pd.to_datetime(calls_data['call_date'])\n",
    "calls_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now I will make a new column and extract the month calls were placed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_93</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>8.52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_145</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>13.66</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_247</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>14.48</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_309</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>5.76</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>4.22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_388</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_510</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>5.75</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_521</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>14.18</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_530</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>5.77</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_544</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id  call_date  duration  month\n",
       "0   1000_93     1000 2018-12-27      8.52     12\n",
       "1  1000_145     1000 2018-12-27     13.66     12\n",
       "2  1000_247     1000 2018-12-27     14.48     12\n",
       "3  1000_309     1000 2018-12-28      5.76     12\n",
       "4  1000_380     1000 2018-12-30      4.22     12\n",
       "5  1000_388     1000 2018-12-31      2.20     12\n",
       "6  1000_510     1000 2018-12-27      5.75     12\n",
       "7  1000_521     1000 2018-12-28     14.18     12\n",
       "8  1000_530     1000 2018-12-28      5.77     12\n",
       "9  1000_544     1000 2018-12-26      4.40     12"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data['month']= calls_data['call_date'].dt.month\n",
    "calls_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    " In this case, we only have data for a year, and the \"month\" method works fine. But for other datasets, this can lead to an error: for example, April 2019 and April 2021 will result in the same number. To display all information, you can use the astype('datetime64[M]') method. It converts the date to the format first day - month - year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enrich data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The duration needs to be rounded to the next minute to be in accordance with megaline's plan requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_93</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_145</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_247</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_309</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137730</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137731</th>\n",
       "      <td>1499_200</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137732</th>\n",
       "      <td>1499_201</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137733</th>\n",
       "      <td>1499_202</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137734</th>\n",
       "      <td>1499_203</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137735 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id  call_date  duration  month\n",
       "0        1000_93     1000 2018-12-27       9.0     12\n",
       "1       1000_145     1000 2018-12-27      14.0     12\n",
       "2       1000_247     1000 2018-12-27      15.0     12\n",
       "3       1000_309     1000 2018-12-28       6.0     12\n",
       "4       1000_380     1000 2018-12-30       5.0     12\n",
       "...          ...      ...        ...       ...    ...\n",
       "137730  1499_199     1499 2018-11-21       9.0     11\n",
       "137731  1499_200     1499 2018-10-20      11.0     10\n",
       "137732  1499_201     1499 2018-09-21       9.0      9\n",
       "137733  1499_202     1499 2018-10-10       1.0     10\n",
       "137734  1499_203     1499 2018-12-29      14.0     12\n",
       "\n",
       "[137735 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data['duration']=np.ceil(calls_data['duration'])\n",
    "calls_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Ok, great job! Good that you added data rounding. Also, great choice of np.ceil () method. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76051 entries, 0 to 76050\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   id            76051 non-null  object\n",
      " 1   user_id       76051 non-null  int64 \n",
      " 2   message_date  76051 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "messages_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>message_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_125</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_160</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_223</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_251</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_255</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_346</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_386</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_502</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_525</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id message_date\n",
       "0  1000_125     1000   2018-12-27\n",
       "1  1000_160     1000   2018-12-31\n",
       "2  1000_223     1000   2018-12-31\n",
       "3  1000_251     1000   2018-12-27\n",
       "4  1000_255     1000   2018-12-26\n",
       "5  1000_346     1000   2018-12-29\n",
       "6  1000_386     1000   2018-12-30\n",
       "7  1000_413     1000   2018-12-31\n",
       "8  1000_502     1000   2018-12-27\n",
       "9  1000_525     1000   2018-12-28"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "message_date will be changed to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76051 entries, 0 to 76050\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   id            76051 non-null  object        \n",
      " 1   user_id       76051 non-null  int64         \n",
      " 2   message_date  76051 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "messages_data['message_date']=pd.to_datetime(messages_data['message_date'])\n",
    "messages_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enrich data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again I will extract the month the message was placed and make it its own column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>message_date</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_125</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_160</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_223</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_251</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_255</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000_346</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000_386</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000_502</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000_525</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id message_date  month\n",
       "0  1000_125     1000   2018-12-27     12\n",
       "1  1000_160     1000   2018-12-31     12\n",
       "2  1000_223     1000   2018-12-31     12\n",
       "3  1000_251     1000   2018-12-27     12\n",
       "4  1000_255     1000   2018-12-26     12\n",
       "5  1000_346     1000   2018-12-29     12\n",
       "6  1000_386     1000   2018-12-30     12\n",
       "7  1000_413     1000   2018-12-31     12\n",
       "8  1000_502     1000   2018-12-27     12\n",
       "9  1000_525     1000   2018-12-28     12"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_data['month']= messages_data['message_date'].dt.month\n",
    "messages_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104825 entries, 0 to 104824\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            104825 non-null  object \n",
      " 1   user_id       104825 non-null  int64  \n",
      " 2   session_date  104825 non-null  object \n",
      " 3   mb_used       104825 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "internet_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_204</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_379</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001_0</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>284.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001_3</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>656.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1001_4</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-04</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001_10</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>135.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1001_15</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>761.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id session_date  mb_used\n",
       "0   1000_13     1000   2018-12-29    89.86\n",
       "1  1000_204     1000   2018-12-31     0.00\n",
       "2  1000_379     1000   2018-12-28   660.40\n",
       "3  1000_413     1000   2018-12-26   270.99\n",
       "4  1000_442     1000   2018-12-27   880.22\n",
       "5    1001_0     1001   2018-08-24   284.68\n",
       "6    1001_3     1001   2018-12-09   656.04\n",
       "7    1001_4     1001   2018-11-04    16.97\n",
       "8   1001_10     1001   2018-11-27   135.18\n",
       "9   1001_15     1001   2018-12-13   761.92"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will repeat the steps I took before for datetime and extracting the month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104825 entries, 0 to 104824\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   id            104825 non-null  object        \n",
      " 1   user_id       104825 non-null  int64         \n",
      " 2   session_date  104825 non-null  datetime64[ns]\n",
      " 3   mb_used       104825 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(1)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "internet_data['session_date']=pd.to_datetime(internet_data['session_date'])\n",
    "internet_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enrich data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_204</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_379</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001_0</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>284.68</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001_3</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>656.04</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1001_4</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-04</td>\n",
       "      <td>16.97</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001_10</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>135.18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1001_15</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>761.92</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id session_date  mb_used  month\n",
       "0   1000_13     1000   2018-12-29    89.86     12\n",
       "1  1000_204     1000   2018-12-31     0.00     12\n",
       "2  1000_379     1000   2018-12-28   660.40     12\n",
       "3  1000_413     1000   2018-12-26   270.99     12\n",
       "4  1000_442     1000   2018-12-27   880.22     12\n",
       "5    1001_0     1001   2018-08-24   284.68      8\n",
       "6    1001_3     1001   2018-12-09   656.04     12\n",
       "7    1001_4     1001   2018-11-04    16.97     11\n",
       "8   1001_10     1001   2018-11-27   135.18     11\n",
       "9   1001_15     1001   2018-12-13   761.92     12"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_data['month']= internet_data['session_date'].dt.month\n",
    "internet_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study plan conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>ultimate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute plan_name  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plans_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Good start and first view on data!\n",
    "\n",
    "Next time, you can also create a function with all data processing steps to run it through all your datasets and avoid repetitive code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate data per user\n",
    "\n",
    "Now I will aggregate data per user per month in order to have just one record per user per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>calls_count</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",
       "      <th>1000</th>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1001</th>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1499</th>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               calls_count\n",
       "user_id month             \n",
       "1000    12              16\n",
       "1001    8               27\n",
       "        9               49\n",
       "        10              65\n",
       "        11              64\n",
       "...                    ...\n",
       "1498    12              39\n",
       "1499    9               41\n",
       "        10              53\n",
       "        11              45\n",
       "        12              65\n",
       "\n",
       "[2258 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_calls=calls_data.groupby(['user_id','month'])['user_id'].agg(calls_count='count')\n",
    "user_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_minutes</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",
       "      <th>1000</th>\n",
       "      <th>12</th>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1001</th>\n",
       "      <th>8</th>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <th>12</th>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1499</th>\n",
       "      <th>9</th>\n",
       "      <td>346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>496.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_minutes\n",
       "user_id month               \n",
       "1000    12             124.0\n",
       "1001    8              182.0\n",
       "        9              315.0\n",
       "        10             393.0\n",
       "        11             426.0\n",
       "...                      ...\n",
       "1498    12             339.0\n",
       "1499    9              346.0\n",
       "        10             385.0\n",
       "        11             308.0\n",
       "        12             496.0\n",
       "\n",
       "[2258 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_minutes=calls_data.groupby(['user_id','month'])['duration'].agg(total_minutes='sum')\n",
    "user_minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "In pivot_table or groupby it is possible to pass multiple arguments, so here, you can create an aggregated table in one go like this:\n",
    "\n",
    "    data_calls.groupby(['user_id','month']).agg({'duration' : ('count','sum')}).reset_index()\n",
    "\n",
    "or\n",
    "    \n",
    "    data_calls.pivot_table(index = ('user_id','month'), values = 'duration', aggfunc = ('count','sum')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>message_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"4\" valign=\"top\">1001</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1496</th>\n",
       "      <th>9</th>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               message_count\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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_messages=messages_data.groupby(['user_id','month'])['user_id'].agg(message_count='count')\n",
    "user_messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">1001</th>\n",
       "      <th>8</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1499</th>\n",
       "      <th>9</th>\n",
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       "      <th>12</th>\n",
       "      <td>22059.21</td>\n",
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       "</table>\n",
       "<p>2277 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               data_usage_mb\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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_internet=internet_data.groupby(['user_id', 'month'])['mb_used'].agg(data_usage_mb='sum')\n",
    "user_internet\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now aggregate data together into one DataFrame so that one record in it would represent what an unique user consumed in a given month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "               calls_count  total_minutes  message_count  data_usage_mb\n",
       "user_id month                                                          \n",
       "1000    12            16.0          124.0           11.0        1901.47\n",
       "1001    8             27.0          182.0           30.0        6919.15\n",
       "        9             49.0          315.0           44.0       13314.82\n",
       "        10            65.0          393.0           53.0       22330.49\n",
       "        11            64.0          426.0           36.0       18504.30"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_complete_1=user_calls.merge(user_minutes, left_on=['user_id', 'month'], right_on=['user_id', 'month'], how='outer')\n",
    "\n",
    "users_complete_2=user_messages.merge(user_internet, left_on=['user_id', 'month'], right_on=['user_id', 'month'], how='outer')\n",
    "\n",
    "\n",
    "\n",
    "users_complete=users_complete_1.merge(users_complete_2, left_on=['user_id', 'month'], right_on=['user_id', 'month'], how='outer')\n",
    "users_complete.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Indeed `how = 'outer' can be used here to prevent data loss.\n",
    "    \n",
    "By the way, it is also possible to group 3 tables at once using for example reduce. You can read about it here: https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.17</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1498.83</td>\n",
       "      <td>NaN</td>\n",
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       "<p>2293 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "               calls_count  total_minutes  message_count  data_usage_mb  \\\n",
       "user_id month                                                             \n",
       "1000    12            16.0          124.0           11.0        1901.47   \n",
       "1001    8             27.0          182.0           30.0        6919.15   \n",
       "        9             49.0          315.0           44.0       13314.82   \n",
       "        10            65.0          393.0           53.0       22330.49   \n",
       "        11            64.0          426.0           36.0       18504.30   \n",
       "...                    ...            ...            ...            ...   \n",
       "1349    12             NaN            NaN           61.0       13039.91   \n",
       "1361    5              NaN            NaN            2.0        1519.69   \n",
       "1482    10             NaN            NaN            2.0            NaN   \n",
       "1108    12             NaN            NaN            NaN         233.17   \n",
       "1311    6              NaN            NaN            NaN        1498.83   \n",
       "\n",
       "               avg_call_duration  \n",
       "user_id month                     \n",
       "1000    12              7.750000  \n",
       "1001    8               6.740741  \n",
       "        9               6.428571  \n",
       "        10              6.046154  \n",
       "        11              6.656250  \n",
       "...                          ...  \n",
       "1349    12                   NaN  \n",
       "1361    5                    NaN  \n",
       "1482    10                   NaN  \n",
       "1108    12                   NaN  \n",
       "1311    6                    NaN  \n",
       "\n",
       "[2293 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_complete= users_complete.eval('avg_call_duration = total_minutes/calls_count')\n",
    "users_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   user_id  month  calls_count  total_minutes  message_count  data_usage_mb  \\\n",
       "0     1000     12         16.0          124.0           11.0        1901.47   \n",
       "1     1001      8         27.0          182.0           30.0        6919.15   \n",
       "2     1001      9         49.0          315.0           44.0       13314.82   \n",
       "3     1001     10         65.0          393.0           53.0       22330.49   \n",
       "4     1001     11         64.0          426.0           36.0       18504.30   \n",
       "\n",
       "   avg_call_duration  \n",
       "0           7.750000  \n",
       "1           6.740741  \n",
       "2           6.428571  \n",
       "3           6.046154  \n",
       "4           6.656250  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_complete = users_complete.reset_index()\n",
    "users_complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>plan</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>surf</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>surf</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>surf</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>surf</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>surf</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id      plan                                       city\n",
       "0       1000  ultimate      Atlanta-Sandy Springs-Roswell, GA MSA\n",
       "1       1001      surf            Seattle-Tacoma-Bellevue, WA MSA\n",
       "2       1002      surf       Las Vegas-Henderson-Paradise, NV MSA\n",
       "3       1003      surf                              Tulsa, OK MSA\n",
       "4       1004      surf            Seattle-Tacoma-Bellevue, WA MSA\n",
       "..       ...       ...                                        ...\n",
       "495     1495      surf  New York-Newark-Jersey City, NY-NJ-PA MSA\n",
       "496     1496      surf               New Orleans-Metairie, LA MSA\n",
       "497     1497  ultimate     Los Angeles-Long Beach-Anaheim, CA MSA\n",
       "498     1498      surf  New York-Newark-Jersey City, NY-NJ-PA MSA\n",
       "499     1499      surf          Orlando-Kissimmee-Sanford, FL MSA\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_plan=users_data[['user_id','plan','city']]\n",
    "user_plan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_complete = user_plan[['user_id', 'plan','city']].merge(\n",
    "    users_complete,\n",
    "    left_on='user_id',\n",
    "    right_on='user_id',\n",
    "    how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>plan</th>\n",
       "      <th>city</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_count</th>\n",
       "      <th>total_minutes</th>\n",
       "      <th>message_count</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>avg_call_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>6.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>6.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>6.046154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>6.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>1498</td>\n",
       "      <td>surf</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>8.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>8.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>7.264151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>6.844444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22059.21</td>\n",
       "      <td>7.630769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id      plan                                       city  month  \\\n",
       "0        1000  ultimate      Atlanta-Sandy Springs-Roswell, GA MSA   12.0   \n",
       "1        1001      surf            Seattle-Tacoma-Bellevue, WA MSA    8.0   \n",
       "2        1001      surf            Seattle-Tacoma-Bellevue, WA MSA    9.0   \n",
       "3        1001      surf            Seattle-Tacoma-Bellevue, WA MSA   10.0   \n",
       "4        1001      surf            Seattle-Tacoma-Bellevue, WA MSA   11.0   \n",
       "...       ...       ...                                        ...    ...   \n",
       "2298     1498      surf  New York-Newark-Jersey City, NY-NJ-PA MSA   12.0   \n",
       "2299     1499      surf          Orlando-Kissimmee-Sanford, FL MSA    9.0   \n",
       "2300     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   10.0   \n",
       "2301     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   11.0   \n",
       "2302     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   12.0   \n",
       "\n",
       "      calls_count  total_minutes  message_count  data_usage_mb  \\\n",
       "0            16.0          124.0           11.0        1901.47   \n",
       "1            27.0          182.0           30.0        6919.15   \n",
       "2            49.0          315.0           44.0       13314.82   \n",
       "3            65.0          393.0           53.0       22330.49   \n",
       "4            64.0          426.0           36.0       18504.30   \n",
       "...           ...            ...            ...            ...   \n",
       "2298         39.0          339.0            NaN       23137.69   \n",
       "2299         41.0          346.0            NaN       12984.76   \n",
       "2300         53.0          385.0            NaN       19492.43   \n",
       "2301         45.0          308.0            NaN       16813.83   \n",
       "2302         65.0          496.0            NaN       22059.21   \n",
       "\n",
       "      avg_call_duration  \n",
       "0              7.750000  \n",
       "1              6.740741  \n",
       "2              6.428571  \n",
       "3              6.046154  \n",
       "4              6.656250  \n",
       "...                 ...  \n",
       "2298           8.692308  \n",
       "2299           8.439024  \n",
       "2300           7.264151  \n",
       "2301           6.844444  \n",
       "2302           7.630769  \n",
       "\n",
       "[2303 rows x 9 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_complete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will calculate the monthly revenue from each user ( this will be done by subtracting  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). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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></th>\n",
       "      <th>avg_call_duration</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>message_count</th>\n",
       "      <th>total_minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>plan</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <th>12.0</th>\n",
       "      <th>ultimate</th>\n",
       "      <td>7.750000</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1001</th>\n",
       "      <th>8.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.740741</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.428571</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>44.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1002</th>\n",
       "      <th>10.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>5.363636</td>\n",
       "      <td>6552.01</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>7.018182</td>\n",
       "      <td>19345.08</td>\n",
       "      <td>32.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>8.170213</td>\n",
       "      <td>14396.24</td>\n",
       "      <td>41.0</td>\n",
       "      <td>384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <th>12.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>7.409396</td>\n",
       "      <td>27044.14</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1104.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        avg_call_duration  data_usage_mb  message_count  \\\n",
       "user_id month plan                                                        \n",
       "1000    12.0  ultimate           7.750000        1901.47           11.0   \n",
       "1001    8.0   surf               6.740741        6919.15           30.0   \n",
       "        9.0   surf               6.428571       13314.82           44.0   \n",
       "        10.0  surf               6.046154       22330.49           53.0   \n",
       "        11.0  surf               6.656250       18504.30           36.0   \n",
       "        12.0  surf               7.357143       19369.18           44.0   \n",
       "1002    10.0  surf               5.363636        6552.01           15.0   \n",
       "        11.0  surf               7.018182       19345.08           32.0   \n",
       "        12.0  surf               8.170213       14396.24           41.0   \n",
       "1003    12.0  surf               7.409396       27044.14           50.0   \n",
       "\n",
       "                        total_minutes  \n",
       "user_id month plan                     \n",
       "1000    12.0  ultimate          124.0  \n",
       "1001    8.0   surf              182.0  \n",
       "        9.0   surf              315.0  \n",
       "        10.0  surf              393.0  \n",
       "        11.0  surf              426.0  \n",
       "        12.0  surf              412.0  \n",
       "1002    10.0  surf               59.0  \n",
       "        11.0  surf              386.0  \n",
       "        12.0  surf              384.0  \n",
       "1003    12.0  surf             1104.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_table=pd.pivot_table(users_complete, values= ['data_usage_mb','message_count',\n",
    "                                                  'total_minutes', 'avg_call_duration'],index=['user_id','month','plan'], aggfunc=np.sum )\n",
    "sum_table.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_call_duration</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>message_count</th>\n",
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       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>plan</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <th>12.0</th>\n",
       "      <th>ultimate</th>\n",
       "      <td>7.750000</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1001</th>\n",
       "      <th>8.0</th>\n",
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       "      <td>6919.15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.428571</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>44.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <th>12.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>8.692308</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1499</th>\n",
       "      <th>9.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>8.439024</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>7.264151</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>6.844444</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <th>surf</th>\n",
       "      <td>7.630769</td>\n",
       "      <td>22059.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>22.0</td>\n",
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       "</table>\n",
       "<p>2293 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                        avg_call_duration  data_usage_mb  message_count  \\\n",
       "user_id month plan                                                        \n",
       "1000    12.0  ultimate           7.750000        1901.47           11.0   \n",
       "1001    8.0   surf               6.740741        6919.15           30.0   \n",
       "        9.0   surf               6.428571       13314.82           44.0   \n",
       "        10.0  surf               6.046154       22330.49           53.0   \n",
       "        11.0  surf               6.656250       18504.30           36.0   \n",
       "...                                   ...            ...            ...   \n",
       "1498    12.0  surf               8.692308       23137.69            0.0   \n",
       "1499    9.0   surf               8.439024       12984.76            0.0   \n",
       "        10.0  surf               7.264151       19492.43            0.0   \n",
       "        11.0  surf               6.844444       16813.83            0.0   \n",
       "        12.0  surf               7.630769       22059.21            0.0   \n",
       "\n",
       "                        total_minutes  data_usage_gb  \n",
       "user_id month plan                                    \n",
       "1000    12.0  ultimate          124.0            2.0  \n",
       "1001    8.0   surf              182.0            7.0  \n",
       "        9.0   surf              315.0           14.0  \n",
       "        10.0  surf              393.0           22.0  \n",
       "        11.0  surf              426.0           19.0  \n",
       "...                               ...            ...  \n",
       "1498    12.0  surf              339.0           23.0  \n",
       "1499    9.0   surf              346.0           13.0  \n",
       "        10.0  surf              385.0           20.0  \n",
       "        11.0  surf              308.0           17.0  \n",
       "        12.0  surf              496.0           22.0  \n",
       "\n",
       "[2293 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_table=sum_table.eval('data_usage_gb = data_usage_mb/1024')\n",
    "sum_table['data_usage_gb']=np.ceil(sum_table['data_usage_gb'])\n",
    "sum_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Great! Data aggregation and rounding is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_table=sum_table.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>59.0</td>\n",
       "      <td>32.0</td>\n",
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       "      <th>26</th>\n",
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       "      <td>6.704225</td>\n",
       "      <td>17106.99</td>\n",
       "      <td>21.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>24.0</td>\n",
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      ],
      "text/plain": [
       "    user_id  month      plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "0      1000   12.0  ultimate           7.750000        1901.47           11.0   \n",
       "19     1006   11.0  ultimate           5.000000        2068.37           15.0   \n",
       "20     1006   12.0  ultimate           6.555556       32118.82          139.0   \n",
       "26     1008   10.0  ultimate           6.704225       17106.99           21.0   \n",
       "27     1008   11.0  ultimate           7.079365       23676.72           37.0   \n",
       "\n",
       "    total_minutes  data_usage_gb  \n",
       "0           124.0            2.0  \n",
       "19           10.0            3.0  \n",
       "20           59.0           32.0  \n",
       "26          476.0           17.0  \n",
       "27          446.0           24.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table=sum_table[sum_table['plan']=='ultimate']\n",
    "ultimate_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
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      "text/plain": [
       "   user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1     1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2     1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3     1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4     1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5     1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "\n",
       "   total_minutes  data_usage_gb  \n",
       "1          182.0            7.0  \n",
       "2          315.0           14.0  \n",
       "3          393.0           22.0  \n",
       "4          426.0           19.0  \n",
       "5          412.0           19.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table=sum_table[sum_table['plan']=='surf']\n",
    "surf_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2258</th>\n",
       "      <td>1493</td>\n",
       "      <td>9.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.015152</td>\n",
       "      <td>14991.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-2471.0</td>\n",
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       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>1493</td>\n",
       "      <td>10.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>16329.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-2550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>1493</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.849315</td>\n",
       "      <td>15630.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>1493</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.276923</td>\n",
       "      <td>15289.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-2527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>1497</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>11106.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-950.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-2700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month      plan  avg_call_duration  data_usage_mb  \\\n",
       "0        1000   12.0  ultimate           7.750000        1901.47   \n",
       "19       1006   11.0  ultimate           5.000000        2068.37   \n",
       "20       1006   12.0  ultimate           6.555556       32118.82   \n",
       "26       1008   10.0  ultimate           6.704225       17106.99   \n",
       "27       1008   11.0  ultimate           7.079365       23676.72   \n",
       "...       ...    ...       ...                ...            ...   \n",
       "2258     1493    9.0  ultimate           8.015152       14991.17   \n",
       "2259     1493   10.0  ultimate           6.818182       16329.57   \n",
       "2260     1493   11.0  ultimate           6.849315       15630.78   \n",
       "2261     1493   12.0  ultimate           7.276923       15289.09   \n",
       "2277     1497   12.0  ultimate           5.555556       11106.55   \n",
       "\n",
       "      message_count  total_minutes  data_usage_gb  message_cost  data_cost  \\\n",
       "0              11.0          124.0            2.0        -989.0      -28.0   \n",
       "19             15.0           10.0            3.0        -985.0      -27.0   \n",
       "20            139.0           59.0           32.0        -861.0        2.0   \n",
       "26             21.0          476.0           17.0        -979.0      -13.0   \n",
       "27             37.0          446.0           24.0        -963.0       -6.0   \n",
       "...             ...            ...            ...           ...        ...   \n",
       "2258            0.0          529.0           15.0       -1000.0      -15.0   \n",
       "2259            0.0          450.0           16.0       -1000.0      -14.0   \n",
       "2260            0.0          500.0           16.0       -1000.0      -14.0   \n",
       "2261            0.0          473.0           15.0       -1000.0      -15.0   \n",
       "2277           50.0          300.0           11.0        -950.0      -19.0   \n",
       "\n",
       "      minute_cost  \n",
       "0         -2876.0  \n",
       "19        -2990.0  \n",
       "20        -2941.0  \n",
       "26        -2524.0  \n",
       "27        -2554.0  \n",
       "...           ...  \n",
       "2258      -2471.0  \n",
       "2259      -2550.0  \n",
       "2260      -2500.0  \n",
       "2261      -2527.0  \n",
       "2277      -2700.0  \n",
       "\n",
       "[720 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table= ultimate_table.eval(\"message_cost = message_count -1000\")\n",
    "ultimate_table= ultimate_table.eval('data_cost = data_usage_mb - 30720')\n",
    "ultimate_table= ultimate_table.eval('data_cost = data_cost/1024')\n",
    "ultimate_table['data_cost']=np.ceil(ultimate_table['data_cost'])\n",
    "ultimate_table= ultimate_table.eval('minute_cost = total_minutes-3000')\n",
    "ultimate_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>720 rows × 11 columns</p>\n",
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       "      user_id  month      plan  avg_call_duration  data_usage_mb  \\\n",
       "0        1000   12.0  ultimate           7.750000        1901.47   \n",
       "19       1006   11.0  ultimate           5.000000        2068.37   \n",
       "20       1006   12.0  ultimate           6.555556       32118.82   \n",
       "26       1008   10.0  ultimate           6.704225       17106.99   \n",
       "27       1008   11.0  ultimate           7.079365       23676.72   \n",
       "...       ...    ...       ...                ...            ...   \n",
       "2258     1493    9.0  ultimate           8.015152       14991.17   \n",
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       "2261     1493   12.0  ultimate           7.276923       15289.09   \n",
       "2277     1497   12.0  ultimate           5.555556       11106.55   \n",
       "\n",
       "      message_count  total_minutes  data_usage_gb  message_cost  data_cost  \\\n",
       "0              11.0          124.0            2.0           0.0        0.0   \n",
       "19             15.0           10.0            3.0           0.0        0.0   \n",
       "20            139.0           59.0           32.0           0.0        2.0   \n",
       "26             21.0          476.0           17.0           0.0        0.0   \n",
       "27             37.0          446.0           24.0           0.0        0.0   \n",
       "...             ...            ...            ...           ...        ...   \n",
       "2258            0.0          529.0           15.0           0.0        0.0   \n",
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       "2277           50.0          300.0           11.0           0.0        0.0   \n",
       "\n",
       "      minute_cost  \n",
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       "[720 rows x 11 columns]"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table['message_cost']=ultimate_table['message_cost'].clip(lower=0)\n",
    "ultimate_table['data_cost']=ultimate_table['data_cost'].clip(lower=0)\n",
    "ultimate_table['minute_cost']=ultimate_table['minute_cost'].clip(lower=0)\n",
    "ultimate_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>ultimate</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>16329.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>1493</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.849315</td>\n",
       "      <td>15630.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>1493</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.276923</td>\n",
       "      <td>15289.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>1497</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>11106.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month      plan  avg_call_duration  data_usage_mb  \\\n",
       "0        1000   12.0  ultimate           7.750000        1901.47   \n",
       "19       1006   11.0  ultimate           5.000000        2068.37   \n",
       "20       1006   12.0  ultimate           6.555556       32118.82   \n",
       "26       1008   10.0  ultimate           6.704225       17106.99   \n",
       "27       1008   11.0  ultimate           7.079365       23676.72   \n",
       "...       ...    ...       ...                ...            ...   \n",
       "2258     1493    9.0  ultimate           8.015152       14991.17   \n",
       "2259     1493   10.0  ultimate           6.818182       16329.57   \n",
       "2260     1493   11.0  ultimate           6.849315       15630.78   \n",
       "2261     1493   12.0  ultimate           7.276923       15289.09   \n",
       "2277     1497   12.0  ultimate           5.555556       11106.55   \n",
       "\n",
       "      message_count  total_minutes  data_usage_gb  message_cost  data_cost  \\\n",
       "0              11.0          124.0            2.0           0.0        0.0   \n",
       "19             15.0           10.0            3.0           0.0        0.0   \n",
       "20            139.0           59.0           32.0           0.0       14.0   \n",
       "26             21.0          476.0           17.0           0.0        0.0   \n",
       "27             37.0          446.0           24.0           0.0        0.0   \n",
       "...             ...            ...            ...           ...        ...   \n",
       "2258            0.0          529.0           15.0           0.0        0.0   \n",
       "2259            0.0          450.0           16.0           0.0        0.0   \n",
       "2260            0.0          500.0           16.0           0.0        0.0   \n",
       "2261            0.0          473.0           15.0           0.0        0.0   \n",
       "2277           50.0          300.0           11.0           0.0        0.0   \n",
       "\n",
       "      minute_cost  plan_cost  \n",
       "0             0.0         70  \n",
       "19            0.0         70  \n",
       "20            0.0         70  \n",
       "26            0.0         70  \n",
       "27            0.0         70  \n",
       "...           ...        ...  \n",
       "2258          0.0         70  \n",
       "2259          0.0         70  \n",
       "2260          0.0         70  \n",
       "2261          0.0         70  \n",
       "2277          0.0         70  \n",
       "\n",
       "[720 rows x 12 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table=ultimate_table.eval('minute_cost = minute_cost*0.01')\n",
    "ultimate_table=ultimate_table.eval('message_cost = message_cost*0.01')\n",
    "ultimate_table=ultimate_table.eval('data_cost = data_cost*7')\n",
    "ultimate_table=ultimate_table.eval('plan_cost = 70')\n",
    "ultimate_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>plan</th>\n",
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       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <th>27</th>\n",
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       "      <td>7.079365</td>\n",
       "      <td>23676.72</td>\n",
       "      <td>37.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0</td>\n",
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       "      <td>14991.17</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>1493</td>\n",
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       "      <td>6.818182</td>\n",
       "      <td>16329.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>1493</td>\n",
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       "      <td>6.849315</td>\n",
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       "    <tr>\n",
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       "      <td>1493</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month      plan  avg_call_duration  data_usage_mb  \\\n",
       "0        1000   12.0  ultimate           7.750000        1901.47   \n",
       "19       1006   11.0  ultimate           5.000000        2068.37   \n",
       "20       1006   12.0  ultimate           6.555556       32118.82   \n",
       "26       1008   10.0  ultimate           6.704225       17106.99   \n",
       "27       1008   11.0  ultimate           7.079365       23676.72   \n",
       "...       ...    ...       ...                ...            ...   \n",
       "2258     1493    9.0  ultimate           8.015152       14991.17   \n",
       "2259     1493   10.0  ultimate           6.818182       16329.57   \n",
       "2260     1493   11.0  ultimate           6.849315       15630.78   \n",
       "2261     1493   12.0  ultimate           7.276923       15289.09   \n",
       "2277     1497   12.0  ultimate           5.555556       11106.55   \n",
       "\n",
       "      message_count  total_minutes  data_usage_gb  message_cost  data_cost  \\\n",
       "0              11.0          124.0            2.0           0.0        0.0   \n",
       "19             15.0           10.0            3.0           0.0        0.0   \n",
       "20            139.0           59.0           32.0           0.0       14.0   \n",
       "26             21.0          476.0           17.0           0.0        0.0   \n",
       "27             37.0          446.0           24.0           0.0        0.0   \n",
       "...             ...            ...            ...           ...        ...   \n",
       "2258            0.0          529.0           15.0           0.0        0.0   \n",
       "2259            0.0          450.0           16.0           0.0        0.0   \n",
       "2260            0.0          500.0           16.0           0.0        0.0   \n",
       "2261            0.0          473.0           15.0           0.0        0.0   \n",
       "2277           50.0          300.0           11.0           0.0        0.0   \n",
       "\n",
       "      minute_cost  plan_cost  total_cost  \n",
       "0             0.0         70        70.0  \n",
       "19            0.0         70        70.0  \n",
       "20            0.0         70        84.0  \n",
       "26            0.0         70        70.0  \n",
       "27            0.0         70        70.0  \n",
       "...           ...        ...         ...  \n",
       "2258          0.0         70        70.0  \n",
       "2259          0.0         70        70.0  \n",
       "2260          0.0         70        70.0  \n",
       "2261          0.0         70        70.0  \n",
       "2277          0.0         70        70.0  \n",
       "\n",
       "[720 rows x 13 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table=ultimate_table.eval('total_cost= message_cost+data_cost+minute_cost+plan_cost')\n",
    "ultimate_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "  </tbody>\n",
       "</table>\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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plans_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1498</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>8.692308</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1499</td>\n",
       "      <td>9.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>8.439024</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1499</td>\n",
       "      <td>10.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.264151</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-50.0</td>\n",
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       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1499</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.844444</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1499</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.630769</td>\n",
       "      <td>22059.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-50.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1573 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1        1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2        1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3        1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4        1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5        1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "...       ...    ...   ...                ...            ...            ...   \n",
       "2288     1498   12.0  surf           8.692308       23137.69            0.0   \n",
       "2289     1499    9.0  surf           8.439024       12984.76            0.0   \n",
       "2290     1499   10.0  surf           7.264151       19492.43            0.0   \n",
       "2291     1499   11.0  surf           6.844444       16813.83            0.0   \n",
       "2292     1499   12.0  surf           7.630769       22059.21            0.0   \n",
       "\n",
       "      total_minutes  data_usage_gb  message_cost  \n",
       "1             182.0            7.0         -20.0  \n",
       "2             315.0           14.0          -6.0  \n",
       "3             393.0           22.0           3.0  \n",
       "4             426.0           19.0         -14.0  \n",
       "5             412.0           19.0          -6.0  \n",
       "...             ...            ...           ...  \n",
       "2288          339.0           23.0         -50.0  \n",
       "2289          346.0           13.0         -50.0  \n",
       "2290          385.0           20.0         -50.0  \n",
       "2291          308.0           17.0         -50.0  \n",
       "2292          496.0           22.0         -50.0  \n",
       "\n",
       "[1573 rows x 9 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table= surf_table.eval(\"message_cost = message_count - 50\")\n",
    "surf_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>1001</td>\n",
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       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
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       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001</td>\n",
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       "      <td>surf</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>8.692308</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>23.0</td>\n",
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       "      <th>2289</th>\n",
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       "      <td>12984.76</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>surf</td>\n",
       "      <td>7.264151</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>385.0</td>\n",
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       "      <td>-50.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1499</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.844444</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-50.0</td>\n",
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      "text/plain": [
       "      user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1        1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2        1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3        1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4        1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5        1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "...       ...    ...   ...                ...            ...            ...   \n",
       "2288     1498   12.0  surf           8.692308       23137.69            0.0   \n",
       "2289     1499    9.0  surf           8.439024       12984.76            0.0   \n",
       "2290     1499   10.0  surf           7.264151       19492.43            0.0   \n",
       "2291     1499   11.0  surf           6.844444       16813.83            0.0   \n",
       "2292     1499   12.0  surf           7.630769       22059.21            0.0   \n",
       "\n",
       "      total_minutes  data_usage_gb  message_cost  data_cost  \n",
       "1             182.0            7.0         -20.0       -8.0  \n",
       "2             315.0           14.0          -6.0       -1.0  \n",
       "3             393.0           22.0           3.0        7.0  \n",
       "4             426.0           19.0         -14.0        4.0  \n",
       "5             412.0           19.0          -6.0        4.0  \n",
       "...             ...            ...           ...        ...  \n",
       "2288          339.0           23.0         -50.0        8.0  \n",
       "2289          346.0           13.0         -50.0       -2.0  \n",
       "2290          385.0           20.0         -50.0        5.0  \n",
       "2291          308.0           17.0         -50.0        2.0  \n",
       "2292          496.0           22.0         -50.0        7.0  \n",
       "\n",
       "[1573 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table= surf_table.eval('data_cost = data_usage_mb - 15360')\n",
    "surf_table= surf_table.eval('data_cost = data_cost/1024')\n",
    "surf_table['data_cost']=np.ceil(surf_table['data_cost'])\n",
    "surf_table\n",
    "                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>data_usage_gb</th>\n",
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       "      <th>data_cost</th>\n",
       "      <th>minute_cost</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.740741</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-318.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
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       "      <td>6.428571</td>\n",
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       "      <td>44.0</td>\n",
       "      <td>315.0</td>\n",
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       "      <td>-185.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1001</td>\n",
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       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-107.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1     1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2     1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3     1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4     1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5     1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "\n",
       "   total_minutes  data_usage_gb  message_cost  data_cost  minute_cost  \n",
       "1          182.0            7.0         -20.0       -8.0       -318.0  \n",
       "2          315.0           14.0          -6.0       -1.0       -185.0  \n",
       "3          393.0           22.0           3.0        7.0       -107.0  \n",
       "4          426.0           19.0         -14.0        4.0        -74.0  \n",
       "5          412.0           19.0          -6.0        4.0        -88.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table= surf_table.eval('minute_cost = total_minutes-500')\n",
    "surf_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>plan</th>\n",
       "      <th>avg_call_duration</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>message_count</th>\n",
       "      <th>total_minutes</th>\n",
       "      <th>data_usage_gb</th>\n",
       "      <th>message_cost</th>\n",
       "      <th>data_cost</th>\n",
       "      <th>minute_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.740741</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.428571</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>44.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1498</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>8.692308</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1499</td>\n",
       "      <td>9.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>8.439024</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1499</td>\n",
       "      <td>10.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.264151</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1499</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.844444</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1499</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.630769</td>\n",
       "      <td>22059.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1573 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1        1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2        1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3        1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4        1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5        1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "...       ...    ...   ...                ...            ...            ...   \n",
       "2288     1498   12.0  surf           8.692308       23137.69            0.0   \n",
       "2289     1499    9.0  surf           8.439024       12984.76            0.0   \n",
       "2290     1499   10.0  surf           7.264151       19492.43            0.0   \n",
       "2291     1499   11.0  surf           6.844444       16813.83            0.0   \n",
       "2292     1499   12.0  surf           7.630769       22059.21            0.0   \n",
       "\n",
       "      total_minutes  data_usage_gb  message_cost  data_cost  minute_cost  \n",
       "1             182.0            7.0           0.0        0.0          0.0  \n",
       "2             315.0           14.0           0.0        0.0          0.0  \n",
       "3             393.0           22.0           3.0        7.0          0.0  \n",
       "4             426.0           19.0           0.0        4.0          0.0  \n",
       "5             412.0           19.0           0.0        4.0          0.0  \n",
       "...             ...            ...           ...        ...          ...  \n",
       "2288          339.0           23.0           0.0        8.0          0.0  \n",
       "2289          346.0           13.0           0.0        0.0          0.0  \n",
       "2290          385.0           20.0           0.0        5.0          0.0  \n",
       "2291          308.0           17.0           0.0        2.0          0.0  \n",
       "2292          496.0           22.0           0.0        7.0          0.0  \n",
       "\n",
       "[1573 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table['message_cost']=surf_table['message_cost'].clip(lower=0)\n",
    "surf_table['data_cost']=surf_table['data_cost'].clip(lower=0)\n",
    "surf_table['minute_cost']=surf_table['minute_cost'].clip(lower=0)\n",
    "surf_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1573 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "      user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1        1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "2        1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "3        1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "4        1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "5        1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "...       ...    ...   ...                ...            ...            ...   \n",
       "2288     1498   12.0  surf           8.692308       23137.69            0.0   \n",
       "2289     1499    9.0  surf           8.439024       12984.76            0.0   \n",
       "2290     1499   10.0  surf           7.264151       19492.43            0.0   \n",
       "2291     1499   11.0  surf           6.844444       16813.83            0.0   \n",
       "2292     1499   12.0  surf           7.630769       22059.21            0.0   \n",
       "\n",
       "      total_minutes  data_usage_gb  message_cost  data_cost  minute_cost  \\\n",
       "1             182.0            7.0          0.00        0.0          0.0   \n",
       "2             315.0           14.0          0.00        0.0          0.0   \n",
       "3             393.0           22.0          0.09       70.0          0.0   \n",
       "4             426.0           19.0          0.00       40.0          0.0   \n",
       "5             412.0           19.0          0.00       40.0          0.0   \n",
       "...             ...            ...           ...        ...          ...   \n",
       "2288          339.0           23.0          0.00       80.0          0.0   \n",
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       "2291          308.0           17.0          0.00       20.0          0.0   \n",
       "2292          496.0           22.0          0.00       70.0          0.0   \n",
       "\n",
       "      plan_cost  \n",
       "1            20  \n",
       "2            20  \n",
       "3            20  \n",
       "4            20  \n",
       "5            20  \n",
       "...         ...  \n",
       "2288         20  \n",
       "2289         20  \n",
       "2290         20  \n",
       "2291         20  \n",
       "2292         20  \n",
       "\n",
       "[1573 rows x 12 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table=surf_table.eval('minute_cost = minute_cost*0.03')\n",
    "surf_table=surf_table.eval('message_cost = message_cost*0.03')\n",
    "surf_table=surf_table.eval('data_cost = data_cost*10')\n",
    "surf_table=surf_table.eval('plan_cost = 20')\n",
    "surf_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7894.79</td>\n",
       "      <td>21.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1042</td>\n",
       "      <td>1.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>1854.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>1359</td>\n",
       "      <td>2.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.820513</td>\n",
       "      <td>20389.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>1099</td>\n",
       "      <td>2.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>5.571429</td>\n",
       "      <td>599.78</td>\n",
       "      <td>3.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>1362</td>\n",
       "      <td>2.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.650943</td>\n",
       "      <td>10761.20</td>\n",
       "      <td>58.0</td>\n",
       "      <td>811.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.33</td>\n",
       "      <td>20</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "1676     1362    1.0  surf           6.952381        7894.79           21.0   \n",
       "156      1042    1.0  surf           6.333333        1854.93            0.0   \n",
       "1656     1359    2.0  surf           6.820513       20389.87            0.0   \n",
       "462      1099    2.0  surf           5.571429         599.78            3.0   \n",
       "1677     1362    2.0  surf           7.650943       10761.20           58.0   \n",
       "\n",
       "      total_minutes  data_usage_gb  message_cost  data_cost  minute_cost  \\\n",
       "1676          292.0            8.0          0.00        0.0         0.00   \n",
       "156           114.0            2.0          0.00        0.0         0.00   \n",
       "1656          266.0           20.0          0.00       50.0         0.00   \n",
       "462            39.0            1.0          0.00        0.0         0.00   \n",
       "1677          811.0           11.0          0.24        0.0         9.33   \n",
       "\n",
       "      plan_cost  total_cost  \n",
       "1676         20       20.00  \n",
       "156          20       20.00  \n",
       "1656         20       70.00  \n",
       "462          20       20.00  \n",
       "1677         20       29.57  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table=surf_table.eval('total_cost= message_cost+data_cost+minute_cost+plan_cost')\n",
    "surf_table.sort_values(by='month').head()\n",
    "\n",
    "                           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Everything is correct here. Additionally, it could also be nice to create a function for the revenue calculation.\n",
    "\n",
    "The great thing about python is that there are so many libraries and ready-to-use functions. For example, this task can also be solved using np.where - [https://numpy.org/doc/stable/reference/generated/numpy.where.html](https://numpy.org/doc/stable/reference/generated/numpy.where.html)\n",
    "\n",
    "```\n",
    "  np.where ('condition: if exceeding the package limit', 'multiply the difference by the price of additional services', '0')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Also, it is better to avoid hard-coding in your functions of all values from the data plans. These values could be frequently updated and it will be annoyingly inconvenient to update them once again in the function. I would recommend using an actual table with values or create a separate data dictionary - https://towardsdatascience.com/12-examples-to-master-python-dictionaries-5a8bcd688c6d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study user behaviour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to continue by calculating some useful descriptive statistics for the aggregated and merged data, on a per plan basis, which will reveal an overall picture captured by the data and aid in comparing the plans. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>avg_call_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.201915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.053403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.393757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.215266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.121973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.807941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.324718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.018966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.117788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7.024169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6.969518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.971843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  avg_call_duration\n",
       "0     1.0           6.201915\n",
       "1     2.0           6.053403\n",
       "2     3.0           6.393757\n",
       "3     4.0           7.215266\n",
       "4     5.0           7.121973\n",
       "5     6.0           6.807941\n",
       "6     7.0           7.324718\n",
       "7     8.0           7.018966\n",
       "8     9.0           7.117788\n",
       "9    10.0           7.024169\n",
       "10   11.0           6.969518\n",
       "11   12.0           6.971843"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_min_ult=pd.pivot_table(ultimate_table, values='avg_call_duration', index='month', aggfunc='mean')\n",
    "avg_min_ult=avg_min_ult.reset_index()\n",
    "avg_min_ult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>month</th>\n",
       "      <th>avg_call_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.814628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.199957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.040349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.996065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.141114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.969665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.995927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.017420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.953572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.083883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.059456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  avg_call_duration\n",
       "0     1.0           6.642857\n",
       "1     2.0           6.814628\n",
       "2     3.0           7.199957\n",
       "3     4.0           7.040349\n",
       "4     5.0           6.996065\n",
       "5     6.0           7.141114\n",
       "6     7.0           6.969665\n",
       "7     8.0           6.995927\n",
       "8     9.0           7.017420\n",
       "9    10.0           6.953572\n",
       "10   11.0           7.083883\n",
       "11   12.0           7.059456"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare average duration of calls per each plan per each distinct month. Plot a bar plat to visualize it.\n",
    "\n",
    "avg_min_surf=pd.pivot_table(surf_table, values='avg_call_duration', index='month', aggfunc='mean')\n",
    "\n",
    "avg_min_surf=avg_min_surf.reset_index()\n",
    "avg_min_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = np.arange(len(avg_min_surf['month']))\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    "\n",
    "ax.bar(index, avg_min_ult['avg_call_duration'], color='r', align='edge', width=bar_width, label = 'Ultimate')\n",
    "ax.bar(index, avg_min_surf['avg_call_duration'], color='b', align='edge', width=-bar_width,label = 'Surf')\n",
    "\n",
    "# Assign the tick labels\n",
    "ax.set_xticks(index)\n",
    "ax.set_xticklabels(avg_min_ult['month'], rotation=90)\n",
    "\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('avg_call_duration')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>plan</th>\n",
       "      <th>city</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_count</th>\n",
       "      <th>total_minutes</th>\n",
       "      <th>message_count</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>avg_call_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>6.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>6.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>6.046154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>surf</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>6.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>1498</td>\n",
       "      <td>surf</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23137.69</td>\n",
       "      <td>8.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>8.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>7.264151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>11.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>6.844444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1499</td>\n",
       "      <td>surf</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22059.21</td>\n",
       "      <td>7.630769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id      plan                                       city  month  \\\n",
       "0        1000  ultimate      Atlanta-Sandy Springs-Roswell, GA MSA   12.0   \n",
       "1        1001      surf            Seattle-Tacoma-Bellevue, WA MSA    8.0   \n",
       "2        1001      surf            Seattle-Tacoma-Bellevue, WA MSA    9.0   \n",
       "3        1001      surf            Seattle-Tacoma-Bellevue, WA MSA   10.0   \n",
       "4        1001      surf            Seattle-Tacoma-Bellevue, WA MSA   11.0   \n",
       "...       ...       ...                                        ...    ...   \n",
       "2298     1498      surf  New York-Newark-Jersey City, NY-NJ-PA MSA   12.0   \n",
       "2299     1499      surf          Orlando-Kissimmee-Sanford, FL MSA    9.0   \n",
       "2300     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   10.0   \n",
       "2301     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   11.0   \n",
       "2302     1499      surf          Orlando-Kissimmee-Sanford, FL MSA   12.0   \n",
       "\n",
       "      calls_count  total_minutes  message_count  data_usage_mb  \\\n",
       "0            16.0          124.0           11.0        1901.47   \n",
       "1            27.0          182.0           30.0        6919.15   \n",
       "2            49.0          315.0           44.0       13314.82   \n",
       "3            65.0          393.0           53.0       22330.49   \n",
       "4            64.0          426.0           36.0       18504.30   \n",
       "...           ...            ...            ...            ...   \n",
       "2298         39.0          339.0            NaN       23137.69   \n",
       "2299         41.0          346.0            NaN       12984.76   \n",
       "2300         53.0          385.0            NaN       19492.43   \n",
       "2301         45.0          308.0            NaN       16813.83   \n",
       "2302         65.0          496.0            NaN       22059.21   \n",
       "\n",
       "      avg_call_duration  \n",
       "0              7.750000  \n",
       "1              6.740741  \n",
       "2              6.428571  \n",
       "3              6.046154  \n",
       "4              6.656250  \n",
       "...                 ...  \n",
       "2298           8.692308  \n",
       "2299           8.439024  \n",
       "2300           7.264151  \n",
       "2301           6.844444  \n",
       "2302           7.630769  \n",
       "\n",
       "[2303 rows x 9 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_complete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "I can advise to always use .head/.tail/.sample to limit the output from the table, as some tools could try to print all rows in your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": [
    "# Compare the number of minutes users of each plan require each month. Plot a histogram.\n",
    "ultimate=users_complete.loc[users_complete['plan'] == 'ultimate']\n",
    "ultimate.hist('total_minutes', bins=50, label= 'ultimate')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "surf=users_complete.loc[users_complete['plan'] == 'surf']\n",
    "surf.hist('total_minutes', bins=50, label= 'surf')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Can you please add axis labels to your graphs? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will calculate the variance and the means for the average call duration to give me an idea of user behavior between plans. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.158129850659148"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf['avg_call_duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2267282557007997"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(surf['avg_call_duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.078557173137358"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate['avg_call_duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9141908573365181"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(ultimate['avg_call_duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment v1</b>\n",
    " \n",
    "It's great that you managed to calculate all the necessary metrics and display them.\n",
    "\n",
    "There is also a way to display all statistical metrics in one table (you can pass all metrics to aggfunc at once.), using, for example, pivot_table:\n",
    "\n",
    "`data.pivot_table(index= 'tariff', values= '...', aggfunc= ['mean', 'var','std', 'median'])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": [
    "sns.boxplot(y=surf['avg_call_duration'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": [
    "sns.boxplot(y=ultimate['avg_call_duration'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these graphs and the descriptive statistics I do not see any significant difference between user behavior on each plan. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.10965630114566"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf['message_count'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1090.4511506183685"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(surf['message_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.29623287671233"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate['message_count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1083.3249173156314"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(ultimate['message_count'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": [
    "sns.boxplot(y=surf['message_count'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "sns.boxplot(y=ultimate['message_count'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "people with the ultimate plan seem to send a more text messages than surf users. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16717.702137355584"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf['data_usage_mb'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62128323.16300871"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(surf['data_usage_mb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17238.642253129346"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate['data_usage_mb'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61239556.12006475"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(ultimate['data_usage_mb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAADrCAYAAABD2BBHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUM0lEQVR4nO3df5Bd5X3f8fdXK4NEbBAIFWNJRvKsYo9/1IktG1LjjmvAlp2kYlr/IL+QKbb+AEvK4Jka2mRIk9A6k2kcULGDGlOvPB0TardBSYWwhE0cxhAjbCwMxJM1wUUCjLLilyNAkfTtH+eRvBK7qz2cvXvO1b5fM3fufZ5z7r3fywh99JznPOdEZiJJ0ss1q+0CJEn9zSCRJDVikEiSGjFIJEmNGCSSpEYMEklSI7PbLmC6nX766blkyZK2y5CkvnLvvff+Q2YuGGvbjAuSJUuWsH379rbLkKS+EhE/Gm+bh7YkSY0YJJKkRgwSSVIjBokkqRGDROqIkZER1q5dy8jISNulSLUYJFJH3HDDDezYsYMNGza0XYpUi0EidcDIyAjbtm0DYOvWrY5K1FcMEqkDbrjhBg4ePAjAwYMHHZWorxgkUgfcfvvtR7QPjU6kfmCQSB0QERO2pS4zSKQOOO+88yZsS11mkEgdsHr16sOjkIhg9erVLVckTZ5BInXE6CCR+olBInXA0NDQEUGycePGliuSJs8gkTpg27ZtHDhwAIADBw6wdevWliuSJs8gkTrg/PPPZ/bs6vZAs2fP5oILLmi5ImnyDBKpA1atWsWsWdX/jgMDA1x88cUtVyRNnkEidcD8+fNZsWIFEcGKFSuYP39+2yVJkzbjbrUrddWqVat45JFHHI2o7zgikSQ1YpBIHTE0NMT999/vqb/qOwaJ1AEjIyNs2bKFzGTLli1eRl59xSCROmBoaOjwZeQPHDjgqER9xSCROmDbtm3s378fgP3797sgUX3FIJE6wAWJ6mcGidQBLkhUPzNIpA5wQaL6mQsSpY5wQaL6lUEidcT8+fO57rrr2i5Dqs1DW5KkRgwSqSNGRkZYu3atixHVdwwSqSO8RIr61bQESUQMRMR3I+IvS3tpRPxNRAxHxJ9FxAml/8TSHi7bl4z6jKtK/w8i4v2j+leUvuGIuHI6fo801bxEivrZdI1I1gEPjWr/AfDZzBwEngIuLf2XAk+V/s+W/YiINwIXAW8CVgCfK+E0AFwPfAB4I/ArZV+pr3iJFPWzngdJRCwCfhH409IO4L3AV8ouQ8CF5fXK0qZsP6/svxK4KTNfzMy/B4aBd5bHcGY+nJn7gJvKvlJf8RIp6mfTMSL5Y+DfAwdLez7wdGbuL+2dwMLyeiHwKEDZ/kzZ/3D/Ue8Zr1/qK+effz4DAwNAtbLdS6Son/Q0SCLil4AnM/PeXn7PJOpYHRHbI2L77t272yxFGtOqVavITAAy00WJ6iu9HpG8C/jXEfEI1WGn9wLXAvMi4tBiyEXArvJ6F7AYoGw/BRgZ3X/Ue8brP0JmbsjM5Zm5fMGCBVPzyyRJQI+DJDOvysxFmbmEarL865n5a8A3gA+V3VYBt5TXm0qbsv3rWf0zbRNwUTmraymwDPg2cA+wrJwFdkL5jk29/E1SLwwNDR0xInGyXf2krXUknwauiIhhqjmQL5T+LwDzS/8VwJUAmfkAcDPwILAFuDwzD5R5lE8Ct1GdFXZz2VfqK1u3bj0iSL72ta+1XJE0edN2ra3MvAO4o7x+mOqMq6P3eQH48Djvvwa4Zoz+zcDmKSxVmnbz5s3j+eefP9w+9dRTW6xGqseV7VIHPP7440e0H3vssZYqkeozSCRJjRgkUgeceOKJR7TnzJnTUiVSfQaJ1AEvvvjiEe0XXnihpUqk+gwSSVIjBokkqRGDROqA008/fcK21GUGidQBr3nNa45oL1zotUfVPwwSqQN27NhxRPt73/teS5VI9RkkkqRGDBKpAw7di2S8ttRlBokkqRGDRJLUiEEidcC55557RPvd7353S5VI9RkkUgfs27dvwrbUZQaJ1AF33XXXEe1vfetbLVUi1WeQSJIaMUikDoiICdtSlxkkUge8+tWvPqJ95plntlSJVJ9BInXAU089dUR7z549LVUi1WeQSB1w2mmnTdiWuswgkTrgsccem7AtddnstgvQzLZ+/XqGh4fbLqOT1q1b13YJrRocHGTNmjVtl6FJcEQidcAJJ5wwYVvqMkckapX/4qwMDw/z8Y9//HD7c5/7HIODgy1WJE2eIxKpAwYHBw+PQhYvXmyIqK8YJFJHnHXWWcyaNYurr7667VKkWgwSqSNOOukk3vKWtzgaUd+pHSQRcXJEvKoXxUiS+s+kgyQi3hER9wM7gO9HxPci4u29K02S1A/qnLX1BeCyzPxrgIg4F/gfwD/vRWGSpP5Q59DWgUMhApCZdwL7p74kSVI/OeaIJCLeVl7+VUTcAHwZSOCjwB29K02S1A8mc2jrvx7VHn1uYk5hLZKkPnTMIMnMf/VyPzwi5gDfBE4s3/WVzLw6IpYCNwHzgXuB38jMfRFxIrAReDswAnw0Mx8pn3UVcClwAFibmbeV/hXAtcAA8KeZ+ZmXW68kqb5JT7ZHxDzgYmDJ6Pdl5toJ3vYi8N7M/ElEvAK4MyJuBa4APpuZN0XEn1AFxOfL81OZORgRFwF/AHw0It4IXAS8CXgNsC0ifrZ8x/XABcBO4J6I2JSZD072d0mSmqkz2b6ZKkTupxpFHHqMKys/Kc1XlEcC7wW+UvqHgAvL65WlTdl+XlT3HF0J3JSZL2bm3wPDwDvLYzgzH87MfVSjnJU1fpMkqaE6p//Oycwr6n5BRAxQBc4g1ejhh8DTmXnojK+dwMLyeiHwKEBm7o+IZ6gOfy0E7h71saPf8+hR/WfXrVGS9PLVGZF8KSI+ERFnRsRphx7HelNmHsjMnwMWUY0g3vAya33ZImJ1RGyPiO27d++e7q+XpONanSDZB/whcBc/Pay1fbJvzsyngW8AvwDMi4hDo6FFwK7yehewGKBsP4Vq0v1w/1HvGa//6O/ekJnLM3P5ggULJluyJGkS6gTJp4DBzFySmUvL43UTvSEiFpRJeiJiLtWk+ENUgfKhstsq4JbyelNpU7Z/PTOz9F8UESeWM76WAd8G7gGWRcTSiDiBakJ+U43fJElqqM4cyTCwt+bnnwkMlXmSWcDNmfmXEfEgcFNE/D7wXarLr1CevxQRw8AeqmAgMx+IiJuBB6lW01+emQcAIuKTwG1Up//emJkP1KxRktRAnSD5R+C+iPgG1Wm9wMSn/2bmDuDnx+h/mGq+5Oj+F4APj/NZ1wDXjNG/meqMMklSC+oEyZ+XhyRJh006SDJzaKLtEfHVzPy3zUuSJPWTqbxD4oQT75Kk49NUBokXcJSkGch7tkuSGpnKIIkp/CxJUp+oFSQRMTciXj/O5k9PQT2SpD4z6SCJiF8G7gO2lPbPRcThVeSZ+bUpr06S1Hl1RiS/Q7WI8GmAzLwPWDrlFUmS+kqdIPmnzHzmqD7P1JKkGa7OyvYHIuJXgYGIWAasBb7Vm7IkSf2izohkDdWtbl8Evgw8C/xmD2qSJPWROpdI2Qv8x/KQJAmoESQR8Re8dE7kGaqbW91QrtwrSZph6hzaehj4CfDfy+NZ4DngZ0tbkjQD1Zls/xeZ+Y5R7b+IiHsy8x0R4c2kJGmGqjMieWVEvPZQo7x+ZWnum9KqJEl9o86I5FPAnRHxQ6rrai0FLouInwEmvFeJJOn4Veesrc1l/cgbStcPRk2w//FUFyZJ6g91RiQAy4DXA3OAt0YEmblx6suSJPWLOqf/Xg28B3gjsBn4AHAnYJBI0gxWZ7L9Q8B5wBOZeQnwVuCUnlQlSeobdYLk+cw8COyPiJOBJ4HFvSlLktQv6syRbI+IeVSLD++lWpx4Vy+KkiT1jzpnbV1WXv5JRGwBTs7MHb0pS5LUL+rcIfFdZc0IwLnAxyLirN6UJUnqF3XmSD4P7I2It1ItTvwhnrElSTNenSDZn5kJrAT+W2ZeD7yqN2VJkvpFncn25yLiKuDXgX8ZEbOAV/SmLElSv6gzIvko1d0RL83MJ4BFwB/2pCpJUt+oc9bWE8AfjWr/P5wjkaQZr84lUp7jp3dIPIHqsNZPMtPV7ZI0g9UZkRyeWI+IoJp0P6cXRR3v1q9fz/DwcNtlqGMO/ZlYt25dy5WoawYHB1mzZk3bZYyr7tV/AShnb/15uZDjlePtFxGLqQ5/nUE1mtmQmddGxGnAnwFLgEeAj2TmUyWgrgU+COwFPpaZ3ymftQr4rfLRv5+ZQ6X/7cAXgblUF5NcV+rrrOHhYe77/kMcOOm0tktRh8zaV/2xvffhH7dcibpkYO+etks4pjqHtv7NqOYsYDnwwji7H7If+FRmficiXgXcGxFbgY8Bt2fmZyLiSqow+jTVFYWXlcfZVGtXzi7Bc3X5ziyfsykznyr7fAL4G6ogWQHcOtnf1ZYDJ53G82/4YNtlSOq4uX+7ue0SjqnOiOSXR73eTzWSWDnRGzLzceDx8vq5iHgIWFje956y2xBwB1WQrAQ2lhHF3RExLyLOLPtuzcw9ACWMVkTEHVSXarm79G8ELqQPgkSSjhd15kgumWh7RFyVmf9lgu1LgJ+nGjmcUUIG4AmqQ19Qhcyjo962s/RN1L9zjH5J0jSps47kWD483oaIeCXwVeA3M/PZ0dvK6KOncxoRsToitkfE9t27d/fyqyRpxpnKIIkxOyNeQRUi/zMz/3fp/nE5ZEV5frL07+LIe5wsKn0T9S8ao/8ImbkhM5dn5vIFCxbU/V2SpAlMZZC8ZFRRzsL6AvBQZv7RqE2bgFXl9SrgllH9F0flHOCZcgjsNuB9EXFqRJwKvA+4rWx7NiLOKd918ajPkiRNg5d1+u84xhqRvAv4DeD+iLiv9P0H4DPAzRFxKfAj4CNl22aqU3+HqU7/vQQgM/dExO8B95T9fvfQxDtwGT89/fdWnGiXpGk1lUHyv47uyMw7GeeQF9X934/eP4HLx9o5M28Ebhyjfzvw5lqVSpKmTJ11JHOAS4E3AXMO9WfmvyvP/3nKq5MkdV6dOZIvAa8G3g/8FdXE9nO9KEqS1D/qBMlgZv428I/l8iS/SLX6XJI0g9UJkn8qz09HxJuBU4B/NvUlSZL6SZ3J9g3l1NvfojpN95XAb/ekKklS36gTJLeXiyR+E3gdQEQs7UlVkqS+UefQ1lfH6PvKVBUiSepPxxyRRMQbqE75PeWoS8mfzKjTgCVJM9NkDm29HvglYB5HXkr+Oar7gKimXbt2MbD3mb64z4Ckdg3sHWHXrv1tlzGhYwZJZt4C3BIRv5CZd01DTZKkPlJnsv27EXE546xs1+QtXLiQJ16c7R0SJR3T3L/dzMKFZxx7xxa5sl2S1Igr2yVJjbiyXZLUiCvbJUmNTGYdyRWjmpeU5+vL889MeUWSpL4ymRHJq8rz64F3UI1GoFpT8u1eFCVJ6h+TWUfynwAi4pvA2zLzudL+HeD/9rQ6SVLn1ZlsPwPYN6q9r/RJkmawOpPtG4FvR8T/Ke0LgS9OdUGSpP4y6SDJzGsi4lbg3aXrksz8bm/KkiT1izojEjLzO8B3elSLJKkP1ZkjkSTpJQwSSVIjBokkqZFacySaOgN793hjKx1h1gvPAnBwzsktV6IuGdi7h66vtDBIWjA4ONh2Ceqg4eHqrgyDr+v2Xxqabmd0/u8Mg6QFa9asabsEddC6desAuPbaa1uuRKrHORJJUiMGiSSpEYNEktSIQSJJasQgkSQ1YpBIkhrpaZBExI0R8WREfH9U32kRsTUi/q48n1r6IyKui4jhiNgREW8b9Z5VZf+/i4hVo/rfHhH3l/dcFxHRy98jSXqpXo9IvgisOKrvSuD2zFwG3F7aAB8AlpXHauDzUAUPcDVwNvBO4OpD4VP2+cSo9x39XZKkHutpkGTmN4E9R3WvBIbK6yGqG2Qd6t+YlbuBeRFxJvB+YGtm7snMp4CtwIqy7eTMvDszk+rGWxciSZpWbcyRnJGZj5fXT/DTi8gsBB4dtd/O0jdR/84x+iVJ06jVyfYykshef09ErI6I7RGxfffu3b3+OkmaUdoIkh+Xw1KU5ydL/y5g8aj9FpW+ifoXjdH/Epm5ITOXZ+byBQsWTMmPkCRV2giSTcChM69WAbeM6r+4nL11DvBMOQR2G/C+iDi1TLK/D7itbHs2Is4pZ2tdPOqzJEnTpKdX/42ILwPvAU6PiJ1UZ199Brg5Ii4FfgR8pOy+GfggMAzsBS4ByMw9EfF7wD1lv9/NzEMT+JdRnRk2F7i1PCRJ06inQZKZvzLOpvPG2DeBy8f5nBuBG8fo3w68uUmNkqRmXNkuSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBokkqRGDRJLUiEEiSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBokkqRGDRJLUiEEiSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBokkqRGDRJLUiEEiSWrEIJEkNWKQSJIaMUgkSY0YJJKkRgwSSVIjBokkqRGDRJLUiEEiSWrEIJEkNXJcBElErIiIH0TEcERc2XY9kjSTzG67gKYiYgC4HrgA2AncExGbMvPBdivTZKxfv57h4eG2y+iEQ/8d1q1b13Il3TA4OMiaNWvaLkOTcDyMSN4JDGfmw5m5D7gJWNlyTVJtc+fOZe7cuW2XIdXW9yMSYCHw6Kj2TuDs0TtExGpgNcBrX/va6atMx+S/OKX+dzyMSI4pMzdk5vLMXL5gwYK2y5Gk48rxECS7gMWj2otKnyRpGhwPQXIPsCwilkbECcBFwKaWa5KkGaPv50gyc39EfBK4DRgAbszMB1ouS5JmjL4PEoDM3AxsbrsOSZqJjodDW5KkFhkkkqRGDBJJUiORmW3XMK0iYjfwo7brkMZxOvAPbRchjeGszBxzId6MCxKpyyJie2Yub7sOqQ4PbUmSGjFIJEmNGCRSt2xouwCpLudIJEmNOCKRJDVikEiSGjFIJEmNGCSSpEYMEklSI/8fVaq8rGQ1jsoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=ultimate['data_usage_mb'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": [
    "sns.boxplot(y=surf['data_usage_mb'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The behavior between plans is pretty similar for data usage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>total_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>34.396667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>45.941304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>40.710600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>47.628182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>49.276289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>62.674050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>63.931173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>58.271495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>65.421224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>58.004276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>70.557044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_cost\n",
       "month            \n",
       "1.0     20.000000\n",
       "2.0     34.396667\n",
       "3.0     45.941304\n",
       "4.0     40.710600\n",
       "5.0     47.628182\n",
       "6.0     49.276289\n",
       "7.0     62.674050\n",
       "8.0     63.931173\n",
       "9.0     58.271495\n",
       "10.0    65.421224\n",
       "11.0    58.004276\n",
       "12.0    70.557044"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_revenue_surf= pd.pivot_table(surf_table, values='total_cost', index='month', aggfunc='mean')\n",
    "monthly_revenue_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>total_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>74.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>70.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>71.638298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>71.898305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>72.859155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>72.034884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>72.311321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>71.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>73.291391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_cost\n",
       "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     72.859155\n",
       "9.0     72.034884\n",
       "10.0    72.311321\n",
       "11.0    71.708661\n",
       "12.0    73.291391"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_revenue_ult= pd.pivot_table(ultimate_table, values='total_cost', index='month', aggfunc='mean')\n",
    "monthly_revenue_ult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.706408137317226"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_table['total_cost'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.31388888888888"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimate_table['total_cost'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129.66814043209877"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(ultimate_table['total_cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3065.8848438690247"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(surf_table['total_cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": [
    "sns.boxplot(y=surf_table['total_cost'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": [
    "sns.boxplot(y=ultimate_table['total_cost'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "sns.boxplot(y=monthly_revenue_surf['total_cost'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": [
    "sns.boxplot(y=monthly_revenue_ult['total_cost'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the average monthly revenue between plans: The Ultimate plan is bringing in significantly more revenue. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "- I will write overall thoughts about the data analysis section:\n",
    "- Very nice visualization of the data. Great that you used different graph types to check available data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "- Additionally could be interesting to add both plans on one graph.\n",
    "- If you have time and willing to practice, you can display two charts (histogram and a box plot) using a two-column subplot.\n",
    "\n",
    "```\n",
    "    # Create one row with 2 columns\n",
    "\t  fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n",
    "\n",
    "\t  # Create a histogram for the surf plan\n",
    "\t  sns.distplot(... ax=axes[0])\n",
    "\t  # Create a histogram for the second plan\n",
    "\t  sns.distplot(... ax=axes[0])\n",
    "\t\n",
    "\t  # Create a boxplot for both plans\n",
    "\t  sns.boxplot(... ax=axes[1])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test statistical hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " First I will test the hypothesis that the average revenue from users of the Ultimate and Surf calling plans differs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H₀= there is no difference between the average revenue of the Ultimate plan and the average revenue of the Surf plan.\n",
    "\n",
    "H₁=there is a difference between the average revenues in the plans\n",
    "\n",
    "I will use the independent ttest to test these hypothesis.\n",
    "\n",
    "The alpha value will be 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_table=pd.concat([surf_table,ultimate_table], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2293 entries, 0 to 2292\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   user_id            2293 non-null   int64         \n",
      " 1   month              2293 non-null   float64       \n",
      " 2   plan               2293 non-null   object        \n",
      " 3   avg_call_duration  2293 non-null   float64       \n",
      " 4   data_usage_mb      2293 non-null   float64       \n",
      " 5   message_count      2293 non-null   float64       \n",
      " 6   total_minutes      2293 non-null   float64       \n",
      " 7   data_usage_gb      2293 non-null   float64       \n",
      " 8   message_cost       2293 non-null   float64       \n",
      " 9   data_cost          2293 non-null   float64       \n",
      " 10  minute_cost        2293 non-null   float64       \n",
      " 11  plan_cost          2293 non-null   int64         \n",
      " 12  total_cost         2293 non-null   float64       \n",
      " 13  first_name         2293 non-null   object        \n",
      " 14  last_name          2293 non-null   object        \n",
      " 15  age                2293 non-null   int64         \n",
      " 16  city               2293 non-null   object        \n",
      " 17  reg_date           2293 non-null   datetime64[ns]\n",
      " 18  churn_date         2293 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(10), int64(3), object(5)\n",
      "memory usage: 358.3+ KB\n"
     ]
    }
   ],
   "source": [
    "master_table=master_table.merge(users_data)\n",
    "master_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>plan</th>\n",
       "      <th>avg_call_duration</th>\n",
       "      <th>data_usage_mb</th>\n",
       "      <th>message_count</th>\n",
       "      <th>total_minutes</th>\n",
       "      <th>data_usage_gb</th>\n",
       "      <th>message_cost</th>\n",
       "      <th>data_cost</th>\n",
       "      <th>minute_cost</th>\n",
       "      <th>plan_cost</th>\n",
       "      <th>total_cost</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>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>8.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.740741</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20.00</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>9.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.428571</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>44.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20.00</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>10.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.046154</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>53.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>90.09</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>6.656250</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>60.00</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>19369.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>60.00</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  month  plan  avg_call_duration  data_usage_mb  message_count  \\\n",
       "0     1001    8.0  surf           6.740741        6919.15           30.0   \n",
       "1     1001    9.0  surf           6.428571       13314.82           44.0   \n",
       "2     1001   10.0  surf           6.046154       22330.49           53.0   \n",
       "3     1001   11.0  surf           6.656250       18504.30           36.0   \n",
       "4     1001   12.0  surf           7.357143       19369.18           44.0   \n",
       "\n",
       "   total_minutes  data_usage_gb  message_cost  data_cost  minute_cost  \\\n",
       "0          182.0            7.0          0.00        0.0          0.0   \n",
       "1          315.0           14.0          0.00        0.0          0.0   \n",
       "2          393.0           22.0          0.09       70.0          0.0   \n",
       "3          426.0           19.0          0.00       40.0          0.0   \n",
       "4          412.0           19.0          0.00       40.0          0.0   \n",
       "\n",
       "   plan_cost  total_cost first_name  last_name  age  \\\n",
       "0         20       20.00     Mickey  Wilkerson   28   \n",
       "1         20       20.00     Mickey  Wilkerson   28   \n",
       "2         20       90.09     Mickey  Wilkerson   28   \n",
       "3         20       60.00     Mickey  Wilkerson   28   \n",
       "4         20       60.00     Mickey  Wilkerson   28   \n",
       "\n",
       "                              city   reg_date churn_date  \n",
       "0  Seattle-Tacoma-Bellevue, WA MSA 2018-08-13          0  \n",
       "1  Seattle-Tacoma-Bellevue, WA MSA 2018-08-13          0  \n",
       "2  Seattle-Tacoma-Bellevue, WA MSA 2018-08-13          0  \n",
       "3  Seattle-Tacoma-Bellevue, WA MSA 2018-08-13          0  \n",
       "4  Seattle-Tacoma-Bellevue, WA MSA 2018-08-13          0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_avg_revenue= master_table['total_cost'].mean()\n",
    "master_table['total_cost'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "surf_avg_revenue= master_table[master_table['plan']== 'surf']['total_cost'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "ult_avg_revenue=master_table[master_table['plan']== 'ultimate']['total_cost'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 7.83119144714113e-05\n",
      "We reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "surf = monthly_revenue_surf['total_cost']\n",
    "\n",
    "ultimate = monthly_revenue_ult['total_cost']\n",
    "\n",
    "alpha= 0.05\n",
    "\n",
    "results = st.ttest_ind(surf,ultimate)\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 cant reject the null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Additionally, could be interesting to show/visualize selected samples with mean values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can reject the null hypothesis that the average revenue is the same. Therefore we can accept that the alternative hypothesis is true, there is a significant difference in revenue between the ultimate and surf plans. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will test the hypothesis that the average revenue from users in the NY-NJ area is different from that of the users from the other regions\n",
    "H₀= there is no difference between the average revenue of users in the NY-NJ area and that of the users from the other regions\n",
    "\n",
    "H₁=there is a difference between the average revenues of users in the NY-NJ area and that of the users from the other regions\n",
    "\n",
    "I will use the independent ttest to test these hypothesis.\n",
    "\n",
    "The alpha value will be 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "Overall great job! Hypotheses have been formulated and tested correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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>total_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>41.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>41.119286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>47.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>45.941600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>53.747879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>58.053171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>60.115745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>69.107170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>66.402419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>64.565789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_cost\n",
       "month            \n",
       "2.0     45.000000\n",
       "3.0     41.140000\n",
       "4.0     41.119286\n",
       "5.0     47.965000\n",
       "6.0     45.941600\n",
       "7.0     53.747879\n",
       "8.0     58.053171\n",
       "9.0     60.115745\n",
       "10.0    69.107170\n",
       "11.0    66.402419\n",
       "12.0    64.565789"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NY_NJ=master_table.loc[master_table['city'].str.contains('NY-NJ')]\n",
    "NY_NJ_avg_revenue = pd.pivot_table(NY_NJ, values='total_cost', index='month')\n",
    "NY_NJ_avg_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>total_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>53.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>49.973125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>55.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>50.260986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>53.946887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>56.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>65.697556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>66.651717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>62.498821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>67.550525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>62.249293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>71.437399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_cost\n",
       "month            \n",
       "1.0     53.333333\n",
       "2.0     49.973125\n",
       "3.0     55.790000\n",
       "4.0     50.260986\n",
       "5.0     53.946887\n",
       "6.0     56.575000\n",
       "7.0     65.697556\n",
       "8.0     66.651717\n",
       "9.0     62.498821\n",
       "10.0    67.550525\n",
       "11.0    62.249293\n",
       "12.0    71.437399"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_revenue_master= pd.pivot_table(master_table, values='total_cost', index='month')\n",
    "avg_revenue_master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.13479967352645303\n",
      "We cant reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "other = avg_revenue_master['total_cost']\n",
    "\n",
    "NYNJ =NY_NJ_avg_revenue['total_cost']\n",
    "\n",
    "alpha= 0.05\n",
    "\n",
    "results = st.ttest_ind(other,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 cant reject the null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "This is also correct.\n",
    "    \n",
    "You can also additionally compare the variances of the samples before testing hypotheses in order to understand with which equal_var parameter to use the ttest_ind test method (by default, True). Here you can manually calculate variances, or use Levene's statistic test (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html) or Barlett's test (https: //docs.scipy .org / doc / scipy / reference / generated / scipy.stats.bartlett.html # scipy.stats.bartlett)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General conclusion\n",
    "\n",
    "I have cleaned, enriched, and examined these data. I am confident in concluding based on my statisitical tests, descriptive statistics and data visualizations I was able to generate, that the Ultimate plan generated significantly more revenue in 2018 than did the Surf plan. Additionally, there is no significant difference in revenue between users in the NY-NJ area and the users in the other areas of use. \n",
    "\n",
    "Thank you again for your time reviewing my project :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\" role=\"alert\">\n",
    "<b>Reviewer's comment v1:</b>\n",
    "    \n",
    "- Overall you conducted great research with correct interim findings, clean code and nice visualisation.\n",
    "- Nevertheless, general conclusions represent the overall work progress that you achieved. On a real project, this is probably the only thing the business stakeholders will read. Therefore, it is crucial to indicate in a structured way all conclusions that you made on each step in the project. It is also important to provide explanations and interpretations that will be interesting for business based on your analysis (a detailed description of the average user per each plan, which client group brings more additional revenue, which group brings more revenue in the total revenue)."
   ]
  }
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