data-analysis.ipynb

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   "source": [
    "# 🏥 Analyzing No-shows for Scheduled Medical Appointments\n",
    "\n",
    "**Author**: _Emanuel Gabbay_\n",
    "\n",
    "**Publishment Date**: _July 13th, 2022_"
   ]
  },
  {
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   "id": "9845342f-acc8-4e89-8941-ff77b03d8ee8",
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    "tags": []
   },
   "source": [
    "## 📃 Table of Contents"
   ]
  },
  {
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   "id": "e5dbb1f6-83b5-4294-bef7-011fc265aba2",
   "metadata": {},
   "source": [
    "1. [Introduction](#intro)\n",
    "2. [About the Data](#about)\n",
    "3. [Examining the Data](#examine)\n",
    "4. [Cleaning the Data](#clean)\n",
    "5. [Adding Useful Data](#adddata)\n",
    "6. [Exploring the Data](#explore)\n",
    "7. [Conclusions](#conclusions)"
   ]
  },
  {
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   },
   "source": [
    "## 📖 Introduction <a name=\"intro\"></a>"
   ]
  },
  {
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   "id": "3abe28af-1e06-4919-a312-088a5f74b0ca",
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   "source": [
    "In this project, I will wrangle and analyze a dataset about patients who scheduled a doctor's appointment and some of them didn't show (aka no-shows).\n",
    "\n",
    "**Questions**\n",
    "- How many no-shows are there (a proportion estimation)?\n",
    "- What factors are correlated with no-shows?\n",
    "- Does the proportion of no-shows among men is different than the proportion among women?\n",
    "- Is there a difference in no-show proportions between people who got an SMS reminder and people who don't?\n",
    "- How can we predict no-shows?"
   ]
  },
  {
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   },
   "source": [
    "## ℹ About the Data <a name=\"about\"></a>"
   ]
  },
  {
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   "id": "04707ab2-6175-4663-bbc5-213398d096a2",
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   "source": [
    "The dataset contains 110,527 medical appointments that have been scheduled in Brazil, together with patient data, wether they received an SMS reminder for the meeting, and wether they showed up.\n",
    "\n",
    "Data provided by Joni Hoppen and Aquarela Advanced Analytics Aquarela (source: [Kaggle](https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia))\n",
    "\n",
    "The variables:\n",
    "\n",
    "- `PatientId` - The id of the patient\n",
    "- `AppointmentID` - The id of the scheduled appointmnet\n",
    "- `Gender` - The gender of the patient (F=Female; M=Male)\n",
    "- `ScheduledDay` - The the date and time the appointment was scheduled (timestamp)\n",
    "- `AppointmentDay` - The date of the appointment (timestamp; date only)\n",
    "- `Age` - The age of the patient\n",
    "- `Neighbourhood` - The neighbourhood where the appointment takes place\n",
    "- `Scholarship` - Wether the patient is a member of the \"Bolsa Família\" program ([more info](https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia); Binary data type)\n",
    "- `Hipertension` - Wether the patient has hypertension (Binary data type)\n",
    "- `Diabetes` - Wether the patient has diabetes (Binary data type)\n",
    "- `Alcoholism` - Wether the patient is alcoholic (Binary data type)\n",
    "- `Handcap` - The number of disabilities the patient has\n",
    "- `SMS_received` - Wether the patient received an SMS reminder before the appointment (Binary data type)\n",
    "- `No-show` - Wether the patient arrived to the appointment or not (Binary data type)"
   ]
  },
  {
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   "id": "182fb9e8-6e3d-44c2-9ee0-941e422ae5ac",
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   "source": [
    "## 🔬 Examining the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313d1bf7-415b-4f19-8717-48770e7e7617",
   "metadata": {},
   "source": [
    "I will start by going through the data, understand its structure and values, and finally will conclude what should be done in order to clean it, making it ready for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# import libraries\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e131c901-8e55-4e94-9e94-80c10f32d493",
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   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import CSV file to Pandas DataFrame\n",
    "df = pd.read_csv(r'medical_appointments_may_2016.csv')\n",
    "\n",
    "# Validating successful import, looking at the first 5 rows of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b22c844d-39e5-4e51-86fc-c0ca3027e7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the structure of the DataFrame (number of rows and columns)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "045e52b4-1ef8-4dbc-88dc-b9b976cb9982",
   "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": [
    "# Looking at the data types and wether there is a missing data (nulls)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38a7a174-11b0-45e0-bdb6-beef2910b62e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a969f6f-aaed-46ef-bf6e-163ee78318f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values of Gender\n",
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c7d070e-dde8-48bc-81f9-20e036848326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-11-10T07:13:56Z'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking minimum value of ScheduledDay\n",
    "df['ScheduledDay'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b5dae83-b12b-4fba-84f2-7d49f7db91a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-06-08T20:07:23Z'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking maximum value of ScheduledDay\n",
    "df['ScheduledDay'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e27463b-34d4-47a9-b9a5-7074f8f19430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-04-29T00:00:00Z'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking minimum value of AppointmentDay\n",
    "df['AppointmentDay'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7414c726-5b3f-4258-b018-0ddccb6333ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-06-08T00:00:00Z'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking maximum value of AppointmentDay\n",
    "df['AppointmentDay'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35bc289e-7c61-45ee-8088-002e985f3204",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# Checking unique values of Age\n",
    "ages = df['Age'].value_counts().sort_index()\n",
    "for i in ages.index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bca652a-7a95-48c5-a241-4b29c0255cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>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>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06T08:58:13Z</td>\n",
       "      <td>2016-06-06T00:00:00Z</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": [
       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "99832  4.659432e+14        5775010      F  2016-06-06T08:58:13Z   \n",
       "\n",
       "             AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  \\\n",
       "99832  2016-06-06T00:00:00Z   -1         ROMÃO            0             0   \n",
       "\n",
       "       Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "99832         0           0        0             0      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the numbers of rows where age is -1\n",
    "df[df['Age'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c574c750-fd9b-4f40-a8fb-9f6ae6cebfc3",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AEROPORTO\n",
      "ANDORINHAS\n",
      "ANTÔNIO HONÓRIO\n",
      "ARIOVALDO FAVALESSA\n",
      "BARRO VERMELHO\n",
      "BELA VISTA\n",
      "BENTO FERREIRA\n",
      "BOA VISTA\n",
      "BONFIM\n",
      "CARATOÍRA\n",
      "CENTRO\n",
      "COMDUSA\n",
      "CONQUISTA\n",
      "CONSOLAÇÃO\n",
      "CRUZAMENTO\n",
      "DA PENHA\n",
      "DE LOURDES\n",
      "DO CABRAL\n",
      "DO MOSCOSO\n",
      "DO QUADRO\n",
      "ENSEADA DO SUÁ\n",
      "ESTRELINHA\n",
      "FONTE GRANDE\n",
      "FORTE SÃO JOÃO\n",
      "FRADINHOS\n",
      "GOIABEIRAS\n",
      "GRANDE VITÓRIA\n",
      "GURIGICA\n",
      "HORTO\n",
      "ILHA DAS CAIEIRAS\n",
      "ILHA DE SANTA MARIA\n",
      "ILHA DO BOI\n",
      "ILHA DO FRADE\n",
      "ILHA DO PRÍNCIPE\n",
      "ILHAS OCEÂNICAS DE TRINDADE\n",
      "INHANGUETÁ\n",
      "ITARARÉ\n",
      "JABOUR\n",
      "JARDIM CAMBURI\n",
      "JARDIM DA PENHA\n",
      "JESUS DE NAZARETH\n",
      "JOANA D´ARC\n",
      "JUCUTUQUARA\n",
      "MARIA ORTIZ\n",
      "MARUÍPE\n",
      "MATA DA PRAIA\n",
      "MONTE BELO\n",
      "MORADA DE CAMBURI\n",
      "MÁRIO CYPRESTE\n",
      "NAZARETH\n",
      "NOVA PALESTINA\n",
      "PARQUE INDUSTRIAL\n",
      "PARQUE MOSCOSO\n",
      "PIEDADE\n",
      "PONTAL DE CAMBURI\n",
      "PRAIA DO CANTO\n",
      "PRAIA DO SUÁ\n",
      "REDENÇÃO\n",
      "REPÚBLICA\n",
      "RESISTÊNCIA\n",
      "ROMÃO\n",
      "SANTA CECÍLIA\n",
      "SANTA CLARA\n",
      "SANTA HELENA\n",
      "SANTA LUÍZA\n",
      "SANTA LÚCIA\n",
      "SANTA MARTHA\n",
      "SANTA TEREZA\n",
      "SANTO ANDRÉ\n",
      "SANTO ANTÔNIO\n",
      "SANTOS DUMONT\n",
      "SANTOS REIS\n",
      "SEGURANÇA DO LAR\n",
      "SOLON BORGES\n",
      "SÃO BENEDITO\n",
      "SÃO CRISTÓVÃO\n",
      "SÃO JOSÉ\n",
      "SÃO PEDRO\n",
      "TABUAZEIRO\n",
      "UNIVERSITÁRIO\n",
      "VILA RUBIM\n"
     ]
    }
   ],
   "source": [
    "# Examine neighborhood names, check for value format, and correct syntax\n",
    "neighborhoods = df['Neighbourhood'].value_counts().sort_index()\n",
    "for i in neighborhoods.index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80c95575-d54a-411a-b190-4f0da9f44b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99666\n",
       "1    10861\n",
       "Name: Scholarship, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for unique Scholarship values (supposed to be 0 and 1)\n",
    "df['Scholarship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f3bdfc2-67c3-401e-87f0-5eb4d7afbfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88726\n",
       "1    21801\n",
       "Name: Hipertension, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for unique Hipertension values (supposed to be 0 and 1)\n",
    "df['Hipertension'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a2d36434-1b0e-4073-9027-8fe856dea535",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    102584\n",
       "1      7943\n",
       "Name: Diabetes, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for unique Diabetes values (supposed to be 0 and 1)\n",
    "df['Diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1aec4630-d560-47b6-999b-ec5653647444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    107167\n",
       "1      3360\n",
       "Name: Alcoholism, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for unique Alcoholism values (supposed to be 0 and 1)\n",
    "df['Alcoholism'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bee848dc-157e-4c39-8866-0fa2e5ac0a33",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values of Handcap\n",
    "df['Handcap'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e76b041f-6b94-41ce-8a6d-ffa4417c958e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75045\n",
       "1    35482\n",
       "Name: SMS_received, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values of SMS_received (supposed to be 0 and 1)\n",
    "df['SMS_received'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dfde7f6a-dc6a-4ddb-9a57-243d0e16cc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88208\n",
       "Yes    22319\n",
       "Name: No-show, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values of No-show\n",
    "df['No-show'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd97be95-bea7-4016-8ce3-c11f17de08fa",
   "metadata": {},
   "source": [
    "### Examination Conclusions \n",
    "\n",
    "- All columns (labels) should be reanamed to a lowercase format with underscores as word seperators\n",
    "- Column names should better represent the data\n",
    "- `PatientID` and `AppointmentID` columns should be conveted to string\n",
    "- `ScheduledDay` and `AppointmentDay` columns should be converted to datetime\n",
    "- `No-show` column should be converted to int (with zeros and ones instead of Yes and No)\n",
    "- The row where `Age` is -1 should be dropped\n",
    "- `Neighbourhood` column values should be renamed to title case\n",
    "- Columns should be reordered in a more logical way\n",
    "- Rows should be sorted by appointment id\n",
    "- `ScheduledDay` timestamp should be truncated to day, as the time of scheduling is not relevant to this research"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4a96cf-c5d5-4313-b047-ef3b30a77085",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 🧹 Cleaning the Data <a name=\"clean\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "582c9d76-7e97-48ba-ab0c-6b41bddb01ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>age</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>did_receive_sms</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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id  appointment_id gender        time_scheduled  \\\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",
       "       appointment_date  age       neighborhood  has_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",
       "   has_hypertension  has_diabetes  is_alcoholic  num_of_disabilities  \\\n",
       "0                 1             0             0                    0   \n",
       "1                 0             0             0                    0   \n",
       "2                 0             0             0                    0   \n",
       "3                 0             0             0                    0   \n",
       "4                 1             1             0                    0   \n",
       "\n",
       "   did_receive_sms no_show  \n",
       "0                0      No  \n",
       "1                0      No  \n",
       "2                0      No  \n",
       "3                0      No  \n",
       "4                0      No  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming column names\n",
    "new_columns = ['patient_id', 'appointment_id', 'gender', 'time_scheduled', 'appointment_date', 'age',\n",
    "               'neighborhood', 'has_scholarship', 'has_hypertension', 'has_diabetes', 'is_alcoholic', 'num_of_disabilities',\n",
    "               'did_receive_sms', 'no_show']\n",
    "df.columns = new_columns\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "839d858d-ebaa-4764-bbc7-5efabf1ac376",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
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       "      <th>patient_id</th>\n",
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       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
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       "      <th>0</th>\n",
       "      <td>5642903</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>5642503</td>\n",
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       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>56</td>\n",
       "      <td>M</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>5642549</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>62</td>\n",
       "      <td>F</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>5642828</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>8</td>\n",
       "      <td>F</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>5642494</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>56</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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": [
       "   appointment_id        time_scheduled      appointment_date  \\\n",
       "0         5642903  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   \n",
       "1         5642503  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   \n",
       "2         5642549  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   \n",
       "3         5642828  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z   \n",
       "4         5642494  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   \n",
       "\n",
       "        neighborhood    patient_id  age gender  has_hypertension  \\\n",
       "0    JARDIM DA PENHA  2.987250e+13   62      F                 1   \n",
       "1    JARDIM DA PENHA  5.589978e+14   56      M                 0   \n",
       "2      MATA DA PRAIA  4.262962e+12   62      F                 0   \n",
       "3  PONTAL DE CAMBURI  8.679512e+11    8      F                 0   \n",
       "4    JARDIM DA PENHA  8.841186e+12   56      F                 1   \n",
       "\n",
       "   num_of_disabilities  has_diabetes  is_alcoholic  has_scholarship  \\\n",
       "0                    0             0             0                0   \n",
       "1                    0             0             0                0   \n",
       "2                    0             0             0                0   \n",
       "3                    0             0             0                0   \n",
       "4                    0             1             0                0   \n",
       "\n",
       "   did_receive_sms no_show  \n",
       "0                0      No  \n",
       "1                0      No  \n",
       "2                0      No  \n",
       "3                0      No  \n",
       "4                0      No  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorder columns to a more logical order (appointment data, patient data, reminder, show/no-show)\n",
    "\n",
    "df = df[['appointment_id', 'time_scheduled', 'appointment_date', 'neighborhood',\n",
    "         'patient_id', 'age', 'gender', 'has_hypertension', 'num_of_disabilities', 'has_diabetes',\n",
    "        'is_alcoholic', 'has_scholarship', 'did_receive_sms', 'no_show' ]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60edf959-a45e-4d01-adb9-a8e5d13c1ca7",
   "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>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5030230</td>\n",
       "      <td>2015-11-10T07:13:56Z</td>\n",
       "      <td>2016-05-04T00:00:00Z</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>8.322564e+14</td>\n",
       "      <td>51</td>\n",
       "      <td>F</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>1</th>\n",
       "      <td>5122866</td>\n",
       "      <td>2015-12-03T08:17:28Z</td>\n",
       "      <td>2016-05-02T00:00:00Z</td>\n",
       "      <td>VILA RUBIM</td>\n",
       "      <td>9.163747e+13</td>\n",
       "      <td>34</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5134197</td>\n",
       "      <td>2015-12-07T10:40:59Z</td>\n",
       "      <td>2016-06-03T00:00:00Z</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>1.216587e+12</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5134220</td>\n",
       "      <td>2015-12-07T10:42:42Z</td>\n",
       "      <td>2016-06-03T00:00:00Z</td>\n",
       "      <td>MARUÍPE</td>\n",
       "      <td>3.189960e+13</td>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5134223</td>\n",
       "      <td>2015-12-07T10:43:01Z</td>\n",
       "      <td>2016-06-03T00:00:00Z</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>9.582232e+12</td>\n",
       "      <td>80</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   appointment_id        time_scheduled      appointment_date   neighborhood  \\\n",
       "0         5030230  2015-11-10T07:13:56Z  2016-05-04T00:00:00Z    RESISTÊNCIA   \n",
       "1         5122866  2015-12-03T08:17:28Z  2016-05-02T00:00:00Z     VILA RUBIM   \n",
       "2         5134197  2015-12-07T10:40:59Z  2016-06-03T00:00:00Z  SÃO CRISTÓVÃO   \n",
       "3         5134220  2015-12-07T10:42:42Z  2016-06-03T00:00:00Z        MARUÍPE   \n",
       "4         5134223  2015-12-07T10:43:01Z  2016-06-03T00:00:00Z  SÃO CRISTÓVÃO   \n",
       "\n",
       "     patient_id  age gender  has_hypertension  num_of_disabilities  \\\n",
       "0  8.322564e+14   51      F                 0                    0   \n",
       "1  9.163747e+13   34      M                 1                    0   \n",
       "2  1.216587e+12   27      F                 0                    0   \n",
       "3  3.189960e+13   48      F                 1                    0   \n",
       "4  9.582232e+12   80      F                 1                    0   \n",
       "\n",
       "   has_diabetes  is_alcoholic  has_scholarship  did_receive_sms no_show  \n",
       "0             0             0                0                1      No  \n",
       "1             0             0                0                1     Yes  \n",
       "2             0             0                1                1     Yes  \n",
       "3             1             0                0                1      No  \n",
       "4             1             0                0                1      No  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort values by appointment_id\n",
    "df.sort_values(by=['appointment_id'], inplace=True)\n",
    "\n",
    "# Reset index after sort\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Check it has been done right\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15cadeea-2253-4c6f-9ed0-38a4f2ba418e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Non-Null Count   Dtype              \n",
      "---  ------            --------------   -----              \n",
      " 0   time_scheduled    110527 non-null  datetime64[ns, UTC]\n",
      " 1   appointment_date  110527 non-null  datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](2)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "# Convert the date variables to datetime objects\n",
    "\n",
    "df['time_scheduled'] = pd.to_datetime(df['time_scheduled'])\n",
    "df['appointment_date'] = pd.to_datetime(df['appointment_date'])\n",
    "\n",
    "# Check that the column types have been updated\n",
    "df.loc[:, 'time_scheduled':'appointment_date'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc9f0803-22a1-47d7-b5e9-c2c18d389409",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-05-03 00:00:00+00:00    4238\n",
       "2016-05-02 00:00:00+00:00    4216\n",
       "2016-05-16 00:00:00+00:00    4120\n",
       "2016-05-05 00:00:00+00:00    4095\n",
       "2016-05-10 00:00:00+00:00    4024\n",
       "                             ... \n",
       "2016-02-15 00:00:00+00:00       1\n",
       "2016-04-16 00:00:00+00:00       1\n",
       "2016-04-09 00:00:00+00:00       1\n",
       "2016-03-05 00:00:00+00:00       1\n",
       "2015-11-10 00:00:00+00:00       1\n",
       "Name: time_scheduled, Length: 111, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trancuate time_scheduled column to day\n",
    "df['time_scheduled'] = df['time_scheduled'].dt.floor('D')\n",
    "\n",
    "# Validate the data\n",
    "df['time_scheduled'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0c7719a6-9047-46db-84a3-df7c6d882c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert id columns to string types\n",
    "\n",
    "df['appointment_id'] = df['appointment_id'].astype(str)\n",
    "df['patient_id'] = df['patient_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba4dbaae-56e6-4c79-ba37-a5d3d56791fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the appointment_id values\n",
    "type(df['appointment_id'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0352811f-7335-4938-acc2-c4f972eb8594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5030230    1\n",
       "5709283    1\n",
       "5709281    1\n",
       "5709278    1\n",
       "5709275    1\n",
       "          ..\n",
       "5654157    1\n",
       "5654155    1\n",
       "5654152    1\n",
       "5654151    1\n",
       "5790484    1\n",
       "Name: appointment_id, Length: 110527, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print values of appointment_id\n",
    "df['appointment_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25794bb8-1048-46f0-8c67-047cb0d7a25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the patient_id values\n",
    "type(df['patient_id'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "87245456-d479-4858-8cfc-a91659a80409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "822145925426128.0    88\n",
       "99637671331.0        84\n",
       "26886125921145.0     70\n",
       "33534783483176.0     65\n",
       "6264198675331.0      62\n",
       "                     ..\n",
       "556441589989718.0     1\n",
       "55486572438678.0      1\n",
       "49618667315736.0      1\n",
       "87326319339173.0      1\n",
       "5237164264312.0       1\n",
       "Name: patient_id, Length: 62299, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print values of patient_id\n",
    "df['patient_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3147db06-92a8-442e-be3f-1923e301a0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "822145925426128    88\n",
       "99637671331        84\n",
       "26886125921145     70\n",
       "33534783483176     65\n",
       "6264198675331      62\n",
       "                   ..\n",
       "556441589989718     1\n",
       "55486572438678      1\n",
       "49618667315736      1\n",
       "87326319339173      1\n",
       "5237164264312       1\n",
       "Name: patient_id, Length: 62299, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the float \".0\" from the string of patient_id\n",
    "df['patient_id'] = df['patient_id'].apply(lambda x: x[:-2])\n",
    "\n",
    "# Print values of patient_id again\n",
    "df['patient_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2977b778-da73-4a88-a769-3372716b16fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "appointment_id                      object\n",
       "time_scheduled         datetime64[ns, UTC]\n",
       "appointment_date       datetime64[ns, UTC]\n",
       "neighborhood                        object\n",
       "patient_id                          object\n",
       "age                                  int64\n",
       "gender                              object\n",
       "has_hypertension                     int64\n",
       "num_of_disabilities                  int64\n",
       "has_diabetes                         int64\n",
       "is_alcoholic                         int64\n",
       "has_scholarship                      int64\n",
       "did_receive_sms                      int64\n",
       "no_show                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validate all data types again\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "edfe17fe-7652-43fb-bc32-0bb17497d2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88208\n",
       "1    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace no_show values to numeric values\n",
    "df['no_show'].replace('No', 0, inplace=True)\n",
    "df['no_show'].replace('Yes', 1, inplace=True)\n",
    "\n",
    "# Check the replacement went ok\n",
    "df['no_show'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c006ff1f-3330-41d3-806c-dc914ceb296a",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aeroporto\n",
      "Andorinhas\n",
      "Antônio Honório\n",
      "Ariovaldo Favalessa\n",
      "Barro Vermelho\n",
      "Bela Vista\n",
      "Bento Ferreira\n",
      "Boa Vista\n",
      "Bonfim\n",
      "Caratoíra\n",
      "Centro\n",
      "Comdusa\n",
      "Conquista\n",
      "Consolação\n",
      "Cruzamento\n",
      "Da Penha\n",
      "De Lourdes\n",
      "Do Cabral\n",
      "Do Moscoso\n",
      "Do Quadro\n",
      "Enseada Do Suá\n",
      "Estrelinha\n",
      "Fonte Grande\n",
      "Forte São João\n",
      "Fradinhos\n",
      "Goiabeiras\n",
      "Grande Vitória\n",
      "Gurigica\n",
      "Horto\n",
      "Ilha Das Caieiras\n",
      "Ilha De Santa Maria\n",
      "Ilha Do Boi\n",
      "Ilha Do Frade\n",
      "Ilha Do Príncipe\n",
      "Ilhas Oceânicas De Trindade\n",
      "Inhanguetá\n",
      "Itararé\n",
      "Jabour\n",
      "Jardim Camburi\n",
      "Jardim Da Penha\n",
      "Jesus De Nazareth\n",
      "Joana D´Arc\n",
      "Jucutuquara\n",
      "Maria Ortiz\n",
      "Maruípe\n",
      "Mata Da Praia\n",
      "Monte Belo\n",
      "Morada De Camburi\n",
      "Mário Cypreste\n",
      "Nazareth\n",
      "Nova Palestina\n",
      "Parque Industrial\n",
      "Parque Moscoso\n",
      "Piedade\n",
      "Pontal De Camburi\n",
      "Praia Do Canto\n",
      "Praia Do Suá\n",
      "Redenção\n",
      "República\n",
      "Resistência\n",
      "Romão\n",
      "Santa Cecília\n",
      "Santa Clara\n",
      "Santa Helena\n",
      "Santa Luíza\n",
      "Santa Lúcia\n",
      "Santa Martha\n",
      "Santa Tereza\n",
      "Santo André\n",
      "Santo Antônio\n",
      "Santos Dumont\n",
      "Santos Reis\n",
      "Segurança Do Lar\n",
      "Solon Borges\n",
      "São Benedito\n",
      "São Cristóvão\n",
      "São José\n",
      "São Pedro\n",
      "Tabuazeiro\n",
      "Universitário\n",
      "Vila Rubim\n"
     ]
    }
   ],
   "source": [
    "# Change neighborhood values to title format\n",
    "df['neighborhood'] = df['neighborhood'].apply(lambda x: x.title())\n",
    "\n",
    "# Check that it went ok\n",
    "neighborhoods = df['neighborhood'].value_counts().sort_index()\n",
    "for i in neighborhoods.index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3427720b-4e0c-4d4f-b959-e5a5e625d493",
   "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>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105744</th>\n",
       "      <td>5775010</td>\n",
       "      <td>2016-06-06 00:00:00+00:00</td>\n",
       "      <td>2016-06-06 00:00:00+00:00</td>\n",
       "      <td>Romão</td>\n",
       "      <td>465943158731293</td>\n",
       "      <td>-1</td>\n",
       "      <td>F</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       appointment_id            time_scheduled          appointment_date  \\\n",
       "105744        5775010 2016-06-06 00:00:00+00:00 2016-06-06 00:00:00+00:00   \n",
       "\n",
       "       neighborhood       patient_id  age gender  has_hypertension  \\\n",
       "105744        Romão  465943158731293   -1      F                 0   \n",
       "\n",
       "        num_of_disabilities  has_diabetes  is_alcoholic  has_scholarship  \\\n",
       "105744                    0             0             0                0   \n",
       "\n",
       "        did_receive_sms  no_show  \n",
       "105744                0        0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the index of the problematic row where age is -1\n",
    "df[df['age'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bc0abb17-ded6-46fc-96b4-420a1aa65a5f",
   "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>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [appointment_id, time_scheduled, appointment_date, neighborhood, patient_id, age, gender, has_hypertension, num_of_disabilities, has_diabetes, is_alcoholic, has_scholarship, did_receive_sms, no_show]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete problematic row with age -1\n",
    "df.drop(105744, inplace=True)\n",
    "\n",
    "# Check that the column has been deleted\n",
    "df[df['age'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5f814856-9cef-4a03-8ebb-ede4ef5fe0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index after dropping the column\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7f06f8f-f37a-4b49-8453-114540d7a991",
   "metadata": {
    "tags": []
   },
   "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>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5030230</td>\n",
       "      <td>2015-11-10 00:00:00+00:00</td>\n",
       "      <td>2016-05-04 00:00:00+00:00</td>\n",
       "      <td>Resistência</td>\n",
       "      <td>832256398961987</td>\n",
       "      <td>51</td>\n",
       "      <td>F</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5122866</td>\n",
       "      <td>2015-12-03 00:00:00+00:00</td>\n",
       "      <td>2016-05-02 00:00:00+00:00</td>\n",
       "      <td>Vila Rubim</td>\n",
       "      <td>91637474953513</td>\n",
       "      <td>34</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5134197</td>\n",
       "      <td>2015-12-07 00:00:00+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>São Cristóvão</td>\n",
       "      <td>1216586867796</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5134220</td>\n",
       "      <td>2015-12-07 00:00:00+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>Maruípe</td>\n",
       "      <td>31899595421534</td>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5134223</td>\n",
       "      <td>2015-12-07 00:00:00+00:00</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>São Cristóvão</td>\n",
       "      <td>9582232334148</td>\n",
       "      <td>80</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  appointment_id            time_scheduled          appointment_date  \\\n",
       "0        5030230 2015-11-10 00:00:00+00:00 2016-05-04 00:00:00+00:00   \n",
       "1        5122866 2015-12-03 00:00:00+00:00 2016-05-02 00:00:00+00:00   \n",
       "2        5134197 2015-12-07 00:00:00+00:00 2016-06-03 00:00:00+00:00   \n",
       "3        5134220 2015-12-07 00:00:00+00:00 2016-06-03 00:00:00+00:00   \n",
       "4        5134223 2015-12-07 00:00:00+00:00 2016-06-03 00:00:00+00:00   \n",
       "\n",
       "    neighborhood       patient_id  age gender  has_hypertension  \\\n",
       "0    Resistência  832256398961987   51      F                 0   \n",
       "1     Vila Rubim   91637474953513   34      M                 1   \n",
       "2  São Cristóvão    1216586867796   27      F                 0   \n",
       "3        Maruípe   31899595421534   48      F                 1   \n",
       "4  São Cristóvão    9582232334148   80      F                 1   \n",
       "\n",
       "   num_of_disabilities  has_diabetes  is_alcoholic  has_scholarship  \\\n",
       "0                    0             0             0                0   \n",
       "1                    0             0             0                0   \n",
       "2                    0             0             0                1   \n",
       "3                    0             1             0                0   \n",
       "4                    0             1             0                0   \n",
       "\n",
       "   did_receive_sms  no_show  \n",
       "0                1        0  \n",
       "1                1        1  \n",
       "2                1        1  \n",
       "3                1        0  \n",
       "4                1        0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final look on the cleaned data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99627f40-eea3-4ac4-8068-ecef4756290f",
   "metadata": {},
   "source": [
    "**Perfect!** 😌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d98e9ea6-79b0-4874-b319-7c11bf6948d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the cleaned DataFrame for future use\n",
    "df.to_csv('medical_appointments_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779f6c2f-9404-492e-868c-6fb1452ff1a1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## ➕ Adding Useful Data <a name=\"adddata\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f48769e-7563-4350-95c6-ff2aa352e3ee",
   "metadata": {},
   "source": [
    "I would like to add these columns to dive to deeper detail in my analysis:\n",
    "- `days_from_appointment` column that represents the number of days from scheduling the appointment to the appointment\n",
    "- `appointment_day_of_week` column that represents the day of that appointment (Sunday, Monday, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "adf3a750-6de9-4119-acc1-78ca85dc4e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "      <th>days_from_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37824</th>\n",
       "      <td>5655637</td>\n",
       "      <td>2016-05-04 00:00:00+00:00</td>\n",
       "      <td>2016-05-03 00:00:00+00:00</td>\n",
       "      <td>Tabuazeiro</td>\n",
       "      <td>3787481966821</td>\n",
       "      <td>7</td>\n",
       "      <td>M</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>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44332</th>\n",
       "      <td>5664962</td>\n",
       "      <td>2016-05-05 00:00:00+00:00</td>\n",
       "      <td>2016-05-04 00:00:00+00:00</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>24252258389979</td>\n",
       "      <td>22</td>\n",
       "      <td>F</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>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54796</th>\n",
       "      <td>5679978</td>\n",
       "      <td>2016-05-10 00:00:00+00:00</td>\n",
       "      <td>2016-05-09 00:00:00+00:00</td>\n",
       "      <td>Resistência</td>\n",
       "      <td>7839272661752</td>\n",
       "      <td>38</td>\n",
       "      <td>M</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>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59551</th>\n",
       "      <td>5686628</td>\n",
       "      <td>2016-05-11 00:00:00+00:00</td>\n",
       "      <td>2016-05-05 00:00:00+00:00</td>\n",
       "      <td>Santo Antônio</td>\n",
       "      <td>998231581612122</td>\n",
       "      <td>81</td>\n",
       "      <td>F</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>1</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77382</th>\n",
       "      <td>5715660</td>\n",
       "      <td>2016-05-18 00:00:00+00:00</td>\n",
       "      <td>2016-05-17 00:00:00+00:00</td>\n",
       "      <td>Santo Antônio</td>\n",
       "      <td>7896293967868</td>\n",
       "      <td>19</td>\n",
       "      <td>F</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>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      appointment_id            time_scheduled          appointment_date  \\\n",
       "37824        5655637 2016-05-04 00:00:00+00:00 2016-05-03 00:00:00+00:00   \n",
       "44332        5664962 2016-05-05 00:00:00+00:00 2016-05-04 00:00:00+00:00   \n",
       "54796        5679978 2016-05-10 00:00:00+00:00 2016-05-09 00:00:00+00:00   \n",
       "59551        5686628 2016-05-11 00:00:00+00:00 2016-05-05 00:00:00+00:00   \n",
       "77382        5715660 2016-05-18 00:00:00+00:00 2016-05-17 00:00:00+00:00   \n",
       "\n",
       "        neighborhood       patient_id  age gender  has_hypertension  \\\n",
       "37824     Tabuazeiro    3787481966821    7      M                 0   \n",
       "44332     Consolação   24252258389979   22      F                 0   \n",
       "54796    Resistência    7839272661752   38      M                 0   \n",
       "59551  Santo Antônio  998231581612122   81      F                 0   \n",
       "77382  Santo Antônio    7896293967868   19      F                 0   \n",
       "\n",
       "       num_of_disabilities  has_diabetes  is_alcoholic  has_scholarship  \\\n",
       "37824                    0             0             0                0   \n",
       "44332                    0             0             0                0   \n",
       "54796                    1             0             0                0   \n",
       "59551                    0             0             0                0   \n",
       "77382                    1             0             0                0   \n",
       "\n",
       "       did_receive_sms  no_show  days_from_appointment  \n",
       "37824                0        1                   -1.0  \n",
       "44332                0        1                   -1.0  \n",
       "54796                0        1                   -1.0  \n",
       "59551                0        1                   -6.0  \n",
       "77382                0        1                   -1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a column showing the number of days between scheduling the appointment and the appointment date\n",
    "df['days_from_appointment'] = (df['appointment_date']-df['time_scheduled']).astype('timedelta64[D]')\n",
    "\n",
    "# Check if there is any faulty data (a scheduled appointment to a prior date)\n",
    "df.query('days_from_appointment < 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b7c409c-c546-4e1e-ad43-97e3ed0eadb3",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appointment_id</th>\n",
       "      <th>time_scheduled</th>\n",
       "      <th>appointment_date</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_hypertension</th>\n",
       "      <th>num_of_disabilities</th>\n",
       "      <th>has_diabetes</th>\n",
       "      <th>is_alcoholic</th>\n",
       "      <th>has_scholarship</th>\n",
       "      <th>did_receive_sms</th>\n",
       "      <th>no_show</th>\n",
       "      <th>days_from_appointment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [appointment_id, time_scheduled, appointment_date, neighborhood, patient_id, age, gender, has_hypertension, num_of_disabilities, has_diabetes, is_alcoholic, has_scholarship, did_receive_sms, no_show, days_from_appointment]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the faulty data\n",
    "df.drop([37824, 44332, 54796, 59551, 77382], inplace=True)\n",
    "\n",
    "# Restting indices after dropping the rows \n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Check that all went ok\n",
    "df.query('days_from_appointment < 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f3f42c69-f3f2-476a-a0cd-e58dc2b976f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert days_from_appointment data to int\n",
    "df['days_from_appointment'] = df['days_from_appointment'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fec2b2ca-1098-46d6-8134-1774196fcbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a column showing the day of the week (of the appointment)\n",
    "df['appointment_day_of_week'] = df['appointment_date'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "84765cf8-87b8-4dcc-b60e-5d81e4a22ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changes the numerical day of week to the description of the day (where Monday is 0 and Sunday is 6)\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(0, 'Monday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(1, 'Tuesday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(2, 'Wednesday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(3, 'Thursday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(4, 'Friday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(5, 'Saturday')\n",
    "df['appointment_day_of_week'] = df['appointment_day_of_week'].replace(6, 'Sunday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "55cd8d63-8511-46a8-a15f-9c029924bb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the columns with the new ones\n",
    "df = df[['appointment_id', 'time_scheduled', 'days_from_appointment', 'appointment_date', 'appointment_day_of_week',\n",
    "         'neighborhood', 'patient_id', 'age', 'gender', 'has_hypertension', 'num_of_disabilities', 'has_diabetes',\n",
    "        'is_alcoholic', 'has_scholarship', 'did_receive_sms', 'no_show' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "139f4f87-673e-4709-9ea9-b692266262bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the DataFrame with the new columns \n",
    "df.to_csv('medical_appointments_cleaned_v2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e5c9aa-8ba5-487d-a842-2938468cd8bd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 🔎 Exploring the Data <a name=\"explore\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90cd0e75-2728-42ee-9cb2-aa8e0ba3759b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 🔎 Exploring the `time_scheduled` Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c750159a-7253-4824-837e-6ca664052bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-11-10 00:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The schedule date of the first appointment\n",
    "df['time_scheduled'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ce699b02-9f38-477c-86df-58fce48d3985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-06-08 00:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The schedule date of the last appointment\n",
    "df['time_scheduled'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "abae7841-f400-483c-bc4d-733b134f64d7",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Showing the distribution of the variable across the timeline\n",
    "sns.histplot(data=df, x=\"time_scheduled\", kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67e14cd-94e0-4be8-b17a-43504229c821",
   "metadata": {},
   "source": [
    "We observe that the variable is left-skewed. Most of the appointments were scheduled in May."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e69da1f-0122-4367-bdb7-f759b2f6b87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-05-10 00:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the 'median' date (the date in which 50% of the appointments were scheduled before, and 50% after)\n",
    "time_num = np.array(df['time_scheduled'])\n",
    "time_num[int((time_num.size-1)/2)]"
   ]
  },
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   "cell_type": "markdown",
   "id": "422bdac0-0d69-4d04-9d05-e5126f135fa1",
   "metadata": {},
   "source": [
    "50% of the appointments were scheduled between 10/11/2015-10/5/2016\n",
    "\n",
    "The other 50% were scheduled between 10/5/2016-8/6/2016"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f86640b-5b7d-4ad0-8390-9a2448fd8e21",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### 🔎 Exploring the `days_from_appointment` Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "88efbe0c-ef92-4af9-bfc0-182eff9b6d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean         10.184345\n",
       "std          15.255153\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           4.000000\n",
       "75%          15.000000\n",
       "max         179.000000\n",
       "Name: days_from_appointment, dtype: float64"
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['days_from_appointment'].describe()"
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  },
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   "cell_type": "code",
   "execution_count": 52,
   "id": "13259446-9dc9-4a99-ad96-0110c70938f8",
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   ],
   "source": [
    "# Showing the distribution of the variable via histogram\n",
    "sns.histplot(data=df, x=\"days_from_appointment\", kde=True);"
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    "- We see that the variable is skewed to the right\n",
    "- We see that at least 25% of the appointments were scheduled on the same day of the appointment\n",
    "- We see that at least 50% of the appointments were scheduled less than 4 days ahead\n",
    "- We see that at least 75% of the appointments were scheduled less than 15 days ahead"
   ]
  },
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   "id": "c994f8e0-eea1-403c-be27-4d373d12011c",
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   },
   "source": [
    "### 🔎 Exploring the `appointment_day_of_week` Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dca3f4b2-2547-462a-a593-42350a7f73f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wednesday    25866\n",
       "Tuesday      25638\n",
       "Monday       22713\n",
       "Friday       19019\n",
       "Thursday     17246\n",
       "Saturday        39\n",
       "Name: appointment_day_of_week, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['appointment_day_of_week'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb87b0e-e926-4255-87f0-484bf7abe2fe",
   "metadata": {},
   "source": [
    "No appointments were held on Sunday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f97838a9-853f-4c0d-9519-a2840da80fcf",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 650x500 with 1 Axes>"
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   "source": [
    "sns.catplot(x='appointment_day_of_week', kind=\"count\", data=df, order=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], aspect=1.3);"
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   "id": "9ed4375d-f750-4d90-a8a1-b7cc2a018e5d",
   "metadata": {},
   "source": [
    "Appointments are scheduled (and held) between Monday to Friday (only 0.3% on Saturday). Wednesday is the most scheduled day, and Thursday is the least one. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3326377-6e72-4903-8080-e97c0ef1bd63",
   "metadata": {
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    "tags": []
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   "source": [
    "### 🔎 Exploring the `age` Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3cedfc59-9d9e-49b6-9d2c-aaba10e10668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean         37.089386\n",
       "std          23.109885\n",
       "min           0.000000\n",
       "25%          18.000000\n",
       "50%          37.000000\n",
       "75%          55.000000\n",
       "max         115.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['age'].describe()"
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  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6ff67237-653f-424c-a555-7ca1996630fd",
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    "Age is also skewed to the right, with 75% of the appointments are of people who are less than 55."
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       "  appointment_id            time_scheduled  days_from_appointment  \\\n",
       "0        5030230 2015-11-10 00:00:00+00:00                    176   \n",
       "1        5122866 2015-12-03 00:00:00+00:00                    151   \n",
       "2        5134197 2015-12-07 00:00:00+00:00                    179   \n",
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       "4        5134223 2015-12-07 00:00:00+00:00                    179   \n",
       "\n",
       "           appointment_date appointment_day_of_week   neighborhood  \\\n",
       "0 2016-05-04 00:00:00+00:00               Wednesday    Resistência   \n",
       "1 2016-05-02 00:00:00+00:00                  Monday     Vila Rubim   \n",
       "2 2016-06-03 00:00:00+00:00                  Friday  São Cristóvão   \n",
       "3 2016-06-03 00:00:00+00:00                  Friday        Maruípe   \n",
       "4 2016-06-03 00:00:00+00:00                  Friday  São Cristóvão   \n",
       "\n",
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   "source": [
    "### 🔎 Exploring the `gender` Variable"
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    "sns.set(rc={'figure.figsize':(5,5)})\n",
    "sns.countplot(x=\"gender\", data=df);"
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   "id": "58396aed-1fb3-4b14-9601-03f9492b6cfb",
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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
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   ],
   "source": [
    "plt.pie(df['gender'].value_counts(), autopct='%.0f%%', labels=['Women','Men'])\n",
    "plt.show()"
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  },
  {
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   "source": [
    "### 🔎 Exploring `did_receive_sms` Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "247aaedc-f6e3-48cd-844c-dd1a6a7791ff",
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    {
     "data": {
      "text/plain": [
       "count    110521.000000\n",
       "mean          0.321043\n",
       "std           0.466879\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           1.000000\n",
       "max           1.000000\n",
       "Name: did_receive_sms, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['did_receive_sms'].describe()"
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  {
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   "id": "ca1bbfcf-9079-4875-88ba-4938fb4b3f1e",
   "metadata": {},
   "source": [
    "50% of the scheduled appointments did not receive an SMS.\n",
    "\n",
    "Let's check the days before the appointment on which SMS has been received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7af99e12-052b-4839-84b0-2e988ee0b08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       906\n",
       "4      3054\n",
       "5      2054\n",
       "6      2684\n",
       "7      2850\n",
       "       ... \n",
       "151       1\n",
       "155       9\n",
       "169       7\n",
       "176      13\n",
       "179       7\n",
       "Name: days_from_appointment, Length: 121, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df['did_receive_sms'] == 1]['days_from_appointment'].value_counts().sort_index()"
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  },
  {
   "cell_type": "markdown",
   "id": "d82ab525-f19d-4c4d-be42-81f57806c999",
   "metadata": {},
   "source": [
    "We see that SMS did not received when appointments were scheduled less than 3 days ahead.\n",
    "\n",
    "Becuase we saw that 50% of the appointments were scheduled only 4 (or less) days in advance, we need to take this into consideration."
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  },
  {
   "cell_type": "markdown",
   "id": "c6bc8353-7e60-44e0-a1ab-13af1f0ee0e7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
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   "source": [
    "## ❓ Answering Questions"
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  },
  {
   "cell_type": "markdown",
   "id": "a737e373-f3f8-4ec0-b0fa-113a4d4e6a3c",
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   "source": [
    "### ❓ What is the proportion of no-shows?"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1e796d6d-711a-494f-87a4-44c1223c0354",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use boostrapping to estimate the proportion of no-shows\n",
    "no_shows = np.array(df['no_show'])\n",
    "\n",
    "# taking 10000 times a the data (with replacement),\n",
    "# estimating the proportions, and enter it to proporitons variable\n",
    "proportions = [np.random.choice(no_shows , no_shows.size).mean() for _ in range(10000)]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 63,
   "id": "ebe6c591-fc0c-463d-a8e6-159deff35b97",
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   "outputs": [
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       "<Figure size 600x600 with 1 Axes>"
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     "metadata": {},
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    }
   ],
   "source": [
    "# plotting the distribution of the proportions \n",
    "sns.set(rc={'figure.figsize':(6,6)})\n",
    "lower, upper = np.percentile(proportions, 2.5), np.percentile(proportions, 97.5)\n",
    "plt.axvline(lower, color='red')\n",
    "plt.axvline(upper, color='red')\n",
    "sns.histplot(proportions, stat='probability', kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "55368c3c-f001-4da0-b1c7-73777a09d8f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With 95% confidence, the percentage of no-shows is between 19.900000000000002% and 20.4%\n"
     ]
    }
   ],
   "source": [
    "# Print estimations\n",
    "print(\"With 95% confidence, the percentage of no-shows is between \" + str(round(lower, 3)*100) + \"% and \" + str(round(upper, 3)*100) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad982957-f34c-48ab-96ee-ec4445e844c2",
   "metadata": {},
   "source": [
    "We estimate there are about 20% no-shows regardless any other factor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b35944-0607-4ed1-88c0-4ca2af152413",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ❓ What factors are correlated with no-shows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb5f6ede-f801-43e3-b386-b885c43cd952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.18631962066746582, pvalue=0.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if days from appointment is correlated with no_show\n",
    "import scipy.stats as stats\n",
    "x = np.array(df['days_from_appointment'])\n",
    "y = np.array(df['no_show'])\n",
    "#calculate point-biserial correlation\n",
    "stats.pointbiserialr(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16369809-2e97-42c9-baa6-f81885e09d1f",
   "metadata": {},
   "source": [
    "There is a weak correlation between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5c47e954-e578-48c8-b2df-a50564875a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=-0.06031958954185393, pvalue=1.3200839804866788e-89)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if age is correlated with no_show\n",
    "import scipy.stats as stats\n",
    "x = np.array(df['age'])\n",
    "y = np.array(df['no_show'])\n",
    "#calculate point-biserial correlation\n",
    "stats.pointbiserialr(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee20516f-d903-4a49-86e6-503bf74b900c",
   "metadata": {},
   "source": [
    "There is a weak correlation between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "80b9534b-4b69-4eb6-aa31-af33ccfc06ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.004131879616526528, pvalue=0.16955929285365584)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if gender is correlated with no_show\n",
    "import scipy.stats as stats\n",
    "x = np.array(df['gender'].replace(['M', 'F'], [0,1]))\n",
    "y = np.array(df['no_show'])\n",
    "#calculate point-biserial correlation\n",
    "stats.pointbiserialr(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9e8ca4-a784-43d4-acf0-d5c0ec7bbff0",
   "metadata": {},
   "source": [
    "There is no correlation between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8703f80e-49d9-4173-b1aa-5f6bf3f02784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=-0.03566231882102261, pvalue=1.9219118195149742e-32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if hypertension is correlated with no_show\n",
    "import scipy.stats as stats\n",
    "x = np.array(df['has_hypertension'])\n",
    "y = np.array(df['no_show'])\n",
    "#calculate point-biserial correlation\n",
    "stats.pointbiserialr(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85abf73d-5fd3-4846-b271-95c5ed2dfc8e",
   "metadata": {},
   "source": [
    "There is a weak correlation between the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ba1508-c443-4776-8df0-533377dbd1d5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ❓ Does the proportion of no-shows among men is different than the proportion among women?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "31bea284-28fe-4311-8e93-9aee2d3cf085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  no_show\n",
       "F       0          57245\n",
       "        1          14591\n",
       "M       0          30962\n",
       "        1           7723\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will conduct a chi-square test\n",
    "\n",
    "# Show the number of shows and no-shows for men and women in the dataset\n",
    "df.groupby(['gender'])['no_show'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5a8af697-5202-4232-a1c2-f57145b39a3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tab_data = [[14591,57245],[7723,30962]]\n",
    "results = stats.chi2_contingency(tab_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "096e68ca-f441-4247-9f87-797f5651fa45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 17.2%\n"
     ]
    }
   ],
   "source": [
    "# Printing the p-value (if less than 5% we conclude that there is no difference)\n",
    "print(\"The p-value is \" + str(round(results[1], 3)*100) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708f7e1a-a429-43e1-9823-c33baa230222",
   "metadata": {},
   "source": [
    "**Conclusion:** There is no evidence that there is a difference between men and women in not-showing to an appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac5bc78-d505-49bb-b761-9dd8e3e90580",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### ❓ Is there a difference in no-show proportions between people who got an SMS reminder and people who don't?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6833af3f-2b63-4344-88f4-6643de6871ef",
   "metadata": {},
   "source": [
    "Because we saw earlier that SMS is not received when appointments are scheduled less than 3 days ahead, I have decided to analyze data only for days (`days_from_appointmnet`) were both SMS have been received and not have been received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a002eeb5-a056-484a-b452-16fd13200aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Days when SMS was received\n",
    "sms_days = df[df['did_receive_sms']==1]['days_from_appointment'].value_counts().sort_index()\n",
    "# Days when SMS wasn't received\n",
    "no_sms_days = df[df['did_receive_sms']==0]['days_from_appointment'].value_counts().sort_index()\n",
    "\n",
    "# Prepare a list of days (indecies) where sms received and not received\n",
    "lst_1 = list(sms_days.index)\n",
    "lst_2 = list(no_sms_days.index)\n",
    "lst_days = []\n",
    "for i in lst_1:\n",
    "    if i in lst_2:\n",
    "        lst_days.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3eff01f8-af6a-4574-928e-4762f20f7174",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5134220</td>\n",
       "      <td>2015-12-07 00:00:00+00:00</td>\n",
       "      <td>179</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Maruípe</td>\n",
       "      <td>31899595421534</td>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
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       "      <td>179</td>\n",
       "      <td>2016-06-03 00:00:00+00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>São Cristóvão</td>\n",
       "      <td>9582232334148</td>\n",
       "      <td>80</td>\n",
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       "      <th>5</th>\n",
       "      <td>5134224</td>\n",
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       "      <td>Friday</td>\n",
       "      <td>São Cristóvão</td>\n",
       "      <td>3516253533716</td>\n",
       "      <td>74</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  appointment_id            time_scheduled  days_from_appointment  \\\n",
       "0        5030230 2015-11-10 00:00:00+00:00                    176   \n",
       "2        5134197 2015-12-07 00:00:00+00:00                    179   \n",
       "3        5134220 2015-12-07 00:00:00+00:00                    179   \n",
       "4        5134223 2015-12-07 00:00:00+00:00                    179   \n",
       "5        5134224 2015-12-07 00:00:00+00:00                    179   \n",
       "\n",
       "           appointment_date appointment_day_of_week   neighborhood  \\\n",
       "0 2016-05-04 00:00:00+00:00               Wednesday    Resistência   \n",
       "2 2016-06-03 00:00:00+00:00                  Friday  São Cristóvão   \n",
       "3 2016-06-03 00:00:00+00:00                  Friday        Maruípe   \n",
       "4 2016-06-03 00:00:00+00:00                  Friday  São Cristóvão   \n",
       "5 2016-06-03 00:00:00+00:00                  Friday  São Cristóvão   \n",
       "\n",
       "        patient_id  age gender  has_hypertension  num_of_disabilities  \\\n",
       "0  832256398961987   51      F                 0                    0   \n",
       "2    1216586867796   27      F                 0                    0   \n",
       "3   31899595421534   48      F                 1                    0   \n",
       "4    9582232334148   80      F                 1                    0   \n",
       "5    3516253533716   74      F                 1                    0   \n",
       "\n",
       "   has_diabetes  is_alcoholic  has_scholarship  did_receive_sms  no_show  \n",
       "0             0             0                0                1        0  \n",
       "2             0             0                1                1        1  \n",
       "3             1             0                0                1        0  \n",
       "4             1             0                0                1        0  \n",
       "5             1             0                0                1        0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare a new DataFrame with the desired data for analysis\n",
    "df_test = df[df['days_from_appointment'].isin(lst_days)]\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5234a728-fbf8-47cd-a4cb-8005581b274a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "did_receive_sms  no_show\n",
       "0                0          16494\n",
       "                 1           8016\n",
       "1                0          25668\n",
       "                 1           9770\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
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    }
   ],
   "source": [
    "# Check the frequencies for the four groups for chi-square test\n",
    "df_test.groupby(['did_receive_sms'])['no_show'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "abb94feb-6869-4060-acc8-47e63bcef112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.0%\n"
     ]
    }
   ],
   "source": [
    "# Perform a chi-square test\n",
    "tab_data = [[16494,8016],[25668,9770]]\n",
    "results = stats.chi2_contingency(tab_data)\n",
    "\n",
    "# Printing the p-value (if higher than 5% we conclude that there is no difference)\n",
    "print(\"The p-value is \" + str(round(results[1], 3)*100) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92dec42a-44ff-4ba8-bc5b-06556274dc5d",
   "metadata": {},
   "source": [
    "**Conclusion:** There is a statistical evidence for difference in proportion of no-shows between people who got and people who didn't get an SMS.\n",
    "\n",
    "If we calculate the proportions we see 32.7% no-shows for appointments where people didn't get an SMS and 27.6% for appointments were people got."
   ]
  },
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   "execution_count": 76,
   "id": "6a7ff747-d425-4fec-92af-1b733af6f482",
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       "<Figure size 600x600 with 1 Axes>"
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   ],
   "source": [
    "# Scatter plot the data to compare the proportion of no-shows between those who receive SMS and those who don't on the tested DataFrame\n",
    "scat = df_test.groupby(['days_from_appointment','did_receive_sms']).mean()\n",
    "sns.scatterplot(data=scat, x=\"days_from_appointment\", y=\"no_show\", hue='did_receive_sms');"
   ]
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   "id": "0d54dfaa-fd5e-4f2a-99db-4cd10ff5fab0",
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   "source": [
    "We can clearly see that for appointments that were scheduled up to 75 days ahead, the proportion of no-shows for those who received an SMS and those who don't is lower (the orange dots are lower on the Y axis than the blue dots)."
   ]
  },
  {
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   "id": "6c6242f1-b3d6-4f0a-8faa-b64013dcb3db",
   "metadata": {
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    "tags": []
   },
   "source": [
    "### ❓ How can we predict no-shows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "07da168a-893d-4533-900f-53703cbe8e5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.487885\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
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       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>no_show</td>     <th>  No. Observations:  </th>  <td>110521</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>110519</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 12 Sep 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.03009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:05:13</td>     <th>  Log-Likelihood:    </th> <td> -53922.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -55595.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>             <td>   -1.6779</td> <td>    0.010</td> <td> -175.079</td> <td> 0.000</td> <td>   -1.697</td> <td>   -1.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>days_from_appointment</th> <td>    0.0259</td> <td>    0.000</td> <td>   57.537</td> <td> 0.000</td> <td>    0.025</td> <td>    0.027</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                no_show   No. Observations:               110521\n",
       "Model:                          Logit   Df Residuals:                   110519\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Mon, 12 Sep 2022   Pseudo R-squ.:                 0.03009\n",
       "Time:                        13:05:13   Log-Likelihood:                -53922.\n",
       "converged:                       True   LL-Null:                       -55595.\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "=========================================================================================\n",
       "                            coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "intercept                -1.6779      0.010   -175.079      0.000      -1.697      -1.659\n",
       "days_from_appointment     0.0259      0.000     57.537      0.000       0.025       0.027\n",
       "=========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic regression\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Create a copy of the DataFrame\n",
    "df_log = df.copy()\n",
    "\n",
    "df_log['intercept'] = 1\n",
    "logit_mod = sm.Logit(df_log['no_show'], df_log[['intercept','days_from_appointment']])\n",
    "result = logit_mod.fit()\n",
    "\n",
    "result.summary()"
   ]
  }
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