Analysis_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: pandas in /opt/conda/lib/python3.6/site-packages (1.1.5)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /opt/conda/lib/python3.6/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.15.4 in /opt/conda/lib/python3.6/site-packages (from pandas) (1.19.5)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas) (2017.3)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in /opt/conda/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas) (1.11.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import useful libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>show</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08</td>\n",
       "      <td>2016-04-29 00:00:00</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>Yes</td>\n",
       "      <td>old</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>2016-04-29 16:08:27</td>\n",
       "      <td>2016-04-29 00:00:00</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>Yes</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04</td>\n",
       "      <td>2016-04-29 00:00:00</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>Yes</td>\n",
       "      <td>old</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>2016-04-29 17:29:31</td>\n",
       "      <td>2016-04-29 00:00:00</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>Yes</td>\n",
       "      <td>child</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>2016-04-29 16:07:23</td>\n",
       "      <td>2016-04-29 00:00:00</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>Yes</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patientid  appointmentid gender         scheduledday  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29 18:38:08   \n",
       "1  5.589978e+14        5642503      M  2016-04-29 16:08:27   \n",
       "2  4.262962e+12        5642549      F  2016-04-29 16:19:04   \n",
       "3  8.679512e+11        5642828      F  2016-04-29 17:29:31   \n",
       "4  8.841186e+12        5642494      F  2016-04-29 16:07:23   \n",
       "\n",
       "        appointmentday  age      neighbourhood  scholarship  hipertension  \\\n",
       "0  2016-04-29 00:00:00   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29 00:00:00   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29 00:00:00   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29 00:00:00    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29 00:00:00   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received show age_group  \n",
       "0         0           0        0             0  Yes       old  \n",
       "1         0           0        0             0  Yes     adult  \n",
       "2         0           0        0             0  Yes       old  \n",
       "3         0           0        0             0  Yes     child  \n",
       "4         1           0        0             0  Yes     adult  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load in the dataset\n",
    "df_show = pd.read_csv('clean_appointments.csv')\n",
    "df_show.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110526 entries, 0 to 110525\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   patientid       110526 non-null  float64\n",
      " 1   appointmentid   110526 non-null  int64  \n",
      " 2   gender          110526 non-null  object \n",
      " 3   scheduledday    110526 non-null  object \n",
      " 4   appointmentday  110526 non-null  object \n",
      " 5   age             110526 non-null  int64  \n",
      " 6   neighbourhood   110526 non-null  object \n",
      " 7   scholarship     110526 non-null  int64  \n",
      " 8   hipertension    110526 non-null  int64  \n",
      " 9   diabetes        110526 non-null  int64  \n",
      " 10  alcoholism      110526 non-null  int64  \n",
      " 11  handcap         110526 non-null  int64  \n",
      " 12  sms_received    110526 non-null  int64  \n",
      " 13  show            110526 non-null  object \n",
      " 14  age_group       110526 non-null  object \n",
      "dtypes: float64(1), int64(8), object(6)\n",
      "memory usage: 12.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_show.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix data type issue\n",
    "> The dataset has previously been cleaned, but it's data type formatting was lost in converting from a dataframe to CSV, so I'll perform data type transformation on the new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_show['patientid'] = df_show['patientid'].astype('str')\n",
    "type(df_show['patientid'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_show['appointmentid'] = df_show['appointmentid'].astype('str')\n",
    "type(df_show['appointmentid'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### fix problem with scheduled day and appointment day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert feature to datetime for easy manipulation of data\n",
    "df_show.scheduledday = pd.to_datetime(df_show.scheduledday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_show.appointmentday = pd.to_datetime(df_show.appointmentday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### convert categorical variables from other types to category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_var = ['gender','scholarship','hipertension',\n",
    "          'diabetes', 'alcoholism', 'handcap',\n",
    "          'sms_received', 'show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for val in cat_var:\n",
    "    df_show[val] = df_show[val].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 3: What percentage of persons showed up during the period that the research lasted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110526, 15)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_show.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = df_show['show'].value_counts().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Plot of the percentage of Patients that attended vs those Absent')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc56ea498d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fmt(x):\n",
    "    return '{:.2f}%\\n{:.0f}'.format(x, total*x/100)\n",
    "\n",
    "plt.pie(df_show['show'].value_counts().values, labels=df_show['show'].value_counts().index, autopct=fmt);\n",
    "plt.title('Plot of the percentage of Patients that attended vs those Absent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### From the above, we see that:\n",
    "\n",
    "> 79.81% of persons showed up during the entire phase of the research.\n",
    "\n",
    "> 20.19% of persons didn't keep their appointment with the medical personnel."
   ]
  }
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