notebooks/1-analysis.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import researchpy as rp\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7966882",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = Path('../data/')\n",
    "output_folder = Path('../output/')\n",
    "output_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "\n",
    "fn = Path(data_folder, 'assessments.csv')\n",
    "df = pd.read_csv(fn)\n",
    "\n",
    "individuals = df[df['AssessmentName'].str.contains('Individuals')].copy()\n",
    "\n",
    "# Drop any completely empty columns\n",
    "# e.g. columns that are youth survey-specific and not used for adults\n",
    "individuals.dropna(axis=1, how='all', inplace=True)\n",
    "\n",
    "#Convert subscores to an int for math purposes.\n",
    "for col in [c for c in individuals.columns if c.startswith('CALC_')]:\n",
    "    individuals[col] = individuals[col].apply(int)\n",
    "\n",
    "youth = df[df['AssessmentName'].str.contains('Youth')].copy()\n",
    "\n",
    "# Drop any completely empty columns\n",
    "# e.g. columns that are adult survey-specific and not used for youth\n",
    "youth.dropna(axis=1, how='all', inplace=True)\n",
    "\n",
    "# Convert subscores to an int for math purposes.\n",
    "for col in [c for c in youth.columns if c.startswith('CALC_')]:\n",
    "    youth[col] = youth[col].apply(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef68597",
   "metadata": {},
   "source": [
    "## Analysis: Chi-square tests\n",
    "\n",
    "**NOTE:** For each year and assessment type, do two chi-square tests: one that compares Black and White people, and another that compares Latino and White people. In addition to individual years, we also perform chi-square tests on data across all years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d229e1d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "chidata = []\n",
    "\n",
    "years = dict()\n",
    "for year in df['AssessmentYear'].unique():\n",
    "    years[year] = [year]\n",
    "years['Overall'] = df['AssessmentYear'].unique()\n",
    "\n",
    "for y in years.keys():\n",
    "    year = years[y]\n",
    "    for assessment in df['AssessmentName'].unique():\n",
    "        for group in ['Black', 'Latino']:\n",
    "            af = df[\n",
    "                df['AssessmentName'].eq(assessment) & \n",
    "                df['AssessmentYear'].isin(year)].copy()\n",
    "            af['ChiRE'] = df['Race/Ethnicity'].apply(lambda r: r if r in ['White', group] else np.nan)\n",
    "            af['ChiAcuity'] = df['Acuity'].str.contains('High')\n",
    "            \n",
    "            crosstab, test_results, expected = rp.crosstab(af['ChiRE'], af['ChiAcuity'],\n",
    "                                                       test= \"chi-square\",\n",
    "                                                       expected_freqs= True,\n",
    "                                                       prop= \"cell\")        \n",
    "            row = [y, assessment, group]\n",
    "            row.extend(test_results['results'])\n",
    "            chidata.append(row)\n",
    "            \n",
    "chidf = pd.DataFrame(chidata, columns=['year','assessment','group','pearson chi-square','p-value','cramers phi'])            \n",
    "chidf['sig'] = chidf['p-value'].lt(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9027289",
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   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>year</th>\n",
       "      <th>assessment</th>\n",
       "      <th>group</th>\n",
       "      <th>pearson chi-square</th>\n",
       "      <th>p-value</th>\n",
       "      <th>cramers phi</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>47.5027</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>56.0208</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0680</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>145.1486</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>55.6901</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>16.0113</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>45.2605</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0622</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>6.5137</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>376.9853</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0684</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>17.8769</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>24.6483</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>117.1682</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0982</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>31.0615</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>18.1626</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>19.2887</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>3.6135</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>125.4553</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>5.0850</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.1308</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>30.2113</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1781</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>8.6309</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>21.2462</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1273</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>4.5966</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>25.3899</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1724</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>21.3817</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1552</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>86.0766</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>0.3845</td>\n",
       "      <td>0.5352</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>11.5437</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>5.7699</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>11.8084</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>1.5182</td>\n",
       "      <td>0.2179</td>\n",
       "      <td>0.0456</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>6.3314</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>8.6024</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.1133</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>34.3049</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0770</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year                               assessment   group  \\\n",
       "20     2016  CES for Individuals - CES Survey Part 1   Black   \n",
       "0      2017  CES for Individuals - CES Survey Part 1   Black   \n",
       "8      2018  CES for Individuals - CES Survey Part 1   Black   \n",
       "4      2019  CES for Individuals - CES Survey Part 1   Black   \n",
       "16     2020  CES for Individuals - CES Survey Part 1   Black   \n",
       "12     2021  CES for Individuals - CES Survey Part 1   Black   \n",
       "24     2022  CES for Individuals - CES Survey Part 1   Black   \n",
       "28  Overall  CES for Individuals - CES Survey Part 1   Black   \n",
       "21     2016  CES for Individuals - CES Survey Part 1  Latino   \n",
       "1      2017  CES for Individuals - CES Survey Part 1  Latino   \n",
       "9      2018  CES for Individuals - CES Survey Part 1  Latino   \n",
       "5      2019  CES for Individuals - CES Survey Part 1  Latino   \n",
       "17     2020  CES for Individuals - CES Survey Part 1  Latino   \n",
       "13     2021  CES for Individuals - CES Survey Part 1  Latino   \n",
       "25     2022  CES for Individuals - CES Survey Part 1  Latino   \n",
       "29  Overall  CES for Individuals - CES Survey Part 1  Latino   \n",
       "22     2016           CES for Youth - Next Step Tool   Black   \n",
       "2      2017           CES for Youth - Next Step Tool   Black   \n",
       "10     2018           CES for Youth - Next Step Tool   Black   \n",
       "6      2019           CES for Youth - Next Step Tool   Black   \n",
       "18     2020           CES for Youth - Next Step Tool   Black   \n",
       "14     2021           CES for Youth - Next Step Tool   Black   \n",
       "26     2022           CES for Youth - Next Step Tool   Black   \n",
       "30  Overall           CES for Youth - Next Step Tool   Black   \n",
       "23     2016           CES for Youth - Next Step Tool  Latino   \n",
       "3      2017           CES for Youth - Next Step Tool  Latino   \n",
       "11     2018           CES for Youth - Next Step Tool  Latino   \n",
       "7      2019           CES for Youth - Next Step Tool  Latino   \n",
       "19     2020           CES for Youth - Next Step Tool  Latino   \n",
       "15     2021           CES for Youth - Next Step Tool  Latino   \n",
       "27     2022           CES for Youth - Next Step Tool  Latino   \n",
       "31  Overall           CES for Youth - Next Step Tool  Latino   \n",
       "\n",
       "    pearson chi-square  p-value  cramers phi    sig  \n",
       "20             47.5027   0.0000       0.0768   True  \n",
       "0              56.0208   0.0000       0.0680   True  \n",
       "8             145.1486   0.0000       0.0969   True  \n",
       "4              55.6901   0.0000       0.0645   True  \n",
       "16             16.0113   0.0001       0.0381   True  \n",
       "12             45.2605   0.0000       0.0622   True  \n",
       "24              6.5137   0.0107       0.0272   True  \n",
       "28            376.9853   0.0000       0.0684   True  \n",
       "21             17.8769   0.0000       0.0581   True  \n",
       "1              24.6483   0.0000       0.0521   True  \n",
       "9             117.1682   0.0000       0.0982   True  \n",
       "5              31.0615   0.0000       0.0530   True  \n",
       "17             18.1626   0.0000       0.0431   True  \n",
       "13             19.2887   0.0000       0.0411   True  \n",
       "25              3.6135   0.0573       0.0206  False  \n",
       "29            125.4553   0.0000       0.0432   True  \n",
       "22              5.0850   0.0241       0.1308   True  \n",
       "2              30.2113   0.0000       0.1781   True  \n",
       "10              8.6309   0.0033       0.0689   True  \n",
       "6              21.2462   0.0000       0.1273   True  \n",
       "18              4.5966   0.0320       0.0727   True  \n",
       "14             25.3899   0.0000       0.1724   True  \n",
       "26             21.3817   0.0000       0.1552   True  \n",
       "30             86.0766   0.0000       0.1110   True  \n",
       "23              0.3845   0.5352       0.0379  False  \n",
       "3              11.5437   0.0007       0.1141   True  \n",
       "11              5.7699   0.0163       0.0649   True  \n",
       "7              11.8084   0.0006       0.1010   True  \n",
       "19              1.5182   0.2179       0.0456  False  \n",
       "15              6.3314   0.0119       0.0947   True  \n",
       "27              8.6024   0.0034       0.1133   True  \n",
       "31             34.3049   0.0000       0.0770   True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = chidf.sort_values(by=['assessment','group','year']).copy()\n",
    "\n",
    "fn = Path(output_folder, 'chi-outcomes.csv')\n",
    "cf.to_csv(fn, index=False)\n",
    "cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27122524",
   "metadata": {},
   "source": [
    "## Table: Acuity group distribution by race/ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52dce7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "acuity_group_folder = Path(output_folder, \"acuity_group_distribution\")\n",
    "acuity_group_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "acuity_group_distribution_tables = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)\n",
    "    cross_tab = pd.crosstab(af['Race/Ethnicity'], af['Acuity'])\n",
    "    \n",
    "    cross_tab.loc['Overall'] = af.groupby('Acuity').count()['Unique_client_ID']\n",
    "    \n",
    "    cross_tab_prop.loc['Overall'] = cross_tab.loc['Overall'].div(cross_tab.loc['Overall'].sum()).mul(100).round(1)\n",
    "    \n",
    "    cf = cross_tab.merge(cross_tab_prop, left_index=True, right_index=True,suffixes=(' (count)', \" (%)\"))\n",
    "    \n",
    "    cols = list(cf.columns)\n",
    "    cols.sort()\n",
    "    cols.reverse()\n",
    "    acuity_group_distribution_tables[assessment] = cf[cols].copy()\n",
    "    \n",
    "    fn = Path(acuity_group_folder,f'{assessment}.csv')\n",
    "    acuity_group_distribution_tables[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8d9308e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Acuity</th>\n",
       "      <th>3/High Acuity (count)</th>\n",
       "      <th>3/High Acuity (%)</th>\n",
       "      <th>2/Mid Acuity (count)</th>\n",
       "      <th>2/Mid Acuity (%)</th>\n",
       "      <th>1/Low Acuity (count)</th>\n",
       "      <th>1/Low Acuity (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</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>Asian</th>\n",
       "      <td>466</td>\n",
       "      <td>24.5</td>\n",
       "      <td>1245</td>\n",
       "      <td>65.4</td>\n",
       "      <td>193</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>12997</td>\n",
       "      <td>25.7</td>\n",
       "      <td>32297</td>\n",
       "      <td>63.8</td>\n",
       "      <td>5356</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>10503</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24133</td>\n",
       "      <td>64.4</td>\n",
       "      <td>2812</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>985</td>\n",
       "      <td>27.5</td>\n",
       "      <td>2313</td>\n",
       "      <td>64.6</td>\n",
       "      <td>283</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>935</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2683</td>\n",
       "      <td>67.4</td>\n",
       "      <td>364</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>9567</td>\n",
       "      <td>32.0</td>\n",
       "      <td>18556</td>\n",
       "      <td>62.1</td>\n",
       "      <td>1754</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>35453</td>\n",
       "      <td>27.8</td>\n",
       "      <td>81227</td>\n",
       "      <td>63.7</td>\n",
       "      <td>10762</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity (count)  3/High Acuity (%)  \\\n",
       "Race/Ethnicity                                             \n",
       "Asian                             466               24.5   \n",
       "Black                           12997               25.7   \n",
       "Latino                          10503               28.0   \n",
       "Other                             985               27.5   \n",
       "Unknown                           935               23.5   \n",
       "White                            9567               32.0   \n",
       "Overall                         35453               27.8   \n",
       "\n",
       "Acuity          2/Mid Acuity (count)  2/Mid Acuity (%)  1/Low Acuity (count)  \\\n",
       "Race/Ethnicity                                                                 \n",
       "Asian                           1245              65.4                   193   \n",
       "Black                          32297              63.8                  5356   \n",
       "Latino                         24133              64.4                  2812   \n",
       "Other                           2313              64.6                   283   \n",
       "Unknown                         2683              67.4                   364   \n",
       "White                          18556              62.1                  1754   \n",
       "Overall                        81227              63.7                 10762   \n",
       "\n",
       "Acuity          1/Low Acuity (%)  \n",
       "Race/Ethnicity                    \n",
       "Asian                       10.1  \n",
       "Black                       10.6  \n",
       "Latino                       7.5  \n",
       "Other                        7.9  \n",
       "Unknown                      9.1  \n",
       "White                        5.9  \n",
       "Overall                      8.4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuity_group_distribution_tables['CES for Individuals - CES Survey Part 1']#.to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3c5e7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Acuity</th>\n",
       "      <th>3/High Acuity (count)</th>\n",
       "      <th>3/High Acuity (%)</th>\n",
       "      <th>2/Mid Acuity (count)</th>\n",
       "      <th>2/Mid Acuity (%)</th>\n",
       "      <th>1/Low Acuity (count)</th>\n",
       "      <th>1/Low Acuity (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</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>Asian</th>\n",
       "      <td>44</td>\n",
       "      <td>36.1</td>\n",
       "      <td>58</td>\n",
       "      <td>47.5</td>\n",
       "      <td>20</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>2312</td>\n",
       "      <td>41.3</td>\n",
       "      <td>2508</td>\n",
       "      <td>44.8</td>\n",
       "      <td>782</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>2027</td>\n",
       "      <td>46.1</td>\n",
       "      <td>1860</td>\n",
       "      <td>42.3</td>\n",
       "      <td>513</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>266</td>\n",
       "      <td>53.2</td>\n",
       "      <td>185</td>\n",
       "      <td>37.0</td>\n",
       "      <td>49</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>201</td>\n",
       "      <td>42.2</td>\n",
       "      <td>200</td>\n",
       "      <td>42.0</td>\n",
       "      <td>75</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>765</td>\n",
       "      <td>55.1</td>\n",
       "      <td>499</td>\n",
       "      <td>35.9</td>\n",
       "      <td>125</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>5615</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5310</td>\n",
       "      <td>42.5</td>\n",
       "      <td>1564</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity (count)  3/High Acuity (%)  \\\n",
       "Race/Ethnicity                                             \n",
       "Asian                              44               36.1   \n",
       "Black                            2312               41.3   \n",
       "Latino                           2027               46.1   \n",
       "Other                             266               53.2   \n",
       "Unknown                           201               42.2   \n",
       "White                             765               55.1   \n",
       "Overall                          5615               45.0   \n",
       "\n",
       "Acuity          2/Mid Acuity (count)  2/Mid Acuity (%)  1/Low Acuity (count)  \\\n",
       "Race/Ethnicity                                                                 \n",
       "Asian                             58              47.5                    20   \n",
       "Black                           2508              44.8                   782   \n",
       "Latino                          1860              42.3                   513   \n",
       "Other                            185              37.0                    49   \n",
       "Unknown                          200              42.0                    75   \n",
       "White                            499              35.9                   125   \n",
       "Overall                         5310              42.5                  1564   \n",
       "\n",
       "Acuity          1/Low Acuity (%)  \n",
       "Race/Ethnicity                    \n",
       "Asian                       16.4  \n",
       "Black                       14.0  \n",
       "Latino                      11.7  \n",
       "Other                        9.8  \n",
       "Unknown                     15.8  \n",
       "White                        9.0  \n",
       "Overall                     12.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuity_group_distribution_tables['CES for Youth - Next Step Tool']#.to_clipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6964daf3",
   "metadata": {},
   "source": [
    "## Chart: high acuity frequency over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e32d9946",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_acuity_freq_folder = Path(output_folder, \"high_acuity_freq\")\n",
    "high_acuity_freq_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "high_acuity_freq_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    yf = pd.DataFrame()\n",
    "    for year in df['AssessmentYear'].unique():\n",
    "        af = df[df['AssessmentName'].eq(assessment) & df['AssessmentYear'].eq(year)].copy()\n",
    "        cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)   \n",
    "        yf[year] = cross_tab_prop['3/High Acuity'].copy()\n",
    "    high_acuity_freq_charts[assessment] = yf[range(2016, 2023)]\n",
    "    \n",
    "    fn = Path(high_acuity_freq_folder, f'{assessment}.csv')\n",
    "    high_acuity_freq_charts[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1fb57df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</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>Asian</th>\n",
       "      <td>14.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.7</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>11.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>32.8</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>13.3</td>\n",
       "      <td>16.3</td>\n",
       "      <td>21.5</td>\n",
       "      <td>30.3</td>\n",
       "      <td>37.7</td>\n",
       "      <td>34.8</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>10.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>39.7</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>12.6</td>\n",
       "      <td>19.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.2</td>\n",
       "      <td>35.6</td>\n",
       "      <td>29.8</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>17.5</td>\n",
       "      <td>20.3</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2016  2017  2018  2019  2020  2021  2022\n",
       "Race/Ethnicity                                          \n",
       "Asian           14.0  11.2  22.0  26.0  32.6  32.7  29.3\n",
       "Black           11.9  15.0  21.4  29.1  38.1  32.8  32.3\n",
       "Latino          13.3  16.3  21.5  30.3  37.7  34.8  32.9\n",
       "Other           10.5  17.6  26.4  32.3  39.7  30.1  35.2\n",
       "Unknown         12.6  19.1  22.4  20.2  35.6  29.8  32.7\n",
       "White           17.5  20.3  30.1  35.3  42.0  38.8  34.9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_acuity_freq_charts['CES for Individuals - CES Survey Part 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba897f9a",
   "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>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</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>Asian</th>\n",
       "      <td>50.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>30.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>30.3</td>\n",
       "      <td>23.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>45.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>40.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>54.9</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>47.6</td>\n",
       "      <td>33.3</td>\n",
       "      <td>56.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>57.1</td>\n",
       "      <td>56.5</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>42.6</td>\n",
       "      <td>34.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>47.9</td>\n",
       "      <td>47.8</td>\n",
       "      <td>47.7</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>44.3</td>\n",
       "      <td>41.7</td>\n",
       "      <td>47.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>67.1</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2016  2017  2018  2019  2020  2021  2022\n",
       "Race/Ethnicity                                          \n",
       "Asian           50.0  21.1  30.8  36.0  40.0  35.3  66.7\n",
       "Black           30.3  23.1  38.8  45.1  51.0  45.5  48.6\n",
       "Latino          40.2  29.5  40.0  48.9  54.9  56.2  56.0\n",
       "Other           47.6  33.3  56.7  52.9  57.1  56.5  69.2\n",
       "Unknown         42.6  34.1  30.4  47.9  47.8  47.7  43.4\n",
       "White           44.3  41.7  47.4  61.1  60.4  67.1  69.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_acuity_freq_charts['CES for Youth - Next Step Tool']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc56fde",
   "metadata": {},
   "source": [
    "## Chart: subscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de463d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subscore_folder = Path(output_folder, \"subscore_frequency\")\n",
    "subscore_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "subscore_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique(): \n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    af = af.dropna(how='all', axis=1).copy()\n",
    "    \n",
    "    subscores = [c for c in af.columns if c.startswith('CALC_') and not c.endswith('_SCORE')]\n",
    "    subscores.append('GENERAL_SUB_SCORE')\n",
    "    \n",
    "    for subscore in subscores:\n",
    "        af[subscore] = af[subscore].apply(int)\n",
    "    \n",
    "    rates = af[\n",
    "        af['Race/Ethnicity'].isin(['Black','Latino', 'White'])\n",
    "    ].groupby('Race/Ethnicity')[subscores].mean().T\n",
    "    \n",
    "    subscore_charts[assessment] = rates.mul(100).round(1).sort_values(by='Black').copy()\n",
    "    \n",
    "    fn = Path(subscore_folder, f'{assessment}.csv')\n",
    "    subscore_charts[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc1c9aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th>Black</th>\n",
       "      <th>Latino</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_TRIMORBIDITY</th>\n",
       "      <td>17.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENERAL_SUB_SCORE</th>\n",
       "      <td>19.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_SUBSTANCE_USE</th>\n",
       "      <td>23.9</td>\n",
       "      <td>34.1</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_LEGAL_ISSUES</th>\n",
       "      <td>28.6</td>\n",
       "      <td>35.1</td>\n",
       "      <td>35.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MEDICATIONS</th>\n",
       "      <td>35.1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SELFCARE</th>\n",
       "      <td>39.9</td>\n",
       "      <td>40.3</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_EXPLOITATION</th>\n",
       "      <td>45.9</td>\n",
       "      <td>46.9</td>\n",
       "      <td>50.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MENTAL_HEALTH</th>\n",
       "      <td>49.3</td>\n",
       "      <td>50.8</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_EMERGENCY_SERVICE_USE</th>\n",
       "      <td>56.3</td>\n",
       "      <td>58.1</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_HARM</th>\n",
       "      <td>59.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY</th>\n",
       "      <td>59.9</td>\n",
       "      <td>60.4</td>\n",
       "      <td>63.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS</th>\n",
       "      <td>64.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MONEY_MANAGEMENT</th>\n",
       "      <td>65.4</td>\n",
       "      <td>73.7</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_ABUSE_AND_TRAUMA</th>\n",
       "      <td>67.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_PHYSICAL_HEALTH</th>\n",
       "      <td>71.9</td>\n",
       "      <td>73.8</td>\n",
       "      <td>79.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_HISTORY_OF_HOMELESSNESS</th>\n",
       "      <td>76.2</td>\n",
       "      <td>74.8</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_SLEEPING_LOCATION</th>\n",
       "      <td>81.1</td>\n",
       "      <td>85.5</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race/Ethnicity                                Black  Latino  White\n",
       "CALC_WELLNESS_TRIMORBIDITY                     17.6    21.7   24.1\n",
       "GENERAL_SUB_SCORE                              19.7    13.9   21.8\n",
       "CALC_WELLNESS_SUBSTANCE_USE                    23.9    34.1   34.4\n",
       "CALC_RISKS_LEGAL_ISSUES                        28.6    35.1   35.3\n",
       "CALC_WELLNESS_MEDICATIONS                      35.1    34.5   40.4\n",
       "CALC_SOCIALIZATION_SELFCARE                    39.9    40.3   44.3\n",
       "CALC_RISKS_RISK_OF_EXPLOITATION                45.9    46.9   50.9\n",
       "CALC_WELLNESS_MENTAL_HEALTH                    49.3    50.8   56.5\n",
       "CALC_RISKS_EMERGENCY_SERVICE_USE               56.3    58.1   63.3\n",
       "CALC_RISKS_RISK_OF_HARM                        59.0    60.6   63.5\n",
       "CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY   59.9    60.4   63.1\n",
       "CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS        64.5    68.0   65.5\n",
       "CALC_SOCIALIZATION_MONEY_MANAGEMENT            65.4    73.7   70.2\n",
       "CALC_WELLNESS_ABUSE_AND_TRAUMA                 67.2    67.9   70.5\n",
       "CALC_WELLNESS_PHYSICAL_HEALTH                  71.9    73.8   79.7\n",
       "CALC_HISTORY_HISTORY_OF_HOMELESSNESS           76.2    74.8   76.2\n",
       "CALC_HISTORY_SLEEPING_LOCATION                 81.1    85.5   84.7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscore_charts['CES for Individuals - CES Survey Part 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bf89604",
   "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>Race/Ethnicity</th>\n",
       "      <th>Black</th>\n",
       "      <th>Latino</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GENERAL_SUB_SCORE</th>\n",
       "      <td>1.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_TRIMORBIDITY</th>\n",
       "      <td>9.8</td>\n",
       "      <td>13.3</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MEDICATIONS</th>\n",
       "      <td>18.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_SUBSTANCE_USE</th>\n",
       "      <td>26.7</td>\n",
       "      <td>38.2</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SELFCARE</th>\n",
       "      <td>29.1</td>\n",
       "      <td>25.9</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_EXPLOITATION</th>\n",
       "      <td>30.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MENTAL_HEALTH</th>\n",
       "      <td>30.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_LEGAL_ISSUES</th>\n",
       "      <td>33.8</td>\n",
       "      <td>41.0</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_EMERGENCY_SERVICE_USE</th>\n",
       "      <td>35.7</td>\n",
       "      <td>35.7</td>\n",
       "      <td>44.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY</th>\n",
       "      <td>38.8</td>\n",
       "      <td>37.2</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_HARM</th>\n",
       "      <td>44.6</td>\n",
       "      <td>46.1</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_USE_TRAUMA</th>\n",
       "      <td>57.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_HISTORY_OF_HOMELESSNESS</th>\n",
       "      <td>59.2</td>\n",
       "      <td>55.7</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_PHYSICAL_HEALTH</th>\n",
       "      <td>60.2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MONEY_MANAGEMENT</th>\n",
       "      <td>63.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>69.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS</th>\n",
       "      <td>72.7</td>\n",
       "      <td>73.1</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_SLEEPING_LOCATION</th>\n",
       "      <td>81.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race/Ethnicity                                Black  Latino  White\n",
       "GENERAL_SUB_SCORE                               1.8     3.2    2.3\n",
       "CALC_WELLNESS_TRIMORBIDITY                      9.8    13.3   17.4\n",
       "CALC_WELLNESS_MEDICATIONS                      18.9    21.1   29.7\n",
       "CALC_WELLNESS_SUBSTANCE_USE                    26.7    38.2   39.6\n",
       "CALC_SOCIALIZATION_SELFCARE                    29.1    25.9   27.4\n",
       "CALC_RISKS_RISK_OF_EXPLOITATION                30.0    32.9   42.4\n",
       "CALC_WELLNESS_MENTAL_HEALTH                    30.3    33.1   45.3\n",
       "CALC_RISKS_LEGAL_ISSUES                        33.8    41.0   37.1\n",
       "CALC_RISKS_EMERGENCY_SERVICE_USE               35.7    35.7   44.7\n",
       "CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY   38.8    37.2   39.1\n",
       "CALC_RISKS_RISK_OF_HARM                        44.6    46.1   55.4\n",
       "CALC_SOCIALIZATION_USE_TRAUMA                  57.6    62.0   62.5\n",
       "CALC_HISTORY_HISTORY_OF_HOMELESSNESS           59.2    55.7   57.2\n",
       "CALC_WELLNESS_PHYSICAL_HEALTH                  60.2    62.2   65.5\n",
       "CALC_SOCIALIZATION_MONEY_MANAGEMENT            63.0    66.2   69.6\n",
       "CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS        72.7    73.1   75.0\n",
       "CALC_HISTORY_SLEEPING_LOCATION                 81.5    82.5   82.6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscore_charts['CES for Youth - Next Step Tool']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e73b1f4",
   "metadata": {},
   "source": [
    "## Chart: Acuity score distribution by race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1912fdea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_dist_folder = Path(output_folder, \"score_distribution\")\n",
    "score_dist_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "score_dist_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique(): \n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    \n",
    "    score_dist = af[\n",
    "        af['Race/Ethnicity'].isin(['Black','White','Latino'])\n",
    "    ].groupby(['TOTAL_SCORE','Race/Ethnicity'])['Unique_client_ID'].count().unstack().copy()\n",
    "    \n",
    "    score_dist_charts[assessment] = score_dist.div(score_dist.sum()).mul(100)\n",
    "    \n",
    "    fn = Path(score_dist_folder, f'{assessment}.csv')\n",
    "    score_dist_charts[assessment].to_csv(fn)\n",
    "    \n",
    "    # Plot for context\n",
    "    score_dist_charts[assessment].plot(title=assessment, ylabel='Percent')\n",
    "    high_cutoff = af[af['Acuity'].str.contains('High')]['TOTAL_SCORE'].min()\n",
    "    plt.xticks(np.arange(0, int(af['TOTAL_SCORE'].max()) + 1, step=1)) \n",
    "    plt.axvline(high_cutoff, color='red', linestyle = '--') # theoretical \n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f9648c",
   "metadata": {},
   "source": [
    "## Chart: Rate of people matched, but in yet in, permanent housing by race and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cec0e28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df['Intervention'].unique():\n",
    "    df[i] = df['Intervention'].eq(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cca5b25a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Matched to Permanent Housing, Not Yet Housed</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>Total</th>\n",
       "      <th>pct</th>\n",
       "      <th>pctref</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_SCORE</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>363</td>\n",
       "      <td>4</td>\n",
       "      <td>367</td>\n",
       "      <td>0.010899</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>852</td>\n",
       "      <td>15</td>\n",
       "      <td>867</td>\n",
       "      <td>0.017301</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2387</td>\n",
       "      <td>47</td>\n",
       "      <td>2434</td>\n",
       "      <td>0.019310</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3819</td>\n",
       "      <td>102</td>\n",
       "      <td>3921</td>\n",
       "      <td>0.026014</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5144</td>\n",
       "      <td>137</td>\n",
       "      <td>5281</td>\n",
       "      <td>0.025942</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5663</td>\n",
       "      <td>201</td>\n",
       "      <td>5864</td>\n",
       "      <td>0.034277</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5454</td>\n",
       "      <td>240</td>\n",
       "      <td>5694</td>\n",
       "      <td>0.042150</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5325</td>\n",
       "      <td>271</td>\n",
       "      <td>5596</td>\n",
       "      <td>0.048427</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6475</td>\n",
       "      <td>405</td>\n",
       "      <td>6880</td>\n",
       "      <td>0.058866</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6248</td>\n",
       "      <td>443</td>\n",
       "      <td>6691</td>\n",
       "      <td>0.066208</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5750</td>\n",
       "      <td>459</td>\n",
       "      <td>6209</td>\n",
       "      <td>0.073925</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5298</td>\n",
       "      <td>496</td>\n",
       "      <td>5794</td>\n",
       "      <td>0.085606</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4710</td>\n",
       "      <td>603</td>\n",
       "      <td>5313</td>\n",
       "      <td>0.113495</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3921</td>\n",
       "      <td>596</td>\n",
       "      <td>4517</td>\n",
       "      <td>0.131946</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3069</td>\n",
       "      <td>599</td>\n",
       "      <td>3668</td>\n",
       "      <td>0.163304</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2381</td>\n",
       "      <td>638</td>\n",
       "      <td>3019</td>\n",
       "      <td>0.211328</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1502</td>\n",
       "      <td>506</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.251992</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>173</td>\n",
       "      <td>87</td>\n",
       "      <td>260</td>\n",
       "      <td>0.334615</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matched to Permanent Housing, Not Yet Housed  False  True  Total       pct  \\\n",
       "TOTAL_SCORE                                                                  \n",
       "0                                               363     4    367  0.010899   \n",
       "1                                               852    15    867  0.017301   \n",
       "2                                              2387    47   2434  0.019310   \n",
       "3                                              3819   102   3921  0.026014   \n",
       "4                                              5144   137   5281  0.025942   \n",
       "5                                              5663   201   5864  0.034277   \n",
       "6                                              5454   240   5694  0.042150   \n",
       "7                                              5325   271   5596  0.048427   \n",
       "8                                              6475   405   6880  0.058866   \n",
       "9                                              6248   443   6691  0.066208   \n",
       "10                                             5750   459   6209  0.073925   \n",
       "11                                             5298   496   5794  0.085606   \n",
       "12                                             4710   603   5313  0.113495   \n",
       "13                                             3921   596   4517  0.131946   \n",
       "14                                             3069   599   3668  0.163304   \n",
       "15                                             2381   638   3019  0.211328   \n",
       "16                                             1502   506   2008  0.251992   \n",
       "17                                              173    87    260  0.334615   \n",
       "\n",
       "Matched to Permanent Housing, Not Yet Housed  pctref  \n",
       "TOTAL_SCORE                                           \n",
       "0                                                1.1  \n",
       "1                                                1.7  \n",
       "2                                                1.9  \n",
       "3                                                2.6  \n",
       "4                                                2.6  \n",
       "5                                                3.4  \n",
       "6                                                4.2  \n",
       "7                                                4.8  \n",
       "8                                                5.9  \n",
       "9                                                6.6  \n",
       "10                                               7.4  \n",
       "11                                               8.6  \n",
       "12                                              11.3  \n",
       "13                                              13.2  \n",
       "14                                              16.3  \n",
       "15                                              21.1  \n",
       "16                                              25.2  \n",
       "17                                              33.5  "
      ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Matched to Permanent Housing, Not Yet Housed</th>\n",
       "      <th>False</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>5.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>5</td>\n",
       "      <td>103</td>\n",
       "      <td>0.048544</td>\n",
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       "      <th>2</th>\n",
       "      <td>304</td>\n",
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       "      <td>0.037975</td>\n",
       "      <td>3.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>555</td>\n",
       "      <td>23</td>\n",
       "      <td>578</td>\n",
       "      <td>0.039792</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>780</td>\n",
       "      <td>39</td>\n",
       "      <td>819</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>4.8</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>855</td>\n",
       "      <td>55</td>\n",
       "      <td>910</td>\n",
       "      <td>0.060440</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>835</td>\n",
       "      <td>50</td>\n",
       "      <td>885</td>\n",
       "      <td>0.056497</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>703</td>\n",
       "      <td>67</td>\n",
       "      <td>770</td>\n",
       "      <td>0.087013</td>\n",
       "      <td>8.7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>0.100220</td>\n",
       "      <td>10.0</td>\n",
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       "      <th>9</th>\n",
       "      <td>642</td>\n",
       "      <td>79</td>\n",
       "      <td>721</td>\n",
       "      <td>0.109570</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>485</td>\n",
       "      <td>86</td>\n",
       "      <td>571</td>\n",
       "      <td>0.150613</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>342</td>\n",
       "      <td>66</td>\n",
       "      <td>408</td>\n",
       "      <td>0.161765</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>220</td>\n",
       "      <td>57</td>\n",
       "      <td>277</td>\n",
       "      <td>0.205776</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>175</td>\n",
       "      <td>32</td>\n",
       "      <td>207</td>\n",
       "      <td>0.154589</td>\n",
       "      <td>15.5</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>106</td>\n",
       "      <td>34</td>\n",
       "      <td>140</td>\n",
       "      <td>0.242857</td>\n",
       "      <td>24.3</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>69</td>\n",
       "      <td>0.188406</td>\n",
       "      <td>18.8</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>26.2</td>\n",
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      "text/plain": [
       "Matched to Permanent Housing, Not Yet Housed  False  True  Total       pct  \\\n",
       "TOTAL_SCORE                                                                  \n",
       "0                                                18     1     19  0.052632   \n",
       "1                                                98     5    103  0.048544   \n",
       "2                                               304    12    316  0.037975   \n",
       "3                                               555    23    578  0.039792   \n",
       "4                                               780    39    819  0.047619   \n",
       "5                                               855    55    910  0.060440   \n",
       "6                                               835    50    885  0.056497   \n",
       "7                                               703    67    770  0.087013   \n",
       "8                                               817    91    908  0.100220   \n",
       "9                                               642    79    721  0.109570   \n",
       "10                                              485    86    571  0.150613   \n",
       "11                                              342    66    408  0.161765   \n",
       "12                                              220    57    277  0.205776   \n",
       "13                                              175    32    207  0.154589   \n",
       "14                                              106    34    140  0.242857   \n",
       "15                                               56    13     69  0.188406   \n",
       "16                                               31    11     42  0.261905   \n",
       "\n",
       "Matched to Permanent Housing, Not Yet Housed  pctref  \n",
       "TOTAL_SCORE                                           \n",
       "0                                                5.3  \n",
       "1                                                4.9  \n",
       "2                                                3.8  \n",
       "3                                                4.0  \n",
       "4                                                4.8  \n",
       "5                                                6.0  \n",
       "6                                                5.6  \n",
       "7                                                8.7  \n",
       "8                                               10.0  \n",
       "9                                               11.0  \n",
       "10                                              15.1  \n",
       "11                                              16.2  \n",
       "12                                              20.6  \n",
       "13                                              15.5  \n",
       "14                                              24.3  \n",
       "15                                              18.8  \n",
       "16                                              26.2  "
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matched_rate_folder = Path(output_folder, \"matched_rate\")\n",
    "matched_rate_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "perm_charts = dict()\n",
    "perm_overall_chart = dict()\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    intervention = 'Matched to Permanent Housing, Not Yet Housed'\n",
    "    filters = df['AssessmentName'].eq(assessment) & \\\n",
    "              df['AssessmentPredatesIntervention']\n",
    "    re = ['Black', 'White', 'Latino']\n",
    "    \n",
    "    permdf = df[filters & df['Race/Ethnicity'].isin(re)].groupby(['TOTAL_SCORE','Race/Ethnicity',intervention]).count()['Unique_client_ID'].unstack()\n",
    "    permdf['Total'] = permdf.sum(axis=1)\n",
    "    permdf['pct'] = permdf[True].div(permdf['Total'])\n",
    "\n",
    "    perm_charts[assessment] = permdf['pct'].mul(100).copy()\n",
    "\n",
    "    fn = Path(matched_rate_folder, f'{assessment}.csv')\n",
    "    perm_charts[assessment].to_csv(fn)\n",
    "    \n",
    "    # Overall numbers\n",
    "    permoveralldf = df[filters].groupby(['TOTAL_SCORE', intervention]).count()['Unique_client_ID'].unstack()\n",
    "    permoveralldf['Total'] = permoveralldf.sum(axis=1)\n",
    "    permoveralldf['pct'] = permoveralldf[True].div(permoveralldf['Total'])\n",
    "    permoveralldf['pctref'] = permoveralldf['pct'].mul(100).round(1)\n",
    "    display(permoveralldf)\n",
    "    \n",
    "    # Plot for context\n",
    "    perm_charts[assessment].unstack().plot(xlim=(0,17),title=f'{intervention}: {assessment}', ylabel=\"Percent\")\n",
    "pass   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8ea06f",
   "metadata": {},
   "source": [
    "## Chart: 2021 Youth Acuity Group Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63e3e611",
   "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>Acuity</th>\n",
       "      <th>3/High Acuity</th>\n",
       "      <th>2/Mid Acuity</th>\n",
       "      <th>1/Low Acuity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>45.5</td>\n",
       "      <td>45.8</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>56.2</td>\n",
       "      <td>36.6</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>67.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity  2/Mid Acuity  1/Low Acuity\n",
       "Race/Ethnicity                                           \n",
       "Black                    45.5          45.8           8.8\n",
       "Latino                   56.2          36.6           7.3\n",
       "White                    67.1          27.6           5.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "af = df[\n",
    "    df['AssessmentName'].str.contains('Youth') & \n",
    "    df['Race/Ethnicity'].isin(['Black','White','Latino']) &\n",
    "    df['AssessmentYear'].eq(2021)\n",
    "].copy()\n",
    "\n",
    "cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)\n",
    "\n",
    "cols = list(cross_tab_prop.columns)\n",
    "cols.reverse()\n",
    "\n",
    "display(cross_tab_prop[cols])\n",
    "\n",
    "cross_tab_prop[cols].to_csv(Path(output_folder, '2021-youth-acuity-breakdown.csv'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}