Nonportmanteaus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "\n",
    "\n",
    "def fisher_exact(x, func=scipy.stats.fisher_exact):\n",
    "    return pd.Series(func(x), index=('odds', 'p'))\n",
    "\n",
    "\n",
    "def pearsonr(df, left, right, func=scipy.stats.pearsonr):\n",
    "    df = df[[left, right]].dropna()\n",
    "    name = f'{left} & {right}'\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter('ignore', scipy.stats.ConstantInputWarning)\n",
    "        with np.errstate(invalid='ignore'):\n",
    "            result = func(df[left], df[right])\n",
    "    return pd.Series(result, index=('r', 'p'), name=name)\n",
    "\n",
    "\n",
    "plt.style.use('classic')\n",
    "plt.rcParams.update({'figure.figsize': (6, 4), 'figure.facecolor': 'w',\n",
    "                     'figure.subplot.bottom': .125, 'font.size': 10, 'savefig.dpi': 72})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14773 entries, 0 to 14772\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Language    14773 non-null  object\n",
      " 1   Discharger  14773 non-null  object\n",
      " 2   Cell        14773 non-null  object\n",
      " 3   Form        7969 non-null   object\n",
      "dtypes: object(4)\n",
      "memory usage: 461.8+ KB\n"
     ]
    },
    {
     "data": {
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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>Language</th>\n",
       "      <th>Discharger</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s</td>\n",
       "      <td>ku</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1p</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>2s</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>2p</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>3s</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>3p</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>x</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;2s</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;2p</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;3s</td>\n",
       "      <td>ku</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language Discharger    Cell Form\n",
       "0     Ainu       None      1s   ku\n",
       "1     Ainu       None      1p   as\n",
       "2     Ainu       None      2s    e\n",
       "3     Ainu       None      2p  eci\n",
       "4     Ainu       None      3s  NaN\n",
       "5     Ainu       None      3p  NaN\n",
       "6     Ainu       None       x   an\n",
       "7     Ainu       None  1s->2s  eci\n",
       "8     Ainu       None  1s->2p  eci\n",
       "9     Ainu       None  1s->3s   ku"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV = 'nonportmanteau.csv'\n",
    "\n",
    "RENAME = {'Quechua (Ayacucho)': 'Ayacucho', 'Tlachichilco Tepehuan': 'Tepehua', 'Lakhota': 'Lakota'}\n",
    "\n",
    "\n",
    "tb = pd.read_csv(CSV, encoding='utf-8', na_values='', keep_default_na=False)\n",
    "\n",
    "tb['Language'] = tb['Language'].replace(RENAME)\n",
    "tb['Discharger'] = (tb['Discharger']\n",
    "                    .str.replace('Fifty', '50')\n",
    "                    .str.replace('Sixty', '60')\n",
    "                    .str.replace('Hundred', '/100')\n",
    "                    .str.replace('Discharger', ''))\n",
    "\n",
    "tb.info()\n",
    "assert tb.set_index(['Language', 'Discharger', 'Cell']).index.is_unique\n",
    "tb.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Form</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>1133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intrans</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>1343</td>\n",
       "      <td>1343</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Language  Cell  Form\n",
       "Discharger                             \n",
       "All50/100              1343  1343   350\n",
       "All60                  1343  1343   143\n",
       "All60/100              1343  1343   496\n",
       "AllIntransPerfect      1343  1343   615\n",
       "AllPerfect             1343  1343  1050\n",
       "Free50/100             1343  1343   589\n",
       "Free60                 1343  1343   434\n",
       "Free60/100             1343  1343   828\n",
       "FreePerfect            1343  1343  1133\n",
       "Intrans                1343  1343  1014\n",
       "None                   1343  1343  1317"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb.groupby('Discharger').count()"
   ]
  },
  {
   "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>Language</th>\n",
       "      <th>Discharger</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Local</th>\n",
       "      <th>Direct</th>\n",
       "      <th>Form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>ku</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1p</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>2s</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>2p</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>3s</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>3p</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>x</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;2s</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;2p</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>eci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>None</td>\n",
       "      <td>1s-&gt;3s</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>ku</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language Discharger    Cell  Trans  Local  Direct Form\n",
       "0     Ainu       None      1s  False  False    True   ku\n",
       "1     Ainu       None      1p  False  False    True   as\n",
       "2     Ainu       None      2s  False  False    True    e\n",
       "3     Ainu       None      2p  False  False    True  eci\n",
       "4     Ainu       None      3s  False  False    True  NaN\n",
       "5     Ainu       None      3p  False  False    True  NaN\n",
       "6     Ainu       None       x  False  False    True   an\n",
       "7     Ainu       None  1s->2s   True   True    True  eci\n",
       "8     Ainu       None  1s->2p   True   True    True  eci\n",
       "9     Ainu       None  1s->3s   True  False    True   ku"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "SEP = '->'\n",
    "\n",
    "\n",
    "def is_local(cellkey, sep=SEP, persons=('1', '2')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    return any(p in subj for p in persons) and any(p in obj for p in persons)\n",
    "\n",
    "\n",
    "def is_inverse(cellkey, sep=SEP, hierarchy=('1', '2', '3', '4', 'x')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    for high, low in itertools.combinations(hierarchy, 2):\n",
    "        if low in subj and high in obj:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "\n",
    "tb.insert(3, 'Trans', tb['Cell'].str.contains(SEP))\n",
    "tb.insert(4, 'Local', tb['Cell'].map(is_local))\n",
    "tb.insert(5, 'Direct', ~tb['Cell'].map(is_inverse))\n",
    "\n",
    "tb.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Intrans</td>\n",
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       "      <th>2</th>\n",
       "      <td>FreePerfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Free60/100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free50/100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Free60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AllPerfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AllIntransPerfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>All60/100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>All50/100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>All60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Discharger\n",
       "0                None\n",
       "1             Intrans\n",
       "2         FreePerfect\n",
       "3          Free60/100\n",
       "4          Free50/100\n",
       "5              Free60\n",
       "6          AllPerfect\n",
       "7   AllIntransPerfect\n",
       "8           All60/100\n",
       "9           All50/100\n",
       "10              All60"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc = tb['Discharger'].unique()\n",
    "\n",
    "pd.DataFrame(disc, columns=['Discharger'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intrans</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FreePerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free50/100</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllIntransPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All50/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>...</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>47</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>91</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>79</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger   None       Intrans       FreePerfect       Free60/100        \\\n",
       "Local       False True    False True        False True       False True    \n",
       "Language                                                                   \n",
       "Ainu           29     8      19     4          25     6         19     6   \n",
       "Aleut          45    18      27     0          39    18         26    15   \n",
       "Ayacucho       24     8      10     8          19     8          9     6   \n",
       "Bella Coola    20     8      15     6          18     8          3     5   \n",
       "Chuckchi       19     8      19     4          19     8          5     6   \n",
       "Darai          20     8      14     2          16     2         16     2   \n",
       "Fox            32     8      32     8          32     8         30     8   \n",
       "Hixkaryana     22     6       8     2          20     6         16     4   \n",
       "Jaqaru          7     3       3     3           6     3          4     3   \n",
       "Jumjum         28     8      18     8          24     8         22     8   \n",
       "Karuk          20     8       9     6          19     7         15     5   \n",
       "Ket            84     8      74     8          76     8         52     8   \n",
       "Kunama         57    18      47    16          52    18         42    12   \n",
       "Lakota         23     8      13     6          18     7          0     2   \n",
       "Maricopa       16     8       4     8          16     8          4     4   \n",
       "Maung          96     8      80     8          96     8         91     7   \n",
       "Mordvin        20     8      20     7          20     8         11     2   \n",
       "Nocte          20     8      12     8          18     4         12     2   \n",
       "Reyesano       20     8       0     0          16     0          0     0   \n",
       "Sahu           96     8      79     6          54     5         52     5   \n",
       "Siuslawan      57    18      57    18          53    18         53    18   \n",
       "Tepehua        23     8      17     8          22     8         14     8   \n",
       "Thangmi        20     8      10     2          18     6          6     1   \n",
       "Turkana        20     8       2     2          20     8         14     8   \n",
       "Wardaman       27     8      20     8          23     8         20     8   \n",
       "Yimas          45    18      45    18          45    18         45    18   \n",
       "\n",
       "Discharger  Free50/100        ... AllPerfect       AllIntransPerfect        \\\n",
       "Local            False True   ...      False True              False True    \n",
       "Language                      ...                                            \n",
       "Ainu                 9     0  ...         25     6                15     0   \n",
       "Aleut               20     6  ...         39    18                27     0   \n",
       "Ayacucho             9     6  ...         19     7                 4     6   \n",
       "Bella Coola          3     5  ...         18     8                14     5   \n",
       "Chuckchi             5     6  ...         19     8                19     4   \n",
       "Darai               16     2  ...         14     2                14     2   \n",
       "Fox                 24     8  ...         32     8                32     8   \n",
       "Hixkaryana          16     4  ...         20     6                 8     2   \n",
       "Jaqaru               4     3  ...          4     3                 2     0   \n",
       "Jumjum              22     8  ...         24     8                16     8   \n",
       "Karuk               11     5  ...         19     7                 8     5   \n",
       "Ket                 52     8  ...         64     0                10     0   \n",
       "Kunama              28     4  ...         32     8                12     4   \n",
       "Lakota               0     2  ...         18     7                 4     4   \n",
       "Maricopa             0     0  ...         16     8                 4     8   \n",
       "Maung               63     1  ...         95     8                80     8   \n",
       "Mordvin             11     2  ...         14     5                18     7   \n",
       "Nocte               12     2  ...         18     4                 8     4   \n",
       "Reyesano             0     0  ...         16     0                 0     0   \n",
       "Sahu                36     5  ...         54     5                33     0   \n",
       "Siuslawan            6     2  ...         47    11                57    18   \n",
       "Tepehua             10     8  ...         22     8                17     8   \n",
       "Thangmi              6     1  ...         18     6                 8     0   \n",
       "Turkana              6     2  ...         20     8                 2     2   \n",
       "Wardaman            15     7  ...         21     8                14     8   \n",
       "Yimas               45    18  ...         43    18                39    11   \n",
       "\n",
       "Discharger  All60/100       All50/100       All60        \n",
       "Local           False True      False True  False True   \n",
       "Language                                                 \n",
       "Ainu               19     6         9     0     0     0  \n",
       "Aleut              26    15        20     6    14     3  \n",
       "Ayacucho            4     4         4     4     0     4  \n",
       "Bella Coola         3     5         3     5     1     0  \n",
       "Chuckchi            5     6         5     6     3     4  \n",
       "Darai               0     0         0     0     0     0  \n",
       "Fox                14     8         0     8     0     0  \n",
       "Hixkaryana         16     4        16     4     8     0  \n",
       "Jaqaru              1     1         1     1     1     1  \n",
       "Jumjum             12     5        12     5     5     2  \n",
       "Karuk              14     4         7     2     2     0  \n",
       "Ket                20     0        10     0    10     0  \n",
       "Kunama             30     6        28     4    27     0  \n",
       "Lakota              0     2         0     2     0     0  \n",
       "Maricopa            4     4         0     0     0     0  \n",
       "Maung              22     0        10     0    20     0  \n",
       "Mordvin            10     2        10     2     8     1  \n",
       "Nocte              12     2        12     2     6     2  \n",
       "Reyesano            0     0         0     0     0     0  \n",
       "Sahu               52     5        36     5     0     0  \n",
       "Siuslawan           0     0         0     0     0     0  \n",
       "Tepehua            14     8        10     8     0     6  \n",
       "Thangmi             6     1         4     1     0     1  \n",
       "Turkana             4     0         4     0     4     0  \n",
       "Wardaman            6     4         4     2     1     0  \n",
       "Yimas              34    16        30     9     3     6  \n",
       "\n",
       "[26 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff = (tb[tb['Trans']]\n",
    "      .pivot_table('Form', 'Language', ['Discharger', 'Local'],\n",
    "                   aggfunc='count', fill_value=0))\n",
    "\n",
    "ff[disc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>890</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intrans</th>\n",
       "      <td>654</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>784</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>581</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>429</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>340</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>731</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>465</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>328</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>235</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>113</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local              False  True \n",
       "Discharger                     \n",
       "None                 890    241\n",
       "Intrans              654    174\n",
       "FreePerfect          784    214\n",
       "Free60/100           581    171\n",
       "Free50/100           429    115\n",
       "Free60               340     94\n",
       "AllPerfect           731    185\n",
       "AllIntransPerfect    465    122\n",
       "All60/100            328    108\n",
       "All50/100            235     76\n",
       "All60                113     30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff.sum().unstack('Local').loc[disc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intrans</th>\n",
       "      <td>64.860601</td>\n",
       "      <td>72.489316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>89.592801</td>\n",
       "      <td>87.019231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>58.145222</td>\n",
       "      <td>68.429487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>45.055730</td>\n",
       "      <td>50.320513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>34.662631</td>\n",
       "      <td>39.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>84.155698</td>\n",
       "      <td>77.617521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>50.613743</td>\n",
       "      <td>50.160256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>35.464207</td>\n",
       "      <td>43.482906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>26.239827</td>\n",
       "      <td>32.905983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>11.957076</td>\n",
       "      <td>12.820513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local                   False       True \n",
       "Discharger                               \n",
       "None               100.000000  100.000000\n",
       "Intrans             64.860601   72.489316\n",
       "FreePerfect         89.592801   87.019231\n",
       "Free60/100          58.145222   68.429487\n",
       "Free50/100          45.055730   50.320513\n",
       "Free60              34.662631   39.583333\n",
       "AllPerfect          84.155698   77.617521\n",
       "AllIntransPerfect   50.613743   50.160256\n",
       "All60/100           35.464207   43.482906\n",
       "All50/100           26.239827   32.905983\n",
       "All60               11.957076   12.820513"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = 100 * ff / ff['None']\n",
    "\n",
    "nf = df / df.groupby(axis=1, level='Discharger').sum()\n",
    "\n",
    "xf = df.join(nf, rsuffix=' (norm)')\n",
    "\n",
    "df.mean().unstack('Local').loc[disc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intrans</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FreePerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free50/100</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllIntransPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All50/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
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       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
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       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
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       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>50</td>\n",
       "      <td>86</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>75</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>100</td>\n",
       "      <td>58</td>\n",
       "      <td>83</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>83</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>100</td>\n",
       "      <td>38</td>\n",
       "      <td>75</td>\n",
       "      <td>38</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>26</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>26</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>25</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>44</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>57</td>\n",
       "      <td>100</td>\n",
       "      <td>57</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>100</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>57</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "      <td>62</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>82</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>44</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>53</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>57</td>\n",
       "      <td>75</td>\n",
       "      <td>78</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>56</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>62</td>\n",
       "      <td>38</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>62</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>62</td>\n",
       "      <td>38</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>100</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>76</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger   None       Intrans       FreePerfect       Free60/100        \\\n",
       "Local       False True    False True        False True       False True    \n",
       "Language                                                                   \n",
       "Ainu          100   100      66    50          86    75         66    75   \n",
       "Aleut         100   100      60     0          87   100         58    83   \n",
       "Ayacucho      100   100      42   100          79   100         38    75   \n",
       "Bella Coola   100   100      75    75          90   100         15    62   \n",
       "Chuckchi      100   100     100    50         100   100         26    75   \n",
       "Darai         100   100      70    25          80    25         80    25   \n",
       "Fox           100   100     100   100         100   100         94   100   \n",
       "Hixkaryana    100   100      36    33          91   100         73    67   \n",
       "Jaqaru        100   100      43   100          86   100         57   100   \n",
       "Jumjum        100   100      64   100          86   100         79   100   \n",
       "Karuk         100   100      45    75          95    88         75    62   \n",
       "Ket           100   100      88   100          90   100         62   100   \n",
       "Kunama        100   100      82    89          91   100         74    67   \n",
       "Lakota        100   100      57    75          78    88          0    25   \n",
       "Maricopa      100   100      25   100         100   100         25    50   \n",
       "Maung         100   100      83   100         100   100         95    88   \n",
       "Mordvin       100   100     100    88         100   100         55    25   \n",
       "Nocte         100   100      60   100          90    50         60    25   \n",
       "Reyesano      100   100       0     0          80     0          0     0   \n",
       "Sahu          100   100      82    75          56    62         54    62   \n",
       "Siuslawan     100   100     100   100          93   100         93   100   \n",
       "Tepehua       100   100      74   100          96   100         61   100   \n",
       "Thangmi       100   100      50    25          90    75         30    12   \n",
       "Turkana       100   100      10    25         100   100         70   100   \n",
       "Wardaman      100   100      74   100          85   100         74   100   \n",
       "Yimas         100   100     100   100         100   100        100   100   \n",
       "\n",
       "Discharger  Free50/100        ... AllPerfect       AllIntransPerfect        \\\n",
       "Local            False True   ...      False True              False True    \n",
       "Language                      ...                                            \n",
       "Ainu                31     0  ...         86    75                52     0   \n",
       "Aleut               44    33  ...         87   100                60     0   \n",
       "Ayacucho            38    75  ...         79    88                17    75   \n",
       "Bella Coola         15    62  ...         90   100                70    62   \n",
       "Chuckchi            26    75  ...        100   100               100    50   \n",
       "Darai               80    25  ...         70    25                70    25   \n",
       "Fox                 75   100  ...        100   100               100   100   \n",
       "Hixkaryana          73    67  ...         91   100                36    33   \n",
       "Jaqaru              57   100  ...         57   100                29     0   \n",
       "Jumjum              79   100  ...         86   100                57   100   \n",
       "Karuk               55    62  ...         95    88                40    62   \n",
       "Ket                 62   100  ...         76     0                12     0   \n",
       "Kunama              49    22  ...         56    44                21    22   \n",
       "Lakota               0    25  ...         78    88                17    50   \n",
       "Maricopa             0     0  ...        100   100                25   100   \n",
       "Maung               66    12  ...         99   100                83   100   \n",
       "Mordvin             55    25  ...         70    62                90    88   \n",
       "Nocte               60    25  ...         90    50                40    50   \n",
       "Reyesano             0     0  ...         80     0                 0     0   \n",
       "Sahu                38    62  ...         56    62                34     0   \n",
       "Siuslawan           11    11  ...         82    61               100   100   \n",
       "Tepehua             43   100  ...         96   100                74   100   \n",
       "Thangmi             30    12  ...         90    75                40     0   \n",
       "Turkana             30    25  ...        100   100                10    25   \n",
       "Wardaman            56    88  ...         78   100                52   100   \n",
       "Yimas              100   100  ...         96   100                87    61   \n",
       "\n",
       "Discharger  All60/100       All50/100       All60        \n",
       "Local           False True      False True  False True   \n",
       "Language                                                 \n",
       "Ainu               66    75        31     0     0     0  \n",
       "Aleut              58    83        44    33    31    17  \n",
       "Ayacucho           17    50        17    50     0    50  \n",
       "Bella Coola        15    62        15    62     5     0  \n",
       "Chuckchi           26    75        26    75    16    50  \n",
       "Darai               0     0         0     0     0     0  \n",
       "Fox                44   100         0   100     0     0  \n",
       "Hixkaryana         73    67        73    67    36     0  \n",
       "Jaqaru             14    33        14    33    14    33  \n",
       "Jumjum             43    62        43    62    18    25  \n",
       "Karuk              70    50        35    25    10     0  \n",
       "Ket                24     0        12     0    12     0  \n",
       "Kunama             53    33        49    22    47     0  \n",
       "Lakota              0    25         0    25     0     0  \n",
       "Maricopa           25    50         0     0     0     0  \n",
       "Maung              23     0        10     0    21     0  \n",
       "Mordvin            50    25        50    25    40    12  \n",
       "Nocte              60    25        60    25    30    25  \n",
       "Reyesano            0     0         0     0     0     0  \n",
       "Sahu               54    62        38    62     0     0  \n",
       "Siuslawan           0     0         0     0     0     0  \n",
       "Tepehua            61   100        43   100     0    75  \n",
       "Thangmi            30    12        20    12     0    12  \n",
       "Turkana            20     0        20     0    20     0  \n",
       "Wardaman           22    50        15    25     4     0  \n",
       "Yimas              76    89        67    50     7    33  \n",
       "\n",
       "[26 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.round().astype(int)[disc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xf.sort_values(by=('Intrans (norm)', True), kind='mergesort')['Intrans']\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xf.sort_values(by=('AllPerfect (norm)', True), kind='mergesort')['AllPerfect']\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xf.sort_values(by=('All60/100 (norm)', True), kind='mergesort')['All60/100']\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xf.sort_values(by=('All50/100 (norm)', True), kind='mergesort')['All50/100']\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8K8vPvx8FMQAAAACYwMvLS76+vvL39zc7SpHn6+srLy+vv/11FMQAAAAAYAJX7jd8q7vZ/ZQpiAEAAADAJOw3bC6aagEAAAAA3BIFMQAAAADALVEQAwAAAADcEgUxAAAAAMAt5bkgTklJ0ZAhQ1SjRg3Vq1dPvXr1kiTFxsaqWbNmCg4OVlhYmH7//fdCCwsAAAAAQEHJc5fpV155RTabTfv375fNZlNCQoIkaeDAgRowYICioqK0ePFiRUVFKSYmptACAwAAAABQEPJUEP/111+aPXu2jh07JpvNJunKxsenTp3Sli1b9PPPP0uSunbtqiFDhujAgQOqXr164aUGAAAAACCf8jRl+uDBgypXrpwmTpyoRo0a6b777tOKFSsUHx+vSpUqydPzSl1ts9kUEBCgo0ePFmpoAAAAAADyK08jxGlpaTpy5Ihq166tt956S9u3b1e7du30/fff/+0Ljhw5Ul5eXpKkyMhIRUZG/u1zAAAAAACQk2XLlmnZsmWSJIfDcd3PzVNBHBAQIA8PDz3xxBOSpNDQUFWtWlVHjhzRyZMnlZaWJk9PTxmGoaNHjyogICDXc02cOFE+Pj55/V4AAAAAAMizrAOvycnJmjFjRq6fm6cp0xUqVFCbNm2cVXZcXJzi4uJ07733qkGDBoqOjpYkLVmyRH5+fqwfBgAAAABYXp67TM+cOVP9+vXTiBEj5OHhoVmzZqlKlSqaNWuWoqKinCO/c+fOLcy8AAAAAAAUiDwXxNWqVdOqVauuOV6zZk1t2LChQEMBAAAAAFDY8jRlGgAAAACAWw0FMQAAAADALVEQAwAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC1REAMAAAAA3BIFMQAAAADALVEQAwAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC1REAMAAAAA3BIFMQAAAADALXmaHQAAYF12u10OhyPf5/Hy8pK3t3cBJAIAACg4FMQAgBzZ7XZVrVpVCQkJ+T6Xr6+v4uLiKIoBAIClUBADAHLkcDiUkJCg+Ph4+fj43PR5kpOT5e/vL4fDQUEMAAAshYIYAHBdPj4++SqIAQAArIqmWgAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEl2kAbs9ut8vhcOT7PF5eXmwrBAAAUIRQEANwa3a7XVWrVlVCQkK+z+Xr66u4uDiKYgAAgCKCghiAW3M4HEpISFB8fHy+9tpNTk6Wv7+/HA4HBTEAAEARQUEMAJJ8fHzyVRADAACg6KGpFgAAAADALVEQAwAAAADcUp4L4qCgINWsWVMhISEKCQnRokWLJEmxsbFq1qyZgoODFRYWpt9//73QwgIAAAAAUFD+1hriRYsWKSQkJNuxgQMHasCAAYqKitLixYsVFRWlmJiYgswIAAAAAECBy9eU6VOnTmnLli3q1auXJKlr166Kj4/XgQMHCiQcAAAAAACF5W8VxH369FG9evXUr18/nT59WvHx8apUqZI8Pa8MNNtsNgUEBOjo0aOFEhYAAAAAgIKS5ynTa9euVUBAgFJTUzVq1Cj17dtX48aNK8xsAAAABcJut8vhcOT7PF5eXuw1DgC3kDwXxAEBAZKk4sWL67nnnlNwcLD8/f118uRJpaWlydPTU4Zh6OjRo87PzcnIkSPl5eUlSYqMjFRkZGQ+vwUAAIDc2e12Va1aVQkJCfk+l6+vr+Li4iiKAcDCli1bpmXLlknSDW+G5qkg/uuvv5SamqoyZcpIkhYuXKjQ0FDdddddatCggaKjoxUVFaUlS5bIz89P1atXz/VcEydOlI+PTx6/FQAAgPxxOBxKSEhQfHx8vt6DJCcny9/fXw6Hg4IYACws68BrcnKyZsyYkevn5qkg/vPPP9W1a1elp6fLMAxVq1ZN8+fPlyTNmjVLUVFRzkJ37ty5BfAtAAAAFCwfHx9uygMAsslTQVytWjVt3749x8dq1qypDRs2FGgoAAAAAAAKW762XQIAAAAAoKiiIAYAAAAAuCUKYgAAAACAW8rztksAgMLHXqkAAACuQ0EMABbBXqlFDzcwAAAo2iiIccsoiDemvCmFmdgrtWjhBgYAAEUfBTFuCXa7XVWqVFFiYmK+zlOuXDkdP36cN6UwFXulFg3cwAAAoOijIMYtweFw5LsYlqTExETelAL4W7iBAQBA0UWXaQAAAACAW6IgBgAAAAC4JQpiAAAAAIBboiAGAAAAALglCmIAAAAAgFuiIAYAAAAAuCUKYgAAAACAW6IgBgAAAAC4JQpiAAAAAIBboiAGAAAAALglCmIAAAAAgFuiIAYAAAAAuCUKYgAAAACAW6IgBgAAAAC4JQpiAAAAAIBb8jQ7AAAAeWW32+VwOPJ9Hi8vL3l7exdAIgAAUJRREAMAigS73a6qVasqISEh3+fy9fVVXFwcRTEAAG6OghgAUCQ4HA4lJCQoPj5ePj4+N32e5ORk+fv7y+FwUBADAODmKIgBAEWKj49PvgpiAACATDTVAgAAAAC4JQpiAAAAAIBboiAGAAAAALilv10Qz507VzabTUuXLpUknTp1Sh06dFCNGjVUt25drV27tqAzAgAAAABQ4P5WQXz48GF99NFHCg8Pdx575ZVXFB4ertjYWM2dO1c9e/ZUampqgQcFAAAAAKAg5bkgzsjI0NNPP63p06erRIkSzuNffPGFBg0aJEkKCwtT5cqVtWbNmoJPCgAAAABAAcpzQTxlyhTde++9atiwofPY2bNnlZqaKl9fX+exoKAgHT16tGBTAgAAAABQwPK0D/Hu3bu1ZMkS1gcDuGUlJyeb+vXArcZut8vhcOT7PF5eXvL29i6ARAAAXCtPBfH//vc/HT58WDVq1JAkJSQkaMCAARo7dqw8PT2VkJDgHCU+fPiwAgICcj3XyJEj5eXlJUmKjIxUZGRkfr8HALhpGRkZKlGihPz9/fN9rhIlSigjI6MAUgFFm91uV9WqVZWQkJDvc/n6+iouLo6iGACQZ8uWLdOyZcsk6YY3Z/NUEA8ePFiDBw92/r1ly5Z67rnn1LlzZ23atEkzZ87UmDFjFBMTo+PHjysiIiLXc02cOFE+Pj55uSwAFDoPDw+lpKQUyLlSUlLk4cFudoDD4VBCQoLi4+Pz9ZqfnJwsf39/ORwOCmIAQJ5lHXhNTk7WjBkzcv3cPBXE1/P222+rd+/eqlGjhry8vBQdHa3ixYvn97QAAKCI8/Hx4SY4AMDSbqogXr16tfPjihUr6ueffy6oPAAAAAAAuARz+wAAAAAAbomCGAAAAADgliiIAQAAAABuiYIYAAAAAOCWKIgBAAAAAG6JghgAAAAA4JYoiAEAAAAAbomCGAAAAADgliiIAQAAAABuydPsAFZmt9vlcDjyfR4vLy95e3sXQCIAAAAAQEGhIM6F3W5XlSpVlJiYmO9zlStXTsePH6coBgAAAAALYcp0LhwOR4EUw5KUmJhYICPNAAAAAICCQ0EMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwS55mBwAAZJecnGzq1wMAALiLPBfE7du3V0JCgjw8PFSqVCm99957Cg0NVWxsrPr27aszZ86odOnSmjdvnurUqVOYmQHglpSRkaESJUrI398/3+cqUaKEMjIyCiAVAADArSvPU6a/+OIL7dq1Szt27NALL7ygqKgoSdLAgQM1YMAA7d+/XyNGjHAeBwD8PR4eHkpJSSmQc6WkpMjDg1UxAAAA15Pnd0tlypRxfnz+/HnZbDadOnVKW7ZsUa9evSRJXbt2VXx8vA4cOFDgQQEAAAAAKEh/aw1xnz59tGrVKknSDz/8oPj4eFWqVEmenldOY7PZFBAQoKNHj6p69eoFnxYA4HKsaQYAALeqv1UQz58/X5L0ySefaMSIERo3btzfvuDIkSPl5eUlSYqMjFRkZOTfPgcAoPCxphkAABRFy5Yt07JlyyRJDofjup97U12m+/btq0GDBsnPz08nT55UWlqaPD09ZRiGjh49qoCAgFy/duLEifLx8bmZywIAXIg1zQAAoCjKOvCanJysGTNm5Pq5eXp3cu7cOZ04ccL596VLl6p8+fK666671KBBA0VHR0uSlixZIj8/P6ZLAwAAAAAsL08jxOfPn9djjz2my5cvy8PDQ3feeae+++472Ww2zZo1S1FRUc6R37lz5xZ2ZgAAAAAA8i1PBXFgYKA2b96c42M1a9bUhg0bCjQUAAAAAACFjQVdAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt0RBDAAAAABwSxTEAAAAAAC3REEMAAAAAHBLFMQAAAAAALdEQQwAAAAAcEsUxAAAAAAAt5Sngthut6tz584KDg5W/fr11a5dOx04cECSdOrUKXXo0EE1atRQ3bp1tXbt2kINDAAAAABAQfDM6ycOGDBAHTt2lM1m0/vvv6+nn35aq1ev1iuvvKLw8HD99NNPiomJUZcuXRQXF6fixYsXZm7A0ux2uxwOR77P4+XlJW9v7wJIBAAAAOBqeSqIvb29df/99zv/Hh4erkmTJkmSvvjiC+docVhYmCpXrqw1a9aobdu2hRAXsD673a4qVaooMTEx3+cqV66cjh8/TlEMAAAAFIKbWkM8bdo0derUSWfPnlVqaqp8fX2djwUFBeno0aMFFhAoahwOR4EUw5KUmJhYICPNAAAAAK6V5ynTmSZOnKgDBw5oxYoVunz5cmFkAlDAmMINAAAAXOtvFcSTJk3SV199peXLl+v222/X7bffLk9PTyUkJDhHiQ8fPqyAgIBczzFy5Eh5eXlJkiIjIxUZGZmP+ABuhCncAAAAcCfLli3TsmXLJOmGg0J5LoinTJmihQsXavny5SpTpozz+GOPPaaZM2dqzJgxiomJ0fHjxxUREZHreSZOnCgfH5+8XhZAPhXGFG4KYgAAAFhV1oHX5ORkzZgxI9fPzVNBfOzYMQ0fPlzVqlVTq1atJEklSpTQpk2b9Pbbb6t3796qUaOGvLy8FB0dTYdpAAAAAIDl5akg9vPzk2EYOT5WsWJF/fzzzwUaCgAAAACAwnZTXaYBAAAAACjqKIgBAAAAAG6JghgAAAAA4Jb+9j7EAACYKTk52dSvBwAAtw4KYgBAkZCRkaESJUrI398/3+cqUaKEMjIyCiAVAAAoypgyDQAoEjw8PJSSklIg50pJSZGHBy+BAAC4O94NAAAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC1REAMAAAAA3BLbLgEAcAvJzz7L7NEMAHA3FMQAANwCCmqfZvZoBgC4E6ZMAwBwCyiofZrZoxkA4E54xQMAAAAAuCUKYgAAAACAW6IgBgAAAAC4JZpqAQCQD/ntzExnZwAAzENBDADATSiors4SnZ0BADALU6YBALgJBdXVWaKzMwAAZuHVFwAAAADgliiIAQAAAABuiYIYAAAAAOCWKIgBAAAAAG6JLtMAAABuzm63y+Fw5OscXl5e8vb2LqBEAOAaFMQAAABuzG63q2rVqkpISMjXeXx9fRUXF0dRDKBIoSDGTSuIu8kSd5QBADCTw+FQQkKC4uPj5ePjc1PnSE5Olr+/vxwOB6/pAIoUCmLcFLvdripVqigxMTHf5ypXrpyOHz/OCygAACby8fG56YIYAIoqmmrhpjgcjgIphiUpMTGxQEaaAQAAAODvoCAGAAAAALilPBXEQ4cOVVBQkGw2m3bs2OE8Hhsbq2bNmik4OFhhYWH6/fffCysnAAAAAAAFKk8F8aOPPqp169YpMDAw2/GBAwdqwIAB2r9/v0aMGKGoqKjCyAgAAAAAQIHLU0HcokUL+fn5ZTt26tQpbdmyRb169ZIkde3aVfHx8Tpw4EDBpwQAAAAAoIDd9Bri+Ph4VapUSZ6eVxpV22w2BQQE6OjRowUWDgAAAACAwsK2S0UI+/4CAAAAQMG56YLY399fJ0+eVFpamjw9PWUYho4ePaqAgIDrft3IkSPl5eUlSYqMjFRkZOTNRnAr7PsLAAAAADe2bNkyLVu2TJJuOKB40wXxXXfdpQYNGig6OlpRUVFasmSJ/Pz8VL169et+3cSJE9n0/SYUxr6/FMQAAAAAbjVZB16Tk5M1Y8aMXD83T2uIBw4cKD8/Px07dkyRkZHOonfWrFmaNWuWgoOD9dZbb2nu3LkFEB8AAAAAgMKXpxHiWbNm5Xi8Zs2a2rBhQ4EGAgAAAADAFW66yzQAAAAAAEUZBTEAAAAAwC1REAMAAAAA3BIFMQAAAADALVEQAwAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC15mh0AAADcmpKTk039egAAboSCGAAAFKiMjAyVKFFC/v7++T5XiRIllJGRUQCpAAC4FlOmAQBAgfLw8FBKSkqBnCslJUUeHrxdAQAUDl5hAAAAAABuiYIYAAAAAOCWKIgBAAAAAG7Jck217Ha7HA5Hvs7h5eUlb2/vAkoEAAAAALgVWaogttvtqlKlihITE/N1nnLlyun48eMUxQAAAACAXFlqyrTD4ch3MSxJiYmJ+R5lBgAAAADc2ixVEAMAAAAA4CoUxAAAAAAAt2SpNcQA3ENBNM+TaKAHAACA/KEgBuBSBdU8T6KBHgAAAPKHKdMAXKqgmudJNNADAABA/lAQAwAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC1REAMAAAAA3BIFMQAAAADALVEQAwAAAADcEgUxAAAAAMAtFUhBHBsbq2bNmik4OFhhYWH6/fffC+K0AAAAAAAUmgIpiAcOHKgBAwZo//79GjFihKKiogritAAAAC6zbNkysyNkQ57cWSmLRJ4bsVIeK2WRyHMjrsiT74L41KlT2rJli3r16iVJ6tq1q+Lj43XgwIF8hwMAAHAVd3wj+HdYKY+VskjkuREr5bFSFok8N1IkCuL4+HhVqlRJnp6ekiSbzaaAgAAdPXo03+EAAAAAACgsnq66kGEYkqTk5ORcP+d6j/1d+T1XQWYpiPORp3C/viDPdSv/2xTE+chTOF9bGOcjT+F+fUGfj+fBG3/98ePHr3uuCxcu6NixY9d9nDw55ymoLJnnyDxfbhITE/XHH39c93NKlSolHx8f8rhRHitlIY918mQ+L2XWo1ezGbk9kkenTp1S9erVlZiYKE9PTxmGoUqVKmndunWqXr268/OOHTsmf3///FwKAAAAAIC/LT4+Xn5+ftccz/cI8V133aUGDRooOjpaUVFRWrJkifz8/LIVw5JUuXJlxcfHq1SpUrLZbPm9LAAAAAAA12UYhi5cuKDKlSvn+Hi+R4glad++fYqKitLZs2fl4+OjuXPnql69evk9LQAAAAAAhaZACmIAAAAAAIqaAtmHGAAAAACAooaCGAAAAADglly27VJ+nDhxQrt375bdbncee/jhh01MBBR9p06d0uuvv66dO3dm+93atm2biamAv+/HH39UbGys0tLSnMdeeOEFU7JUrVo1x8aRhw4dMiGN9cTHx6tixYry8vLSr7/+qu3bt6tv374qVaqUqbkyV4+Z3fTz8uXLmj59unbs2JHtefmrr75yaY6IiAitWbNGZcuWzfZvYhiGbDabEhMTXZoHAAqT5QviOXPm6I033lBiYqJq1KihnTt3Kjw8nILYwp555hn95z//ueExmKtfv35q3ry5VqxYocmTJ2vWrFkKDQ01NdOmTZt08ODBbIVNnz59TExkDZ07d9bSpUtveMwdPfHEE9qzZ49CQ0NVrFgxSeYWNd99953zY7vdrk8//VTly5c3JYsVi5pOnTpp/fr1On78uLp3767mzZtrzZo1+vLLL12eRZJOnjypfv36aeXKlbLZbGrTpo0++ugjVapUyZQ8/fv3l4+Pj9avX6/hw4dr3rx5atGihctzfP7555KkHTt2uPzaufHw8Mjxdzs9Pd2ENNYUGxurGjVqZDu2YcMGNW3a1KREKCrWrl2b43Eznn+kK8/NcXFx2d4PFmYWyzfVqlevntauXavWrVtr+/btWrt2rebNm6c5c+aYmstKo9bz5s1TVFRUtmMjRozQ22+/bUqeBg0aXDPKGBISYuoL6xdffHHNHfcpU6a4PEdOo0dlypRR06ZNNW7cOJUrV85lWTL/T+rVq6fffvtNDodDERER2rBhg8syZDV48GAtW7ZMISEh2QqbL774wpQ8knT48GG9/fbb1xTpK1eudGmOnH6n7rnnHu3atculObKKj4/X4MGDdezYMe3YsUM7duzQqlWr9Pzzz7s0R61atfT77787f2asqFmzZlq/fr3Lr3vy5ElVqlRJR44cyfHxwMBAFyf6v5/lDz/8UKdOndKoUaNUv3597dy50+VZJOmhhx5S06ZN9cwzz0iSZs6cqV9//VXffvutKXkyn48zf78vXLigBx54INc3q+7kr7/+cn58+fJlzZ8/X+np6XrppZdcnsWKN5skyd/fX5MnT9bjjz8uSfr3v/+tjz76SLGxsabkkaTU1FTFxcVle/91zz33mJIlNjZWQ4cOvWZmnFn/X5s3b9by5cslSe3atVNYWJgpOSRlu7bdbte+fftUt25dU2YNTpgwQe+8846qVauW7f3g5s2bC+2alh8h9vLyUtmyZZ1vRlu0aKHnnnvO1ExWG7X+z3/+I39/f7Vp00aSNG7cOFPeKC9atEiff/654uLi9MgjjziPnz9/XiVLlnR5nkxDhw5VXFyctm7dqh49eujLL79Uu3btTMnSq1cvHT9+XP369ZMkzZ07V2XKlJFhGBo0aJBLiz8vLy9Jkre3t86ePauyZcvqzJkzLrv+1ZYvX649e/bI29vbtAxXe/zxx9WmTRsNGTLElIJr1qxZmjlzpvbv368GDRo4j58/f1516tRxeZ6sBg4cqJ49e+qdd96RJNWtW1e9e/d2eUEcFBSklJQU3X777S69bl6dPXtWCQkJplw7c5TTjMI3NykpKUpJSdEvv/xi+mu5dOXGTtbi95VXXlFISIhpeW677TZJkqenp/766y+VKlVKp0+fNi3P2rVr9eKLL+rAgQNKS0tzFnzJyckuz3LHHXdk+/iFF15QkyZNTCmIrTiCLkmrV69Wt27dtHLlSh0/flzSlZlXZvnuu+/Uv39/JSUl6Y477tC5c+cUEBCguLg4U/L0799fgwcP1htvvKHPP/9c06dPV1BQkClZPvzwQ40fP16PPPKIbDabHn30UY0ePVpPP/20KXliYmKy/X3z5s2aN2+eKVnmzJmjgwcPunR2leUL4hIlSsgwDAUHB+vdd99VYGCgLl68aGqmqVOnavv27WrdurW2bt3qHLU2y9KlS9W+fXstXLhQK1as0IoVK/TTTz+5PEetWrXUqVMnbdu2TZ06dXIe9/HxcRbrZli1apV27typ0NBQTZ48WS+99JL69u1rSpaff/4524tTs2bN1KRJE23evFm1a9d2aZbg4GCdPXtWvXr1UpMmTeTj46OGDRu6NENWlSpVUokSJUy7fk7sdrvefPNN067foUMH1axZU4MHD9bUqVOdx318fEy7w57p1KlT6tWrlyZPnizpyht4T0/Xv6RMnjxZbdu2VcuWLbPdTHnttddcnkWSQkNDnSNG6enpOnLkiF5++WVTsmTatm2bRo4cqUOHDmWb6WDGuuYePXrI19dXwcHBatasmU6ePGnqzQzDMJSQkCBfX19JUkJCgsycOFeuXDklJSXp/vvvV2RkpCpUqCA/Pz/T8vTv318TJkxQ48aNLTcLY+/evabdxLXizSZJ+sc//qHJkyerffv2qlChgrZv3+7SmWdXGz16tDZu3KjOnTtr+/btio6ONm02iCQlJyerW7duGj9+vOrVq6dZs2apSZMmGjlypMuzvP/++9q6davuvPNOSdLIkSPVpk0b0wriqzVu3FgDBw405doVK1Z0+VIjyxfE48ePV3Jysv79739r0KBBOnfunOlrUa02al25cmV9/vnnevjhh+Xr66tffvnFlFG2+vXrq379+nrggQd05513KiUlxRIFjre3t3PtUWpqqnx9fXXixAlTsiQmJurSpUvON4CXLl3SuXPnnDldKTo6WpI0bNgwNWzYUOfOnVOHDh1cmiGrJk2a6NFHH1W3bt2y/VuY2S+gbt26Onr0qAICAky5fmBgoAIDA/XHH3+Ycv3r8fT0zFY4JCUlmVJI/Otf/5KXl5fsdrtSU1Ndfv2rvfvuu86PPT09Va1aNdPWo2bq27evhgwZoqZNm5pe1IwaNUpDhgyRj4+PbDabSpUqpcWLF5uW58UXX1RoaKg6duwoSfrpp5+csx7M8P3336tYsWIaN26cPvvsM507d87UPgo+Pj569NFHTbt+VlmnJ6enp8swDE2fPt3UTFZrTjl58mT95z//0apVq7Rt2zaFh4dr7ty5ioiIMCWPh4eHAgMDne+Xe/Xqle3mrqsVL15cklSqVCkdPnxYvr6+ps6MyyyGr/7YDFlnlqanp2vTpk2mvaa2a9dOzz33nHr27Jnt/WBhDgRYviBu3bq1JKl06dL65ZdfTE5zhVVGrbt06ZJt7Yqnp6e8vLzUu3dvSa7vSpkpISFBrVq10rlz53Ts2DFt3bpVixYt0r///W9T8pQqVUqXLl1S8+bN1atXL/n6+po2ItGzZ0+Fh4frsccekyQtWbJEPXr00MWLF02btiNJzZs3N+3ambZs2SJJ+uCDD5zHbDabqQXx6dOnVb9+fTVt2jTbk7Krf7datWqVYzMZV69lzuqxxx7TwIEDlZycrI8//lgzZ8405c72vn37tG/fPpdfNyfp6en6+OOP9emnn5odJZtixYqZdqc/J3fccYcOHDiQrYCoUqWKKVl69+6t0NBQrV69WpI0fPhwU5cjZF0v16tXL9NyZOratas+/fRTdevWzbnMxixZpyd7enrK19fX9Bs8VmtOuXz5cm3evFnly5dXs2bN1KxZM/Xs2VN79+41JU9mAern56evv/5aQUFBSkpKMiWLdGUA6+zZsxoyZIgaNmwoLy8vde/e3ZQsNWrU0Kuvvup8bv7oo4+uaYjmSllndnp6eqpGjRr65JNPTMkyf/58SdI333zjPGaz2Qp1VpNlm2q9995713186NChLkpyrZUrV6phw4Y6c+aMc9T6zTffVNu2bV2a40Y/qGZNC27ZsqXGjx+vZ599Vtu3b5dhGKpbt65+//13U/L8+eefKlOmjDIyMjR58mSdO3dOw4YNk7+/vyl5vv/+e2ch07p1az3wwAOm5Pjpp5/03HPP6dChQ8677TabjY6dWeT2O+bq363vv//e+bHdbteCBQsUHBxs6nRuSVq4cKGWLl0qwzDUuXNn9ezZ0+UZHn74YUVHR8vHx8fl185JkyZNTF2zl5N//vOfevLJJ9WoUSOzo1huTaHVWG3brm+++Ua9evXSpUuXJInXiatYrTll5v9P5iy4ypUr68KFC6Zta7Zw4UJ16NBBhw4dUvfu3XXu3Dm9++67euKJJ0zJk1V8fLzOnz+vunXrmnL906dP69lnn83WVGvatGm66667TMnj7ixbED/55JO5Pmaz2UzvMo3cNWrUSFu2bFFoaKi2b98uSdk+hjUEBwdr+vTp10yjzNq4xNWs1I3SytLS0tS6dWtTO8/+9NNP10yxz+lYYevWrZu2bt2q9u3bZxvFN6OLvHSlKVNiYqKioqKyNRM04+c4cz1zamqq9u3bp+rVq2f7NzJjWmdoaKiWLl16zZpCs6YpW2l9taRsN46zbts1evRoU/JUq1ZNH3/8sRo1amT668TV/1eZxZ+Ze3w3btxYmzdvVlhYmH766SeVLVtWNWvWNK2r8969e9W1a1dnQezn56fFixerZs2apuQ5c+aMKlSocMNjrrRx40YtX77cuc1aeHi4aVmsJiMjQwkJCdmeC81aMib9XxPGTIV549uyU6bnzp1rdoRcpaWlacmSJddsxWJWE5ennnoqx+Nm3TTw9PRUamqq8y53fHy8qdOa9uzZozFjxji7ZGYyoxO3Vbbxka48sURGRrr8urm5euQoKSlJgYGBpowcTZ48WcOHD9cLL7yQ4+NmFVuZ0tPTTVsHn2nkyJHXFL85HStstWvXdnlDuutZtGiRJGVb4mPWm/as65mtwmprCq20vlrSNdO1GzZsqGbNmplWEN91113OpWtms9r/lWS95pSDBw/Wq6++6pyt8/nnn2vQoEFatWqVKXnat29/zY23nI65yqRJkzR9+nTnTijdu3fX0KFDc32tL2ybNm265v2gWT0D5s2bp6FDh6p48eLy8PCQdOW169SpUy7PsnHjRj355JPav39/tuOFOTPFsgVxprS0NE2dOtX55iIyMlLDhg0zpZtppu7duyshIcEyXRezPvna7XYtWbIk2xYtrpKWliZPT08NGTJEnTt31unTpzVq1Ch9+umnzj0ezdC9e3f16dPHtK1zsjJ7G5+sHnzwQedIjRVYqRtl5she6dKlTbn+1bL2C0hPT9euXbt0//33m5Jl//792rt3r86fP6///ve/zuPnz593Tqt0pddff93l17weK039zWykY7fbVaJECefPUEZGhhwOhymZrLam0Grrq69m5rZd0pUlCe+//74ef/zxbLMLzFiiYMX/q6zNKRs1aqSkpCRTm1MmJSVlW7rSvXt3vfXWWy7P4XA4ZLfblZ6ergsXLjgbLp4/fz7bftKu9uGHH2rbtm3ODsajR49WeHi4KQXx4MGDtWzZMoWEhGTrHWBWQTxu3DjFxMSYNpsgq2HDhmnevHkaNGiQ1q5dq/fee6/QG89adsp0pqFDh+rgwYPq37+/bDabPv74Y1WtWvWGa4wLU82aNbV3794c1/lYwaVLl/Twww871yW4Srdu3ZyjI+vXr9c333wjwzD04IMPKioqyrRpTVaarn3PPfeYMjKdk7Jly+r8+fO67bbbnI3ibDabaRvUN2zYUFu3bnWuxcp6zCzp6emm37iQsq9l9vT0VPXq1dWkSRPTssybN09btmzJtibVx8dHAwYMcPma+DfeeCPH42bN2MnkyqleN9KsWTP9+OOPzhs858+f1wMPPKB169a5PIvV1hRaaX21lPu2XWZsCyPJOVIkXXmzbuYaYqv9X6Wnp6tevXras2eP2VGcGjZsqE8//dQ5a2bPnj3q3bu3y19Hx44dq7Fjxzp/ZjL5+Pho+PDhps14yFzSd6NjrlCjRg399ttvpuwKkxMr9b9o0KCBtm3blu39YFhY2DV7JRcky48Qr169Wjt27HA+KT/wwAOmjH5m5e/vL4fDYYkthXLi7e2tY8eOufy6KSkpGjx4sD744ANnd8P4+Hi1atXKtOko0pUOvWvXrlWLFi1My5DJ7G18ssrasdMKrDZylJmld+/eevLJJ3X33XebliOziZcVtjLr27ev+vbtq9mzZ6tfv36mZpGkCxcuOD+22+364Ycf1LRpU9PymDHV60YuXbqUbbZD6dKlTdkZQbqyD7F05Y27Wesspezrqz/66CNLrK+WrLdtV0ZGhmnXvtratWst9X9VrFgx3Xnnndm2UjTbxIkT1aJFC2fPgt9++02fffaZy3O8/vrrev31153vCa2idevWioqKcr52zZs3T23btnUOVLiy10OlSpVMfz2XruzNLEmdO3fWu+++e81WR2bczM18P1i+fHlt27ZN/v7+On36dKFe0/IFsWEYysjIcBbEhmGYstdlVtWrV1fLli3VpUuXbD80ZnW+fv7557PdUd6yZYspXfMWLVqkjh076tVXX9WECRN07NgxtW7dWs8++6yGDBni8jyZHn30UUVGRqpUqVLy9vY2tRGHVbbxka7scWslw4YNU1JSksaPH59t5MhMGzZs0Lx585x7az/11FPq0aOHy18gfvvtN/Xo0cMyW5lJV55rEhMTVa5cOUlXpnZ+9dVX6t+/v0tzXN2MacyYMYqKinJphqzMmOp1IxkZGbp48aJzKUBycnK2NWuu9uOPPyo2NjZbBlffNDX7uSU3Zu0XWxS8//77Zke4RvXq1XXvvffqsccey9ZEz9XvBwcMGKAPP/xQkZGR+s9//uMs0MPDw01tYPXBBx8oPj5e//vf/yRd+fk2a4s1Sfryyy8lSWvWrMl2fNGiRS5/X9ikSRM9+uij6tatW7bXCFdvNVmmTJlsI/kvvPCC6bNBunfvrrNnz2rkyJGKiIhQamqqxo8fX6jXtPyU6Zdeeklbt251vsGZP3++GjRoYOobwZw6YJvZ+Xrs2LHOjzOnUz7yyCPOOyyudOHCBbVp00bt2rXT4sWLNWjQID3//PMuz5FVzZo1NWLEiGu6ZJqx16RVtvGRpFOnTun111/Xzp07s3V1Nutuu9WtXr1aM2fO1LfffuvyNVBW28pM+r/tRrKyyvKEOnXqmPZvY8ZUrxt5++239fXXX2vQoEGSpJkzZ6pr16566aWXXJ7liSee0J49exQaGppt3dyHH37o8ixS7uurzbqJYaXGi9KVKdNXLw8rXbq0mjZtqhkzZigoKMiUXFZhlfeDmc87V39stm+++ca5V7PNZtOvv/6q2bNn66GHHjI7mulatWp1zTGbzWba77pVpaamym63F/rWYZYfIX777bc1a9YsZ/OWRx99VAMGDDA1k5U6YKenp6ts2bKm7sucKXPKyVtvvaVu3brpwQcfVJs2bUyZipJVyZIlc+3E7Wpm7Q2dk8wXqRUrVmjy5MmaNWuWQkNDTc1khZGjnGzdulVffvmlVq5cmeOLWGG7ePGimjdv7vy7zWaTl5eXy3NkldO9VDPuJGftJ5Genq7NmzfL19fX5TkymTHV60ZGjBghX19f537WQ4YMUa9evUzJsnXrVv3++++WWJsvXZlCmXV99YULF0xbXy1Zq/GidGWNflpamnPmx+zZs5WSkqKKFStq4MCBWrZsWaFnGD58uCZPnpytuWBWZsywymSl94OZrDTONXbsWG3cuFHVq1eXJB04cECPP/64qQXx5s2bnT122rdvb9qadLM6fxcFTz/9tNq2bavWrVvrrrvucskAn+ULYg8PDw0ePNh5Z9sqjaxOnDih3bt3ZxtZc/U0B+nKGpb58+dboiDu1KmT8+OSJUtq9erVWr16tSTzth2Rrqw7//bbby1xR9JKW2TFx8drxIgRio6O1kMPPaTIyEhFRERo3LhxLs8i5T5yZKbJkydr3rx5Sk9P15NPPqmdO3easp7PaluZSVfWP33xxRd6/PHHJV2ZcmbGv03WEWlPT0+FhISYetPUjKleN3L27Fnn2u9M+/fvV3BwsMuzBAUFKSUlxTJrLq20vlq6MmL95ptvmnb9qy1dujRbw6HXXnvN2YTIVaP6LVu2lKRrdkRITU019f8qkxXeD16+fFm//fabDMOQ3W53fpzJrAGJ9PR0ZzEsXZlibua69A8//FDjx4/XI488IpvNpq5du2r06NF6+umnTcmTmpqquLi4bD87Zv1fxcbGaujQodfMGjSj0WpERIR++OEHvfjiiypXrpzatm2rtm3bFu7uGobFHT9+3OjYsaPh7e1teHt7Gw888IBx4sQJUzPNnj3bCAwMNEqVKmU0aNDAKFasmHHvvfealmfEiBFGdHS0ade3ujJlyhg2m824/fbbjbJlyxplypQxypYta0qW999/3/ln0qRJRtOmTY1//vOfpmQJCwszDMMwGjVqZJw5c8ZIT083qlevbkoWwzCMmjVrGmlpaaZdPyf9+/c31q9fb3YM49NPPzXuv/9+w8/Pz3j11VeNwMBAY9GiRaZm+uOPP4zg4GAjMDDQCAwMNO6++25j//79pmayGofDYSQnJ5sdw2jevLmRkpLi/HtcXJwRHBxsSpbdu3cbTZs2Nf71r38ZY8eOdf4xS7169YwLFy44/37+/HmjTp06puXp0aOHceTIEdOuf7Xg4GDj1KlTzr+fOnXK+bMTEhJiSqa9e/caL774olGxYkWjYcOGpmTIZJX3g4GBgUZQUFCOf6pWreryPJnatm1rfPTRR0Z6erqRnp5ufPzxx0bbtm1Ny1OvXr1rfp7r1atnSpZvv/3W8PX1NUqUKGGUK1fOsNlsRlBQkClZDMMwIiIijM8//9yoXbu2sWvXLqN///7GhAkTTMtjGIaRkZFhzJ8/3wgKCjI8PDwK9VqWHyEeOHCgmjdvrgULFki6svZpwIAB+vbbb03LNHXqVG3fvl2tW7fW1q1btXbtWs2bN8+0PLNmzdL58+fVr18/3X777aZvn2M1Vuqm/M9//jPb3wcPHmzKzAJJCg4O1tmzZ9WrVy81adJEPj4+2fa0djWrjRxJMm1d49V69eqlatWq6ZtvvpHD4VB0dHS2KdRmqFWrlvbs2aN9+/ZJurJW35Wj1mvWrFFERES2vZAz2Ww2VahQQeHh4S6fZbB27drrPm5Gt/vHH39cPXr00JIlS3Ty5El17NhRU6dOdXkOSfrXv/4lLy8v2e12paammpIhqyeeeEJt27bNtr7ajKUtmdOBL1y4YJnGi9KVJSv169dXx44dJUnLli3TqFGjdPHiRd17770uy3Hp0iUtWrRIs2fP1qFDh3T58mVt2LBBtWrVclmGnFjl/eDhw4ddfs28mDlzpp544gnne58GDRqY0vU6qzvvvDPHj11t9OjR2rhxozp37qzt27crOjpaO3fuNC1PcnKyunXrpvHjx6tevXqaNWuWmjRpYsqWbx9//LGWL1/u7Mfx0ksvqV27doV6Tcs31cqpcUtOx1wpp/1SzWxicOTIkRyPW62LMK6VkZGh2rVra+/evabm+PXXX5WUlKSOHTuaNhX3999/V//+/dWyZctsbwTN2E+2atWq1y2kzJr+bzVmrsXq37+/Pvroo1zXdJ8+fVo1a9bUkiVLXJZJutJYbNeuXc6foUOHDql+/foqVqyYbDabNm/e7NI8mYYPH66LFy9q06ZNGj16tLp27WpKjpo1azpvoljFJ5984lxf/fDDD5uyvjq3houZzOw/sXv3bud6x1atWrl8F4v+/fvrq6++UosWLfTUU0+pY8eOqlGjhuLi4lyaIydWez9oNWfPnlX58uWdU9tLlixp2nINSeratatq1aqlgQMHSpI++ugj7dmzx+WvE1LOPzuZx8yQuQ9xs2bNtGDBAvn6+qp27dqmvN/x8vJS48aN9corr6hNmza67bbbCv2alh8hNgxDCQkJziYpCQkJpjcMKFGihAzDUHBwsN59910FBgaauo6Fwvf6rNRNOWuDKDO3yJKk8ePHa9SoUZKke++9V4ZhqFevXqbdvbXSyNF3330nSfr88891+PDhbC+ervx9y62JTCYzm8mYvRbro48+knT9xiRmjB6Fhobq3//+t/Nu9vLly/X555/r448/dnmWzIaG0pVZBgMGDFC7du1Uo0YN7dq1y5S1ajVr1lRycrIpe1vm5ur11WZlyMoKe45nqlu3rmmvU9KV5+FGjRpp4MCBioyMlM1mM72/RCarvR+0ms6dO2vFihXOLamOHDmihx56yLSbYjNnztSzzz6rBg0ayGazqW3btqbtk5zZKMrPz09ff/21goKClJSUZEoW6crspbNnz2rIkCFq2LChvLy81K1bN1OynD17VqtWrdLPP/+s0aNHq0yZMmrbtq1effXVQrum5UeIP/30U7388svO6To//fST3nnnHT3xxBOmZVq5cqUaNmyoM2fOaNCgQTp37pzefPNNtW3b1pQ8Vir4rOihhx5S8+bNNXv27GzdlM1oHmWlLbIiIyPVp08f5+/SgAEDlJaWZtr2YVYcObp6uxzDMNS4cWOXbaGT26hRZjOZ5557ziU5cnLPPfdoxYoVzilnp0+fztZV3pX++OOPa7qTP/LIIy7PIV35d7n636B+/fqmTIWrWrVqro+Z1eiwW7du2rp1q9q3b59tJsiUKVNcnkW6sm1OTsWVWc+DVttzfNu2bRo5cqQOHTqU7ffLlT87Fy9edE6XPnbsmPr06aNPPvlE8fHxLsuQG6u9H7Sa6dOna/Xq1c7lGq1bt9bkyZMLtzlSEbFw4UJ16NBBhw4dUvfu3XXu3DlNmzZNPXv2NDXXoUOH9Nlnn6lMmTJ69tlnTcuRmpqqdevWafny5VqwYIGSk5N19uzZQrue5Qti6cpUyqzTdczYPzarjIwMeXh4mJohKysVfFaUOcU+c1qKw+FQRESENmzYYHY0UyUnJ6t169Z655139O233+rEiRNasGCBaT/bDz/8sKKjoy01chQcHKzt27frjjvukHTljVmDBg20f/9+U/Ls27dPH3/8sT799FP5+fll6/7qajkVfjkdK2zDhw/XZ599ptq1a2frTv7zzz+7NEem0NBQTZkyxTmVe82aNXruuecssT+zFWS9KZjV66+/7uIkV8yYMcP5sd1u15IlS9SgQQO9//77puSx2p7j9erV05AhQ9S0adNsy2nMeh+2Z88ezZkzR59++qn+8Y9/qFevXnrmmWdMyRITE6N33nlHe/bskXRlJH348OEKCwszJY9VWWW5hnTleWbo0KEqX768JOnMmTOaMWOGac8/VtC2bVtNmjRJISEhOnHihOrWrasmTZooLi5OTz75pEaMGOHyTJGRkdq6datCQkLUtm1btWnTRo0aNSrUmSGWLYiPHTumhISEa9akbdmyRZUqVVKVKlVMSiYFBARo0KBB6t+/v6kL8jNR8F1f48aNtXnzZoWFhemnn35S2bJlVbNmTcXGxpqSZ/PmzdqxY0e20Xyzts06duyYWrRooXr16umrr74ydSsfq40cSdKYMWO0ZMkS59ZCX375pR599FGXrmu2ajOZq9diffjhh/rjjz9cvharevXq2rVrl2Wasa1fv17du3d3zvpIS0vTokWLFB4eblqmb7/9Vvfdd5/KlCkjSUpKStL69ev1wAMPmJbJqi5duqSHH37YuTbe1TK3NAoNDXXeRMn6sauZ3bMlN2lpafrmm280Z84c5/pvV9qwYYPuv/9+DRo0SE2aNJFhGIqJidHMmTP1448/qkmTJi7PZCVZb4ymp6drwIABatmypXr37i3JvK2Fcvp5NmvN96xZs9S9e3eVLl1aQ4YM0caNGzVlyhSXN168++679ccff0iSJk2apF9//VVff/21kpKSFBERYcqsr19++UX33XdftveChc2ya4hHjBiR41q0ixcvOvdONcsvv/yiDz74QHXr1lX79u01ZMgQU5/8vLy8JEne3t46e/asypYtqzNnzpiWx2qs1E154sSJWrx4sY4ePaqIiAj98ssvatOmjUsL4tDQ0Gx32ex2u2JjY513tc2aal+7dm3Vrl3blGvnZsyYMQoLC9PKlSslSW+//bZz+YYrZG0mM2LECGczGbOLYSnntVgzZ850eQ5/f3+XvmjeSLNmzXTw4EFno7xatWqZsiQiq9GjR2d7E1imTBmNHj3apQXxwoUL1aNHD7333ns5Pm7WTcGreXt769ixY6Zd32p7jt97773asmWLSxvm5YWnp6e6du1q2mjjv//9b82ZM0ddunRxHuvSpYuaNGmiN998U0uXLjUll1V06tTpmmOLFy/W4sWLTVuuISnHPZAdDocJSa7MThk4cKB+/fVX/fbbb5owYYJefPFFlzdezNqwav369c7p7GXLlpWnpzllYmYPjpSUFKWkpDiPF+YMQsuOEGfeJc1J3bp1tXv3bhcnutalS5cUHR2tCRMm6K677tLzzz+vHj16uKzZQ1pamjw9PdWrVy9NmzZN0dHRmj59unx8fFSzZk0tXLjQJTmKknXr1uncuXPq0KGDKb/odevW1ZYtWxQeHq4dO3Zo3759GjlypEtH1dasWXPdxyMiIlyUxNrS09NVr14953Q4M5QqVUqNGjXSiBEjnM1kqlWrZmqX66u3Fcp8Ccl83nP13e2YmBi99dZbioyMzFYY9+nTx6U5rubKF/IbyWlUxNXrmkuWLKmLFy/qySefvOYxm81m2prdnBodVqpUSYsXLzYlT3R0tBYuXKhdu3apb9++io6O1r///W/nLBVXq1evnvbt26fq1atn+/1y9x4lwcHBuS6dud5jMNdjjz2msLAwDR8+XIZhaPLkyYqJiTHl9z1zZHrcuHGqXLmy+vXrZ8podaNGjbR06VKVKVNGgYGB2rBhg7MLeK1atUzZBWXTpk2Kioq65vcoPT290K5p2RHiy5cv5/qYFWp4wzD0888/a9GiRSpZsqR69OihhQsX6ssvv9TXX3/tkgxPPPGEFi1a5BwtHzZsmBo1aqSDBw8Waie2oszsvVu9vb3l7e2tjIwMGYahmjVr6uDBgy7NEBERofT0dEVFRenTTz916bVvxErTyYsVK6Y777xTly5dMm067smTJ7Vo0SK98cYbGjBggPr06WN6B+7hw4dfc8xms+nEiRM6efJkob5g5WTmzJnatWuXDMPItobYrIJ448aNevLJJ136Qn4jpUqV0vr169WsWTNJV7ZZK1WqlEszREREKCIiQp988omCgoJceu3rKV26tPNjT09PDR061LSGbJL19hw3ay211V3vNSGz5wT+j1VuEE6bNk29evXSqFGjZLPZ1KJFC82fP9+ULB4eHlq0aJEWLVrknPZvxmj1yJEjFRoaKk9PT7Vq1cpZDK9fv9605+qhQ4dq3rx5GjRokNauXav33nuv8GeCGRZVt25d48SJE9ccP378uFG3bl0TEv2fiRMnGkFBQcYDDzxgLFu2LNtj1atXd1mOTp06GYMGDcp27OjRo0a1atWM6dOnuyyH1f34449GzZo1jeLFixseHh6GzWYzPDw8TMnSvHlzw+FwGL179zZeeOEFY8qUKab9PDdu3NiU6+ZmwoQJRmhoqFG+fHnjkUceMUqVKmV07tzZ1ExPPfWUERISYkyYMMGYNm2a848Zfv/9d2P48OHGXXfdZTRt2tSYMWOGKTmudvbsWeP555837rzzTmPcuHEuv361atWM1NRUl183N40bNzY2btxohISEGMnJycb48eONSZMmmZpp/fr1RsWKFY2IiAgjIiLCqFy5srFp0yaX5/j4448NPz8/46OPPnL5tYua+Ph449ixY2bHQC5q1apl7Nq1y9i5c+c1f2rVqmV2PMvYsGGDUatWLcPDwyPbH7NdvHjRuHjxoqkZNm7caHTq1Ml49913DcMwjH379hnPPvusKVlOnjxp7Nixw8jIyHAeO378uHHkyBFT8oSGhhqGYWR7f9yoUaNCvaZlp0zPmDFDCxYs0Jw5c1SzZk1J0t69e9W/f389/vjjprYCf/bZZ/Xss8/muLH41q1bXbY+NSUlRR07dlTTpk01YcIEHTt2TK1atdKQIUM0bNgwl2QoCoKDgzV9+vRrumSacRd39+7dqlq1qi5duqSRI0fq3LlzGjVqlOrXr+/yLK+88ooSExMVFRXl3CNQMq/ZhRWmk1/NatM7JfObyWSy2+2aOnWqc5uIV1991dm505XatWunH374wfR1upkyp7xlNjmUrt2+ywxJSUnORovNmjVzNthytQMHDigsLEyGYcjDw0OGYchmsykxMdGUPE8//bTefvvtbF1nX331Vc2aNcuUPDt37lT37t31559/SpJ8fX21cOFCU14jpCuz9aZPn37NzB0z90C3gqCgoFyXx5m5RtZqmjRpovfee++akb6cZhoVptjYWOf+6zkx632PZK09x62iSZMm2rRpk1q2bKkpU6bI399fYWFhOnz4cKFd07JTpv/5z3/q1KlTCg0Ndf6gOBwOvfjii6YWw+np6frjjz9yLIYlubRZU4kSJfTNN9+oTZs2kq40LHjmmWcohq/i4+OjyMhIUzNkXaeWqVSpUipZsqQ++eQTUzopL1q0SNKVJnGZzHwht8J08qvNnTvX1OvnxOxmMhkZGfr44481btw4tW7dWps2bVJgYKApWSSpWrVqatmypTp16pRtSpVZU+0zexOUL19e27Ztk7+/v06fPm1KlqzKli1r+t6fW7duVVRUlLp3766XXnrJ1GZRWTNlvZFToUIFU29ePP3003rjjTf02GOPSbryuv7000+blql///7y8fHR+vXrNXz4cM2bN8/lfQKsqDDfmN9KUlNT1aRJE6WlpalUqVJ69dVXnWt4Xen555/Xd999l2OzL7Pe9+zatUs9e/a0zJ7jVtK9e3edPXtWI0eOVEREhFJTUzV+/PhCvaZlR4gzXbp0ybn/Xu3atS2xNqNZs2Zat26d6XsRZ97pOnPmjLp166YHH3xQzz//vPNxM+94WcmYMWMUEhKizp07m5bBw8NDYWFh6tChQ44/N+68B16m++67TytXrlS/fv105513ys/PT3PmzHGOspkhLS1NU6dOdd40iIyM1LBhw0zrvGgFtWvXVkpKisaMGZPjqJWrn3esNoo/depU9enTR1u3blXXrl2dL+QvvviiKXmkK88/OY1muXJd86uvvqoFCxZo5syZpt+gzCrrSL50pT9IvXr1TGvceXUeyZz9va/Ok5nhwoULeuCBB65prgfkxIyRvtwYhqGTJ0+qcuXKLr92Tqy257hVpaamym63F3rfC8u/q7v99tstt8l5WFiYHnzwQfXq1SvbVNOHH37YpTmy3ukqWbKkVq9erdWrV0tiyk5W06ZN0/nz53XbbbepRIkSpkzRW7FihebMmaMFCxbo8ccf11NPPaV//OMfLrt+To4ePZrj8YCAABcnueKDDz6Qw+HQ5MmTNXLkSP3666+mN/164YUXdPDgQT3zzDOy2Wz6+OOPdeTIkVy3jnEHly5dks1my3EvZjOed6wyip9ZsLRp00bHjx+Xr6+vVq5cKbvdrooVK8put5u2PdSFCxecH1++fFnz5893eZOvI0eOaPv27aZN1c5NeHi4hgwZopdeekmGYWjSpEmm7hndoEEDrV69Wi1btpR0ZVcAs7YJlP5vSxZPT0/99ddfKlWqlCVmPKBoyGmkb9SoUablad++vSV2qZGubCObtWGezWZzbqPqrm50468wb7hbfoTYilq1anXNMZvN5tyrFNZy5MiRHI+bMc0zOTlZCxcu1Ny5c3XbbbfprbfeMm0P6zvvvFM2m02GYchut+vSpUsqX768Tp06ZUoeK7rnnnu0Y8cO56h+WlqaGjRoYNpoDa6VW4dQV3eZrlq16jXHMkdlU1NTdeHCBU2ePFn9+vVzaa7cZI7cuLvk5GQ999xz+u6772Sz2fTwww9rypQpLu/CnSlzq7fM7q6HDx9W7dq1nWvkXb0lS4cOHbRw4UJNnjxZq1evVoUKFfTXX39lW2oD5EVqaqouX76sunXr5npDvrB16NBB0dHRqlChginXzyo8PFz/+9//1KRJE23btk3x8fHq0qVLrlvOuoOcXkczFfYNd8uPEFvRqlWrzI6Av8HM9Y1X8/HxUadOnZSYmKj33ntPe/fuNa0gvvou/1dffeXSfUkzPfnkk9fdu9vMBlaGYSgjI8NZEBuGYYlt3/B/vv32W+fHdrtd69atU3h4uMsL4ri4uOs+fvLkSbVu3doSBfHevXt15swZs2NYgo+Pj6nPMVez2jZH33//vYoVK6Zx48bps88+07lz50zf4xtFU/HixVW8eHFTX0NLliypkJAQ3X///dlmeJrRx2XIkCHq3LmzTp8+rVGjRjn3HHdnN3odLUwUxDcpNTVVcXFx2bousmbXmqpWrZpjweXKqZ3p6en673//q9mzZ+vIkSPq3bu3tm3bpkqVKrksw4088sgjmjhxosaOHevS6zZq1EiS9Ntvv2nt2rXq2bOnbDabFi5cqPvuu8+lWa7WoUMHtW/fXlFRUZKujEZ26NDB1EzI7ssvv8z297i4OEvuw16pUiUNGDDAlGuXLVvW+RyYnp4uwzA0ffp0U7JY0YkTJ7R79+5sr+euXgKVKXNa6dGjR01fViMp297evXr1MjkNbgXXuwFe2OrVq6d69eqZdv2srLbnuJXExMSoTp06uv322/XFF19o8+bNeuGFFwp1/XeRmDL9xRdfXNPy34y7OZm+++479e/fX0lJSbrjjjuUlJSkwMBAU+9sIHdZGxTY7XZ9+umnKl++vEaPHu2yDL6+vgoICNCTTz6pe++995rHzbiZkpyc7Pw4PT1dmzZt0tChQ7V//36XZ5GkFi1a6LvvvpOPj48z34MPPmhq85aMjAx9+OGHWr58uWw2m9q2basBAwaY+oKOG6tfv74psx2sKuuyEU9PT/n6+lqiw7MVzJkzR2+88YYSExNVo0YN7dy5U+Hh4Vq3bp0peVavXq2ePXvK09NTR48eVUxMjKZNm6bo6GhT8mzbtk0jR47UoUOHlJaW5jxOjxJcz/WWFUVGRurkyZMuTGNNe/fuVa1atcyOYUn169fXtm3bdOjQId1///169NFHtW3bNi1btqzQrmn5gnjo0KGKi4vT1q1b1aNHD3355Zdq166dZs+ebVqm0NBQLV26VJ07d9b27dsVHR2tnTt36p133jEtE/6eZs2aaf369S67XtY9CzPX7WZydSOiHj16aOHChc7Os4ZhqFixYqpRo4amTJli2gjo3XffrT/++OOGx1zh6m2yMv+/Mv8Pzbwhh+z++9//Oj9OT0/Xxo0btWrVKm3evNnEVCgq6tWrp7Vr16p169bavn271q5dq3nz5pk2jTo8PFyfffaZHn30UW3fvl2SVKdOHdM6z9arV09DhgxR06ZNs91EqVOnjil5UDSYuRb0RjZv3nzNIJsZ2/QFBQWpRo0aevbZZ/XQQw9xoz2LBg0aaNu2bXrvvfeUlpamF154QaGhoc7nxMJg+SnTq1at0s6dOxUaGqrJkyfrpZdeUt++fU3N5OHhocDAQOfd0l69emnq1KmmZkLenT17VgkJCS69ppX2LNy7d6+kK6OfVnLPPfcoKirKucZy7ty5OW7r4wrvvvvudbfJgnVkfe719PRU9erVnXts44qrR/kyO+0zyid5eXmpbNmyztfzFi1a6LnnnjMtT3p6+jVTpc3sPFusWDENHDjQtOujaLLqjMmJEydq8eLFOnr0qCIiIvTLL7+oTZs2phTEhw4d0jfffKPp06frueee06BBg/T000+rXLlyLs9iNSkpKfrzzz/17bff6u2335ZU+NsEWr4g9vb2do5kpaamytfXVydOnDA1U2a3Rz8/P3399dcKCgpSUlKSqZlwrbS0NHl6eio0NDTb+rkjR47opZdeMjmdeax6F3L27Nl64403nG9G27Zt69Jp7VlZcZssZJc5JW/atGnXPJa5b6FZ2xxZTd++fXMc5YOcW/EFBwfr3XffVWBgoC5evGhaHm9vb128eNH5PP3bb785tz4yw7333qstW7Y4ez0ARdmCBQu0ZcsWhYeHa8mSJdq3b59GjhxpShYPDw916dJFXbp00datW9WlSxeNGTNGTzzxhMaOHWuZ/ZLN8Pzzz6tmzZpq27atGjRooIMHD6ps2bKFek3LT5lu3bq1vvvuO7388ss6ffq0fH19tXHjRlO3i1i4cKE6dOigQ4cOqXv37jp37pymTZumnj17mpYJ1+rWrZsWLVqkNWvWOI95enrK399fLVu2dNvREU9PT+c63azM2J85U3p6uqKiokzfd/hqVtomC9llTsm7+gaPYRhKS0uz3DZHZgoJCdGOHTvMjmFJK1euVMOGDXXmzBkNGjRI586d05tvvqm2bdu6NEfmUpZly5Zp7NixOnjwoNq1a6fly5drwYIFat26tUvzZN5ITk1N1b59+1S9evVsN5hcvf0TUBAaNWqkLVu26J577tHOnTtls9lMfX6MjY3VjBkz9OWXX+rhhx9W//79tXLlSs2fP5/tHbPIyMhQWlpaoc6WsfwI8cKFC1WsWDG98847mjJlipKSkrR48WJTM7Vv315ly5ZVw4YNFRsbK0mmNSJC7lJSUjR48GB98MEHzmPx8fFq1arVNWtE3UnNmjX1ww8/mB0jm2LFilnyd8hK22Qhu6K0zZHZGOXLXcuWLeXh4aHSpUuburdu5lKWyMhIBQcH66effpJhGBo7dqwps1Peffddl18TKGy33XabUlNTFRISohdffFF+fn6FPhU3N5GRkYqNjdXgwYP1+++/q0yZMpKurJ+dN2+eKZmsYv78+TkeL8wt3yw/QmxF9913n1asWOG8U3HkyBG1b99e+/btMzkZskpJSVHHjh3VtGlTTZgwQceOHVOrVq00ZMgQDRs2zOx4pslsVmA1r7zyihITExUVFZVtf0AzOnDntE1W7969LbVNFm5s6tSpev75582OYRpG+W4sICBAgwYNUv/+/XXnnXealsNqz8uZI9bArWT37t2qWrWqLl26pJEjR+rcuXMaNWqUKf1KlixZoi5dutCnJAePPfaY82O73a5169YpPDxcP/74Y6Fd07IFcZcuXa671vGrr75yYZrspk+frtWrV2vJkiXOUYjJkyfr/vvvNy0TcnbhwgW1adNG7dq10+LFizVo0CC3foMsqdA79d2snLpSmtX4x4rbZAF/1zfffKPExERVq1Yt2/FDhw6pXLly6tSpk0nJrGPfvn364IMPtHDhQrVv315DhgwxZRaI1ZayWK1AB/Ijt1mBmb9fZu0ckXUd8/Hjx5WYmGiZfZKtJC4uTq+++qoWLFhQaNewbEH8ySefXPdxsztNDx8+XBcvXtSmTZs0evRode3a1dQ8uFbm+oszZ86oW7duevDBB7MVwxQ1yI2VtskCbtagQYPUrl27a16fvvrqK/3yyy/ZlpO4u0uXLik6OloTJkzQXXfdpeeff149evRwWRPCOnXqXHcpS2BgoEtyZKIgxq3Ew8PjujtHvP766y7P9NprrykmJkYHDx7U/v37dfLkSXXt2tWlW4IWJfXr19fOnTsL7fyWLYitKOsC9/T0dA0YMEAtW7ZU7969JVFgWY2V98FD7lJSUpSSkuL8e06jJgBu7HpFjZl721qNYRjO7U8SEhLUr18/rVq1Sp6envr6669dksFqBajVRqyB/Fi1apXmzJmjjRs3WmbniPr162vbtm1q1KiRc9bePffcQzMtSf/973+dH6enp2vTpk1auXKlNm/eXGjXtGxTrRs1PTJjekNO08sWL16sxYsXU2BZkFX3wUPONm7cqCeffPKa5lpmNbwAirrMvXVzwrq1K9588019+OGHqlOnjl555RW1atVK3333nV544QXVqFHDZTmsNjZhxeaLwM1q1aqVWrVq5dw54oknnjB954jbbrvtmm3wrPY8YJapU6c6P/b09FT16tW1aNGiQr2mZQvi0qVLmx3hGhRYQOEZNmyY5s2bp0GDBmnt2rV677332EcWyIfU1FQlJydfM9J3/vx5paammpTKWk6cOKGff/5Z6enpmjNnjvr06aMqVaqoc+fO+vzzz12Ww2p9HUqUKOHyadpAYbPSzhGBgYH63//+J5vNJofDoTfffFMhISGmZLGaVatWufyaTJm+CevWrVPz5s2zHfvyyy+zdUUD8PdkThmsV6+efvvtN0lSWFiYYmJiTE4GFE1jx47Vtm3bNG/ePJUtW1aSlJSUpH79+umee+7RmDFjzA1oskuXLunzzz/XnDlzdOjQIV2+fFkbNmxQrVq1zI5mOqs2XwRuhhV3jvjzzz/Vt29frVixQjabTa1atdL48eMVFhZmWiazxcbGqkaNGrlOGy/MpamWL4jj4+M1ePBgHTt2TDt27NCOHTu0atUqUzsFV69eXQMGDNDLL7+s1NRUDRs2TDExMbxxB/KhcePG2rx5s1q2bKkpU6bI399fYWFhOnz4sNnRgCIpPT1dTz31lJYsWeKc/hsbG6uuXbtqzpw510zXcyf9+/fXV199pRYtWuipp55Sx44dVaNGDWaCAbcgq+0csWXLFh05ckQtW7bUbbfdpkOHDmnUqFH69ddfdfr0aZdmsZIHHnhA33//fbYeQJmNTQt7aarlC+L7779fPXv21DvvvKOdO3cqLS1NoaGhzhEkM2TulZqWlqZTp04pPDxcU6ZMce5LDCDvXn/9dbVu3VobNmxQ//79tW3bNj3yyCNKTU3VuHHj9NJLL5kdESjSDh486GzY1KBBA9ObyVhBqVKl1KhRI40YMUKRkZGy2WyqVq0avUCAW5CVdo54++239eabb6pWrVpKTEzUs88+q1deeUWDBw/WqFGjVKZMGZdlsZrMwjfTwYMH9d///lfVq1fXQw89VKjXtnxB3KhRI23ZsiXb9B0rTOVZvHixnn76aZUsWVLLly9nihVwkwYPHqxVq1bp6NGjatasmVq1aqUWLVqofv36dJgGUCguXryoRYsWafbs2Tp27Jj69OmjTz75RPHx8WZHA3ALu/vuu7VixQpVrlxZe/fuVd26dbVs2TK1adPG7Gima9u2rSZNmqSQkBCdOHFCdevWVZMmTXT48GFFRUVpxIgRhXZty7eZ9PT0zHYnJykpyfQubM8++6wmTpyoLVu2aObMmerQoYOio6NNzQQUVR988IH27t2rQ4cOqV+/fjp69Kiefvpp+fn56YEHHjA7HoBbUMmSJdWvXz+tX79eP/30k+x2uxwOh5o1a6b//Oc/ZscDcIvy9vZW5cqVJUm1atVScHAwxfD/d/z4cWdjsQULFigiIkI//vij1q9fr88++6xQr235gvixxx7TwIEDlZycrI8//ljt2rXT008/bWqmlJQUbdiwQdWrV9eDDz6otWvX6oMPPjA1E1DU+fr6qmvXrurRo4e6d+8uX19f7dixw+xYAG5xtWvX1qRJk3T8+HENHz5c33//vdmRANyi7Ha7fvvtN+3atUu7du2SYRjZ/u7ObrvtNufH69ev1/333y9JKlu2rDw9C3djJMtPmZakhQsXaunSpTIMQ507d1bPnj3NjnSN9PR0t25QAtystWvXavXq1Vq1apWOHz+u8PBwtWjRQhERES7dBxQAAKAwZV3PfDVXr2e2mkaNGmnp0qUqU6aMAgMDtWHDBgUHB0u6Mpq+d+/eQrt2kSiIrWLhwoXq0aOH3nvvvRwfHzp0qIsTAUWfh4eHwsPD9dprr6lDhw5mxwEAAICLffXVVxo4cKA8PT117733avHixZKujBa/8cYb+umnnwrt2oU7/pwPly9fdu6d+Pjjj2vEiBH66aefVLNmTU2bNk1VqlRxeabMOxM5NfTK7W4PgOvLHCGeNGmShgwZosaNG6tly5Zq2bKl884gAAAAbl2PPPKImjVrpj///DPbVlhBQUH68MMPC/Xalh0h7tWrl86dO6dLly6pWLFiCgwMVNeuXbVy5Urt3btX3377rcsz/fe//73u4w8//LCLkgC3JofDoU2bNmnVqlVasGCBLl68qGPHjpkdCwAAALcoyxbEtWvX1p49e2S321WpUiWdPXtWHh5XeoDVq1fPlH2IW7Vq5fx469atatSokbPjtc1m08qVK12eCbhVnDhxQqtWrdLq1au1cuVKnTp1Ss2bN9ePP/5odjQAAADcoiw7ZbpEiRKSrrQnDwoKchbDklS8eHFTMq1atcr5cWhoKAUwUAD69++vNWvW6MSJE2ratKlatWql6OhohYWFFXpXQQAAALg3y77bzGxLbhhGto+lK+uLzcaaYaBg+Pv7a/bs2QoPDzftZhcAAADck2WnTFu9LXmDBg20bds2UzMAAAAAAG6eZUeIDx8+bHaEa2TdMPvy5cvZRq0lZeuIBgAAAACwNsuOEFtR1apVc33MCqPWAAAAAIC8oyAGAAAAALgljxt/CgAAAAAAtx4KYgAAAACAW6IgBgAAAAC4JQpiAABuUlBQkHbs2GF2DAAAcJMoiAEAAAAAbomCGACAAjRlyhSFhYUpJCREYWFh2rBhg/OxoKAgvfbaa2ratKmqVq2q8ePHOx/bu3evmjZtqjp16uiRRx5R+/btNW/ePElSVFSU3n33XefnvvjiixozZowkacWKFWratKlCQ0NVp04dzZ492/l5J0+eVPv27VW7dm21b99e3bt3d35damqqXnnlFTVu3FghISF6/PHHlZSUVGj/LgAAWBEFMQAABah3796KiYnRjh07NH36dD355JPZHj937pw2bNigmJgYvfPOOzp+/Ljz6wYMGKDff/9dEyZM0Nq1a/N0vQYNGmjdunXavn27/ve//+mNN97QsWPHJElDhw5V06ZNtWfPHs2fP1+rV692ft0777yjO+64Q5s3b9aOHTtUr149jRo1qmD+EQAAKCI8zQ4AAMCtZPv27ZowYYLOnj0rT09P7du3T5cvX9Ztt90mSerZs6ckqUKFCqpWrZri4uJUqlQp7dixQ3369JEk3X333WrevHmernf27Fn169dP+/fvl6enp86ePavdu3fLz89PK1as0KRJkyRJvr6+evDBB51ft3TpUp0/f15LliyRJDkcDgUFBRXUPwMAAEUCBTEAAAXE4XDokUce0apVqxQWFqbk5GSVLl1aKSkpzoLY29vb+fnFihVTWlpajuey2WzOjz09PZWenu78u91uV8mSJSVJgwYN0v33368lS5bIZrOpQYMGstvtNzynYRiaPn262rdvf/PfMAAARRxTpgEAKCB2u10Oh0MBAQGSpOnTp+fp63x8fFS/fn1FR0dLkvbt26d169Y5H69evbo2b94s6cqI8A8//OB8LCkpSYGBgbLZbFq7dq127tzpfKx169bOdch//vmnvvvuO+djnTt31tSpU3Xp0iVJ0qVLl/T777/fxHcNAEDRxQgxAAD5EBkZqeLFizv/PmrUKDVu3FgVKlRQ9+7d83ye+fPn66mnntI777yj6tWrKywsTGXKlJEkDRgwQI8++qjuvvtuVatWTeHh4c6ve+utt/TMM89o3LhxCgkJUZMmTZyPTZs2TX379lXt2rVVuXJlNWnSxHnOESNGKCUlRU2aNHGOHI8YMUJ16tTJx78GAABFi80wDMPsEAAAuLuLFy/qjjvukM1mU1xcnJo2baqYmBj5+/vf9DkvX76s4sWLO9cWh4eHKzo6OlvRDACAO2OEGAAAC1i/fr1eeuklSVJ6erqmTp2ar2JYkmJjY9WnTx8ZhiGHw6FnnnmGYhgAgCwYIQYAAAAAuCWaagEAAAAA3BIFMQAAAADALVEQAwAAAADcEgUxAAAAAMAtURADAAAAANwSBTEAAAAAwC1REAMAAAAA3NL/A2FvRDvH34qFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xf.sort_values(by=('All60 (norm)', True), kind='mergesort')['All60']\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Discharger</th>\n",
       "      <th>Intrans</th>\n",
       "      <th>FreePerfect</th>\n",
       "      <th>Free60/100</th>\n",
       "      <th>Free50/100</th>\n",
       "      <th>Free60</th>\n",
       "      <th>AllPerfect</th>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <th>All60/100</th>\n",
       "      <th>All50/100</th>\n",
       "      <th>All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</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>Intrans</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385622</td>\n",
       "      <td>0.450496</td>\n",
       "      <td>0.422237</td>\n",
       "      <td>0.409808</td>\n",
       "      <td>0.100372</td>\n",
       "      <td>0.675281</td>\n",
       "      <td>0.181019</td>\n",
       "      <td>0.211517</td>\n",
       "      <td>0.172892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>0.385622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.535841</td>\n",
       "      <td>0.347679</td>\n",
       "      <td>0.350159</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.412036</td>\n",
       "      <td>0.302966</td>\n",
       "      <td>0.256339</td>\n",
       "      <td>0.217824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>0.450496</td>\n",
       "      <td>0.535841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670234</td>\n",
       "      <td>0.624852</td>\n",
       "      <td>0.306996</td>\n",
       "      <td>0.348054</td>\n",
       "      <td>0.407894</td>\n",
       "      <td>0.326037</td>\n",
       "      <td>0.234178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>0.422237</td>\n",
       "      <td>0.347679</td>\n",
       "      <td>0.670234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840166</td>\n",
       "      <td>0.208151</td>\n",
       "      <td>0.242204</td>\n",
       "      <td>0.522919</td>\n",
       "      <td>0.638048</td>\n",
       "      <td>0.428336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>0.409808</td>\n",
       "      <td>0.350159</td>\n",
       "      <td>0.624852</td>\n",
       "      <td>0.840166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.163024</td>\n",
       "      <td>0.251307</td>\n",
       "      <td>0.249879</td>\n",
       "      <td>0.320272</td>\n",
       "      <td>0.457837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>0.100372</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.306996</td>\n",
       "      <td>0.208151</td>\n",
       "      <td>0.163024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404220</td>\n",
       "      <td>0.446834</td>\n",
       "      <td>0.339513</td>\n",
       "      <td>0.183914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>0.675281</td>\n",
       "      <td>0.412036</td>\n",
       "      <td>0.348054</td>\n",
       "      <td>0.242204</td>\n",
       "      <td>0.251307</td>\n",
       "      <td>0.404220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195540</td>\n",
       "      <td>0.222130</td>\n",
       "      <td>0.119517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>0.181019</td>\n",
       "      <td>0.302966</td>\n",
       "      <td>0.407894</td>\n",
       "      <td>0.522919</td>\n",
       "      <td>0.249879</td>\n",
       "      <td>0.446834</td>\n",
       "      <td>0.195540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.820271</td>\n",
       "      <td>0.425964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>0.211517</td>\n",
       "      <td>0.256339</td>\n",
       "      <td>0.326037</td>\n",
       "      <td>0.638048</td>\n",
       "      <td>0.320272</td>\n",
       "      <td>0.339513</td>\n",
       "      <td>0.222130</td>\n",
       "      <td>0.820271</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.573646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>0.172892</td>\n",
       "      <td>0.217824</td>\n",
       "      <td>0.234178</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.457837</td>\n",
       "      <td>0.183914</td>\n",
       "      <td>0.119517</td>\n",
       "      <td>0.425964</td>\n",
       "      <td>0.573646</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger          Intrans  FreePerfect  Free60/100  Free50/100    Free60  \\\n",
       "Discharger                                                                   \n",
       "Intrans            1.000000     0.385622    0.450496    0.422237  0.409808   \n",
       "FreePerfect        0.385622     1.000000    0.535841    0.347679  0.350159   \n",
       "Free60/100         0.450496     0.535841    1.000000    0.670234  0.624852   \n",
       "Free50/100         0.422237     0.347679    0.670234    1.000000  0.840166   \n",
       "Free60             0.409808     0.350159    0.624852    0.840166  1.000000   \n",
       "AllPerfect         0.100372     0.670971    0.306996    0.208151  0.163024   \n",
       "AllIntransPerfect  0.675281     0.412036    0.348054    0.242204  0.251307   \n",
       "All60/100          0.181019     0.302966    0.407894    0.522919  0.249879   \n",
       "All50/100          0.211517     0.256339    0.326037    0.638048  0.320272   \n",
       "All60              0.172892     0.217824    0.234178    0.428336  0.457837   \n",
       "\n",
       "Discharger         AllPerfect  AllIntransPerfect  All60/100  All50/100  \\\n",
       "Discharger                                                               \n",
       "Intrans              0.100372           0.675281   0.181019   0.211517   \n",
       "FreePerfect          0.670971           0.412036   0.302966   0.256339   \n",
       "Free60/100           0.306996           0.348054   0.407894   0.326037   \n",
       "Free50/100           0.208151           0.242204   0.522919   0.638048   \n",
       "Free60               0.163024           0.251307   0.249879   0.320272   \n",
       "AllPerfect           1.000000           0.404220   0.446834   0.339513   \n",
       "AllIntransPerfect    0.404220           1.000000   0.195540   0.222130   \n",
       "All60/100            0.446834           0.195540   1.000000   0.820271   \n",
       "All50/100            0.339513           0.222130   0.820271   1.000000   \n",
       "All60                0.183914           0.119517   0.425964   0.573646   \n",
       "\n",
       "Discharger            All60  \n",
       "Discharger                   \n",
       "Intrans            0.172892  \n",
       "FreePerfect        0.217824  \n",
       "Free60/100         0.234178  \n",
       "Free50/100         0.428336  \n",
       "Free60             0.457837  \n",
       "AllPerfect         0.183914  \n",
       "AllIntransPerfect  0.119517  \n",
       "All60/100          0.425964  \n",
       "All50/100          0.573646  \n",
       "All60              1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stack('Local').corr().loc[disc[1:], disc[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Local &amp; Ratio</th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intrans</th>\n",
       "      <td>0.123610</td>\n",
       "      <td>0.382633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>-0.066892</td>\n",
       "      <td>0.637520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>0.171901</td>\n",
       "      <td>0.223012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>0.082854</td>\n",
       "      <td>0.559254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>0.075947</td>\n",
       "      <td>0.592564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>-0.137421</td>\n",
       "      <td>0.331306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>-0.006503</td>\n",
       "      <td>0.963507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>0.139123</td>\n",
       "      <td>0.325293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>0.124082</td>\n",
       "      <td>0.380807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>0.024868</td>\n",
       "      <td>0.861083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local & Ratio             r         p\n",
       "Discharger                           \n",
       "None                    NaN       NaN\n",
       "Intrans            0.123610  0.382633\n",
       "FreePerfect       -0.066892  0.637520\n",
       "Free60/100         0.171901  0.223012\n",
       "Free50/100         0.082854  0.559254\n",
       "Free60             0.075947  0.592564\n",
       "AllPerfect        -0.137421  0.331306\n",
       "AllIntransPerfect -0.006503  0.963507\n",
       "All60/100          0.139123  0.325293\n",
       "All50/100          0.124082  0.380807\n",
       "All60              0.024868  0.861083"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.stack(['Local', 'Discharger'])\n",
    " .to_frame('Ratio')\n",
    " .reset_index('Local')\n",
    " .groupby(level='Discharger')\n",
    " .apply(lambda x: pearsonr(x, 'Local', 'Ratio'))\n",
    " .loc[disc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Local &amp; Ratio</th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intrans</th>\n",
       "      <td>0.140772</td>\n",
       "      <td>0.329511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FreePerfect</th>\n",
       "      <td>-0.066892</td>\n",
       "      <td>0.637520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60/100</th>\n",
       "      <td>0.193480</td>\n",
       "      <td>0.178217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free50/100</th>\n",
       "      <td>0.095686</td>\n",
       "      <td>0.517669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free60</th>\n",
       "      <td>0.094629</td>\n",
       "      <td>0.541198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllPerfect</th>\n",
       "      <td>-0.137421</td>\n",
       "      <td>0.331306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AllIntransPerfect</th>\n",
       "      <td>-0.006927</td>\n",
       "      <td>0.961920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60/100</th>\n",
       "      <td>0.170203</td>\n",
       "      <td>0.258107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All50/100</th>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.322164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All60</th>\n",
       "      <td>0.033980</td>\n",
       "      <td>0.844030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local & Ratio             r         p\n",
       "Discharger                           \n",
       "None                    NaN       NaN\n",
       "Intrans            0.140772  0.329511\n",
       "FreePerfect       -0.066892  0.637520\n",
       "Free60/100         0.193480  0.178217\n",
       "Free50/100         0.095686  0.517669\n",
       "Free60             0.094629  0.541198\n",
       "AllPerfect        -0.137421  0.331306\n",
       "AllIntransPerfect -0.006927  0.961920\n",
       "All60/100          0.170203  0.258107\n",
       "All50/100          0.152771  0.322164\n",
       "All60              0.033980  0.844030"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.where(df.groupby(axis=1, level='Discharger').any())\n",
    " .stack(['Local', 'Discharger'])\n",
    " .to_frame('Ratio')\n",
    " .reset_index('Local')\n",
    " .groupby(level='Discharger')\n",
    " .apply(lambda x: pearsonr(x, 'Local', 'Ratio'))\n",
    " .loc[disc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intrans</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FreePerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free50/100</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllIntransPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All50/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
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       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger   None       Intrans       FreePerfect       Free60/100        \\\n",
       "Local       False True    False True        False True       False True    \n",
       "Language                                                                   \n",
       "Ainu          0.5   0.5    0.57  0.43        0.53  0.47       0.47  0.53   \n",
       "Aleut         0.5   0.5    1.00  0.00        0.46  0.54       0.41  0.59   \n",
       "Ayacucho      0.5   0.5    0.29  0.71        0.44  0.56       0.33  0.67   \n",
       "Bella Coola   0.5   0.5    0.50  0.50        0.47  0.53       0.19  0.81   \n",
       "Chuckchi      0.5   0.5    0.67  0.33        0.50  0.50       0.26  0.74   \n",
       "Darai         0.5   0.5    0.74  0.26        0.76  0.24       0.76  0.24   \n",
       "Fox           0.5   0.5    0.50  0.50        0.50  0.50       0.48  0.52   \n",
       "Hixkaryana    0.5   0.5    0.52  0.48        0.48  0.52       0.52  0.48   \n",
       "Jaqaru        0.5   0.5    0.30  0.70        0.46  0.54       0.36  0.64   \n",
       "Jumjum        0.5   0.5    0.39  0.61        0.46  0.54       0.44  0.56   \n",
       "Karuk         0.5   0.5    0.38  0.62        0.52  0.48       0.55  0.45   \n",
       "Ket           0.5   0.5    0.47  0.53        0.48  0.52       0.38  0.62   \n",
       "Kunama        0.5   0.5    0.48  0.52        0.48  0.52       0.52  0.48   \n",
       "Lakota        0.5   0.5    0.43  0.57        0.47  0.53       0.00  1.00   \n",
       "Maricopa      0.5   0.5    0.20  0.80        0.50  0.50       0.33  0.67   \n",
       "Maung         0.5   0.5    0.45  0.55        0.50  0.50       0.52  0.48   \n",
       "Mordvin       0.5   0.5    0.53  0.47        0.50  0.50       0.69  0.31   \n",
       "Nocte         0.5   0.5    0.38  0.62        0.64  0.36       0.71  0.29   \n",
       "Reyesano      0.5   0.5     NaN   NaN        1.00  0.00        NaN   NaN   \n",
       "Sahu          0.5   0.5    0.52  0.48        0.47  0.53       0.46  0.54   \n",
       "Siuslawan     0.5   0.5    0.50  0.50        0.48  0.52       0.48  0.52   \n",
       "Tepehua       0.5   0.5    0.42  0.57        0.49  0.51       0.38  0.62   \n",
       "Thangmi       0.5   0.5    0.67  0.33        0.55  0.45       0.71  0.29   \n",
       "Turkana       0.5   0.5    0.29  0.71        0.50  0.50       0.41  0.59   \n",
       "Wardaman      0.5   0.5    0.43  0.57        0.46  0.54       0.43  0.57   \n",
       "Yimas         0.5   0.5    0.50  0.50        0.50  0.50       0.50  0.50   \n",
       "\n",
       "Discharger  Free50/100        ... AllPerfect       AllIntransPerfect        \\\n",
       "Local            False True   ...      False True              False True    \n",
       "Language                      ...                                            \n",
       "Ainu              1.00  0.00  ...       0.53  0.47              1.00  0.00   \n",
       "Aleut             0.57  0.43  ...       0.46  0.54              1.00  0.00   \n",
       "Ayacucho          0.33  0.67  ...       0.48  0.52              0.18  0.82   \n",
       "Bella Coola       0.19  0.81  ...       0.47  0.53              0.53  0.47   \n",
       "Chuckchi          0.26  0.74  ...       0.50  0.50              0.67  0.33   \n",
       "Darai             0.76  0.24  ...       0.74  0.26              0.74  0.26   \n",
       "Fox               0.43  0.57  ...       0.50  0.50              0.50  0.50   \n",
       "Hixkaryana        0.52  0.48  ...       0.48  0.52              0.52  0.48   \n",
       "Jaqaru            0.36  0.64  ...       0.36  0.64              1.00  0.00   \n",
       "Jumjum            0.44  0.56  ...       0.46  0.54              0.36  0.64   \n",
       "Karuk             0.47  0.53  ...       0.52  0.48              0.39  0.61   \n",
       "Ket               0.38  0.62  ...       1.00  0.00              1.00  0.00   \n",
       "Kunama            0.69  0.31  ...       0.56  0.44              0.49  0.51   \n",
       "Lakota            0.00  1.00  ...       0.47  0.53              0.26  0.74   \n",
       "Maricopa           NaN   NaN  ...       0.50  0.50              0.20  0.80   \n",
       "Maung             0.84  0.16  ...       0.50  0.50              0.45  0.55   \n",
       "Mordvin           0.69  0.31  ...       0.53  0.47              0.51  0.49   \n",
       "Nocte             0.71  0.29  ...       0.64  0.36              0.44  0.56   \n",
       "Reyesano           NaN   NaN  ...       1.00  0.00               NaN   NaN   \n",
       "Sahu              0.38  0.62  ...       0.47  0.53              1.00  0.00   \n",
       "Siuslawan         0.49  0.51  ...       0.57  0.43              0.50  0.50   \n",
       "Tepehua           0.30  0.70  ...       0.49  0.51              0.42  0.57   \n",
       "Thangmi           0.71  0.29  ...       0.55  0.45              1.00  0.00   \n",
       "Turkana           0.55  0.45  ...       0.50  0.50              0.29  0.71   \n",
       "Wardaman          0.39  0.61  ...       0.44  0.56              0.34  0.66   \n",
       "Yimas             0.50  0.50  ...       0.49  0.51              0.59  0.41   \n",
       "\n",
       "Discharger  All60/100       All50/100       All60        \n",
       "Local           False True      False True  False True   \n",
       "Language                                                 \n",
       "Ainu             0.47  0.53      1.00  0.00   NaN   NaN  \n",
       "Aleut            0.41  0.59      0.57  0.43  0.65  0.35  \n",
       "Ayacucho         0.25  0.75      0.25  0.75  0.00  1.00  \n",
       "Bella Coola      0.19  0.81      0.19  0.81  1.00  0.00  \n",
       "Chuckchi         0.26  0.74      0.26  0.74  0.24  0.76  \n",
       "Darai             NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Fox              0.30  0.70      0.00  1.00   NaN   NaN  \n",
       "Hixkaryana       0.52  0.48      0.52  0.48  1.00  0.00  \n",
       "Jaqaru           0.30  0.70      0.30  0.70  0.30  0.70  \n",
       "Jumjum           0.41  0.59      0.41  0.59  0.42  0.58  \n",
       "Karuk            0.58  0.42      0.58  0.42  1.00  0.00  \n",
       "Ket              1.00  0.00      1.00  0.00  1.00  0.00  \n",
       "Kunama           0.61  0.39      0.69  0.31  1.00  0.00  \n",
       "Lakota           0.00  1.00      0.00  1.00   NaN   NaN  \n",
       "Maricopa         0.33  0.67       NaN   NaN   NaN   NaN  \n",
       "Maung            1.00  0.00      1.00  0.00  1.00  0.00  \n",
       "Mordvin          0.67  0.33      0.67  0.33  0.76  0.24  \n",
       "Nocte            0.71  0.29      0.71  0.29  0.55  0.45  \n",
       "Reyesano          NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Sahu             0.46  0.54      0.38  0.62   NaN   NaN  \n",
       "Siuslawan         NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Tepehua          0.38  0.62      0.30  0.70  0.00  1.00  \n",
       "Thangmi          0.71  0.29      0.62  0.38  0.00  1.00  \n",
       "Turkana          1.00  0.00      1.00  0.00  1.00  0.00  \n",
       "Wardaman         0.31  0.69      0.37  0.63  1.00  0.00  \n",
       "Yimas            0.46  0.54      0.57  0.43  0.17  0.83  \n",
       "\n",
       "[26 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with np.errstate(invalid='ignore'):\n",
    "    _ = nf.round(2)[disc]\n",
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_16_row3_col1, #T_16_row6_col1 {\n",
       "  background-color: lime;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_16\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Local & Ratio</th>\n",
       "      <th id=\"T_16_level0_col0\" class=\"col_heading level0 col0\" >r</th>\n",
       "      <th id=\"T_16_level0_col1\" class=\"col_heading level0 col1\" >p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Discharger</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row0\" class=\"row_heading level0 row0\" >None</th>\n",
       "      <td id=\"T_16_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_16_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row1\" class=\"row_heading level0 row1\" >Intrans</th>\n",
       "      <td id=\"T_16_row1_col0\" class=\"data row1 col0\" >0.094043</td>\n",
       "      <td id=\"T_16_row1_col1\" class=\"data row1 col1\" >0.515945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row2\" class=\"row_heading level0 row2\" >FreePerfect</th>\n",
       "      <td id=\"T_16_row2_col0\" class=\"data row2 col0\" >-0.201486</td>\n",
       "      <td id=\"T_16_row2_col1\" class=\"data row2 col1\" >0.152042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row3\" class=\"row_heading level0 row3\" >Free60/100</th>\n",
       "      <td id=\"T_16_row3_col0\" class=\"data row3 col0\" >0.281779</td>\n",
       "      <td id=\"T_16_row3_col1\" class=\"data row3 col1\" >0.047429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row4\" class=\"row_heading level0 row4\" >Free50/100</th>\n",
       "      <td id=\"T_16_row4_col0\" class=\"data row4 col0\" >0.009490</td>\n",
       "      <td id=\"T_16_row4_col1\" class=\"data row4 col1\" >0.948958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row5\" class=\"row_heading level0 row5\" >Free60</th>\n",
       "      <td id=\"T_16_row5_col0\" class=\"data row5 col0\" >0.122306</td>\n",
       "      <td id=\"T_16_row5_col1\" class=\"data row5 col1\" >0.428998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row6\" class=\"row_heading level0 row6\" >AllPerfect</th>\n",
       "      <td id=\"T_16_row6_col0\" class=\"data row6 col0\" >-0.302617</td>\n",
       "      <td id=\"T_16_row6_col1\" class=\"data row6 col1\" >0.029216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row7\" class=\"row_heading level0 row7\" >AllIntransPerfect</th>\n",
       "      <td id=\"T_16_row7_col0\" class=\"data row7 col0\" >-0.268690</td>\n",
       "      <td id=\"T_16_row7_col1\" class=\"data row7 col1\" >0.059197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row8\" class=\"row_heading level0 row8\" >All60/100</th>\n",
       "      <td id=\"T_16_row8_col0\" class=\"data row8 col0\" >0.028865</td>\n",
       "      <td id=\"T_16_row8_col1\" class=\"data row8 col1\" >0.848976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row9\" class=\"row_heading level0 row9\" >All50/100</th>\n",
       "      <td id=\"T_16_row9_col0\" class=\"data row9 col0\" >-0.058414</td>\n",
       "      <td id=\"T_16_row9_col1\" class=\"data row9 col1\" >0.706442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16_level0_row10\" class=\"row_heading level0 row10\" >All60</th>\n",
       "      <td id=\"T_16_row10_col0\" class=\"data row10 col0\" >-0.280094</td>\n",
       "      <td id=\"T_16_row10_col1\" class=\"data row10 col1\" >0.098013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x278286c9b90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nf.stack(['Local', 'Discharger'])\n",
    " .to_frame('Ratio')\n",
    " .reset_index('Local')\n",
    " .groupby(level='Discharger')\n",
    " .apply(lambda x: pearsonr(x, 'Local', 'Ratio'))\n",
    " .loc[disc]\n",
    " .style.applymap(lambda x: x < .05 and 'background-color: lime' or '', subset=['p'])\n",
    " .set_uuid('16'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2VP78+TMjIwAAAAAAWS7DBXKvXr3Us2dPHThwQEOHDlV4ePhtnztp0iT961//yox8AAAAAACYIkMF8tmzZ7V582aFhYVJktq2bauYmBgdOnQozXP37NmjJUuW6NVXX83cpAAAAAAAZKEMFcgxMTEqVKiQPD09JUkOh0PFihVTdHS02/MSExMVERGhadOmKUeOHJmfFgAAAACALJKpTbpGjx6tNm3a6LHHHsvMlwUAAAAAIMt5ZuRJ/v7+On36tJKSkuTp6SnDMBQdHa1ixYq5Pe+XX35RdHS0PvzwQyUlJSk2NlaBgYHatGmTChQokOZ1hw0bJi8vL0lSaGioQkNDM+FTAgAAAABAWrp0qZYuXSpJSkhIuOvzM1QgFyxYUJUrV1ZUVJTCw8O1ePFiFS1aVCVLlnR73q+//up6+9ixYwoODtaxY8du+7pvvvmmfHx8MhIBAAAAAIC/JPVAbGxsrD766KM7Pj/DU6ynTZumadOmKSgoSG+//bbmzJkjSerRo4e+/vrrfxAZAAAAAADrZWgEWZJKly6tdevWpTk+c+bMdJ8fGBioy5cv/+1gAAAAAACYKVObdAEAAAAAkF1RIAMAAAAAIApkAAAAAAAkUSADAAAAACCJAhkAAAAAAEkUyAAAAAAASPoL2zwBAAAAAJAVYmNjLf14JwpkAAAAAIAlUlJSlCtXLvn7+//j18qVK5dSUlL+0WswxRoAAAAAYAkPDw/Fx8dnymvFx8fLw+OflbgUyAAAAAAAiAIZAAAAAABJFMgAAAAAAEiiQAYAAAAAQBIFMgAAAAAAkiiQAQAAAACQRIEMAAAAAIAkCmQAAAAAACRRIAMAAAAAIIkCGQAAAAAASRTIAAAAAABIokAGAAAAAEASBTIAAAAAAJIokAEAAAAAkESBDAAAAACAJApkAAAAAAAkUSADAAAAACCJAhkAAAAAAEkUyAAAAAAASKJABgAAAABAEgUyAAAAAACSKJABAAAAAJBEgQwAAAAAgCQKZAAAAAAAJFEgAwAAAAAgiQIZAAAAAABJFMgAAAAAAEiiQAYAAAAAQBIFMgAAAAAAkiiQAQAAAACQRIEMAAAAAIAkCmQAAAAAACRRIAMAAAAAIIkCGQAAAAAASRTIAAAAAABIokAGAAAAAEASBTIAAAAAAJIokAEAAAAAkESBDAAAAACAJApkAAAAAAAkUSADAAAAACCJAhkAAAAAAEkUyAAAAAAASJI8rQ4AAICdxMXFKSEh4S99TGxsbKZm+KevZ4c8Xl5e8vb2ztQcAABktQwXyAcPHlTXrl11/vx55cuXT5GRkSpXrpzbc1asWKFXX31V165dk8PhUIsWLfT222/Lw4OBagCA/cXFxal48eI6c+aMpTn8/f0tPf+t/k4ePz8/HT16lCIZAJCtZLhA7tWrl3r27Knw8HAtWrRI4eHh2rRpk9tzfH19tWDBApUoUUJxcXFq3Lix5s6dq/Dw8MzODQBApktISNCZM2cUExMjHx8fq+NkW7GxsfL391dCQgIFMgAgW8lQgXz27Flt3rxZP/30kySpbdu26tu3rw4dOqSSJUu6nhcSEuJ629vbW8HBwTp27FjmJgYAIIv5+PhQIAMAcB/K0NznmJgYFSpUSJ6eN+tph8OhYsWKKTo6+rYfc+bMGS1atEhPPfVU5iQFAAAAACALZcni4NjYWD399NN65ZVXVLVq1aw4BQAAAAAAmSpDU6z9/f11+vRpJSUlydPTU4ZhKDo6WsWKFUvz3KtXr6pZs2Zq1aqVBg4ceMfXHTZsmLy8vCRJoaGhCg0N/RufAgAAAAAAaS1dulRLly6VpAztUpGhArlgwYKqXLmyoqKiFB4ersWLF6to0aJu648l6dq1a2rWrJmaNWumESNG3PV133zzTdZ4AQDua4GBgVqyZImCg4Oz5PVXrVqll156Sdu3b8+S1wcAwM5SD8TGxsbqo48+uuPzMzzFetq0aZo2bZqCgoL09ttva86cOZKkHj166Ouvv5YkTZ48WRs3btSXX36p4OBgBQcHa9y4cX/3cwEAAAAAwDQZ3uapdOnSWrduXZrjM2fOdL09fPhwDR8+PHOSAQBwn1q6dKn+85//KCkpSb6+vvrkk09UtmxZSdKcOXM0efJkGYahnDlzatGiRSpatKhatGihCxcu6MaNG6pUqZJmzJihBx980OLPBACA7CVLmnQBAIC/5+zZs+rYsaM+/fRT7dy5Uz179tSzzz4rwzC0atUqvfHGG/rhhx+0Y8cOrV69WgULFlSOHDk0b948bd68Wbt371a+fPk0ZcoUqz8VAACyHQpkAABsZMOGDapQoYIqVKggSerUqZNOnTqlkydP6rvvvlPnzp1VqFAhSdIDDzygBx54QIZhaNKkSQoJCVHFihX13XffseYYAIC/gQIZAIBsbt68eVqxYoV++eUX7dq1S4MHD1ZcXJzVsQAAyHYokAEAsJGaNWtq165d2r17tyRpwYIFKlKkiIoUKaKnn35aUVFROn36tCTp+vXrun79ui5duqRHHnlEPj4+unr1qiIjIy38DAAAyL4y3KQLAABkjdDQUOXMmdP1/uTJk9WlSxdXk64vvvhCDodDdevW1euvv67Q0FA5HA55eXlp0aJF6tKli7766iuVLl1aBQoU0BNPPKHjx49b+BkBAJA9USADAGChY8eOpXs8LCws3eNdu3ZV165d0xxftmxZus+vX78+65EBAMggplgDAAAAACAKZAAAAAAAJFEgAwAAAAAgiQIZAAAAAABJFMgAAAAAAEiiQAYAAAAAQBIFMgAAAAAAktgHGQCADImLi1NCQkKWn8fLy0ve3t5Zeo5jx46pePHiOnjwoEqWLJml5wIAIDuhQAYA4C7i4uJUpEgRXbx4McvP9dBDD+nkyZMZLpLr16+vtWvXysvLy3WscuXKWr16dVZFBADgnsUUawAA7iIhIcGU4liSLl68+JdHql955RVdu3bN9Y/iGACAv4cCGQCAe8zu3bvVqFEjFShQQPny5VONGjW0YsWK2z5/x44dqlevnvLnzy9fX19VqVJF+/fvdz0+d+5cVapUSfny5VO5cuW0YMECMz4NAABMR4EMAMA96NVXX1V0dLTOnj2r5s2b65lnntHZs2fTfe6LL76oRo0a6fz58zp37pxmzZql/PnzS5IiIyM1YsQIzZo1S5cuXdK0adPUs2dPrVmzxsTPBgAAc1AgAwCQzb333nvKnz+/69+2bdvUpEkT5c6dW7ly5dKoUaPkcDi0YcOGdD/ey8tL0dHROn78uDw9PRUcHKxHH31UkjRx4kQNHz5cVatWlYeHh+rUqaN27dopMjLSxM8QAABzUCADAJDNDR48WJcvX3b9q1evntq3b69ixYrJx8dH+fPnV2xs7G1HkCMjI+VwONSwYUMVLVpUL730kq5duyZJOnjwoAYNGuRWgM+fP1+nTp0y81MEAMAUdLEGAOAeExERoXz58mnTpk169NFHZRiGfH19ZRhGus8PCAjQjBkzJEmHDh1Sq1at9OCDD2rcuHHy8/PT6NGj1aVLFzM/BQAALMEIMgAA95grV64oT5488vX11Z9//qn//Oc/rhHh9ERGRurEiRMyDEM+Pj7y9PSUp+fNe+gvvfSSxowZo02bNiklJUXx8fHatGmTtmzZYtanAwCAaSiQAQC4x3zwwQfasWOHfH19VbZsWRUpUkRFixa97fNXrlyp6tWrK0+ePKpUqZJq1aqloUOHSpIGDBigUaNGqXfv3nrooYdUpEgRDRkyRH/++adZnw4AAKZhijUAAHfh5eWlhx56yJS9kB966CF5eXll+PmrVq1Kc6x69eppRnj79evnejswMNBtuvWnn356x3N06tRJnTp1ynAmAACyKwpkAADuwtvbWydPnlRCQkKWn8vLy0ve3t5Zfh4AAJAWBTIAABng7e1N4QoAwD2ONcgAAAAAAIgCGQAAAAAASRTIAAAAAABIokAGAAAAAEASBTIAAAAAAJIokAEAAAAAkMQ2TwAAZEhcXBz7IAMAcI+jQAYA4C7i4uJUvHhxnTlzJsvP5efnp6NHj1IkAwBgAQpkAADuIiEhQWfOnFFMTIx8fHyy7DyxsbHy9/dXQkJChgrkPHnyuGVMTk5W7ty5Xcd++OEHPfHEE1mSFQCAexEFMgAAGeTj45OlBfJfde3aNdfbI0aM0Jo1a7Rq1ap0n5uQkCAvLy+TkgEAkD3RpAsAgHvQqFGjVKdOHY0cOVKFCxdWcHCwJMnhcGjZsmWu5x07dkwOh0OHDh1yHfv+++9Vo0YN+fr6qlSpUvrggw/Mjg8AgCUYQQYA4B61fv16NW3aVEeOHFFKSkqGPmblypXq2LGjFi9erAYNGmjv3r1q3ry5Hn74YXXq1CmLEwMAYC1GkAEAuEc9+uijGjlypLy9vfXAAw9k6GMmTZqkPn36qFGjRvLw8FD58uXVu3dvzZkzJ4vTAgBgPUaQAQC4RwUEBMjhcPyljzl48KCWLVumTz75xHUsOTlZxYoVy+x4AADYDgUyAAD3KA+PtBPF8uTJoz///NP1/qlTp9we9/PzU4cOHfTaa69leT4AAOyGKdYAANxHqlatqsjISMXFxemPP/7Q6NGj3R4fMGCApkyZouXLlyspKUlJSUnavXu3Vq9ebVFiAADMwwgyAAAZFBsbm61fX5I++ugjde/eXY888ohKlCihV155RT/99JPr8datW8vb21uvvfaafv/9d0lSUFCQXnnllSzPBgCA1SiQAQC4Cy8vL/n5+cnf3z/Lz+Xn5/e39iseO3as2/ujRo3SqFGj0jyvbNmyWrdunduxsLAwt/ebNWumZs2a/eUMAABkdxTIAADchbe3t44ePaqEhIQsP5eXl5e8vb2z/DwAACAtCmQAADLA29ubwhUAgHscTboAAAAAABAFMgAAAAAAkiiQAQAAAACQRIEMAAAAAIAkmnQBAJCGGfsR38v4+gEAsisKZAAA/o+Z+x3f6/7ufs4AAFiJAhkAgP/zd/c7jo2NzdSiOiYmRj4+Pn/74+2Qh/2cAQDZUYYL5IMHD6pr1646f/688uXLp8jISJUrVy7N82bNmqW3335bKSkpatiwoT7++GPlzJkzU0MDAJBV7LDfsY+Pzz8qkDOb3fIAAJBVMtykq1evXurZs6cOHDigoUOHKjw8PM1zjh49qpEjR+rXX3/VoUOH9Mcff2j69OmZmRcAAAAAgCyRoQL57Nmz2rx5s8LCwiRJbdu2VUxMjA4dOuT2vEWLFqlly5by8/OTw+FQ7969NX/+/MxPDQAAAABAJstQgRwTE6NChQrJ0/PmjGyHw6FixYopOjra7XnR0dEKCAhwvR8YGJjmOQAAAAAA2JElTboMw5B0520gMnuLiH/6euTJmo/NitcjT9Z+fGa/XmbmsVOWzHg98mTtx2fma93LX5vMeD3yZM3HZsXrkSdrPz6zX4/fg1n38Zn9evdyHjtludvrOR9z1qPpcRh3evT/nD17ViVLltTFixfl6ekpwzBUqFAhrVmzRiVLlnQ9b/z48Tp8+LCmTp0qSfr+++/15ptvas2aNW6vd+LECbbQAAAAAACYLiYmRkWLFk33sQyNIBcsWFCVK1dWVFSUwsPDtXjxYhUtWtStOJZurk2uU6eORo0apUcffVRTp05V+/bt07xe4cKFFRMTo7x588rhcPyNTwkAAAAAgIwzDENXr15V4cKFb/ucDI0gS9L+/fsVHh6uCxcuyMfHR3PmzFGFChXUo0cPtWzZUi1btpQkzZgxQ2+//bYkqX79+po6dSrbPAEAAAAAbC/DBTIAAAAAAPeyDO+DDAAAAADAvYwCGQAAAAAAWbTNEwD7qFevnn755Rf5+vq6Nc0zDEMOh0MXL160MB2AzHT27Fm9/vrr2rFjh+Li4lzHt27damEq+7hx44amTJmi7du3u319vvzyS9OzxMTE6NFHH5WXl5d+++03bdu2TV27dlXevHlNz2JnzpWCdmj6eurUKe3evdvte8fZowe4nR9++EEHDx5UUlKS69jAgQMtTGQfxYsXT/dn+8iRI1l63mxZIL/44ov6+OOP73oMSM/p06d19OhRt19EdevWtTCRtRYsWCBJ2r59u7VBbuHh4ZHuL8Xk5GQL0iA9dr65cvDgQZUqVcrt2Lp161SrVi2LEtlD9+7dVadOHS1fvlwTJkzQtGnTFBISYnUs24iIiJCPj4/Wrl2rQYMGKTIy0rK/D61atdLatWt18uRJtW/fXnXq1NEvv/yiL774wpI8dnP69Gl1795dK1askMPhUKNGjTRjxgwVKlTIkjyzZ8/WG2+8oYsXL6pUqVLasWOHatasaVmBvHr16nSP38/XO06tW7fWkiVL7nrMDJ06ddLevXsVEhKiHDlySLLHzZ4NGzbo8OHDbtfKXbp0MT3Ht99+63o7Li5On332mR5++OEsP2+2bNJVuXLlNHe7g4ODLb3A37hxo5YtWyZJatKkiapVq2Z6BrterMbExKhPnz46ceKEtm/fru3bt2vlypV6+eWXTc8ybtw4jR8/XiVKlHD7RbRx40bTszj997//TTNaMXHiRMvy2MWff/7pevvGjRuaO3eukpOTNWTIEEvyHDt2TO+8806aPxgrVqywJE9kZKTCw8Pdjg0dOlTvvPOOaRlOnz6tQoUK6fjx4+k+HhAQYFqWW/n7+2vChAl6/vnnJUnvvvuuZsyYoYMHD5qe5eDBg+rfv3+aUVsrfic7/1ZWqFBBu3btUkJCgurVq6d169aZnsUpvRGC/Pnzq1atWhozZoweeugh07I4vy4VK1bUzp07dfXqVbVo0eK2xUZWcl7rTJ8+XWfPntWIESNUqVIl7dixw/QsqdlllPTpp59WrVq19OKLL0qSpk6dqt9++03ffPON6Vmkm987q1evVsOGDbVt2zatXr1akZGRmj17tiV5Ul+HxsXFaf/+/Spfvryls0USExN19OhRt++dihUrmp4jvTrC+TNvtjJlymjPnj2ua1I76NOnj5YuXarg4GC3a+X//ve/Fie7qXbt2lq7dm2WniNbjSAvXLhQCxYs0NGjR9WmTRvX8StXrihPnjyW5Zo+fbrGjh2rNm3ayOFw6Nlnn9XIkSPVo0cPU3PYdSSwV69e6tixo8aPHy9JKl++vDp37mxJgTx79mwdPnzYlLtPGdG/f38dPXpUW7ZsUYcOHfTFF1+oSZMmlmRZvXq1Bg8erEOHDikpKcl1YyU2NtaSPA8++KDb2wMHDlSNGjUsK5Cff/55NWrUSH379rXFH7KPP/5Y/v7+atSokSRpzJgxpv9xd47UWFkI386qVavUrl07rVixQidPnpR08464FSIiItSnTx+98cYbWrBggaZMmaLAwEBLsnh5eUmSvL29deHCBfn6+ur8+fOWZHEKCwvTyZMn1b17d0nSnDlzlD9/fhmGod69e5t6UZY7d25Jkqenp/7880/lzZtX586dM+38qcXHxys+Pl4///yzXnrpJUsy3MpOo6QxMTFuxfCrr76q4OBg03M4eXl5ydfX13UDtW7dupb+v23atMnt/Y0bNyoyMtKaMLo5EhgREaFLly7pwQcf1OXLl1WsWDEdPXrUtAzTpk3T1KlTdeDAAVWuXNl1/MqVKypXrpxpOVILDAxUfHy8HnjgAUvOn55ly5Zp79698vb2tjpKGhcuXNCZM2ey/DzZqkAuU6aMWrVqpa1bt6pVq1au4z4+Pq6LRCt8+OGH2rJliwoUKCBJGjZsmBo1amR6gWzXi9WzZ88qLCxMEyZMkHTzwsPT05pvvUcffdQ2xbEkrVy5Ujt27FBISIgmTJigIUOGqGvXrpZkiYiI0Lhx41S9enVbFIC32rdvn6UX8nFxcXrrrbcsO/+tlixZoqZNm2r+/Plavny5li9frh9//NGSLFu3btWwYcN05MgRt9H1rF4jdCf/+te/NGHCBDVt2lSPPPKItm3bZupIZGqxsbFq166dxo4dqwoVKmjatGmqUaOGhg0bZnqWoKAgXbhwQWFhYapRo4Z8fHxUpUoV03Ok9tNPP7ndvKhdu7Zq1KihjRs3qmzZsqZmeeihh3Tp0iU9+eSTCg0N1SOPPKKiRYuamsGpQ4cO8vPzU1BQkGrXrq3Tp09bfhE9adIkbdu2TQ0bNtSWLVtco6RWMAxDZ86ckZ+fnyTpzJkzsnJSZK5cuWQYhoKCgvT+++8rICBA165dsyzPrapXr65evXpZdv6RI0dq/fr1at26tbZt26aoqCjTZ0M0a9ZMpUuXVp8+fTRp0iTXcR8fH0tGsiVpwoQJaty4serXr+9WkL722muW5JFu1hO5cuWy7PyphYSEuGYYJScn6/jx43rllVey/LzZqkCuVKmSKlWqpBYtWqhAgQKKj4+3zX+gszi+9W0r2K0Ji6enp9sfrUuXLln2R6xJkyZ66aWX1LFjR7dfRFb9YvT29nattU1MTJSfn59OnTplSRYfHx89++yzlpw7PamXCiQnJ8swDE2ZMsWyPOXLl1d0dLSKFStmWYbUChcurAULFqhly5by8/PTzz//bNnd3q5du6pv376qVauWbW6uTJgwQR9//LFWrlyprVu3qmbNmpozZ47q1atnepacOXNKkvLmzatjx47Jz8/Psps9UVFRkqQBAwaoSpUqunz5spo1a2ZJFqeLFy/q+vXrruLv+vXrunz5siSZ/j393XffKUeOHBozZow+//xzXb582ZJ1d5I0YsQI9e3bVz4+PnI4HMqbN68WLVpkSRYnO42SDh48WCEhIWrevLkk6ccff3TNVLPC2LFjFRsbq3fffVe9e/fW5cuXLe2Nk3pGUXJysjZs2KDExETL8nh4eCggIMD1vRMWFuZWpJohICBAAQEB+v33300975385z//kZeXl+Li4iz9/0mtRo0aevbZZ9WuXTu338FWzBR5//33XW97enqqRIkSpvQZyFYFstOZM2fUoEEDXb58WSdOnNCWLVu0cOFCvfvuu5bkKVWqlIYPH+66Mzdjxow0zWHMZLcmLM8995x69eql2NhYzZw5U1OnTjV9dN1p7ty5kqSvvvrKdczhcFg20pU3b15dv35dderUUVhYmPz8/CwbIWjbtq0+++wztWvXzjUN00qplwp4enrKz8/P0uLr3LlzqlSpkmrVquX2B8Ps7rbPPPOM23pNT09PeXl5qXPnzpbkkaQcOXJYOjKRnmXLlmnjxo16+OGHVbt2bdWuXVsdO3bUvn37TM9St25dXbhwQX379lWVKlXk5eWl9u3bm57jVnXq1LE6giSpY8eOqlmzpp577jlJ0uLFi9WhQwddu3bN9KnoqdfbhYWFmXru9Dz44IM6dOiQ283uIkWKWJbHTqOknTt3VkhIiFatWiVJGjRokGXTZCWpYcOGkqR8+fLp559/tiyHU+qZlp6enipVqpQ+/fRTy/I4bxQWLVpU//vf/xQYGKhLly5ZkqVBgwbpNsKyoqfI/v37tX//ftPPeyebN2+WJH3yySeuYw6Hw/QCOTk5WTNnztRnn31m6nmlbNqkq379+ho7dqz69eunbdu2yTAMlS9fXnv27LEkz7lz59SvXz+3Jl2TJ09WwYIFLcljxyYs8+fP15IlS2QYhlq3bq2OHTtalsVO/vjjD+XPn18pKSmaMGGCLl++rAEDBsjf39/0LF999ZXCwsJ0/fp1Sf+/uRtdo2+63YWF2VPi73aBY8UU/X//+9/q1q2bqlatavq5b8f5/euckVG4cGFdvXrV8i1yYmJidOXKFZUvX96S8//444966aWXdOTIEdfMDDv8nH/33Xeui9OGDRuqRYsWluSwakuR9NhhzeatVqxYoSpVquj8+fOuUdK33npLjRs3tiyT1T744IM7Pt6/f3+Tktjb/Pnz1axZMx05ckTt27fX5cuX9f7776tTp06mZ/nuu+9cb8fFxWnevHkKCgqyZBlVy5YtFRUVJR8fH9PPnR3UqFHDkv4h2bJArlq1qjZv3qyQkBBt27ZNktzevt9Vr15dGzduVLVq1fTjjz/K19dXpUuXtqR7q3TzguzWKXzpHTOTs/mJE7+YpBIlSmjmzJmqWrWq20ht6mZZZrp1XavzQt7Kda1w51wblJiYqP3796tkyZJuo+tWdkvdt2+f2rZt6yqQixYtqkWLFql06dKW5Fm/fr2WLVvm2o6mZs2aluQICgrSlClT0kyHt+rn3G5S32hPvaXIyJEjTc8SEhKiJUuWpFmzaeU0YjuxS++Dbt263fYxh8NhWRdrSUpJSdGZM2fcvj5WLRM6f/68Hnnkkbses0JSUpIaNmxoSbf6du3aacuWLWratKnb30+rdzOxS8fxV199VRcvXlR4eLhbQ+aszpItp1h7enoqMTHRdZc3JibG8nVvdtkvTLJfE5Zhw4alKYbTO2aG9evXq1u3bjpw4IDbcatGT/bu3atRo0a5Okc7WbHVQMGCBV1TxOzALutaJ0yYoEGDBmngwIHpPm7VH7EXXngh3eNmXoylXhtkN3369NHw4cNds1UWLFig3r17a+XKlaZnee+99zRlyhTX7gvt27dX//79b/s9lZV8fHwUGhpq+nnvxE5bqN06RbdKlSqqXbu2JQWyHdZs3iopKUmLFy9O839lRUMhu/yNmDNnjmXnvpPIyEj1799fOXPmlIeHh6SbBfvZs2ctydO0adM0N03TO2aF5ORky/q/lC1b1vRmhHdz6+yVS5cuKSAgwJLZKwsXLpQkt2ULZgyWZKsCOSkpSZ6enurbt69at26tc+fOacSIEfrss89c++BZ4Xb7hVlVIKduwlK1alVdunTJkmL0wIED2rdvn65cuaKvv/7adfzKlSuuabxmGzBggCIjI9W7d2+tXr1aH3zwgaVt7Nu3b68uXbrYYuugli1b6sMPP9Tzzz/v9jWxanTdLutanXcs8+XLZ3ESd6lvesXFxWnx4sVu21aYwdnwKi4uTrly5XLdtExJSVFCQoKpWW516dIlt6Uc7du319tvv21JlunTp2vr1q2uDvojR45UzZo1LSmQn3rqKdeopF3YbQu11MzaUiQ9dlqz6dS+fXudOXPGFrsd2OVvhFNSUpImTZrkupAPDQ3VgAEDLNu1Y8yYMdq0aZNls2acEhISFBcXp+TkZF29etXVpPXKlSv6888/LcmUupdHcnKydu7cqSeffNKSLK+//rol570TO3Qcd7JqSUm2mmLdrl07152EtWvX6quvvpJhGHrqqacUHh5u2dTLUqVKadeuXbbYLyw5OVkVKlTQ3r17rY6iTz/9VJGRkdq8ebPb2kQfHx/17NnTkjVmzs3hneuzJalatWpp9gs0i52WBjjvMEs3b/BYvTbRbutak5OTLb8gvJPr16+rZcuWrl4IZqpdu7Z++OEH102EK1euqEWLFlqzZo3pWZyqVKmizz77zHVnfu/evercubO2bNliehbnsqC7HTODr6+vrly5oty5c7saLjkcDl28eNH0LE4VK1a0ZNZMem63pYgVW3LZac2mU+nSpbVv375012mbzW5/I/r376/Dhw8rIiJCDodDM2fOVPHixe+6RjmrWLV281ajR4/W6NGjXdcVTj4+Pho0aJAlszNS9/Lw9PRUyZIlVaNGDdNzSNIbb7yR7nErt3mqUqWKtmzZ4nat7DxmFbOXRmarEeT4+Hj16dNHn3zyiasraUxMjBo0aGDJnXgnO+0XliNHDhUoUMBtywyrdO3aVV27dtWsWbPUvXt3S7M4Oe/IP/zww9q6dav8/f117tw5y/I0aNBAq1evVt26dS3L4JSSkmJ1BDerV6/WjBkzbLOutWjRourcubO6deumxx57zJIMd+Lt7a0TJ05Ycu7r16+7jbDny5fP8v0/33zzTdWtW9e1TmnXrl36/PPPLcnSsGFDhYeHu34PRkZGqnHjxq6i0Mx1Xam7w9uFnbZQs2pLkfR06NBB0s0LU6t6iNzK399fCQkJll7zpO59YKe/EatWrdL27dtdN5tbtGhh+qwe6ea+65LUunVrvf/++2m2tTR7Vtjrr7+u119/3XX9bgfOZpZ22C726tWrrrfj4uL0/fffq1atWhYmstfsFauWRmarAnnhwoVq3ry5hg8frnHjxunEiRNq2LCh+vXrp759+1qWy077hUlSyZIl9fjjj+u5555zW9BuVSfF5ORkXbx4UQ899JCkm1PWvvzyS0VERJiepX379rpw4YKGDRumevXqKTExUWPHjjU9h9Ozzz6r0NBQ5c2bV97e3jSiSuXDDz+0OoKbdevWKTIy0rUP+wsvvKAOHTpYNgX95Zdfdhvp2rx5s2WdkVNSUnTt2jXX75vY2Fi39Ylm6tmzp6ZPn67Q0FB9/PHHrhuFNWvWtKwZzBdffCFJ+uWXX9yOL1y40PSf94CAANPOlVF22UJNkiX7ZN/JDz/8oIMHD7r9PFk5IFCyZEnVr19fzzzzjNv/lZnXF3btfWAYhlJSUlwFsmEYsmKSZv78+d1GawcOHGiLWWGffPKJYmJi9Ouvv0q6+bNm1ZZlu3btUocOHWyxXeytTfdGjRql8PBw03OkNmDAAF26dEljx451m71iVRYrlkZmqynW0s07LY0aNVKTJk20aNEi9e7dWy+//LKlmRo0aJDmmMPhsKTBiJR+R0UrOyk6t51KzQ5TixMTExUXF2fpti+lS5fW0KFD03SOtmIvRw8PjzTT5vLly6datWrpo48+Mn0/UjtbtWqVpk6dqm+++cayNVSjR492ve2cItamTRvXnV8zvfPOO/rf//6n3r17S5KmTp2qtm3basiQIaZncS6juPVt3HT27Fm9/vrr2rFjh1t3Uiu/TnbZQk2yV8OwTp06ae/evQoJCXHrbzJ9+nTTszjZ6fridr0PrFruNmTIEG3ZssVV3MydO1eVK1e2pOiyo6+++krdu3dXnTp15HA49Ntvv2nWrFl6+umnTc9it+1ib1WuXDnbZLGaVUsjs9UIsnM62ttvv6127drpqaeeUqNGjSyZppaaFV1R78RuHRXTuwdj1R3MHj16qHHjxmrYsKEKFixoSTGRWp48eW7bjdhsb7zxhpKSklwj+7NmzVJ8fLweffRR9erVS0uXLjUlx6BBgzRhwgS3JhqpWTGqlNqWLVv0xRdfaMWKFeneHDNDcnKyfH19bbO/5tChQ+Xn5+faW7Jv374KCwuzOFX6v3ussnHjRtf68KZNm1q2btJ5gbp8+XJNmDBB06ZNU0hIiCVZnKwohG/HTg3DtmzZoj179lieIzU7XV80bNjQrffB1atXLe198M4772jatGmupqTPPvusevbsaUkWOxo9erTWr1+vkiVLSpIOHTqk559/3pIC+dq1a6pTp47rfYfDIS8vL9NzSO77aCcnJ2vjxo3y8/OzJEtqdpm9YtXSyGxVILdq1cr1dp48ebRq1SqtWrVKkjktv+/ELvuFOZ06dUq7d+92y2PVlO9ChQrpv//9r55//nlJN6cVWrWmq169evr+++81ePBgPfTQQ2rcuLEaN25sWffCFi1a6JtvvrHkD8StlixZ4tY06LXXXnM1EjJzxKJ+/fqSlKbLbmJioqXrWidMmKDIyEglJyerW7du2rFjh2Xfxzly5NDcuXNtUyBfuHDB1XPA6cCBAwoKCjI9y40bN7Rr1y4ZhqG4uDjX205W/F6ePn26xo4dqzZt2sjhcKht27YaOXKkevToYXqWmJgYDR06VFFRUXr66acVGhqqevXqacyYMaZncbLDlmVOcXFxeuutt0w/b3oCAwMVHx9veT+RW9nl+sJuvQ88PDzUp08f10waqxuZHTx4UP37908zW8SqhnzJycmu4li6OV3fqt4ndtouNvVsSk9PTwUHB1t+Y+V2s1esYNnSSAP/2DfffGP4+fkZuXLlMh566CHD4XAYgYGBluWZNWuWERAQYOTNm9eoXLmykSNHDuPxxx+3LM/vv/9uBAUFGQEBAUZAQIDx2GOPGQcOHLAsj2EYRkpKijF37lwjMDDQ8PDwsCxH/vz5DYfDYTzwwAOGr6+vkT9/fsPX19eSLEFBQcbZs2dd7589e9YICgoyDMMwgoODLclkGIaxb98+Y/Dgwcajjz5qVKlSxbIcERERxtq1ay07/62GDh1qREVFWR3DMAzDqFOnjhEfH+96/+jRo67vHbMFBAQYgYGB6f4rXry4JZkqVKiQ5merQoUKlmSpVq2aYRiGUbVqVeP8+fNGcnKyUbJkSUuyOH344Yeuf++9955Rq1Yt49///rclWTp06GAcP37cknPfavfu3UatWrWM//znP8bo0aNd/6xkp+uLChUqGFevXnW9f+XKFaNcuXKWZDEMwzh58qTRvHlzw9vb2/D29jZatGhhnDp1yrI89erVMxYsWGCULVvW2LlzpxEREWGMGzfOsjyNGzc2ZsyYYSQnJxvJycnGzJkzjcaNG1uS5bPPPjOefPJJo2jRosbw4cONgIAAY+HChZZksaPSpUsbSUlJVsdIIyEhwYiNjTXlXNlqBNmu7LRfmCRNmjRJ27ZtU8OGDbVlyxatXr1akZGRluUpU6aM9u7dq/3790u6ue7Wqjt1M2fO1LJly1zrGYYMGaImTZpYkkWyV0fZgQMHqlKlSmrevLkkaenSpRoxYoSuXbumxx9/3NQs169f18KFCzVr1iwdOXJEN27c0Lp161SmTBlTc6Rm5bq/9EybNk1XrlxR9+7d9cADD1i6Xc/zzz+vDh06aPHixTp9+rSaN2+uSZMmmZ5DurmG1I4KFCiQ7ttmCwoK0oULFxQWFqYaNWrIx8fHbU9tK/z73/92e79Pnz6mj0g6l3RcvXrVNg3D/vOf/8jLy0txcXFKTEw0/fzpsdP1RadOndS4cWO33gdWTtfv1auX6tSpo3nz5rny9OzZU998840leWJjY9WuXTuNHTtWFSpU0LRp01SjRg1LtiyTbn49OnXq5Pp5r1y5smU7C4SFhalEiRL66quvlJCQoKioKLcp12b45ZdfVK9ePdeU/NQcDoceeeQR1axZ05KRWzvNXlm9evUdH8+qXWCyXZMuO7LbfmHp5bG6WY1d1t95eXmpevXqevXVV9WoUSPlzp3bkhx2tXv3btea+gYNGljSFTkiIkJffvml6tatqxdeeEHNmzdXqVKlLNssvnjx4nf8A2XV0o7jx4+ne9yqLsWDBg3StWvXtGHDBo0cOVJt27a1JIcdtW3bVmXKlFGvXr0kSTNmzNDevXu1ePFiS3P99ttvunTpkpo3b26rda4pKSkqW7as9u3bZ9o5b9cozMmKwqt06dKuG8t2Ybfri08//dTV+6Bly5aW9j5IryFpesfM4twHuXbt2po3b578/PxUtmxZy/5mXbhwQQ8//LBrGnyePHksW4pjBxEREZoxY8Zte5mcO3dOpUuXtuTvxJ49exQREaH69eu73Si0Ym/mkJAQ7dy503UtduTIEVWqVEk5cuSQw+HQxo0bs+S8jCBnAjvtFyZJuXLlkmEYCgoK0vvvv6+AgABL1+XYaf3dhQsXtHLlSv30008aOXKk8ufPr8aNG2v48OGmZ5Hs11G2fPnylm0V5LRgwQJVrVpVvXr1UmhoqBwOh6Vrub799ltXrmPHjrkVOVZumWOH7XqcDRKlm3fke/bsqSZNmqhUqVLauXOnpX0Y7GTq1Knq16+fKleuLIfDocaNG1u2H+jYsWM1YsQISdLjjz8uwzAUFhZm2UiO5N74xaoty24tgO2wP2rp0qUVGxtr2VZy6bHb9cWtvQ+sZBiGzpw542qwdObMGUsbBdatW1cXLlxQ3759VaVKFXl5ealdu3aW5WndurWWL1/u2g7w+PHjevrpp029CXS75p9OZs4UmTFjhqQ7N/q1atacnWavhISE6N1333XN9ly2bJkWLFigmTNnZul5GUHOBPPnz1ezZs105MgR135hkydPVseOHS3Js2LFClWpUkXnz59X7969dfnyZb311ltq3LixJXkqVqyo5cuXu6YVnjt3zq37uNkSExO1Zs0aLVu2TPPmzVNsbKwuXLhgSZann35aderU0axZs9w6ylrRMGfr1q0aNmyYjhw54ta10Oy7zdeuXXNNrz5x4oS6dOmiTz/9VDExMabmuNWt2woYhqHq1atn+VYDt2OHmyvFixe/7WNWN05E+kJDQ9WlSxd16tRJ0s19o5OSkizbBlCy15ZldtoftV27dtqyZYuaNm3qNoozceJE07M42en6olu3bukWO1Z9L3/22Wd65ZVXXMuUfvzxR40fP971s2aVI0eO6PPPP1f+/PnVr18/y3JMmTJFq1atci3FadiwoSZMmGBqk9TbzRRxNgF96aWXTMuS2u+//56mY3SbNm0sySLZa/ZKxYoV09QLlSpVyvKlrBTI95hNmzZp/Pjx2rt3r6SbI4KDBg1StWrVLMuU3jd3esfMEBoaqi1btig4OFiNGzdWo0aNVLVqVctGKJ3Tr5zT1RISElSvXj2tW7fO9CwVKlRQ3759VatWLcv3ZHbau3evZs+erc8++0z/+te/FBYWphdffNGSLEFBQdq2bZsefPBBSTcL+cqVK+vAgQOW5LHTzRXc2euvv67+/fvr4YcfliSdP39eH330kV5//XXTs8TGxqphw4YaP368vvnmG506dUrz5s2Th4eH6VnsyE77o6a+cZCaFd83TikpKbb5Xvnoo49cb8fFxWnx4sWqXLmyPvzwQ8sy7dmzx22ZkhV/Pxs3bqz33ntPwcHBOnXqlMqXL68aNWro6NGj6tatm4YOHWp6Jie7LcXZv3+/Zs6cqc8++0xFixZ128nDLIMGDdLnn3+usmXLunWM/umnn0zP4tSyZUtFRUXZYvZKSEiIJk6c6JqK/ssvv+ill15y6/6dFSiQM8G0adPUvn175cuXT3379tX69es1ceLELFs4fjvr1q3Tk08+qd69e6tGjRoyDEObNm3S1KlT9cMPP6hGjRqm5nG6df3d9OnT9fvvv1uyruLnn3/WE0884XY33krVq1fXxo0bVa1aNf3444/y9fVV6dKldfDgQdOzWLlW6m6SkpL01Vdfafbs2a71ZmYbNWqUFi9e7Nqu7IsvvtCzzz5ryZocyV43V7755hs98cQTyp8/vyTp0qVLWrt2rVq0aGF6FjtK72fLynWbJ06cUN26dVWhQgV9+eWXtlh/vHHjRm3fvt1tNoQV25g5t7YLCQlxXYClfvt+V6xYMfXu3VsRERGWNptLz/Xr19WyZUtXvxOznDhxQmfOnEnTW2Xz5s0qVKiQihQpYmqexx57TL///rsk6b333tNvv/2m//3vf7p06ZLq1atn+uBE6vMlJyerZ8+eql+/vjp37izJ/K337NYEtGTJktq5c6ctGmI52Wn2ytq1a9W+fXvXjKKkpCQtXLhQNWvWzNLzsgY5E3z00Ufq1auXfvvtN+3atUvjxo3T4MGDs2zh+O28++67mj17tp555hnXsWeeeUY1atTQW2+9pSVLlpiaxym99XdTp061JItzDUN8fLzi4+Ndx626S2anjrKPP/64Nm/ebFkDtTvx9PRU27ZtLb3bPGrUKFWrVk0rVqyQJL3zzjuuqXRW8PLykiR5e3vrwoUL8vX11fnz5y3JMnLkSLcCMH/+/Bo5ciQF8v9Jb6/PhIQEUzOEhIS4zZSJi4vTwYMHXbOLrGzi+Oabb2rRokWKjo5WvXr19PPPP6tRo0aWFMh22B91/vz56tChgz744IN0H7dy//Off/5Zn3zyicqXL6+mTZuqb9++lt18v5W3t7dOnDhh+nmHDh2abk+Va9euufYcN1Pq5qNr1651TWH29fWVp6f5l/2tWrVKc2zRokVatGiR6UtxUjcBHTp0qKsJqJU7ZPj7+9tm0MapbNmyKlu2rNUxJEm1a9fW4cOHXU0by5QpY8ryG0aQM4FzJGDMmDEqXLiwunfvbsnoQFBQ0G2ne97psaxya2t257ea88LD7BF2SdqwYYPCw8PTfC2Sk5NNz3KrNWvW6PLly2rWrJklf8QqVKig/fv3q2TJkm6/rK28cLaL5ORkVahQwbV0wUpJSUny9PRUWFiYJk+erKioKE2ZMkU+Pj4qXbq05s+fb3qm9EZIzVgjlF0899xzqlatmgYNGiTDMDRhwgRt2rRJixYtMi3DL7/8csfH69WrZ1KStMqXL6/NmzerZs2a2r59u/bv369hw4ZZMssoKipK8+fP186dO9W1a1dFRUXp3Xffdc0cMUOePHl07do1devWLc1jDofD0vXiTtevX1dUVJTGjRunggUL6uWXX1aHDh1MXa6UXnO3QoUKmfpzJf3/WQfpKV++vHbv3m16niVLlih//vwKCAjQunXrXJ2iy5QpY2p3eLvJmzevqlatqqFDh7qagJYoUcLSfhmbNm3S22+/rdDQULdrry5duliWyY7MHthiBDkTeHh4aOHChVq4cKFr+qfZowOS7jg9w7lu0kyDBg1Kc8zhcOjUqVM6ffq0JUVp//79FRkZqd69e2v16tX64IMPbHPnzuw9+G5l5botu8uRI4cKFCig69evWz4NqlOnTlq4cKFrVGLAgAGqWrWqDh8+bFk39rx582rt2rWqXbu2pJvbB+XNm9eSLHY0efJkhYWFacSIEXI4HKpbt67mzp1raoZ69eopOTlZ4eHh+uyzz0w99914e3vL29tbKSkpMgxDpUuX1uHDhy3JYof9UevVq6d69erp008/VWBgoKnnzgjDMPTTTz9p4cKFypMnjzp06KD58+friy++0P/+9z/TcuTLl8/1tqenp/r3729JY6MbN27c9jErxqCGDRumkJAQeXp6qkGDBq7ieO3atbb4frJyBt/p06e1cOFCvfHGG+rZs6e6dOlieZfmqVOnaufOnTIMw20NstUFsl2Wvaxfv17dunUzf2DLwD+2fv16o1WrVsb7779vGIZh7N+/3+jXr5/pOcqUKWPs3LnT2LFjR5p/ZcqUMT3PrS5cuGC8/PLLRoECBYwxY8ZYkiEkJMQwDMMoX76861jVqlUtyWIYhvHDDz8YpUuXNnLmzGl4eHgYDofD8PDwsCwPbu+FF14wgoODjXHjxhmTJ092/TNbq1atjN69e7sdi46ONkqUKGFMmTLF9DyGYRhr1641Hn30UaNevXpGvXr1jMKFCxsbNmywJIudXbt2zbh27ZqlGapXr27p+dNTp04dIyEhwejcubMxcOBAY+LEiW6/o60QExNjnDhxwrLzz5w50yhatKgxY8YMyzKk58033zQCAwONFi1aGEuXLnV7rGTJkhalslb58uWNU6dOpTl+8uRJy76PT58+bWzfvt1ISUlxy3P8+HFL8hiGYaxbt84oU6aM4eHh4fbPKnv27DEGDRpkFCxY0KhVq5bx0UcfWZKjRIkSRmJioiXnvp1x48YZISEhxsMPP2y0adPGyJs3r9G6dWtLslSvXt1Yv369ERwcbMTGxhpjx4413nvvvSw/L1OsM5HV+yYGBgbednqTlVuuxMXFadKkSa6tr4YPH+7q5mq2GjVqaMOGDapfv74mTpwof39/VatWTceOHbMkT1BQkKZMmZKmc7QVI/43btzQlClT0twxNHNfQDuzy3TH+Ph4NW/eXLVq1dK4ceN04sQJNWjQQH379tWAAQNMzZLapUuXXA3Cateu7WrYdT87ePCga0/o9FixT/Srr76qixcvKjw83LUfqVVZnHbv3q3ixYvr+vXrGjZsmC5fvqwRI0aoUqVKpmfZsWOH2rdvrz/++EOS5Ofnp/nz51uS5dChQ6pWrZoMw5CHh4cMw5DD4dDFixdNz+LUr18/9evXzzUqmdqWLVtM7aHRo0cPvfPOO27d4YcPH65p06aZlkG62Ydm3rx5mj17tkqXLi1J2rdvnyIiIvT8889burWSndSoUUMffPBBmhl86c02NJPVTUCbNGmi77//3pJt7W7HTstenEtWnQ1JpbTbbmYFplhngp07d6pjx46W75toVZF3OykpKZo5c6bGjBmjhg0basOGDQoICLA0U/v27XXhwgUNGzZM9erVU2JiosaOHWtZHh8fH4WGhlp2/tQiIiLk4+OjtWvXatCgQYqMjLRknbhdzZkzx+oIkqRcuXLpq6++UqNGjSTdbHby4osvWlocSzcbwJi5n2V28PLLL+vbb79Nt0mNVTctFy5cKOlmsyWrs6ReQ+qUN29e5cmTR59++qklHVN79OihN954Q88995ykmz9fPXr0MH2/8y1btig8PFzt27fXkCFDbNFpPDk5Wb///nu6xbEk0xtMbtmyxe1m+yOPPGLJvvT//ve/dfbsWYWEhLgGSRISEjR48GCK41QSExNVo0YNJSUlKW/evBo+fLirN4OVrG4CWqJECdWvX1+tWrVyW/JnZTM+Oy17cfbkefjhh7V161b5+/vr3LlzWX5eRpAzgZ32TbSTsmXLKj4+XqNGjUr37ruVIxbSzV/WcXFxlq6VHDVqlIKDg9W6dWvLMjg5784596i+evWqWrRokabZ2v0qKSlJkyZNchUWoaGhGjBggOkN1ZyjkefPn1e7du301FNP6eWXX3Y9bsXPlYeHR7qzV+zQ/M5qhmHo9OnTKly4sNVRbMfDw0PVqlVTs2bN0t1b14r9flOPUjg5fyeaZfjw4Zo3b56mTp1qmxuoTrVr19aaNWtssRfyrf9XhmGoQoUKpjfFcrp+/brruq9s2bKWzASzM7vN4LMLu8xOS+2JJ57QihUr1L17dxUoUEBFixbV7Nmz0/xuNMOkSZPUpUsXbdmyRW3btnUNbA0ePDhLz8sIcia4du2aWxMPh8Ph2oLlfnb9+nU5HI5094k1e8Tibhc3VhXrkydP1pUrV5Q7d27lypXL0il0zq0hPD099eeffypv3rym3KXLLgYOHKjDhw/rxRdflMPh0MyZM3X8+PHbbsWSVVKPRubJk0erVq3SqlWrJFk3Enj16lXX2zdu3NDcuXMpjlNp2rSpZRftt4qOjk73eLFixUxOIi1fvlyzZ8/WvHnz9Pzzz+uFF17Qv/71L9NzpFa5cmWtWrVK9evXl3Sz+7fZI6PHjx/Xtm3bbLlMoVq1anrqqacUFhbmNkW/ZcuWpmepWbOm+vbtqyFDhsgwDL333ntZvjfqnTzwwAOubdOQVnoz+EaMGGF1LMvZZXZaap988okSEhI0YcIEDRs2TL/99pvpzR2d1+2NGjXSyZMn5efnpxUrViguLk6PPvqo4uLisrTJLiPImaBmzZr69ddfVaNGDW3dulUxMTF65plnbtv2H+YrXrz4bR+zcn328ePH0z1uxVT0Zs2aaf78+ZowYYJWrVqlRx55RH/++afbVMz7WcWKFbV9+3bXyElSUpIqV65s6shSduIcLcDNn62oqCg98sgjVkdRgQIF5HA4ZBiG4uLidP36dT388MM6e/asZZliY2M1f/58zZkzR7lz59bbb79t2d66zu3cnN1+jx07prJly7rWB97v2941aNAgzTGHw+HaH95MsbGxeumll/Ttt9/K4XCoZcuWmjhxIh30s4HExETduHFD5cuXv+1Nu/vF7XY0sLqLtdXSu253zlRLTEzU1atXNWHCBHXv3j1Lzs8Icibo27evWrdurXPnzmnEiBGufRNhH0ePHrU6QrqsXpOd2nfffaccOXJozJgx+vzzz3X58uX7/hd0aoZhKCUlxVUgG4ZhyRYe2cG+fft0/vx5q2PYRp48eRQcHKwnn3zSbdTNijW2t84K+fLLLy3fr9rHx0etWrXSxYsX9cEHH2jfvn2WFchsd3dnK1eutDqCi4+Pjy32hMZflzNnTuXMmZO/oZK++eYb19txcXFas2aNatasacn1V7du3e64l7mZP293u24/ffq0GjZsSIFsZ3bYNxEZs2nTJpUrV04PPPCA/vvf/2rjxo0aOHCgZesDixcvnu4vIytGtFPvvxcWFmb6+e2uWbNmatq0qcLDwyXdvOvbrFkza0PZhK+vr+v7ODk5WYZhaMqUKRanso8KFSqoQoUKVsdIV5s2bfTmm29q9OjRpp87OTlZX3/9tWbNmqXjx4+rc+fO2rp1qwoVKmR6Fifn1M/o6GjLp3vbVWJioo4ePeq224FVy5ROnTql3bt3u2WxYro3/p47FWP3iy+++MLt/aNHj2r48OGWZKlataokadeuXVq9erU6duwoh8Oh+fPn64knnrAk0+0UKlRIPXv2zLLXZ4p1Jti3b5/KlCljdQxkQKVKlbR161YdOXJETz75pJ599llt3bpVS5cutSRP6kZucXFx+uyzz/Twww9r5MiRpmfZunWrhg0bpiNHjigpKcl13Krp53aTkpKi6dOna9myZXI4HGrcuLF69uzJH3i5LxXw9PSUn5+fLbruIq3Y2FjX28nJydqwYYP69++vAwcOmJ7Fz89PxYoVU7du3fT444+nedyKomvVqlXq2LGjPD09FR0drU2bNmny5MmKiooyPYsdffvtt4qIiNClS5f04IMP6tKlSwoICLBkltbs2bP1xhtv6OLFiypVqpR27NihmjVras2aNaZncfrvf/+bZqtEK2aK2MmdliGFhobq9OnTJqbJHipVqmTpzJ66devq22+/lY+Pj6Sbfzeeeuqp+6ppKwVyJggMDFSpUqXUr18/Pf3001ww25hzP7UPPvhASUlJGjhwoEJCQrRt2zaro7nUrl1ba9euNf28FSpUUN++fdPsyVyuXDnTs9jJrVvROH9lOn/O7/eLH2TMxo0b01w4m7mNR4cOHTR//nxXx3HDMJQjRw6VKlVKEydOtGQ2RGBgoOvnyJnJyareEDVr1tTnn3+uZ5991vV3oVy5cvf9rhROISEhWrJkiVq3bq1t27YpKipKO3bs0Pjx403PUqFCBa1evVoNGzbUtm3btHr1akVGRlo27bp///46evSotmzZog4dOuiLL75QkyZNNGvWLEvy2IVde8DYxddff+16Ozk5WevXr9fKlSu1ceNGyzI99thj+v333+967F7GFOtMcOTIEX311VeaMmWKXnrpJfXu3Vs9evTQQw89ZHU03CI+Pl5//PGHvvnmG73zzjuS7LUVzYULF3TmzBlLzp0jRw716tXLknPb2fvvv3/HrWiQdvaBsxv7/X7h4/Tmm29q0aJFio6OVr169fTzzz+rUaNGphbI+/btk3RzJoRd2HF7l+Tk5DRTq9mV4v/z8PBQQECAa5ZRWFiYJk2aZEkWLy8v+fr6urLUrVtXL730kiVZpJvrs3fs2KGQkBBNmDBBQ4YMUdeuXS3LYxd27QFjF6l/fjw9PVWyZEnXfvVWqVixosLDw13re+fMmZPudq33MgrkTODh4aFnnnlGzzzzjLZs2aJnnnlGo0aNUqdOnTR69Gj2v7SRl19+WaVLl1bjxo1VuXJlHT58WL6+vqbnSEpKkqenp0JCQtzWbh4/flxDhgwxPY8kPf7449q8ebNrDQpusuNWNHbTtWvXdGcf4KZ58+Zp8+bNqlmzphYvXqz9+/dr2LBhpmZgZlPGeHt769q1a66v165du1xb4EGubt5FixbV//73PwUGBurSpUuWZHFujRgUFKT3339fAQEBunbtmiVZpJvfO84ZGomJifLz89OpU6csywN7c049nzx5cprHEhMTs3wbozuZNWuW3njjDdcNp8aNG1uy9M9KTLHOJAcPHtRHH32kL774Qi1btlRERIRWrFihuXPnsg2MjaWkpCgpKcn0EYJ27dpp4cKF+uWXX1zHPD095e/vr/r165s68uYs0hMTE7V//36VLFnS7Zfy/b6tiZOdtqKxm+DgYG3fvt3qGLZVtWpVbd68WRUrVtSOHTvkcDhM/5p5enq61pOlZuXe63binIK+dOlSjR49WocPH1aTJk20bNkyzZs3Tw0bNrQ6oi3Mnz9fzZo105EjR9S+fXtdvnxZkydPVseOHU3PsmLFClWpUkXnz59X7969dfnyZb311ltq3Lix6VkkqWHDhvr222/1yiuv6Ny5c/Lz89P69evZ7g7pck49v/XmpWEYSkpKyvJtjG4nOTlZ4eHhpu97bDeMIGeC0NBQHTx4UH369NGePXuUP39+STfXu0ZGRlqaDe7sst9cfHy8+vTpo08++cR1LCYmRg0aNEiz5jWrvf/++6aeL7uy01Y0dsPsgzvLnTu3EhMTFRwcrMGDB6to0aKmL+0oXbq0vv/+e1PPmZ04p6CHhoYqKChIP/74owzD0OjRo5kxkkrTpk3l6+urKlWq6ODBg5JkSYM3Sapfv748PDyUL18+/fzzz5ZkSG3+/PnKkSOHxo8fr4kTJ+rSpUtatGiR1bFgU1ZvY3Q7OXLksOxn2k4YQc4Eixcv1jPPPMP6xGzgueeec72der+5H374wdQc8fHxat68uWrVqqVx48bpxIkTatCggfr27asBAwaYmsU5coL0pbcVTefOnS3disYumH2QMbt371bx4sV1/fp1DRs2TJcvX9aIESNMXdPlbFCI9PH1yZgnnnhCy5cvd826On78uJo2bar9+/ebnqVYsWLq3bu3IiIiVKBAAdPPD2S1SZMm6eWXXzb9vK+++qouXryo8PBw5cmTx3Xcqu3crECBnElSrys7efKkLl68aNt9L/H/Ofebmzdvnunnvnr1qho1aqQmTZpo0aJF6t27tyW/CLkwvDM7bkVjF1999ZUuXryoEiVKuB0/cuSIHnroIbVq1cqiZPZwu9kgzmnNZnZAt1u3frthCnrGTJkyRatWrdLixYtdI1wTJkzQk08+aXqW/fv365NPPtH8+fPVtGlT9e3b15JZPc8888wd1/h/+eWXJqYB/rn0Oo/fb403KZAzwWuvvaZNmzbp8OHDOnDggE6fPq22bdtaslUP/jor9ptzrks/f/682rVrp6eeesqtODaz6KJAvjM7bkVjF71791aTJk3Utm1bt+Nffvmlfv75Z7clBPcjDw+PO3ZAf/311y1IhfSUK1fujlPQAwICTExjb4MGDdK1a9e0YcMGjRw5Ms3Pv9muX7+uqKgojRs3TgULFtTLL7+sDh06mNaY7tNPP73j43SyBrIfCuRMUKlSJW3dulVVq1Z13aGvWLEizbls6Nb95jZs2KAVK1aYvt+cnfYFZOQEf9edbq6wd+zNbV9mz56t9evX0wHd5rhReGepr2eSk5PVs2dP1a9fX507d5Zk3UwawzBc22yeOXNG3bt318qVK+Xp6an//e9/lmQC7hXx8fGKj493vZ/eteK9iiZdmSB37txptjbhvoM92WW/OTvtC0jzHvxdzv1H00NPBqlBgwZq0KCBqwN6p06d6IBuU/zNvrP0lkssWrRIixYtsmwmzVtvvaXp06erXLlyevXVV9WgQQN9++23GjhwoEqVKmVajrs11jRzKQWQGdavX69u3bqladZldnNJK1EgZ4KAgAD9+uuvcjgcSkhI0FtvvaXg4GCrYyEdK1eutDqC7eTKlYvpg/hbEhMTFRsbm+au8pUrV5SYmGhRKvuhA7r9sT77zux0U9fp1KlT+umnn5ScnKzZs2erS5cuKlKkiFq3bq0FCxaYliNfvnymnQsww4ABAxQZGanevXtr9erV+uCDDyzbk9kqTLHOBH/88Ye6du2q5cuXy+FwqEGDBho7dqyqVatmdTT8n4MHD6pUqVK3nfZ+PzdaonkP/q7Ro0dr69atioyMlK+vryTp0qVL6t69uypWrKhRo0ZZG9BidEDHvWbNmjWqU6eO27EvvvjCbYcIM1y/fl0LFizQ7NmzdeTIEd24cUPr1q1TmTJlTM0B3IucS04qVKigXbt2SZKqVaumTZs2WZzMPBTI/9DmzZt1/Phx1a9fX7lz59aRI0c0YsQI/fbbbzp37pzV8fB/WrRooe+++85t7a+z4dL93mgJ+LuSk5P1wgsvaPHixa4pjQcPHlTbtm01e/bsNEtP7jd0QMe9pmTJkurZs6deeeUVJSYmasCAAdq0aZOpF84RERH68ssvVbduXb3wwgtq3ry5SpUqZfkod0xMjPr06aMTJ05o+/bt2r59u1auXGnJ7hTAP1G9enVt3LhR9evX18SJE+Xv769q1arp2LFjVkczDQXyP/DOO+/orbfeUpkyZXTx4kX169dPr776qvr06aMRI0Yof/78VkfE/3EWwk6HDx/W119/rZIlS+rpp5+2MBmQ/R0+fNjV4Khy5co0ovo/dEDHvca5N2pSUpLOnj2rmjVrauLEia59kc2QN29eVa1aVUOHDlVoaKgcDodKlChh+c/Tk08+qY4dO2r8+PHasWOHkpKSFBIS4hqBA+zu9ddfV8OGDbVu3TpFRERo69atatOmjRITEzVmzBgNGTLE6oimoUD+Bx577DEtX75chQsX1r59+1S+fHktXbpUjRo1sjoabtG4cWO99957Cg4O1qlTp1S+fHnVqFFDx44dU3h4uIYOHWp1RAAAbG/RokXq0aOH8uTJo2XLlpk+rfnatWtauHChZs2apRMnTqhLly769NNPFRMTY2qOW1WtWlWbN292W7bEEiZkJ3369NHKlSsVHR2t2rVrq0GDBqpbt64qVap0X3WwliTajP4D3t7eKly4sCSpTJkyCgoKoji2qZMnT7oap82bN0/16tXTDz/8oLVr1+rzzz+3NhwAANlAv3799Oabb2rz5s2aOnWqmjVrpqioKFMz5MmTR927d9fatWv1448/Ki4uTgkJCapdu7Y+/vhjU7Ok5unp6TZL5NKlS3RHR7byySefaN++fTpy5Ii6d++u6Oho9ejRQ0WLFlWLFi2sjmcqCuR/IC4uTrt27dLOnTu1c+dOGYbh9j7sI3fu3K63165dqyeffFKS5OvrK09PmrkDAHA38fHxWrdunUqWLKmnnnpKq1ev1ieffGJZnrJly+q9997TyZMnNWjQIH333XeWZXnuuefUq1cvxcbGaubMmWrSpIl69OhhWR7g7/Lz81Pbtm3VoUMHtW/fXn5+ftq+fbvVsUzFFOt/IPX6sluxvsxeqlatqiVLlih//vwKCAjQunXrFBQUJOnm6P++ffssTggAQPaTnJx83zfkc5o/f76WLFkiwzDUunVrdezY0epIQIatXr1aq1at0sqVK3Xy5EnVrFlTdevWVb169UzdW9wOKJBxX/jyyy/Vq1cveXp66vHHH9eiRYsk3RxNfuONN/Tjjz9anBAAAHuaP3++OnTooA8++CDdx/v3729yIgCZzcPDQzVr1tRrr72mZs2aWR3HUswtxX2hTZs2ql27tv744w+3rVUCAwM1ffp0C5MBAGBvzllW6TWcut1MuvvFjRs3XHvBP//88xo6dKh+/PFHlS5dWpMnT1aRIkWsjghkiHME+b333lPfvn1VvXp11a9fX/Xr13fNurxfMIIMAACA2/r666/v+HjLli1NSmI/YWFhunz5sq5fv64cOXIoICBAbdu21YoVK7Rv3z598803VkcE/rKEhARt2LBBK1eu1Lx583Tt2jWdOHHC6limoUAGAADAbTVo0MD19pYtW1S1alVXh2aHw6EVK1ZYFc1yZcuW1d69exUXF6dChQrpwoUL8vC42QO3QoUK7IOMbOfUqVNauXKlVq1apRUrVujs2bOqU6eOfvjhB6ujmYYp1gAAALitlStXut4OCQm5rwviW+XKlUvSza0/AwMDXcWxJOXMmdOqWMBfFhERoV9++UWnTp1SrVq11KBBA0VFRalatWr33Y4v99dnCwAAgL/tfl9zfCvnlp+GYbi9Ld1cnwxkF/7+/po1a5Zq1qx539/cYYo1AAAAMqRy5craunWr1TFsgy0/gXsPI8gAAAC4rZ07d7revnHjhtsoqSS33SHuN8eOHbM6AoBMxggyAAAAbqt48eK3fYxRUgD3GgpkAAAAAAAkedz9KQAAAAAA3PsokAEAAAAAEAUyAAAAAACSKJABAMg0gYGB2r59u9UxAADA30SBDAAAAACAKJABAMhSEydOVLVq1RQcHKxq1app3bp1rscCAwP12muvqVatWipevLjGjh3remzfvn2qVauWypUrpzZt2qhp06aKjIyUJIWHh+v99993PXfw4MEaNWqUJGn58uWqVauWQkJCVK5cOc2aNcv1vNOnT6tp06YqW7asmjZtqvbt27s+LjExUa+++qqqV6+u4OBgPf/887p06VKWfV0AALAjCmQAALJQ586dtWnTJm3fvl1TpkxRt27d3B6/fPmy1q1bp02bNmn8+PE6efKk6+N69uypPXv2aNy4cVq9enWGzle5cmWtWbNG27Zt06+//qo33nhDJ06ckCT1799ftWrV0t69ezV37lytWrXK9XHjx4/Xgw8+qI0bN2r79u2qUKGCRowYkTlfBAAAsglPqwMAAHAv27Ztm8aNG6cLFy7I09NT+/fv140bN5Q7d25JUseOHSVJjzzyiEqUKKGjR48qb9682r59u7p06SJJeuyxx1SnTp0Mne/ChQvq3r27Dhw4IE9PT124cEG7d+9W0aJFtXz5cr333nuSJD8/Pz311FOuj1uyZImuXLmixYsXS5ISEhIUGBiYWV8GAACyBQpkAACySEJCgtq0aaOVK1eqWrVqio2NVb58+RQfH+8qkL29vV3Pz5Ejh5KSktJ9LYfD4Xrb09NTycnJrvfj4uKUJ08eSVLv3r315JNPavHixXI4HKpcubLi4uLu+pqGYWjKlClq2rTp3/+EAQDI5phiDQBAFomLi1NCQoKKFSsmSZoyZUqGPs7Hx0eVKlVSVFSUJGn//v1as2aN6/GSJUtq48aNkm6OGH///feuxy5duqSAgAA5HA6tXr1aO3bscD3WsGFD1zrmP/74Q99++63rsdatW2vSpEm6fv26JOn69evas2fP3/isAQDIvhhBBgAgE4WGhipnzpyu90eMGKHq1avrkUceUfv27TP8OnPnztULL7yg8ePHq2TJkqpWrZry588vSerZs6eeffZZPfbYYypRooRq1qzp+ri3335bL774osaMGaPg4GDVqFHD9djkyZPVtWtXlS1bVoULF1aNGjVcrzl06FDFx8erRo0arpHloUOHqly5cv/gqwEAQPbiMAzDsDoEAABwd+3aNT344INyOBw6evSoatWqpU2bNsnf3/9vv+aNGzeUM2dO19rkmjVrKioqyq2IBgDgfsYIMgAANrR27VoNGTJEkpScnKxJkyb9o+JYkg4ePKguXbrIMAwlJCToxRdfpDgGACAVRpABAAAAABBNugAAAAAAkESBDAAAAACAJApkAAAAAAAkUSADAAAAACCJAhkAAAAAAEkUyAAAAAAASKJABgAAAABAkvT/AJiSUMwmMQtCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(nf['AllPerfect'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(nf['All60/100'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(nf['All50/100'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(nf['All60'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff = (tb[tb['Trans']]\n",
    "       .pivot_table('Form', 'Language', ['Discharger', 'Direct'],\n",
    "                    aggfunc='count', fill_value=0))\n",
    "\n",
    "dff['All50/100'].plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Discharger</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intrans</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FreePerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free50/100</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllIntransPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All50/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>...</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>...</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger   None       Intrans       FreePerfect       Free60/100        \\\n",
       "Direct      False True    False True        False True       False True    \n",
       "Language                                                                   \n",
       "Ainu          0.5   0.5    0.62  0.38        0.40  0.60       0.33  0.67   \n",
       "Aleut         0.5   0.5    0.00  1.00        0.55  0.45       0.36  0.64   \n",
       "Ayacucho      0.5   0.5    0.82  0.18        0.47  0.53       0.66  0.34   \n",
       "Bella Coola   0.5   0.5    0.55  0.45        0.53  0.47       0.69  0.31   \n",
       "Chuckchi      0.5   0.5    0.39  0.61        0.50  0.50       0.35  0.65   \n",
       "Darai         0.5   0.5    0.31  0.69        0.40  0.60       0.40  0.60   \n",
       "Fox           0.5   0.5    0.50  0.50        0.50  0.50       0.47  0.53   \n",
       "Hixkaryana    0.5   0.5    0.73  0.27        0.53  0.47       0.55  0.45   \n",
       "Jaqaru        0.5   0.5    0.83  0.17        0.56  0.44       0.71  0.29   \n",
       "Jumjum        0.5   0.5    0.67  0.33        0.56  0.44       0.59  0.41   \n",
       "Karuk         0.5   0.5    0.84  0.16        0.45  0.55       0.47  0.53   \n",
       "Ket           0.5   0.5    0.54  0.46        0.53  0.47       0.45  0.55   \n",
       "Kunama        0.5   0.5    0.58  0.42        0.52  0.48       0.65  0.35   \n",
       "Lakota        0.5   0.5    0.47  0.53        0.53  0.47       0.00  1.00   \n",
       "Maricopa      0.5   0.5    0.67  0.33        0.50  0.50       0.50  0.50   \n",
       "Maung         0.5   0.5    0.56  0.44        0.50  0.50       0.48  0.52   \n",
       "Mordvin       0.5   0.5    0.52  0.48        0.50  0.50       0.29  0.71   \n",
       "Nocte         0.5   0.5    0.67  0.33        0.62  0.38       0.50  0.50   \n",
       "Reyesano      0.5   0.5     NaN   NaN        0.57  0.43        NaN   NaN   \n",
       "Sahu          0.5   0.5    0.50  0.50        0.24  0.76       0.25  0.75   \n",
       "Siuslawan     0.5   0.5    0.50  0.50        0.47  0.53       0.47  0.53   \n",
       "Tepehua       0.5   0.5    0.61  0.39        0.52  0.48       0.59  0.41   \n",
       "Thangmi       0.5   0.5    0.00  1.00        0.57  0.43       0.50  0.50   \n",
       "Turkana       0.5   0.5    0.57  0.43        0.50  0.50       0.62  0.38   \n",
       "Wardaman      0.5   0.5    0.61  0.39        0.49  0.51       0.51  0.49   \n",
       "Yimas         0.5   0.5    0.50  0.50        0.50  0.50       0.50  0.50   \n",
       "\n",
       "Discharger  Free50/100        ... AllPerfect       AllIntransPerfect        \\\n",
       "Direct           False True   ...      False True              False True    \n",
       "Language                      ...                                            \n",
       "Ainu              0.00  1.00  ...       0.40  0.60              0.41  0.59   \n",
       "Aleut             0.29  0.71  ...       0.55  0.45              0.00  1.00   \n",
       "Ayacucho          0.66  0.34  ...       0.45  0.55              0.66  0.34   \n",
       "Bella Coola       0.69  0.31  ...       0.53  0.47              0.55  0.45   \n",
       "Chuckchi          0.35  0.65  ...       0.50  0.50              0.39  0.61   \n",
       "Darai             0.40  0.60  ...       0.31  0.69              0.31  0.69   \n",
       "Fox               0.49  0.51  ...       0.50  0.50              0.50  0.50   \n",
       "Hixkaryana        0.55  0.45  ...       0.53  0.47              0.73  0.27   \n",
       "Jaqaru            0.71  0.29  ...       0.43  0.57              1.00  0.00   \n",
       "Jumjum            0.59  0.41  ...       0.56  0.44              0.64  0.36   \n",
       "Karuk             0.44  0.56  ...       0.45  0.55              0.82  0.18   \n",
       "Ket               0.45  0.55  ...       0.58  0.42              0.00  1.00   \n",
       "Kunama            0.50  0.50  ...       0.41  0.59              0.90  0.10   \n",
       "Lakota            0.00  1.00  ...       0.53  0.47              0.55  0.45   \n",
       "Maricopa           NaN   NaN  ...       0.50  0.50              0.67  0.33   \n",
       "Maung             0.39  0.61  ...       0.50  0.50              0.56  0.44   \n",
       "Mordvin           0.29  0.71  ...       0.32  0.68              0.51  0.49   \n",
       "Nocte             0.50  0.50  ...       0.62  0.38              1.00  0.00   \n",
       "Reyesano           NaN   NaN  ...       0.57  0.43               NaN   NaN   \n",
       "Sahu              0.33  0.67  ...       0.24  0.76              0.00  1.00   \n",
       "Siuslawan         0.90  0.10  ...       0.40  0.60              0.50  0.50   \n",
       "Tepehua           0.71  0.29  ...       0.52  0.48              0.61  0.39   \n",
       "Thangmi           0.50  0.50  ...       0.57  0.43              0.00  1.00   \n",
       "Turkana           0.31  0.69  ...       0.50  0.50              0.57  0.43   \n",
       "Wardaman          0.45  0.55  ...       0.53  0.47              0.76  0.24   \n",
       "Yimas             0.50  0.50  ...       0.48  0.52              0.43  0.57   \n",
       "\n",
       "Discharger  All60/100       All50/100       All60        \n",
       "Direct          False True      False True  False True   \n",
       "Language                                                 \n",
       "Ainu             0.33  0.67      0.00  1.00   NaN   NaN  \n",
       "Aleut            0.36  0.64      0.29  0.71  0.00  1.00  \n",
       "Ayacucho         0.56  0.44      0.56  0.44  0.00  1.00  \n",
       "Bella Coola      0.69  0.31      0.69  0.31  1.00  0.00  \n",
       "Chuckchi         0.35  0.65      0.35  0.65  0.20  0.80  \n",
       "Darai             NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Fox              0.50  0.50      0.55  0.45   NaN   NaN  \n",
       "Hixkaryana       0.55  0.45      0.55  0.45  0.38  0.62  \n",
       "Jaqaru           0.00  1.00      0.00  1.00  0.00  1.00  \n",
       "Jumjum           0.47  0.53      0.47  0.53  0.33  0.67  \n",
       "Karuk            0.40  0.60      0.00  1.00  0.00  1.00  \n",
       "Ket              0.36  0.64      0.00  1.00  0.60  0.40  \n",
       "Kunama           0.45  0.55      0.50  0.50  0.39  0.61  \n",
       "Lakota           0.00  1.00      0.00  1.00   NaN   NaN  \n",
       "Maricopa         0.50  0.50       NaN   NaN   NaN   NaN  \n",
       "Maung            0.00  1.00      0.00  1.00  0.00  1.00  \n",
       "Mordvin          0.21  0.79      0.21  0.79  0.28  0.72  \n",
       "Nocte            0.50  0.50      0.50  0.50  0.57  0.43  \n",
       "Reyesano          NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Sahu             0.25  0.75      0.33  0.67   NaN   NaN  \n",
       "Siuslawan         NaN   NaN       NaN   NaN   NaN   NaN  \n",
       "Tepehua          0.59  0.41      0.71  0.29  0.55  0.45  \n",
       "Thangmi          0.50  0.50      0.67  0.33  1.00  0.00  \n",
       "Turkana          0.00  1.00      0.00  1.00  0.00  1.00  \n",
       "Wardaman         0.54  0.46      0.37  0.63  1.00  0.00  \n",
       "Yimas            0.47  0.53      0.37  0.63  0.73  0.27  \n",
       "\n",
       "[26 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf = 100 * dff / dff['None']\n",
    "dnf = ddf / ddf.groupby(axis=1, level='Discharger').sum()\n",
    "with np.errstate(invalid='ignore'):\n",
    "    _ = dnf.round(2)[disc]\n",
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_21_row5_col1, #T_21_row8_col1, #T_21_row9_col1 {\n",
       "  background-color: lime;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_21\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Direct & Ratio</th>\n",
       "      <th id=\"T_21_level0_col0\" class=\"col_heading level0 col0\" >r</th>\n",
       "      <th id=\"T_21_level0_col1\" class=\"col_heading level0 col1\" >p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Discharger</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row0\" class=\"row_heading level0 row0\" >None</th>\n",
       "      <td id=\"T_21_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_21_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row1\" class=\"row_heading level0 row1\" >Intrans</th>\n",
       "      <td id=\"T_21_row1_col0\" class=\"data row1 col0\" >-0.204242</td>\n",
       "      <td id=\"T_21_row1_col1\" class=\"data row1 col1\" >0.154814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row2\" class=\"row_heading level0 row2\" >FreePerfect</th>\n",
       "      <td id=\"T_21_row2_col0\" class=\"data row2 col0\" >-0.001038</td>\n",
       "      <td id=\"T_21_row2_col1\" class=\"data row2 col1\" >0.994175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row3\" class=\"row_heading level0 row3\" >Free60/100</th>\n",
       "      <td id=\"T_21_row3_col0\" class=\"data row3 col0\" >0.160060</td>\n",
       "      <td id=\"T_21_row3_col1\" class=\"data row3 col1\" >0.266849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row4\" class=\"row_heading level0 row4\" >Free50/100</th>\n",
       "      <td id=\"T_21_row4_col0\" class=\"data row4 col0\" >0.203384</td>\n",
       "      <td id=\"T_21_row4_col1\" class=\"data row4 col1\" >0.165599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row5\" class=\"row_heading level0 row5\" >Free60</th>\n",
       "      <td id=\"T_21_row5_col0\" class=\"data row5 col0\" >0.306644</td>\n",
       "      <td id=\"T_21_row5_col1\" class=\"data row5 col1\" >0.042913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row6\" class=\"row_heading level0 row6\" >AllPerfect</th>\n",
       "      <td id=\"T_21_row6_col0\" class=\"data row6 col0\" >0.228250</td>\n",
       "      <td id=\"T_21_row6_col1\" class=\"data row6 col1\" >0.103633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row7\" class=\"row_heading level0 row7\" >AllIntransPerfect</th>\n",
       "      <td id=\"T_21_row7_col0\" class=\"data row7 col0\" >-0.077528</td>\n",
       "      <td id=\"T_21_row7_col1\" class=\"data row7 col1\" >0.592546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row8\" class=\"row_heading level0 row8\" >All60/100</th>\n",
       "      <td id=\"T_21_row8_col0\" class=\"data row8 col0\" >0.531955</td>\n",
       "      <td id=\"T_21_row8_col1\" class=\"data row8 col1\" >0.000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row9\" class=\"row_heading level0 row9\" >All50/100</th>\n",
       "      <td id=\"T_21_row9_col0\" class=\"data row9 col0\" >0.574728</td>\n",
       "      <td id=\"T_21_row9_col1\" class=\"data row9 col1\" >0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21_level0_row10\" class=\"row_heading level0 row10\" >All60</th>\n",
       "      <td id=\"T_21_row10_col0\" class=\"data row10 col0\" >0.294196</td>\n",
       "      <td id=\"T_21_row10_col1\" class=\"data row10 col1\" >0.081569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27829700a90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dnf.stack(['Direct', 'Discharger'])\n",
    " .to_frame('Ratio')\n",
    " .reset_index('Direct')\n",
    " .groupby(level='Discharger')\n",
    " .apply(lambda x: pearsonr(x, 'Direct', 'Ratio'))\n",
    " .loc[disc]\n",
    " .style.applymap(lambda x: x < .05 and 'background-color: lime' or '', subset=['p'])\n",
    " .set_uuid('21'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dnf['Intrans'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dnf['AllPerfect'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dnf['All60/100'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dnf['All50/100'].sort_values(by=True, kind='mergesort')\n",
    " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local & form contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Discharger</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intrans</th>\n",
       "      <th colspan=\"2\" halign=\"left\">FreePerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Free50/100</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AllIntransPerfect</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All50/100</th>\n",
       "      <th colspan=\"2\" halign=\"left\">All60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>...</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Form</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ainu</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aleut</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ayacucho</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bella Coola</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Chuckchi</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Darai</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fox</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hixkaryana</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Jaqaru</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Jumjum</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Karuk</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ket</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kunama</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>47</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lakota</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Maricopa</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Maung</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>91</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mordvin</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nocte</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Reyesano</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sahu</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>79</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Siuslawan</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tepehua</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thangmi</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Turkana</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wardaman</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yimas</th>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Discharger         None       Intrans       FreePerfect       Free60/100  \\\n",
       "Local             False True    False True        False True       False   \n",
       "Language    Form                                                           \n",
       "Ainu        False     0     0      10     4           4     2         10   \n",
       "            True     29     8      19     4          25     6         19   \n",
       "Aleut       False     0     0      18    18           6     0         19   \n",
       "            True     45    18      27     0          39    18         26   \n",
       "Ayacucho    False     0     0      14     0           5     0         15   \n",
       "            True     24     8      10     8          19     8          9   \n",
       "Bella Coola False     0     0       5     2           2     0         17   \n",
       "            True     20     8      15     6          18     8          3   \n",
       "Chuckchi    False     0     0       0     4           0     0         14   \n",
       "            True     19     8      19     4          19     8          5   \n",
       "Darai       False     0     0       6     6           4     6          4   \n",
       "            True     20     8      14     2          16     2         16   \n",
       "Fox         False     0     0       0     0           0     0          2   \n",
       "            True     32     8      32     8          32     8         30   \n",
       "Hixkaryana  False     0     0      14     4           2     0          6   \n",
       "            True     22     6       8     2          20     6         16   \n",
       "Jaqaru      False     0     0       4     0           1     0          3   \n",
       "            True      7     3       3     3           6     3          4   \n",
       "Jumjum      False     0     0      10     0           4     0          6   \n",
       "            True     28     8      18     8          24     8         22   \n",
       "Karuk       False     0     0      11     2           1     1          5   \n",
       "            True     20     8       9     6          19     7         15   \n",
       "Ket         False     0     0      10     0           8     0         32   \n",
       "            True     84     8      74     8          76     8         52   \n",
       "Kunama      False     0     0      10     2           5     0         15   \n",
       "            True     57    18      47    16          52    18         42   \n",
       "Lakota      False     0     0      10     2           5     1         23   \n",
       "            True     23     8      13     6          18     7          0   \n",
       "Maricopa    False     0     0      12     0           0     0         12   \n",
       "            True     16     8       4     8          16     8          4   \n",
       "Maung       False     0     0      16     0           0     0          5   \n",
       "            True     96     8      80     8          96     8         91   \n",
       "Mordvin     False     0     0       0     1           0     0          9   \n",
       "            True     20     8      20     7          20     8         11   \n",
       "Nocte       False     0     0       8     0           2     4          8   \n",
       "            True     20     8      12     8          18     4         12   \n",
       "Reyesano    False     0     0      20     8           4     8         20   \n",
       "            True     20     8       0     0          16     0          0   \n",
       "Sahu        False     0     0      17     2          42     3         44   \n",
       "            True     96     8      79     6          54     5         52   \n",
       "Siuslawan   False     0     0       0     0           4     0          4   \n",
       "            True     57    18      57    18          53    18         53   \n",
       "Tepehua     False     0     0       6     0           1     0          9   \n",
       "            True     23     8      17     8          22     8         14   \n",
       "Thangmi     False     0     0      10     6           2     2         14   \n",
       "            True     20     8      10     2          18     6          6   \n",
       "Turkana     False     0     0      18     6           0     0          6   \n",
       "            True     20     8       2     2          20     8         14   \n",
       "Wardaman    False     0     0       7     0           4     0          7   \n",
       "            True     27     8      20     8          23     8         20   \n",
       "Yimas       False     0     0       0     0           0     0          0   \n",
       "            True     45    18      45    18          45    18         45   \n",
       "\n",
       "Discharger              Free50/100        ... AllPerfect        \\\n",
       "Local             True       False True   ...      False True    \n",
       "Language    Form                          ...                    \n",
       "Ainu        False     2         20     8  ...          4     2   \n",
       "            True      6          9     0  ...         25     6   \n",
       "Aleut       False     3         25    12  ...          6     0   \n",
       "            True     15         20     6  ...         39    18   \n",
       "Ayacucho    False     2         15     2  ...          5     1   \n",
       "            True      6          9     6  ...         19     7   \n",
       "Bella Coola False     3         17     3  ...          2     0   \n",
       "            True      5          3     5  ...         18     8   \n",
       "Chuckchi    False     2         14     2  ...          0     0   \n",
       "            True      6          5     6  ...         19     8   \n",
       "Darai       False     6          4     6  ...          6     6   \n",
       "            True      2         16     2  ...         14     2   \n",
       "Fox         False     0          8     0  ...          0     0   \n",
       "            True      8         24     8  ...         32     8   \n",
       "Hixkaryana  False     2          6     2  ...          2     0   \n",
       "            True      4         16     4  ...         20     6   \n",
       "Jaqaru      False     0          3     0  ...          3     0   \n",
       "            True      3          4     3  ...          4     3   \n",
       "Jumjum      False     0          6     0  ...          4     0   \n",
       "            True      8         22     8  ...         24     8   \n",
       "Karuk       False     3          9     3  ...          1     1   \n",
       "            True      5         11     5  ...         19     7   \n",
       "Ket         False     0         32     0  ...         20     8   \n",
       "            True      8         52     8  ...         64     0   \n",
       "Kunama      False     6         29    14  ...         25    10   \n",
       "            True     12         28     4  ...         32     8   \n",
       "Lakota      False     6         23     6  ...          5     1   \n",
       "            True      2          0     2  ...         18     7   \n",
       "Maricopa    False     4         16     8  ...          0     0   \n",
       "            True      4          0     0  ...         16     8   \n",
       "Maung       False     1         33     7  ...          1     0   \n",
       "            True      7         63     1  ...         95     8   \n",
       "Mordvin     False     6          9     6  ...          6     3   \n",
       "            True      2         11     2  ...         14     5   \n",
       "Nocte       False     6          8     6  ...          2     4   \n",
       "            True      2         12     2  ...         18     4   \n",
       "Reyesano    False     8         20     8  ...          4     8   \n",
       "            True      0          0     0  ...         16     0   \n",
       "Sahu        False     3         60     3  ...         42     3   \n",
       "            True      5         36     5  ...         54     5   \n",
       "Siuslawan   False     0         51    16  ...         10     7   \n",
       "            True     18          6     2  ...         47    11   \n",
       "Tepehua     False     0         13     0  ...          1     0   \n",
       "            True      8         10     8  ...         22     8   \n",
       "Thangmi     False     7         14     7  ...          2     2   \n",
       "            True      1          6     1  ...         18     6   \n",
       "Turkana     False     0         14     6  ...          0     0   \n",
       "            True      8          6     2  ...         20     8   \n",
       "Wardaman    False     0         12     1  ...          6     0   \n",
       "            True      8         15     7  ...         21     8   \n",
       "Yimas       False     0          0     0  ...          2     0   \n",
       "            True     18         45    18  ...         43    18   \n",
       "\n",
       "Discharger        AllIntransPerfect       All60/100       All50/100        \\\n",
       "Local                         False True      False True      False True    \n",
       "Language    Form                                                            \n",
       "Ainu        False                14     8        10     2        20     8   \n",
       "            True                 15     0        19     6         9     0   \n",
       "Aleut       False                18    18        19     3        25    12   \n",
       "            True                 27     0        26    15        20     6   \n",
       "Ayacucho    False                20     2        20     4        20     4   \n",
       "            True                  4     6         4     4         4     4   \n",
       "Bella Coola False                 6     3        17     3        17     3   \n",
       "            True                 14     5         3     5         3     5   \n",
       "Chuckchi    False                 0     4        14     2        14     2   \n",
       "            True                 19     4         5     6         5     6   \n",
       "Darai       False                 6     6        20     8        20     8   \n",
       "            True                 14     2         0     0         0     0   \n",
       "Fox         False                 0     0        18     0        32     0   \n",
       "            True                 32     8        14     8         0     8   \n",
       "Hixkaryana  False                14     4         6     2         6     2   \n",
       "            True                  8     2        16     4        16     4   \n",
       "Jaqaru      False                 5     3         6     2         6     2   \n",
       "            True                  2     0         1     1         1     1   \n",
       "Jumjum      False                12     0        16     3        16     3   \n",
       "            True                 16     8        12     5        12     5   \n",
       "Karuk       False                12     3         6     4        13     6   \n",
       "            True                  8     5        14     4         7     2   \n",
       "Ket         False                74     8        64     8        74     8   \n",
       "            True                 10     0        20     0        10     0   \n",
       "Kunama      False                45    14        27    12        29    14   \n",
       "            True                 12     4        30     6        28     4   \n",
       "Lakota      False                19     4        23     6        23     6   \n",
       "            True                  4     4         0     2         0     2   \n",
       "Maricopa    False                12     0        12     4        16     8   \n",
       "            True                  4     8         4     4         0     0   \n",
       "Maung       False                16     0        74     8        86     8   \n",
       "            True                 80     8        22     0        10     0   \n",
       "Mordvin     False                 2     1        10     6        10     6   \n",
       "            True                 18     7        10     2        10     2   \n",
       "Nocte       False                12     4         8     6         8     6   \n",
       "            True                  8     4        12     2        12     2   \n",
       "Reyesano    False                20     8        20     8        20     8   \n",
       "            True                  0     0         0     0         0     0   \n",
       "Sahu        False                63     8        44     3        60     3   \n",
       "            True                 33     0        52     5        36     5   \n",
       "Siuslawan   False                 0     0        57    18        57    18   \n",
       "            True                 57    18         0     0         0     0   \n",
       "Tepehua     False                 6     0         9     0        13     0   \n",
       "            True                 17     8        14     8        10     8   \n",
       "Thangmi     False                12     8        14     7        16     7   \n",
       "            True                  8     0         6     1         4     1   \n",
       "Turkana     False                18     6        16     8        16     8   \n",
       "            True                  2     2         4     0         4     0   \n",
       "Wardaman    False                13     0        21     4        23     6   \n",
       "            True                 14     8         6     4         4     2   \n",
       "Yimas       False                 6     7        11     2        15     9   \n",
       "            True                 39    11        34    16        30     9   \n",
       "\n",
       "Discharger        All60        \n",
       "Local             False True   \n",
       "Language    Form               \n",
       "Ainu        False    29     8  \n",
       "            True      0     0  \n",
       "Aleut       False    31    15  \n",
       "            True     14     3  \n",
       "Ayacucho    False    24     4  \n",
       "            True      0     4  \n",
       "Bella Coola False    19     8  \n",
       "            True      1     0  \n",
       "Chuckchi    False    16     4  \n",
       "            True      3     4  \n",
       "Darai       False    20     8  \n",
       "            True      0     0  \n",
       "Fox         False    32     8  \n",
       "            True      0     0  \n",
       "Hixkaryana  False    14     6  \n",
       "            True      8     0  \n",
       "Jaqaru      False     6     2  \n",
       "            True      1     1  \n",
       "Jumjum      False    23     6  \n",
       "            True      5     2  \n",
       "Karuk       False    18     8  \n",
       "            True      2     0  \n",
       "Ket         False    74     8  \n",
       "            True     10     0  \n",
       "Kunama      False    30    18  \n",
       "            True     27     0  \n",
       "Lakota      False    23     8  \n",
       "            True      0     0  \n",
       "Maricopa    False    16     8  \n",
       "            True      0     0  \n",
       "Maung       False    76     8  \n",
       "            True     20     0  \n",
       "Mordvin     False    12     7  \n",
       "            True      8     1  \n",
       "Nocte       False    14     6  \n",
       "            True      6     2  \n",
       "Reyesano    False    20     8  \n",
       "            True      0     0  \n",
       "Sahu        False    96     8  \n",
       "            True      0     0  \n",
       "Siuslawan   False    57    18  \n",
       "            True      0     0  \n",
       "Tepehua     False    23     2  \n",
       "            True      0     6  \n",
       "Thangmi     False    20     7  \n",
       "            True      0     1  \n",
       "Turkana     False    16     8  \n",
       "            True      4     0  \n",
       "Wardaman    False    26     8  \n",
       "            True      1     0  \n",
       "Yimas       False    42    12  \n",
       "            True      3     6  \n",
       "\n",
       "[52 rows x 22 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctl = (tb[tb['Trans']]\n",
    "       .set_index(['Language', 'Discharger', 'Cell', 'Local'])['Form'].notnull()\n",
    "       .unstack('Discharger')\n",
    "       .rename(columns={'None': 'Base'})\n",
    "       .query('Base == 1')\n",
    "       .rename(columns={'Base': 'None'})\n",
    "       .stack('Discharger')\n",
    "       .to_frame('Form')\n",
    "       .reset_index()\n",
    "       .pivot_table('Cell', ['Language', 'Form'], ['Discharger', 'Local'], aggfunc=len, fill_value=0))\n",
    "\n",
    "ctl[disc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th>Discharger</th>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Intrans</th>\n",
       "      <th>Aleut</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.797740e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>inf</td>\n",
       "      <td>4.445680e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.988604e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>4.405160e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.346076e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">FreePerfect</th>\n",
       "      <th>Darai</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.104692e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>3.835006e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.592610e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">Free60/100</th>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>2.230330e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.322185e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.104692e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>inf</td>\n",
       "      <td>4.700267e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">4</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">Free50/100</th>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>2.230330e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.322185e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.104692e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>inf</td>\n",
       "      <td>4.700267e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>0.074830</td>\n",
       "      <td>4.930952e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>inf</td>\n",
       "      <td>9.625510e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">5</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">Free60</th>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>11.400000</td>\n",
       "      <td>9.898463e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>2.230330e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.104692e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.902499e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.205725e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>0.093333</td>\n",
       "      <td>3.636695e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.208441e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>inf</td>\n",
       "      <td>3.802896e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">6</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">AllPerfect</th>\n",
       "      <th>Darai</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>4.405160e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.339144e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>3.835006e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.592610e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">7</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">AllIntransPerfect</th>\n",
       "      <th>Ainu</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.211601e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.797740e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.867231e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.988604e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>4.405160e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>inf</td>\n",
       "      <td>3.337695e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.346076e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.524523e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>0.241758</td>\n",
       "      <td>3.759624e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">All60/100</th>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>2.230330e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.322185e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>inf</td>\n",
       "      <td>4.726994e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">9</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">All50/100</th>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9.444444</td>\n",
       "      <td>2.230330e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.322185e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.300311e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>inf</td>\n",
       "      <td>9.625510e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">All60</th>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.946607e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.205725e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>inf</td>\n",
       "      <td>3.802896e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.254650e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       odds             p\n",
       "i  Discharger        Language                            \n",
       "1  Intrans           Aleut         0.000000  6.797740e-06\n",
       "                     Ayacucho           inf  4.445680e-03\n",
       "                     Chuckchi      0.000000  3.988604e-03\n",
       "                     Darai         0.142857  4.405160e-02\n",
       "                     Maricopa           inf  1.346076e-03\n",
       "2  FreePerfect       Darai         0.083333  1.104692e-02\n",
       "                     Nocte         0.111111  3.835006e-02\n",
       "                     Reyesano      0.000000  1.592610e-04\n",
       "3  Free60/100        Bella Coola   9.444444  2.230330e-02\n",
       "                     Chuckchi      8.400000  3.322185e-02\n",
       "                     Darai         0.083333  1.104692e-02\n",
       "                     Ket                inf  4.700267e-02\n",
       "4  Free50/100        Bella Coola   9.444444  2.230330e-02\n",
       "                     Chuckchi      8.400000  3.322185e-02\n",
       "                     Darai         0.083333  1.104692e-02\n",
       "                     Ket                inf  4.700267e-02\n",
       "                     Maung         0.074830  4.930952e-03\n",
       "                     Tepehua            inf  9.625510e-03\n",
       "5  Free60            Ayacucho     11.400000  9.898463e-03\n",
       "                     Bella Coola   9.444444  2.230330e-02\n",
       "                     Darai         0.083333  1.104692e-02\n",
       "                     Ket                inf  1.902499e-02\n",
       "                     Kunama        0.000000  1.205725e-04\n",
       "                     Maung         0.093333  3.636695e-03\n",
       "                     Siuslawan          inf  1.208441e-02\n",
       "                     Tepehua            inf  3.802896e-05\n",
       "6  AllPerfect        Darai         0.142857  4.405160e-02\n",
       "                     Ket           0.000000  3.339144e-05\n",
       "                     Nocte         0.111111  3.835006e-02\n",
       "                     Reyesano      0.000000  1.592610e-04\n",
       "7  AllIntransPerfect Ainu          0.000000  1.211601e-02\n",
       "                     Aleut         0.000000  6.797740e-06\n",
       "                     Ayacucho     15.000000  4.867231e-03\n",
       "                     Chuckchi      0.000000  3.988604e-03\n",
       "                     Darai         0.142857  4.405160e-02\n",
       "                     Jumjum             inf  3.337695e-02\n",
       "                     Maricopa           inf  1.346076e-03\n",
       "                     Wardaman           inf  1.524523e-02\n",
       "                     Yimas         0.241758  3.759624e-02\n",
       "8  All60/100         Bella Coola   9.444444  2.230330e-02\n",
       "                     Chuckchi      8.400000  3.322185e-02\n",
       "                     Fox                inf  4.726994e-03\n",
       "9  All50/100         Bella Coola   9.444444  2.230330e-02\n",
       "                     Chuckchi      8.400000  3.322185e-02\n",
       "                     Fox                inf  1.300311e-08\n",
       "                     Tepehua            inf  9.625510e-03\n",
       "10 All60             Ayacucho           inf  1.946607e-03\n",
       "                     Kunama        0.000000  1.205725e-04\n",
       "                     Tepehua            inf  3.802896e-05\n",
       "                     Yimas         7.000000  1.254650e-02"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ctl.stack('Discharger')\n",
    " .groupby(level=['Discharger', 'Language'])\n",
    " .apply(fisher_exact).query('p <= .05')\n",
    " .assign(i=lambda x: list(map(disc.tolist().index, x.index.get_level_values('Discharger'))))\n",
    " .reset_index()\n",
    " .set_index(['i', 'Discharger', 'Language'])\n",
    " .sort_index())"
   ]
  }
 ],
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