MCQ-Reweighter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Re-weight marks of OMR responses\n",
    "\n",
    "by [T. Kam](https://phantomachine.github.io/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Purpose:\n",
    "\n",
    "1. Import OMR scanner report - (``input.xlsx`` file)\n",
    "\n",
    "2. Match/compare and convert ``string`` data in table of OMR Report (responses with green/pink or CORRECT/WRONG table)\n",
    "\n",
    "3. Export to new ``output.xlsx`` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify file path(s) and names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Users can modify this!\n",
    "\n",
    "# Source file\n",
    "source_name = \"ECON9101_MCQ_GRADED_07062024\"\n",
    "file_source = source_name + \".xlsx\"\n",
    "\n",
    "# Target output file\n",
    "file_out = source_name + \"-reweighted\" + \".xlsx\"\n",
    "stats_out = source_name + \"-reweighted-stats\" + \".xlsx\"\n",
    "\n",
    "# Specify your question weights / scores\n",
    "weights = [5, 5, 5, 5, 5, 5, 10, 2, 2, 6]\n",
    "\n",
    "# What stats would you like to report?\n",
    "stat_au_choix = [np.mean, np.median, np.std, min, max]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import OMR report (xlsx format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# See file_source for where the table row begins!\n",
    "start_row = 5\n",
    "\n",
    "# Import as dataframe\n",
    "df = pd.read_excel(file_source, \n",
    "                   sheet_name=\"Student Response Report\", \n",
    "                   header=start_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNum</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Answer Key</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1234567</td>\n",
       "      <td>8.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1234568</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1234569</td>\n",
       "      <td>8.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1234570</td>\n",
       "      <td>8.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UNum  Total  Percent Grade Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10\n",
       "0  Answer Key   10.0      NaN   NaN  D  B  D  B  C  B  A  B  D   B\n",
       "1     1234567    8.0     80.0     B  D  B  D  B  A  B  B  B  D   B\n",
       "2     1234568    3.0     30.0     F  B  B  B  A  D  B  C  B  C   D\n",
       "3     1234569    8.0     80.0     B  B  B  D  B  C  B  C  B  D   B\n",
       "4     1234570    8.0     80.0     B  B  B  D  B  C  B  C  B  D   B"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick peek \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNum</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1234649</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1234650</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1234651</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1234652</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Mean</td>\n",
       "      <td>6.209302</td>\n",
       "      <td>62.093023</td>\n",
       "      <td>D</td>\n",
       "      <td>51.162791</td>\n",
       "      <td>88.372093</td>\n",
       "      <td>56.976744</td>\n",
       "      <td>94.186047</td>\n",
       "      <td>39.534884</td>\n",
       "      <td>55.813953</td>\n",
       "      <td>9.302326</td>\n",
       "      <td>76.744186</td>\n",
       "      <td>77.906977</td>\n",
       "      <td>70.930233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UNum     Total    Percent Grade         Q1         Q2         Q3  \\\n",
       "83  1234649  8.000000  80.000000     B          B          B          D   \n",
       "84  1234650  5.000000  50.000000     F          D          B          A   \n",
       "85  1234651  6.000000  60.000000     D          B          B          D   \n",
       "86  1234652  8.000000  80.000000     B          D          B          D   \n",
       "87     Mean  6.209302  62.093023     D  51.162791  88.372093  56.976744   \n",
       "\n",
       "           Q4         Q5         Q6        Q7         Q8         Q9        Q10  \n",
       "83          B          C          B         C          B          D          B  \n",
       "84          B          C          A         B          C          B          B  \n",
       "85          B          D          C         C          B          D          B  \n",
       "86          B          D          B         A          B          D          D  \n",
       "87  94.186047  39.534884  55.813953  9.302326  76.744186  77.906977  70.930233  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another look-see\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clone, clean, set index to ``UNum``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clone to new dataframe dg, drop last row (Mean)\n",
    "dg = df.copy()\n",
    "dg.drop(dg.index[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UNum', 'Total', 'Percent', 'Grade', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6',\n",
       "       'Q7', 'Q8', 'Q9', 'Q10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect key names\n",
    "dg.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dg = dg.drop([\"Total\", \"Percent\", \"Grade\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
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       "    <tr>\n",
       "      <th>UNum</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Answer Key</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234567</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234568</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234569</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234570</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234648</th>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>1234649</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234650</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234651</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234652</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10\n",
       "UNum                                     \n",
       "Answer Key  D  B  D  B  C  B  A  B  D   B\n",
       "1234567     D  B  D  B  A  B  B  B  D   B\n",
       "1234568     B  B  B  A  D  B  C  B  C   D\n",
       "1234569     B  B  D  B  C  B  C  B  D   B\n",
       "1234570     B  B  D  B  C  B  C  B  D   B\n",
       "...        .. .. .. .. .. .. .. .. ..  ..\n",
       "1234648     B  D  B  B  C  B  B  C  D   B\n",
       "1234649     B  B  D  B  C  B  C  B  D   B\n",
       "1234650     D  B  A  B  C  A  B  C  B   B\n",
       "1234651     B  B  D  B  D  C  C  B  D   B\n",
       "1234652     D  B  D  B  D  B  A  B  D   D\n",
       "\n",
       "[87 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set series \"UNum\" as index\n",
    "dg = dg.set_index('UNum')\n",
    "dg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get ``Answer Key`` and create comparison dataframe ``dt``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['D', 'B', 'D', 'B', 'C', 'B', 'A', 'B', 'D', 'B'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg.loc['Answer Key',:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make array tiled from Answer Key row\n",
    "d_testval = np.tile(dg.loc['Answer Key', :].values, (dg.shape[0], 1))\n",
    "# create test dataframe with values from d_testval\n",
    "dt = dg.copy()\n",
    "dt.loc[:, :] = d_testval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNum</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Answer Key</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234567</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234568</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234569</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234570</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234648</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234649</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234650</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234651</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234652</th>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10\n",
       "UNum                                     \n",
       "Answer Key  D  B  D  B  C  B  A  B  D   B\n",
       "1234567     D  B  D  B  C  B  A  B  D   B\n",
       "1234568     D  B  D  B  C  B  A  B  D   B\n",
       "1234569     D  B  D  B  C  B  A  B  D   B\n",
       "1234570     D  B  D  B  C  B  A  B  D   B\n",
       "...        .. .. .. .. .. .. .. .. ..  ..\n",
       "1234648     D  B  D  B  C  B  A  B  D   B\n",
       "1234649     D  B  D  B  C  B  A  B  D   B\n",
       "1234650     D  B  D  B  C  B  A  B  D   B\n",
       "1234651     D  B  D  B  C  B  A  B  D   B\n",
       "1234652     D  B  D  B  C  B  A  B  D   B\n",
       "\n",
       "[87 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equality test (boolean) ``df_bool``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
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       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNum</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Answer Key</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234567</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234568</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234569</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234570</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234648</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234649</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1234650</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234651</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234652</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Q1     Q2     Q3     Q4     Q5     Q6     Q7     Q8     Q9  \\\n",
       "UNum                                                                        \n",
       "Answer Key   True   True   True   True   True   True   True   True   True   \n",
       "1234567      True   True   True   True  False   True  False   True   True   \n",
       "1234568     False   True  False  False  False   True  False   True  False   \n",
       "1234569     False   True   True   True   True   True  False   True   True   \n",
       "1234570     False   True   True   True   True   True  False   True   True   \n",
       "...           ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "1234648     False  False  False   True   True   True  False  False   True   \n",
       "1234649     False   True   True   True   True   True  False   True   True   \n",
       "1234650      True   True  False   True   True  False  False  False  False   \n",
       "1234651     False   True   True   True  False  False  False   True   True   \n",
       "1234652      True   True   True   True  False   True   True   True   True   \n",
       "\n",
       "              Q10  \n",
       "UNum               \n",
       "Answer Key   True  \n",
       "1234567      True  \n",
       "1234568     False  \n",
       "1234569      True  \n",
       "1234570      True  \n",
       "...           ...  \n",
       "1234648      True  \n",
       "1234649      True  \n",
       "1234650      True  \n",
       "1234651      True  \n",
       "1234652     False  \n",
       "\n",
       "[87 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test equality between element-wise values of dg and dt\n",
    "df_bool = dg.eq(dt)\n",
    "df_bool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute re-weighted index based on ``weights`` (user specified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Answer Key</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>10</td>\n",
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       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234567</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234568</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234569</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234570</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234648</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234649</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234650</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234651</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234652</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Q1  Q2  Q3  Q4  Q5  Q6  Q7  Q8  Q9  Q10\n",
       "UNum                                               \n",
       "Answer Key   5   5   5   5   5   5  10   2   2    6\n",
       "1234567      5   5   5   5   0   5   0   2   2    6\n",
       "1234568      0   5   0   0   0   5   0   2   0    0\n",
       "1234569      0   5   5   5   5   5   0   2   2    6\n",
       "1234570      0   5   5   5   5   5   0   2   2    6\n",
       "...         ..  ..  ..  ..  ..  ..  ..  ..  ..  ...\n",
       "1234648      0   0   0   5   5   5   0   0   2    6\n",
       "1234649      0   5   5   5   5   5   0   2   2    6\n",
       "1234650      5   5   0   5   5   0   0   0   0    6\n",
       "1234651      0   5   5   5   0   0   0   2   2    6\n",
       "1234652      5   5   5   5   0   5  10   2   2    0\n",
       "\n",
       "[87 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Custom weights matrix\n",
    "W = np.tile(weights, (df_bool.shape[0], 1))\n",
    "\n",
    "# Generate re-weighted OMR results\n",
    "df_reweighted = df_bool*W\n",
    "df_reweighted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write output to Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some lipstick on this piggy\n",
    "def pink_or_green(val):\n",
    "    if val == 0:\n",
    "        color = 'pink' \n",
    "    else:\n",
    "        color = 'turquoise'\n",
    "    return 'background-color: %s' % color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add totals and percentage totals columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maximal mark that can be earned\n",
    "max_total = df_reweighted.loc[\"Answer Key\"].sum()\n",
    "\n",
    "# series: totals\n",
    "totals = df_reweighted.sum(axis=1)\n",
    "\n",
    "# series: percentage totals\n",
    "totals_percent = 100*totals/max_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new columns\n",
    "df_reweighted[\"Total\"] = totals\n",
    "df_reweighted[\"Percent\"] = totals_percent.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to excel\n",
    "df_reweighted.style.applymap(pink_or_green).to_excel(file_out, sheet_name=\"Scores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is what you should see in the output file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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#T_c34e6_row22_col0, #T_c34e6_row22_col1, #T_c34e6_row22_col6, #T_c34e6_row22_col7, #T_c34e6_row22_col8, #T_c34e6_row22_col9, #T_c34e6_row23_col5, #T_c34e6_row23_col6, #T_c34e6_row24_col2, #T_c34e6_row24_col4, #T_c34e6_row24_col5, #T_c34e6_row24_col6, #T_c34e6_row24_col9, #T_c34e6_row25_col0, #T_c34e6_row25_col2, #T_c34e6_row25_col6, #T_c34e6_row25_col9, #T_c34e6_row26_col0, #T_c34e6_row26_col6, #T_c34e6_row26_col7, #T_c34e6_row27_col0, #T_c34e6_row27_col2, #T_c34e6_row27_col4, #T_c34e6_row27_col5, #T_c34e6_row27_col7, #T_c34e6_row27_col8, #T_c34e6_row27_col9, #T_c34e6_row28_col1, #T_c34e6_row28_col6, #T_c34e6_row28_col7, #T_c34e6_row28_col8, #T_c34e6_row28_col9, #T_c34e6_row29_col0, #T_c34e6_row29_col5, #T_c34e6_row30_col4, #T_c34e6_row30_col6, #T_c34e6_row31_col2, #T_c34e6_row31_col4, #T_c34e6_row31_col5, #T_c34e6_row31_col6, #T_c34e6_row31_col8, #T_c34e6_row31_col9, #T_c34e6_row32_col0, #T_c34e6_row32_col2, #T_c34e6_row32_col6, #T_c34e6_row32_col7, #T_c34e6_row33_col4, #T_c34e6_row33_col6, #T_c34e6_row33_col9, #T_c34e6_row34_col0, #T_c34e6_row34_col4, #T_c34e6_row34_col6, #T_c34e6_row35_col2, #T_c34e6_row35_col4, #T_c34e6_row35_col5, #T_c34e6_row35_col6, #T_c34e6_row36_col4, #T_c34e6_row36_col6, #T_c34e6_row36_col8, #T_c34e6_row37_col6, #T_c34e6_row38_col0, #T_c34e6_row38_col4, #T_c34e6_row38_col5, #T_c34e6_row38_col6, #T_c34e6_row39_col4, #T_c34e6_row39_col6, #T_c34e6_row40_col5, #T_c34e6_row40_col6, #T_c34e6_row40_col7, #T_c34e6_row40_col9, #T_c34e6_row41_col4, #T_c34e6_row41_col6, #T_c34e6_row41_col7, #T_c34e6_row42_col0, #T_c34e6_row42_col2, #T_c34e6_row42_col4, #T_c34e6_row42_col6, #T_c34e6_row43_col4, #T_c34e6_row43_col6, #T_c34e6_row43_col8, #T_c34e6_row44_col0, #T_c34e6_row44_col2, #T_c34e6_row44_col5, #T_c34e6_row44_col6, #T_c34e6_row45_col0, #T_c34e6_row45_col4, #T_c34e6_row45_col6, #T_c34e6_row46_col0, #T_c34e6_row46_col2, #T_c34e6_row46_col6, #T_c34e6_row47_col2, #T_c34e6_row47_col4, #T_c34e6_row47_col6, #T_c34e6_row48_col2, #T_c34e6_row48_col6, #T_c34e6_row49_col0, #T_c34e6_row49_col4, #T_c34e6_row49_col5, #T_c34e6_row49_col6, #T_c34e6_row49_col8, #T_c34e6_row49_col9, #T_c34e6_row50_col4, #T_c34e6_row50_col5, #T_c34e6_row50_col6, #T_c34e6_row51_col0, #T_c34e6_row51_col6, #T_c34e6_row52_col0, #T_c34e6_row52_col4, #T_c34e6_row52_col6, #T_c34e6_row53_col4, #T_c34e6_row53_col6, #T_c34e6_row54_col0, #T_c34e6_row54_col2, #T_c34e6_row54_col5, #T_c34e6_row55_col4, #T_c34e6_row55_col5, #T_c34e6_row55_col6, #T_c34e6_row56_col0, #T_c34e6_row56_col1, #T_c34e6_row56_col2, #T_c34e6_row56_col4, #T_c34e6_row56_col6, #T_c34e6_row57_col2, #T_c34e6_row57_col6, #T_c34e6_row58_col5, #T_c34e6_row58_col6, #T_c34e6_row58_col9, #T_c34e6_row59_col4, #T_c34e6_row59_col5, #T_c34e6_row59_col6, #T_c34e6_row59_col9, #T_c34e6_row60_col4, #T_c34e6_row60_col5, #T_c34e6_row60_col6, #T_c34e6_row61_col0, #T_c34e6_row61_col2, #T_c34e6_row61_col4, #T_c34e6_row61_col6, #T_c34e6_row62_col4, #T_c34e6_row62_col5, #T_c34e6_row62_col6, #T_c34e6_row62_col8, #T_c34e6_row62_col9, #T_c34e6_row63_col0, #T_c34e6_row63_col1, #T_c34e6_row63_col5, #T_c34e6_row63_col6, #T_c34e6_row63_col8, #T_c34e6_row64_col5, #T_c34e6_row64_col6, #T_c34e6_row64_col9, #T_c34e6_row65_col2, #T_c34e6_row65_col4, #T_c34e6_row65_col6, #T_c34e6_row66_col0, #T_c34e6_row66_col2, #T_c34e6_row66_col5, #T_c34e6_row66_col6, #T_c34e6_row66_col7, #T_c34e6_row67_col6, #T_c34e6_row68_col0, #T_c34e6_row68_col2, #T_c34e6_row68_col3, #T_c34e6_row68_col4, #T_c34e6_row68_col5, #T_c34e6_row68_col6, #T_c34e6_row69_col0, #T_c34e6_row69_col6, #T_c34e6_row70_col0, #T_c34e6_row70_col2, #T_c34e6_row70_col4, #T_c34e6_row70_col6, #T_c34e6_row70_col9, #T_c34e6_row71_col5, #T_c34e6_row72_col0, #T_c34e6_row72_col1, #T_c34e6_row72_col6, #T_c34e6_row72_col7, #T_c34e6_row72_col8, #T_c34e6_row73_col1, #T_c34e6_row73_col4, #T_c34e6_row73_col7, #T_c34e6_row74_col0, #T_c34e6_row74_col2, #T_c34e6_row74_col6, #T_c34e6_row75_col0, #T_c34e6_row75_col4, #T_c34e6_row75_col5, #T_c34e6_row75_col6, #T_c34e6_row75_col9, #T_c34e6_row76_col4, #T_c34e6_row76_col6, #T_c34e6_row77_col4, #T_c34e6_row77_col5, #T_c34e6_row77_col6, #T_c34e6_row78_col0, #T_c34e6_row78_col2, #T_c34e6_row78_col3, #T_c34e6_row78_col4, #T_c34e6_row78_col6, #T_c34e6_row79_col0, #T_c34e6_row79_col2, #T_c34e6_row79_col4, #T_c34e6_row79_col6, #T_c34e6_row80_col0, #T_c34e6_row80_col2, #T_c34e6_row80_col5, #T_c34e6_row80_col6, #T_c34e6_row80_col7, #T_c34e6_row81_col2, #T_c34e6_row81_col6, #T_c34e6_row81_col9, #T_c34e6_row82_col0, #T_c34e6_row82_col1, #T_c34e6_row82_col2, #T_c34e6_row82_col6, #T_c34e6_row82_col7, #T_c34e6_row83_col0, #T_c34e6_row83_col6, #T_c34e6_row84_col2, #T_c34e6_row84_col5, #T_c34e6_row84_col6, #T_c34e6_row84_col7, #T_c34e6_row84_col8, #T_c34e6_row85_col0, #T_c34e6_row85_col4, #T_c34e6_row85_col5, #T_c34e6_row85_col6, #T_c34e6_row86_col4, #T_c34e6_row86_col9 {\n",
       "  background-color: pink;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c34e6_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Q1</th>\n",
       "      <th class=\"col_heading level0 col1\" >Q2</th>\n",
       "      <th class=\"col_heading level0 col2\" >Q3</th>\n",
       "      <th class=\"col_heading level0 col3\" >Q4</th>\n",
       "      <th class=\"col_heading level0 col4\" >Q5</th>\n",
       "      <th class=\"col_heading level0 col5\" >Q6</th>\n",
       "      <th class=\"col_heading level0 col6\" >Q7</th>\n",
       "      <th class=\"col_heading level0 col7\" >Q8</th>\n",
       "      <th class=\"col_heading level0 col8\" >Q9</th>\n",
       "      <th class=\"col_heading level0 col9\" >Q10</th>\n",
       "      <th class=\"col_heading level0 col10\" >Total</th>\n",
       "      <th class=\"col_heading level0 col11\" >Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >UNum</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row0\" class=\"row_heading level0 row0\" >Answer Key</th>\n",
       "      <td id=\"T_c34e6_row0_col0\" class=\"data row0 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col1\" class=\"data row0 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col2\" class=\"data row0 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col3\" class=\"data row0 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col4\" class=\"data row0 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col5\" class=\"data row0 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row0_col6\" class=\"data row0 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row0_col7\" class=\"data row0 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row0_col8\" class=\"data row0 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row0_col9\" class=\"data row0 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row0_col10\" class=\"data row0 col10\" >50</td>\n",
       "      <td id=\"T_c34e6_row0_col11\" class=\"data row0 col11\" >100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row1\" class=\"row_heading level0 row1\" >1234567</th>\n",
       "      <td id=\"T_c34e6_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row1_col1\" class=\"data row1 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row1_col2\" class=\"data row1 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row1_col3\" class=\"data row1 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row1_col5\" class=\"data row1 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row1_col7\" class=\"data row1 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row1_col8\" class=\"data row1 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row1_col9\" class=\"data row1 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row1_col10\" class=\"data row1 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row1_col11\" class=\"data row1 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row2\" class=\"row_heading level0 row2\" >1234568</th>\n",
       "      <td id=\"T_c34e6_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col5\" class=\"data row2 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col7\" class=\"data row2 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row2_col8\" class=\"data row2 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col9\" class=\"data row2 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row2_col10\" class=\"data row2 col10\" >12</td>\n",
       "      <td id=\"T_c34e6_row2_col11\" class=\"data row2 col11\" >24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row3\" class=\"row_heading level0 row3\" >1234569</th>\n",
       "      <td id=\"T_c34e6_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row3_col1\" class=\"data row3 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row3_col2\" class=\"data row3 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row3_col3\" class=\"data row3 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row3_col4\" class=\"data row3 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row3_col5\" class=\"data row3 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row3_col7\" class=\"data row3 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row3_col8\" class=\"data row3 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row3_col9\" class=\"data row3 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row3_col10\" class=\"data row3 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row3_col11\" class=\"data row3 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row4\" class=\"row_heading level0 row4\" >1234570</th>\n",
       "      <td id=\"T_c34e6_row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row4_col3\" class=\"data row4 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row4_col5\" class=\"data row4 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row4_col7\" class=\"data row4 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row4_col8\" class=\"data row4 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row4_col9\" class=\"data row4 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row4_col10\" class=\"data row4 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row4_col11\" class=\"data row4 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row5\" class=\"row_heading level0 row5\" >1234571</th>\n",
       "      <td id=\"T_c34e6_row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row5_col1\" class=\"data row5 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row5_col3\" class=\"data row5 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row5_col4\" class=\"data row5 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row5_col5\" class=\"data row5 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row5_col6\" class=\"data row5 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row5_col7\" class=\"data row5 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row5_col8\" class=\"data row5 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row5_col9\" class=\"data row5 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row5_col10\" class=\"data row5 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row5_col11\" class=\"data row5 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row6\" class=\"row_heading level0 row6\" >1234572</th>\n",
       "      <td id=\"T_c34e6_row6_col0\" class=\"data row6 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row6_col3\" class=\"data row6 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row6_col4\" class=\"data row6 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row6_col6\" class=\"data row6 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row6_col7\" class=\"data row6 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row6_col8\" class=\"data row6 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row6_col9\" class=\"data row6 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row6_col10\" class=\"data row6 col10\" >36</td>\n",
       "      <td id=\"T_c34e6_row6_col11\" class=\"data row6 col11\" >72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row7\" class=\"row_heading level0 row7\" >1234573</th>\n",
       "      <td id=\"T_c34e6_row7_col0\" class=\"data row7 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row7_col1\" class=\"data row7 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row7_col2\" class=\"data row7 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row7_col3\" class=\"data row7 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row7_col4\" class=\"data row7 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row7_col5\" class=\"data row7 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row7_col6\" class=\"data row7 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row7_col7\" class=\"data row7 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row7_col8\" class=\"data row7 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row7_col9\" class=\"data row7 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row7_col10\" class=\"data row7 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row7_col11\" class=\"data row7 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row8\" class=\"row_heading level0 row8\" >1234574</th>\n",
       "      <td id=\"T_c34e6_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col2\" class=\"data row8 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row8_col3\" class=\"data row8 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row8_col4\" class=\"data row8 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col5\" class=\"data row8 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col6\" class=\"data row8 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col7\" class=\"data row8 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col8\" class=\"data row8 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row8_col9\" class=\"data row8 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row8_col10\" class=\"data row8 col10\" >12</td>\n",
       "      <td id=\"T_c34e6_row8_col11\" class=\"data row8 col11\" >24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row9\" class=\"row_heading level0 row9\" >1234575</th>\n",
       "      <td id=\"T_c34e6_row9_col0\" class=\"data row9 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row9_col1\" class=\"data row9 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row9_col2\" class=\"data row9 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col3\" class=\"data row9 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row9_col4\" class=\"data row9 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col5\" class=\"data row9 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col6\" class=\"data row9 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col7\" class=\"data row9 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col8\" class=\"data row9 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col9\" class=\"data row9 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row9_col10\" class=\"data row9 col10\" >15</td>\n",
       "      <td id=\"T_c34e6_row9_col11\" class=\"data row9 col11\" >30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row10\" class=\"row_heading level0 row10\" >1234576</th>\n",
       "      <td id=\"T_c34e6_row10_col0\" class=\"data row10 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col1\" class=\"data row10 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row10_col2\" class=\"data row10 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col3\" class=\"data row10 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row10_col4\" class=\"data row10 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col5\" class=\"data row10 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col6\" class=\"data row10 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col7\" class=\"data row10 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row10_col8\" class=\"data row10 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row10_col9\" class=\"data row10 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row10_col10\" class=\"data row10 col10\" >18</td>\n",
       "      <td id=\"T_c34e6_row10_col11\" class=\"data row10 col11\" >36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row11\" class=\"row_heading level0 row11\" >1234577</th>\n",
       "      <td id=\"T_c34e6_row11_col0\" class=\"data row11 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row11_col1\" class=\"data row11 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row11_col2\" class=\"data row11 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row11_col3\" class=\"data row11 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row11_col4\" class=\"data row11 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row11_col5\" class=\"data row11 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row11_col6\" class=\"data row11 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row11_col7\" class=\"data row11 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row11_col8\" class=\"data row11 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row11_col9\" class=\"data row11 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row11_col10\" class=\"data row11 col10\" >28</td>\n",
       "      <td id=\"T_c34e6_row11_col11\" class=\"data row11 col11\" >56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row12\" class=\"row_heading level0 row12\" >1234578</th>\n",
       "      <td id=\"T_c34e6_row12_col0\" class=\"data row12 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row12_col1\" class=\"data row12 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row12_col2\" class=\"data row12 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row12_col3\" class=\"data row12 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row12_col4\" class=\"data row12 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row12_col5\" class=\"data row12 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row12_col6\" class=\"data row12 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row12_col7\" class=\"data row12 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row12_col8\" class=\"data row12 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row12_col9\" class=\"data row12 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row12_col10\" class=\"data row12 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row12_col11\" class=\"data row12 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row13\" class=\"row_heading level0 row13\" >1234579</th>\n",
       "      <td id=\"T_c34e6_row13_col0\" class=\"data row13 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row13_col1\" class=\"data row13 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row13_col2\" class=\"data row13 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row13_col3\" class=\"data row13 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row13_col4\" class=\"data row13 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row13_col5\" class=\"data row13 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row13_col6\" class=\"data row13 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row13_col7\" class=\"data row13 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row13_col8\" class=\"data row13 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row13_col9\" class=\"data row13 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row13_col10\" class=\"data row13 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row13_col11\" class=\"data row13 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row14\" class=\"row_heading level0 row14\" >1234580</th>\n",
       "      <td id=\"T_c34e6_row14_col0\" class=\"data row14 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row14_col1\" class=\"data row14 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row14_col2\" class=\"data row14 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row14_col3\" class=\"data row14 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row14_col4\" class=\"data row14 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row14_col5\" class=\"data row14 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row14_col6\" class=\"data row14 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row14_col7\" class=\"data row14 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row14_col8\" class=\"data row14 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row14_col9\" class=\"data row14 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row14_col10\" class=\"data row14 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row14_col11\" class=\"data row14 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row15\" class=\"row_heading level0 row15\" >1234581</th>\n",
       "      <td id=\"T_c34e6_row15_col0\" class=\"data row15 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row15_col1\" class=\"data row15 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row15_col2\" class=\"data row15 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row15_col3\" class=\"data row15 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row15_col4\" class=\"data row15 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row15_col5\" class=\"data row15 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row15_col6\" class=\"data row15 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row15_col7\" class=\"data row15 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row15_col8\" class=\"data row15 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row15_col9\" class=\"data row15 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row15_col10\" class=\"data row15 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row15_col11\" class=\"data row15 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row16\" class=\"row_heading level0 row16\" >1234582</th>\n",
       "      <td id=\"T_c34e6_row16_col0\" class=\"data row16 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row16_col1\" class=\"data row16 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row16_col2\" class=\"data row16 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col3\" class=\"data row16 col3\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col4\" class=\"data row16 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row16_col5\" class=\"data row16 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col6\" class=\"data row16 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col7\" class=\"data row16 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row16_col8\" class=\"data row16 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col9\" class=\"data row16 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row16_col10\" class=\"data row16 col10\" >17</td>\n",
       "      <td id=\"T_c34e6_row16_col11\" class=\"data row16 col11\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row17\" class=\"row_heading level0 row17\" >1234583</th>\n",
       "      <td id=\"T_c34e6_row17_col0\" class=\"data row17 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row17_col1\" class=\"data row17 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row17_col2\" class=\"data row17 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row17_col3\" class=\"data row17 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row17_col4\" class=\"data row17 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row17_col5\" class=\"data row17 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row17_col6\" class=\"data row17 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row17_col7\" class=\"data row17 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row17_col8\" class=\"data row17 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row17_col9\" class=\"data row17 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row17_col10\" class=\"data row17 col10\" >28</td>\n",
       "      <td id=\"T_c34e6_row17_col11\" class=\"data row17 col11\" >56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row18\" class=\"row_heading level0 row18\" >1234584</th>\n",
       "      <td id=\"T_c34e6_row18_col0\" class=\"data row18 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row18_col1\" class=\"data row18 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row18_col2\" class=\"data row18 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row18_col3\" class=\"data row18 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row18_col4\" class=\"data row18 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row18_col5\" class=\"data row18 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row18_col6\" class=\"data row18 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row18_col7\" class=\"data row18 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row18_col8\" class=\"data row18 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row18_col9\" class=\"data row18 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row18_col10\" class=\"data row18 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row18_col11\" class=\"data row18 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row19\" class=\"row_heading level0 row19\" >1234585</th>\n",
       "      <td id=\"T_c34e6_row19_col0\" class=\"data row19 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row19_col1\" class=\"data row19 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row19_col2\" class=\"data row19 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row19_col3\" class=\"data row19 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row19_col4\" class=\"data row19 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row19_col5\" class=\"data row19 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row19_col6\" class=\"data row19 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row19_col7\" class=\"data row19 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row19_col8\" class=\"data row19 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row19_col9\" class=\"data row19 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row19_col10\" class=\"data row19 col10\" >26</td>\n",
       "      <td id=\"T_c34e6_row19_col11\" class=\"data row19 col11\" >52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row20\" class=\"row_heading level0 row20\" >1234586</th>\n",
       "      <td id=\"T_c34e6_row20_col0\" class=\"data row20 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row20_col1\" class=\"data row20 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row20_col2\" class=\"data row20 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row20_col3\" class=\"data row20 col3\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col4\" class=\"data row20 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col5\" class=\"data row20 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col6\" class=\"data row20 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col7\" class=\"data row20 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row20_col8\" class=\"data row20 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col9\" class=\"data row20 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row20_col10\" class=\"data row20 col10\" >17</td>\n",
       "      <td id=\"T_c34e6_row20_col11\" class=\"data row20 col11\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row21\" class=\"row_heading level0 row21\" >1234587</th>\n",
       "      <td id=\"T_c34e6_row21_col0\" class=\"data row21 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row21_col1\" class=\"data row21 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row21_col2\" class=\"data row21 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row21_col3\" class=\"data row21 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row21_col4\" class=\"data row21 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row21_col5\" class=\"data row21 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row21_col6\" class=\"data row21 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row21_col7\" class=\"data row21 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row21_col8\" class=\"data row21 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row21_col9\" class=\"data row21 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row21_col10\" class=\"data row21 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row21_col11\" class=\"data row21 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row22\" class=\"row_heading level0 row22\" >1234588</th>\n",
       "      <td id=\"T_c34e6_row22_col0\" class=\"data row22 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col1\" class=\"data row22 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col2\" class=\"data row22 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row22_col3\" class=\"data row22 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row22_col4\" class=\"data row22 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row22_col5\" class=\"data row22 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row22_col6\" class=\"data row22 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col7\" class=\"data row22 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col8\" class=\"data row22 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col9\" class=\"data row22 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row22_col10\" class=\"data row22 col10\" >20</td>\n",
       "      <td id=\"T_c34e6_row22_col11\" class=\"data row22 col11\" >40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row23\" class=\"row_heading level0 row23\" >1234589</th>\n",
       "      <td id=\"T_c34e6_row23_col0\" class=\"data row23 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row23_col1\" class=\"data row23 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row23_col2\" class=\"data row23 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row23_col3\" class=\"data row23 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row23_col4\" class=\"data row23 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row23_col5\" class=\"data row23 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row23_col6\" class=\"data row23 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row23_col7\" class=\"data row23 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row23_col8\" class=\"data row23 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row23_col9\" class=\"data row23 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row23_col10\" class=\"data row23 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row23_col11\" class=\"data row23 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row24\" class=\"row_heading level0 row24\" >1234590</th>\n",
       "      <td id=\"T_c34e6_row24_col0\" class=\"data row24 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row24_col1\" class=\"data row24 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row24_col2\" class=\"data row24 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row24_col3\" class=\"data row24 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row24_col4\" class=\"data row24 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row24_col5\" class=\"data row24 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row24_col6\" class=\"data row24 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row24_col7\" class=\"data row24 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row24_col8\" class=\"data row24 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row24_col9\" class=\"data row24 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row24_col10\" class=\"data row24 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row24_col11\" class=\"data row24 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row25\" class=\"row_heading level0 row25\" >1234591</th>\n",
       "      <td id=\"T_c34e6_row25_col0\" class=\"data row25 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row25_col1\" class=\"data row25 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row25_col2\" class=\"data row25 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row25_col3\" class=\"data row25 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row25_col4\" class=\"data row25 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row25_col5\" class=\"data row25 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row25_col6\" class=\"data row25 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row25_col7\" class=\"data row25 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row25_col8\" class=\"data row25 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row25_col9\" class=\"data row25 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row25_col10\" class=\"data row25 col10\" >24</td>\n",
       "      <td id=\"T_c34e6_row25_col11\" class=\"data row25 col11\" >48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row26\" class=\"row_heading level0 row26\" >1234592</th>\n",
       "      <td id=\"T_c34e6_row26_col0\" class=\"data row26 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row26_col1\" class=\"data row26 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row26_col2\" class=\"data row26 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row26_col3\" class=\"data row26 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row26_col4\" class=\"data row26 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row26_col5\" class=\"data row26 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row26_col6\" class=\"data row26 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row26_col7\" class=\"data row26 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row26_col8\" class=\"data row26 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row26_col9\" class=\"data row26 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row26_col10\" class=\"data row26 col10\" >33</td>\n",
       "      <td id=\"T_c34e6_row26_col11\" class=\"data row26 col11\" >66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row27\" class=\"row_heading level0 row27\" >1234593</th>\n",
       "      <td id=\"T_c34e6_row27_col0\" class=\"data row27 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col1\" class=\"data row27 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row27_col2\" class=\"data row27 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col3\" class=\"data row27 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row27_col4\" class=\"data row27 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col5\" class=\"data row27 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col6\" class=\"data row27 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row27_col7\" class=\"data row27 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col8\" class=\"data row27 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col9\" class=\"data row27 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row27_col10\" class=\"data row27 col10\" >20</td>\n",
       "      <td id=\"T_c34e6_row27_col11\" class=\"data row27 col11\" >40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row28\" class=\"row_heading level0 row28\" >1234594</th>\n",
       "      <td id=\"T_c34e6_row28_col0\" class=\"data row28 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row28_col1\" class=\"data row28 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row28_col2\" class=\"data row28 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row28_col3\" class=\"data row28 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row28_col4\" class=\"data row28 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row28_col5\" class=\"data row28 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row28_col6\" class=\"data row28 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row28_col7\" class=\"data row28 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row28_col8\" class=\"data row28 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row28_col9\" class=\"data row28 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row28_col10\" class=\"data row28 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row28_col11\" class=\"data row28 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row29\" class=\"row_heading level0 row29\" >1234595</th>\n",
       "      <td id=\"T_c34e6_row29_col0\" class=\"data row29 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row29_col1\" class=\"data row29 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row29_col2\" class=\"data row29 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row29_col3\" class=\"data row29 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row29_col4\" class=\"data row29 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row29_col5\" class=\"data row29 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row29_col6\" class=\"data row29 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row29_col7\" class=\"data row29 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row29_col8\" class=\"data row29 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row29_col9\" class=\"data row29 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row29_col10\" class=\"data row29 col10\" >40</td>\n",
       "      <td id=\"T_c34e6_row29_col11\" class=\"data row29 col11\" >80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row30\" class=\"row_heading level0 row30\" >1234596</th>\n",
       "      <td id=\"T_c34e6_row30_col0\" class=\"data row30 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row30_col1\" class=\"data row30 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row30_col2\" class=\"data row30 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row30_col3\" class=\"data row30 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row30_col4\" class=\"data row30 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row30_col5\" class=\"data row30 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row30_col6\" class=\"data row30 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row30_col7\" class=\"data row30 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row30_col8\" class=\"data row30 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row30_col9\" class=\"data row30 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row30_col10\" class=\"data row30 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row30_col11\" class=\"data row30 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row31\" class=\"row_heading level0 row31\" >1234597</th>\n",
       "      <td id=\"T_c34e6_row31_col0\" class=\"data row31 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row31_col1\" class=\"data row31 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row31_col2\" class=\"data row31 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col3\" class=\"data row31 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row31_col4\" class=\"data row31 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col5\" class=\"data row31 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col6\" class=\"data row31 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col7\" class=\"data row31 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row31_col8\" class=\"data row31 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col9\" class=\"data row31 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row31_col10\" class=\"data row31 col10\" >17</td>\n",
       "      <td id=\"T_c34e6_row31_col11\" class=\"data row31 col11\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row32\" class=\"row_heading level0 row32\" >1234598</th>\n",
       "      <td id=\"T_c34e6_row32_col0\" class=\"data row32 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row32_col1\" class=\"data row32 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row32_col2\" class=\"data row32 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row32_col3\" class=\"data row32 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row32_col4\" class=\"data row32 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row32_col5\" class=\"data row32 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row32_col6\" class=\"data row32 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row32_col7\" class=\"data row32 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row32_col8\" class=\"data row32 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row32_col9\" class=\"data row32 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row32_col10\" class=\"data row32 col10\" >28</td>\n",
       "      <td id=\"T_c34e6_row32_col11\" class=\"data row32 col11\" >56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row33\" class=\"row_heading level0 row33\" >1234599</th>\n",
       "      <td id=\"T_c34e6_row33_col0\" class=\"data row33 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row33_col1\" class=\"data row33 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row33_col2\" class=\"data row33 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row33_col3\" class=\"data row33 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row33_col4\" class=\"data row33 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row33_col5\" class=\"data row33 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row33_col6\" class=\"data row33 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row33_col7\" class=\"data row33 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row33_col8\" class=\"data row33 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row33_col9\" class=\"data row33 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row33_col10\" class=\"data row33 col10\" >29</td>\n",
       "      <td id=\"T_c34e6_row33_col11\" class=\"data row33 col11\" >58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row34\" class=\"row_heading level0 row34\" >1234600</th>\n",
       "      <td id=\"T_c34e6_row34_col0\" class=\"data row34 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row34_col1\" class=\"data row34 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row34_col2\" class=\"data row34 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row34_col3\" class=\"data row34 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row34_col4\" class=\"data row34 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row34_col5\" class=\"data row34 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row34_col6\" class=\"data row34 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row34_col7\" class=\"data row34 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row34_col8\" class=\"data row34 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row34_col9\" class=\"data row34 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row34_col10\" class=\"data row34 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row34_col11\" class=\"data row34 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row35\" class=\"row_heading level0 row35\" >1234601</th>\n",
       "      <td id=\"T_c34e6_row35_col0\" class=\"data row35 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row35_col1\" class=\"data row35 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row35_col2\" class=\"data row35 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row35_col3\" class=\"data row35 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row35_col4\" class=\"data row35 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row35_col5\" class=\"data row35 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row35_col6\" class=\"data row35 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row35_col7\" class=\"data row35 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row35_col8\" class=\"data row35 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row35_col9\" class=\"data row35 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row35_col10\" class=\"data row35 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row35_col11\" class=\"data row35 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row36\" class=\"row_heading level0 row36\" >1234602</th>\n",
       "      <td id=\"T_c34e6_row36_col0\" class=\"data row36 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row36_col1\" class=\"data row36 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row36_col2\" class=\"data row36 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row36_col3\" class=\"data row36 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row36_col4\" class=\"data row36 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row36_col5\" class=\"data row36 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row36_col6\" class=\"data row36 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row36_col7\" class=\"data row36 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row36_col8\" class=\"data row36 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row36_col9\" class=\"data row36 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row36_col10\" class=\"data row36 col10\" >33</td>\n",
       "      <td id=\"T_c34e6_row36_col11\" class=\"data row36 col11\" >66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row37\" class=\"row_heading level0 row37\" >1234603</th>\n",
       "      <td id=\"T_c34e6_row37_col0\" class=\"data row37 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col1\" class=\"data row37 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col2\" class=\"data row37 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col3\" class=\"data row37 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col4\" class=\"data row37 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col5\" class=\"data row37 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row37_col6\" class=\"data row37 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row37_col7\" class=\"data row37 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row37_col8\" class=\"data row37 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row37_col9\" class=\"data row37 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row37_col10\" class=\"data row37 col10\" >40</td>\n",
       "      <td id=\"T_c34e6_row37_col11\" class=\"data row37 col11\" >80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row38\" class=\"row_heading level0 row38\" >1234604</th>\n",
       "      <td id=\"T_c34e6_row38_col0\" class=\"data row38 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row38_col1\" class=\"data row38 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row38_col2\" class=\"data row38 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row38_col3\" class=\"data row38 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row38_col4\" class=\"data row38 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row38_col5\" class=\"data row38 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row38_col6\" class=\"data row38 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row38_col7\" class=\"data row38 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row38_col8\" class=\"data row38 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row38_col9\" class=\"data row38 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row38_col10\" class=\"data row38 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row38_col11\" class=\"data row38 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row39\" class=\"row_heading level0 row39\" >1234605</th>\n",
       "      <td id=\"T_c34e6_row39_col0\" class=\"data row39 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row39_col1\" class=\"data row39 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row39_col2\" class=\"data row39 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row39_col3\" class=\"data row39 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row39_col4\" class=\"data row39 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row39_col5\" class=\"data row39 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row39_col6\" class=\"data row39 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row39_col7\" class=\"data row39 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row39_col8\" class=\"data row39 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row39_col9\" class=\"data row39 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row39_col10\" class=\"data row39 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row39_col11\" class=\"data row39 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row40\" class=\"row_heading level0 row40\" >1234606</th>\n",
       "      <td id=\"T_c34e6_row40_col0\" class=\"data row40 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row40_col1\" class=\"data row40 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row40_col2\" class=\"data row40 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row40_col3\" class=\"data row40 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row40_col4\" class=\"data row40 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row40_col5\" class=\"data row40 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row40_col6\" class=\"data row40 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row40_col7\" class=\"data row40 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row40_col8\" class=\"data row40 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row40_col9\" class=\"data row40 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row40_col10\" class=\"data row40 col10\" >27</td>\n",
       "      <td id=\"T_c34e6_row40_col11\" class=\"data row40 col11\" >54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row41\" class=\"row_heading level0 row41\" >1234607</th>\n",
       "      <td id=\"T_c34e6_row41_col0\" class=\"data row41 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row41_col1\" class=\"data row41 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row41_col2\" class=\"data row41 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row41_col3\" class=\"data row41 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row41_col4\" class=\"data row41 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row41_col5\" class=\"data row41 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row41_col6\" class=\"data row41 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row41_col7\" class=\"data row41 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row41_col8\" class=\"data row41 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row41_col9\" class=\"data row41 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row41_col10\" class=\"data row41 col10\" >33</td>\n",
       "      <td id=\"T_c34e6_row41_col11\" class=\"data row41 col11\" >66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row42\" class=\"row_heading level0 row42\" >1234608</th>\n",
       "      <td id=\"T_c34e6_row42_col0\" class=\"data row42 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row42_col1\" class=\"data row42 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row42_col2\" class=\"data row42 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row42_col3\" class=\"data row42 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row42_col4\" class=\"data row42 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row42_col5\" class=\"data row42 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row42_col6\" class=\"data row42 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row42_col7\" class=\"data row42 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row42_col8\" class=\"data row42 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row42_col9\" class=\"data row42 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row42_col10\" class=\"data row42 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row42_col11\" class=\"data row42 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row43\" class=\"row_heading level0 row43\" >1234609</th>\n",
       "      <td id=\"T_c34e6_row43_col0\" class=\"data row43 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row43_col1\" class=\"data row43 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row43_col2\" class=\"data row43 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row43_col3\" class=\"data row43 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row43_col4\" class=\"data row43 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row43_col5\" class=\"data row43 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row43_col6\" class=\"data row43 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row43_col7\" class=\"data row43 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row43_col8\" class=\"data row43 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row43_col9\" class=\"data row43 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row43_col10\" class=\"data row43 col10\" >33</td>\n",
       "      <td id=\"T_c34e6_row43_col11\" class=\"data row43 col11\" >66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row44\" class=\"row_heading level0 row44\" >1234610</th>\n",
       "      <td id=\"T_c34e6_row44_col0\" class=\"data row44 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row44_col1\" class=\"data row44 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row44_col2\" class=\"data row44 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row44_col3\" class=\"data row44 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row44_col4\" class=\"data row44 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row44_col5\" class=\"data row44 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row44_col6\" class=\"data row44 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row44_col7\" class=\"data row44 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row44_col8\" class=\"data row44 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row44_col9\" class=\"data row44 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row44_col10\" class=\"data row44 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row44_col11\" class=\"data row44 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row45\" class=\"row_heading level0 row45\" >1234611</th>\n",
       "      <td id=\"T_c34e6_row45_col0\" class=\"data row45 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row45_col1\" class=\"data row45 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row45_col2\" class=\"data row45 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row45_col3\" class=\"data row45 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row45_col4\" class=\"data row45 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row45_col5\" class=\"data row45 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row45_col6\" class=\"data row45 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row45_col7\" class=\"data row45 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row45_col8\" class=\"data row45 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row45_col9\" class=\"data row45 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row45_col10\" class=\"data row45 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row45_col11\" class=\"data row45 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row46\" class=\"row_heading level0 row46\" >1234612</th>\n",
       "      <td id=\"T_c34e6_row46_col0\" class=\"data row46 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row46_col1\" class=\"data row46 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row46_col2\" class=\"data row46 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row46_col3\" class=\"data row46 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row46_col4\" class=\"data row46 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row46_col5\" class=\"data row46 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row46_col6\" class=\"data row46 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row46_col7\" class=\"data row46 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row46_col8\" class=\"data row46 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row46_col9\" class=\"data row46 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row46_col10\" class=\"data row46 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row46_col11\" class=\"data row46 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row47\" class=\"row_heading level0 row47\" >1234613</th>\n",
       "      <td id=\"T_c34e6_row47_col0\" class=\"data row47 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row47_col1\" class=\"data row47 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row47_col2\" class=\"data row47 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row47_col3\" class=\"data row47 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row47_col4\" class=\"data row47 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row47_col5\" class=\"data row47 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row47_col6\" class=\"data row47 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row47_col7\" class=\"data row47 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row47_col8\" class=\"data row47 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row47_col9\" class=\"data row47 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row47_col10\" class=\"data row47 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row47_col11\" class=\"data row47 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row48\" class=\"row_heading level0 row48\" >1234614</th>\n",
       "      <td id=\"T_c34e6_row48_col0\" class=\"data row48 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row48_col1\" class=\"data row48 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row48_col2\" class=\"data row48 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row48_col3\" class=\"data row48 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row48_col4\" class=\"data row48 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row48_col5\" class=\"data row48 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row48_col6\" class=\"data row48 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row48_col7\" class=\"data row48 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row48_col8\" class=\"data row48 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row48_col9\" class=\"data row48 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row48_col10\" class=\"data row48 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row48_col11\" class=\"data row48 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row49\" class=\"row_heading level0 row49\" >1234615</th>\n",
       "      <td id=\"T_c34e6_row49_col0\" class=\"data row49 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col1\" class=\"data row49 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row49_col2\" class=\"data row49 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row49_col3\" class=\"data row49 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row49_col4\" class=\"data row49 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col5\" class=\"data row49 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col6\" class=\"data row49 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col7\" class=\"data row49 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row49_col8\" class=\"data row49 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col9\" class=\"data row49 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row49_col10\" class=\"data row49 col10\" >17</td>\n",
       "      <td id=\"T_c34e6_row49_col11\" class=\"data row49 col11\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row50\" class=\"row_heading level0 row50\" >1234616</th>\n",
       "      <td id=\"T_c34e6_row50_col0\" class=\"data row50 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row50_col1\" class=\"data row50 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row50_col2\" class=\"data row50 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row50_col3\" class=\"data row50 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row50_col4\" class=\"data row50 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row50_col5\" class=\"data row50 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row50_col6\" class=\"data row50 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row50_col7\" class=\"data row50 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row50_col8\" class=\"data row50 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row50_col9\" class=\"data row50 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row50_col10\" class=\"data row50 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row50_col11\" class=\"data row50 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row51\" class=\"row_heading level0 row51\" >1234617</th>\n",
       "      <td id=\"T_c34e6_row51_col0\" class=\"data row51 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row51_col1\" class=\"data row51 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row51_col2\" class=\"data row51 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row51_col3\" class=\"data row51 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row51_col4\" class=\"data row51 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row51_col5\" class=\"data row51 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row51_col6\" class=\"data row51 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row51_col7\" class=\"data row51 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row51_col8\" class=\"data row51 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row51_col9\" class=\"data row51 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row51_col10\" class=\"data row51 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row51_col11\" class=\"data row51 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row52\" class=\"row_heading level0 row52\" >1234618</th>\n",
       "      <td id=\"T_c34e6_row52_col0\" class=\"data row52 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row52_col1\" class=\"data row52 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row52_col2\" class=\"data row52 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row52_col3\" class=\"data row52 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row52_col4\" class=\"data row52 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row52_col5\" class=\"data row52 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row52_col6\" class=\"data row52 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row52_col7\" class=\"data row52 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row52_col8\" class=\"data row52 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row52_col9\" class=\"data row52 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row52_col10\" class=\"data row52 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row52_col11\" class=\"data row52 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row53\" class=\"row_heading level0 row53\" >1234619</th>\n",
       "      <td id=\"T_c34e6_row53_col0\" class=\"data row53 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row53_col1\" class=\"data row53 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row53_col2\" class=\"data row53 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row53_col3\" class=\"data row53 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row53_col4\" class=\"data row53 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row53_col5\" class=\"data row53 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row53_col6\" class=\"data row53 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row53_col7\" class=\"data row53 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row53_col8\" class=\"data row53 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row53_col9\" class=\"data row53 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row53_col10\" class=\"data row53 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row53_col11\" class=\"data row53 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row54\" class=\"row_heading level0 row54\" >1234620</th>\n",
       "      <td id=\"T_c34e6_row54_col0\" class=\"data row54 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row54_col1\" class=\"data row54 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row54_col2\" class=\"data row54 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row54_col3\" class=\"data row54 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row54_col4\" class=\"data row54 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row54_col5\" class=\"data row54 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row54_col6\" class=\"data row54 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row54_col7\" class=\"data row54 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row54_col8\" class=\"data row54 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row54_col9\" class=\"data row54 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row54_col10\" class=\"data row54 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row54_col11\" class=\"data row54 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row55\" class=\"row_heading level0 row55\" >1234621</th>\n",
       "      <td id=\"T_c34e6_row55_col0\" class=\"data row55 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row55_col1\" class=\"data row55 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row55_col2\" class=\"data row55 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row55_col3\" class=\"data row55 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row55_col4\" class=\"data row55 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row55_col5\" class=\"data row55 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row55_col6\" class=\"data row55 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row55_col7\" class=\"data row55 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row55_col8\" class=\"data row55 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row55_col9\" class=\"data row55 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row55_col10\" class=\"data row55 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row55_col11\" class=\"data row55 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row56\" class=\"row_heading level0 row56\" >1234622</th>\n",
       "      <td id=\"T_c34e6_row56_col0\" class=\"data row56 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row56_col1\" class=\"data row56 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row56_col2\" class=\"data row56 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row56_col3\" class=\"data row56 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row56_col4\" class=\"data row56 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row56_col5\" class=\"data row56 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row56_col6\" class=\"data row56 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row56_col7\" class=\"data row56 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row56_col8\" class=\"data row56 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row56_col9\" class=\"data row56 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row56_col10\" class=\"data row56 col10\" >20</td>\n",
       "      <td id=\"T_c34e6_row56_col11\" class=\"data row56 col11\" >40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row57\" class=\"row_heading level0 row57\" >1234623</th>\n",
       "      <td id=\"T_c34e6_row57_col0\" class=\"data row57 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row57_col1\" class=\"data row57 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row57_col2\" class=\"data row57 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row57_col3\" class=\"data row57 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row57_col4\" class=\"data row57 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row57_col5\" class=\"data row57 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row57_col6\" class=\"data row57 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row57_col7\" class=\"data row57 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row57_col8\" class=\"data row57 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row57_col9\" class=\"data row57 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row57_col10\" class=\"data row57 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row57_col11\" class=\"data row57 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row58\" class=\"row_heading level0 row58\" >1234624</th>\n",
       "      <td id=\"T_c34e6_row58_col0\" class=\"data row58 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row58_col1\" class=\"data row58 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row58_col2\" class=\"data row58 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row58_col3\" class=\"data row58 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row58_col4\" class=\"data row58 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row58_col5\" class=\"data row58 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row58_col6\" class=\"data row58 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row58_col7\" class=\"data row58 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row58_col8\" class=\"data row58 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row58_col9\" class=\"data row58 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row58_col10\" class=\"data row58 col10\" >29</td>\n",
       "      <td id=\"T_c34e6_row58_col11\" class=\"data row58 col11\" >58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row59\" class=\"row_heading level0 row59\" >1234625</th>\n",
       "      <td id=\"T_c34e6_row59_col0\" class=\"data row59 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row59_col1\" class=\"data row59 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row59_col2\" class=\"data row59 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row59_col3\" class=\"data row59 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row59_col4\" class=\"data row59 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row59_col5\" class=\"data row59 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row59_col6\" class=\"data row59 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row59_col7\" class=\"data row59 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row59_col8\" class=\"data row59 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row59_col9\" class=\"data row59 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row59_col10\" class=\"data row59 col10\" >24</td>\n",
       "      <td id=\"T_c34e6_row59_col11\" class=\"data row59 col11\" >48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row60\" class=\"row_heading level0 row60\" >1234626</th>\n",
       "      <td id=\"T_c34e6_row60_col0\" class=\"data row60 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row60_col1\" class=\"data row60 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row60_col2\" class=\"data row60 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row60_col3\" class=\"data row60 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row60_col4\" class=\"data row60 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row60_col5\" class=\"data row60 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row60_col6\" class=\"data row60 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row60_col7\" class=\"data row60 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row60_col8\" class=\"data row60 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row60_col9\" class=\"data row60 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row60_col10\" class=\"data row60 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row60_col11\" class=\"data row60 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row61\" class=\"row_heading level0 row61\" >1234627</th>\n",
       "      <td id=\"T_c34e6_row61_col0\" class=\"data row61 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row61_col1\" class=\"data row61 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row61_col2\" class=\"data row61 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row61_col3\" class=\"data row61 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row61_col4\" class=\"data row61 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row61_col5\" class=\"data row61 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row61_col6\" class=\"data row61 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row61_col7\" class=\"data row61 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row61_col8\" class=\"data row61 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row61_col9\" class=\"data row61 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row61_col10\" class=\"data row61 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row61_col11\" class=\"data row61 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row62\" class=\"row_heading level0 row62\" >1234628</th>\n",
       "      <td id=\"T_c34e6_row62_col0\" class=\"data row62 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row62_col1\" class=\"data row62 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row62_col2\" class=\"data row62 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row62_col3\" class=\"data row62 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row62_col4\" class=\"data row62 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row62_col5\" class=\"data row62 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row62_col6\" class=\"data row62 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row62_col7\" class=\"data row62 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row62_col8\" class=\"data row62 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row62_col9\" class=\"data row62 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row62_col10\" class=\"data row62 col10\" >22</td>\n",
       "      <td id=\"T_c34e6_row62_col11\" class=\"data row62 col11\" >44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row63\" class=\"row_heading level0 row63\" >1234629</th>\n",
       "      <td id=\"T_c34e6_row63_col0\" class=\"data row63 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row63_col1\" class=\"data row63 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row63_col2\" class=\"data row63 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row63_col3\" class=\"data row63 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row63_col4\" class=\"data row63 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row63_col5\" class=\"data row63 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row63_col6\" class=\"data row63 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row63_col7\" class=\"data row63 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row63_col8\" class=\"data row63 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row63_col9\" class=\"data row63 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row63_col10\" class=\"data row63 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row63_col11\" class=\"data row63 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row64\" class=\"row_heading level0 row64\" >1234630</th>\n",
       "      <td id=\"T_c34e6_row64_col0\" class=\"data row64 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row64_col1\" class=\"data row64 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row64_col2\" class=\"data row64 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row64_col3\" class=\"data row64 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row64_col4\" class=\"data row64 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row64_col5\" class=\"data row64 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row64_col6\" class=\"data row64 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row64_col7\" class=\"data row64 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row64_col8\" class=\"data row64 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row64_col9\" class=\"data row64 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row64_col10\" class=\"data row64 col10\" >29</td>\n",
       "      <td id=\"T_c34e6_row64_col11\" class=\"data row64 col11\" >58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row65\" class=\"row_heading level0 row65\" >1234631</th>\n",
       "      <td id=\"T_c34e6_row65_col0\" class=\"data row65 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row65_col1\" class=\"data row65 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row65_col2\" class=\"data row65 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row65_col3\" class=\"data row65 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row65_col4\" class=\"data row65 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row65_col5\" class=\"data row65 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row65_col6\" class=\"data row65 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row65_col7\" class=\"data row65 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row65_col8\" class=\"data row65 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row65_col9\" class=\"data row65 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row65_col10\" class=\"data row65 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row65_col11\" class=\"data row65 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row66\" class=\"row_heading level0 row66\" >1234632</th>\n",
       "      <td id=\"T_c34e6_row66_col0\" class=\"data row66 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row66_col1\" class=\"data row66 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row66_col2\" class=\"data row66 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row66_col3\" class=\"data row66 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row66_col4\" class=\"data row66 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row66_col5\" class=\"data row66 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row66_col6\" class=\"data row66 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row66_col7\" class=\"data row66 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row66_col8\" class=\"data row66 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row66_col9\" class=\"data row66 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row66_col10\" class=\"data row66 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row66_col11\" class=\"data row66 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row67\" class=\"row_heading level0 row67\" >1234633</th>\n",
       "      <td id=\"T_c34e6_row67_col0\" class=\"data row67 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col1\" class=\"data row67 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col2\" class=\"data row67 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col3\" class=\"data row67 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col4\" class=\"data row67 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col5\" class=\"data row67 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row67_col6\" class=\"data row67 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row67_col7\" class=\"data row67 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row67_col8\" class=\"data row67 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row67_col9\" class=\"data row67 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row67_col10\" class=\"data row67 col10\" >40</td>\n",
       "      <td id=\"T_c34e6_row67_col11\" class=\"data row67 col11\" >80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row68\" class=\"row_heading level0 row68\" >1234634</th>\n",
       "      <td id=\"T_c34e6_row68_col0\" class=\"data row68 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col1\" class=\"data row68 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row68_col2\" class=\"data row68 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col3\" class=\"data row68 col3\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col4\" class=\"data row68 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col5\" class=\"data row68 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col6\" class=\"data row68 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row68_col7\" class=\"data row68 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row68_col8\" class=\"data row68 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row68_col9\" class=\"data row68 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row68_col10\" class=\"data row68 col10\" >15</td>\n",
       "      <td id=\"T_c34e6_row68_col11\" class=\"data row68 col11\" >30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row69\" class=\"row_heading level0 row69\" >1234635</th>\n",
       "      <td id=\"T_c34e6_row69_col0\" class=\"data row69 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row69_col1\" class=\"data row69 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row69_col2\" class=\"data row69 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row69_col3\" class=\"data row69 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row69_col4\" class=\"data row69 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row69_col5\" class=\"data row69 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row69_col6\" class=\"data row69 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row69_col7\" class=\"data row69 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row69_col8\" class=\"data row69 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row69_col9\" class=\"data row69 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row69_col10\" class=\"data row69 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row69_col11\" class=\"data row69 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row70\" class=\"row_heading level0 row70\" >1234636</th>\n",
       "      <td id=\"T_c34e6_row70_col0\" class=\"data row70 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row70_col1\" class=\"data row70 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row70_col2\" class=\"data row70 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row70_col3\" class=\"data row70 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row70_col4\" class=\"data row70 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row70_col5\" class=\"data row70 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row70_col6\" class=\"data row70 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row70_col7\" class=\"data row70 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row70_col8\" class=\"data row70 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row70_col9\" class=\"data row70 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row70_col10\" class=\"data row70 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row70_col11\" class=\"data row70 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row71\" class=\"row_heading level0 row71\" >1234637</th>\n",
       "      <td id=\"T_c34e6_row71_col0\" class=\"data row71 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row71_col1\" class=\"data row71 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row71_col2\" class=\"data row71 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row71_col3\" class=\"data row71 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row71_col4\" class=\"data row71 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row71_col5\" class=\"data row71 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row71_col6\" class=\"data row71 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row71_col7\" class=\"data row71 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row71_col8\" class=\"data row71 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row71_col9\" class=\"data row71 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row71_col10\" class=\"data row71 col10\" >45</td>\n",
       "      <td id=\"T_c34e6_row71_col11\" class=\"data row71 col11\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row72\" class=\"row_heading level0 row72\" >1234638</th>\n",
       "      <td id=\"T_c34e6_row72_col0\" class=\"data row72 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row72_col1\" class=\"data row72 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row72_col2\" class=\"data row72 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row72_col3\" class=\"data row72 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row72_col4\" class=\"data row72 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row72_col5\" class=\"data row72 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row72_col6\" class=\"data row72 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row72_col7\" class=\"data row72 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row72_col8\" class=\"data row72 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row72_col9\" class=\"data row72 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row72_col10\" class=\"data row72 col10\" >26</td>\n",
       "      <td id=\"T_c34e6_row72_col11\" class=\"data row72 col11\" >52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row73\" class=\"row_heading level0 row73\" >1234639</th>\n",
       "      <td id=\"T_c34e6_row73_col0\" class=\"data row73 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row73_col1\" class=\"data row73 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row73_col2\" class=\"data row73 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row73_col3\" class=\"data row73 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row73_col4\" class=\"data row73 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row73_col5\" class=\"data row73 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row73_col6\" class=\"data row73 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row73_col7\" class=\"data row73 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row73_col8\" class=\"data row73 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row73_col9\" class=\"data row73 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row73_col10\" class=\"data row73 col10\" >38</td>\n",
       "      <td id=\"T_c34e6_row73_col11\" class=\"data row73 col11\" >76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row74\" class=\"row_heading level0 row74\" >1234640</th>\n",
       "      <td id=\"T_c34e6_row74_col0\" class=\"data row74 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row74_col1\" class=\"data row74 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row74_col2\" class=\"data row74 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row74_col3\" class=\"data row74 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row74_col4\" class=\"data row74 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row74_col5\" class=\"data row74 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row74_col6\" class=\"data row74 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row74_col7\" class=\"data row74 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row74_col8\" class=\"data row74 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row74_col9\" class=\"data row74 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row74_col10\" class=\"data row74 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row74_col11\" class=\"data row74 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row75\" class=\"row_heading level0 row75\" >1234641</th>\n",
       "      <td id=\"T_c34e6_row75_col0\" class=\"data row75 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row75_col1\" class=\"data row75 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row75_col2\" class=\"data row75 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row75_col3\" class=\"data row75 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row75_col4\" class=\"data row75 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row75_col5\" class=\"data row75 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row75_col6\" class=\"data row75 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row75_col7\" class=\"data row75 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row75_col8\" class=\"data row75 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row75_col9\" class=\"data row75 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row75_col10\" class=\"data row75 col10\" >19</td>\n",
       "      <td id=\"T_c34e6_row75_col11\" class=\"data row75 col11\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row76\" class=\"row_heading level0 row76\" >1234642</th>\n",
       "      <td id=\"T_c34e6_row76_col0\" class=\"data row76 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row76_col1\" class=\"data row76 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row76_col2\" class=\"data row76 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row76_col3\" class=\"data row76 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row76_col4\" class=\"data row76 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row76_col5\" class=\"data row76 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row76_col6\" class=\"data row76 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row76_col7\" class=\"data row76 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row76_col8\" class=\"data row76 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row76_col9\" class=\"data row76 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row76_col10\" class=\"data row76 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row76_col11\" class=\"data row76 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row77\" class=\"row_heading level0 row77\" >1234643</th>\n",
       "      <td id=\"T_c34e6_row77_col0\" class=\"data row77 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row77_col1\" class=\"data row77 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row77_col2\" class=\"data row77 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row77_col3\" class=\"data row77 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row77_col4\" class=\"data row77 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row77_col5\" class=\"data row77 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row77_col6\" class=\"data row77 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row77_col7\" class=\"data row77 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row77_col8\" class=\"data row77 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row77_col9\" class=\"data row77 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row77_col10\" class=\"data row77 col10\" >30</td>\n",
       "      <td id=\"T_c34e6_row77_col11\" class=\"data row77 col11\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row78\" class=\"row_heading level0 row78\" >1234644</th>\n",
       "      <td id=\"T_c34e6_row78_col0\" class=\"data row78 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row78_col1\" class=\"data row78 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row78_col2\" class=\"data row78 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row78_col3\" class=\"data row78 col3\" >0</td>\n",
       "      <td id=\"T_c34e6_row78_col4\" class=\"data row78 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row78_col5\" class=\"data row78 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row78_col6\" class=\"data row78 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row78_col7\" class=\"data row78 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row78_col8\" class=\"data row78 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row78_col9\" class=\"data row78 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row78_col10\" class=\"data row78 col10\" >20</td>\n",
       "      <td id=\"T_c34e6_row78_col11\" class=\"data row78 col11\" >40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row79\" class=\"row_heading level0 row79\" >1234645</th>\n",
       "      <td id=\"T_c34e6_row79_col0\" class=\"data row79 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row79_col1\" class=\"data row79 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row79_col2\" class=\"data row79 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row79_col3\" class=\"data row79 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row79_col4\" class=\"data row79 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row79_col5\" class=\"data row79 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row79_col6\" class=\"data row79 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row79_col7\" class=\"data row79 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row79_col8\" class=\"data row79 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row79_col9\" class=\"data row79 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row79_col10\" class=\"data row79 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row79_col11\" class=\"data row79 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row80\" class=\"row_heading level0 row80\" >1234646</th>\n",
       "      <td id=\"T_c34e6_row80_col0\" class=\"data row80 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row80_col1\" class=\"data row80 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row80_col2\" class=\"data row80 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row80_col3\" class=\"data row80 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row80_col4\" class=\"data row80 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row80_col5\" class=\"data row80 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row80_col6\" class=\"data row80 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row80_col7\" class=\"data row80 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row80_col8\" class=\"data row80 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row80_col9\" class=\"data row80 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row80_col10\" class=\"data row80 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row80_col11\" class=\"data row80 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row81\" class=\"row_heading level0 row81\" >1234647</th>\n",
       "      <td id=\"T_c34e6_row81_col0\" class=\"data row81 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row81_col1\" class=\"data row81 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row81_col2\" class=\"data row81 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row81_col3\" class=\"data row81 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row81_col4\" class=\"data row81 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row81_col5\" class=\"data row81 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row81_col6\" class=\"data row81 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row81_col7\" class=\"data row81 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row81_col8\" class=\"data row81 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row81_col9\" class=\"data row81 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row81_col10\" class=\"data row81 col10\" >29</td>\n",
       "      <td id=\"T_c34e6_row81_col11\" class=\"data row81 col11\" >58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row82\" class=\"row_heading level0 row82\" >1234648</th>\n",
       "      <td id=\"T_c34e6_row82_col0\" class=\"data row82 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row82_col1\" class=\"data row82 col1\" >0</td>\n",
       "      <td id=\"T_c34e6_row82_col2\" class=\"data row82 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row82_col3\" class=\"data row82 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row82_col4\" class=\"data row82 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row82_col5\" class=\"data row82 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row82_col6\" class=\"data row82 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row82_col7\" class=\"data row82 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row82_col8\" class=\"data row82 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row82_col9\" class=\"data row82 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row82_col10\" class=\"data row82 col10\" >23</td>\n",
       "      <td id=\"T_c34e6_row82_col11\" class=\"data row82 col11\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row83\" class=\"row_heading level0 row83\" >1234649</th>\n",
       "      <td id=\"T_c34e6_row83_col0\" class=\"data row83 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row83_col1\" class=\"data row83 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row83_col2\" class=\"data row83 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row83_col3\" class=\"data row83 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row83_col4\" class=\"data row83 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row83_col5\" class=\"data row83 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row83_col6\" class=\"data row83 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row83_col7\" class=\"data row83 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row83_col8\" class=\"data row83 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row83_col9\" class=\"data row83 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row83_col10\" class=\"data row83 col10\" >35</td>\n",
       "      <td id=\"T_c34e6_row83_col11\" class=\"data row83 col11\" >70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row84\" class=\"row_heading level0 row84\" >1234650</th>\n",
       "      <td id=\"T_c34e6_row84_col0\" class=\"data row84 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row84_col1\" class=\"data row84 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row84_col2\" class=\"data row84 col2\" >0</td>\n",
       "      <td id=\"T_c34e6_row84_col3\" class=\"data row84 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row84_col4\" class=\"data row84 col4\" >5</td>\n",
       "      <td id=\"T_c34e6_row84_col5\" class=\"data row84 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row84_col6\" class=\"data row84 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row84_col7\" class=\"data row84 col7\" >0</td>\n",
       "      <td id=\"T_c34e6_row84_col8\" class=\"data row84 col8\" >0</td>\n",
       "      <td id=\"T_c34e6_row84_col9\" class=\"data row84 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row84_col10\" class=\"data row84 col10\" >26</td>\n",
       "      <td id=\"T_c34e6_row84_col11\" class=\"data row84 col11\" >52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row85\" class=\"row_heading level0 row85\" >1234651</th>\n",
       "      <td id=\"T_c34e6_row85_col0\" class=\"data row85 col0\" >0</td>\n",
       "      <td id=\"T_c34e6_row85_col1\" class=\"data row85 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row85_col2\" class=\"data row85 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row85_col3\" class=\"data row85 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row85_col4\" class=\"data row85 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row85_col5\" class=\"data row85 col5\" >0</td>\n",
       "      <td id=\"T_c34e6_row85_col6\" class=\"data row85 col6\" >0</td>\n",
       "      <td id=\"T_c34e6_row85_col7\" class=\"data row85 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row85_col8\" class=\"data row85 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row85_col9\" class=\"data row85 col9\" >6</td>\n",
       "      <td id=\"T_c34e6_row85_col10\" class=\"data row85 col10\" >25</td>\n",
       "      <td id=\"T_c34e6_row85_col11\" class=\"data row85 col11\" >50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c34e6_level0_row86\" class=\"row_heading level0 row86\" >1234652</th>\n",
       "      <td id=\"T_c34e6_row86_col0\" class=\"data row86 col0\" >5</td>\n",
       "      <td id=\"T_c34e6_row86_col1\" class=\"data row86 col1\" >5</td>\n",
       "      <td id=\"T_c34e6_row86_col2\" class=\"data row86 col2\" >5</td>\n",
       "      <td id=\"T_c34e6_row86_col3\" class=\"data row86 col3\" >5</td>\n",
       "      <td id=\"T_c34e6_row86_col4\" class=\"data row86 col4\" >0</td>\n",
       "      <td id=\"T_c34e6_row86_col5\" class=\"data row86 col5\" >5</td>\n",
       "      <td id=\"T_c34e6_row86_col6\" class=\"data row86 col6\" >10</td>\n",
       "      <td id=\"T_c34e6_row86_col7\" class=\"data row86 col7\" >2</td>\n",
       "      <td id=\"T_c34e6_row86_col8\" class=\"data row86 col8\" >2</td>\n",
       "      <td id=\"T_c34e6_row86_col9\" class=\"data row86 col9\" >0</td>\n",
       "      <td id=\"T_c34e6_row86_col10\" class=\"data row86 col10\" >39</td>\n",
       "      <td id=\"T_c34e6_row86_col11\" class=\"data row86 col11\" >78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe1308d2350>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reweighted.style.applymap(pink_or_green)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "      <th>Q8</th>\n",
       "      <th>Q9</th>\n",
       "      <th>Q10</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>27.6</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Q1   Q2   Q3   Q4   Q5   Q6    Q7   Q8   Q9  Q10  Total  Percent\n",
       "mean    2.6  4.4  2.8  4.7  2.0  2.8   0.9  1.5  1.6  4.3   27.6     55.2\n",
       "median  5.0  5.0  5.0  5.0  0.0  5.0   0.0  2.0  2.0  6.0   28.0     56.0\n",
       "std     2.5  1.6  2.5  1.2  2.5  2.5   2.9  0.8  0.8  2.7    7.2     14.4\n",
       "min     0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0  0.0   12.0     24.0\n",
       "max     5.0  5.0  5.0  5.0  5.0  5.0  10.0  2.0  2.0  6.0   45.0     90.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Drop answer key (so you don't bias up the stats!)\n",
    "df_reweighted_nomaster = df_reweighted.drop(index=(\"Answer Key\"))\n",
    "\n",
    "# Compute basic stats\n",
    "df_stats = df_reweighted_nomaster.apply(stat_au_choix).round(1)\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to new excel file\n",
    "df_stats.to_excel(stats_out, sheet_name=\"Statistics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution by question and total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = len(df_reweighted_nomaster.columns)\n",
    "n = 3\n",
    "m = (k - 1) // n + 1\n",
    "fig, axes = plt.subplots(m, n, figsize=(n * 3, m * 2))\n",
    "for i, (key, series) in enumerate(df_reweighted_nomaster.iteritems()):\n",
    "    r, c = i // n, i % n\n",
    "    ax = axes[r, c]\n",
    "    series.hist(ax=ax)\n",
    "    ax2 = series.plot.kde(ax=ax, secondary_y=True, title=key)\n",
    "    ax2.set_ylim(0)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kolmogorov-Smirnov test\n",
    "\n",
    "Use default of null hypothesis that the empirical distribution is identical to a Normal distribution. The alternative is that they are not identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject null of normality!\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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test Statistic</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                value\n",
       "Test Statistic    1.0\n",
       "p-value           0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run K-S test against a normal distro\n",
    "result = stats.kstest(df_reweighted_nomaster[\"Total\"], \n",
    "                                                    stats.norm.cdf)\n",
    "\n",
    "# Make a dict of tuple, results, parse as pandas df\n",
    "val = dict(zip((\"Test Statistic\", \"p-value\"), result))\n",
    "df_ks = pd.DataFrame.from_dict(val, orient=\"index\", columns=[\"value\",])\n",
    "\n",
    "# Message\n",
    "if df_ks.loc[\"p-value\"].item() <= 0.05:\n",
    "    print(\"Reject null of normality!\")\n",
    "else:\n",
    "    print(\"Cannot reject null (at 95% confidence): You're normal like all of us!\")\n",
    "    \n",
    "# Show table\n",
    "df_ks"
   ]
  }
 ],
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   "language": "python",
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