getting_started_with_great_tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "---\n",
    "\n",
    "This repo showcases how I use the `Great Tables` package in daily data science projects. I have been using the `gt` (and `gtExtras`) since May 2018 and I'm now transitioning to `Python` and will be using the `Great Tables` library, instead, for personal and professional tabulation needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Required Libraries\n",
    "import polars as pl \n",
    "import great_tables \n",
    "from great_tables import GT, style, loc, md, html\n",
    "import polars.selectors as cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset\n",
    "url = 'https://raw.githubusercontent.com/tongakuot/polars-orbit/main/data/ss_2008_census_data_raw.csv'\n",
    "age_mapping = {\n",
    "    \"0 to 4\": \"0-14\",\n",
    "    \"5 to 9\": \"0-14\",\n",
    "    \"10 to 14\": \"0-14\",\n",
    "    \"15 to 19\": \"15-24\",\n",
    "    \"20 to 24\": \"15-24\",\n",
    "    \"25 to 29\": \"25-34\",\n",
    "    \"30 to 34\": \"25-34\",\n",
    "    \"35 to 39\": \"35-44\",\n",
    "    \"40 to 44\": \"35-44\",\n",
    "    \"45 to 49\": \"45-54\",\n",
    "    \"50 to 54\": \"45-54\",\n",
    "    \"55 to 59\": \"55-64\",\n",
    "    \"60 to 64\": \"55-64\",\n",
    "    \"65+\": \"65 and above\",\n",
    "}\n",
    "census = (\n",
    "    pl.read_csv(url, null_values='NA')\n",
    "    .select(\n",
    "        [\"Region Name\", \"Variable Name\", \"Age Name\", \"2008\"]\n",
    "    )\n",
    "    .rename({\n",
    "            \"Region Name\": \"state\",\n",
    "            \"Variable Name\": \"gender\",\n",
    "            \"Age Name\": \"age_category\",\n",
    "            \"2008\": \"population\",\n",
    "    })\n",
    "    .with_columns(\n",
    "        gender=pl.col(\"gender\").str.split(\" \").list.get(1),\n",
    "        age_category=pl.col(\"age_category\").replace(age_mapping),\n",
    "    )\n",
    "    .filter(\n",
    "        (pl.col(\"gender\") != \"Total\") & (pl.col(\"age_category\") != \"Total\")\n",
    "    )\n",
    "    .group_by(['state', 'gender', 'age_category'])\n",
    "    .agg(total=pl.col('population').sum())\n",
    "    .sort('total', descending=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (5, 4)\n",
      "┌───────────────────┬────────┬──────────────┬────────┐\n",
      "│ state             ┆ gender ┆ age_category ┆ total  │\n",
      "│ ---               ┆ ---    ┆ ---          ┆ ---    │\n",
      "│ str               ┆ str    ┆ str          ┆ i64    │\n",
      "╞═══════════════════╪════════╪══════════════╪════════╡\n",
      "│ Jonglei           ┆ Male   ┆ 0-14         ┆ 338443 │\n",
      "│ Jonglei           ┆ Female ┆ 0-14         ┆ 263646 │\n",
      "│ Central Equatoria ┆ Male   ┆ 0-14         ┆ 242247 │\n",
      "│ Upper Nile        ┆ Male   ┆ 0-14         ┆ 237461 │\n",
      "│ Warrap            ┆ Male   ┆ 0-14         ┆ 230854 │\n",
      "└───────────────────┴────────┴──────────────┴────────┘\n"
     ]
    }
   ],
   "source": [
    "# Inspect the first few rows\n",
    "print(census.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tabulating Data with the Great Tables Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (10, 3)\n",
      "┌─────────────────────────┬────────┬────────┐\n",
      "│ state                   ┆ Male   ┆ Female │\n",
      "│ ---                     ┆ ---    ┆ ---    │\n",
      "│ str                     ┆ i64    ┆ i64    │\n",
      "╞═════════════════════════╪════════╪════════╡\n",
      "│ Jonglei                 ┆ 734327 ┆ 624275 │\n",
      "│ Central Equatoria       ┆ 581722 ┆ 521835 │\n",
      "│ Upper Nile              ┆ 525430 ┆ 438923 │\n",
      "│ Warrap                  ┆ 470734 ┆ 502194 │\n",
      "│ Eastern Equatoria       ┆ 465187 ┆ 440974 │\n",
      "│ Northern Bahr el Ghazal ┆ 348290 ┆ 372608 │\n",
      "│ Lakes                   ┆ 365880 ┆ 329850 │\n",
      "│ Western Equatoria       ┆ 318443 ┆ 300586 │\n",
      "│ Unity                   ┆ 300247 ┆ 285554 │\n",
      "│ Western Bahr el Ghazal  ┆ 177040 ┆ 156391 │\n",
      "└─────────────────────────┴────────┴────────┘\n"
     ]
    }
   ],
   "source": [
    "# Summarize data by state\n",
    "state = (\n",
    "    census\n",
    "    .group_by(['state', 'gender'])\n",
    "    .agg(total=pl.col('total').sum())\n",
    "    .sort('total', descending=True)\n",
    "    .pivot(index='state', columns='gender', values='total')\n",
    ")\n",
    "\n",
    "# Dispay output\n",
    "print(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
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       " \n",
       "</style>\n",
       "<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n",
       "<thead class=\"gt_header\">\n",
       "  <tr>\n",
       "    <th colspan=\"3\" class=\"gt_heading gt_title gt_font_normal\">Population Distribution by State and Gender in South Sudan</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th colspan=\"3\" class=\"gt_heading gt_subtitle gt_font_normal gt_bottom_border\"><strong>Insights from the 2008 Census:</strong> <br> Jonglei State Leads with Over 13.6 Million Persons,<br> while Western Bahr el Ghazal State Has the Lowest Population</th>\n",
       "  </tr>\n",
       "</thead>\n",
       "<tr class=\"gt_col_headings gt_spanner_row\">\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"2\" colspan=\"1\" scope=\"col\" id=\"\"></th>\n",
       "  <th class=\"gt_center gt_columns_top_border gt_column_spanner_outer\" rowspan=\"1\" colspan=\"2\" scope=\"colgroup\" id=\"Population by Gender\">\n",
       "    <span class=\"gt_column_spanner\">Population by Gender</span>\n",
       "  </th>\n",
       "</tr>\n",
       "<tr class=\"gt_col_headings\">\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Male\">Male</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"Female\">Female</th>\n",
       "</tr>\n",
       "<tbody class=\"gt_table_body\">\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Jonglei</th>\n",
       "    <td class=\"gt_row gt_center\">734,327</td>\n",
       "    <td class=\"gt_row gt_center\">624,275</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Central Equatoria</th>\n",
       "    <td class=\"gt_row gt_center\">581,722</td>\n",
       "    <td class=\"gt_row gt_center\">521,835</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Upper Nile</th>\n",
       "    <td class=\"gt_row gt_center\">525,430</td>\n",
       "    <td class=\"gt_row gt_center\">438,923</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Warrap</th>\n",
       "    <td class=\"gt_row gt_center\">470,734</td>\n",
       "    <td class=\"gt_row gt_center\">502,194</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Eastern Equatoria</th>\n",
       "    <td class=\"gt_row gt_center\">465,187</td>\n",
       "    <td class=\"gt_row gt_center\">440,974</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Northern Bahr el Ghazal</th>\n",
       "    <td class=\"gt_row gt_center\">348,290</td>\n",
       "    <td class=\"gt_row gt_center\">372,608</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Lakes</th>\n",
       "    <td class=\"gt_row gt_center\">365,880</td>\n",
       "    <td class=\"gt_row gt_center\">329,850</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Western Equatoria</th>\n",
       "    <td class=\"gt_row gt_center\">318,443</td>\n",
       "    <td class=\"gt_row gt_center\">300,586</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Unity</th>\n",
       "    <td class=\"gt_row gt_center\">300,247</td>\n",
       "    <td class=\"gt_row gt_center\">285,554</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Western Bahr el Ghazal</th>\n",
       "    <td class=\"gt_row gt_center\">177,040</td>\n",
       "    <td class=\"gt_row gt_center\">156,391</td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "  <tfoot class=\"gt_sourcenotes\">\n",
       "  \n",
       "  <tr>\n",
       "    <td class=\"gt_sourcenote\" colspan=\"3\"><a href=\"http://southsudan.opendataforafrica.org/fvjqdpe/population-by-age-and-sex-2008-south-sudan\"><strong>2008 Census: National Bureau of Statistics, South Sudan</strong></a></td>\n",
       "  </tr>\n",
       "\n",
       "</tfoot>\n",
       "\n",
       "</table>\n",
       "\n",
       "</div>\n",
       "        "
      ],
      "text/plain": [
       "GT(_tbl_data=shape: (10, 3)\n",
       "┌─────────────────────────┬────────┬────────┐\n",
       "│ state                   ┆ Male   ┆ Female │\n",
       "│ ---                     ┆ ---    ┆ ---    │\n",
       "│ str                     ┆ i64    ┆ i64    │\n",
       "╞═════════════════════════╪════════╪════════╡\n",
       "│ Jonglei                 ┆ 734327 ┆ 624275 │\n",
       "│ Central Equatoria       ┆ 581722 ┆ 521835 │\n",
       "│ Upper Nile              ┆ 525430 ┆ 438923 │\n",
       "│ Warrap                  ┆ 470734 ┆ 502194 │\n",
       "│ Eastern Equatoria       ┆ 465187 ┆ 440974 │\n",
       "│ Northern Bahr el Ghazal ┆ 348290 ┆ 372608 │\n",
       "│ Lakes                   ┆ 365880 ┆ 329850 │\n",
       "│ Western Equatoria       ┆ 318443 ┆ 300586 │\n",
       "│ Unity                   ┆ 300247 ┆ 285554 │\n",
       "│ Western Bahr el Ghazal  ┆ 177040 ┆ 156391 │\n",
       "└─────────────────────────┴────────┴────────┘, _body=<great_tables._gt_data.Body object at 0x16b666570>, _boxhead=Boxhead([ColInfo(var='state', type=<ColInfoTypeEnum.stub: 2>, column_label='State', column_align='left', column_width=None), ColInfo(var='Male', type=<ColInfoTypeEnum.default: 1>, column_label='Male', column_align='center', column_width=None), ColInfo(var='Female', type=<ColInfoTypeEnum.default: 1>, column_label='Female', column_align='center', column_width=None)]), _stub=Stub([RowInfo(rownum_i=0, group_id=None, rowname='Jonglei', group_label=None, built=False), RowInfo(rownum_i=1, group_id=None, rowname='Central Equatoria', group_label=None, built=False), RowInfo(rownum_i=2, group_id=None, rowname='Upper Nile', group_label=None, built=False), RowInfo(rownum_i=3, group_id=None, rowname='Warrap', group_label=None, built=False), RowInfo(rownum_i=4, group_id=None, rowname='Eastern Equatoria', group_label=None, built=False), RowInfo(rownum_i=5, group_id=None, 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      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabulate state data frame\n",
    "(\n",
    "    GT(state, rowname_col='state')\n",
    "    .tab_header(\n",
    "        title='Population Distribution by State and Gender in South Sudan',\n",
    "        subtitle=md(\n",
    "            '**Insights from the 2008 Census:** <br> Jonglei State Leads with Over 13.6 Million Persons,<br> while Western Bahr el Ghazal State Has the Lowest Population'\n",
    "        )\n",
    "    )\n",
    "    .fmt_integer(columns=cs.integer(), use_seps=True)\n",
    "    .cols_align(align='center', columns=['Male', 'Female'])\n",
    "    .opt_align_table_header(align=\"left\")\n",
    "    .cols_label(state='State')\n",
    "    .tab_spanner(columns=cs.integer(), label='Population by Gender')\n",
    "    .tab_source_note(source_note=html('<a href=\"http://southsudan.opendataforafrica.org/fvjqdpe/population-by-age-and-sex-2008-south-sudan\"><strong>2008 Census: National Bureau of Statistics, South Sudan</strong></a>'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (20, 9)\n",
      "┌────────────────────────┬────────┬────────┬────────┬───┬───────┬───────┬───────┬──────────────┐\n",
      "│ state                  ┆ gender ┆ 0-14   ┆ 15-24  ┆ … ┆ 35-44 ┆ 45-54 ┆ 55-64 ┆ 65 and above │\n",
      "│ ---                    ┆ ---    ┆ ---    ┆ ---    ┆   ┆ ---   ┆ ---   ┆ ---   ┆ ---          │\n",
      "│ str                    ┆ str    ┆ i64    ┆ i64    ┆   ┆ i64   ┆ i64   ┆ i64   ┆ i64          │\n",
      "╞════════════════════════╪════════╪════════╪════════╪═══╪═══════╪═══════╪═══════╪══════════════╡\n",
      "│ Jonglei                ┆ Male   ┆ 338443 ┆ 142786 ┆ … ┆ 66018 ┆ 44620 ┆ 24530 ┆ 22658        │\n",
      "│ Jonglei                ┆ Female ┆ 263646 ┆ 125241 ┆ … ┆ 66971 ┆ 35955 ┆ 15724 ┆ 12384        │\n",
      "│ Central Equatoria      ┆ Male   ┆ 242247 ┆ 124513 ┆ … ┆ 59775 ┆ 32567 ┆ 15704 ┆ 11409        │\n",
      "│ Upper Nile             ┆ Male   ┆ 237461 ┆ 99908  ┆ … ┆ 52518 ┆ 31790 ┆ 16976 ┆ 15746        │\n",
      "│ Warrap                 ┆ Male   ┆ 230854 ┆ 79293  ┆ … ┆ 45602 ┆ 28227 ┆ 13867 ┆ 12345        │\n",
      "│ …                      ┆ …      ┆ …      ┆ …      ┆ … ┆ …     ┆ …     ┆ …     ┆ …            │\n",
      "│ Unity                  ┆ Female ┆ 134815 ┆ 54670  ┆ … ┆ 25152 ┆ 14448 ┆ 7518  ┆ 7801         │\n",
      "│ Western Equatoria      ┆ Male   ┆ 125766 ┆ 65642  ┆ … ┆ 34746 ┆ 21753 ┆ 10882 ┆ 11541        │\n",
      "│ Western Equatoria      ┆ Female ┆ 111841 ┆ 68412  ┆ … ┆ 34048 ┆ 18749 ┆ 8769  ┆ 7369         │\n",
      "│ Western Bahr el Ghazal ┆ Male   ┆ 74421  ┆ 34102  ┆ … ┆ 19933 ┆ 10364 ┆ 4981  ┆ 4171         │\n",
      "│ Western Bahr el Ghazal ┆ Female ┆ 67293  ┆ 31389  ┆ … ┆ 15844 ┆ 8323  ┆ 4079  ┆ 3527         │\n",
      "└────────────────────────┴────────┴────────┴────────┴───┴───────┴───────┴───────┴──────────────┘\n"
     ]
    }
   ],
   "source": [
    "# Summarize data by state\n",
    "state_by_gender_and_age = (\n",
    "    census\n",
    "    .group_by(['state', 'gender', 'age_category'])\n",
    "    .agg(total=pl.col('total').sum())\n",
    "    .sort('total', descending=True)\n",
    "    .pivot(\n",
    "        index=['state', 'gender'], \n",
    "        columns='age_category', \n",
    "        values='total', \n",
    "        aggregate_function='sum'\n",
    "    )\n",
    ")\n",
    "\n",
    "# Dispay output\n",
    "print(state_by_gender_and_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       " #pofgchhbuf .gt_group_heading { padding-top: 8px !important; padding-bottom: 8px !important; padding-left: 5px !important; padding-right: 5px !important; color: #333333 !important; background-color: #FFFFFF !important; font-size: 100% !important; font-weight: initial !important; text-transform: inherit !important; border-top-style: solid !important; border-top-width: 2px !important; border-top-color: #D3D3D3 !important; border-bottom-style: solid !important; border-bottom-width: 2px !important; border-bottom-color: #D3D3D3 !important; border-left-style: none !important; border-left-width: 1px !important; border-left-color: #D3D3D3 !important; border-right-style: none !important; border-right-width: 1px !important; border-right-color: #D3D3D3 !important; vertical-align: middle !important; text-align: left !important; }\n",
       " #pofgchhbuf .gt_empty_group_heading { padding: 0.5px !important; color: #333333 !important; background-color: #FFFFFF !important; font-size: 100% !important; font-weight: initial !important; border-top-style: solid !important; border-top-width: 2px !important; border-top-color: #D3D3D3 !important; border-bottom-style: solid !important; border-bottom-width: 2px !important; border-bottom-color: #D3D3D3 !important; vertical-align: middle !important; }\n",
       " #pofgchhbuf .gt_from_md> :first-child { margin-top: 0 !important; }\n",
       " #pofgchhbuf .gt_from_md> :last-child { margin-bottom: 0 !important; }\n",
       " #pofgchhbuf .gt_row { padding-top: 8px !important; padding-bottom: 8px !important; padding-left: 5px !important; padding-right: 5px !important; margin: 10px !important; border-top-style: solid !important; border-top-width: 1px !important; border-top-color: #D3D3D3 !important; border-left-style: none !important; border-left-width: 1px !important; border-left-color: #D3D3D3 !important; border-right-style: none !important; border-right-width: 1px !important; border-right-color: #D3D3D3 !important; vertical-align: middle !important; overflow-x: hidden !important; }\n",
       " #pofgchhbuf .gt_stub { color: #333333 !important; background-color: #FFFFFF !important; font-size: 100% !important; font-weight: initial !important; text-transform: inherit !important; border-right-style: solid !important; border-right-width: 2px !important; border-right-color: #D3D3D3 !important; padding-left: 5px !important; padding-right: 5px !important; }\n",
       " #pofgchhbuf .gt_stub_row_group { color: #333333 !important; background-color: #FFFFFF !important; font-size: 100% !important; font-weight: initial !important; text-transform: inherit !important; border-right-style: solid !important; border-right-width: 2px !important; border-right-color: #D3D3D3 !important; padding-left: 5px !important; padding-right: 5px !important; vertical-align: top !important; }\n",
       " #pofgchhbuf .gt_row_group_first td { border-top-width: 2px !important; }\n",
       " #pofgchhbuf .gt_row_group_first th { border-top-width: 2px !important; }\n",
       " #pofgchhbuf .gt_table_body { border-top-style: solid !important; border-top-width: 2px !important; border-top-color: #D3D3D3 !important; border-bottom-style: solid !important; border-bottom-width: 2px !important; border-bottom-color: #D3D3D3 !important; }\n",
       " #pofgchhbuf .gt_sourcenotes { color: #333333 !important; background-color: #FFFFFF !important; border-bottom-style: none !important; border-bottom-width: 2px !important; border-bottom-color: #D3D3D3 !important; border-left-style: none !important; border-left-width: 2px !important; border-left-color: #D3D3D3 !important; border-right-style: none !important; border-right-width: 2px !important; border-right-color: #D3D3D3 !important; }\n",
       " #pofgchhbuf .gt_sourcenote { font-size: 90% !important; padding-top: 4px !important; padding-bottom: 4px !important; padding-left: 5px !important; padding-right: 5px !important; text-align: left !important; }\n",
       " #pofgchhbuf .gt_left { text-align: left !important; }\n",
       " #pofgchhbuf .gt_center { text-align: center !important; }\n",
       " #pofgchhbuf .gt_right { text-align: right !important; font-variant-numeric: tabular-nums !important; }\n",
       " #pofgchhbuf .gt_font_normal { font-weight: normal !important; }\n",
       " #pofgchhbuf .gt_font_bold { font-weight: bold !important; }\n",
       " #pofgchhbuf .gt_font_italic { font-style: italic !important; }\n",
       " #pofgchhbuf .gt_super { font-size: 65% !important; }\n",
       " #pofgchhbuf .gt_footnote_marks { font-size: 75% !important; vertical-align: 0.4em !important; position: initial !important; }\n",
       " #pofgchhbuf .gt_asterisk { font-size: 100% !important; vertical-align: 0 !important; }\n",
       " \n",
       "</style>\n",
       "<table class=\"gt_table\" data-quarto-disable-processing=\"false\" data-quarto-bootstrap=\"false\">\n",
       "<thead class=\"gt_header\">\n",
       "  <tr>\n",
       "    <th colspan=\"8\" class=\"gt_heading gt_title gt_font_normal\">Population Distribution by State, Gender, and Age in South Sudan</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th colspan=\"8\" class=\"gt_heading gt_subtitle gt_font_normal gt_bottom_border\"><strong>Insights from the 2008 Census:</strong> <br> Jonglei State Leads with Over 13.6 Million Persons,<br> while Western Bahr el Ghazal State Has the Lowest Population</th>\n",
       "  </tr>\n",
       "</thead>\n",
       "<tr class=\"gt_col_headings gt_spanner_row\">\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"2\" colspan=\"1\" scope=\"col\" id=\"\"></th>\n",
       "  <th class=\"gt_center gt_columns_top_border gt_column_spanner_outer\" rowspan=\"1\" colspan=\"7\" scope=\"colgroup\" id=\"Age Group\">\n",
       "    <span class=\"gt_column_spanner\">Age Group</span>\n",
       "  </th>\n",
       "</tr>\n",
       "<tr class=\"gt_col_headings\">\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"0-14\">0-14</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"15-24\">15-24</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"25-34\">25-34</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"35-44\">35-44</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"45-54\">45-54</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"55-64\">55-64</th>\n",
       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"65 and above\">65 and above</th>\n",
       "</tr>\n",
       "<tbody class=\"gt_table_body\">\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Jonglei</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">338,443</td>\n",
       "    <td class=\"gt_row gt_center\">142,786</td>\n",
       "    <td class=\"gt_row gt_center\">95,272</td>\n",
       "    <td class=\"gt_row gt_center\">66,018</td>\n",
       "    <td class=\"gt_row gt_center\">44,620</td>\n",
       "    <td class=\"gt_row gt_center\">24,530</td>\n",
       "    <td class=\"gt_row gt_center\">22,658</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">263,646</td>\n",
       "    <td class=\"gt_row gt_center\">125,241</td>\n",
       "    <td class=\"gt_row gt_center\">104,354</td>\n",
       "    <td class=\"gt_row gt_center\">66,971</td>\n",
       "    <td class=\"gt_row gt_center\">35,955</td>\n",
       "    <td class=\"gt_row gt_center\">15,724</td>\n",
       "    <td class=\"gt_row gt_center\">12,384</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Central Equatoria</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">242,247</td>\n",
       "    <td class=\"gt_row gt_center\">124,513</td>\n",
       "    <td class=\"gt_row gt_center\">95,507</td>\n",
       "    <td class=\"gt_row gt_center\">59,775</td>\n",
       "    <td class=\"gt_row gt_center\">32,567</td>\n",
       "    <td class=\"gt_row gt_center\">15,704</td>\n",
       "    <td class=\"gt_row gt_center\">11,409</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">221,216</td>\n",
       "    <td class=\"gt_row gt_center\">115,726</td>\n",
       "    <td class=\"gt_row gt_center\">86,092</td>\n",
       "    <td class=\"gt_row gt_center\">50,764</td>\n",
       "    <td class=\"gt_row gt_center\">27,182</td>\n",
       "    <td class=\"gt_row gt_center\">12,259</td>\n",
       "    <td class=\"gt_row gt_center\">8,596</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Upper Nile</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">237,461</td>\n",
       "    <td class=\"gt_row gt_center\">99,908</td>\n",
       "    <td class=\"gt_row gt_center\">71,031</td>\n",
       "    <td class=\"gt_row gt_center\">52,518</td>\n",
       "    <td class=\"gt_row gt_center\">31,790</td>\n",
       "    <td class=\"gt_row gt_center\">16,976</td>\n",
       "    <td class=\"gt_row gt_center\">15,746</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">191,018</td>\n",
       "    <td class=\"gt_row gt_center\">86,484</td>\n",
       "    <td class=\"gt_row gt_center\">68,857</td>\n",
       "    <td class=\"gt_row gt_center\">46,427</td>\n",
       "    <td class=\"gt_row gt_center\">24,277</td>\n",
       "    <td class=\"gt_row gt_center\">11,716</td>\n",
       "    <td class=\"gt_row gt_center\">10,144</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Warrap</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">230,854</td>\n",
       "    <td class=\"gt_row gt_center\">79,293</td>\n",
       "    <td class=\"gt_row gt_center\">60,546</td>\n",
       "    <td class=\"gt_row gt_center\">45,602</td>\n",
       "    <td class=\"gt_row gt_center\">28,227</td>\n",
       "    <td class=\"gt_row gt_center\">13,867</td>\n",
       "    <td class=\"gt_row gt_center\">12,345</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">222,745</td>\n",
       "    <td class=\"gt_row gt_center\">96,100</td>\n",
       "    <td class=\"gt_row gt_center\">81,722</td>\n",
       "    <td class=\"gt_row gt_center\">50,875</td>\n",
       "    <td class=\"gt_row gt_center\">27,042</td>\n",
       "    <td class=\"gt_row gt_center\">13,085</td>\n",
       "    <td class=\"gt_row gt_center\">10,625</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Eastern Equatoria</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">212,728</td>\n",
       "    <td class=\"gt_row gt_center\">100,730</td>\n",
       "    <td class=\"gt_row gt_center\">60,808</td>\n",
       "    <td class=\"gt_row gt_center\">40,638</td>\n",
       "    <td class=\"gt_row gt_center\">24,710</td>\n",
       "    <td class=\"gt_row gt_center\">13,045</td>\n",
       "    <td class=\"gt_row gt_center\">12,528</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">187,961</td>\n",
       "    <td class=\"gt_row gt_center\">96,850</td>\n",
       "    <td class=\"gt_row gt_center\">69,910</td>\n",
       "    <td class=\"gt_row gt_center\">43,393</td>\n",
       "    <td class=\"gt_row gt_center\">23,209</td>\n",
       "    <td class=\"gt_row gt_center\">11,014</td>\n",
       "    <td class=\"gt_row gt_center\">8,637</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Northern Bahr el Ghazal</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">175,357</td>\n",
       "    <td class=\"gt_row gt_center\">51,538</td>\n",
       "    <td class=\"gt_row gt_center\">41,105</td>\n",
       "    <td class=\"gt_row gt_center\">32,672</td>\n",
       "    <td class=\"gt_row gt_center\">21,982</td>\n",
       "    <td class=\"gt_row gt_center\">12,113</td>\n",
       "    <td class=\"gt_row gt_center\">13,523</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">168,282</td>\n",
       "    <td class=\"gt_row gt_center\">64,698</td>\n",
       "    <td class=\"gt_row gt_center\">56,574</td>\n",
       "    <td class=\"gt_row gt_center\">37,080</td>\n",
       "    <td class=\"gt_row gt_center\">21,263</td>\n",
       "    <td class=\"gt_row gt_center\">12,126</td>\n",
       "    <td class=\"gt_row gt_center\">12,585</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Lakes</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">160,815</td>\n",
       "    <td class=\"gt_row gt_center\">70,657</td>\n",
       "    <td class=\"gt_row gt_center\">54,328</td>\n",
       "    <td class=\"gt_row gt_center\">36,257</td>\n",
       "    <td class=\"gt_row gt_center\">22,397</td>\n",
       "    <td class=\"gt_row gt_center\">11,326</td>\n",
       "    <td class=\"gt_row gt_center\">10,100</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">143,171</td>\n",
       "    <td class=\"gt_row gt_center\">65,575</td>\n",
       "    <td class=\"gt_row gt_center\">55,004</td>\n",
       "    <td class=\"gt_row gt_center\">32,485</td>\n",
       "    <td class=\"gt_row gt_center\">18,164</td>\n",
       "    <td class=\"gt_row gt_center\">9,055</td>\n",
       "    <td class=\"gt_row gt_center\">6,396</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Unity</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">149,257</td>\n",
       "    <td class=\"gt_row gt_center\">54,521</td>\n",
       "    <td class=\"gt_row gt_center\">38,151</td>\n",
       "    <td class=\"gt_row gt_center\">24,319</td>\n",
       "    <td class=\"gt_row gt_center\">16,343</td>\n",
       "    <td class=\"gt_row gt_center\">8,657</td>\n",
       "    <td class=\"gt_row gt_center\">8,999</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">134,815</td>\n",
       "    <td class=\"gt_row gt_center\">54,670</td>\n",
       "    <td class=\"gt_row gt_center\">41,150</td>\n",
       "    <td class=\"gt_row gt_center\">25,152</td>\n",
       "    <td class=\"gt_row gt_center\">14,448</td>\n",
       "    <td class=\"gt_row gt_center\">7,518</td>\n",
       "    <td class=\"gt_row gt_center\">7,801</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Western Equatoria</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">125,766</td>\n",
       "    <td class=\"gt_row gt_center\">65,642</td>\n",
       "    <td class=\"gt_row gt_center\">48,113</td>\n",
       "    <td class=\"gt_row gt_center\">34,746</td>\n",
       "    <td class=\"gt_row gt_center\">21,753</td>\n",
       "    <td class=\"gt_row gt_center\">10,882</td>\n",
       "    <td class=\"gt_row gt_center\">11,541</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">111,841</td>\n",
       "    <td class=\"gt_row gt_center\">68,412</td>\n",
       "    <td class=\"gt_row gt_center\">51,398</td>\n",
       "    <td class=\"gt_row gt_center\">34,048</td>\n",
       "    <td class=\"gt_row gt_center\">18,749</td>\n",
       "    <td class=\"gt_row gt_center\">8,769</td>\n",
       "    <td class=\"gt_row gt_center\">7,369</td>\n",
       "  </tr>\n",
       "  <tr class=\"gt_group_heading_row\">\n",
       "    <th class=\"gt_group_heading\" colspan=\"8\">Western Bahr el Ghazal</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Male</th>\n",
       "    <td class=\"gt_row gt_center\">74,421</td>\n",
       "    <td class=\"gt_row gt_center\">34,102</td>\n",
       "    <td class=\"gt_row gt_center\">29,068</td>\n",
       "    <td class=\"gt_row gt_center\">19,933</td>\n",
       "    <td class=\"gt_row gt_center\">10,364</td>\n",
       "    <td class=\"gt_row gt_center\">4,981</td>\n",
       "    <td class=\"gt_row gt_center\">4,171</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Female</th>\n",
       "    <td class=\"gt_row gt_center\">67,293</td>\n",
       "    <td class=\"gt_row gt_center\">31,389</td>\n",
       "    <td class=\"gt_row gt_center\">25,936</td>\n",
       "    <td class=\"gt_row gt_center\">15,844</td>\n",
       "    <td class=\"gt_row gt_center\">8,323</td>\n",
       "    <td class=\"gt_row gt_center\">4,079</td>\n",
       "    <td class=\"gt_row gt_center\">3,527</td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "  <tfoot class=\"gt_sourcenotes\">\n",
       "  \n",
       "  <tr>\n",
       "    <td class=\"gt_sourcenote\" colspan=\"8\"><a href=\"http://southsudan.opendataforafrica.org/fvjqdpe/population-by-age-and-sex-2008-south-sudan\"><strong>2008 Census: National Bureau of Statistics, South Sudan</strong></a></td>\n",
       "  </tr>\n",
       "\n",
       "</tfoot>\n",
       "\n",
       "</table>\n",
       "\n",
       "</div>\n",
       "        "
      ],
      "text/plain": [
       "GT(_tbl_data=shape: (20, 9)\n",
       "┌────────────────────────┬────────┬────────┬────────┬───┬───────┬───────┬───────┬──────────────┐\n",
       "│ state                  ┆ gender ┆ 0-14   ┆ 15-24  ┆ … ┆ 35-44 ┆ 45-54 ┆ 55-64 ┆ 65 and above │\n",
       "│ ---                    ┆ ---    ┆ ---    ┆ ---    ┆   ┆ ---   ┆ ---   ┆ ---   ┆ ---          │\n",
       "│ str                    ┆ str    ┆ i64    ┆ i64    ┆   ┆ i64   ┆ i64   ┆ i64   ┆ i64          │\n",
       "╞════════════════════════╪════════╪════════╪════════╪═══╪═══════╪═══════╪═══════╪══════════════╡\n",
       "│ Jonglei                ┆ Male   ┆ 338443 ┆ 142786 ┆ … ┆ 66018 ┆ 44620 ┆ 24530 ┆ 22658        │\n",
       "│ Jonglei                ┆ Female ┆ 263646 ┆ 125241 ┆ … ┆ 66971 ┆ 35955 ┆ 15724 ┆ 12384        │\n",
       "│ Central Equatoria      ┆ Male   ┆ 242247 ┆ 124513 ┆ … ┆ 59775 ┆ 32567 ┆ 15704 ┆ 11409        │\n",
       "│ Upper Nile             ┆ Male   ┆ 237461 ┆ 99908  ┆ … ┆ 52518 ┆ 31790 ┆ 16976 ┆ 15746        │\n",
       "│ Warrap                 ┆ Male   ┆ 230854 ┆ 79293  ┆ … ┆ 45602 ┆ 28227 ┆ 13867 ┆ 12345        │\n",
       "│ …                      ┆ …      ┆ …      ┆ …      ┆ … ┆ …     ┆ …     ┆ …     ┆ …            │\n",
       "│ Unity                  ┆ Female ┆ 134815 ┆ 54670  ┆ … ┆ 25152 ┆ 14448 ┆ 7518  ┆ 7801         │\n",
       "│ Western Equatoria      ┆ Male   ┆ 125766 ┆ 65642  ┆ … ┆ 34746 ┆ 21753 ┆ 10882 ┆ 11541        │\n",
       "│ Western Equatoria      ┆ Female ┆ 111841 ┆ 68412  ┆ … ┆ 34048 ┆ 18749 ┆ 8769  ┆ 7369         │\n",
       "│ Western Bahr el Ghazal ┆ Male   ┆ 74421  ┆ 34102  ┆ … ┆ 19933 ┆ 10364 ┆ 4981  ┆ 4171         │\n",
       "│ Western Bahr el Ghazal ┆ Female ┆ 67293  ┆ 31389  ┆ … ┆ 15844 ┆ 8323  ┆ 4079  ┆ 3527         │\n",
       "└────────────────────────┴────────┴────────┴────────┴───┴───────┴───────┴───────┴──────────────┘, _body=<great_tables._gt_data.Body object at 0x14f0aa5d0>, _boxhead=Boxhead([ColInfo(var='state', type=<ColInfoTypeEnum.row_group: 3>, column_label='State', column_align='center', column_width=None), ColInfo(var='gender', type=<ColInfoTypeEnum.stub: 2>, column_label='gender', column_align='center', column_width=None), ColInfo(var='0-14', type=<ColInfoTypeEnum.default: 1>, column_label='0-14', column_align='center', column_width=None), ColInfo(var='15-24', type=<ColInfoTypeEnum.default: 1>, column_label='15-24', column_align='center', column_width=None), ColInfo(var='25-34', type=<ColInfoTypeEnum.default: 1>, column_label='25-34', column_align='center', column_width=None), ColInfo(var='35-44', type=<ColInfoTypeEnum.default: 1>, column_label='35-44', column_align='center', column_width=None), ColInfo(var='45-54', type=<ColInfoTypeEnum.default: 1>, column_label='45-54', column_align='center', column_width=None), ColInfo(var='55-64', type=<ColInfoTypeEnum.default: 1>, column_label='55-64', column_align='center', column_width=None), ColInfo(var='65 and above', type=<ColInfoTypeEnum.default: 1>, column_label='65 and above', column_align='center', column_width=None)]), _stub=Stub([RowInfo(rownum_i=0, group_id='Jonglei', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=1, group_id='Jonglei', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=2, group_id='Central Equatoria', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=3, group_id='Upper Nile', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=4, group_id='Warrap', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=5, group_id='Warrap', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=6, group_id='Central Equatoria', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=7, group_id='Eastern Equatoria', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=8, group_id='Upper Nile', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=9, group_id='Eastern Equatoria', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=10, group_id='Northern Bahr el Ghazal', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=11, group_id='Northern Bahr el Ghazal', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=12, group_id='Lakes', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=13, group_id='Unity', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=14, group_id='Lakes', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=15, group_id='Unity', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=16, group_id='Western Equatoria', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=17, group_id='Western Equatoria', rowname='Female', group_label=None, built=False), RowInfo(rownum_i=18, group_id='Western Bahr el Ghazal', rowname='Male', group_label=None, built=False), RowInfo(rownum_i=19, group_id='Western Bahr el Ghazal', rowname='Female', group_label=None, built=False)]), _row_groups=['Jonglei', 'Central Equatoria', 'Upper Nile', 'Warrap', 'Eastern Equatoria', 'Northern Bahr el Ghazal', 'Lakes', 'Unity', 'Western Equatoria', 'Western Bahr el Ghazal'], _group_rows=GroupRows([GroupRowInfo(group_id='Jonglei', group_label=None, indices=[0, 1], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Central Equatoria', group_label=None, indices=[2, 6], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Upper Nile', group_label=None, indices=[3, 8], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Warrap', group_label=None, indices=[4, 5], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Eastern Equatoria', group_label=None, indices=[7, 9], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Northern Bahr el Ghazal', group_label=None, indices=[10, 11], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Lakes', group_label=None, indices=[12, 14], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Unity', group_label=None, indices=[13, 15], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Western Equatoria', group_label=None, indices=[16, 17], has_summary_rows=False, summary_row_side=None), GroupRowInfo(group_id='Western Bahr el Ghazal', group_label=None, indices=[18, 19], has_summary_rows=False, summary_row_side=None)]), _spanners=Spanners([SpannerInfo(spanner_id='Age Group', spanner_level=0, spanner_label='Age Group', spanner_units=None, spanner_pattern=None, vars=['0-14', '15-24', '25-34', '35-44', '45-54', '55-64', '65 and above'], built=None)]), _heading=Heading(title='Population Distribution by State, Gender, and Age in South Sudan', subtitle=Text(text='**Insights from the 2008 Census:** <br> Jonglei State Leads with Over 13.6 Million Persons,<br> while Western Bahr el Ghazal State Has the Lowest Population', type='from_markdown'), preheader=None), _stubhead=None, _source_notes=[Text(text='<a href=\"http://southsudan.opendataforafrica.org/fvjqdpe/population-by-age-and-sex-2008-south-sudan\"><strong>2008 Census: National Bureau of Statistics, South Sudan</strong></a>', type='html')], _footnotes=[], _styles=[], _locale=<great_tables._gt_data.Locale object at 0x16b6baa20>, _formats=[<great_tables._gt_data.FormatInfo object at 0x16b6b9340>], _substitutions=[], _options=Options(table_id=OptionsInfo(scss=False, category='table', type='value', value=None), table_caption=OptionsInfo(scss=False, category='table', type='value', value=None), table_width=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_layout=OptionsInfo(scss=True, category='table', type='value', value='fixed'), 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container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    GT(state_by_gender_and_age, rowname_col='gender', groupname_col='state')\n",
    "    .tab_header(\n",
    "        title='Population Distribution by State, Gender, and Age in South Sudan',\n",
    "        subtitle=md(\n",
    "            '**Insights from the 2008 Census:** <br> Jonglei State Leads with Over 13.6 Million Persons,<br> while Western Bahr el Ghazal State Has the Lowest Population'\n",
    "        )\n",
    "    )\n",
    "    .fmt_integer(columns=cs.integer(), use_seps=True)\n",
    "    .cols_align(align='center', columns=cs.all())\n",
    "    .opt_align_table_header(align=\"left\")\n",
    "    .cols_label(state='State')\n",
    "    .tab_spanner(columns=cs.integer(), label='Age Group')\n",
    "    .tab_source_note(source_note=html('<a href=\"http://southsudan.opendataforafrica.org/fvjqdpe/population-by-age-and-sex-2008-south-sudan\"><strong>2008 Census: National Bureau of Statistics, South Sudan</strong></a>'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>test</th><th>units</th><th>day_3</th><th>day_4</th><th>day_5</th><th>day_6</th><th>day_7</th><th>day_8</th><th>day_9</th><th>values</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>&quot;WBC&quot;</td><td>&quot;x10^9 / L&quot;</td><td>5.26</td><td>4.26</td><td>9.92</td><td>10.49</td><td>24.77</td><td>30.26</td><td>19.03</td><td>&quot;5.26 4.26 9.92 10.49 24.77 30.…</td></tr><tr><td>&quot;Neutrophils&quot;</td><td>&quot;x10^9 / L&quot;</td><td>4.87</td><td>4.72</td><td>7.92</td><td>18.21</td><td>22.08</td><td>27.17</td><td>16.59</td><td>&quot;4.87 4.72 7.92 18.21 22.08 27.…</td></tr><tr><td>&quot;RBC&quot;</td><td>&quot;x10^12 / L&quot;</td><td>5.72</td><td>5.98</td><td>4.23</td><td>4.83</td><td>4.12</td><td>2.68</td><td>3.32</td><td>&quot;5.72 5.98 4.23 4.83 4.12 2.68 …</td></tr><tr><td>&quot;Hb&quot;</td><td>&quot;g / L&quot;</td><td>153.0</td><td>135.0</td><td>126.0</td><td>115.0</td><td>75.0</td><td>87.0</td><td>95.0</td><td>&quot;153.0 135.0 126.0 115.0 75.0 8…</td></tr><tr><td>&quot;PLT&quot;</td><td>&quot;x10^9 / L&quot;</td><td>67.0</td><td>38.6</td><td>27.4</td><td>26.2</td><td>74.1</td><td>36.2</td><td>25.6</td><td>&quot;67.0 38.6 27.4 26.2 74.1 36.2 …</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 10)\n",
       "┌─────────────┬────────────┬───────┬───────┬───┬───────┬───────┬───────┬───────────────────────────┐\n",
       "│ test        ┆ units      ┆ day_3 ┆ day_4 ┆ … ┆ day_7 ┆ day_8 ┆ day_9 ┆ values                    │\n",
       "│ ---         ┆ ---        ┆ ---   ┆ ---   ┆   ┆ ---   ┆ ---   ┆ ---   ┆ ---                       │\n",
       "│ str         ┆ str        ┆ f64   ┆ f64   ┆   ┆ f64   ┆ f64   ┆ f64   ┆ str                       │\n",
       "╞═════════════╪════════════╪═══════╪═══════╪═══╪═══════╪═══════╪═══════╪═══════════════════════════╡\n",
       "│ WBC         ┆ x10^9 / L  ┆ 5.26  ┆ 4.26  ┆ … ┆ 24.77 ┆ 30.26 ┆ 19.03 ┆ 5.26 4.26 9.92 10.49      │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 24.77 30.…                │\n",
       "│ Neutrophils ┆ x10^9 / L  ┆ 4.87  ┆ 4.72  ┆ … ┆ 22.08 ┆ 27.17 ┆ 16.59 ┆ 4.87 4.72 7.92 18.21      │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 22.08 27.…                │\n",
       "│ RBC         ┆ x10^12 / L ┆ 5.72  ┆ 5.98  ┆ … ┆ 4.12  ┆ 2.68  ┆ 3.32  ┆ 5.72 5.98 4.23 4.83 4.12  │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 2.68 …                    │\n",
       "│ Hb          ┆ g / L      ┆ 153.0 ┆ 135.0 ┆ … ┆ 75.0  ┆ 87.0  ┆ 95.0  ┆ 153.0 135.0 126.0 115.0   │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 75.0 8…                   │\n",
       "│ PLT         ┆ x10^9 / L  ┆ 67.0  ┆ 38.6  ┆ … ┆ 74.1  ┆ 36.2  ┆ 25.6  ┆ 67.0 38.6 27.4 26.2 74.1  │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 36.2 …                    │\n",
       "└─────────────┴────────────┴───────┴───────┴───┴───────┴───────┴───────┴───────────────────────────┘"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reproducing great_tables example\n",
    "# Import required libraries and illness dataset\n",
    "from great_tables.data import illness\n",
    "\n",
    "# Convert illness dataset into a polars dataframe (ensure that you've installed pyarrow)\n",
    "illness_pl = pl.from_pandas(illness)\n",
    "\n",
    "# Transform the dataset to include a nested or list column\n",
    "illness_df = (\n",
    "    illness_pl\n",
    "    .join(\n",
    "        illness_pl\n",
    "        .select(\n",
    "            'test', \n",
    "            values=pl.concat_str(\n",
    "                pl.exclude('test', 'units'), \n",
    "                separator=' ', \n",
    "                ignore_nulls=True\n",
    "            )\n",
    "        ), \n",
    "        on='test'\n",
    "    )\n",
    "    .select(\n",
    "        'test', 'units', 'day_3', 'day_4', \n",
    "        'day_5', 'day_6', 'day_7',\n",
    "        'day_8', 'day_9', 'values'\n",
    "    )\n",
    "    .slice(1, 5)\n",
    ")\n",
    "\n",
    "# inspect output\n",
    "illness_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <th colspan=\"10\" class=\"gt_heading gt_title gt_font_normal\">My Reproduction of the Summary of Daily Tests<br>Performed on YF Patient</th>\n",
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       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_left\" rowspan=\"2\" colspan=\"1\" scope=\"col\" id=\"&lt;strong&gt;Test&lt;/strong&gt;\"><strong>Test</strong></th>\n",
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       "    <span class=\"gt_column_spanner\">Day of Disease progression</span>\n",
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       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_center\" rowspan=\"2\" colspan=\"1\" scope=\"col\" id=\"&lt;em&gt;Progression&lt;/em&gt; Trends\"><em>Progression</em> Trends</th>\n",
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       "  <th class=\"gt_col_heading gt_columns_bottom_border gt_right\" rowspan=\"1\" colspan=\"1\" scope=\"col\" id=\"3\">3</th>\n",
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       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">WBC</th>\n",
       "    <td style=\"background-color: #F9E3D6; font-style: italic;\" class=\"gt_row gt_center\">x10^9 / L</td>\n",
       "    <td class=\"gt_row gt_right\">5.26</td>\n",
       "    <td class=\"gt_row gt_right\">4.26</td>\n",
       "    <td class=\"gt_row gt_right\">9.92</td>\n",
       "    <td class=\"gt_row gt_right\">10.49</td>\n",
       "    <td class=\"gt_row gt_right\">24.77</td>\n",
       "    <td class=\"gt_row gt_right\">30.26</td>\n",
       "    <td class=\"gt_row gt_right\">19.03</td>\n",
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       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Neutrophils</th>\n",
       "    <td style=\"background-color: #F9E3D6; font-style: italic;\" class=\"gt_row gt_center\">x10^9 / L</td>\n",
       "    <td class=\"gt_row gt_right\">4.87</td>\n",
       "    <td class=\"gt_row gt_right\">4.72</td>\n",
       "    <td class=\"gt_row gt_right\">7.92</td>\n",
       "    <td class=\"gt_row gt_right\">18.21</td>\n",
       "    <td class=\"gt_row gt_right\">22.08</td>\n",
       "    <td class=\"gt_row gt_right\">27.17</td>\n",
       "    <td class=\"gt_row gt_right\">16.59</td>\n",
       "    <td class=\"gt_row gt_center\"><div><svg role=\"img\" viewBox=\"0 0 550 130\" style=\"height: 2em; margin-left: auto; margin-right: auto; font-size: inherit; overflow: visible; vertical-align: middle; position:relative;\"><defs><pattern id=\"area_pattern\" width=\"8\" height=\"8\" patternUnits=\"userSpaceOnUse\"><path class=\"pattern-line\" d=\"M 0,8 l 8,-8 M -1,1 l 4,-4 M 6,10 l 4,-4\" stroke=\"#FF0000\" stroke-width=\"1.5\" stroke-linecap=\"round\" shape-rendering=\"geometricPrecision\"></path></pattern></defs><style> text { font-family: ui-monospace, 'Cascadia Code', 'Source Code Pro', Menlo, Consolas, 'DejaVu Sans Mono', monospace; stroke-width: 0.15em; paint-order: stroke; stroke-linejoin: round; cursor: default; } .vert-line:hover rect { fill: #911EB4; fill-opacity: 40%; stroke: #FFFFFF60; color: red; } .vert-line:hover text { stroke: white; fill: #212427; } .horizontal-line:hover text {stroke: white; fill: #212427; } .ref-line:hover rect { stroke: #FFFFFF60; } .ref-line:hover line { stroke: #FF0000; } .ref-line:hover text { stroke: white; fill: #212427; } .y-axis-line:hover rect { fill: #EDEDED; fill-opacity: 60%; stroke: #FFFFFF60; color: red; } .y-axis-line:hover text { stroke: white; stroke-width: 0.20em; fill: #1A1C1F; } </style><path class=\"area-closed\" d=\"M 50.0,103.59753675009932 106.25,104.19348430671434 162.5,91.47993643226063 218.75,50.59793404847041 275.0,35.22248708780295 331.25,15.0 387.5,57.034167659912605 443.75,115.0 500.0,91.16209773539929 500.0,125 50.0,125 Z\" stroke=\"transparent\" stroke-width=\"2\" fill=\"url(#area_pattern)\" fill-opacity=\"0.7\"></path><path d=\"M 50.0,103.59753675009932 C 75.0,103.59753675009932 81.25,104.19348430671434 106.25,104.19348430671434 C 131.25,104.19348430671434 137.5,91.47993643226063 162.5,91.47993643226063 C 187.5,91.47993643226063 193.75,50.59793404847041 218.75,50.59793404847041 C 243.75,50.59793404847041 250.0,35.22248708780295 275.0,35.22248708780295 C 300.0,35.22248708780295 306.25,15.0 331.25,15.0 C 356.25,15.0 362.5,57.034167659912605 387.5,57.034167659912605 C 412.5,57.034167659912605 418.75,115.0 443.75,115.0 C 468.75,115.0 475.0,91.16209773539929 500.0,91.16209773539929\" stroke=\"#4682B4\" stroke-width=\"8\" fill=\"none\"></path><circle cx=\"50.0\" cy=\"103.59753675009932\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"106.25\" cy=\"104.19348430671434\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"162.5\" cy=\"91.47993643226063\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"218.75\" cy=\"50.59793404847041\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"275.0\" cy=\"35.22248708780295\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"331.25\" cy=\"15.0\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"387.5\" cy=\"57.034167659912605\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"443.75\" cy=\"115.0\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><circle cx=\"500.0\" cy=\"91.16209773539929\" r=\"10\" stroke=\"#FFFFFF\" stroke-width=\"4\" fill=\"#FF0000\"></circle><g class=\"y-axis-line\"><rect x=\"0\" y=\"0\" width=\"65\" height=\"130\" stroke=\"transparent\" stroke-width=\"0\" fill=\"transparent\"></rect><text x=\"0\" y=\"19.0\" fill=\"transparent\" stroke=\"transparent\" font-size=\"25\">27.2</text><text x=\"0\" y=\"126.0\" fill=\"transparent\" stroke=\"transparent\" font-size=\"25\">2.00</text></g><g class=\"vert-line\"><rect x=\"40.0\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"60.0\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">4.87</text></g><g class=\"vert-line\"><rect x=\"96.25\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"116.25\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">4.72</text></g><g class=\"vert-line\"><rect x=\"152.5\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"172.5\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">7.92</text></g><g class=\"vert-line\"><rect x=\"208.75\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"228.75\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">18.2</text></g><g class=\"vert-line\"><rect x=\"265.0\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"285.0\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">22.1</text></g><g class=\"vert-line\"><rect x=\"321.25\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"341.25\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">27.2</text></g><g class=\"vert-line\"><rect x=\"377.5\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"397.5\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">16.6</text></g><g class=\"vert-line\"><rect x=\"433.75\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"453.75\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">2.00</text></g><g class=\"vert-line\"><rect x=\"490.0\" y=\"0\" width=\"20\" height=\"130\" stroke=\"transparent\" stroke-width=\"12\" fill=\"transparent\"></rect><text x=\"510.0\" y=\"20\" fill=\"transparent\" stroke=\"transparent\" font-size=\"30px\">8.00</text></g></svg></div></td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">RBC</th>\n",
       "    <td style=\"background-color: #F9E3D6; font-style: italic;\" class=\"gt_row gt_center\">x10^12 / L</td>\n",
       "    <td class=\"gt_row gt_right\">5.72</td>\n",
       "    <td class=\"gt_row gt_right\">5.98</td>\n",
       "    <td class=\"gt_row gt_right\">4.23</td>\n",
       "    <td class=\"gt_row gt_right\">4.83</td>\n",
       "    <td class=\"gt_row gt_right\">4.12</td>\n",
       "    <td class=\"gt_row gt_right\">2.68</td>\n",
       "    <td class=\"gt_row gt_right\">3.32</td>\n",
       "    <td class=\"gt_row gt_center\"><div><svg role=\"img\" viewBox=\"0 0 550 130\" style=\"height: 2em; margin-left: auto; margin-right: auto; font-size: inherit; overflow: visible; vertical-align: middle; position:relative;\"><defs><pattern id=\"area_pattern\" width=\"8\" height=\"8\" patternUnits=\"userSpaceOnUse\"><path class=\"pattern-line\" d=\"M 0,8 l 8,-8 M -1,1 l 4,-4 M 6,10 l 4,-4\" stroke=\"#FF0000\" stroke-width=\"1.5\" stroke-linecap=\"round\" shape-rendering=\"geometricPrecision\"></path></pattern></defs><style> text { font-family: ui-monospace, 'Cascadia Code', 'Source Code Pro', Menlo, Consolas, 'DejaVu Sans Mono', monospace; stroke-width: 0.15em; paint-order: stroke; stroke-linejoin: round; cursor: default; } .vert-line:hover rect { fill: #911EB4; fill-opacity: 40%; stroke: #FFFFFF60; color: red; } .vert-line:hover text { stroke: white; fill: #212427; } .horizontal-line:hover text {stroke: white; fill: #212427; } .ref-line:hover rect { stroke: #FFFFFF60; } .ref-line:hover line { stroke: #FF0000; 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       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">Hb</th>\n",
       "    <td style=\"background-color: #F9E3D6; font-style: italic;\" class=\"gt_row gt_center\">g / L</td>\n",
       "    <td class=\"gt_row gt_right\">153.0</td>\n",
       "    <td class=\"gt_row gt_right\">135.0</td>\n",
       "    <td class=\"gt_row gt_right\">126.0</td>\n",
       "    <td class=\"gt_row gt_right\">115.0</td>\n",
       "    <td class=\"gt_row gt_right\">75.0</td>\n",
       "    <td class=\"gt_row gt_right\">87.0</td>\n",
       "    <td class=\"gt_row gt_right\">95.0</td>\n",
       "    <td class=\"gt_row gt_center\"><div><svg role=\"img\" viewBox=\"0 0 550 130\" style=\"height: 2em; margin-left: auto; margin-right: auto; font-size: inherit; overflow: visible; vertical-align: middle; position:relative;\"><defs><pattern id=\"area_pattern\" width=\"8\" height=\"8\" patternUnits=\"userSpaceOnUse\"><path class=\"pattern-line\" d=\"M 0,8 l 8,-8 M -1,1 l 4,-4 M 6,10 l 4,-4\" stroke=\"#FF0000\" stroke-width=\"1.5\" stroke-linecap=\"round\" shape-rendering=\"geometricPrecision\"></path></pattern></defs><style> text { font-family: ui-monospace, 'Cascadia Code', 'Source Code Pro', Menlo, Consolas, 'DejaVu Sans Mono', monospace; stroke-width: 0.15em; paint-order: stroke; stroke-linejoin: round; cursor: default; } .vert-line:hover rect { fill: #911EB4; fill-opacity: 40%; stroke: #FFFFFF60; color: red; } .vert-line:hover text { stroke: white; fill: #212427; } .horizontal-line:hover text {stroke: white; fill: #212427; } .ref-line:hover rect { stroke: #FFFFFF60; } .ref-line:hover line { stroke: #FF0000; 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       "  </tr>\n",
       "  <tr>\n",
       "    <th class=\"gt_row gt_left gt_stub\">PLT</th>\n",
       "    <td style=\"background-color: #F9E3D6; font-style: italic;\" class=\"gt_row gt_center\">x10^9 / L</td>\n",
       "    <td class=\"gt_row gt_right\">67.0</td>\n",
       "    <td class=\"gt_row gt_right\">38.6</td>\n",
       "    <td class=\"gt_row gt_right\">27.4</td>\n",
       "    <td class=\"gt_row gt_right\">26.2</td>\n",
       "    <td class=\"gt_row gt_right\">74.1</td>\n",
       "    <td class=\"gt_row gt_right\">36.2</td>\n",
       "    <td class=\"gt_row gt_right\">25.6</td>\n",
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       "  </tr>\n",
       "</tbody>\n",
       "  <tfoot class=\"gt_sourcenotes\">\n",
       "  \n",
       "  <tr>\n",
       "    <td class=\"gt_sourcenote\" colspan=\"10\">Measurements from Day 3 through to Day 9</td>\n",
       "  </tr>\n",
       "\n",
       "</tfoot>\n",
       "\n",
       "</table>\n",
       "\n",
       "</div>\n",
       "        "
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       "GT(_tbl_data=shape: (5, 10)\n",
       "┌─────────────┬────────────┬───────┬───────┬───┬───────┬───────┬───────┬───────────────────────────┐\n",
       "│ test        ┆ units      ┆ day_3 ┆ day_4 ┆ … ┆ day_7 ┆ day_8 ┆ day_9 ┆ values                    │\n",
       "│ ---         ┆ ---        ┆ ---   ┆ ---   ┆   ┆ ---   ┆ ---   ┆ ---   ┆ ---                       │\n",
       "│ str         ┆ str        ┆ f64   ┆ f64   ┆   ┆ f64   ┆ f64   ┆ f64   ┆ str                       │\n",
       "╞═════════════╪════════════╪═══════╪═══════╪═══╪═══════╪═══════╪═══════╪═══════════════════════════╡\n",
       "│ WBC         ┆ x10^9 / L  ┆ 5.26  ┆ 4.26  ┆ … ┆ 24.77 ┆ 30.26 ┆ 19.03 ┆ 5.26 4.26 9.92 10.49      │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 24.77 30.…                │\n",
       "│ Neutrophils ┆ x10^9 / L  ┆ 4.87  ┆ 4.72  ┆ … ┆ 22.08 ┆ 27.17 ┆ 16.59 ┆ 4.87 4.72 7.92 18.21      │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 22.08 27.…                │\n",
       "│ RBC         ┆ x10^12 / L ┆ 5.72  ┆ 5.98  ┆ … ┆ 4.12  ┆ 2.68  ┆ 3.32  ┆ 5.72 5.98 4.23 4.83 4.12  │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 2.68 …                    │\n",
       "│ Hb          ┆ g / L      ┆ 153.0 ┆ 135.0 ┆ … ┆ 75.0  ┆ 87.0  ┆ 95.0  ┆ 153.0 135.0 126.0 115.0   │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 75.0 8…                   │\n",
       "│ PLT         ┆ x10^9 / L  ┆ 67.0  ┆ 38.6  ┆ … ┆ 74.1  ┆ 36.2  ┆ 25.6  ┆ 67.0 38.6 27.4 26.2 74.1  │\n",
       "│             ┆            ┆       ┆       ┆   ┆       ┆       ┆       ┆ 36.2 …                    │\n",
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     },
     "execution_count": 127,
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   ],
   "source": [
    "# Table the results\n",
    "(\n",
    "    GT(illness_df, rowname_col='test')\n",
    "    # .cols_hide('units')\n",
    "    .fmt_nanoplot(columns='values')\n",
    "    .tab_header(md('My Reproduction of the Summary of Daily Tests<br>Performed on YF Patient'))\n",
    "    .tab_stubhead(label=md('**Test**'))\n",
    "    .cols_label(\n",
    "        units='Units',\n",
    "        day_3='3',\n",
    "        day_4='4',\n",
    "        day_5='5',\n",
    "        day_6='6',\n",
    "        day_7='7',\n",
    "        day_8='8',\n",
    "        day_9='9',\n",
    "        values=md('*Progression* Trends')\n",
    "    )\n",
    "    .cols_align(columns=['units', 'values'], align='center')\n",
    "    .tab_spanner(label='Day of Disease progression', columns=cs.starts_with('day'))\n",
    "    .sub_missing()\n",
    "    .tab_style(\n",
    "        style=[\n",
    "            style.fill(color='#F9E3D6'),\n",
    "            style.text(style='italic')\n",
    "        ],\n",
    "        locations=loc.body(columns='units')\n",
    "    )\n",
    "    .tab_source_note(source_note='Measurements from Day 3 through to Day 9')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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