notebooks/report_weight.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display, Markdown, Image\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CONFIGURE ###\n",
    "definitions = ['weight', 'weight_backend']\n",
    "covariates = ['_age_band','_sex','_ethnicity','_region','_imd','_dementia','_diabetes','_hypertension','_learning_disability']\n",
    "output_path = 'phenotype_validation_weight'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weight\n",
    "## Count of Patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>weight</th>\n",
       "      <th>weight_backend</th>\n",
       "      <th>all_filled</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <th>with records</th>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0 (100.0)</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_band</th>\n",
       "      <th>0-19</th>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0 (100.0)</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0 (100.0)</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0 (100.0)</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0 (100.0)</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0 (100.0)</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>505.0 (100.0)</td>\n",
       "      <td>505.0 (100.0)</td>\n",
       "      <td>505.0 (100.0)</td>\n",
       "      <td>505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ethnicity</th>\n",
       "      <th>Asian</th>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0 (100.0)</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>270.0 (100.0)</td>\n",
       "      <td>270.0 (100.0)</td>\n",
       "      <td>270.0 (100.0)</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0 (100.0)</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0 (100.0)</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0 (100.0)</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0 (100.0)</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0 (100.0)</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0 (100.0)</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0 (100.0)</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0 (100.0)</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0 (100.0)</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0 (100.0)</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0 (100.0)</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dementia</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">diabetes</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hypertension</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">learning_disability</th>\n",
       "      <th>False</th>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0 (100.0)</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0 (100.0)</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      weight  weight_backend  \\\n",
       "group               subgroup                                                   \n",
       "all                 with records              1000.0 (100.0)  1000.0 (100.0)   \n",
       "age_band            0-19                       125.0 (100.0)   125.0 (100.0)   \n",
       "                    20-29                      145.0 (100.0)   145.0 (100.0)   \n",
       "                    30-39                      110.0 (100.0)   110.0 (100.0)   \n",
       "                    40-49                      130.0 (100.0)   130.0 (100.0)   \n",
       "                    50-59                      120.0 (100.0)   120.0 (100.0)   \n",
       "                    60-69                      130.0 (100.0)   130.0 (100.0)   \n",
       "                    70-79                      130.0 (100.0)   130.0 (100.0)   \n",
       "                    80+                        110.0 (100.0)   110.0 (100.0)   \n",
       "sex                 F                          505.0 (100.0)   505.0 (100.0)   \n",
       "                    M                          495.0 (100.0)   495.0 (100.0)   \n",
       "ethnicity           Asian                      135.0 (100.0)   135.0 (100.0)   \n",
       "                    Black                       75.0 (100.0)    75.0 (100.0)   \n",
       "                    Mixed                      270.0 (100.0)   270.0 (100.0)   \n",
       "                    Other                       30.0 (100.0)    30.0 (100.0)   \n",
       "                    White                      495.0 (100.0)   495.0 (100.0)   \n",
       "region              East Midlands               70.0 (100.0)    70.0 (100.0)   \n",
       "                    East of England             65.0 (100.0)    65.0 (100.0)   \n",
       "                    London                     140.0 (100.0)   140.0 (100.0)   \n",
       "                    North East                  65.0 (100.0)    65.0 (100.0)   \n",
       "                    North West                  60.0 (100.0)    60.0 (100.0)   \n",
       "                    South East                 115.0 (100.0)   115.0 (100.0)   \n",
       "                    West Midlands               60.0 (100.0)    60.0 (100.0)   \n",
       "                    Yorkshire and the Humber    75.0 (100.0)    75.0 (100.0)   \n",
       "imd                 1 Most deprived            215.0 (100.0)   215.0 (100.0)   \n",
       "                    2                          190.0 (100.0)   190.0 (100.0)   \n",
       "                    3                          215.0 (100.0)   215.0 (100.0)   \n",
       "                    4                          190.0 (100.0)   190.0 (100.0)   \n",
       "                    5 Least deprived           185.0 (100.0)   185.0 (100.0)   \n",
       "                    Unknown                     10.0 (100.0)    10.0 (100.0)   \n",
       "dementia            False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "diabetes            False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "hypertension        False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "learning_disability False                      990.0 (100.0)   990.0 (100.0)   \n",
       "                    True                        10.0 (100.0)    10.0 (100.0)   \n",
       "\n",
       "                                                  all_filled  population  \n",
       "group               subgroup                                              \n",
       "all                 with records              1000.0 (100.0)      1000.0  \n",
       "age_band            0-19                       125.0 (100.0)       125.0  \n",
       "                    20-29                      145.0 (100.0)       145.0  \n",
       "                    30-39                      110.0 (100.0)       110.0  \n",
       "                    40-49                      130.0 (100.0)       130.0  \n",
       "                    50-59                      120.0 (100.0)       120.0  \n",
       "                    60-69                      130.0 (100.0)       130.0  \n",
       "                    70-79                      130.0 (100.0)       130.0  \n",
       "                    80+                        110.0 (100.0)       110.0  \n",
       "sex                 F                          505.0 (100.0)       505.0  \n",
       "                    M                          495.0 (100.0)       495.0  \n",
       "ethnicity           Asian                      135.0 (100.0)       135.0  \n",
       "                    Black                       75.0 (100.0)        75.0  \n",
       "                    Mixed                      270.0 (100.0)       270.0  \n",
       "                    Other                       30.0 (100.0)        30.0  \n",
       "                    White                      495.0 (100.0)       495.0  \n",
       "region              East Midlands               70.0 (100.0)        70.0  \n",
       "                    East of England             65.0 (100.0)        65.0  \n",
       "                    London                     140.0 (100.0)       140.0  \n",
       "                    North East                  65.0 (100.0)        65.0  \n",
       "                    North West                  60.0 (100.0)        60.0  \n",
       "                    South East                 115.0 (100.0)       115.0  \n",
       "                    West Midlands               60.0 (100.0)        60.0  \n",
       "                    Yorkshire and the Humber    75.0 (100.0)        75.0  \n",
       "imd                 1 Most deprived            215.0 (100.0)       215.0  \n",
       "                    2                          190.0 (100.0)       190.0  \n",
       "                    3                          215.0 (100.0)       215.0  \n",
       "                    4                          190.0 (100.0)       190.0  \n",
       "                    5 Least deprived           185.0 (100.0)       185.0  \n",
       "                    Unknown                     10.0 (100.0)        10.0  \n",
       "dementia            False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "diabetes            False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "hypertension        False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  \n",
       "learning_disability False                      990.0 (100.0)       990.0  \n",
       "                    True                        10.0 (100.0)        10.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient_counts = pd.read_csv(f'../output/{output_path}/tables/patient_counts_filled.csv').set_index(['group','subgroup'])\n",
    "display(df_patient_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count of Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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></th>\n",
       "      <th>weight</th>\n",
       "      <th>weight_backend</th>\n",
       "      <th>all_missing</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <th>missing records</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age_band</th>\n",
       "      <th>0-19</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
       "      <th>F</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ethnicity</th>\n",
       "      <th>Asian</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">region</th>\n",
       "      <th>East Midlands</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">imd</th>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dementia</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">diabetes</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hypertension</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">learning_disability</th>\n",
       "      <th>False</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>- (-)</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             weight weight_backend  \\\n",
       "group               subgroup                                         \n",
       "all                 missing records           - (-)          - (-)   \n",
       "age_band            0-19                      - (-)          - (-)   \n",
       "                    20-29                     - (-)          - (-)   \n",
       "                    30-39                     - (-)          - (-)   \n",
       "                    40-49                     - (-)          - (-)   \n",
       "                    50-59                     - (-)          - (-)   \n",
       "                    60-69                     - (-)          - (-)   \n",
       "                    70-79                     - (-)          - (-)   \n",
       "                    80+                       - (-)          - (-)   \n",
       "sex                 F                         - (-)          - (-)   \n",
       "                    M                         - (-)          - (-)   \n",
       "ethnicity           Asian                     - (-)          - (-)   \n",
       "                    Black                     - (-)          - (-)   \n",
       "                    Mixed                     - (-)          - (-)   \n",
       "                    Other                     - (-)          - (-)   \n",
       "                    White                     - (-)          - (-)   \n",
       "region              East Midlands             - (-)          - (-)   \n",
       "                    East of England           - (-)          - (-)   \n",
       "                    London                    - (-)          - (-)   \n",
       "                    North East                - (-)          - (-)   \n",
       "                    North West                - (-)          - (-)   \n",
       "                    South East                - (-)          - (-)   \n",
       "                    West Midlands             - (-)          - (-)   \n",
       "                    Yorkshire and the Humber  - (-)          - (-)   \n",
       "imd                 1 Most deprived           - (-)          - (-)   \n",
       "                    2                         - (-)          - (-)   \n",
       "                    3                         - (-)          - (-)   \n",
       "                    4                         - (-)          - (-)   \n",
       "                    5 Least deprived          - (-)          - (-)   \n",
       "                    Unknown                   - (-)          - (-)   \n",
       "dementia            False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "diabetes            False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "hypertension        False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "learning_disability False                     - (-)          - (-)   \n",
       "                    True                      - (-)          - (-)   \n",
       "\n",
       "                                             all_missing  population  \n",
       "group               subgroup                                          \n",
       "all                 missing records                - (-)        1000  \n",
       "age_band            0-19                           - (-)         125  \n",
       "                    20-29                          - (-)         145  \n",
       "                    30-39                          - (-)         110  \n",
       "                    40-49                          - (-)         130  \n",
       "                    50-59                          - (-)         120  \n",
       "                    60-69                          - (-)         130  \n",
       "                    70-79                          - (-)         130  \n",
       "                    80+                            - (-)         110  \n",
       "sex                 F                              - (-)         505  \n",
       "                    M                              - (-)         495  \n",
       "ethnicity           Asian                          - (-)         135  \n",
       "                    Black                          - (-)          75  \n",
       "                    Mixed                          - (-)         270  \n",
       "                    Other                          - (-)          30  \n",
       "                    White                          - (-)         495  \n",
       "region              East Midlands                  - (-)          70  \n",
       "                    East of England                - (-)          65  \n",
       "                    London                         - (-)         140  \n",
       "                    North East                     - (-)          65  \n",
       "                    North West                     - (-)          60  \n",
       "                    South East                     - (-)         115  \n",
       "                    West Midlands                  - (-)          60  \n",
       "                    Yorkshire and the Humber       - (-)          75  \n",
       "imd                 1 Most deprived                - (-)         215  \n",
       "                    2                              - (-)         190  \n",
       "                    3                              - (-)         215  \n",
       "                    4                              - (-)         190  \n",
       "                    5 Least deprived               - (-)         185  \n",
       "                    Unknown                        - (-)          10  \n",
       "dementia            False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "diabetes            False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "hypertension        False                          - (-)         990  \n",
       "                    True                           - (-)          10  \n",
       "learning_disability False                          - (-)         990  \n",
       "                    True                           - (-)          10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient_counts_missing = pd.read_csv(f'../output/{output_path}/tables/patient_counts_missing.csv').set_index(['group','subgroup'])\n",
    "display(df_patient_counts_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Values\n",
    "\n",
    "### Comparison of Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>69.911242</td>\n",
       "      <td>5570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight_backend</th>\n",
       "      <td>69.944520</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean  count\n",
       "weight          69.911242   5570\n",
       "weight_backend  69.944520   5545"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>70.509948</td>\n",
       "      <td>69.943371</td>\n",
       "      <td>705</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>70.111694</td>\n",
       "      <td>70.070888</td>\n",
       "      <td>810</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>68.993233</td>\n",
       "      <td>69.691171</td>\n",
       "      <td>610</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>69.479428</td>\n",
       "      <td>69.981564</td>\n",
       "      <td>720</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>70.156375</td>\n",
       "      <td>70.080769</td>\n",
       "      <td>685</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>70.077610</td>\n",
       "      <td>70.285071</td>\n",
       "      <td>705</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>70.272724</td>\n",
       "      <td>69.525654</td>\n",
       "      <td>715</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>69.492941</td>\n",
       "      <td>69.949048</td>\n",
       "      <td>620</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "age_band                                                              \n",
       "0-19       70.509948           69.943371        705                710\n",
       "20-29      70.111694           70.070888        810                815\n",
       "30-39      68.993233           69.691171        610                620\n",
       "40-49      69.479428           69.981564        720                705\n",
       "50-59      70.156375           70.080769        685                675\n",
       "60-69      70.077610           70.285071        705                690\n",
       "70-79      70.272724           69.525654        715                720\n",
       "80+        69.492941           69.949048        620                615"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>70.065780</td>\n",
       "      <td>69.971693</td>\n",
       "      <td>2830</td>\n",
       "      <td>2825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>69.751858</td>\n",
       "      <td>69.916288</td>\n",
       "      <td>2745</td>\n",
       "      <td>2720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "sex                                                              \n",
       "F     70.065780           69.971693       2830               2825\n",
       "M     69.751858           69.916288       2745               2720"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity</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>69.745766</td>\n",
       "      <td>70.734172</td>\n",
       "      <td>750</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>70.039338</td>\n",
       "      <td>69.007680</td>\n",
       "      <td>405</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>70.356730</td>\n",
       "      <td>70.019228</td>\n",
       "      <td>1495</td>\n",
       "      <td>1485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>69.035531</td>\n",
       "      <td>70.342251</td>\n",
       "      <td>170</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>69.750277</td>\n",
       "      <td>69.807205</td>\n",
       "      <td>2750</td>\n",
       "      <td>2745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "ethnicity                                                              \n",
       "Asian       69.745766           70.734172        750                745\n",
       "Black       70.039338           69.007680        405                410\n",
       "Mixed       70.356730           70.019228       1495               1485\n",
       "Other       69.035531           70.342251        170                160\n",
       "White       69.750277           69.807205       2750               2745"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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TdHS0PvzwQ+3YsaPE/ktHeV3M29u7xGdwsccee0wbNmxQYmKi6tSpU+7PGAAAVLxqzi4AAAA4x7p16/TNN9/o/Pnzys7O1ubNm5WSkqImTZron//8p+x2+2WPnTlzpj799FPdf//9atKkiY4dO6bXXntNt9xyi7p06SLpQpjk7++vJUuWqGbNmvL29lbHjh0VEhJyTfXWrl1bXbp00dChQ5Wdna3ExEQ1bdpUI0aMMNsMHz5cq1atUo8ePdSvXz/t27dPK1ascFhUvby19erVS/fee68mT56sAwcOqE2bNtqwYYM++OADxcXFlej7avLy8nTLLbfo4YcfVps2beTj46ONGzcqIyND8+fPL1Mf+/fvV+/evdWjRw9t375dK1as0KBBg9SmTRuHdu3atdPtt9+u999/Xy1bttSdd9551b59fX21ePFiPfbYY7rzzjs1YMAA1atXTwcPHtTatWt1zz33mGHeXXfdJUkaO3asoqKi5O7urgEDBpTrflyP559/Xhs2bFB4eLhGjhypli1b6siRI3r//feVlpZmPmzgUnfddZfee+89/eUvf1GHDh3k4+OjXr16mfsHDRqkZ555RmvWrNETTzyh6tWrV9YlAQCAyyDAAgDARU2bNk2SVKNGDdWuXVt33HGHEhMTNXTo0Csu4C5JvXv31oEDB/Tmm2/ql19+Ud26dRUeHq74+HhzraDq1atr+fLlmjhxokaPHq3z589r6dKl1xxgTZo0Sbt371ZCQoLy8vJ033336bXXXpOXl5fZJioqSvPnz9eCBQsUFxen9u3b66OPPtK4ceMc+ipPbW5ubvrnP/+padOm6b333tPSpUsVHBysuXPnlui3LLy8vDRmzBht2LBBq1evVlFRkZo2barXXntNTzzxRJn6eO+99zRt2jQ9++yzqlatmmJjYzV37txS2w4ePFjPPPNMuabBDRo0SEFBQXrhhRc0d+5cnT17Vg0bNlTXrl0dnhz54IMP6s9//rP+/ve/a8WKFTIMo1IDrIYNGyo9PV1Tp05VUlKScnNz1bBhQ/Xs2dPhe3GpMWPGKDMzU0uXLtXChQvVpEkThwArICBA//M//6OPP/6Y6YMAAFiEzbjS+GoAAADc1F566SU99dRTOnDgQKlPSETp+vbtq//85z/KyspydikAAECsgQUAAFBlGYahv/3tbwoPDye8KocjR45o7dq1jL4CAMBCmEIIAABQxZw+fVr//Oc/tWXLFv3nP//RBx984OySbgr79+/Xtm3b9Ne//lXVq1fXqFGjnF0SAAD4PwRYAAAAVczPP/+sQYMGyd/fX5MmTVLv3r2dXdJNYevWrRo6dKgaN26s5cuXKzAw0NklAQCA/8MaWAAAAAAAALA01sCqQj799FP16tVLQUFBstlsSk5OdthvGIamTZumBg0ayNPTU5GRkfruu+8c2pw4cUIxMTHy9fWVv7+/hg0bpvz8/Mq8DAAAAAAAAAcEWFXI6dOn1aZNG7366qul7p8zZ44WLVqkJUuWKD09Xd7e3oqKilJBQYHZJiYmRl9//bVSUlL00Ucf6dNPP9XIkSMr6xIAAAAAAABKYAphFWWz2bRmzRpFR0dLujD6KigoSOPGjdP48eMlSTk5OQoICNCyZcs0YMAA7d27V61atVJGRobat28vSVq/fr3+8Ic/6Mcff1RQUFCZzl1UVKTDhw+rZs2astlsN+YCAQAAAAD4P4ZhKC8vT0FBQXJzY6xOVcQi7i5i//79Onr0qCIjI81tfn5+6tixo7Zv364BAwZo+/bt8vf3N8MrSYqMjJSbm5vS09PVt2/fUvs+e/aszp49a77/6aef1KpVqxt3MQAAAAAAlOLQoUO65ZZbnF0GbgACLBdx9OhRSVJAQIDD9oCAAHPf0aNHVb9+fYf91apVU+3atc02pUlISFB8fHyJ7YcOHZKvr+/1lg4AAAAAwBXl5uaqUaNGqlmzprNLwQ1CgIXrNnHiRP3lL38x3xf/4PD19SXAAgAAAABUGpaxqbqYGOoiAgMDJUnZ2dkO27Ozs819gYGBOnbsmMP+8+fP68SJE2ab0nh4eJhhFaEVAAAAAACoaARYLiIkJESBgYHatGmTuS03N1fp6enq1KmTJKlTp046deqUvvjiC7PN5s2bVVRUpI4dO1Z6zQAAAAAAABJTCKuU/Px8ZWVlme/379+vzMxM1a5dW40bN1ZcXJxmzZqlsLAwhYSEaOrUqQoKCjKfVNiyZUv16NFDI0aM0JIlS3Tu3DnFxsZqwIABZX4CIQAAAAAAQEUjwKpCduzYoXvvvdd8X7wu1ZAhQ7Rs2TI988wzOn36tEaOHKlTp06pS5cuWr9+vex2u3lMUlKSYmNjdd9998nNzU0PPfSQFi1aVOnXAgAAAMB6CgsLde7cOWeXARdUvXp1ubu7O7sMOJHNMAzD2UWgasnNzZWfn59ycnJYDwsAAACoAgzD0NGjR3Xq1ClnlwIX5u/vr8DAwFIXaufv0KqPEVgAAAAAgCsqDq/q168vLy8vnvSGSmUYhs6cOWM+dKxBgwZOrgjOQIAFAAAAALiswsJCM7yqU6eOs8uBi/L09JQkHTt2TPXr12c6oQviKYQAAAAAgMsqXvPKy8vLyZXA1RV/B1mHzTURYAEAAAAAroppg3A2voOujQALAAAAAAAAlkaABQAAAABABTpw4IBsNpsyMzPLfMzjjz+u6OjoK7aJiIhQXFzc9ZYH3JRYxB0AAAAAgArUqFEjHTlyRHXr1nV2KUCVQYAFAAAAAEAF+e2331SjRg0FBgY6uxSgSmEKIQAAAADAJb3++usKCgpSUVGRw/Y+ffroj3/8o/bt26c+ffooICBAPj4+6tChgzZu3OjQNjg4WM8995wGDx4sX19fjRw5ssQUwsLCQg0bNkwhISHy9PRU8+bN9dJLL5VaU3x8vOrVqydfX1+NHj1av/3222XrP3v2rMaPH6+GDRvK29tbHTt2VGpq6vXdFMCiCLAAAAAAAC7pkUce0fHjx7VlyxZz24kTJ7R+/XrFxMQoPz9ff/jDH7Rp0ybt3LlTPXr0UK9evXTw4EGHfubNm6c2bdpo586dmjp1aonzFBUV6ZZbbtH777+vPXv2aNq0aZo0aZJWrlzp0G7Tpk3au3evUlNT9e6772r16tWKj4+/bP2xsbHavn27/v73v2v37t165JFH1KNHD3333XfXeWcA67EZhmE4uwhULbm5ufLz81NOTo58fX2dXQ4AAACA61BQUKD9+/crJCREdrvd2eVUuOjoaNWpU0d/+9vfJF0YlRUfH69Dhw7Jza3kmI/bb79do0ePVmxsrKQLI7DatWunNWvWmG0OHDigkJAQ7dy5U23bti31vLGxsTp69KhWrVol6cIi7h9++KEOHTokLy8vSdKSJUv09NNPKycnR25uboqIiFDbtm2VmJiogwcP6tZbb9XBgwcVFBRk9hsZGam7775bzz//fMXcIAu50neRv0OrPkZgAQAAAABcVkxMjP7xj3/o7NmzkqSkpCQNGDBAbm5uys/P1/jx49WyZUv5+/vLx8dHe/fuLTECq3379lc9z6uvvqq77rpL9erVk4+Pj15//fUS/bRp08YMrySpU6dOys/P16FDh0r095///EeFhYVq1qyZfHx8zNfWrVu1b9++a7kVgKWxiDsAAAAAwGX16tVLhmFo7dq16tChgz777DMtXLhQkjR+/HilpKRo3rx5atq0qTw9PfXwww+XWJfK29v7iuf4+9//rvHjx2v+/Pnq1KmTatasqblz5yo9Pf2a687Pz5e7u7u++OILubu7O+zz8fG55n4BqyLAAgAAAAC4LLvdrgcffFBJSUnKyspS8+bNdeedd0qStm3bpscff1x9+/aVdCE0OnDgQLnPsW3bNnXu3Fljxowxt5U2SmrXrl369ddf5enpKUn6/PPP5ePjo0aNGpVo265dOxUWFurYsWPq2rVruWsCbjZMIQQAAAAAuLSYmBitXbtWb775pmJiYsztYWFhWr16tTIzM7Vr1y4NGjSoxBMLyyIsLEw7duzQJ598ov/+97+aOnWqMjIySrT77bffNGzYMO3Zs0cff/yxpk+frtjY2FLX4mrWrJliYmI0ePBgrV69Wvv379e///1vJSQkaO3ateWuEbA6AiwAAAAAgEv7/e9/r9q1a+vbb7/VoEGDzO0LFixQrVq11LlzZ/Xq1UtRUVHm6KzyGDVqlB588EH1799fHTt21PHjxx1GYxW77777FBYWpm7duql///7q3bu3ZsyYcdl+ly5dqsGDB2vcuHFq3ry5oqOjlZGRocaNG5e7RsDqeAohKhxPfwAAAACqjqr+FELcPHgKoWtjBBYAAAAAAAAsjUXcAaCKMAxDBQUFFdJP8WOkPTw8ZLPZrrtPu91eIf0AAAAAcE0EWABQRRQUFKhnz57OLqNU69atM5+mAwAAAADlRYAFAABQxTAiEwAAVDUEWABQRdjtdq1bt+66+ykoKFDfvn0lSWvWrKmQxVpZ8BWoXIzIBAAAVQ0BFgBUETabrcL/KLTb7fyhCQAAAMDpCLAAAACqGEZkAgCAqoYACwDgUlgbCK6AEZkAAKCqIcACALgU1gYCAADXIjg4WHFxcYqLiytT+wMHDigkJEQ7d+5U27Ztb3B1QNVHgAUAAAAAKLfCwkIZhlFp57PZbHJ3d6+0810qIyND3t7eFdrnsmXLFBcXp1OnTlVov0BVRIAFAHAprA0EAMD1Kyws1IMPP6Kckycq7Zx+tWpr9ar3nRZi1atXzynnBXABARYAwKWwNhAAVA2saehchmEo5+QJ5d05WLK5VcIJi6Qv3yrXiK+PPvpIjz76qI4fPy53d3dlZmaqXbt2mjBhgl544QVJ0vDhw1VQUKAVK1YoLS1NEydO1I4dO1S3bl317dtXCQkJ5qirS6cQfvPNNxo+fLh27NihW2+9VYsWLVL37t21Zs0aRUdHm3V8//33euqpp5Senq6wsDAtWbJEnTp1UmpqqoYOHSpJ5vdl+vTpmjFjRkXcMaDKIcCCS+AXHAAAgKqFNQ0twuYmuVVCgFVU/kO6du2qvLw87dy5U+3bt9fWrVtVt25dpaammm22bt2qCRMmaN++ferRo4dmzZqlN998Uz///LNiY2MVGxurpUuXlui7sLBQ0dHRaty4sdLT05WXl6dx48aVWsfkyZM1b948hYWFafLkyRo4cKCysrLUuXNnJSYmatq0afr2228lST4+PuW/UMBFEGDBJfALDgAAAOBa/Pz81LZtW6Wmpqp9+/ZKTU3VU089pfj4eOXn5ysnJ0dZWVkKDw9XQkKCYmJizNFVYWFhWrRokcLDw7V48eIS0/xTUlK0b98+paamKjAwUJI0e/Zsde/evUQd48eP1/333y9Jio+P12233aasrCy1aNFCfn5+stlsZh8ALo8ACwAA3DQYUQugGGsaoizCw8OVmpqqcePG6bPPPlNCQoJWrlyptLQ0nThxQkFBQQoLC9OuXbu0e/duJSUlmccahqGioiLt379fLVu2dOj322+/VaNGjRyCp7vvvrvUGlq3bm3+u0GDBpKkY8eOqUWLFhV5qUCVR4AFl8AvOABQNTCiFkAx1jREWUREROjNN9/Url27VL16dbVo0UIRERFKTU3VyZMnFR4eLknKz8/XqFGjNHbs2BJ9NG7c+LpqqF69uvnv4v/RUVR0DXMiARdHgAWXwC84AAAAgOspXgdr4cKFZlgVERGhF154QSdPnjTXrbrzzju1Z88eNW3atEz9Nm/eXIcOHVJ2drYCAgIkSRkZGeWur0aNGiosLCz3cYArIsACAAA3DUbUAgDKo1atWmrdurWSkpL0yiuvSJK6deumfv366dy5c2aoNWHCBP3ud79TbGyshg8fLm9vb+3Zs0cpKSnmcRfr3r27QkNDNWTIEM2ZM0d5eXmaMmWKJJVrOnlwcLDy8/O1adMmtWnTRl5eXvLy8qqAKweqHgIsACbWlgFgdYyoBQCLMYqu6QmB13SeaxQeHq7MzExFRERIkmrXrq1WrVopOztbzZs3l3RhnaqtW7dq8uTJ6tq1qwzDUGhoqPr3719qn+7u7kpOTtbw4cPVoUMH3XrrrZo7d6569epVrjSzsEoAACAASURBVP+h0blzZ40ePVr9+/fX8ePHNX36dM2YMeOarxWoygiwAJhYWwYAAABlYbPZ5FertvTlW5V2Tr9ata/pf2gmJiYqMTHRYVtmZmaJdh06dNCGDRsu28+BAwcc3rdo0UJpaWnm+23btkmSOQ0xODhYhmE4HOPv719i2+LFi7V48eKrXwjg4giwAAAAAADl4u7urtWr3i8RxtxINptN7u7ulXa+q1mzZo18fHwUFhamrKwsPfnkk7rnnnsUGhrq7NKAKokAC4CJtWUAAABQVlYKk5whLy9PEyZM0MGDB1W3bl1FRkZq/vz5zi4LqLIIsACYWFsGAAAAKJvBgwdr8ODBzi4DcBluzi4AAAAAAAAAuBICLAAAAAAAAFgaARYAAAAAAAAsjQALAAAAAAAAlkaABQAAAAAAAEsjwAIAAAAAAIClEWABAAAAAADA0giwAAAAAADlVlhYqPPnz1faq7Cw0KnXGxwcrMTExDK3P3DggGw2mzIzM51ax7WIiIhQXFzcDT1HaZYtWyZ/f/9KPy9uDtWcXQAAAAAA4OZSWFio/o88qF9O5FTaOevW9tN776+Wu7t7pZ3zYhkZGfL29q7QPpctW6a4uDidOnWqQvsFqiICLAAAAABAuRiGoV9O5OiN8ONyt9348xUa0oitF87rLPXq1XPauQEwhRAAAAAAcI3cbVI1txv/upaQ7KOPPpK/v7859TAzM1M2m03PPvus2Wb48OF69NFHJUlpaWnq2rWrPD091ahRI40dO1anT5822146de+bb75Rly5dZLfb1apVK23cuFE2m03JyckOdXz//fe699575eXlpTZt2mj79u2SpNTUVA0dOlQ5OTmy2Wyy2WyaMWNGma4tLy9PAwcOlLe3txo2bKhXX33VYf+CBQt0xx13yNvbW40aNdKYMWOUn5/v0Gbbtm2KiIiQl5eXatWqpaioKJ08ebLU861du1Z+fn5KSkoyt/31r39Vy5YtZbfb1aJFC7322mvmvuLpk6tXry712ostW7ZMjRs3lpeXl/r27avjx4+X6frhmgiwAAAAAABVTteuXZWXl6edO3dKkrZu3aq6desqNTXVbLN161ZFRERo37596tGjhx566CHt3r1b7733ntLS0hQbG1tq34WFhYqOjpaXl5fS09P1+uuva/LkyaW2nTx5ssaPH6/MzEw1a9ZMAwcO1Pnz59W5c2clJibK19dXR44c0ZEjRzR+/PgyXdvcuXPVpk0b7dy5U88++6yefPJJpaSkmPvd3Ny0aNEiff3111q+fLk2b96sZ555xtyfmZmp++67T61atdL27duVlpamXr16lbrO2DvvvKOBAwcqKSlJMTExkqSkpCRNmzZNs2fP1t69e/X8889r6tSpWr58eZmuXZLS09M1bNgwxcbGKjMzU/fee69mzZpVpuuHa2IKIQAAAGAhhmGooKCgQvo5e/asJMnDw0M22/XP87Lb7RXSD1AZ/Pz81LZtW6Wmpqp9+/ZKTU3VU089pfj4eOXn5ysnJ0dZWVkKDw9XQkKCYmJizIXLw8LCtGjRIoWHh2vx4sWy2+0OfaekpGjfvn1KTU1VYGCgJGn27Nnq3r17iTrGjx+v+++/X5IUHx+v2267TVlZWWrRooX8/Pxks9nMPsrqnnvuMUeSNWvWTNu2bdPChQvN81+8AHtwcLBmzZql0aNHm6Ok5syZo/bt2zuMmrrttttKnOfVV1/V5MmT9eGHHyo8PNzcPn36dM2fP18PPvigJCkkJER79uzR//7v/2rIkCFluvaXXnpJPXr0MIO1Zs2a6V//+pfWr19frnsB10GABQAAAFhIQUGBevbs6ewySrVu3Tp5eno6uwygzMLDw5Wamqpx48bps88+U0JCglauXKm0tDSdOHFCQUFBCgsL065du7R7926HKXKGYaioqEj79+9Xy5YtHfr99ttv1ahRI4fg6e677y61htatW5v/btCggSTp2LFjatGixTVfV6dOnUq8v3h648aNG5WQkKBvvvlGubm5On/+vAoKCnTmzBl5eXkpMzNTjzzyyBXPsWrVKh07dkzbtm1Thw4dzO2nT5/Wvn37NGzYMI0YMcLcfv78efn5+Tn0caVr37t3r/r27VviOgiwcDkEWAAAAACAKikiIkJvvvmmdu3aperVq6tFixaKiIhQamqqTp48aY4qys/P16hRozR27NgSfTRu3Pi6aqhevbr57+IRjEVFRdfV55UcOHBADzzwgJ544gnNnj1btWvXVlpamoYNG6bffvtNXl5eZQqi27Vrpy+//FJvvvmm2rdvb9ZevJbWG2+8oY4dOzocc+kTIiv72lG1EWABAAAAFmK327Vu3brr7qegoMAc3bBmzZoSU6CuRUX0AVSm4nWwFi5caIZVEREReuGFF3Ty5EmNGzdOknTnnXdqz549atq0aZn6bd68uQ4dOqTs7GwFBARIkjIyMspdX40aNUpdd+pqPv/88xLvi0eJffHFFyoqKtL8+fPl5nZh2euVK1c6tG/durU2bdqk+Pj4y54jNDRU8+fPV0REhNzd3fXKK69IkgICAhQUFKTvv//eXBPrWrRs2VLp6elXvC7gYgRYAAAAgIXYbLYKn6Znt9uZ+ocbotCQVAkDagqNazuuVq1aat26tZKSkswAplu3burXr5/OnTtnhloTJkzQ7373O8XGxmr48OHy9vbWnj17lJKSYh53se7duys0NFRDhgzRnDlzlJeXpylTpkhSudaJCw4OVn5+vjZt2qQ2bdrIy8tLXl5eVz1u27ZtmjNnjqKjo5WSkqL3339fa9eulSQ1bdpU586d08svv6xevXpp27ZtWrJkicPxEydO1B133KExY8Zo9OjRqlGjhrZs2aJHHnlEdevWNds1a9ZMW7ZsUUREhKpVq2ZOU4yPj9fYsWPl5+enHj166OzZs9qxY4dOnjypv/zlL2W69rFjx+qee+7RvHnz1KdPH33yySdMH8QVEWABAAAAAMrFZrOpbm0/jdhaeeesW9vvmh4iEB4erszMTEVEREiSateurVatWik7O1vNmzeXdGFE0tatWzV58mR17dpVhmEoNDRU/fv3L7VPd3d3JScna/jw4erQoYNuvfVWzZ07V7169SrXSMXOnTtr9OjR6t+/v44fP67p06drxowZVz1u3Lhx2rFjh+Lj4+Xr66sFCxYoKipKktSmTRstWLBAL774oiZOnKhu3bopISFBgwcPNo9v1qyZNmzYoEmTJunuu++Wp6enOnbsqIEDB5Y4V/PmzbV582ZzJNb8+fM1fPhweXl5ae7cuXr66afl7e2tO+64w2Hx+Kv53e9+pzfeeEPTp0/XtGnTFBkZqSlTpui5554rcx9wLTbDMK4xywZKl5ubKz8/P+Xk5MjX19fZ5VSoX3/91VxUlUVML4/7dHPj8ysb7tPNjc+vbLhPNzc+v7LhPl1dQUGB9u/fr5CQEIdwprCwUJX556TNZiuxxpKVbNu2TV26dFFWVpZCQ0OdXU6VdLnvolS1/w7FBYzAAgAAAACUm5XDpMqwZs0a+fj4KCwsTFlZWXryySd1zz33EF4BNwgBFgAAAAAA5ZSXl6cJEybo4MGDqlu3riIjIzV//vzr6vOzzz4zRwSWpvgJgIArIsACAAAAAKCcBg8e7LCuVEVo3769MjMzK7RPoKogwAIAAAAAwAI8PT3VtGlTZ5cBWJKbswsAAAAAAFgfz/+Cs/EddG0EWAAAAACAy6pevbok6cyZM06uBK6u+DtY/J2Ea2EKIQAAAADgstzd3eXv769jx45Jkry8vGSz2a6rT8MwKmQ0zcX92Gy2666rIvtBxTEMQ2fOnNGxY8fk7+/v8k/AdFUEWAAAAACAKwoMDJQkM8S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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>70.782597</td>\n",
       "      <td>70.256322</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>69.812061</td>\n",
       "      <td>70.838677</td>\n",
       "      <td>380</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>69.815377</td>\n",
       "      <td>70.259629</td>\n",
       "      <td>775</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>70.150924</td>\n",
       "      <td>69.817507</td>\n",
       "      <td>385</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>70.331588</td>\n",
       "      <td>69.339616</td>\n",
       "      <td>340</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>69.881764</td>\n",
       "      <td>69.302950</td>\n",
       "      <td>645</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>69.434793</td>\n",
       "      <td>70.570271</td>\n",
       "      <td>320</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>69.621553</td>\n",
       "      <td>69.819890</td>\n",
       "      <td>435</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_weight  avg_weight_backend  ct_weight  \\\n",
       "region                                                                \n",
       "East Midlands              70.782597           70.256322        375   \n",
       "East of England            69.812061           70.838677        380   \n",
       "London                     69.815377           70.259629        775   \n",
       "North East                 70.150924           69.817507        385   \n",
       "North West                 70.331588           69.339616        340   \n",
       "South East                 69.881764           69.302950        645   \n",
       "West Midlands              69.434793           70.570271        320   \n",
       "Yorkshire and the Humber   69.621553           69.819890        435   \n",
       "\n",
       "                          ct_weight_backend  \n",
       "region                                       \n",
       "East Midlands                           375  \n",
       "East of England                         390  \n",
       "London                                  785  \n",
       "North East                              330  \n",
       "North West                              335  \n",
       "South East                              620  \n",
       "West Midlands                           340  \n",
       "Yorkshire and the Humber                440  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>69.863431</td>\n",
       "      <td>69.936204</td>\n",
       "      <td>1185</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.999594</td>\n",
       "      <td>70.425283</td>\n",
       "      <td>1045</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.028015</td>\n",
       "      <td>69.638366</td>\n",
       "      <td>1215</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.704823</td>\n",
       "      <td>70.020630</td>\n",
       "      <td>1040</td>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>70.069979</td>\n",
       "      <td>69.700321</td>\n",
       "      <td>1025</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>67.774363</td>\n",
       "      <td>71.294859</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "imd                                                                           \n",
       "1 Most deprived    69.863431           69.936204       1185               1175\n",
       "2                  69.999594           70.425283       1045               1035\n",
       "3                  70.028015           69.638366       1215               1200\n",
       "4                  69.704823           70.020630       1040               1025\n",
       "5 Least deprived   70.069979           69.700321       1025               1065\n",
       "Unknown            67.774363           71.294859         60                 45"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
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       "<IPython.core.display.Image object>"
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     "output_type": "display_data"
    },
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dementia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>69.892191</td>\n",
       "      <td>69.943617</td>\n",
       "      <td>5525</td>\n",
       "      <td>5490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>72.150672</td>\n",
       "      <td>70.029973</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "dementia                                                              \n",
       "False      69.892191           69.943617       5525               5490\n",
       "True       72.150672           70.029973         45                 60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>69.899015</td>\n",
       "      <td>69.924171</td>\n",
       "      <td>5520</td>\n",
       "      <td>5495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>71.184475</td>\n",
       "      <td>72.137065</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "diabetes                                                              \n",
       "False      69.899015           69.924171       5520               5495\n",
       "True       71.184475           72.137065         55                 50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
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     "output_type": "display_data"
    },
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>69.883275</td>\n",
       "      <td>69.944326</td>\n",
       "      <td>5515</td>\n",
       "      <td>5490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>72.666003</td>\n",
       "      <td>69.964299</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              avg_weight  avg_weight_backend  ct_weight  ct_weight_backend\n",
       "hypertension                                                              \n",
       "False          69.883275           69.944326       5515               5490\n",
       "True           72.666003           69.964299         55                 55"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_weight</th>\n",
       "      <th>avg_weight_backend</th>\n",
       "      <th>ct_weight</th>\n",
       "      <th>ct_weight_backend</th>\n",
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       "      <th>learning_disability</th>\n",
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       "      <th>False</th>\n",
       "      <td>69.919723</td>\n",
       "      <td>69.942526</td>\n",
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       "      <td>5490</td>\n",
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       "    <tr>\n",
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       "      <td>69.157496</td>\n",
       "      <td>70.133227</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
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      "text/plain": [
       "                     avg_weight  avg_weight_backend  ct_weight  \\\n",
       "learning_disability                                              \n",
       "False                 69.919723           69.942526       5510   \n",
       "True                  69.157496           70.133227         60   \n",
       "\n",
       "                     ct_weight_backend  \n",
       "learning_disability                     \n",
       "False                             5490  \n",
       "True                                60  "
      ]
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for suffix in [''] + covariates:    \n",
    "    df_distribution = pd.read_csv(f'../output/{output_path}/tables/avg_value{suffix}.csv', index_col=0)\n",
    "    display(df_distribution)\n",
    "    \n",
    "    img_exist = os.path.exists(f'../output/{output_path}/figures/distribution{suffix}.png')\n",
    "    \n",
    "    if img_exist:\n",
    "        img_distribution = Image(f'../output/{output_path}/figures/distribution{suffix}.png')\n",
    "        display(img_distribution)\n",
    "    else: \n",
    "        print('Plot redacted due to low counts.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Range Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>oor_weight</th>\n",
       "      <th>oor_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct25</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct75</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      oor_weight oor_weight_backend\n",
       "index                              \n",
       "count          -                  -\n",
       "mean           -                  -\n",
       "pct25          -                  -\n",
       "pct75          -                  -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
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    },
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_weight</th>\n",
       "      <th>mean_weight</th>\n",
       "      <th>pct25_weight</th>\n",
       "      <th>pct75_weight</th>\n",
       "      <th>count_weight_backend</th>\n",
       "      <th>mean_weight_backend</th>\n",
       "      <th>pct25_weight_backend</th>\n",
       "      <th>pct75_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <th>30-39</th>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>40-49</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "age_band                                                      \n",
       "0-19                -           -            -            -   \n",
       "20-29               -           -            -            -   \n",
       "30-39               -           -            -            -   \n",
       "40-49               -           -            -            -   \n",
       "50-59               -           -            -            -   \n",
       "60-69               -           -            -            -   \n",
       "70-79               -           -            -            -   \n",
       "80+                 -           -            -            -   \n",
       "\n",
       "         count_weight_backend mean_weight_backend pct25_weight_backend  \\\n",
       "age_band                                                                 \n",
       "0-19                        -                   -                    -   \n",
       "20-29                       -                   -                    -   \n",
       "30-39                       -                   -                    -   \n",
       "40-49                       -                   -                    -   \n",
       "50-59                       -                   -                    -   \n",
       "60-69                       -                   -                    -   \n",
       "70-79                       -                   -                    -   \n",
       "80+                         -                   -                    -   \n",
       "\n",
       "         pct75_weight_backend  \n",
       "age_band                       \n",
       "0-19                        -  \n",
       "20-29                       -  \n",
       "30-39                       -  \n",
       "40-49                       -  \n",
       "50-59                       -  \n",
       "60-69                       -  \n",
       "70-79                       -  \n",
       "80+                         -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
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       "    count_weight mean_weight pct25_weight pct75_weight count_weight_backend  \\\n",
       "sex                                                                           \n",
       "F              -           -            -            -                    -   \n",
       "M              -           -            -            -                    -   \n",
       "\n",
       "    mean_weight_backend pct25_weight_backend pct75_weight_backend  \n",
       "sex                                                                \n",
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       "M                     -                    -                    -  "
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       "          count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "ethnicity                                                      \n",
       "Asian                -           -            -            -   \n",
       "Black                -           -            -            -   \n",
       "Mixed                -           -            -            -   \n",
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       "White                -           -            -            -   \n",
       "\n",
       "          count_weight_backend mean_weight_backend pct25_weight_backend  \\\n",
       "ethnicity                                                                 \n",
       "Asian                        -                   -                    -   \n",
       "Black                        -                   -                    -   \n",
       "Mixed                        -                   -                    -   \n",
       "Other                        -                   -                    -   \n",
       "White                        -                   -                    -   \n",
       "\n",
       "          pct75_weight_backend  \n",
       "ethnicity                       \n",
       "Asian                        -  \n",
       "Black                        -  \n",
       "Mixed                        -  \n",
       "Other                        -  \n",
       "White                        -  "
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       "                         count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "region                                                                        \n",
       "East Midlands                       -           -            -            -   \n",
       "East of England                     -           -            -            -   \n",
       "London                              -           -            -            -   \n",
       "North East                          -           -            -            -   \n",
       "North West                          -           -            -            -   \n",
       "South East                          -           -            -            -   \n",
       "West Midlands                       -           -            -            -   \n",
       "Yorkshire and the Humber            -           -            -            -   \n",
       "\n",
       "                         count_weight_backend mean_weight_backend  \\\n",
       "region                                                              \n",
       "East Midlands                               -                   -   \n",
       "East of England                             -                   -   \n",
       "London                                      -                   -   \n",
       "North East                                  -                   -   \n",
       "North West                                  -                   -   \n",
       "South East                                  -                   -   \n",
       "West Midlands                               -                   -   \n",
       "Yorkshire and the Humber                    -                   -   \n",
       "\n",
       "                         pct25_weight_backend pct75_weight_backend  \n",
       "region                                                              \n",
       "East Midlands                               -                    -  \n",
       "East of England                             -                    -  \n",
       "London                                      -                    -  \n",
       "North East                                  -                    -  \n",
       "North West                                  -                    -  \n",
       "South East                                  -                    -  \n",
       "West Midlands                               -                    -  \n",
       "Yorkshire and the Humber                    -                    -  "
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       "                 count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "imd                                                                   \n",
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       "2                           -           -            -            -   \n",
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       "4                           -           -            -            -   \n",
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       "Unknown                     -           -            -            -   \n",
       "\n",
       "                 count_weight_backend mean_weight_backend  \\\n",
       "imd                                                         \n",
       "1 Most deprived                     -                   -   \n",
       "2                                   -                   -   \n",
       "3                                   -                   -   \n",
       "4                                   -                   -   \n",
       "5 Least deprived                    -                   -   \n",
       "Unknown                             -                   -   \n",
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       "imd                                                         \n",
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       "         count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "dementia                                                      \n",
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       "dementia                                                                 \n",
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       "      <th></th>\n",
       "      <th>count_weight</th>\n",
       "      <th>mean_weight</th>\n",
       "      <th>pct25_weight</th>\n",
       "      <th>pct75_weight</th>\n",
       "      <th>count_weight_backend</th>\n",
       "      <th>mean_weight_backend</th>\n",
       "      <th>pct25_weight_backend</th>\n",
       "      <th>pct75_weight_backend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</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",
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       "      <th>False</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "diabetes                                                      \n",
       "False               -           -            -            -   \n",
       "True                -           -            -            -   \n",
       "\n",
       "         count_weight_backend mean_weight_backend pct25_weight_backend  \\\n",
       "diabetes                                                                 \n",
       "False                       -                   -                    -   \n",
       "True                        -                   -                    -   \n",
       "\n",
       "         pct75_weight_backend  \n",
       "diabetes                       \n",
       "False                       -  \n",
       "True                        -  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
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       "             count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "hypertension                                                      \n",
       "False                   -           -            -            -   \n",
       "True                    -           -            -            -   \n",
       "\n",
       "             count_weight_backend mean_weight_backend pct25_weight_backend  \\\n",
       "hypertension                                                                 \n",
       "False                           -                   -                    -   \n",
       "True                            -                   -                    -   \n",
       "\n",
       "             pct75_weight_backend  \n",
       "hypertension                       \n",
       "False                           -  \n",
       "True                            -  "
      ]
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     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
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       "      <th>learning_disability</th>\n",
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      "text/plain": [
       "                    count_weight mean_weight pct25_weight pct75_weight  \\\n",
       "learning_disability                                                      \n",
       "False                          -           -            -            -   \n",
       "True                           -           -            -            -   \n",
       "\n",
       "                    count_weight_backend mean_weight_backend  \\\n",
       "learning_disability                                            \n",
       "False                                  -                   -   \n",
       "True                                   -                   -   \n",
       "\n",
       "                    pct25_weight_backend pct75_weight_backend  \n",
       "learning_disability                                            \n",
       "False                                  -                    -  \n",
       "True                                   -                    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    }
   ],
   "source": [
    "for suffix in [''] + covariates: \n",
    "    df_oor = pd.read_csv(f'../output/{output_path}/tables/out_of_range{suffix}.csv', index_col=0)\n",
    "    display(df_oor)\n",
    "    \n",
    "    img_exist = os.path.exists(f'../output/{output_path}/figures/out_of_range{suffix}.png')\n",
    "    \n",
    "    if img_exist:\n",
    "        img_oor = Image(f'../output/{output_path}/figures/out_of_range{suffix}.png')\n",
    "        display(img_oor)\n",
    "    else: \n",
    "        print('Plot redacted due to low counts.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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