Activity_Course 2 Waze project lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Waze Project**\n",
    "**Course 2 - Get Started with Python**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zJCatj3xzrQZ"
   },
   "source": [
    "Welcome to the Waze Project!\n",
    "\n",
    "Your Waze data analytics team is still in the early stages of their user churn project. Previously, you were asked to complete a project proposal by your supervisor, May Santner. You have received notice that your project proposal has been approved and that your team has been given access to Waze's user data. To get clear insights, the user data must be inspected and prepared for the upcoming process of exploratory data analysis (EDA).\n",
    "\n",
    "A Python notebook has been prepared to guide you through this project. Answer the questions and create an executive summary for the Waze data team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# **Course 2 End-of-course project: Inspect and analyze data**\n",
    "\n",
    "In this activity, you will examine data provided and prepare it for analysis. This activity will help ensure the information is,\n",
    "\n",
    "1.   Ready to answer questions and yield insights\n",
    "\n",
    "2.   Ready for visualizations\n",
    "\n",
    "3.   Ready for future hypothesis testing and statistical methods\n",
    "<br/>\n",
    "\n",
    "**The purpose** of this project is to investigate and understand the data provided.\n",
    "\n",
    "**The goal** is to use a dataframe contructed within Python, perform a cursory inspection of the provided dataset, and inform team members of your findings.\n",
    "<br/>\n",
    "\n",
    "*This activity has three parts:*\n",
    "\n",
    "**Part 1:** Understand the situation\n",
    "* How can you best prepare to understand and organize the provided information?\n",
    "\n",
    "**Part 2:** Understand the data\n",
    "\n",
    "* Create a pandas dataframe for data learning, future exploratory data analysis (EDA), and statistical activities\n",
    "\n",
    "* Compile summary information about the data to inform next steps\n",
    "\n",
    "**Part 3:** Understand the variables\n",
    "\n",
    "* Use insights from your examination of the summary data to guide deeper investigation into variables\n",
    "\n",
    "\n",
    "<br/>\n",
    "\n",
    "Follow the instructions and answer the following questions to complete the activity. Then, you will complete an Executive Summary using the questions listed on the PACE Strategy Document.\n",
    "\n",
    "Be sure to complete this activity before moving on. The next course item will provide you with a completed exemplar to compare to your own work.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjFGokxv2pc5"
   },
   "source": [
    "# **Identify data types and compile summary information**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRUYfzCb4vop"
   },
   "source": [
    "<img src=\"images/Pace.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "# **PACE stages**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b4B47DQPcSQu"
   },
   "source": [
    "Throughout these project notebooks, you'll see references to the problem-solving framework, PACE. The following notebook components are labeled with the respective PACE stages: Plan, Analyze, Construct, and Execute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zRHb2QQWj99m"
   },
   "source": [
    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "\n",
    "## **PACE: Plan**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document and those below to craft your response:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pWEfG5zJV5oG"
   },
   "source": [
    "### **Task 1. Understand the situation**\n",
    "\n",
    "*   How can you best prepare to understand and organize the provided driver data?\n",
    "\n",
    "\n",
    "*Begin by exploring your dataset and consider reviewing the Data Dictionary.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irvqnKSe6Z80"
   },
   "source": [
    "Answer:\n",
    "Acquire first a high level view of the data by reviewing the columns, data types, how many rows, etc. This would give one an understanding on the data before cleaning it and before proceeding to the next steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1E9Y5aC0IAA-"
   },
   "source": [
    "<img src=\"images/Analyze.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## **PACE: Analyze**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Analyze stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D4WK_AxP_S__"
   },
   "source": [
    "### **Task 2a. Imports and data loading**\n",
    "\n",
    "Start by importing the packages that you will need to load and explore the dataset. Make sure to use the following import statements:\n",
    "\n",
    "*   `import pandas as pd`\n",
    "\n",
    "*   `import numpy as np`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "OZSXM4q5zrQh"
   },
   "outputs": [],
   "source": [
    "# Import packages for data manipulation\n",
    "### YOUR CODE HERE ###\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2-hT-EQA67v3"
   },
   "source": [
    "Then, load the dataset into a dataframe. Creating a dataframe will help you conduct data manipulation, exploratory data analysis (EDA), and statistical activities.\n",
    "\n",
    "**Note:** As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset into dataframe\n",
    "df = pd.read_csv('waze_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gYx1emvno7U_"
   },
   "source": [
    "### **Task 2b. Summary information**\n",
    "\n",
    "View and inspect summary information about the dataframe by **coding the following:**\n",
    "\n",
    "1.   df.head(10)\n",
    "2.   df.info()\n",
    "\n",
    "*Consider the following questions:*\n",
    "\n",
    "1. When reviewing the `df.head()` output, are there any variables that have missing values?\n",
    "\n",
    "2. When reviewing the `df.info()` output, what are the data types? How many rows and columns do you have?\n",
    "\n",
    "3. Does the dataset have any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "t7Nck2hh4R6J"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>label</th>\n",
       "      <th>sessions</th>\n",
       "      <th>drives</th>\n",
       "      <th>total_sessions</th>\n",
       "      <th>n_days_after_onboarding</th>\n",
       "      <th>total_navigations_fav1</th>\n",
       "      <th>total_navigations_fav2</th>\n",
       "      <th>driven_km_drives</th>\n",
       "      <th>duration_minutes_drives</th>\n",
       "      <th>activity_days</th>\n",
       "      <th>driving_days</th>\n",
       "      <th>device</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>283</td>\n",
       "      <td>226</td>\n",
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       "      <td>2276</td>\n",
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       "      <td>Android</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>retained</td>\n",
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       "      <td>107</td>\n",
       "      <td>326.896596</td>\n",
       "      <td>1225</td>\n",
       "      <td>19</td>\n",
       "      <td>64</td>\n",
       "      <td>13715.920550</td>\n",
       "      <td>3160.472914</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>iPhone</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>retained</td>\n",
       "      <td>114</td>\n",
       "      <td>95</td>\n",
       "      <td>135.522926</td>\n",
       "      <td>2651</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3059.148818</td>\n",
       "      <td>1610.735904</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>Android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>retained</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>67.589221</td>\n",
       "      <td>15</td>\n",
       "      <td>322</td>\n",
       "      <td>7</td>\n",
       "      <td>913.591123</td>\n",
       "      <td>587.196542</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>retained</td>\n",
       "      <td>84</td>\n",
       "      <td>68</td>\n",
       "      <td>168.247020</td>\n",
       "      <td>1562</td>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "      <td>3950.202008</td>\n",
       "      <td>1219.555924</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>Android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>retained</td>\n",
       "      <td>113</td>\n",
       "      <td>103</td>\n",
       "      <td>279.544437</td>\n",
       "      <td>2637</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>901.238699</td>\n",
       "      <td>439.101397</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>iPhone</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>retained</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>236.725314</td>\n",
       "      <td>360</td>\n",
       "      <td>185</td>\n",
       "      <td>18</td>\n",
       "      <td>5249.172828</td>\n",
       "      <td>726.577205</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>retained</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>176.072845</td>\n",
       "      <td>2999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7892.052468</td>\n",
       "      <td>2466.981741</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>retained</td>\n",
       "      <td>57</td>\n",
       "      <td>46</td>\n",
       "      <td>183.532018</td>\n",
       "      <td>424</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2651.709764</td>\n",
       "      <td>1594.342984</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>Android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>churned</td>\n",
       "      <td>84</td>\n",
       "      <td>68</td>\n",
       "      <td>244.802115</td>\n",
       "      <td>2997</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>6043.460295</td>\n",
       "      <td>2341.838528</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID     label  sessions  drives  total_sessions  n_days_after_onboarding  \\\n",
       "0   0  retained       283     226      296.748273                     2276   \n",
       "1   1  retained       133     107      326.896596                     1225   \n",
       "2   2  retained       114      95      135.522926                     2651   \n",
       "3   3  retained        49      40       67.589221                       15   \n",
       "4   4  retained        84      68      168.247020                     1562   \n",
       "5   5  retained       113     103      279.544437                     2637   \n",
       "6   6  retained         3       2      236.725314                      360   \n",
       "7   7  retained        39      35      176.072845                     2999   \n",
       "8   8  retained        57      46      183.532018                      424   \n",
       "9   9   churned        84      68      244.802115                     2997   \n",
       "\n",
       "   total_navigations_fav1  total_navigations_fav2  driven_km_drives  \\\n",
       "0                     208                       0       2628.845068   \n",
       "1                      19                      64      13715.920550   \n",
       "2                       0                       0       3059.148818   \n",
       "3                     322                       7        913.591123   \n",
       "4                     166                       5       3950.202008   \n",
       "5                       0                       0        901.238699   \n",
       "6                     185                      18       5249.172828   \n",
       "7                       0                       0       7892.052468   \n",
       "8                       0                      26       2651.709764   \n",
       "9                      72                       0       6043.460295   \n",
       "\n",
       "   duration_minutes_drives  activity_days  driving_days   device  \n",
       "0              1985.775061             28            19  Android  \n",
       "1              3160.472914             13            11   iPhone  \n",
       "2              1610.735904             14             8  Android  \n",
       "3               587.196542              7             3   iPhone  \n",
       "4              1219.555924             27            18  Android  \n",
       "5               439.101397             15            11   iPhone  \n",
       "6               726.577205             28            23   iPhone  \n",
       "7              2466.981741             22            20   iPhone  \n",
       "8              1594.342984             25            20  Android  \n",
       "9              2341.838528              7             3   iPhone  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3NctoTSAvGGD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   ID                       14999 non-null  int64  \n",
      " 1   label                    14299 non-null  object \n",
      " 2   sessions                 14999 non-null  int64  \n",
      " 3   drives                   14999 non-null  int64  \n",
      " 4   total_sessions           14999 non-null  float64\n",
      " 5   n_days_after_onboarding  14999 non-null  int64  \n",
      " 6   total_navigations_fav1   14999 non-null  int64  \n",
      " 7   total_navigations_fav2   14999 non-null  int64  \n",
      " 8   driven_km_drives         14999 non-null  float64\n",
      " 9   duration_minutes_drives  14999 non-null  float64\n",
      " 10  activity_days            14999 non-null  int64  \n",
      " 11  driving_days             14999 non-null  int64  \n",
      " 12  device                   14999 non-null  object \n",
      "dtypes: float64(3), int64(8), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JscqNfr6ZVsE"
   },
   "source": [
    "Answers:\n",
    "1. There are columns having 0 as the entry.\n",
    "2. There are integer, float, and object data types. Upon reviewing the expected data type for each column, everything seems to be in perfect order. There are 14999 rows and 13 columns.\n",
    "3. The dataset has missing values, if the 0 are considered as \"missing\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BMNnIoc51_1N"
   },
   "source": [
    "### **Task 2c. Null values and summary statistics**\n",
    "\n",
    "Compare the summary statistics of the 700 rows that are missing labels with summary statistics of the rows that are not missing any values.\n",
    "\n",
    "**Question:** Is there a discernible difference between the two populations?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "bAQeHW-d2S1-"
   },
   "outputs": [
    {
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       "      <th>total_navigations_fav1</th>\n",
       "      <th>total_navigations_fav2</th>\n",
       "      <th>driven_km_drives</th>\n",
       "      <th>duration_minutes_drives</th>\n",
       "      <th>activity_days</th>\n",
       "      <th>driving_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
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       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>7405.584286</td>\n",
       "      <td>80.837143</td>\n",
       "      <td>67.798571</td>\n",
       "      <td>198.483348</td>\n",
       "      <td>1709.295714</td>\n",
       "      <td>118.717143</td>\n",
       "      <td>30.371429</td>\n",
       "      <td>3935.967029</td>\n",
       "      <td>1795.123358</td>\n",
       "      <td>15.382857</td>\n",
       "      <td>12.125714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4306.900234</td>\n",
       "      <td>79.987440</td>\n",
       "      <td>65.271926</td>\n",
       "      <td>140.561715</td>\n",
       "      <td>1005.306562</td>\n",
       "      <td>156.308140</td>\n",
       "      <td>46.306984</td>\n",
       "      <td>2443.107121</td>\n",
       "      <td>1419.242246</td>\n",
       "      <td>8.772714</td>\n",
       "      <td>7.626373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.582648</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.119811</td>\n",
       "      <td>66.588493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3744.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>94.056340</td>\n",
       "      <td>869.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2119.344818</td>\n",
       "      <td>779.009271</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7443.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>177.255925</td>\n",
       "      <td>1650.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3421.156721</td>\n",
       "      <td>1414.966279</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11007.000000</td>\n",
       "      <td>112.250000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>266.058022</td>\n",
       "      <td>2508.750000</td>\n",
       "      <td>169.250000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5166.097373</td>\n",
       "      <td>2443.955404</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14993.000000</td>\n",
       "      <td>556.000000</td>\n",
       "      <td>445.000000</td>\n",
       "      <td>1076.879741</td>\n",
       "      <td>3498.000000</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>15135.391280</td>\n",
       "      <td>9746.253023</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID    sessions      drives  total_sessions  \\\n",
       "count    700.000000  700.000000  700.000000      700.000000   \n",
       "mean    7405.584286   80.837143   67.798571      198.483348   \n",
       "std     4306.900234   79.987440   65.271926      140.561715   \n",
       "min       77.000000    0.000000    0.000000        5.582648   \n",
       "25%     3744.500000   23.000000   20.000000       94.056340   \n",
       "50%     7443.000000   56.000000   47.500000      177.255925   \n",
       "75%    11007.000000  112.250000   94.000000      266.058022   \n",
       "max    14993.000000  556.000000  445.000000     1076.879741   \n",
       "\n",
       "       n_days_after_onboarding  total_navigations_fav1  \\\n",
       "count               700.000000              700.000000   \n",
       "mean               1709.295714              118.717143   \n",
       "std                1005.306562              156.308140   \n",
       "min                  16.000000                0.000000   \n",
       "25%                 869.000000                4.000000   \n",
       "50%                1650.500000               62.500000   \n",
       "75%                2508.750000              169.250000   \n",
       "max                3498.000000             1096.000000   \n",
       "\n",
       "       total_navigations_fav2  driven_km_drives  duration_minutes_drives  \\\n",
       "count              700.000000        700.000000               700.000000   \n",
       "mean                30.371429       3935.967029              1795.123358   \n",
       "std                 46.306984       2443.107121              1419.242246   \n",
       "min                  0.000000        290.119811                66.588493   \n",
       "25%                  0.000000       2119.344818               779.009271   \n",
       "50%                 10.000000       3421.156721              1414.966279   \n",
       "75%                 43.000000       5166.097373              2443.955404   \n",
       "max                352.000000      15135.391280              9746.253023   \n",
       "\n",
       "       activity_days  driving_days  \n",
       "count     700.000000    700.000000  \n",
       "mean       15.382857     12.125714  \n",
       "std         8.772714      7.626373  \n",
       "min         0.000000      0.000000  \n",
       "25%         8.000000      6.000000  \n",
       "50%        15.000000     12.000000  \n",
       "75%        23.000000     18.000000  \n",
       "max        31.000000     30.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolate rows with null values\n",
    "### YOUR CODE HERE ###\n",
    "df_null = df[df.isnull().any(axis=1)]\n",
    "\n",
    "# Display summary stats of rows with null values\n",
    "### YOUR CODE HERE ###\n",
    "df_null.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "W77hp8q3w-zi"
   },
   "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>sessions</th>\n",
       "      <th>drives</th>\n",
       "      <th>total_sessions</th>\n",
       "      <th>n_days_after_onboarding</th>\n",
       "      <th>total_navigations_fav1</th>\n",
       "      <th>total_navigations_fav2</th>\n",
       "      <th>driven_km_drives</th>\n",
       "      <th>duration_minutes_drives</th>\n",
       "      <th>activity_days</th>\n",
       "      <th>driving_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7499.000000</td>\n",
       "      <td>80.633776</td>\n",
       "      <td>67.281152</td>\n",
       "      <td>189.964447</td>\n",
       "      <td>1749.837789</td>\n",
       "      <td>121.605974</td>\n",
       "      <td>29.672512</td>\n",
       "      <td>4039.340921</td>\n",
       "      <td>1860.976012</td>\n",
       "      <td>15.537102</td>\n",
       "      <td>12.179879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4329.982679</td>\n",
       "      <td>80.699065</td>\n",
       "      <td>65.913872</td>\n",
       "      <td>136.405128</td>\n",
       "      <td>1008.513876</td>\n",
       "      <td>148.121544</td>\n",
       "      <td>45.394651</td>\n",
       "      <td>2502.149334</td>\n",
       "      <td>1446.702288</td>\n",
       "      <td>9.004655</td>\n",
       "      <td>7.824036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.220211</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.441250</td>\n",
       "      <td>18.282082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3749.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>90.661156</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2212.600607</td>\n",
       "      <td>835.996260</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7499.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>159.568115</td>\n",
       "      <td>1741.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3493.858085</td>\n",
       "      <td>1478.249859</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11248.500000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>254.192341</td>\n",
       "      <td>2623.500000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5289.861262</td>\n",
       "      <td>2464.362632</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14998.000000</td>\n",
       "      <td>743.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>1216.154633</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>1236.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>21183.401890</td>\n",
       "      <td>15851.727160</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID      sessions        drives  total_sessions  \\\n",
       "count  14999.000000  14999.000000  14999.000000    14999.000000   \n",
       "mean    7499.000000     80.633776     67.281152      189.964447   \n",
       "std     4329.982679     80.699065     65.913872      136.405128   \n",
       "min        0.000000      0.000000      0.000000        0.220211   \n",
       "25%     3749.500000     23.000000     20.000000       90.661156   \n",
       "50%     7499.000000     56.000000     48.000000      159.568115   \n",
       "75%    11248.500000    112.000000     93.000000      254.192341   \n",
       "max    14998.000000    743.000000    596.000000     1216.154633   \n",
       "\n",
       "       n_days_after_onboarding  total_navigations_fav1  \\\n",
       "count             14999.000000            14999.000000   \n",
       "mean               1749.837789              121.605974   \n",
       "std                1008.513876              148.121544   \n",
       "min                   4.000000                0.000000   \n",
       "25%                 878.000000                9.000000   \n",
       "50%                1741.000000               71.000000   \n",
       "75%                2623.500000              178.000000   \n",
       "max                3500.000000             1236.000000   \n",
       "\n",
       "       total_navigations_fav2  driven_km_drives  duration_minutes_drives  \\\n",
       "count            14999.000000      14999.000000             14999.000000   \n",
       "mean                29.672512       4039.340921              1860.976012   \n",
       "std                 45.394651       2502.149334              1446.702288   \n",
       "min                  0.000000         60.441250                18.282082   \n",
       "25%                  0.000000       2212.600607               835.996260   \n",
       "50%                  9.000000       3493.858085              1478.249859   \n",
       "75%                 43.000000       5289.861262              2464.362632   \n",
       "max                415.000000      21183.401890             15851.727160   \n",
       "\n",
       "       activity_days  driving_days  \n",
       "count   14999.000000  14999.000000  \n",
       "mean       15.537102     12.179879  \n",
       "std         9.004655      7.824036  \n",
       "min         0.000000      0.000000  \n",
       "25%         8.000000      5.000000  \n",
       "50%        16.000000     12.000000  \n",
       "75%        23.000000     19.000000  \n",
       "max        31.000000     30.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolate rows without null values\n",
    "### YOUR CODE HERE ###\n",
    "df_wo_null = df[df.notnull().any(axis=1)]\n",
    "\n",
    "# Display summary stats of rows without null values\n",
    "### YOUR CODE HERE ###\n",
    "df_wo_null.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sJpXfBZUlWC5"
   },
   "source": [
    "Answer: \n",
    "The count is definitely different from one to the other. As for other differences, the rows without null values are slightly less in terms of summary statistics that that of rows with null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S2CupDgSlpm4"
   },
   "source": [
    "### **Task 2d. Null values - device counts**\n",
    "\n",
    "Next, check the two populations with respect to the `device` variable.\n",
    "\n",
    "**Question:** How many iPhone users had null values and how many Android users had null values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "IbCnokO8lsq3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of iPhone users that had null values:  447\n"
     ]
    }
   ],
   "source": [
    "# Get count of null values by device\n",
    "### YOUR CODE HERE ###\n",
    "print(\"Number of iPhone users that had null values: \", len(df_null[df_null[\"device\"] == \"iPhone\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Android users that had null values:  253\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Android users that had null values: \", len(df_null[df_null[\"device\"] == \"Android\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JKfLfQQUltQk"
   },
   "source": [
    "Answer:\n",
    "There are 447 iPhone users that had rows with null values, and 253 Android users that had rows with null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xodMNO1Ql5PZ"
   },
   "source": [
    "Now, of the rows with null values, calculate the percentage with each device&mdash;Android and iPhone. You can do this directly with the [`value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "ajlCljYHmCTa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iPhone     0.638571\n",
       "Android    0.361429\n",
       "Name: device, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate % of iPhone nulls and Android nulls\n",
    "### YOUR CODE HERE ###\n",
    "df_null[\"device\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA_ps_fA3xn9"
   },
   "source": [
    "How does this compare to the device ratio in the full dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Dm-qKyQNmCsQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iPhone     0.644843\n",
       "Android    0.355157\n",
       "Name: device, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate % of iPhone users and Android users in full dataset\n",
    "### YOUR CODE HERE ###\n",
    "df[\"device\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eEIeGZdgmRh9"
   },
   "source": [
    "The percentage of missing values by each device is consistent with their representation in the data overall.\n",
    "\n",
    "There is nothing to suggest a non-random cause of the missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uIzg4fXtmSTe"
   },
   "source": [
    "Examine the counts and percentages of users who churned vs. those who were retained. How many of each group are represented in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "zQ1mu8g9maYX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "retained    0.822645\n",
       "churned     0.177355\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate counts of churned vs. retained\n",
    "### YOUR CODE HERE ###\n",
    "df[\"label\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYTZIIOKmfIz"
   },
   "source": [
    "This dataset contains 82% retained users and 18% churned users.\n",
    "\n",
    "Next, compare the medians of each variable for churned and retained users. The reason for calculating the median and not the mean is that you don't want outliers to unduly affect the portrayal of a typical user. Notice, for example, that the maximum value in the `driven_km_drives` column is 21,183 km. That's more than half the circumference of the earth!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "jzngebHRmmFA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                         7477.500000\n",
       "sessions                     59.000000\n",
       "drives                       50.000000\n",
       "total_sessions              164.339042\n",
       "n_days_after_onboarding    1321.000000\n",
       "total_navigations_fav1       84.500000\n",
       "total_navigations_fav2       11.000000\n",
       "driven_km_drives           3652.655666\n",
       "duration_minutes_drives    1607.183785\n",
       "activity_days                 8.000000\n",
       "driving_days                  6.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate median values of all columns for churned and retained users\n",
    "### YOUR CODE HERE ###\n",
    "df[df[\"label\"] == \"churned\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                         7509.000000\n",
       "sessions                     56.000000\n",
       "drives                       47.000000\n",
       "total_sessions              157.586756\n",
       "n_days_after_onboarding    1843.000000\n",
       "total_navigations_fav1       68.000000\n",
       "total_navigations_fav2        9.000000\n",
       "driven_km_drives           3464.684614\n",
       "duration_minutes_drives    1458.046141\n",
       "activity_days                17.000000\n",
       "driving_days                 14.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"label\"] == \"retained\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NvnPFKS3mm71"
   },
   "source": [
    "This offers an interesting snapshot of the two groups, churned vs. retained:\n",
    "\n",
    "Users who churned averaged ~3 more drives in the last month than retained users, but retained users used the app on over twice as many days as churned users in the same time period.\n",
    "\n",
    "The median churned user drove ~200 more kilometers and 2.5 more hours during the last month than the median retained user.\n",
    "\n",
    "It seems that churned users had more drives in fewer days, and their trips were farther and longer in duration. Perhaps this is suggestive of a user profile. Continue exploring!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cUAkU-JInALK"
   },
   "source": [
    "Calculate the median kilometers per drive in the last month for both retained and churned users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "TVcP2PPhnBMZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.05311332"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by `label` and calculate the medians\n",
    "### YOUR CODE HERE ###\n",
    "df_median = df.groupby(\"label\").median()\n",
    "\n",
    "# Divide the median distance by median number of drives\n",
    "### YOUR CODE HERE ###\n",
    "df_median.loc[\"churned\"][\"driven_km_drives\"] / df_median.loc[\"churned\"][\"drives\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.71669391489361"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median.loc[\"retained\"][\"driven_km_drives\"] / df_median.loc[\"retained\"][\"drives\"] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6VicaRVnFzq"
   },
   "source": [
    "The median user from both groups drove ~73 km/drive. How many kilometers per driving day was this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "I6lD33kfnGQb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "608.7759443333333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide the median distance by median number of driving days\n",
    "### YOUR CODE HERE ###\n",
    "df_median.loc[\"churned\"][\"driven_km_drives\"] / df_median.loc[\"churned\"][\"driving_days\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247.47747242857142"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median.loc[\"retained\"][\"driven_km_drives\"] / df_median.loc[\"retained\"][\"driving_days\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kIfSmukAnVSs"
   },
   "source": [
    "Now, calculate the median number of drives per driving day for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "VAHqOO8endWX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.333333333333334"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide the median number of drives by median number of driving days\n",
    "### YOUR CODE HERE ###\n",
    "df_median.loc[\"churned\"][\"drives\"] / df_median.loc[\"churned\"][\"driving_days\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.357142857142857"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median.loc[\"retained\"][\"drives\"] / df_median.loc[\"retained\"][\"driving_days\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LVRAwsb1nv2L"
   },
   "source": [
    "The median user who churned drove 608 kilometers each day they drove last month, which is almost 250% the per-drive-day distance of retained users. The median churned user had a similarly disproporionate number of drives per drive day compared to retained users.\n",
    "\n",
    "It is clear from these figures that, regardless of whether a user churned or not, the users represented in this data are serious drivers! It would probably be safe to assume that this data does not represent typical drivers at large. Perhaps the data&mdash;and in particular the sample of churned users&mdash;contains a high proportion of long-haul truckers.\n",
    "\n",
    "In consideration of how much these users drive, it would be worthwhile to recommend to Waze that they gather more data on these super-drivers. It's possible that the reason for their driving so much is also the reason why the Waze app does not meet their specific set of needs, which may differ from the needs of a more typical driver, such as a commuter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xc7Q6elLoD1R"
   },
   "source": [
    "Finally, examine whether there is an imbalance in how many users churned by device type.\n",
    "\n",
    "Begin by getting the overall counts of each device type for each group, churned and retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "LGkODIILoEp-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "churned      2536\n",
       "retained    11763\n",
       "Name: device, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each label, calculate the number of Android users and iPhone users\n",
    "### YOUR CODE HERE ###\n",
    "df.groupby(\"label\")[\"device\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yTVM6qFkoJs4"
   },
   "source": [
    "Now, within each group, churned and retained, calculate what percent was Android and what percent was iPhone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "rx0ElsS6oO7y"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     device \n",
       "churned   iPhone     0.648659\n",
       "          Android    0.351341\n",
       "retained  iPhone     0.644393\n",
       "          Android    0.355607\n",
       "Name: device, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each label, calculate the percentage of Android users and iPhone users\n",
    "### YOUR CODE HERE ###\n",
    "df.groupby(\"label\")[\"device\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQVIMPzroavO"
   },
   "source": [
    "The ratio of iPhone users and Android users is consistent between the churned group and the retained group, and those ratios are both consistent with the ratio found in the overall dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tF_82VLgzrQm"
   },
   "source": [
    "<img src=\"images/Construct.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## **PACE: Construct**\n",
    "\n",
    "**Note**: The Construct stage does not apply to this workflow. The PACE framework can be adapted to fit the specific requirements of any project.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BMHV86A6zrQo"
   },
   "source": [
    "<img src=\"images/Execute.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## **PACE: Execute**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document and those below to craft your response:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u3HxcMZgz6iW"
   },
   "source": [
    "### **Task 3. Conclusion**\n",
    "\n",
    "Recall that your supervisor, May Santer, asked you to share your findings with the data team in an executive summary. Consider the following questions as you prepare to write your summary. Think about key points you may want to share with the team, and what information is most relevant to the user churn project.\n",
    "\n",
    "**Questions:**\n",
    "\n",
    "1. Did the data contain any missing values? How many, and which variables were affected? Was there a pattern to the missing data?\n",
    "\n",
    "2. What is a benefit of using the median value of a sample instead of the mean?\n",
    "\n",
    "3. Did your investigation give rise to further questions that you would like to explore or ask the Waze team about?\n",
    "\n",
    "4. What percentage of the users in the dataset were Android users and what percentage were iPhone users?\n",
    "\n",
    "5. What were some distinguishing characteristics of users who churned vs. users who were retained?\n",
    "\n",
    "6. Was there an appreciable difference in churn rate between iPhone users vs. Android users?\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iMHlK7k5_2PV"
   },
   "source": [
    "Answers:\n",
    "1. The data did contain missing values. Upon investigation, it shows that there is nothing to suggest a non-random cause of the missing data.\n",
    "2. The benefit of using the median value of a sample instead of the mean is that the median is more robust against the presence of outliers compared to that of the mean.\n",
    "3. Yes, the preliminary investigation to the data gave rise to further questions in that it might be worthwhile to investigate the app users who are \"super drives\" (long-haul truck drivers) in that probably the app doesn't really address their specific needs.\n",
    "4. It was consistent that there is approximately 64% iPhone users and 36% Android users.\n",
    "5. The 64-36 ratio of iPhone vs. Android users is retained across churned vs. retained groups.\n",
    "6. There was no appreciable difference in churn rate between iPhone and Android users since 64-36 ratio remains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations!** You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged."
   ]
  }
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