Megaline EDA.ipynb

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    "**Table of contents**<a id='toc0_'></a>    \n",
    "- [Which one is a better plan?](#toc1_)    \n",
    "  - [Purpose](#toc1_1_)    \n",
    "  - [Hypotheses](#toc1_2_)    \n",
    "  - [Initialization](#toc1_3_)    \n",
    "  - [Load data](#toc1_4_)    \n",
    "      - [Initial Thoughts](#toc1_4_1_1_)    \n",
    "  - [Prepare the data](#toc1_5_)    \n",
    "  - [Plans](#toc1_6_)    \n",
    "  - [Fix data](#toc1_7_)    \n",
    "  - [Users](#toc1_8_)    \n",
    "    - [Fix Data](#toc1_8_1_)    \n",
    "    - [Enrich Data](#toc1_8_2_)    \n",
    "  - [Calls](#toc1_9_)    \n",
    "    - [Fix data](#toc1_9_1_)    \n",
    "    - [Enrich data](#toc1_9_2_)    \n",
    "  - [Messages](#toc1_10_)    \n",
    "    - [Fix data](#toc1_10_1_)    \n",
    "    - [Enrich data](#toc1_10_2_)    \n",
    "  - [Internet](#toc1_11_)    \n",
    "    - [Fix data](#toc1_11_1_)    \n",
    "    - [Enrich data](#toc1_11_2_)    \n",
    "  - [Study plan conditions](#toc1_12_)    \n",
    "  - [Aggregate data per user](#toc1_13_)    \n",
    "  - [Study User Behavior](#toc1_14_)    \n",
    "    - [Calls](#toc1_14_1_)    \n",
    "        - [Null hypothesis that the mean call durations for both plans is similar](#toc1_14_1_1_1_)    \n",
    "    - [Messages](#toc1_14_2_)    \n",
    "        - [Null hypothesis is the mean number of messages for the plans are similar](#toc1_14_2_1_1_)    \n",
    "    - [Internet](#toc1_14_3_)    \n",
    "        - [Null hypothesis is that the two mean data traffic numbers are the same](#toc1_14_3_1_1_)    \n",
    "        - [Null hypothesis that mean data usage is similar](#toc1_14_3_1_2_)    \n",
    "  - [Revenue](#toc1_15_)    \n",
    "  - [Test statistical hypotheses](#toc1_16_)    \n",
    "        - [Testing the hypothesis that the average revenues from users of the Ultimate and Surf calling plans differ.](#toc1_16_1_1_1_)    \n",
    "        - [Null hypothesis is the mean revenues  of the Surf and Ultimate plans are similar](#toc1_16_1_1_2_)    \n",
    "    - [Testing the hypothesis that the average revenue from users in the NY-NJ area is different from that of the users from the other regions.](#toc1_16_2_)    \n",
    "        - [Null hypothesis that the mean revenue of Ny vs the other cities is similar](#toc1_16_2_1_1_)    \n",
    "      - [Relationship between revenue and age](#toc1_16_2_2_)    \n",
    "      - [Comparing revenue and age](#toc1_16_2_3_)    \n",
    "- [Conclusions](#toc2_)    \n",
    "\n",
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   "source": [
    "# <a id='toc1_'></a>[Which one is a better plan?](#toc0_)\n"
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   "source": [
    "## <a id='toc1_1_'></a>[Purpose](#toc0_)"
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    "The purpose of this project remains to analyze the data provided by the telecom operator Megaline. With an offering of two plans, Surf and Ultimate, the goal of this project is to determine optimal capital allocation. We will determine which plan brings in more revenue. This will result in an adjustment of the advertising budget, as a means to further increase revenue. The dataset provided is a sample of the population of Megaline customers, across different cities in 2018. We will conduct further analysis on the client behavior, as well as look at other important insights found in the data. "
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   "source": [
    "## <a id='toc1_2_'></a>[Hypotheses](#toc0_)"
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    "Initial thoughts suggest the Surf plan would bring in more revenue, as the overage charges, combined with the limited plan allotment, would lead to many customers paying fees. The Ultimate plan is more than double the price of the Surf plan, and the company lacks and middle tier plan. As a result, we hypothesize that the Surf plan would be far more popular than the Ultimate plan, further contributing to the differences in revenue. Yet another factor could be the overages charges on the Ultimate plan, as they are far lower than those of the Surf plan. We expect to see differences in plan preference based on age, as well as revenue when looking across age groups. "
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   "source": [
    "## <a id='toc1_3_'></a>[Initialization](#toc0_)"
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   "source": [
    "First, we need to load useful libraries that will aid us in evaluating the data."
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    "# Loading all the libraries\n",
    "\n",
    "import pandas as pd\n",
    "import math as mt\n",
    "import numpy as np\n",
    "from scipy import stats as st\n",
    "from matplotlib import pyplot as plt"
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   "source": [
    "## <a id='toc1_4_'></a>[Load data](#toc0_)"
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    "# Load the data files into different DataFrames\n",
    "df_calls = pd.read_csv('datasets/megaline_calls.csv')\n",
    "df_int = pd.read_csv('datasets/megaline_internet.csv')\n",
    "df_msg = pd.read_csv('datasets/megaline_messages.csv')\n",
    "df_plans = pd.read_csv('datasets/megaline_plans.csv')\n",
    "df_users = pd.read_csv('datasets/megaline_users.csv')"
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       "</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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>Fidel</td>\n",
       "      <td>Sharpe</td>\n",
       "      <td>67</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>Ariel</td>\n",
       "      <td>Shepherd</td>\n",
       "      <td>49</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>Donte</td>\n",
       "      <td>Barrera</td>\n",
       "      <td>49</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>Scot</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>51</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>Shena</td>\n",
       "      <td>Dickson</td>\n",
       "      <td>37</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name   last_name  age  \\\n",
       "0       1000   Anamaria       Bauer   45   \n",
       "1       1001     Mickey   Wilkerson   28   \n",
       "2       1002     Carlee     Hoffman   36   \n",
       "3       1003   Reynaldo     Jenkins   52   \n",
       "4       1004    Leonila    Thompson   40   \n",
       "..       ...        ...         ...  ...   \n",
       "495     1495      Fidel      Sharpe   67   \n",
       "496     1496      Ariel    Shepherd   49   \n",
       "497     1497      Donte     Barrera   49   \n",
       "498     1498       Scot  Williamson   51   \n",
       "499     1499      Shena     Dickson   37   \n",
       "\n",
       "                                          city    reg_date      plan  \\\n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA  2018-12-24  ultimate   \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA  2018-10-21      surf   \n",
       "3                                Tulsa, OK MSA  2018-01-28      surf   \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA  2018-05-23      surf   \n",
       "..                                         ...         ...       ...   \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-09-04      surf   \n",
       "496               New Orleans-Metairie, LA MSA  2018-02-20      surf   \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA  2018-12-10  ultimate   \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-02-04      surf   \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA  2018-05-06      surf   \n",
       "\n",
       "    churn_date  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "..         ...  \n",
       "495        NaN  \n",
       "496        NaN  \n",
       "497        NaN  \n",
       "498        NaN  \n",
       "499        NaN  \n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display files to have a visual of the data\n",
    "display(df_calls)\n",
    "display(df_int)\n",
    "display(df_msg)\n",
    "display(df_plans)\n",
    "display(df_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='toc1_4_1_1_'></a>[Initial Thoughts](#toc0_)\n",
    "\n",
    "Looking at the data we see all the information needed to calculate total revenue of the two plans. However, several dataframes would need to be merged, grouped, and appended with summary columns. The user ID column appears in many tables, so that can be the key to merging the different dataframes. We would also need to categorize the data by the specific plans: Surf and Unlimited. The call, message, and session dates can be used to append the data tables with a column that specifies the month. Then, we can categorize data by month to month. Analyzing the data based on a year would me a logical fallacy, as fluctuations in usage are expected on a monthly basis. In other words, some users may exceed their plan some months, and be charged fees, while also under use their allotment another month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_5_'></a>[Prepare the data](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data will be explored to determine the need for removing duplicates, missing values, or unnecessary columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_6_'></a>[Plans](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# Print the general/summary information about the plans' DataFrame\n",
    "df_plans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
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       "  <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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a sample of data for plans\n",
    "display(df_plans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a dataset that provides the variables for the two plans. We will use this data to calculate the monthly cost per customer, including overages charged to the customer. No missing values are present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_7_'></a>[Fix data](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Fix obvious issues with the data given the initial observations.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan</th>\n",
       "    </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",
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       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>ultimate</td>\n",
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      "text/plain": [
       "   messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute      plan  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at column names\n",
    "df_plans.columns = ['messages_included', 'mb_per_month_included', 'minutes_included',\n",
    "       'usd_monthly_pay', 'usd_per_gb', 'usd_per_message', 'usd_per_minute',\n",
    "       'plan']\n",
    "df_plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nothing needs to be fixed continue to the next dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_8_'></a>[Users](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "# Print the general/summary information about the users' DataFrame\n",
    "df_users.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two date columns are not in the date/time format. We need to change at least the reg date to extract data from the column. We are less concerned with churn date at the moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "df_users.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id       500\n",
       "first_name    458\n",
       "last_name     399\n",
       "age            58\n",
       "city           73\n",
       "reg_date      266\n",
       "plan            2\n",
       "churn_date     29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at unique values\n",
    "df_users.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>458</td>\n",
       "      <td>399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>266</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>David</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>surf</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>5</td>\n",
       "      <td>339</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1249.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.486000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.972269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1124.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1249.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1374.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id first_name last_name         age  \\\n",
       "count    500.000000        500       500  500.000000   \n",
       "unique          NaN        458       399         NaN   \n",
       "top             NaN    Leonila     David         NaN   \n",
       "freq            NaN          3         3         NaN   \n",
       "mean    1249.500000        NaN       NaN   45.486000   \n",
       "std      144.481833        NaN       NaN   16.972269   \n",
       "min     1000.000000        NaN       NaN   18.000000   \n",
       "25%     1124.750000        NaN       NaN   30.000000   \n",
       "50%     1249.500000        NaN       NaN   46.000000   \n",
       "75%     1374.250000        NaN       NaN   61.000000   \n",
       "max     1499.000000        NaN       NaN   75.000000   \n",
       "\n",
       "                                             city    reg_date  plan  \\\n",
       "count                                         500         500   500   \n",
       "unique                                         73         266     2   \n",
       "top     New York-Newark-Jersey City, NY-NJ-PA MSA  2018-03-08  surf   \n",
       "freq                                           80           5   339   \n",
       "mean                                          NaN         NaN   NaN   \n",
       "std                                           NaN         NaN   NaN   \n",
       "min                                           NaN         NaN   NaN   \n",
       "25%                                           NaN         NaN   NaN   \n",
       "50%                                           NaN         NaN   NaN   \n",
       "75%                                           NaN         NaN   NaN   \n",
       "max                                           NaN         NaN   NaN   \n",
       "\n",
       "        churn_date  \n",
       "count           34  \n",
       "unique          29  \n",
       "top     2018-12-18  \n",
       "freq             3  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick overview of tables and values\n",
    "df_users.describe(include='all',datetime_is_numeric=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice all user ID's are unique, so there are no duplicates in the data. Although there are only 458 unique first names, we expect some people could have the same name. This also applies to last names. We see this data is distributed among 73 cities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id  churn_date\n",
       "1006     2018-12-18    1\n",
       "1363     2018-08-16    1\n",
       "1281     2018-11-14    1\n",
       "1296     2018-12-18    1\n",
       "1298     2018-12-19    1\n",
       "1300     2018-12-19    1\n",
       "1315     2018-10-03    1\n",
       "1358     2018-10-22    1\n",
       "1402     2018-12-26    1\n",
       "1012     2018-11-16    1\n",
       "1414     2018-09-01    1\n",
       "1416     2018-11-21    1\n",
       "1441     2018-08-19    1\n",
       "1451     2018-12-10    1\n",
       "1466     2018-09-17    1\n",
       "1467     2018-11-18    1\n",
       "1269     2018-12-15    1\n",
       "1246     2018-07-31    1\n",
       "1220     2018-10-13    1\n",
       "1191     2018-11-30    1\n",
       "1186     2018-12-31    1\n",
       "1180     2018-12-22    1\n",
       "1172     2018-11-29    1\n",
       "1129     2018-12-27    1\n",
       "1106     2018-11-14    1\n",
       "1094     2018-12-12    1\n",
       "1084     2018-11-11    1\n",
       "1083     2018-12-18    1\n",
       "1067     2018-11-24    1\n",
       "1054     2018-12-31    1\n",
       "1050     2018-10-07    1\n",
       "1040     2018-12-30    1\n",
       "1022     2018-09-07    1\n",
       "1491     2018-09-18    1\n",
       "Name: churn_date, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Users who cancelled their plans \n",
    "df_users.groupby('user_id')['churn_date'].value_counts().nlargest(35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>Fidel</td>\n",
       "      <td>Sharpe</td>\n",
       "      <td>67</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>Ariel</td>\n",
       "      <td>Shepherd</td>\n",
       "      <td>49</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>Donte</td>\n",
       "      <td>Barrera</td>\n",
       "      <td>49</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>Scot</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>51</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>Shena</td>\n",
       "      <td>Dickson</td>\n",
       "      <td>37</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name   last_name  age  \\\n",
       "0       1000   Anamaria       Bauer   45   \n",
       "1       1001     Mickey   Wilkerson   28   \n",
       "2       1002     Carlee     Hoffman   36   \n",
       "3       1003   Reynaldo     Jenkins   52   \n",
       "4       1004    Leonila    Thompson   40   \n",
       "..       ...        ...         ...  ...   \n",
       "495     1495      Fidel      Sharpe   67   \n",
       "496     1496      Ariel    Shepherd   49   \n",
       "497     1497      Donte     Barrera   49   \n",
       "498     1498       Scot  Williamson   51   \n",
       "499     1499      Shena     Dickson   37   \n",
       "\n",
       "                                          city    reg_date      plan  \\\n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA  2018-12-24  ultimate   \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA  2018-08-13      surf   \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA  2018-10-21      surf   \n",
       "3                                Tulsa, OK MSA  2018-01-28      surf   \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA  2018-05-23      surf   \n",
       "..                                         ...         ...       ...   \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-09-04      surf   \n",
       "496               New Orleans-Metairie, LA MSA  2018-02-20      surf   \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA  2018-12-10  ultimate   \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA  2018-02-04      surf   \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA  2018-05-06      surf   \n",
       "\n",
       "    churn_date  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "..         ...  \n",
       "495        NaN  \n",
       "496        NaN  \n",
       "497        NaN  \n",
       "498        NaN  \n",
       "499        NaN  \n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a sample of data for users\n",
    "display(df_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset includes customers, their location, registration date, plan, and churn date. We have missing values in the churn date column we do not need to fix. We could set the missing values to 'active', to denote the plans are still active, yet that will convert the column data type. However, we need to change reg date to date/time format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_8_1_'></a>[Fix Data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert churn date column format to date/time\n",
    "df_users['churn_date'] = pd.to_datetime(df_users['churn_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert reg date column format to date/time \n",
    "df_users['reg_date'] = pd.to_datetime(df_users['reg_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                int64\n",
       "first_name            object\n",
       "last_name             object\n",
       "age                    int64\n",
       "city                  object\n",
       "reg_date      datetime64[ns]\n",
       "plan                  object\n",
       "churn_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm type change\n",
    "df_users.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not changing value to active, as this will change the column type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>52</td>\n",
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       "      <td>surf</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>Fidel</td>\n",
       "      <td>Sharpe</td>\n",
       "      <td>67</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>Ariel</td>\n",
       "      <td>Shepherd</td>\n",
       "      <td>49</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>Donte</td>\n",
       "      <td>Barrera</td>\n",
       "      <td>49</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>Scot</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>51</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>Shena</td>\n",
       "      <td>Dickson</td>\n",
       "      <td>37</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name   last_name  age  \\\n",
       "0       1000   Anamaria       Bauer   45   \n",
       "1       1001     Mickey   Wilkerson   28   \n",
       "2       1002     Carlee     Hoffman   36   \n",
       "3       1003   Reynaldo     Jenkins   52   \n",
       "4       1004    Leonila    Thompson   40   \n",
       "..       ...        ...         ...  ...   \n",
       "495     1495      Fidel      Sharpe   67   \n",
       "496     1496      Ariel    Shepherd   49   \n",
       "497     1497      Donte     Barrera   49   \n",
       "498     1498       Scot  Williamson   51   \n",
       "499     1499      Shena     Dickson   37   \n",
       "\n",
       "                                          city   reg_date      plan churn_date  \n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA 2018-12-24  ultimate        NaT  \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA 2018-08-13      surf        NaT  \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA 2018-10-21      surf        NaT  \n",
       "3                                Tulsa, OK MSA 2018-01-28      surf        NaT  \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA 2018-05-23      surf        NaT  \n",
       "..                                         ...        ...       ...        ...  \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-09-04      surf        NaT  \n",
       "496               New Orleans-Metairie, LA MSA 2018-02-20      surf        NaT  \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA 2018-12-10  ultimate        NaT  \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-02-04      surf        NaT  \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA 2018-05-06      surf        NaT  \n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at users dataframe\n",
    "display(df_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do not need to delete data based on churn date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_8_2_'></a>[Enrich Data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a month start column \n",
    "df_users['month_start'] = df_users['reg_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>month_start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaT</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>Fidel</td>\n",
       "      <td>Sharpe</td>\n",
       "      <td>67</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>Ariel</td>\n",
       "      <td>Shepherd</td>\n",
       "      <td>49</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>Donte</td>\n",
       "      <td>Barrera</td>\n",
       "      <td>49</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaT</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>Scot</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>51</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>Shena</td>\n",
       "      <td>Dickson</td>\n",
       "      <td>37</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name   last_name  age  \\\n",
       "0       1000   Anamaria       Bauer   45   \n",
       "1       1001     Mickey   Wilkerson   28   \n",
       "2       1002     Carlee     Hoffman   36   \n",
       "3       1003   Reynaldo     Jenkins   52   \n",
       "4       1004    Leonila    Thompson   40   \n",
       "..       ...        ...         ...  ...   \n",
       "495     1495      Fidel      Sharpe   67   \n",
       "496     1496      Ariel    Shepherd   49   \n",
       "497     1497      Donte     Barrera   49   \n",
       "498     1498       Scot  Williamson   51   \n",
       "499     1499      Shena     Dickson   37   \n",
       "\n",
       "                                          city   reg_date      plan  \\\n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA 2018-12-24  ultimate   \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA 2018-08-13      surf   \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA 2018-10-21      surf   \n",
       "3                                Tulsa, OK MSA 2018-01-28      surf   \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA 2018-05-23      surf   \n",
       "..                                         ...        ...       ...   \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-09-04      surf   \n",
       "496               New Orleans-Metairie, LA MSA 2018-02-20      surf   \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA 2018-12-10  ultimate   \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-02-04      surf   \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA 2018-05-06      surf   \n",
       "\n",
       "    churn_date  month_start  \n",
       "0          NaT           12  \n",
       "1          NaT            8  \n",
       "2          NaT           10  \n",
       "3          NaT            1  \n",
       "4          NaT            5  \n",
       "..         ...          ...  \n",
       "495        NaT            9  \n",
       "496        NaT            2  \n",
       "497        NaT           12  \n",
       "498        NaT            2  \n",
       "499        NaT            5  \n",
       "\n",
       "[500 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check proper implementation\n",
    "display(df_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Added the month start colum for ease in merging data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_9_'></a>[Calls](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "# Print the general/summary information about the calls' DataFrame\n",
    "df_calls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "    </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",
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       "    <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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137730</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137731</th>\n",
       "      <td>1499_200</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>10.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137732</th>\n",
       "      <td>1499_201</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137733</th>\n",
       "      <td>1499_202</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137734</th>\n",
       "      <td>1499_203</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137735 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id   call_date  duration\n",
       "0        1000_93     1000  2018-12-27      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",
       "...          ...      ...         ...       ...\n",
       "137730  1499_199     1499  2018-11-21      8.72\n",
       "137731  1499_200     1499  2018-10-20     10.89\n",
       "137732  1499_201     1499  2018-09-21      8.12\n",
       "137733  1499_202     1499  2018-10-10      0.37\n",
       "137734  1499_203     1499  2018-12-29     13.86\n",
       "\n",
       "[137735 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a sample of data for calls\n",
    "display(df_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id           137735\n",
       "user_id         481\n",
       "call_date       351\n",
       "duration       2802\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique values per column\n",
    "df_calls.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>count</th>\n",
       "      <td>137735</td>\n",
       "      <td>137735.000000</td>\n",
       "      <td>137735</td>\n",
       "      <td>137735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>137735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>351</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1000_93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1091</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1247.658046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.745927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>139.416268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.839241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1128.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id        user_id   call_date       duration\n",
       "count    137735  137735.000000      137735  137735.000000\n",
       "unique   137735            NaN         351            NaN\n",
       "top     1000_93            NaN  2018-12-27            NaN\n",
       "freq          1            NaN        1091            NaN\n",
       "mean        NaN    1247.658046         NaN       6.745927\n",
       "std         NaN     139.416268         NaN       5.839241\n",
       "min         NaN    1000.000000         NaN       0.000000\n",
       "25%         NaN    1128.000000         NaN       1.290000\n",
       "50%         NaN    1247.000000         NaN       5.980000\n",
       "75%         NaN    1365.000000         NaN      10.690000\n",
       "max         NaN    1499.000000         NaN      37.600000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick summary of data in columns\n",
    "df_calls.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calls data is complete with no missing values. We are not concerned with duplicates. The call date column should be changed to date/time format. We would also need to extract the month from the date, and categorize our values by user id and month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_9_1_'></a>[Fix data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing call date colum format to date/time\n",
    "df_calls['call_date'] = pd.to_datetime(df_calls['call_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   object\n",
       "user_id               int64\n",
       "call_date    datetime64[ns]\n",
       "duration            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for proper implementation\n",
    "df_calls.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_9_2_'></a>[Enrich data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a month column \n",
    "df_calls['month'] = df_calls['call_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137730</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>8.72</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137731</th>\n",
       "      <td>1499_200</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137732</th>\n",
       "      <td>1499_201</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>8.12</td>\n",
       "      <td>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>0.37</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>13.86</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      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",
       "...          ...      ...        ...       ...    ...\n",
       "137730  1499_199     1499 2018-11-21      8.72     11\n",
       "137731  1499_200     1499 2018-10-20     10.89     10\n",
       "137732  1499_201     1499 2018-09-21      8.12      9\n",
       "137733  1499_202     1499 2018-10-10      0.37     10\n",
       "137734  1499_203     1499 2018-12-29     13.86     12\n",
       "\n",
       "[137735 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for proper implementation\n",
    "display(df_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id  month\n",
       "1000     12       116.83\n",
       "1001     8        171.14\n",
       "         9        297.69\n",
       "         10       374.11\n",
       "         11       404.59\n",
       "                   ...  \n",
       "1498     12       324.77\n",
       "1499     9        330.37\n",
       "         10       363.28\n",
       "         11       288.56\n",
       "         12       468.10\n",
       "Name: duration, Length: 2258, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group data by user ID and month, then take the sum of the call duration\n",
    "df_calls_mo = df_calls.groupby(['user_id','month'])['duration'].sum()\n",
    "display(df_calls_mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>8</th>\n",
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       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.14</td>\n",
       "      <td>297.69</td>\n",
       "      <td>374.11</td>\n",
       "      <td>404.59</td>\n",
       "      <td>392.93</td>\n",
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       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.13</td>\n",
       "      <td>359.76</td>\n",
       "      <td>363.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1041.00</td>\n",
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       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.58</td>\n",
       "      <td>261.32</td>\n",
       "      <td>358.45</td>\n",
       "      <td>334.86</td>\n",
       "      <td>284.60</td>\n",
       "      <td>341.63</td>\n",
       "      <td>452.98</td>\n",
       "      <td>403.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.46</td>\n",
       "      <td>578.03</td>\n",
       "      <td>337.45</td>\n",
       "      <td>467.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.62</td>\n",
       "      <td>389.94</td>\n",
       "      <td>301.16</td>\n",
       "      <td>291.88</td>\n",
       "      <td>278.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>NaN</td>\n",
       "      <td>231.87</td>\n",
       "      <td>247.72</td>\n",
       "      <td>344.18</td>\n",
       "      <td>275.13</td>\n",
       "      <td>225.57</td>\n",
       "      <td>304.49</td>\n",
       "      <td>244.57</td>\n",
       "      <td>344.62</td>\n",
       "      <td>278.06</td>\n",
       "      <td>208.99</td>\n",
       "      <td>324.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330.37</td>\n",
       "      <td>363.28</td>\n",
       "      <td>288.56</td>\n",
       "      <td>468.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "month    1       2       3       4       5       6       7       8       9   \\\n",
       "user_id                                                                       \n",
       "1000    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1001    NaN     NaN     NaN     NaN     NaN     NaN     NaN  171.14  297.69   \n",
       "1002    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1003    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1004    NaN     NaN     NaN     NaN  181.58  261.32  358.45  334.86  284.60   \n",
       "...      ..     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "1495    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  283.46   \n",
       "1496    NaN     NaN     NaN     NaN     NaN     NaN     NaN  114.62  389.94   \n",
       "1497    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1498    NaN  231.87  247.72  344.18  275.13  225.57  304.49  244.57  344.62   \n",
       "1499    NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  330.37   \n",
       "\n",
       "month        10      11       12  \n",
       "user_id                           \n",
       "1000        NaN     NaN   116.83  \n",
       "1001     374.11  404.59   392.93  \n",
       "1002      54.13  359.76   363.24  \n",
       "1003        NaN     NaN  1041.00  \n",
       "1004     341.63  452.98   403.53  \n",
       "...         ...     ...      ...  \n",
       "1495     578.03  337.45   467.47  \n",
       "1496     301.16  291.88   278.61  \n",
       "1497        NaN     NaN   276.53  \n",
       "1498     278.06  208.99   324.77  \n",
       "1499     363.28  288.56   468.10  \n",
       "\n",
       "[481 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pivot table illustrating call duration\n",
    "df_calls_pivot = df_calls.pivot_table(index='user_id',\n",
    "                            columns='month',\n",
    "                            values='duration',\n",
    "                            aggfunc='sum'\n",
    "                                     )\n",
    "display(df_calls_pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We changed the call date colum to the date/time type, and extracted the month to create a column that distinguishes the months of the data. We notice some missing values in the data, but we will keep them. It is not unusual for some months to have no data, as users could start in different months. Users could also not make any calls in a particular month, which is less likely, but still a possibility. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_10_'></a>[Messages](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# Print the general/summary information about the messages' DataFrame\n",
    "df_msg.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76046</th>\n",
       "      <td>1497_526</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76047</th>\n",
       "      <td>1497_536</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76048</th>\n",
       "      <td>1497_547</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76049</th>\n",
       "      <td>1497_558</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76050</th>\n",
       "      <td>1497_613</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76051 rows × 3 columns</p>\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",
       "...         ...      ...          ...\n",
       "76046  1497_526     1497   2018-12-24\n",
       "76047  1497_536     1497   2018-12-24\n",
       "76048  1497_547     1497   2018-12-31\n",
       "76049  1497_558     1497   2018-12-24\n",
       "76050  1497_613     1497   2018-12-23\n",
       "\n",
       "[76051 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a sample of data for messages\n",
    "display(df_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>76051.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1245.972768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>139.843635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1362.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1497.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id\n",
       "count  76051.000000\n",
       "mean    1245.972768\n",
       "std      139.843635\n",
       "min     1000.000000\n",
       "25%     1123.000000\n",
       "50%     1251.000000\n",
       "75%     1362.000000\n",
       "max     1497.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick overview of the data in user ID column\n",
    "df_msg.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Message dataframe has a message date column that needs to be changed to date/time format. We do not see missing values, and are not concerned with duplicates. We would need to extract the month from the message day, group by user id, and count the number of messages for that month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_10_1_'></a>[Fix data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert column message date format to date/time\n",
    "df_msg['message_date'] = pd.to_datetime(df_msg['message_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      object\n",
       "user_id                  int64\n",
       "message_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure proper implementation\n",
    "df_msg.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_10_2_'></a>[Enrich data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a month column from message date\n",
    "df_msg['month'] = df_msg['message_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>month</th>\n",
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       "      <th>0</th>\n",
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       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>76047</th>\n",
       "      <td>1497_536</td>\n",
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       "      <td>2018-12-24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76048</th>\n",
       "      <td>1497_547</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76049</th>\n",
       "      <td>1497_558</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>76050</th>\n",
       "      <td>1497_613</td>\n",
       "      <td>1497</td>\n",
       "      <td>2018-12-23</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76051 rows × 4 columns</p>\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",
       "...         ...      ...          ...    ...\n",
       "76046  1497_526     1497   2018-12-24     12\n",
       "76047  1497_536     1497   2018-12-24     12\n",
       "76048  1497_547     1497   2018-12-31     12\n",
       "76049  1497_558     1497   2018-12-24     12\n",
       "76050  1497_613     1497   2018-12-23     12\n",
       "\n",
       "[76051 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual of new column, month\n",
    "display(df_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>1801</th>\n",
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       "      <th>1802</th>\n",
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       "      <th>1803</th>\n",
       "      <td>1496</td>\n",
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       "      <th>1804</th>\n",
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       "      <th>1805</th>\n",
       "      <td>1497</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  message_count\n",
       "0        1000     12             11\n",
       "1        1001      8             30\n",
       "2        1001      9             44\n",
       "3        1001     10             53\n",
       "4        1001     11             36\n",
       "...       ...    ...            ...\n",
       "1801     1496      9             21\n",
       "1802     1496     10             18\n",
       "1803     1496     11             13\n",
       "1804     1496     12             11\n",
       "1805     1497     12             50\n",
       "\n",
       "[1806 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grouping data by user ID and month, then counting the number of times a message was sent in that month\n",
    "df_msg_mo = df_msg.groupby(['user_id','month'])['message_date'].count().reset_index() \n",
    "df_msg_mo.columns = ['user_id', 'month', 'message_count']\n",
    "display(df_msg_mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>402 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                                   ...  \\\n",
       "month    1   2   3    4     5     6     7     8     9     10  ...   \n",
       "user_id                                                       ...   \n",
       "1000    NaN NaN NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "1001    NaN NaN NaN  NaN   NaN   NaN   NaN  30.0  44.0  53.0  ...   \n",
       "1002    NaN NaN NaN  NaN   NaN   NaN   NaN   NaN   NaN  15.0  ...   \n",
       "1003    NaN NaN NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "1004    NaN NaN NaN  NaN   7.0  18.0  26.0  25.0  21.0  24.0  ...   \n",
       "...      ..  ..  ..  ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "1491    NaN NaN NaN  6.0  45.0  54.0  64.0  50.0  50.0  51.0  ...   \n",
       "1492    NaN NaN NaN  NaN   NaN   NaN   NaN   NaN  11.0  29.0  ...   \n",
       "1494    NaN NaN NaN  NaN   NaN   NaN  20.0  27.0  21.0  38.0  ...   \n",
       "1496    NaN NaN NaN  NaN   NaN   NaN   NaN   2.0  21.0  18.0  ...   \n",
       "1497    NaN NaN NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "        message_date                                                       \n",
       "month             3    4     5     6     7     8     9     10    11    12  \n",
       "user_id                                                                    \n",
       "1000             NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  11.0  \n",
       "1001             NaN  NaN   NaN   NaN   NaN  30.0  44.0  53.0  36.0  44.0  \n",
       "1002             NaN  NaN   NaN   NaN   NaN   NaN   NaN  15.0  32.0  41.0  \n",
       "1003             NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  50.0  \n",
       "1004             NaN  NaN   7.0  18.0  26.0  25.0  21.0  24.0  25.0  31.0  \n",
       "...              ...  ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "1491             NaN  6.0  45.0  54.0  64.0  50.0  50.0  51.0  46.0  43.0  \n",
       "1492             NaN  NaN   NaN   NaN   NaN   NaN  11.0  29.0  31.0  37.0  \n",
       "1494             NaN  NaN   NaN   NaN  20.0  27.0  21.0  38.0  35.0  33.0  \n",
       "1496             NaN  NaN   NaN   NaN   NaN   2.0  21.0  18.0  13.0  11.0  \n",
       "1497             NaN  NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  50.0  \n",
       "\n",
       "[402 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pivot table showing the number of messages\n",
    "df_msg_pivot = df_msg.pivot_table(index='user_id',\n",
    "                            columns='month',\n",
    "                            aggfunc='count'\n",
    "                                     )\n",
    "display(df_msg_pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see some missing values, as expected, due to people starting their plans in different months. We will not fill in the missing data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_11_'></a>[Internet](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "# Print the general/summary information about the internet DataFrame\n",
    "df_int.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_204</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_379</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id session_date  mb_used\n",
       "0   1000_13     1000   2018-12-29    89.86\n",
       "1  1000_204     1000   2018-12-31     0.00\n",
       "2  1000_379     1000   2018-12-28   660.40\n",
       "3  1000_413     1000   2018-12-26   270.99\n",
       "4  1000_442     1000   2018-12-27   880.22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a sample of data for the internet traffic\n",
    "display(df_int.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The internet data does not contain missing values, and we are not concerned with duplicates. We would need to perform similar strategies with the previous tables. We should extract the month, and organize the mb used by user id and month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_11_1_'></a>[Fix data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert session date format to date/time\n",
    "df_int['session_date'] = pd.to_datetime(df_int['session_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      object\n",
       "user_id                  int64\n",
       "session_date    datetime64[ns]\n",
       "mb_used                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check proper implementation\n",
    "df_int.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converted session date to date/time format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_11_2_'></a>[Enrich data](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Created a month column from session date\n",
    "df_int['month'] = df_int['session_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check proper implementation\n",
    "display(df_int.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Added a month column to group data based on month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: mb_used, Length: 2277, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped the data based on user ID and month, then took the sum of the data used \n",
    "df_int_mo = df_int.groupby(['user_id', 'month'])['mb_used'].sum()\n",
    "display(df_int_mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1901.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6919.15</td>\n",
       "      <td>13314.82</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>18504.30</td>\n",
       "      <td>19369.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6552.01</td>\n",
       "      <td>19345.08</td>\n",
       "      <td>14396.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27044.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6547.21</td>\n",
       "      <td>20672.82</td>\n",
       "      <td>24516.62</td>\n",
       "      <td>27981.74</td>\n",
       "      <td>18852.72</td>\n",
       "      <td>14541.63</td>\n",
       "      <td>21850.78</td>\n",
       "      <td>21389.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22981.37</td>\n",
       "      <td>26899.41</td>\n",
       "      <td>24912.78</td>\n",
       "      <td>24097.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8605.66</td>\n",
       "      <td>16389.27</td>\n",
       "      <td>14287.36</td>\n",
       "      <td>8547.36</td>\n",
       "      <td>16438.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11106.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19822.04</td>\n",
       "      <td>19744.34</td>\n",
       "      <td>19878.86</td>\n",
       "      <td>22462.17</td>\n",
       "      <td>14807.18</td>\n",
       "      <td>24834.37</td>\n",
       "      <td>20261.89</td>\n",
       "      <td>22827.28</td>\n",
       "      <td>20580.76</td>\n",
       "      <td>19168.55</td>\n",
       "      <td>23137.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12984.76</td>\n",
       "      <td>19492.43</td>\n",
       "      <td>16813.83</td>\n",
       "      <td>22059.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>489 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "month    1         2         3         4         5         6         7   \\\n",
       "user_id                                                                   \n",
       "1000    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1001    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1002    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1003    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1004    NaN       NaN       NaN       NaN   6547.21  20672.82  24516.62   \n",
       "...      ..       ...       ...       ...       ...       ...       ...   \n",
       "1495    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1496    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1497    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1498    NaN  19822.04  19744.34  19878.86  22462.17  14807.18  24834.37   \n",
       "1499    NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "month          8         9         10        11        12  \n",
       "user_id                                                    \n",
       "1000          NaN       NaN       NaN       NaN   1901.47  \n",
       "1001      6919.15  13314.82  22330.49  18504.30  19369.18  \n",
       "1002          NaN       NaN   6552.01  19345.08  14396.24  \n",
       "1003          NaN       NaN       NaN       NaN  27044.14  \n",
       "1004     27981.74  18852.72  14541.63  21850.78  21389.29  \n",
       "...           ...       ...       ...       ...       ...  \n",
       "1495          NaN  22981.37  26899.41  24912.78  24097.40  \n",
       "1496      8605.66  16389.27  14287.36   8547.36  16438.99  \n",
       "1497          NaN       NaN       NaN       NaN  11106.55  \n",
       "1498     20261.89  22827.28  20580.76  19168.55  23137.69  \n",
       "1499          NaN  12984.76  19492.43  16813.83  22059.21  \n",
       "\n",
       "[489 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pivot table of data used per month\n",
    "df_int_pivot = df_int.pivot_table(index='user_id',\n",
    "                            columns='month',\n",
    "                            values='mb_used',\n",
    "                            aggfunc='sum'\n",
    "                                     )\n",
    "display(df_int_pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot table groups data by user id and month, then sums the data used in megabytes. We will keep the missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_12_'></a>[Study plan conditions](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "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>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan</th>\n",
       "    </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  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the plan conditions\n",
    "df_plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two plans: Surf and Ultimate. Surf costs 20 dollars per month, and includes overage fees. The ultimate plan is 70 dollars per month and also includes overage fees, but they are less than that of Surf. Data included in the plans is in megabytes, yet the cost is in gigabytes. We will use the conversion factor of 1 gb = 1024 mb. We note that the currency is US dollars. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_13_'></a>[Aggregate data per user](#toc0_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate the number of calls made by each user per month. Save the result.\n",
    "\n",
    "df_calls_num = df_calls.groupby(['user_id','month'])['duration'].count().reset_index()\n",
    "df_calls_num.columns = ['user_id', 'month', 'calls']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
       "        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>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>116.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>171.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>297.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>374.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>404.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>324.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>330.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>363.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>288.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>468.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  duration\n",
       "0        1000     12    116.83\n",
       "1        1001      8    171.14\n",
       "2        1001      9    297.69\n",
       "3        1001     10    374.11\n",
       "4        1001     11    404.59\n",
       "...       ...    ...       ...\n",
       "2253     1498     12    324.77\n",
       "2254     1499      9    330.37\n",
       "2255     1499     10    363.28\n",
       "2256     1499     11    288.56\n",
       "2257     1499     12    468.10\n",
       "\n",
       "[2258 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset index\n",
    "df_calls_mo.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Reset index\n",
    "df_msg = df_msg.loc[:,'user_id':]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>message_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
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       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
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       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>1496</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>1496</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>1496</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>1496</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1805</th>\n",
       "      <td>1497</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  message_count\n",
       "0        1000     12             11\n",
       "1        1001      8             30\n",
       "2        1001      9             44\n",
       "3        1001     10             53\n",
       "4        1001     11             36\n",
       "...       ...    ...            ...\n",
       "1801     1496      9             21\n",
       "1802     1496     10             18\n",
       "1803     1496     11             13\n",
       "1804     1496     12             11\n",
       "1805     1497     12             50\n",
       "\n",
       "[1806 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of messages sent by each user per month. Save the result.\n",
    "df_msg_mo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>1901.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>6919.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>13314.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>22330.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>18504.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>23137.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>12984.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>19492.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>16813.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>22059.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2277 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month   mb_used\n",
       "0        1000     12   1901.47\n",
       "1        1001      8   6919.15\n",
       "2        1001      9  13314.82\n",
       "3        1001     10  22330.49\n",
       "4        1001     11  18504.30\n",
       "...       ...    ...       ...\n",
       "2272     1498     12  23137.69\n",
       "2273     1499      9  12984.76\n",
       "2274     1499     10  19492.43\n",
       "2275     1499     11  16813.83\n",
       "2276     1499     12  22059.21\n",
       "\n",
       "[2277 rows x 3 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the volume of internet traffic used by each user per month. Save the result.\n",
    "df_int_mo.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merge the data for calls, minutes, messages, internet based on user_id and month\n",
    "df_1 = df_calls_num.merge(df_calls_mo, on=('user_id', 'month'), how='outer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Appending data column\n",
    "df_2 = df_1.merge(df_int_mo, on=('user_id', 'month'), how='outer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appending number of messages column \n",
    "df_3= df_2.merge(df_msg_mo, on=('user_id', 'month'), how='outer')\n",
    "df_3.columns = ['user_id', 'month', 'num_calls', 'call_duration', 'mb_used', 'num_messages']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User ID and plan table \n",
    "df_user_plan = df_users[['user_id', 'plan']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Add the plan information, merge on user ID\n",
    "df_4 = df_3.merge(df_user_plan, on='user_id', how='outer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan</th>\n",
       "    </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  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual of plans table\n",
    "df_plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Merging for final dataset\n",
    "df_merged = df_4.merge(df_plans, on='plan', how='outer')\n",
    "df_merged = df_merged.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                  0\n",
       "month                    0\n",
       "num_calls                0\n",
       "call_duration            0\n",
       "mb_used                  0\n",
       "num_messages             0\n",
       "plan                     0\n",
       "messages_included        0\n",
       "mb_per_month_included    0\n",
       "minutes_included         0\n",
       "usd_monthly_pay          0\n",
       "usd_per_gb               0\n",
       "usd_per_message          0\n",
       "usd_per_minute           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df_merged.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Calculate the monthly revenue from each user (subtract the free package limit from the total number of calls, text messages, and data; multiply the result by the calling plan value; add the monthly charge depending on the calling plan).]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to round up minutes, gb data\n",
    "def round_up(n, decimals=0):\n",
    "    multiplier = 10 ** decimals\n",
    "    return mt.ceil(n * multiplier) / multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking functionality of round function\n",
    "round_up(31.366)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the monthly revenue for each user\n",
    "# using megabytes instead of gigabytes, converted cost to appropriate value\n",
    "\n",
    "def revenue(row) :\n",
    "    \n",
    "    additional_mins = 0                      # to add additional minutes\n",
    "    additional_messages = 0                  # to add additional messages\n",
    "    additional_gb = 0                        # to add additional data\n",
    "    surf = 20                                # base price of Surf plan\n",
    "    ultimate = 70                            # base price of Ultimate Plan\n",
    "    \n",
    "    plan = row['plan']                       # looking at the plan row\n",
    "    call = round_up(row['call_duration'])    # rounding the call duration to the nearest minute\n",
    "    gb = round_up(row['mb_used'] / 1024)     # rounding the data to the nearest gigabyte\n",
    "    \n",
    "    \n",
    "    if plan == 'surf' :\n",
    "        if call > 500 :                      \n",
    "            additional_mins = call - 500\n",
    "        if row['num_messages'] > 50 :\n",
    "            additional_messages = row['num_messages'] - 50\n",
    "        if gb > 15 :\n",
    "            additional_gb = gb - 15\n",
    "        profit = (additional_mins * 0.03) + (additional_messages * 0.03) + (additional_gb * 10 )\n",
    "        if profit == 0 :\n",
    "            return surf\n",
    "        else :\n",
    "            return profit + surf\n",
    "\n",
    "        \n",
    "    if plan == 'ultimate' :\n",
    "        if call > 3000 :\n",
    "            additional_mins = call - 3000\n",
    "        if row['num_messages'] > 1000 :\n",
    "            additional_messages = row['num_messages'] - 1000      \n",
    "        if gb > 30 :\n",
    "            additional_gb = gb - 30\n",
    "        profit = (additional_mins * 0.01) + (additional_messages * 0.01) + (additional_gb * 7 )\n",
    "        if profit == 0 :\n",
    "            return ultimate\n",
    "        else :\n",
    "            return profit + ultimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_14_'></a>[Study User Behavior](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will calculate some useful descriptive statistics for the aggregated and merged data, to reveal an overall picture captured by the data. We will display useful plots to help with the understanding of the given data. Given that the main task is to compare the plans and decide on which one is more profitable, the statistics and the plots will be calculated on a per-plan, and on a per month basis. Insights will be given on the relationships of the data among the various parameters, including age and location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_14_1_'></a>[Calls](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plan</th>\n",
       "      <th>month</th>\n",
       "      <th>call_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>surf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>192.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>280.851111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>310.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surf</td>\n",
       "      <td>4.0</td>\n",
       "      <td>332.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>377.053247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>surf</td>\n",
       "      <td>6.0</td>\n",
       "      <td>407.208866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>424.523223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>surf</td>\n",
       "      <td>8.0</td>\n",
       "      <td>387.169630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>390.992062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>surf</td>\n",
       "      <td>10.0</td>\n",
       "      <td>405.692363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>surf</td>\n",
       "      <td>11.0</td>\n",
       "      <td>399.599823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surf</td>\n",
       "      <td>12.0</td>\n",
       "      <td>447.475283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>183.162500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.0</td>\n",
       "      <td>379.861429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>3.0</td>\n",
       "      <td>285.701667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>316.508095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.0</td>\n",
       "      <td>383.664828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>349.811064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>403.767288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.0</td>\n",
       "      <td>397.274789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>413.287326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>425.168019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>11.0</td>\n",
       "      <td>420.477559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>12.0</td>\n",
       "      <td>433.012583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        plan  month  call_duration\n",
       "0       surf    0.0       0.000000\n",
       "1       surf    1.0     192.840000\n",
       "2       surf    2.0     280.851111\n",
       "3       surf    3.0     310.970000\n",
       "4       surf    4.0     332.380000\n",
       "5       surf    5.0     377.053247\n",
       "6       surf    6.0     407.208866\n",
       "7       surf    7.0     424.523223\n",
       "8       surf    8.0     387.169630\n",
       "9       surf    9.0     390.992062\n",
       "10      surf   10.0     405.692363\n",
       "11      surf   11.0     399.599823\n",
       "12      surf   12.0     447.475283\n",
       "13  ultimate    0.0       0.000000\n",
       "14  ultimate    1.0     183.162500\n",
       "15  ultimate    2.0     379.861429\n",
       "16  ultimate    3.0     285.701667\n",
       "17  ultimate    4.0     316.508095\n",
       "18  ultimate    5.0     383.664828\n",
       "19  ultimate    6.0     349.811064\n",
       "20  ultimate    7.0     403.767288\n",
       "21  ultimate    8.0     397.274789\n",
       "22  ultimate    9.0     413.287326\n",
       "23  ultimate   10.0     425.168019\n",
       "24  ultimate   11.0     420.477559\n",
       "25  ultimate   12.0     433.012583"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare average duration of calls per each plan per each distinct month. Plot a bar plat to visualize it.\n",
    "df_merged_calls = df_merged.groupby(['plan', 'month'])['call_duration'].mean()\n",
    "display(df_merged_calls.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separated calls on Surf plan\n",
    "df_surf_calls = df_merged_calls[1:13].reset_index('plan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separated calls on Ultimate plan\n",
    "df_ultimate_calls = df_merged_calls.reset_index('plan').tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged Surf and Ultimate call plans side by side\n",
    "df_all_calls = df_surf_calls.merge(df_ultimate_calls, on='month', how='outer')\n",
    "df_all_calls.columns = ['plan_s', 'surf', 'plan_u', 'ultimate'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AADAcH80DwA5ltT7RI/GpHgCwkQmzAACwhlbtY3RX5zQwLMuMAQAAGI4wCwAAwHAsMwYA7h8XKgOwDmY+M1tVu1bVBVX1ocnzh1fVOVX1pcn3h80be0pVXV5Vl1XVMbOuDQAAgDGtxTLjk5JcOu/5a5J8vLsPTPLxyfNU1eOSHJfk8UmeleQ/V9Wua1AfAAAAg5lpmK2qLUmem+QP5m1+fpLTJo9PS/KCedvf1923dPdXklye5IhZ1gcAAMCYZj0z+7Ykr05yx7xt+3T31Uky+f7Iyfb9klw5b9z2yba7qaoTq2pbVW279tprZ1I0AACwvqpW74sd08zCbFUdm+Sa7j5/2kMW2XaPu0B099u7+/DuPnzvvfe+XzUCAAAwplnezfjIJM+rquck2ZzkIVX1riRfr6pHdffVVfWoJNdMxm9Psv+847ckuWqG9QEAADComc3Mdvcp3b2lu7dm7sZOf9bdL0lyVpITJsNOSPLByeOzkhxXVQ+oqgOSHJjkvFnVBwCwpqyZBFhV6/E5s29JckZVvTzJ15K8MEm6++KqOiPJJUluS/LK7r59HeoDAABgg6se+MPJDz/88N62bdt6lwHABrKak1YD/xW5olXt06K3vbivJ9P0qezIfUpWr1cbtE+r9vZ28D97G/L/U/p0L062Or2qqvO7+/DF9q3F58wCAADAqhJmAQAAGI4wCwAAwHCEWQAAAIazHnczBgBgB7O6N6EBWJmZWQAAAIYjzAIAADAcYRYAAIDhCLMAAAAMxw2gAACWsVo3NnJTI4DVZWYWAACA4QizAAAADEeYBQAAYDiumQXY2azWBYBJ0q4CBADWh5lZAAAAhiPMAgAAMBxhFgAAgOG4Zpb147o9AADgPjIzCwAAwHCEWQAAAIYjzAIAADAc18wCDGK1LjN3hTkAsCMwMwsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDib1rsAANiwqlbnPN2rcx4A4C5mZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGs2m9CwB2blWrd67u1TsXAAAbm5lZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMJxN610AwKqpWp3zdK/OeQAAmBkzswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcNzNGGbEjXUBAGB2zMwCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwnJmF2araXFXnVdVfVdXFVfWGyfaHV9U5VfWlyfeHzTvmlKq6vKouq6pjZlUbAAAAY5vlzOwtSZ7e3U9McmiSZ1XVU5O8JsnHu/vAJB+fPE9VPS7JcUken+RZSf5zVe06w/oAAAAY1MzCbM/5h8nT3SZfneT5SU6bbD8tyQsmj5+f5H3dfUt3fyXJ5UmOmFV9AAAAjGum18xW1a5VdWGSa5Kc092fS7JPd1+dJJPvj5wM3y/JlfMO3z7ZtvCcJ1bVtqradu21186yfAAAADaomYbZ7r69uw9NsiXJEVV18DLDa7FTLHLOt3f34d19+N57771KlQIAADCSNbmbcXd/M8knMnct7Ner6lFJMvl+zWTY9iT7zztsS5Kr1qI+AAAAxjLLuxnvXVV7Th7vnuSHknwxyVlJTpgMOyHJByePz0pyXFU9oKoOSHJgkvNmVR8AAADj2jTDcz8qyWmTOxLvkuSM7v5QVf1FkjOq6uVJvpbkhUnS3RdX1RlJLklyW5JXdvftM6wPAACAQc0szHb3RUkOW2T79UmescQxb0ryplnVBAAAwI5hTa6ZBQAAgNUkzAIAADAcYRYAAIDhCLMAAAAMR5gFAABgOMIsAAAAwxFmAQAAGI4wCwAAwHCEWQAAAIYjzAIAADCcTetdALCCqtU7V/fqnQsAANaRmVkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjuZsy9sqo31l29UwEAADuZFcNsVe2d5KeTbJ0/vrtfNruyAAAAYGnTzMx+MMmnk3wsye2zLQcAAABWNk2YfWB3nzzzSgAAAGBK09wA6kNV9ZyZVwIAAABTmibMnpS5QHtzVd04+frWrAsDAACApay4zLi791iLQgAAAGBaU300T1U9L8kPTp5+ors/NLuSAAAAYHkrLjOuqrdkbqnxJZOvkybbAAAAYF1MMzP7nCSHdvcdSVJVpyW5IMlrZlkYAAAALGWaG0AlyZ7zHj90BnUAAADA1KaZmf3/k1xQVecmqcxdO3vKTKsCAACAZUxzN+P3VtUnkjw5c2H25O7+u1kXBgAAAEtZcplxVR00+f6kJI9Ksj3JlUn2nWwDAACAdbHczOwvJDkxya8vsq+TPH0mFQEAAMAKlgyz3X3i5OGzu/vm+fuqavNMqwIAAIBlTHM34z+fchsAAACsiSVnZqvqe5Psl2T3qjosczd/SpKHJHngGtQGAAAAi1rumtljkvxkki1JfmPe9huTvHaGNQEAAMCylrtm9rQkp1XVj3b3mWtYEwAAACxrms+ZPbOqnpvk8Uk2z9v+xlkWBgAAAEtZ8QZQVfVfkrwoyb/N3HWzL0zy6BnXBQAAAEua5m7GT+vulyb5Rne/Ick/SbL/bMsCAACApU0TZu/8jNlvV9W+SW5NcsDsSgIAAIDlrXjNbJL/XlV7Jvm1JJ9P0kl+f5ZFAQAAwHKWDbNVtUuSj3f3N5OcWVUfSrK5u29Yi+IAAABgMcsuM+7uO5L8+rzntwiyAAAArLdprpn9aFX9aFXVzKsBAACAKUxzzewvJHlQktuq6ubMfTxPd/dDZloZAAAALGHFMNvde6xFIQAAADCtFcNsVf3gYtu7+1OrXw4AAACsbJplxq+a93hzkiOSnJ/k6TOpCAAAAFYwzTLjH57/vKr2T/IfZlYRAAAArGCauxkvtD3JwatdCAAAAExrmmtmfztJT57ukuTQJH81w5oAAABgWdNcM7tt3uPbkry3u//XjOoBAACAFU1zzexpVbX35PG1sy8JAAAAlrfkNbM159Squi7JF5P876q6tqpev3blAQAAwD0tdwOon0tyZJInd/de3f2wJE9JcmRV/fxaFAcAAACLWS7MvjTJ8d39lTs3dPeXk7xksg8AAADWxXJhdrfuvm7hxsl1s7vNriQAAABY3nJh9rv3cR8AAADM1HJ3M35iVX1rke2VZPOM6gEAAIAVLRlmu3vXtSwEAAAAprXcMmMAAADYkIRZAAAAhiPMAgAAMBxhFgAAgOEseQOoqroxSS+2K0l390NmVhUAAAAsY7m7Ge+xloUAAADAtJabmX34cgd299+vfjkAAACwsiXDbJLzM7fMuBbZ10m+byYVAQAAwAqWW2Z8wFoWAgAAANNabmb2LlX1sCQHJtl857bu/tSsigIAAIDlrBhmq+oVSU5KsiXJhUmemuQvkjx9ppUBAADAEqb5nNmTkjw5yVe7++gkhyW5dqZVAQAAwDKmCbM3d/fNSVJVD+juLyZ5zGzLAgAAgKVNc83s9qraM8l/S3JOVX0jyVWzLAoAAACWs2KY7e4fmTw8tarOTfLQJB+ZaVUAAACwjCWXGVfVk6vq2fO3dfcnJw8PmWlVAAAAsIzlrpn9tSSXLrL9ksk+AAAAWBfLhdm9uvuKhRu7+/Ike82sIgAAAFjBcmF292X2PWi1CwEAAIBpLRdmP1ZVb6qqmr+xqt6Q5M9mWxYAAAAsbbm7Gf9ikj9IcnlVXTjZ9sQk25K8YsZ1AQAAwJKWDLPdfVOS46vq+5I8frL54u7+8ppUBgAAAEuY5nNmv5xEgAUAAGDDWO6aWQAAANiQhFkAAACGM1WYrap/WlU/NXm8d1UdMNuyAAAAYGkrhtmq+pUkJyc5ZbJptyTvmmVRAAAAsJxpZmZ/JMnzktyUJN19VZI9ZlkUAAAALGeaMPvd7u4knSRV9aDZlgQAAADLmybMnlFVv5dkz6r66SQfS/L7sy0LAAAAljbN58y+tar+RZJvJXlMktd39zkzrwwAAACWsGKYTZJJeBVgAQAA2BBWDLNVdWMm18vOc0OSbUl+sbu/PIvCAAAAYCnTzMz+RpKrkrwnSSU5Lsn3JrksyTuSHDWr4gAAAGAx09wA6lnd/XvdfWN3f6u7357kOd19epKHzbg+AAAAuIdpwuwdVfVjVbXL5OvH5u1buPwYAAAAZm6aMPviJD+R5JokX588fklV7Z7kZ2ZYGwAAACxqmo/m+XKSH15i92dWtxwAAABY2TR3M96c5OVJHp9k853bu/tlM6wLAAAAljTNMuM/ztzdi49J8skkW5LcOMuiAAAAYDnThNl/3N2vS3JTd5+W5LlJDpltWQAAALC0acLsrZPv36yqg5M8NMnWmVUEAAAAK1jxmtkkb6+qhyX55SRnJXlwktfNtCoAAABYxrJhtqp2SfKt7v5Gkk8l+b41qQoAAACWsewy4+6+Iz5LFgAAgA1mmmtmz6mqX6qq/avq4Xd+zbwyAAAAWMI0YfZlSV6ZuWXG50++tq100CT8nltVl1bVxVV10mT7w6vqnKr60uT7w+Ydc0pVXV5Vl1XVMfftLQEAALCjW/EGUN19wH08921JfrG7P19VeyQ5v6rOSfKTST7e3W+pqtckeU2Sk6vqcUmOS/L4JPsm+VhV/UB3334fXx8AAIAd1Iozs1X1wKr65ap6++T5gVV17ErHdffV3f35yeMbk1yaZL8kz09y2mTYaUleMHn8/CTv6+5buvsrSS5PcsS9fD8AAADsBKZZZvxHSb6b5GmT59uT/Oq9eZGq2prksCSfS7JPd1+dzAXeJI+cDNsvyZXzDts+2bbwXCdW1baq2nbttdfemzIAAADYQUwTZr+/u/9DkluTpLu/k6SmfYGqenCSM5P8XHd/a7mhi2zre2zofnt3H97dh++9997TlgEAAMAOZJow+92q2j2TYFlV35/klmlOXlW7ZS7Ivru7/3Sy+etV9ajJ/kcluWayfXuS/ecdviXJVdO8DgAAADuXacLsqUk+kmT/qnp3ko8nefVKB1VVJfnDJJd292/M23VWkhMmj09I8sF524+rqgdU1QFJDkxy3jRvAgAAgJ3LNHcz/mhVnZ/kqZlbCnxSd183xbmPTPITSb5QVRdOtr02yVuSnFFVL0/ytSQvnLzOxVV1RpJLMncn5Fe6kzEAAACLWTHMVtVZSd6b5KzuvmnaE3f3Z7L0tbXPWOKYNyV507SvAQAAwM5pmmXGv57knyW5pKr+pKr+ZVVtnnFdAAAAsKRplhl/Msknq2rXJE9P8tNJ3pHkITOuDQAAABa1YphNksndjH84yYuSPCnJabMsCgAAAJYzzTWzpyd5SubuaPw7ST7R3XfMurC1VlN/cu7K+h6fjgsAAMBqmmZm9o+S/PiddxauqiOr6se7+5WzLQ0AAAAWN801sx+pqkOr6vjMLTP+SpI/nXllAAAAsIQlw2xV/UCS45Icn+T6JKcnqe4+eo1qAwAAgEUtNzP7xSSfTvLD3X15klTVz69JVQAAALCM5T5n9keT/F2Sc6vq96vqGUlW8TZJAAAAcN8sGWa7+wPd/aIkByX5RJKfT7JPVf1uVT1zjeoDAACAe1huZjZJ0t03dfe7u/vYJFuSXJjkNbMuDAAAAJayYpidr7v/vrt/r7ufPquCAAAAYCX3KswCAADARiDMAgAAMJzlPpqH+6pW6abP3atzHgAAgB2MmVkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADEeYBQAAYDjCLAAAAMMRZgEAABiOMAsAAMBwhFkAAACGI8wCAAAwHGEWAACA4QizAAAADGdmYbaq3lFV11TVX8/b9vCqOqeqvjT5/rB5+06pqsur6rKqOmZWdQEAADC+Wc7MvjPJsxZse02Sj3f3gUk+PnmeqnpckuOSPH5yzH+uql1nWBsAAAADm1mY7e5PJfn7BZufn+S0yePTkrxg3vb3dfct3f2VJJcnOWJWtQEAADC2tb5mdp/uvjpJJt8fOdm+X5Ir543bPtl2D1V1YlVtq6pt11577UyLBQAAYGPaKDeAqkW29WIDu/vt3X14dx++9957z7gsAAAANqK1DrNfr6pHJcnk+zWT7duT7D9v3JYkV61xbQAAAAxircPsWUlOmDw+IckH520/rqoeUFUHJDkwyXlrXBsAAACD2DSrE1fVe5McleQRVbU9ya8keUuSM6rq5Um+luSFSdLdF1fVGUkuSXJbkld29+2zqg0AAICxzSzMdvfxS+x6xhLj35TkTbOqBwAAgB3HRrkBFAAAAExNmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAxHmAUAAGA4wiwAAADD2XBhtqqeVVWXVdXlVfWa9a4HAACAjWdDhdmq2jXJ7yR5dpLHJTm+qh63vlUBAACw0WyoMJvkiCSXd/eXu/u7Sd6X5PnrXBMAAAAbzKb1LmCB/ZJcOe/59iRPmT+gqk5McuLk6T9U1WVrVNvUKnlEkuvu/4nq/hezga1an5Idulf6ND1/9qbjZ2p6fqam42dqOvo0PX/2puNnanp+pqazQX+mHr3Ujo0WZhd7x323J91vT/L2tSnnvqmqbd19+HrXsdHp03T0aXp6NR19mp5eTUefpqNP09Or6ejT9PRqOqP1aaMtM96eZP95z7ckuWqdagEAAGCD2mhh9i+THFhVB1TV9yQ5LslZ61wTAAAAG8yGWmbc3bdV1c8k+Z9Jdk3yju6+eJ3Lui829DLoDUSfpqNP09Or6ejT9PRqOvo0HX2anl5NR5+mp1fTGapP1d0rjwIAAIANZKMtMwYAAIAVCbMAAAAMR5i9j6rqHVV1TVX99RL7q6p+q6our6qLqupJa13jRlBV+1fVuVV1aVVdXFUnLTJmp+9VVW2uqvOq6q8mfXrDImN2+j7dqap2raoLqupDi+zTp4mquqKqvlBVF1bVtkX261WSqtqzqt5fVV+c/L/qnyzYr09Jquoxk5+lO7++VVU/t2CMXiWpqp+f/L/8r6vqvVW1ecF+fUpSVSdNenTxwp+lyf6dtk+L/Z5ZVQ+vqnOq6kuT7w9b4thnVdVlk769Zu2qXntL9OmFk5+pO6pqyY+Y2Zn6lCzZq1+b/N13UVV9oKr2XOLYDdsrYfa+e2eSZy2z/9lJDpx8nZjkd9egpo3otiS/2N2PTfLUJK+sqsctGKNXyS1Jnt7dT0xyaJJnVdVTF4zRp//rpCSXLrFPn+7u6O4+dInPjNOrOf8xyUe6+6AkT8w9f7b0KUl3Xzb5WTo0yf+T5NtJPrBg2E7fq6raL8nPJjm8uw/O3A0tj1swTJ+qDk7y00mOyNyfu2Or6sAFw3bmPr0z9/w98zVJPt7dByb5+OT53VTVrkl+J3O9e1yS4xf5vWtH8s7cs09/neT/TfKppQ7aCfuULN6rc5Ic3N1PSPK/k5yy8KCN3ith9j7q7k8l+ftlhjw/yX/tOZ9NsmdVPWptqts4uvvq7v785PGNmfslcb8Fw3b6Xk3e+z9Mnu42+Vp4d7advk9JUlVbkjw3yR8sMUSfprfT96qqHpLkB5P8YZJ093e7+5sLhu30fVrEM5L8TXd/dcF2vZqzKcnuVbUpyQOTXLVgvz4lj03y2e7+dnffluSTSX5kwZidtk9L/J75/CSnTR6fluQFixx6RJLLu/vL3f3dJO+bHLdDWqxP3X1pd1+2wqE7VZ+SJXv10cmfvyT5bJItixy6oXslzM7OfkmunPd8e+4Z4nYqVbU1yWFJPrdgl17lrqWzFya5Jsk53a1Pi3tbklcnuWOJ/fr0f3WSj1bV+VV14iL79Sr5viTXJvmjmlu6/gdV9aAFY/Tpno5L8t5Ftu/0veruv03y1iRfS3J1khu6+6MLhu30fcrc7NkPVtVeVfXAJM9Jsv+CMfp0d/t099XJ3GRBkkcuMkbPpqNP9/SyJP9jke0bulfC7OzUItt22s9BqqoHJzkzyc9197cW7l7kkJ2uV919+2T53pYkR0yWYM230/epqo5Nck13n7/csEW27VR9mufI7n5S5pYGvbKqfnDBfr2am0F7UpLf7e7DktyUey7d06d5qup7kjwvyZ8stnuRbTtVrybXMT4/yQFJ9k3yoKp6ycJhixy6U/Wpuy9N8u8zt8zxI0n+KnOXJs230/fpPtCz6ejTPFX17zL35+/di+1eZNuG6ZUwOzvbc/d/YdySey4z2ilU1W6ZC7Lv7u4/XWSIXs0zWeL4idzzugZ9So5M8ryquiJzy1yeXlXvWjBGnya6+6rJ92syd23jEQuG6NVcD7bPWwnx/syF24VjdvY+zffsJJ/v7q8vsk+vkh9K8pXuvra7b03yp0metmCMPiXp7j/s7id19w9mbvnjlxYM0ae7+/qdy6wn369ZZIyeTUefJqrqhCTHJnlxdy8WUjd0r4TZ2TkryUsnd+J7auaWGV293kWttaqqzF2Ldml3/8YSw3b6XlXV3nfeQa6qds/cL0NfXDBsp+9Td5/S3Vu6e2vmljn+WXcvnPHY6fuUJFX1oKra487HSZ6ZuWV98+30veruv0tyZVU9ZrLpGUkuWTBsp+/TAsdn8SXGiV4lc8uLn1pVD5z8HfiM3POmYvqUpKoeOfn+jzJ3w56FP1f6dHdnJTlh8viEJB9cZMxfJjmwqg6YrKI4bnIcd6dPmbtLcZKTkzyvu7+9xLAN3atN613AqKrqvUmOSvKIqtqe5Fcyd9OedPd/SXJ25q7/uDxzd3z8qfWpdN0dmeQnknxhcj1okrw2yT9K9GqeRyU5bXLHuF2SnNHdH6qqf53o00r0aVH7JPnA3O/S2ZTkPd39Eb1a1L9N8u7JX9JfTvJT+rS4ybWN/yLJv5q3Ta/m6e7PVdX7k3w+c8v2Lkjydn1a1JlVtVeSW5O8sru/oU9zlvg98y1Jzqiql2fuH01eOBm7b5I/6O7ndPdtVfUzSf5n5u6k/Y7uvng93sNaWKJPf5/kt5PsneTDVXVhdx+zM/cpWbJXpyR5QJJzJr8vfLa7//VIvarFZ5MBAABg47LMGAAAgOEIswAAAAxHmAUAAGA4wiwAAADDEWYBAAAYjjALAGusqrqq/nje801VdW1Vfeg+nm/Pqvo3854fdV/PBQCjEGYBYO3dlOTgqtp98vxfJPnb+3G+PZP8m5UGAcCORJgFgPXxP5I8d/L4+CTvvXNHVT28qv5bVV1UVZ+tqidMtp9aVe+oqk9U1Zer6mcnh7wlyfdX1YVV9WuTbQ+uqvdX1Rer6t1VVWv1xgBgLQizALA+3pfkuKranOQJST43b98bklzQ3U9I8tok/3XevoOSHJPkiCS/UlW7JXlNkr/p7kO7+1WTcYcl+bkkj0vyfUmOnOF7AYA1J8wCwDro7ouSbM3crOzZC3b/0yR/PBn3Z0n2qqqHTvZ9uLtv6e7rklyTZJ8lXuK87t7e3XckuXDyWgCww9i03gUAwE7srCRvTXJUkr3mbV9sSXBPvt8yb9vtWfrv8mnHAcCQzMwCwPp5R5I3dvcXFmz/VJIXJ3N3Jk5yXXd/a5nz3Jhkj1kUCAAblX+lBYB10t3bk/zHRXadmuSPquqiJN9OcsIK57m+qv5XVf115m4s9eHVrhUANprq7pVHAQAAwAZimTEAAADDEWYBAAAYjjALAADAcIRZAAAAhiPMAgAAMBxhFgAAgOEIswAAAAzn/wB0L8UMnko9ewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot call duration on Surf and Ultimate plans, by month\n",
    "df_all_calls.plot(kind='bar',\n",
    "                    title='Average Call Duration',\n",
    "                  xlabel='Month',\n",
    "                  ylabel='Average Call Duration',\n",
    "                  color=('blue', 'red'),\n",
    "                  rot=0,\n",
    "                  figsize= (16,10)\n",
    "                    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "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",
    "df_surf_calls.hist(bins=5)\n",
    "df_ultimate_calls.hist(bins=5)\n",
    "plt.title='Minutes Required'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The merged histograms\n",
    "df_surf_calls['call_duration'].plot(kind='hist', bins=15, title='Minutes Required', ylabel='Frequency', figsize=(15,8))\n",
    "df_ultimate_calls['call_duration'].plot(kind='hist', bins=15, alpha=0.3)\n",
    "\n",
    "plt.legend(['Surf', 'Ultimate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Calculate the mean and the variable of the call duration to reason on whether users on the different plans have different behaviours for their calls.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "call_duration    363.062967\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "call_duration    5177.321155\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3673763875.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_calls.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3673763875.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_calls.var())\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean and the variance of the monthly call duration, Surf plan\n",
    "print('mean')\n",
    "print(df_surf_calls.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_surf_calls.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "call_duration    365.974762\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "call_duration    5335.727136\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\848826890.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_calls.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\848826890.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_calls.var())\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean and the variance of the monthly call duration, Ultimate plan\n",
    "print('mean')\n",
    "print(df_ultimate_calls.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_ultimate_calls.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Monthly Call Duration'}>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly call duration\n",
    "df_all_calls.plot(kind='box', title='Monthly Call Duration', figsize=(15,5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# surf call durations\n",
    "surf_calls = df_surf_calls['call_duration'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ultimate call durations\n",
    "ultimate_calls = df_ultimate_calls['call_duration'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_14_1_1_1_'></a>[Null hypothesis that the mean call durations for both plans is similar](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.9225249438414813\n",
      "We can't reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "# Null hypothesis that the two means are the same\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(surf_calls, ultimate_calls)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average call durations differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The users of the two plans seem to have similar calling behavior, when considering call durations. In general, Surf plan customers and Ultimate plan customers have more messages 6 months each, out of the year. The mean call durations for both plans appeared to be similar, further visualized by the box plots, but we will test this hypothesis statistically. The Ultimate plan sees a greater variance in call duration compared to the Surf plan. This may be attributed to the outlier we see in the box plot of the Ultimate plan, yet the Surf plan has an outlier as well. Hypothesis testing suggests we cannot reject the null hypothesis that the mean call duration of both plans is similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_14_2_'></a>[Messages](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>num_calls</th>\n",
       "      <th>call_duration</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>num_messages</th>\n",
       "      <th>plan</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>116.83</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1006</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>2068.37</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>54.79</td>\n",
       "      <td>32118.82</td>\n",
       "      <td>139.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1008</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>450.21</td>\n",
       "      <td>17106.99</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1008</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>422.81</td>\n",
       "      <td>23676.72</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>1143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>1319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  num_calls  call_duration   mb_used  num_messages  \\\n",
       "0        1000   12.0       16.0         116.83   1901.47          11.0   \n",
       "1        1006   11.0        2.0           9.32   2068.37          15.0   \n",
       "2        1006   12.0        9.0          54.79  32118.82         139.0   \n",
       "3        1008   10.0       71.0         450.21  17106.99          21.0   \n",
       "4        1008   11.0       63.0         422.81  23676.72          37.0   \n",
       "...       ...    ...        ...            ...       ...           ...   \n",
       "2298     1143    0.0        0.0           0.00      0.00           0.0   \n",
       "2299     1307    0.0        0.0           0.00      0.00           0.0   \n",
       "2300     1319    0.0        0.0           0.00      0.00           0.0   \n",
       "2301     1378    0.0        0.0           0.00      0.00           0.0   \n",
       "2302     1473    0.0        0.0           0.00      0.00           0.0   \n",
       "\n",
       "          plan  messages_included  mb_per_month_included  minutes_included  \\\n",
       "0     ultimate               1000                  30720              3000   \n",
       "1     ultimate               1000                  30720              3000   \n",
       "2     ultimate               1000                  30720              3000   \n",
       "3     ultimate               1000                  30720              3000   \n",
       "4     ultimate               1000                  30720              3000   \n",
       "...        ...                ...                    ...               ...   \n",
       "2298      surf                 50                  15360               500   \n",
       "2299      surf                 50                  15360               500   \n",
       "2300      surf                 50                  15360               500   \n",
       "2301      surf                 50                  15360               500   \n",
       "2302      surf                 50                  15360               500   \n",
       "\n",
       "      usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute  \n",
       "0                  70           7             0.01            0.01  \n",
       "1                  70           7             0.01            0.01  \n",
       "2                  70           7             0.01            0.01  \n",
       "3                  70           7             0.01            0.01  \n",
       "4                  70           7             0.01            0.01  \n",
       "...               ...         ...              ...             ...  \n",
       "2298               20          10             0.03            0.03  \n",
       "2299               20          10             0.03            0.03  \n",
       "2300               20          10             0.03            0.03  \n",
       "2301               20          10             0.03            0.03  \n",
       "2302               20          10             0.03            0.03  \n",
       "\n",
       "[2303 rows x 14 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual of dataset we are working with\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plan</th>\n",
       "      <th>month</th>\n",
       "      <th>num_messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>surf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surf</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>surf</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.298969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.033058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>surf</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.762887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>surf</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.839662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>surf</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surf</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.600629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.340426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.830508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.478873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>37.895349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>39.443396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>11.0</td>\n",
       "      <td>38.606299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>12.0</td>\n",
       "      <td>45.006623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        plan  month  num_messages\n",
       "0       surf    0.0      0.000000\n",
       "1       surf    1.0     10.500000\n",
       "2       surf    2.0     12.000000\n",
       "3       surf    3.0     15.260870\n",
       "4       surf    4.0     17.400000\n",
       "5       surf    5.0     24.012987\n",
       "6       surf    6.0     25.298969\n",
       "7       surf    7.0     27.033058\n",
       "8       surf    8.0     28.777778\n",
       "9       surf    9.0     30.762887\n",
       "10      surf   10.0     33.839662\n",
       "11      surf   11.0     32.385159\n",
       "12      surf   12.0     38.600629\n",
       "13  ultimate    0.0      0.000000\n",
       "14  ultimate    1.0     15.500000\n",
       "15  ultimate    2.0     21.571429\n",
       "16  ultimate    3.0     20.250000\n",
       "17  ultimate    4.0     22.047619\n",
       "18  ultimate    5.0     32.103448\n",
       "19  ultimate    6.0     29.340426\n",
       "20  ultimate    7.0     32.830508\n",
       "21  ultimate    8.0     38.478873\n",
       "22  ultimate    9.0     37.895349\n",
       "23  ultimate   10.0     39.443396\n",
       "24  ultimate   11.0     38.606299\n",
       "25  ultimate   12.0     45.006623"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the number of messages users of each plan tend to send each month\n",
    "df_merged_msg = df_merged.groupby(['plan', 'month'])['num_messages'].mean()\n",
    "display(df_merged_msg.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# number of messages sent per month, Surf plan\n",
    "df_surf_msgs = df_merged_msg[1:13].reset_index('plan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# number of messages sent per month, Ultimate plan\n",
    "df_ultimate_msgs = df_merged_msg.reset_index('plan').tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merged the dataset of the two plans side by side\n",
    "df_all_msgs = df_surf_msgs.merge(df_ultimate_msgs, on='month', how='outer')\n",
    "df_all_msgs.columns = ['plan_s', 'surf', 'plan_u', 'ultimate'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying chart of the number of messages per plan, per month\n",
    "df_all_msgs.plot(kind='bar',\n",
    "                    title='Number of Messages',\n",
    "                  xlabel='Month',\n",
    "                  ylabel='Average Number of Messages',\n",
    "                  color=('blue', 'red'),\n",
    "                 rot=0,\n",
    "                  figsize= (16,10)\n",
    "                    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "num_messages    24.656\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "num_messages    81.888846\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\2921719458.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_msgs.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\2921719458.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_msgs.var())\n"
     ]
    }
   ],
   "source": [
    "# showing statistical metrics\n",
    "print('mean')\n",
    "print(df_surf_msgs.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_surf_msgs.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "num_messages    31.089497\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "num_messages    87.353963\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\1981465832.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_msgs.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\1981465832.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_msgs.var())\n"
     ]
    }
   ],
   "source": [
    "# Mean and variance of ultimate messages\n",
    "print('mean')\n",
    "print(df_ultimate_msgs.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_ultimate_msgs.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of surf messages\n",
    "df_surf_msgs.hist(bins=10)\n",
    "df_ultimate_msgs.hist(bins=10)\n",
    "plt.title='Messages'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# surf and ultimate messages\n",
    "df_surf_msgs['num_messages'].plot(kind='hist', bins=15, title='Number of Messages', ylabel='Frequency', figsize=(15,8))\n",
    "df_ultimate_msgs['num_messages'].plot(kind='hist', bins=15, alpha=0.5)\n",
    "\n",
    "plt.legend(['Surf', 'Ultimate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Monthly Number of Messages'}>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAEsG2dVdWRVfaCqPlJVV1bVi6fl51TVtVV1+fR16tqPCwAAsD5tWsE2X07yiO7+QlVtTvK+qvqDad0ru/vlazceAADAxrBsnHV3J/nC9HTz9NVrORQAAMBGs6LPnFXVEVV1eZIbklzc3e+fVp1dVR+tqvOq6th97HtWVe2sqp27d+9enakBAADWmRXFWXff2t0nJzkhySlV9cAkr05y3yQnJ7kuySv2se+53b21u7du2bJlVYYGAABYb/brbo3dfWOSS5M8truvn6LttiSvSXLK6o8HAACwMazkbo1bquou0+Ojkjwqycer6vhFmz0xyRVrMiEAAMAGsJK7NR6fZEdVHZFZzL25uy+sqvOr6uTMbg6yK8mz1mxKAACAdW4ld2v8aJKH7GX5GWsyEQAAwAa0X585AwAAYG2IMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAFsmvcAAACs3NEnbcuDdmyb9xhzcfRJSXLavMeANSPOAAAOIzddtT27tm/MQFnYdtG8R4A15bJGAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAYgzAACAAWya9wBwuHnwi9+Vz938lbkdf2HbRXM79jFHbc5HXvSYuR0fAGA9E2ewnz5381eya/tp8x5jLuYZhgAA653LGgEAAAYgzgAAAAYgzgAAAAYgzgAAAAYgzgAAAAYgzgAAAAYgzgAAAAYgzgAAAAawbJxV1ZFV9YGq+khVXVlVL56W37WqLq6qq6fvx679uAAAAOvTSs6cfTnJI7r7wUlOTvLYqnpokm1JLunu+yW5ZHoOAADAAVg2znrmC9PTzdNXJzk9yY5p+Y4kT1iLAQEAADaCFX3mrKqOqKrLk9yQ5OLufn+Se3T3dUkyfb/7mk0JAACwzq0ozrr71u4+OckJSU6pqgeu9ABVdVZV7ayqnbt37z7AMQEAANa3/bpbY3ffmOTSJI9Ncn1VHZ8k0/cb9rHPud29tbu3btmy5eCmBQAAWKdWcrfGLVV1l+nxUUkeleTjSd6R5MxpszOTvH2NZgQAAFj3Nq1gm+OT7KiqIzKLuTd394VV9edJ3lxVz0zyySRPXsM5AQAA1rVl46y7P5rkIXtZ/vdJHrkWQwEAAGw0+/WZMwAAANaGOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABiAOAMAABjApnkPAIebo0/algft2DbvMebi6JOS5LR5jwEAsC6JM9hPN121Pbu2b8xAWdh20bxHAABYt1zWCAAAMABxBgAAMABxBgAAMABxBgAAMABxBgAAMABxBgAAMABxBgAAMIBl46yq7l1V76mqq6rqyqp69rT8nKq6tqoun75OXftxAQAA1qeV/BHqW5I8t7s/VFVHJ7msqi6e1r2yu1++duMBAABsDMvGWXdfl+S66fFNVXVVknut9WAAAAAbyX595qyqFpI8JMn7p0VnV9VHq+q8qjp2tYcDAADYKFYcZ1V15yRvTfKc7v58klcnuW+SkzM7s/aKfex3VlXtrKqdu3fvPviJAQAA1qEVxVlVbc4szN7Y3W9Lku6+vrtv7e7bkrwmySl727e7z+3urd29dcuWLas1NwAAwLqykrs1VpLXJbmqu3950fLjF232xCRXrP54AAAAG8NK7tb4XUnOSPKxqrp8WvaCJE+rqpOTdJJdSZ61BvMBAABsCCu5W+P7ktReVr1z9ccBAADYmPbrbo0AAACsDXEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwAHEGAAAwgE3zHgAAgP2zsO2ieY8wF8cctXneI8CaEmcAAIeRXdtPm+vxF7ZdNPcZYL1yWSMAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAxBkAAMAAlo2zqrp3Vb2nqq6qqiur6tnT8rtW1cVVdfX0/di1HxcAAGB9WsmZs1uSPLe7T0ry0CT/raoekGRbkku6+35JLpmeAwAAcACWjbPuvq67PzQ9vinJVUnuleT0JDumzXYkecIazQgAALDu7ddnzqpqIclDkrw/yT26+7pkFnBJ7r7q0wEAAGwQK46zqrpzkrcmeU53f34/9jurqnZW1c7du3cfyIwAAADr3orirKo2ZxZmb+zut02Lr6+q46f1xye5YW/7dve53b21u7du2bJlNWYGAABYd1Zyt8ZK8rokV3X3Ly9a9Y4kZ06Pz0zy9tUfDwAAYGPYtIJtvivJGUk+VlWXT8tekGR7kjdX1TOTfDLJk9dkQgAAgA1g2Tjr7vclqX2sfuTqjgMAALAx7dfdGgEAAFgb4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAA4gwAAGAAm+Y9AByOFrZdNO8R5uKYozbPewQAgHVLnMF+2rX9tLkde2HbRXM9PgAAa8dljQAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAMQZwAAAAPYNO8BAAA4dBa2XTTX19i1/bSDPj6sV+IMAGADEUcwrmUva6yq86rqhqq6YtGyc6rq2qq6fPo6dW3HBAAAWN9W8pmz1yd57F6Wv7K7T56+3rm6YwEAAGwsy8ZZd/9Jkn84BLMAAABsWAdzt8azq+qj02WPx+5ro6o6q6p2VtXO3bt3H8ThAAAA1q8DjbNXJ7lvkpOTXJfkFfvasLvP7e6t3b11y5YtB3g4AACA9e2A4qy7r+/uW7v7tiSvSXLK6o4FAACwsRxQnFXV8YuePjHJFfvaFgAAgOUt+3fOqupNSR6e5Liq+nSSFyV5eFWdnKST7EryrLUbEQAAYP1bNs66+2l7Wfy6NZgFAABgwzqYuzUCAACwSsQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAAMQZAADAADbNewDYaBa2XTTX/XdtP+2g9gcAYG2IMzjExBEAAHuz7GWNVXVeVd1QVVcsWnbXqrq4qq6evh+7tmMCAACsbyv5zNnrkzx2ybJtSS7p7vsluWR6DgAAwAFaNs66+0+S/MOSxacn2TE93pHkCas7FgAAwMZyoHdrvEd3X5ck0/e7r95IAAAAG8+a30q/qs6qqp1VtXP37t1rfTgAAIDD0oHG2fVVdXySTN9v2NeG3X1ud2/t7q1btmw5wMMBAACsbwcaZ+9Icub0+Mwkb1+dcQAAADamldxK/01J/jzJ/avq01X1zCTbkzy6qq5O8ujpOQAAAAdo2T9C3d1P28eqR67yLAAAABvWmt8QBAAAgOWJMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAGIMwAAgAFUdx+6g1XtTnLNITsgrD/HJfnsvIcAYEPzuwgOzondvWVvKw5pnAEHp6p2dvfWec8BwMbldxGsHZc1AgAADECcAQAADECcweHl3HkPAMCG53cRrBGfOQMAABiAM2cAAAADEGewTlTVm6rqo1X13+c9CwDjq6rXV9UPTI+fU1V3XLTunVV1l1U4xslVderBvg5sFJvmPQBwcKpqU2Z/c+bfd/eJ854HgMPSc5JckORLSdLdqxVUJyfZmuSdq/R6sK45cwaDqKo7VdVFVfWRqrqiqp5SVbuq6rhp/daqunR6fE5VnVtV70ryhiTvSnL3qrq8qr57fj8FAKOpqoWqumLR8+dV1TmLnv9kknsmeU9VvWdatquqjpv2/XhVvXb63fTGqnpUVf1pVV1dVadM259SVX9WVR+evt+/qr4uyc8lecr0++kp0++686rqg9O2px/SNwMG58wZjOOxSf6uu09Lkqo6JsnLbmf7b0/ysO6+uaoWklzY3Sev+ZQArCvd/atV9VNJ/mN3f3Yvm3xjkicnOSvJB5P8UJKHJXl8khckeUKSjyf5nu6+paoeleSl3f2kqvrZJFu7++wkqaqXJnl3d//odNnkB6rqj7r7i2v7U8LhQZzBOD6W5OVV9bLMQuu9VXV727+ju28+NKMBsIH9bXd/LEmq6sokl3R3V9XHkixM2xyTZEdV3S9JJ9m8j9d6TJLHV9XzpudHJrlPkqvWang4nIgzGER3f6Kqvj3JqUl+cbpk8ZZ89fLjI5fs4v9lBGAlFv8uSf7175PlfHnR49sWPb8tX/1vyZ9P8p7ufuJ0Ncel+3itSvKk7v6r/ZwBNgSfOYNBVNU9k3ypuy9I8vIk35ZkV2aXLybJk+Y0GgCHt+sz+1zy3arqDkket5dtbkpy9EEc45gk106Pn3E7r/uHSX6ipktDquohB3FMWHfEGYzjQZlde395kp9J8gtJXpzkVVX13iS3znE2AA5T3f2VzG7M8f4kF2b2+bClzk3yB3tuCHIAfimzqz7+NMkRi5a/J8kD9twQJLMzbJuTfHS6ScnPH+DxYF2q7p73DAAAABueM2cAAAADEGcAAAADEGcAAAADEGcAAAADEGcAAAADEGcAAAADEGcAAAADEGcAAAAD+P9dmWZvgU+gUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly call duration\n",
    "df_all_msgs.plot(kind='box', title='Monthly Number of Messages', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# surf message values\n",
    "surf_msgs = df_surf_msgs['num_messages'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ultimate message values\n",
    "ultimate_msgs = df_ultimate_msgs['num_messages'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_14_2_1_1_'></a>[Null hypothesis is the mean number of messages for the plans are similar](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.10075353966021278\n",
      "We can't reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "# Null hypothesis that the two means are the same\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(surf_msgs, ultimate_msgs)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average number of messages differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of messages sent by customers of the Ultimate plan is consistently greater than that of Surf customers. The mean of the number of messages of the two plans appear different, however, we need to test this. Looking at the boxplot, we see the two plans are similar, and both have wide upper and lower bounds. Hypothesis testing suggests the average number of messages does not differ, contrary to our earlier thoughts.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_14_3_'></a>[Internet](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>num_calls</th>\n",
       "      <th>call_duration</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>num_messages</th>\n",
       "      <th>plan</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>116.83</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
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       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1006</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>2068.37</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>54.79</td>\n",
       "      <td>32118.82</td>\n",
       "      <td>139.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1008</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>450.21</td>\n",
       "      <td>17106.99</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1008</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>422.81</td>\n",
       "      <td>23676.72</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>1143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>1319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  num_calls  call_duration   mb_used  num_messages  \\\n",
       "0        1000   12.0       16.0         116.83   1901.47          11.0   \n",
       "1        1006   11.0        2.0           9.32   2068.37          15.0   \n",
       "2        1006   12.0        9.0          54.79  32118.82         139.0   \n",
       "3        1008   10.0       71.0         450.21  17106.99          21.0   \n",
       "4        1008   11.0       63.0         422.81  23676.72          37.0   \n",
       "...       ...    ...        ...            ...       ...           ...   \n",
       "2298     1143    0.0        0.0           0.00      0.00           0.0   \n",
       "2299     1307    0.0        0.0           0.00      0.00           0.0   \n",
       "2300     1319    0.0        0.0           0.00      0.00           0.0   \n",
       "2301     1378    0.0        0.0           0.00      0.00           0.0   \n",
       "2302     1473    0.0        0.0           0.00      0.00           0.0   \n",
       "\n",
       "          plan  messages_included  mb_per_month_included  minutes_included  \\\n",
       "0     ultimate               1000                  30720              3000   \n",
       "1     ultimate               1000                  30720              3000   \n",
       "2     ultimate               1000                  30720              3000   \n",
       "3     ultimate               1000                  30720              3000   \n",
       "4     ultimate               1000                  30720              3000   \n",
       "...        ...                ...                    ...               ...   \n",
       "2298      surf                 50                  15360               500   \n",
       "2299      surf                 50                  15360               500   \n",
       "2300      surf                 50                  15360               500   \n",
       "2301      surf                 50                  15360               500   \n",
       "2302      surf                 50                  15360               500   \n",
       "\n",
       "      usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute  \n",
       "0                  70           7             0.01            0.01  \n",
       "1                  70           7             0.01            0.01  \n",
       "2                  70           7             0.01            0.01  \n",
       "3                  70           7             0.01            0.01  \n",
       "4                  70           7             0.01            0.01  \n",
       "...               ...         ...              ...             ...  \n",
       "2298               20          10             0.03            0.03  \n",
       "2299               20          10             0.03            0.03  \n",
       "2300               20          10             0.03            0.03  \n",
       "2301               20          10             0.03            0.03  \n",
       "2302               20          10             0.03            0.03  \n",
       "\n",
       "[2303 rows x 14 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare the amount of internet traffic consumed by users per plan\n",
    "df_merged\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plan</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>surf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surf</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>surf</td>\n",
       "      <td>6.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>surf</td>\n",
       "      <td>8.0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>surf</td>\n",
       "      <td>10.0</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>surf</td>\n",
       "      <td>11.0</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surf</td>\n",
       "      <td>12.0</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>11.0</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>12.0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        plan  month  mb_used\n",
       "0       surf    0.0        6\n",
       "1       surf    1.0        2\n",
       "2       surf    2.0        9\n",
       "3       surf    3.0       23\n",
       "4       surf    4.0       50\n",
       "5       surf    5.0       77\n",
       "6       surf    6.0       97\n",
       "7       surf    7.0      121\n",
       "8       surf    8.0      162\n",
       "9       surf    9.0      194\n",
       "10      surf   10.0      237\n",
       "11      surf   11.0      283\n",
       "12      surf   12.0      318\n",
       "13  ultimate    0.0        4\n",
       "14  ultimate    1.0        4\n",
       "15  ultimate    2.0        7\n",
       "16  ultimate    3.0       12\n",
       "17  ultimate    4.0       21\n",
       "18  ultimate    5.0       29\n",
       "19  ultimate    6.0       47\n",
       "20  ultimate    7.0       59\n",
       "21  ultimate    8.0       71\n",
       "22  ultimate    9.0       86\n",
       "23  ultimate   10.0      106\n",
       "24  ultimate   11.0      127\n",
       "25  ultimate   12.0      151"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the amount of internet traffic consumed by users per plan\n",
    "df_ints_traffic = df_merged.groupby(['plan', 'month'])['mb_used'].count()\n",
    "\n",
    "df_ints_traffic.columns = ['plan', 'month', 'internet_traffic']\n",
    "display(df_ints_traffic.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Internet traffic for Surf customers\n",
    "df_surf_ints_traffic = df_ints_traffic[1:13].reset_index('plan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Internet traffic for Ultimate customers \n",
    "df_ultimate_ints_traffic = df_ints_traffic.reset_index('plan').tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# internet trffic for both Surf and Ultiate customers, merged on month\n",
    "df_all_ints_traffic = df_surf_ints_traffic.merge(df_ultimate_ints_traffic, on='month', how='outer')\n",
    "df_all_ints_traffic.columns = ['plan_s', 'surf', 'plan_u', 'ultimate'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display chart on Internet traffic by plan, by month\n",
    "df_all_ints_traffic.plot(kind='bar',\n",
    "                    title='Internet Traffic Count',\n",
    "                  xlabel='Month',\n",
    "                  ylabel='Internet Traffic',\n",
    "                  color=('blue', 'red'),\n",
    "                 rot=0,\n",
    "                  figsize= (16,10)\n",
    "                    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "mb_used    131.083333\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "mb_used    11650.992424\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3245096143.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_ints_traffic.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3245096143.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_ints_traffic.var())\n"
     ]
    }
   ],
   "source": [
    "# Surf internet traffic mean and variance\n",
    "print('mean')\n",
    "print(df_surf_ints_traffic.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_surf_ints_traffic.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "mb_used    60.0\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "mb_used    2416.727273\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\94670807.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_ints_traffic.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\94670807.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_ints_traffic.var())\n"
     ]
    }
   ],
   "source": [
    "# Ultimate internet traffic mean and variance\n",
    "print('mean')\n",
    "print(df_ultimate_ints_traffic.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_ultimate_ints_traffic.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display histogram of internet traffic per month, per plan\n",
    "df_surf_ints_traffic['mb_used'].plot(kind='hist', bins=15, title='Internet Traffic', ylabel='Frequency', figsize=(15,8))\n",
    "df_ultimate_ints_traffic['mb_used'].plot(kind='hist', bins=15, alpha=0.4)\n",
    "\n",
    "\n",
    "plt.legend(['Surf', 'Ultimate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Monthly Internet Traffic'}>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5QnffmSyEXZLHT6udkOT2mc32TMv239dFVbWrqnbt3bt3GaMDAACsf0uOtqp6dJJ3JHlJd//DoVY9wLL+kgXdl3b3lu7esmnTpqWOAQAAsKEsKdqq6qgsBNvbuvt3p8V3VdXx0/vHJ7l7Wr4nyUkzm5+Y5I7VGRcAAGBjWcrdIyvJG5Pc3N2/PPPWFUkunJ5fmOSdM8svqKqjq+pJSU5Jcu3qjQwAALBxLOWXaz8tyfOTfLCqrp+WvSzJ9iSXV9ULknwsyflJ0t03VtXlSW7Kwp0nX9TdD6724AAAABvBotHW3e/Jgb+nliTPOMg225JsW8FcAAAA5DDvHgkAAMBDS7QBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMbNFoq6o3VdXdVXXDzLJLqurjVXX99Oc5M+9dXFW3VNWHq+qstRocAABgI1jKmbY3Jzn7AMt/pbvPmP5cmSRV9eQkFyQ5bdrm9VV1xGoNCwAAsNEsGm3d/WdJPrXE/Z2b5LLuvq+7b01yS5IzVzAfAADAhnbkCrZ9cVX9QJJdSX66uz+d5IQkfz2zzp5pGQAAA9u8dee8R8ju7efMewQY0nKj7TeS/HySnh5fk+SHk9QB1u0D7aCqLkpyUZKcfPLJyxwDAIDVsNJg2rx1p+iCNbKsu0d2913d/WB3fz7Jb+YLl0DuSXLSzKonJrnjIPu4tLu3dPeWTZs2LWcMAACAdW9Z0VZVx8+8/O4k++4seUWSC6rq6Kp6UpJTkly7shEBAAA2rkUvj6yqtyd5epLjqmpPkpcneXpVnZGFSx93J3lhknT3jVV1eZKbkjyQ5EXd/eCaTA4AALABLBpt3f28Ayx+4yHW35Zk20qGAgAAYMGyLo8EAADgoSHaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABibaAAAABnbkvAeA9eKYU7fmKTu2znuMuTjm1CQ5Z95jAACsS6INVsk9N2/P7u0bM1w2b9057xEAANYtl0cCAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMTLQBAAAMbNFoq6o3VdXdVXXDzLLHVdVVVfWR6fGxM+9dXFW3VNWHq+qstRocAABgI1jKmbY3Jzl7v2Vbk1zd3ackuXp6nap6cpILkpw2bfP6qjpi1aYFAADYYBaNtu7+sySf2m/xuUl2TM93JDlvZvll3X1fd9+a5JYkZ67OqAAAABvPcr/T9oTuvjNJpsfHT8tPSHL7zHp7pmVfoqouqqpdVbVr7969yxwDAABgfVvtG5HUAZb1gVbs7ku7e0t3b9m0adMqjwEAALA+LDfa7qqq45Nkerx7Wr4nyUkz652Y5I7ljwcAALCxLTfarkhy4fT8wiTvnFl+QVUdXVVPSnJKkmtXNiIAAMDGdeRiK1TV25M8PclxVbUnycuTbE9yeVW9IMnHkpyfJN19Y1VdnuSmJA8keVF3P7hGswMAAKx7i0Zbdz/vIG894yDrb0uybSVDAQAAsGC1b0QCAADAKhJtAAAAAxNtAAAAAxNtAAAAAxNtAAAAAxNtAAAAA1v0lv8AADw8nP6Kd+Wz994/t+Nv3rpzLsc99lFH5f0vf9Zcjg0PBdEGALBOfPbe+7N7+znzHuMhN69YhIeKyyMBAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGJtoAAAAGduRKNq6q3UnuSfJgkge6e0tVPS7J7yTZnGR3ku/r7k+vbEwAABZzzKlb85QdW+c9xkPumFOT5Jx5jwFrZkXRNvnW7v7EzOutSa7u7u1VtXV6/V9X4TgAABzCPTdvz+7tGy9eNm/dOe8RYE2txeWR5ybZMT3fkeS8NTgGAADAhrDSaOsk76qq91XVRdOyJ3T3nUkyPT5+hccAAADYsFZ6eeTTuvuOqnp8kquq6kNL3XCKvIuS5OSTT17hGAAAAOvTis60dfcd0+PdSX4vyZlJ7qqq45Nkerz7INte2t1bunvLpk2bVjIGAADAurXsaKuqL6+qY/Y9T/KsJDckuSLJhdNqFyZ550qHBAAA2KhWcnnkE5L8XlXt289vd/f/qqrrklxeVS9I8rEk5698TAAAgI1p2dHW3R9NcvoBln8yyTNWMhQAAAAL1uKW/wAAAKwS0QYAADAw0QYAADCwlf6eNmDG5q075z3CXBz7qKPmPQIAwLol2mCV7N5+zlyPv3nrzrnPAADA6nN5JAAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMCOnPcAAACsns1bd857hIfcsY86at4jwJoSbQAA68Tu7efM7dibt+6c6/FhPXN5JAAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMBEGwAAwMCOnPcAAADM3+atO+e+j93bz1nxDLAerVm0VdXZSV6b5Igkb+ju7Wt1LAAAVkYwwbjWJNqq6ogkv57k25PsSXJdVV3R3TetxfFgPZj3/+H0wxoAYExrdabtzCS3dPdHk6SqLktybhLRBgchmgAAOJC1uhHJCUlun3m9Z1oGAADAYViraKsDLOsvWqHqoqraVVW79u7du0ZjAAAAPLytVbTtSXLSzOsTk9wxu0J3X9rdW7p7y6ZNm9ZoDAAAgIe3tYq265KcUlVPqqpHJLkgyRVrdCwAAIB1a01uRNLdD1TVi5P8cRZu+f+m7r5xLY4FAACwnq3Z72nr7iuTXLlW+wcAANgI1urySAAAAFaBaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABiYaAMAABhYdfe8Z0hV7U1y27zngIe545J8Yt5DALBh+TkEK/PE7t50oDeGiDZg5apqV3dvmfccAGxMfg7B2nF5JAAAwMBEGwAAwMBEG6wfl857AAA2ND+HYI34ThsAAMDAnGkDAAAYmGiDda6q3l5VH6iq/zTvWQAYX1W9uaq+d3r+kqr6FzPvXVlVj1mFY5xRVc9Z6X5gozhy3gMAa6OqjszC78z5t939xHnPA8DD0kuSvDXJ55Kku1crtM5IsiXJlau0P1jXnGmDwVXVl1fVzqp6f1XdUFXPrardVXXc9P6Wqnr39PySqrq0qt6V5LeSvCvJ46vq+qr65vl9CgBGU1Wbq+qGmdcvrapLZl7/RJKvSnJNVV0zLdtdVcdN236oqt4w/Wx6W1U9s6r+oqo+UlVnTuufWVV/WVX/Z3r8l1X1iCQ/l+S508+n504/695UVddN6577kP5lwOCcaYPxnZ3kju4+J0mq6tgkv3SI9b8hyTd1971VtTnJH3b3GWs+JQDrSnf/WlX9VJJv7e5PHGCVr01yfpKLklyX5PuTfFOS70rysiTnJflQkm/p7geq6plJXtnd31NV/y3Jlu5+cZJU1SuT/Gl3//B0+eW1VfUn3f1Pa/sp4eFBtMH4Ppjk1VX1S1kIsD+vqkOtf0V33/vQjAbABnZrd38wSarqxiRXd3dX1QeTbJ7WOTbJjqo6JUknOeog+3pWku+qqpdOrx+Z5OQkN6/V8PBwItpgcN39t1X1DUmek+QXp0sfH8gXLm9+5H6b+L+SACzF7M+S5Et/nizmvpnnn595/fl84b8xfz7JNd393dPVH+8+yL4qyfd094cPcwbYEHynDQZXVV+V5HPd/dYkr07y9Ul2Z+EyyCT5njmNBsDD211Z+N7zV1bV0Um+4wDr3JPkmBUc49gkH5+e/+Ah9vvHSX68pktJquqpKzgmrDuiDcb3lCxc2399kp9J8gtJXpHktVX150kenONsADxMdff9WbghyHuT/GEWvn+2v0uT/NG+G5Esw6uycJXIXyQ5Ymb5NUmevO9GJFk4I3dUkg9MN0f5+WUeD9al6u55zwAAAMBBONMGAAAwMNEGAAAwMNEGAAAwMNEGAAAwMNEGAAAwMNEGAAAwMNEGAAAwMNEGAAAwsP8PFAfmskGzeIUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly internet traffic\n",
    "df_all_ints_traffic.plot(kind='box', title='Monthly Internet Traffic', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# surf traffic values\n",
    "surf_ints_traffic = df_surf_ints_traffic['mb_used'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ultimate traffic values\n",
    "ultimate_ints_traffic = df_ultimate_ints_traffic['mb_used'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_14_3_1_1_'></a>[Null hypothesis is that the two mean data traffic numbers are the same](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.04977590665959082\n",
      "We reject the null hypothesis, the average data traffic numbers differ\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(surf_ints_traffic, ultimate_ints_traffic)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average data traffic numbers differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Surf plan consistently sees more internet traffic than the Ultimate plan, with one exception. The mean internet traffic values appears to be quite different, further emphasized by the box plot. The upper and lower bounds of the Surf plan are wider than that of the Ultimate plan. This is made evident by the variance in the Surf data usage. Hypothesis testing further supports that the average traffic data numbers differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "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>plan</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>surf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4874.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12178.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13345.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surf</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11984.203000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13936.354935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>surf</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15301.529175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16783.600579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>surf</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16795.331358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16591.431289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>surf</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17311.335063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>surf</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16339.254417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surf</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18132.469371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6918.092500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17128.808571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18321.518333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16121.654762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16624.482414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15337.921064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16344.744407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17814.720141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16969.869535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17612.553396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17033.685354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18323.191854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        plan  month       mb_used\n",
       "0       surf    0.0      0.000000\n",
       "1       surf    1.0   4874.860000\n",
       "2       surf    2.0  12178.843333\n",
       "3       surf    3.0  13345.440000\n",
       "4       surf    4.0  11984.203000\n",
       "5       surf    5.0  13936.354935\n",
       "6       surf    6.0  15301.529175\n",
       "7       surf    7.0  16783.600579\n",
       "8       surf    8.0  16795.331358\n",
       "9       surf    9.0  16591.431289\n",
       "10      surf   10.0  17311.335063\n",
       "11      surf   11.0  16339.254417\n",
       "12      surf   12.0  18132.469371\n",
       "13  ultimate    0.0      0.000000\n",
       "14  ultimate    1.0   6918.092500\n",
       "15  ultimate    2.0  17128.808571\n",
       "16  ultimate    3.0  18321.518333\n",
       "17  ultimate    4.0  16121.654762\n",
       "18  ultimate    5.0  16624.482414\n",
       "19  ultimate    6.0  15337.921064\n",
       "20  ultimate    7.0  16344.744407\n",
       "21  ultimate    8.0  17814.720141\n",
       "22  ultimate    9.0  16969.869535\n",
       "23  ultimate   10.0  17612.553396\n",
       "24  ultimate   11.0  17033.685354\n",
       "25  ultimate   12.0  18323.191854"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare average mb used per each plan per each distinct month. Plot a bar plat to visualize it.\n",
    "df_merged_ints = df_merged.groupby(['plan', 'month'])['mb_used'].mean()\n",
    "display(df_merged_ints.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Data used per month in Surf plan\n",
    "df_surf_ints = df_merged_ints[1:13].reset_index('plan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Data used per month in Ultimate plan\n",
    "df_ultimate_ints = df_merged_ints.reset_index('plan').tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merging of Surf and Ultimate data usage, per month\n",
    "df_all_ints = df_surf_ints.merge(df_ultimate_ints, on='month', how='outer')\n",
    "df_all_ints.columns = ['plan_s', 'surf', 'plan_u', 'ultimate'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display visual of internet usage per month, per plan\n",
    "df_all_ints.plot(kind='bar',\n",
    "                    title='Megabytes Used',\n",
    "                  xlabel='Month',\n",
    "                  ylabel='Average Number of Megabytes',\n",
    "                  color=('blue', 'red'),\n",
    "                 rot=0,\n",
    "                  figsize= (16,10)\n",
    "                    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "mb_used    14464.554377\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "mb_used    1.327065e+07\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3548975033.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_ints.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3548975033.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_ints.var())\n"
     ]
    }
   ],
   "source": [
    "# Mean and variance of Surf data usage\n",
    "print('mean')\n",
    "print(df_surf_ints.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_surf_ints.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "mb_used    16212.603528\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "mb_used    9.351289e+06\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3184717699.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_ints.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3184717699.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_ints.var())\n"
     ]
    }
   ],
   "source": [
    "# Mean and variance of Ultimate data usage\n",
    "print('mean')\n",
    "print(df_ultimate_ints.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_ultimate_ints.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogras of Surf and Ultimate data usage\n",
    "df_surf_ints.hist(bins=10)\n",
    "df_ultimate_ints.hist(bins=10)\n",
    "plt.title='Megabytes'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merged histogram of Surf and Ultimate dat usage\n",
    "df_surf_ints['mb_used'].plot(kind='hist', bins=5, title='Megabytes Used', ylabel='Frequency', figsize=(15,8))\n",
    "df_ultimate_ints['mb_used'].plot(kind='hist', bins=5, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.legend(['Surf', 'Ultimate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Monthly Megabytes Used'}>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly call duration\n",
    "df_all_ints.plot(kind='box', title='Monthly Megabytes Used', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# surf mb values\n",
    "surf_ints = df_surf_ints['mb_used'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# surf megabyte values\n",
    "ultimate_ints = df_ultimate_ints['mb_used'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_14_3_1_2_'></a>[Null hypothesis that mean data usage is similar](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.21625434664424556\n",
      "We can't reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "# Null hypothesis that the two means are the same\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(surf_ints, ultimate_ints)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average data usages differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Ultimate plan customers consistently used more data than those of the Surf plan, with the exception of one month out of the year. The mean data usage of both plans appears to be quite similar, further emphasized by the box  plot. The Surf plan has an outlier on the lower side of data usage, and the upper and lower bounds are quite wide. The box plot of the Ultimate plan is tighter, with one outlier as well. Hypothesis testing suggests the mean data usage between plans is not different. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_15_'></a>[Revenue](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistically describe the revenue between the plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a monthly revenue column\n",
    "df_merged['monthly_revenue'] = df_merged.apply(revenue, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "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>num_calls</th>\n",
       "      <th>call_duration</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>num_messages</th>\n",
       "      <th>plan</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>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>116.83</td>\n",
       "      <td>1901.47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1006</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>2068.37</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>54.79</td>\n",
       "      <td>32118.82</td>\n",
       "      <td>139.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1008</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>450.21</td>\n",
       "      <td>17106.99</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1008</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>422.81</td>\n",
       "      <td>23676.72</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>1143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>1319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  num_calls  call_duration   mb_used  num_messages  \\\n",
       "0        1000   12.0       16.0         116.83   1901.47          11.0   \n",
       "1        1006   11.0        2.0           9.32   2068.37          15.0   \n",
       "2        1006   12.0        9.0          54.79  32118.82         139.0   \n",
       "3        1008   10.0       71.0         450.21  17106.99          21.0   \n",
       "4        1008   11.0       63.0         422.81  23676.72          37.0   \n",
       "...       ...    ...        ...            ...       ...           ...   \n",
       "2298     1143    0.0        0.0           0.00      0.00           0.0   \n",
       "2299     1307    0.0        0.0           0.00      0.00           0.0   \n",
       "2300     1319    0.0        0.0           0.00      0.00           0.0   \n",
       "2301     1378    0.0        0.0           0.00      0.00           0.0   \n",
       "2302     1473    0.0        0.0           0.00      0.00           0.0   \n",
       "\n",
       "          plan  messages_included  mb_per_month_included  minutes_included  \\\n",
       "0     ultimate               1000                  30720              3000   \n",
       "1     ultimate               1000                  30720              3000   \n",
       "2     ultimate               1000                  30720              3000   \n",
       "3     ultimate               1000                  30720              3000   \n",
       "4     ultimate               1000                  30720              3000   \n",
       "...        ...                ...                    ...               ...   \n",
       "2298      surf                 50                  15360               500   \n",
       "2299      surf                 50                  15360               500   \n",
       "2300      surf                 50                  15360               500   \n",
       "2301      surf                 50                  15360               500   \n",
       "2302      surf                 50                  15360               500   \n",
       "\n",
       "      usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute  \\\n",
       "0                  70           7             0.01            0.01   \n",
       "1                  70           7             0.01            0.01   \n",
       "2                  70           7             0.01            0.01   \n",
       "3                  70           7             0.01            0.01   \n",
       "4                  70           7             0.01            0.01   \n",
       "...               ...         ...              ...             ...   \n",
       "2298               20          10             0.03            0.03   \n",
       "2299               20          10             0.03            0.03   \n",
       "2300               20          10             0.03            0.03   \n",
       "2301               20          10             0.03            0.03   \n",
       "2302               20          10             0.03            0.03   \n",
       "\n",
       "      monthly_revenue  \n",
       "0                70.0  \n",
       "1                70.0  \n",
       "2                84.0  \n",
       "3                70.0  \n",
       "4                70.0  \n",
       "...               ...  \n",
       "2298             20.0  \n",
       "2299             20.0  \n",
       "2300             20.0  \n",
       "2301             20.0  \n",
       "2302             20.0  \n",
       "\n",
       "[2303 rows x 15 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual of the new column\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_calls</th>\n",
       "      <th>call_duration</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>num_messages</th>\n",
       "      <th>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_calls</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980393</td>\n",
       "      <td>0.134690</td>\n",
       "      <td>0.341054</td>\n",
       "      <td>0.247941</td>\n",
       "      <td>0.227388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>call_duration</th>\n",
       "      <td>0.980393</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.135112</td>\n",
       "      <td>0.331108</td>\n",
       "      <td>0.246878</td>\n",
       "      <td>0.218230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.134690</td>\n",
       "      <td>0.135112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157701</td>\n",
       "      <td>0.170708</td>\n",
       "      <td>0.098734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mb_used</th>\n",
       "      <td>0.341054</td>\n",
       "      <td>0.331108</td>\n",
       "      <td>0.157701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226509</td>\n",
       "      <td>0.774237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_messages</th>\n",
       "      <td>0.247941</td>\n",
       "      <td>0.246878</td>\n",
       "      <td>0.170708</td>\n",
       "      <td>0.226509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.158293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_revenue</th>\n",
       "      <td>0.227388</td>\n",
       "      <td>0.218230</td>\n",
       "      <td>0.098734</td>\n",
       "      <td>0.774237</td>\n",
       "      <td>0.158293</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 num_calls  call_duration     month   mb_used  num_messages  \\\n",
       "num_calls         1.000000       0.980393  0.134690  0.341054      0.247941   \n",
       "call_duration     0.980393       1.000000  0.135112  0.331108      0.246878   \n",
       "month             0.134690       0.135112  1.000000  0.157701      0.170708   \n",
       "mb_used           0.341054       0.331108  0.157701  1.000000      0.226509   \n",
       "num_messages      0.247941       0.246878  0.170708  0.226509      1.000000   \n",
       "monthly_revenue   0.227388       0.218230  0.098734  0.774237      0.158293   \n",
       "\n",
       "                 monthly_revenue  \n",
       "num_calls               0.227388  \n",
       "call_duration           0.218230  \n",
       "month                   0.098734  \n",
       "mb_used                 0.774237  \n",
       "num_messages            0.158293  \n",
       "monthly_revenue         1.000000  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Greatest contributor to revenue\n",
    "df_merged[['num_calls', 'call_duration','month', 'mb_used', 'num_messages', 'monthly_revenue']] .corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plan</th>\n",
       "      <th>month</th>\n",
       "      <th>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>surf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>surf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>surf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>surf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.792609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surf</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.458600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.240130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>surf</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48.935155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>surf</td>\n",
       "      <td>7.0</td>\n",
       "      <td>62.226281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>surf</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63.576728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>surf</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57.952320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>surf</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65.051097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>surf</td>\n",
       "      <td>11.0</td>\n",
       "      <td>57.633463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>surf</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.108176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>71.638298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.898305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72.859155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>72.034884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72.311321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>11.0</td>\n",
       "      <td>71.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ultimate</td>\n",
       "      <td>12.0</td>\n",
       "      <td>73.291391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        plan  month  monthly_revenue\n",
       "0       surf    0.0        20.000000\n",
       "1       surf    1.0        20.000000\n",
       "2       surf    2.0        34.260000\n",
       "3       surf    3.0        45.792609\n",
       "4       surf    4.0        40.458600\n",
       "5       surf    5.0        47.240130\n",
       "6       surf    6.0        48.935155\n",
       "7       surf    7.0        62.226281\n",
       "8       surf    8.0        63.576728\n",
       "9       surf    9.0        57.952320\n",
       "10      surf   10.0        65.051097\n",
       "11      surf   11.0        57.633463\n",
       "12      surf   12.0        70.108176\n",
       "13  ultimate    0.0        70.000000\n",
       "14  ultimate    1.0        70.000000\n",
       "15  ultimate    2.0        70.000000\n",
       "16  ultimate    3.0        74.666667\n",
       "17  ultimate    4.0        73.000000\n",
       "18  ultimate    5.0        70.724138\n",
       "19  ultimate    6.0        71.638298\n",
       "20  ultimate    7.0        71.898305\n",
       "21  ultimate    8.0        72.859155\n",
       "22  ultimate    9.0        72.034884\n",
       "23  ultimate   10.0        72.311321\n",
       "24  ultimate   11.0        71.708661\n",
       "25  ultimate   12.0        73.291391"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# revenues of both plans\n",
    "df_merged_revs = df_merged.groupby(['plan', 'month'])['monthly_revenue'].mean()\n",
    "display(df_merged_revs.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate monthly revenue of Surf plan\n",
    "df_surf_revs = df_merged_revs[1:13].reset_index('plan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separate monthly revenue of Ultimate plan\n",
    "df_ultimate_revs = df_merged_revs.reset_index('plan').tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Recombine revenue of both plans, per month\n",
    "df_all_revs = df_surf_revs.merge(df_ultimate_revs, on='month', how='outer')\n",
    "df_all_revs.columns = ['plan_s', 'surf', 'plan_u', 'ultimate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot revenue per month, per plan\n",
    "df_all_revs.plot(kind='bar',\n",
    "                    title='Revenue',\n",
    "                  xlabel='Month',\n",
    "                  ylabel='Total Revenue',\n",
    "                  color=('blue', 'red'),\n",
    "                 rot=0,\n",
    "                  figsize= (16,10)\n",
    "                    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "monthly_revenue    51.10288\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "monthly_revenue    212.439602\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3926635512.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_revs.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3926635512.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_surf_revs.var())\n"
     ]
    }
   ],
   "source": [
    "# Surf revenue mean and variance\n",
    "print('mean')\n",
    "print(df_surf_revs.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_surf_revs.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "monthly_revenue    72.011068\n",
      "dtype: float64\n",
      "\n",
      "variance\n",
      "monthly_revenue    1.860681\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3365555136.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_revs.mean())\n",
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\3365555136.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(df_ultimate_revs.var())\n"
     ]
    }
   ],
   "source": [
    "# Surf revenue mean and variance\n",
    "print('mean')\n",
    "print(df_ultimate_revs.mean())\n",
    "print()\n",
    "print('variance')\n",
    "print(df_ultimate_revs.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Monthly Revenue'}>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly call duration\n",
    "df_all_revs.plot(kind='box', title='Monthly Revenue', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Ultimate plan consistently sees more revenue on a monthly basis. The mean revenues for the two plans appears to be different, but this will be further explored statistically. Looking at the box plot, we see the differences in the mean revenues. The Ultimate plan has a tighter range, while the Surf plan has a wider range and variance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_16_'></a>[Test statistical hypotheses](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_16_1_1_1_'></a>[Testing the hypothesis that the average revenues from users of the Ultimate and Surf calling plans differ.](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extracting the revenues from the Surf plan\n",
    "surf_revs = df_surf_revs['monthly_revenue'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the revenues from the Ultimate Plan\n",
    "ultimate_revs = df_ultimate_revs['monthly_revenue'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_16_1_1_2_'></a>[Null hypothesis is the mean revenues  of the Surf and Ultimate plans are similar](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  5.981445309161515e-05\n",
      "We reject the null hypothesis, the average revenues differ\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "# Null hypothesis that the two means are the same\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(surf_revs, ultimate_revs)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average revenues differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our earlier thoughts were wrong. Statistically, the mean revenues of both plans are different, but it is the Ultimate plan that brings in more revenue.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_16_2_'></a>[Testing the hypothesis that the average revenue from users in the NY-NJ area is different from that of the users from the other regions.](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Separating data based on user ID and city\n",
    "df_user_city = df_users[['user_id','city']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Adding data on plan and monthly revenue, merged by user ID\n",
    "df_all_cities = df_merged[['user_id', 'plan','monthly_revenue']].merge(df_user_city, on='user_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Grouping the monthly revenue by city\n",
    "df_cities = df_all_cities.groupby('city')['monthly_revenue'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany-Schenectady-Troy, NY MSA</td>\n",
       "      <td>147.794000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Colorado Springs, CO MSA</td>\n",
       "      <td>135.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Urban Honolulu, HI MSA</td>\n",
       "      <td>112.408095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Baton Rouge, LA MSA</td>\n",
       "      <td>104.012500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fresno, CA MSA</td>\n",
       "      <td>99.405789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dayton-Kettering, OH MSA</td>\n",
       "      <td>95.582222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA MSA</td>\n",
       "      <td>94.625806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT MSA</td>\n",
       "      <td>89.955789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Sacramento–Roseville–Folsom, CA MSA</td>\n",
       "      <td>87.781667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Jacksonville, FL MSA</td>\n",
       "      <td>86.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Providence-Warwick, RI-MA MSA</td>\n",
       "      <td>82.198182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Minneapolis-St. Paul-Bloomington, MN-WI MSA</td>\n",
       "      <td>80.859500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>St. Louis, MO-IL MSA</td>\n",
       "      <td>79.145455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>77.181765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Louisville/Jefferson County, KY-IN MSA</td>\n",
       "      <td>76.929706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>75.393333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Grand Rapids-Kentwood, MI MSA</td>\n",
       "      <td>75.176667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>74.823469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Miami-Fort Lauderdale-West Palm Beach, FL MSA</td>\n",
       "      <td>72.100656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Charleston-North Charleston, SC MSA</td>\n",
       "      <td>71.615385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             city  monthly_revenue\n",
       "0                 Albany-Schenectady-Troy, NY MSA       147.794000\n",
       "17                       Colorado Springs, CO MSA       135.017500\n",
       "70                         Urban Honolulu, HI MSA       112.408095\n",
       "6                             Baton Rouge, LA MSA       104.012500\n",
       "25                                 Fresno, CA MSA        99.405789\n",
       "21                       Dayton-Kettering, OH MSA        95.582222\n",
       "63         San Jose-Sunnyvale-Santa Clara, CA MSA        94.625806\n",
       "9             Bridgeport-Stamford-Norwalk, CT MSA        89.955789\n",
       "58            Sacramento–Roseville–Folsom, CA MSA        87.781667\n",
       "30                           Jacksonville, FL MSA        86.170000\n",
       "53                  Providence-Warwick, RI-MA MSA        82.198182\n",
       "39    Minneapolis-St. Paul-Bloomington, MN-WI MSA        80.859500\n",
       "65                           St. Louis, MO-IL MSA        79.145455\n",
       "47              Orlando-Kissimmee-Sanford, FL MSA        77.181765\n",
       "35         Louisville/Jefferson County, KY-IN MSA        76.929706\n",
       "69                                  Tulsa, OK MSA        75.393333\n",
       "26                  Grand Rapids-Kentwood, MI MSA        75.176667\n",
       "2           Atlanta-Sandy Springs-Roswell, GA MSA        74.823469\n",
       "37  Miami-Fort Lauderdale-West Palm Beach, FL MSA        72.100656\n",
       "12            Charleston-North Charleston, SC MSA        71.615385"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sorting monthly revenue in descending order, by city\n",
    "df_cities_sorted = df_cities.sort_values(by='monthly_revenue', ascending=False)\n",
    "display(df_cities_sorted.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>user_id</th>\n",
       "      <th>plan</th>\n",
       "      <th>monthly_revenue</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>1031</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>1031</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>1031</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1031</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>1031</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>2270</td>\n",
       "      <td>1080</td>\n",
       "      <td>surf</td>\n",
       "      <td>80.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>2271</td>\n",
       "      <td>1080</td>\n",
       "      <td>surf</td>\n",
       "      <td>120.42</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>2272</td>\n",
       "      <td>1080</td>\n",
       "      <td>surf</td>\n",
       "      <td>170.27</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>2273</td>\n",
       "      <td>1080</td>\n",
       "      <td>surf</td>\n",
       "      <td>200.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2274</td>\n",
       "      <td>1080</td>\n",
       "      <td>surf</td>\n",
       "      <td>110.33</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>378 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  user_id      plan  monthly_revenue  \\\n",
       "0       30     1031  ultimate            70.00   \n",
       "1       31     1031  ultimate            70.00   \n",
       "2       32     1031  ultimate            70.00   \n",
       "3       33     1031  ultimate            70.00   \n",
       "4       34     1031  ultimate            70.00   \n",
       "..     ...      ...       ...              ...   \n",
       "373   2270     1080      surf            80.00   \n",
       "374   2271     1080      surf           120.42   \n",
       "375   2272     1080      surf           170.27   \n",
       "376   2273     1080      surf           200.00   \n",
       "377   2274     1080      surf           110.33   \n",
       "\n",
       "                                          city  \n",
       "0    New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "1    New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "2    New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "3    New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "4    New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "..                                         ...  \n",
       "373  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "374  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "375  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "376  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "377  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "\n",
       "[378 rows x 5 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting rows based on the keywords that distinguish NY\n",
    "df_ny = df_all_cities[df_all_cities['city'].str.contains('New York-Newark-Jersey City, NY-NJ-PA MSA')]\n",
    "df_ny.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1014</td>\n",
       "      <td>28.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1022</td>\n",
       "      <td>55.738750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1024</td>\n",
       "      <td>20.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1027</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1031</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1469</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1482</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1494</td>\n",
       "      <td>30.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1495</td>\n",
       "      <td>118.092500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1498</td>\n",
       "      <td>77.272727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  monthly_revenue\n",
       "0      1014        28.475000\n",
       "1      1022        55.738750\n",
       "2      1024        20.930000\n",
       "3      1027        33.333333\n",
       "4      1031        70.000000\n",
       "..      ...              ...\n",
       "75     1469       100.000000\n",
       "76     1482        70.000000\n",
       "77     1494        30.430000\n",
       "78     1495       118.092500\n",
       "79     1498        77.272727\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping NY revenue by user ID and monthly revenues\n",
    "df_ny_rev = df_ny.groupby('user_id')['monthly_revenue'].mean()\n",
    "df_ny_rev.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extracting the mean revenues from the NY data\n",
    "ny = df_ny_rev.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "59.72249310064935"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of NY revenue\n",
    "print('Mean: ')\n",
    "df_ny_rev.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "32.5551036141766"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation of NY revenue\n",
    "print('Standard Deviation: ')\n",
    "df_ny_rev.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Creating cities data without NY, by index\n",
    "# Should see total rows drop from 73 to 72\n",
    "df_cities_2 = df_cities.drop(labels=43, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean :\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\4283063849.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_cities_2.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "monthly_revenue    65.443134\n",
       "dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean revenue of all the other cities\n",
    "print('Mean :')\n",
    "df_cities_2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation :\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_24004\\2224394961.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_cities_2.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "monthly_revenue    21.740895\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard deviation of revenue of all the other cities\n",
    "print('Standard Deviation :')\n",
    "df_cities_2.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>35.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>North Port-Sarasota-Bradenton, FL MSA</td>\n",
       "      <td>58.922500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Oklahoma City, OK MSA</td>\n",
       "      <td>67.980500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Omaha-Council Bluffs, NE-IA MSA</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>77.181765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Oxnard-Thousand Oaks-Ventura, CA MSA</td>\n",
       "      <td>38.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA</td>\n",
       "      <td>63.490732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Phoenix-Mesa-Chandler, AZ MSA</td>\n",
       "      <td>66.703250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Pittsburgh, PA MSA</td>\n",
       "      <td>66.506667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Portland-Vancouver-Hillsboro, OR-WA MSA</td>\n",
       "      <td>49.870909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Providence-Warwick, RI-MA MSA</td>\n",
       "      <td>82.198182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Raleigh-Cary, NC MSA</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Richmond, VA MSA</td>\n",
       "      <td>50.638000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA MSA</td>\n",
       "      <td>53.825111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Rochester, NY MSA</td>\n",
       "      <td>58.898667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Sacramento–Roseville–Folsom, CA MSA</td>\n",
       "      <td>87.781667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Salt Lake City, UT MSA</td>\n",
       "      <td>63.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>San Antonio-New Braunfels, TX MSA</td>\n",
       "      <td>70.331176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>San Diego-Chula Vista-Carlsbad, CA MSA</td>\n",
       "      <td>56.351250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA MSA</td>\n",
       "      <td>55.998772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA MSA</td>\n",
       "      <td>94.625806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>62.128113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>St. Louis, MO-IL MSA</td>\n",
       "      <td>79.145455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Stockton, CA MSA</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL MSA</td>\n",
       "      <td>61.511875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Tucson, AZ MSA</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>75.393333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Urban Honolulu, HI MSA</td>\n",
       "      <td>112.408095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Virginia Beach-Norfolk-Newport News, VA-NC MSA</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV MSA</td>\n",
       "      <td>57.576290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                city  monthly_revenue\n",
       "42                      New Orleans-Metairie, LA MSA        35.015000\n",
       "44             North Port-Sarasota-Bradenton, FL MSA        58.922500\n",
       "45                             Oklahoma City, OK MSA        67.980500\n",
       "46                   Omaha-Council Bluffs, NE-IA MSA        20.000000\n",
       "47                 Orlando-Kissimmee-Sanford, FL MSA        77.181765\n",
       "48              Oxnard-Thousand Oaks-Ventura, CA MSA        38.520000\n",
       "49   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA        63.490732\n",
       "50                     Phoenix-Mesa-Chandler, AZ MSA        66.703250\n",
       "51                                Pittsburgh, PA MSA        66.506667\n",
       "52           Portland-Vancouver-Hillsboro, OR-WA MSA        49.870909\n",
       "53                     Providence-Warwick, RI-MA MSA        82.198182\n",
       "54                              Raleigh-Cary, NC MSA        20.000000\n",
       "55                                  Richmond, VA MSA        50.638000\n",
       "56          Riverside-San Bernardino-Ontario, CA MSA        53.825111\n",
       "57                                 Rochester, NY MSA        58.898667\n",
       "58               Sacramento–Roseville–Folsom, CA MSA        87.781667\n",
       "59                            Salt Lake City, UT MSA        63.750000\n",
       "60                 San Antonio-New Braunfels, TX MSA        70.331176\n",
       "61            San Diego-Chula Vista-Carlsbad, CA MSA        56.351250\n",
       "62            San Francisco-Oakland-Berkeley, CA MSA        55.998772\n",
       "63            San Jose-Sunnyvale-Santa Clara, CA MSA        94.625806\n",
       "64                   Seattle-Tacoma-Bellevue, WA MSA        62.128113\n",
       "65                              St. Louis, MO-IL MSA        79.145455\n",
       "66                                  Stockton, CA MSA        35.000000\n",
       "67           Tampa-St. Petersburg-Clearwater, FL MSA        61.511875\n",
       "68                                    Tucson, AZ MSA        65.000000\n",
       "69                                     Tulsa, OK MSA        75.393333\n",
       "70                            Urban Honolulu, HI MSA       112.408095\n",
       "71    Virginia Beach-Norfolk-Newport News, VA-NC MSA        65.000000\n",
       "72  Washington-Arlington-Alexandria, DC-VA-MD-WV MSA        57.576290"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirming NY is not in cities 2 data \n",
    "df_cities_2.tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Other City Monthly Revenue'}>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot to visualize the distribution of the monthly call duration\n",
    "df_ny_rev.plot(kind='box', title='NY Monthly Revenue', figsize=(15,8))\n",
    "df_cities_2.plot(kind='box', title='Other City Monthly Revenue',figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ny_rev.plot(kind='hist', title='NY Monthly Revenue', figsize=(15,8), color='darkgreen')\n",
    "df_cities_2.plot(kind='hist', title='Other Cities Monthly Revenue',figsize=(15,8), color='darkblue', alpha=.8, legend=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extracting mean revenues from cities 2 data \n",
    "cities_2 = df_cities_2['monthly_revenue'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <a id='toc1_16_2_1_1_'></a>[Null hypothesis that the mean revenue of Ny vs the other cities is similar](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.2098498653029106\n",
      "We can't reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Test the hypotheses\n",
    "# Null hypothesis that the mean of NY is the same as the mean of the other cities\n",
    "alpha = 0.05  # critical statistical significance level\n",
    "# if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(ny, cities_2)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"We reject the null hypothesis, the average revenues differ\")\n",
    "else:\n",
    "    print(\"We can't reject the null hypothesis\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After statistical testing, we can not reject the null hypothesis that the mean of NY revenue is similar the the mean revenue of all the other cities. This suggests that they are indeed similar. This finding is further supported by the box plots, and histograms. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='toc1_16_2_2_'></a>[Relationship between revenue and age](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Making a dataset of the customers plan and monthly revenue, based on age\n",
    "df_age_rev = df_users[['age', 'user_id']].merge(df_merged, on='user_id', how='outer')\n",
    "df_age= df_age_rev[['age', 'user_id', 'plan', 'monthly_revenue']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Getting a count of the number of customers at each age \n",
    "df_customers = df_age.groupby('age')['age'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Age of Customers'}, xlabel='age', ylabel='Age'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the distribution of customer ages, across both plans\n",
    "df_customers.plot(kind='bar', figsize=(20,6), ylabel='Age', title='Age of Customers', color='black', alpha=.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Grouping by ages and plans\n",
    "df_customer_plans = df_age.groupby(['age', 'plan'])['user_id'].count()\n",
    "df_customer_plans = pd.DataFrame(df_customer_plans).reset_index(level=1)\n",
    "df_customer_plans.columns = ['plan', 'customers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extracting all age groups with Surf plan\n",
    "df_surf_customers = df_customer_plans[df_customer_plans['plan'].str.contains('surf')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extracting all age groups with Ultimate plan\n",
    "df_ultimate_customers = df_customer_plans[df_customer_plans['plan'].str.contains('ultimate')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recombining Surf and Ultimate customers, based on age\n",
    "df_plan_ages = df_surf_customers.merge(df_ultimate_customers, on='age', how='left')\n",
    "df_plan_ages.columns = ['plan_s', 'surf_customers', 'plan_u', 'ultimate_customers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Age of Customers'}, xlabel='Age', ylabel='Number of Customers'>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing the distributon of plan choices, based on age\n",
    "df_plan_ages.plot(kind='bar', figsize=(20,8), ylabel='Number of Customers', xlabel='Age',\n",
    "                  title='Age of Customers', color=('blue', 'red'), alpha=.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see most age groups prefer the Surf plan, with a few exceptions that prefer the Ultimate plan. We were expecting to see a pattern that suggests younger customers prefer the cheaper plan, yet the data does not suggest that. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='toc1_16_2_3_'></a>[Comparing revenue and age](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Mean Revenue based on age\n",
    "age_rev_mean = df_age.groupby('age')['monthly_revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Total monthly revenue based on age\n",
    "age_rev_sum = df_age.groupby('age')['monthly_revenue'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total Revenue'}, xlabel='Age', ylabel='Total Revenue'>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying total revenue, based on age\n",
    "age_rev_sum.plot(kind='bar', figsize=(20,8), ylabel='Total Revenue', xlabel='Age', title='Total Revenue', color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Average Revenue'}, xlabel='Age', ylabel='Mean Revenue'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying mean revenue, based on age\n",
    "age_rev_mean.plot(kind='bar', figsize=(20,8), ylabel='Mean Revenue', xlabel='Age', title='Average Revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that looking at total revenue based on age could be misleading, as we demonstrated the distribution of the number of customers in each age group earlier. Therefore, mean revenue would be better for making comparisons across age groups. We do not see any noticeable pattern in the data. We anticipated that younger customers would show a spike in revenue, due to their perceived lack of maturity. However, that was not the case. We only see spikes in the data with Ultimate plan customers in a few age groups. The age groups with the smallest mean revenue are 22, 35, and 63 year olds. Those with the highest revenue are 33, 42, and 71 year olds. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='toc2_'></a>[Conclusions](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data shows statistical differences in mean revenue among the two plans, as the Ultimate plan brings in more revenue. Our significance level was set to 5%, and our p value was much higher. In simpler words, we reject our null hypothesis that the mean revenues were similar. \n",
    "\n",
    "The data shows us that capital allocation to marketing the Ultimate plan would likely yield a better cash on cash return, not based on popularity, but on revenue. As the Surf plan is more popular, new customers should be lead to the Ultimate plan instead. We saw many Surf customers would experience overages on their plan. These would be the prime customer base to push towards the ultimate plan. \n",
    "\n",
    "We see that the mean revenue of customers in New York appears to be similar to that of all the other cities combined.  Yet, Honolulu, Albany, and Colorado Springs are the cities with the highest average revenue. A marketing push may also be a good idea in those areas, to further increase revenue, while also considering market saturation. We did not see a preference of plans of customers of different age groups, as most preferred the Surf plan. \n",
    "\n",
    "Overall, the Ultimate plan is not very popular. As such, maybe it would be beneficial to test a middle tier plan, in order to capture customers who may be dismayed by the gap in plan prices. Another method that would lead to increased revenue would be to slightly increase the overage fees on the Surf plan. Yet, a smart revenue strategy remains in rounding up minutes, and more substantially, rounding up data used to the nearest gigabyte. Data usage appears to be the largest contributor to revenue.\n",
    "\n",
    "Finally, Hypothesis testing suggests the mean of the call durations and number of messages were not different. On the other hand, internet traffic is different, when conducting statistical tests on the means."
   ]
  }
 ],
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    "duration": 828,
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