Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos los paquetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos los datos necesarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "demographic = pd.read_csv('../data/national-health-and-nutrition-examination-survey/demographic.csv', sep=',')\n",
    "diet = pd.read_csv('../data/national-health-and-nutrition-examination-survey/diet.csv', sep=',')\n",
    "examination = pd.read_csv('../data/national-health-and-nutrition-examination-survey/examination.csv', sep=',')\n",
    "labs = pd.read_csv('../data/national-health-and-nutrition-examination-survey/labs.csv', sep=',')\n",
    "medications = pd.read_csv('../data/national-health-and-nutrition-examination-survey/medications.csv', sep=',',\n",
    "                          encoding='iso-8859-1')\n",
    "questionnaire = pd.read_csv('../data/national-health-and-nutrition-examination-survey/questionnaire.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos una limpieza de los datos de medications. Nos quedamos solo con los que sean no nulos.\n",
    "Agrupamos por SEQN tras descategorizar, ya que en medications aparece más de un registro por paciente, debido a que un paciente puede haber sido tratado por más de un motivo.\n",
    "\n",
    "Para ello creamos un registro por paciente que tenga como variable cada una de las\n",
    "enfermedades del conjunto de datos como variable binarizada. Seleccionamos solo las variables de interés. \n",
    "\n",
    "Convertimos el nombre de las columnas de medications2 a lista para poder tenerlas identificadas y poder tratarlas después."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "medications = medications[medications.RXDRSD1.notnull()]\n",
    "\n",
    "medications2 = pd.get_dummies(medications[['SEQN', 'RXDRSC1']]).groupby(['SEQN']).sum()\n",
    "for i in medications2.columns:\n",
    "    medications2.loc[medications2[i] > 1, i] = 1\n",
    "\n",
    "medications2=medications2.reset_index()\n",
    "# medications2\n",
    "    \n",
    "variablesMedications = medications2.columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unimos los archivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(demographic, examination, how='outer', on='SEQN')\n",
    "df = pd.merge(df, diet, how='outer', on='SEQN')\n",
    "df = pd.merge(df, labs, how='outer', on='SEQN')\n",
    "df = pd.merge(df, questionnaire, how='outer', on='SEQN')\n",
    "df = pd.merge(df, medications2, how='inner', on='SEQN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputamos los Nan.\n",
    "\n",
    "\n",
    "Se eliminan las filas y columnas que tengan todos los elementos nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0, how='all', inplace=True)\n",
    "df.dropna(axis=1, how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputamos RIDAGEMN. Esta variable es la edad en meses y en pacientes mayores de 2 años no aparece este dato. \n",
    "Es una posible variable de interés para poder tener una edad más exacta.\n",
    "Así que en los registros que no existan, multiplicamos la edad en años, RIDAGEYR, por 12 para obtener los meses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.RIDAGEMN = df.RIDAGEMN.replace(np.nan, df.RIDAGEYR*12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputamos RIDEXAGM. Esta variable es la edad en meses en el momento del examen. Si el valor es nulo, \n",
    "lo imputamos con el valor de RIDAGEMN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.RIDEXAGM = df.RIDEXAGM.replace(np.nan, df.RIDAGEMN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputamos de nan de variablesMedications como el valor que más se repite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for v in variablesMedications:\n",
    "    if df[v].isnull().sum() != 0:\n",
    "        df[v].fillna(value=df[v].value_counts().index[0], inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el resto de valores, hacemos distinción entre si la variable es numérica o categórica, para imputar por la media o por el valor más repetido repectivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if df[column].isnull().sum() != 0:\n",
    "        if df[column].dtype==object:\n",
    "            df[column].fillna(value=df[column].value_counts().index[0],inplace =True)\n",
    "        else:\n",
    "            df[column].fillna(df[column].median(),inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hallamos la matriz de correlación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrizCorrelacion = df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sacamos las variables que están relacionadas como aquellas cuya correlación sea mayor o igual a 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[['RIDAGEYR', 'RIDAGEMN'], 0.9999940551689572],\n",
       " [['RIDAGEYR', 'RIDEXAGM'], 0.9999710262472322],\n",
       " [['RIDAGEMN', 'RIDEXAGM'], 0.9999710894656363],\n",
       " [['RIDRETH1', 'RIDRETH3'], 0.964629711400703],\n",
       " [['DMDHHSIZ', 'DMDFMSIZ'], 0.9391253962527536],\n",
       " [['WTINT2YR', 'WTMEC2YR'], 0.9760850763771678],\n",
       " [['INDHHIN2', 'INDFMIN2'], 0.9156842864809795],\n",
       " [['INDFMPIR', 'INDFMMPI'], 0.8735477671894537],\n",
       " [['BPXSY1', 'BPXSY2'], 0.9051119483569185],\n",
       " [['BPXSY1', 'BPXSY3'], 0.8877822089285669],\n",
       " [['BPXDI1', 'BPXDI2'], 0.8078652602599443],\n",
       " [['BPXSY2', 'BPXSY3'], 0.9354793985707626],\n",
       " [['BPXDI2', 'BPXDI3'], 0.8688796129690664],\n",
       " [['BMXWT', 'BMXBMI'], 0.8476111025441124],\n",
       " [['BMXWT', 'BMXARMC'], 0.9137446163891016],\n",
       " [['BMXWT', 'BMXWAIST'], 0.8289945865634065],\n",
       " [['BMXBMI', 'BMXARMC'], 0.8184739155144255],\n",
       " [['BMXBMI', 'BMXWAIST'], 0.8675863340983514],\n",
       " [['BMXARMC', 'BMXWAIST'], 0.8182883495432837],\n",
       " [['BMXWAIST', 'BMXSAD1'], 0.8134443383579767],\n",
       " [['BMXWAIST', 'BMXSAD2'], 0.8130006201321396],\n",
       " [['BMXWAIST', 'BMDAVSAD'], 0.813835135212468],\n",
       " [['BMXSAD1', 'BMXSAD2'], 0.9983148543786615],\n",
       " [['BMXSAD1', 'BMDAVSAD'], 0.9991412584461937],\n",
       " [['BMXSAD2', 'BMDAVSAD'], 0.999507508693463],\n",
       " [['BMXSAD3', 'BMXSAD4'], 0.99789142158628],\n",
       " [['MGAPHAND', 'MGATHAND'], -0.9640874607413764],\n",
       " [['MGXH1T1', 'MGXH2T1'], 0.900693642607672],\n",
       " [['MGXH1T1', 'MGXH1T2'], 0.9587587087053344],\n",
       " [['MGXH1T1', 'MGXH2T2'], 0.8859570388178145],\n",
       " [['MGXH1T1', 'MGXH1T3'], 0.9487333507967385],\n",
       " [['MGXH1T1', 'MGXH2T3'], 0.8804519254127321],\n",
       " [['MGXH1T1', 'MGDCGSZ'], 0.9420215214610282],\n",
       " [['MGXH2T1', 'MGXH1T2'], 0.9065997633743844],\n",
       " [['MGXH2T1', 'MGXH2T2'], 0.968975319519792],\n",
       " [['MGXH2T1', 'MGXH1T3'], 0.9016213461092901],\n",
       " [['MGXH2T1', 'MGXH2T3'], 0.9614934123862545],\n",
       " [['MGXH2T1', 'MGDCGSZ'], 0.9656159288033679],\n",
       " [['MGXH1T2', 'MGXH2T2'], 0.9083520333580501],\n",
       " [['MGXH1T2', 'MGXH1T3'], 0.9733780850185499],\n",
       " [['MGXH1T2', 'MGXH2T3'], 0.8993364690009],\n",
       " [['MGXH1T2', 'MGDCGSZ'], 0.960224747031587],\n",
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       " [['MGXH2T2', 'MGXH2T3'], 0.9751519760726205],\n",
       " [['MGXH2T2', 'MGDCGSZ'], 0.9714972878720733],\n",
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       " [['MGXH1T3', 'MGDCGSZ'], 0.9651871588957947],\n",
       " [['MGXH2T3', 'MGDCGSZ'], 0.9694717548947711],\n",
       " [['OHDEXSTS', 'OHDDESTS'], 0.869111335594469],\n",
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       " [['OHX07TC', 'OHX08TC'], 0.8409961726062734],\n",
       " [['OHX07TC', 'OHX09TC'], 0.8291196706476439],\n",
       " [['OHX07TC', 'OHX10TC'], 0.853418056748699],\n",
       " [['OHX08TC', 'OHX09TC'], 0.8809671598502122],\n",
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       " [['OHX23TC', 'OHX24TC'], 0.8776874244134938],\n",
       " [['OHX23TC', 'OHX25TC'], 0.86058993304382],\n",
       " [['OHX23TC', 'OHX26TC'], 0.8918569150981227],\n",
       " [['OHX24TC', 'OHX25TC'], 0.9258374278895476],\n",
       " [['OHX24TC', 'OHX26TC'], 0.8599098450309847],\n",
       " [['OHX25TC', 'OHX26TC'], 0.8653451576935931],\n",
       " [['OHX02SE', 'OHX15SE'], 0.974620348053586],\n",
       " [['OHX02SE', 'OHX18SE'], 0.8877695179394285],\n",
       " [['OHX02SE', 'OHX31SE'], 0.8886820467487079],\n",
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       " [['OHX07SE', 'OHX10SE'], 0.9653834817687666],\n",
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       " [['OHX15SE', 'OHX31SE'], 0.8954089029845196],\n",
       " [['OHX18SE', 'OHX31SE'], 0.964673144017023],\n",
       " [['OHX19SE', 'OHX30SE'], 0.9150787368917225],\n",
       " [['DR1TKCAL', 'DR1TCARB'], 0.8836201252562111],\n",
       " [['DR1TKCAL', 'DR1TTFAT'], 0.8965570002918656],\n",
       " [['DR1TKCAL', 'DR1TSFAT'], 0.8346930382794032],\n",
       " [['DR1TKCAL', 'DR1TMFAT'], 0.8476812620318654],\n",
       " [['DR1TKCAL', 'DR1TPHOS'], 0.8422369669551835],\n",
       " [['DR1TKCAL', 'DR1TS160'], 0.8561690179377299],\n",
       " [['DR1TKCAL', 'DR1TS180'], 0.8305754785131776],\n",
       " [['DR1TKCAL', 'DR1TM181'], 0.8343841514527328],\n",
       " [['DR1TPROT', 'DR1TCHL'], 0.8167025100470946],\n",
       " [['DR1TPROT', 'DR1TPHOS'], 0.8918814364281406],\n",
       " [['DR1TPROT', 'DR1TSELE'], 0.8586357304851469],\n",
       " [['DR1TCARB', 'DR1TSUGR'], 0.8565457786093096],\n",
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       " [['DR1TTFAT', 'DR1TMFAT'], 0.9645656818619478],\n",
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       " [['DR1TSFAT', 'DR1TM181'], 0.8024208053512673],\n",
       " [['DR1TMFAT', 'DR1TPFAT'], 0.8156484014153618],\n",
       " [['DR1TMFAT', 'DR1TS160'], 0.8630030043598175],\n",
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       " [['DR1TPFAT', 'DR1TP183'], 0.8882325783553329],\n",
       " [['DR1TCHOL', 'DR1TCHL'], 0.88030065049964],\n",
       " [['DR1TCHOL', 'DR1TP204'], 0.9071426818854488],\n",
       " [['DR1TLZ', 'DR1TVK'], 0.9186642625551914],\n",
       " [['DR1TNIAC', 'DR1TVB6'], 0.8423270057040203],\n",
       " [['DR1TFOLA', 'DR1TFA'], 0.8163829812334826],\n",
       " [['DR1TFOLA', 'DR1TFDFE'], 0.9791433034841055],\n",
       " [['DR1TFA', 'DR1TFDFE'], 0.9166653459610049],\n",
       " [['DR1TCHL', 'DR1TP204'], 0.8081693839519434],\n",
       " [['DR1TPHOS', 'DR1TPOTA'], 0.800999077210129],\n",
       " [['DR1TMAGN', 'DR1TPOTA'], 0.8425388269944881],\n",
       " [['DR1TS040', 'DR1TS060'], 0.9433130516277528],\n",
       " [['DR1TS040', 'DR1TS140'], 0.9024999663299825],\n",
       " [['DR1TS060', 'DR1TS140'], 0.9072792186026819],\n",
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       " [['DR1TM181', 'DR1TP182'], 0.834445200685459],\n",
       " [['DR1TP182', 'DR1TP183'], 0.8641165097162119],\n",
       " [['DR1TP205', 'DR1TP226'], 0.9071001159701894],\n",
       " [['DR1TP225', 'DR1TP226'], 0.841283761357697],\n",
       " [['DRD370PQ', 'RXDRSC1_A15'], 0.8660254067685492],\n",
       " [['URXUMA', 'URXUMS'], 1.0],\n",
       " [['URXUMA', 'URDACT'], 0.8450295121912799],\n",
       " [['URXUMS', 'URDACT'], 0.8450295121912799],\n",
       " [['URXUCR.x', 'URXCRS'], 1.0000000000000009],\n",
       " [['WTSAF2YR.x', 'WTSAF2YR.y'], 1.0],\n",
       " [['WTSAF2YR.x', 'WTSAF2YR'], 1.0],\n",
       " [['WTSAF2YR.x', 'WTSOG2YR'], 0.8600060337274194],\n",
       " [['LBXAPB', 'LBDAPBSI'], 1.000000000000049],\n",
       " [['LBXAPB', 'LBDLDL'], 0.8619224929630864],\n",
       " [['LBXAPB', 'LBDLDLSI'], 0.8619184599146442],\n",
       " [['LBDAPBSI', 'LBDLDL'], 0.8619224929630249],\n",
       " [['LBDAPBSI', 'LBDLDLSI'], 0.861918459914588],\n",
       " [['LBXSAL', 'LBDSALSI'], 1.0000000000000222],\n",
       " [['LBXSBU', 'LBDSBUSI'], 0.9999993665304715],\n",
       " [['LBXSCA', 'LBDSCASI'], 1.0],\n",
       " [['LBXSCH', 'LBDSCHSI'], 0.9999999627281284],\n",
       " [['LBXSCH', 'LBXTC'], 0.9310969530661578],\n",
       " [['LBXSCH', 'LBDTCSI'], 0.9311090716361883],\n",
       " [['LBDSCHSI', 'LBXTC'], 0.9311042341046439],\n",
       " [['LBDSCHSI', 'LBDTCSI'], 0.931116369552859],\n",
       " [['LBXSCR', 'LBDSCRSI'], 0.9999999982970765],\n",
       " [['LBXSGB', 'LBDSGBSI'], 1.0000000000000382],\n",
       " [['LBXSGL', 'LBDSGLSI'], 0.9999991231642787],\n",
       " [['LBXSIR', 'LBDSIRSI'], 0.9999886880027609],\n",
       " [['LBXSPH', 'LBDSPHSI'], 0.9999989937025406],\n",
       " [['LBXSTB', 'LBDSTBSI'], 0.9999999995992939],\n",
       " [['LBXSTP', 'LBDSTPSI'], 1.0000000000000226],\n",
       " [['LBXSTR', 'LBDSTRSI'], 0.9999999860267343],\n",
       " [['LBXSUA', 'LBDSUASI'], 0.9999999230435047],\n",
       " [['LBXWBCSI', 'LBDNENO'], 0.8375700094920386],\n",
       " [['LBXLYPCT', 'LBXNEPCT'], -0.9401646050203134],\n",
       " [['LBXEOPCT', 'LBDEONO'], 0.8925797047538557],\n",
       " [['LBXHGB', 'LBXHCT'], 0.9638261635920841],\n",
       " [['LBXMCVSI', 'LBXMCHSI'], 0.9173008992908753],\n",
       " [['WTSA2YR.x', 'WTSA2YR.y'], 1.0],\n",
       " [['WTSA2YR.x', 'WTSA2YR'], 1.0],\n",
       " [['LBXSCU', 'LBDSCUSI'], 0.9999997932322131],\n",
       " [['LBXSSE', 'LBDSSESI'], 0.9999166227512865],\n",
       " [['LBXSZN', 'LBDSZNSI'], 0.9999991024517785],\n",
       " [['WTSB2YR.x', 'WTSB2YR.y'], 0.9915417645619736],\n",
       " [['WTSB2YR.x', 'WTSB2YR'], 1.0],\n",
       " [['URX14D', 'URXDCB'], 0.8913204717295244],\n",
       " [['LBDHDD', 'LBDHDDSI'], 0.9999766821661253],\n",
       " [['ORXGH', 'ORXGL'], 1.0],\n",
       " [['ORXGH', 'ORXH06'], 0.9730590524170353],\n",
       " [['ORXGH', 'ORXH11'], 1.0],\n",
       " [['ORXGH', 'ORXH16'], 0.9341748120072596],\n",
       " [['ORXGH', 'ORXH18'], 0.9862118583924375],\n",
       " [['ORXGH', 'ORXH26'], 1.0],\n",
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       " [['ORXGH', 'ORXH42'], 0.9895974291541869],\n",
       " [['ORXGH', 'ORXH45'], 0.9964907030815249],\n",
       " [['ORXGH', 'ORXH51'], 0.979559021716113],\n",
       " [['ORXGH', 'ORXH52'], 0.9895974291541869],\n",
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       " [['ORXGH', 'ORXH54'], 0.9964907030815237],\n",
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       " [['ORXGH', 'ORXH56'], 0.9698646362086787],\n",
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       " [['ORXGH', 'ORXH59'], 0.9514348276390532],\n",
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       " [['LBDR40.x', 'LBDR58.x'], 0.8531758456666361],\n",
       " [['LBDR40.x', 'LBDR64.x'], 0.9066354586273542],\n",
       " [['LBDR40.x', 'LBDR67.x'], 0.8436128371875712],\n",
       " [['LBDR40.x', 'LBDR68.x'], 0.8003548560876369],\n",
       " [['LBDR40.x', 'LBDR69.x'], 0.906635458627355],\n",
       " [['LBDR40.x', 'LBDR71.x'], 0.8436128371875692],\n",
       " [['LBDR40.x', 'LBDR72.x'], 0.834376359872394],\n",
       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variablesRelacionadas = list()\n",
    "for i in range(len(matrizCorrelacion)):\n",
    "    var1 = matrizCorrelacion.columns[i]\n",
    "    for j in range(len(matrizCorrelacion)):\n",
    "        var2 = matrizCorrelacion.columns[j]\n",
    "        elemento = matrizCorrelacion.iloc[i,j]\n",
    "        if (i<j) & (abs(elemento)>=0.8):\n",
    "            l = list()\n",
    "            l.append([var1, var2])\n",
    "            l.append(elemento)\n",
    "            variablesRelacionadas.append(l)\n",
    "variablesRelacionadas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminamos las variables que están relacionadas, excepto si son enfermedades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in variablesRelacionadas:\n",
    "    if (i[0][1] in df.columns) & (i[0][1] not in variablesMedications):\n",
    "        df.drop([i[0][1]], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convertimos las variables categóricas a numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizamos los datos con StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Maria Jose- Javier\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\Maria Jose- Javier\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "dfN = scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementamos la regla del codo para poder seleccionar visualmente cual es la mejor k para aplicar kMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b35498630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def regla_del_codo(k_max, datos):\n",
    "    SSE = []\n",
    "    for i in range(1, k_max):\n",
    "        kmeans = KMeans(n_clusters = i)\n",
    "        kmeans.fit(datos)\n",
    "        SSE.append(kmeans.inertia_)\n",
    "    plt.plot(range(1, k_max), SSE, 'bd-')\n",
    "    plt.title('Método del codo')\n",
    "    plt.xlabel('k')\n",
    "    plt.ylabel('SSE') \n",
    "plt.show()\n",
    "\n",
    "regla_del_codo(10, dfN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según la gráfica, escogemos k = 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b357cd278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colores = ['r', 'yellowgreen', 'pink', 'yellow', 'b', 'aquamarine']\n",
    "\n",
    "plt.figure(figsize=(20, 10)) \n",
    "\n",
    "colorCluster=[]\n",
    "kmeans = KMeans(n_clusters=6).fit(dfN)\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(dfN)\n",
    "projectedPCA = pca.fit(dfN).transform(dfN)\n",
    "        \n",
    "Lab = kmeans.labels_\n",
    "for i in Lab:\n",
    "    colorCluster.append(colores[i])\n",
    "plt.scatter(projectedPCA[:, 0], projectedPCA[:, 1], c=colorCluster)\n",
    "plt.scatter(pca.transform(kmeans.cluster_centers_)[:, 0], pca.transform(kmeans.cluster_centers_)[:, 1], marker='*', c='k')\n",
    "plt.title('k-Means Para k = 6', fontsize=20)\n",
    "plt.xlabel('Componente 1', fontsize=14)\n",
    "plt.ylabel('Componente 2', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a realizar una predicción de los clústeres con Random Forest para poder obtener que variables son las más importantes para asignar los clústeres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfN = pd.DataFrame(data=dfN, columns=df.columns)\n",
    "X = dfN.copy()\n",
    "y = kmeans.labels_\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=158)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para empezar, elegimos los mejores parámetros para max_depth y n_estimators con RandomizedSearchCV con validación cruzada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Maria Jose- Javier\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'max_depth': range(20, 100), 'n_estimators': range(50, 200)},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parametrosRF = {'max_depth': range(20, 100), 'n_estimators': range(50, 200)}\n",
    "RF = RandomForestClassifier()\n",
    "\n",
    "RSRF = RandomizedSearchCV(RF, param_distributions = parametrosRF, cv = 10)\n",
    "\n",
    "RSRF.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor óptimo para max_depth: 91\n",
      "Valor óptimo para n_estimators: 136\n"
     ]
    }
   ],
   "source": [
    "print('Valor óptimo para max_depth: {}'.format(RSRF.best_params_['max_depth']))\n",
    "print('Valor óptimo para n_estimators: {}'.format(RSRF.best_params_['n_estimators']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtenemos el mejor modelo con los mejores parámetros obtenidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión en el conjunto de test: 94.55%\n"
     ]
    }
   ],
   "source": [
    "RFBestRS = RandomForestClassifier(max_depth=RSRF.best_params_['max_depth'], \n",
    "                                       n_estimators=RSRF.best_params_['n_estimators'])\n",
    "RFBestRS.fit(X_train, y_train)\n",
    "\n",
    "predsRFBestRS = RFBestRS.predict(X_test)\n",
    "\n",
    "accuracy = np.true_divide(np.sum(predsRFBestRS == y_test), predsRFBestRS.shape[0])*100\n",
    "print('Precisión en el conjunto de test: {:.2f}%'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hayamos las variables más importantes y guardamos las 50 más importantes entre todas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La variable en 1ª posición es RIDAGEYR, con una importancia de 0.016688\n",
      "La variable en 2ª posición es OHX04CTC_S, con una importancia de 0.013943\n",
      "La variable en 3ª posición es OHX29CTC_S, con una importancia de 0.01197\n",
      "La variable en 4ª posición es OHX13CTC_S, con una importancia de 0.011789\n",
      "La variable en 5ª posición es OHX20CTC_S, con una importancia de 0.011515\n",
      "La variable en 6ª posición es OHX18TC, con una importancia de 0.010943\n",
      "La variable en 7ª posición es OHX11CTC_P, con una importancia de 0.010511\n",
      "La variable en 8ª posición es OHX23TC, con una importancia de 0.010231\n",
      "La variable en 9ª posición es OHX09CTC_S, con una importancia de 0.01016\n",
      "La variable en 10ª posición es OHX02CTC_U, con una importancia de 0.010122\n",
      "La variable en 11ª posición es OHX02CTC_P, con una importancia de 0.010093\n",
      "La variable en 12ª posición es OHX07TC, con una importancia de 0.009758\n",
      "La variable en 13ª posición es OHX06TC, con una importancia de 0.009281\n",
      "La variable en 14ª posición es OHX02TC, con una importancia de 0.009176\n",
      "La variable en 15ª posición es OHX19TC, con una importancia de 0.009013\n",
      "La variable en 16ª posición es OHX07CTC_S, con una importancia de 0.008967\n",
      "La variable en 17ª posición es OHX11CTC_S, con una importancia de 0.008652\n",
      "La variable en 18ª posición es OHX06CTC_S, con una importancia de 0.00846\n",
      "La variable en 19ª posición es OHX30TC, con una importancia de 0.008162\n",
      "La variable en 20ª posición es OHX14TC, con una importancia de 0.008108\n",
      "La variable en 21ª posición es OHX04CTC_P, con una importancia de 0.008077\n",
      "La variable en 22ª posición es OHX10CTC_P, con una importancia de 0.007913\n",
      "La variable en 23ª posición es SIAPROXY, con una importancia de 0.007816\n",
      "La variable en 24ª posición es OHX08CTC_S, con una importancia de 0.007575\n",
      "La variable en 25ª posición es OHX26CTC_S, con una importancia de 0.00755\n",
      "La variable en 26ª posición es OHX15CTC_P, con una importancia de 0.007449\n",
      "La variable en 27ª posición es WHQ150, con una importancia de 0.007313\n",
      "La variable en 28ª posición es OHX25CTC_S, con una importancia de 0.007193\n",
      "La variable en 29ª posición es OHX12TC, con una importancia de 0.006996\n",
      "La variable en 30ª posición es HSAQUEX, con una importancia de 0.006935\n",
      "La variable en 31ª posición es PAQ706, con una importancia de 0.006694\n",
      "La variable en 32ª posición es DMDHRAGE, con una importancia de 0.006679\n",
      "La variable en 33ª posición es OHX22CTC_S, con una importancia de 0.006565\n",
      "La variable en 34ª posición es OHX05CTC_S, con una importancia de 0.006536\n",
      "La variable en 35ª posición es OHX09CTC_P, con una importancia de 0.006421\n",
      "La variable en 36ª posición es OHX04TC, con una importancia de 0.00617\n",
      "La variable en 37ª posición es OHX31TC, con una importancia de 0.006044\n",
      "La variable en 38ª posición es OHX29TC, con una importancia de 0.005898\n",
      "La variable en 39ª posición es OHX05CTC_P, con una importancia de 0.005707\n",
      "La variable en 40ª posición es OHX15CTC_U, con una importancia de 0.00554\n",
      "La variable en 41ª posición es OHX12CTC_S, con una importancia de 0.005415\n",
      "La variable en 42ª posición es SMAQUEX.x, con una importancia de 0.005283\n",
      "La variable en 43ª posición es OHX30CTC_P, con una importancia de 0.005198\n",
      "La variable en 44ª posición es OHX10CTC_S, con una importancia de 0.005194\n",
      "La variable en 45ª posición es OHX27CTC_S, con una importancia de 0.005038\n",
      "La variable en 46ª posición es OHX05TC, con una importancia de 0.005022\n",
      "La variable en 47ª posición es OHX03CTC_P, con una importancia de 0.005014\n",
      "La variable en 48ª posición es OHX13TC, con una importancia de 0.004973\n",
      "La variable en 49ª posición es OHX06CTC_P, con una importancia de 0.004903\n",
      "La variable en 50ª posición es OHX03TC, con una importancia de 0.004902\n"
     ]
    }
   ],
   "source": [
    "importancias_variables = RFBestRS.feature_importances_\n",
    "mejores = np.argsort(importancias_variables)[-50:][::-1]\n",
    "mejoresTodas = np.argsort(importancias_variables)[::-1]\n",
    "\n",
    "posicion=1\n",
    "for variable in mejores:\n",
    "    print ('La variable en {}ª posición es {}, con una importancia de {}'.format(posicion, X.iloc[:,variable].name,\n",
    "                                                                                   round(importancias_variables[variable], 6)))\n",
    "    posicion+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos que enfermedades tienen por clústeres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster1 = list()\n",
    "cluster2 = list()\n",
    "cluster3 = list()\n",
    "cluster4 = list()\n",
    "cluster5 = list()\n",
    "cluster6 = list()\n",
    "\n",
    "for p in range(len(Lab)):\n",
    "    if Lab[p]==0:\n",
    "        cluster1.append(df.iloc[p])\n",
    "    elif Lab[p]==1:\n",
    "        cluster2.append(df.iloc[p])\n",
    "    elif Lab[p]==2:\n",
    "        cluster3.append(df.iloc[p])\n",
    "    elif Lab[p]==3:\n",
    "        cluster4.append(df.iloc[p])\n",
    "    elif Lab[p]==4:\n",
    "        cluster5.append(df.iloc[p])\n",
    "    elif Lab[p]==5:\n",
    "        cluster6.append(df.iloc[p])\n",
    "\n",
    "cluster1df = pd.DataFrame(data=cluster1)\n",
    "cluster2df = pd.DataFrame(data=cluster2)\n",
    "cluster3df = pd.DataFrame(data=cluster3)\n",
    "cluster4df = pd.DataFrame(data=cluster4)\n",
    "cluster5df = pd.DataFrame(data=cluster5)\n",
    "cluster6df = pd.DataFrame(data=cluster6)\n",
    "\n",
    "#Genero csv para poder visualizar todos los registros.\n",
    "# cluster1df.to_csv('cluster1df.csv', index=False, header=True)\n",
    "# cluster2df.to_csv('cluster2df.csv', index=False, header=True)\n",
    "# cluster3df.to_csv('cluster3df.csv', index=False, header=True)\n",
    "# cluster4df.to_csv('cluster4df.csv', index=False, header=True)\n",
    "# cluster5df.to_csv('cluster5df.csv', index=False, header=True)\n",
    "# cluster6df.to_csv('cluster6df.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster1Enfdf = cluster1df[variablesMedications]\n",
    "cluster2Enfdf = cluster2df[variablesMedications]\n",
    "cluster3Enfdf = cluster3df[variablesMedications]\n",
    "cluster4Enfdf = cluster4df[variablesMedications]\n",
    "cluster5Enfdf = cluster5df[variablesMedications]\n",
    "cluster6Enfdf = cluster6df[variablesMedications]\n",
    "\n",
    "\n",
    "# cluster1Enfdf.to_csv('cluster1Enfdf.csv', index=False, header=True)\n",
    "# cluster2Enfdf.to_csv('cluster2Enfdf.csv', index=False, header=True)\n",
    "# cluster3Enfdf.to_csv('cluster3Enfdf.csv', index=False, header=True)\n",
    "# cluster4Enfdf.to_csv('cluster4Enfdf.csv', index=False, header=True)\n",
    "# cluster5Enfdf.to_csv('cluster5Enfdf.csv', index=False, header=True)\n",
    "# cluster6Enfdf.to_csv('cluster6Enfdf.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "[1 rows x 471 columns]"
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster1Enfdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "      <th>RXDRSC1_Z91.030</th>\n",
       "      <th>RXDRSC1_Z94.0</th>\n",
       "      <th>RXDRSC1_Z95.0</th>\n",
       "      <th>RXDRSC1_Z95.2</th>\n",
       "      <th>RXDRSC1_Z95.5</th>\n",
       "      <th>RXDRSC1_Z95.9</th>\n",
       "      <th>RXDRSC1_Z96.64</th>\n",
       "      <th>RXDRSC1_Z96.65</th>\n",
       "      <th>RXDRSC1_Z98.84</th>\n",
       "      <th>RXDRSC1_Z99.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73583.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>73630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>73680.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>73686.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 471 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEQN  RXDRSC1_A08.4  RXDRSC1_A09  RXDRSC1_A15  RXDRSC1_A15.P  \\\n",
       "9   73572.0            0.0          0.0          0.0            0.0   \n",
       "11  73583.0            0.0          0.0          0.0            0.0   \n",
       "28  73630.0            0.0          0.0          0.0            0.0   \n",
       "52  73680.0            0.0          0.0          0.0            0.0   \n",
       "54  73686.0            0.0          0.0          0.0            0.0   \n",
       "\n",
       "    RXDRSC1_A41.9  RXDRSC1_A44.9  RXDRSC1_A49.02  RXDRSC1_A49.9  \\\n",
       "9             0.0            0.0             0.0            0.0   \n",
       "11            0.0            0.0             0.0            0.0   \n",
       "28            0.0            0.0             0.0            0.0   \n",
       "52            0.0            0.0             0.0            0.0   \n",
       "54            0.0            0.0             0.0            0.0   \n",
       "\n",
       "    RXDRSC1_A49.9P  ...  RXDRSC1_Z91.030  RXDRSC1_Z94.0  RXDRSC1_Z95.0  \\\n",
       "9              0.0  ...              0.0            0.0            0.0   \n",
       "11             0.0  ...              0.0            0.0            0.0   \n",
       "28             0.0  ...              0.0            0.0            0.0   \n",
       "52             0.0  ...              0.0            0.0            0.0   \n",
       "54             0.0  ...              0.0            0.0            0.0   \n",
       "\n",
       "    RXDRSC1_Z95.2  RXDRSC1_Z95.5  RXDRSC1_Z95.9  RXDRSC1_Z96.64  \\\n",
       "9             0.0            0.0            0.0             0.0   \n",
       "11            0.0            0.0            0.0             0.0   \n",
       "28            0.0            0.0            0.0             0.0   \n",
       "52            0.0            0.0            0.0             0.0   \n",
       "54            0.0            0.0            0.0             0.0   \n",
       "\n",
       "    RXDRSC1_Z96.65  RXDRSC1_Z98.84  RXDRSC1_Z99.2  \n",
       "9              0.0             0.0            0.0  \n",
       "11             0.0             0.0            0.0  \n",
       "28             0.0             0.0            0.0  \n",
       "52             0.0             0.0            0.0  \n",
       "54             0.0             0.0            0.0  \n",
       "\n",
       "[5 rows x 471 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster5Enfdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQN</th>\n",
       "      <th>RXDRSC1_A08.4</th>\n",
       "      <th>RXDRSC1_A09</th>\n",
       "      <th>RXDRSC1_A15</th>\n",
       "      <th>RXDRSC1_A15.P</th>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>73612.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>73620.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>73635.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>73679.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>73838.0</td>\n",
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       "      <td>0.0</td>\n",
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       "<p>5 rows × 471 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SEQN  RXDRSC1_A08.4  RXDRSC1_A09  RXDRSC1_A15  RXDRSC1_A15.P  \\\n",
       "16   73612.0            0.0          0.0          0.0            0.0   \n",
       "21   73620.0            0.0          0.0          0.0            0.0   \n",
       "31   73635.0            0.0          0.0          0.0            0.0   \n",
       "51   73679.0            0.0          0.0          0.0            0.0   \n",
       "120  73838.0            0.0          0.0          0.0            0.0   \n",
       "\n",
       "     RXDRSC1_A41.9  RXDRSC1_A44.9  RXDRSC1_A49.02  RXDRSC1_A49.9  \\\n",
       "16             0.0            0.0             0.0            0.0   \n",
       "21             0.0            0.0             0.0            0.0   \n",
       "31             0.0            0.0             0.0            0.0   \n",
       "51             0.0            0.0             0.0            0.0   \n",
       "120            0.0            0.0             0.0            0.0   \n",
       "\n",
       "     RXDRSC1_A49.9P  ...  RXDRSC1_Z91.030  RXDRSC1_Z94.0  RXDRSC1_Z95.0  \\\n",
       "16              0.0  ...              0.0            0.0            0.0   \n",
       "21              0.0  ...              0.0            0.0            0.0   \n",
       "31              0.0  ...              0.0            0.0            0.0   \n",
       "51              0.0  ...              0.0            0.0            0.0   \n",
       "120             0.0  ...              0.0            0.0            0.0   \n",
       "\n",
       "     RXDRSC1_Z95.2  RXDRSC1_Z95.5  RXDRSC1_Z95.9  RXDRSC1_Z96.64  \\\n",
       "16             0.0            0.0            0.0             0.0   \n",
       "21             0.0            0.0            0.0             0.0   \n",
       "31             0.0            0.0            0.0             0.0   \n",
       "51             0.0            0.0            0.0             0.0   \n",
       "120            0.0            0.0            0.0             0.0   \n",
       "\n",
       "     RXDRSC1_Z96.65  RXDRSC1_Z98.84  RXDRSC1_Z99.2  \n",
       "16              0.0             0.0            0.0  \n",
       "21              0.0             0.0            0.0  \n",
       "31              0.0             0.0            0.0  \n",
       "51              0.0             0.0            0.0  \n",
       "120             0.0             0.0            0.0  \n",
       "\n",
       "[5 rows x 471 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster6Enfdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contador (clusterdf):\n",
    "    clusterContador = clusterdf.astype(bool).sum(axis=0).sort_values(ascending=False)\n",
    "    clusterContador = clusterContador[clusterContador!=0]\n",
    "    return clusterContador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster1Contador = contador(cluster1Enfdf)\n",
    "cluster2Contador = contador(cluster2Enfdf)\n",
    "cluster3Contador = contador(cluster3Enfdf)\n",
    "cluster4Contador = contador(cluster4Enfdf)\n",
    "cluster5Contador = contador(cluster5Enfdf)\n",
    "cluster6Contador = contador(cluster6Enfdf)\n",
    "\n",
    "# cluster1Contador.to_csv('cluster1Contador.csv', index=True, header=True)\n",
    "# cluster2Contador.to_csv('cluster2Contador.csv', index=True, header=True)\n",
    "# cluster3Contador.to_csv('cluster3Contador.csv', index=True, header=True)\n",
    "# cluster4Contador.to_csv('cluster4Contador.csv', index=True, header=True)\n",
    "# cluster5Contador.to_csv('cluster5Contador.csv', index=True, header=True)\n",
    "# cluster6Contador.to_csv('cluster6Contador.csv', index=True, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos que tienen en común cada clúster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tamaño de cada cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamaño del cluster1: 1\n",
      "Tamaño del cluster2: 1683\n",
      "Tamaño del cluster3: 611\n",
      "Tamaño del cluster4: 1199\n",
      "Tamaño del cluster5: 330\n",
      "Tamaño del cluster6: 192\n"
     ]
    }
   ],
   "source": [
    "print('Tamaño del cluster1: {}'.format(len(cluster1)))\n",
    "print('Tamaño del cluster2: {}'.format(len(cluster2)))\n",
    "print('Tamaño del cluster3: {}'.format(len(cluster3)))\n",
    "print('Tamaño del cluster4: {}'.format(len(cluster4)))\n",
    "print('Tamaño del cluster5: {}'.format(len(cluster5)))\n",
    "print('Tamaño del cluster6: {}'.format(len(cluster6)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos escogido $k = 6$, sin embargo en la gráfica del clustering solamente distinguimos 5. Esto es debido a que el cluster1 tiene un único registro y es el centroide del cluster, que en la representación tiene la forma de estrella en negro. Por eso no vemos ningún punto en rojo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hallamos los datos en su descomposición en PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterPCA1=[]\n",
    "clusterPCA2=[]\n",
    "clusterPCA3=[]\n",
    "clusterPCA4=[]\n",
    "clusterPCA5=[]\n",
    "clusterPCA6=[]\n",
    "\n",
    "for p in range(len(Lab)):\n",
    "    if Lab[p]==0:\n",
    "        clusterPCA1.append(projectedPCA[p])\n",
    "    elif Lab[p]==1:\n",
    "        clusterPCA2.append(projectedPCA[p])\n",
    "    elif Lab[p]==2:\n",
    "        clusterPCA3.append(projectedPCA[p])\n",
    "    elif Lab[p]==3:\n",
    "        clusterPCA4.append(projectedPCA[p])\n",
    "    elif Lab[p]==4:\n",
    "        clusterPCA5.append(projectedPCA[p])\n",
    "    elif Lab[p]==5:\n",
    "        clusterPCA6.append(projectedPCA[p])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El elemento del cluster1 en PCA es:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 3.34917147, -2.08719642])]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterPCA1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la gráfica podemos ver que es cercano al cluster amarillo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.49604152, -2.20411391])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.transform(kmeans.cluster_centers_)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mejoresNombres = [X.iloc[:,m].name for m in mejores]\n",
    "d1 = cluster1df[mejoresNombres].describe()\n",
    "d2 = cluster2df[mejoresNombres].describe()\n",
    "d3 = cluster3df[mejoresNombres].describe()\n",
    "d4 = cluster4df[mejoresNombres].describe()\n",
    "d5 = cluster5df[mejoresNombres].describe()\n",
    "d6 = cluster6df[mejoresNombres].describe()\n",
    "\n",
    "d1.to_csv('cluster1Describe.csv', index=True, header=True)\n",
    "d2.to_csv('cluster2Describe.csv', index=True, header=True)\n",
    "d3.to_csv('cluster3Describe.csv', index=True, header=True)\n",
    "d4.to_csv('cluster4Describe.csv', index=True, header=True)\n",
    "d5.to_csv('cluster5Describe.csv', index=True, header=True)\n",
    "d6.to_csv('cluster6Describe.csv', index=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
       "      <th>OHX29CTC_S</th>\n",
       "      <th>OHX13CTC_S</th>\n",
       "      <th>OHX20CTC_S</th>\n",
       "      <th>OHX18TC</th>\n",
       "      <th>OHX11CTC_P</th>\n",
       "      <th>OHX23TC</th>\n",
       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
       "      <th>OHX03TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RIDAGEYR  OHX04CTC_S  OHX29CTC_S  OHX13CTC_S  OHX20CTC_S  OHX18TC  \\\n",
       "count       1.0         1.0         1.0         1.0         1.0      1.0   \n",
       "mean       56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "std         NaN         NaN         NaN         NaN         NaN      NaN   \n",
       "min        56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "25%        56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "50%        56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "75%        56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "max        56.0         0.0         0.0         0.0         1.0      2.0   \n",
       "\n",
       "       OHX11CTC_P  OHX23TC  OHX09CTC_S  OHX02CTC_U  ...  OHX12CTC_S  \\\n",
       "count         1.0      1.0         1.0         1.0  ...         1.0   \n",
       "mean          0.0      2.0         1.0         0.0  ...         0.0   \n",
       "std           NaN      NaN         NaN         NaN  ...         NaN   \n",
       "min           0.0      2.0         1.0         0.0  ...         0.0   \n",
       "25%           0.0      2.0         1.0         0.0  ...         0.0   \n",
       "50%           0.0      2.0         1.0         0.0  ...         0.0   \n",
       "75%           0.0      2.0         1.0         0.0  ...         0.0   \n",
       "max           0.0      2.0         1.0         0.0  ...         0.0   \n",
       "\n",
       "       SMAQUEX.x  OHX30CTC_P  OHX10CTC_S  OHX27CTC_S  OHX05TC  OHX03CTC_P  \\\n",
       "count        1.0         1.0         1.0         1.0      1.0         1.0   \n",
       "mean         2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "std          NaN         NaN         NaN         NaN      NaN         NaN   \n",
       "min          2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "25%          2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "50%          2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "75%          2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "max          2.0         0.0         1.0         1.0      4.0         0.0   \n",
       "\n",
       "       OHX13TC  OHX06CTC_P  OHX03TC  \n",
       "count      1.0         1.0      1.0  \n",
       "mean       4.0         0.0      4.0  \n",
       "std        NaN         NaN      NaN  \n",
       "min        4.0         0.0      4.0  \n",
       "25%        4.0         0.0      4.0  \n",
       "50%        4.0         0.0      4.0  \n",
       "75%        4.0         0.0      4.0  \n",
       "max        4.0         0.0      4.0  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
       "      <th>OHX29CTC_S</th>\n",
       "      <th>OHX13CTC_S</th>\n",
       "      <th>OHX20CTC_S</th>\n",
       "      <th>OHX18TC</th>\n",
       "      <th>OHX11CTC_P</th>\n",
       "      <th>OHX23TC</th>\n",
       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1683.00000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "      <td>1683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.463458</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.816399</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.816993</td>\n",
       "      <td>2.168746</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>2.008318</td>\n",
       "      <td>0.897802</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.78372</td>\n",
       "      <td>1.995247</td>\n",
       "      <td>0.004159</td>\n",
       "      <td>0.906120</td>\n",
       "      <td>0.986334</td>\n",
       "      <td>2.164587</td>\n",
       "      <td>0.004753</td>\n",
       "      <td>2.086750</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>2.141414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.436130</td>\n",
       "      <td>0.408932</td>\n",
       "      <td>0.387273</td>\n",
       "      <td>0.424309</td>\n",
       "      <td>0.386787</td>\n",
       "      <td>0.568744</td>\n",
       "      <td>0.042195</td>\n",
       "      <td>0.128754</td>\n",
       "      <td>0.302999</td>\n",
       "      <td>0.024376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41183</td>\n",
       "      <td>0.316004</td>\n",
       "      <td>0.064377</td>\n",
       "      <td>0.291748</td>\n",
       "      <td>0.116135</td>\n",
       "      <td>0.554631</td>\n",
       "      <td>0.068801</td>\n",
       "      <td>0.439797</td>\n",
       "      <td>0.034462</td>\n",
       "      <td>0.536616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RIDAGEYR   OHX04CTC_S   OHX29CTC_S   OHX13CTC_S   OHX20CTC_S  \\\n",
       "count  1683.000000  1683.000000  1683.000000  1683.000000  1683.000000   \n",
       "mean     40.463458     0.787879     0.816399     0.764706     0.816993   \n",
       "std      18.436130     0.408932     0.387273     0.424309     0.386787   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      27.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "50%      41.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "75%      53.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "max      80.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           OHX18TC   OHX11CTC_P      OHX23TC   OHX09CTC_S   OHX02CTC_U  ...  \\\n",
       "count  1683.000000  1683.000000  1683.000000  1683.000000  1683.000000  ...   \n",
       "mean      2.168746     0.001783     2.008318     0.897802     0.000594  ...   \n",
       "std       0.568744     0.042195     0.128754     0.302999     0.024376  ...   \n",
       "min       2.000000     0.000000     2.000000     0.000000     0.000000  ...   \n",
       "25%       2.000000     0.000000     2.000000     1.000000     0.000000  ...   \n",
       "50%       2.000000     0.000000     2.000000     1.000000     0.000000  ...   \n",
       "75%       2.000000     0.000000     2.000000     1.000000     0.000000  ...   \n",
       "max       5.000000     1.000000     4.000000     1.000000     1.000000  ...   \n",
       "\n",
       "       OHX12CTC_S    SMAQUEX.x   OHX30CTC_P   OHX10CTC_S   OHX27CTC_S  \\\n",
       "count  1683.00000  1683.000000  1683.000000  1683.000000  1683.000000   \n",
       "mean      0.78372     1.995247     0.004159     0.906120     0.986334   \n",
       "std       0.41183     0.316004     0.064377     0.291748     0.116135   \n",
       "min       0.00000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.00000     2.000000     0.000000     1.000000     1.000000   \n",
       "50%       1.00000     2.000000     0.000000     1.000000     1.000000   \n",
       "75%       1.00000     2.000000     0.000000     1.000000     1.000000   \n",
       "max       1.00000     3.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           OHX05TC   OHX03CTC_P      OHX13TC   OHX06CTC_P      OHX03TC  \n",
       "count  1683.000000  1683.000000  1683.000000  1683.000000  1683.000000  \n",
       "mean      2.164587     0.004753     2.086750     0.001188     2.141414  \n",
       "std       0.554631     0.068801     0.439797     0.034462     0.536616  \n",
       "min       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "25%       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "50%       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "75%       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "max       5.000000     1.000000     5.000000     1.000000     5.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
       "      <th>OHX29CTC_S</th>\n",
       "      <th>OHX13CTC_S</th>\n",
       "      <th>OHX20CTC_S</th>\n",
       "      <th>OHX18TC</th>\n",
       "      <th>OHX11CTC_P</th>\n",
       "      <th>OHX23TC</th>\n",
       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
       "      <th>OHX03TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.0</td>\n",
       "      <td>...</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.0</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.806874</td>\n",
       "      <td>0.014730</td>\n",
       "      <td>0.047463</td>\n",
       "      <td>0.009820</td>\n",
       "      <td>0.054010</td>\n",
       "      <td>3.914894</td>\n",
       "      <td>0.772504</td>\n",
       "      <td>3.448445</td>\n",
       "      <td>0.027823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016367</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.672668</td>\n",
       "      <td>0.022913</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>3.932897</td>\n",
       "      <td>0.828151</td>\n",
       "      <td>3.924714</td>\n",
       "      <td>0.793781</td>\n",
       "      <td>3.967267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.455488</td>\n",
       "      <td>0.120569</td>\n",
       "      <td>0.212802</td>\n",
       "      <td>0.098689</td>\n",
       "      <td>0.226222</td>\n",
       "      <td>0.427679</td>\n",
       "      <td>0.419559</td>\n",
       "      <td>0.931354</td>\n",
       "      <td>0.164601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469624</td>\n",
       "      <td>0.149750</td>\n",
       "      <td>0.409599</td>\n",
       "      <td>0.417347</td>\n",
       "      <td>0.377559</td>\n",
       "      <td>0.405973</td>\n",
       "      <td>0.404921</td>\n",
       "      <td>0.311909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         RIDAGEYR  OHX04CTC_S  OHX29CTC_S  OHX13CTC_S  OHX20CTC_S     OHX18TC  \\\n",
       "count  611.000000  611.000000  611.000000  611.000000  611.000000  611.000000   \n",
       "mean    66.806874    0.014730    0.047463    0.009820    0.054010    3.914894   \n",
       "std     11.455488    0.120569    0.212802    0.098689    0.226222    0.427679   \n",
       "min     30.000000    0.000000    0.000000    0.000000    0.000000    2.000000   \n",
       "25%     60.000000    0.000000    0.000000    0.000000    0.000000    4.000000   \n",
       "50%     69.000000    0.000000    0.000000    0.000000    0.000000    4.000000   \n",
       "75%     76.000000    0.000000    0.000000    0.000000    0.000000    4.000000   \n",
       "max     80.000000    1.000000    1.000000    1.000000    1.000000    5.000000   \n",
       "\n",
       "       OHX11CTC_P     OHX23TC  OHX09CTC_S  OHX02CTC_U  ...  OHX12CTC_S  \\\n",
       "count  611.000000  611.000000  611.000000       611.0  ...  611.000000   \n",
       "mean     0.772504    3.448445    0.027823         0.0  ...    0.016367   \n",
       "std      0.419559    0.931354    0.164601         0.0  ...    0.126985   \n",
       "min      0.000000    2.000000    0.000000         0.0  ...    0.000000   \n",
       "25%      1.000000    2.000000    0.000000         0.0  ...    0.000000   \n",
       "50%      1.000000    4.000000    0.000000         0.0  ...    0.000000   \n",
       "75%      1.000000    4.000000    0.000000         0.0  ...    0.000000   \n",
       "max      1.000000    5.000000    1.000000         0.0  ...    1.000000   \n",
       "\n",
       "       SMAQUEX.x  OHX30CTC_P  OHX10CTC_S  OHX27CTC_S     OHX05TC  OHX03CTC_P  \\\n",
       "count      611.0  611.000000  611.000000  611.000000  611.000000  611.000000   \n",
       "mean         2.0    0.672668    0.022913    0.212766    3.932897    0.828151   \n",
       "std          0.0    0.469624    0.149750    0.409599    0.417347    0.377559   \n",
       "min          2.0    0.000000    0.000000    0.000000    2.000000    0.000000   \n",
       "25%          2.0    0.000000    0.000000    0.000000    4.000000    1.000000   \n",
       "50%          2.0    1.000000    0.000000    0.000000    4.000000    1.000000   \n",
       "75%          2.0    1.000000    0.000000    0.000000    4.000000    1.000000   \n",
       "max          2.0    1.000000    1.000000    1.000000    5.000000    1.000000   \n",
       "\n",
       "          OHX13TC  OHX06CTC_P     OHX03TC  \n",
       "count  611.000000  611.000000  611.000000  \n",
       "mean     3.924714    0.793781    3.967267  \n",
       "std      0.405973    0.404921    0.311909  \n",
       "min      2.000000    0.000000    2.000000  \n",
       "25%      4.000000    1.000000    4.000000  \n",
       "50%      4.000000    1.000000    4.000000  \n",
       "75%      4.000000    1.000000    4.000000  \n",
       "max      5.000000    1.000000    5.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
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       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
       "      <th>OHX03TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.572977</td>\n",
       "      <td>0.166806</td>\n",
       "      <td>0.253545</td>\n",
       "      <td>0.177648</td>\n",
       "      <td>0.254379</td>\n",
       "      <td>2.908257</td>\n",
       "      <td>0.012510</td>\n",
       "      <td>2.083403</td>\n",
       "      <td>0.446205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215179</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.048374</td>\n",
       "      <td>0.440367</td>\n",
       "      <td>0.851543</td>\n",
       "      <td>2.612177</td>\n",
       "      <td>0.058382</td>\n",
       "      <td>2.637198</td>\n",
       "      <td>0.015013</td>\n",
       "      <td>2.754796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.175836</td>\n",
       "      <td>0.372958</td>\n",
       "      <td>0.435221</td>\n",
       "      <td>0.382376</td>\n",
       "      <td>0.435693</td>\n",
       "      <td>1.022252</td>\n",
       "      <td>0.111195</td>\n",
       "      <td>0.399979</td>\n",
       "      <td>0.497305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.411118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214644</td>\n",
       "      <td>0.496638</td>\n",
       "      <td>0.355701</td>\n",
       "      <td>0.978339</td>\n",
       "      <td>0.234562</td>\n",
       "      <td>0.985361</td>\n",
       "      <td>0.121653</td>\n",
       "      <td>1.011581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RIDAGEYR   OHX04CTC_S   OHX29CTC_S   OHX13CTC_S   OHX20CTC_S  \\\n",
       "count  1199.000000  1199.000000  1199.000000  1199.000000  1199.000000   \n",
       "mean     64.572977     0.166806     0.253545     0.177648     0.254379   \n",
       "std      11.175836     0.372958     0.435221     0.382376     0.435693   \n",
       "min      22.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      57.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%      65.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%      74.000000     0.000000     1.000000     0.000000     1.000000   \n",
       "max      80.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           OHX18TC   OHX11CTC_P      OHX23TC   OHX09CTC_S  OHX02CTC_U  ...  \\\n",
       "count  1199.000000  1199.000000  1199.000000  1199.000000      1199.0  ...   \n",
       "mean      2.908257     0.012510     2.083403     0.446205         0.0  ...   \n",
       "std       1.022252     0.111195     0.399979     0.497305         0.0  ...   \n",
       "min       2.000000     0.000000     2.000000     0.000000         0.0  ...   \n",
       "25%       2.000000     0.000000     2.000000     0.000000         0.0  ...   \n",
       "50%       2.000000     0.000000     2.000000     0.000000         0.0  ...   \n",
       "75%       4.000000     0.000000     2.000000     1.000000         0.0  ...   \n",
       "max       5.000000     1.000000     4.000000     1.000000         0.0  ...   \n",
       "\n",
       "        OHX12CTC_S  SMAQUEX.x   OHX30CTC_P   OHX10CTC_S   OHX27CTC_S  \\\n",
       "count  1199.000000     1199.0  1199.000000  1199.000000  1199.000000   \n",
       "mean      0.215179        2.0     0.048374     0.440367     0.851543   \n",
       "std       0.411118        0.0     0.214644     0.496638     0.355701   \n",
       "min       0.000000        2.0     0.000000     0.000000     0.000000   \n",
       "25%       0.000000        2.0     0.000000     0.000000     1.000000   \n",
       "50%       0.000000        2.0     0.000000     0.000000     1.000000   \n",
       "75%       0.000000        2.0     0.000000     1.000000     1.000000   \n",
       "max       1.000000        2.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "           OHX05TC   OHX03CTC_P      OHX13TC   OHX06CTC_P      OHX03TC  \n",
       "count  1199.000000  1199.000000  1199.000000  1199.000000  1199.000000  \n",
       "mean      2.612177     0.058382     2.637198     0.015013     2.754796  \n",
       "std       0.978339     0.234562     0.985361     0.121653     1.011581  \n",
       "min       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "25%       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "50%       2.000000     0.000000     2.000000     0.000000     2.000000  \n",
       "75%       4.000000     0.000000     4.000000     0.000000     4.000000  \n",
       "max       5.000000     1.000000     5.000000     1.000000     5.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
       "      <th>OHX29CTC_S</th>\n",
       "      <th>OHX13CTC_S</th>\n",
       "      <th>OHX20CTC_S</th>\n",
       "      <th>OHX18TC</th>\n",
       "      <th>OHX11CTC_P</th>\n",
       "      <th>OHX23TC</th>\n",
       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
       "      <th>OHX03TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.0</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.0</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.0</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>330.0</td>\n",
       "      <td>330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.503030</td>\n",
       "      <td>0.463636</td>\n",
       "      <td>0.478788</td>\n",
       "      <td>0.457576</td>\n",
       "      <td>0.451515</td>\n",
       "      <td>3.115152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.987879</td>\n",
       "      <td>0.942424</td>\n",
       "      <td>0.630303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.536364</td>\n",
       "      <td>2.290909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.827273</td>\n",
       "      <td>0.551515</td>\n",
       "      <td>1.639394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.521212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.006061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.616581</td>\n",
       "      <td>0.499433</td>\n",
       "      <td>0.500308</td>\n",
       "      <td>0.498953</td>\n",
       "      <td>0.498399</td>\n",
       "      <td>0.994856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220193</td>\n",
       "      <td>0.233293</td>\n",
       "      <td>0.483456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499433</td>\n",
       "      <td>0.958204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.378585</td>\n",
       "      <td>0.498094</td>\n",
       "      <td>0.628811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.568557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         RIDAGEYR  OHX04CTC_S  OHX29CTC_S  OHX13CTC_S  OHX20CTC_S     OHX18TC  \\\n",
       "count  330.000000  330.000000  330.000000  330.000000  330.000000  330.000000   \n",
       "mean    10.503030    0.463636    0.478788    0.457576    0.451515    3.115152   \n",
       "std      2.616581    0.499433    0.500308    0.498953    0.498399    0.994856   \n",
       "min      5.000000    0.000000    0.000000    0.000000    0.000000    2.000000   \n",
       "25%      8.000000    0.000000    0.000000    0.000000    0.000000    2.000000   \n",
       "50%     10.000000    0.000000    0.000000    0.000000    0.000000    4.000000   \n",
       "75%     12.000000    1.000000    1.000000    1.000000    1.000000    4.000000   \n",
       "max     16.000000    1.000000    1.000000    1.000000    1.000000    4.000000   \n",
       "\n",
       "       OHX11CTC_P     OHX23TC  OHX09CTC_S  OHX02CTC_U  ...  OHX12CTC_S  \\\n",
       "count       330.0  330.000000  330.000000  330.000000  ...  330.000000   \n",
       "mean          0.0    1.987879    0.942424    0.630303  ...    0.536364   \n",
       "std           0.0    0.220193    0.233293    0.483456  ...    0.499433   \n",
       "min           0.0    1.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%           0.0    2.000000    1.000000    0.000000  ...    0.000000   \n",
       "50%           0.0    2.000000    1.000000    1.000000  ...    1.000000   \n",
       "75%           0.0    2.000000    1.000000    1.000000  ...    1.000000   \n",
       "max           0.0    4.000000    1.000000    1.000000  ...    1.000000   \n",
       "\n",
       "        SMAQUEX.x  OHX30CTC_P  OHX10CTC_S  OHX27CTC_S     OHX05TC  OHX03CTC_P  \\\n",
       "count  330.000000       330.0  330.000000  330.000000  330.000000       330.0   \n",
       "mean     2.290909         0.0    0.827273    0.551515    1.639394         0.0   \n",
       "std      0.958204         0.0    0.378585    0.498094    0.628811         0.0   \n",
       "min      1.000000         0.0    0.000000    0.000000    1.000000         0.0   \n",
       "25%      1.000000         0.0    1.000000    0.000000    1.000000         0.0   \n",
       "50%      3.000000         0.0    1.000000    1.000000    2.000000         0.0   \n",
       "75%      3.000000         0.0    1.000000    1.000000    2.000000         0.0   \n",
       "max      3.000000         0.0    1.000000    1.000000    4.000000         0.0   \n",
       "\n",
       "          OHX13TC  OHX06CTC_P     OHX03TC  \n",
       "count  330.000000       330.0  330.000000  \n",
       "mean     1.521212         0.0    2.006061  \n",
       "std      0.568557         0.0    0.110096  \n",
       "min      1.000000         0.0    2.000000  \n",
       "25%      1.000000         0.0    2.000000  \n",
       "50%      1.000000         0.0    2.000000  \n",
       "75%      2.000000         0.0    2.000000  \n",
       "max      4.000000         0.0    4.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>RIDAGEYR</th>\n",
       "      <th>OHX04CTC_S</th>\n",
       "      <th>OHX29CTC_S</th>\n",
       "      <th>OHX13CTC_S</th>\n",
       "      <th>OHX20CTC_S</th>\n",
       "      <th>OHX18TC</th>\n",
       "      <th>OHX11CTC_P</th>\n",
       "      <th>OHX23TC</th>\n",
       "      <th>OHX09CTC_S</th>\n",
       "      <th>OHX02CTC_U</th>\n",
       "      <th>...</th>\n",
       "      <th>OHX12CTC_S</th>\n",
       "      <th>SMAQUEX.x</th>\n",
       "      <th>OHX30CTC_P</th>\n",
       "      <th>OHX10CTC_S</th>\n",
       "      <th>OHX27CTC_S</th>\n",
       "      <th>OHX05TC</th>\n",
       "      <th>OHX03CTC_P</th>\n",
       "      <th>OHX13TC</th>\n",
       "      <th>OHX06CTC_P</th>\n",
       "      <th>OHX03TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>192.00000</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>192.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>192.0</td>\n",
       "      <td>192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.145833</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.609375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.84192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.613618</td>\n",
       "      <td>0.143200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.210130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RIDAGEYR  OHX04CTC_S  OHX29CTC_S  OHX13CTC_S  OHX20CTC_S  OHX18TC  \\\n",
       "count  192.00000       192.0       192.0       192.0       192.0    192.0   \n",
       "mean     3.50000         0.0         0.0         0.0         0.0      4.0   \n",
       "std      1.84192         0.0         0.0         0.0         0.0      0.0   \n",
       "min      1.00000         0.0         0.0         0.0         0.0      4.0   \n",
       "25%      2.00000         0.0         0.0         0.0         0.0      4.0   \n",
       "50%      3.00000         0.0         0.0         0.0         0.0      4.0   \n",
       "75%      5.00000         0.0         0.0         0.0         0.0      4.0   \n",
       "max      8.00000         0.0         0.0         0.0         0.0      4.0   \n",
       "\n",
       "       OHX11CTC_P     OHX23TC  OHX09CTC_S  OHX02CTC_U  ...  OHX12CTC_S  \\\n",
       "count       192.0  192.000000  192.000000       192.0  ...       192.0   \n",
       "mean          0.0    1.145833    0.020833         1.0  ...         0.0   \n",
       "std           0.0    0.613618    0.143200         0.0  ...         0.0   \n",
       "min           0.0    1.000000    0.000000         1.0  ...         0.0   \n",
       "25%           0.0    1.000000    0.000000         1.0  ...         0.0   \n",
       "50%           0.0    1.000000    0.000000         1.0  ...         0.0   \n",
       "75%           0.0    1.000000    0.000000         1.0  ...         0.0   \n",
       "max           0.0    4.000000    1.000000         1.0  ...         0.0   \n",
       "\n",
       "       SMAQUEX.x  OHX30CTC_P  OHX10CTC_S  OHX27CTC_S     OHX05TC  OHX03CTC_P  \\\n",
       "count      192.0       192.0       192.0       192.0  192.000000       192.0   \n",
       "mean         3.0         0.0         0.0         0.0    1.281250         0.0   \n",
       "std          0.0         0.0         0.0         0.0    0.876728         0.0   \n",
       "min          3.0         0.0         0.0         0.0    1.000000         0.0   \n",
       "25%          3.0         0.0         0.0         0.0    1.000000         0.0   \n",
       "50%          3.0         0.0         0.0         0.0    1.000000         0.0   \n",
       "75%          3.0         0.0         0.0         0.0    1.000000         0.0   \n",
       "max          3.0         0.0         0.0         0.0    4.000000         0.0   \n",
       "\n",
       "          OHX13TC  OHX06CTC_P     OHX03TC  \n",
       "count  192.000000       192.0  192.000000  \n",
       "mean     1.609375         0.0    3.750000  \n",
       "std      1.210130         0.0    0.663167  \n",
       "min      1.000000         0.0    2.000000  \n",
       "25%      1.000000         0.0    4.000000  \n",
       "50%      1.000000         0.0    4.000000  \n",
       "75%      1.000000         0.0    4.000000  \n",
       "max      4.000000         0.0    4.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6"
   ]
  }
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