HCA 203 PUF Aggregator - Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning Notebook for Combined PUF File\n",
    "\n",
    "Greatly revised to solve some data problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.rcsetup as mrc\n",
    "\n",
    "#For standardizing variables\n",
    "import sklearn.preprocessing as pre\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with the Elixhauser Score files.\n",
    "\n",
    "By averaging, produce 1 score for each DeSYNPuf ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "elix = pd.read_csv(\"/home/kylier/python/DS/data/PUF/IPANDOP_FIPS_ELIX_ALL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1332236, 57)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0\n",
      "Unnamed: 1\n",
      "DESYNPUF_ID\n",
      "BENE_SEX_IDENT_CD\n",
      "BENE_RACE_CD\n",
      "SP_STATE_CODE\n",
      "BENE_COUNTY_CD\n",
      "FIPS_COUNTY_CODE\n",
      "FULL_FIPS_CODE\n",
      "FIPS_STATE_CODE\n",
      "ICD9_DGNS_CD_1\n",
      "ICD9_DGNS_CD_2\n",
      "ICD9_DGNS_CD_3\n",
      "ICD9_DGNS_CD_4\n",
      "ICD9_DGNS_CD_5\n",
      "ICD9_DGNS_CD_6\n",
      "ICD9_DGNS_CD_7\n",
      "ICD9_DGNS_CD_8\n",
      "ICD9_DGNS_CD_9\n",
      "ICD9_DGNS_CD_10\n",
      "CLAIM_TYPE\n",
      "BENE_AGE\n",
      "YEAR\n",
      "CHF\n",
      "Car_Arrhythmia\n",
      "Valve_Disease\n",
      "Pulm_Cir_Disorder\n",
      "PVD\n",
      "HT_Uncomp\n",
      "HT_Comp\n",
      "Paralysis\n",
      "Neuro_disorder_other\n",
      "CPD\n",
      "DM_Uncomp\n",
      "DM_Comp\n",
      "Hypo_thyroid\n",
      "Renal_Failure\n",
      "Liver_Disease\n",
      "Peptic_Ulcer_WO_bleeding\n",
      "AIDS_HIV\n",
      "Lymphoma\n",
      "Metastatic_Cancer\n",
      "Tumor_WO_Metastasis\n",
      "RA\n",
      "Coagulopathy\n",
      "Obesity\n",
      "Weight_Loss\n",
      "Fluid_Electrolyte_Disorder\n",
      "Blood_Loss_Anemia\n",
      "Deficiency_Anemia\n",
      "Alcohol_Abuse\n",
      "Drug_Abuse\n",
      "Psychoses\n",
      "Depression\n",
      "i\n",
      "TOT_GRP\n",
      "elx_WEIGHT_SCORE\n"
     ]
    }
   ],
   "source": [
    "for c in elix.columns:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the first 2 columns (created when the csv file was written out)\n",
    "\n",
    "Show nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DESYNPUF_ID                         0\n",
       "BENE_SEX_IDENT_CD                   0\n",
       "BENE_RACE_CD                        0\n",
       "SP_STATE_CODE                       0\n",
       "BENE_COUNTY_CD                      0\n",
       "FIPS_COUNTY_CODE                14272\n",
       "FULL_FIPS_CODE                  14272\n",
       "FIPS_STATE_CODE                 14272\n",
       "ICD9_DGNS_CD_1                 187985\n",
       "ICD9_DGNS_CD_2                 576088\n",
       "ICD9_DGNS_CD_3                 822826\n",
       "ICD9_DGNS_CD_4                 986948\n",
       "ICD9_DGNS_CD_5                1097620\n",
       "ICD9_DGNS_CD_6                1155237\n",
       "ICD9_DGNS_CD_7                1192973\n",
       "ICD9_DGNS_CD_8                1219278\n",
       "ICD9_DGNS_CD_9                1239874\n",
       "ICD9_DGNS_CD_10               1322764\n",
       "CLAIM_TYPE                     187912\n",
       "BENE_AGE                       187912\n",
       "YEAR                                0\n",
       "CHF                                 0\n",
       "Car_Arrhythmia                      0\n",
       "Valve_Disease                       0\n",
       "Pulm_Cir_Disorder                   0\n",
       "PVD                                 0\n",
       "HT_Uncomp                           0\n",
       "HT_Comp                             0\n",
       "Paralysis                           0\n",
       "Neuro_disorder_other                0\n",
       "CPD                                 0\n",
       "DM_Uncomp                           0\n",
       "DM_Comp                             0\n",
       "Hypo_thyroid                        0\n",
       "Renal_Failure                       0\n",
       "Liver_Disease                       0\n",
       "Peptic_Ulcer_WO_bleeding            0\n",
       "AIDS_HIV                            0\n",
       "Lymphoma                            0\n",
       "Metastatic_Cancer                   0\n",
       "Tumor_WO_Metastasis                 0\n",
       "RA                                  0\n",
       "Coagulopathy                        0\n",
       "Obesity                             0\n",
       "Weight_Loss                         0\n",
       "Fluid_Electrolyte_Disorder          0\n",
       "Blood_Loss_Anemia                   0\n",
       "Deficiency_Anemia                   0\n",
       "Alcohol_Abuse                       0\n",
       "Drug_Abuse                          0\n",
       "Psychoses                           0\n",
       "Depression                          0\n",
       "i                                   0\n",
       "TOT_GRP                             0\n",
       "elx_WEIGHT_SCORE                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elix.drop(columns=elix.columns[0], axis=1, inplace=True)\n",
    "elix.drop(columns=elix.columns[0], axis=1, inplace=True)\n",
    "elix.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1332236 entries, 0 to 1332235\n",
      "Data columns (total 57 columns):\n",
      " #   Column                      Non-Null Count    Dtype  \n",
      "---  ------                      --------------    -----  \n",
      " 0   DESYNPUF_ID                 1332236 non-null  object \n",
      " 1   BENE_SEX_IDENT_CD           1332236 non-null  int64  \n",
      " 2   BENE_RACE_CD                1332236 non-null  int64  \n",
      " 3   SP_STATE_CODE               1332236 non-null  int64  \n",
      " 4   BENE_COUNTY_CD              1332236 non-null  int64  \n",
      " 5   FIPS_COUNTY_CODE            1317964 non-null  float64\n",
      " 6   FULL_FIPS_CODE              1317964 non-null  float64\n",
      " 7   FIPS_STATE_CODE             1317964 non-null  float64\n",
      " 8   ICD9_DGNS_CD_1              1144251 non-null  object \n",
      " 9   ICD9_DGNS_CD_2              756148 non-null   object \n",
      " 10  ICD9_DGNS_CD_3              509410 non-null   object \n",
      " 11  ICD9_DGNS_CD_4              345288 non-null   object \n",
      " 12  ICD9_DGNS_CD_5              234616 non-null   object \n",
      " 13  ICD9_DGNS_CD_6              176999 non-null   object \n",
      " 14  ICD9_DGNS_CD_7              139263 non-null   object \n",
      " 15  ICD9_DGNS_CD_8              112958 non-null   object \n",
      " 16  ICD9_DGNS_CD_9              92362 non-null    object \n",
      " 17  ICD9_DGNS_CD_10             9472 non-null     object \n",
      " 18  CLAIM_TYPE                  1144324 non-null  object \n",
      " 19  BENE_AGE                    1144324 non-null  float64\n",
      " 20  YEAR                        1332236 non-null  int64  \n",
      " 21  CHF                         1332236 non-null  int64  \n",
      " 22  Car_Arrhythmia              1332236 non-null  int64  \n",
      " 23  Valve_Disease               1332236 non-null  int64  \n",
      " 24  Pulm_Cir_Disorder           1332236 non-null  int64  \n",
      " 25  PVD                         1332236 non-null  int64  \n",
      " 26  HT_Uncomp                   1332236 non-null  int64  \n",
      " 27  HT_Comp                     1332236 non-null  int64  \n",
      " 28  Paralysis                   1332236 non-null  int64  \n",
      " 29  Neuro_disorder_other        1332236 non-null  int64  \n",
      " 30  CPD                         1332236 non-null  int64  \n",
      " 31  DM_Uncomp                   1332236 non-null  int64  \n",
      " 32  DM_Comp                     1332236 non-null  int64  \n",
      " 33  Hypo_thyroid                1332236 non-null  int64  \n",
      " 34  Renal_Failure               1332236 non-null  int64  \n",
      " 35  Liver_Disease               1332236 non-null  int64  \n",
      " 36  Peptic_Ulcer_WO_bleeding    1332236 non-null  int64  \n",
      " 37  AIDS_HIV                    1332236 non-null  int64  \n",
      " 38  Lymphoma                    1332236 non-null  int64  \n",
      " 39  Metastatic_Cancer           1332236 non-null  int64  \n",
      " 40  Tumor_WO_Metastasis         1332236 non-null  int64  \n",
      " 41  RA                          1332236 non-null  int64  \n",
      " 42  Coagulopathy                1332236 non-null  int64  \n",
      " 43  Obesity                     1332236 non-null  int64  \n",
      " 44  Weight_Loss                 1332236 non-null  int64  \n",
      " 45  Fluid_Electrolyte_Disorder  1332236 non-null  int64  \n",
      " 46  Blood_Loss_Anemia           1332236 non-null  int64  \n",
      " 47  Deficiency_Anemia           1332236 non-null  int64  \n",
      " 48  Alcohol_Abuse               1332236 non-null  int64  \n",
      " 49  Drug_Abuse                  1332236 non-null  int64  \n",
      " 50  Psychoses                   1332236 non-null  int64  \n",
      " 51  Depression                  1332236 non-null  int64  \n",
      " 52  i                           1332236 non-null  int64  \n",
      " 53  TOT_GRP                     1332236 non-null  int64  \n",
      " 54  elx_WEIGHT_SCORE            1332236 non-null  int64  \n",
      " 55  ELIX_GRP_TOTAL              1332236 non-null  int64  \n",
      " 56  ELIX_SCORE                  1332236 non-null  int64  \n",
      "dtypes: float64(4), int64(41), object(12)\n",
      "memory usage: 579.4+ MB\n"
     ]
    }
   ],
   "source": [
    "elix[\"ELIX_GRP_TOTAL\"] = elix[\"TOT_GRP\"].copy()\n",
    "elix[\"ELIX_SCORE\"] = elix[\"elx_WEIGHT_SCORE\"].copy()\n",
    "\n",
    "elix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th>SP_STATE_CODE</th>\n",
       "      <th>BENE_COUNTY_CD</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>ELIX_GRP_TOTAL</th>\n",
       "      <th>ELIX_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>10</td>\n",
       "      <td>280</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>34</td>\n",
       "      <td>590</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>34</td>\n",
       "      <td>590</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>34</td>\n",
       "      <td>590</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DESYNPUF_ID  SP_STATE_CODE  BENE_COUNTY_CD  YEAR  ELIX_GRP_TOTAL  \\\n",
       "0  0000438E79D01BEA             10             280  2008               0   \n",
       "1  00010E7BEA69142C              8              10  2008               0   \n",
       "2  00015BF6509E1DF7             34             590  2008               0   \n",
       "3  00015BF6509E1DF7             34             590  2008               1   \n",
       "4  00015BF6509E1DF7             34             590  2008               0   \n",
       "\n",
       "   ELIX_SCORE  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elix = elix.drop(elix.columns[[1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, \n",
    "                                   24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, \n",
    "                                   43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]], axis=1, errors=\"ignore\")\n",
    "elix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SP_STATE_CODE</th>\n",
       "      <th>BENE_COUNTY_CD</th>\n",
       "      <th>MEAN_ELIX_GRP_TOT</th>\n",
       "      <th>MEAN_ELIX_SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0000438E79D01BEA</th>\n",
       "      <th>2008</th>\n",
       "      <td>10</td>\n",
       "      <td>280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>10</td>\n",
       "      <td>280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>10</td>\n",
       "      <td>280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">00010E7BEA69142C</th>\n",
       "      <th>2008</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP_STATE_CODE  BENE_COUNTY_CD  MEAN_ELIX_GRP_TOT  \\\n",
       "DESYNPUF_ID      YEAR                                                     \n",
       "0000438E79D01BEA 2008             10             280           0.000000   \n",
       "                 2009             10             280           0.000000   \n",
       "                 2010             10             280           0.000000   \n",
       "00010E7BEA69142C 2008              8              10           0.000000   \n",
       "                 2009              8              10           0.666667   \n",
       "\n",
       "                       MEAN_ELIX_SCORE  \n",
       "DESYNPUF_ID      YEAR                   \n",
       "0000438E79D01BEA 2008              0.0  \n",
       "                 2009              0.0  \n",
       "                 2010              0.0  \n",
       "00010E7BEA69142C 2008              0.0  \n",
       "                 2009              1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate Scores by DeSYNPuf ID and Year\n",
    "elix = elix.groupby([\"DESYNPUF_ID\", \"YEAR\"]).aggregate({\"SP_STATE_CODE\":\"mean\",\n",
    "                                                             \"BENE_COUNTY_CD\":\"mean\",\n",
    "                                                             \"ELIX_GRP_TOTAL\":\"mean\",\n",
    "                                                             \"ELIX_SCORE\":\"mean\"})\n",
    "elix.columns = [\"SP_STATE_CODE\", \"BENE_COUNTY_CD\", \"MEAN_ELIX_GRP_TOT\", \"MEAN_ELIX_SCORE\"]\n",
    "elix.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 116351 entries, 0 to 116350\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   DESYNPUF_ID        116351 non-null  object \n",
      " 1   SP_STATE_CODE      116351 non-null  int64  \n",
      " 2   BENE_COUNTY_CD     116351 non-null  int64  \n",
      " 3   MEAN_ELIX_GRP_TOT  116351 non-null  float64\n",
      " 4   MEAN_ELIX_SCORE    116351 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Aggregate Scores to 1 Score per Beneficiary\n",
    "elix = elix.reset_index()\n",
    "elix = elix.groupby(\"DESYNPUF_ID\").aggregate({\"SP_STATE_CODE\":\"mean\",\n",
    "                                                \"BENE_COUNTY_CD\":\"mean\",\n",
    "                                                \"MEAN_ELIX_GRP_TOT\":\"mean\",\n",
    "                                                \"MEAN_ELIX_SCORE\":\"mean\"})\n",
    "elix.columns = [\"SP_STATE_CODE\", \"BENE_COUNTY_CD\", \"MEAN_ELIX_GRP_TOT\", \"MEAN_ELIX_SCORE\"]\n",
    "elix = elix.reset_index()\n",
    "elix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>MEAN_ELIX_GRP_TOT</th>\n",
       "      <th>MEAN_ELIX_SCORE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0000438E79D01BEA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00010E7BEA69142C</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00015BF6509E1DF7</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>119.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00018DD17736A661</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000240D599ED789C</th>\n",
       "      <td>0.766667</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>35.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFFDFD377BF9EC59</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFFF1E240FFD97D8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFFF58406F9882DD</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>469.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFFFCA5AFABA1CF6</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFFFF4859B6D7402</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116351 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MEAN_ELIX_GRP_TOT  MEAN_ELIX_SCORE  FIPS_COUNTY_CODE  \\\n",
       "DESYNPUF_ID                                                              \n",
       "0000438E79D01BEA           0.000000         0.000000              57.0   \n",
       "00010E7BEA69142C           0.333333         0.500000               3.0   \n",
       "00015BF6509E1DF7           0.555556         0.555556             119.0   \n",
       "00018DD17736A661           0.428571         1.571429             101.0   \n",
       "000240D599ED789C           0.766667         2.016667              35.0   \n",
       "...                             ...              ...               ...   \n",
       "FFFDFD377BF9EC59           0.000000         0.000000              11.0   \n",
       "FFFF1E240FFD97D8           0.000000         0.000000              19.0   \n",
       "FFFF58406F9882DD           0.555556         3.333333             469.0   \n",
       "FFFFCA5AFABA1CF6           1.500000         2.500000              59.0   \n",
       "FFFFF4859B6D7402           0.333333         0.666667               5.0   \n",
       "\n",
       "                  FIPS_STATE_CODE  \n",
       "DESYNPUF_ID                        \n",
       "0000438E79D01BEA             12.0  \n",
       "00010E7BEA69142C             10.0  \n",
       "00015BF6509E1DF7             37.0  \n",
       "00018DD17736A661             26.0  \n",
       "000240D599ED789C             55.0  \n",
       "...                           ...  \n",
       "FFFDFD377BF9EC59             41.0  \n",
       "FFFF1E240FFD97D8             27.0  \n",
       "FFFF58406F9882DD             48.0  \n",
       "FFFFCA5AFABA1CF6             51.0  \n",
       "FFFFF4859B6D7402             25.0  \n",
       "\n",
       "[116351 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load FIPS data for join\n",
    "\n",
    "SSA2FIPS = pd.read_csv(\"/home/kylier/python/DS/data/ssa_fips_state_county2011_augmented.csv\")\n",
    "\n",
    "ELIX_FIPS = pd.merge(elix, SSA2FIPS, on=['SP_STATE_CODE', 'BENE_COUNTY_CD'], how='left')\n",
    "ELIX_FIPS.drop([\"SP_STATE_CODE\", \"BENE_COUNTY_CD\", \"FULL_FIPS_CODE\", \"CBSA\", \"CBSA_NAME\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "ELIX_FIPS = ELIX_FIPS.set_index(\"DESYNPUF_ID\")\n",
    "ELIX_FIPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kylier/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (59,69,71,72,74,75,95,96,98,102,104,115) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# Load All Inpatient and Outpatient Data (1/3 GB)\n",
    "desynpuf = pd.read_csv(\"/home/kylier/python/DS/data/PUF/IPANDOP_FIPS_ALL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DESYNPUF_ID               0\n",
       "BENE_BIRTH_DT             0\n",
       "BENE_DEATH_DT        981851\n",
       "BENE_SEX_IDENT_CD         0\n",
       "BENE_RACE_CD              0\n",
       "                      ...  \n",
       "HCPCS_CD_44          981241\n",
       "HCPCS_CD_45          991604\n",
       "CLAIM_TYPE           149316\n",
       "BENE_AGE             149316\n",
       "YEAR                      0\n",
       "Length: 122, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.drop(columns=desynpuf.columns[0], axis=1, inplace=True)\n",
    "desynpuf.drop(columns=desynpuf.columns[0], axis=1, inplace=True)\n",
    "desynpuf.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(991604, 122)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost 1 million rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DESYNPUF_ID\n",
      "BENE_BIRTH_DT\n",
      "BENE_DEATH_DT\n",
      "BENE_SEX_IDENT_CD\n",
      "BENE_RACE_CD\n",
      "BENE_ESRD_IND\n",
      "SP_STATE_CODE\n",
      "BENE_COUNTY_CD\n",
      "BENE_HI_CVRAGE_TOT_MONS\n",
      "BENE_SMI_CVRAGE_TOT_MONS\n",
      "BENE_HMO_CVRAGE_TOT_MONS\n",
      "PLAN_CVRG_MOS_NUM\n",
      "SP_ALZHDMTA\n",
      "SP_CHF\n",
      "SP_CHRNKIDN\n",
      "SP_CNCR\n",
      "SP_COPD\n",
      "SP_DEPRESSN\n",
      "SP_DIABETES\n",
      "SP_ISCHMCHT\n",
      "SP_OSTEOPRS\n",
      "SP_RA_OA\n",
      "SP_STRKETIA\n",
      "MEDREIMB_IP\n",
      "BENRES_IP\n",
      "PPPYMT_IP\n",
      "MEDREIMB_OP\n",
      "BENRES_OP\n",
      "PPPYMT_OP\n",
      "MEDREIMB_CAR\n",
      "BENRES_CAR\n",
      "PPPYMT_CAR\n",
      "FIPS_COUNTY_CODE\n",
      "FULL_FIPS_CODE\n",
      "CBSA\n",
      "CBSA_NAME\n",
      "FIPS_STATE_CODE\n",
      "CLM_ID\n",
      "SEGMENT\n",
      "CLM_FROM_DT\n",
      "CLM_THRU_DT\n",
      "PRVDR_NUM\n",
      "CLM_PMT_AMT\n",
      "NCH_PRMRY_PYR_CLM_PD_AMT\n",
      "AT_PHYSN_NPI\n",
      "OP_PHYSN_NPI\n",
      "OT_PHYSN_NPI\n",
      "CLM_ADMSN_DT\n",
      "ADMTNG_ICD9_DGNS_CD\n",
      "CLM_PASS_THRU_PER_DIEM_AMT\n",
      "NCH_BENE_IP_DDCTBL_AMT\n",
      "NCH_BENE_PTB_DDCTBL_AMT\n",
      "NCH_BENE_PTA_COINSRNC_LBLTY_AM\n",
      "NCH_BENE_PTB_COINSRNC_AMT\n",
      "NCH_BENE_BLOOD_DDCTBL_LBLTY_AM\n",
      "CLM_UTLZTN_DAY_CNT\n",
      "NCH_BENE_DSCHRG_DT\n",
      "CLM_DRG_CD\n",
      "ICD9_DGNS_CD_1\n",
      "ICD9_DGNS_CD_2\n",
      "ICD9_DGNS_CD_3\n",
      "ICD9_DGNS_CD_4\n",
      "ICD9_DGNS_CD_5\n",
      "ICD9_DGNS_CD_6\n",
      "ICD9_DGNS_CD_7\n",
      "ICD9_DGNS_CD_8\n",
      "ICD9_DGNS_CD_9\n",
      "ICD9_DGNS_CD_10\n",
      "ICD9_PRCDR_CD_1\n",
      "ICD9_PRCDR_CD_2\n",
      "ICD9_PRCDR_CD_3\n",
      "ICD9_PRCDR_CD_4\n",
      "ICD9_PRCDR_CD_5\n",
      "ICD9_PRCDR_CD_6\n",
      "HCPCS_CD_1\n",
      "HCPCS_CD_2\n",
      "HCPCS_CD_3\n",
      "HCPCS_CD_4\n",
      "HCPCS_CD_5\n",
      "HCPCS_CD_6\n",
      "HCPCS_CD_7\n",
      "HCPCS_CD_8\n",
      "HCPCS_CD_9\n",
      "HCPCS_CD_10\n",
      "HCPCS_CD_11\n",
      "HCPCS_CD_12\n",
      "HCPCS_CD_13\n",
      "HCPCS_CD_14\n",
      "HCPCS_CD_15\n",
      "HCPCS_CD_16\n",
      "HCPCS_CD_17\n",
      "HCPCS_CD_18\n",
      "HCPCS_CD_19\n",
      "HCPCS_CD_20\n",
      "HCPCS_CD_21\n",
      "HCPCS_CD_22\n",
      "HCPCS_CD_23\n",
      "HCPCS_CD_24\n",
      "HCPCS_CD_25\n",
      "HCPCS_CD_26\n",
      "HCPCS_CD_27\n",
      "HCPCS_CD_28\n",
      "HCPCS_CD_29\n",
      "HCPCS_CD_30\n",
      "HCPCS_CD_31\n",
      "HCPCS_CD_32\n",
      "HCPCS_CD_33\n",
      "HCPCS_CD_34\n",
      "HCPCS_CD_35\n",
      "HCPCS_CD_36\n",
      "HCPCS_CD_37\n",
      "HCPCS_CD_38\n",
      "HCPCS_CD_39\n",
      "HCPCS_CD_40\n",
      "HCPCS_CD_41\n",
      "HCPCS_CD_42\n",
      "HCPCS_CD_43\n",
      "HCPCS_CD_44\n",
      "HCPCS_CD_45\n",
      "CLAIM_TYPE\n",
      "BENE_AGE\n",
      "YEAR\n"
     ]
    }
   ],
   "source": [
    "for c in desynpuf.columns:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age\n",
    "\n",
    "This data is slightly more normally-distributed.  The mean age is 73, the median age is 74. IQR is 15, making anyone under 67 years old in the <25th percentile.  \n",
    "\n",
    "Not surprising - these are Medicare beneficiaries.  Only those who are disabled are eligible at <65 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='BENE_AGE', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.histplot(x=round(desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"BENE_AGE\"].mean()), binwidth=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    194068.000000\n",
       "mean         73.159626\n",
       "std          12.768693\n",
       "min          24.000000\n",
       "25%          67.666667\n",
       "50%          74.000000\n",
       "75%          81.625000\n",
       "max         102.000000\n",
       "Name: BENE_AGE, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"BENE_AGE\"].mean().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Race Codes\n",
    "\n",
    "NaN\tUNKNOWN\n",
    "\n",
    "1\tNON-HISPANIC WHITE\n",
    "\n",
    "2\tBLACK (OR AFRICAN-AMERICAN)\n",
    "\n",
    "3\tOTHER\n",
    "\n",
    "4\tASIAN/PACIFIC ISLANDER\n",
    "\n",
    "5\tHISPANIC\n",
    "\n",
    "6\tAMERICAN INDIAN / ALASKA NATIVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='BENE_RACE_CD', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.countplot(x=round(desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"BENE_RACE_CD\"].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this data set, the beneficiary population is 84% white, 11% Black or African American, 2.3% Hispanic, and 4% Other.  \n",
    "\n",
    "Asians / Pacific Islanders and American Indians / AK Natives are not represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.826797\n",
       "2    0.107737\n",
       "3    0.042233\n",
       "5    0.023233\n",
       "Name: BENE_RACE_CD, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"BENE_RACE_CD\"].mean().value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sex\n",
    "\n",
    "1 MALE, 2 FEMALE recoded to 1 MALE, 0 FEMALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='B_SEX', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desynpuf[\"B_SEX\"] = desynpuf[\"BENE_SEX_IDENT_CD\"].replace(2, 0)\n",
    "\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.countplot(x=desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"B_SEX\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.556415\n",
       "1    0.443585\n",
       "Name: B_SEX, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.groupby([\"DESYNPUF_ID\",\"YEAR\"])[\"B_SEX\"].mean().value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chronic Conditions\n",
    "\n",
    "Alzheimer's & Related SP_ALZHDMTA 1=Yes, 2=No\n",
    "\n",
    "Congestive Heart Failure SP_CHF 1=Yes, 2=No\n",
    "\n",
    "Cancer SP_CNCR 1=Yes, 2=No\n",
    "\n",
    "Depression SP_DEPRESSN 1=Yes, 2=No\n",
    "\n",
    "Diabetes SP_DIABETES 1=Yes, 2=No\n",
    "\n",
    "Ischemic Heart Disease SP_ISCHMCHT 1=Yes, 2=No\n",
    "\n",
    "Osteoporosis SP_OSTEOPRS 1=Yes, 2=No\n",
    "\n",
    "COPD SP_COPD 1=Yes, 2=No\n",
    "\n",
    "Chronic Kidney Disease SP_CHRNKIDN 1=Yes, 2=No\n",
    "\n",
    "Rheumatoid or Osteoarthritis SP_RA_OA 1=Yes, 2=No\n",
    "\n",
    "Stroke or TIA SP_STRKETIA 1=Yes, 2=No\n",
    "\n",
    "ESRD Y=Yes, 0=No, recoded to 1=Yes, 2=No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "desynpuf[\"SP_ESRD\"] = desynpuf[\"BENE_ESRD_IND\"].replace(\"Y\", 1).astype(int)\n",
    "desynpuf[\"SP_ESRD\"] = desynpuf[\"SP_ESRD\"].replace(0, 2).astype(int)\n",
    "desynpuf[\"CLM_ADMSN_DT\"] = pd.to_datetime(desynpuf[\"CLM_ADMSN_DT\"], format=\"%Y%m%d\")\n",
    "desynpuf[\"NCH_BENE_DSCHRG_DT\"] = pd.to_datetime(desynpuf[\"NCH_BENE_DSCHRG_DT\"], format=\"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conditions = desynpuf[[\"SP_ALZHDMTA\", \"SP_CHF\", \"SP_CNCR\", \"SP_DEPRESSN\", \"SP_DIABETES\", \"SP_ISCHMCHT\", \n",
    "                        \"SP_OSTEOPRS\", \"SP_COPD\", \"SP_CHRNKIDN\", \"SP_RA_OA\", \"SP_STRKETIA\", \"SP_ESRD\"]]\n",
    "conditions_corr = conditions.corr(method=\"pearson\")\n",
    "lower_corr = conditions_corr.where(np.tril(np.ones(conditions_corr.shape)).astype(np.bool))\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "conditions_hmap=sb.heatmap(lower_corr, annot=True, cmap=\"mako\", fmt=\".2f\")\n",
    "fig = conditions_hmap.get_figure()\n",
    "fig.savefig(\"desynpuf_cc_heatmap.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the clinically understood correlations between diabetes, ischemic heart disease, CHF and CKD.\n",
    "\n",
    "They are known to be causally connected on the basis of their pathophysiology.\n",
    "\n",
    "Obviously, so are ESRD and CKD, which are two points on the same disease continuum.\n",
    "\n",
    "There are also moderate correlations between depression, dementia, COPD and the DM/CHF/Heart Disease/CKD group.\n",
    "\n",
    "Strangly, there appears to be a weaker correlation between stroke / TIA and these diseases.\n",
    "\n",
    "While RA & OA produce similar disability in advanced stages, they have almost nothing in common in terms of pathophysiology or disease course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-code the conditions indicators as dummy variables\n",
    "# \"SP_ALZHDMTA\", \"SP_CHF\", \"SP_CNCR\", \"SP_DEPRESSN\", \"SP_DIABETES\", \"SP_ISCHMCHT\", SP_OSTEOPRS\"\n",
    "# \"SP_COPD\", \"SP_CHRNKIDN\", \"SP_RA_OA\" and \"SP_STRKETIA\"  \n",
    "\n",
    "desynpuf[\"B_ALZHDMTA\"] = desynpuf[\"SP_ALZHDMTA\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_CHF\"] = desynpuf[\"SP_CHF\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_CNCR\"] = desynpuf[\"SP_CNCR\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_COPD\"] = desynpuf[\"SP_COPD\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_DEPRESSN\"] = desynpuf[\"SP_DEPRESSN\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_DIABETES\"] = desynpuf[\"SP_DIABETES\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_ISCHMCHT\"] = desynpuf[\"SP_ISCHMCHT\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_OSTEOPRS\"] = desynpuf[\"SP_OSTEOPRS\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_CHRNKIDN\"] = desynpuf[\"SP_CHRNKIDN\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_RA_OA\"] = desynpuf[\"SP_RA_OA\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_STRKETIA\"] = desynpuf[\"SP_STRKETIA\"].replace(2, 0).astype(int)\n",
    "desynpuf[\"B_ESRD\"] = desynpuf[\"SP_ESRD\"].replace(2, 0).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>DESYNPUF_ID</th>\n",
       "      <th>BENE_BIRTH_DT</th>\n",
       "      <th>BENE_DEATH_DT</th>\n",
       "      <th>BENE_HI_CVRAGE_TOT_MONS</th>\n",
       "      <th>BENE_SMI_CVRAGE_TOT_MONS</th>\n",
       "      <th>BENE_HMO_CVRAGE_TOT_MONS</th>\n",
       "      <th>PLAN_CVRG_MOS_NUM</th>\n",
       "      <th>SP_ALZHDMTA</th>\n",
       "      <th>SP_CHF</th>\n",
       "      <th>SP_CHRNKIDN</th>\n",
       "      <th>...</th>\n",
       "      <th>B_ISCHMCHT</th>\n",
       "      <th>B_OSTEOPRS</th>\n",
       "      <th>B_CHRNKIDN</th>\n",
       "      <th>B_RA_OA</th>\n",
       "      <th>B_STRKETIA</th>\n",
       "      <th>B_ESRD</th>\n",
       "      <th>NH_WHITE</th>\n",
       "      <th>AA_BLACK</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>NW_HISPANIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>1937-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>1938-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DESYNPUF_ID BENE_BIRTH_DT BENE_DEATH_DT  BENE_HI_CVRAGE_TOT_MONS  \\\n",
       "0  0000438E79D01BEA    1937-05-01           NaN                       12   \n",
       "1  00010E7BEA69142C    1938-04-01           NaN                       12   \n",
       "2  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "3  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "4  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "\n",
       "   BENE_SMI_CVRAGE_TOT_MONS  BENE_HMO_CVRAGE_TOT_MONS  PLAN_CVRG_MOS_NUM  \\\n",
       "0                        12                         0                 12   \n",
       "1                        12                        12                 12   \n",
       "2                        12                         0                  0   \n",
       "3                        12                         0                  0   \n",
       "4                        12                         0                  0   \n",
       "\n",
       "   SP_ALZHDMTA  SP_CHF  SP_CHRNKIDN  ...  B_ISCHMCHT  B_OSTEOPRS  B_CHRNKIDN  \\\n",
       "0            2       2            2  ...           0           0           0   \n",
       "1            1       2            2  ...           0           0           0   \n",
       "2            1       1            2  ...           0           1           0   \n",
       "3            1       1            2  ...           0           1           0   \n",
       "4            1       1            2  ...           0           1           0   \n",
       "\n",
       "   B_RA_OA  B_STRKETIA  B_ESRD  NH_WHITE  AA_BLACK  OTHER  NW_HISPANIC  \n",
       "0        0           0       0         1         0      0            0  \n",
       "1        0           0       0         1         0      0            0  \n",
       "2        0           0       0         1         0      0            0  \n",
       "3        0           0       0         1         0      0            0  \n",
       "4        0           0       0         1         0      0            0  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb = pre.LabelBinarizer()\n",
    "BinarizedRace = pd.DataFrame(lb.fit_transform(desynpuf[\"BENE_RACE_CD\"]), \n",
    "                             columns=[\"NH_WHITE\", \"AA_BLACK\", \"OTHER\", \"NW_HISPANIC\"])\n",
    "desynpuf = desynpuf.join(BinarizedRace)\n",
    "desynpuf.drop([\"BENE_SEX_IDENT_CD\", \"BENE_RACE_CD\", \"BENE_ESRD_IND\", \"SP_STATE_CODE\", \n",
    "               \"BENE_COUNTY_CD\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "desynpuf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "desynpuf[\"IP_VISITS\"] = desynpuf[\"CLAIM_TYPE\"].replace(\"INPATIENT\", 1)\n",
    "desynpuf[\"IP_VISITS\"] = desynpuf[\"IP_VISITS\"].replace(\"OUTPATIENT\", 0)\n",
    "desynpuf[\"OP_VISITS\"] = desynpuf[\"CLAIM_TYPE\"].replace(\"OUTPATIENT\", 1)\n",
    "desynpuf[\"OP_VISITS\"] = desynpuf[\"OP_VISITS\"].replace(\"INPATIENT\", 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "desynpuf[\"IP_VISITS\"] = desynpuf.IP_VISITS.fillna(0).astype(int)\n",
    "desynpuf[\"OP_VISITS\"] = desynpuf.OP_VISITS.fillna(0).astype(int)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Codes per Claim\n",
    "\n",
    "Count ICD9 Codes per Claim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes = desynpuf[[\"ICD9_DGNS_CD_1\",\"ICD9_DGNS_CD_2\",\"ICD9_DGNS_CD_3\",\"ICD9_DGNS_CD_4\",\"ICD9_DGNS_CD_5\",\n",
    "                  \"ICD9_DGNS_CD_6\",\"ICD9_DGNS_CD_7\",\"ICD9_DGNS_CD_8\",\"ICD9_DGNS_CD_9\",\"ICD9_DGNS_CD_10\",\n",
    "                  \"ICD9_PRCDR_CD_1\",\"ICD9_PRCDR_CD_2\",\"ICD9_PRCDR_CD_3\",\"ICD9_PRCDR_CD_4\",\"ICD9_PRCDR_CD_5\",\n",
    "                  \"ICD9_PRCDR_CD_6\"]]\n",
    "\n",
    "desynpuf[\"CODES_COUNT\"] = codes.count(axis=1)\n",
    "desynpuf[\"CODES_COUNT\"].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedures\n",
    "\n",
    "HCPCS codes of the Level I Type are Procedure codes; these are 5 digit numbers.\n",
    "\n",
    "Level II codes start with a letter.\n",
    "\n",
    "Here, we will store the count of Level I / CPT codes for each beneficiary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# First, replace any L2-formatted codes with None / NULL\n",
    "\n",
    "import re\n",
    "\n",
    "procedures = desynpuf[[\"HCPCS_CD_1\", \"HCPCS_CD_2\",\"HCPCS_CD_3\",\"HCPCS_CD_4\",\"HCPCS_CD_5\",\"HCPCS_CD_6\",\"HCPCS_CD_7\",\n",
    "                      \"HCPCS_CD_8\",\"HCPCS_CD_9\",\"HCPCS_CD_10\",\"HCPCS_CD_11\",\"HCPCS_CD_12\",\"HCPCS_CD_13\",\n",
    "                      \"HCPCS_CD_14\",\"HCPCS_CD_15\",\"HCPCS_CD_16\",\"HCPCS_CD_17\",\"HCPCS_CD_18\",\"HCPCS_CD_19\",\n",
    "                      \"HCPCS_CD_20\",\"HCPCS_CD_21\",\"HCPCS_CD_22\",\"HCPCS_CD_23\",\"HCPCS_CD_24\",\"HCPCS_CD_25\",\n",
    "                      \"HCPCS_CD_26\",\"HCPCS_CD_27\",\"HCPCS_CD_28\",\"HCPCS_CD_29\",\"HCPCS_CD_30\",\"HCPCS_CD_31\",\n",
    "                      \"HCPCS_CD_32\",\"HCPCS_CD_33\",\"HCPCS_CD_34\",\"HCPCS_CD_35\",\"HCPCS_CD_36\",\"HCPCS_CD_37\",\n",
    "                      \"HCPCS_CD_38\",\"HCPCS_CD_39\",\"HCPCS_CD_40\",\"HCPCS_CD_41\",\"HCPCS_CD_42\",\"HCPCS_CD_43\",\n",
    "                      \"HCPCS_CD_44\",\"HCPCS_CD_45\"]]\n",
    "\n",
    "# Drop rows with NO procedures: new rowcount = 741,581\n",
    "procedures = procedures.dropna(how=\"all\")\n",
    "\n",
    "# Drop columns with NO codes: new column count = 44 (0-43)\n",
    "procedures = procedures.dropna(axis=1, how=\"all\")\n",
    "\n",
    "# Cast all to type \"string\"\n",
    "procedures = procedures.astype(\"string\")\n",
    "\n",
    "# Replace L2 codes with NULL / NaN because we do not count them as procedures.\n",
    "procedures = procedures.replace(r\"([A-Z]\\d{4})\", np.nan,regex=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that we know we only have L1 / CPT codes, count them for each beneficiary claim\n",
    "\n",
    "desynpuf[\"HCPCS_COUNT\"] = procedures.count(axis=1)\n",
    "desynpuf[\"HCPCS_COUNT\"].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th>BENE_BIRTH_DT</th>\n",
       "      <th>BENE_DEATH_DT</th>\n",
       "      <th>BENE_HI_CVRAGE_TOT_MONS</th>\n",
       "      <th>BENE_SMI_CVRAGE_TOT_MONS</th>\n",
       "      <th>BENE_HMO_CVRAGE_TOT_MONS</th>\n",
       "      <th>PLAN_CVRG_MOS_NUM</th>\n",
       "      <th>SP_ALZHDMTA</th>\n",
       "      <th>SP_CHF</th>\n",
       "      <th>SP_CHRNKIDN</th>\n",
       "      <th>...</th>\n",
       "      <th>B_STRKETIA</th>\n",
       "      <th>B_ESRD</th>\n",
       "      <th>NH_WHITE</th>\n",
       "      <th>AA_BLACK</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>NW_HISPANIC</th>\n",
       "      <th>IP_VISITS</th>\n",
       "      <th>OP_VISITS</th>\n",
       "      <th>CODES_COUNT</th>\n",
       "      <th>HCPCS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>1937-05-01</td>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>1938-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DESYNPUF_ID BENE_BIRTH_DT BENE_DEATH_DT  BENE_HI_CVRAGE_TOT_MONS  \\\n",
       "0  0000438E79D01BEA    1937-05-01           NaN                       12   \n",
       "1  00010E7BEA69142C    1938-04-01           NaN                       12   \n",
       "2  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "3  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "4  00015BF6509E1DF7    1934-05-01           NaN                       12   \n",
       "\n",
       "   BENE_SMI_CVRAGE_TOT_MONS  BENE_HMO_CVRAGE_TOT_MONS  PLAN_CVRG_MOS_NUM  \\\n",
       "0                        12                         0                 12   \n",
       "1                        12                        12                 12   \n",
       "2                        12                         0                  0   \n",
       "3                        12                         0                  0   \n",
       "4                        12                         0                  0   \n",
       "\n",
       "   SP_ALZHDMTA  SP_CHF  SP_CHRNKIDN  ...  B_STRKETIA  B_ESRD  NH_WHITE  \\\n",
       "0            2       2            2  ...           0       0         1   \n",
       "1            1       2            2  ...           0       0         1   \n",
       "2            1       1            2  ...           0       0         1   \n",
       "3            1       1            2  ...           0       0         1   \n",
       "4            1       1            2  ...           0       0         1   \n",
       "\n",
       "   AA_BLACK  OTHER  NW_HISPANIC  IP_VISITS  OP_VISITS  CODES_COUNT  \\\n",
       "0         0      0            0          0          0            0   \n",
       "1         0      0            0          0          0            0   \n",
       "2         0      0            0          0          1            2   \n",
       "3         0      0            0          0          1            7   \n",
       "4         0      0            0          0          1            1   \n",
       "\n",
       "   HCPCS_COUNT  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          4.0  \n",
       "3          5.0  \n",
       "4          3.0  \n",
       "\n",
       "[5 rows x 139 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Summaries\n",
    "\n",
    "For each year;\n",
    "For all 3 years\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary = desynpuf.groupby([\"DESYNPUF_ID\", \"YEAR\"]).aggregate({\"BENE_AGE\":\"mean\",\n",
    "                                                                                  \"B_SEX\":\"mean\",\n",
    "                                                                                  \"NH_WHITE\": \"mean\",\n",
    "                                                                                  \"AA_BLACK\" : \"mean\",\n",
    "                                                                                  \"OTHER\": \"mean\",\n",
    "                                                                                  \"NW_HISPANIC\": \"mean\",\n",
    "                                                                                  \"B_ALZHDMTA\":\"sum\",\n",
    "                                                                                  \"B_CHF\":\"sum\" ,\n",
    "                                                                                  \"B_CNCR\":\"sum\",\n",
    "                                                                                  \"B_COPD\":\"sum\",\n",
    "                                                                                  \"B_DEPRESSN\":\"sum\",\n",
    "                                                                                  \"B_DIABETES\":\"sum\",\n",
    "                                                                                  \"B_ISCHMCHT\":\"sum\",\n",
    "                                                                                  \"B_OSTEOPRS\":\"sum\",\n",
    "                                                                                  \"B_CHRNKIDN\":\"sum\",\n",
    "                                                                                  \"B_RA_OA\":\"sum\",\n",
    "                                                                                  \"B_STRKETIA\":\"sum\",\n",
    "                                                                                  \"B_ESRD\":\"sum\",\n",
    "                                                                                  \"BENE_HI_CVRAGE_TOT_MONS\":\"mean\",\n",
    "                                                                                  \"BENE_SMI_CVRAGE_TOT_MONS\":\"mean\",\n",
    "                                                                                  \"BENE_HMO_CVRAGE_TOT_MONS\":\"mean\",\n",
    "                                                                                  \"PLAN_CVRG_MOS_NUM\":\"mean\",\n",
    "                                                                                  \"CLAIM_TYPE\":\"count\",\n",
    "                                                                                  \"IP_VISITS\":\"sum\",\n",
    "                                                                                  \"OP_VISITS\":\"sum\",\n",
    "                                                                                  \"CODES_COUNT\":\"sum\",\n",
    "                                                                                  \"HCPCS_COUNT\":\"sum\",\n",
    "                                                                                  \"PRVDR_NUM\":\"nunique\",\n",
    "                                                                                  \"AT_PHYSN_NPI\":\"nunique\",\n",
    "                                                                                  \"OP_PHYSN_NPI\":\"nunique\",\n",
    "                                                                                  \"OT_PHYSN_NPI\":\"nunique\",\n",
    "                                                                                  \"CLM_UTLZTN_DAY_CNT\":[\"sum\",\"mean\"],\n",
    "                                                                                  \"MEDREIMB_IP\":\"sum\",\n",
    "                                                                                  \"BENRES_IP\":\"sum\",\n",
    "                                                                                  \"PPPYMT_IP\":\"sum\",\n",
    "                                                                                  \"MEDREIMB_OP\":\"sum\",\n",
    "                                                                                  \"BENRES_OP\":\"sum\",\n",
    "                                                                                  \"PPPYMT_OP\":\"sum\",\n",
    "                                                                                  \"CBSA\":\"mean\",\n",
    "                                                                                  \"FIPS_STATE_CODE\":\"mean\",\n",
    "                                                                                  \"FIPS_COUNTY_CODE\":\"mean\"})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary.columns = [\"MEAN_AGE\", \"PROP_SEX\", \"NH_WHITE\", \"AA_BLACK\", \"OTHER\", \"NW_HISPANIC\", \"B_ALZHDMTA\", \n",
    "                      \"B_CHF\", \"B_CNCR\", \"B_COPD\", \"B_DEPRESSN\", \"B_DIABETES\", \"B_ISCHMCHT\", \"B_OSTEOPRS\", \n",
    "                      \"B_CHRNKIDN\", \"B_RA_OA\",\"B_STRKETIA\", \"B_ESRD\", \"MEAN_HI_CVRAGE_TOT_MONS\", \n",
    "                      \"MEAN_SMI_CVRAGE_TOT_MONS\",\"MEAN_HMO_CVRAGE_TOT_MONS\", \"MEAN_CVRG_MOS_NUM\", \n",
    "                      \"TOTAL_VISITS\", \"IP_VISITS\", \"OP_VISITS\", \"CODES_CT\", \"PROCEDURE_CT\", \"NUM_PRVDRS\", \n",
    "                      \"NUM_AT_PHYSNS\", \"NUM_OP_PHYSNS\",\"NUM_OT_PHYSNS\", \"TOTAL_INP_DAYS\", \"MEAN_INP_STAY\", \n",
    "                      \"TOT_MEDREIMB_IP\", \"TOT_BENRES_IP\", \"TOT_PPPYMT_IP\", \"TOT_MEDREIMB_OP\",\n",
    "                      \"TOT_BENRES_OP\",\"TOT_PPPYMT_OP\",\"CBSA\", \"FIPS_STATE_CODE\",\"FIPS_COUNTY_CODE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cc_summary.loc[cc_summary[\"B_ALZHDMTA\"]>1, \"B_ALZHDMTA\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_CHF\"]>1, \"B_CHF\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_CNCR\"]>1, \"B_CNCR\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_COPD\"]>1, \"B_COPD\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_DEPRESSN\"]>1, \"B_DEPRESSN\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_DIABETES\"]>1, \"B_DIABETES\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_ISCHMCHT\"]>1, \"B_ISCHMCHT\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_OSTEOPRS\"]>1, \"B_OSTEOPRS\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_CHRNKIDN\"]>1, \"B_CHRNKIDN\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_RA_OA\"]>1, \"B_RA_OA\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_STRKETIA\"]>1, \"B_STRKETIA\"] = 1\n",
    "cc_summary.loc[cc_summary[\"B_ESRD\"]>1, \"B_ESRD\"] = 1\n",
    "\n",
    "cc_summary[\"TOTCHRONIC\"] = cc_summary[\"B_ALZHDMTA\"] + cc_summary[\"B_CHF\"] + cc_summary[\"B_CNCR\"] + cc_summary[\"B_COPD\"] + cc_summary[\"B_DEPRESSN\"] + cc_summary[\"B_DIABETES\"] + cc_summary[\"B_ISCHMCHT\"] + cc_summary[\"B_OSTEOPRS\"] + cc_summary[\"B_CHRNKIDN\"] + cc_summary[\"B_RA_OA\"] + cc_summary[\"B_STRKETIA\"]\n",
    "cc_summary[\"TOTIPCOST\"] = cc_summary[\"TOT_MEDREIMB_IP\"] + cc_summary[\"TOT_BENRES_IP\"] + cc_summary[\"TOT_PPPYMT_IP\"]\n",
    "cc_summary[\"TOTOPCOST\"] = cc_summary[\"TOT_MEDREIMB_OP\"] + cc_summary[\"TOT_BENRES_OP\"] + cc_summary[\"TOT_PPPYMT_OP\"]\n",
    "cc_summary[\"ALLCOSTS\"] = cc_summary[\"TOTIPCOST\"] + cc_summary[\"TOTOPCOST\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Variable\n",
    "\n",
    "NEW_CHRONIC: Number of new conditions during the period (2008-2010)\n",
    "\n",
    "Will become an aggregated incidence measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = cc_summary.copy()\n",
    "c = c.reset_index()\n",
    "CCs_2008 = c.loc[c[\"YEAR\"] == 2008, \"TOTCHRONIC\"]\n",
    "CCs_2010 = c.loc[c[\"YEAR\"] == 2010, \"TOTCHRONIC\"]\n",
    "\n",
    "c.loc[c[\"YEAR\"] == 2008, \"NEW_CHRONIC\"] = 0\n",
    "c.loc[c[\"YEAR\"] == 2009, \"NEW_CHRONIC\"] = 0\n",
    "c.loc[c[\"YEAR\"] == 2010, \"NEW_CHRONIC\"] = CCs_2010.subtract(CCs_2008, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine Coverage Data and Number of Providers\n",
    "c[\"MEAN_CVRG_MOS\"] = c[\"MEAN_HI_CVRAGE_TOT_MONS\"].copy()\n",
    "c[\"MEAN_RX_CVRG_MOS\"] = c[\"MEAN_CVRG_MOS_NUM\"].copy()\n",
    "c.drop([\"MEAN_HI_CVRAGE_TOT_MONS\", \"MEAN_SMI_CVRAGE_TOT_MONS\", \"MEAN_HMO_CVRAGE_TOT_MONS\", \"MEAN_CVRG_MOS_NUM\",\n",
    "       \"NUM_AT_PHYSNS\", \"NUM_OP_PHYSNS\", \"NUM_OT_PHYSNS\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all = c.groupby(\"DESYNPUF_ID\").aggregate({\"YEAR\":\"count\",\n",
    "                                                     \"MEAN_AGE\":\"mean\",\n",
    "                                                     \"PROP_SEX\":\"mean\",\n",
    "                                                     \"NH_WHITE\":\"mean\",\n",
    "                                                     \"AA_BLACK\":\"mean\",\n",
    "                                                     \"OTHER\":\"mean\",\n",
    "                                                     \"NW_HISPANIC\":\"mean\",\n",
    "                                                     \"B_ALZHDMTA\":\"mean\",\n",
    "                                                     \"B_CHF\":\"mean\",\n",
    "                                                     \"B_CNCR\":\"mean\",\n",
    "                                                     \"B_COPD\":\"mean\",\n",
    "                                                     \"B_DEPRESSN\":\"mean\",\n",
    "                                                     \"B_DIABETES\":\"mean\",\n",
    "                                                     \"B_ISCHMCHT\":\"mean\",\n",
    "                                                     \"B_OSTEOPRS\":\"mean\",\n",
    "                                                     \"B_CHRNKIDN\":\"mean\",\n",
    "                                                     \"B_RA_OA\":\"mean\",\n",
    "                                                     \"B_STRKETIA\":\"mean\",\n",
    "                                                     \"B_ESRD\":\"mean\",\n",
    "                                                     \"MEAN_CVRG_MOS\":\"mean\",\n",
    "                                                     \"MEAN_RX_CVRG_MOS\":\"mean\",\n",
    "                                                     \"TOTAL_VISITS\":\"sum\",\n",
    "                                                     \"IP_VISITS\":\"sum\",\n",
    "                                                     \"OP_VISITS\":\"sum\",\n",
    "                                                     \"CODES_CT\":\"mean\",\n",
    "                                                     \"PROCEDURE_CT\":\"mean\",\n",
    "                                                     \"NUM_PRVDRS\":\"mean\",\n",
    "                                                     \"TOTAL_INP_DAYS\":\"sum\",\n",
    "                                                     \"MEAN_INP_STAY\": \"mean\",\n",
    "                                                     \"TOT_MEDREIMB_IP\":\"sum\",\n",
    "                                                     \"TOT_BENRES_IP\":\"sum\",\n",
    "                                                     \"TOT_PPPYMT_IP\":\"sum\",\n",
    "                                                     \"TOT_MEDREIMB_OP\":\"sum\",\n",
    "                                                     \"TOT_BENRES_OP\":\"sum\",\n",
    "                                                     \"TOT_PPPYMT_OP\":\"sum\",\n",
    "                                                     \"CBSA\":\"mean\",\n",
    "                                                     \"FIPS_STATE_CODE\":\"mean\",\n",
    "                                                     \"FIPS_COUNTY_CODE\":\"mean\", \n",
    "                                                     \"TOTIPCOST\":\"sum\",\n",
    "                                                     \"TOTOPCOST\":\"sum\",\n",
    "                                                     \"ALLCOSTS\":\"sum\",\n",
    "                                                     \"NEW_CHRONIC\":\"sum\"})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all.columns = [\"YEAR_CNT\", \"MEAN_AGE\", \"B_SEX\", \"NH_WHITE\", \"AA_BLACK\", \"OTHER\", \"NW_HISPANIC\",\n",
    "                          \"B_ALZHDMTA\", \"B_CHF\", \"B_CNCR\", \"B_COPD\",\"B_DEPRESSN\", \"B_DIABETES\", \n",
    "                          \"B_ISCHMCHT\", \"B_OSTEOPRS\", \"B_CHRNKIDN\", \"B_RA_OA\",\"B_STRKETIA\", \"B_ESRD\", \n",
    "                          \"MEAN_CVRG_MOS_PY\", \"MEAN_RX_CVRG_MOS_PY\", \"TOTAL_VISITS\", \"IP_VISITS\", \n",
    "                           \"OP_VISITS\", \"MEAN_CODES_CT\", \"MEAN_PROCEDURE_CT\", \"MEAN_NUM_PRVDRS\", \n",
    "                          \"TOTAL_INP_DAYS\", \"MEAN_INP_STAY\", \"TOT_MEDREIMB_IP\", \"TOT_BENRES_IP\", \"TOT_PPPYMT_IP\", \n",
    "                          \"TOT_MEDREIMB_OP\", \"TOT_BENRES_OP\",\"TOT_PPPYMT_OP\", \"CBSA\", \"FIPS_STATE_CODE\",\n",
    "                          \"FIPS_COUNTY_CODE\", \"TOTIPCOST\", \"TOTOPCOST\", \"ALLCOSTS\", \"NEW_CHRONIC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all.loc[cc_summary_all[\"B_ALZHDMTA\"]>0, \"B_ALZHDMTA\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_CHF\"]>0, \"B_CHF\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_CNCR\"]>0, \"B_CNCR\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_COPD\"]>0, \"B_COPD\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_DEPRESSN\"]>0, \"B_DEPRESSN\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_DIABETES\"]>0, \"B_DIABETES\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_ISCHMCHT\"]>0, \"B_ISCHMCHT\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_OSTEOPRS\"]>0, \"B_OSTEOPRS\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_CHRNKIDN\"]>0, \"B_CHRNKIDN\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_RA_OA\"]>0, \"B_RA_OA\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_STRKETIA\"]>0, \"B_STRKETIA\"] = 1\n",
    "cc_summary_all.loc[cc_summary_all[\"B_ESRD\"]>0, \"B_ESRD\"] = 1\n",
    "\n",
    "cc_summary_all[\"TOTCHRONIC\"] = cc_summary_all[\"B_ALZHDMTA\"] + cc_summary_all[\"B_CHF\"] + cc_summary_all[\"B_CNCR\"] + cc_summary_all[\"B_COPD\"] + cc_summary_all[\"B_DEPRESSN\"] + cc_summary_all[\"B_DIABETES\"] + cc_summary_all[\"B_ISCHMCHT\"] + cc_summary_all[\"B_OSTEOPRS\"] + cc_summary_all[\"B_CHRNKIDN\"] + cc_summary_all[\"B_RA_OA\"] + cc_summary_all[\"B_STRKETIA\"]\n",
    "cc_summary_all[\"MEAN_AGE\"] = cc_summary_all[\"MEAN_AGE\"].round(decimals=1)\n",
    "cc_summary_all[\"VISITS_FLAG\"] = 0\n",
    "cc_summary_all.loc[cc_summary_all[\"TOTAL_VISITS\"]>0, \"VISITS_FLAG\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TOTCHRONIC', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.countplot(data=cc_summary_all, x=\"TOTCHRONIC\", palette=\"mako\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NEW_CHRONIC', ylabel='count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.countplot(data=cc_summary_all, x=\"NEW_CHRONIC\", palette=\"mako\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prevalence\n",
    "\n",
    "Over the three year period, 25% of the beneficiaries had none of the noted chronic conditions.\n",
    "\n",
    "7% had 1, \n",
    "\n",
    "7% had 2, \n",
    "\n",
    "8% had 3, \n",
    "\n",
    "8% had 4, \n",
    "\n",
    "9% had 5, \n",
    "\n",
    "10% had 6,\n",
    "\n",
    "9% had 7,\n",
    "\n",
    "8% had 8,\n",
    "\n",
    "and the remaining 9% had 9 or more.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     0.248240\n",
       "6.0     0.095205\n",
       "7.0     0.090657\n",
       "5.0     0.089600\n",
       "4.0     0.084022\n",
       "8.0     0.081856\n",
       "3.0     0.075366\n",
       "1.0     0.070320\n",
       "2.0     0.066659\n",
       "9.0     0.061097\n",
       "10.0    0.030342\n",
       "11.0    0.006636\n",
       "Name: TOTCHRONIC, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[\"TOTCHRONIC\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incidence\n",
    "\n",
    "New chronic conditions during the three year period.\n",
    "\n",
    "41% had no new chronic conditions.\n",
    "\n",
    "15% had 1 new chronic condition.\n",
    "\n",
    "13% had 2 new chronic conditions.\n",
    "\n",
    "11% had 3 new chronic conditions.\n",
    "\n",
    "8.5% had 4 new chronic conditions.\n",
    "\n",
    "5.8% had 5 new chronic conditions.\n",
    "\n",
    "3.4% had 6 new chronic conditions.\n",
    "\n",
    "1.6% had 7 new chronic conditions.\n",
    "\n",
    "<1% had 8-10 new chronic conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     0.408152\n",
       "1.0     0.149400\n",
       "2.0     0.131254\n",
       "3.0     0.109233\n",
       "4.0     0.084838\n",
       "5.0     0.058347\n",
       "6.0     0.034236\n",
       "7.0     0.016418\n",
       "8.0     0.006258\n",
       "9.0     0.001633\n",
       "10.0    0.000232\n",
       "Name: NEW_CHRONIC, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[\"NEW_CHRONIC\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 991604 entries, 0 to 991603\n",
      "Columns: 127 entries, DESYNPUF_ID to HCPCS_COUNT\n",
      "dtypes: datetime64[ns](2), float64(31), int64(25), object(69)\n",
      "memory usage: 960.8+ MB\n"
     ]
    }
   ],
   "source": [
    "desynpuf.drop([\"SP_ALZHDMTA\", \"SP_CHF\", \"SP_CNCR\", \"SP_DEPRESSN\", \"SP_DIABETES\", \"SP_ISCHMCHT\", \"SP_ESRD\",\n",
    "              \"SP_OSTEOPRS\", \"SP_COPD\", \"SP_CHRNKIDN\", \"SP_RA_OA\", \"SP_STRKETIA\"], inplace=True, axis=1)\n",
    "\n",
    "desynpuf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visits 2008-2010\n",
    "\n",
    "Total Inpatient Claims: 66,029\n",
    "\n",
    "Total Outpatient Claims: 776,259"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th>CLAIM_TYPE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
       "      <th>INPATIENT</th>\n",
       "      <td>27240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTPATIENT</th>\n",
       "      <td>282069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th>INPATIENT</th>\n",
       "      <td>24873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTPATIENT</th>\n",
       "      <td>320990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>INPATIENT</th>\n",
       "      <td>13916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTPATIENT</th>\n",
       "      <td>173200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  COUNT\n",
       "YEAR CLAIM_TYPE        \n",
       "2008 INPATIENT    27240\n",
       "     OUTPATIENT  282069\n",
       "2009 INPATIENT    24873\n",
       "     OUTPATIENT  320990\n",
       "2010 INPATIENT    13916\n",
       "     OUTPATIENT  173200"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claims_breakdown = desynpuf.groupby([\"YEAR\", \"CLAIM_TYPE\"])[\"CLAIM_TYPE\"].count().to_frame()\n",
    "claims_breakdown.columns = [\"COUNT\"]\n",
    "claims_breakdown.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OUTPATIENT    776259\n",
       "INPATIENT      66029\n",
       "Name: CLAIM_TYPE, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desynpuf[\"CLAIM_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the total number of chronic conditions increases, the total visits increases.\n",
    "\n",
    "No surprises there!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\")\n",
    "sb.barplot(x=\"TOTCHRONIC\", y=\"TOTAL_VISITS\", data=cc_summary_all, palette=\"mako\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Costs also increased as number of conditions increased, really beginning to take off after 6 chronic conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\")\n",
    "sb.barplot(x=\"TOTCHRONIC\", y=\"ALLCOSTS\", data=cc_summary_all, palette=\"mako\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "SUM_TOTAL = cc_summary_all[\"ALLCOSTS\"].sum()\n",
    "cc_summary_all[\"PERCENT_COSTS\"] = cc_summary_all[\"ALLCOSTS\"] / SUM_TOTAL * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join Elixhauser Scores\n",
    "\n",
    "Add scores from elix DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all = cc_summary_all.reset_index()\n",
    "ELIX_FIPS = ELIX_FIPS.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cc_summary_all = pd.merge(cc_summary_all, ELIX_FIPS, on=[\"DESYNPUF_ID\"], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>DESYNPUF_ID</th>\n",
       "      <th>YEAR_CNT</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>B_SEX</th>\n",
       "      <th>NH_WHITE</th>\n",
       "      <th>AA_BLACK</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>NW_HISPANIC</th>\n",
       "      <th>B_ALZHDMTA</th>\n",
       "      <th>B_CHF</th>\n",
       "      <th>...</th>\n",
       "      <th>TOTOPCOST</th>\n",
       "      <th>ALLCOSTS</th>\n",
       "      <th>NEW_CHRONIC</th>\n",
       "      <th>TOTCHRONIC</th>\n",
       "      <th>VISITS_FLAG</th>\n",
       "      <th>PERCENT_COSTS</th>\n",
       "      <th>MEAN_ELIX_GRP_TOT</th>\n",
       "      <th>MEAN_ELIX_SCORE</th>\n",
       "      <th>FIPS_COUNTY_CODE_y</th>\n",
       "      <th>FIPS_STATE_CODE_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>3</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>119.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00018DD17736A661</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6930.0</td>\n",
       "      <td>6930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000240D599ED789C</td>\n",
       "      <td>3</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47330.0</td>\n",
       "      <td>509056.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007014</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>35.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0003E73AFBA840A8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00040FBA47F90D4F</td>\n",
       "      <td>3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21730.0</td>\n",
       "      <td>21730.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000588F33B7B0753</td>\n",
       "      <td>3</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>128750.0</td>\n",
       "      <td>508222.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007002</td>\n",
       "      <td>0.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>113.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0006071C876CEADF</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0006CB5289978FD8</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>380784.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005246</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DESYNPUF_ID  YEAR_CNT  MEAN_AGE  B_SEX  NH_WHITE  AA_BLACK  OTHER  \\\n",
       "0  0000438E79D01BEA         3       NaN      1         1         0      0   \n",
       "1  00010E7BEA69142C         2      71.0      0         1         0      0   \n",
       "2  00015BF6509E1DF7         3      75.1      0         1         0      0   \n",
       "3  00018DD17736A661         1      73.0      0         1         0      0   \n",
       "4  000240D599ED789C         3      67.0      0         0         1      0   \n",
       "5  0003E73AFBA840A8         3       NaN      0         1         0      0   \n",
       "6  00040FBA47F90D4F         3      84.4      0         1         0      0   \n",
       "7  000588F33B7B0753         3      77.7      0         1         0      0   \n",
       "8  0006071C876CEADF         3      78.0      0         1         0      0   \n",
       "9  0006CB5289978FD8         3      90.0      0         0         1      0   \n",
       "\n",
       "   NW_HISPANIC  B_ALZHDMTA  B_CHF  ...  TOTOPCOST  ALLCOSTS  NEW_CHRONIC  \\\n",
       "0            0         0.0    0.0  ...        0.0       0.0          0.0   \n",
       "1            0         1.0    0.0  ...     1620.0    1620.0          0.0   \n",
       "2            0         1.0    1.0  ...    25860.0   25860.0          3.0   \n",
       "3            0         0.0    0.0  ...     6930.0    6930.0          0.0   \n",
       "4            0         1.0    1.0  ...    47330.0  509056.0          5.0   \n",
       "5            0         0.0    0.0  ...        0.0       0.0          0.0   \n",
       "6            0         1.0    1.0  ...    21730.0   21730.0          3.0   \n",
       "7            0         0.0    1.0  ...   128750.0  508222.0          2.0   \n",
       "8            0         0.0    1.0  ...     3980.0    3980.0          0.0   \n",
       "9            0         1.0    1.0  ...    24800.0  380784.0          3.0   \n",
       "\n",
       "   TOTCHRONIC  VISITS_FLAG  PERCENT_COSTS  MEAN_ELIX_GRP_TOT  MEAN_ELIX_SCORE  \\\n",
       "0         0.0            0       0.000000           0.000000         0.000000   \n",
       "1         2.0            1       0.000022           0.333333         0.500000   \n",
       "2         7.0            1       0.000356           0.555556         0.555556   \n",
       "3         2.0            1       0.000095           0.428571         1.571429   \n",
       "4         6.0            1       0.007014           0.766667         2.016667   \n",
       "5         0.0            0       0.000000           0.000000         0.000000   \n",
       "6         6.0            1       0.000299           0.333333         1.266667   \n",
       "7         7.0            1       0.007002           0.604167         1.000000   \n",
       "8         6.0            1       0.000055           0.166667         0.000000   \n",
       "9         9.0            1       0.005246           1.375000         3.875000   \n",
       "\n",
       "   FIPS_COUNTY_CODE_y  FIPS_STATE_CODE_y  \n",
       "0                57.0               12.0  \n",
       "1                 3.0               10.0  \n",
       "2               119.0               37.0  \n",
       "3               101.0               26.0  \n",
       "4                35.0               55.0  \n",
       "5                55.0               36.0  \n",
       "6                35.0               34.0  \n",
       "7               113.0               19.0  \n",
       "8               141.0               48.0  \n",
       "9                75.0                6.0  \n",
       "\n",
       "[10 rows x 50 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th>YEAR_CNT</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>B_SEX</th>\n",
       "      <th>NH_WHITE</th>\n",
       "      <th>AA_BLACK</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>NW_HISPANIC</th>\n",
       "      <th>B_ALZHDMTA</th>\n",
       "      <th>B_CHF</th>\n",
       "      <th>...</th>\n",
       "      <th>ALLCOSTS</th>\n",
       "      <th>NEW_CHRONIC</th>\n",
       "      <th>TOTCHRONIC</th>\n",
       "      <th>VISITS_FLAG</th>\n",
       "      <th>PERCENT_COSTS</th>\n",
       "      <th>MEAN_ELIX_GRP_TOT</th>\n",
       "      <th>MEAN_ELIX_SCORE</th>\n",
       "      <th>FIPS_COUNTY_CODE_y</th>\n",
       "      <th>FIPS_STATE_CODE_y</th>\n",
       "      <th>IP_VISIT_PROP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>3</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>119.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00018DD17736A661</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000240D599ED789C</td>\n",
       "      <td>3</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>509056.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007014</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>35.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0003E73AFBA840A8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00040FBA47F90D4F</td>\n",
       "      <td>3</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21730.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000588F33B7B0753</td>\n",
       "      <td>3</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>508222.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007002</td>\n",
       "      <td>0.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>113.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0006071C876CEADF</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0006CB5289978FD8</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>380784.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005246</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DESYNPUF_ID  YEAR_CNT  MEAN_AGE  B_SEX  NH_WHITE  AA_BLACK  OTHER  \\\n",
       "0  0000438E79D01BEA         3       NaN      1         1         0      0   \n",
       "1  00010E7BEA69142C         2      71.0      0         1         0      0   \n",
       "2  00015BF6509E1DF7         3      75.1      0         1         0      0   \n",
       "3  00018DD17736A661         1      73.0      0         1         0      0   \n",
       "4  000240D599ED789C         3      67.0      0         0         1      0   \n",
       "5  0003E73AFBA840A8         3       NaN      0         1         0      0   \n",
       "6  00040FBA47F90D4F         3      84.4      0         1         0      0   \n",
       "7  000588F33B7B0753         3      77.7      0         1         0      0   \n",
       "8  0006071C876CEADF         3      78.0      0         1         0      0   \n",
       "9  0006CB5289978FD8         3      90.0      0         0         1      0   \n",
       "\n",
       "   NW_HISPANIC  B_ALZHDMTA  B_CHF  ...  ALLCOSTS  NEW_CHRONIC  TOTCHRONIC  \\\n",
       "0            0         0.0    0.0  ...       0.0          0.0         0.0   \n",
       "1            0         1.0    0.0  ...    1620.0          0.0         2.0   \n",
       "2            0         1.0    1.0  ...   25860.0          3.0         7.0   \n",
       "3            0         0.0    0.0  ...    6930.0          0.0         2.0   \n",
       "4            0         1.0    1.0  ...  509056.0          5.0         6.0   \n",
       "5            0         0.0    0.0  ...       0.0          0.0         0.0   \n",
       "6            0         1.0    1.0  ...   21730.0          3.0         6.0   \n",
       "7            0         0.0    1.0  ...  508222.0          2.0         7.0   \n",
       "8            0         0.0    1.0  ...    3980.0          0.0         6.0   \n",
       "9            0         1.0    1.0  ...  380784.0          3.0         9.0   \n",
       "\n",
       "   VISITS_FLAG  PERCENT_COSTS  MEAN_ELIX_GRP_TOT  MEAN_ELIX_SCORE  \\\n",
       "0            0       0.000000           0.000000         0.000000   \n",
       "1            1       0.000022           0.333333         0.500000   \n",
       "2            1       0.000356           0.555556         0.555556   \n",
       "3            1       0.000095           0.428571         1.571429   \n",
       "4            1       0.007014           0.766667         2.016667   \n",
       "5            0       0.000000           0.000000         0.000000   \n",
       "6            1       0.000299           0.333333         1.266667   \n",
       "7            1       0.007002           0.604167         1.000000   \n",
       "8            1       0.000055           0.166667         0.000000   \n",
       "9            1       0.005246           1.375000         3.875000   \n",
       "\n",
       "   FIPS_COUNTY_CODE_y  FIPS_STATE_CODE_y  IP_VISIT_PROP  \n",
       "0                57.0               12.0       0.000000  \n",
       "1                 3.0               10.0       0.000000  \n",
       "2               119.0               37.0       0.000000  \n",
       "3               101.0               26.0       0.000000  \n",
       "4                35.0               55.0       0.150000  \n",
       "5                55.0               36.0       0.000000  \n",
       "6                35.0               34.0       0.000000  \n",
       "7               113.0               19.0       0.121212  \n",
       "8               141.0               48.0       0.000000  \n",
       "9                75.0                6.0       0.250000  \n",
       "\n",
       "[10 rows x 51 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[\"IP_VISIT_PROP\"] = cc_summary_all[\"IP_VISITS\"]/cc_summary_all[\"TOTAL_VISITS\"]\n",
    "cc_summary_all[\"IP_VISIT_PROP\"].fillna(0, inplace=True)\n",
    "cc_summary_all.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary.to_csv(\"/home/kylier/python/DS/data/PUF/PUF_BENE_SUMMARY_YEARS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all.to_csv(\"/home/kylier/python/DS/data/PUF/PUF_BENE_SUMMARY_ALL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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