HCA 203 PUF Counties Aggregate - tSNE Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.set_printoptions(precision=3, suppress = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data\n",
    "\n",
    "This data has already been run through the Factor Analysis notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/home/kylier/python/DS/data/PUF/PUF_RWJF_MERGED_CLUSTERED_NOOUTLIERS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2694, 82)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th>CNTY_BENE_CT</th>\n",
       "      <th>CNTY_BENE_USRS</th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>CNTY_SEX_PROP</th>\n",
       "      <th>TOT_CNCR_CASES</th>\n",
       "      <th>TOT_STRKETIA_CASES</th>\n",
       "      <th>TOT_ESRD_CASES</th>\n",
       "      <th>CNTY_TOT_VSTS</th>\n",
       "      <th>...</th>\n",
       "      <th>HIGH_SEV</th>\n",
       "      <th>HIGH_PROCS</th>\n",
       "      <th>DIVERSE_PROP</th>\n",
       "      <th>Utilization FACTOR</th>\n",
       "      <th>Illness FACTOR</th>\n",
       "      <th>Hospitalization FACTOR</th>\n",
       "      <th>FACTORS_1</th>\n",
       "      <th>FACTORS_2</th>\n",
       "      <th>FACTORS_3</th>\n",
       "      <th>KM_FACTORS</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 73 columns</p>\n",
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      "text/plain": [
       "   FIPS_STATE_CODE  FIPS_COUNTY_CODE  CNTY_BENE_CT  CNTY_BENE_USRS  \\\n",
       "0                1                 1            16              13   \n",
       "1                1                 3            95              65   \n",
       "2                1                 5            14              12   \n",
       "3                1                 7            17              12   \n",
       "4                1                11             5               2   \n",
       "\n",
       "   CNTY_MEAN_AGE  CNTY_SEX_PROP  TOT_CNCR_CASES  TOT_STRKETIA_CASES  \\\n",
       "0           65.4       0.187500               3                   3   \n",
       "1           70.3       0.463158              18                   8   \n",
       "2           72.4       0.642857               0                   0   \n",
       "3           69.0       0.411765               2                   3   \n",
       "4           71.5       0.200000               0                   1   \n",
       "\n",
       "   TOT_ESRD_CASES  CNTY_TOT_VSTS  ...  HIGH_SEV  HIGH_PROCS  DIVERSE_PROP  \\\n",
       "0               5            112  ...         0           1      0.312500   \n",
       "1              18            720  ...         1           1      0.368421   \n",
       "2               4             92  ...         1           0      0.500000   \n",
       "3               4            152  ...         1           1      0.294118   \n",
       "4               2             41  ...         0           1      0.200000   \n",
       "\n",
       "   Utilization FACTOR  Illness FACTOR  Hospitalization FACTOR  FACTORS_1  \\\n",
       "0           -0.250884        0.785873               -0.122812  -0.295489   \n",
       "1            0.659104       -0.431479                0.216796   0.929719   \n",
       "2           -0.230425       -0.469385                0.223178  -0.186919   \n",
       "3            0.538031        0.253332               -0.200188   0.707499   \n",
       "4            2.903910       -1.412240               -0.246765   3.388853   \n",
       "\n",
       "   FACTORS_2  FACTORS_3  KM_FACTORS  \n",
       "0   0.822719   0.093919           3  \n",
       "1  -0.441932   0.235808           4  \n",
       "2  -0.534365   0.630980           0  \n",
       "3   0.360124  -0.366061           3  \n",
       "4  -1.407649  -0.853064           4  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the extra index column created when the data is loaded\n",
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "\n",
    "# Drop the TSNE columns, if they are present\n",
    "df.drop([\"TSNE_1\", \"TSNE_2\", \"TSNE_3\", \"KM_TSNE\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "\n",
    "# SAVE SOME GLOBAL VARIABLES\n",
    "DF_COL_NAMES = df.columns\n",
    "\n",
    "FIPS_STATE = df['FIPS_STATE_CODE']\n",
    "FIPS_COUNTY = df['FIPS_COUNTY_CODE']\n",
    "FIPS = df['FIPS']\n",
    "STATE = df[\"STATE\"]\n",
    "COUNTY = df[\"COUNTY\"]\n",
    "CBSA = df[\"CBSA\"]\n",
    "\n",
    "# We won't be clustering object / string data\n",
    "df.drop([\"STATE\", \"COUNTY\", \"FIPS\", \"CBSA\"], axis=1, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tSNE\n",
    "\n",
    "Apply tSNE Dimension Reduction to the same subset of variables used for Factor Analysis.\n",
    "\n",
    "TOT_VSTS_PB\n",
    "Total Visits per Beneficiary\n",
    "\n",
    "CNTY_IP_PROP\n",
    "The Proportion of Visits that are Inpatient\n",
    "\n",
    "CODES_PU\n",
    "The Number of Codes per User (A User is a beneficiary with >0 claims for the period (2008-2010))\n",
    "\n",
    "PROCS_PU\n",
    "The Number of Procedures per User (based on HCPCS Level 1 Codes)\n",
    "\n",
    "PRVDRS_PU\n",
    "The Number of Providers per User\n",
    "\n",
    "SR[TOT_IPDAYS_PU] The Square Root of the Total Number of Inpatient Days per User\n",
    "\n",
    "TOT_CC_PREV\n",
    "The Total Number of Chronic Conditions / The Number of Beneficiaries in the County\n",
    "\n",
    "TOT_CC_INCD\n",
    "The Total # of New Chronic Conditions during the period / The # of Beneficiaries in the County\n",
    "\n",
    "TOT_ELIX_PU\n",
    "The Sum of all the Avg Elixhauser Scores for all the Users in the County / # of Users\n",
    "\n",
    "SR[TOT_COST_PB]\n",
    "The Square Root of the Total Cost / Number of Beneficiaries in the County\n",
    "\n",
    "DIVERSE_PROP\n",
    "The Proportion of people in the county whose self-identified race is not white."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2694, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_ds = df[[\"TOT_VSTS_PB\", \"CNTY_IP_PROP\",\"CODES_PU\", \"PROCS_PU\", \"PRVDRS_PU\", \n",
    "                  \"SR[TOT_IPDAYS_PU]\", \"TOT_CC_PREV\", \"TOT_CC_INCD\", \"TOT_ELIX_PU\", \"SR[TOT_COST_PB]\", \n",
    "                  \"DIVERSE_PROP\"]]\n",
    "\n",
    "analysis_ds.columns = [\"TOT_VSTS_PB\", \"CNTY_IP_PROP\", \"CODES_PU\", \"PROCS_PU\", \"PRVDRS_PU\", \n",
    "                       \"SR_TOT_IPDAYS\", \"TOT_CC_PREV\", \"TOT_CC_INCD\", \"TOT_ELIX_PU\", \"SR_TOT_COST_PB\", \n",
    "                       \"DIVERSE_PROP\"]\n",
    "analysis_ds = analysis_ds.dropna()\n",
    "analysis_ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_ds_copy = analysis_ds.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.preprocessing as pre\n",
    "\n",
    "s = pre.StandardScaler()\n",
    "analysis_ds = s.fit_transform(analysis_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply tSNE Reduction\n",
    "\n",
    "Approximate execution time: 34 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total run time = 32.405 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=3, random_state=123)\n",
    "reduced_ds = tsne.fit_transform(analysis_ds)\n",
    "end_time = time.time()\n",
    "print(\"Total run time = %.3f seconds\" %(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.242,  -5.11 , -11.567],\n",
       "       [  5.334, -12.483, -12.75 ],\n",
       "       [-11.138,   4.038,  -0.76 ],\n",
       "       ...,\n",
       "       [ -4.905,   2.888,  -3.679],\n",
       "       [ -4.071,  17.235,   4.916],\n",
       "       [  3.493,   7.677,  -5.938]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "TSNE = pd.DataFrame(reduced_ds, columns = [\"TSNE_1\", \"TSNE_2\", \"TSNE_3\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2694, 73)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <th>Illness FACTOR</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.538031</td>\n",
       "      <td>0.253332</td>\n",
       "      <td>-0.200188</td>\n",
       "      <td>0.707499</td>\n",
       "      <td>0.360124</td>\n",
       "      <td>-0.366061</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.903910</td>\n",
       "      <td>-1.412240</td>\n",
       "      <td>-0.246765</td>\n",
       "      <td>3.388853</td>\n",
       "      <td>-1.407649</td>\n",
       "      <td>-0.853064</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>70.6</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>-0.178433</td>\n",
       "      <td>-0.138656</td>\n",
       "      <td>-0.176684</td>\n",
       "      <td>-0.764856</td>\n",
       "      <td>-0.644227</td>\n",
       "      <td>-0.378574</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>55</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>73.3</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-0.264102</td>\n",
       "      <td>-0.817504</td>\n",
       "      <td>-0.880140</td>\n",
       "      <td>0.130767</td>\n",
       "      <td>0.156742</td>\n",
       "      <td>-0.143448</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691</th>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>183</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.078960</td>\n",
       "      <td>-0.276524</td>\n",
       "      <td>-0.505371</td>\n",
       "      <td>0.940358</td>\n",
       "      <td>-1.014010</td>\n",
       "      <td>-0.249721</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692</th>\n",
       "      <td>55</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>56.8</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-0.675425</td>\n",
       "      <td>-0.676739</td>\n",
       "      <td>0.551757</td>\n",
       "      <td>-0.643502</td>\n",
       "      <td>-1.059212</td>\n",
       "      <td>-0.203979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>36</td>\n",
       "      <td>72.3</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>298</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>-0.731424</td>\n",
       "      <td>-0.216995</td>\n",
       "      <td>-0.167369</td>\n",
       "      <td>-0.220061</td>\n",
       "      <td>-0.803552</td>\n",
       "      <td>0.232388</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2694 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPS_STATE_CODE  FIPS_COUNTY_CODE  CNTY_BENE_CT  CNTY_BENE_USRS  \\\n",
       "0                   1                 1            16              13   \n",
       "1                   1                 3            95              65   \n",
       "2                   1                 5            14              12   \n",
       "3                   1                 7            17              12   \n",
       "4                   1                11             5               2   \n",
       "...               ...               ...           ...             ...   \n",
       "2689               55                31            23              14   \n",
       "2690               55                33            17              15   \n",
       "2691               55                35            30              15   \n",
       "2692               55                37             4               2   \n",
       "2693               55                39            56              36   \n",
       "\n",
       "      CNTY_MEAN_AGE  CNTY_SEX_PROP  TOT_CNCR_CASES  TOT_STRKETIA_CASES  \\\n",
       "0              65.4       0.187500               3                   3   \n",
       "1              70.3       0.463158              18                   8   \n",
       "2              72.4       0.642857               0                   0   \n",
       "3              69.0       0.411765               2                   3   \n",
       "4              71.5       0.200000               0                   1   \n",
       "...             ...            ...             ...                 ...   \n",
       "2689           70.6       0.434783               2                   1   \n",
       "2690           73.3       0.470588               4                   3   \n",
       "2691           72.7       0.366667               0                   5   \n",
       "2692           56.8       0.250000               1                   1   \n",
       "2693           72.3       0.357143               6                   1   \n",
       "\n",
       "      TOT_ESRD_CASES  CNTY_TOT_VSTS  ...  HIGH_SEV  HIGH_PROCS  DIVERSE_PROP  \\\n",
       "0                  5            112  ...         0           1      0.312500   \n",
       "1                 18            720  ...         1           1      0.368421   \n",
       "2                  4             92  ...         1           0      0.500000   \n",
       "3                  4            152  ...         1           1      0.294118   \n",
       "4                  2             41  ...         0           1      0.200000   \n",
       "...              ...            ...  ...       ...         ...           ...   \n",
       "2689               3            102  ...         0           0      0.043478   \n",
       "2690               3            146  ...         1           1      0.058824   \n",
       "2691               6            183  ...         1           1      0.166667   \n",
       "2692               1             15  ...         0           0      0.250000   \n",
       "2693               5            298  ...         0           1      0.107143   \n",
       "\n",
       "      Utilization FACTOR  Illness FACTOR  Hospitalization FACTOR  FACTORS_1  \\\n",
       "0              -0.250884        0.785873               -0.122812  -0.295489   \n",
       "1               0.659104       -0.431479                0.216796   0.929719   \n",
       "2              -0.230425       -0.469385                0.223178  -0.186919   \n",
       "3               0.538031        0.253332               -0.200188   0.707499   \n",
       "4               2.903910       -1.412240               -0.246765   3.388853   \n",
       "...                  ...             ...                     ...        ...   \n",
       "2689           -0.178433       -0.138656               -0.176684  -0.764856   \n",
       "2690           -0.264102       -0.817504               -0.880140   0.130767   \n",
       "2691            0.078960       -0.276524               -0.505371   0.940358   \n",
       "2692           -0.675425       -0.676739                0.551757  -0.643502   \n",
       "2693           -0.731424       -0.216995               -0.167369  -0.220061   \n",
       "\n",
       "      FACTORS_2  FACTORS_3  KM_FACTORS  \n",
       "0      0.822719   0.093919           3  \n",
       "1     -0.441932   0.235808           4  \n",
       "2     -0.534365   0.630980           0  \n",
       "3      0.360124  -0.366061           3  \n",
       "4     -1.407649  -0.853064           4  \n",
       "...         ...        ...         ...  \n",
       "2689  -0.644227  -0.378574           1  \n",
       "2690   0.156742  -0.143448           3  \n",
       "2691  -1.014010  -0.249721           4  \n",
       "2692  -1.059212  -0.203979           1  \n",
       "2693  -0.803552   0.232388           3  \n",
       "\n",
       "[2694 rows x 73 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 0:73]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th>CNTY_BENE_CT</th>\n",
       "      <th>CNTY_BENE_USRS</th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>CNTY_SEX_PROP</th>\n",
       "      <th>TOT_CNCR_CASES</th>\n",
       "      <th>TOT_STRKETIA_CASES</th>\n",
       "      <th>TOT_ESRD_CASES</th>\n",
       "      <th>CNTY_TOT_VSTS</th>\n",
       "      <th>...</th>\n",
       "      <th>Utilization FACTOR</th>\n",
       "      <th>Illness FACTOR</th>\n",
       "      <th>Hospitalization FACTOR</th>\n",
       "      <th>FACTORS_1</th>\n",
       "      <th>FACTORS_2</th>\n",
       "      <th>FACTORS_3</th>\n",
       "      <th>KM_FACTORS</th>\n",
       "      <th>TSNE_1</th>\n",
       "      <th>TSNE_2</th>\n",
       "      <th>TSNE_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>65.4</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.250884</td>\n",
       "      <td>0.785873</td>\n",
       "      <td>-0.122812</td>\n",
       "      <td>-0.295489</td>\n",
       "      <td>0.822719</td>\n",
       "      <td>0.093919</td>\n",
       "      <td>3</td>\n",
       "      <td>9.242106</td>\n",
       "      <td>-5.110165</td>\n",
       "      <td>-11.567027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>70.3</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>720</td>\n",
       "      <td>...</td>\n",
       "      <td>0.659104</td>\n",
       "      <td>-0.431479</td>\n",
       "      <td>0.216796</td>\n",
       "      <td>0.929719</td>\n",
       "      <td>-0.441932</td>\n",
       "      <td>0.235808</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333952</td>\n",
       "      <td>-12.482608</td>\n",
       "      <td>-12.750140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.230425</td>\n",
       "      <td>-0.469385</td>\n",
       "      <td>0.223178</td>\n",
       "      <td>-0.186919</td>\n",
       "      <td>-0.534365</td>\n",
       "      <td>0.630980</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.138145</td>\n",
       "      <td>4.038353</td>\n",
       "      <td>-0.760248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.538031</td>\n",
       "      <td>0.253332</td>\n",
       "      <td>-0.200188</td>\n",
       "      <td>0.707499</td>\n",
       "      <td>0.360124</td>\n",
       "      <td>-0.366061</td>\n",
       "      <td>3</td>\n",
       "      <td>1.813346</td>\n",
       "      <td>-10.088836</td>\n",
       "      <td>3.131184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>2.903910</td>\n",
       "      <td>-1.412240</td>\n",
       "      <td>-0.246765</td>\n",
       "      <td>3.388853</td>\n",
       "      <td>-1.407649</td>\n",
       "      <td>-0.853064</td>\n",
       "      <td>4</td>\n",
       "      <td>1.725450</td>\n",
       "      <td>-16.089573</td>\n",
       "      <td>-15.685245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>70.6</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178433</td>\n",
       "      <td>-0.138656</td>\n",
       "      <td>-0.176684</td>\n",
       "      <td>-0.764856</td>\n",
       "      <td>-0.644227</td>\n",
       "      <td>-0.378574</td>\n",
       "      <td>1</td>\n",
       "      <td>1.927621</td>\n",
       "      <td>12.133542</td>\n",
       "      <td>9.169892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>55</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>73.3</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.264102</td>\n",
       "      <td>-0.817504</td>\n",
       "      <td>-0.880140</td>\n",
       "      <td>0.130767</td>\n",
       "      <td>0.156742</td>\n",
       "      <td>-0.143448</td>\n",
       "      <td>3</td>\n",
       "      <td>1.190519</td>\n",
       "      <td>-7.473377</td>\n",
       "      <td>-0.055672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691</th>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078960</td>\n",
       "      <td>-0.276524</td>\n",
       "      <td>-0.505371</td>\n",
       "      <td>0.940358</td>\n",
       "      <td>-1.014010</td>\n",
       "      <td>-0.249721</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.905205</td>\n",
       "      <td>2.887669</td>\n",
       "      <td>-3.678633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692</th>\n",
       "      <td>55</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>56.8</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.675425</td>\n",
       "      <td>-0.676739</td>\n",
       "      <td>0.551757</td>\n",
       "      <td>-0.643502</td>\n",
       "      <td>-1.059212</td>\n",
       "      <td>-0.203979</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.070534</td>\n",
       "      <td>17.235117</td>\n",
       "      <td>4.915854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>36</td>\n",
       "      <td>72.3</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>298</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.731424</td>\n",
       "      <td>-0.216995</td>\n",
       "      <td>-0.167369</td>\n",
       "      <td>-0.220061</td>\n",
       "      <td>-0.803552</td>\n",
       "      <td>0.232388</td>\n",
       "      <td>3</td>\n",
       "      <td>3.493206</td>\n",
       "      <td>7.677205</td>\n",
       "      <td>-5.937979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2694 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPS_STATE_CODE  FIPS_COUNTY_CODE  CNTY_BENE_CT  CNTY_BENE_USRS  \\\n",
       "0                   1                 1            16              13   \n",
       "1                   1                 3            95              65   \n",
       "2                   1                 5            14              12   \n",
       "3                   1                 7            17              12   \n",
       "4                   1                11             5               2   \n",
       "...               ...               ...           ...             ...   \n",
       "2689               55                31            23              14   \n",
       "2690               55                33            17              15   \n",
       "2691               55                35            30              15   \n",
       "2692               55                37             4               2   \n",
       "2693               55                39            56              36   \n",
       "\n",
       "      CNTY_MEAN_AGE  CNTY_SEX_PROP  TOT_CNCR_CASES  TOT_STRKETIA_CASES  \\\n",
       "0              65.4       0.187500               3                   3   \n",
       "1              70.3       0.463158              18                   8   \n",
       "2              72.4       0.642857               0                   0   \n",
       "3              69.0       0.411765               2                   3   \n",
       "4              71.5       0.200000               0                   1   \n",
       "...             ...            ...             ...                 ...   \n",
       "2689           70.6       0.434783               2                   1   \n",
       "2690           73.3       0.470588               4                   3   \n",
       "2691           72.7       0.366667               0                   5   \n",
       "2692           56.8       0.250000               1                   1   \n",
       "2693           72.3       0.357143               6                   1   \n",
       "\n",
       "      TOT_ESRD_CASES  CNTY_TOT_VSTS  ...  Utilization FACTOR  Illness FACTOR  \\\n",
       "0                  5            112  ...           -0.250884        0.785873   \n",
       "1                 18            720  ...            0.659104       -0.431479   \n",
       "2                  4             92  ...           -0.230425       -0.469385   \n",
       "3                  4            152  ...            0.538031        0.253332   \n",
       "4                  2             41  ...            2.903910       -1.412240   \n",
       "...              ...            ...  ...                 ...             ...   \n",
       "2689               3            102  ...           -0.178433       -0.138656   \n",
       "2690               3            146  ...           -0.264102       -0.817504   \n",
       "2691               6            183  ...            0.078960       -0.276524   \n",
       "2692               1             15  ...           -0.675425       -0.676739   \n",
       "2693               5            298  ...           -0.731424       -0.216995   \n",
       "\n",
       "      Hospitalization FACTOR  FACTORS_1  FACTORS_2  FACTORS_3  KM_FACTORS  \\\n",
       "0                  -0.122812  -0.295489   0.822719   0.093919           3   \n",
       "1                   0.216796   0.929719  -0.441932   0.235808           4   \n",
       "2                   0.223178  -0.186919  -0.534365   0.630980           0   \n",
       "3                  -0.200188   0.707499   0.360124  -0.366061           3   \n",
       "4                  -0.246765   3.388853  -1.407649  -0.853064           4   \n",
       "...                      ...        ...        ...        ...         ...   \n",
       "2689               -0.176684  -0.764856  -0.644227  -0.378574           1   \n",
       "2690               -0.880140   0.130767   0.156742  -0.143448           3   \n",
       "2691               -0.505371   0.940358  -1.014010  -0.249721           4   \n",
       "2692                0.551757  -0.643502  -1.059212  -0.203979           1   \n",
       "2693               -0.167369  -0.220061  -0.803552   0.232388           3   \n",
       "\n",
       "         TSNE_1     TSNE_2     TSNE_3  \n",
       "0      9.242106  -5.110165 -11.567027  \n",
       "1      5.333952 -12.482608 -12.750140  \n",
       "2    -11.138145   4.038353  -0.760248  \n",
       "3      1.813346 -10.088836   3.131184  \n",
       "4      1.725450 -16.089573 -15.685245  \n",
       "...         ...        ...        ...  \n",
       "2689   1.927621  12.133542   9.169892  \n",
       "2690   1.190519  -7.473377  -0.055672  \n",
       "2691  -4.905205   2.887669  -3.678633  \n",
       "2692  -4.070534  17.235117   4.915854  \n",
       "2693   3.493206   7.677205  -5.937979  \n",
       "\n",
       "[2694 rows x 76 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = df.merge(TSNE, left_index=True, right_index=True)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_embeddings = merged[[\"FACTORS_1\", \"FACTORS_2\", \"FACTORS_3\", \"TSNE_1\", \"TSNE_2\", \"TSNE_3\"]].corr()\n",
    "lower_corr = corr_embeddings.where(np.tril(np.ones(corr_embeddings.shape)).astype(np.bool))\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "corr_hmap=sb.heatmap(lower_corr, annot=True, cmap=\"mako\", fmt=\".2f\")\n",
    "fig = corr_hmap.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"embeddings_heatmap.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Analysis vs. tSNE\n",
    "\n",
    "Looks like these two things may be getting at different aspects of the data, for the most part.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2694, 76)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans Clustering on tSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KM_TSNE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2694 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      KM_TSNE\n",
       "0           4\n",
       "1           2\n",
       "2           1\n",
       "3           2\n",
       "4           2\n",
       "...       ...\n",
       "2689        3\n",
       "2690        0\n",
       "2691        1\n",
       "2692        3\n",
       "2693        4\n",
       "\n",
       "[2694 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "k = 5\n",
    "\n",
    "clustered_df = pd.DataFrame()\n",
    "kmeans = KMeans(n_clusters=k)\n",
    "KM_TSNE = kmeans.fit_predict(TSNE)\n",
    "clustered_df[\"KM_TSNE\"] = KM_TSNE\n",
    "clustered_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th>CNTY_BENE_CT</th>\n",
       "      <th>CNTY_BENE_USRS</th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>CNTY_SEX_PROP</th>\n",
       "      <th>TOT_CNCR_CASES</th>\n",
       "      <th>TOT_STRKETIA_CASES</th>\n",
       "      <th>TOT_ESRD_CASES</th>\n",
       "      <th>CNTY_TOT_VSTS</th>\n",
       "      <th>...</th>\n",
       "      <th>Illness FACTOR</th>\n",
       "      <th>Hospitalization FACTOR</th>\n",
       "      <th>FACTORS_1</th>\n",
       "      <th>FACTORS_2</th>\n",
       "      <th>FACTORS_3</th>\n",
       "      <th>KM_FACTORS</th>\n",
       "      <th>TSNE_1</th>\n",
       "      <th>TSNE_2</th>\n",
       "      <th>TSNE_3</th>\n",
       "      <th>KM_TSNE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>65.4</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.785873</td>\n",
       "      <td>-0.122812</td>\n",
       "      <td>-0.295489</td>\n",
       "      <td>0.822719</td>\n",
       "      <td>0.093919</td>\n",
       "      <td>3</td>\n",
       "      <td>9.242106</td>\n",
       "      <td>-5.110165</td>\n",
       "      <td>-11.567027</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>70.3</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>720</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.431479</td>\n",
       "      <td>0.216796</td>\n",
       "      <td>0.929719</td>\n",
       "      <td>-0.441932</td>\n",
       "      <td>0.235808</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333952</td>\n",
       "      <td>-12.482608</td>\n",
       "      <td>-12.750140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.469385</td>\n",
       "      <td>0.223178</td>\n",
       "      <td>-0.186919</td>\n",
       "      <td>-0.534365</td>\n",
       "      <td>0.630980</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.138145</td>\n",
       "      <td>4.038353</td>\n",
       "      <td>-0.760248</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253332</td>\n",
       "      <td>-0.200188</td>\n",
       "      <td>0.707499</td>\n",
       "      <td>0.360124</td>\n",
       "      <td>-0.366061</td>\n",
       "      <td>3</td>\n",
       "      <td>1.813346</td>\n",
       "      <td>-10.088836</td>\n",
       "      <td>3.131184</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.412240</td>\n",
       "      <td>-0.246765</td>\n",
       "      <td>3.388853</td>\n",
       "      <td>-1.407649</td>\n",
       "      <td>-0.853064</td>\n",
       "      <td>4</td>\n",
       "      <td>1.725450</td>\n",
       "      <td>-16.089573</td>\n",
       "      <td>-15.685245</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS_STATE_CODE  FIPS_COUNTY_CODE  CNTY_BENE_CT  CNTY_BENE_USRS  \\\n",
       "0                1                 1            16              13   \n",
       "1                1                 3            95              65   \n",
       "2                1                 5            14              12   \n",
       "3                1                 7            17              12   \n",
       "4                1                11             5               2   \n",
       "\n",
       "   CNTY_MEAN_AGE  CNTY_SEX_PROP  TOT_CNCR_CASES  TOT_STRKETIA_CASES  \\\n",
       "0           65.4       0.187500               3                   3   \n",
       "1           70.3       0.463158              18                   8   \n",
       "2           72.4       0.642857               0                   0   \n",
       "3           69.0       0.411765               2                   3   \n",
       "4           71.5       0.200000               0                   1   \n",
       "\n",
       "   TOT_ESRD_CASES  CNTY_TOT_VSTS  ...  Illness FACTOR  Hospitalization FACTOR  \\\n",
       "0               5            112  ...        0.785873               -0.122812   \n",
       "1              18            720  ...       -0.431479                0.216796   \n",
       "2               4             92  ...       -0.469385                0.223178   \n",
       "3               4            152  ...        0.253332               -0.200188   \n",
       "4               2             41  ...       -1.412240               -0.246765   \n",
       "\n",
       "   FACTORS_1  FACTORS_2  FACTORS_3  KM_FACTORS     TSNE_1     TSNE_2  \\\n",
       "0  -0.295489   0.822719   0.093919           3   9.242106  -5.110165   \n",
       "1   0.929719  -0.441932   0.235808           4   5.333952 -12.482608   \n",
       "2  -0.186919  -0.534365   0.630980           0 -11.138145   4.038353   \n",
       "3   0.707499   0.360124  -0.366061           3   1.813346 -10.088836   \n",
       "4   3.388853  -1.407649  -0.853064           4   1.725450 -16.089573   \n",
       "\n",
       "      TSNE_3  KM_TSNE  \n",
       "0 -11.567027        4  \n",
       "1 -12.750140        2  \n",
       "2  -0.760248        1  \n",
       "3   3.131184        2  \n",
       "4 -15.685245        2  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.iloc[:,0:76]\n",
    "merged = merged.merge(clustered_df,  left_index=True, right_index=True)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "z = merged[\"TSNE_3\"]\n",
    "x = merged[\"TSNE_1\"]\n",
    "y = merged[\"TSNE_2\"]\n",
    "c = merged[\"KM_TSNE\"]\n",
    "ax.scatter(x, y, z, c=c)\n",
    "ax.set_title('KMeans - tSNE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2694 entries, 0 to 2693\n",
      "Data columns (total 81 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   FIPS_STATE_CODE         2694 non-null   int64  \n",
      " 1   FIPS_COUNTY_CODE        2694 non-null   int64  \n",
      " 2   CNTY_BENE_CT            2694 non-null   int64  \n",
      " 3   CNTY_BENE_USRS          2694 non-null   int64  \n",
      " 4   CNTY_MEAN_AGE           2694 non-null   float64\n",
      " 5   CNTY_SEX_PROP           2694 non-null   float64\n",
      " 6   TOT_CNCR_CASES          2694 non-null   int64  \n",
      " 7   TOT_STRKETIA_CASES      2694 non-null   int64  \n",
      " 8   TOT_ESRD_CASES          2694 non-null   int64  \n",
      " 9   CNTY_TOT_VSTS           2694 non-null   int64  \n",
      " 10  CNTY_TOT_IP_VSTS        2694 non-null   int64  \n",
      " 11  CNTY_TOT_OP_VSTS        2694 non-null   int64  \n",
      " 12  CNTY_TOT_CODES          2694 non-null   float64\n",
      " 13  CNTY_TOT_PROCS          2694 non-null   float64\n",
      " 14  CNTY_MEAN_IP_STAY       2694 non-null   float64\n",
      " 15  CNTY_TOT_CCS            2694 non-null   int64  \n",
      " 16  CNTY_MEAN_CCS           2694 non-null   float64\n",
      " 17  CNTY_TOT_NEW_CCS        2694 non-null   int64  \n",
      " 18  CNTY_MEAN_NEW_CCS       2694 non-null   float64\n",
      " 19  CNTY_SUM_ELIX_SCORES    2694 non-null   float64\n",
      " 20  CNTY_TOT_IP_COST        2694 non-null   int64  \n",
      " 21  CNTY_TOT_OP_COST        2694 non-null   int64  \n",
      " 22  CNTY_ALL_COSTS          2694 non-null   int64  \n",
      " 23  CNTY_PERCENT_COSTS      2694 non-null   float64\n",
      " 24  CNTY_USR_PROP           2694 non-null   float64\n",
      " 25  CNTY_IP_PROP            2694 non-null   float64\n",
      " 26  CNTY_AGE_95CIL          2585 non-null   float64\n",
      " 27  CNTY_AGE_95CIU          2585 non-null   float64\n",
      " 28  CNTY_PROP_NH_WHITE      2694 non-null   float64\n",
      " 29  CNTY_PROP_AA_BLACK      2694 non-null   float64\n",
      " 30  CNTY_PROP_OTHER         2694 non-null   float64\n",
      " 31  CNTY_PROP_NW_HISPANIC   2694 non-null   float64\n",
      " 32  TOT_CNCR_PREV           2694 non-null   float64\n",
      " 33  TOT_STRK_PREV           2694 non-null   float64\n",
      " 34  TOT_ESRD_PREV           2694 non-null   float64\n",
      " 35  TOT_CVRG_MOS_PB_PY      2694 non-null   float64\n",
      " 36  TOT_RXCVRG_MOS_PB_PY    2694 non-null   float64\n",
      " 37  TOT_VSTS_PB             2694 non-null   float64\n",
      " 38  TOT_VSTS_PU             2694 non-null   float64\n",
      " 39  TOT_IP_VSTS_PU          2694 non-null   float64\n",
      " 40  TOT_OP_VSTS_PU          2694 non-null   float64\n",
      " 41  CODES_PU                2694 non-null   float64\n",
      " 42  PROCS_PU                2694 non-null   float64\n",
      " 43  PRVDRS_PU               2694 non-null   float64\n",
      " 44  TOT_IPDAYS_PU           2694 non-null   float64\n",
      " 45  SR[TOT_IPDAYS_PU]       2694 non-null   float64\n",
      " 46  TOT_CC_PREV             2694 non-null   float64\n",
      " 47  TOT_CCS_PU              2694 non-null   float64\n",
      " 48  TOT_CC_INCD             2694 non-null   float64\n",
      " 49  TOT_NEW_CC_PU           2694 non-null   float64\n",
      " 50  TOT_ELIX_PU             2694 non-null   float64\n",
      " 51  TOT_IP_COST_PB          2694 non-null   float64\n",
      " 52  TOT_OP_COST_PB          2694 non-null   float64\n",
      " 53  TOT_COST_PB             2694 non-null   float64\n",
      " 54  SR[TOT_COST_PB]         2694 non-null   float64\n",
      " 55  NUM_COUNTIES            2694 non-null   int64  \n",
      " 56  HO_RANK                 2694 non-null   int64  \n",
      " 57  HO_PROP_RANK            2694 non-null   float64\n",
      " 58  HO_QUARTILE             2694 non-null   int64  \n",
      " 59  HF_RANK                 2694 non-null   int64  \n",
      " 60  HF_PROP_RANK            2694 non-null   float64\n",
      " 61  HF_QUARTILE             2694 non-null   int64  \n",
      " 62  HF_HIGH                 2694 non-null   int64  \n",
      " 63  HIGH_SEV                2694 non-null   int64  \n",
      " 64  HIGH_PROCS              2694 non-null   int64  \n",
      " 65  DIVERSE_PROP            2694 non-null   float64\n",
      " 66  Utilization FACTOR      2694 non-null   float64\n",
      " 67  Illness FACTOR          2694 non-null   float64\n",
      " 68  Hospitalization FACTOR  2694 non-null   float64\n",
      " 69  FACTORS_1               2694 non-null   float64\n",
      " 70  FACTORS_2               2694 non-null   float64\n",
      " 71  FACTORS_3               2694 non-null   float64\n",
      " 72  KM_FACTORS              2694 non-null   int64  \n",
      " 73  TSNE_1                  2694 non-null   float32\n",
      " 74  TSNE_2                  2694 non-null   float32\n",
      " 75  TSNE_3                  2694 non-null   float32\n",
      " 76  KM_TSNE                 2694 non-null   int32  \n",
      " 77  FIPS                    2694 non-null   int64  \n",
      " 78  STATE                   2694 non-null   object \n",
      " 79  COUNTY                  2694 non-null   object \n",
      " 80  CBSA                    1041 non-null   float64\n",
      "dtypes: float32(3), float64(50), int32(1), int64(25), object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "merged[\"FIPS\"] = FIPS\n",
    "merged[\"STATE\"] = STATE\n",
    "merged[\"COUNTY\"] = COUNTY\n",
    "merged['FIPS_STATE_CODE'] = FIPS_STATE\n",
    "merged['FIPS_COUNTY_CODE'] = FIPS_COUNTY\n",
    "merged['CBSA'] = CBSA\n",
    "\n",
    "merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.to_csv(\"/home/kylier/python/DS/data/PUF/PUF_RWJF_MERGED_CLUSTERED_NOOUTLIERS2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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