baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', None)   \n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/2003393883.py:2: DtypeWarning: Columns (2,64,91,94,97,106,108,109,110,111,112,114,116,119,120,122,127,139,142,143,144,145,152,153,154,155,156,157,159,160,161,163,164,165,169,170,171,172,175,178,179,180,187,189,215,216,217,218,219,317,318) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  d = pd.read_csv(f)\n"
     ]
    },
    {
     "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>wl_org</th>\n",
       "      <th>cod_wl</th>\n",
       "      <th>cod_ostxt_wl</th>\n",
       "      <th>num_prev_tx</th>\n",
       "      <th>thoracic_dgn</th>\n",
       "      <th>tah</th>\n",
       "      <th>vas</th>\n",
       "      <th>onvent</th>\n",
       "      <th>icu</th>\n",
       "      <th>inotropic</th>\n",
       "      <th>gender</th>\n",
       "      <th>abo</th>\n",
       "      <th>wgt_kg_tcr</th>\n",
       "      <th>hgt_cm_tcr</th>\n",
       "      <th>bmi_tcr</th>\n",
       "      <th>education</th>\n",
       "      <th>ecmo_tcr</th>\n",
       "      <th>iabp_tcr</th>\n",
       "      <th>inhaled_no</th>\n",
       "      <th>inotropes_tcr</th>\n",
       "      <th>vad_device_ty_tcr</th>\n",
       "      <th>vad_brand1_tcr</th>\n",
       "      <th>vad_brand2_tcr</th>\n",
       "      <th>vad_tah_tcr</th>\n",
       "      <th>func_stat_tcr</th>\n",
       "      <th>tcr_dgn</th>\n",
       "      <th>diab</th>\n",
       "      <th>dial_ty_tcr</th>\n",
       "      <th>cereb_vasc</th>\n",
       "      <th>malig_tcr</th>\n",
       "      <th>malig_ty_tcr</th>\n",
       "      <th>most_rcnt_creat</th>\n",
       "      <th>tot_serum_album</th>\n",
       "      <th>sud_death</th>\n",
       "      <th>impl_defibril</th>\n",
       "      <th>hemo_sys_tcr</th>\n",
       "      <th>hemo_pa_dia_tcr</th>\n",
       "      <th>hemo_pa_mn_tcr</th>\n",
       "      <th>hemo_pcw_tcr</th>\n",
       "      <th>hemo_co_tcr</th>\n",
       "      <th>cig_use</th>\n",
       "      <th>tcr_dur_abstain</th>\n",
       "      <th>prior_card_surg_tcr</th>\n",
       "      <th>histry_cig_old</th>\n",
       "      <th>sternotomy_tcr</th>\n",
       "      <th>ssdmf_death_date</th>\n",
       "      <th>days_stat1</th>\n",
       "      <th>days_stat1a</th>\n",
       "      <th>days_stat2</th>\n",
       "      <th>days_stat1b</th>\n",
       "      <th>days_stata4</th>\n",
       "      <th>days_stata5</th>\n",
       "      <th>days_stata2</th>\n",
       "      <th>days_stata3</th>\n",
       "      <th>days_stata1</th>\n",
       "      <th>days_stata6</th>\n",
       "      <th>init_stat</th>\n",
       "      <th>init_creat</th>\n",
       "      <th>rem_cd</th>\n",
       "      <th>dayswait_chron</th>\n",
       "      <th>end_stat</th>\n",
       "      <th>init_age</th>\n",
       "      <th>life_sup_tcr</th>\n",
       "      <th>activate_date</th>\n",
       "      <th>death_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>init_date</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>ethcat</th>\n",
       "      <th>pt_code</th>\n",
       "      <th>init_hgt_cm_calc</th>\n",
       "      <th>init_wgt_kg_calc</th>\n",
       "      <th>init_bmi_calc</th>\n",
       "      <th>end_hgt_cm_calc</th>\n",
       "      <th>end_wgt_kg_calc</th>\n",
       "      <th>end_bmi_calc</th>\n",
       "      <th>composite_death_date</th>\n",
       "      <th>ventilator_tcr</th>\n",
       "      <th>lvad_at_listing</th>\n",
       "      <th>lvad_while_listed</th>\n",
       "      <th>rvad_at_listing</th>\n",
       "      <th>rvad_while_listed</th>\n",
       "      <th>work_income_tcr</th>\n",
       "      <th>academic_prg_tcr</th>\n",
       "      <th>academic_level_tcr</th>\n",
       "      <th>exercise_o2</th>\n",
       "      <th>academic_level_trr</th>\n",
       "      <th>academic_prg_trr</th>\n",
       "      <th>func_stat_trr</th>\n",
       "      <th>med_cond_trr</th>\n",
       "      <th>ecmo_trr</th>\n",
       "      <th>work_income_trr</th>\n",
       "      <th>cognitive_dev_trr</th>\n",
       "      <th>motor_dev_trr</th>\n",
       "      <th>measurement_date_trr</th>\n",
       "      <th>pge_trr</th>\n",
       "      <th>creat_trr</th>\n",
       "      <th>dial_after_list</th>\n",
       "      <th>fev1_trr</th>\n",
       "      <th>fvc_trr</th>\n",
       "      <th>hemo_co_trr</th>\n",
       "      <th>hemo_pa_dia_trr</th>\n",
       "      <th>hemo_pa_mn_trr</th>\n",
       "      <th>hemo_pcw_trr</th>\n",
       "      <th>hemo_sys_trr</th>\n",
       "      <th>iabp_trr</th>\n",
       "      <th>infect_iv_drug_trr</th>\n",
       "      <th>inotropes_trr</th>\n",
       "      <th>inotrop_vaso_co_trr</th>\n",
       "      <th>inotrop_vaso_dia_trr</th>\n",
       "      <th>inotrop_vaso_mn_trr</th>\n",
       "      <th>inotrop_vaso_pcw_trr</th>\n",
       "      <th>inotrop_vaso_sys_trr</th>\n",
       "      <th>pco2_trr</th>\n",
       "      <th>steroid</th>\n",
       "      <th>tbili</th>\n",
       "      <th>transfusions</th>\n",
       "      <th>vad_device_ty_trr</th>\n",
       "      <th>prior_card_surg_type_trr</th>\n",
       "      <th>prior_card_surg_type_ostxt_trr</th>\n",
       "      <th>cmv_status</th>\n",
       "      <th>cod</th>\n",
       "      <th>cod_ostxt</th>\n",
       "      <th>gstatus</th>\n",
       "      <th>gtime</th>\n",
       "      <th>lastfuno</th>\n",
       "      <th>ptime</th>\n",
       "      <th>px_stat</th>\n",
       "      <th>func_stat_trf</th>\n",
       "      <th>amis</th>\n",
       "      <th>bmis</th>\n",
       "      <th>drmis</th>\n",
       "      <th>hlamis</th>\n",
       "      <th>pramr</th>\n",
       "      <th>prapk</th>\n",
       "      <th>pramr_cl1</th>\n",
       "      <th>pramr_cl2</th>\n",
       "      <th>prapk_cl1</th>\n",
       "      <th>prapk_cl2</th>\n",
       "      <th>hist_cocaine_don</th>\n",
       "      <th>age_don</th>\n",
       "      <th>ethcat_don</th>\n",
       "      <th>abo_don</th>\n",
       "      <th>alcohol_heavy_don</th>\n",
       "      <th>gender_don</th>\n",
       "      <th>cod_ostxt_don</th>\n",
       "      <th>bun_don</th>\n",
       "      <th>creat_don</th>\n",
       "      <th>sgot_don</th>\n",
       "      <th>sgpt_don</th>\n",
       "      <th>tbili_don</th>\n",
       "      <th>hypertens_dur_don</th>\n",
       "      <th>cancer_oth_ostxt_don</th>\n",
       "      <th>contin_cig_don</th>\n",
       "      <th>contin_cocaine_don</th>\n",
       "      <th>contin_oth_drug_don</th>\n",
       "      <th>diet_don</th>\n",
       "      <th>diuretics_don</th>\n",
       "      <th>diabdur_don</th>\n",
       "      <th>hist_hypertens_don</th>\n",
       "      <th>hist_cancer_don</th>\n",
       "      <th>hist_insulin_dep_don</th>\n",
       "      <th>hist_diabetes_don</th>\n",
       "      <th>diabetes_don</th>\n",
       "      <th>hist_oth_drug_don</th>\n",
       "      <th>insulin_don</th>\n",
       "      <th>hgt_cm_don_calc</th>\n",
       "      <th>wgt_kg_don_calc</th>\n",
       "      <th>bmi_don_calc</th>\n",
       "      <th>px_stat_date</th>\n",
       "      <th>tx_date</th>\n",
       "      <th>discharge_date</th>\n",
       "      <th>tx_type</th>\n",
       "      <th>abo_mat</th>\n",
       "      <th>age</th>\n",
       "      <th>dial_prior_tx</th>\n",
       "      <th>ischtime</th>\n",
       "      <th>grf_fail_cause</th>\n",
       "      <th>grf_fail_date</th>\n",
       "      <th>grf_stat</th>\n",
       "      <th>age_group</th>\n",
       "      <th>hgt_cm_calc</th>\n",
       "      <th>wgt_kg_calc</th>\n",
       "      <th>bmi_calc</th>\n",
       "      <th>distance</th>\n",
       "      <th>vad_tah_trr</th>\n",
       "      <th>tx_year</th>\n",
       "      <th>referral_date</th>\n",
       "      <th>pco2_don</th>\n",
       "      <th>pulm_cath_don</th>\n",
       "      <th>map_init_don</th>\n",
       "      <th>map_post_don</th>\n",
       "      <th>cvp_cath_init_don</th>\n",
       "      <th>cvp_cath_post_don</th>\n",
       "      <th>cvp_cath_old_don</th>\n",
       "      <th>pcwp_init_don</th>\n",
       "      <th>pcwp_post_don</th>\n",
       "      <th>svr_init_don</th>\n",
       "      <th>svr_post_don</th>\n",
       "      <th>syst_pa_cath_init_don</th>\n",
       "      <th>syst_pa_cath_post_don</th>\n",
       "      <th>systolic_pa_cath_old_don</th>\n",
       "      <th>diast_pa_cath_init_don</th>\n",
       "      <th>diast_pa_cath_post_don</th>\n",
       "      <th>diastolic_pa_cath_old_don</th>\n",
       "      <th>cardiac_output_cath_init_don</th>\n",
       "      <th>cardiac_output_cath_post_don</th>\n",
       "      <th>cardiac_output_cath_old_don</th>\n",
       "      <th>card_idx_init_don</th>\n",
       "      <th>card_idx_post_don</th>\n",
       "      <th>broncho_lt_don</th>\n",
       "      <th>broncho_rt_don</th>\n",
       "      <th>chest_xray_don</th>\n",
       "      <th>ph_don</th>\n",
       "      <th>hematocrit_don</th>\n",
       "      <th>abn_valves_don</th>\n",
       "      <th>abn_lvh_don</th>\n",
       "      <th>abn_congen_don</th>\n",
       "      <th>wall_abn_seg_don</th>\n",
       "      <th>wall_abn_glob_don</th>\n",
       "      <th>data_transplant</th>\n",
       "      <th>data_waitlist</th>\n",
       "      <th>alg_ind</th>\n",
       "      <th>alg_maint</th>\n",
       "      <th>cyclosporin_ind</th>\n",
       "      <th>cyclosporin_maint</th>\n",
       "      <th>okt4_ind</th>\n",
       "      <th>okt4_maint</th>\n",
       "      <th>cytoxan_ind</th>\n",
       "      <th>cytoxan_maint</th>\n",
       "      <th>methotrexate_ind</th>\n",
       "      <th>methotrexate_maint</th>\n",
       "      <th>brequinarsodium_ind</th>\n",
       "      <th>brequinarsodium_maint</th>\n",
       "      <th>mizoribine_ind</th>\n",
       "      <th>mizoribine_maint</th>\n",
       "      <th>atgam_ind</th>\n",
       "      <th>atgam_maint</th>\n",
       "      <th>nratgnrats_ind</th>\n",
       "      <th>nratgnrats_maint</th>\n",
       "      <th>okt3_ind</th>\n",
       "      <th>okt3_maint</th>\n",
       "      <th>xomazymecd5_ind</th>\n",
       "      <th>xomazymecd5_maint</th>\n",
       "      <th>dab486il2_ind</th>\n",
       "      <th>dab486il2_maint</th>\n",
       "      <th>antilfa1_ind</th>\n",
       "      <th>antilfa1_maint</th>\n",
       "      <th>antiicam1_ind</th>\n",
       "      <th>antiicam1_maint</th>\n",
       "      <th>il1receptorantagonist_ind</th>\n",
       "      <th>il1receptorantagonist_maint</th>\n",
       "      <th>antiil6_ind</th>\n",
       "      <th>antiil6_maint</th>\n",
       "      <th>antitnf_ind</th>\n",
       "      <th>antitnf_maint</th>\n",
       "      <th>solubleil1receptor_ind</th>\n",
       "      <th>solubleil1receptor_maint</th>\n",
       "      <th>aldesleukin_ind</th>\n",
       "      <th>aldesleukin_maint</th>\n",
       "      <th>t10b9_ind</th>\n",
       "      <th>t10b9_maint</th>\n",
       "      <th>sandimmune_ind</th>\n",
       "      <th>sandimmune_maint</th>\n",
       "      <th>deoxyspergualin_ind</th>\n",
       "      <th>deoxyspergualin_maint</th>\n",
       "      <th>neoral_ind</th>\n",
       "      <th>neoral_maint</th>\n",
       "      <th>sangcya_ind</th>\n",
       "      <th>sangcya_maint</th>\n",
       "      <th>thymoglobulin_ind</th>\n",
       "      <th>thymoglobulin_maint</th>\n",
       "      <th>zenapax_ind</th>\n",
       "      <th>zenapax_maint</th>\n",
       "      <th>simulect_ind</th>\n",
       "      <th>simulect_maint</th>\n",
       "      <th>gengraf_ind</th>\n",
       "      <th>gengraf_maint</th>\n",
       "      <th>zortress_ind</th>\n",
       "      <th>zortress_maint</th>\n",
       "      <th>eon_ind</th>\n",
       "      <th>eon_maint</th>\n",
       "      <th>myfortic_ind</th>\n",
       "      <th>myfortic_maint</th>\n",
       "      <th>genericcyclosporin_ind</th>\n",
       "      <th>genericcyclosporin_maint</th>\n",
       "      <th>steroids_ind</th>\n",
       "      <th>steroids_maint</th>\n",
       "      <th>prograf_ind</th>\n",
       "      <th>prograf_maint</th>\n",
       "      <th>campath_ind</th>\n",
       "      <th>campath_maint</th>\n",
       "      <th>fty720_ind</th>\n",
       "      <th>fty720_maint</th>\n",
       "      <th>rituxan_ind</th>\n",
       "      <th>astagrafxl_maint</th>\n",
       "      <th>generictacrolimus_maint</th>\n",
       "      <th>genericmmf_maint</th>\n",
       "      <th>nulojix_maint</th>\n",
       "      <th>genericmyfortic_ind</th>\n",
       "      <th>genericmyfortic_maint</th>\n",
       "      <th>genericsirolimus_ind</th>\n",
       "      <th>genericsirolimus_maint</th>\n",
       "      <th>envarsusxr_ind</th>\n",
       "      <th>envarsusxr_maint</th>\n",
       "      <th>rapamune_ind</th>\n",
       "      <th>rapamune_maint</th>\n",
       "      <th>leflunomide_ind</th>\n",
       "      <th>leflunomide_maint</th>\n",
       "      <th>imuran_ind</th>\n",
       "      <th>imuran_maint</th>\n",
       "      <th>cellcept_ind</th>\n",
       "      <th>cellcept_maint</th>\n",
       "      <th>oth_drug_ind1</th>\n",
       "      <th>oth_drug_maint1</th>\n",
       "      <th>oth_drug_ind2</th>\n",
       "      <th>oth_drug_maint2</th>\n",
       "      <th>oth_drug_ostxt1</th>\n",
       "      <th>oth_drug_ostxt2</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1201</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>O</td>\n",
       "      <td>57.60620</td>\n",
       "      <td>165.10000</td>\n",
       "      <td>21.13370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>2130.00000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>2130.00000</td>\n",
       "      <td>61.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05jun2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12jun2020</td>\n",
       "      <td>05jun2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1351175</td>\n",
       "      <td>165.10000</td>\n",
       "      <td>57.60620</td>\n",
       "      <td>21.13368</td>\n",
       "      <td>165.10000</td>\n",
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       "      <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>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>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>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>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>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>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>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>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>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>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>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>N</td>\n",
       "      <td>Y</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",
       "      <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>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>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>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>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>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>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>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>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>Master only (1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>O</td>\n",
       "      <td>80.28600</td>\n",
       "      <td>172.72000</td>\n",
       "      <td>26.91250</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2040.00000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>43.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>4.02000</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>2030.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>631.00000</td>\n",
       "      <td>2160.00000</td>\n",
       "      <td>48.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>20sep2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12jun2020</td>\n",
       "      <td>20sep2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1257236</td>\n",
       "      <td>172.72000</td>\n",
       "      <td>80.28600</td>\n",
       "      <td>26.91253</td>\n",
       "      <td>172.72000</td>\n",
       "      <td>80.28600</td>\n",
       "      <td>26.91253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</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",
       "      <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>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>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>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>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>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>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>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>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>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>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>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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</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",
       "      <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>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>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>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>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>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>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>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>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>Master only (1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>185.00000</td>\n",
       "      <td>34.18550</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>224.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2070.00000</td>\n",
       "      <td>1007.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.21000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>31.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>Y</td>\n",
       "      <td>998.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2140.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578.00000</td>\n",
       "      <td>2140.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>Y</td>\n",
       "      <td>12nov2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12jun2020</td>\n",
       "      <td>12nov2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1266592</td>\n",
       "      <td>185.00000</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>34.18554</td>\n",
       "      <td>185.00000</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>34.18554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>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>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>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>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>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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master only (1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>O</td>\n",
       "      <td>96.36000</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>28.46170</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2080.00000</td>\n",
       "      <td>1049.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.48000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>5.40000</td>\n",
       "      <td>Y</td>\n",
       "      <td>998.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2160.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>2160.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>04jun2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12jun2020</td>\n",
       "      <td>04jun2020</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1351661</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>96.36000</td>\n",
       "      <td>28.46172</td>\n",
       "      <td>184.00000</td>\n",
       "      <td>96.36000</td>\n",
       "      <td>28.46172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</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",
       "      <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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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>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>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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master only (1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>O</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>29.76080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>996.00000</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",
       "      <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>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.00000</td>\n",
       "      <td>12nov2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2030.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>356.00000</td>\n",
       "      <td>2999.00000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>N</td>\n",
       "      <td>04may1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25apr1991</td>\n",
       "      <td>04may1990</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>524595</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>29.76084</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>29.76084</td>\n",
       "      <td>12nov2000</td>\n",
       "      <td>0</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",
       "      <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>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>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>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>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>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>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>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>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>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>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>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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</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",
       "      <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>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>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>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>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>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>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>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>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>Master only (1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  wl_org  cod_wl cod_ostxt_wl  num_prev_tx  thoracic_dgn  tah  vas onvent  \\\n",
       "0     HR     NaN          NaN            0          1201  NaN  NaN    NaN   \n",
       "1     HR     NaN          NaN            0          1000  NaN  NaN    NaN   \n",
       "2     HR     NaN          NaN            0          1007  NaN  NaN    NaN   \n",
       "3     HR     NaN          NaN            0          1049  NaN  NaN    NaN   \n",
       "4     HR     NaN          NaN            0          1999    N    N      N   \n",
       "\n",
       "   icu inotropic gender abo  wgt_kg_tcr  hgt_cm_tcr  bmi_tcr  education  \\\n",
       "0  NaN       NaN      M   O    57.60620   165.10000 21.13370        NaN   \n",
       "1  NaN       NaN      F   O    80.28600   172.72000 26.91250    6.00000   \n",
       "2  NaN       NaN      M   A   117.00000   185.00000 34.18550    5.00000   \n",
       "3  NaN       NaN      M   O    96.36000   184.00000 28.46170    4.00000   \n",
       "4    N       NaN      M   O    83.00000   167.00000 29.76080        NaN   \n",
       "\n",
       "   ecmo_tcr  iabp_tcr  inhaled_no  inotropes_tcr  vad_device_ty_tcr  \\\n",
       "0         0         0           0              0                NaN   \n",
       "1         0         0           0              0            1.00000   \n",
       "2         0         0           0              0            2.00000   \n",
       "3         0         0           0              0            1.00000   \n",
       "4         0         0           0              0                NaN   \n",
       "\n",
       "   vad_brand1_tcr  vad_brand2_tcr  vad_tah_tcr  func_stat_tcr    tcr_dgn  \\\n",
       "0             NaN             NaN          NaN            NaN        NaN   \n",
       "1             NaN             NaN          NaN     2040.00000 1000.00000   \n",
       "2       224.00000             NaN          NaN     2070.00000 1007.00000   \n",
       "3             NaN             NaN          NaN     2080.00000 1049.00000   \n",
       "4             NaN             NaN          NaN      996.00000        NaN   \n",
       "\n",
       "     diab  dial_ty_tcr cereb_vasc malig_tcr  malig_ty_tcr  most_rcnt_creat  \\\n",
       "0     NaN          NaN        NaN       NaN           NaN              NaN   \n",
       "1 1.00000      1.00000          N         N           NaN          0.80000   \n",
       "2 3.00000      1.00000          N         N           NaN          2.21000   \n",
       "3 1.00000      1.00000          N         N           NaN          1.48000   \n",
       "4     NaN          NaN        NaN       NaN           NaN              NaN   \n",
       "\n",
       "   tot_serum_album sud_death impl_defibril  hemo_sys_tcr  hemo_pa_dia_tcr  \\\n",
       "0              NaN       NaN           NaN           NaN              NaN   \n",
       "1              NaN       NaN             Y      43.00000         13.00000   \n",
       "2              NaN       NaN             Y      47.00000         20.00000   \n",
       "3              NaN       NaN             Y      23.00000          5.00000   \n",
       "4              NaN       NaN           NaN           NaN              NaN   \n",
       "\n",
       "   hemo_pa_mn_tcr  hemo_pcw_tcr  hemo_co_tcr cig_use  tcr_dur_abstain  \\\n",
       "0             NaN           NaN          NaN     NaN              NaN   \n",
       "1        26.00000      19.00000      4.02000       N              NaN   \n",
       "2        31.00000      23.00000      5.00000       Y        998.00000   \n",
       "3        13.00000       7.00000      5.40000       Y        998.00000   \n",
       "4             NaN           NaN          NaN     NaN              NaN   \n",
       "\n",
       "  prior_card_surg_tcr histry_cig_old  sternotomy_tcr ssdmf_death_date  \\\n",
       "0                 NaN            NaN             NaN              NaN   \n",
       "1                   N            NaN             NaN              NaN   \n",
       "2                   N            NaN             NaN              NaN   \n",
       "3                   N            NaN             NaN              NaN   \n",
       "4                   U            NaN       998.00000        12nov2000   \n",
       "\n",
       "   days_stat1  days_stat1a  days_stat2  days_stat1b  days_stata4  days_stata5  \\\n",
       "0           0            0           0            0            0            0   \n",
       "1           0            0          28            0            0            0   \n",
       "2           0            0           0            0          548            0   \n",
       "3           0            0           0            0            0            0   \n",
       "4           0            0         210            0            0            0   \n",
       "\n",
       "   days_stata2  days_stata3  days_stata1  days_stata6  init_stat  init_creat  \\\n",
       "0            0            7            0            0 2130.00000         NaN   \n",
       "1            0            0            0          598 2030.00000         NaN   \n",
       "2            0           30            0            0 2140.00000         NaN   \n",
       "3            0            0            0            8 2160.00000         NaN   \n",
       "4            0            0            0            0 2030.00000         NaN   \n",
       "\n",
       "   rem_cd  dayswait_chron   end_stat  init_age life_sup_tcr activate_date  \\\n",
       "0     NaN         7.00000 2130.00000  61.00000          NaN     05jun2020   \n",
       "1     NaN       631.00000 2160.00000  48.00000            N     20sep2018   \n",
       "2     NaN       578.00000 2140.00000  66.00000            Y     12nov2018   \n",
       "3     NaN         8.00000 2160.00000  66.00000            N     04jun2020   \n",
       "4 9.00000       356.00000 2999.00000  52.00000            N     04may1990   \n",
       "\n",
       "  death_date   end_date  init_date  ethnicity  ethcat  pt_code  \\\n",
       "0        NaN  12jun2020  05jun2020          0       1  1351175   \n",
       "1        NaN  12jun2020  20sep2018          0       1  1257236   \n",
       "2        NaN  12jun2020  12nov2018          0       1  1266592   \n",
       "3        NaN  12jun2020  04jun2020          0       2  1351661   \n",
       "4        NaN  25apr1991  04may1990          0       2   524595   \n",
       "\n",
       "   init_hgt_cm_calc  init_wgt_kg_calc  init_bmi_calc  end_hgt_cm_calc  \\\n",
       "0         165.10000          57.60620       21.13368        165.10000   \n",
       "1         172.72000          80.28600       26.91253        172.72000   \n",
       "2         185.00000         117.00000       34.18554        185.00000   \n",
       "3         184.00000          96.36000       28.46172        184.00000   \n",
       "4         167.00000          83.00000       29.76084        167.00000   \n",
       "\n",
       "   end_wgt_kg_calc  end_bmi_calc composite_death_date  ventilator_tcr  \\\n",
       "0         57.60620      21.13368                  NaN               0   \n",
       "1         80.28600      26.91253                  NaN               0   \n",
       "2        117.00000      34.18554                  NaN               0   \n",
       "3         96.36000      28.46172                  NaN               0   \n",
       "4         83.00000      29.76084            12nov2000               0   \n",
       "\n",
       "   lvad_at_listing  lvad_while_listed  rvad_at_listing  rvad_while_listed  \\\n",
       "0              NaN                NaN              NaN                NaN   \n",
       "1              NaN                NaN              NaN                NaN   \n",
       "2              NaN                NaN              NaN                NaN   \n",
       "3              NaN                NaN              NaN                NaN   \n",
       "4              NaN                NaN              NaN                NaN   \n",
       "\n",
       "  work_income_tcr  academic_prg_tcr  academic_level_tcr  exercise_o2  \\\n",
       "0             NaN               NaN                 NaN          NaN   \n",
       "1             NaN               NaN                 NaN          NaN   \n",
       "2             NaN               NaN                 NaN          NaN   \n",
       "3             NaN               NaN                 NaN          NaN   \n",
       "4             NaN               NaN                 NaN          NaN   \n",
       "\n",
       "   academic_level_trr  academic_prg_trr  func_stat_trr  med_cond_trr  \\\n",
       "0                 NaN               NaN            NaN           NaN   \n",
       "1                 NaN               NaN            NaN           NaN   \n",
       "2                 NaN               NaN            NaN           NaN   \n",
       "3                 NaN               NaN            NaN           NaN   \n",
       "4                 NaN               NaN            NaN           NaN   \n",
       "\n",
       "   ecmo_trr work_income_trr  cognitive_dev_trr  motor_dev_trr  \\\n",
       "0       NaN             NaN                NaN            NaN   \n",
       "1       NaN             NaN                NaN            NaN   \n",
       "2       NaN             NaN                NaN            NaN   \n",
       "3       NaN             NaN                NaN            NaN   \n",
       "4       NaN             NaN                NaN            NaN   \n",
       "\n",
       "  measurement_date_trr  pge_trr  creat_trr dial_after_list  fev1_trr  fvc_trr  \\\n",
       "0                  NaN      NaN        NaN             NaN       NaN      NaN   \n",
       "1                  NaN      NaN        NaN             NaN       NaN      NaN   \n",
       "2                  NaN      NaN        NaN             NaN       NaN      NaN   \n",
       "3                  NaN      NaN        NaN             NaN       NaN      NaN   \n",
       "4                  NaN      NaN        NaN             NaN       NaN      NaN   \n",
       "\n",
       "   hemo_co_trr  hemo_pa_dia_trr  hemo_pa_mn_trr  hemo_pcw_trr  hemo_sys_trr  \\\n",
       "0          NaN              NaN             NaN           NaN           NaN   \n",
       "1          NaN              NaN             NaN           NaN           NaN   \n",
       "2          NaN              NaN             NaN           NaN           NaN   \n",
       "3          NaN              NaN             NaN           NaN           NaN   \n",
       "4          NaN              NaN             NaN           NaN           NaN   \n",
       "\n",
       "   iabp_trr infect_iv_drug_trr  inotropes_trr inotrop_vaso_co_trr  \\\n",
       "0       NaN                NaN            NaN                 NaN   \n",
       "1       NaN                NaN            NaN                 NaN   \n",
       "2       NaN                NaN            NaN                 NaN   \n",
       "3       NaN                NaN            NaN                 NaN   \n",
       "4       NaN                NaN            NaN                 NaN   \n",
       "\n",
       "  inotrop_vaso_dia_trr inotrop_vaso_mn_trr inotrop_vaso_pcw_trr  \\\n",
       "0                  NaN                 NaN                  NaN   \n",
       "1                  NaN                 NaN                  NaN   \n",
       "2                  NaN                 NaN                  NaN   \n",
       "3                  NaN                 NaN                  NaN   \n",
       "4                  NaN                 NaN                  NaN   \n",
       "\n",
       "  inotrop_vaso_sys_trr  pco2_trr steroid  tbili transfusions  \\\n",
       "0                  NaN       NaN     NaN    NaN          NaN   \n",
       "1                  NaN       NaN     NaN    NaN          NaN   \n",
       "2                  NaN       NaN     NaN    NaN          NaN   \n",
       "3                  NaN       NaN     NaN    NaN          NaN   \n",
       "4                  NaN       NaN     NaN    NaN          NaN   \n",
       "\n",
       "   vad_device_ty_trr  prior_card_surg_type_trr prior_card_surg_type_ostxt_trr  \\\n",
       "0                NaN                       NaN                            NaN   \n",
       "1                NaN                       NaN                            NaN   \n",
       "2                NaN                       NaN                            NaN   \n",
       "3                NaN                       NaN                            NaN   \n",
       "4                NaN                       NaN                            NaN   \n",
       "\n",
       "  cmv_status  cod cod_ostxt  gstatus  gtime  lastfuno  ptime px_stat  \\\n",
       "0        NaN  NaN       NaN      NaN    NaN       NaN    NaN     NaN   \n",
       "1        NaN  NaN       NaN      NaN    NaN       NaN    NaN     NaN   \n",
       "2        NaN  NaN       NaN      NaN    NaN       NaN    NaN     NaN   \n",
       "3        NaN  NaN       NaN      NaN    NaN       NaN    NaN     NaN   \n",
       "4        NaN  NaN       NaN      NaN    NaN       NaN    NaN     NaN   \n",
       "\n",
       "   func_stat_trf  amis  bmis  drmis  hlamis  pramr  prapk  pramr_cl1  \\\n",
       "0            NaN   NaN   NaN    NaN     NaN    NaN    NaN        NaN   \n",
       "1            NaN   NaN   NaN    NaN     NaN    NaN    NaN        NaN   \n",
       "2            NaN   NaN   NaN    NaN     NaN    NaN    NaN        NaN   \n",
       "3            NaN   NaN   NaN    NaN     NaN    NaN    NaN        NaN   \n",
       "4            NaN   NaN   NaN    NaN     NaN    NaN    NaN        NaN   \n",
       "\n",
       "   pramr_cl2  prapk_cl1  prapk_cl2 hist_cocaine_don  age_don  ethcat_don  \\\n",
       "0        NaN        NaN        NaN              NaN      NaN         NaN   \n",
       "1        NaN        NaN        NaN              NaN      NaN         NaN   \n",
       "2        NaN        NaN        NaN              NaN      NaN         NaN   \n",
       "3        NaN        NaN        NaN              NaN      NaN         NaN   \n",
       "4        NaN        NaN        NaN              NaN      NaN         NaN   \n",
       "\n",
       "  abo_don alcohol_heavy_don gender_don cod_ostxt_don  bun_don  creat_don  \\\n",
       "0     NaN               NaN        NaN           NaN      NaN        NaN   \n",
       "1     NaN               NaN        NaN           NaN      NaN        NaN   \n",
       "2     NaN               NaN        NaN           NaN      NaN        NaN   \n",
       "3     NaN               NaN        NaN           NaN      NaN        NaN   \n",
       "4     NaN               NaN        NaN           NaN      NaN        NaN   \n",
       "\n",
       "   sgot_don  sgpt_don  tbili_don  hypertens_dur_don cancer_oth_ostxt_don  \\\n",
       "0       NaN       NaN        NaN                NaN                  NaN   \n",
       "1       NaN       NaN        NaN                NaN                  NaN   \n",
       "2       NaN       NaN        NaN                NaN                  NaN   \n",
       "3       NaN       NaN        NaN                NaN                  NaN   \n",
       "4       NaN       NaN        NaN                NaN                  NaN   \n",
       "\n",
       "  contin_cig_don contin_cocaine_don contin_oth_drug_don diet_don  \\\n",
       "0            NaN                NaN                 NaN      NaN   \n",
       "1            NaN                NaN                 NaN      NaN   \n",
       "2            NaN                NaN                 NaN      NaN   \n",
       "3            NaN                NaN                 NaN      NaN   \n",
       "4            NaN                NaN                 NaN      NaN   \n",
       "\n",
       "  diuretics_don  diabdur_don hist_hypertens_don hist_cancer_don  \\\n",
       "0           NaN          NaN                NaN             NaN   \n",
       "1           NaN          NaN                NaN             NaN   \n",
       "2           NaN          NaN                NaN             NaN   \n",
       "3           NaN          NaN                NaN             NaN   \n",
       "4           NaN          NaN                NaN             NaN   \n",
       "\n",
       "  hist_insulin_dep_don  hist_diabetes_don diabetes_don hist_oth_drug_don  \\\n",
       "0                  NaN                NaN          NaN               NaN   \n",
       "1                  NaN                NaN          NaN               NaN   \n",
       "2                  NaN                NaN          NaN               NaN   \n",
       "3                  NaN                NaN          NaN               NaN   \n",
       "4                  NaN                NaN          NaN               NaN   \n",
       "\n",
       "  insulin_don  hgt_cm_don_calc  wgt_kg_don_calc  bmi_don_calc px_stat_date  \\\n",
       "0         NaN              NaN              NaN           NaN          NaN   \n",
       "1         NaN              NaN              NaN           NaN          NaN   \n",
       "2         NaN              NaN              NaN           NaN          NaN   \n",
       "3         NaN              NaN              NaN           NaN          NaN   \n",
       "4         NaN              NaN              NaN           NaN          NaN   \n",
       "\n",
       "  tx_date discharge_date tx_type  abo_mat  age dial_prior_tx  ischtime  \\\n",
       "0     NaN            NaN     NaN      NaN  NaN           NaN       NaN   \n",
       "1     NaN            NaN     NaN      NaN  NaN           NaN       NaN   \n",
       "2     NaN            NaN     NaN      NaN  NaN           NaN       NaN   \n",
       "3     NaN            NaN     NaN      NaN  NaN           NaN       NaN   \n",
       "4     NaN            NaN     NaN      NaN  NaN           NaN       NaN   \n",
       "\n",
       "   grf_fail_cause grf_fail_date grf_stat age_group  hgt_cm_calc  wgt_kg_calc  \\\n",
       "0             NaN           NaN      NaN       NaN          NaN          NaN   \n",
       "1             NaN           NaN      NaN       NaN          NaN          NaN   \n",
       "2             NaN           NaN      NaN       NaN          NaN          NaN   \n",
       "3             NaN           NaN      NaN       NaN          NaN          NaN   \n",
       "4             NaN           NaN      NaN       NaN          NaN          NaN   \n",
       "\n",
       "   bmi_calc  distance  vad_tah_trr  tx_year referral_date  pco2_don  \\\n",
       "0       NaN       NaN          NaN      NaN           NaN       NaN   \n",
       "1       NaN       NaN          NaN      NaN           NaN       NaN   \n",
       "2       NaN       NaN          NaN      NaN           NaN       NaN   \n",
       "3       NaN       NaN          NaN      NaN           NaN       NaN   \n",
       "4       NaN       NaN          NaN      NaN           NaN       NaN   \n",
       "\n",
       "  pulm_cath_don  map_init_don  map_post_don  cvp_cath_init_don  \\\n",
       "0           NaN           NaN           NaN                NaN   \n",
       "1           NaN           NaN           NaN                NaN   \n",
       "2           NaN           NaN           NaN                NaN   \n",
       "3           NaN           NaN           NaN                NaN   \n",
       "4           NaN           NaN           NaN                NaN   \n",
       "\n",
       "   cvp_cath_post_don  cvp_cath_old_don  pcwp_init_don  pcwp_post_don  \\\n",
       "0                NaN               NaN            NaN            NaN   \n",
       "1                NaN               NaN            NaN            NaN   \n",
       "2                NaN               NaN            NaN            NaN   \n",
       "3                NaN               NaN            NaN            NaN   \n",
       "4                NaN               NaN            NaN            NaN   \n",
       "\n",
       "   svr_init_don  svr_post_don  syst_pa_cath_init_don  syst_pa_cath_post_don  \\\n",
       "0           NaN           NaN                    NaN                    NaN   \n",
       "1           NaN           NaN                    NaN                    NaN   \n",
       "2           NaN           NaN                    NaN                    NaN   \n",
       "3           NaN           NaN                    NaN                    NaN   \n",
       "4           NaN           NaN                    NaN                    NaN   \n",
       "\n",
       "   systolic_pa_cath_old_don  diast_pa_cath_init_don  diast_pa_cath_post_don  \\\n",
       "0                       NaN                     NaN                     NaN   \n",
       "1                       NaN                     NaN                     NaN   \n",
       "2                       NaN                     NaN                     NaN   \n",
       "3                       NaN                     NaN                     NaN   \n",
       "4                       NaN                     NaN                     NaN   \n",
       "\n",
       "   diastolic_pa_cath_old_don  cardiac_output_cath_init_don  \\\n",
       "0                        NaN                           NaN   \n",
       "1                        NaN                           NaN   \n",
       "2                        NaN                           NaN   \n",
       "3                        NaN                           NaN   \n",
       "4                        NaN                           NaN   \n",
       "\n",
       "   cardiac_output_cath_post_don  cardiac_output_cath_old_don  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "\n",
       "   card_idx_init_don  card_idx_post_don  broncho_lt_don  broncho_rt_don  \\\n",
       "0                NaN                NaN             NaN             NaN   \n",
       "1                NaN                NaN             NaN             NaN   \n",
       "2                NaN                NaN             NaN             NaN   \n",
       "3                NaN                NaN             NaN             NaN   \n",
       "4                NaN                NaN             NaN             NaN   \n",
       "\n",
       "   chest_xray_don  ph_don  hematocrit_don abn_valves_don abn_lvh_don  \\\n",
       "0             NaN     NaN             NaN            NaN         NaN   \n",
       "1             NaN     NaN             NaN            NaN         NaN   \n",
       "2             NaN     NaN             NaN            NaN         NaN   \n",
       "3             NaN     NaN             NaN            NaN         NaN   \n",
       "4             NaN     NaN             NaN            NaN         NaN   \n",
       "\n",
       "  abn_congen_don wall_abn_seg_don wall_abn_glob_don data_transplant  \\\n",
       "0            NaN              NaN               NaN               N   \n",
       "1            NaN              NaN               NaN               N   \n",
       "2            NaN              NaN               NaN               N   \n",
       "3            NaN              NaN               NaN               N   \n",
       "4            NaN              NaN               NaN               N   \n",
       "\n",
       "  data_waitlist  alg_ind  alg_maint  cyclosporin_ind  cyclosporin_maint  \\\n",
       "0             Y      NaN        NaN              NaN                NaN   \n",
       "1             Y      NaN        NaN              NaN                NaN   \n",
       "2             Y      NaN        NaN              NaN                NaN   \n",
       "3             Y      NaN        NaN              NaN                NaN   \n",
       "4             Y      NaN        NaN              NaN                NaN   \n",
       "\n",
       "   okt4_ind  okt4_maint  cytoxan_ind  cytoxan_maint  methotrexate_ind  \\\n",
       "0       NaN         NaN          NaN            NaN               NaN   \n",
       "1       NaN         NaN          NaN            NaN               NaN   \n",
       "2       NaN         NaN          NaN            NaN               NaN   \n",
       "3       NaN         NaN          NaN            NaN               NaN   \n",
       "4       NaN         NaN          NaN            NaN               NaN   \n",
       "\n",
       "   methotrexate_maint  brequinarsodium_ind  brequinarsodium_maint  \\\n",
       "0                 NaN                  NaN                    NaN   \n",
       "1                 NaN                  NaN                    NaN   \n",
       "2                 NaN                  NaN                    NaN   \n",
       "3                 NaN                  NaN                    NaN   \n",
       "4                 NaN                  NaN                    NaN   \n",
       "\n",
       "   mizoribine_ind  mizoribine_maint  atgam_ind  atgam_maint  nratgnrats_ind  \\\n",
       "0             NaN               NaN        NaN          NaN             NaN   \n",
       "1             NaN               NaN        NaN          NaN             NaN   \n",
       "2             NaN               NaN        NaN          NaN             NaN   \n",
       "3             NaN               NaN        NaN          NaN             NaN   \n",
       "4             NaN               NaN        NaN          NaN             NaN   \n",
       "\n",
       "   nratgnrats_maint  okt3_ind  okt3_maint  xomazymecd5_ind  xomazymecd5_maint  \\\n",
       "0               NaN       NaN         NaN              NaN                NaN   \n",
       "1               NaN       NaN         NaN              NaN                NaN   \n",
       "2               NaN       NaN         NaN              NaN                NaN   \n",
       "3               NaN       NaN         NaN              NaN                NaN   \n",
       "4               NaN       NaN         NaN              NaN                NaN   \n",
       "\n",
       "   dab486il2_ind  dab486il2_maint  antilfa1_ind  antilfa1_maint  \\\n",
       "0            NaN              NaN           NaN             NaN   \n",
       "1            NaN              NaN           NaN             NaN   \n",
       "2            NaN              NaN           NaN             NaN   \n",
       "3            NaN              NaN           NaN             NaN   \n",
       "4            NaN              NaN           NaN             NaN   \n",
       "\n",
       "   antiicam1_ind  antiicam1_maint  il1receptorantagonist_ind  \\\n",
       "0            NaN              NaN                        NaN   \n",
       "1            NaN              NaN                        NaN   \n",
       "2            NaN              NaN                        NaN   \n",
       "3            NaN              NaN                        NaN   \n",
       "4            NaN              NaN                        NaN   \n",
       "\n",
       "   il1receptorantagonist_maint  antiil6_ind  antiil6_maint  antitnf_ind  \\\n",
       "0                          NaN          NaN            NaN          NaN   \n",
       "1                          NaN          NaN            NaN          NaN   \n",
       "2                          NaN          NaN            NaN          NaN   \n",
       "3                          NaN          NaN            NaN          NaN   \n",
       "4                          NaN          NaN            NaN          NaN   \n",
       "\n",
       "   antitnf_maint  solubleil1receptor_ind  solubleil1receptor_maint  \\\n",
       "0            NaN                     NaN                       NaN   \n",
       "1            NaN                     NaN                       NaN   \n",
       "2            NaN                     NaN                       NaN   \n",
       "3            NaN                     NaN                       NaN   \n",
       "4            NaN                     NaN                       NaN   \n",
       "\n",
       "   aldesleukin_ind  aldesleukin_maint  t10b9_ind  t10b9_maint  sandimmune_ind  \\\n",
       "0              NaN                NaN        NaN          NaN             NaN   \n",
       "1              NaN                NaN        NaN          NaN             NaN   \n",
       "2              NaN                NaN        NaN          NaN             NaN   \n",
       "3              NaN                NaN        NaN          NaN             NaN   \n",
       "4              NaN                NaN        NaN          NaN             NaN   \n",
       "\n",
       "   sandimmune_maint  deoxyspergualin_ind  deoxyspergualin_maint  neoral_ind  \\\n",
       "0               NaN                  NaN                    NaN         NaN   \n",
       "1               NaN                  NaN                    NaN         NaN   \n",
       "2               NaN                  NaN                    NaN         NaN   \n",
       "3               NaN                  NaN                    NaN         NaN   \n",
       "4               NaN                  NaN                    NaN         NaN   \n",
       "\n",
       "   neoral_maint  sangcya_ind  sangcya_maint  thymoglobulin_ind  \\\n",
       "0           NaN          NaN            NaN                NaN   \n",
       "1           NaN          NaN            NaN                NaN   \n",
       "2           NaN          NaN            NaN                NaN   \n",
       "3           NaN          NaN            NaN                NaN   \n",
       "4           NaN          NaN            NaN                NaN   \n",
       "\n",
       "   thymoglobulin_maint  zenapax_ind  zenapax_maint  simulect_ind  \\\n",
       "0                  NaN          NaN            NaN           NaN   \n",
       "1                  NaN          NaN            NaN           NaN   \n",
       "2                  NaN          NaN            NaN           NaN   \n",
       "3                  NaN          NaN            NaN           NaN   \n",
       "4                  NaN          NaN            NaN           NaN   \n",
       "\n",
       "   simulect_maint  gengraf_ind  gengraf_maint  zortress_ind  zortress_maint  \\\n",
       "0             NaN          NaN            NaN           NaN             NaN   \n",
       "1             NaN          NaN            NaN           NaN             NaN   \n",
       "2             NaN          NaN            NaN           NaN             NaN   \n",
       "3             NaN          NaN            NaN           NaN             NaN   \n",
       "4             NaN          NaN            NaN           NaN             NaN   \n",
       "\n",
       "   eon_ind  eon_maint  myfortic_ind  myfortic_maint  genericcyclosporin_ind  \\\n",
       "0      NaN        NaN           NaN             NaN                     NaN   \n",
       "1      NaN        NaN           NaN             NaN                     NaN   \n",
       "2      NaN        NaN           NaN             NaN                     NaN   \n",
       "3      NaN        NaN           NaN             NaN                     NaN   \n",
       "4      NaN        NaN           NaN             NaN                     NaN   \n",
       "\n",
       "   genericcyclosporin_maint  steroids_ind  steroids_maint  prograf_ind  \\\n",
       "0                       NaN           NaN             NaN          NaN   \n",
       "1                       NaN           NaN             NaN          NaN   \n",
       "2                       NaN           NaN             NaN          NaN   \n",
       "3                       NaN           NaN             NaN          NaN   \n",
       "4                       NaN           NaN             NaN          NaN   \n",
       "\n",
       "   prograf_maint  campath_ind  campath_maint  fty720_ind  fty720_maint  \\\n",
       "0            NaN          NaN            NaN         NaN           NaN   \n",
       "1            NaN          NaN            NaN         NaN           NaN   \n",
       "2            NaN          NaN            NaN         NaN           NaN   \n",
       "3            NaN          NaN            NaN         NaN           NaN   \n",
       "4            NaN          NaN            NaN         NaN           NaN   \n",
       "\n",
       "   rituxan_ind  astagrafxl_maint  generictacrolimus_maint  genericmmf_maint  \\\n",
       "0          NaN               NaN                      NaN               NaN   \n",
       "1          NaN               NaN                      NaN               NaN   \n",
       "2          NaN               NaN                      NaN               NaN   \n",
       "3          NaN               NaN                      NaN               NaN   \n",
       "4          NaN               NaN                      NaN               NaN   \n",
       "\n",
       "   nulojix_maint  genericmyfortic_ind  genericmyfortic_maint  \\\n",
       "0            NaN                  NaN                    NaN   \n",
       "1            NaN                  NaN                    NaN   \n",
       "2            NaN                  NaN                    NaN   \n",
       "3            NaN                  NaN                    NaN   \n",
       "4            NaN                  NaN                    NaN   \n",
       "\n",
       "   genericsirolimus_ind  genericsirolimus_maint  envarsusxr_ind  \\\n",
       "0                   NaN                     NaN             NaN   \n",
       "1                   NaN                     NaN             NaN   \n",
       "2                   NaN                     NaN             NaN   \n",
       "3                   NaN                     NaN             NaN   \n",
       "4                   NaN                     NaN             NaN   \n",
       "\n",
       "   envarsusxr_maint  rapamune_ind  rapamune_maint  leflunomide_ind  \\\n",
       "0               NaN           NaN             NaN              NaN   \n",
       "1               NaN           NaN             NaN              NaN   \n",
       "2               NaN           NaN             NaN              NaN   \n",
       "3               NaN           NaN             NaN              NaN   \n",
       "4               NaN           NaN             NaN              NaN   \n",
       "\n",
       "   leflunomide_maint  imuran_ind  imuran_maint  cellcept_ind  cellcept_maint  \\\n",
       "0                NaN         NaN           NaN           NaN             NaN   \n",
       "1                NaN         NaN           NaN           NaN             NaN   \n",
       "2                NaN         NaN           NaN           NaN             NaN   \n",
       "3                NaN         NaN           NaN           NaN             NaN   \n",
       "4                NaN         NaN           NaN           NaN             NaN   \n",
       "\n",
       "   oth_drug_ind1  oth_drug_maint1  oth_drug_ind2  oth_drug_maint2  \\\n",
       "0            NaN              NaN            NaN              NaN   \n",
       "1            NaN              NaN            NaN              NaN   \n",
       "2            NaN              NaN            NaN              NaN   \n",
       "3            NaN              NaN            NaN              NaN   \n",
       "4            NaN              NaN            NaN              NaN   \n",
       "\n",
       "  oth_drug_ostxt1 oth_drug_ostxt2           _merge  \n",
       "0             NaN             NaN  Master only (1)  \n",
       "1             NaN             NaN  Master only (1)  \n",
       "2             NaN             NaN  Master only (1)  \n",
       "3             NaN             NaN  Master only (1)  \n",
       "4             NaN             NaN  Master only (1)  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"data/Data_120294_2023-03-29.csv\", \"r\") as f:\n",
    "    d = pd.read_csv(f)\n",
    "\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_columns = {\n",
    "    \"gender\": \"gender\", \n",
    "    \"wgt_kg_tcr\": \"weight\", \n",
    "    \"bmi_tcr\": \"bmi\", \n",
    "    \"hgt_cm_tcr\": \"height\", \n",
    "    \"abo\": \"blood_type\", \n",
    "    \"init_age\": \"init_age\",\n",
    "    \"data_transplant\": \"transplanted\",\n",
    "    #\"init_date\": \"start_date\",\n",
    "    #\"end_date\": \"end_date\",\n",
    "    \"rem_cd\": \"reason_removed\"\n",
    "}\n",
    "\n",
    "d = d[sample_columns.keys()].rename(sample_columns, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date_columns = [\"start_date\", \"end_date\"]\n",
    "\n",
    "# for col in date_columns:\n",
    "#     d[col] = pd.to_datetime(d[col], format=\"%d%b%Y\")\n",
    "\n",
    "# d[\"duration\"] = (d.end_date - d.start_date).dt.days\n",
    "# d.drop(d[d.duration < 0].index, inplace=True)\n",
    "# d.drop([\"start_date\", \"end_date\"], axis=1, inplace=True)\n",
    "\n",
    "# d.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   115677.00000\n",
       "mean        34.62568\n",
       "std       1472.43018\n",
       "min          1.92530\n",
       "25%         22.00720\n",
       "50%         25.76430\n",
       "75%         29.70150\n",
       "max     318008.31020\n",
       "Name: bmi, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.bmi.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115520   318008.31020\n",
       "114754   276457.06370\n",
       "114853   260207.61250\n",
       "1303      50833.33330\n",
       "111478    33257.99950\n",
       "6730      33058.18180\n",
       "4715      18611.11110\n",
       "112048    18140.00000\n",
       "8113       7920.96830\n",
       "1228       5554.34310\n",
       "34208      5509.64190\n",
       "29988       755.36390\n",
       "9498        224.48980\n",
       "7961        204.47530\n",
       "8662        196.79930\n",
       "12300       185.17170\n",
       "17751       175.31040\n",
       "98691       169.19190\n",
       "41303       161.22450\n",
       "2382        158.16010\n",
       "Name: bmi, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.bmi.sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/2649699995.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(d.bmi)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove outliers\n",
    "print(len(d[d.bmi > 60]))\n",
    "\n",
    "d.drop(d[d.bmi > 60].index, inplace=True)\n",
    "\n",
    "sns.distplot(d.bmi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/4185854147.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(d[col])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/4185854147.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(d[col])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/4185854147.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(d[col])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4917/4185854147.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(d[col])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_normalize = [\"weight\", \"bmi\", \"height\", \"init_age\"]\n",
    "\n",
    "for col in to_normalize:\n",
    "    # plot distribution\n",
    "    sns.distplot(d[col])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>weight</th>\n",
       "      <th>bmi</th>\n",
       "      <th>height</th>\n",
       "      <th>init_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116586.00000</td>\n",
       "      <td>115553.00000</td>\n",
       "      <td>115687.00000</td>\n",
       "      <td>120133.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.36868</td>\n",
       "      <td>0.40904</td>\n",
       "      <td>0.71465</td>\n",
       "      <td>0.52631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.13762</td>\n",
       "      <td>0.10431</td>\n",
       "      <td>0.14782</td>\n",
       "      <td>0.21954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.31407</td>\n",
       "      <td>0.34569</td>\n",
       "      <td>0.70584</td>\n",
       "      <td>0.43182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.38693</td>\n",
       "      <td>0.41040</td>\n",
       "      <td>0.75309</td>\n",
       "      <td>0.60227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.45377</td>\n",
       "      <td>0.47827</td>\n",
       "      <td>0.79012</td>\n",
       "      <td>0.68182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            weight          bmi       height     init_age\n",
       "count 116586.00000 115553.00000 115687.00000 120133.00000\n",
       "mean       0.36868      0.40904      0.71465      0.52631\n",
       "std        0.13762      0.10431      0.14782      0.21954\n",
       "min        0.00000      0.00000      0.00000      0.00000\n",
       "25%        0.31407      0.34569      0.70584      0.43182\n",
       "50%        0.38693      0.41040      0.75309      0.60227\n",
       "75%        0.45377      0.47827      0.79012      0.68182\n",
       "max        1.00000      1.00000      1.00000      1.00000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in to_normalize:\n",
    "    # normalize\n",
    "    d[col] = (d[col] - d[col].min()) / (d[col].max() - d[col].min())\n",
    "\n",
    "d[to_normalize].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M    88679\n",
      "F    31461\n",
      "Name: gender, dtype: int64\n",
      "O      52860\n",
      "A      46185\n",
      "B      15598\n",
      "AB      5162\n",
      "A1       241\n",
      "A2        55\n",
      "A1B       20\n",
      "A2B       14\n",
      "Z          5\n",
      "Name: blood_type, dtype: int64\n",
      "Y    77341\n",
      "N    42799\n",
      "Name: transplanted, dtype: int64\n",
      "4.00000     76683\n",
      "8.00000     16888\n",
      "9.00000      6316\n",
      "12.00000     6212\n",
      "13.00000     4999\n",
      "7.00000      1899\n",
      "5.00000       943\n",
      "6.00000       846\n",
      "14.00000      614\n",
      "3.00000       505\n",
      "24.00000      373\n",
      "16.00000      114\n",
      "21.00000       96\n",
      "18.00000       49\n",
      "15.00000       43\n",
      "20.00000        5\n",
      "22.00000        3\n",
      "2.00000         3\n",
      "Name: reason_removed, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "label_columns = [\"gender\", \"blood_type\", \"transplanted\", \"reason_removed\"]\n",
    "\n",
    "for col in label_columns:\n",
    "    print(d[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[\"is_male\"] = d.apply(lambda row: row[\"gender\"] == \"M\", axis=1)\n",
    "\n",
    "d[\"blood_A\"] = d.apply(lambda row: row[\"blood_type\"] == \"A\", axis=1)\n",
    "d[\"blood_B\"] = d.apply(lambda row: row[\"blood_type\"] == \"B\", axis=1)\n",
    "d[\"blood_AB\"] = d.apply(lambda row: row[\"blood_type\"] == \"AB\", axis=1)\n",
    "d[\"blood_O\"] = d.apply(lambda row: row[\"blood_type\"] == \"O\", axis=1)\n",
    "\n",
    "d[\"transplanted\"] = d.apply(lambda row: row[\"transplanted\"] == \"Y\", axis=1)\n",
    "d[\"died\"] = d.apply(lambda row: row[\"reason_removed\"] == 8, axis=1)\n",
    "d[\"result_label\"] = d.apply(lambda row: 0 if row[\"transplanted\"] else 1 if row[\"died\"] else 2, axis=1)\n",
    "\n",
    "d = d.drop([\"gender\", \"blood_type\", \"reason_removed\", \"transplanted\", \"died\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 120140 entries, 0 to 120263\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   weight        116586 non-null  float64\n",
      " 1   bmi           115553 non-null  float64\n",
      " 2   height        115687 non-null  float64\n",
      " 3   init_age      120133 non-null  float64\n",
      " 4   is_male       120140 non-null  bool   \n",
      " 5   blood_A       120140 non-null  bool   \n",
      " 6   blood_B       120140 non-null  bool   \n",
      " 7   blood_AB      120140 non-null  bool   \n",
      " 8   blood_O       120140 non-null  bool   \n",
      " 9   result_label  120140 non-null  int64  \n",
      "dtypes: bool(5), float64(4), int64(1)\n",
      "memory usage: 6.1 MB\n"
     ]
    }
   ],
   "source": [
    "d.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight         2.95822\n",
       "bmi            3.81805\n",
       "height         3.70651\n",
       "init_age       0.00583\n",
       "is_male        0.00000\n",
       "blood_A        0.00000\n",
       "blood_B        0.00000\n",
       "blood_AB       0.00000\n",
       "blood_O        0.00000\n",
       "result_label   0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print percentage of missing values\n",
    "d.isnull().sum() * 100 / len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with missing values\n",
    "d.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75798\n",
       "2    24304\n",
       "1    15449\n",
       "Name: result_label, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"result_label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6564490445859873\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      1.00      0.79     37941\n",
      "           1       0.00      0.00      0.00      7738\n",
      "           2       0.36      0.01      0.02     12097\n",
      "\n",
      "    accuracy                           0.66     57776\n",
      "   macro avg       0.34      0.33      0.27     57776\n",
      "weighted avg       0.51      0.66      0.52     57776\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# fit multinomial logistic regression model (transplant/death/neither)\n",
    "X = d.drop([\"result_label\"], axis=1)\n",
    "y = d[\"result_label\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n",
    "\n",
    "lr = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\", max_iter=10000)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "print(\"Accuracy: \", accuracy_score(y_test, y_pred))\n",
    "\n",
    "# plot confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Classification Report: \", classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6564836610357242\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      1.00      0.79     37941\n",
      "           1       0.00      0.00      0.00      7738\n",
      "           2       0.38      0.01      0.02     12097\n",
      "\n",
      "    accuracy                           0.66     57776\n",
      "   macro avg       0.34      0.33      0.27     57776\n",
      "weighted avg       0.51      0.66      0.52     57776\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random forest classification\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, max_depth=8, criterion=\"entropy\", min_samples_leaf=1, min_samples_split=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print(\"Accuracy: \", accuracy_score(y_test, y_pred))\n",
    "\n",
    "# Plot confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Classification Report: \", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6566913597341457\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      1.00      0.79     37941\n",
      "           1       0.00      0.00      0.00      7738\n",
      "           2       0.00      0.00      0.00     12097\n",
      "\n",
      "    accuracy                           0.66     57776\n",
      "   macro avg       0.22      0.33      0.26     57776\n",
      "weighted avg       0.43      0.66      0.52     57776\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/wilmer/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Support vector machine classification\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel=\"rbf\", gamma=\"auto\")\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "print(\"Accuracy: \", accuracy_score(y_test, y_pred))\n",
    "\n",
    "# Plot confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Classification Report: \", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "\n",
    "corr = d.corr()\n",
    "sns.heatmap(corr, annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}