Variables_cuantitativas_cualitativas_diabetic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis Preliminar de Datasets Diabetic_data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - **Primera opción:** diabetic_data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  - **Revisar su estructura y clasificar las variables**\n",
    "  \n",
    "### Cualitativas:\n",
    "* **encounter_id:** Código de identificación según llegada del paciente.\n",
    "* **patient_nbr:** Código de cama odontológica del paciente.\n",
    "* **race:** raza del paciente.\n",
    "* **gender:** genero del paciente.\n",
    "* **discharge_disposition_id:** código de tipo frecuencia de secreción. \n",
    "* **admission_source_id:** código,  tipo de precedencia del paciente; \n",
    "                1\tDischarged to home\n",
    "                2   Discharged/transferred to another short term hospital\n",
    "                3\tDischarged/transferred to SNF\n",
    "                4\tDischarged/transferred to ICF\n",
    "                5\tDischarged/transferred to another type of inpatient care institution\n",
    "                6\tDischarged/transferred to home with home health service\n",
    "                7\tLeft AMA\n",
    "                8\tDischarged/transferred to home under care of Home IV provider\n",
    "                9\tAdmitted as an inpatient to this hospital\n",
    "                10\tNeonate discharged to another hospital for neonatal aftercare\n",
    "                11\tExpired\n",
    "                12\tStill patient or expected to return for outpatient services\n",
    "                13\tHospice / home\n",
    "                14\tHospice / medical facility\n",
    "                15\tDischarged/transferred within this institution to Medicare approved swing bed\n",
    "                16\tDischarged/transferred/referred another institution for outpatient services\n",
    "                17\tDischarged/transferred/referred to this institution for outpatient services\n",
    "                18\tNULL\n",
    "                19\tExpired at home. Medicaid only, hospice.\n",
    "                20\tExpired in a medical facility. Medicaid only, hospice.\n",
    "                21\tExpired, place unknown. Medicaid only, hospice.\n",
    "                22\tDischarged/transferred to another rehab fac including rehab units of a hospital.\n",
    "                23\tDischarged/transferred to a long term care hospital.\n",
    "                24\tDischarged/transferred to a nursing facility certified under Medicaid but not certified under Medicare.\n",
    "                25\tNot Mapped\n",
    "                26\tUnknown/Invalid\n",
    "                30\tDischarged/transferred to another Type of Health Care Institution not defined Elsewhere\n",
    "                27\tDischarged/transferred to a federal health care facility.\n",
    "                28\tDischarged/transferred/referred to a psychiatric hospital of psychiatric distinct part unit of a hospital\n",
    "                29\tDischarged/transferred to a Critical Access Hospital (CAH).\n",
    " \n",
    "* **payer_code:** código de pago del paciente.\n",
    "* **medical_specialty:** Especialidad médica que el paciente requirió.\n",
    "* **max_glu_serum:** docis maxima de gluten que el paciente puede ingerir.\n",
    "* **insulin:** estabilidad de los niveles de insulina. \n",
    "* **glyburide-metformin:** Uso o no uso de este tipo de medicamento.\n",
    "* **glipizide-metformin:** Uso o no uno de este tipo de medicamento.\n",
    "* **change:** Cambios en el uso de medicamento. \n",
    "* **diabetesMed:** uso de medicina para la diabetes.\n",
    "\n",
    "\t\n",
    "### Cuantitativas:\n",
    "* **age:** rango de edad del paciente.\n",
    "* **weight:** peso del paciente.\n",
    "* **admission_type_id:** Sala a la que ingreso el paciente; \n",
    "            (1)Emergency\n",
    "            (2)Urgent\n",
    "            (3)Elective\n",
    "            (4)Newborn\n",
    "            (5)Not Available\n",
    "            (6)NULL\n",
    "            (7)Trauma Center\n",
    "            (8)Not Mapped.\n",
    "* **time_in_hospital:** días de permanencia en el hospital.\n",
    "* **num_lab_procedures:** cantidad de laboratorios realizados.\n",
    "* **num_procedures:** cantidad de procedimientos quirurgicos.\n",
    "* **num_medications:** cantidad de medicamentos.\n",
    "* **number_outpatient:** cantidad de consultas con el médico general.\n",
    "* **number_emergency:** número de emergencias.\n",
    "* **number_inpatient:** tipo de paciente interno \n",
    "* **diag_1:**\n",
    "* **diag_2:**\n",
    "* **diag_3:**\n",
    "* **number_diagnoses:** número que indica la intensidad de la diabetes diagnosticada.  \n",
    "* **readmitted** Cantidad aproximada de las veces que el paciente a sido interno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  - **Realizar un resumen estadístico de todas las variables**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Datos = pd.read_csv(\"diabetic_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender      age weight  \\\n",
       "0       2278392      8222157        Caucasian  Female   [0-10)      ?   \n",
       "1        149190     55629189        Caucasian  Female  [10-20)      ?   \n",
       "2         64410     86047875  AfricanAmerican  Female  [20-30)      ?   \n",
       "3        500364     82442376        Caucasian    Male  [30-40)      ?   \n",
       "4         16680     42519267        Caucasian    Male  [40-50)      ?   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "2                  1                         1                    7   \n",
       "3                  1                         1                    7   \n",
       "4                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital    ...     citoglipton insulin  glyburide-metformin  \\\n",
       "0                 1    ...              No      No                   No   \n",
       "1                 3    ...              No      Up                   No   \n",
       "2                 2    ...              No      No                   No   \n",
       "3                 2    ...              No      Up                   No   \n",
       "4                 1    ...              No  Steady                   No   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "0                   No                        No                       No   \n",
       "1                   No                        No                       No   \n",
       "2                   No                        No                       No   \n",
       "3                   No                        No                       No   \n",
       "4                   No                        No                       No   \n",
       "\n",
       "   metformin-pioglitazone  change diabetesMed readmitted  \n",
       "0                      No      No          No         NO  \n",
       "1                      No      Ch         Yes        >30  \n",
       "2                      No      No         Yes         NO  \n",
       "3                      No      Ch         Yes         NO  \n",
       "4                      No      Ch         Yes         NO  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Datos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>number_diagnoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.017660e+05</td>\n",
       "      <td>1.017660e+05</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.652016e+08</td>\n",
       "      <td>5.433040e+07</td>\n",
       "      <td>2.024006</td>\n",
       "      <td>3.715642</td>\n",
       "      <td>5.754437</td>\n",
       "      <td>4.395987</td>\n",
       "      <td>43.095641</td>\n",
       "      <td>1.339730</td>\n",
       "      <td>16.021844</td>\n",
       "      <td>0.369357</td>\n",
       "      <td>0.197836</td>\n",
       "      <td>0.635566</td>\n",
       "      <td>7.422607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026403e+08</td>\n",
       "      <td>3.869636e+07</td>\n",
       "      <td>1.445403</td>\n",
       "      <td>5.280166</td>\n",
       "      <td>4.064081</td>\n",
       "      <td>2.985108</td>\n",
       "      <td>19.674362</td>\n",
       "      <td>1.705807</td>\n",
       "      <td>8.127566</td>\n",
       "      <td>1.267265</td>\n",
       "      <td>0.930472</td>\n",
       "      <td>1.262863</td>\n",
       "      <td>1.933600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.252200e+04</td>\n",
       "      <td>1.350000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.496119e+07</td>\n",
       "      <td>2.341322e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.523890e+08</td>\n",
       "      <td>4.550514e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.302709e+08</td>\n",
       "      <td>8.754595e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.438672e+08</td>\n",
       "      <td>1.895026e+08</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       encounter_id   patient_nbr  admission_type_id  \\\n",
       "count  1.017660e+05  1.017660e+05      101766.000000   \n",
       "mean   1.652016e+08  5.433040e+07           2.024006   \n",
       "std    1.026403e+08  3.869636e+07           1.445403   \n",
       "min    1.252200e+04  1.350000e+02           1.000000   \n",
       "25%    8.496119e+07  2.341322e+07           1.000000   \n",
       "50%    1.523890e+08  4.550514e+07           1.000000   \n",
       "75%    2.302709e+08  8.754595e+07           3.000000   \n",
       "max    4.438672e+08  1.895026e+08           8.000000   \n",
       "\n",
       "       discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
       "count             101766.000000        101766.000000     101766.000000   \n",
       "mean                   3.715642             5.754437          4.395987   \n",
       "std                    5.280166             4.064081          2.985108   \n",
       "min                    1.000000             1.000000          1.000000   \n",
       "25%                    1.000000             1.000000          2.000000   \n",
       "50%                    1.000000             7.000000          4.000000   \n",
       "75%                    4.000000             7.000000          6.000000   \n",
       "max                   28.000000            25.000000         14.000000   \n",
       "\n",
       "       num_lab_procedures  num_procedures  num_medications  number_outpatient  \\\n",
       "count       101766.000000   101766.000000    101766.000000      101766.000000   \n",
       "mean            43.095641        1.339730        16.021844           0.369357   \n",
       "std             19.674362        1.705807         8.127566           1.267265   \n",
       "min              1.000000        0.000000         1.000000           0.000000   \n",
       "25%             31.000000        0.000000        10.000000           0.000000   \n",
       "50%             44.000000        1.000000        15.000000           0.000000   \n",
       "75%             57.000000        2.000000        20.000000           0.000000   \n",
       "max            132.000000        6.000000        81.000000          42.000000   \n",
       "\n",
       "       number_emergency  number_inpatient  number_diagnoses  \n",
       "count     101766.000000     101766.000000     101766.000000  \n",
       "mean           0.197836          0.635566          7.422607  \n",
       "std            0.930472          1.262863          1.933600  \n",
       "min            0.000000          0.000000          1.000000  \n",
       "25%            0.000000          0.000000          6.000000  \n",
       "50%            0.000000          0.000000          8.000000  \n",
       "75%            0.000000          1.000000          9.000000  \n",
       "max           76.000000         21.000000         16.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Datos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        101766 non-null object\n",
      "gender                      101766 non-null object\n",
      "age                         101766 non-null object\n",
      "weight                      101766 non-null object\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
      "time_in_hospital            101766 non-null int64\n",
      "payer_code                  101766 non-null object\n",
      "medical_specialty           101766 non-null object\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101766 non-null object\n",
      "diag_2                      101766 non-null object\n",
      "diag_3                      101766 non-null object\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null object\n",
      "A1Cresult                   101766 non-null object\n",
      "metformin                   101766 non-null object\n",
      "repaglinide                 101766 non-null object\n",
      "nateglinide                 101766 non-null object\n",
      "chlorpropamide              101766 non-null object\n",
      "glimepiride                 101766 non-null object\n",
      "acetohexamide               101766 non-null object\n",
      "glipizide                   101766 non-null object\n",
      "glyburide                   101766 non-null object\n",
      "tolbutamide                 101766 non-null object\n",
      "pioglitazone                101766 non-null object\n",
      "rosiglitazone               101766 non-null object\n",
      "acarbose                    101766 non-null object\n",
      "miglitol                    101766 non-null object\n",
      "troglitazone                101766 non-null object\n",
      "tolazamide                  101766 non-null object\n",
      "examide                     101766 non-null object\n",
      "citoglipton                 101766 non-null object\n",
      "insulin                     101766 non-null object\n",
      "glyburide-metformin         101766 non-null object\n",
      "glipizide-metformin         101766 non-null object\n",
      "glimepiride-pioglitazone    101766 non-null object\n",
      "metformin-rosiglitazone     101766 non-null object\n",
      "metformin-pioglitazone      101766 non-null object\n",
      "change                      101766 non-null object\n",
      "diabetesMed                 101766 non-null object\n",
      "readmitted                  101766 non-null object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "Datos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Datos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - **Después del resumen determinar 10 variables de interés, describirlas por separado (structure y summarize)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cualitativas:\n",
    "* **Race:** raza del paciente.\n",
    "* **Gender:** genero del paciente.\n",
    "* **admission_source_id:** código, tipo de precedencia del paciente.\n",
    "* **medical_specialty:** Especialidad médica que el paciente requirió.\n",
    "\n",
    "### Cuantitativas:\n",
    "* **Age:** rango de edad del paciente.\n",
    "* **time_in_hospital:** días de permanencia en el hospital.\n",
    "* **num_lab_procedures:** cantidad de laboratorios realizados.\n",
    "* **num_medications:** cantidad de medicamentos.\n",
    "* **number_outpatient:** cantidad de consultas con el médico general.\n",
    "* **readmitted** Cantidad aproximada de las veces que el paciente a sido interno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>age</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              race  gender  admission_type_id         medical_specialty  \\\n",
       "0        Caucasian  Female                  6  Pediatrics-Endocrinology   \n",
       "1        Caucasian  Female                  1                         ?   \n",
       "2  AfricanAmerican  Female                  1                         ?   \n",
       "3        Caucasian    Male                  1                         ?   \n",
       "4        Caucasian    Male                  1                         ?   \n",
       "\n",
       "       age  time_in_hospital  num_lab_procedures  num_medications  \\\n",
       "0   [0-10)                 1                  41                1   \n",
       "1  [10-20)                 3                  59               18   \n",
       "2  [20-30)                 2                  11               13   \n",
       "3  [30-40)                 2                  44               16   \n",
       "4  [40-50)                 1                  51                8   \n",
       "\n",
       "   number_outpatient readmitted  \n",
       "0                  0         NO  \n",
       "1                  0        >30  \n",
       "2                  2         NO  \n",
       "3                  0         NO  \n",
       "4                  0         NO  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Importantes = Datos[['race', 'gender', 'admission_type_id', 'medical_specialty', 'age', 'time_in_hospital', 'num_lab_procedures', 'num_medications', 'number_outpatient', 'readmitted']]\n",
    "Importantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "      <td>101766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.024006</td>\n",
       "      <td>4.395987</td>\n",
       "      <td>43.095641</td>\n",
       "      <td>16.021844</td>\n",
       "      <td>0.369357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.445403</td>\n",
       "      <td>2.985108</td>\n",
       "      <td>19.674362</td>\n",
       "      <td>8.127566</td>\n",
       "      <td>1.267265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       admission_type_id  time_in_hospital  num_lab_procedures  \\\n",
       "count      101766.000000     101766.000000       101766.000000   \n",
       "mean            2.024006          4.395987           43.095641   \n",
       "std             1.445403          2.985108           19.674362   \n",
       "min             1.000000          1.000000            1.000000   \n",
       "25%             1.000000          2.000000           31.000000   \n",
       "50%             1.000000          4.000000           44.000000   \n",
       "75%             3.000000          6.000000           57.000000   \n",
       "max             8.000000         14.000000          132.000000   \n",
       "\n",
       "       num_medications  number_outpatient  \n",
       "count    101766.000000      101766.000000  \n",
       "mean         16.021844           0.369357  \n",
       "std           8.127566           1.267265  \n",
       "min           1.000000           0.000000  \n",
       "25%          10.000000           0.000000  \n",
       "50%          15.000000           0.000000  \n",
       "75%          20.000000           0.000000  \n",
       "max          81.000000          42.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Importantes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  - **Gráficas**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.39900000e+04,   1.84800000e+04,   1.88690000e+04,\n",
       "          0.00000000e+00,   1.00000000e+01,   4.78500000e+03,\n",
       "          0.00000000e+00,   5.29100000e+03,   2.10000000e+01,\n",
       "          3.20000000e+02]),\n",
       " array([ 1. ,  1.7,  2.4,  3.1,  3.8,  4.5,  5.2,  5.9,  6.6,  7.3,  8. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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12rx5s6LRqAoLC7Vs2bJpXxYAgNnGEYvFYnYPMRllz/n1yZyldo8xaaOXzshf+YCWLPFO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\nwf4fZWYkw7GikNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdce36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Importantes.admission_type_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 31432.,  17756.,  13924.,  17505.,   5859.,   4391.,   5344.,\n",
       "          1855.,   1448.,   2252.]),\n",
       " array([  1. ,   2.3,   3.6,   4.9,   6.2,   7.5,   8.8,  10.1,  11.4,\n",
       "         12.7,  14. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ab3YFAACX6fKe+pYtW/TZZ5+pvLxcmzdvVlRUlFasWKF169bJ7/dr9OjRmjFjhqKiopST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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcfc9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Importantes.time_in_hospital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.35500000e+03,   8.91000000e+02,   2.13700000e+03,\n",
       "          1.93500000e+03,   1.74800000e+03,   2.47000000e+03,\n",
       "          2.20600000e+03,   3.71700000e+03,   3.74200000e+03,\n",
       "          3.94400000e+03,   7.62500000e+03,   6.51500000e+03,\n",
       "          7.03400000e+03,   7.06100000e+03,   8.15400000e+03,\n",
       "          5.56500000e+03,   5.56300000e+03,   5.07900000e+03,\n",
       "          6.14200000e+03,   3.77400000e+03,   3.12700000e+03,\n",
       "          3.03400000e+03,   1.60300000e+03,   1.11200000e+03,\n",
       "          8.01000000e+02,   6.74000000e+02,   2.66000000e+02,\n",
       "          1.74000000e+02,   1.47000000e+02,   9.40000000e+01,\n",
       "          3.40000000e+01,   1.50000000e+01,   1.40000000e+01,\n",
       "          3.00000000e+00,   5.00000000e+00,   1.00000000e+00,\n",
       "          2.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          2.00000000e+00]),\n",
       " array([   1.   ,    4.275,    7.55 ,   10.825,   14.1  ,   17.375,\n",
       "          20.65 ,   23.925,   27.2  ,   30.475,   33.75 ,   37.025,\n",
       "          40.3  ,   43.575,   46.85 ,   50.125,   53.4  ,   56.675,\n",
       "          59.95 ,   63.225,   66.5  ,   69.775,   73.05 ,   76.325,\n",
       "          79.6  ,   82.875,   86.15 ,   89.425,   92.7  ,   95.975,\n",
       "          99.25 ,  102.525,  105.8  ,  109.075,  112.35 ,  115.625,\n",
       "         118.9  ,  122.175,  125.45 ,  128.725,  132.   ]),\n",
       " <a list of 40 Patch objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TqqurVV9fr7S0NHm9XjU2NkqSOjo61N7erszMTFVXV6usrEyRSES1tbUaGhqSZVlqampS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\nV+SnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfa6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Importantes.num_lab_procedures, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.04900000e+03,   1.25530000e+04,   2.20580000e+04,\n",
       "          2.30150000e+04,   1.72110000e+04,   1.06330000e+04,\n",
       "          6.16100000e+03,   3.18400000e+03,   1.63200000e+03,\n",
       "          9.09000000e+02,   5.01000000e+02,   3.14000000e+02,\n",
       "          2.13000000e+02,   1.42000000e+02,   9.40000000e+01,\n",
       "          5.10000000e+01,   3.10000000e+01,   1.00000000e+01,\n",
       "          3.00000000e+00,   2.00000000e+00]),\n",
       " array([  1.,   5.,   9.,  13.,  17.,  21.,  25.,  29.,  33.,  37.,  41.,\n",
       "         45.,  49.,  53.,  57.,  61.,  65.,  69.,  73.,  77.,  81.]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PBADA0NRj1Ddt2qSysjIFg0FJ0tq1a1VcXKzNmzeru7tbO3fuVGtrq6qrq1VTU6NNmzap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\n/wOqxdicX1rkoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf28630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Importantes.num_medications, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.00309000e+05,   1.08900000e+03,   2.12000000e+02,\n",
       "          9.40000000e+01,   2.30000000e+01,   1.90000000e+01,\n",
       "          8.00000000e+00,   2.00000000e+00,   6.00000000e+00,\n",
       "          4.00000000e+00]),\n",
       " array([  0. ,   4.2,   8.4,  12.6,  16.8,  21. ,  25.2,  29.4,  33.6,\n",
       "         37.8,  42. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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INuZI5GYlocf4hYWF+t3vfidJOn36tP72t79pypQpOnTokCRp3759KiwsVH5+vtrb29Xb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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcda09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Importantes.number_outpatient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\linda\\Anaconda2\\lib\\site-packages\\seaborn\\categorical.py:2125: UserWarning: The boxplot API has been changed. Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xca8d9b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNm2q48eP649//KPPFc5t27Zp6tSpZywHysvLlZqaqjffPHUxoqSkJM2bN0+L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\nCGQAACzw/2QENMUu18IQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca8d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro = Importantes.race != '?'\n",
    "RACE = Importantes[filtro]\n",
    "sns.boxplot(RACE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd848b70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBg2KAQMGxPLlyyMiYuXKlVFVVRUVFRXx2muvxaWXXhrLly+Pfv36xdSpU+Pw4cMxceLE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8bea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(Importantes.admission_type_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd85af60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nPve5WL58eSxevDhWr14dhw8fjm7dusX8+fPjfe97X4e/XBwqjZd/qXhz5syJ/v37x+zZ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\nAJL8H31cTZOPZ5HeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9ba7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(Importantes.time_in_hospital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdc293c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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37e93qK+/pKREn3zyiVatWqXFixdr9uzZYbX+2NhYff755xo2bJgyMjKUlpYWFj//d955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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdafd278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(Importantes.num_lab_procedures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9d06780>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdea0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(Importantes.num_medications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc6cd710>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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84Q/p06fPGm/7yiuv5KCDDsovf/nLJMmvf/3r3HDDDfnmN7+ZH//4x12322yzzXLzzTfn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\nVQ/CC1AlAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda8c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(Importantes.number_outpatient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd098c50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiIh/CuQg2XGgBoCxCmQREekEBXKQ7DhQjd1mZdSwBLNLERGREKBADoKGZhdf\nljUyalgCjgib2eWIiEgIUCAHwc7C9uFqXe4kIiKdpUAOgq+uP9bxYxER6RwFchDs+rKWaKedzEFx\nZpciIiIhQoEcYG0uL+U1LWQMisVq1XSZIiLSOQrkADtU1QRAWkqsyZWIiEgoUSAHWFFFIwBpKTEm\nVyIiIqFEgRxgxRXtPeRhyeohi4hI5ymQA6y4sj2QhyarhywiIp2nQA6w4opGkuKdREfazS5FRERC\niAI5gBpb3NQ2ukjTcLWIiHSRAjmADh0erh6mE7pERKSLFMgBVKwzrEVEpJsUyAFUdLiHrCFrERHp\nKgVyABVXNGGxwJCB0WaXIiIiIUaBHCCGYVBc0UjqgGgtuSgiIl2mQA6QuiYXTa0ehun6YxER6Qa/\ngWwYBvPnzycvL48bb7yRgwcPnnS/+++/n0cffTTgBYaKIzN06YQuERHpDr+BvHbtWlwuF8uWLWPe\nvHksXrz4hH2WLVvGnj17glJgqPhqDmud0CUiIl3nN5A3b95MTk4OABMmTCA/P/+Y7Z999hnbt28n\nLy8vOBWGiI4esoasRUSkG/wGcmNjI3FxcR237XY7Pp8PgIqKCh5//HHuv/9+DMMIXpUhoLiyEbvN\nQuqAKLNLERGREOR3wuXY2Fiampo6bvt8PqzW9hx/8803qa2t5dZbb6WiooK2tjbOOOMMrrvuutM+\nZ0pK3Gm3h4oj7TAMg5KqZoalxjFkcILJVXVNuP0sQl04tCMc2gDh0Y5waAOETzv88RvIkyZN4p13\n3uHKK69ky5YtZGdnd2ybO3cuc+fOBeDVV1+loKDAbxgDVFQ09KDkviElJa6jHfVNLlpdXpLinCHV\ntqPbEMrUjr4jHNoA4dGOcGgDhFc7/PEbyNOnT2fDhg0dx4gXL17M6tWraWlpITc3t+dVhoGK2hYA\nUhIjTa5ERERCld9AtlgsLFy48Jj7srKyTthv5syZgasqxFTUtQdycoKOH4uISPdoYpAAqKhtBSAl\nUYEsIiLdo0AOgEoNWYuISA8pkAPgyDHk5AQFsoiIdI8COQAqaltJjHUQYdeiEiIi0j0K5B7yeH1U\nN7Tq+LGIiPSIArmHqutbMQydYS0iIj2jQO6hirojZ1jr+LGIiHSfArmHvpoURD1kERHpPgVyD1Xq\nGmQREQkABXIP6ZInEREJBAVyD1XWtWC3WUiMc5pdioiIhDAFcg9V1LYyMCEKq8VidikiIhLCFMg9\n0NLmobHbCVCiAAAO50lEQVTFrTOsRUSkxxTIPdBxhrWuQRYRkR5SIPdAZZ3OsBYRkcBQIPeAzrAW\nEZFAUSD3gK5BFhGRQFEg90BFndZBFhGRwFAg90BFbQsxkXaiIyPMLkVEREKcArmbDMOgsq5VqzyJ\niEhAKJC7qbahDbfHpxO6REQkIBTI3XTkDOuBCmQREQkABXI3VdS0B3JSvAJZRER6ToHcTRW1zQAM\njNeiEiIi0nMK5G460kPWkLWIiASCArmbjhxD1pC1iIgEggK5mypqmomwW4mL0jXIIiLScwrkbqqo\nbSEpPhKL1kEWEZEAUCB3g8vtpa7RpRO6REQkYBTI3VDd0Abo+LGIiASOArkbqurbV3kaqEAWEZEA\nUSB3Q3VdeyAnachaREQCRIHcDeohi4hIoCmQu6G6vv0YsiYFERGRQFEgd8ORHnJSnIasRUQkMBTI\n3VBV30pinJMIu83sUkREJEwokLvIZxhU17eRkhhldikiIhJGFMhd1NDsxuP1kTJAgSwiIoGjQO6i\n6sPHj1MSo02uREREwokCuYuqDl+DrB6yiIgEkgK5i77qISuQRUQkcBTIXVR1+Bpk9ZBFRCSQFMhd\ndKSHnDpAx5BFRCRwFMhdVFXfSoTdSnyMw+xSREQkjCiQu6i6vpWk+EgsFovZpYiISBhRIHeBy+2l\nvtnNQK3yJCIiAaZA7oLqhvYTupK0ypOIiASYArkLKutaAEjWKk8iIhJgCuQuODIpiAJZREQCTYHc\nBZWHA3mghqxFRCTAFMhd8FUPWZOCiIhIYCmQu6CyrhWb1UJinK5BFhGRwFIgd0FlXQsD4pzYrPq2\niYhIYClZOsnt8VHb6NIJXSIiEhR2fzsYhsGCBQvYvXs3DoeDRYsWkZ6e3rF99erVvPjii9jtdrKz\ns1mwYEEw6zVNdYOOH4uISPD47SGvXbsWl8vFsmXLmDdvHosXL+7Y1tbWxmOPPcZLL73EX/7yFxoa\nGnjnnXeCWrBZOs6wVg9ZRESCwG8gb968mZycHAAmTJhAfn5+xzaHw8GyZctwONpPcvJ4PDid4Tmt\npK5BFhGRYPI7ZN3Y2EhcXNxXD7Db8fl8WK1WLBYLSUlJACxdupSWlhamTJni90VTUuL87tPXNLt9\nAIzITOqoPxTbcbxwaAOoHX1JOLQBwqMd4dAGCJ92+OM3kGNjY2lqauq4fSSMjzAMg0ceeYTCwkIe\nf/zxTr1oRUVDN0o115cldQDYDR8VFQ2kpMSFZDuOFg5tALWjLwmHNkB4tCMc2gDh1Q5//A5ZT5o0\nifXr1wOwZcsWsrOzj9n+X//1X7jdbp588smOoetwVFnXitViYUBceA7Ji4iIufz2kKdPn86GDRvI\ny8sDYPHixaxevZqWlhbGjh3LihUrmDx5MnPnzsVisXDjjTcybdq0oBfe26rqWkmK1zXIIiISHH4D\n2WKxsHDhwmPuy8rK6vh6x44dga+qj/F4fdQ2tJGdnmh2KSIiEqbU3euE6vpWDHSGtYiIBI8CuRN0\nDbKIiASbArkTKrXKk4iIBJkCuRMqNSmIiIgEmQK5EzRLl4iIBJsCuROq6lqwWCBR1yCLiEiQKJA7\nobK+laQ4J3abvl0iIhIcShg/PF4fNQ1tDNQJXSIiEkQKZD+qG9owDB0/FhGR4FIg+1FR0wIokEVE\nJLgUyH6UVjcDMHhgtMmViIhIOFMg+1FS1b705JCkGJMrERGRcKZA9qOjh5ykHrKIiASPAtmPkqpm\nkuKdOB02s0sREZEwpkA+jZY2DzUNbQxR71hERIJMgXwaZTVHhqt1/FhERIJLgXwapVU6w1pERHqH\nAvk0Sg4H8hAFsoiIBJkC+TRKqo8EsoasRUQkuBTIp1Fa1YzTYSMx1mF2KSIiEuYUyKfg8xmU1TQz\nOCkai8VidjkiIhLmFMinUFXfitvj0/FjERHpFQrkUzhyQpdm6BIRkd6gQD6FUp3QJSIivUiBfAql\nHYtKqIcsIiLBp0A+hZKqZixA6oAos0sREZF+QIF8CqXVzQxMiMQRoUUlREQk+BTIJ9Hc6qauyaXj\nxyIi0msUyCfx1QxdOn4sIiK9Q4F8EgfLGgEYmqwesoiI9A4F8knsLa4DYMTQeJMrERGR/kKBfBJ7\ni+uIdtoZoh6yiIj0EgXyceqaXJTXtDAiLQGr5rAWEZFeokA+zr7Dw9Uj0zRcLSIivUeBfJy9RYcD\neViiyZWIiEh/okA+zt7iOqwWC2cMUQ9ZRER6jwL5KG6PjwOl9aQPisXp0AxdIiLSexTIRyksbcDj\nNRiZlmB2KSIi0s8okI+yt+OELgWyiIj0LgXyUb4oqgVg1DAFsoiI9C4F8mGGYbCvuI4BcU6S4iPN\nLkdERPoZBfJhFbUt1De7NVwtIiKmUCAftuvL9uFqBbKIiJhBgXzYB9tLAJgwcqDJlYiISH+kQAZK\nqprYU1TH6MwBpA7QGsgiItL7FMjAv7YeAuDr5ww1uRIREemv+n0guz0+NmwvJTYqgomjUswuR0RE\n+ql+H8iffVFBY4ubi84eTIS93387RETEJP0+gdZvaR+uvmSChqtFRMQ8/TqQy2ua2VlYQ/awBIYM\njDG7HBER6cf6bSAbhsH/rt8PwCU6mUtEREzWbwP5Hx8W8smuckYNS+C80YPMLkdERPq5fhnI2/ZV\nsmL9fgbEObl95tnYbf3y2yAiIn2I3yQyDIP58+eTl5fHjTfeyMGDB4/Zvm7dOubMmUNeXh7Lly8P\nWqGB8mVZA0+v2oHdbuXHs88mIcZhdkkiIiLY/e2wdu1aXC4Xy5YtY+vWrSxevJgnn3wSAI/Hw5Il\nS1ixYgVOp5Prr7+eyy+/nKSkpKAX3lVlNc2ser+AD3eUYRhw67VjGD443uyyREREgE4E8ubNm8nJ\nyQFgwoQJ5Ofnd2zbt28fmZmZxMbGAjB58mQ2bdrEjBkzglRu5zU0u9jyRSUHKxopKm9kz8E6fIbB\nsJRYZn39DM4ZmWx2iSIiIh38BnJjYyNxcXFfPcBux+fzYbVaT9gWExNDQ0NDcCrtohff3M3mPRUA\nWID01FiuvjCTr52VitViMbc4ERGR4/gN5NjYWJqamjpuHwnjI9saGxs7tjU1NREf738YOCUlzu8+\nPbXgB1OC/hq90Y5gC4c2gNrRl4RDGyA82hEObYDwaYc/fk/qmjRpEuvXrwdgy5YtZGdnd2wbMWIE\nhYWF1NfX43K52LRpE+ecc07wqhUREQlTFsMwjNPtYBgGCxYsYPfu3QAsXryYzz//nJaWFnJzc3n3\n3Xd5/PHHMQyDOXPmcP311/dK4SIiIuHEbyCLiIhI8GlGDBERkT5AgSwiItIHKJBFRET6AAWyiIhI\nH+D3OuRAOfpsbYfDwaJFi0hPT++tlw+orVu38pvf/IalS5eaXUq3eDwe7r33XoqLi3G73fzwhz/k\nsssuM7usLvP5fNx3330UFBRgtVpZuHAhI0eONLusbqmqqmL27Nk8//zzZGVlmV1Ot8yaNatj1r5h\nw4bx0EMPmVxR1z3zzDOsW7cOt9vNDTfcwOzZs80uqcteffVVVqxYgcVioa2tjV27drFhw4aOn02o\n8Hg83H333RQXF2O323nggQdC7m/D5XJxzz33UFRURGxsLPPnzycjI+OU+/daIJ9uTuxQ8uyzz7Jy\n5UpiYmLMLqXbVq1axYABA3jkkUeoq6vjuuuuC8lAXrduHRaLhVdeeYWPP/6YRx99NCR/pzweD/Pn\nzycyMtLsUrrN5XIB8OKLL5pcSfd9/PHHfPbZZyxbtozm5maee+45s0vqlpkzZzJz5kwAfvWrXzFn\nzpyQC2OA9evX4/P5WLZsGR988AG/+93veOyxx8wuq0uWL19OTEwMf/3rXykoKGDhwoX86U9/OuX+\nvTZkfbo5sUNJZmYmTzzxhNll9MhVV13FnXfeCbT3Mu32XvtcFlDTpk3jgQceAKC4uJiEhASTK+qe\nhx9+mOuvv57U1FSzS+m2Xbt20dzczC233MJNN93E1q1bzS6py95//32ys7O5/fbbue2227j00kvN\nLqlHtm/fzt69e8nNzTW7lG4ZPnw4Xq8XwzBoaGggIiLC7JK6bO/evVxyySUAZGVlsX///tPu32vv\nxKebEzuUTJ8+neLiYrPL6JGoqCig/Wdy55138tOf/tTkirrParXyi1/8grVr14bcp2eAFStWMHDg\nQC666CKeeuops8vptsjISG655RZyc3M5cOAAt956K2vWrAmpv++amhoOHTrE008/zcGDB7ntttt4\n8803zS6r25555hnuuOMOs8votpiYGIqKirjyyiupra3l6aefNrukLhs9ejTvvvsu06ZNY8uWLZSX\nl2MYBpZTrKfQa38tp5sTW3pfSUkJ3/ve95g5cyZXX3212eX0yJIlS1izZg333Xcfra2tZpfTJStW\nrGDDhg3MnTuXXbt2cffdd1NVVWV2WV02fPhwvvnNb3Z8nZiYSEVFhclVdU1iYiI5OTnY7XaysrJw\nOp1UV1ebXVa3NDQ0cODAAc477zyzS+m2F154gZycHNasWcOqVau4++67Ow6NhIrZs2cTExPDd77z\nHd5++23Gjh17yjCGXgzk082JHYpCeYKzyspKbrnlFu66666OY02haOXKlTzzzDMAOJ1OrFZryH3I\ne+mll1i6dClLly7lrLPO4uGHH2bgwIFml9Vlf//731myZAkAZWVlNDU1kZKSYnJVXTN58mTee+89\noL0Nra2tDBgwwOSqumfTpk1ccMEFZpfRIwkJCR3HvuPi4vB4PPh8PpOr6prt27dz4YUX8vLLLzNj\nxgy/JzL32pD19OnT2bBhA3l5eUD7nNih7HSfcvq6p59+mvr6ep588kmeeOIJLBYLzz77LA6Hw+zS\nuuSKK67gnnvu4bvf/S4ej4df/vKXIdeGo4Xy79ScOXO45557uOGGG7BarTz00EMh9+Fo6tSpfPLJ\nJ8yZMwfDMJg/f37I/kwKCgpC9iqWI773ve9x77338p3vfAePx8O8efNC7sTHzMxM/vCHP/DUU08R\nHx/PokWLTru/5rIWERHpA0LrI6yIiEiYUiCLiIj0AQpkERGRPkCBLCIi0gcokEVERPoABbKIiEgf\noEAWERHpA/4/Z3vpst+ri5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5af860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.admission_type_id, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9d8a780>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EqU6MHxqJmRkDxY5DRERgOXu1H4824at9tRgQ5o/7bxsCCffOJiJyCSxnL1Xb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e3fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.time_in_hospital, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e8fc18>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jkADcN60/VEq+HBA5Iv5kErmpr/eWobKuFZOGRqBPRIDccYjoCljURG6ouqEVWXtKEOCr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\nvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa123550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.num_lab_procedures, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbfb5f98>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sz3wwAJiFo2+M+vj6YIaiAcAshHCMOr5eNNcHA4B5COEYVX/ANTgfPJ75YAAw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfc0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.num_medications, cumulative=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Densidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe424358>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HbJL1u1hCYzQaEGaFrLtTbR6sfXIXDKKAj8ybhIs+VIPzZlVDFPUd5SAiKhRW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\nT5JG0H+xygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd482710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.admission_type_id, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd2342e8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LrKIgGEmip9VR9HUx0tbjrLbW0HbaZWQVFYlUtmTlNCIiuvixuIrOZqJpqOrCwiqCGGmP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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec001d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.time_in_hospital, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xec334e0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RuGKoU0TqGxxFU6cFmUlaqFU8B9yf5ntWwVdyCp4o1DDUKSKV13cDALKStBJX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\noaUuIiLiEVrqIiIiHqGlLiIi4hG/AKvnc9+rQaH+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121ba630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.num_lab_procedures, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed2a908>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KCt56twt/2nUCADC3rPCKn1cQBHxoQQkAwKgX8IlbSzG9wAJFAQa9XKCEiGgy2HPOAYFg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed35e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.num_medications, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed01550>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6d3Whey5E8jSvjANDCkxEi3AiGhERTTAFQ9vtdsPn82mfn0/4juceAKiu9mQ9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\ngqJVswsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123ca1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(Importantes.number_outpatient, shade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12522b70>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HIXtqYc7sF7fdaYq/BZ6yz5CVlYWHHnoSubl5UV2nW7d8DBs2Etu2fQ5P3WGY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\nacAgJSIi0oBBSkREpAGDlIiISIP/DzB7V/v1EmncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12536390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(Importantes.admission_type_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1295d710>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vplksIU5QgLXp3LkWAAyChwqcSGZEXyn88+6C6K/A0aOH8dhjf4bBwX4oJUsy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\nEbGAAlZERMQC/w8dmOZwPNMAQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1282c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(Importantes.time_in_hospital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1282c7f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yGQBjC0DXC0s4e/mCCM0Dr7MEmrO0ZiKV8NghvGf/RSo0NAzRzVsgJibmhAlU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12da48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(Importantes.num_lab_procedures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d4c4a8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y5vf70a/fAFjBKlh6k+w4CSVSsQ1G3QH06tUH99//HV5piohswdKNcZxibn+R\n6k8RqdqFnJxcfPe734fb7ZEdiYiSBEs3xl1yyWUAAINTzO1Crz+McPlH8PvT8fDDj8Lv98uORERJ\nhKUb4zIzM9G3byHMpgpYRkh2nLhmNFUgdHQ93G43HnxwDrKzc2RHIqIkw9KNA5xivniWHkSobC1U\nReCBBx5CQUFP2ZGIKAmxdOPAZ6XLKea2EMJEqGwthBHCzTffhkGDhsiORERJiqUbBzp3zkZBQS+Y\nTScgzIjsOHEnXL4VZrASX/rSaEyderXsOESUxFi6cWL48EsAIWA0lsuOElf0ugPQa/aga9duuPPO\ne3guLhFJxdKNE0OHDgcAmIGjkpPEDyvSgPDxzXB7PHjggQd5ahARScfSjRMFBb2QmpYGo/EYhBCy\n48Q8IUwEyz6EsAx8447Z6NIlT3YkIiKWbrxQVRVDBg+DMEKwwrWy48S8SMV2WKFqXH75eIwePVZ2\nHCIiACzduNIyxWxwivmcjMbjiFTtRk5OF3zta/8hOw4RURRLN44MGjQUiqLADByTHSVmCTOC0NEN\nUFUV9977bXi9XtmRiIiiWLpxJDU1FX36FMIMVkGYYdlxYlK4fCuEEcS1196AXr16y45DRHQKlm6c\nGTJkOACeOnQmRuAY9LpSdO9egGuuuVZ2HCKi07B04wz3656ZMHWEjm+CqqqYPfte3qqPiGISSzfO\n9OhRAL8/HWaApw61Fj7xCYTehBkzrkOPHj1lxyEiOiOWbpxRFAVDhgyDMMOwQjWy48QEM1wHvXYf\n8vK7YsaM62THISI6K5ZuHBo4cDAAwGw6ITlJbIic2AZA4Cs33cppZSKKaSzdONS//yAA4MFUaL5H\nrhEoQ2FhPwwbNkJ2HCKic2LpxqHMzEzk5ubDClZACEt2HGmEEIic+AQA8JWv3MqbGRBRzGPpxqkB\nAwZBWAasYLXsKNKYgaMwg5UYMWIk+vYtkh2HiOi8WLpxasCAk1PMTck5xSyEQLhiGxRFwQ033CI7\nDhHRBWHpxqn+/QcAAMwk3a9rBo7CCtdh9Oix6Nq1m+w4REQXhKUbp1JT09C9ewHMYCWEZcqOY7tI\n1S4A4JWniCiusHTj2IABgwBhwQxWyo5iK6OpAmawEsOGjeBWLhHFFZZuHOvffyCA5JtibtnKvfrq\nmZKTEBF9MSzdONavX3+oqgojiS6SYYbrYAaOok+fQhQW9pMdh4joC2HpxjGvNwU9e/aCFaqCMHXZ\ncWwRqdquGb1CAAAUdElEQVQNALjmmpk8L5eI4g5LN84179cVSbFf19KbYNQfRG5uPoYNu0R2HCKi\nL4ylG+cKC/sDAMymCslJOp5e8ykgLEyf/mWoKn90iSj+8J0rzvXtWwhFUWAGE7t0halDr9kHvz8d\nY8aMlR2HiKhNWLpxLiXFh65du8MMVkOIxD1fV68thbB0XHnlVXA6XbLjEBG1CUs3ARQW9gOECStU\nKztKhxDCQqSmBE6nC5MnT5Edh4iozVi6CaDl1JlE3a9r1B+G0JswfvxEpKamyY5DRNRmLN0EUFSU\nuKUrhECkejcURcHUqVfLjkNEdFFYugkgK6sTsrI6NV+HWQjZcdqV2VQBK1SDESMuRZcuubLjEBFd\nFJZugigq6gdhhiEiDbKjtCu95lMAwLRp10hOQkR08Vi6CaJlv66RQKcOWZEAjIYjKCjoxZvUE1FC\nYOkmiM8OpkqcK1NFavYAAK666mpe8pGIEgJLN0Hk53eD15sCK0G2dIWpw6gthd+fjssuGy07DhFR\nu2DpJghVVVFYWAQrEoBlBGXHuWh63X4IS8cVV0yFpmmy4xARtQuWbgJJlClmIQT0mj3QNA2TJl0p\nOw4RUbth6SaQaOnG+RSzGTgGK9KA0aPHwu9Plx2HiKjdsHQTSM+eveFwOOJ+Szdy8jShKVOmS05C\nRNS+WLoJxOVyoWfP3rBCNRCWITtOm5jhOpiNx9Gv3wD06FEgOw4RUbti6SaYwsIiAAJmsEp2lDbR\nq5tPE5oyZZrkJERE7Y+lm2Di+eYHwozAqD+A7OxsDB8+UnYcIqJ2x9JNMH36NF+5yQzG337d5nvm\nGpgxYwYcDofsOERE7Y6lm2D8fj9yc/NgBSshhCU7zgUTwoJeswdOpwtTp06VHYeIqEOwdBNQYWE/\nCMuAFa6THeWCGYGjsPRGjB07HmlpvGcuESUmlm4Cisf9unp182lCV17JA6iIKHGxdBNQ8xHM8bNf\n1wzVwmw6gYEDB6Nr126y4xARdRiWbgLKyclFWpofZlNFXNzUXq8uAQBMnXq15CRERB2LpZuAFEVp\n3q9rBCGMJtlxzskyQtDrDyInJxdDhgyTHYeIqEOxdBNUdIo5xvfr6rX7AGFhypRpUFX+OBJRYuO7\nXIIqKuoPADAbT0hOcnZCmNBr9sLj8WLs2Amy4xARdTiWboIqKOgFn88Ho+l4zO7XNeoPQxhBjB8/\nCV6vV3YcIqIOx9JNUKqqYuDAIRB6E6xIvew4ZxSp3gNFUXDllVfJjkJEZAuWbgIbPHgoAMBsPC45\nyenMYBWsUBWGDRuBnJwusuMQEdmCpZvABg0aAgAwArFXuhFeDIOIkhBLN4FlZXVCfn5XmE0nICxT\ndpwoywjCaDiMvLx8DBw4WHYcIiLbsHQT3KBBQwFhwgzGzqlDes1npwkpiiI7DhGRbVi6CS7W9usK\nYUKv3Qev14sxY8bLjkNEZCuWboIrKuoPTdNiZr+uUX8kepqQx+ORHYeIyFYs3QTndrtRVNQfVrgW\nlhGUHQeRmk+hKAquuIKnCRFR8mHpJoGWo5hlTzGboRpYwSoMGTKMpwkRUVJi6SaBwYObbyRgBI5J\nzaFX7wEAXHHFVKk5iIhkYekmgW7duiMnpwvMwFEIy5CSQZhhGA0H0Tk7J/pLABFRsmHpJgFFUTBq\n1OUQlgGjoUxKBr12P4Rl4orJU3k3ISJKWnz3SxKjRl0OANDrD9q+bCEE9Nq9cDqdGDduou3LJyKK\nFSzdJJGf3xXduxfAbDwGYYRtXbbZeAxWJIBRo8YiNTXV1mUTEcUSlm4SGT36ckAI6A2HbV1upGYv\nAODKK3kAFRElN5ZuEmmZYjZsnGK2IgGYgaPo3bsPCgp62bZcIqJYxNJNIllZnVBY2A9mUwUsvcmW\nZeq1+wAAkydzK5eIiKWbZEaPHgsAMOoPdfiyhGVCry2Fz+fDl740usOXR0QU61i6SebSS78EVXVA\nr9sPIUSHLstoOAJhhjFu3CQ4na4OXRYRUTxg6SaZtDQ/LrlkJKxwHcxgZYcuSz95ANWkSVd06HKI\niOIFSzcJtexfbSnFjmCGamEGKzBo0BB06ZLXYcshIoonLN0k1L//QOTm5sNoOAzLCHXIMvTalq3c\nKR0yPhFRPGLpJiFFUTB58hRAWNBr97f7+MIIw6g7gIyMTAwffkm7j09EFK9Yuklq7NjxcLlc0Gv3\nQgirXceOVO+GsAxMnz4DDoejXccmIopnLN0klZLiw+jRYyH0xna9z65lBKFXf4qMjExMnnxlu41L\nRJQIWLpJbPLk5v2tkXY8oCpSuQtCmJg583qeJkRE9Dks3SRWUNALffsWwQwchRmquejxLL0Reu1e\ndOrUGePHT7r4gERECYalm+RmzboRABCp2HHRY0UqdwLCwqxZN0LTtIsej4go0bB0k9zAgYNRWNgP\nRqAMZrCqzePoDUeg15aiS5dcjBkzrh0TEhElDpZuklMUBddddxMAINzGrV29/ghCZWvhcrlw5533\n8IhlIqKzYOkSBgwYhP79B8JsPAaz6YtdGlKvP4zQ0bVwOV146KE5KCrq30EpiYjiH0uXAKDV1u62\nC7oRghACkaoShMrWwe1y4eGHH2XhEhGdB0uXAABFRf0xePAwmE0nEKnaec7nClNHqGwdwic+ht/v\nxyOPPIbCwn42JSUiil8sXYqaPfs+ZGV1QqRiO/Sz3G/XDNeh6cBiGA2H0bdvEX7846fRp0+hzUmJ\niOITS5ei0tPT8b3vfR9utwfhoxtOO5pZr92P4IHFsCL1mDJlOn7wgx8iMzNTUloiovjDkynpFN26\n9cD9938H//Vfv0Lw0AqoKdlweDJh6Y0w6g7A4/Hirru+hUsvHSU7KhFR3OGWLp1m6NDhuPPOe+BP\n88EMHEWkshhG3QH06NETP/nJ0yxcIqI24pYundG4cRMxbtxE1NXV4dChA2hsDGDkyMt4PWUioovA\n0qVzSk9Px5Ahw2THICJKCJxeJiIisglLl4iIyCYsXSIiIpuwdImIiGzC0iUiIrIJS5eIiMgmLF0i\nIiKbsHSJiIhswtIlIiKyCUuXiIjIJixdIiIim7B0iYiIbMLSJSIisglLl4iIyCYsXSIiIpuwdImI\niGzC0iUiIrIJS5eIiMgmLF0iIiKbsHSJiIhswtIlIiKyCUuXiIjIJixdIiIim7B0iYiIbMLSJSIi\nsglLl4iIyCYsXSIiIpuwdImIiGyiCCGE7BBERETJgFu6RERENmHpEhER2YSlS0REZBOWLhERkU1Y\nukRERDZh6RIREdlEa8uLAoEAHnnkETQ2NkLXdcydOxfDhg3D1q1b8fTTT0PTNFx++eV44IEH2jtv\n3BFC4Cc/+QlKSkrgcrnw1FNPoXv37rJjxQTDMPDYY4+hrKwMuq7jvvvuQ9++ffHoo49CVVUUFhbi\nxz/+seyYMaOqqgo33ngjXn75ZTgcDq6ns/jjH/+IZcuWQdd13Hbbbbjsssu4rs7AMAzMmTMHZWVl\n0DQNTz75JH+uPueTTz7Br3/9a7z22ms4dOjQGdfNm2++iTfeeANOpxP33XcfJk2adO5BRRv87ne/\nE6+88ooQQojS0lJx/fXXCyGEmDVrljh8+LAQQoi7775b7Nq1qy3DJ5RFixaJRx99VAghxNatW8X9\n998vOVHs+Mc//iGefvppIYQQdXV1YtKkSeK+++4TmzZtEkII8aMf/UgsXrxYZsSYoeu6+Na3viWm\nTZsmSktLuZ7OYsOGDeK+++4TQgjR2Ngofv/733NdncWSJUvE9773PSGEEGvXrhXf/va3ua5a+dOf\n/iRmzJghbrnlFiGEOOO6qaioEDNmzBC6rouGhgYxY8YMEYlEzjlum6aX77zzTnz1q18F0Pzbktvt\nRiAQgK7r6NatGwBg3LhxWLduXVuGTyhbtmzB+PHjAQDDhg3Djh07JCeKHVdffTW++93vAgBM04TD\n4cDOnTtx6aWXAgAmTJiADz/8UGbEmPHLX/4St956K3JyciCE4Ho6izVr1qCoqAjf/OY3cf/992PS\npElcV2fRs2dPmKYJIQQaGhqgaRrXVSsFBQV4/vnno18XFxefsm7WrVuHbdu2YeTIkdA0DampqejZ\nsydKSkrOOe55p5ffeustvPLKK6d87+c//zkGDx6MiooK/OAHP8Djjz+OxsZGpKamRp/j8/lw5MiR\nL/SXTESBQABpaWnRrzVNg2VZUFXuTvd6vQCa19F3v/tdPPjgg/jlL38Zfdzn86GhoUFWvJgxf/58\ndOrUCWPHjsWLL74IALAsK/o419NnampqcPToUbz00ks4fPgw7r//fq6rs2h5j54+fTpqa2vx4osv\nYvPmzac8nszraurUqSgrK4t+LVpdvNHn8yEQCKCxsfGU9/eUlJTzrrPzlu5NN92Em2666bTvl5SU\n4JFHHsGcOXNw6aWXIhAIIBAIRB9vbGyE3+8/3/AJLzU1FY2NjdGvWbinOnbsGB544AF8/etfx5e/\n/GX86le/ij7Gn6Fm8+fPh6IoWLt2LUpKSjBnzhzU1NREH+d6+kxGRgb69OkDTdPQq1cvuN1ulJeX\nRx/nuvrMX/7yF4wfPx4PPvggysvLcfvtt0PX9ejjXFenav2+3bJuUlNTv3Dvtendf+/evfje976H\nX//61xg3bhyA5nJxuVw4fPgwhBBYs2YNRo4c2ZbhE8oll1yClStXAgC2bt2KoqIiyYliR2VlJWbP\nno3vf//7uP766wEAAwYMwKZNmwAAq1at4s8QgHnz5uG1117Da6+9hv79++OZZ57B+PHjuZ7OYOTI\nkVi9ejUAoLy8HMFgEKNHj8bGjRsBcF21lp6eHp2dTEtLg2EYGDhwINfVWQwcOPC0/3NDhgzBli1b\nEIlE0NDQgNLSUhQWFp5znDYdvfzss88iEongqaeeghACfr8fzz//PH7yk5/gkUcegWVZGDt2LIYO\nHdqW4RPK1KlTsXbt2ug+8J///OeSE8WOl156CfX19fjv//5vPP/881AUBY8//jh+9rOfQdd19OnT\nB9OnT5cdMybNmTMHTzzxBNfT50yaNAmbN2/GTTfdFD1zoGvXrvjhD3/IdfU53/jGN/DYY4/ha1/7\nGgzDwCOPPIJBgwZxXZ3Fmf7PKYqC22+/HbfddhuEEHjooYfgcrnOOQ7vMkRERGQT7lwkIiKyCUuX\niIjIJixdIiIim7B0iYiIbMLSJSIisglLl4iIyCYsXaIEMHfuXCxYsAAnTpzAvffe2+Yxjh07BgC4\n9957UVFR0Z4RiQhtvDgGEcWmnJwcvPTSS2167YYNG6LXl23rGER0bixdolY2btyIl156CR6PB/v2\n7UO/fv3w4IMP4q677sKyZcsAAH/4wx8AAA888ADGjRuHyZMnY/PmzcjOzsZtt92G1157DeXl5fjF\nL34RvSvJmcydOxderxdbtmxBQ0MDHnvsMfzrX/9CSUkJrrzySsyZMweWZeGZZ57Bxo0bYVkWrr/+\nenzjG98A0Hx1sxUrViAnJweWZWHUqFEoKyvD7bffjmXLluHo0aOYO3cuqqur4fV68bOf/QxFRUV4\n7rnnsH79etTV1SEzMxO///3vMX/+fJw4cQL33HMP5s2bhxtuuAHz5s1DXl4ennrqKaxfvx6KouDa\na6/F3Xfffcb19Jvf/AahUAgPP/wwKisro+to8uTJHfyvRhQ/WLpEn/Pxxx9j4cKFyMnJwVe+8hWs\nWbMGiqKc8bmVlZW44oor8OSTT+KOO+7AkiVL8Prrr2PBggV45ZVXzlm6AFBRUYF//etfWLBgAebO\nnYtFixbB5XJhwoQJ+Na3voV33nkHiqJg/vz5iEQimD17NgYPHozKykrs3r0b77//Pmpra3HttddG\nx2zJ+tOf/hTTp0/HrbfeilWrVuGFF17Agw8+iP379+ONN94A0Hxpu3feeQf33HMP/v73v+NPf/oT\nMjIyomP87W9/Q3l5Od555x2EQiHcfvvtKCoqgtfrja6n7Oxs3HzzzVizZg1qa2vRrVs3vPTSS9i3\nbx/mz5/P0iVqhaVL9DlFRUXIyckBAPTp0we1tbXnfH7L/ZK7du0avUB8fn4+6urqzrusCRMmRJ9f\nVFSEzMxMAM13y6mvr8e6detQUlISva9pMBjEp59+ir179+Kqq66CqqrIysrCxIkTTxt748aNePbZ\nZ6PLaVnWnDlz8Oabb2L//v3YunUrevToEX3N568Ku379+ujNKDweD2bOnIn169dj8uTJZ1xPI0aM\nwHPPPYfjx49j0qRJ+OY3v3nedUCUTFi6RJ/T+oLlLVt8rctI13U4nc7o15qmnfHzC9F6HIfDcdrj\nlmXh+9//PqZMmQIAqK2thdfrxa9+9atT7hN7pte2HhsA9u3bh1AohIceegh33XUXpk+fDlVVTyva\n1j7/mBAChmEAOPN6KigowPvvv4/Vq1dj2bJl+POf/4z333//rOMTJRsevUx0Hn6/H/X19aipqUEk\nEoneOq4jtZTd6NGj8cYbb8AwDDQ2NuLWW2/Ftm3bMGbMGCxcuBCRSAR1dXVYs2bNaWNcdtlleO+9\n9wAAa9euxRNPPIFNmzZh1KhRuOWWW9C7d2+sXbs2Wt6apsE0zdOWv2DBAliWhWAwiLfffhujRo06\na+7XX38dv/vd7zBt2jT86Ec/QnV19Sn3GyVKdtzSJToPv9+P2bNn48Ybb0R+fj6GDRsWfexs+3ov\nVsu4X/3qV3Hw4EFcf/31ME0TN910Ey677DIAwPbt2zFz5kxkZ2ejb9++p43xxBNP4PHHH8frr78O\nr9eLp556Cj6fD9/+9rcxa9YsaJqG/v3748iRIwCab4t3991343/+53+iy7/llluwf/9+zJo1C4Zh\nYNasWZgyZUr0nqufd9111+Ghhx7CzJkz4XQ68Z3vfCd6z1Yi4q39iIiIbMMtXaIO9Mwzz2DdunWn\nbREPHjwYTz75pKRURCQLt3SJiIhswgOpiIiIbMLSJSIisglLl4iIyCYsXSIiIpuwdImIiGzC0iUi\nIrLJ/wfOHFfFftOaPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12de7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(Importantes.num_medications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x132af0f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9erQeeOABSdLWrVs1ZswYTZ06VaNGjdINN9yghoYGLViwQDt37tTNN9+shoaG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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12daec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(Importantes.number_outpatient)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}