Longitudinal Descriptive - Averages and Standard Deviations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e531279a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c257a72",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_visits = pd.read_csv(\"../Data/AllVisitsLong.csv\", parse_dates=[\"service_date\"], low_memory=False)\n",
    "demo = pd.read_csv(\"../Data/new_demo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d04f14b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16730 entries, 0 to 16729\n",
      "Data columns (total 59 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   service_date      16730 non-null  datetime64[ns]\n",
      " 1   patient_id        16730 non-null  int64         \n",
      " 2   location          16730 non-null  object        \n",
      " 3   visit_type        16730 non-null  object        \n",
      " 4   visit_level       13086 non-null  float64       \n",
      " 5   admitted          16730 non-null  int64         \n",
      " 6   non_emergent_dx   16730 non-null  int64         \n",
      " 7   deceased          34 non-null     float64       \n",
      " 8   deceased_date     34 non-null     object        \n",
      " 9   CVD               16730 non-null  int64         \n",
      " 10  HF                16730 non-null  int64         \n",
      " 11  HTN               16730 non-null  int64         \n",
      " 12  URI/PNA           16730 non-null  int64         \n",
      " 13  AS/COPD           16730 non-null  int64         \n",
      " 14  NEURO             16730 non-null  int64         \n",
      " 15  SUD               16730 non-null  int64         \n",
      " 16  MHD               16730 non-null  int64         \n",
      " 17  CA                16730 non-null  int64         \n",
      " 18  PREG              16730 non-null  int64         \n",
      " 19  DM                16730 non-null  int64         \n",
      " 20  INF               16730 non-null  int64         \n",
      " 21  LIV               16730 non-null  int64         \n",
      " 22  REN               16730 non-null  int64         \n",
      " 23  COG               16730 non-null  int64         \n",
      " 24  SENS              16730 non-null  int64         \n",
      " 25  PAIN              16730 non-null  int64         \n",
      " 26  TOB               16730 non-null  int64         \n",
      " 27  ACC/INJ           16730 non-null  int64         \n",
      " 28  MH_VISIT          16730 non-null  int64         \n",
      " 29  SU_VISIT          16730 non-null  int64         \n",
      " 30  TREAT_VISIT       16730 non-null  int64         \n",
      " 31  PREV_VISIT        16730 non-null  int64         \n",
      " 32  raw_text          11173 non-null  object        \n",
      " 33  date_time         11173 non-null  object        \n",
      " 34  height            9148 non-null   float64       \n",
      " 35  weight            8324 non-null   float64       \n",
      " 36  BMI               8279 non-null   float64       \n",
      " 37  temp              8903 non-null   float64       \n",
      " 38  pulse             9121 non-null   float64       \n",
      " 39  bp                9000 non-null   object        \n",
      " 40  o2                7131 non-null   float64       \n",
      " 41  chief_complaint   11173 non-null  object        \n",
      " 42  pain_count        11173 non-null  float64       \n",
      " 43  mat_count         11173 non-null  float64       \n",
      " 44  jail_count        11173 non-null  float64       \n",
      " 45  tobacco           10884 non-null  float64       \n",
      " 46  cigpday           2922 non-null   float64       \n",
      " 47  disability_count  11173 non-null  float64       \n",
      " 48  nida              3260 non-null   float64       \n",
      " 49  a1c               501 non-null    float64       \n",
      " 50  obg               1100 non-null   float64       \n",
      " 51  phq2              2213 non-null   float64       \n",
      " 52  sra               11173 non-null  float64       \n",
      " 53  hark              586 non-null    float64       \n",
      " 54  medcount          11172 non-null  float64       \n",
      " 55  nurse             11172 non-null  object        \n",
      " 56  provider          11172 non-null  object        \n",
      " 57  mh_provider       11172 non-null  object        \n",
      " 58  extreme_day       16730 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(21), int64(27), object(10)\n",
      "memory usage: 7.5+ MB\n"
     ]
    }
   ],
   "source": [
    "long_visits.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03e5fbb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2273 entries, 0 to 2272\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Unnamed: 0    2273 non-null   int64 \n",
      " 1   patient_id    2273 non-null   int64 \n",
      " 2   veteran       2273 non-null   int64 \n",
      " 3   age           2273 non-null   int64 \n",
      " 4   age_group     2273 non-null   object\n",
      " 5   sex           2273 non-null   int64 \n",
      " 6   housing       2273 non-null   object\n",
      " 7   education     2273 non-null   object\n",
      " 8   si_race       2273 non-null   object\n",
      " 9   si_ethnicity  2273 non-null   object\n",
      " 10  language      2273 non-null   int64 \n",
      " 11  insured       2273 non-null   int64 \n",
      " 12  incarcerated  2273 non-null   int64 \n",
      "dtypes: int64(8), object(5)\n",
      "memory usage: 231.0+ KB\n"
     ]
    }
   ],
   "source": [
    "demo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01ef48f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             NaN\n",
       "1             NaN\n",
       "2             NaN\n",
       "3             NaN\n",
       "4             NaN\n",
       "           ...   \n",
       "16725     97 / 66\n",
       "16726     97 / 66\n",
       "16727         NaN\n",
       "16728      133/81\n",
       "16729    124 / 74\n",
       "Name: bp, Length: 16730, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_visits[\"bp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39f81ede",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
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       "      <th>4</th>\n",
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       "      <th>16726</th>\n",
       "      <td>97</td>\n",
       "      <td>66</td>\n",
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       "    <tr>\n",
       "      <th>16727</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16728</th>\n",
       "      <td>133</td>\n",
       "      <td>81</td>\n",
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       "    <tr>\n",
       "      <th>16729</th>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>16730 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "      systolic diastolic\n",
       "0          NaN       NaN\n",
       "1          NaN       NaN\n",
       "2          NaN       NaN\n",
       "3          NaN       NaN\n",
       "4          NaN       NaN\n",
       "...        ...       ...\n",
       "16725       97        66\n",
       "16726       97        66\n",
       "16727      NaN       NaN\n",
       "16728      133        81\n",
       "16729      124        74\n",
       "\n",
       "[16730 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bps = long_visits[\"bp\"].str.split(\" / |/\", expand=True)\n",
    "bps.columns = [\"systolic\", \"diastolic\"]\n",
    "bps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c89857d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bug work-around\n",
    "bps[\"systolic\"] = bps[\"systolic\"].astype(\"float\")\n",
    "bps[\"diastolic\"] = bps[\"diastolic\"].astype(\"float\")\n",
    "\n",
    "bps[\"systolic\"] = bps[\"systolic\"].astype(\"Int64\")\n",
    "bps[\"diastolic\"] = bps[\"diastolic\"].astype(\"Int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e0978e4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1558625</th>\n",
       "      <td>137.363636</td>\n",
       "      <td>72.909091</td>\n",
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       "      <th>1558631</th>\n",
       "      <td>132.428571</td>\n",
       "      <td>77.714286</td>\n",
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       "      <th>1558651</th>\n",
       "      <td>98.0</td>\n",
       "      <td>65.0</td>\n",
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       "      <td>113.217391</td>\n",
       "      <td>72.826087</td>\n",
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       "      <th>1568735</th>\n",
       "      <td>124.0</td>\n",
       "      <td>68.0</td>\n",
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       "      <th>1568736</th>\n",
       "      <td>99.0</td>\n",
       "      <td>75.0</td>\n",
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       "      <th>1568738</th>\n",
       "      <td>135.0</td>\n",
       "      <td>90.0</td>\n",
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       "      <td>134.0</td>\n",
       "      <td>78.0</td>\n",
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       "      <th>1568741</th>\n",
       "      <td>124.0</td>\n",
       "      <td>74.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2186 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              systolic  diastolic\n",
       "patient_id                       \n",
       "1558625     137.363636  72.909091\n",
       "1558631     132.428571  77.714286\n",
       "1558651           98.0       65.0\n",
       "1558659     113.217391  72.826087\n",
       "1558661          129.7       71.9\n",
       "...                ...        ...\n",
       "1568735          124.0       68.0\n",
       "1568736           99.0       75.0\n",
       "1568738          135.0       90.0\n",
       "1568739          134.0       78.0\n",
       "1568741          124.0       74.0\n",
       "\n",
       "[2186 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bps = bps.copy()\n",
    "p_bps[\"patient_id\"] = long_visits[\"patient_id\"].copy()\n",
    "mean_bps = p_bps.groupby(\"patient_id\").mean()\n",
    "mean_bps[(mean_bps[\"systolic\"].notnull()) & (mean_bps[\"diastolic\"].notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3710006b",
   "metadata": {},
   "outputs": [
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       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
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       "      <th>16725</th>\n",
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       "      <td>66</td>\n",
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       "      <th>16726</th>\n",
       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>16728</th>\n",
       "      <td>133</td>\n",
       "      <td>81</td>\n",
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       "      <th>16729</th>\n",
       "      <td>124</td>\n",
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       "<p>9000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       systolic  diastolic\n",
       "5           149         92\n",
       "6           125         73\n",
       "7           128         82\n",
       "10          111         65\n",
       "12          107         77\n",
       "...         ...        ...\n",
       "16724       116         68\n",
       "16725        97         66\n",
       "16726        97         66\n",
       "16728       133         81\n",
       "16729       124         74\n",
       "\n",
       "[9000 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bps[(bps[\"systolic\"].notnull()) & (bps[\"diastolic\"].notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd98a78e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1751</th>\n",
       "      <td>1110</td>\n",
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      "text/plain": [
       "      systolic  diastolic\n",
       "1751      1110         64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bps[bps[\"systolic\"]==1110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02e825e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bps.at[1751, \"systolic\"] = 111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c70660b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>service_date</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>location</th>\n",
       "      <th>visit_type</th>\n",
       "      <th>visit_level</th>\n",
       "      <th>admitted</th>\n",
       "      <th>non_emergent_dx</th>\n",
       "      <th>deceased</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>CVD</th>\n",
       "      <th>...</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>phq2</th>\n",
       "      <th>sra</th>\n",
       "      <th>hark</th>\n",
       "      <th>medcount</th>\n",
       "      <th>nurse</th>\n",
       "      <th>provider</th>\n",
       "      <th>mh_provider</th>\n",
       "      <th>extreme_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [service_date, patient_id, location, visit_type, visit_level, admitted, non_emergent_dx, deceased, deceased_date, CVD, HF, HTN, URI/PNA, AS/COPD, NEURO, SUD, MHD, CA, PREG, DM, INF, LIV, REN, COG, SENS, PAIN, TOB, ACC/INJ, MH_VISIT, SU_VISIT, TREAT_VISIT, PREV_VISIT, raw_text, date_time, height, weight, BMI, temp, pulse, bp, o2, chief_complaint, pain_count, mat_count, jail_count, tobacco, cigpday, disability_count, nida, a1c, obg, phq2, sra, hark, medcount, nurse, provider, mh_provider, extreme_day]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 59 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_visits[long_visits[\"height\"]<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e667b80c",
   "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>service_date</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>location</th>\n",
       "      <th>visit_type</th>\n",
       "      <th>visit_level</th>\n",
       "      <th>admitted</th>\n",
       "      <th>non_emergent_dx</th>\n",
       "      <th>deceased</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>CVD</th>\n",
       "      <th>...</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>phq2</th>\n",
       "      <th>sra</th>\n",
       "      <th>hark</th>\n",
       "      <th>medcount</th>\n",
       "      <th>nurse</th>\n",
       "      <th>provider</th>\n",
       "      <th>mh_provider</th>\n",
       "      <th>extreme_day</th>\n",
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       "<p>0 rows × 59 columns</p>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [service_date, patient_id, location, visit_type, visit_level, admitted, non_emergent_dx, deceased, deceased_date, CVD, HF, HTN, URI/PNA, AS/COPD, NEURO, SUD, MHD, CA, PREG, DM, INF, LIV, REN, COG, SENS, PAIN, TOB, ACC/INJ, MH_VISIT, SU_VISIT, TREAT_VISIT, PREV_VISIT, raw_text, date_time, height, weight, BMI, temp, pulse, bp, o2, chief_complaint, pain_count, mat_count, jail_count, tobacco, cigpday, disability_count, nida, a1c, obg, phq2, sra, hark, medcount, nurse, provider, mh_provider, extreme_day]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 59 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_visits[long_visits[\"weight\"]<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a4c4ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>service_date</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>location</th>\n",
       "      <th>visit_type</th>\n",
       "      <th>visit_level</th>\n",
       "      <th>admitted</th>\n",
       "      <th>non_emergent_dx</th>\n",
       "      <th>deceased</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>CVD</th>\n",
       "      <th>...</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>phq2</th>\n",
       "      <th>sra</th>\n",
       "      <th>hark</th>\n",
       "      <th>medcount</th>\n",
       "      <th>nurse</th>\n",
       "      <th>provider</th>\n",
       "      <th>mh_provider</th>\n",
       "      <th>extreme_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>2018-05-16</td>\n",
       "      <td>1560669</td>\n",
       "      <td>New Horizons</td>\n",
       "      <td>outpatient</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0xdc00335</td>\n",
       "      <td>0x39e0184</td>\n",
       "      <td>0x1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6097</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>1559434</td>\n",
       "      <td>New Horizons</td>\n",
       "      <td>outpatient</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0xdc00335</td>\n",
       "      <td>0xb1402d6</td>\n",
       "      <td>0x1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16062</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>1568174</td>\n",
       "      <td>New Horizons</td>\n",
       "      <td>outpatient</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0x27d6056c</td>\n",
       "      <td>0x1</td>\n",
       "      <td>0x1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      service_date  patient_id      location  visit_type  visit_level  \\\n",
       "2507    2018-05-16     1560669  New Horizons  outpatient          3.0   \n",
       "6097    2018-11-01     1559434  New Horizons  outpatient          3.0   \n",
       "16062   2019-12-06     1568174  New Horizons  outpatient          1.0   \n",
       "\n",
       "       admitted  non_emergent_dx  deceased deceased_date  CVD  ...  a1c  obg  \\\n",
       "2507          0                0       NaN           NaN    0  ...  NaN  NaN   \n",
       "6097          0                0       NaN           NaN    0  ...  NaN  NaN   \n",
       "16062         0                0       NaN           NaN    0  ...  NaN  NaN   \n",
       "\n",
       "       phq2  sra  hark  medcount       nurse   provider  mh_provider  \\\n",
       "2507    NaN  0.0   NaN       2.0   0xdc00335  0x39e0184          0x1   \n",
       "6097    0.0  0.0   NaN       0.0   0xdc00335  0xb1402d6          0x1   \n",
       "16062   NaN  0.0   NaN       0.0  0x27d6056c        0x1          0x1   \n",
       "\n",
       "       extreme_day  \n",
       "2507             0  \n",
       "6097             0  \n",
       "16062            1  \n",
       "\n",
       "[3 rows x 59 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_visits[long_visits[\"weight\"]>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "152b59cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_visits.at[[2507, 6097, 16062], \"weight\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "542c9046",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_visits.loc[long_visits[\"height\"]>84, \"height\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ad80699",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_visits.at[[5508], \"height\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "13f9fb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_visits.at[[2507, 5508, 6097, 16062], \"calc_BMI\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ae5b14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub1 = long_visits[[\"height\", \"weight\"]].copy()\n",
    "sub2 = bps.copy()\n",
    "sub3 = long_visits[[\"pain_count\", \"jail_count\", \"disability_count\"]].copy()\n",
    "sub4 = long_visits[[\"nida\", \"phq2\", \"hark\"]].copy()\n",
    "\n",
    "sub1.columns = [\"Avg Height\", \"Avg Weight\"]\n",
    "sub2.columns = [\"Avg Systolic BP\", \"Avg Diastolic BP\"]\n",
    "sub3.columns = [\"Avg Pain Count\", \"Avg Jail Count\", \"Avg Disability Count\"]\n",
    "sub4.columns = [\"Avg NIDA TAPS Score\", \"Avg PHQ-2 Score\", \"Avg HARK Score\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1dba850e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.boxplot(data=sub1, x=\"Avg Height\", palette=\"dark\", showmeans=True,\n",
    "            meanprops={\"marker\": \"x\",\n",
    "                       \"markeredgecolor\": \"white\",\n",
    "                       \"markersize\": \"10\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff755220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.boxplot(data=sub1, x=\"Avg Weight\", palette=\"dark\", showmeans=True,\n",
    "            meanprops={\"marker\": \"x\",\n",
    "                       \"markeredgecolor\": \"white\",\n",
    "                       \"markersize\": \"10\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "571bc910",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub2 = sub2[(sub2[\"Avg Systolic BP\"].notnull()) & (sub2[\"Avg Diastolic BP\"].notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28113947",
   "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>Avg Systolic BP</th>\n",
       "      <th>Avg Diastolic BP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3438</th>\n",
       "      <td>124</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg Systolic BP  Avg Diastolic BP\n",
       "3438              124               767"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub2[sub2[\"Avg Diastolic BP\"]>200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08530415",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub2.at[3438, \"Avg Diastolic BP\"] = 76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f08c814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.boxplot(data=sub2, palette=\"dark\", showmeans=True,\n",
    "            meanprops={\"marker\": \"x\",\n",
    "                       \"markeredgecolor\": \"white\",\n",
    "                       \"markersize\": \"10\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14ad2756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.boxplot(data=sub3, palette=\"dark\", showmeans=True,\n",
    "            meanprops={\"marker\": \"x\",\n",
    "                       \"markeredgecolor\": \"white\",\n",
    "                       \"markersize\": \"10\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "197e07f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.boxplot(data=sub4, palette=\"dark\", showmeans=True,\n",
    "            meanprops={\"marker\": \"x\",\n",
    "                       \"markeredgecolor\": \"white\",\n",
    "                       \"markersize\": \"10\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "233cb982",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_vars = long_visits[[\"patient_id\", \"height\", \"weight\", \"pain_count\", \"nida\", \"phq2\", \"hark\", \"a1c\", \"obg\", \"HTN\", \"DM\"]].copy()\n",
    "h_vars = pd.concat([h_vars, bps], axis=1)\n",
    "pt_means = h_vars.groupby(\"patient_id\").mean()\n",
    "\n",
    "heights = pt_means[pt_means[\"height\"].notnull()][\"height\"].copy()\n",
    "weights = pt_means[pt_means[\"weight\"].notnull()][\"weight\"].copy()\n",
    "pain = pt_means[pt_means[\"pain_count\"].notnull()][\"pain_count\"].copy()\n",
    "nida = pt_means[pt_means[\"nida\"].notnull()][\"nida\"].copy()\n",
    "phq2 = pt_means[pt_means[\"phq2\"].notnull()][\"phq2\"].copy()\n",
    "hark = pt_means[pt_means[\"hark\"].notnull()][\"hark\"].copy()\n",
    "aic = pt_means[pt_means[\"a1c\"].notnull()][\"a1c\"].copy()\n",
    "obg = pt_means[pt_means[\"obg\"].notnull()][\"obg\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96c40242",
   "metadata": {},
   "outputs": [],
   "source": [
    "demo = demo.set_index(\"patient_id\")\n",
    "joined = pt_means.join(demo, how=\"right\").reset_index()\n",
    "# joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f244e580",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined2 = joined[[\"patient_id\", \"height\", \"weight\", \"pain_count\", \"nida\", \"phq2\", \"hark\", \"a1c\", \"obg\", \"HTN\", \"DM\", \"systolic\", \"diastolic\", \"sex\", \"language\", \"age\"]].copy()\n",
    "# joined2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2113915f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2272 entries, 0 to 2271\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   index       2272 non-null   int64  \n",
      " 1   patient_id  2272 non-null   int64  \n",
      " 2   height      2198 non-null   float64\n",
      " 3   weight      2185 non-null   float64\n",
      " 4   pain_count  2239 non-null   float64\n",
      " 5   nida        1846 non-null   float64\n",
      " 6   phq2        1664 non-null   float64\n",
      " 7   hark        456 non-null    float64\n",
      " 8   a1c         231 non-null    float64\n",
      " 9   obg         230 non-null    float64\n",
      " 10  HTN         2265 non-null   float64\n",
      " 11  DM          2265 non-null   float64\n",
      " 12  systolic    2179 non-null   Float64\n",
      " 13  diastolic   2179 non-null   Float64\n",
      " 14  sex         2272 non-null   int64  \n",
      " 15  language    2272 non-null   int64  \n",
      " 16  age         2272 non-null   int64  \n",
      "dtypes: Float64(2), float64(10), int64(5)\n",
      "memory usage: 306.3 KB\n"
     ]
    }
   ],
   "source": [
    "women_heights = joined2[joined2[\"sex\"]==1][\"height\"].copy()\n",
    "refugee_heights = joined2[joined2[\"language\"]==1][\"height\"].copy()\n",
    "children_heights = joined2[joined2[\"age\"]<18][\"height\"].copy()\n",
    "\n",
    "#plt.figure(figsize=[10, 10])\n",
    "#plt.grid(axis='y', color = 'olive', linestyle = '--')\n",
    "#col_map = plt.get_cmap('tab20b')\n",
    "comp = joined2[joined2[\"sex\"]<2].copy()\n",
    "comp = comp[(comp[\"language\"].notnull()) & (comp[\"sex\"].notnull())].copy().reset_index()\n",
    "comp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a271f570",
   "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>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>HTN</th>\n",
       "      <th>DM</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>sex</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>Language</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1558625</td>\n",
       "      <td>57.0</td>\n",
       "      <td>104.700000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137.363636</td>\n",
       "      <td>72.909091</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Not Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1558631</td>\n",
       "      <td>70.0</td>\n",
       "      <td>254.585714</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.428571</td>\n",
       "      <td>77.714286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1558651</td>\n",
       "      <td>69.0</td>\n",
       "      <td>225.300000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1558659</td>\n",
       "      <td>70.0</td>\n",
       "      <td>165.145455</td>\n",
       "      <td>1.153846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.217391</td>\n",
       "      <td>72.826087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1558661</td>\n",
       "      <td>72.0</td>\n",
       "      <td>189.800000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>173.444444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>129.7</td>\n",
       "      <td>71.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  patient_id  height      weight  pain_count  nida  phq2  hark  \\\n",
       "0      0     1558625    57.0  104.700000    5.000000   4.0   0.0   0.0   \n",
       "1      1     1558631    70.0  254.585714    1.500000   5.0   0.0   NaN   \n",
       "2      2     1558651    69.0  225.300000    7.000000   8.0   6.0   NaN   \n",
       "3      3     1558659    70.0  165.145455    1.153846   0.0   NaN   NaN   \n",
       "4      4     1558661    72.0  189.800000    5.300000   2.0   0.0   NaN   \n",
       "\n",
       "        a1c         obg  HTN   DM    systolic  diastolic  sex  language  age  \\\n",
       "0  5.000000   80.000000  1.0  1.0  137.363636  72.909091    1         0   59   \n",
       "1       NaN         NaN  0.0  0.0  132.428571  77.714286    0         0   62   \n",
       "2       NaN         NaN  0.0  0.0        98.0       65.0    0         0   55   \n",
       "3       NaN         NaN  1.0  0.0  113.217391  72.826087    0         0   55   \n",
       "4  6.333333  173.444444  1.0  1.0       129.7       71.9    0         0   65   \n",
       "\n",
       "          Language    Gender  Age Group  \n",
       "0  English-speaker  Not Male  18+ years  \n",
       "1  English-speaker      Male  18+ years  \n",
       "2  English-speaker      Male  18+ years  \n",
       "3  English-speaker      Male  18+ years  \n",
       "4  English-speaker      Male  18+ years  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp[\"Language\"] = \"\"\n",
    "comp[\"Gender\"] = \"\"\n",
    "comp[\"Age Group\"] = \"\"\n",
    "\n",
    "for i in range(len(comp)):\n",
    "    if comp.at[i, \"language\"]==0:\n",
    "        comp.at[i, \"Language\"] = \"English-speaker\"\n",
    "    else:\n",
    "        comp.at[i, \"Language\"] = \"Another primary language\"\n",
    "    if comp.at[i, \"sex\"]==0:\n",
    "        comp.at[i, \"Gender\"] = \"Male\"\n",
    "    else:\n",
    "        comp.at[i, \"Gender\"] = \"Not Male\"\n",
    "    if comp.at[i, \"age\"]<18:\n",
    "        comp.at[i, \"Age Group\"] = \"< 18 years\"\n",
    "    else:\n",
    "        comp.at[i, \"Age Group\"] = \"18+ years\"\n",
    "    if comp.at[i, \"DM\"]>0:\n",
    "        comp.at[i, \"DM\"] = 1\n",
    "    else: \n",
    "        comp.at[i, \"DM\"] = 0\n",
    "    if comp.at[i, \"HTN\"]>0:\n",
    "        comp.at[i, \"HTN\"] = 1\n",
    "    else: \n",
    "        comp.at[i, \"HTN\"] = 0   \n",
    "comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5bdc7bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 887.75x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 887.75x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_e_mean = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"18+ years\")][\"height\"].mean()\n",
    "f_ne_mean = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"18+ years\")][\"height\"].mean()\n",
    "\n",
    "m_e_mean = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"18+ years\")][\"height\"].mean()\n",
    "m_ne_mean = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"18+ years\")][\"height\"].mean()\n",
    "\n",
    "fc_e_mean = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"height\"].mean()\n",
    "fc_ne_mean = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"height\"].mean()\n",
    "\n",
    "mc_e_mean = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"height\"].mean()\n",
    "mc_ne_mean = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"height\"].mean()\n",
    "\n",
    "f_e_mean = np.round(f_e_mean, 0)\n",
    "f_ne_mean = np.round(f_ne_mean, 0)\n",
    "m_e_mean = np.round(m_e_mean, 0)\n",
    "m_ne_mean = np.round(m_ne_mean, 0)\n",
    "\n",
    "fc_e_mean = np.round(fc_e_mean, 0)\n",
    "fc_ne_mean = np.round(fc_ne_mean, 0)\n",
    "mc_e_mean = np.round(mc_e_mean, 0)\n",
    "mc_ne_mean = np.round(mc_ne_mean, 0)\n",
    "\n",
    "comp1 = comp[comp[\"Age Group\"]==\"18+ years\"].copy()\n",
    "comp2 = comp[comp[\"Age Group\"]==\"< 18 years\"].copy()\n",
    "\n",
    "g1 = sb.displot(data=comp1, x=\"height\", hue=\"Language\", col=\"Gender\", kind=\"kde\")\n",
    "g1.set_titles(\"{col_name} - Adults\")\n",
    "line_position1 = [[f_e_mean, f_ne_mean], [m_e_mean, m_ne_mean]]\n",
    "\n",
    "for ax, pos_list in zip(g1.axes.flat, line_position1):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "#plt.xticks(fontsize=14)\n",
    "#plt.yticks(fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "g2 = sb.displot(data=comp2, x=\"height\", hue=\"Language\", col=\"Gender\", kind=\"kde\")\n",
    "g2.set_titles(\"{col_name} - Children\")\n",
    "line_position2 = [[fc_e_mean, fc_ne_mean], [mc_e_mean, mc_ne_mean]]\n",
    "\n",
    "for ax, pos_list in zip(g2.axes.flat, line_position2):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "#plt.xticks(fontsize=14)\n",
    "#plt.yticks(fontsize=14)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "475b578f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.0\n",
      "61.0\n",
      "70.0\n",
      "65.0\n",
      "\n",
      "50.0\n",
      "48.0\n",
      "48.0\n",
      "49.0\n"
     ]
    }
   ],
   "source": [
    "print(f_e_mean)\n",
    "print(f_ne_mean)\n",
    "print(m_e_mean)\n",
    "print(m_ne_mean)\n",
    "print()\n",
    "print(fc_e_mean)\n",
    "print(fc_ne_mean)\n",
    "print(mc_e_mean)\n",
    "print(mc_ne_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e6771f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.0\n",
      "9.0\n",
      "8.0\n",
      "10.0\n"
     ]
    }
   ],
   "source": [
    "fc_e_meanage = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"age\"].mean()\n",
    "fc_ne_meanage = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"age\"].mean()\n",
    "\n",
    "mc_e_meanage = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"age\"].mean()\n",
    "mc_ne_meanage = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"age\"].mean()\n",
    "\n",
    "fc_e_meanage = np.round(fc_e_meanage, 0)\n",
    "fc_ne_meanage = np.round(fc_ne_meanage, 0)\n",
    "mc_e_meanage = np.round(mc_e_meanage, 0)\n",
    "mc_ne_meanage = np.round(mc_ne_meanage, 0)\n",
    "\n",
    "print(fc_e_meanage)\n",
    "print(fc_ne_meanage)\n",
    "print(mc_e_meanage)\n",
    "print(mc_ne_meanage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b569acaa",
   "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>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>HTN</th>\n",
       "      <th>DM</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>sex</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>Language</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, patient_id, height, weight, pain_count, nida, phq2, hark, a1c, obg, HTN, DM, systolic, diastolic, sex, language, age, Language, Gender, Age Group]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp[comp[\"weight\"]<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f54ebda8",
   "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>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>HTN</th>\n",
       "      <th>DM</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>sex</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>Language</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>1561700</td>\n",
       "      <td>70.9</td>\n",
       "      <td>407.372222</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.727273</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>133.764706</td>\n",
       "      <td>81.058824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>1163</td>\n",
       "      <td>1566259</td>\n",
       "      <td>70.0</td>\n",
       "      <td>441.738462</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>162.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.538462</td>\n",
       "      <td>73.230769</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>1566467</td>\n",
       "      <td>70.0</td>\n",
       "      <td>400.266667</td>\n",
       "      <td>5.466667</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.5</td>\n",
       "      <td>87.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>1479</td>\n",
       "      <td>1566694</td>\n",
       "      <td>70.0</td>\n",
       "      <td>467.460000</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.727273</td>\n",
       "      <td>84.545455</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  patient_id  height      weight  pain_count      nida  phq2  hark  \\\n",
       "297     298     1561700    70.9  407.372222    6.000000  7.727273   6.0   0.0   \n",
       "1162   1163     1566259    70.0  441.738462    5.000000  0.666667   NaN   NaN   \n",
       "1308   1309     1566467    70.0  400.266667    5.466667  4.166667   4.5   NaN   \n",
       "1478   1479     1566694    70.0  467.460000    2.833333  0.500000   3.0   NaN   \n",
       "\n",
       "      a1c         obg  HTN   DM    systolic  diastolic  sex  language  age  \\\n",
       "297   5.0  106.000000  0.0  1.0  133.764706  81.058824    0         0   39   \n",
       "1162  6.0  162.833333  1.0  1.0  140.538462  73.230769    0         0   69   \n",
       "1308  NaN         NaN  1.0  0.0       143.5  87.428571    0         0   41   \n",
       "1478  NaN         NaN  1.0  0.0  136.727273  84.545455    0         0   57   \n",
       "\n",
       "             Language Gender  Age Group  \n",
       "297   English-speaker   Male  18+ years  \n",
       "1162  English-speaker   Male  18+ years  \n",
       "1308  English-speaker   Male  18+ years  \n",
       "1478  English-speaker   Male  18+ years  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp[comp[\"weight\"]>400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc1a7901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 887.75x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 887.75x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_e_meanw = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"18+ years\")][\"weight\"].mean()\n",
    "f_ne_meanw = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"18+ years\")][\"weight\"].mean()\n",
    "\n",
    "m_e_meanw = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"18+ years\")][\"weight\"].mean()\n",
    "m_ne_meanw = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"18+ years\")][\"weight\"].mean()\n",
    "\n",
    "fc_e_meanw = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"weight\"].mean()\n",
    "fc_ne_meanw = comp[(comp[\"sex\"]!=0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"weight\"].mean()\n",
    "\n",
    "mc_e_meanw = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==0) & (comp[\"Age Group\"]==\"< 18 years\")][\"weight\"].mean()\n",
    "mc_ne_meanw = comp[(comp[\"sex\"]==0) & (comp[\"language\"]==1) & (comp[\"Age Group\"]==\"< 18 years\")][\"weight\"].mean()\n",
    "\n",
    "f_e_meanw = np.round(f_e_meanw, 0)\n",
    "f_ne_meanw = np.round(f_ne_meanw, 0)\n",
    "m_e_meanw = np.round(m_e_meanw, 0)\n",
    "m_ne_meanw = np.round(m_ne_meanw, 0)\n",
    "\n",
    "fc_e_meanw = np.round(fc_e_meanw, 0)\n",
    "fc_ne_meanw = np.round(fc_ne_meanw, 0)\n",
    "mc_e_meanw = np.round(mc_e_meanw, 0)\n",
    "mc_ne_meanw = np.round(mc_ne_meanw, 0)\n",
    "\n",
    "w1 = sb.displot(data=comp1, x=\"weight\", hue=\"Language\", col=\"Gender\", kind=\"kde\")\n",
    "w1.set_titles(\"{col_name} - Adults\")\n",
    "line_position1 = [[f_e_meanw, f_ne_meanw], [m_e_meanw, m_ne_meanw]]\n",
    "\n",
    "for ax, pos_list in zip(w1.axes.flat, line_position1):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "w2 = sb.displot(data=comp2, x=\"weight\", hue=\"Language\", col=\"Gender\", kind=\"kde\")\n",
    "w2.set_titles(\"{col_name} - Children\")\n",
    "line_position2 = [[fc_e_meanw, fc_ne_meanw], [mc_e_meanw, mc_ne_meanw]]\n",
    "\n",
    "for ax, pos_list in zip(w2.axes.flat, line_position2):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b35839b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188.0\n",
      "167.0\n",
      "\n",
      "158.0\n",
      "153.0\n",
      "\n",
      "49.0\n",
      "72.0\n",
      "\n",
      "67.0\n",
      "57.0\n"
     ]
    }
   ],
   "source": [
    "print(m_e_meanw)\n",
    "print(f_e_meanw)\n",
    "print()\n",
    "print(m_ne_meanw)\n",
    "print(f_ne_meanw)\n",
    "print()\n",
    "print(mc_e_meanw)\n",
    "print(fc_e_meanw)\n",
    "print()\n",
    "print(mc_ne_meanw)\n",
    "print(fc_ne_meanw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "55ba819e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "767.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"diastolic\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b6e16dd3",
   "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>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>HTN</th>\n",
       "      <th>DM</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>sex</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>Language</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>1566461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td>Not Male</td>\n",
       "      <td>18+ years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  patient_id  height  weight  pain_count  nida  phq2  hark  a1c  \\\n",
       "1304   1305     1566461     NaN     NaN         0.0   NaN   NaN   NaN  NaN   \n",
       "\n",
       "      obg  HTN   DM  systolic  diastolic  sex  language  age         Language  \\\n",
       "1304  NaN  0.0  0.0     124.0      767.0    1         0   44  English-speaker   \n",
       "\n",
       "        Gender  Age Group  \n",
       "1304  Not Male  18+ years  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"diastolic\"]==767]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "841e9493",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[1304, \"diastolic\"] = 76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "afd2fb8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"diastolic\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "10c80544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"systolic\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4381049a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.0\n",
      "47.0\n"
     ]
    }
   ],
   "source": [
    "print(comp1[\"systolic\"].min())\n",
    "print(comp1[\"diastolic\"].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "08a68ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>veteran</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_group 2</th>\n",
       "      <th>sex</th>\n",
       "      <th>housing</th>\n",
       "      <th>housing 2</th>\n",
       "      <th>...</th>\n",
       "      <th>mf_mhprovider</th>\n",
       "      <th>mf_mhprovider 2</th>\n",
       "      <th>avg_elix_op</th>\n",
       "      <th>avg_elix_ed</th>\n",
       "      <th>HSCS_ed</th>\n",
       "      <th>HSCS_ed_std</th>\n",
       "      <th>HSCS_op</th>\n",
       "      <th>HSCS_op_std</th>\n",
       "      <th>extreme_days</th>\n",
       "      <th>UMAP_KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>59</td>\n",
       "      <td>55-65</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>DOUBLE-UP</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.997914</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1558631</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>55-65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>OTHER/SRO</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0xa2702a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.307692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.834523</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1558651</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>46-55</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>OTHER/SRO</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1558659</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>46-55</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>OTHER/SRO</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0x15420400</td>\n",
       "      <td>2</td>\n",
       "      <td>0.432836</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.498767</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1558661</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>55-65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>OTHER/SRO</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2.947368</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.545268</td>\n",
       "      <td>2.363636</td>\n",
       "      <td>1.911687</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  patient_id  veteran  age age_group  age_group 2  \\\n",
       "0           0             0     1558625        0   59     55-65            3   \n",
       "1           1             1     1558631        0   62     55-65            3   \n",
       "2           2             2     1558651        0   55     46-55            2   \n",
       "3           3             3     1558659        0   55     46-55            2   \n",
       "4           4             4     1558661        0   65     55-65            3   \n",
       "\n",
       "   sex    housing  housing 2  ... mf_mhprovider  mf_mhprovider 2 avg_elix_op  \\\n",
       "0    1  DOUBLE-UP          3  ...           NaN               10    0.476190   \n",
       "1    0  OTHER/SRO          4  ...     0xa2702a4                0    1.307692   \n",
       "2    0  OTHER/SRO          4  ...           NaN               10    1.000000   \n",
       "3    0  OTHER/SRO          4  ...    0x15420400                2    0.432836   \n",
       "4    0  OTHER/SRO          4  ...           NaN               10    2.947368   \n",
       "\n",
       "   avg_elix_ed HSCS_ed  HSCS_ed_std   HSCS_op  HSCS_op_std  extreme_days  \\\n",
       "0     1.000000     3.0          NaN  0.937500     0.997914             3   \n",
       "1          NaN     NaN          NaN  1.125000     0.834523             1   \n",
       "2          NaN     NaN          NaN  1.000000          NaN             0   \n",
       "3     1.000000     1.0          NaN  0.965517     0.498767             5   \n",
       "4     4.222222     8.0     5.545268  2.363636     1.911687             2   \n",
       "\n",
       "   UMAP_KNN  \n",
       "0         5  \n",
       "1         0  \n",
       "2         2  \n",
       "3         5  \n",
       "4         3  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = pd.read_csv(\"../Data/aggregate_patients_base.csv\")\n",
    "base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b0afbf90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2183.000000\n",
       "mean      172.565447\n",
       "std        51.011099\n",
       "min         9.220000\n",
       "25%       143.705000\n",
       "50%       167.000000\n",
       "75%       199.250000\n",
       "max       467.300000\n",
       "Name: avg_weight, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[\"avg_weight\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b4908fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2185.000000\n",
       "mean       66.255228\n",
       "std         6.454869\n",
       "min        21.665000\n",
       "25%        64.000000\n",
       "50%        67.000000\n",
       "75%        70.000000\n",
       "max        84.000000\n",
       "Name: avg_height, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[\"avg_height\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a87a1121",
   "metadata": {},
   "outputs": [],
   "source": [
    "base.loc[base[\"avg_height\"]>84, \"avg_height\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "caf25d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    837\n",
       "3    420\n",
       "5    360\n",
       "0    289\n",
       "2    206\n",
       "4    153\n",
       "Name: UMAP_KNN, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl = pd.read_csv(\"../Data/Spectral_Clusters4.csv\")\n",
    "base[\"UMAP_KNN\"] = cl[\"UMAP_KNN\"].copy()\n",
    "base[\"UMAP_KNN\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e2f5b880",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">calc_BMI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UMAP_KNN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>275.0</td>\n",
       "      <td>29.308073</td>\n",
       "      <td>6.947936</td>\n",
       "      <td>16.00</td>\n",
       "      <td>23.9850</td>\n",
       "      <td>28.170</td>\n",
       "      <td>33.3000</td>\n",
       "      <td>60.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>791.0</td>\n",
       "      <td>26.788647</td>\n",
       "      <td>5.748292</td>\n",
       "      <td>14.48</td>\n",
       "      <td>22.9000</td>\n",
       "      <td>25.640</td>\n",
       "      <td>29.3750</td>\n",
       "      <td>57.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>197.0</td>\n",
       "      <td>25.735178</td>\n",
       "      <td>5.050027</td>\n",
       "      <td>14.69</td>\n",
       "      <td>22.1600</td>\n",
       "      <td>25.120</td>\n",
       "      <td>28.0200</td>\n",
       "      <td>47.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>408.0</td>\n",
       "      <td>28.735172</td>\n",
       "      <td>8.007004</td>\n",
       "      <td>13.37</td>\n",
       "      <td>23.8425</td>\n",
       "      <td>27.025</td>\n",
       "      <td>31.6500</td>\n",
       "      <td>78.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130.0</td>\n",
       "      <td>19.199231</td>\n",
       "      <td>5.522440</td>\n",
       "      <td>10.72</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>17.960</td>\n",
       "      <td>21.7425</td>\n",
       "      <td>38.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>354.0</td>\n",
       "      <td>29.211299</td>\n",
       "      <td>6.518696</td>\n",
       "      <td>15.36</td>\n",
       "      <td>24.8925</td>\n",
       "      <td>27.965</td>\n",
       "      <td>32.4475</td>\n",
       "      <td>63.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calc_BMI                                                             \n",
       "            count       mean       std    min      25%     50%      75%    max\n",
       "UMAP_KNN                                                                      \n",
       "0           275.0  29.308073  6.947936  16.00  23.9850  28.170  33.3000  60.91\n",
       "1           791.0  26.788647  5.748292  14.48  22.9000  25.640  29.3750  57.11\n",
       "2           197.0  25.735178  5.050027  14.69  22.1600  25.120  28.0200  47.69\n",
       "3           408.0  28.735172  8.007004  13.37  23.8425  27.025  31.6500  78.27\n",
       "4           130.0  19.199231  5.522440  10.72  14.9800  17.960  21.7425  38.67\n",
       "5           354.0  29.211299  6.518696  15.36  24.8925  27.965  32.4475  63.34"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = base[[\"UMAP_KNN\", \"calc_BMI\"]].groupby(\"UMAP_KNN\").describe()\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "53a2c055",
   "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>UMAP_KNN</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">calc_BMI</th>\n",
       "      <th>count</th>\n",
       "      <td>275.000000</td>\n",
       "      <td>791.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.308073</td>\n",
       "      <td>26.788647</td>\n",
       "      <td>25.735178</td>\n",
       "      <td>28.735172</td>\n",
       "      <td>19.199231</td>\n",
       "      <td>29.211299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.947936</td>\n",
       "      <td>5.748292</td>\n",
       "      <td>5.050027</td>\n",
       "      <td>8.007004</td>\n",
       "      <td>5.522440</td>\n",
       "      <td>6.518696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.480000</td>\n",
       "      <td>14.690000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>10.720000</td>\n",
       "      <td>15.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.985000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>22.160000</td>\n",
       "      <td>23.842500</td>\n",
       "      <td>14.980000</td>\n",
       "      <td>24.892500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.170000</td>\n",
       "      <td>25.640000</td>\n",
       "      <td>25.120000</td>\n",
       "      <td>27.025000</td>\n",
       "      <td>17.960000</td>\n",
       "      <td>27.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.300000</td>\n",
       "      <td>29.375000</td>\n",
       "      <td>28.020000</td>\n",
       "      <td>31.650000</td>\n",
       "      <td>21.742500</td>\n",
       "      <td>32.447500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.910000</td>\n",
       "      <td>57.110000</td>\n",
       "      <td>47.690000</td>\n",
       "      <td>78.270000</td>\n",
       "      <td>38.670000</td>\n",
       "      <td>63.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQR</th>\n",
       "      <td>9.315000</td>\n",
       "      <td>6.475000</td>\n",
       "      <td>5.860000</td>\n",
       "      <td>7.807500</td>\n",
       "      <td>6.762500</td>\n",
       "      <td>7.555000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "UMAP_KNN                 0           1           2           3           4  \\\n",
       "calc_BMI count  275.000000  791.000000  197.000000  408.000000  130.000000   \n",
       "         mean    29.308073   26.788647   25.735178   28.735172   19.199231   \n",
       "         std      6.947936    5.748292    5.050027    8.007004    5.522440   \n",
       "         min     16.000000   14.480000   14.690000   13.370000   10.720000   \n",
       "         25%     23.985000   22.900000   22.160000   23.842500   14.980000   \n",
       "         50%     28.170000   25.640000   25.120000   27.025000   17.960000   \n",
       "         75%     33.300000   29.375000   28.020000   31.650000   21.742500   \n",
       "         max     60.910000   57.110000   47.690000   78.270000   38.670000   \n",
       "         IQR      9.315000    6.475000    5.860000    7.807500    6.762500   \n",
       "\n",
       "UMAP_KNN                 5  \n",
       "calc_BMI count  354.000000  \n",
       "         mean    29.211299  \n",
       "         std      6.518696  \n",
       "         min     15.360000  \n",
       "         25%     24.892500  \n",
       "         50%     27.965000  \n",
       "         75%     32.447500  \n",
       "         max     63.340000  \n",
       "         IQR      7.555000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[\"calc_BMI\", \"IQR\"] = w[\"calc_BMI\", \"75%\"] - w[\"calc_BMI\", \"25%\"]\n",
    "w.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c394de5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sys_mean = comp1[comp1[\"HTN\"]==0][\"systolic\"].mean()\n",
    "dia_mean = comp1[comp1[\"HTN\"]==0][\"diastolic\"].mean()\n",
    "\n",
    "sys_mean_htn = comp1[comp1[\"HTN\"]==1][\"systolic\"].mean()\n",
    "dia_mean_htn = comp1[comp1[\"HTN\"]==1][\"diastolic\"].mean()\n",
    "\n",
    "sys_mean = np.round(sys_mean, 0)\n",
    "dia_mean = np.round(dia_mean, 0)\n",
    "\n",
    "sys_mean_htn = np.round(sys_mean_htn, 0)\n",
    "dia_mean_htn = np.round(dia_mean_htn, 0)\n",
    "\n",
    "bp1 = sb.displot(data=comp1, x=\"systolic\", hue=\"HTN\", kind=\"kde\")\n",
    "line_position1 = [[sys_mean, sys_mean_htn]]\n",
    "\n",
    "for ax, pos_list in zip(bp1.axes.flat, line_position1):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "bp2 = sb.displot(data=comp1, x=\"diastolic\", hue=\"HTN\", kind=\"kde\")\n",
    "line_position2 = [[dia_mean, dia_mean_htn]]\n",
    "\n",
    "for ax, pos_list in zip(bp2.axes.flat, line_position2):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6ae7c047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "542"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"HTN\"]==1][\"HTN\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8bdc5144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(391, 95)\n",
      "(783, 95)\n",
      "(57, 95)\n"
     ]
    }
   ],
   "source": [
    "pain_both = base[((base[\"PAIN_op\"]>0) | (base[\"PAIN_ed\"]>0)) & (base[\"avg_pain_ct\"]>4)].copy().reset_index()\n",
    "pain_dx = base[(base[\"PAIN_op\"]>0) | (base[\"PAIN_ed\"]>0)].copy().reset_index()\n",
    "pain_complaint = base[((base[\"PAIN_op\"]==0) & (base[\"PAIN_ed\"]==0)) & (base[\"avg_pain_ct\"]>4)].copy().reset_index()\n",
    "\n",
    "print(pain_both.shape)\n",
    "print(pain_dx.shape)\n",
    "print(pain_complaint.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7c6499bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx[\"PAIN\"] = (pain_dx[\"PAIN_ed\"] + pain_dx[\"PAIN_op\"])/2\n",
    "pain_dx[\"PAIN\"] = pain_dx[\"PAIN\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "afbdbff0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pain_dx[\"SENSORY\"] = (pain_dx[\"SENS_ed\"] + pain_dx[\"SENS_op\"])/2\n",
    "pain_dx[\"SENSORY\"] = pain_dx[\"SENSORY\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4f12af28",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_sens = pain_dx[(pain_dx[\"PAIN\"]>0) & (pain_dx[\"SENSORY\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "222499f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx[\"RESPIRATORY\"] = (pain_dx[\"URI/PNA_ed\"] + pain_dx[\"URI/PNA_op\"])/2\n",
    "pain_dx[\"RESPIRATORY\"] = pain_dx[\"RESPIRATORY\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "074bd19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_resp = pain_dx[(pain_dx[\"PAIN\"]>0) & (pain_dx[\"RESPIRATORY\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6eed6248",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx[\"INFECTION\"] = (pain_dx[\"INF_ed\"] + pain_dx[\"INF_op\"])/2\n",
    "pain_dx[\"INFECTION\"] = pain_dx[\"INFECTION\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "834209d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_inf = pain_dx[(pain_dx[\"PAIN\"]>0) & (pain_dx[\"INFECTION\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "277c22bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx[\"DIABETES\"] = (pain_dx[\"DM_ed\"] + pain_dx[\"DM_op\"])/2\n",
    "pain_dx[\"DIABETES\"] = pain_dx[\"DIABETES\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "672ca647",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dm = pain_dx[(pain_dx[\"PAIN\"]>0) & (pain_dx[\"DIABETES\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f0a02de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx[\"HYPERTENSION\"] = (pain_dx[\"HTN_ed\"] + pain_dx[\"HTN_op\"])/2\n",
    "pain_dx[\"HYPERTENSION\"] = pain_dx[\"HYPERTENSION\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2ede350f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_htn = pain_dx[(pain_dx[\"PAIN\"]>0) & (pain_dx[\"HYPERTENSION\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f69973d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_dx.loc[pain_dx[\"avg_pain_ct\"]>4, \"High Pain Count\"] = 1\n",
    "pain_dx.loc[pain_dx[\"avg_pain_ct\"]<5, \"High Pain Count\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "779952fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "sb.set_palette(\"tab10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "62c72bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeTUlEQVR4nO3df7BU5Z3n8feXn5dfEtSLg14JOlBuEAWp6y9MjMbVuMYC4rooSVaJWkwSfyWTqDi1qZiqZGUmTBxX3XEwKhIVy8XfBkzczBh1MxpAcSQQhYqoF24UNCJEELj3u39097Vpuvuc0326T3efz6uK8vY5p8/znNvl53nuc55+jrk7IiKSHv2SroCIiNSXgl9EJGUU/CIiKaPgFxFJGQW/iEjKDEi6AmEcfPDBPm7cuKSrISLSVFatWrXV3dsLtzdF8I8bN46VK1cmXQ0RkaZiZm8W266hHhGRlFHwi4ikjIJfRCRlmmKMX0Sa3549e+jq6mLXrl1JV6XltLW10dHRwcCBA0Mdr+AXkbro6upixIgRjBs3DjNLujotw91577336Orq4ogjjgj1Hg31iEhd7Nq1i4MOOkihHzMz46CDDor0l5SCX0TqRqFfG1F/rwp+EZGUqVnwm9ldZvauma3J23agmT1tZuuz/x1Vq/JFpPENHz58n9eLFi3iiiuuAOD2229n8eLFZd+ff3w5p512GkcddRSTJ0/mlFNO4bXXXit57ObNmzn//PND1P4Te/bsYd68eUyYMIFJkyZxwgknsHz58kjnCLJx40buv//+WM5Vyx7/IuDsgm3zgF+7+wTg19nXIiL7+cY3vsFFF10U2/nuu+8+XnnlFS6++GKuueaakscdeuihLF26NNK5v//979Pd3c2aNWtYs2YNTzzxBNu3b6+2yvtoiuB392eB9ws2zwDuyf58DzCzVuWLSHO74YYbWLBgAQArVqzg2GOP5eSTT+aaa65h0qRJfcdt3ryZs88+mwkTJnDttdcGnvfUU09lw4YNbNy4kc997nNMnTqVqVOn8tvf/hbIBGzu/IsWLeK8884re/6PPvqIO+64g1tuuYXBgwcDcMghhzBr1iwAlixZwjHHHMOkSZO47rrr+t6X/9fO0qVLmTNnDgBz5szhqquuYtq0aRx55JF9jdC8efN47rnnmDJlCjfddFPo32Mx9Z7OeYi7dwO4e7eZjS51oJnNBeYCjB07tk7VE5F62rlzJ1OmTOl7/f777zN9+vT9jvv617/OwoULmTZtGvPm7TtQsHr1al5++WUGDx7MUUcdxZVXXsnhhx9esswnnniCY445htGjR/P000/T1tbG+vXrmT17dtE1wYLOv2HDBsaOHcsBBxyw33s3b97Mddddx6pVqxg1ahRnnXUWjz76KDNnziz7e+nu7ub555/nD3/4A9OnT+f8889n/vz5LFiwgCeffLLse8No2Hn87r4QWAjQ2dmpBwOLtKAhQ4awevXqvteLFi3aL3w/+OADtm/fzrRp0wD4yle+sk/4nXHGGYwcORKAiRMn8uabbxYN/q9+9asMGTKEcePGccstt7Bnzx6uuOIKVq9eTf/+/Xn99deL1jHs+YtZsWIFp512Gu3t7X11ePbZZwODf+bMmfTr14+JEyfyzjvvhCorinoH/ztmNibb2x8DvFvn8kWkybiX7/flhlcA+vfvz969e4sed99999HZ2dn3+oYbbuCQQw7hlVdeobe3l7a2torOP378eN566y22b9/OiBEjQtc9fwpm4Rz8/DKDrr8S9Z7O+Thwcfbni4HH6ly+iDSZUaNGMWLECF544QUAHnjggVjOu23bNsaMGUO/fv34+c9/Tk9PT0XnGTp0KJdeeilXXXUVu3fvBjJDNffeey8nnngiv/nNb9i6dSs9PT0sWbKEz3/+80DmPsC6devo7e3lkUceCSxnxIgRsd0wruV0ziXAvwNHmVmXmV0KzAfONLP1wJnZ1yIiZd15553MnTuXk08+GXfvG3qpxre+9S3uueceTjrpJF5//XWGDRtW8bl+9KMf0d7ezsSJE5k0aRIzZ86kvb2dMWPGcOONN3L66aczefJkpk6dyowZMwCYP38+5557Ll/4whcYM2ZMYBnHHnssAwYMYPLkyVXf3LVa/BkRt87OTteDWESa27p16/jMZz5T0Xt37NjRNwtm/vz5dHd3c/PNN8dZvaZX7PdrZqvcvbPw2Ia9uSsikvOLX/yCG2+8kb179/LpT3+aRYsWJV2lpqbgF5GGd8EFF3DBBRckXY2WobV6RERSRsEvIpIyCn4RkZRR8IuIpIxu7opIU+v80dNs3bE7tvMdPHwQK//HmYHHPfXUU1x99dX09PRw2WWX7beGkLtz9dVXs2zZMoYOHcqiRYuYOnVqbPWshnr8ItLU4gz9sOfr6enh8ssvZ/ny5axdu5YlS5awdu3afY5Zvnw569evZ/369SxcuJBvfvObsdazGgp+EZGIfve73zF+/HiOPPJIBg0axIUXXshjj+27As1jjz3GRRddhJlx0kkn8cEHH9Dd3Z1Qjfel4BcRiWjTpk37rNDZ0dHBpk2bIh+TFAW/iEhExZa6KXzgeZhjkqLgFxGJqKOjg7fffrvvdVdXF4ceemjkY5Ki4BcRiej4449n/fr1vPHGG+zevZsHHnhgvyeHTZ8+ncWLF+PuvPDCC4wcOTLUKpz1oOmcItLUDh4+KPbpnEEGDBjArbfeyhe/+EV6enq45JJLOProo7n99tuBzIPizznnHJYtW8b48eMZOnQod999d2x1rJaWZRaRuqhmWWYJFmVZZg31iIikjIJfRCRlFPwiIimj4BcRSRkFv4hIyij4RURSRvP4RaS5/WQC/OXd+M43bDRcs77sIZdccglPPvkko0ePZs2aNfvtb+QlmUE9fhFpdnGGfsjzzZkzh6eeeqrk/kZekhkU/CIikZ166qkceOCBJfc38pLMoOAXEYldIy/JDAp+EZHYNfKSzKDgFxGJXSMvyQwKfhGR2DXyksyQ0HROM/sOcBngwKvA1919VxJ1EZEmN2x0/NM5A8yePZtnnnmGrVu30tHRwQ9/+EP27NkDNP6SzJBA8JvZYcBVwER332lmDwIXAovqXRcRaQEBc+5rYcmSJWX3mxm33XZbnWoTXVJDPQOAIWY2ABgKbE6oHiIiqVP34Hf3TcAC4C2gG9jm7r8qPM7M5prZSjNbuWXLlnpXU0SkZdU9+M1sFDADOAI4FBhmZl8rPM7dF7p7p7t3tre317uaIlIDzfDEv2YU9feaxFDPfwbecPct7r4HeBiYlkA9RKSO2traeO+99xT+MXN33nvvPdra2kK/J4lZPW8BJ5nZUGAncAagB+qKtLiOjg66urrQ0G382tra6OjoCH183YPf3V80s6XAS8Be4GVgYb3rISL1NXDgQI444oikqyEkNI/f3X8A/CCJskVE0k7f3BURSRkFv4hIyij4RURSRsEvIpIyCn4RkZRR8IuIpIyCX0QkZRT8IiIpo+AXEUkZBb+ISMoo+EVEUkbBLyKSMgp+EZGUUfCLiKSMgl9EJGUU/CIiKaPgFxFJGQW/iEjKKPhFRFJGwS8ikjIKfhGRlFHwi4ikjIJfRCRlFPwiIimj4BcRSRkFv4hIyij4RURSJpHgN7NPmdlSM/uDma0zs5OTqIeISBoNSKjcm4Gn3P18MxsEDE2oHiIiqVP34DezA4BTgTkA7r4b2F3veoiIpFUSQz1HAluAu83sZTP7mZkNKzzIzOaa2UozW7lly5b611JEpEUlEfwDgKnAP7v7ccBfgHmFB7n7QnfvdPfO9vb2etdRRKRllQ3+7LBM3LqALnd/Mft6KZmGQERE6iCox/+ymV0YZ4Hu/ifgbTM7KrvpDGBtnGWIiEhpQcH/BeACM3vazMbHWO6VwH1m9h/AFOB/xnhuEREpo+ysHnd/E/iymZ0N/D8zWwH05u2fXkmh7r4a6KzkvSIiUp3A6ZzZIZlrgeeA28gLfhERaT5lg9/M5gPTge+6+/L6VElERGopaIy/BziuMPTNrL+ZfbV21RIRkVoJCv5/AP7WzG41s7Ms40rgj8Cs2ldPRETiFjTGvxj4M/DvwGXANcAgYEb2Bq2IiDSZoOA/0t2PATCznwFbgbHuvr3mNZO6e/DFP5fcN+vEUXWsiYjUUlDw78n94O49ZvaGQr91lAv6UseqARBpfkHBP9nMPgQs+3pI3mt391os6SA1FiXwi71X4S/S3IK+wNW/XhWR2qsm8AvPo/AXaV6h1uM3s9OBowEHfu/uz9SyUhK/uEJfRJpf0Be4DgMeBnYBq8gM8cwysyHAl919U+2rKNVQ4ItIoaAe/61k1s1flL/RzC4C/jcwo0b1khgo9EWkmKAvcE0sDH0Ad18M/Kea1EhiodAXkVKCgr/ozV0z61dqnyRPoS8i5QQF/xNmdkf+M3GzP98OLKtpzaQiCn0RCRIU/NcC24A3zWyVma0CNgIfAt+rcd0kIoW+iIQRNI9/D/A9M/s+MJ7MrJ4N7v5RPSon4Sn0RSSsoIetH29mf+XuO939VTKPSVxiZv/LzA6sSw0lUL1DX1/eEmluQUM9/wLsBjCzU4H5ZFbs3AYsrG3VRESkFoLm8fd39/ezP18ALHT3h4CHzGx1TWsmoWiIR0SiCpzOaWa5xuEM4F/z9oVa7kFqJ4nQ1zCPSPMLCu8lwG/MbCuwk8wD1zGz8WSGeyQh6umLSKWCZvX82Mx+DYwBfuXunt3VD7iy1pWT4pIKffX2RVpD4HCNu79QZNvrtamOBFFPX0SqFbQ653YySzHDJw9j8ez7Brm7xvnrSKEvInEIGuoZkf/azEYA3wL+BnikhvWSAkmHvoZ5RFpH2AexfAr4NnARcD9wvLu/V7tqSU7SgS8irSdoqOdg4Ltk5vDfBRzn7prNUycKfRGphaAe/5vAFuBu4CPgUjPr2+nuP620YDPrD6wENrn7uZWepxU1WuBrmEektQQF/0/45ObuiHIHVuBqYB1wQMznbVqNFvgi0pqCbu7eUItCzawD+BLwY+Bva1FGs1Hoi0i9BK3O+WDez39fsO9XVZT7T2TW+u8tU/ZcM1tpZiu3bNlSRVGN7cEX/6zQF5G6ClqrZ0Lez2cW7GuvpEAzOxd4191XlTvO3Re6e6e7d7a3V1RUQ1Pgi0hSgsb4vcJ95ZwCTDezc4A24AAzu9fdv1bh+ZqKwl5EkhYU/EPN7DgyfxkMMbOp2e0GDKmkQHe/HrgewMxOA76n0BcRqZ+g4O8G/pFM0P8JWJC370+1qlSrUeCLSCMJCv7rgLfdvRvAzC4G/iuZB67fUG3h7v4M8Ey152lktQ79XXut5L62AZWOxolIKwu6uXs78DH0PXrxRuAe9OjFQLW8ebtrr/X9C3OciEg+PXqxBmoR+NUE+K69VnHvX9/aFWk9gcFvZgPcfS+ZRy/OjfDe1Ik78OPsrVcT/iLSWvToxZjEFfoamhGRWtOjF2MQR+jXI/DV6xcR0KMXq1Zt6KuHLyL1pnH6KlQT+s0Q+LqxK9KaFPwVqjT0myHwRaS1Bc3jlyIU+iLSzBT8EaUl9DXMI9K6NNQTQSWh30iBrxk9IgLq8YfW7KEfhXr7Iq1NwR9CmkJfRFqfgj9Aq4R+2GEe9fZFWp+CP2aNGPoiIvkU/GVE7e0r9EWkGSj4S0jjU7M0zCOSDgr+mDRyb1/TOEUkn+bxF5HUEM/2PeHa4REDe2MpT0TSScFfpWpCP2zQl3qfGgARqYSCv0CU3n4loV9p2Jc7V1ADoHX4RSSfxvgrFCX0t+/p1/evFuI6bxpvaIukkXr8ecIGX9jQr1XQlyorzNDPOR/8ouz+Hb8svn34F79WSbVEpAEp+CMKE/r1DPzCci/++PGanHvHL+/db5saA5HmpODPCtPbDwr9XOBXGr43MbPo9gMH79uTr1W4R5XfGKgREGkeCv4qnbfjydjO9R0eLb7j49iKqJlcI6AGQKTxKfiJ3tuPM+zrxXbvjPweHzQk8nvUAIg0vroHv5kdDiwG/groBRa6+831rkcUudBvlsCvJOSDzlNJIyAijSmJHv9e4Lvu/pKZjQBWmdnT7r42gboE9vZ37bWmCPy4wr7c+aOE/45f3qtev0iDqvv0E3fvdveXsj9vB9YBh9W7HmE0eujb7p19/+pVnog0v0TH+M1sHHAc8GKRfXOBuQBjx46tSfnlevtxhX7YsAzqTSt0RSQuiX1z18yGAw8B33b3Dwv3u/tCd+9098729vbYyw8K/WpU0hPPf0+xf81I3wQWaUyJ9PjNbCCZ0L/P3R9Oog6lVHMjt1kDOqxKbvDmwl9r/Ys0jiRm9RhwJ7DO3X9a7/KhdE+0mp5+K4d+JYG/7FNf2ud1/u88TCNQ7JvC+XTjWKRySfT4TwH+O/Cqma3Obvs7d19Wj8IV+tHEEfqFcp9B0LpB5WgJCZHK1T343f15oKEeV1UY+lGGeVo19Cudtx8U+tWEfRAtISESTqq+uVust18Y+lEWWKsm9Pvt3Bbp+N4hIysuK4pmDPxi9D0CkdJSE/xhZphEWd0ySuhHDfmo54ijUahV4EP9Qz9Hy0eIFJea4C8mv7cfNvTrHfhRyqm0AYga+g8PPzf0E72SCn0RKS0VT+AKM8QTRpjQ77dzW9+/equkzEpCH+J7wHw9BM0QEkmblg/+sOP6Qb39oNBPKuyL1SOsKKH/8PBz+0I/R+Ev0pxaPvgL1Sr0G0k969NM4S8iGS0d/EE3dKsN/Th6+f13bNnnX1zC1C3s/YrCnn6hZgl/9fpFMlr25m7QEE9Q6AcFfhRRAj3o2J7h0dYt6rdzW9Wzfs7b8WTo8A970zcpmuYp0uI9/nyFvdJahn6tevGF5w6rXJ3D9vrDfqmtWXr/ImnWkj3+oCGeciFWKgjDBn495ZcX9JdAuZ5/2Ies5P/eyv0F0OjPMVCvX9Ku5YI/aIgn7tCvNOz77egqu793eEek8+XqUa4BKDffP3ftYWf6NHKwi0h5LRf85cQZ+mECPyjcK3lvUIMQVwMAes6uSKtqqeCv9ItacYZ+NWEfRuH5SzUEYYaB8q8vqBHIaYXGQMM8knYtFfyFwg7xFBM19IMCv9+21ZHKz9c7ckrp8+aVG1cjAKWXfyhsDFqhIRBJm5YJ/kof81esV1ss9KMGfjVBH+ZcxRqDqI0AxN8QQGM3Burti7RQ8BeqtLcfNvSLBX65sP/oow2h65Bv6NDxRbfnlxXUCED9GgJo3MZAoS+S0RLBH1dvv9LQLxb4lQZ9mPMUNgaF5de7IQjzBbEkGwMFvsi+WiL4KxHmi0uFIVht4G/9S0+4ygEHD+tfcl9+GcX+Igj6awDqe5O4lKhTSKNS4IsU15LBX8kwT9RlGApDv1TgRwn7cu8r1RDkyq10SKjvuAj3B4KmikZdIiLsF8jCUuCLlNf0wV/pME+Qcr39MKFfaeCXsvUvPaH+CijVAMAn9S7XAEDmWst9X6D/ji2xh38cFPgi4bTcWj2VzNsP6u1HnZsfd+jHfd4wM45q/X2EuCn0RcJr+h5/OVEenJ4vyjIMcd3ETbNqhnkU+CLRNXXw12qYp2d4e+jwHzp0/H7hf/Cw/rH3+ssN8+TXJS5R1wqqVKWhr8AXqVxTB3+99A7viDz0EVf4xxn4QWP7fceVCf2gVUDDju0r8EWS01LBH3YteB80ZJ9x/t4hIyPN6ukdOWWfcfJc8Bbr+Rcq1hiECfd8cQc9hOvhxxH6CnyR5DVt8Id5rGIUheFfONyTC8Zczz8XqsUaACg99h8l5GsR8BBtGCfJsAcFvkgtNG3wh3XP4OlFn7ZV2OuH4uEPhGoAcnINQazj7TUM9kJhHu2osBdpbokEv5mdDdwM9Ad+5u7za1leufAHAod9yjUAUPDlp4ghHVW1N12jPrMXah/0oLAXqae6B7+Z9QduA84EuoAVZva4u6+tZbk3MZPv8GjRfcXG/HOK/QWQk2sI6jUDpphKgrycWt+cBYW8SNKS6PGfAGxw9z8CmNkDwAygpsEPmfAHijYA+UEWphGA8qFb6SMZ4w5yiLZ+TjG6ISvSWpII/sOAt/NedwEn1rMC1+8+D4AbBz1cdH+YRiBfsRlBSfXEK1HtMM2yT32JWSeOiqk2IlJrSQR/sTmXvt9BZnOBuQBjx46tSUWu330eG3dlZv8sOWBp0WOKhWKxm8JJqtfyxqUCflZdSheRuCQR/F3A4XmvO4DNhQe5+0JgIUBnZ+d+DUM1Rg/q5d3dmcAf19bLxl39mP3h+QC8vXM3AM8fsv/N4L66NcBDRWohqOeugBdpDUkE/wpggpkdAWwCLgS+EnchIwb27jOX/8DBvbz/cfDc/sOHDOLtnbv57DvT99tXrjFI2sPDz93ndduA8G1lLuwV7CLpUPfgd/e9ZnYF8Esy0znvcvff17sehb1+oG/Y5/Ahg4BPev85xRqDsHLnzJcrt7BehQ4cvP82yDRuxYQJfY3Ji6RXIvP43X0ZsKze5Rb2+vPDHz4Z9snJD+vCRqCcYiGfX0YxCnwRqZeW+uZu2wDfZ72ewuGeYoqFP7BPAwDlw7ycUkGfK7tQqbCHygNfYS8i+Voq+MMoNtafC+BiDUC+wsYgX7mALyynVL2KUe9eROLWtME/68RRRRdqC9Prz4VsmAYgX5hwL3a+UmrRuwcFvoiU17TBH0WpIZ9SM32CArtS5YIeSoc9KPBFJD4tGfyFvX4oH/45YaZ7RhEU9Ll6lRJ2SqYCX0SiaOrgLzXcA6XDH0qv1V8sqMs1BmGCvVC5oAeFvYjUXlMHP0QPfwhuAPJVEu7Fyiqnki9biYhUqumDP0guVMs1ADlRn9oVdL4w9QpDYS8icWqJ4C/X688p1wDkRAnuqKIEPSjsRaR2WiL4IVz4Q7gGoFpRQz5HYS8i9dAywQ+fBGeUBqBQmAah0mAvpKAXkSS0VPDnRGkACsUV6qUo7EUkaS0Z/DnVNABxli8i0khaOvhz8gO4Vo2AQl5EmkUqgj+fAlpE0i7eNQpERKThKfhFRFLG3Gs7iyUOZrYFeDPpemQdDGxNuhJ1kIbrTMM1QjquU9dY3Kfdvb1wY1MEfyMxs5Xu3pl0PWotDdeZhmuEdFynrjEaDfWIiKSMgl9EJGUU/NEtTLoCdZKG60zDNUI6rlPXGIHG+EVEUkY9fhGRlFHwi4ikjII/AjM728xeM7MNZjYv6frUgpltNLNXzWy1ma1Muj5xMbO7zOxdM1uTt+1AM3vazNZn/9vU63mUuMYbzGxT9vNcbWbnJFnHapnZ4Wb2b2a2zsx+b2ZXZ7e32mdZ6jpj+Tw1xh+SmfUHXgfOBLqAFcBsd1+baMViZmYbgU53b6kvw5jZqcAOYLG7T8pu+wfgfXefn23IR7n7dUnWsxolrvEGYIe7L0iybnExszHAGHd/ycxGAKuAmcAcWuuzLHWds4jh81SPP7wTgA3u/kd33w08AMxIuE4Skrs/C7xfsHkGcE/253vI/I/VtEpcY0tx9253fyn783ZgHXAYrfdZlrrOWCj4wzsMeDvvdRcxfhANxIFfmdkqM5ubdGVq7BB374bM/2jA6ITrUytXmNl/ZIeCmnoIJJ+ZjQOOA16khT/LguuEGD5PBX94xZ7J2IrjZKe4+1TgvwCXZ4cPpHn9M/DXwBSgG/jHRGsTEzMbDjwEfNvdP0y6PrVS5Dpj+TwV/OF1AYfnve4ANidUl5px983Z/74LPEJmiKtVvZMdS82Nqb6bcH1i5+7vuHuPu/cCd9ACn6eZDSQThve5+8PZzS33WRa7zrg+TwV/eCuACWZ2hJkNAi4EHk+4TrEys2HZG0mY2TDgLGBN+Xc1tceBi7M/Xww8lmBdaiIXhllfpsk/TzMz4E5gnbv/NG9XS32Wpa4zrs9Ts3oiyE6d+iegP3CXu/842RrFy8yOJNPLh8zT2e5vlWs0syXAaWSWtn0H+AHwKPAgMBZ4C/hv7t60N0dLXONpZIYFHNgI/E1uLLwZmdlngeeAV4He7Oa/IzP+3UqfZanrnE0Mn6eCX0QkZTTUIyKSMgp+EZGUUfCLiKSMgl9EJGUU/CIiKaPgFynCzHqyqx+uMbP/Y2ZDs9sHmNlWM7ux4PhnzKwz+/NGM3sob9/5ZraorhcgUoaCX6S4ne4+JbvK5W7gG9ntZwGvAbOyX7IppdPMjq51JUUqoeAXCfYcMD7782zgZjJfEjqpzHsWkPnCjUjDUfCLlGFmA8gsWPeqmQ0BzgCeBJaQaQRKeRCYambjyxwjkggFv0hxQ8xsNbCSTO/+TuBc4N/c/SMyi2d9OfuAnmJ6gJ8A19ehriKRDEi6AiINaqe7T8nfYGazgVOyTykDOAg4Hfi/Jc7xczLB//sa1VGkIurxi4RgZgcAnwXGuvs4dx8HXE6Z4R533wPcBHy7HnUUCUvBLxLOecC/uvvHedseA6ab2eAy77sT/WUtDUarc4qIpIx6/CIiKaPgFxFJGQW/iEjKKPhFRFJGwS8ikjIKfhGRlFHwi4ikzP8HSLv5d3RR8AcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/4H/EWzSRnhQSIskIa3r6HHCAu79hZiOAFcAR7v54/EUTeY9CQiQ5YTWKd939DQB3fxl4QSEhjaaQEElWWI2izcy+W/B+SOF7d78snmJJ0u544s2y66ZMKDk7fKqoI1skOmFBcUXR+8VxFUTSISggkqDahEjyAoPC3bunBMfMBuYWaUhsVqUtJGql2oRItEJnjzWzi8zsZeAl4GUze8nMPh9/0aSRFBIiUk7Yndn/ChwGTHT3F/PL9gOuNbPd3f0bDSijxCgrAQEKCZG4hNUoPgOc3B0SAPnXU4Cz4yyYxK+WkEhrR7ZCQiQ+oU1P7v5uiWUbeG9yQGkRSYREWAAMPPYshYRIzMJGPXWY2VHu/uvChWb2cUATBDaxamsTSdYkFAQiyQoLisuAu83sMXJDYx34CHA4cFLMZZOYNFNIiEjywh5c9CwwGngEGAnsl389Wg8Tag0KCREJG/X0kLsfA9zYoPJIiigkRATCm54GN6QUkirFAVHu7mj1HYi0hrCg2CXo2djuXvZRqNJ8CgOi0ocDgQJDJOtCgwI4AbAS6wKfmS3pFdSkVMvcSnqanEi2hQXFS+5+XkNKIomqd/I9hYVIdoXdcFeqJoGZ9TWzU2s5oJkdYGZLCn7eNrMvFG0z0czeKtjmq7UcS8Ktf/AWzdAqIoHCahSf6X5hZr2BY4CpwLHAo8DPqj2guz9P7lnc3ftcBdxVYtNH3f2EavcvlVNAiEglwu6jWGpmR5jZbGAlcAG5sNjX3U+J4PhHAX9195ci2JdUIY6QUPCIZFNgUJhZBzAT+D1woLt/Gtjg7u9EdPzTgXll1h1qZk+Z2QIz+1BAGaeb2SIzW7R69eqIipVtcV3Q1Uchkk1hfRQ/B4YDpwGfNLMB5EY71c3MdgROpHTz1ZPAPu4+BrgO+GW5/bj7HHdvd/f2wYN120cY/dUvItUKa3q6nNzUHd8GjgReAAab2ZT8E+/qcTzwpLu/WuK4b7v7+vzr+cAOZrZnncdreQoJEalFJdOMu7v/l7t/llxonAlMJtdnUY+plGl2MrP3mZnlX4/Pl/P1Oo/X0hQSIlKrsFFPPbj7JuAeM3sB2O45FZUys/7A0cDnCpZdmD/GbOAU4CIz2wxsAE5390iavEREpDphkwIeDMwChpHrJ7gO+E9gAvAftR403xm+R9Gy2QWvrweur3X/zarc9N/1Ts7XiNqEOrJFsius6emHwG3Ap4HV5DqZXwT2d/drYi5by7jjiTcDnxGRpedai0jzCWt62snd5+ZfP29mXwRmuPuWeIvVOuIOAdUmRKReYUHR18w+zHtTeawHDu7uaHb3J+MsXJZlpZagkBDJvrCg+Bu5obGl3jvw8TgKlXWNehSpRjqJSBQCg8LdJzaoHC0jKzUJUG1CpFWEjXoq+9Ai0IOLqlVLSKT1caStGBKF319avxeROIQ1PX0yYJ0eXFQFhURzKve93fHEm6n9fkSiFtb0dG6jCiI9pfUi1CohUUmwKyykVYQ1PU0A5gDvB54BznP3ZY0oWJY0qvM6Tq0SEJCtfiSRKIQ1PX0P+CLwCLmZXr9D7qFFEpMoQ2LgsWdFMvIpqZAIumDHFabVhEQaA10kDmFB0cvdH86//pmZfTnuArWqNF50kgiISi/UcTT7qCYhUlpYUOxaNPKpx3uNeopGnCFRS60izQFR/JmofndZaB4UiUtYUPyOniOfCt9r1FOFpkzYbbsLUSMvNJWGRRqbmFrh+CJpp1FPDZL0X6Bp7IxOwwU6S8OWReIS9szsT5rZPgXvv5p/jvU9ZrZv/MWTrEpDSNRCISGtKKzp6ZvAIQBmdgJwFrkn030YmI1GQEmVFBAizSfseRSef8gQwMnADe6+2N1/BAyOt2iSNQoJkeYUVqMwMxsIvAMcRe7pdt36xlYqyZxmDAkFhEhOWFB8B1gCvA0sc/dFAPlnVHTFWjLJjDSHRHcYdJdR4SCyvbBRTzea2YPAEOCpglV/AzQiKgGVXHTTdLGLMySiPM80/c5E0iZsrqez3P0WYJWZHQ78HsDdu8zsEuD6BpSxZdV6kU3yno2gcohIcwprevqfQPedWtcB4wrWnUeNQWFmK4F1wBZgs7u3F6034FpgErn+kWmt8NjVuC6sSTxHQSEhkh2hndllXpd6X60j3X1NmXXHA6PyPxOA7+f/zZxGX1DjDo1GnY+aikQaJywovMzrUu+jdBJws7s78LiZ7WpmQ909Mx3oafiLO+oO3DSck4hELywoPmBmT5OrPbw//5r8+/3qOK4DD5mZAz9w9zlF64cDrxS878gv2y4ozGw6MB1gxIgRdRSpMaK4mE5ae39V28/f9ROB66MIjEaGhGoTIo0VFhQfjOm4h7t7p5kNAR42s7+4+yMF60s1a5WsweRDZg5Ae3t7nLWcutRzIa02GMp9vpLAqOUirJAQybaw4bEvlVpuZr2B04GS68O4e2f+39fM7C5gPLmHI3XrAPYueN8GdNZyrKTVehGtNxzK7TPqsFBzk0j2hU0KuLOZfdnMrjezYyznUuBFYEotBzSzAWY2qPs1cAywtGize4Cz88c7BHirGfsnqr2ITlp7/7afuFSy72oeHtRIqk2IJCOs6eknwJvAH4ELgCuAHYGT3H1JjcfcC7grNwKWPsBt7v6AmV0I4O6zgfnkhsauIDc8tqlu7qslIBqpkppFGIWESOsIC4r93P0gADP7EbAGGOHu62o9oLu/CIwpsXx2wWsHLq71GEmq5gLa6ICoRrkmKDU1ibSesNljN3W/cPctwH/XExJZdscTb1Z8Ed2w2VIdEt2KzyepkFBtQiRZYTWKMWb2dv61Af3y743cH/47x1q6JlFNQHx6/X0xl6YylTY/JV2DUEiIJC9s1FPvRhWkWVVyId2wOTfaNy0h0SwUEiLpENb0JAEUEvFRSIikR1jTk5QRFhLdAQEKiWopJETSRUFRpWpqEVB7SNg/3glc7zv1r2m/haIYJhs1hYRI+igoqlBNLQKqD4mwcCi1bRSBISISRH0UFUpTSEQp7jvBq6HahEg6KSgqEGdI2D/eqSskogqYpANDISGSXmp6qlNxSFQjqVpEkErDIqq+DQWESPopKEIE1SZKhUSltYkoQ8L+8U7D+ypKBUq14aGQEGkOanoK0AwhkSbVNF8pJESah4KijGpDYt1G/Sq7hYWFQkKkuejqVkItITFt4z0V7Tuu2kTaainlahcKCZHmo6Ao0sw1ibSFBfSsXSgkRJpTeq5yTag7JJKuTRQfI22BMWnt/QoJkSamUU8Fqq1NpFlhWKTh7u31D94CwMBjzyq5PEzx50SkcRQUebU2OaWpNlHpsZMMjkqDodTnFBYiyVBQhEh7v0Qt0lbbEJF0U1AQ/1Pc0tZnUEihISJhGv6nsZntbWa/MbNlZvasmV1eYpuJZvaWmS3J/3w1rvLUM8qpkmanNIdEsTR2hBeqtdlKROqTRI1iM/C/3P1JMxsELDazh939uaLtHnX3ExIoX1mNaHLqtWFt4Pqt/XaNvQxpncJcfRQiyWh4ULh7F9CVf73OzJYBw4HioIhdPaOcoqpNhAVDtdtHGSRJzCElIumTaK+smY0EPgw8UWL1oWb2lJktMLMPBexjupktMrNFq1evjquokTc59dqwtuqQqET3fqPad5qbokSkMRILCjMbCPwc+IK7v120+klgH3cfA1wH/LLcftx9jru3u3v74MGDIylbvfdMBF1c4wqIOI+VhrBQs5NIchIJCjPbgVxI3Oruvyhe7+5vu/v6/Ov5wA5mtmeUZah1pFNYbaLcRbWRAZGmY4tI80ti1JMBNwDL3P3bZbZ5X347zGw8uXK+3rhS9lTpzXVBIZEG9ZQjyVqFahMiyUpi1NPhwGeAZ8xsSX7ZvwAjANx9NnAKcJGZbQY2AKe7u0dVgFo6sWsJiUouzL3/Xnu/ypYB0TS1VUqd2yKtKYlRT48BgZ0A7n49cH1jShSskiGx1YZEPeFQbj+VhkavDWsbMsQ2KqpNiCRPd2ZXIKg2URwS5QIiqnAop3v/cdcyGlmrmL/rJ+CJNzXzrEjCmnvSohpU2+xULiRK3cWcVEhUe6y09JkEKXz+dtxTrIhIMNUoApR7BnalTU3lLtq91nXUXKatg9pCt+n999Wx1izUVyHSWlquRlGpciFRSnFI9P776pIh0WtdR10hEdU+IN21isLaRDfVKkSSo6DIq/Qmu7DmprgCotp9xt3cFddw2VIhISLJaqmmp0r/Kq20ySksJMpdyHu9/XRF5QDYuvPBget7reuoqDmqGSgkRNKppYKiElGERKmAqCYcSn0uLDBq2nedQ2Wj7KtQSIikl5qeeK/ZKY6Q6PX20zWHRPF+yq6LuFmr0SoJCQ2RFUmOahQh6g2JcjZs+Ot2y/r1e39gWXq9/XRVNYu4Rz+JSGtQUORVMsqp0pAoFxClwqHc+nKhUW1YhEn6Tm01OYmkX8s0PZXryN6w2aq6X6JbNSGxYcNfQ0Oi1GfSrt7+iUpDQs1OIslSjaKMoCanSkMi6Yt9mpudqqlJBD0rW3NBicSvZWoU5VTb5BQ2uqlb0iGRZpWGxKS19zNp7f2B2wSFiIhEoyVqFEHNTqXUcjNZYW2iFUIizik8wsJBRBqrpWsUUdUmohj+WqlSHdn13HBXS0d2PSERVptQSIikT8sGRZS1iR77TUltIo39EwoJkebUskGR9tpEqeGxldYmKg2JRtYmNAxWpHllPihK9U/EVZuISj0hUam0hYRqEyLplfmgaDb1hkQltYm0hUQ9NDxWJH6ZHvVUbrRTLXdhN0KWQqIRTU0KCZHGaLkaRaXPnahV2HxN5T5Tb59EWEhs7bdr1SHhO/VvSEjozmuRdEskKMzsODN73sxWmNmMEuvNzL6bX/+0mY2LvUw19k+UupiXu/AXrw8KiCg7rtPc1FRrSKg2IdI4DW96MrPewPeAo4EOYKGZ3ePuzxVsdjwwKv8zAfh+/t+K1dPsFJVqaxflJvurtalJASEiUUiij2I8sMLdXwQws9uBk4DCoDgJuNndHXjczHY1s6Hu3lXPgeNqdtq688F1DZNt5oAAhYRI1iURFMOBVwred7B9baHUNsOB7YLCzKYD0wFGjBgRaUHL2Tqobbt5nrov9pUERtg04VkNCCgfEt0hUGruJgWESLKSCIpSf9Z7DdvkFrrPAeYAtLe3l9wmDqXCAmp/ZGnQPRFpCwiIrxahUBBJnySCogPYu+B9G9BZwzZlVXOTXaW2DBjc4+5sKB8WlQq7YS7qgIhiIj+NaBJpPUkExUJglJntC6wCTgfOKNrmHuCSfP/FBOCtevsnqrW1367b3UtRLiyKbdcsVcUd1GkMh27VhIQCQiQ7Gh4U7r7ZzC4BHgR6Aze6+7NmdmF+/WxgPjAJWAG8A5wbe7l26l/RENlSYVGs2qk1oh7iGvUU4HoSnUhrS+TObHefTy4MCpfNLnjtwMW17LvcsNhalKpVwHsX9rDAKCeOSfvieD5EUEAoFERaR6an8IhCubCAeKbyrjQc4nxwUHeH8pTYjiAizURBUaBc81P3xTuu+Z+SrjmARhuJSHktFxQ/H3hC4N3ZQX0VUQRGEkNZgyggRCRMywVFJcI6tmu52Fd7/LgoGESkWi0ZFGG1Cqh8FFRUFA4iklYtGRSVKrx4RxkaCgURaSYtExT9+niPu7MrqVUUKndxLw6QOEOgkAJBRBqlZYIC6g+LUhQMIpJ1LRUUpUQRFnFQMIhIWmQuKKZM2C3w7ux+fXITzBbXLKCxDzUqNn/XT+huZxFJpcwFRaWSDIzCqTG6w0F3QYtIWmUyKMJqFYW6A6PQ/F0/0SNA+vVxJq29v+Ljh02ip5qDiDSTTAYFVBcWpRQHSC1Pcysuj4hIM8psUED9YVHvsUVEsiDTQQGNCwsFg4hkVeaDAnpexKMIDYWCiLSSlgiKQrrIi4hUx3IPk8sGM1sNvNTAQ+4JrGng8ZLWaucLrXfOrXa+0HrnXHy++7h74FPYMhUUjWZmi9y9PelyNEqrnS+03jm32vlC651zLefbK67CiIhINigoREQkkIKiPnOSLkCDtdr5Quudc6udL7TeOVd9vuqjEBGRQKpRiIhIIAWFiIgEUlDUwMyOM7PnzWyFmc1IujyNYGYrzewZM1tiZouSLk8czOxGM3vNzJYWLNvdzB42s+X5fzNzx2aZ873KzFblv+clZjYpyTJGycz2NrPfmNkyM3vWzC7PL8/yd1zunKv6ntVHUSUz6w28ABwNdAALganu/lyiBYuZma0E2t09szcmmdkRwHrgZncfnV/278Ab7j4z/0fBbu5+ZZLljEqZ870KWO/us5IsWxzMbCgw1N2fNLNBwGJgMjCN7H7H5c55ClV8z6pRVG88sMLdX3T3jcDtwEkJl0ki4O6PAG8ULT4JuCn/+iZy/8kyocz5Zpa7d7n7k/nX64BlwHCy/R2XO+eqKCiqNxx4peB9BzX84puQAw+Z2WIzm550YRpoL3fvgtx/OmBIwuVphEvM7Ol801RmmmEKmdlI4MPAE7TId1x0zlDF96ygqJ6VWNYK7XeHu/s44Hjg4nyzhWTP94H3A2OBLuA/Ei1NDMxsIPBz4Avu/nbS5WmEEudc1fesoKheB7B3wfs2oDOhsjSMu3fm/30NuItcE1wreDXfztvd3vtawuWJlbu/6u5b3H0r8EMy9j2b2Q7kLpi3uvsv8osz/R2XOudqv2cFRfUWAqPMbF8z2xE4Hbgn4TLFyswG5DvCMLMBwDHA0uBPZcY9wDn51+cAdydYlth1XzDzPkWGvmczM+AGYJm7f7tgVWa/43LnXO33rFFPNcgPJfsO0Bu40d2/mWyJ4mVm+5GrRUDuGSa3ZfGczWweMJHcNMyvAv8G/BK4AxgBvAyc6u6Z6AAuc74TyTVHOLAS+Fx3+32zM7N/Ah4FngG25hf/C7k2+6x+x+XOeSpVfM8KChERCaSmJxERCaSgEBGRQAoKEREJpKAQEZFACgoREQmkoBCpg5ltyc++udTMfmZm/fPL+5jZGjO7umj735pZe/71SjP7ecG6U8xsbkNPQKQCCgqR+mxw97H52Vc3Ahfmlx8DPA9Myd/0VE67mX0o7kKK1ENBIRKdR4H986+nAteSu4HrkIDPzCJ3A5RIaikoRCJgZn3ITZj4jJn1A44C7gPmkQuNcu4AxpnZ/gHbiCRKQSFSn35mtgRYRK72cANwAvAbd3+H3GRsn8o/8KqULcC3gC83oKwiNemTdAFEmtwGdx9buMDMpgKH558KCLAHcCTwqzL7+Am5oHg2pjKK1EU1CpEImdnOwD8BI9x9pLuPBC4moPnJ3TcB1wBfaEQZRaqloBCJ1snAf7n7PwqW3Q2caGY7BXzuBlTDl5TS7LEiIhJINQoREQmkoBARkUAKChERCaSgEBGRQAoKEREJpKAQEZFACgoREQn0/wFm+fmvhBaFMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "paina = sb.kdeplot(data=pain_sens, x=\"PAIN\", y=\"SENSORY\", hue=\"High Pain Count\", fill=True, palette=\"tab10\")\n",
    "plt.show()\n",
    "pain2a = sb.kdeplot(data=pain_resp, x=\"PAIN\", y=\"RESPIRATORY\", hue=\"High Pain Count\", fill=True, palette=\"tab10\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cbaced94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oN/ybLfTrsWrkbCDY1BAKfZH41SoAFwEqAAHVE/6tEvxRhoJ6C0GWU0WL5FGtAtBmZiOpcMWPu78cf5Oaj4JfRJpRrQLw18BayhcAB06JvUVNJmr4Jx382/e3RX7vuEHdMbZERBpVrQLwrLu/I5WWNKFGCv96Ar/SttIuBCt+s1vDQCIpyvKWkE0tSvjHGfxxBn61fehoQKR11SoAt1R6wcz6u/uhmNvTFLIK/zRCv9w+VQREWlO/Gq8fueOXmd1V8tq/xt+cxpdF+G/f35ZJ+GdhxW92Zz5nkkhe1CoAQ4sev73ktdxdvpJ2+Ocp+EupCIgkL8xcQGFeE6KHf6OFflbDQKVFQCeIReJVqwCMMLPL6DlSGGFmHygsN+C4RFvWYML2SLMM/y37Kh/YnTj4cN3bz4oKgki8ahWAXwKzih5fWvTaE4m0qAGlEf71BH+1wK+0blKFIM0J4Xq/FxUCkWiqFgB3/1haDWlUjRr+YUK/0vub+WigmH4/IBJNzRQxs/ea2ZmFx1eY2TfN7HPFt4iU6MKG/5Z9/eoO/+JttQqdNBYJr+oRgJl9CzgTGGRmzwHDgEeBGcAdwEcSb2GGku79hwn/pMK6lY4ERCScWucAznf3yWY2CNgKjHX3bjP7LvBvQXZgZncAlwAvufuUwrLjgR8Ak4DNwBXu/kq0j9AYkgr/SsF/y8AfHrVswYEPhmqDiORbrQKwH8Dd95vZC+7eXXjuZnYw4D6WAd8E7ixathD4mbsvNrOFhec3hmp5wpIcUogS/uUCv1TpOioIIlJNrQIw1sz+Kz2XffY+pvB8TJAduPsTZjapZPFsYGbh8XLgcRqsAIQRpvcfNvyDBH8lve+tVQjiHAbK4taQIhJNrQJwGzC8zGOA/1PHfse5exeAu3eZ2dhKK5rZfGA+wMSJE+vYZXBZn1Dcsq9fXcFfKmghEJF8qXUZ6FcrvWZm74y/OWXbsBRYCtDR0dHUXcsgvf8vdD8AA5PZfz2FQJPCibSeUNNBm9lk4EPAXOBVoCPifreb2fhC73888FLE7WQuzimev9D9QGzbqqbcuQJdDSSSPzULgJmdRE/gzwUOAScBHe6+uY79/hiYBywu/F37ruApSWr4p1rvP63gr+RIQajVwX+98kt3DZ1z5HEW5wH0QzCR8Gr9DuDX9Mz5cy9wubtvNLO/hAl/M1tBzwnf0WbWCXyFnuC/z8w+DrwItPTgdCOHf1w++vpDfZ6vGjg7m4aISGC1jgB2AO3AOHqu+tlIyFlA3X1uhZcuCLOdVtQq4V/Opa+8eVC3amSyxUC9f5Foqv681N1nA2cAvwO+amZ/AUaa2VlpNK7RBRn/r9T7b+XwL3XpKyv7FIQ4KfxFoqs5v4C7v+rud7j7+4Gz6RnC+Scz25J461KW1uWfeQr/YkkVARGJJtQEM+6+3d3/2d1nAOcl1KaWUa73n9fw7xVnEVDvX6Q+tU4Cr6L6mP+sKq+JlHXpKyvrPi+g8BepX62TwEtSaUUTinL9f957/8XqKQIKf5F41Pol8C97H5vZmMKyHUk3qhWUDv80Qvi3vb4r1u11Dx0V6/aCUPiLxCfIDWG+YmY7gT8Bz5vZDjP7H8k3TaJoe31XxT9J7iuKsOcDFP4i8ap1DuBz9Jzsfae7/6Ww7BTg22b2OXe/OYU2Np00ev9JBHo9etuT1FGBwl8kfrWOAK4C5vaGP4C7/xn4u8JruRTn/D9hJNmbj0sSbVP4iySjVgEY4O47SxcWzgMMSKZJzS2p3n8jh36pONuq8BdJTq2rgA5EfE1i0kzBX6zt9V2BhoOqXQ3U7OEf5IeFzf4ZpbnVKgBTzey1MssNGJRAexpeteGfuHv/aYR/22vbyi7vPnZC4vuupJlDMeyvyVf8ZndTf15pbrUuAw12/8IWkPVdwErFHf6Vgj7M+mGLQtCjgGLNGob1/PejIiBZCXVDGKks6L1+0xI28MNsM6mjg2YMwbg6DioCkgUVgBDSuvqnnt5/EsFfbh9xF4FmC79GO2IUiSLUZHBSXrne/5Z90f5po4Z/22vbUgn/4v3FReEvkg0dAQQUpve/ZV+/o+67m5Q0Q7/cvrM8WZw2Bb+0GhUA6vsfO86x/zC9/yyDP48U/tKKVAACCNv7T1qU8G/b/XzNdbpH/FWU5tRl2MXzUt9nWGmEf7MNg0lrUAGoQ9q9/zDBHyTwq70naDFo9WEg9fylleX+JHCt/8Er9f7TPvEbNPzbdj8fKfzLbSfvFP7S6nQEEJMkh36ChL8CO15phr+GfyQruT4CiLP3X2zD/uD/rLV6/7XCP64ef6Vt19KKwz8Kf8mLTI8AzGwzsAfoBg65e0eW7YmquPcfJvxrqRb+zdDjrzUNRCOeANawj+RJIwwBnV9uyumsBe391zP0U6333+zhL7Wp9y9Zy+0QUBI9vd7e/0+P/UHNdRX+jUdDP5I3WR8BOPBTM3Pgu+6+tHQFM5sPzAeYOHFiKo1KuvcfJfyDBr+/+mTF1+y48wJtI6hq4/9Z3DC+WSj8pVFkXQDOdfdtZjYWeMzM/uTuTxSvUCgKSwE6Ojo8jp0m2fuvR9Twrxb6pevFXQSiarTx/7R6/wp/aSSZFgB331b4+yUzexA4C3ii+ruyUa33Xxz+tYZ/KvX+w4Z/0NAv976gRSCLXwa3MoW/NJrMzgGY2VAzG977GLgQWJ9Ve3olOeVz1uEf1/tBwz9hKfylEWV5BDAOeNDMettxj7s/mvROoxzqx9H7DxP+SQV/s9m7enmk9zXS8JKCXxpZZgXA3f8MTM1q/+WkdcOXXkHDP4vgrzb8E0fvf+/q5UcFddTAL7dtyL4QKPyl0WV9EjhVjdT7b+TwT0tcgV9t+1kUAQW/NIvc/g6gVD29/yTC3199MrPwT7r3n6agRSaO0J57zgiFvzSVXB0BhBWk959U+Jeze9/uivsqNmLwiEDr5UXSRwIKfWlWuSkAac/xEiX86w3+4vWTKALN1vsPqzfIg/y3otCXVpCbAlBN1OGfSr3/IOEfpNcfNvhL36sjgTeFOQpQuEte6BxABbWGf5IM/937dtcV/sXbCavS+H8rTvsskne5KABxD/8EmeytV5TwT0OcJ5hbYfhHJI9yPwQUZPjnqJO/A8uvV9r7rxb+YYL/5X0VdggcP/hAxdd6t6uhIBEpJ/cFoJxqd/wKOvRTb/hXC/1y69UqBPXQ8I9Ia2r5AhDn8M8tA39YdnnU8K8n+Mu9r94i0AiTv9W6RSZoyEkkLrk4B1BJmOGfoOF/1OsphH+t9yd5XiGuMG57fVeg8A+7rohU1vJHAGHVuuF7sVpX/AQN/3qDv5kpyEWy09IFIK7hn0q9/1Jxhv9/vDG64n7eMqTyLZTjGAoqluT4v8JfJFu5HQIKOvwTZdy/nvD/jzdGVw3/3nXqlfWdweIIfxUQkfrktgCUUzr8E/Wkb68w4R8k+EvXb1YKbpHGoAIQUtCTvmHDP2tRrgCKcgI4q/DP+t4AIo2oZQtAveP/X+h+INB6lYZ+eiUZ/kkXjrjH/+MOf10OKlKfli0A1ZQb/y8e/qkU/lHG/Ys1Ys8/LVkO+6j3L1JeLgtAFEEDrNLQT5bhXzoVROkJ4Eb4AZiIpE8FoERSQz/F4gr/apeD1qvW8E+Y4Zckev8a/hGpX0sWgGrj/7WGf8qJctVPsTR/6JXknECNIkz4a/hHpLKWLABRBe39F8uq9x9UlOGfOHv/WVL4i1SnAlBDo/b+yw3/qPcvImFkWgDM7CIze87MNpnZwizbElfvPw1Bw7/Vev8a+hGJV2YFwMzagG8BfwNMBuaa2eR6txt2/L+aKCcvk76jl8K/NoW/SDBZHgGcBWxy9z+7+wHgXmB22o3Yvr8t0pU/xcLcXjHqlTtvGbIz0fBPUj2Fo3voKIW/SEKyLAAnAFuKnncWlvVhZvPNbI2ZrdmxY0dqjaslreGfSsEPwcI/qKR7/1Her/F+kWRlOR10ufEYP2qB+1JgKUBHR8dRryclyV+u9gZ61CmfIVrPH7Id+ukeOirRO36p9y8STpYFoBM4seh5O1B+jCVBUU7+hnH84AMVrwSKMhxU6UqfRg//pLbXS+EvEl6WBeBp4DQzOxnYCnwI+HA9G4zzBHCpSuP/pUYMHpHIieCgwQ+NG/5JUfiLRJNZAXD3Q2Z2HfB/gTbgDnf/Y1btSVJveIf9TUCt6/qDhH+lE76tEP4KfpH6ZHpLSHdfDazOav8fff2hWLZjx53X50qgSkcBxYFeWgzC/Iirnl4/VA//Zgh+UPiLxKGl7wmcpVpDQWF/tVvp6p64gh8U/iJ5owIQUPexE/qcB+ge8Vd9LgUtPQqA+s8HVLuks9I9fVs5/BX8IvFSAYhRpSJQrFxBCHPtftzBDwp/kbxSAahD6VEAlC8CxaL8UKtS6Pe2oeJr6vWLSBUqAHWqVAQg3BQR5d5fbZ9VX2+BXr+CXyR5uSkAIwZ63b8FKD0PcGR5mSIAtYM81L4DzN+TVPAHCeO9q5eH3m7UfYlIPHJTAOJSrQhAvHMEBZ20LcvgL7dumGKgwBfJjgpABdXmralUBODo0A5SEKLMzhkk9CH54E/i/SKSDhWAiKoVgT7rxTj1ctDQh2jBv2rkbOaeMyL0+0SkOeWqAJSeB7hr6JyqvwauNXtlbyAHnScorDCBf+Q9dQT/3NDvFJFmlqsCEEWQKYyLgzpqMYgS9n3er+AXkZByVwCiXA0UdB57qD/Iw4h6KaeCX0Qg45vCxy3o+PWIgW/eV+auoXMCvadRrpvvvUVi1B7/sIvnaZxfRIAcHgH0inokAMneLazSPuuhHr+IlGPuqd1lsW4dHR2+Zs2amutVuzFMqd4iEHVq6LiKQdxHGKtGzgaCHxWJSOsys7Xu3lG6PLdHAL16h4PuovoVQZU0ytBQLwW/iATVkkcAEO4ooNilr6yM9L4sKfRFpJrcHQHMPWdEpCKwauTspigCxaGvsX0RiaJlCwDUVwSgsY4GetsECn0RiUdLFwCIXgSgb+imXQwU+CKStJYvAFBfEehVHMgQT0Eo3Sa8OY6vwBeRpOWiAEDfE6T1FgMoH95h6aStiGQpNwWgWG/wxlEIwuxPRKSRZFIAzGwR8F+AHYVFX3L31Wm3o1IwhykMCncRaVZZHgHc7O5LMtx/RQp1EcmDlpoMTkREgsuyAFxnZv9mZneY2cgM2yEikkuJFQAz+xczW1/mz2zg28BbgWlAF/D1KtuZb2ZrzGzNjh07Kq0mIiIhZT4XkJlNAh529ym11g0zF5CIiPSoNBdQJkNAZja+6OllwPos2iEikmdZXQX0D2Y2DXBgM/DJjNohIpJbmQ8BhWFmO4AXsm5HSKOBnVk3Imb6TM2jFT+XPlN4J7n7mNKFTVUAmpGZrSk39tbM9JmaRyt+Ln2m+Oh3ACIiOaUCICKSUyoAyVuadQMSoM/UPFrxc+kzxUTnAEREckpHACIiOaUCICKSUyoACTGzi8zsOTPbZGYLs25PXMxss5n9wczWmVlTzstRmIDwJTNbX7TseDN7zMw2Fv5uqgkKK3ymRWa2tfBdrTOzi7NsY1hmdqKZ/cLMNpjZH81sQWF5s39XlT5X6t+XzgEkwMzagOeB9wOdwNPAXHd/NtOGxcDMNgMd7t60P8Qxs/cAe4E7e+egMrN/AF5298WFgj3S3W/Msp1hVPhMi4C9jXrfjVoKU8aMd/ffmdlwYC0wB7ia5v6uKn2uK0j5+9IRQDLOAja5+5/d/QBwL1D/TYQlFu7+BPByyeLZwPLC4+X0/A/ZNCp8pqbm7l3u/rvC4z3ABuAEmv+7qvS5UqcCkIwTgC1FzzvJ6AtOgAM/NbO1ZjY/68bEaJy7d0HP/6DA2IzbE5eWuO9GYdbgdwC/pYW+q5LPBSl/XyoAybAyy1plrO1cd58O/A1wbWHoQRpT4PtuNDIzGwY8AHzW3V/Luj1xKfO5Uv++VACS0QmcWPS8HdiWUVti5e7bCn+/BDxIz3BXK9jeO0154e+XMm5P3dx9u7t3u/th4Daa8LsyswH0hOTd7v6jwuKm/67Kfa4svi8VgGQ8DZxmZieb2UDgQ8CPM25T3cxsaOGkFWY2FLiQ1rmXw4+BeYXH84CVGbYlFs1+3w0zM+B2YIO7f6Popab+rip9riy+L10FlJDCJVz/BLQBd7j7/8q2RfUzs1Po6fVDz70k7mnGz2VmK4CZ9EzBux34CvAQcB8wEXgR+KC7N81J1QqfaSY9wwlH7rvRO3beDMzsPOBXwB+Aw4XFX6JnvLyZv6tKn2suKX9fKgAiIjmlISARkZxSARARySkVABGRnFIBEBHJKRUAEZGcUgEQqcLMugszM643sx+a2ZDC8v5mttPMbipZ/3Ez6yg83mxmDxS9drmZLUv1A4hUoQIgUt0+d59WmGHzAHBNYfmFwHPAFYUf9lTSYWZvT7qRIlGoAIgE9yvg1MLjucAt9PwQ6ewq71lCz498RBqOCoBIAGbWn54J8P5gZoOBC4CHgRX0FINK7gOmm9mpVdYRyYQKgEh1g81sHbCGnt7+7cAlwC/c/Q16JvS6rHAToHK6gX8E/j6FtoqE0j/rBog0uH3uPq14gZnNBc4t3B0NYBRwPvAvFbZxFz0F4I8JtVEkEh0BiIRgZscC5wET3X2Su08CrqXKMJC7HwRuBj6bRhtFglIBEAnnA8DP3f3/FS1bCcwys2OqvO92dMQtDUazgYqI5JSOAEREckoFQEQkp1QARERySgVARCSnVABERHJKBUBEJKdUAEREcur/AzoppcilnvpVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pain3a = sb.kdeplot(data=pain_inf, x=\"PAIN\", y=\"INFECTION\", hue=\"High Pain Count\", fill=True, palette=\"tab10\")\n",
    "plt.show()\n",
    "pain4a = sb.kdeplot(data=pain_dm, x=\"PAIN\", y=\"DIABETES\", hue=\"High Pain Count\", fill=True, palette=\"tab10\")\n",
    "plt.show()\n",
    "pain5a = sb.kdeplot(data=pain_htn, x=\"PAIN\", y=\"HYPERTENSION\", hue=\"High Pain Count\", fill=True, palette=\"tab10\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "58c3bdf9",
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>veteran</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_group 2</th>\n",
       "      <th>sex</th>\n",
       "      <th>housing</th>\n",
       "      <th>housing 2</th>\n",
       "      <th>...</th>\n",
       "      <th>mf_mhprovider</th>\n",
       "      <th>mf_mhprovider 2</th>\n",
       "      <th>avg_elix_op</th>\n",
       "      <th>avg_elix_ed</th>\n",
       "      <th>HSCS_ed</th>\n",
       "      <th>HSCS_ed_std</th>\n",
       "      <th>HSCS_op</th>\n",
       "      <th>HSCS_op_std</th>\n",
       "      <th>extreme_days</th>\n",
       "      <th>UMAP_KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1304</td>\n",
       "      <td>1304</td>\n",
       "      <td>1566461</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0xa2702a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Unnamed: 0.1  patient_id  veteran  age age_group  \\\n",
       "1304        1304          1304     1566461        0   44     36-45   \n",
       "\n",
       "      age_group 2  sex  housing  housing 2  ... mf_mhprovider  \\\n",
       "1304            1    1  UNKNOWN          0  ...     0xa2702a4   \n",
       "\n",
       "      mf_mhprovider 2 avg_elix_op  avg_elix_ed HSCS_ed  HSCS_ed_std  HSCS_op  \\\n",
       "1304                0         1.0          NaN     NaN          NaN      2.0   \n",
       "\n",
       "      HSCS_op_std  extreme_days  UMAP_KNN  \n",
       "1304          NaN             1         1  \n",
       "\n",
       "[1 rows x 94 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[base[\"avg_diastolic_bp\"]>300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0ff3dff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "base.at[1304, \"avg_diastolic_bp\"] = 76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b75ae184",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">avg_diastolic_bp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UMAP_KNN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>281.0</td>\n",
       "      <td>76.494662</td>\n",
       "      <td>9.104991</td>\n",
       "      <td>54.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>813.0</td>\n",
       "      <td>74.687577</td>\n",
       "      <td>9.539434</td>\n",
       "      <td>47.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199.0</td>\n",
       "      <td>76.266332</td>\n",
       "      <td>10.640803</td>\n",
       "      <td>53.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>411.0</td>\n",
       "      <td>75.661800</td>\n",
       "      <td>9.412756</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118.0</td>\n",
       "      <td>63.627119</td>\n",
       "      <td>7.573906</td>\n",
       "      <td>41.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>358.0</td>\n",
       "      <td>77.656425</td>\n",
       "      <td>7.537533</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_diastolic_bp                                                     \n",
       "                    count       mean        std   min   25%   50%   75%    max\n",
       "UMAP_KNN                                                                      \n",
       "0                   281.0  76.494662   9.104991  54.0  70.0  76.0  82.0  109.0\n",
       "1                   813.0  74.687577   9.539434  47.0  68.0  74.0  80.0  114.0\n",
       "2                   199.0  76.266332  10.640803  53.0  69.0  75.0  83.0  109.0\n",
       "3                   411.0  75.661800   9.412756  50.0  69.0  75.0  82.0  104.0\n",
       "4                   118.0  63.627119   7.573906  41.0  58.0  63.0  69.0   86.0\n",
       "5                   358.0  77.656425   7.537533  57.0  73.0  78.0  83.0  109.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = base[[\"UMAP_KNN\", \"avg_diastolic_bp\"]].groupby(\"UMAP_KNN\").describe()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2eb76d97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UMAP_KNN</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">avg_diastolic_bp</th>\n",
       "      <th>count</th>\n",
       "      <td>281.000000</td>\n",
       "      <td>813.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>411.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>358.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>76.494662</td>\n",
       "      <td>74.687577</td>\n",
       "      <td>76.266332</td>\n",
       "      <td>75.661800</td>\n",
       "      <td>63.627119</td>\n",
       "      <td>77.656425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.104991</td>\n",
       "      <td>9.539434</td>\n",
       "      <td>10.640803</td>\n",
       "      <td>9.412756</td>\n",
       "      <td>7.573906</td>\n",
       "      <td>7.537533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>109.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQR</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "UMAP_KNN                         0           1           2           3  \\\n",
       "avg_diastolic_bp count  281.000000  813.000000  199.000000  411.000000   \n",
       "                 mean    76.494662   74.687577   76.266332   75.661800   \n",
       "                 std      9.104991    9.539434   10.640803    9.412756   \n",
       "                 min     54.000000   47.000000   53.000000   50.000000   \n",
       "                 25%     70.000000   68.000000   69.000000   69.000000   \n",
       "                 50%     76.000000   74.000000   75.000000   75.000000   \n",
       "                 75%     82.000000   80.000000   83.000000   82.000000   \n",
       "                 max    109.000000  114.000000  109.000000  104.000000   \n",
       "                 IQR     12.000000   12.000000   14.000000   13.000000   \n",
       "\n",
       "UMAP_KNN                         4           5  \n",
       "avg_diastolic_bp count  118.000000  358.000000  \n",
       "                 mean    63.627119   77.656425  \n",
       "                 std      7.573906    7.537533  \n",
       "                 min     41.000000   57.000000  \n",
       "                 25%     58.000000   73.000000  \n",
       "                 50%     63.000000   78.000000  \n",
       "                 75%     69.000000   83.000000  \n",
       "                 max     86.000000  109.000000  \n",
       "                 IQR     11.000000   10.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"avg_diastolic_bp\", \"IQR\"] = d[\"avg_diastolic_bp\", \"75%\"] - d[\"avg_diastolic_bp\", \"25%\"]\n",
    "d.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cb3e0300",
   "metadata": {},
   "outputs": [
    {
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       "      <th>patient_id</th>\n",
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       "      <th>age_group 2</th>\n",
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       "      <th>9</th>\n",
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       "      <td>1558709</td>\n",
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       "      <td>10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>10</td>\n",
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       "    Unnamed: 0  Unnamed: 0.1  patient_id  veteran  age age_group  age_group 2  \\\n",
       "0            0             0     1558625        0   59     55-65            3   \n",
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       "9            9             9     1558709        0   63     55-65            3   \n",
       "10          10            10     1558726        0   59     55-65            3   \n",
       "\n",
       "    sex    housing  housing 2  ... mf_mhprovider  mf_mhprovider 2 avg_elix_op  \\\n",
       "0     1  DOUBLE-UP          3  ...           NaN               10    0.476190   \n",
       "3     0  OTHER/SRO          4  ...    0x15420400                2    0.432836   \n",
       "4     0  OTHER/SRO          4  ...           NaN               10    2.947368   \n",
       "9     1  OTHER/SRO          4  ...           NaN               10    0.842105   \n",
       "10    1  OTHER/SRO          4  ...           NaN               10    1.609218   \n",
       "\n",
       "    avg_elix_ed   HSCS_ed  HSCS_ed_std   HSCS_op  HSCS_op_std  extreme_days  \\\n",
       "0      1.000000  3.000000          NaN  0.937500     0.997914             3   \n",
       "3      1.000000  1.000000          NaN  0.965517     0.498767             5   \n",
       "4      4.222222  8.000000     5.545268  2.363636     1.911687             2   \n",
       "9           NaN       NaN          NaN  0.888889     0.781736             2   \n",
       "10     0.500000  2.166667     1.602082  1.421053     1.609548             4   \n",
       "\n",
       "    UMAP_KNN  \n",
       "0          5  \n",
       "3          5  \n",
       "4          3  \n",
       "9          5  \n",
       "10         3  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htn_pt_visits = base[(base[\"age\"]>18) & ((base[\"HTN_ed\"]>0) | (base[\"HTN_op\"]>0))].copy()\n",
    "htn_pt_visits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "17b1d930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543, 92)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htn_pt_visits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "99d25135",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>543 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  HTN_ed  HTN_op\n",
       "0        1558625     1.0     7.0\n",
       "3        1558659     1.0     0.0\n",
       "4        1558661     6.0     3.0\n",
       "9        1558709     0.0     5.0\n",
       "10       1558726     1.0     5.0\n",
       "...          ...     ...     ...\n",
       "2243     1568710     0.0     1.0\n",
       "2244     1568712     0.0     1.0\n",
       "2245     1568714     1.0     0.0\n",
       "2252     1568727     0.0     3.0\n",
       "2256     1568731     1.0     0.0\n",
       "\n",
       "[543 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htn_visits_by_pt = htn_pt_visits[[\"patient_id\", \"HTN_ed\", \"HTN_op\"]].copy()\n",
    "htn_visits_by_pt[\"HTN_ed\"] = htn_visits_by_pt[\"HTN_ed\"].fillna(0)\n",
    "htn_visits_by_pt[\"HTN_op\"] = htn_visits_by_pt[\"HTN_op\"].fillna(0)\n",
    "htn_visits_by_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c56bea52",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>HTN_ed</th>\n",
       "      <th>HTN_op</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1558659</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1558765</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1559028</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1559241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1559491</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1559774</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1559836</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1560167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1560497</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1560770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1560956</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1561300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1562559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1562861</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>1563110</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>1563477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>1563684</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>1563693</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>1563739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1564103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>1564721</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1564756</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>1565161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>1565232</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>1565477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1566162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>1566318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1566519</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1566650</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>1566872</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>1566891</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>1568054</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>1568068</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>1568125</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>1568174</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>1568196</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>1568207</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>1568333</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1568363</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>1568402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>1568432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>1568444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>1568450</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2078</th>\n",
       "      <td>1568469</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>1568546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>1568550</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>1568577</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2170</th>\n",
       "      <td>1568600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>1568629</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>1568648</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>1568675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>1568700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>1568714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>1568731</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  HTN_ed  HTN_op\n",
       "3        1558659     1.0     0.0\n",
       "16       1558765     1.0     0.0\n",
       "41       1559028    12.0     0.0\n",
       "62       1559241     1.0     0.0\n",
       "88       1559491     3.0     0.0\n",
       "104      1559774     2.0     0.0\n",
       "109      1559836     1.0     0.0\n",
       "147      1560167     1.0     0.0\n",
       "180      1560497     4.0     0.0\n",
       "206      1560770     3.0     0.0\n",
       "219      1560956     4.0     0.0\n",
       "258      1561300     1.0     0.0\n",
       "389      1562559     1.0     0.0\n",
       "426      1562861     2.0     0.0\n",
       "449      1563110     3.0     0.0\n",
       "521      1563477     1.0     0.0\n",
       "571      1563684     2.0     0.0\n",
       "574      1563693     3.0     0.0\n",
       "583      1563739     1.0     0.0\n",
       "611      1564103     1.0     0.0\n",
       "713      1564721     7.0     0.0\n",
       "723      1564756     2.0     0.0\n",
       "776      1565161     1.0     0.0\n",
       "784      1565232     1.0     0.0\n",
       "840      1565477     1.0     0.0\n",
       "1080     1566162     1.0     0.0\n",
       "1208     1566318     1.0     0.0\n",
       "1345     1566519     4.0     0.0\n",
       "1441     1566650     6.0     0.0\n",
       "1607     1566872     1.0     0.0\n",
       "1621     1566891     1.0     0.0\n",
       "1757     1568054     1.0     0.0\n",
       "1767     1568068     2.0     0.0\n",
       "1813     1568125     7.0     0.0\n",
       "1847     1568174     6.0     0.0\n",
       "1863     1568196     1.0     0.0\n",
       "1871     1568207     1.0     0.0\n",
       "1975     1568333     7.0     0.0\n",
       "1996     1568363     2.0     0.0\n",
       "2025     1568402     1.0     0.0\n",
       "2048     1568432     1.0     0.0\n",
       "2058     1568444     4.0     0.0\n",
       "2064     1568450     6.0     0.0\n",
       "2078     1568469     2.0     0.0\n",
       "2131     1568546     1.0     0.0\n",
       "2134     1568550     2.0     0.0\n",
       "2154     1568577     2.0     0.0\n",
       "2170     1568600     3.0     0.0\n",
       "2187     1568629     1.0     0.0\n",
       "2199     1568648     1.0     0.0\n",
       "2219     1568675     1.0     0.0\n",
       "2235     1568700     1.0     0.0\n",
       "2245     1568714     1.0     0.0\n",
       "2256     1568731     1.0     0.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htn_visits_by_pt[htn_visits_by_pt[\"HTN_op\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5456314c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVD</th>\n",
       "      <th>HF</th>\n",
       "      <th>HTN</th>\n",
       "      <th>URI/PNA</th>\n",
       "      <th>AS/COPD</th>\n",
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       "      <th>PAIN</th>\n",
       "      <th>TOB</th>\n",
       "      <th>ACC/INJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3571</th>\n",
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       "      <th>9067</th>\n",
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      ],
      "text/plain": [
       "      CVD  HF  HTN  URI/PNA  AS/COPD  NEURO  SUD  MHD  CA  PREG  DM  INF  LIV  \\\n",
       "3571    0   0    0        0        0      0    0    0   0     0   0    1    0   \n",
       "8344    0   0    0        0        0      0    0    0   0     0   0    1    0   \n",
       "9067    0   0    0        0        0      0    0    0   0     0   0    0    0   \n",
       "\n",
       "      REN  COG  SENS  PAIN  TOB  ACC/INJ  \n",
       "3571    0    0     0     0    0        0  \n",
       "8344    0    0     0     0    0        0  \n",
       "9067    0    0     0     0    0        0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient1559028 = long_visits[(long_visits[\"patient_id\"]==1559028) & (long_visits[\"visit_type\"]==\"outpatient\")]\n",
    "patient1559028 = patient1559028.fillna(0)\n",
    "patient1559028[['CVD', 'HF', 'HTN', 'URI/PNA', 'AS/COPD', 'NEURO', 'SUD', 'MHD',\n",
    "           'CA', 'PREG', 'DM', 'INF', 'LIV', 'REN', 'COG', 'SENS', 'PAIN', 'TOB', \n",
    "           'ACC/INJ']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76517d66",
   "metadata": {},
   "source": [
    "This patient had 12 ED visits for hypertension, but 0 clinic visits for same.  One clinic visit appears to have been preventative or a physical (no diagnosis codes), while the others were for an infection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "abd3d65a",
   "metadata": {},
   "outputs": [
    {
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       "    .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>patient_id</th>\n",
       "      <th>HTN_ed</th>\n",
       "      <th>HTN_op</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1558625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1558709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1558726</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1558772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1558833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>1568538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>1568588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>1568593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>1568624</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>1568727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>308 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  HTN_ed  HTN_op\n",
       "0        1558625     1.0     7.0\n",
       "9        1558709     0.0     5.0\n",
       "10       1558726     1.0     5.0\n",
       "18       1558772     0.0     6.0\n",
       "22       1558833     0.0     2.0\n",
       "...          ...     ...     ...\n",
       "2125     1568538     1.0     2.0\n",
       "2161     1568588     0.0     2.0\n",
       "2165     1568593     0.0     2.0\n",
       "2183     1568624     0.0     2.0\n",
       "2252     1568727     0.0     3.0\n",
       "\n",
       "[308 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htn_visits_by_pt[(htn_visits_by_pt[\"HTN_ed\"]<htn_visits_by_pt[\"HTN_op\"]) & (htn_visits_by_pt[\"HTN_op\"]>1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab5d521a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 92)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[((base[\"HTN_ed\"]==0) & (base[\"HTN_op\"]==0)) & ((base[\"avg_systolic_bp\"]>130) & (base[\"avg_diastolic_bp\"]>80))].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "26dc8a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 92)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[((base[\"HTN_ed\"]>0) | (base[\"HTN_op\"]>0)) & ((base[\"avg_systolic_bp\"]>130) & (base[\"avg_diastolic_bp\"]>80))].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "825ce1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(154, 92)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[((base[\"HTN_ed\"]>0) | (base[\"HTN_op\"]>0)) & ((base[\"avg_systolic_bp\"]<=130) & (base[\"avg_diastolic_bp\"]<=80))].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7c12bec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543, 92)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[((base[\"HTN_ed\"]>0) | (base[\"HTN_op\"]>0))].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "77176a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "369\n",
      "22\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "elevated = base[((base[\"avg_systolic_bp\"]>120) & (base[\"avg_systolic_bp\"]<130)) & (base[\"avg_diastolic_bp\"]<=80)]\n",
    "print(len(elevated))\n",
    "print(elevated[elevated[\"HTN_ed\"]>0][\"HTN_ed\"].count())\n",
    "print(elevated[elevated[\"HTN_op\"]>0][\"HTN_op\"].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "741bf581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "657\n",
      "78\n",
      "255\n"
     ]
    }
   ],
   "source": [
    "stage1 = base[((base[\"avg_systolic_bp\"]>=130) & (base[\"avg_systolic_bp\"]<140)) | \n",
    "             ((base[\"avg_diastolic_bp\"]>80) & (base[\"avg_diastolic_bp\"]<90))]\n",
    "print(len(stage1))\n",
    "print(stage1[stage1[\"HTN_ed\"]>0][\"HTN_ed\"].count())\n",
    "print(stage1[stage1[\"HTN_op\"]>0][\"HTN_op\"].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "be8925d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291\n",
      "50\n",
      "182\n"
     ]
    }
   ],
   "source": [
    "stage2 = base[((base[\"avg_systolic_bp\"]>=140) & (base[\"avg_systolic_bp\"]<181)) | \n",
    "             ((base[\"avg_diastolic_bp\"]>90) & (base[\"avg_diastolic_bp\"]<120))]\n",
    "print(len(stage2))\n",
    "print(stage2[stage2[\"HTN_ed\"]>0][\"HTN_ed\"].count())\n",
    "print(stage2[stage2[\"HTN_op\"]>0][\"HTN_op\"].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6f3ccb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 92)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[(base[\"avg_systolic_bp\"]>180) | (base[\"avg_diastolic_bp\"]>120)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1a3fbebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119.0\n",
      "74.0\n",
      "\n",
      "135.0\n",
      "82.0\n"
     ]
    }
   ],
   "source": [
    "print(sys_mean)\n",
    "print(dia_mean)\n",
    "print()\n",
    "print(sys_mean_htn)\n",
    "print(dia_mean_htn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710e5072",
   "metadata": {},
   "source": [
    "Cleaning some a1c rows by using eAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3d61b1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"a1c\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cdadb015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>...</th>\n",
       "      <th>DM</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>sex</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>lang</th>\n",
       "      <th>sx</th>\n",
       "      <th>age_group</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>2092</td>\n",
       "      <td>1568484</td>\n",
       "      <td>62.5</td>\n",
       "      <td>190.916667</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.0</td>\n",
       "      <td>208.75</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.714286</td>\n",
       "      <td>70.571429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td></td>\n",
       "      <td>18+ years</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  patient_id  height      weight  pain_count  nida  phq2  hark  \\\n",
       "2091   2092     1568484    62.5  190.916667         3.5   NaN   NaN   NaN   \n",
       "\n",
       "        a1c     obg  ...   DM    systolic  diastolic  sex  language  age  \\\n",
       "2091  245.0  208.75  ...  1.0  110.714286  70.571429    1         0   60   \n",
       "\n",
       "                 lang sx  age_group       s  \n",
       "2091  English-speaker     18+ years  Female  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"a1c\"]==245]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4bcc26c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[2091, \"a1c\"] = 8.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "53a249b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"a1c\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b6a963be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
       "      <th>hark</th>\n",
       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>...</th>\n",
       "      <th>DM</th>\n",
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       "      <th>diastolic</th>\n",
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       "      <th>language</th>\n",
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       "      <th>age_group</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>1514</td>\n",
       "      <td>1566742</td>\n",
       "      <td>67.0</td>\n",
       "      <td>151.05</td>\n",
       "      <td>7.75</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td></td>\n",
       "      <td>18+ years</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  patient_id  height  weight  pain_count      nida  phq2  hark  \\\n",
       "1513   1514     1566742    67.0  151.05        7.75  2.333333   5.0   NaN   \n",
       "\n",
       "       a1c    obg  ...   DM  systolic  diastolic  sex  language  age  \\\n",
       "1513  95.0  174.0  ...  1.0     140.0       73.0    0         0   64   \n",
       "\n",
       "                 lang sx  age_group     s  \n",
       "1513  English-speaker     18+ years  Male  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"a1c\"]==95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "97d08613",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[1513, \"a1c\"] = 7.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "028e3168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.66666666666667"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"a1c\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "af71f5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>patient_id</th>\n",
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       "      <th>nida</th>\n",
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       "      <th>obg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>826</td>\n",
       "      <td>1565415</td>\n",
       "      <td>57.84375</td>\n",
       "      <td>131.94</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.666667</td>\n",
       "      <td>223.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>81.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td></td>\n",
       "      <td>18+ years</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  patient_id    height  weight  pain_count  nida  phq2  hark  \\\n",
       "825    826     1565415  57.84375  131.94       0.625   0.0   0.5   NaN   \n",
       "\n",
       "           a1c         obg  ...   DM  systolic  diastolic  sex  language  age  \\\n",
       "825  77.666667  223.333333  ...  1.0     126.0       81.8    1         0   26   \n",
       "\n",
       "                lang sx  age_group       s  \n",
       "825  English-speaker     18+ years  Female  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"a1c\"]==77.66666666666667]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "841a49d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[825, \"a1c\"] = 9.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a5c960d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.4"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"a1c\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "097a2704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>pain_count</th>\n",
       "      <th>nida</th>\n",
       "      <th>phq2</th>\n",
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       "      <th>a1c</th>\n",
       "      <th>obg</th>\n",
       "      <th>...</th>\n",
       "      <th>DM</th>\n",
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       "      <th>diastolic</th>\n",
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       "      <th>lang</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>690</td>\n",
       "      <td>1564589</td>\n",
       "      <td>67.0</td>\n",
       "      <td>229.075</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.4</td>\n",
       "      <td>267.909091</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>127.153846</td>\n",
       "      <td>83.846154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>English-speaker</td>\n",
       "      <td></td>\n",
       "      <td>18+ years</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "     index  patient_id  height   weight  pain_count      nida  phq2  hark  \\\n",
       "689    690     1564589    67.0  229.075    4.142857  0.333333   4.0   0.0   \n",
       "\n",
       "      a1c         obg  ...   DM    systolic  diastolic  sex  language  age  \\\n",
       "689  59.4  267.909091  ...  1.0  127.153846  83.846154    0         0   61   \n",
       "\n",
       "                lang sx  age_group     s  \n",
       "689  English-speaker     18+ years  Male  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
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   "source": [
    "comp1[comp1[\"a1c\"]==59.4]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cb3c9575",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[689, \"a1c\"] = 10.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c6a15030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.666666666666664"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "comp1[\"a1c\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "08173fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>patient_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>977</td>\n",
       "      <td>1565963</td>\n",
       "      <td>68.290909</td>\n",
       "      <td>157.455556</td>\n",
       "      <td>3.538462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>171.888889</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>123.727273</td>\n",
       "      <td>75.363636</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>Another primary language</td>\n",
       "      <td></td>\n",
       "      <td>18+ years</td>\n",
       "      <td>Female</td>\n",
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       "<p>1 rows × 21 columns</p>\n",
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       "     index  patient_id     height      weight  pain_count  nida  phq2  hark  \\\n",
       "976    977     1565963  68.290909  157.455556    3.538462   0.0   0.0   NaN   \n",
       "\n",
       "           a1c         obg  ...   DM    systolic  diastolic  sex  language  \\\n",
       "976  44.666667  171.888889  ...  1.0  123.727273  75.363636    1         1   \n",
       "\n",
       "     age                      lang sx  age_group       s  \n",
       "976   69  Another primary language     18+ years  Female  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "comp1[comp1[\"a1c\"]==44.666666666666664]"
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  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "69bad02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp1.at[976, \"a1c\"] = 7.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "664fa0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    231.000000\n",
       "mean       6.782694\n",
       "std        2.008280\n",
       "min        4.000000\n",
       "25%        5.000000\n",
       "50%        6.000000\n",
       "75%        8.000000\n",
       "max       13.000000\n",
       "Name: a1c, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"a1c\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "07e43b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    230.000000\n",
       "mean     192.625204\n",
       "std       88.845716\n",
       "min       70.000000\n",
       "25%      124.833333\n",
       "50%      172.344444\n",
       "75%      242.830357\n",
       "max      569.000000\n",
       "Name: obg, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[\"obg\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bfb6a71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    229\n",
       "1     77\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"DM\"]==1][\"language\"].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "12370d06",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    164\n",
       "1     67\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"a1c\"].notnull()][\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b1d34f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    76\n",
       "1    40\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"a1c\"].notnull()) & (comp1[\"a1c\"]<=6)][\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4662e9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    178\n",
       "1     52\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"obg\"].notnull()][\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e595229c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    70\n",
       "1    21\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"obg\"].notnull()) & (comp1[\"obg\"]<=150)][\"language\"].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "86a0b02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77,)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==1)][\"obg\"].shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a8862a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(229,)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==0)][\"obg\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e5e551b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "306"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "229 + 77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "452f5241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 21)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==1) & (comp1[\"obg\"].isnull())].shape"
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  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "fdd769de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 21)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==1) & (comp1[\"a1c\"].isnull())].shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3f248ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69, 21)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==0) & (comp1[\"obg\"].isnull())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c1b9331d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82, 21)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"DM\"]==1) & (comp1[\"language\"]==0) & (comp1[\"a1c\"].isnull())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4d3e836b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 527.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 527.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obg_mean = comp1[\"obg\"].mean()\n",
    "a1c_mean = comp1[\"a1c\"].mean()\n",
    "\n",
    "obg_meane = comp1[comp1[\"language\"]==0][\"obg\"].mean()\n",
    "a1c_meane = comp1[comp1[\"language\"]==0][\"a1c\"].mean()\n",
    "\n",
    "obg_meanne = comp1[comp1[\"language\"]==1][\"obg\"].mean()\n",
    "a1c_meanne = comp1[comp1[\"language\"]==1][\"a1c\"].mean()\n",
    "\n",
    "obg_mean = np.round(obg_mean, 0)\n",
    "a1c_mean = np.round(a1c_mean, 0)\n",
    "\n",
    "obg_meane = np.round(obg_meane, 0)\n",
    "a1c_meane = np.round(a1c_meane, 0)\n",
    "\n",
    "obg_meanne = np.round(obg_meanne, 0)\n",
    "a1c_meanne = np.round(a1c_meanne, 0)\n",
    "\n",
    "\n",
    "obg = sb.displot(data=comp1, x=\"obg\", hue=\"lang\", kind=\"kde\")\n",
    "line_position1 = [[obg_meane, obg_meanne]]\n",
    "\n",
    "for ax, pos_list in zip(obg.axes.flat, line_position1):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.xlabel(\"Random Blood Glucose (n=232)\")\n",
    "plt.show()\n",
    "\n",
    "a1c = sb.displot(data=comp1, x=\"a1c\", hue=\"lang\", kind=\"kde\")\n",
    "line_position2 = [[a1c_meane, a1c_meanne]]\n",
    "\n",
    "for ax, pos_list in zip(a1c.axes.flat, line_position2):\n",
    "    ax.axvline(x=pos_list[0], color='blue', linestyle=':')\n",
    "    ax.axvline(x=pos_list[1], color='orange', linestyle=':')\n",
    "    \n",
    "plt.xlabel(\"Hemoglobin A1C (n=233)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "336930e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193.0\n",
      "7.0\n",
      "\n",
      "200.0\n",
      "7.0\n",
      "\n",
      "168.0\n",
      "6.0\n"
     ]
    }
   ],
   "source": [
    "print(obg_mean)\n",
    "print(a1c_mean)\n",
    "print()\n",
    "print(obg_meane)\n",
    "print(a1c_meane)\n",
    "print()\n",
    "print(obg_meanne)\n",
    "print(a1c_meanne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b830b21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(306, 21)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[comp1[\"DM\"]==1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9ab12c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 21)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[((comp1[\"a1c\"].isnull()) | (comp1[\"obg\"].isnull())) & (comp1[\"DM\"]==1)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "54af2cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 21)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[((comp1[\"a1c\"].notnull()) | (comp1[\"obg\"].notnull())) & (comp1[\"DM\"]==0)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "482265b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 21)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"a1c\"].notnull()) & (comp1[\"DM\"]==0)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "31a1d3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 21)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp1[(comp1[\"obg\"].notnull()) & (comp1[\"DM\"]==0)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f5b5d18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_notes = base[base[\"avg_pain_ct\"]>2].copy()\n",
    "pain_notes = pain_notes[[\"PAIN_ed\", \"PAIN_op\", \"avg_pain_ct\"]].copy().reset_index()\n",
    "pain_notes[\"PAIN_ed\"] = pain_notes[\"PAIN_ed\"].fillna(0)\n",
    "pain_notes[\"PAIN_op\"] = pain_notes[\"PAIN_op\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "283cb7ea",
   "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>index</th>\n",
       "      <th>PAIN_ed</th>\n",
       "      <th>PAIN_op</th>\n",
       "      <th>avg_pain_ct</th>\n",
       "      <th>Diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>ED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.83</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>OP/Clinic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  PAIN_ed  PAIN_op  avg_pain_ct  Diagnosis\n",
       "0      0      0.0      0.0         5.00       None\n",
       "1      2      0.0      0.0         7.00       None\n",
       "2      4      6.0      0.0         5.30         ED\n",
       "3      5      0.0      0.0         6.83       None\n",
       "4      6      0.0      4.0         3.75  OP/Clinic"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pain_notes[\"Diagnosis\"] = \"\"\n",
    "\n",
    "for i in range(len(pain_notes)):\n",
    "    if pain_notes.at[i, \"PAIN_ed\"]>0:\n",
    "        if pain_notes.at[i, \"PAIN_op\"]>0:\n",
    "            pain_notes.at[i, \"Diagnosis\"] = \"Both\"\n",
    "        else:\n",
    "            pain_notes.at[i, \"Diagnosis\"] = \"ED\"\n",
    "    else:\n",
    "        if pain_notes.at[i, \"PAIN_op\"]>0:\n",
    "            pain_notes.at[i, \"Diagnosis\"] = \"OP/Clinic\"\n",
    "        else:\n",
    "            pain_notes.at[i, \"Diagnosis\"] = \"None\"\n",
    "\n",
    "pain_notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b1d77531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1562, 5)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pain_notes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e2b71ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 440.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pain_ed_mean = pain_notes[pain_notes[\"Diagnosis\"]==\"ED\"][\"avg_pain_ct\"].mean()\n",
    "pain_op_mean = pain_notes[pain_notes[\"Diagnosis\"]==\"OP/Clinic\"][\"avg_pain_ct\"].mean()\n",
    "pain_both_mean = pain_notes[pain_notes[\"Diagnosis\"]==\"Both\"][\"avg_pain_ct\"].mean()\n",
    "pain_nodx_mean = pain_notes[pain_notes[\"Diagnosis\"]==\"None\"][\"avg_pain_ct\"].mean()\n",
    "\n",
    "pain_ed_mean = np.round(pain_ed_mean, 0)\n",
    "pain_op_mean = np.round(pain_op_mean, 0)\n",
    "pain_both_mean = np.round(pain_both_mean, 0)\n",
    "pain_nodx_mean = np.round(pain_nodx_mean, 0)\n",
    "\n",
    "pain = sb.displot(data=pain_notes, x=\"avg_pain_ct\", hue=\"Diagnosis\", kind=\"kde\")\n",
    "positions = [[pain_nodx_mean, pain_ed_mean, pain_op_mean, pain_both_mean]]\n",
    "\n",
    "for ax, pos in zip(pain.axes.flat, positions):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle=':')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle='solid')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "plt.xlabel(\"Average Pain Count by Diagnosis (n=1562)\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1d6c3627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "4.0\n",
      "5.0\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "print(pain_nodx_mean)\n",
    "print(pain_ed_mean)\n",
    "print(pain_op_mean)\n",
    "print(pain_both_mean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a199ff98",
   "metadata": {},
   "outputs": [],
   "source": [
    "jail_notes = base[base[\"avg_jail_ct\"]>2].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "02cdfc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 92)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jail_notes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dc601b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    61\n",
       "1    11\n",
       "Name: incarcerated, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jail_notes[\"incarcerated\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1acde8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1938\n",
       "1     335\n",
       "Name: incarcerated, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo[\"incarcerated\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3de36149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "335 + 61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "151f7e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17483443708609273"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "396/2265"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "792b56f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jail_notes[\"avg_jail_ct\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "47cf9099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(896, 92)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base1 = base[base[\"avg_jail_ct\"]>0].copy()\n",
    "base1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4973f96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jail_inc_mean = base1[base1[\"incarcerated\"]==1][\"avg_jail_ct\"].mean()\n",
    "jail_noinc_mean = base1[base1[\"incarcerated\"]==0][\"avg_jail_ct\"].mean()\n",
    "\n",
    "jail_inc_mean = np.round(jail_inc_mean, 0)\n",
    "jail_noinc_mean = np.round(jail_noinc_mean, 0)\n",
    "\n",
    "jail = sb.displot(data=base1, x=\"avg_jail_ct\", hue=\"incarcerated\", kind=\"kde\")\n",
    "positions = [[jail_noinc_mean, jail_inc_mean]]\n",
    "\n",
    "for ax, pos in zip(jail.axes.flat, positions):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average Jail Count by Incarceration Status (n=896)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "562635db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(jail_noinc_mean)\n",
    "print(jail_inc_mean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fa73b3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>veteran</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_group 2</th>\n",
       "      <th>sex</th>\n",
       "      <th>housing</th>\n",
       "      <th>housing 2</th>\n",
       "      <th>education</th>\n",
       "      <th>...</th>\n",
       "      <th>mf_provider 2</th>\n",
       "      <th>mf_mhprovider</th>\n",
       "      <th>mf_mhprovider 2</th>\n",
       "      <th>avg_elix_op</th>\n",
       "      <th>avg_elix_ed</th>\n",
       "      <th>HSCS_ed</th>\n",
       "      <th>HSCS_ed_std</th>\n",
       "      <th>HSCS_op</th>\n",
       "      <th>HSCS_op_std</th>\n",
       "      <th>extreme_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>1569</td>\n",
       "      <td>1566820</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>46-55</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>1683</td>\n",
       "      <td>1567966</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>46-55</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/CG</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  patient_id  veteran  age age_group  age_group 2  sex  \\\n",
       "1569        1569     1566820        0   52     46-55            2    1   \n",
       "1683        1683     1567966        0   55     46-55            2    1   \n",
       "\n",
       "      housing  housing 2 education  ...  mf_provider 2 mf_mhprovider  \\\n",
       "1569  UNKNOWN          0   UNKNOWN  ...              7           NaN   \n",
       "1683  UNKNOWN          0     SC/CG  ...              2           NaN   \n",
       "\n",
       "      mf_mhprovider 2 avg_elix_op  avg_elix_ed  HSCS_ed  HSCS_ed_std  HSCS_op  \\\n",
       "1569               10        0.00          NaN      NaN          NaN      2.0   \n",
       "1683               10        0.75          NaN      NaN          NaN      2.0   \n",
       "\n",
       "      HSCS_op_std  extreme_days  \n",
       "1569          NaN             0  \n",
       "1683     1.414214             0  \n",
       "\n",
       "[2 rows x 92 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base[base[\"avg_disability_ct\"]>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "13e5eadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "adults = base[base[\"age\"]>18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "df5a43bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1843, 93)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_nida = adults[adults[\"avg_nida\"].notnull()].copy().reset_index()\n",
    "have_nida.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f49d6acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "have_nida[\"SUD\"] = \"\"\n",
    "have_nida[\"MHD\"] = \"\"\n",
    "\n",
    "for i in range(len(have_nida)):\n",
    "    if pd.notnull(have_nida.at[i, \"SUD_ed\"]):\n",
    "        if have_nida.at[i, \"SUD_ed\"]==1:\n",
    "            if pd.notnull(have_nida.at[i, \"SUD_op\"]):\n",
    "                if have_nida.at[i, \"SUD_op\"]==1:\n",
    "                    have_nida.at[i, \"SUD\"] = \"both\"\n",
    "                else:\n",
    "                    have_nida.at[i, \"SUD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_nida.at[i, \"SUD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_nida.at[i, \"SUD_op\"]):\n",
    "                if have_nida.at[i, \"SUD_op\"]==1: \n",
    "                    have_nida.at[i, \"SUD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_nida.at[i, \"SUD\"] = \"none\"\n",
    "        else:\n",
    "            have_nida.at[i, \"SUD\"] = \"none\"\n",
    "    elif pd.notnull(have_nida.at[i, \"SUD_op\"]):\n",
    "        if have_nida.at[i, \"SUD_op\"]==1: \n",
    "            have_nida.at[i, \"SUD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_nida.at[i, \"SUD\"] = \"none\"\n",
    "    else:\n",
    "        have_nida.at[i, \"SUD\"] = \"none\"\n",
    "        \n",
    "        \n",
    "    if pd.notnull(have_nida.at[i, \"MHD_ed\"]):\n",
    "        if have_nida.at[i, \"MHD_ed\"]==1:\n",
    "            if pd.notnull(have_nida.at[i, \"MHD_op\"]):\n",
    "                if have_nida.at[i, \"MHD_op\"]==1:\n",
    "                    have_nida.at[i, \"MHD\"] = \"both\"\n",
    "                else:\n",
    "                    have_nida.at[i, \"MHD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_nida.at[i, \"MHD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_nida.at[i, \"MHD_op\"]):\n",
    "                if have_nida.at[i, \"MHD_op\"]==1: \n",
    "                    have_nida.at[i, \"MHD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_nida.at[i, \"MHD\"] = \"none\"\n",
    "        else:\n",
    "            have_nida.at[i, \"MHD\"] = \"none\"\n",
    "    elif pd.notnull(have_nida.at[i, \"MHD_op\"]):\n",
    "        if have_nida.at[i, \"MHD_op\"]==1: \n",
    "            have_nida.at[i, \"MHD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_nida.at[i, \"MHD\"] = \"none\"\n",
    "    else:\n",
    "        have_nida.at[i, \"MHD\"] = \"none\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "cec62e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nida_mean = have_nida[\"avg_nida\"].mean()\n",
    "sud_nida_meaned = have_nida[have_nida[\"SUD\"]==\"emergency\"][\"avg_nida\"].mean()\n",
    "sud_nida_meanop = have_nida[have_nida[\"SUD\"]==\"outpatient\"][\"avg_nida\"].mean()\n",
    "sud_nida_meanboth = have_nida[have_nida[\"SUD\"]==\"both\"][\"avg_nida\"].mean()\n",
    "nosud_nida_mean = have_nida[have_nida[\"SUD\"]==\"none\"][\"avg_nida\"].mean()\n",
    "\n",
    "mhd_nida_meaned = have_nida[have_nida[\"MHD\"]==\"emergency\"][\"avg_nida\"].mean()\n",
    "mhd_nida_meanop = have_nida[have_nida[\"MHD\"]==\"outpatient\"][\"avg_nida\"].mean()\n",
    "mhd_nida_meanboth = have_nida[have_nida[\"MHD\"]==\"both\"][\"avg_nida\"].mean()\n",
    "nomhd_nida_mean = have_nida[have_nida[\"MHD\"]==\"none\"][\"avg_nida\"].mean()\n",
    "\n",
    "nida_mean = np.round(nida_mean, 0)\n",
    "sud_nida_meaned = np.round(sud_nida_meaned, 0)\n",
    "sud_nida_meanop = np.round(sud_nida_meanop, 0)\n",
    "sud_nida_meanboth = np.round(sud_nida_meanboth, 0)\n",
    "nosud_nida_mean = np.round(nosud_nida_mean, 0)\n",
    "\n",
    "mhd_nida_meaned = np.round(mhd_nida_meaned, 0)\n",
    "mhd_nida_meanop = np.round(mhd_nida_meanop, 0)\n",
    "mhd_nida_meanboth = np.round(mhd_nida_meanboth, 0)\n",
    "nomhd_nida_mean = np.round(nomhd_nida_mean, 0)\n",
    "\n",
    "\n",
    "sud = sb.displot(data=have_nida, x=\"avg_nida\", hue=\"SUD\", kind=\"kde\")\n",
    "positions1 = [[nosud_nida_mean, sud_nida_meanop, sud_nida_meaned, sud_nida_meanboth]]\n",
    "\n",
    "for ax, pos in zip(sud.axes.flat, positions1):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-.')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle=':')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average TAPS Score by Substance Use Diagnosis (n=1843)\")\n",
    "plt.show()\n",
    "\n",
    "mhd = sb.displot(data=have_nida, x=\"avg_nida\", hue=\"MHD\", kind=\"kde\")\n",
    "positions2 = [[nomhd_nida_mean, mhd_nida_meaned, mhd_nida_meanop, mhd_nida_meanboth]]\n",
    "\n",
    "for ax, pos in zip(mhd.axes.flat, positions2):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-.')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle=':')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average TAPS Score by Mental Health Diagnosis (n=1843)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "54c081ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "\n",
      "7.0\n",
      "5.0\n",
      "4.0\n",
      "6.0\n",
      "\n",
      "5.0\n",
      "5.0\n",
      "6.0\n",
      "8.0\n"
     ]
    }
   ],
   "source": [
    "print(nida_mean)\n",
    "print()\n",
    "print(sud_nida_meanop)\n",
    "print(sud_nida_meaned)\n",
    "print(nosud_nida_mean)\n",
    "print(sud_nida_meanboth)\n",
    "print()\n",
    "print(mhd_nida_meanop)\n",
    "print(nomhd_nida_mean)\n",
    "print(mhd_nida_meaned)\n",
    "print(mhd_nida_meanboth)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4a903a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1665, 93)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_phq2 = adults[adults[\"avg_phq2\"].notnull()].copy().reset_index()\n",
    "have_phq2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2b509491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    475\n",
       "2.0    357\n",
       "1.0    193\n",
       "3.0    193\n",
       "4.0    191\n",
       "6.0    170\n",
       "5.0     86\n",
       "Name: avg_phq2, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_phq2[\"avg_phq2\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1e2f4105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 93)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_phq2[have_phq2[\"avg_phq2\"]>2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6ac11201",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAZY HACK\n",
    "have_phq2[\"SUD\"] = \"\"\n",
    "have_phq2[\"MHD\"] = \"\"\n",
    "\n",
    "for i in range(len(have_phq2)):\n",
    "    if pd.notnull(have_phq2.at[i, \"SUD_ed\"]):\n",
    "        if have_phq2.at[i, \"SUD_ed\"]==1:\n",
    "            if pd.notnull(have_phq2.at[i, \"SUD_op\"]):\n",
    "                if have_phq2.at[i, \"SUD_op\"]==1:\n",
    "                    have_phq2.at[i, \"SUD\"] = \"both\"\n",
    "                else:\n",
    "                    have_phq2.at[i, \"SUD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_phq2.at[i, \"SUD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_phq2.at[i, \"SUD_op\"]):\n",
    "                if have_phq2.at[i, \"SUD_op\"]==1: \n",
    "                    have_phq2.at[i, \"SUD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_phq2.at[i, \"SUD\"] = \"none\"\n",
    "        else:\n",
    "            have_phq2.at[i, \"SUD\"] = \"none\"\n",
    "    elif pd.notnull(have_phq2.at[i, \"SUD_op\"]):\n",
    "        if have_phq2.at[i, \"SUD_op\"]==1: \n",
    "            have_phq2.at[i, \"SUD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_phq2.at[i, \"SUD\"] = \"none\"\n",
    "    else:\n",
    "        have_phq2.at[i, \"SUD\"] = \"none\"\n",
    "        \n",
    "        \n",
    "    if pd.notnull(have_phq2.at[i, \"MHD_ed\"]):\n",
    "        if have_phq2.at[i, \"MHD_ed\"]==1:\n",
    "            if pd.notnull(have_phq2.at[i, \"MHD_op\"]):\n",
    "                if have_phq2.at[i, \"MHD_op\"]==1:\n",
    "                    have_phq2.at[i, \"MHD\"] = \"both\"\n",
    "                else:\n",
    "                    have_phq2.at[i, \"MHD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_phq2.at[i, \"MHD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_phq2.at[i, \"MHD_op\"]):\n",
    "                if have_phq2.at[i, \"MHD_op\"]==1: \n",
    "                    have_phq2.at[i, \"MHD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_phq2.at[i, \"MHD\"] = \"none\"\n",
    "        else:\n",
    "            have_phq2.at[i, \"MHD\"] = \"none\"\n",
    "    elif pd.notnull(have_phq2.at[i, \"MHD_op\"]):\n",
    "        if have_phq2.at[i, \"MHD_op\"]==1: \n",
    "            have_phq2.at[i, \"MHD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_phq2.at[i, \"MHD\"] = \"none\"\n",
    "    else:\n",
    "        have_phq2.at[i, \"MHD\"] = \"none\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "feab836f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wFMgD/CJyI66HaQnwdxEJlPHPqvov79S3Ag+JyFXANuDiaL2GRNHYHpkaYWpyEkVZadY0aowZVaI6oF5VnwGe6bHu7qD7Vbgm054agCN7OWcNcEYEi5nwGoc4BVOw0tx0S7xtjBlVbPYJ032NcKhNo+ACYbVdIzTGjCIWCA1N7T6SBLLSkod8rjG5GdY0aowZVSwQGhrbfOSkp+Bdkx2SMXnp7G1qx+8fyEiZodnT2DagcTnGGBPMkm4bGtuGnl4toDQnnc4upa61k6LstIicsy//ereKG/6yjNb0Bsblp0fuxJZn1JiEYTVCQ+MQJ+UNNpxjCWua2vnK35Zz+IR8iguF6vZm3t0RobRolmvUmIRhgdDQ1O6LXCD08o0OR8/R3766mTZfFz+56EjGZORARwr3vR6htGiWa9SYhGGB0HRfI4yE4Uqz1tnl529LtnHe4eOYPiaHrVsEbcjm6ZW7qGvpGPoTWK5RYxKGBUJDU7uPnAhdIxyuptE3NtZQ29LJhXNdHveZM2H6DOjw+fn3+xHIunfjTLcYY+KeBUIT0WuEWWkp5GemsrOuNSLn680zK3eRm57CKTNdQvWsLCgpSKYkJ42X10VgtpFJWW4xxsQ96zVqXK/RCDWNAkwoyGRnXfRqhKrKy+uqOWVmKRmpbuxjTY3bdtaMUv6zdg9dfiU5aQjDQd7c627PHGJhjTEjntUIE1yHz0+7zx+xGiHA+IJMdtRGr0a4sbqZqoY2TppR0r1u+3a3nDKzlNqWTt6vGuIsvY9UusUYE/csECa4wKS8keosAzCxMDOqTaOvb3C1tZOmlxy07egphQAs21YXtec3xsQXC4QJbv9chJHpLAOuabSx3Ud9a2fEzhnszY01TCzMZFLRwdfwJhZmUpKTzjtba6Py3MaY+GOBMME1RGh2+mDjCzIBotI8qqq8va2WY8qLQm4XEeZNLuCd7XURf25jTHyyQJjgmiI0F2GwCYUuEEajebSytpXqxnbmeU2gocydXMDmvc3Ut0SnRmqMiS8WCBNcYHb63PTINo0C7IhCIHzba/I8enLvgfDw8S412nu7hthhxhiTECwQJrim9sg3jRZnp5GWkhSVGuE722rJSkvmkHG5ve5zWFkeYIHQGBMeC4QJrrtGGMFAmJQkTCjIpDIKgfDdnQ3MHp/X5xjB0tx0xuSm895OC4TGmP5ZIExwjRGcnT7YhCiMJezyK+/tbODwCf3PCjFrfJ7VCI0xYbHMMgmuqd1HWnJSd4aWSBlfkMFLayOQ6izIxuomWju7uq8BBpvZIy3ooePyeH3DJnxdflKSB/F7z/KMGpMwrEaY4BrbOiN6fTBgQkEWexrbafd1ReycgbkG50w8OBBmZbklYPqYHDq7lG37Wgb3ZJZr1JiEYYEwwTW1RW4uwmDjC9y8hFX1kcs5umpHPRmpSUwrzTloW03N/nyj4AIhwIY9TYN7sjf37s83aoyJaxYIE1wk5yIMFhhLWBnB64Tv7qhnVlnojjKBXKMBU0uzAdhQPchAaLlGjUkYdo0wwTVGcHb6YOXFLhBtqWnmxBA5QQfK71dW72zg4qMnhtw+e/aBj/MyUhmblz74GuF3Zve/jzEmLlggTHCNbb7uAfCRNC4vg8zUZDZVN0fkfJv2NtPS0cXsXnqMpobIBzB9TA4bB/v8+ZFLMGCMGdmsaTTBNbVHblLeYElJQnlJNpv3RiYQrt7pdZTpJRBWVbkl2PTSHDbuaUJVB/6Ez1a5xRgT9ywQJrjGKHWWAZhaks2mwV6j62FVZT3pKUnMGHNwRxkIHQinjcmhqd3H7ob2gT/hc1VuMcbEvagGQhE5V0TWisgGEbk5xPZDReRNEWkXkZuC1k8Skf+IyBoRWS0iXw7a9j0R2SEiy73l/Gi+hnimqjRFqbMMuA4r22tb6fD5h3yuVTvqObQsb0BjAqeXDrHnqDEmIUQtEIpIMnAncB4wC7hURGb12G0f8CXgth7rfcDXVPUwYAFwXY9jf6aqc73lmei8gvjX7vPj82tE5yIMVlGSTZd/CGP5PH4vo8ycCXkDOi4whGJjhGqlxpj4FM0a4bHABlXdpKodwF+BhcE7qOoeVV0CdPZYv0tVl3n3G4E1wIQoljUhRWMuwmBTvRrZUK8Tbt3XQmO7r9frg70pzU0nNz3FaoTGmD5FMxBOAIJGdlHJIIKZiJQDRwFvBa2+XkRWish9IhJyPh4RuVpElorI0urqyKb6iheB2enzohQIK7whFEO9TrjKyygzO0Rqtb6ICNPG5FggNMb0KZqBMNT0AAPqviciOcCjwI2qGsigfBcwDZgL7AJ+GupYVb1HVeer6vzS0tKBPG3CiFbC7YD8rFSKs9OGXCNcVVlHWkpSn1Mv9WZqSTZbaiLTc9UYE5+iGQgrgUlBjycCO8M9WERScUHwT6r6WGC9qu5W1S5V9QO/xTXBmkHYPzt99MbMTS3NHvJYwlU76jmsLI/UQSTPrijJZld9G60dkct5aoyJL9EMhEuAGSJSISJpwCXAk+EcKCIC/A5Yo6q399hWFvTwI8C7ESpvwmkMXCOMUo0QXCDaNIQaod+vvLtj4B1lAspL9me4McaYUKIWCFXVB1wPPIvr7PKQqq4WkWtE5BoAERknIpXAV4Fvi0iliOQBJwKXAR8IMUzixyKySkRWAqcDX4nWa4h30ZiUt6eppTnsbWqnvrWz/51D2FLTTFO7jyMmFAzq+AovEEZqYL8xJv5ENcWaN7ThmR7r7g66X4VrMu3pNUJfY0RVL4tkGRPZcATCmWNdz9G1VY0cW1E04ONX9TH1UrCeuUYDygcbCC3XqDEJwzLLJLDANcLsKDaNzipzAWzNIGeL7y+jTEBqauh8oznpKYzJTR94IMxPtXyjxiQIS7qdwJrafWSmJg+qE0q4xualU5iVOuhAuHJHPbPG959Rpmd6tWDlJdlsGWggDOQZPXNghxljRh+rESawaM1OH0xEOKwsj/cGEQj3Z5Tpf/xgqFyjAVMHk/zbco2aBCci3/JSXK70+mkcJyJbRKQkaJ/TRORp7/6VIlItIu+IyHoReVZETojdKwif1QgTWDQTbgebVZbHHxZtpbPLP6Da5/tVjTS1+5g7qaDffefO7X1bRUk2Nc0d1Ld2kp8ZZnPnT/s4oTFxTkSOBz4IzFPVdi/4pYVx6N9U9XrvHKcDj4nI6aq6JorFHTKrESawxjYfuVG8PhhwxKQC2n1+1lY1Dui4JVv2AXBM+cA72QTrHkJhPUeNCVcZsFdV2wFUda+qhj0O3DvmP8A9wNVRKF9EhRUIReRREblARCxwxpHGts6oDqYPOMqr0b2zvW5Axy3eso+y/AwmFvY/cfD27W4JZepgxhI+vN0txiSm54BJIrJORH4tIqcO8jzLgEMjWK6oCDew3QV8ElgvIreKyIh/YaZ/Te3Rm4Ip2MTCTEpy0nhnW23Yx6gqSzbv45jyIlx+hb7V1LgllElFWYgwsAw3i2rcYkwCUtUm4Ghcba4a+JuIXEnoNJl9pc7s/593BAjrW1BVXwBeEJF84FLgeRHZjktx9kdVHdxoaRNTw3WNUESYO6mQ5dvqwj5m+75W9jS2c8wgxh72lJGazISCTMsuY8wAqGoX8BLwkoisAq4AaoBCYK+3W1HQ/VCOwiVUGdHCbuoUkWLgSuBzwDvAz4F5wPNRKZmJuqY237A0jQIcW1HIpr3N7G5oC2v/xd71wWOHeH0woGIwPUeNSVAicoiIzAhaNRfYiguMl3n7JAOfBv7TyzlOxdUofxvNskZCWNUBEXkM1877B+BDqrrL2/Q3EVkarcKZ6PH7laYOX9SHTwScMM31uH5zYw0XHtX/bFxLNu8jPzO134H04aooyebv7+xAVcNqajUmweUAvxSRAtxE6RtwQa0TuEtEVuCaPf8F/DHouE+IyElAFrAZ+NhI7zEK4Q+fuLfnTPAikq6q7ao6PwrlMlHW1OFDNXpzEfZ0WFke+ZmpvLFxb7+BUFVZtLmGY8oLSUqKTNAqL86msc1HTXMHJTnpETmnMfFKVd8GehsD+MlejrkfuD9KRYqqcJtG/zfEujcjWRAzvJqGIc9osOQk4YRpxby8rhq/v+9pKTftbWZrTQunzozcPJIVpTaEwhgTWp+B0Jsd4mggU0SOEpF53nIaruprRqn9k/IOXz7Ns2aNZXdDe3ci7d785/09AJx+6JiIPXdFsQuEQ5kSyhgTn/qrDpyD6yAzEQieF7AR+GaUymSGQWAuwuGqEQJ84NAxJCcJz66u4sg+ssU8995uDhmby8TCyP3WmliYSUqSWI3QGHOQPmuEqvqAqp4OXKmqpwctHw6eNd6MPo3ezBPD1VkGoCArjROmFfPE8p29No/uqGtl8eZ9fPCIspDbByslOYnJRVnWc9QYc5A+vwVF5NOq+kegXES+2nN7z9njzegRaBodrs4yAR+fP4kb/vIOr23YyykhrgE+/s4OABbO7b9nabC+co0GDGgIheUaNSZh9NdZJtu7zQFyQyxmlNrfNDq8c+6dPXssRdlp/P71zQdt6+zy8+e3tnFcRRGTiyN/Cbq8JJstNc39dtYxxiSWPqsDqvob7/b7w1McM1yaujvLDG+NMD0lmatOquAnz65l2bZa5k0u7N72xPKd7Khr5QcXDnx2+N7yjAarKMmmrdPP7sY2yvL7yV8ayDNq8xEaE/fCTbr9YxHJE5FUEXlRRPaKyKejXTgTPY1tPpKThKy05GF/7itPKKc0N53/fnQVbZ1dANQ2d3DrP9cwZ0I+px8y8N6iDQ1u6UuFl3x7czg5R99rcIsxJu6FO47wbFVtwM1PVQnMBP4raqUyUdfY1klOekpMsqxkp6dw28VHsnZ3I1f+fjH/XLWLy+57i/rWTn580RGDKtPs2W7pS3cgDCfn6Hdnu8WYBCAi5SKyRkR+603G+5yIZIrIXBFZ5E3O+3cRKfT2f0lEfiQii70ZKk721ieLyE9EZIl3zBdi+8rCE267WOBC0vnAX1R1n6WpGt0ah2nmid6cOrOU2z9+JP/92CoWbVpGTnoK91w2n8PK8qL2nOPyMkhPSQqvRmhMjJTf/I87cLk9I2n5llsvuLGffWYAl6rq50XkIeBjwNeBG1T1ZRG5BfguEDhPiqoeKyLne+vPBK4C6lX1GBFJB14XkedU9eBOASNIuN+ET4nI+0Ar8EURKQXCy55sRqThmnmiLx+dN5EzZ41lXVUjs8bnkZU2+PJsCuPfLClJqPA6zPTrd5vcrV0jNIljs6ou9+6/DUwDClT1ZW/dA8DDQfs/FrRvuXf/bOAIEbnIe5yPC7CjPxCq6s0i8iOgQVW7RKQZWBjdoploamzrJG+Ye4yGkpeRyvwIzDDR0Heymm7lxdms29PY/452fdDESBg1t2hpD7rfBRSEuX8X+2OJ4GqQz0a2aNE1kBnnD8NlFr8cuAgX+c0o1dQ+fDNPjCQVpdls39eCr8sf66IYM9LVA7WB63+46Zde7mN/gGeBa0UkFUBEZopIdj/HxFy40zD9AVdNXo6L/uBmJX4wOsUy0dbY5mNaaQIGwpJsOruUHXWtTCke8f+fxsTaFcDdIpIFbAI+08/+9+KaSZeJ60hSDVwYzQJGQrjfhPOBWapqI5HjxEi4RhgLU72eo5uqmy0QGuNR1S3A4UGPbwvavCDE/qcF3d+Ld41QVf24PNSjKhd1uE2j7wLjolkQM7ya2nzDOvPESDFjjEuItG53GNcJjTEJIdxAWAK8JyLPisiTgaW/g0TkXBFZKyIbROTmENsPFZE3RaRdRG4K51gRKRKR50VkvXdb2PO8pm9tnV10dPkTskaYn5XK2Lx01logNMZ4wv0m/N5ATywiycCdwFm4QfhLRORJVX0vaLd9wJfo0Ybcz7E3Ay+q6q1egLwZ+MZAy5fIYpVwe6SYOTaX9bubYl0MY8wIEVaN0BtHsgVI9e4vAZb1c9ixwAZV3aSqHcBf6THkQlX3qOoSoHMAxy7EjWfBu70wnNdg9muKwRRMI8nMsbms39NoybeNMUD4uUY/DzwC/MZbNQF4vJ/DJgDBqZArvXXh6OvYsaq6C8C7DZmYUkSuFpGlIrK0uro6zKdNDN0zT8TRNcLUVLeE45CxubR1+tle29L7TnmpbjHGxL1wqwTX4WppbwGo6noR6S8zcqgcbOH+BB/KsW5n1XuAewDmz59vP/2DBJpG4+kaYX95RoPNGJsDwNqqxt57jlqeUWMSRridZdq9JkoARCSF/gNTJTAp6PFEYGeYz9fXsbtFpMwrRxmwJ8xzGk8gECZq0+iMsdZz1JihEJErRWT8EI4/TUROCHp8jZesZTDnKheRTw62LBB+IHxZRL4JZIrIWbh8c0/1c8wSYIaIVIhIGnAJ0G9P0zCOfRI3yBPv9okwz2k8gabRkZBiLVI2bQ4v3yi4ORgnFmayrq8OM7/btD/fqDGmpyuBQQdC4DSgOxCq6t2qOtgELeXAkAJhuFWCm3FZxVcBXwCewWUQ6JWq+kTkelzKnWTgPlVdLSLXeNvvFpFxwFIgD/CLyI24gfsNoY71Tn0r8JCIXAVsAy4O+9UaID6bRn09u1v1Y+bY3L5rhA2+oRXImFFGRL4KfNZ7eC+uH8jTqnq4t/0mIAc3rnw+8CcRaQWOB9YAfwNO947/pKpuEJEPAd8G0oAa4FNAJnAN0OXNa3sDcAbQpKq3icg03KiBUqAF+Lyqvi8i9wMN3nOPA76uqo/gYsJhIrIceEBVfzbQ1x5u0m2/iDwOPK6qYfc8UdVncEEzeN3dQfercM2eYR3rra/BvWlmkAK9RrNjOA1TpM2cOcD9x+by6vpqOrv8pCaHaBj5ygBPaEykfC//DqIwDRPfq7+xt40icjQufdpxuD4ab9FLXlFVfcSrqNykqku948FNynCs18R5B27+2teABaqqIvI5XPD6mojcjRf4vOODv9PvAa7x+qIcB/wa+IC3rQw4CTgU1zr4CK6idpOqfnCA70m3Pr8JvVxx3wWux705IiJdwC9V9ZbBPqmJrca2TjJTk0MHgARxWFkunV3Khj1NUZ0D0ZhR4iTg76raDCAijwEn933IQf4SdBuolU0E/ub150ijn+mYRCQH12T6cNCct+lBuzzupXF7T0TGDrB8veqvSnAjcCJwTGBiRRGZCtwlIl8ZTBXUxF485hldt25g+8+ZkA/Aqsr60IHwZ94JbT5CM9z6qLlFUaie+gUc2I8ko59zaIj7vwRuV9UnReQ0+k/OkgTUqercXrYHTxUVsdnh+6sSXI6bsbg7iqvqJuDT3jYzCjXG4RRMLS1uCVd5cTa56Sms3FEXeofKFrcYkxheAS4UkSxv2qSPAP8ExohIsTfbfHDTYyOQ2+Mcnwi6fdO7nw/s8O5fEbRvqONR1QZgs4hcDK4JUkSO7KfsIc81EP0FwlQvs/gBvOuE8dPlMMG4GmFi//mSkoQ5E/NZWRnmjL7GxDFVXQbcDyzGXR+818v6dYv3+Gng/aBD7sdNz7RcRDK9deki8hbwZeAr3rrv4Zo5XwWCY8lTwEe843s2wX4KuEpEVgCr6X8S+JWAT0RWiMhX+tk3pP6qBR2D3GZGsKa2zoTNMxpszsR87nttM+2+LtJTkmNdHGNiSlVvB27vse4XwC9C7Pso8GjgsXc9705V/X6P/Z4gxBA3VV0HHBG06tWgbZuBc0Mcc2WPxznebSdD7EDZ37fhkSLSEGK90H97sRmhGtt8jM2zP98REwro7FLWVTUxZ2J+rItjjImRPgOhqtrP5DgUj51lBuMIL/it3FFngdCYIVDV8liXYSgSt/98Amtqt2uEABMLMynISmXldrtOaEwis0CYYLr8SlO7j5w4Gkw/WCLCnAn5rKisi3VRjDExZIEwwQSyyljTqDNvciFrdzdS3zrAHG3GmLhhgTDBxGsgzMpyy0AdP60YVVi8ed+BGyZmucUYE/fi69vQ9Kt7Ut44u0Y40FyjAXMnFZCWksSiTTWcNSsoY5PlGjUmYViNMMHE48wTQ5GRmszRkwtZtKkm1kUxJm6JyIgegWCBMME0BSbljbPOMuvWDTzfaMCCqcW8t6uBupagHBE/W7c/36gxCUBEPi0ii71sL78RkWQRaRKRH4nI2yLygogcKyIvicgmEfmwd1yyiPxERJaIyEoR+YK3/jQR+Y+I/BlYJSJJIvJrEVktIk+LyDMicpG379Ei8rL3PM8GTb7+kvf8i0VkXSALjfect4nIKu85bxCRM0Tk70Gv5ywveXi/4uvb0PSrIU6bRlOG8HKOn1bMz15w1wnPnj3Orcyzfw0TG3MemHMHUZiGadUVq27sbaOIHIbLEXqiqnaKyK9xqc6ygZdU9RtekPlf4CxgFvAAbiqkq4B6VT3Gy0n6uog85536WOBwVd3sBb1yYA4wBjeH4X0ikopLzr1QVatF5BPA/7F/bsQUb3qn83GzIZ0JXA1UAEd5c98WAbXAnSJS6qUB/Qzw+3DeHPtvTzCBptF4S7E2tWLwxx45KZ/0lCTe3FSzPxBeNTUyBTNmdDgDOBpY4qVLywT24FJp/svbZxXQ7gXKVbigBnA2cESgdodLtD3DO3Zx0KQNJwEPe9MoVYnIf7z1hwCHA897z50M7AoqW6BW93bQc54J3K2qPgBV3QcgIn8APi0iv8dNGBzW5BDx9W1o+hUIhPE2+8RQpKckc2xFES+vrYYPxbo0JtH1VXOLIsHN7v7fB6wUuUlVA1Mq+fGmQfIma08JOvYGVX22x7GnAc09nqO3516tqsf3sj0w9VIX+2OWcOC0TwG/xyX0bsMFXV8v5zyAXSNMMA1tnaQmC5mpI/ra9YCtXu2WwTpr1lg27W1mw54mt+L7q91iTGJ4EbhIRMYAiEiRiEwJ89hngWu9Jk5EZKY3lVNPrwEf864VjgVO89avBUpF5Hjv+FQRmd3Pcz4HXBMIxl7TKKq6E9gJfBs3Q0ZYLBAmmPrWTvIzUwPZ4uNGZ6dbBuvMw9zQieff2+1WNHS6xZgEoKrv4YLHcyKyEngeKAvz8HuB94BlIvIu8BtCtzY+ClQCgX3ewl1b7AAuAn7kTb20HDdLfX/PuQ1Y6R3zyaBtfwK2e68pLNY+lmAaWjvJi7OOMpEwviCTORPyef69Kq49bVqsi2PMsFPVvwF/67E6J2j793rsH5gGyQ9801uCveQtgf39XlNrk4gU4+Y+XOVtWw6cEqJMpwXd34t3jdBr8vyqt/R0EvDb0K8yNKsRJpiGNh+5mRYIQzlr1lje2V5HdWN7/zsbYwbjaRFZjpt/8AeqWhXJk4vI27h5Dv84kOMsECaYQNOoOdhZs8aiCi+s2R3rohgTl1T1NFWdq6qzVPX+KJz/aFU9RVUH9GvWAmGCaWy12el7c+i4XCpKsnly+c5YF8UYM4wsECYYqxH2TkS4cO4E3txUgz9Ux2xjTFyyQJhAVJWGtk7yLBD26iNHTQCgs8sf45IYY4aLBcIE0trZRWeXWo2wD5OLszimvJDOLn/I0brGmPhjgTCBNLQG0qvFXyDMy3dLJHzkqInUV2TSOTOn/52NiQMiUu6NAQx3/xtFJCvocVN0SjY8ohoIReRcEVkrIhtE5OYQ20VEfuFtXyki87z1h3gZ0ANLg4jc6G37nojsCNp2fjRfQzwJJNzOy4y/zjJTK4aWbzTYBUeUseOjZVR+NNzxxMYknBuBuJm5OmqB0Jt/6k7gPFym8ktFZFaP3c7DJWedgcsmfheAqq71utjOxSWCbQH+HnTczwLbVfWZaL2GeFPf6gKhNY32LT8zleKcdPY2tXe/Z8YkgBQRecCrlDwiIlne1EbveNMd3Sci6SLyJWA88J+gxNmIyP+JyAoRWeSlUBs1olk1OBbYoKqbAETkr8BCXCqegIXAg15S10UiUiAiZaoanHn8DGCjqm6NYlkTQoP3pR6PTaNDyTMaypRfbaagtZPHZlXymRMjVNU0JgxrDj3sDqIwDdNh76+5sZ99DgGuUtXXReQ+XNaWLwBnqOo6EXkQuFZV7xCRrwKne9lewE3XtEhVvyUiPwY+j5uyaVSIZtPoBGB70ONKb91A97kE+EuPddd7v1ruE5HCUE8uIleLyFIRWVpdXT3w0seheK4R5uW5JVJSDs+nY2YOf1y0lf3J942Ja9tV9XXv/h9xlZDNqhqYofoBQqRB83QAT3v3g6dLGhWiWSMMldW55zdKn/uISBrwYSB4apC7gB94+/0A+Cn7J3DcfxLVe4B7AObPn2/fZATVCOMwEE6aFOETXjyJlMZ2Nr7VzJubajhhWkmEn8CY0MKouUXLUL4nO4OmawqeLmlUiGaNsBII/nqaiJseYyD7nAcsU9XunFequltVu7xEr7/FNcGaMNR7vUZzLbNMWIpz0sjPTOVPi7bFuijGDIfJgamQgEuBF4ByEZnurbsMeNm73wjkDnP5oiaagXAJMENEKrya3SXAkz32eRK43Os9ugA3JUfw9cFL6dEsKiLBXfk+gpvSw4Shoa2T7LRkUpPjb9TM8uVuiZivLSfpphV8fP5Enl1dxc661gie3JgRaQ1whTcNUxHwM+AzwMPejPR+4G5v33uAfwZ3lhnNolY1UFWfiFyPm7QxGbhPVVeLyDXe9ruBZ4DzgQ24nqGfCRzvjVE5C3exNtiPRWQurhq/JcR204uGVssqM1BXnFDOfa9v4b7XNvPtD/bs9GxMfFDVLbje/T29CBwVYv9fAr8Mehw8XdMjwCORL2X0RLWNzBva8EyPdXcH3Vfgul6ObQGKQ6y/LMLFTBiWZ3TgJhZmccGcMv6yeBs3nDHD3j9j4lD8tZGZXjW02aS8g3H1KVNp7ujiz2/ZtUJj4pEFwgRS3+qzptFBOHxCPidNL+H3r2+m3dcV6+IYYyLMAmECcdcIrcfoYFx9ylT2NLbzhM1VaEzcsUCYQKxpdPBOnlHCYWV53PXSRpuiyZg4Y4EwQXT5lcY2n3X2GCQR4atnzWTz3mYeXloZ6+IYYyLIAmGCaGrzpmCyQDhoZx42hqOnFPLzF9fR2mHXCo2JFxYIE0Q85xkFKC52S8QsKHZLEBHhG+ceyu6Gdu59dVMEn8wYE0vWcyJBdM9FGKfp1aKRazSUYyuKuGBOGb/89wbOnj2OQ8bFTZYpYxKW1QgTRH0cJ9webrcsnE1uRgo3PbzCOs4YEwcsECaI2pYOAAqz0mJckuiIRq5Rvhb6hMU56fzvhYezakc9P3j6PZumyZhRLj7bycxBaltcjbAwKz5rhOPGRfiEZ/d9wvPmlPH5kyv47aubEeBbF8wiLcV+VxozGlkgTBB1za5GWBCnNcKIB8Jz+j/hf593GKpw72ubeXXDXj557GQOK8sjIzWJ5KQkkgTG5WUwJi8jwoUzxkSSBcIEsa+lg5z0lLittXR2RviE9f2fMClJ+PYHZ3H8tGJ++tw6/vcfa0Lud+i4XL50xgzOn1MWcrsxJrYsECaIupZOCuK0WRRg9eoIn/AW74Qf63/XMw4byxmHjWVHXSvb97XQ7vPT5ffT5YetNW4A/hf/tIwrTyjnOx+cRVKSRLiwxpihsECYIGpbOuK2o8xIMaEgkwkFmQetv/KEcm795/vc+9pmctJTuOmcQ2JQOmNMbywQJojaOK8RjmQpyUl864LDaGzz8av/bGDB1GJOmlES62IZYzzxecHIHKTOaoQxJSJ8f+FsppZk841HV9LWaSnajBkpLBAmiNrmjrgdOjFaZKQm878fOZwdda38/vUtsS6OMcZjgTAB+Lr8NLT5KMy2GmGsnTCthA8cOoa7X95IS4cv1sUxxmCBMCHUtQYG01sgHAmuO30a9a2dNp2TMSOEBcIEUNcSGExvTaMjwdFTipg3uYDfvbaZLr+lZzMm1iwQJoD96dWsRjhSXH3KVLbta+G51VWxLooxCc8CYQKobY7vhNvgUqxFNM3a2eP6zTc6FGfNGsfkoizuf2NL1J7DGBMeC4QJoDYBmkYjHgjPGRdWvtHBSk4SPnHMJN7avI+tNc1Rex5jTP8sECaAQNNoURz3Gu3sjHC+0frOsPKNDsXH5k0kSbBOM8bEmAXCBFDb0kFachJZacmxLkrUrF4d4Xyjt6zen280SsblZ3DqzFIeebvSOs0YE0MWCBNAXbNLryYSv8meJ01yS8RcNNEtUfbx+ZOoamjjlXXVUX8uY0xoUQ2EInKuiKwVkQ0icnOI7SIiv/C2rxSReUHbtojIKhFZLiJLg9YXicjzIrLeuy2M5muIB4mQcLu42C0Rc3yJW6LsjMPGUpiVyiPLYtc8umFPIz/+1/v85Nn32bLXrleaxBO1QCgiycCdwHnALOBSEZnVY7fzgBnecjVwV4/tp6vqXFWdH7TuZuBFVZ0BvOg9Nn2I9ymYAFpa3BIx21vcEmVpKUl8+MjxPP/ebupbontNMpS/v1PJeT9/lXte2cTdL2/igl+8yhsb9w57OYyJpWjWCI8FNqjqJlXtAP4KLOyxz0LgQXUWAQUi0t/spQuBB7z7DwAXRrDMcWlfAtQI161zS8Tcsc4tw+BjR0+kw+fn6VU7h+X5Al5bv5ebHl7J0VMKWfTNM3j166czoTCT6/60jD2NbcNaFmNiKZqBcAKwPehxpbcu3H0UeE5E3haRq4P2GauquwC82zGhnlxErhaRpSKytLo6sa+/1LV0WJ7REWzOhHxmjMnh0beHr3m0rqWDrz60nIqSbH53xTGU5KQzviCTX39qHs0dXfzwH2uGrSzGxFo0A2Gonhk9u8b1tc+JqjoP13x6nYicMpAnV9V7VHW+qs4vLS0dyKFxRVWpa+m0mSdGMBHho/MmsmxbHZuH6RrdT59bx96mdu74xFyy0/dPSzp9TC6fObGcJ1fsZGN107CUxZhYi2YgrASC+/FNBHq2/fS6j6oGbvcAf8c1tQLsDjSferd7Il7yONLY7sPn17hvGh3tPnLUBJIEHhuGTjMb9jTyp7e2ctmCKRw+If+g7Z8/eSppKUnc++rmqJfFmJEgmoFwCTBDRCpEJA24BHiyxz5PApd7vUcXAPWquktEskUkF0BEsoGzgXeDjrnCu38F8EQUX8Oot7exHYCSXAuEI9m4/AxOnF7CY8t24I/ymMJfvLiBjNRkvnTGjJDbS3LSuWDOeJ5cvoPmdpsqysS/qAVCVfUB1wPPAmuAh1R1tYhcIyLXeLs9A2wCNgC/Bb7orR8LvCYiK4DFwD9U9V/etluBs0RkPXCW99j0Ym+TS69WkpMe45KY/lx09ER21LWyaHNN1J5jw55Gnlq5k8uPL6e4j8/EpcdOormji2dW7YpaWYwZKVL632XwVPUZXLALXnd30H0Frgtx3CbgyF7OWQOcEdmSxq9qr0ZYmmuBcKQ7e9Y4ctJTeGzZDk6YFp0xjL/89wYyUpL5/MkVfe539JRCJhVl8o9Vu7h4fiQzFRgz8lhmmTi3t8lrGrUa4YiXmZbMBXPKeGbVLpqi0CS5YU8TT67YyeUnTOmzNgiuA8/5h5fx+oa9MRnfaMxwskAY56ob20lOEussM0p84thJtHR0RaXTzC//vZ6MlGSuPnlqWPufN6eMzi7l32t3R7wsxowkFgjj3N6mdoqy00hOit88ozB6c432dNSkAo6cmM8Db2zBXTmIjA17mnhqxU4uP77/2mDAERPyKcpO49V1lmnGxDcLhHGuurGd0gRoFh2tuUZ7EhGuOKGcjdXNvLYhcgHol/9eT3pKMp8/JbzaIEBSknDS9BJeWb83okHZmJHGAmGcq25qpyQBOsockGu0qxN2vwfbF0PlUmjYCQP9Ih+mXKOhXHBEGSU5adz/+paInO/9qgaeXLGTK04oH/C14pNnlLC3qZ01uxojUhZjRqKo9ho1sbe3sZ0ZY3JjXYyoW7cOFwB/fwFULoGu9gN3SMuFScfCtNNh6ukwdjb0NS1VIM/oZ6JW5F6lpyRz2YJyfvbCOlZW1nHExIIhne+2Z9eSk57CtadOG/CxJ89wWZleXV/NrPF5QyqHMSOVBcI4pqrsbeqI/8H0W99gat466GyFpmlw7OehbC5kFoLfB/XbYc8a2PIqPPdtd0xuGcw4C2acDVNPg/QePxY+2/fwgmj77Enl3P/GZm57bh0PfvbY/g/oxdtba3lhzR7+65xDyB9Emr1x+RkcMjaXV9fv5QuDCKTGjAYWCONYQ6uPji5//F4jVIU374Tn/4e8tA4YWw5fXNJ3Ta9+B2z8N2x4HlY/DssehKRUmLzA1RjL5kLZkTArr/fz+LugeS80VLrzNex095v3ulppUjLkjoOiqa72WThlwC8tNyOVa0+bxg+feZ9Fm2pYMHXgF0B9XX6+++S7lOam85kTywd8fMDJM0p4cNFWWju6yExLHvR5jBmpLBDGseomN5VOXA6m93fBU1+Gd/4Ah32Yhl1boSup7yAIkD8B5l3mlq5O2P4WrH8ONvwbXrsDtMvt19YEySmw7gSQJEDd/i01bumZPz45HXLGQHIa+DuhsQq6XFYfxh4OJ94Ih3/UBckwXX58Ob97bTP/+4/3ePyLJ5KSPLBL+r9/fQvv7mjgzk/OIytt8P/qJ04v4d7XNvPOtlpOmD78HYiMiTYLhHGsutF9EcddjVAVnv6KC4In3wSnf4tNX/3AwM+TnArlJ7nlrFtc0+ru1VC1Eq64DtQP36lwt4gLYlnFLuBll0LeBMgbD/kT3frgIKwKe9fDhhdcrfOxz8GrP4WP/sbVOMOQkZrMdz44m+v+vIzfvLKJ606fHvZL21rTzO3Pr+PMw8Zw/pxxA3xjDjRvSiEisHjLPguEJi5ZIIxj1YGsMvFWI3z+O7DsATjpq3DG/0TuvKmZMHG+W1K+6tZd8qfBnUsESme65bhr4P2n4J/fgN+eAWd9HxZ8sf/aK3D+nHFcMKeM259fx7zJhRw/rf8m0taOLq794zJSk4VbFh6OhPE8fcnPTOXQcXks3VI7pPMYM1LZ8Ik4Fph5Iq5qhKsegTd+AfOvgjO+E+vShCcpCWYthGvfgJnnwLPfhGe/BX5/v4eKCLd+bA7lxVlc/eBSVmyv63P/ts4uvvDHt3m/qoGfX3IU4wsyI/ISji0vZNm2Wnxd/ZfZmNHGAmEcq25qJyVJyM+Mk0l5q9fCk1+CSQvgvB+FVaMaUbKK4ON/cDXERXfCkzeEFQxzM1J58KrjyM9K5ZJ7FvG3JdtCTtW0qbqJT/zmTV5dX82tHz2C0w8dE7Gizy8voqWji/d2NUTsnMaMFNY0Gsf2NrZTkpNOUjykV2tvgr9dBmlZcPHv3fW90SgpCc69FTIK4OVbISMfzvm/foP6hIJMHvviCXzpL+/wjUdXce+rm7ngiDIqSrJpavfxxsYann23iqy0ZH7z6aM5e/bQrgv2dEx5EQCLN+8b8rhGY0YaC4RxzGWViYMxhKquh2jNerjscddBZTQTgdNuhrZ6VzPMGQMn3djvYWNyM/jz5xbw5Iqd3P/GFu54YX33tuLsNC47fgrXnjqNMXkZES/yuPwMJhVlsmTLPj4XZtJuY0YLC4RxbG9TnOQZXXIvvPsIfOB/YOqpsS5NZIjAOT+E5j3wwneh9BA45Lx+D0tKEi48agIXHjWBpnYfVfVtZKYlU5aXEfWa/zHlRby8thpVHXIHHGNGErtGGMf2NLSP/jGElUvhX/8NM891vUTjSVISLLwTxh8Fj37eXQMdgJz0FKaPyWFCQeawNH8fU15ETXMHW2tik4PVmGixQBinOrv8VDe1My4/Mr0GY6K5Bh66AvLK4CN3u8ARb1Iz4RN/hNQM+Mul0FoX6xL16ugphQAs3WrDKEx8icNvFgOwp7EdVSjLj/z1omHh73KD0Jv3wMcfdHlD+zB1qlsi5rMVw5dvNH+ie411W+Gxz7vXPgJNL80hNyOFty0Qmjhj1wjjVFV9K+A6OQyZrx1WPQxr/wm1W13tZezhMPtCqDg1OsMYXv6Rywn6wTtc02E/8iI9McLs/AifsB9TToDzfgz/+Cr85/9G5BjJpCRh3uRCllkgNHHGaoRxale9yzM6fqhNo1vfgF8dA09c51KP5U+AlAw3sP3BhXDvmbD5lQiUOMh7T7hAOPdTcPSVYR3S0OCWiFld75bhNP+zMO8Kl4pt9ePD+9xhOnpKIev2NFLf2hnrohgTMVYjjFO76lwgHFKNcMXf4PFroWAyfPpRmHbG/tpfZxusegheuhUe+BAcdRmc+/8Ons5ooKpWwd+vgYnHwAW3h13b3LRpaE97kPs2u9svR/i8fRGB83/ipox6/ItQMsPNmziCHD2lEFVYvr2OU2eWxro4xkSE1Qjj1K76NrLSksnLGORvnfeegL9/wTXZfeEVmH7mgUEpNQPmXQ43LIOTvgLv/BHuPsnNCj9YNRvhjx9zg80DHUjCNHOmWyLmxpluGW4p6fCJP7gfFH/9JLTsG/4y9GHupAKSBN7eMrLKZcxQWCCMU1UNrYzLzxjceK+qVfDY1S759Kcehow+LsClZsCZ34PPPONmabjvHHjxB+DrGNhz1m6BBz7sJtK97O9uPr8ByMpyS8RMynJLLOSOcz8EGnbCQ5e72nc0+DrcfIpN1S5pQRiy01M4rCyPt7fZdUITPywQxqmddW2D6zHa0QIPX+lqZZf8xXXvD8eUE+Ca1+HIT8Krt8HvzoQ974d37JbX3bXGjkaXOWbMoQMudk2NWyLmzb1uiZVJx8DCX8OWV+HRq6DLF5nzttbB4t+69/uHZfCzWXDbdPjJNJfHtWZjv6c4ekohy7fVWQJuEzcsEMapytpWJhYMokbz8o+gZgN89B7IGeA1oIw8uPBOV5upr4TfnOwGwzf3EqF87fDKbfDgh13Ozaueh7IjBl5mYPt2t0TMI5VuiaUjLoZzfwTvP+2u1XYNoYNKax288H24/TB45ibwtcEJN7heuef+yF3/XfUw3HksvPKTPpOBHz2lkOaOLtbubhx8eYwZQayzTBxq6+xib1M7EwsH2GN092p481dw1KeHlsrssA/BpOPgxVvgrbth6e9h+hluCqL8iS6BduUSWPkQNFW5KYo+/EsXDM2BFlwDnc3uvWzZ68YbDqRDUkcLLL4HXvuZy2065yI4/noYP/fgfRt3w79uhn//L+x+z/0YCpHcfN5kN6Zz2dZaZo+3v5kZ/aIaCEXkXODnQDJwr6re2mO7eNvPB1qAK1V1mYhMAh4ExgF+4B5V/bl3zPeAzwPV3mm+qarPRPN1jDaVtS4F1qSiAdQI/X546kYXjM76wdALkTMGFv7K1ToW/9bVat5/ev/2pFSYdjoc/xuYetrQny+enfw1yC51f597TnNNppOP6/sYX4ebvPiVn0DTbph+Fpz5XRg3p/djcsfCRfe5IPn8d1yz+MI7D+q5O7Ewk7F56by9tZbLji8f4oszJvaiFghFJBm4EzgLqASWiMiTqvpe0G7nATO85TjgLu/WB3zNC4q5wNsi8nzQsT9T1duiVfbRbnutG0w/oBrh6segcrH7ks0qilxhSg+BC25zg8X3bXKZYlKz3NCAtOzIPU+8m3c5FJa7YRX3nQPzLoPjroWxs/bvo+qy06x8CJY9CPXbYfLxcPH97hpuOETgxC+7muTLt7qhM6fd3GMX4egphZZqzcSNaNYIjwU2qOomABH5K7AQCA6EC4EHVVWBRSJSICJlqroL2AWgqo0isgaY0ONY04vKfQOsEXZ1uuawsYfDkZdGp1BJSVAy3S1mcCpOgS++6f5WS3/vgl3ueCia6gJY9fvQXL1/3w/+7OBhL+E67WYXSF/6f1A2Fw4594DN8yYX8syqKnY3tDE2CtM+GTOcotlZZgIQ3H2h0ls3oH1EpBw4CngraPX1IrJSRO4TkZBJKEXkahFZKiJLq6urQ+0StyprW0lLTgp/CqZlD0LtZpfWKx4TW8eT9Fw470fw1ffggp9C+YmgXW7YyYyz4byfwJdXwBVPwYyzBp/+TsQF0rGHw5M3QPOBPWgDCbgt3ZqJB9H81gv1H9hzsFKf+4hIDvAocKOqBhJo3QVMA+biao0/DfXkqnqPqs5X1fmlpYmVAWN7bQsTCsOcmqez1fUUnXy8+yI1o0N2CRzzOfjYvfDZf7nlwl/DcVe7JtRISEmHj/wG2urcxMhBYw1nj88nLSXJEnCbuBDNQFgJTAp6PBHYGe4+IpKKC4J/UtXHAjuo6m5V7VJVP/BbXBOsCbJlbwvlxWE2i77zR9eZ4gPfjk7ybDO6jTscTv/mQZ2d0lKSOHJivg2sN3EhmoFwCTBDRCpEJA24BHiyxz5PApeLswCoV9VdXm/S3wFrVPX24ANEpCzo4UeAd6P3EkYfVWXz3mbKS8LoiNLVCa//HCYtgCknRr9wZnQ6/gYYMwv+9U3XguA5ekoR7+6op61zZE4bZUy4ohYIVdUHXA88C6wBHlLV1SJyjYhc4+32DLAJ2ICr3X3RW38icBnwARFZ7i3ne9t+LCKrRGQlcDrwlWi9htFod0M7rZ1dTA0nEK562HWIOPlro742GDe5Rkei5BTX67d+G7x2R/fqo6cU0tmlrNoxzLN0GBNhUR1H6I3ve6bHuruD7itwXYjjXiP09UNU9bIIFzOubN7bDEBFSU7fO/q74NXbYewc16lilItonlGIXZ7RkariZDj8Y/D6HS7hQsEk5k0uAGDJln0cUx7BITfGDDPrIhhnugNhaT81wvefhpr1cPJXR31tEOIw1+hIdNYtrsPMSy4vRnFOOjPH5rBok81EYUY3C4RxZvPeJtJTkijra2yXqpv8tWiaS28WB+Iy1+hIkz8Rjv08rPizmzMROGFaCUs276PDZwm4zehlgTDOrN/TxLTSnL6HTmx8EXatcPMIJiUPX+GiaPZst0TMd2a7xRzo5K9BWo7LfQocP62Y1s4ulm+vi225jBkCC4RxZl1VI4eM6ycp86u3Q94EOOITw1OoYZCa6paIyU91izlQVpFLwbb2Gdi2iAUVxYjAGxutGdmMXhYI40hDWyc769uYObaPQLj1Tdj6OpzwJUhJG77CRVlVlVsi5tkqt5iDLbgWcsbCC98jPzOFw8fn88bGSF6gNWZ4WSCMI+u9+eEOGddHj9HXboesYpfEOY5EPBA+V+UWc7C0bDj1G7DtTVj3LCdMK+adbbW0dth4QjM6WSCMI2urmgB6rxHufAfWPwcLvghpNjzADMG8y11nqxe/z/FT3XjCxVus96gZnSwQxpHVO+vJTU9hQkEv0y+9/BM33+CxVw9vwUz8SU6FM/4H9rzHgqZ/k5GaxL/X7I51qYwZFAuEcWTVjnrmTMxHQo0L3LUS1v4DFlwHGXnDXzgTf2ZdCOPnkfHK/3HStCJeWLMH1Z559Y0Z+SwQxol2XxdrdjUwZ2J+6B1e+TGk58NxXxjegpn4JQJnfR8aKjkz7T121LXyflVjrEtlzIBZIIwTa6sa6exSjpxYcPDGqndhzVOw4BrIDLHdmMGqOAWmn8kHtrjc+C9a86gZhSwQxonAgOY5E0LUCF/5CaTlwnHXHLzNmKE683uM6djOkXlNvLBmT6xLY8yAWSCME4s372NcXgYTC3t0lNmxDN573NUGsywxsomCcXPgiE9wZutzrNheR1V9W6xLZMyAWCCMA6rKW5v3cdzUogM7yqjCc/8DWSVuAL0x0XLGd/hg2lIUeGL5jliXxpgBieo0TGZ4bKlpobqxnWMretT41v0Ltr4G598Wlz1FfX4f7V3ttHe1M+PQDhRlU/0mMpMzSU9JJyM5g4yUDJJkEL/3LM/owORPoOK0K5j3zDoeXSRcfcrU0L2XjRmBLBDGgdfWVwNw/NTi/St97a42WDwdjr4yas/d5mujvr2eho4Gt7Q30O5vp8vfRZd20eXvwqc+d+v34fP76PR3HnTb6e+ko6ujO7C1dbW5x772g9a1+dytT33d5dhUtwmAhY8fOJuGIOSl51GYXkhhRmH37ZisMZRll1GWU0ZZdhnjsseRnpy+/0DLMzpwx1/Hx167gW/VzuTdrdXMKR8T6xIZExYLhHHgxff3UF6cRUXwrPSv3eHmG/zUI27w8xA1dzbzXs17rNq7inW169jeuJ3Kxkr2tQ0+m0iyJJOSlEJqUiopSSmkJaeRnpxOerKrzaUlp5GTlkNRchEZyRnd2wK1vbTkNLc+JZ2bHroNEeFHJ99Ce1c7rb5W2rvaafG1UNtWS117HbVttWxr3MaK6hXsa9uHcuCYt6KMIsqyyxifM55vPfEeqUmpvD97MZPzJjMma8zgapaJJCWdDy78BN9/sIFHn36KOddfFesSGRMWC4SjXEuHjzc21vDp46bsb4raux5evQ1mf3TQs8/7/D5WVK/gtR2v8dqO11i7b2134CjLLmNy7mROn3Q643PGU5BeQF56HnlpeeSn5ZOenE5ykgtyKZJCclLyQUEvJSklooHl2n2/AeD8qeeHtX9nVye7W3azq3mXW5p2dd9fX7uetBdqUOCqee7LPC0pjUm5k5iUN4nJuZOZlOvd5k2iLLuMlCT7VwLIn3UGZxXfy+OVOXx98xKyKo6JdZGM6Zf9945y/35/Dx0+P2ce5jVD+bvgqS9Daiace+uAz7etYRuPrHuExzc8Tm17LcmSzNwxc7n2yGuZUzqH2cWzKcwojPCrGLq5cwe2f2pyKhNzJzIxd2LI7ZpzKh3+Tn57xO1sa9jG9sbt7rZpO4t2LqKta3/PyBRJYULuBCryK5iaP5VpBdOYmj+VivwKslOzQ54/nn32wnP4x+9W8pe/PshVX5vlknQbM4JZIBzl/r5sB2Pz0jkucH3wlZ+4aZYuvAtyx4Z1Dp/fx8vbX+ahdQ/xxs43SJZkTp90OudWnMvx448nLy3+Otr0R0RIT05jQdkCFpQtOGCbqlLdWt0dILc3bmdLwxY212/mtR2v4fPvv3Y5LnscU/OndgfI6QXTmV4wnZy0PmYIGeWOnjGJBWXv8ttdx/LpJ75K+kV3uyw0xoxQFghHserGdl5eV81VJ1WQnCSw+RV46VY48lKY+8l+j69qruKx9Y/x6LpH2dO6hzFZY/ji3C/y0ekfZWx2eEF0pNi+PcInfNg74ZkHbxIRxmSNYUzWGOaPm3/ANp/fx/bG7Wyq38Smuk3utn4Tj65/lFZfa/d+47LHMb1gOjMKZrgAWTidqflTyUzpJWH6KHPd+fO57HeLeWzFbi4t/x0c87lYF8mYXlkgHMX+8OYWulT5xDGToGYjPPwZ10v0/Nt6Pcavft7c+SYPrX2Ilytfxq9+TphwAt+a+S1OmXjKqL3WVRPpeWEXDe6EKUkpVORXUJFfwRmTz+he71c/u5p3saF2A+vr1rOxbiMb6jaweNdiOvwdgOvhOjF3YnetcXrBdKYXTqcir4LUCHR4Gk4nTS/hyIn5/KLqUhY+cyNZ+ZNg5jmxLpYxIY3Obz1DY1snf1i0lTMOHcvUjCb43YWAwqV/hfSDm932te3j8Q2P8/Dah6lsqqQoo4grZl/BRTMvYlLupGEvf6JJkiQm5ExgQs4ETp10avf6QA1yY91G1tetZ0PtBjbWbeSVylfoUjfRbYqkMDlvcndgnJo/lfHZ4xmXPY7izOIR2ZtVRPifD87iorvr+UX257j5ocvhssdhyvGxLpoxB7FAOErd9dJGals6ueGYbHjgw9BcA1c+BSXTu/dRVZbtWcZDax/i+a3P0+nv5OixR3PDUTdw5pQzSUtOi+ErMHBgDfLMKfvbYTu6OtjSsMUFyNr1bKjbwJp9a3h+6/MHDPtISUphbNZYxmaNpTizmKKMIgrSC7rHTBZkFHSvK8ooGta/+fzyIi45ZhL3LIUzildzzB8/BhffDzPPHrYyGBMOC4Sj0Hs7G7j31c1cODODI//xYehqh089DBOOBty1v6c2PsUTG59ga8NWclNzuXjmxXz8kI8zrWBajEtvwpGWnMbMwpnMLJzJeRXnda9v9bWytWErVc1V3cuu5l1UNVexvnY9de111LfXHzRGMiA7Nbs7KAaCZVFGEQUZBZRmljI+Zzzjs8dTmlUakWbyb39wFm9uquHa1i/wRFE7E/7yCTjn/7npwKwDjRkhJBEm0pw/f74uXbo01sWIiH3NHVx01+s0NtTzbPKXKcrLg08/wr7cUl7e/jLPbn2WN3e+iV/9zB87nwunX8hZU84iKzUr1kV3k7Z2duLv6EADS3s72tGBv6MDOjvR/paO0OtnbnkMFNYUndFjWwf4fEGlkP1fwAfdgqSkIqmpjP/nXSDC7o9/HUl16yQtFVJS9j8OXlJSD9hv//qD9yelx34pKZCURHdYENm/BD0ON2VZl7+L+o566trqqG2vpbatln1t+7qTCvS8X9tW232dMiBZkhmbNZaynDIm5ExgSt4UyvPKKc8vZ0relAOz8PRj3e5GPvbrNyjOTuUPY/7MpC0Pw7QPwId/Cfmhh6+MchbhRxkLhKOI+jr5xM//yfJqP39M+yEzDp3M0zNP5sW97/DOnnfwq5+y7DI+NO1DXDjtQiblhX/tT1XR9nb8zc34m5roamry7je72+Ymtz5o3f4g1u4FtQMDnL/z4HUR5wWXY+e57563N+YdHKSSk10wUXUL7K8xBT7+3jbt6kI7Oyh//1lQZdP4Ew4KuoyE/5ngYBkUNKWXbQesByQ5GUlPR9LTIT2V9qw09uUK1XlKdY6f6mwfezJ9VGe0U5Xayt7klv1PjVCWWsyUrImU506homgaFWMOpbygotcMPG9vreXK3y9GgJ8etYez3v06qB/mXwUnfjnsoT6jhAXCUSaqgVBEzgV+DiQD96rqrT22i7f9fKAFuFJVl/V1rIgUAX8DyoEtwMdVtbavcozqQOjroH37W7Ss+hsZ7/+DdxrHsTm9hLdLN/F8hvvCmZ5bwanFCzgtZy4z/KVoS8uBgcy739XctD+wdW/3gltzc4+aUy+SkkjKySEpK4uk9HQkLc19oaaleffTSEpLQ1IPXJ+UnrZ/n7TA+tT950hN3X/bc+llPamp3bWkghtPA6Dujpci874fWeBuV9QdsFpVoavLBUWfzwV4n29/bdXnBcuetdbAPsG12qB1+P2BZ3ABOfB/qeqCdfBjvO29bOs+PrCt+weA7n8NPhfw/e3t7sdKezva0Y4/cL+9HX9rq/vcNDXRRie7imBHsbCzSNhZDLuKhJ1F0J62/3s/zQfjmlMZ357J+K48xlPAhJQSxmWU0pk2nu9XjeX9liROLPZzbdaLLKh+kGQBpp+FHP5RmHpaPARFC4SjTNQCoYgkA+uAs4BKYAlwqaq+F7TP+cANuEB4HPBzVT2ur2NF5MfAPlW9VURuBgpV9Rt9lWWogVBV3ReV33/gfe/24PXqfu36/d1fll0d7bS0NdLW0UJrexNtHS20dbTS1tlCu6+Nps4m6jqbyGreRFFLJbmdtRR0NTLZ10qmKp3Aa1mZPJmVzeb2XKZXCbM3+Th0s4+Cln5fAknZ2S6Add9mkZyTQ1J2To9t2d76bLftgH2zkczMETmrwHAFwkSkqq7m39SEv7GRrqZm/E2N+Jua8NU3sLtpF1tbKtnWsZsdXTXsSKpnZ2ozVZntdCQf+P2S1paM7DmBuuZT8ZFDpjZwmi7ix2kPkpvifgw0+dJp9ufSIgX4UorpyJiIP2caaWlZpKdnkpqWSUpaBsnpbklJzyApI7P7xxYpKa7Gm5wMyclIUpJbF3zbc3tS0oCan/sx8v5BTJ+i2VnmWGCDqm4CEJG/AguB94L2WQg8qC4aLxKRAhEpw9X2ejt2IXCad/wDwEtAn4FwoDacfQ6du3ZBV1fQL/Wh2VoK//W5/t/ur7fWcmZjIzWSRBVpLPbns7ezgLaOsUyoK+VLFJOekU1SbhZJJ2SRdFZQIMvJJvmggJdDUlam+2c3ZhBEBElPJyk9HYqLD9peCBwa4ji/+qluqWZ743aqmnaxp34Hu+t3smd8FbuaHqFyXwn76it4MSmfE8dN4NCOTha0tnFYRwfTO+op79xLKht4OG01t+QFTTHW6S3NQWX0Kz98oItpVRF5wd1LSlERM159JQInNSNZNAPhBCA430clrtbX3z4T+jl2rKruAlDVXSIScq4XEbkauNp72CQiawfzIiJmLfBar1tLgL0Al3vLgbZFqVAD0l3GEaxEfi6RLWNka7+j4j0kRmVcBTwccksDsDN4RcgyfigahYLBfAb+parnRqMoJjqiGQhDfXp6tsP2tk84x/ZJVe8B7hnIMbEiIktVdX7/e8aOlXHoRnr5wMpoElM028sqgeBuixPp8bOuj336Ona313yKd7sngmU2xhiTYKIZCJcAM0SkQkTSgEuAJ3vs8yRwuTgLgHqv2bOvY58ErvDuXwE8EcXXYIwxJs5FrWlUVX0icj3wLG4IxH2qulpErvG23w08g+sxugE3fOIzfR3rnfpW4CERuQp38eziaL2GYTQamnCtjEM30ssHVkaTgBJiQL0xxhjTG+tTb4wxJqFZIDTGGJPQLBCOACLyExF5X0RWisjfRaQg1mUKEJFzRWStiGzwMvmMKCIySUT+IyJrRGS1iHw51mXqjYgki8g7IvJ0rMsSipfQ4hHvs7hGREbc5IEi8hXv7/yuiPxFRDJiXSYz+lkgHBmeBw5X1SNwqeX+O8blAbrT5N0JnAfMAi4VkVmxLdVBfMDXVPUwYAFw3QgsY8CXgTWxLkQffo4bDH4ocCQjrKwiMgH4EjBfVQ/HdaS7JLalMvHAAuEIoKrPqWog4/Ui3LjJkaA7TZ6qdgCBVHcjhqruCiRqV9VG3Jf3hNiW6mAiMhG4ALg31mUJRUTygFOA3wGoaoeq1sW0UKGlAJkikgJkcfDYZGMGzALhyPNZ4J+xLoSntxR4I5KIlANHAW/FuCih3AF8HYhM8trImwpUA7/3mm/vFZHsWBcqmKruAG7DDZvahRt3/FxsS2XigQXCYSIiL3jXNXouC4P2+Rauqe9PsSvpAYac6m64iEgO8Chwo6o2xLo8wUTkg8AeVX071mXpQwowD7hLVY/CpbQeUdeERaQQ1yJRAYwHskXk07EtlYkH0cw1aoKo6pl9bReRK4APAmfoyBncGU6avJgTkVRcEPyTqj4W6/KEcCLwYW/asQwgT0T+qKoj6Uu8EqhU1UBt+hFGWCAEzgQ2q2o1gIg8BpwA/DGmpTKjntUIRwBvEuJvAB9W1TBmFxw24aTJiylvcuffAWtU9fZYlycUVf1vVZ2oquW49/DfIywIoqpVwHYROcRbdQYHTpk2EmwDFohIlvd3P4MR1qHHjE5WIxwZfgWkA897E4MuUtVrYlukflPdjRQnApcBq0Rkubfum6r6TOyKNGrdAPzJ+9GzCS/l4Uihqm+JyCPAMtwlhHewdGsmAizFmjHGmIRmTaPGGGMSmgVCY4wxCc0CoTHGmIRmgdAYY0xCs0BojDEmocVNIBSRj4iIisihsS5Lf0Rki4isEpEVIvKciIwLWl8StN9pwTMViMiF3gwV73tZaS7q4zn+5M0a8a6I3OcNOu+5T5a33ypvv9e8DC1RJyLfE5GbBnlskoj8wivzKhFZIiIV/Rxzf1/vV499C0Tki4MpWySISFOPx1eKyK8icN4uEVnuzd6wQkS+KiJJ3rb5IvKLoT7HEMs3oDKISKaIvOwlhx/K8x4qIm+KSHvPz2RfM3KIyA3e/9hqEfmxt65cRFq993m5iNwdtP8LXnYcM8LE0zjCS4HXcAOWvzfUk4lIsqp2DfU8fThdVfeKyA+Bb+Ky6vdVniNxeRbPUtXN3hf/CyKyuZfUXX8CAoO2/wx8Drirxz5fBnar6hzvOQ4BOgf9itw5UoISiEfLJ3Apto5QVb+X0Lo5gucvAL4I/DqC5xwJWlV1LoCIjMF9LvKB76rqUmBpDMvGIMrwWeCxCPyf7sP9/10YYltgRo6LvPGVWQAicjou3dsRqtruvZ8BGwPvcw9/wH2u/m+I5TURFhc1Qq8WcyJwFd60LCJynog8FLTPaSLylHf/bO8X4DIReThQC/JqZN8RkdeAi0Xk815tY4WIPCoigX+CaSKyyNt2S/AveBH5L2/9ShH5fhjFfwWYHsZ+NwE/VNXNAN7tD4GvhdpZVZ9RD7CY0DNalAE7go5Zq6rt3uu43HsNK0TkD966KSLyorf+RRGZ7K2/X0RuF5H/AD/y3p9/icjbIvKq9F5LP1JE/i0i60Xk8965/iAH5l/9k4h8OES5d6mq3yt3parWevsH/y0uEpH7g4470yvPOnH5PxGR2SKy2Pv1vlJEZgC3AtO8dT8RkRzv9S7zaqALvWPLvVrCb71awXMikultm+7VAFZ4x03z1g/083EAEblYXE14hYi84q1L9soZOO8X+juPqu4BrgauF6e79UFEjhWRN8Ql337D+4EUaEF4yHuOv4nIWyIyP/C+i8j/eeVaJCJjvfW9fWZCvY7gMpwq+2tV74hIboiX8SngiaBjX5L9tbc/iUioXLkh3wtVXUKPH4HS94wc1wK3Bv5fvPezP0/ifrCbkUZVR/2Cq/n8zrv/Bi55cAouJVO2t/4ub78SXPAJrP8G8B3v/hbg60HnLQ66/7/ADd79p4FLvfvXAE3e/bNxmS4E9yPjaeCUEOXdApR4938F/Cho/SpgubdsAJ72ti0DjuxxniOB5f28N6nesSeH2DYX2AO86b2+Gd762cDaoDIWebdPAVd49z8LPO7dv997rcne4xeDznUcLqVYz+f+HrACyPT+JttxtbxTg86bD2wGUnocO9F7r5YDPwWOCtrWFHT/IuD+oDL+y/u7zMDl1swAfgl8ytsnzStPOfBu0HlSgDzvfon3dxFvPx8w19v2EPBp7/5bwEe8+xm4mkS4n4+mHo+vBH7l3V8FTPDuF3i3VwPf9u6n42pVFf2d11tXC4wFTmP/Zy0v8J7j8ns+6t2/CfiNd/9w77XP9x4r8CHv/o+DytPbZybU6wguw1PAid79nBCfgTSgKujxaUC999lIwn2mT/K2/Yz9/1PBy80hPpM39fj/WIz77LyDm0Ir8L2xHPi+93d+GTjGW1+Oa514x1t/co/nWE/Q94otI2OJixoh7lfWX737f8UFKR/ui+9D4uYuuwD363EBbpLZ18Wl5LoCmBJ0rr8F3T/cq0Gswv36nO2tPx542Lv/56D9z/aWd3DB51Dcl24o//GePw/4f0HrT1fVueqaVj4XtF44eOaHcH7x/hp4RVVf7blBVZfjpt/5CVAELBGRw4APAI+o6l5vv33eIcez//X+ATgp6HQPq2qXuNr1CcDD3uv7Da4GF8oTqtrqPc9/gGNV9WVgurimpktxX8IHNLWqaiVwCG4CYz/wooicEcZ78ZCq+lV1PS6F2KG4L8xvisg3gCmq2hriOAF+KCIrgRdwU1GN9bZt9t5HgLeBcq/2MkFV/+6Vt01dDtmBfD56CvztXwfu92rQgWtjZwOXe+/3W0DxAM4b6jOUj/v7vYsLIoHP/Ul4/2eq+i6wMuiYDlxgB+998O739pkJ9TqCvQ7cLiJfwgXKns3tJUBdj3WL1bUO+HGBqtwr61cC/1M9lltDPG+wvmbkSAEKcd8n/wU85NVAdwGTvf2/CvzZq1kG7MH94DMjyKi/Rigixbgv7sNFRHH/VCoiX8cFtetw1wCWqGqj92F9XlV7a6IIvtZ0P3Chqq4QkStxvzr7LA7w/1T1N2EU/fRAoAnTamA+B375zMO7piIiz+K+nJeq6ue8dd8FSoFem8pUtQl4DHhMRPzA+bgmonBy7wXvE3jfkoA6DX2NpK/jgx//AffD4xJcLSJUudtx8zb+U0R2467vvNjjnBn9PZ+q/llE3sL9UHpWRD6HC5LBPoV7H49W1U4R2RJ07vag/bpwNcrefqCE+/loFZE0dZMhg/uREvhRco2IHOeVd7mIzPXOe4OqPtvPeQ8sjMhUr8x7gMOCNv0A+I+qfkTcHI8vBZW/N52qGnh/u+j9u0X7eB37d1K9VUT+gfs8LhKRM1X1/aBdWjn479vzb5Hivc6fAaeHKMtf+wmGfc3IUYm7PqnAYu9/p0TdzBiB5tK3RWQjMJP91z4zvLKbESQeaoQXAQ+q6hRVLVfVSbjmtJNw/8DzgM+zv6a3CDhRRKZD93WPmb2cOxfYJa7H5aeC1i8CPubdvyRo/bPAZ2X/NccJcuBF9KG4Dfhv74spMAntjbjaHKp6jvcrNxAEPwecg6sdh5wMVkROFK8Xm7iOALOArbiA8nHvRwYiUuQd8kbQ6/0UrnPSAdTNBbhZRC72jhVxHX1CWSgiGd7znIab7QLcD5AbvfMdlORbROaJyHjvfhJwhFdugN0icpi3/iM9Dr1YXI/Tabia8FovGGxS1V/gruEcATTi/vYB+bj5BDvFdZKYQh+896BSRC70ypgu7vpyuJ+Pl/E6Oom75vhxXI0ZEZmmqm+p6ndwwXGSd95rvc8pIjJT+plUV0RKgbtxTa49fyDks//a8ZVB61/zyoKIzALm9PUcnpCfmV5eR3D5pqnqKlX9ES6IHHCdWd014WQR6RkMDzLYGqH2PSPH47gf4HjfH2nAXhEpFa8Xq/fZmoH3w8r7ET4O16xvRpBRXyPENZ/1/EA/CnxSVV/1Lr5fiWsCRVWrvdrdX0Qk3dv/28C6EOf+H1xT01bcNY3Al+ONwB9F5GvAP3DXJlDV57ymxTe96/RNuC+0cC6k90lVl3vNd0955S7H1SrX9nLI3V65A2V5TFVv6bHPNOAu7x80yXstj6qqisj/AS+LSBeuKe9KXM+6+0Tkv3Czmfc2O8GnvPN+G3eN8q+464E9LfaeczLwA1Xd6b3W3SKyBvdlE8oY4LdBf7/FuGut4H6xP4275vgu7vpSwFpckBkLXKOqbSLyCeDTItIJVAG3qOo+EXndaxr8J/Aj3Pu+FNfkFlwz6c1lwG9E5BZcDfviAXw+vuwd+yVcLexBVX3F2/YTcR16BPeDZQWulaAcWOb9LasJ3QMy02s+TcVd3/sDEGrqqh8DD4jIV4F/B63/tbd+Je4zsRLvs9+H3j4zoV7HqUHH3ej96OjCBZ9/hjj3c7gfvC/0U4Y+iRu+tBR3mcIvIjcCs7wfNL3NyHGf97rexTULX+H935wC3CIiPq/s1wRdWjgaN7NMtHtVmwGy2ScGwft13+p98C/B1boW9ndchMtwK64jyjlBTWhxwXt/VwHzVLW/L1ozTLyaTqr3A2IaLoDNjNXnT0SOAr6qqpfF4vkHSkR+Djypqi/GuizmQPFQI4yFo4Ffeb++6+jlOlY0qepImz08IkTkTNyv7dstCI44WbhOXqm4mty1sfwRpqrviMh/JPpjfiPlXQuCI5PVCI0xxiS0eOgsY4wxxgyaBUJjjDEJzQKhMcaYhGaB0BhjTEKzQGiMMSah/X+7LMgrp1ts4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "phq2_mean = have_phq2[\"avg_phq2\"].mean()\n",
    "sud_phq2_meaned = have_phq2[have_phq2[\"SUD\"]==\"emergency\"][\"avg_phq2\"].mean()\n",
    "sud_phq2_meanop = have_phq2[have_phq2[\"SUD\"]==\"outpatient\"][\"avg_phq2\"].mean()\n",
    "sud_phq2_meanboth = have_phq2[have_phq2[\"SUD\"]==\"both\"][\"avg_phq2\"].mean()\n",
    "nosud_phq2_mean = have_phq2[have_phq2[\"SUD\"]==\"none\"][\"avg_phq2\"].mean()\n",
    "\n",
    "mhd_phq2_meaned = have_phq2[have_phq2[\"MHD\"]==\"emergency\"][\"avg_phq2\"].mean()\n",
    "mhd_phq2_meanop = have_phq2[have_phq2[\"MHD\"]==\"outpatient\"][\"avg_phq2\"].mean()\n",
    "mhd_phq2_meanboth = have_phq2[have_phq2[\"MHD\"]==\"both\"][\"avg_phq2\"].mean()\n",
    "nomhd_phq2_mean = have_phq2[have_phq2[\"MHD\"]==\"none\"][\"avg_phq2\"].mean()\n",
    "\n",
    "phq2_mean = np.round(phq2_mean, 0)\n",
    "sud_phq2_meaned = np.round(sud_phq2_meaned, 0)\n",
    "sud_phq2_meanop = np.round(sud_phq2_meanop, 0)\n",
    "sud_phq2_meanboth = np.round(sud_phq2_meanboth, 0)\n",
    "nosud_phq2_mean = np.round(nosud_phq2_mean, 0)\n",
    "\n",
    "mhd_phq2_meaned = np.round(mhd_phq2_meaned, 0)\n",
    "mhd_phq2_meanop = np.round(mhd_phq2_meanop, 0)\n",
    "mhd_phq2_meanboth = np.round(mhd_phq2_meanboth, 0)\n",
    "nomhd_phq2_mean = np.round(nomhd_phq2_mean, 0)\n",
    "\n",
    "\n",
    "phq2a = sb.displot(data=have_phq2, x=\"avg_phq2\", hue=\"SUD\", kind=\"kde\")\n",
    "positions1 = [[nosud_phq2_mean, sud_phq2_meanop, sud_phq2_meaned, sud_phq2_meanboth]]\n",
    "\n",
    "for ax, pos in zip(phq2a.axes.flat, positions1):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-.')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle='-')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average PHQ-2 Score by Substance Use Diagnosis (n=1665)\")\n",
    "plt.show()\n",
    "\n",
    "phq2b = sb.displot(data=have_phq2, x=\"avg_phq2\", hue=\"MHD\", kind=\"kde\")\n",
    "positions2 = [[nomhd_phq2_mean, mhd_phq2_meaned, mhd_phq2_meanop, mhd_phq2_meanboth]]\n",
    "\n",
    "for ax, pos in zip(phq2b.axes.flat, positions2):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-.')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle='-')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average PHQ-2 Score by Mental Health Diagnosis (n=1665)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "b32f7663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "\n",
      "3.0\n",
      "2.0\n",
      "2.0\n",
      "3.0\n",
      "\n",
      "3.0\n",
      "2.0\n",
      "3.0\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "print(phq2_mean)\n",
    "print()\n",
    "print(sud_phq2_meanop)\n",
    "print(sud_phq2_meaned)\n",
    "print(nosud_phq2_mean)\n",
    "print(sud_phq2_meanboth)\n",
    "print()\n",
    "print(mhd_phq2_meanop)\n",
    "print(nomhd_phq2_mean)\n",
    "print(mhd_phq2_meaned)\n",
    "print(mhd_phq2_meanboth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "df77d620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(454, 93)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark = adults[adults[\"avg_hark\"].notnull()].copy().reset_index()\n",
    "have_hark.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f1c2391b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    277\n",
       "1    177\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "14fc5011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    392\n",
       "1     62\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f0b75be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    348\n",
       "1.0     51\n",
       "2.0     24\n",
       "3.0     18\n",
       "4.0     13\n",
       "Name: avg_hark, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[\"avg_hark\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "431da793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "51+24+18+13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "0967ddf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    63\n",
       "0    43\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[have_hark[\"avg_hark\"]>0][\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4cdb2ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    103\n",
       "1      3\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[have_hark[\"avg_hark\"]>0][\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "da199a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAZY HACK AGAIN\n",
    "have_hark[\"SUD\"] = \"\"\n",
    "have_hark[\"MHD\"] = \"\"\n",
    "have_hark[\"ACC\"] = \"\"\n",
    "\n",
    "for i in range(len(have_hark)):\n",
    "    if pd.notnull(have_hark.at[i, \"SUD_ed\"]):\n",
    "        if have_hark.at[i, \"SUD_ed\"]==1:\n",
    "            if pd.notnull(have_hark.at[i, \"SUD_op\"]):\n",
    "                if have_hark.at[i, \"SUD_op\"]==1:\n",
    "                    have_hark.at[i, \"SUD\"] = \"both\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"SUD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_hark.at[i, \"SUD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_hark.at[i, \"SUD_op\"]):\n",
    "                if have_hark.at[i, \"SUD_op\"]==1: \n",
    "                    have_hark.at[i, \"SUD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"SUD\"] = \"none\"\n",
    "        else:\n",
    "            have_hark.at[i, \"SUD\"] = \"none\"\n",
    "    elif pd.notnull(have_hark.at[i, \"SUD_op\"]):\n",
    "        if have_hark.at[i, \"SUD_op\"]==1: \n",
    "            have_hark.at[i, \"SUD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_hark.at[i, \"SUD\"] = \"none\"\n",
    "    else:\n",
    "        have_hark.at[i, \"SUD\"] = \"none\"\n",
    "        \n",
    "        \n",
    "    if pd.notnull(have_hark.at[i, \"MHD_ed\"]):\n",
    "        if have_hark.at[i, \"MHD_ed\"]==1:\n",
    "            if pd.notnull(have_hark.at[i, \"MHD_op\"]):\n",
    "                if have_hark.at[i, \"MHD_op\"]==1:\n",
    "                    have_hark.at[i, \"MHD\"] = \"both\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"MHD\"] = \"emergency\"\n",
    "            else:\n",
    "                have_hark.at[i, \"MHD\"] = \"emergency\"\n",
    "        elif pd.notnull(have_hark.at[i, \"MHD_op\"]):\n",
    "                if have_hark.at[i, \"MHD_op\"]==1: \n",
    "                    have_hark.at[i, \"MHD\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"MHD\"] = \"none\"\n",
    "        else:\n",
    "            have_hark.at[i, \"MHD\"] = \"none\"\n",
    "    elif pd.notnull(have_hark.at[i, \"MHD_op\"]):\n",
    "        if have_hark.at[i, \"MHD_op\"]==1: \n",
    "            have_hark.at[i, \"MHD\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_hark.at[i, \"MHD\"] = \"none\"\n",
    "    else:\n",
    "        have_hark.at[i, \"MHD\"] = \"none\"\n",
    "        \n",
    "    if pd.notnull(have_hark.at[i, \"ACC/INJ_ed\"]):\n",
    "        if have_hark.at[i, \"ACC/INJ_ed\"]==1:\n",
    "            if pd.notnull(have_hark.at[i, \"ACC/INJ_op\"]):\n",
    "                if have_hark.at[i, \"ACC/INJ_op\"]==1:\n",
    "                    have_hark.at[i, \"ACC\"] = \"both\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"ACC\"] = \"emergency\"\n",
    "            else:\n",
    "                have_hark.at[i, \"ACC\"] = \"emergency\"\n",
    "        elif pd.notnull(have_hark.at[i, \"ACC/INJ_op\"]):\n",
    "                if have_hark.at[i, \"ACC/INJ_op\"]==1: \n",
    "                    have_hark.at[i, \"ACC\"] = \"outpatient\"\n",
    "                else:\n",
    "                    have_hark.at[i, \"ACC\"] = \"none\"\n",
    "        else:\n",
    "            have_hark.at[i, \"ACC\"] = \"none\"\n",
    "    elif pd.notnull(have_hark.at[i, \"ACC/INJ_op\"]):\n",
    "        if have_hark.at[i, \"ACC/INJ_op\"]==1: \n",
    "            have_hark.at[i, \"ACC\"] = \"outpatient\"\n",
    "        else:\n",
    "            have_hark.at[i, \"ACC\"] = \"none\"\n",
    "    else:\n",
    "        have_hark.at[i, \"ACC\"] = \"none\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "69116653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krash\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:316: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_mean = have_hark[\"avg_hark\"].mean()\n",
    "sud_h_meaned = have_hark[have_hark[\"SUD\"]==\"emergency\"][\"avg_hark\"].mean()\n",
    "sud_h_meanop = have_hark[have_hark[\"SUD\"]==\"outpatient\"][\"avg_hark\"].mean()\n",
    "sud_h_meanboth = have_hark[have_hark[\"SUD\"]==\"both\"][\"avg_hark\"].mean()\n",
    "nosud_h_mean = have_hark[have_hark[\"SUD\"]==\"none\"][\"avg_hark\"].mean()\n",
    "\n",
    "mhd_h_meaned = have_hark[have_hark[\"MHD\"]==\"emergency\"][\"avg_hark\"].mean()\n",
    "mhd_h_meanop = have_hark[have_hark[\"MHD\"]==\"outpatient\"][\"avg_hark\"].mean()\n",
    "mhd_h_meanboth = have_hark[have_hark[\"MHD\"]==\"both\"][\"avg_hark\"].mean()\n",
    "nomhd_h_mean = have_hark[have_hark[\"MHD\"]==\"none\"][\"avg_hark\"].mean()\n",
    "\n",
    "acc_h_meaned = have_hark[have_hark[\"ACC\"]==\"emergency\"][\"avg_hark\"].mean()\n",
    "acc_h_meanop = have_hark[have_hark[\"ACC\"]==\"outpatient\"][\"avg_hark\"].mean()\n",
    "acc_h_meanboth = have_hark[have_hark[\"ACC\"]==\"both\"][\"avg_hark\"].mean()\n",
    "noacc_h_mean = have_hark[have_hark[\"ACC\"]==\"none\"][\"avg_hark\"].mean()\n",
    "\n",
    "h_mean = np.round(h_mean, 0)\n",
    "sud_h_meaned = np.round(sud_h_meaned, 0)\n",
    "sud_h_meanop = np.round(sud_h_meanop, 0)\n",
    "sud_h_meanboth = np.round(sud_h_meanboth, 0)\n",
    "nosud_h_mean = np.round(nosud_h_mean, 0)\n",
    "\n",
    "mhd_h_meaned = np.round(mhd_h_meaned, 0)\n",
    "mhd_h_meanop = np.round(mhd_h_meanop, 0)\n",
    "mhd_h_meanboth = np.round(mhd_h_meanboth, 0)\n",
    "nomhd_h_mean = np.round(nomhd_h_mean, 0)\n",
    "\n",
    "acc_h_meaned = np.round(mhd_h_meaned, 0)\n",
    "acc_h_meanop = np.round(mhd_h_meanop, 0)\n",
    "acc_h_meanboth = np.round(mhd_h_meanboth, 0)\n",
    "noacc_h_mean = np.round(nomhd_h_mean, 0)\n",
    "\n",
    "\n",
    "ha = sb.displot(data=have_hark, x=\"avg_hark\", hue=\"SUD\", kind=\"kde\")\n",
    "positions1 = [[nosud_h_mean, sud_h_meaned, sud_h_meanop, sud_h_meanboth]]\n",
    "\n",
    "for ax, pos in zip(ha.axes.flat, positions1):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='-.')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle='-')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average HARK Score by Substance Use Diagnosis (n=454)\")\n",
    "plt.show()\n",
    "\n",
    "hb = sb.displot(data=have_hark, x=\"avg_hark\", hue=\"MHD\", kind=\"kde\")\n",
    "positions2 = [[nomhd_h_mean, mhd_h_meanop, mhd_h_meaned, mhd_h_meanboth]]\n",
    "\n",
    "for ax, pos in zip(hb.axes.flat, positions2):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='solid')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle=':')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average HARK Score by Mental Health Diagnosis (n=454)\")\n",
    "plt.show()\n",
    "\n",
    "hc = sb.displot(data=have_hark, x=\"avg_hark\", hue=\"ACC\", kind=\"kde\")\n",
    "positions3 = [[noacc_h_mean, acc_h_meanop, acc_h_meaned, acc_h_meanboth]]\n",
    "\n",
    "for ax, pos in zip(hc.axes.flat, positions3):\n",
    "    ax.axvline(x=pos[0], color=\"blue\", linestyle='solid')\n",
    "    ax.axvline(x=pos[1], color='orange', linestyle='-')\n",
    "    ax.axvline(x=pos[2], color='green', linestyle=':')\n",
    "    ax.axvline(x=pos[3], color='red', linestyle='-.')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Average HARK Score by Accident/Injury Diagnosis (n=454)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "faccf692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "\n",
      "0.0\n",
      "1.0\n",
      "0.0\n",
      "1.0\n",
      "\n",
      "1.0\n",
      "0.0\n",
      "1.0\n",
      "0.0\n",
      "\n",
      "1.0\n",
      "0.0\n",
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(h_mean)\n",
    "print()\n",
    "print(sud_h_meanop)\n",
    "print(sud_h_meaned)\n",
    "print(nosud_h_mean)\n",
    "print(sud_h_meanboth)\n",
    "print()\n",
    "print(mhd_h_meanop)\n",
    "print(nomhd_h_mean)\n",
    "print(mhd_h_meaned)\n",
    "print(mhd_h_meanboth)\n",
    "print()\n",
    "print(acc_h_meanop)\n",
    "print(noacc_h_mean)\n",
    "print(acc_h_meaned)\n",
    "print(acc_h_meanboth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c84f4e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    52\n",
       "1.0    11\n",
       "3.0     7\n",
       "4.0     4\n",
       "2.0     4\n",
       "Name: avg_hark, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[(have_hark[\"MHD\"]==\"emergency\") | (have_hark[\"MHD\"]==\"outpatient\")][\"avg_hark\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "7d55e782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    44\n",
       "1.0     8\n",
       "2.0     8\n",
       "4.0     7\n",
       "3.0     2\n",
       "Name: avg_hark, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[(have_hark[\"ACC\"]==\"emergency\") | (have_hark[\"ACC\"]==\"outpatient\")][\"avg_hark\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "697339f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    31\n",
       "1.0     9\n",
       "4.0     4\n",
       "3.0     2\n",
       "2.0     1\n",
       "Name: avg_hark, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "have_hark[(have_hark[\"SUD\"]==\"emergency\") | (have_hark[\"SUD\"]==\"both\")][\"avg_hark\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "aa323bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11+7+4+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "98976132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8+8+7+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "43c6736b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9+4+2+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "ea1ed3ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25+44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "50f49cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "26+52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "3f6d3a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "16+31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c3914e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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