notebooks/report_height_weight.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from lib import *\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = '../output/data/input_processed.feather'\n",
    "\n",
    "definitions = ['weight','height']\n",
    "\n",
    "other_vars = []\n",
    "\n",
    "null = ['0',0,np.nan]\n",
    "\n",
    "date_min = '2015-03-01'\n",
    "date_max = '2022-03-01'\n",
    "time_delta = 'M'\n",
    "code_dict = {\n",
    "    'ethnicity': {1:'White', 2:'Mixed', 3:'Asian', 4:'Black', 5:'Other', np.nan: 'Unknown', 0: 'Unknown'},\n",
    "    'imd': {0: 'Unknown', 1: '1 Most deprived', 2: '2', 3: '3', 4: '4', 5: '5 Least deprived'}\n",
    "}\n",
    "\n",
    "demographic_covariates = ['age_band', 'sex', 'ethnicity', 'region', 'imd']\n",
    "clinical_covariates =  ['dementia', 'diabetes', 'hypertension', 'learning_disability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occ = import_clean(input_path, definitions, other_vars, demographic_covariates, \n",
    "                      clinical_covariates, null, date_min, date_max, \n",
    "                      time_delta, code_dict, dates=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average height:\n",
      "\n"
     ]
    },
    {
     "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>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9215.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       height\n",
       "mean     1.65\n",
       "count 9215.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by age_band:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>1.65</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean count\n",
       "age_band           \n",
       "0-19     1.65  1165\n",
       "20-29    1.65  1090\n",
       "30-39    1.65  1290\n",
       "40-49    1.65  1255\n",
       "50-59    1.65  1070\n",
       "60-69    1.65  1205\n",
       "70-79    1.65   945\n",
       "80+      1.65  1195"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by sex:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1.65</td>\n",
       "      <td>4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1.65</td>\n",
       "      <td>4665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean count\n",
       "sex           \n",
       "F   1.65  4555\n",
       "M   1.65  4665"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AINXd855hmM2bN/eWLVvm9vynnXZatm7dOrfnZ2Xa8Wdi06ZNc56ElWbTpk058cQT5z3G3Pneyc743sliVsL3zqq6qLs37+zczF7n+pvR1q1bc8lln8qd+x0871FYQe739clfYC+68ktznoSVZM0t1897hBVj69at+efL/182PujOeY/CCnL/2yf/uH7bVfNbNGPlufqmNfMeYZfE9WB37ndwbn3cC+Y9BrDC7XuF98laaOOD7sxrn3rDvMcAVrg3XHzgvEfYJXuuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAgywrrqvqHcs5BgAA38yWu3L9+IVfVNWaJE8bPw4AAKxeS8Z1Vb2mqm5M8qSqumH6cWOSa5K8d49MCAAAq8SScd3db+zuA5Kc0t0HTj8O6O5v6e7X7KEZAQBgVVi7nIu6+zVVtSHJoQsf090XzGowAABYbZYV11X1m0mOTfLJJHdOD3cScQ0AAFPLiusk35vksd192yyHAQCA1Wy5rxZyZZJ9ZjkIAACsdkuuXFfVaZls/7glySVV9VdJ/m31urt/erbjAQDA6rGrbSFbpr9elOS83blxVZ2V5Jgk13T3Exa55qgkv5PJqvh13f2s6fHPJrkxk/3dd3T35t15bgAAmIcl47q733Yf7n12ktOTvH1nJ6vqwUl+P8nR3X11VT30Hpc8u7uvuw/PDwAAe9RyXy3k0ky2hyz0tUxWtn+9u798z8d09wVVddgSt/2hJO/p7qun11+znFkAAGClWu6rhbwvky0a75p+fWyS/ZJ8MZMV6hfei+d+TJJ9quojSQ5Icmp371jl7iQfrKpO8ubuPnOxm1TVCUlOSJKNGzfeizHG2b59e9bc8rXse8X5c50DWPnW3PLlbN9+x7zHWBG2b9+em29ckzdcfOC8RwFWuKtuXJP9t2+f9xhLWm5cf1d3P3XB15dW1cXd/dSqeul9eO6nJXlOkn2T/F1V/X13fzrJM7t7+3SryIeq6orF3rBmGt5nJsnmzZvvuboOAAB7zHLjek1VHdnd/5gkVfUdSdZMz93bpZdtSb7c3TcnubmqLkjy5CSf7u7tyWSrSFWdm+TIrII3rNmwYUO+eNva3Pq4F8x7FGCF2/eK87NhwyHzHmNF2LBhQ2674wt57VNvmPcowAr3hosPzAM2bJj3GEta7utcvyLJW6vqM9NX8nhrkh+vqv2TvPFePvd7kzyzqtZW1X5Jnp7kU1W1f1UdkCTT+z83yWX38jkAAGCPWdbKdXdfmOSJVXXQ9OuvLTj9Rzt7TFWdk+SoJOuqaluSkzN9I5ruPqO7P1VV70/yiSR3JfmD7r6sqh6V5Nyq2jHfu7r7/ffmPw4AAPakXb2JzEu7+51V9TP3OJ4k6e43LfbY7j5uV0/e3ackOeUex67MZHsIAACsKrtaud5/+usBsx4EAABWu129icybp7/+2p4ZBwAAVq9l/UBjVT2mqv6qqi6bfv2kqnrdbEcDAIDVZbmvFvKWJK9JcnuSdPcnMnkjGQAAYGq5cb3fjte4XsBbiwEAwALLjevrqurRmbwtearqB5J8YWZTAQDAKrTcd2j8yUzeYvxxVbU9yWeSvGRmUwEAwCq03LjenuQPk/x1koOT3JDk+CSvn9FcAACw6iw3rt+b5KtJLk7y+dmNAwAAq9dy4/rh3X30TCcBAIBVbrk/0Pi3VfXEmU4CAACr3JIr11V1aSavELI2ycur6soktyWpJN3dT5r9iAAAsDrsalvIMXtkCgAA2AssGdfdfdWeGgQAAFa75e65BgAAdkFcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADDI2nkPAABX37Qmb7j4wHmPwQrypVsm63+H7HfXnCdhJbn6pjU5fN5D7IK4BmCuNm3aNO8RWIG+vnVrkuQBh/rzwd0Oz8r/niGuB1tzy/XZ94rz5z0GK8j9/vWGJMldD7Qqx93W3HJ9kkPmPcaKcOKJJ857BFagk046KUly6qmnznkS2D3ieqCV/jcp5mPr1huTJJseJaRY6BDfMwD2QuJ6IKsv7IzVFwD45uHVQgAAYBBxDQAAg4hrAAAYRFwDAMAgM4vrqjqrqq6pqsuWuOaoqrqkqi6vqo8uOH50Vf1TVW2tqlfPakYAABhplivXZyc5erGTVfXgJL+f5Hu6+/FJ/sv0+Jokv5fk+UmOSHJcVR0xwzkBAGCImcV1d1+Q5PolLvmhJO/p7qun118zPX5kkq3dfWV3fz3Ju5O8aFZzAgDAKPPcc/2YJA+pqo9U1UVV9cPT4xuSfG7BddumxwAAYEWb55vIrE3ytCTPSbJvkr+rqr/f3ZtU1QlJTkiSjRs3Dh0QAAB2xzxXrrcl+UB339zd1yW5IMmTk2xP8ogF1z18emynuvvM7t7c3ZvXr18/04EBAGAp84zr9yZ5ZlWtrar9kjw9yaeSXJjk8Kp6ZFXdP8mxSc6b45wAALAsM9sWUlXnJDkqybqq2pbk5CT7JEl3n9Hdn6qq9yf5RJK7kvxBd182fexPJflAkjVJzuruy2c1JwAAjDKzuO7u45ZxzSlJTtnJ8fOTnD+LuQAAYFa8QyMAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYZGZxXVVnVdU1VXXZIuePqqqvVdUl049fWXDus1V16fT4llnNCAAAI62d4b3PTnJ6krcvcc3HuvuYRc49u7uvGz4VAADMyMxWrrv7giTXz+r+AACw0sx7z/UzqurjVfW+qnr8guOd5INVdVFVnTCv4QAAYHfMclvIrlyc5NDuvqmqXpDkT5McPj33zO7eXlUPTfKhqrpiuhL+DabxfUKSbNy4cU/MDQAAOzW3levuvqG7b5p+fn6Sfapq3fTr7dNfr0lybpIjl7jPmd29ubs3r1+/fg9MDgAAOze3uK6qb62qmn5+5HSWL1fV/lV1wPT4/kmem2SnrzgCAAArycy2hVTVOUmOSrKuqrYlOTnJPknS3Wck+YEkr6qqO5LcmuTY7u6qOiTJudPuXpvkXd39/lnNCQAAo8wsrrv7uF2cPz2Tl+q75/Erkzx5VnMBAMCszPvVQgAAYK8hrgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYZGZxXVVnVdU1VXXZIuePqqqvVdUl049fWXDu6Kr6p6raWlWvntWMAAAw0ixXrs9OcvQurvlYdz9l+vH6JKmqNUl+L8nzkxyR5LiqOmKGcwIAwBAzi+vuviDJ9ffioUcm2drdV3b315O8O8mLhg4HAAAzMO8918+oqo9X1fuq6vHTYxuSfG7BNdumxwAAYEVbO8fnvjjJod19U1W9IMmfJjl8d29SVSckOSFJNm7cOHZCAADYDXNbue7uG7r7punn5yfZp6rWJdme5BELLn349Nhi9zmzuzd39+b169fPdGYAAFjK3OK6qr61qmr6+ZHTWb6c5MIkh1fVI6vq/kmOTXLevOYEAIDlmtm2kKo6J8lRSdZV1bYkJyfZJ0m6+4wkP5DkVVV1R5Jbkxzb3Z3kjqr6qSQfSLImyVndffms5gQAgFFmFtfdfdwuzp+e5PRFzp2f5PxZzAUAALMy71cLAQCAvYa4BgCAQcQ1AAAMIq4BAGCQeb6JDACwwGmnnZatW7fOe4wVYcfvw0knnTTnSVaGTZs25cQTT5z3GCyDuAYAVpx999133iPAvSKuAWCFsDIJq5891wAAMIi4BgCAQcQ1AAAMYs81M+En3u/mJ97/PT/xDsDeTFzDjPmJdwD45iGumQkrkwDANyN7rgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDVHfPe4ZhquraJFfNew7YiXVJrpv3EACrjO+drFSHdvf6nZ3Yq+IaVqqq2tLdm+c9B8Bq4nsnq5FtIQAAMIi4BgCAQcQ17BlnznsAgFXI905WHXuuAQBgECvXAAAwiLiGGaqqO6vqkgUfh817JoCVqqq6qt654Ou1VXVtVf35POeC3bF23gPAXu7W7n7KvIcAWCVuTvKEqtq3u29N8p+TbJ/zTLBbrFwDACvJ+Um+e/r5cUnOmeMssNvENczWvgu2hJw772EAVoF3Jzm2qh6Y5ElJ/mHO88BusS0EZsu2EIDd0N2fmP58ynGZrGLDqiKuAYCV5rwk/zPJUUm+Zb6jwO4R1wDASnNWkq9296VVddS8h4HdIa4BgBWlu7cl+d15zwH3hndoBACAQbxaCAAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgG2ItV1f5V9RdV9fGquqyqXlxVT6uqj1bVRVX1gar6tqpaW1UX7ng3vKp6Y1X9xpzHB1h1vEMjwN7t6CSf7+7vTpKqOijJ+5K8qLuvraoXJ/mN7v7RqvqRJH9cVSdOH/f0eQ0NsFqJa4C926VJfruqfivJnyf5SpInJPlQVSXJmiRfSJLuvryq3jG97hnd/fX5jAyweolrgL1Yd3+6qp6a5AVJfj3Jh5Nc3t3PWOQhT0zy1SQP3UMjAuxV7LkG2ItV1cOS3NLd70xySiZbPdZX1TOm5/epqsdPP/++JAcn+U9JTquqB89pbIBVq7p73jMAMCNV9bxMovquJLcneVWSO5L8bpKDMvkXzN9Jcm6Sv03ynO7+XFX9dJKndffxcxkcYJUS1wAAMIhtIQAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAG+f/dju/vCyuZgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by ethnicity:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>1.65</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>1.65</td>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>1.65</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>1.65</td>\n",
       "      <td>4635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean count\n",
       "ethnicity           \n",
       "Asian     1.65  1330\n",
       "Black     1.65   575\n",
       "Mixed     1.65  2275\n",
       "Other     1.65   400\n",
       "White     1.65  4635"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by region:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>1.65</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>1.64</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>1.65</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>1.65</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>1.65</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>1.65</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean count\n",
       "region                             \n",
       "East Midlands            1.65   500\n",
       "East of England          1.64   640\n",
       "London                   1.65  1195\n",
       "North East               1.65   645\n",
       "North West               1.65   705\n",
       "South East               1.65  1240\n",
       "West Midlands            1.65   540\n",
       "Yorkshire and the Humber 1.65   545"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by imd:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>imd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>1.65</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean count\n",
       "imd                        \n",
       "1 Most deprived  1.65  1920\n",
       "2                1.65  1710\n",
       "3                1.65  1895\n",
       "4                1.65  1715\n",
       "5 Least deprived 1.65  1870\n",
       "Unknown          1.65   110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_distribution(df_occ, ['height'], 1, group='')\n",
    "for group in demographic_covariates:\n",
    "    report_distribution(df_occ, ['height'], 1, group)"
   ]
  },
  {
   "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>oor_height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct25</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct75</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      oor_height\n",
       "index           \n",
       "count          -\n",
       "mean           -\n",
       "pct25          -\n",
       "pct75          -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_height</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>pct25_height</th>\n",
       "      <th>pct75_height</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>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count_height mean_height pct25_height pct75_height\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": {
      "text/html": [
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       "      <th>pct75_height</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>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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      "text/plain": [
       "    count_height mean_height pct25_height pct75_height\n",
       "sex                                                   \n",
       "F              -           -            -            -\n",
       "M              -           -            -            -"
      ]
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      "Plot redacted due to low counts.\n"
     ]
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
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       "      <th>Other</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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      "text/plain": [
       "          count_height mean_height pct25_height pct75_height\n",
       "ethnicity                                                   \n",
       "Asian                -           -            -            -\n",
       "Black                -           -            -            -\n",
       "Mixed                -           -            -            -\n",
       "Other                -           -            -            -\n",
       "White                -           -            -            -"
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      "Plot redacted due to low counts.\n"
     ]
    },
    {
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       "      <th>East Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                         count_height mean_height pct25_height pct75_height\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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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
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       "      <th>imd</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count_height mean_height pct25_height pct75_height\n",
       "imd                                                                \n",
       "1 Most deprived             -           -            -            -\n",
       "2                           -           -            -            -\n",
       "3                           -           -            -            -\n",
       "4                           -           -            -            -\n",
       "5 Least deprived            -           -            -            -\n",
       "Unknown                     -           -            -            -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    }
   ],
   "source": [
    "report_out_of_range(df_occ, ['height'], 0.5, 2.8, 1, [0,'0'], group='')\n",
    "for group in demographic_covariates:\n",
    "    report_out_of_range(df_occ, ['height'], 0.5, 2.8, 1, [0,'0'], group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average weight:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5475.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight\n",
       "mean    70.11\n",
       "count 5475.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by age_band:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>age_band</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-19</th>\n",
       "      <td>69.40</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>70.48</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>70.16</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>70.43</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>69.89</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>69.97</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>70.50</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80+</th>\n",
       "      <td>70.10</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean count\n",
       "age_band            \n",
       "0-19     69.40   705\n",
       "20-29    70.48   640\n",
       "30-39    70.16   820\n",
       "40-49    70.43   730\n",
       "50-59    69.89   635\n",
       "60-69    69.97   705\n",
       "70-79    70.50   535\n",
       "80+      70.10   710"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by sex:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>70.17</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>70.05</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean count\n",
       "sex            \n",
       "F   70.17  2675\n",
       "M   70.05  2800"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by ethnicity:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>70.31</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>70.49</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed</th>\n",
       "      <td>69.74</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>70.91</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>70.11</td>\n",
       "      <td>2745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean count\n",
       "ethnicity            \n",
       "Asian     70.31   765\n",
       "Black     70.49   350\n",
       "Mixed     69.74  1370\n",
       "Other     70.91   245\n",
       "White     70.11  2745"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by region:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>69.98</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>69.74</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>69.81</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>70.29</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>70.31</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>69.76</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>69.96</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>69.93</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean count\n",
       "region                              \n",
       "East Midlands            69.98   315\n",
       "East of England          69.74   375\n",
       "London                   69.81   710\n",
       "North East               70.29   380\n",
       "North West               70.31   415\n",
       "South East               69.76   750\n",
       "West Midlands            69.96   315\n",
       "Yorkshire and the Humber 69.93   325"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages by imd:\n",
      "\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>imd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Most deprived</th>\n",
       "      <td>70.20</td>\n",
       "      <td>1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.08</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.37</td>\n",
       "      <td>1120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.12</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>69.77</td>\n",
       "      <td>1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>69.81</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean count\n",
       "imd                         \n",
       "1 Most deprived  70.20  1115\n",
       "2                70.08  1035\n",
       "3                70.37  1120\n",
       "4                70.12  1030\n",
       "5 Least deprived 69.77  1105\n",
       "Unknown          69.81    70"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_distribution(df_occ, ['weight'], 1, group='')\n",
    "for group in demographic_covariates:\n",
    "    report_distribution(df_occ, ['weight'], 1, group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct25</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct75</th>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      oor_weight\n",
       "index           \n",
       "count          -\n",
       "mean           -\n",
       "pct25          -\n",
       "pct75          -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "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>count_weight</th>\n",
       "      <th>mean_weight</th>\n",
       "      <th>pct25_weight</th>\n",
       "      <th>pct75_weight</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>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <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",
       "    </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+                 -           -            -            -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>pct25_weight</th>\n",
       "      <th>pct75_weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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      "text/plain": [
       "    count_weight mean_weight pct25_weight pct75_weight\n",
       "sex                                                   \n",
       "F              -           -            -            -\n",
       "M              -           -            -            -"
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      "Plot redacted due to low counts.\n"
     ]
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <th>Other</th>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>-</td>\n",
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       "          count_weight mean_weight pct25_weight pct75_weight\n",
       "ethnicity                                                   \n",
       "Asian                -           -            -            -\n",
       "Black                -           -            -            -\n",
       "Mixed                -           -            -            -\n",
       "Other                -           -            -            -\n",
       "White                -           -            -            -"
      ]
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      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East of England</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire and the Humber</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "                         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            -           -            -            -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>pct75_weight</th>\n",
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       "    <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>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Least deprived</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\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",
       "imd                                                                \n",
       "1 Most deprived             -           -            -            -\n",
       "2                           -           -            -            -\n",
       "3                           -           -            -            -\n",
       "4                           -           -            -            -\n",
       "5 Least deprived            -           -            -            -\n",
       "Unknown                     -           -            -            -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot redacted due to low counts.\n"
     ]
    }
   ],
   "source": [
    "report_out_of_range(df_occ, ['weight'], 3, 500, 1, [0,'0'], group='')\n",
    "for group in demographic_covariates:\n",
    "    report_out_of_range(df_occ, ['weight'], 3, 500, 1, [0,'0'], group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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