Investigate_a_Dataset (James Gabriel).ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Project: Investigate a Dataset - [Medical Appointment Attendance]\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "The Medical appointment attendance dataset to be investigated contains information from over 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment, and the reason behind the attendance . A number of characteristics about the patient are included in each row.\n",
    "\n",
    "<li>‘ScheduledDay’ tells us on what day the patient set up their appointment.</li>\n",
    "<li>‘Neighborhood’ indicates the location of the hospital.</li>\n",
    "<li>‘Scholarship’ indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.</li>\n",
    "<li>The 'SMS_received' columns shows patient that got an SMS reminder of their appointments.</li>\n",
    "<li>The 'No-show' column can be confusing: it says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.</li>\n",
    "\n",
    "\n",
    "### Question(s) for Analysis\n",
    "We would be trying to find pointers that might indicate why certain patients showed up to their appointment and if some variables directly influence their ability or decision to attend.\n",
    "<li>What role does the gender of the patient have to play in their ability to show up for their appointment?</li>\n",
    "<li>Does the length between the schedule day and appointment day influence the appointment attendance?</li>\n",
    "<li>Is age a factor in the patients ability to attend their appointment?</li>\n",
    "<li>Are some neighbourhoods more difficult to access the appointment venue than others?</li>\n",
    "<li>Are patients enrolled in the 'Bosia Familia' more likely to attend their appointments?</li>\n",
    "<li>Does receiving an SMS reminder influence patients attendance?</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the packages to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    ">In this section, we would try to point out instances and occurences in the dataset that might need some cleaning and wrangling. To do this we would proceed by first loading the dataset into the workspace and then reading it to have a first view of what the data look like.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110522</th>\n",
       "      <td>2.572134e+12</td>\n",
       "      <td>5651768</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03T09:15:35Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110523</th>\n",
       "      <td>3.596266e+12</td>\n",
       "      <td>5650093</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03T07:27:33Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>51</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>1.557663e+13</td>\n",
       "      <td>5630692</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T16:03:52Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>9.213493e+13</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:09:23Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T13:30:56Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110527 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0       2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1       5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2       4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3       8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4       8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "...              ...            ...    ...                   ...   \n",
       "110522  2.572134e+12        5651768      F  2016-05-03T09:15:35Z   \n",
       "110523  3.596266e+12        5650093      F  2016-05-03T07:27:33Z   \n",
       "110524  1.557663e+13        5630692      F  2016-04-27T16:03:52Z   \n",
       "110525  9.213493e+13        5630323      F  2016-04-27T15:09:23Z   \n",
       "110526  3.775115e+14        5629448      F  2016-04-27T13:30:56Z   \n",
       "\n",
       "              AppointmentDay  Age      Neighbourhood  Scholarship  \\\n",
       "0       2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0   \n",
       "1       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
       "2       2016-04-29T00:00:00Z   62      MATA DA PRAIA            0   \n",
       "3       2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0   \n",
       "4       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
       "...                      ...  ...                ...          ...   \n",
       "110522  2016-06-07T00:00:00Z   56        MARIA ORTIZ            0   \n",
       "110523  2016-06-07T00:00:00Z   51        MARIA ORTIZ            0   \n",
       "110524  2016-06-07T00:00:00Z   21        MARIA ORTIZ            0   \n",
       "110525  2016-06-07T00:00:00Z   38        MARIA ORTIZ            0   \n",
       "110526  2016-06-07T00:00:00Z   54        MARIA ORTIZ            0   \n",
       "\n",
       "        Hipertension  Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0                  1         0           0        0             0      No  \n",
       "1                  0         0           0        0             0      No  \n",
       "2                  0         0           0        0             0      No  \n",
       "3                  0         0           0        0             0      No  \n",
       "4                  1         1           0        0             0      No  \n",
       "...              ...       ...         ...      ...           ...     ...  \n",
       "110522             0         0           0        0             1      No  \n",
       "110523             0         0           0        0             1      No  \n",
       "110524             0         0           0        0             1      No  \n",
       "110525             0         0           0        0             1      No  \n",
       "110526             0         0           0        0             1      No  \n",
       "\n",
       "[110527 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the data using pandas read_csv function\n",
    "# We would refer to the variable where the dataframe is loaded into as 'med_apt' to denote medical appointment and better\n",
    "# reflect the content of the dataframe\n",
    "\n",
    "med_apt = pd.read_csv('med_apt.csv')\n",
    "med_apt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "From the cell above, we can tell that the dataset contains 110527 rows and 14 columns. We can also draw some insights into the\n",
    "wrangling and cleansing procedures we might adopt to make the dataset suitale for analysis. Before we proceed to wrangling, we need to establish the columns that would be necessary for our exploration in order to know what columns needs to be gotten rid of:\n",
    "<li>The 'PatientId' column would serve as a pointer to how many unique patients we have in the dataset.</li>\n",
    "<li>The 'AppointmentID' column let's us know how many appointments were made in total</li>\n",
    "<li>'Gender' and 'Age' would be extremely helpful in determining if the sex of the patient had a role to play in the attendance</li>\n",
    "<li>'ScheduledDay' and 'AppointmentDay' do not seem necessary as the information provided are not consistent(most of the \n",
    "appoinments are set for an earlier time than they are scheduled)</li>\n",
    "<li>We could use the Neighbourhood column to analyze if the area had a role to play in attendance</li>\n",
    "<li>'Scholarship' denotes family that are beneficiaries of a social welfare program, and this would most likely influence attendance</li>\n",
    "<li>'Hipertension','Diabetes','Alcoholism', and 'Handcap' might come in handy when deciding correlation between multiple illnesses.</li>\n",
    "<li>'SMS_received' serves as some sort of reminder and should influence attendance</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let's proceed to checking the dataset in more depth in order to further draft out wrangling and cleansing techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking the non null values and data types of the data set\n",
    "med_apt.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Looking above, there are no null values in the entire dataset. As for the data types, virtually all the columns are in their appropriate data type except the 'ScheduleDay' and 'AppointmentDay' columns which are objects instead of dates. We would be dropping those two columns as data in them are not consistent or relevant, therefore there would be no need to change their data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId          62299\n",
       "AppointmentID     110527\n",
       "Gender                 2\n",
       "ScheduledDay      103549\n",
       "AppointmentDay        27\n",
       "Age                  104\n",
       "Neighbourhood         81\n",
       "Scholarship            2\n",
       "Hipertension           2\n",
       "Diabetes               2\n",
       "Alcoholism             2\n",
       "Handcap                5\n",
       "SMS_received           2\n",
       "No-show                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for unique values in each column\n",
    "med_apt.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The cell above shows 62299 unique patient IDs, and 110527 unique Appointment IDs, this means that a single patient can make multiple appointments.\n",
    "Also, all of the 110527 appointments were scheduled for just 27 days in 81 neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108286\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: Handcap, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the count of different values in the handcap columns to confirm if something is off\n",
    "med_apt.Handcap.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The 'Handcap' columns has 5 unique values instead of just 2 to signify 'yes' and 'no'. We might need to drop the incorrect rows with values '2,3 and 4'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the basic statistics and aggregations of the numerical columns in the dataset\n",
    "med_apt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Fromm the cell above, checking the count, min, standard deviation and percentiles for all the columns with numbers in them, the 'Age' column stands out. It is impossible for someone to be '-1' years old. The max age being '115' also raises some concerns, but for the purpose of this analysis, we might decide to overlook it. 'Handcap' has a max value of '4' but that has been addressed in the previous cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking just the headers\n",
    "med_apt.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The column names are also either incorrectly spelt or not properly capitalized, we would also rename the columns and then convert them all to lower case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    "\n",
    "Now that we've figured out the process to take in cleaning the data to make it suitable for analysis in the data wrangling section above, let's dive right into cleaning the data. Before we do that, let's outline all the steps that we identified earlier so we can reference anytime and also use as a pointer for comppletion:\n",
    "<li>Drop the 'ScheduledDay' and 'AppointmentDay' columns.</li>\n",
    "<li>Drop the rows in the 'Handcap' column that arent consistent with the results(containing 2,3, and 4).</li>\n",
    "<li>Drop the row in the 'Age' column that has '-1' as it's value.</li>\n",
    "<li>Make all column names lowercase.</li>\n",
    "<li>Rename the columns and make them consistent.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    ">Now let's dive right into cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, Age, Neighbourhood, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We would use the pandas drop function and specify the axis to be the column (1)\n",
    "# and make inplace to be True so the change is registered\n",
    "# Then we view the changed columns\n",
    "\n",
    "med_apt.drop(['ScheduledDay', 'AppointmentDay'], axis=1, inplace=True)\n",
    "med_apt.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the columns all lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "    }\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>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patientid, appointmentid, gender, age, neighbourhood, scholarship, hipertension, diabetes, alcoholism, handcap, sms_received, no-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The lambda function is employed here in other to go through the entire list of column names at once\n",
    "\n",
    "med_apt.rename(columns = lambda x : x.lower(), inplace=True)\n",
    "med_apt.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming the misspelt columns and making them consistent with an '_'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "\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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [patient_id, appointment_id, gender, age, neighbourhood, scholarship, hypertension, diabetes, alcoholism, handicap, sms_received, no_show]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The pandas rename function is used here to rename more than one column in the dataframe\n",
    "\n",
    "med_apt.rename(columns = {'patientid':'patient_id', 'appointmentid':'appointment_id', 'handcap':'handicap',\\\n",
    "                          'hipertension':'hypertension','no-show':'no_show'}, inplace=True)\n",
    "med_apt.head(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the row in the 'Age' column that has '-1' as it's value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> First, we would find the row with the value for age being '-1' so we can identify the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id gender  age neighbourhood  scholarship  \\\n",
       "99832  4.659432e+14         5775010      F   -1         ROMÃO            0   \n",
       "\n",
       "       hypertension  diabetes  alcoholism  handicap  sms_received no_show  \n",
       "99832             0         0           0         0             0      No  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use query to see the row that has the age as -1\n",
    "med_apt.query('age == -1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Next, we would drop the row with the index that aligns with the value above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use the drop function and set axis to 0 to drop the row with the wrong value\n",
    "med_apt.drop(99832, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now, let's check the min value of ages in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105260e+05</td>\n",
       "      <td>1.105260e+05</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "      <td>110526.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474934e+14</td>\n",
       "      <td>5.675304e+06</td>\n",
       "      <td>37.089219</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197248</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560943e+14</td>\n",
       "      <td>7.129544e+04</td>\n",
       "      <td>23.110026</td>\n",
       "      <td>0.297676</td>\n",
       "      <td>0.397923</td>\n",
       "      <td>0.258266</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172536e+12</td>\n",
       "      <td>5.640285e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680572e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.438963e+13</td>\n",
       "      <td>5.725523e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.105260e+05    1.105260e+05  110526.000000  110526.000000   \n",
       "mean   1.474934e+14    5.675304e+06      37.089219       0.098266   \n",
       "std    2.560943e+14    7.129544e+04      23.110026       0.297676   \n",
       "min    3.921784e+04    5.030230e+06       0.000000       0.000000   \n",
       "25%    4.172536e+12    5.640285e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13    5.680572e+06      37.000000       0.000000   \n",
       "75%    9.438963e+13    5.725523e+06      55.000000       0.000000   \n",
       "max    9.999816e+14    5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        hypertension       diabetes     alcoholism       handicap  \\\n",
       "count  110526.000000  110526.000000  110526.000000  110526.000000   \n",
       "mean        0.197248       0.071865       0.030400       0.022248   \n",
       "std         0.397923       0.258266       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        sms_received  \n",
       "count  110526.000000  \n",
       "mean        0.321029  \n",
       "std         0.466874  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is apparent that something is still wrong with the 'age' column because the minimun value of age now says '0' which is equally as wrong as '-1'. A patient's age being '0' can mean that the patient is only a couple of months old and the appointment was made either by a parent or guidance. To be on the safe side, we would exclude any patient with an age of '0' and make '1' the only permitted minimum age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<p>3539 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "          patient_id  appointment_id gender  age      neighbourhood  \\\n",
       "59      7.184428e+13         5638545      F    0          CONQUISTA   \n",
       "63      2.366233e+14         5628286      M    0       SÃO BENEDITO   \n",
       "64      1.885174e+14         5616082      M    0  ILHA DAS CAIEIRAS   \n",
       "65      2.718818e+14         5628321      M    0          CONQUISTA   \n",
       "67      8.647128e+13         5639264      F    0     NOVA PALESTINA   \n",
       "...              ...             ...    ...  ...                ...   \n",
       "110345  1.473952e+14         5702537      F    0        RESISTÊNCIA   \n",
       "110346  5.577525e+12         5777724      M    0        RESISTÊNCIA   \n",
       "110454  6.142460e+11         5772400      F    0        RESISTÊNCIA   \n",
       "110460  4.321846e+13         5769545      F    0        RESISTÊNCIA   \n",
       "110507  4.769462e+14         5786918      F    0        MARIA ORTIZ   \n",
       "\n",
       "        scholarship  hypertension  diabetes  alcoholism  handicap  \\\n",
       "59                0             0         0           0         0   \n",
       "63                0             0         0           0         0   \n",
       "64                0             0         0           0         0   \n",
       "65                0             0         0           0         0   \n",
       "67                0             0         0           0         0   \n",
       "...             ...           ...       ...         ...       ...   \n",
       "110345            0             0         0           0         0   \n",
       "110346            0             0         0           0         0   \n",
       "110454            0             0         0           0         0   \n",
       "110460            0             0         0           0         0   \n",
       "110507            0             0         0           0         0   \n",
       "\n",
       "        sms_received no_show  \n",
       "59                 0      No  \n",
       "63                 0      No  \n",
       "64                 1      No  \n",
       "65                 0      No  \n",
       "67                 0      No  \n",
       "...              ...     ...  \n",
       "110345             0      No  \n",
       "110346             0      No  \n",
       "110454             0      No  \n",
       "110460             0      No  \n",
       "110507             0      No  \n",
       "\n",
       "[3539 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the query function to see all rows with a value of 0 as the age\n",
    "med_apt.query('age == 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We have 3539 rows with 'age being '0'. Let's proceed to dropping those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "med_apt.drop(med_apt[med_apt.age==0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>1.557663e+13</td>\n",
       "      <td>5630692</td>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>9.213493e+13</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106987 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  appointment_id gender  age      neighbourhood  \\\n",
       "0       2.987250e+13         5642903      F   62    JARDIM DA PENHA   \n",
       "1       5.589978e+14         5642503      M   56    JARDIM DA PENHA   \n",
       "2       4.262962e+12         5642549      F   62      MATA DA PRAIA   \n",
       "3       8.679512e+11         5642828      F    8  PONTAL DE CAMBURI   \n",
       "4       8.841186e+12         5642494      F   56    JARDIM DA PENHA   \n",
       "...              ...             ...    ...  ...                ...   \n",
       "110522  2.572134e+12         5651768      F   56        MARIA ORTIZ   \n",
       "110523  3.596266e+12         5650093      F   51        MARIA ORTIZ   \n",
       "110524  1.557663e+13         5630692      F   21        MARIA ORTIZ   \n",
       "110525  9.213493e+13         5630323      F   38        MARIA ORTIZ   \n",
       "110526  3.775115e+14         5629448      F   54        MARIA ORTIZ   \n",
       "\n",
       "        scholarship  hypertension  diabetes  alcoholism  handicap  \\\n",
       "0                 0             1         0           0         0   \n",
       "1                 0             0         0           0         0   \n",
       "2                 0             0         0           0         0   \n",
       "3                 0             0         0           0         0   \n",
       "4                 0             1         1           0         0   \n",
       "...             ...           ...       ...         ...       ...   \n",
       "110522            0             0         0           0         0   \n",
       "110523            0             0         0           0         0   \n",
       "110524            0             0         0           0         0   \n",
       "110525            0             0         0           0         0   \n",
       "110526            0             0         0           0         0   \n",
       "\n",
       "        sms_received no_show  \n",
       "0                  0      No  \n",
       "1                  0      No  \n",
       "2                  0      No  \n",
       "3                  0      No  \n",
       "4                  0      No  \n",
       "...              ...     ...  \n",
       "110522             1      No  \n",
       "110523             1      No  \n",
       "110524             1      No  \n",
       "110525             1      No  \n",
       "110526             1      No  \n",
       "\n",
       "[106987 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The new dataframe has 106987 rows with 12 columns after dropping the rows that contains patient with age 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the rows in the 'Handcap' column that arent consistent with the results(containing 2,3, and 4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, we would try to drop the rows with values (2,3, and 4) in their 'handicap' column as the column should only contain values 0 and 1 which signify 'No' and 'Yes' respectively. \n",
    "Let's take a look at the count for each value in the 'handicap' column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    104747\n",
       "1      2041\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: handicap, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.handicap.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>7.282582e+13</td>\n",
       "      <td>5582483</td>\n",
       "      <td>M</td>\n",
       "      <td>94</td>\n",
       "      <td>BELA VISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>1.242919e+13</td>\n",
       "      <td>5525497</td>\n",
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       "      <td>64</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>1.242919e+13</td>\n",
       "      <td>5525498</td>\n",
       "      <td>M</td>\n",
       "      <td>64</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>1.242919e+13</td>\n",
       "      <td>5640120</td>\n",
       "      <td>M</td>\n",
       "      <td>64</td>\n",
       "      <td>SANTA MARTHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>1.491829e+13</td>\n",
       "      <td>5638626</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108376</th>\n",
       "      <td>2.387212e+14</td>\n",
       "      <td>5756931</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109484</th>\n",
       "      <td>3.589495e+14</td>\n",
       "      <td>5752442</td>\n",
       "      <td>M</td>\n",
       "      <td>64</td>\n",
       "      <td>DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109733</th>\n",
       "      <td>5.662976e+14</td>\n",
       "      <td>5772744</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>JUCUTUQUARA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109975</th>\n",
       "      <td>4.522185e+13</td>\n",
       "      <td>5766970</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>PRAIA DO SUÁ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110107</th>\n",
       "      <td>7.143714e+14</td>\n",
       "      <td>5761625</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  appointment_id gender  age neighbourhood  scholarship  \\\n",
       "946     7.282582e+13         5582483      M   94    BELA VISTA            0   \n",
       "1665    1.242919e+13         5525497      M   64  SANTA MARTHA            0   \n",
       "1666    1.242919e+13         5525498      M   64  SANTA MARTHA            0   \n",
       "2071    1.242919e+13         5640120      M   64  SANTA MARTHA            0   \n",
       "2091    1.491829e+13         5638626      F   11    ANDORINHAS            0   \n",
       "...              ...             ...    ...  ...           ...          ...   \n",
       "108376  2.387212e+14         5756931      F   44         ROMÃO            0   \n",
       "109484  3.589495e+14         5752442      M   64      DA PENHA            0   \n",
       "109733  5.662976e+14         5772744      F   34   JUCUTUQUARA            0   \n",
       "109975  4.522185e+13         5766970      M   39  PRAIA DO SUÁ            1   \n",
       "110107  7.143714e+14         5761625      F   44   RESISTÊNCIA            0   \n",
       "\n",
       "        hypertension  diabetes  alcoholism  handicap  sms_received no_show  \n",
       "946                1         1           0         2             1      No  \n",
       "1665               1         0           1         2             1      No  \n",
       "1666               1         0           1         2             0      No  \n",
       "2071               1         0           1         2             0      No  \n",
       "2091               0         0           0         2             0      No  \n",
       "...              ...       ...         ...       ...           ...     ...  \n",
       "108376             1         1           0         2             1      No  \n",
       "109484             1         1           0         2             0      No  \n",
       "109733             0         0           0         2             1      No  \n",
       "109975             0         0           0         2             1      No  \n",
       "110107             0         0           0         2             1      No  \n",
       "\n",
       "[199 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking rows with their handicap value greater than 1\n",
    "med_apt[med_apt.handicap >1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We have 199 rows of data with values above '1' in the handicap column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
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       "      <td>JARDIM DA PENHA</td>\n",
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       "      <td>0</td>\n",
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       "      <td>No</td>\n",
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       "      <th>110522</th>\n",
       "      <td>2.572134e+12</td>\n",
       "      <td>5651768</td>\n",
       "      <td>F</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110523</th>\n",
       "      <td>3.596266e+12</td>\n",
       "      <td>5650093</td>\n",
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       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>1.557663e+13</td>\n",
       "      <td>5630692</td>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>9.213493e+13</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106788 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  appointment_id gender  age      neighbourhood  \\\n",
       "0       2.987250e+13         5642903      F   62    JARDIM DA PENHA   \n",
       "1       5.589978e+14         5642503      M   56    JARDIM DA PENHA   \n",
       "2       4.262962e+12         5642549      F   62      MATA DA PRAIA   \n",
       "3       8.679512e+11         5642828      F    8  PONTAL DE CAMBURI   \n",
       "4       8.841186e+12         5642494      F   56    JARDIM DA PENHA   \n",
       "...              ...             ...    ...  ...                ...   \n",
       "110522  2.572134e+12         5651768      F   56        MARIA ORTIZ   \n",
       "110523  3.596266e+12         5650093      F   51        MARIA ORTIZ   \n",
       "110524  1.557663e+13         5630692      F   21        MARIA ORTIZ   \n",
       "110525  9.213493e+13         5630323      F   38        MARIA ORTIZ   \n",
       "110526  3.775115e+14         5629448      F   54        MARIA ORTIZ   \n",
       "\n",
       "        scholarship  hypertension  diabetes  alcoholism  handicap  \\\n",
       "0                 0             1         0           0         0   \n",
       "1                 0             0         0           0         0   \n",
       "2                 0             0         0           0         0   \n",
       "3                 0             0         0           0         0   \n",
       "4                 0             1         1           0         0   \n",
       "...             ...           ...       ...         ...       ...   \n",
       "110522            0             0         0           0         0   \n",
       "110523            0             0         0           0         0   \n",
       "110524            0             0         0           0         0   \n",
       "110525            0             0         0           0         0   \n",
       "110526            0             0         0           0         0   \n",
       "\n",
       "        sms_received no_show  \n",
       "0                  0      No  \n",
       "1                  0      No  \n",
       "2                  0      No  \n",
       "3                  0      No  \n",
       "4                  0      No  \n",
       "...              ...     ...  \n",
       "110522             1      No  \n",
       "110523             1      No  \n",
       "110524             1      No  \n",
       "110525             1      No  \n",
       "110526             1      No  \n",
       "\n",
       "[106788 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.drop(med_apt[med_apt.handicap >1].index, inplace=True)\n",
    "med_apt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This brings us to the end of the data wrangling and cleaning process. We now have our final dataframe with all the information we needed for the exploration clean and ready for analysis.\n",
    "106788 rows and 12 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> In this section we would be diving deep into the questions that were posed earlier and how we can answer the questions with the data available using apporpriate visuals and chart types. Firstly, let's plot a basic histogram with the numerical variables available in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a simple histogram of the entire dataset using the hist function\n",
    "med_apt.hist(figsize=[12,12]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.067880e+05</td>\n",
       "      <td>1.067880e+05</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.473131e+14</td>\n",
       "      <td>5.675428e+06</td>\n",
       "      <td>38.299294</td>\n",
       "      <td>0.101051</td>\n",
       "      <td>0.203356</td>\n",
       "      <td>0.073978</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.019113</td>\n",
       "      <td>0.323426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.558722e+14</td>\n",
       "      <td>7.133632e+04</td>\n",
       "      <td>22.453253</td>\n",
       "      <td>0.301397</td>\n",
       "      <td>0.402497</td>\n",
       "      <td>0.261737</td>\n",
       "      <td>0.174368</td>\n",
       "      <td>0.136922</td>\n",
       "      <td>0.467786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.175523e+12</td>\n",
       "      <td>5.640484e+06</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173700e+13</td>\n",
       "      <td>5.680740e+06</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.433654e+13</td>\n",
       "      <td>5.725627e+06</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.067880e+05    1.067880e+05  106788.000000  106788.000000   \n",
       "mean   1.473131e+14    5.675428e+06      38.299294       0.101051   \n",
       "std    2.558722e+14    7.133632e+04      22.453253       0.301397   \n",
       "min    3.921784e+04    5.030230e+06       1.000000       0.000000   \n",
       "25%    4.175523e+12    5.640484e+06      19.000000       0.000000   \n",
       "50%    3.173700e+13    5.680740e+06      38.000000       0.000000   \n",
       "75%    9.433654e+13    5.725627e+06      56.000000       0.000000   \n",
       "max    9.999816e+14    5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        hypertension       diabetes     alcoholism       handicap  \\\n",
       "count  106788.000000  106788.000000  106788.000000  106788.000000   \n",
       "mean        0.203356       0.073978       0.031389       0.019113   \n",
       "std         0.402497       0.261737       0.174368       0.136922   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "        sms_received  \n",
       "count  106788.000000  \n",
       "mean        0.323426  \n",
       "std         0.467786  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the simple histograms above, complemented with the table beneath it that describes the numerical columns of the data set, we can tell that the mean age is 38, majority of the population are not enrolled in the governments social welfare program, majority of the population did not receive a reminder SMS, minority of the population are disabled, and finally, alcoholism, diabetes and hypertension only affect a handful of the population. \n",
    "In the following cells, we would be evaluating how these variables go hand in hand in determining if patients showed up for their appointments or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 1 (What role does gender have to play in appointment attendance?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We would like to evaluate if the gender of a patient is a pointer to their ability or negligence to attend appointments. Firstly, we'd examine their numbers and then go ahead to plot a graph to show the relation of the two variables ('gender' and 'no_show')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    70009\n",
       "M    36779\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J0jnUoC6jXNk89N+WPudKXRUC66RDjNTs9ikvlowdL51DDehZ4HrpEANxstRx6K8GrpTOMRJfbfnN3D1Kr+lTKrPtfMoVieez18TJUlddDiyRDjEcu7DmlYtaZ75HOoca1N2UK6L3IBuKs6WOQ38T8EUgU98hDmb2qCnLSoZx0jnUgN4EzpMOMRRnSw0Qh/58cnIYfl7L7Q/vatZ8QDqHGtQ0ypVYOsRQnC511RTSf4jfsOxuVv3t/Nbb3yudQw1qEXCFdIhaOF/qOPQ7SQ7DM7uwcUf7lJeMoUM6hxpQJ3Am5UourlZ0vtQAceg/TrJwljkXts6as7OpvE86hxrUtylXFkmHqFUhSl11CckhVGbsZV5ecW7L3YdK51CDegj4L+kQw1GYUsehv5lMHYZbO7t96mvGsL10EjWgCnCW5HOxRqIwpQaIQ///gO9J5wCY0nrjnLeb9YdI51CD+jLlyjLpEMNVqFJXTQN+LxlgH7Ny6dkt971fMoMaUki5cpt0iJEoXKnj0N8CnA78WWJ8Q0/P7e3lijFsJzG+qslvgW9LhxipwpUaIA79CnAyySb3VF3Wet2cceaNg9IeV9XsL8BnKVd6pIOMVCFLDRCH/grgY6S4m+vdZtmLZ7Y8qJs1sms9cCrlylrpIPUobKkB4tBfCHyKFG6BVKKn+9b2SzYaw+hmj6VGpJOk0E9LB6lXoUsNEIf+A8A5zR7n8rZr5rzNbNQdWNnUTXLILbqA2iiFLzVAHPrXA+Vmff57zYvPn1H641HN+nxVt8mUK3dIh2gULXVVHPqXAL9o9Oe20N01q/2yLmNob/Rnq4a4mHLlZ9IhGklL/VZfBX7TyA+8qm3Gw2NN5/6N/EzVMGXKlVA6RKNpqfuIQ78LOA24thGfd6h5fsnHS/P0RvzZ9A3KlUukQzSDsTZXl7Wmxgui7wCXjfSfb6Nr88JRX47HmM16V9Bs6SE5h27IH9xZpDP1AOLQn0ayAWREX3fNaJv+Jy105nQBn3e50KClHlQc+jcwggtUDjeLnzmp9IQ+djZb1gGfoFy5WTpIs+nhdw28IDoYuBcY8oHv7WzpXDjqyytGmy37ND+ZqtELwCmUK4ulg6RBZ+oaxKH/FHAkNWwCuabtynla6Ex5CDi8KIUGLXXN4tBfDhxD8pukX8eUFi06vrRQD7uz42rgI5Qra6SDpEkPv4fJC6JWYCpwMdDS+/5oOjcuHHXOy+2m611i4VSvdcC5RTh/7o+WeoS8IDoauAnwAGa2TfvjB1ueOU40lAKYR3Id91+lg0jRw+8RikN/LnAwcMOHSgueOrL0jB52y+ohuavNsUUuNOhM3RCvTd3j1Alm3X8DO0tnKaglwDmUK3Okg2SBlrpRyh3jgauAzwknKZJOkhtJhll9VrQELXWjlTuOA6YDhwgncd2DJIthz0sHyRotdTOUO0okN16YBkwQTuOa5STbJX8lHSSrtNTNVO4YR3LzhXNB91PXaS0QAtMpVzYJZ8k0LXUayh17AAHwJWCUcJq8eYPkdOYKypXXpcPkgZY6TeWO3YCLSA7NxwinybrVwE+BH1GuvCodJk+01BLKHbsAXyOZud8hnCZrlpB8i3A95cpG4Sy5pKWWVO5oA84gOecu8tVoPSSr2T8C7hnOA+mMMRa4yVr7+erPrcBLwHxr7cebETbrtNRZUe54D8k90j5NcWbvxcD1wE2UKyN6WooxZgPwPHCUtXajMeZk4PvACi21yobk67DjgH8kmcVdK/hy4E7gBsqVx+v9sGqpfwgssNbeZoy5AXgaOFZLrbJna8E/CZwIHAgY0UzD102yySICIsqVhY388GqpjwKmkFzNNw84H/imllplX7ljZ+AEkoKfCOwtG6hfW4AngUeAPwEPNHM/szFmg7V2e2PM48AMYF/gd2ipVS6VOyaQXI56CHBo9TWRPvu8m2wD8BzJivUCkiI/kebFIX1KPQU4D5gEjEdLrZxR7hgN7AXsWf2197UH8HZgHNABjAVa+/kESzLbbgRWVV+v9vnr5SQlXjLSxa1G6lPq3YEzrLXTjTGT0FKrQkq+UhtNct7bBXTl7bnMvaXe5r1JaKmVUq7QO58o5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5RgttVKO0VIr5Zj/B3x1s8qIqBX5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simple pie chart showing the percentage of male to female\n",
    "med_apt.gender.value_counts().plot(kind='pie');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is apparent that women are almost twice the size of men in the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting an unstacked bar chart using the plot function, and setting the legend and x label\n",
    "plt.style.use('ggplot')\n",
    "med_apt.groupby(['no_show', 'gender']).gender.count().unstack().plot.bar(legend=True, xlabel='appointment missed');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the bar chart above, it's obvious that the gender has nothing to do with the attendance of the appointment, as the size of women who showed up for the appointment is almost double the size of men, and this also applies to the number of people who didn't show up. This figure is synonymous to the overall size of the population, with the female being almost double of the male size. So it can be deduced that the reason more women showed up is because there were more women in the sample population and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  (Is age a factor in the patient's ability to attend their appointment?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Are younger people more likely to attend their appointment? Does the age of the patient hinder them from making their appointment? Let's take a look at the data to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.age.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>age</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.067880e+05</td>\n",
       "      <td>1.067880e+05</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "      <td>106788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.473131e+14</td>\n",
       "      <td>5.675428e+06</td>\n",
       "      <td>38.299294</td>\n",
       "      <td>0.101051</td>\n",
       "      <td>0.203356</td>\n",
       "      <td>0.073978</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.019113</td>\n",
       "      <td>0.323426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.558722e+14</td>\n",
       "      <td>7.133632e+04</td>\n",
       "      <td>22.453253</td>\n",
       "      <td>0.301397</td>\n",
       "      <td>0.402497</td>\n",
       "      <td>0.261737</td>\n",
       "      <td>0.174368</td>\n",
       "      <td>0.136922</td>\n",
       "      <td>0.467786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.175523e+12</td>\n",
       "      <td>5.640484e+06</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173700e+13</td>\n",
       "      <td>5.680740e+06</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.433654e+13</td>\n",
       "      <td>5.725627e+06</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patient_id  appointment_id            age    scholarship  \\\n",
       "count  1.067880e+05    1.067880e+05  106788.000000  106788.000000   \n",
       "mean   1.473131e+14    5.675428e+06      38.299294       0.101051   \n",
       "std    2.558722e+14    7.133632e+04      22.453253       0.301397   \n",
       "min    3.921784e+04    5.030230e+06       1.000000       0.000000   \n",
       "25%    4.175523e+12    5.640484e+06      19.000000       0.000000   \n",
       "50%    3.173700e+13    5.680740e+06      38.000000       0.000000   \n",
       "75%    9.433654e+13    5.725627e+06      56.000000       0.000000   \n",
       "max    9.999816e+14    5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        hypertension       diabetes     alcoholism       handicap  \\\n",
       "count  106788.000000  106788.000000  106788.000000  106788.000000   \n",
       "mean        0.203356       0.073978       0.031389       0.019113   \n",
       "std         0.402497       0.261737       0.174368       0.136922   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "        sms_received  \n",
       "count  106788.000000  \n",
       "mean        0.323426  \n",
       "std         0.467786  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For the sole reason that we have 102 unique ages with the minimun being '1' and the maximum being '115', we definitely have to create a new column that contains age ranges so we can narrow down the ages into ranges to better plot our chart and get better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting conditions to be used to create the new column for age group\n",
    "conditions = [(med_apt.age > 60), (med_apt.age > 47), (med_apt.age > 33), (med_apt.age > 19),\\\n",
    "              (med_apt.age > 12), (med_apt.age > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = ['elderly','senior adult','middle age adult','adult','teenager','children']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "med_apt['age_group'] = np.select(conditions, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "middle age adult    20316\n",
       "senior adult        20299\n",
       "elderly             19691\n",
       "adult               19660\n",
       "children            17475\n",
       "teenager             9347\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We need to make sure the age groups are spread evenly between themselves so that a single group can be represented acccurately. Therefore the 4 top groups have been structured in such a way that they contain around 20,000 values. The exceptions being children and teenagers which have a fixed age range of '1-12' and '13-19' respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handicap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>elderly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>senior adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>elderly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>senior adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id  appointment_id gender  age      neighbourhood  scholarship  \\\n",
       "0  2.987250e+13         5642903      F   62    JARDIM DA PENHA            0   \n",
       "1  5.589978e+14         5642503      M   56    JARDIM DA PENHA            0   \n",
       "2  4.262962e+12         5642549      F   62      MATA DA PRAIA            0   \n",
       "3  8.679512e+11         5642828      F    8  PONTAL DE CAMBURI            0   \n",
       "4  8.841186e+12         5642494      F   56    JARDIM DA PENHA            0   \n",
       "\n",
       "   hypertension  diabetes  alcoholism  handicap  sms_received no_show  \\\n",
       "0             1         0           0         0             0      No   \n",
       "1             0         0           0         0             0      No   \n",
       "2             0         0           0         0             0      No   \n",
       "3             0         0           0         0             0      No   \n",
       "4             1         1           0         0             0      No   \n",
       "\n",
       "      age_group  \n",
       "0       elderly  \n",
       "1  senior adult  \n",
       "2       elderly  \n",
       "3      children  \n",
       "4  senior adult  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_apt.groupby(['no_show', 'age_group']).age_group.count().unstack().plot.bar(legend=True, xlabel='appointment missed');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Even if the correlation is not entirely strong, it is necessary to note that older people tend to attend their appointments than younger people, with the 'elderly' and 'senior adult' topping the chart for attendance. The 'elderly' also has the lowest number of people who missed their appointments after teenagers, strongly because the teengers population isn't well represented. However, these variables are not strongly correlated enough to be trusted to infer a cause."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x183be6a4eb0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_apt.groupby(['age_group', 'no_show']).no_show.count().unstack().plot.line(legend=True, figsize=(12,6), xlabel='Age Group')\n",
    "plt.legend(['No', 'Yes'],\n",
    "            loc='upper right', title='Missed Appointmeent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is more obvious from the line chat the information conveyed above. Elderly people are more likely to attend their appointment than younger people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  (Are patients enrolled in the 'Bosia Familia' more likely to attend their appointments?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The Bosia Familia is a social welfare program organised by the government of Brazil, aimed at providing financial aid to poor Brazilian families. We would be analysing to see if the enrollment in this program has an influence on the appointment attendance>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95997\n",
       "1    10791\n",
       "Name: scholarship, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.scholarship.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> '1' signifying being enrolled in the scholarship program and '0' showing patients who aren't enrolled, it shows that only about 10% of the entire population were enrolled in the program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show  scholarship\n",
       "No       0              76928\n",
       "         1               8221\n",
       "Yes      0              19069\n",
       "         1               2570\n",
       "Name: scholarship, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.groupby(['no_show','scholarship']).scholarship.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_apt.groupby(['scholarship','no_show']).scholarship.count().unstack().plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Of the 95,997 patient who didn't have the scholarship, almost 20% didn't show up. Meanwhile for the ones who got the scholarship, 2570 people accounting for 30% of their population didn't show up. It's almost as if the patients who were on a scholarship didn't take the attendance of the appointment seriously. It is shown more clearly in the pie chart below, the percentage of those who got the scholatship represented by '1' and those who didn't represented by '0'. More people (blue) missed the appointment in the '1' chart than the '0'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using a pie chart to better visualize the difference in attendance\n",
    "med_apt.groupby(['no_show','scholarship']).no_show.count().unstack().plot.pie(subplots=True,\\\n",
    "                figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4  (Does receiving an SMS reminder influence patients attendance?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, we are going to evaluate the relationship, if any, between receiving an SMS reminder of the appointment and the attendance of the appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    72250\n",
       "1    34538\n",
       "Name: sms_received, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.sms_received.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can see that the people who didn't receive any SMS are twice the size of people who did. We'd use the same method as we used in the previous question above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show  sms_received\n",
       "No       0               60170\n",
       "         1               24979\n",
       "Yes      0               12080\n",
       "         1                9559\n",
       "Name: sms_received, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_apt.groupby(['no_show','sms_received']).sms_received.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "med_apt.groupby(['no_show','sms_received']).no_show.count().unstack().plot.pie(subplots=True,\\\n",
    "                figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the above chart, we can tell the SMS did not play any role in the attendance of the appointment, as more people who got an SMS were absent on the day of their appointment compared to people who didn't get the SMS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> The results of the analysis are inconclusive, owing majorly to the fact that the dataset does not capture the population correctly. The major limitation being that the percentages of people who for instance received the Bolsa Familia scholarship, or received an SMS reminder was almost half the size of the population of the people who didn't. This doesn't help for the make of an unbiased conclusion or analysis. Also the women in the dataset were twice the population of men.\n",
    "\n",
    "> That being said, a couple of useful insights were drawn from the questions posed earlier at the top of the page which are as follows:\n",
    "\n",
    "<li>Gender had nothing to do with the attendance. Both men and women alike missed their appointents and vice versa</li>\n",
    "<li>Elderly people tended to take their appointments more seriously, unlike children and teenagers. This could have been due to the fact that they most likely forgot their appointment or their parents didn't remember.It could also been because elderly people are more likely to take their health more seriously than children considering health at that age is really fickle. It would also be worthy to note that the population for children and teenagers were generally lower than that of other age groups, thereby making the analysis not entirely conclusive.</li>\n",
    "<li>The enrollment of patients in the social welfare program had no effect on their attendance as even people who were enrolled missed more appointments than their counterparts who weren't enrolled.</li>\n",
    "<li>Also, the percentage of people who missed their appointments were more for people who received an SMS remind compared to those who didn't, which makes the SMS reminder and ineffective tool.</li>\n",
    "\n",
    "> After all the analysis, it was realized that there were no strong pointers or variables in the dataset that influenced the dependent 'no_show' variable columns of patients showing up to their appointments or not. The strongest pointer being the age of the patient.\n",
    "\n",
    "> More effort should be done in creating a balanced sample population that includes equal amount of patient's gender, scholarship enrollment and SMS receipient. This would enable a more balanced analysis and create more confident reports and conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "med_apt.to_csv('med_apt_final.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}