affixes.ipynb

{
 "cells": [
  {
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   "id": "liquid-column",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pre</th>\n",
       "      <th>suff</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cum</td>\n",
       "      <td>wad</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cum</td>\n",
       "      <td>bag</td>\n",
       "      <td>1608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cum</td>\n",
       "      <td>bucket</td>\n",
       "      <td>4441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cum</td>\n",
       "      <td>sack</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cum</td>\n",
       "      <td>ball</td>\n",
       "      <td>654.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   pre    suff   count\n",
       "0  cum     wad   372.0\n",
       "1  cum     bag  1608.0\n",
       "2  cum  bucket  4441.0\n",
       "3  cum    sack   212.0\n",
       "4  cum    ball   654.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from heatmap import make_heatmap, matricize_df\n",
    "import viz_helpers\n",
    "\n",
    "# Seaborn style defaults. Relevant changes for us are default font, and no ticks for heatmaps\n",
    "sns.set_theme()\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.options.display.precision = 3\n",
    "\n",
    "# Default (all reddit) counts\n",
    "df = viz_helpers.load_df(wikt=False)\n",
    "\n",
    "mat = matricize_df(df)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "operational-indonesian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122 affixes. 66 prefixes, 73 suffixes, overlap=17.\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>726783</td>\n",
       "      <td>162.383</td>\n",
       "      <td>-1.117</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "      <td>52</td>\n",
       "      <td>hat</td>\n",
       "      <td>618186</td>\n",
       "      <td>85.058</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bastard</th>\n",
       "      <td>192</td>\n",
       "      <td>20.587</td>\n",
       "      <td>-4.230</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>face</td>\n",
       "      <td>39</td>\n",
       "      <td>20.312</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>20197</td>\n",
       "      <td>81.706</td>\n",
       "      <td>-3.141</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>shit</td>\n",
       "      <td>6010</td>\n",
       "      <td>29.757</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>67527</td>\n",
       "      <td>106.457</td>\n",
       "      <td>-2.169</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>ass</td>\n",
       "      <td>35551</td>\n",
       "      <td>52.647</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bum</th>\n",
       "      <td>108346</td>\n",
       "      <td>88.323</td>\n",
       "      <td>-0.728</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>fuck</td>\n",
       "      <td>98471</td>\n",
       "      <td>90.886</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "ass      726783  162.383   -1.117           9       62            52      hat   \n",
       "bastard     192   20.587   -4.230          64        4             6     face   \n",
       "bird      20197   81.706   -3.141          30       39            30     shit   \n",
       "bitch     67527  106.457   -2.169          20       55            40      ass   \n",
       "bum      108346   88.323   -0.728          16       44            55     fuck   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables  \n",
       "ass        618186       85.058       2          1  \n",
       "bastard        39       20.312      41          2  \n",
       "bird         6010       29.757      13          1  \n",
       "bitch       35551       52.647       6          1  \n",
       "bum         98471       90.886      11          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "def shannon_entropy(vec, pseudocount=1):\n",
    "    \"\"\"Compute the shannon entropy for given vector of counts for an affix, with\n",
    "    additive smoothing to deal with 0s.\n",
    "    \n",
    "    This is the average number of bits required to communicate a pairing of the affix.\n",
    "    A higher value means the term is more flexible.\n",
    "    \"\"\"\n",
    "    smoothed = vec + pseudocount\n",
    "    total = smoothed.sum()\n",
    "    probs = smoothed / total\n",
    "    bits = np.log2(probs)\n",
    "    return -1 * (bits * probs).sum()\n",
    "\n",
    "# FWIW, did a side-by-side comparison, and the unbiased version is *almost* indistinguishable from\n",
    "# the basic version, with the exception of the rarest prefix and suffix (harpy- and -mitten, resp)\n",
    "# which appear approx 10^1-10^2 times. And even then, the displacement is fairly small.\n",
    "UNBIASED = 1\n",
    "\n",
    "def collision_probability(vec):\n",
    "    probs = vec / vec.sum()\n",
    "    if UNBIASED:\n",
    "        # probabilities after one item is removed and not replaced\n",
    "        post_numerators = vec.apply(lambda n: max(0, n-1))\n",
    "        post_denom = vec.sum() - 1\n",
    "        post_probs = post_numerators / post_denom\n",
    "        return (probs * post_probs).sum()\n",
    "    else:\n",
    "        return (probs**2).sum()\n",
    "    \n",
    "def collision_entropy(vec):\n",
    "    # zzz\n",
    "    pseudocount = 1\n",
    "    vec += pseudocount\n",
    "    probs = vec / vec.sum()\n",
    "    if UNBIASED:\n",
    "        # probabilities after one item is removed and not replaced\n",
    "        post_numerators = vec.apply(lambda n: max(0, n-1))\n",
    "        post_denom = vec.sum() - 1\n",
    "        post_probs = post_numerators / post_denom\n",
    "        return (-1 * np.log2(probs * post_probs)).sum()\n",
    "    else:\n",
    "        return (-1 * np.log2(probs**2)).sum()\n",
    "    \n",
    "def logsum(vec):\n",
    "    return np.log10(vec+1).sum()\n",
    "\n",
    "# XXX: being lazy here\n",
    "collision_probability = logsum\n",
    "\n",
    "pre_entropy = mat.apply(lambda vec: shannon_entropy(vec, 0.1), axis=1).rename_axis(index='entropy')\n",
    "suff_entropy = mat.apply(lambda vec: shannon_entropy(vec, 0.1), axis=0).rename_axis(index='entropy')\n",
    "\n",
    "suff_cp = mat.apply(collision_probability, axis=0).rename_axis(index='cp')\n",
    "pre_cp = mat.apply(collision_probability, axis=1).rename_axis(index='cp')\n",
    "ENTROPY = dict(\n",
    "    pre=pre_entropy,\n",
    "    suff=suff_entropy,\n",
    ")\n",
    "CP = dict(\n",
    "    pre=pre_cp,\n",
    "    suff=suff_cp,\n",
    ")\n",
    "# XXX: above code copy-pasted from flexibility.ipynb. The two might drift apart.\n",
    "ALL_PRES = set(df.pre.unique())\n",
    "ALL_SUFFS = set(df.suff.unique())\n",
    "ALL_AFFIXES = set.union(ALL_PRES, ALL_SUFFS)\n",
    "BOTHS = set.intersection(ALL_PRES, ALL_SUFFS)\n",
    "print(f\"{len(ALL_AFFIXES)} affixes. {len(ALL_PRES)} prefixes, {len(ALL_SUFFS)} suffixes, overlap={len(BOTHS)}.\")\n",
    "COUNTS = dict(\n",
    "    pre = df.groupby('pre')['count'].sum(),\n",
    "    suff = df.groupby('suff')['count'].sum()\n",
    ")\n",
    "\n",
    "ALL_DISYLLABLES = set(\n",
    "    (\"pecker,pussy,homo,nazi,monkey,weasel,bastard,donkey,moron,idiot,loser,harpy,faggot\"\n",
    "               \",bucket,weasel,monkey,goblin,puffin,licker,muncher,fucker,sucker,pirate,burglar,bandit,waffle\"\n",
    "               \",burger,nugget,nozzle,wagon,canoe,socket,trumpet,mitten,knuckle,azoid\"\n",
    "    ).split(',')\n",
    ")\n",
    "DISYLLABLES = dict(\n",
    "    pre = set.intersection(ALL_DISYLLABLES, ALL_PRES),\n",
    "    suff = set.intersection(ALL_DISYLLABLES, ALL_SUFFS),\n",
    ")\n",
    "\n",
    "AFFIX_FORMATS = dict(\n",
    "    count='{:,}',\n",
    ") # NVM?\n",
    "\"\"\"\n",
    "Other info to add:\n",
    "\"\"\"\n",
    "def make_affix_df(atype):\n",
    "    assert atype in ('pre', 'suff')\n",
    "    _swapper = dict(\n",
    "        pre='suff',\n",
    "        suff='pre',\n",
    "    )\n",
    "    othertype = _swapper[atype]\n",
    "    af = pd.concat([\n",
    "        COUNTS[atype].round().astype(int), # round to nearest int for simplicity. We only have non-int values for counts above >~30k anyways.\n",
    "        CP[atype],\n",
    "        ENTROPY[atype] * -1, # so direction is same as cp\n",
    "    ], axis=1)\n",
    "    af.columns = ['count', 'cp', 'entropy']\n",
    "    af['count_rank'] = af['count'].rank(ascending=False).astype(int)\n",
    "    # We'll rank the most 'flexible' (lowest collision prob) at number 1.\n",
    "    af['cp_rank'] = af['cp'].rank(ascending=True).astype(int)\n",
    "    af['entropy_rank'] = af['entropy'].rank(ascending=True).astype(int)\n",
    "    \n",
    "    af['topmatch'] = af.apply(lambda row: top_compound_row(row.name, atype)[othertype],\n",
    "                              axis=1,\n",
    "                             )\n",
    "    af['topcount'] = af.apply(lambda row: top_compound_row(row.name, atype)['count'],\n",
    "                              axis=1,\n",
    "                             ).round().astype(int)\n",
    "    af['topcountpct'] = 100 * (af['topcount'] / af['count'])\n",
    "    \n",
    "    af['nzeros'] = af.apply(\n",
    "        lambda row: ((df[atype]==row.name) & (df['count']==0)).sum(),\n",
    "        axis=1,\n",
    "    )\n",
    "    \n",
    "    af['syllables'] = 1\n",
    "    DSs = DISYLLABLES[atype]\n",
    "    af.loc[DSs, 'syllables'] =  2\n",
    "    return af\n",
    "\n",
    "def top_compound_row(aff, col):\n",
    "    match = df[\n",
    "        df[col] == aff\n",
    "    ]\n",
    "    return match.loc[\n",
    "        match['count'].idxmax()\n",
    "    ]\n",
    "\n",
    "def getpre(pre):\n",
    "    return df[df.pre == pre].sort_values(by='count', ascending=False)\n",
    "\n",
    "def getsuff(suff):\n",
    "    return df[df.suff == suff].sort_values(by='count', ascending=False)\n",
    "\n",
    "def info(affix):\n",
    "    if affix[0] == '-':\n",
    "        col = 'suff'\n",
    "        aff = affix[1:]\n",
    "    elif affix[-1] == '-':\n",
    "        col = 'pre'\n",
    "        aff = affix[:-1]\n",
    "    else:\n",
    "        assert affix in ALL_AFFIXES\n",
    "        if affix in ALL_PRES:\n",
    "            info(affix + '-')\n",
    "        if affix in ALL_SUFFS:\n",
    "            info('-' + affix)\n",
    "        return\n",
    "    \n",
    "    \n",
    "\n",
    "#getsuff('mitten');\n",
    "pf = make_affix_df('pre')\n",
    "pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "modified-justice",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>3754308</td>\n",
       "      <td>136.608</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>dumb</td>\n",
       "      <td>3631048</td>\n",
       "      <td>96.717</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>azoid</th>\n",
       "      <td>2469</td>\n",
       "      <td>27.496</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>creep</td>\n",
       "      <td>1999</td>\n",
       "      <td>80.964</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bag</th>\n",
       "      <td>2939955</td>\n",
       "      <td>140.726</td>\n",
       "      <td>-1.796</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>scum</td>\n",
       "      <td>1347188</td>\n",
       "      <td>45.823</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>93812</td>\n",
       "      <td>132.159</td>\n",
       "      <td>-2.923</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>slime</td>\n",
       "      <td>33254</td>\n",
       "      <td>35.447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balls</th>\n",
       "      <td>82160</td>\n",
       "      <td>116.156</td>\n",
       "      <td>-2.856</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>25</td>\n",
       "      <td>shit</td>\n",
       "      <td>36333</td>\n",
       "      <td>44.222</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count       cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "ass    3754308  136.608   -0.322           1       68            69     dumb   \n",
       "azoid     2469   27.496   -1.402          63       11            56    creep   \n",
       "bag    2939955  140.726   -1.796           2       70            50     scum   \n",
       "ball     93812  132.159   -2.923          16       66            23    slime   \n",
       "balls    82160  116.156   -2.856          17       64            25     shit   \n",
       "\n",
       "       topcount  topcountpct  nzeros  syllables  \n",
       "ass     3631048       96.717       4          1  \n",
       "azoid      1999       80.964      38          2  \n",
       "bag     1347188       45.823       4          1  \n",
       "ball      33254       35.447       1          1  \n",
       "balls     36333       44.222       5          1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf = make_affix_df('suff')\n",
    "sf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "successful-guard",
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.to_csv('prefixes.csv')\n",
    "sf.to_csv('suffixes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sensitive-stockholm",
   "metadata": {},
   "source": [
    "## Flexibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "institutional-hawaiian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from adjustText import adjust_text\n",
    "FIG_GEN = 0\n",
    "\n",
    "FS = (18, 8)\n",
    "if FIG_GEN:\n",
    "    FS = (9, 6)\n",
    "\n",
    "def scatter(af, xcol='count', ycol='cp',\n",
    "            labels=True, arrow=True, adjust=True,\n",
    "            **sns_kwargs,\n",
    "           ):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=FS)\n",
    "\n",
    "    sns.scatterplot(\n",
    "        data=af,\n",
    "        x=xcol,\n",
    "        y=ycol,\n",
    "        ax=ax,\n",
    "        **sns_kwargs\n",
    "    )\n",
    "    ax.set_xscale('log')\n",
    "    if ycol == 'cp' and False:\n",
    "        ax.invert_yaxis()\n",
    "    if labels:\n",
    "        texts = []\n",
    "        for aff, row in af.iterrows():\n",
    "            x = row[xcol]\n",
    "            y = row[ycol]\n",
    "            texts.append( ax.text(x, y, aff) )\n",
    "        if adjust:\n",
    "            iters = adjust_text(\n",
    "                texts,\n",
    "                x=af[xcol].values,\n",
    "                y=af[ycol].values,\n",
    "                expand_text=(1.05, 1.2),\n",
    "                expand_points=(1.2, 1.2),\n",
    "                arrowprops=dict(arrowstyle='->', color='black'),\n",
    "                lim=100, # slooooow\n",
    "            )\n",
    "            print(f\"adjust_text took {iters} iterations.\")\n",
    "    #ax.set_title(f\"Prefixes: count vs. collision prob\")\n",
    "    #ax.set_ylabel('')\n",
    "    if arrow:\n",
    "        xarrow = .05\n",
    "        # arrow up\n",
    "        ax.annotate(\n",
    "            '',\n",
    "            xytext=(xarrow, .45),\n",
    "            xy=(xarrow, .85),\n",
    "            xycoords='figure fraction',\n",
    "            textcoords='figure fraction',\n",
    "            arrowprops=dict(),\n",
    "        )\n",
    "        ax.text(\n",
    "            .065,\n",
    "            .58,\n",
    "            'More flexible',\n",
    "            transform=fig.transFigure,\n",
    "            rotation=90,\n",
    "            fontsize=14,\n",
    "        )\n",
    "    return fig, ax\n",
    "\n",
    "scatter(pf, \n",
    "        labels=1,\n",
    "        hue='syllables', palette='deep', s=120,\n",
    "        adjust=False,\n",
    "       );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "legitimate-setup",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(pf, \n",
    "        labels=0,\n",
    "        hue='syllables', palette='deep', s=120, marker='+',\n",
    "        adjust=False,\n",
    "       );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "handled-weight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(sf, \n",
    "        labels=False,\n",
    "        hue='syllables', palette='deep', s=120, marker='+',\n",
    "       );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "nervous-amount",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adjust_text took 6 iterations.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FIG_GEN = 1\n",
    "\n",
    "# Set to 1 if we're doing the \"collision_probability = logsum\" hack. Bleh.\n",
    "LOGSUM = 1\n",
    "\n",
    "FS = (18, 8)\n",
    "if FIG_GEN:\n",
    "    FS = (9, 8)\n",
    "\n",
    "def scatter_text(af, xcol='count', ycol='cp',\n",
    "            adjust=True, arrow=True,\n",
    "                 min=500,\n",
    "                 textbg='orangered',\n",
    "            **sns_kwargs,\n",
    "           ):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=FS)\n",
    "    if min:\n",
    "        af = af[af['count'] >= min]\n",
    "\n",
    "    sns.scatterplot(\n",
    "        data=af,\n",
    "        x=xcol,\n",
    "        y=ycol,\n",
    "        ax=ax,\n",
    "        alpha=0,\n",
    "        **sns_kwargs\n",
    "    )\n",
    "    ax.set_xscale('log')\n",
    "    if ycol == 'cp' and not LOGSUM:\n",
    "        ax.invert_yaxis()\n",
    "    \n",
    "    texts = []\n",
    "    for aff, row in af.iterrows():\n",
    "        x = row[xcol]\n",
    "        y = row[ycol]\n",
    "        texts.append( ax.annotate(aff, (x, y), \n",
    "                                  bbox=dict(boxstyle='round', alpha=0.2, facecolor=textbg),\n",
    "                                 ) )\n",
    "    if adjust:\n",
    "        iters = adjust_text(\n",
    "            texts,\n",
    "            expand_text=(1.05, 1.2),\n",
    "            lim=100, # slooooow\n",
    "        )\n",
    "        print(f\"adjust_text took {iters} iterations.\")\n",
    "    if arrow:\n",
    "        xarrow = .03\n",
    "        # arrow up\n",
    "        ax.annotate(\n",
    "            '',\n",
    "            xytext=(xarrow, .65),\n",
    "            xy=(xarrow, .89),\n",
    "            xycoords='figure fraction',\n",
    "            textcoords='figure fraction',\n",
    "            arrowprops=dict(),\n",
    "        )\n",
    "        ax.text(\n",
    "            xarrow - .015,\n",
    "            .665,\n",
    "            'More flexible',\n",
    "            transform=fig.transFigure,\n",
    "            rotation=90,\n",
    "            fontsize=12,\n",
    "            fontstyle='italic',\n",
    "        )\n",
    "    return fig, ax\n",
    "\n",
    "fig, ax = scatter_text(pf, \n",
    "        adjust=1,\n",
    "        min=1055,\n",
    "       );\n",
    "ax.set_ylabel(\"sum of log counts\")\n",
    "ax.set_xlabel(\"sum of counts\")\n",
    "ax.set_title(\"Prefix flexibility\")\n",
    "if FIG_GEN:\n",
    "    viz_helpers.savefig(fig, 'prefix_collisions', bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "possible-fraud",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adjust_text took 100 iterations.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = scatter_text(sf, \n",
    "                       adjust=1, \n",
    "                       min=500,\n",
    "                       textbg='green',\n",
    "                       \n",
    "       );\n",
    "ax.set_ylabel(\"sum of log counts\")\n",
    "ax.set_xlabel(\"sum of counts\")\n",
    "ax.set_title(\"Suffix flexibility\")\n",
    "if FIG_GEN:\n",
    "    viz_helpers.savefig(fig, 'suffix_collisions', bbox_inches='tight', dpi=fig.dpi*2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "artificial-ordering",
   "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>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>726783</td>\n",
       "      <td>0.728</td>\n",
       "      <td>-1.117</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>hat</td>\n",
       "      <td>618186</td>\n",
       "      <td>85.058</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bastard</th>\n",
       "      <td>192</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-4.230</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>face</td>\n",
       "      <td>39</td>\n",
       "      <td>20.312</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>20197</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-3.141</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>shit</td>\n",
       "      <td>6010</td>\n",
       "      <td>29.757</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>67527</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-2.169</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>ass</td>\n",
       "      <td>35551</td>\n",
       "      <td>52.647</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "ass      726783  0.728   -1.117           9       52            52      hat   \n",
       "bastard     192  0.087   -4.230          64        4             6     face   \n",
       "bird      20197  0.169   -3.141          30       21            30     shit   \n",
       "bitch     67527  0.345   -2.169          20       37            40      ass   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables  \n",
       "ass        618186       85.058       2          1  \n",
       "bastard        39       20.312      41          2  \n",
       "bird         6010       29.757      13          1  \n",
       "bitch       35551       52.647       6          1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "japanese-collection",
   "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>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "      <th>atype</th>\n",
       "      <th>logcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>726783</td>\n",
       "      <td>0.728</td>\n",
       "      <td>-1.117</td>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>hat</td>\n",
       "      <td>618186</td>\n",
       "      <td>85.058</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>pre</td>\n",
       "      <td>5.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bastard</th>\n",
       "      <td>192</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-4.230</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>face</td>\n",
       "      <td>39</td>\n",
       "      <td>20.312</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>pre</td>\n",
       "      <td>2.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>20197</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-3.141</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>shit</td>\n",
       "      <td>6010</td>\n",
       "      <td>29.757</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>pre</td>\n",
       "      <td>4.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>67527</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-2.169</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ass</td>\n",
       "      <td>35551</td>\n",
       "      <td>52.647</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>pre</td>\n",
       "      <td>4.829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bum</th>\n",
       "      <td>108346</td>\n",
       "      <td>0.828</td>\n",
       "      <td>-0.728</td>\n",
       "      <td>16.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>fuck</td>\n",
       "      <td>98471</td>\n",
       "      <td>90.886</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>pre</td>\n",
       "      <td>5.035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "ass      726783  0.728   -1.117         9.0     53.0          53.0      hat   \n",
       "bastard     192  0.087   -4.230        65.0      4.0           6.0     face   \n",
       "bird      20197  0.169   -3.141        30.0     22.0          31.0     shit   \n",
       "bitch     67527  0.345   -2.169        20.0     38.0          41.0      ass   \n",
       "bum      108346  0.828   -0.728        16.0     56.0          56.0     fuck   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables atype  logcount  \n",
       "ass        618186       85.058       2          1   pre     5.861  \n",
       "bastard        39       20.312      41          2   pre     2.283  \n",
       "bird         6010       29.757      13          1   pre     4.305  \n",
       "bitch       35551       52.647       6          1   pre     4.829  \n",
       "bum         98471       90.886      11          1   pre     5.035  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppf = pf.copy()\n",
    "atcol = 'atype'\n",
    "ppf[atcol] = 'pre'\n",
    "ssf = sf.copy()\n",
    "ssf[atcol] = 'suff'\n",
    "mega = pd.concat([ppf, ssf])\n",
    "\n",
    "# Bleh, hack\n",
    "mega['logcount'] = np.log10(mega['count'])\n",
    "\n",
    "mega.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "impaired-connecticut",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=mega,\n",
    "    x='syllables',\n",
    "    y='logcount',\n",
    "    col=atcol,\n",
    "    kind='swarm',\n",
    "    aspect=.5,\n",
    "    height=7,\n",
    "    sharey=True,\n",
    ");\n",
    "#g.set(yscale='log');\n",
    "#for ax in g.axes.flatten():\n",
    "#    ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "noted-bankruptcy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=FS)\n",
    "af = pf\n",
    "sns.stripplot(\n",
    "    data=af,\n",
    "    x=\"syllables\",\n",
    "    y=\"count\",\n",
    "    ax=ax,\n",
    ");\n",
    "ax.set_yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "controlled-tucson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.476902410809349, 7356323.645535747)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.get_ylim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "egyptian-proposal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.700e+01\n",
       "mean     2.167e+05\n",
       "std      5.777e+05\n",
       "min      1.400e+01\n",
       "25%      3.710e+03\n",
       "50%      1.493e+04\n",
       "75%      8.013e+04\n",
       "max      3.929e+06\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf['count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "comfortable-indianapolis",
   "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>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dumb</th>\n",
       "      <td>3928751</td>\n",
       "      <td>0.857</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>ass</td>\n",
       "      <td>3631048</td>\n",
       "      <td>92.422</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scum</th>\n",
       "      <td>1370798</td>\n",
       "      <td>0.966</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>bag</td>\n",
       "      <td>1347188</td>\n",
       "      <td>98.278</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shit</th>\n",
       "      <td>1266231</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-2.996</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>head</td>\n",
       "      <td>450560</td>\n",
       "      <td>35.583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>douche</th>\n",
       "      <td>1265155</td>\n",
       "      <td>0.853</td>\n",
       "      <td>-0.597</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>bag</td>\n",
       "      <td>1167302</td>\n",
       "      <td>92.266</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuck</th>\n",
       "      <td>1113336</td>\n",
       "      <td>0.131</td>\n",
       "      <td>-3.393</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>tard</td>\n",
       "      <td>234588</td>\n",
       "      <td>21.071</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "dumb    3928751  0.857   -0.494           1       61            62      ass   \n",
       "scum    1370798  0.966   -0.169           2       66            66      bag   \n",
       "shit    1266231  0.211   -2.996           3       31            33     head   \n",
       "douche  1265155  0.853   -0.597           4       60            59      bag   \n",
       "fuck    1113336  0.131   -3.393           5       19            27     tard   \n",
       "\n",
       "        topcount  topcountpct  nzeros  syllables  \n",
       "dumb     3631048       92.422      13          1  \n",
       "scum     1347188       98.278      14          1  \n",
       "shit      450560       35.583       0          1  \n",
       "douche   1167302       92.266       2          1  \n",
       "fuck      234588       21.071       0          1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.sort_values(by='count_rank').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "consecutive-egypt",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "mexican-tuesday",
   "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>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>3754308</td>\n",
       "      <td>0.936</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>dumb</td>\n",
       "      <td>3631048</td>\n",
       "      <td>96.717</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bag</th>\n",
       "      <td>2939955</td>\n",
       "      <td>0.374</td>\n",
       "      <td>-1.796</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>50</td>\n",
       "      <td>scum</td>\n",
       "      <td>1347188</td>\n",
       "      <td>45.823</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shit</th>\n",
       "      <td>1667150</td>\n",
       "      <td>0.437</td>\n",
       "      <td>-1.445</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>dip</td>\n",
       "      <td>863707</td>\n",
       "      <td>51.807</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>head</th>\n",
       "      <td>1611393</td>\n",
       "      <td>0.371</td>\n",
       "      <td>-1.875</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>48</td>\n",
       "      <td>dick</td>\n",
       "      <td>855756</td>\n",
       "      <td>53.107</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hat</th>\n",
       "      <td>626150</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>ass</td>\n",
       "      <td>618186</td>\n",
       "      <td>98.728</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tard</th>\n",
       "      <td>579832</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-1.441</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>lib</td>\n",
       "      <td>302488</td>\n",
       "      <td>52.168</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuck</th>\n",
       "      <td>447426</td>\n",
       "      <td>0.255</td>\n",
       "      <td>-2.585</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>dumb</td>\n",
       "      <td>191951</td>\n",
       "      <td>42.901</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boy</th>\n",
       "      <td>395352</td>\n",
       "      <td>0.270</td>\n",
       "      <td>-2.318</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>fem</td>\n",
       "      <td>133608</td>\n",
       "      <td>33.795</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lord</th>\n",
       "      <td>367655</td>\n",
       "      <td>0.685</td>\n",
       "      <td>-1.091</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>shit</td>\n",
       "      <td>300942</td>\n",
       "      <td>81.854</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face</th>\n",
       "      <td>256361</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-3.007</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>fuck</td>\n",
       "      <td>125768</td>\n",
       "      <td>49.059</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wit</th>\n",
       "      <td>231981</td>\n",
       "      <td>0.968</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>fuck</td>\n",
       "      <td>228196</td>\n",
       "      <td>98.368</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wad</th>\n",
       "      <td>172516</td>\n",
       "      <td>0.375</td>\n",
       "      <td>-1.941</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>fuck</td>\n",
       "      <td>84336</td>\n",
       "      <td>48.886</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sucker</th>\n",
       "      <td>150612</td>\n",
       "      <td>0.901</td>\n",
       "      <td>-0.408</td>\n",
       "      <td>13</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>cock</td>\n",
       "      <td>142886</td>\n",
       "      <td>94.870</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stain</th>\n",
       "      <td>120289</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-1.386</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>shit</td>\n",
       "      <td>92128</td>\n",
       "      <td>76.589</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stick</th>\n",
       "      <td>109843</td>\n",
       "      <td>0.450</td>\n",
       "      <td>-1.460</td>\n",
       "      <td>15</td>\n",
       "      <td>47</td>\n",
       "      <td>52</td>\n",
       "      <td>dip</td>\n",
       "      <td>59925</td>\n",
       "      <td>54.555</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>93812</td>\n",
       "      <td>0.219</td>\n",
       "      <td>-2.923</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>slime</td>\n",
       "      <td>33254</td>\n",
       "      <td>35.447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balls</th>\n",
       "      <td>82160</td>\n",
       "      <td>0.246</td>\n",
       "      <td>-2.856</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>shit</td>\n",
       "      <td>36333</td>\n",
       "      <td>44.222</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boi</th>\n",
       "      <td>75496</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-2.048</td>\n",
       "      <td>18</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "      <td>fuck</td>\n",
       "      <td>49881</td>\n",
       "      <td>66.071</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pile</th>\n",
       "      <td>66674</td>\n",
       "      <td>0.507</td>\n",
       "      <td>-1.325</td>\n",
       "      <td>19</td>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>dog</td>\n",
       "      <td>42953</td>\n",
       "      <td>64.422</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ster</th>\n",
       "      <td>65214</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-2.325</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>trump</td>\n",
       "      <td>31005</td>\n",
       "      <td>47.543</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nozzle</th>\n",
       "      <td>48803</td>\n",
       "      <td>0.893</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>21</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>douche</td>\n",
       "      <td>46107</td>\n",
       "      <td>94.476</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clown</th>\n",
       "      <td>45367</td>\n",
       "      <td>0.950</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>ass</td>\n",
       "      <td>44220</td>\n",
       "      <td>97.472</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slut</th>\n",
       "      <td>40923</td>\n",
       "      <td>0.713</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>cum</td>\n",
       "      <td>34414</td>\n",
       "      <td>84.095</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oid</th>\n",
       "      <td>40304</td>\n",
       "      <td>0.501</td>\n",
       "      <td>-1.277</td>\n",
       "      <td>24</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>fem</td>\n",
       "      <td>25000</td>\n",
       "      <td>62.029</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>38306</td>\n",
       "      <td>0.809</td>\n",
       "      <td>-0.844</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>shit</td>\n",
       "      <td>34414</td>\n",
       "      <td>89.840</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weed</th>\n",
       "      <td>33286</td>\n",
       "      <td>0.604</td>\n",
       "      <td>-1.324</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>dick</td>\n",
       "      <td>25307</td>\n",
       "      <td>76.029</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nuts</th>\n",
       "      <td>26112</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-1.642</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>51</td>\n",
       "      <td>fuck</td>\n",
       "      <td>20122</td>\n",
       "      <td>77.060</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canoe</th>\n",
       "      <td>26035</td>\n",
       "      <td>0.967</td>\n",
       "      <td>-0.177</td>\n",
       "      <td>28</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>douche</td>\n",
       "      <td>25607</td>\n",
       "      <td>98.356</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waffle</th>\n",
       "      <td>25903</td>\n",
       "      <td>0.469</td>\n",
       "      <td>-1.913</td>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>twat</td>\n",
       "      <td>17379</td>\n",
       "      <td>67.093</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cunt</th>\n",
       "      <td>24796</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-2.353</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>shit</td>\n",
       "      <td>15170</td>\n",
       "      <td>61.179</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bro</th>\n",
       "      <td>23464</td>\n",
       "      <td>0.342</td>\n",
       "      <td>-2.293</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>gay</td>\n",
       "      <td>12267</td>\n",
       "      <td>52.280</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib</th>\n",
       "      <td>22792</td>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.356</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>shit</td>\n",
       "      <td>21815</td>\n",
       "      <td>95.713</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fucker</th>\n",
       "      <td>22557</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-3.196</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>tit</td>\n",
       "      <td>6827</td>\n",
       "      <td>30.266</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rag</th>\n",
       "      <td>18943</td>\n",
       "      <td>0.343</td>\n",
       "      <td>-2.121</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>cum</td>\n",
       "      <td>8266</td>\n",
       "      <td>43.636</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nugget</th>\n",
       "      <td>18480</td>\n",
       "      <td>0.368</td>\n",
       "      <td>-2.427</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>fuck</td>\n",
       "      <td>10859</td>\n",
       "      <td>58.761</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brain</th>\n",
       "      <td>17616</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-3.437</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>bird</td>\n",
       "      <td>4353</td>\n",
       "      <td>24.710</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <td>16729</td>\n",
       "      <td>0.203</td>\n",
       "      <td>-3.077</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>shit</td>\n",
       "      <td>5669</td>\n",
       "      <td>33.887</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>licker</th>\n",
       "      <td>16561</td>\n",
       "      <td>0.390</td>\n",
       "      <td>-2.180</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>butt</td>\n",
       "      <td>9815</td>\n",
       "      <td>59.266</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dick</th>\n",
       "      <td>15748</td>\n",
       "      <td>0.226</td>\n",
       "      <td>-3.357</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>shit</td>\n",
       "      <td>6999</td>\n",
       "      <td>44.444</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>let</th>\n",
       "      <td>14937</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-3.241</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>dick</td>\n",
       "      <td>6552</td>\n",
       "      <td>43.864</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <td>12666</td>\n",
       "      <td>0.168</td>\n",
       "      <td>-3.324</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>rat</td>\n",
       "      <td>3548</td>\n",
       "      <td>28.012</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sack</th>\n",
       "      <td>10653</td>\n",
       "      <td>0.273</td>\n",
       "      <td>-2.726</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>crap</td>\n",
       "      <td>4698</td>\n",
       "      <td>44.100</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fart</th>\n",
       "      <td>10161</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-3.599</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "      <td>2639</td>\n",
       "      <td>25.972</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth</th>\n",
       "      <td>9886</td>\n",
       "      <td>0.257</td>\n",
       "      <td>-2.875</td>\n",
       "      <td>44</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>dirt</td>\n",
       "      <td>4175</td>\n",
       "      <td>42.231</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burger</th>\n",
       "      <td>9375</td>\n",
       "      <td>0.161</td>\n",
       "      <td>-3.501</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>ass</td>\n",
       "      <td>2588</td>\n",
       "      <td>27.605</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>9220</td>\n",
       "      <td>0.493</td>\n",
       "      <td>-2.219</td>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>dumb</td>\n",
       "      <td>6430</td>\n",
       "      <td>69.740</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brains</th>\n",
       "      <td>8653</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-3.343</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>cum</td>\n",
       "      <td>1594</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose</th>\n",
       "      <td>8575</td>\n",
       "      <td>0.485</td>\n",
       "      <td>-2.014</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "      <td>dick</td>\n",
       "      <td>5890</td>\n",
       "      <td>68.688</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cock</th>\n",
       "      <td>7032</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-3.492</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>shit</td>\n",
       "      <td>3034</td>\n",
       "      <td>43.146</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pot</th>\n",
       "      <td>6966</td>\n",
       "      <td>0.255</td>\n",
       "      <td>-2.528</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>shit</td>\n",
       "      <td>2391</td>\n",
       "      <td>34.324</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weasel</th>\n",
       "      <td>6386</td>\n",
       "      <td>0.244</td>\n",
       "      <td>-2.767</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>shit</td>\n",
       "      <td>2716</td>\n",
       "      <td>42.531</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nazi</th>\n",
       "      <td>6047</td>\n",
       "      <td>0.864</td>\n",
       "      <td>-0.627</td>\n",
       "      <td>52</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>fem</td>\n",
       "      <td>5617</td>\n",
       "      <td>92.889</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>5985</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-3.760</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>shit</td>\n",
       "      <td>1083</td>\n",
       "      <td>18.095</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cake</th>\n",
       "      <td>5917</td>\n",
       "      <td>0.301</td>\n",
       "      <td>-3.152</td>\n",
       "      <td>54</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>shit</td>\n",
       "      <td>3175</td>\n",
       "      <td>53.659</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twat</th>\n",
       "      <td>4896</td>\n",
       "      <td>0.472</td>\n",
       "      <td>-2.027</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>43</td>\n",
       "      <td>fuck</td>\n",
       "      <td>3296</td>\n",
       "      <td>67.320</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whore</th>\n",
       "      <td>4486</td>\n",
       "      <td>0.149</td>\n",
       "      <td>-3.347</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>cum</td>\n",
       "      <td>1240</td>\n",
       "      <td>27.642</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goblin</th>\n",
       "      <td>4371</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-3.205</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>cock</td>\n",
       "      <td>1276</td>\n",
       "      <td>29.192</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monkey</th>\n",
       "      <td>4232</td>\n",
       "      <td>0.163</td>\n",
       "      <td>-3.461</td>\n",
       "      <td>58</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>butt</td>\n",
       "      <td>1403</td>\n",
       "      <td>33.152</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breath</th>\n",
       "      <td>3926</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-3.296</td>\n",
       "      <td>59</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>dick</td>\n",
       "      <td>1220</td>\n",
       "      <td>31.075</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rat</th>\n",
       "      <td>3614</td>\n",
       "      <td>0.138</td>\n",
       "      <td>-3.838</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>shit</td>\n",
       "      <td>1134</td>\n",
       "      <td>31.378</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muncher</th>\n",
       "      <td>3270</td>\n",
       "      <td>0.172</td>\n",
       "      <td>-3.021</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>cock</td>\n",
       "      <td>885</td>\n",
       "      <td>27.064</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knuckle</th>\n",
       "      <td>2783</td>\n",
       "      <td>0.405</td>\n",
       "      <td>-2.278</td>\n",
       "      <td>62</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>fuck</td>\n",
       "      <td>1723</td>\n",
       "      <td>61.912</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>azoid</th>\n",
       "      <td>2469</td>\n",
       "      <td>0.660</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>63</td>\n",
       "      <td>59</td>\n",
       "      <td>56</td>\n",
       "      <td>creep</td>\n",
       "      <td>1999</td>\n",
       "      <td>80.964</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trumpet</th>\n",
       "      <td>2349</td>\n",
       "      <td>0.347</td>\n",
       "      <td>-2.311</td>\n",
       "      <td>64</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>fuck</td>\n",
       "      <td>1290</td>\n",
       "      <td>54.917</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boat</th>\n",
       "      <td>1548</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-4.080</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>shit</td>\n",
       "      <td>345</td>\n",
       "      <td>22.287</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burglar</th>\n",
       "      <td>1471</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-1.453</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>53</td>\n",
       "      <td>turd</td>\n",
       "      <td>1196</td>\n",
       "      <td>81.305</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pirate</th>\n",
       "      <td>1132</td>\n",
       "      <td>0.442</td>\n",
       "      <td>-1.889</td>\n",
       "      <td>67</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>butt</td>\n",
       "      <td>688</td>\n",
       "      <td>60.777</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skull</th>\n",
       "      <td>1012</td>\n",
       "      <td>0.168</td>\n",
       "      <td>-3.321</td>\n",
       "      <td>68</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>gay</td>\n",
       "      <td>335</td>\n",
       "      <td>33.103</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splash</th>\n",
       "      <td>991</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-2.200</td>\n",
       "      <td>69</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>dick</td>\n",
       "      <td>638</td>\n",
       "      <td>64.379</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>socket</th>\n",
       "      <td>868</td>\n",
       "      <td>0.554</td>\n",
       "      <td>-1.807</td>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>cock</td>\n",
       "      <td>641</td>\n",
       "      <td>73.848</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puffin</th>\n",
       "      <td>630</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-1.213</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>wank</td>\n",
       "      <td>532</td>\n",
       "      <td>84.444</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bandit</th>\n",
       "      <td>500</td>\n",
       "      <td>0.326</td>\n",
       "      <td>-2.839</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>ass</td>\n",
       "      <td>279</td>\n",
       "      <td>55.800</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitten</th>\n",
       "      <td>129</td>\n",
       "      <td>0.187</td>\n",
       "      <td>-3.340</td>\n",
       "      <td>73</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>dick</td>\n",
       "      <td>45</td>\n",
       "      <td>34.884</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "ass      3754308  0.936   -0.322           1       69            69     dumb   \n",
       "bag      2939955  0.374   -1.796           2       38            50     scum   \n",
       "shit     1667150  0.437   -1.445           3       44            54      dip   \n",
       "head     1611393  0.371   -1.875           4       37            48     dick   \n",
       "hat       626150  0.975   -0.139           5       73            73      ass   \n",
       "tard      579832  0.439   -1.441           6       45            55      lib   \n",
       "fuck      447426  0.255   -2.585           7       25            29     dumb   \n",
       "boy       395352  0.270   -2.318           8       28            34      fem   \n",
       "lord      367655  0.685   -1.091           9       61            62     shit   \n",
       "face      256361  0.267   -3.007          10       27            22     fuck   \n",
       "wit       231981  0.968   -0.164          11       72            72     fuck   \n",
       "wad       172516  0.375   -1.941          12       39            45     fuck   \n",
       "sucker    150612  0.901   -0.408          13       67            67     cock   \n",
       "stain     120289  0.600   -1.386          14       56            57     shit   \n",
       "stick     109843  0.450   -1.460          15       47            52      dip   \n",
       "ball       93812  0.219   -2.923          16       19            23    slime   \n",
       "balls      82160  0.246   -2.856          17       23            25     shit   \n",
       "boi        75496  0.454   -2.048          18       48            42     fuck   \n",
       "pile       66674  0.507   -1.325          19       54            58      dog   \n",
       "ster       65214  0.328   -2.325          20       32            33    trump   \n",
       "nozzle     48803  0.893   -0.477          21       66            66   douche   \n",
       "clown      45367  0.950   -0.258          22       70            70      ass   \n",
       "slut       40923  0.713   -1.088          23       62            63      cum   \n",
       "oid        40304  0.501   -1.277          24       53            60      fem   \n",
       "bird       38306  0.809   -0.844          25       64            64     shit   \n",
       "weed       33286  0.604   -1.324          26       58            59     dick   \n",
       "nuts       26112  0.600   -1.642          27       57            51     fuck   \n",
       "canoe      26035  0.967   -0.177          28       71            71   douche   \n",
       "waffle     25903  0.469   -1.913          29       49            46     twat   \n",
       "cunt       24796  0.401   -2.353          30       41            32     shit   \n",
       "bro        23464  0.342   -2.293          31       33            36      gay   \n",
       "lib        22792  0.917   -0.356          32       68            68     shit   \n",
       "fucker     22557  0.169   -3.196          33       12            18      tit   \n",
       "rag        18943  0.343   -2.121          34       34            41      cum   \n",
       "nugget     18480  0.368   -2.427          35       36            31     fuck   \n",
       "brain      17616  0.151   -3.437          36        7             8     bird   \n",
       "bucket     16729  0.203   -3.077          37       17            20     shit   \n",
       "licker     16561  0.390   -2.180          38       40            40     butt   \n",
       "dick       15748  0.226   -3.357          39       21             9     shit   \n",
       "let        14937  0.222   -3.241          40       20            16     dick   \n",
       "dog        12666  0.168   -3.324          41       10            13      rat   \n",
       "sack       10653  0.273   -2.726          42       29            28     crap   \n",
       "fart       10161  0.132   -3.599          43        4             4      dog   \n",
       "mouth       9886  0.257   -2.875          44       26            24     dirt   \n",
       "burger      9375  0.161   -3.501          45        8             5      ass   \n",
       "bitch       9220  0.493   -2.219          46       52            38     dumb   \n",
       "brains      8653  0.132   -3.343          47        3            11      cum   \n",
       "nose        8575  0.485   -2.014          48       51            44     dick   \n",
       "cock        7032  0.211   -3.492          49       18             6     shit   \n",
       "pot         6966  0.255   -2.528          50       24            30     shit   \n",
       "weasel      6386  0.244   -2.767          51       22            27     shit   \n",
       "nazi        6047  0.864   -0.627          52       65            65      fem   \n",
       "wagon       5985  0.103   -3.760          53        2             3     shit   \n",
       "cake        5917  0.301   -3.152          54       30            19     shit   \n",
       "twat        4896  0.472   -2.027          55       50            43     fuck   \n",
       "whore       4486  0.149   -3.347          56        6            10      cum   \n",
       "goblin      4371  0.170   -3.205          57       14            17     cock   \n",
       "monkey      4232  0.163   -3.461          58        9             7     butt   \n",
       "breath      3926  0.170   -3.296          59       13            15     dick   \n",
       "rat         3614  0.138   -3.838          60        5             2     shit   \n",
       "muncher     3270  0.172   -3.021          61       15            21     cock   \n",
       "knuckle     2783  0.405   -2.278          62       42            37     fuck   \n",
       "azoid       2469  0.660   -1.402          63       59            56    creep   \n",
       "trumpet     2349  0.347   -2.311          64       35            35     fuck   \n",
       "boat        1548  0.101   -4.080          65        1             1     shit   \n",
       "burglar     1471  0.664   -1.453          66       60            53     turd   \n",
       "pirate      1132  0.442   -1.889          67       46            47     butt   \n",
       "skull       1012  0.168   -3.321          68       11            14      gay   \n",
       "splash       991  0.431   -2.200          69       43            39     dick   \n",
       "socket       868  0.554   -1.807          70       55            49     cock   \n",
       "puffin       630  0.716   -1.213          71       63            61     wank   \n",
       "bandit       500  0.326   -2.839          72       31            26      ass   \n",
       "mitten       129  0.187   -3.340          73       16            12     dick   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables  \n",
       "ass       3631048       96.717       4          1  \n",
       "bag       1347188       45.823       4          1  \n",
       "shit       863707       51.807       1          1  \n",
       "head       855756       53.107       3          1  \n",
       "hat        618186       98.728      13          1  \n",
       "tard       302488       52.168       7          1  \n",
       "fuck       191951       42.901       2          1  \n",
       "boy        133608       33.795       2          1  \n",
       "lord       300942       81.854       1          1  \n",
       "face       125768       49.059       1          1  \n",
       "wit        228196       98.368      28          1  \n",
       "wad         84336       48.886       7          1  \n",
       "sucker     142886       94.870      16          2  \n",
       "stain       92128       76.589       9          1  \n",
       "stick       59925       54.555       7          1  \n",
       "ball        33254       35.447       1          1  \n",
       "balls       36333       44.222       5          1  \n",
       "boi         49881       66.071       6          1  \n",
       "pile        42953       64.422      19          1  \n",
       "ster        31005       47.543       8          1  \n",
       "nozzle      46107       94.476      33          2  \n",
       "clown       44220       97.472      22          1  \n",
       "slut        34414       84.095      15          1  \n",
       "oid         25000       62.029      12          1  \n",
       "bird        34414       89.840      12          1  \n",
       "weed        25307       76.029      15          1  \n",
       "nuts        20122       77.060      10          1  \n",
       "canoe       25607       98.356      40          2  \n",
       "waffle      17379       67.093      17          2  \n",
       "cunt        15170       61.179       5          1  \n",
       "bro         12267       52.280       6          1  \n",
       "lib         21815       95.713      36          1  \n",
       "fucker       6827       30.266       9          2  \n",
       "rag          8266       43.636      20          1  \n",
       "nugget      10859       58.761      14          2  \n",
       "brain        4353       24.710       6          1  \n",
       "bucket       5669       33.887      13          2  \n",
       "licker       9815       59.266      18          2  \n",
       "dick         6999       44.444       4          1  \n",
       "let          6552       43.864       9          1  \n",
       "dog          3548       28.012       5          1  \n",
       "sack         4698       44.100      14          1  \n",
       "fart         2639       25.972      14          1  \n",
       "mouth        4175       42.231      12          1  \n",
       "burger       2588       27.605       6          2  \n",
       "bitch        6430       69.740       9          1  \n",
       "brains       1594       18.421      10          1  \n",
       "nose         5890       68.688      15          1  \n",
       "cock         3034       43.146       9          1  \n",
       "pot          2391       34.324      17          1  \n",
       "weasel       2716       42.531      19          2  \n",
       "nazi         5617       92.889      36          2  \n",
       "wagon        1083       18.095      11          2  \n",
       "cake         3175       53.659       6          1  \n",
       "twat         3296       67.320      17          1  \n",
       "whore        1240       27.642      17          1  \n",
       "goblin       1276       29.192      24          2  \n",
       "monkey       1403       33.152      12          2  \n",
       "breath       1220       31.075      21          1  \n",
       "rat          1134       31.378       8          1  \n",
       "muncher       885       27.064      29          2  \n",
       "knuckle      1723       61.912      32          2  \n",
       "azoid        1999       80.964      38          2  \n",
       "trumpet      1290       54.917      43          2  \n",
       "boat          345       22.287      16          1  \n",
       "burglar      1196       81.305      40          2  \n",
       "pirate        688       60.777      35          2  \n",
       "skull         335       33.103      29          1  \n",
       "splash        638       64.379      42          1  \n",
       "socket        641       73.848      42          2  \n",
       "puffin        532       84.444      53          2  \n",
       "bandit        279       55.800      40          2  \n",
       "mitten         45       34.884      48          2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.sort_values(by='count_rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "bearing-douglas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lib</th>\n",
       "      <td>312305</td>\n",
       "      <td>0.939</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>10</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>tard</td>\n",
       "      <td>302488</td>\n",
       "      <td>96.857</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>14959</td>\n",
       "      <td>0.846</td>\n",
       "      <td>-0.687</td>\n",
       "      <td>33</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>oid</td>\n",
       "      <td>13748</td>\n",
       "      <td>91.905</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soy</th>\n",
       "      <td>119354</td>\n",
       "      <td>0.850</td>\n",
       "      <td>-0.591</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>boy</td>\n",
       "      <td>109940</td>\n",
       "      <td>92.113</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trump</th>\n",
       "      <td>68645</td>\n",
       "      <td>0.410</td>\n",
       "      <td>-1.782</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>tard</td>\n",
       "      <td>31073</td>\n",
       "      <td>45.266</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "lib    312305  0.939   -0.258          10       66            66     tard   \n",
       "right   14959  0.846   -0.687          33       59            58      oid   \n",
       "soy    119354  0.850   -0.591          15       60            61      boy   \n",
       "trump   68645  0.410   -1.782          19       43            45     tard   \n",
       "\n",
       "       topcount  topcountpct  nzeros  syllables  \n",
       "lib      302488       96.857      21          1  \n",
       "right     13748       91.905      31          1  \n",
       "soy      109940       92.113      18          1  \n",
       "trump     31073       45.266       9          1  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poli_pres = ['lib', 'right', 'soy', 'trump']\n",
    "pf.loc[poli_pres]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "level-annual",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "polyphonic-niger",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4263</th>\n",
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       "      <th>4248</th>\n",
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       "      <th>4247</th>\n",
       "      <td>trump</td>\n",
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       "    <tr>\n",
       "      <th>4306</th>\n",
       "      <td>trump</td>\n",
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       "    <tr>\n",
       "      <th>4239</th>\n",
       "      <td>trump</td>\n",
       "      <td>splash</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>trump</td>\n",
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       "    <tr>\n",
       "      <th>4241</th>\n",
       "      <td>trump</td>\n",
       "      <td>pile</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4262</th>\n",
       "      <td>trump</td>\n",
       "      <td>burglar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4278</th>\n",
       "      <td>trump</td>\n",
       "      <td>socket</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4240</th>\n",
       "      <td>trump</td>\n",
       "      <td>pot</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4269</th>\n",
       "      <td>trump</td>\n",
       "      <td>rag</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4250</th>\n",
       "      <td>trump</td>\n",
       "      <td>muncher</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>trump</td>\n",
       "      <td>brains</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4279</th>\n",
       "      <td>trump</td>\n",
       "      <td>trumpet</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>trump</td>\n",
       "      <td>bucket</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292</th>\n",
       "      <td>trump</td>\n",
       "      <td>knuckle</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pre     suff  count   wikt\n",
       "4270  trump   nozzle    0.0  False\n",
       "4280  trump   mitten    0.0  False\n",
       "4305  trump      lib    0.0  False\n",
       "4263  trump   bandit    0.0  False\n",
       "4256  trump   pirate    0.0  False\n",
       "4248  trump   puffin    0.0  False\n",
       "4247  trump   goblin    0.0  False\n",
       "4306  trump   breath    0.0  False\n",
       "4239  trump   splash    0.0  False\n",
       "4294  trump     nose    1.0  False\n",
       "4241  trump     pile    1.0  False\n",
       "4262  trump  burglar    1.0  False\n",
       "4278  trump   socket    1.0  False\n",
       "4240  trump      pot    1.0  False\n",
       "4269  trump      rag    1.0  False\n",
       "4250  trump  muncher    1.0  False\n",
       "4291  trump   brains    2.0  False\n",
       "4279  trump  trumpet    2.0  False\n",
       "4236  trump   bucket    2.0  False\n",
       "4292  trump  knuckle    4.0  False"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.pre == 'trump'].sort_values(by='count').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "spanish-haiti",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>turd</th>\n",
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       "      <td>36</td>\n",
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       "      <th>moron</th>\n",
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       "      <td>16.779</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>puke</th>\n",
       "      <td>1005</td>\n",
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       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>face</td>\n",
       "      <td>183</td>\n",
       "      <td>18.209</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bastard</th>\n",
       "      <td>192</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-4.230</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>face</td>\n",
       "      <td>39</td>\n",
       "      <td>20.312</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monkey</th>\n",
       "      <td>8489</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-4.009</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>ball</td>\n",
       "      <td>1323</td>\n",
       "      <td>15.585</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>snot</th>\n",
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       "      <td>0.100</td>\n",
       "      <td>-3.876</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>rag</td>\n",
       "      <td>600</td>\n",
       "      <td>15.936</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homo</th>\n",
       "      <td>833</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-3.962</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>goblin</td>\n",
       "      <td>165</td>\n",
       "      <td>19.808</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jizz</th>\n",
       "      <td>4118</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-4.220</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>rag</td>\n",
       "      <td>993</td>\n",
       "      <td>24.114</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prick</th>\n",
       "      <td>1256</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-4.036</td>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>face</td>\n",
       "      <td>247</td>\n",
       "      <td>19.666</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pussy</th>\n",
       "      <td>15350</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-3.859</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>hat</td>\n",
       "      <td>2875</td>\n",
       "      <td>18.730</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fart</th>\n",
       "      <td>6480</td>\n",
       "      <td>0.104</td>\n",
       "      <td>-4.378</td>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>face</td>\n",
       "      <td>1698</td>\n",
       "      <td>26.204</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poop</th>\n",
       "      <td>14931</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-4.144</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>head</td>\n",
       "      <td>3355</td>\n",
       "      <td>22.470</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fag</th>\n",
       "      <td>10627</td>\n",
       "      <td>0.108</td>\n",
       "      <td>-3.960</td>\n",
       "      <td>41</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>lord</td>\n",
       "      <td>2153</td>\n",
       "      <td>20.260</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>queer</th>\n",
       "      <td>962</td>\n",
       "      <td>0.113</td>\n",
       "      <td>-3.940</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>boy</td>\n",
       "      <td>218</td>\n",
       "      <td>22.661</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loser</th>\n",
       "      <td>695</td>\n",
       "      <td>0.116</td>\n",
       "      <td>-3.960</td>\n",
       "      <td>63</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>face</td>\n",
       "      <td>183</td>\n",
       "      <td>26.331</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>faggot</th>\n",
       "      <td>1054</td>\n",
       "      <td>0.122</td>\n",
       "      <td>-4.053</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>ass</td>\n",
       "      <td>319</td>\n",
       "      <td>30.266</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idiot</th>\n",
       "      <td>849</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-3.695</td>\n",
       "      <td>61</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>face</td>\n",
       "      <td>199</td>\n",
       "      <td>23.439</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dork</th>\n",
       "      <td>2181</td>\n",
       "      <td>0.131</td>\n",
       "      <td>-3.684</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>lord</td>\n",
       "      <td>460</td>\n",
       "      <td>21.091</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuck</th>\n",
       "      <td>1113336</td>\n",
       "      <td>0.131</td>\n",
       "      <td>-3.393</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>tard</td>\n",
       "      <td>234588</td>\n",
       "      <td>21.071</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poo</th>\n",
       "      <td>3654</td>\n",
       "      <td>0.133</td>\n",
       "      <td>-3.930</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>head</td>\n",
       "      <td>1066</td>\n",
       "      <td>29.174</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>20197</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-3.141</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>shit</td>\n",
       "      <td>6010</td>\n",
       "      <td>29.757</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geek</th>\n",
       "      <td>1525</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-3.485</td>\n",
       "      <td>53</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>let</td>\n",
       "      <td>563</td>\n",
       "      <td>36.918</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harpy</th>\n",
       "      <td>14</td>\n",
       "      <td>0.176</td>\n",
       "      <td>-4.531</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>bitch</td>\n",
       "      <td>6</td>\n",
       "      <td>42.857</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>donkey</th>\n",
       "      <td>4453</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-3.399</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>balls</td>\n",
       "      <td>1667</td>\n",
       "      <td>37.435</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slut</th>\n",
       "      <td>5043</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-3.489</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>bag</td>\n",
       "      <td>1902</td>\n",
       "      <td>37.716</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whore</th>\n",
       "      <td>3941</td>\n",
       "      <td>0.183</td>\n",
       "      <td>-3.459</td>\n",
       "      <td>48</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>bag</td>\n",
       "      <td>1506</td>\n",
       "      <td>38.214</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rat</th>\n",
       "      <td>45116</td>\n",
       "      <td>0.194</td>\n",
       "      <td>-3.138</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>fuck</td>\n",
       "      <td>17471</td>\n",
       "      <td>38.725</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crap</th>\n",
       "      <td>11464</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-3.456</td>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>sack</td>\n",
       "      <td>4698</td>\n",
       "      <td>40.980</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>piss</th>\n",
       "      <td>16623</td>\n",
       "      <td>0.208</td>\n",
       "      <td>-3.381</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>head</td>\n",
       "      <td>6871</td>\n",
       "      <td>41.334</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spunk</th>\n",
       "      <td>1012</td>\n",
       "      <td>0.209</td>\n",
       "      <td>-3.522</td>\n",
       "      <td>57</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>trumpet</td>\n",
       "      <td>436</td>\n",
       "      <td>43.083</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shit</th>\n",
       "      <td>1266231</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-2.996</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>head</td>\n",
       "      <td>450560</td>\n",
       "      <td>35.583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cum</th>\n",
       "      <td>81554</td>\n",
       "      <td>0.212</td>\n",
       "      <td>-3.303</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>slut</td>\n",
       "      <td>34414</td>\n",
       "      <td>42.198</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weasel</th>\n",
       "      <td>947</td>\n",
       "      <td>0.272</td>\n",
       "      <td>-3.007</td>\n",
       "      <td>60</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>head</td>\n",
       "      <td>469</td>\n",
       "      <td>49.525</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jerk</th>\n",
       "      <td>38509</td>\n",
       "      <td>0.272</td>\n",
       "      <td>-2.287</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>face</td>\n",
       "      <td>13791</td>\n",
       "      <td>35.812</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creep</th>\n",
       "      <td>7335</td>\n",
       "      <td>0.321</td>\n",
       "      <td>-2.351</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>ster</td>\n",
       "      <td>3581</td>\n",
       "      <td>48.821</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stink</th>\n",
       "      <td>11218</td>\n",
       "      <td>0.327</td>\n",
       "      <td>-2.350</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>weed</td>\n",
       "      <td>5320</td>\n",
       "      <td>47.424</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>67527</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-2.169</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>ass</td>\n",
       "      <td>35551</td>\n",
       "      <td>52.647</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>butt</th>\n",
       "      <td>210651</td>\n",
       "      <td>0.355</td>\n",
       "      <td>-2.253</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>head</td>\n",
       "      <td>112887</td>\n",
       "      <td>53.590</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gay</th>\n",
       "      <td>78707</td>\n",
       "      <td>0.363</td>\n",
       "      <td>-2.236</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>lord</td>\n",
       "      <td>44914</td>\n",
       "      <td>57.065</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sleaze</th>\n",
       "      <td>29450</td>\n",
       "      <td>0.398</td>\n",
       "      <td>-1.485</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>ball</td>\n",
       "      <td>13842</td>\n",
       "      <td>47.002</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lame</th>\n",
       "      <td>10734</td>\n",
       "      <td>0.407</td>\n",
       "      <td>-2.278</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>ass</td>\n",
       "      <td>6662</td>\n",
       "      <td>62.064</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trump</th>\n",
       "      <td>68645</td>\n",
       "      <td>0.410</td>\n",
       "      <td>-1.782</td>\n",
       "      <td>19</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>tard</td>\n",
       "      <td>31073</td>\n",
       "      <td>45.266</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skeeze</th>\n",
       "      <td>1399</td>\n",
       "      <td>0.432</td>\n",
       "      <td>-1.584</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>ball</td>\n",
       "      <td>838</td>\n",
       "      <td>59.900</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wank</th>\n",
       "      <td>11100</td>\n",
       "      <td>0.499</td>\n",
       "      <td>-2.079</td>\n",
       "      <td>39</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>stain</td>\n",
       "      <td>7756</td>\n",
       "      <td>69.874</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spit</th>\n",
       "      <td>34730</td>\n",
       "      <td>0.530</td>\n",
       "      <td>-1.355</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>ball</td>\n",
       "      <td>23851</td>\n",
       "      <td>68.675</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slime</th>\n",
       "      <td>47360</td>\n",
       "      <td>0.543</td>\n",
       "      <td>-1.305</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "      <td>ball</td>\n",
       "      <td>33254</td>\n",
       "      <td>70.215</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cock</th>\n",
       "      <td>194723</td>\n",
       "      <td>0.551</td>\n",
       "      <td>-1.877</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>43</td>\n",
       "      <td>sucker</td>\n",
       "      <td>142886</td>\n",
       "      <td>73.379</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twat</th>\n",
       "      <td>23252</td>\n",
       "      <td>0.565</td>\n",
       "      <td>-1.919</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "      <td>waffle</td>\n",
       "      <td>17379</td>\n",
       "      <td>74.742</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tit</th>\n",
       "      <td>40337</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.306</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>fuck</td>\n",
       "      <td>30803</td>\n",
       "      <td>76.364</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fem</th>\n",
       "      <td>174003</td>\n",
       "      <td>0.613</td>\n",
       "      <td>-1.201</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>boy</td>\n",
       "      <td>133608</td>\n",
       "      <td>76.785</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dick</th>\n",
       "      <td>1085733</td>\n",
       "      <td>0.630</td>\n",
       "      <td>-1.429</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "      <td>head</td>\n",
       "      <td>855756</td>\n",
       "      <td>78.818</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>726783</td>\n",
       "      <td>0.728</td>\n",
       "      <td>-1.117</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>hat</td>\n",
       "      <td>618186</td>\n",
       "      <td>85.058</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lard</th>\n",
       "      <td>14680</td>\n",
       "      <td>0.755</td>\n",
       "      <td>-1.061</td>\n",
       "      <td>35</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>ass</td>\n",
       "      <td>12736</td>\n",
       "      <td>86.757</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dirt</th>\n",
       "      <td>133930</td>\n",
       "      <td>0.821</td>\n",
       "      <td>-0.726</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>bag</td>\n",
       "      <td>121166</td>\n",
       "      <td>90.470</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bum</th>\n",
       "      <td>108346</td>\n",
       "      <td>0.828</td>\n",
       "      <td>-0.728</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>fuck</td>\n",
       "      <td>98471</td>\n",
       "      <td>90.886</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pecker</th>\n",
       "      <td>3443</td>\n",
       "      <td>0.833</td>\n",
       "      <td>-0.733</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>54</td>\n",
       "      <td>head</td>\n",
       "      <td>3140</td>\n",
       "      <td>91.200</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <td>743366</td>\n",
       "      <td>0.839</td>\n",
       "      <td>-0.613</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>shit</td>\n",
       "      <td>679523</td>\n",
       "      <td>91.412</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>14959</td>\n",
       "      <td>0.846</td>\n",
       "      <td>-0.687</td>\n",
       "      <td>33</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>oid</td>\n",
       "      <td>13748</td>\n",
       "      <td>91.905</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soy</th>\n",
       "      <td>119354</td>\n",
       "      <td>0.850</td>\n",
       "      <td>-0.591</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>boy</td>\n",
       "      <td>109940</td>\n",
       "      <td>92.113</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>douche</th>\n",
       "      <td>1265155</td>\n",
       "      <td>0.853</td>\n",
       "      <td>-0.597</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>bag</td>\n",
       "      <td>1167302</td>\n",
       "      <td>92.266</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dumb</th>\n",
       "      <td>3928751</td>\n",
       "      <td>0.857</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>ass</td>\n",
       "      <td>3631048</td>\n",
       "      <td>92.422</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dip</th>\n",
       "      <td>931094</td>\n",
       "      <td>0.865</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>shit</td>\n",
       "      <td>863707</td>\n",
       "      <td>92.763</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nazi</th>\n",
       "      <td>21413</td>\n",
       "      <td>0.890</td>\n",
       "      <td>-0.547</td>\n",
       "      <td>29</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>ster</td>\n",
       "      <td>20195</td>\n",
       "      <td>94.312</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knob</th>\n",
       "      <td>24600</td>\n",
       "      <td>0.910</td>\n",
       "      <td>-0.446</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>head</td>\n",
       "      <td>23458</td>\n",
       "      <td>95.358</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib</th>\n",
       "      <td>312305</td>\n",
       "      <td>0.939</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>tard</td>\n",
       "      <td>302488</td>\n",
       "      <td>96.857</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scum</th>\n",
       "      <td>1370798</td>\n",
       "      <td>0.966</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>bag</td>\n",
       "      <td>1347188</td>\n",
       "      <td>98.278</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "turd       11603  0.065   -4.555          36        1             1   burger   \n",
       "moron        149  0.070   -4.434          65        2             4     face   \n",
       "puke        1005  0.074   -4.475          58        3             3     face   \n",
       "bastard      192  0.087   -4.230          64        4             6     face   \n",
       "monkey      8489  0.089   -4.009          42        5            11     ball   \n",
       "snot        3765  0.100   -3.876          49        6            17      rag   \n",
       "homo         833  0.100   -3.962          62        7            12   goblin   \n",
       "jizz        4118  0.100   -4.220          47        8             7      rag   \n",
       "prick       1256  0.102   -4.036          55        9            10     face   \n",
       "pussy      15350  0.102   -3.859          32       10            18      hat   \n",
       "fart        6480  0.104   -4.378          44       11             5     face   \n",
       "poop       14931  0.105   -4.144          34       12             8     head   \n",
       "fag        10627  0.108   -3.960          41       13            14     lord   \n",
       "queer        962  0.113   -3.940          59       14            15      boy   \n",
       "loser        695  0.116   -3.960          63       15            13     face   \n",
       "faggot      1054  0.122   -4.053          56       16             9      ass   \n",
       "idiot        849  0.125   -3.695          61       17            19     face   \n",
       "dork        2181  0.131   -3.684          52       18            20     lord   \n",
       "fuck     1113336  0.131   -3.393           5       19            27     tard   \n",
       "poo         3654  0.133   -3.930          50       20            16     head   \n",
       "bird       20197  0.169   -3.141          30       21            30     shit   \n",
       "geek        1525  0.173   -3.485          53       22            23      let   \n",
       "harpy         14  0.176   -4.531          66       23             2    bitch   \n",
       "donkey      4453  0.181   -3.399          46       24            26    balls   \n",
       "slut        5043  0.181   -3.489          45       25            22      bag   \n",
       "whore       3941  0.183   -3.459          48       26            24      bag   \n",
       "rat        45116  0.194   -3.138          22       27            31     fuck   \n",
       "crap       11464  0.197   -3.456          37       28            25     sack   \n",
       "piss       16623  0.208   -3.381          31       29            28     head   \n",
       "spunk       1012  0.209   -3.522          57       30            21  trumpet   \n",
       "shit     1266231  0.211   -2.996           3       31            33     head   \n",
       "cum        81554  0.212   -3.303          17       32            29     slut   \n",
       "weasel       947  0.272   -3.007          60       33            32     head   \n",
       "jerk       38509  0.272   -2.287          24       34            36     face   \n",
       "creep       7335  0.321   -2.351          43       35            34     ster   \n",
       "stink      11218  0.327   -2.350          38       36            35     weed   \n",
       "bitch      67527  0.345   -2.169          20       37            40      ass   \n",
       "butt      210651  0.355   -2.253          11       38            38     head   \n",
       "gay        78707  0.363   -2.236          18       39            39     lord   \n",
       "sleaze     29450  0.398   -1.485          26       40            46     ball   \n",
       "lame       10734  0.407   -2.278          40       41            37      ass   \n",
       "trump      68645  0.410   -1.782          19       42            44     tard   \n",
       "skeeze      1399  0.432   -1.584          54       43            45     ball   \n",
       "wank       11100  0.499   -2.079          39       44            41    stain   \n",
       "spit       34730  0.530   -1.355          25       45            48     ball   \n",
       "slime      47360  0.543   -1.305          21       46            50     ball   \n",
       "cock      194723  0.551   -1.877          12       47            43   sucker   \n",
       "twat       23252  0.565   -1.919          28       48            42   waffle   \n",
       "tit        40337  0.612   -1.306          23       49            49     fuck   \n",
       "fem       174003  0.613   -1.201          13       50            51      boy   \n",
       "dick     1085733  0.630   -1.429           6       51            47     head   \n",
       "ass       726783  0.728   -1.117           9       52            52      hat   \n",
       "lard       14680  0.755   -1.061          35       53            53      ass   \n",
       "dirt      133930  0.821   -0.726          14       54            56      bag   \n",
       "bum       108346  0.828   -0.728          16       55            55     fuck   \n",
       "pecker      3443  0.833   -0.733          51       56            54     head   \n",
       "dog       743366  0.839   -0.613           8       57            58     shit   \n",
       "right      14959  0.846   -0.687          33       58            57      oid   \n",
       "soy       119354  0.850   -0.591          15       59            60      boy   \n",
       "douche   1265155  0.853   -0.597           4       60            59      bag   \n",
       "dumb     3928751  0.857   -0.494           1       61            62      ass   \n",
       "dip       931094  0.865   -0.434           7       62            64     shit   \n",
       "nazi       21413  0.890   -0.547          29       63            61     ster   \n",
       "knob       24600  0.910   -0.446          27       64            63     head   \n",
       "lib       312305  0.939   -0.258          10       65            65     tard   \n",
       "scum     1370798  0.966   -0.169           2       66            66      bag   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables  \n",
       "turd         1666       14.358       6          1  \n",
       "moron          25       16.779      43          2  \n",
       "puke          183       18.209      20          1  \n",
       "bastard        39       20.312      41          2  \n",
       "monkey       1323       15.585      17          2  \n",
       "snot          600       15.936      16          1  \n",
       "homo          165       19.808      29          2  \n",
       "jizz          993       24.114       3          1  \n",
       "prick         247       19.666      24          1  \n",
       "pussy        2875       18.730      11          2  \n",
       "fart         1698       26.204       4          1  \n",
       "poop         3355       22.470       3          1  \n",
       "fag          2153       20.260       8          1  \n",
       "queer         218       22.661      29          1  \n",
       "loser         183       26.331      34          2  \n",
       "faggot        319       30.266      22          2  \n",
       "idiot         199       23.439      36          2  \n",
       "dork          460       21.091      20          1  \n",
       "fuck       234588       21.071       0          1  \n",
       "poo          1066       29.174      12          1  \n",
       "bird         6010       29.757      13          1  \n",
       "geek          563       36.918      31          1  \n",
       "harpy           6       42.857      65          2  \n",
       "donkey       1667       37.435      18          2  \n",
       "slut         1902       37.716      10          1  \n",
       "whore        1506       38.214      18          1  \n",
       "rat         17471       38.725      11          1  \n",
       "crap         4698       40.980       8          1  \n",
       "piss         6871       41.334       4          1  \n",
       "spunk         436       43.083      29          1  \n",
       "shit       450560       35.583       0          1  \n",
       "cum         34414       42.198       2          1  \n",
       "weasel        469       49.525      38          2  \n",
       "jerk        13791       35.812      14          1  \n",
       "creep        3581       48.821      36          1  \n",
       "stink        5320       47.424      15          1  \n",
       "bitch       35551       52.647       6          1  \n",
       "butt       112887       53.590       3          1  \n",
       "gay         44914       57.065      15          1  \n",
       "sleaze      13842       47.002      43          1  \n",
       "lame         6662       62.064      25          1  \n",
       "trump       31073       45.266       9          1  \n",
       "skeeze        838       59.900      63          1  \n",
       "wank         7756       69.874      10          1  \n",
       "spit        23851       68.675      29          1  \n",
       "slime       33254       70.215      25          1  \n",
       "cock       142886       73.379       1          1  \n",
       "twat        17379       74.742       5          1  \n",
       "tit         30803       76.364      11          1  \n",
       "fem        133608       76.785      24          1  \n",
       "dick       855756       78.818       1          1  \n",
       "ass        618186       85.058       2          1  \n",
       "lard        12736       86.757      25          1  \n",
       "dirt       121166       90.470      15          1  \n",
       "bum         98471       90.886      11          1  \n",
       "pecker       3140       91.200      39          2  \n",
       "dog        679523       91.412       6          1  \n",
       "right       13748       91.905      31          1  \n",
       "soy        109940       92.113      18          1  \n",
       "douche    1167302       92.266       2          1  \n",
       "dumb      3631048       92.422      13          1  \n",
       "dip        863707       92.763      24          1  \n",
       "nazi        20195       94.312      20          2  \n",
       "knob        23458       95.358      28          1  \n",
       "lib        302488       96.857      21          1  \n",
       "scum      1347188       98.278      14          1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.sort_values(by='cp_rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "wired-intensity",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>cp</th>\n",
       "      <th>entropy</th>\n",
       "      <th>count_rank</th>\n",
       "      <th>cp_rank</th>\n",
       "      <th>entropy_rank</th>\n",
       "      <th>topmatch</th>\n",
       "      <th>topcount</th>\n",
       "      <th>topcountpct</th>\n",
       "      <th>nzeros</th>\n",
       "      <th>syllables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>boat</th>\n",
       "      <td>1548</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-4.080</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>shit</td>\n",
       "      <td>345</td>\n",
       "      <td>22.287</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>5985</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-3.760</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>shit</td>\n",
       "      <td>1083</td>\n",
       "      <td>18.095</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brains</th>\n",
       "      <td>8653</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-3.343</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>cum</td>\n",
       "      <td>1594</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fart</th>\n",
       "      <td>10161</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-3.599</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "      <td>2639</td>\n",
       "      <td>25.972</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rat</th>\n",
       "      <td>3614</td>\n",
       "      <td>0.138</td>\n",
       "      <td>-3.838</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>shit</td>\n",
       "      <td>1134</td>\n",
       "      <td>31.378</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whore</th>\n",
       "      <td>4486</td>\n",
       "      <td>0.149</td>\n",
       "      <td>-3.347</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>cum</td>\n",
       "      <td>1240</td>\n",
       "      <td>27.642</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brain</th>\n",
       "      <td>17616</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-3.437</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>bird</td>\n",
       "      <td>4353</td>\n",
       "      <td>24.710</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burger</th>\n",
       "      <td>9375</td>\n",
       "      <td>0.161</td>\n",
       "      <td>-3.501</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>ass</td>\n",
       "      <td>2588</td>\n",
       "      <td>27.605</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monkey</th>\n",
       "      <td>4232</td>\n",
       "      <td>0.163</td>\n",
       "      <td>-3.461</td>\n",
       "      <td>58</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>butt</td>\n",
       "      <td>1403</td>\n",
       "      <td>33.152</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <td>12666</td>\n",
       "      <td>0.168</td>\n",
       "      <td>-3.324</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>rat</td>\n",
       "      <td>3548</td>\n",
       "      <td>28.012</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skull</th>\n",
       "      <td>1012</td>\n",
       "      <td>0.168</td>\n",
       "      <td>-3.321</td>\n",
       "      <td>68</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>gay</td>\n",
       "      <td>335</td>\n",
       "      <td>33.103</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fucker</th>\n",
       "      <td>22557</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-3.196</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>tit</td>\n",
       "      <td>6827</td>\n",
       "      <td>30.266</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breath</th>\n",
       "      <td>3926</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-3.296</td>\n",
       "      <td>59</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>dick</td>\n",
       "      <td>1220</td>\n",
       "      <td>31.075</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goblin</th>\n",
       "      <td>4371</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-3.205</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>cock</td>\n",
       "      <td>1276</td>\n",
       "      <td>29.192</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muncher</th>\n",
       "      <td>3270</td>\n",
       "      <td>0.172</td>\n",
       "      <td>-3.021</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>cock</td>\n",
       "      <td>885</td>\n",
       "      <td>27.064</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitten</th>\n",
       "      <td>129</td>\n",
       "      <td>0.187</td>\n",
       "      <td>-3.340</td>\n",
       "      <td>73</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>dick</td>\n",
       "      <td>45</td>\n",
       "      <td>34.884</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <td>16729</td>\n",
       "      <td>0.203</td>\n",
       "      <td>-3.077</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>shit</td>\n",
       "      <td>5669</td>\n",
       "      <td>33.887</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cock</th>\n",
       "      <td>7032</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-3.492</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>shit</td>\n",
       "      <td>3034</td>\n",
       "      <td>43.146</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>93812</td>\n",
       "      <td>0.219</td>\n",
       "      <td>-2.923</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>slime</td>\n",
       "      <td>33254</td>\n",
       "      <td>35.447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>let</th>\n",
       "      <td>14937</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-3.241</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>dick</td>\n",
       "      <td>6552</td>\n",
       "      <td>43.864</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dick</th>\n",
       "      <td>15748</td>\n",
       "      <td>0.226</td>\n",
       "      <td>-3.357</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>shit</td>\n",
       "      <td>6999</td>\n",
       "      <td>44.444</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weasel</th>\n",
       "      <td>6386</td>\n",
       "      <td>0.244</td>\n",
       "      <td>-2.767</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>shit</td>\n",
       "      <td>2716</td>\n",
       "      <td>42.531</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balls</th>\n",
       "      <td>82160</td>\n",
       "      <td>0.246</td>\n",
       "      <td>-2.856</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>shit</td>\n",
       "      <td>36333</td>\n",
       "      <td>44.222</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pot</th>\n",
       "      <td>6966</td>\n",
       "      <td>0.255</td>\n",
       "      <td>-2.528</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>shit</td>\n",
       "      <td>2391</td>\n",
       "      <td>34.324</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuck</th>\n",
       "      <td>447426</td>\n",
       "      <td>0.255</td>\n",
       "      <td>-2.585</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>dumb</td>\n",
       "      <td>191951</td>\n",
       "      <td>42.901</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mouth</th>\n",
       "      <td>9886</td>\n",
       "      <td>0.257</td>\n",
       "      <td>-2.875</td>\n",
       "      <td>44</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>dirt</td>\n",
       "      <td>4175</td>\n",
       "      <td>42.231</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face</th>\n",
       "      <td>256361</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-3.007</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>fuck</td>\n",
       "      <td>125768</td>\n",
       "      <td>49.059</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boy</th>\n",
       "      <td>395352</td>\n",
       "      <td>0.270</td>\n",
       "      <td>-2.318</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>fem</td>\n",
       "      <td>133608</td>\n",
       "      <td>33.795</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sack</th>\n",
       "      <td>10653</td>\n",
       "      <td>0.273</td>\n",
       "      <td>-2.726</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>crap</td>\n",
       "      <td>4698</td>\n",
       "      <td>44.100</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cake</th>\n",
       "      <td>5917</td>\n",
       "      <td>0.301</td>\n",
       "      <td>-3.152</td>\n",
       "      <td>54</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>shit</td>\n",
       "      <td>3175</td>\n",
       "      <td>53.659</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bandit</th>\n",
       "      <td>500</td>\n",
       "      <td>0.326</td>\n",
       "      <td>-2.839</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>ass</td>\n",
       "      <td>279</td>\n",
       "      <td>55.800</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ster</th>\n",
       "      <td>65214</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-2.325</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>trump</td>\n",
       "      <td>31005</td>\n",
       "      <td>47.543</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bro</th>\n",
       "      <td>23464</td>\n",
       "      <td>0.342</td>\n",
       "      <td>-2.293</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>gay</td>\n",
       "      <td>12267</td>\n",
       "      <td>52.280</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rag</th>\n",
       "      <td>18943</td>\n",
       "      <td>0.343</td>\n",
       "      <td>-2.121</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>cum</td>\n",
       "      <td>8266</td>\n",
       "      <td>43.636</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trumpet</th>\n",
       "      <td>2349</td>\n",
       "      <td>0.347</td>\n",
       "      <td>-2.311</td>\n",
       "      <td>64</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>fuck</td>\n",
       "      <td>1290</td>\n",
       "      <td>54.917</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nugget</th>\n",
       "      <td>18480</td>\n",
       "      <td>0.368</td>\n",
       "      <td>-2.427</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>fuck</td>\n",
       "      <td>10859</td>\n",
       "      <td>58.761</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>head</th>\n",
       "      <td>1611393</td>\n",
       "      <td>0.371</td>\n",
       "      <td>-1.875</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>48</td>\n",
       "      <td>dick</td>\n",
       "      <td>855756</td>\n",
       "      <td>53.107</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bag</th>\n",
       "      <td>2939955</td>\n",
       "      <td>0.374</td>\n",
       "      <td>-1.796</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>50</td>\n",
       "      <td>scum</td>\n",
       "      <td>1347188</td>\n",
       "      <td>45.823</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wad</th>\n",
       "      <td>172516</td>\n",
       "      <td>0.375</td>\n",
       "      <td>-1.941</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>fuck</td>\n",
       "      <td>84336</td>\n",
       "      <td>48.886</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>licker</th>\n",
       "      <td>16561</td>\n",
       "      <td>0.390</td>\n",
       "      <td>-2.180</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>butt</td>\n",
       "      <td>9815</td>\n",
       "      <td>59.266</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cunt</th>\n",
       "      <td>24796</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-2.353</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>shit</td>\n",
       "      <td>15170</td>\n",
       "      <td>61.179</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knuckle</th>\n",
       "      <td>2783</td>\n",
       "      <td>0.405</td>\n",
       "      <td>-2.278</td>\n",
       "      <td>62</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>fuck</td>\n",
       "      <td>1723</td>\n",
       "      <td>61.912</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splash</th>\n",
       "      <td>991</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-2.200</td>\n",
       "      <td>69</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>dick</td>\n",
       "      <td>638</td>\n",
       "      <td>64.379</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shit</th>\n",
       "      <td>1667150</td>\n",
       "      <td>0.437</td>\n",
       "      <td>-1.445</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>dip</td>\n",
       "      <td>863707</td>\n",
       "      <td>51.807</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tard</th>\n",
       "      <td>579832</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-1.441</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>lib</td>\n",
       "      <td>302488</td>\n",
       "      <td>52.168</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pirate</th>\n",
       "      <td>1132</td>\n",
       "      <td>0.442</td>\n",
       "      <td>-1.889</td>\n",
       "      <td>67</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>butt</td>\n",
       "      <td>688</td>\n",
       "      <td>60.777</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stick</th>\n",
       "      <td>109843</td>\n",
       "      <td>0.450</td>\n",
       "      <td>-1.460</td>\n",
       "      <td>15</td>\n",
       "      <td>47</td>\n",
       "      <td>52</td>\n",
       "      <td>dip</td>\n",
       "      <td>59925</td>\n",
       "      <td>54.555</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boi</th>\n",
       "      <td>75496</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-2.048</td>\n",
       "      <td>18</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "      <td>fuck</td>\n",
       "      <td>49881</td>\n",
       "      <td>66.071</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waffle</th>\n",
       "      <td>25903</td>\n",
       "      <td>0.469</td>\n",
       "      <td>-1.913</td>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>twat</td>\n",
       "      <td>17379</td>\n",
       "      <td>67.093</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twat</th>\n",
       "      <td>4896</td>\n",
       "      <td>0.472</td>\n",
       "      <td>-2.027</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>43</td>\n",
       "      <td>fuck</td>\n",
       "      <td>3296</td>\n",
       "      <td>67.320</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose</th>\n",
       "      <td>8575</td>\n",
       "      <td>0.485</td>\n",
       "      <td>-2.014</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "      <td>dick</td>\n",
       "      <td>5890</td>\n",
       "      <td>68.688</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitch</th>\n",
       "      <td>9220</td>\n",
       "      <td>0.493</td>\n",
       "      <td>-2.219</td>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>dumb</td>\n",
       "      <td>6430</td>\n",
       "      <td>69.740</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oid</th>\n",
       "      <td>40304</td>\n",
       "      <td>0.501</td>\n",
       "      <td>-1.277</td>\n",
       "      <td>24</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>fem</td>\n",
       "      <td>25000</td>\n",
       "      <td>62.029</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pile</th>\n",
       "      <td>66674</td>\n",
       "      <td>0.507</td>\n",
       "      <td>-1.325</td>\n",
       "      <td>19</td>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>dog</td>\n",
       "      <td>42953</td>\n",
       "      <td>64.422</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>socket</th>\n",
       "      <td>868</td>\n",
       "      <td>0.554</td>\n",
       "      <td>-1.807</td>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>cock</td>\n",
       "      <td>641</td>\n",
       "      <td>73.848</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stain</th>\n",
       "      <td>120289</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-1.386</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>shit</td>\n",
       "      <td>92128</td>\n",
       "      <td>76.589</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nuts</th>\n",
       "      <td>26112</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-1.642</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>51</td>\n",
       "      <td>fuck</td>\n",
       "      <td>20122</td>\n",
       "      <td>77.060</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weed</th>\n",
       "      <td>33286</td>\n",
       "      <td>0.604</td>\n",
       "      <td>-1.324</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>dick</td>\n",
       "      <td>25307</td>\n",
       "      <td>76.029</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>azoid</th>\n",
       "      <td>2469</td>\n",
       "      <td>0.660</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>63</td>\n",
       "      <td>59</td>\n",
       "      <td>56</td>\n",
       "      <td>creep</td>\n",
       "      <td>1999</td>\n",
       "      <td>80.964</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burglar</th>\n",
       "      <td>1471</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-1.453</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>53</td>\n",
       "      <td>turd</td>\n",
       "      <td>1196</td>\n",
       "      <td>81.305</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lord</th>\n",
       "      <td>367655</td>\n",
       "      <td>0.685</td>\n",
       "      <td>-1.091</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>shit</td>\n",
       "      <td>300942</td>\n",
       "      <td>81.854</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slut</th>\n",
       "      <td>40923</td>\n",
       "      <td>0.713</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>cum</td>\n",
       "      <td>34414</td>\n",
       "      <td>84.095</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puffin</th>\n",
       "      <td>630</td>\n",
       "      <td>0.716</td>\n",
       "      <td>-1.213</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>wank</td>\n",
       "      <td>532</td>\n",
       "      <td>84.444</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>38306</td>\n",
       "      <td>0.809</td>\n",
       "      <td>-0.844</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>shit</td>\n",
       "      <td>34414</td>\n",
       "      <td>89.840</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nazi</th>\n",
       "      <td>6047</td>\n",
       "      <td>0.864</td>\n",
       "      <td>-0.627</td>\n",
       "      <td>52</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>fem</td>\n",
       "      <td>5617</td>\n",
       "      <td>92.889</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nozzle</th>\n",
       "      <td>48803</td>\n",
       "      <td>0.893</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>21</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>douche</td>\n",
       "      <td>46107</td>\n",
       "      <td>94.476</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sucker</th>\n",
       "      <td>150612</td>\n",
       "      <td>0.901</td>\n",
       "      <td>-0.408</td>\n",
       "      <td>13</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>cock</td>\n",
       "      <td>142886</td>\n",
       "      <td>94.870</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib</th>\n",
       "      <td>22792</td>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.356</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>shit</td>\n",
       "      <td>21815</td>\n",
       "      <td>95.713</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ass</th>\n",
       "      <td>3754308</td>\n",
       "      <td>0.936</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>dumb</td>\n",
       "      <td>3631048</td>\n",
       "      <td>96.717</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clown</th>\n",
       "      <td>45367</td>\n",
       "      <td>0.950</td>\n",
       "      <td>-0.258</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>ass</td>\n",
       "      <td>44220</td>\n",
       "      <td>97.472</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canoe</th>\n",
       "      <td>26035</td>\n",
       "      <td>0.967</td>\n",
       "      <td>-0.177</td>\n",
       "      <td>28</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>douche</td>\n",
       "      <td>25607</td>\n",
       "      <td>98.356</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wit</th>\n",
       "      <td>231981</td>\n",
       "      <td>0.968</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>fuck</td>\n",
       "      <td>228196</td>\n",
       "      <td>98.368</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hat</th>\n",
       "      <td>626150</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>ass</td>\n",
       "      <td>618186</td>\n",
       "      <td>98.728</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     cp  entropy  count_rank  cp_rank  entropy_rank topmatch  \\\n",
       "boat        1548  0.101   -4.080          65        1             1     shit   \n",
       "wagon       5985  0.103   -3.760          53        2             3     shit   \n",
       "brains      8653  0.132   -3.343          47        3            11      cum   \n",
       "fart       10161  0.132   -3.599          43        4             4      dog   \n",
       "rat         3614  0.138   -3.838          60        5             2     shit   \n",
       "whore       4486  0.149   -3.347          56        6            10      cum   \n",
       "brain      17616  0.151   -3.437          36        7             8     bird   \n",
       "burger      9375  0.161   -3.501          45        8             5      ass   \n",
       "monkey      4232  0.163   -3.461          58        9             7     butt   \n",
       "dog        12666  0.168   -3.324          41       10            13      rat   \n",
       "skull       1012  0.168   -3.321          68       11            14      gay   \n",
       "fucker     22557  0.169   -3.196          33       12            18      tit   \n",
       "breath      3926  0.170   -3.296          59       13            15     dick   \n",
       "goblin      4371  0.170   -3.205          57       14            17     cock   \n",
       "muncher     3270  0.172   -3.021          61       15            21     cock   \n",
       "mitten       129  0.187   -3.340          73       16            12     dick   \n",
       "bucket     16729  0.203   -3.077          37       17            20     shit   \n",
       "cock        7032  0.211   -3.492          49       18             6     shit   \n",
       "ball       93812  0.219   -2.923          16       19            23    slime   \n",
       "let        14937  0.222   -3.241          40       20            16     dick   \n",
       "dick       15748  0.226   -3.357          39       21             9     shit   \n",
       "weasel      6386  0.244   -2.767          51       22            27     shit   \n",
       "balls      82160  0.246   -2.856          17       23            25     shit   \n",
       "pot         6966  0.255   -2.528          50       24            30     shit   \n",
       "fuck      447426  0.255   -2.585           7       25            29     dumb   \n",
       "mouth       9886  0.257   -2.875          44       26            24     dirt   \n",
       "face      256361  0.267   -3.007          10       27            22     fuck   \n",
       "boy       395352  0.270   -2.318           8       28            34      fem   \n",
       "sack       10653  0.273   -2.726          42       29            28     crap   \n",
       "cake        5917  0.301   -3.152          54       30            19     shit   \n",
       "bandit       500  0.326   -2.839          72       31            26      ass   \n",
       "ster       65214  0.328   -2.325          20       32            33    trump   \n",
       "bro        23464  0.342   -2.293          31       33            36      gay   \n",
       "rag        18943  0.343   -2.121          34       34            41      cum   \n",
       "trumpet     2349  0.347   -2.311          64       35            35     fuck   \n",
       "nugget     18480  0.368   -2.427          35       36            31     fuck   \n",
       "head     1611393  0.371   -1.875           4       37            48     dick   \n",
       "bag      2939955  0.374   -1.796           2       38            50     scum   \n",
       "wad       172516  0.375   -1.941          12       39            45     fuck   \n",
       "licker     16561  0.390   -2.180          38       40            40     butt   \n",
       "cunt       24796  0.401   -2.353          30       41            32     shit   \n",
       "knuckle     2783  0.405   -2.278          62       42            37     fuck   \n",
       "splash       991  0.431   -2.200          69       43            39     dick   \n",
       "shit     1667150  0.437   -1.445           3       44            54      dip   \n",
       "tard      579832  0.439   -1.441           6       45            55      lib   \n",
       "pirate      1132  0.442   -1.889          67       46            47     butt   \n",
       "stick     109843  0.450   -1.460          15       47            52      dip   \n",
       "boi        75496  0.454   -2.048          18       48            42     fuck   \n",
       "waffle     25903  0.469   -1.913          29       49            46     twat   \n",
       "twat        4896  0.472   -2.027          55       50            43     fuck   \n",
       "nose        8575  0.485   -2.014          48       51            44     dick   \n",
       "bitch       9220  0.493   -2.219          46       52            38     dumb   \n",
       "oid        40304  0.501   -1.277          24       53            60      fem   \n",
       "pile       66674  0.507   -1.325          19       54            58      dog   \n",
       "socket       868  0.554   -1.807          70       55            49     cock   \n",
       "stain     120289  0.600   -1.386          14       56            57     shit   \n",
       "nuts       26112  0.600   -1.642          27       57            51     fuck   \n",
       "weed       33286  0.604   -1.324          26       58            59     dick   \n",
       "azoid       2469  0.660   -1.402          63       59            56    creep   \n",
       "burglar     1471  0.664   -1.453          66       60            53     turd   \n",
       "lord      367655  0.685   -1.091           9       61            62     shit   \n",
       "slut       40923  0.713   -1.088          23       62            63      cum   \n",
       "puffin       630  0.716   -1.213          71       63            61     wank   \n",
       "bird       38306  0.809   -0.844          25       64            64     shit   \n",
       "nazi        6047  0.864   -0.627          52       65            65      fem   \n",
       "nozzle     48803  0.893   -0.477          21       66            66   douche   \n",
       "sucker    150612  0.901   -0.408          13       67            67     cock   \n",
       "lib        22792  0.917   -0.356          32       68            68     shit   \n",
       "ass      3754308  0.936   -0.322           1       69            69     dumb   \n",
       "clown      45367  0.950   -0.258          22       70            70      ass   \n",
       "canoe      26035  0.967   -0.177          28       71            71   douche   \n",
       "wit       231981  0.968   -0.164          11       72            72     fuck   \n",
       "hat       626150  0.975   -0.139           5       73            73      ass   \n",
       "\n",
       "         topcount  topcountpct  nzeros  syllables  \n",
       "boat          345       22.287      16          1  \n",
       "wagon        1083       18.095      11          2  \n",
       "brains       1594       18.421      10          1  \n",
       "fart         2639       25.972      14          1  \n",
       "rat          1134       31.378       8          1  \n",
       "whore        1240       27.642      17          1  \n",
       "brain        4353       24.710       6          1  \n",
       "burger       2588       27.605       6          2  \n",
       "monkey       1403       33.152      12          2  \n",
       "dog          3548       28.012       5          1  \n",
       "skull         335       33.103      29          1  \n",
       "fucker       6827       30.266       9          2  \n",
       "breath       1220       31.075      21          1  \n",
       "goblin       1276       29.192      24          2  \n",
       "muncher       885       27.064      29          2  \n",
       "mitten         45       34.884      48          2  \n",
       "bucket       5669       33.887      13          2  \n",
       "cock         3034       43.146       9          1  \n",
       "ball        33254       35.447       1          1  \n",
       "let          6552       43.864       9          1  \n",
       "dick         6999       44.444       4          1  \n",
       "weasel       2716       42.531      19          2  \n",
       "balls       36333       44.222       5          1  \n",
       "pot          2391       34.324      17          1  \n",
       "fuck       191951       42.901       2          1  \n",
       "mouth        4175       42.231      12          1  \n",
       "face       125768       49.059       1          1  \n",
       "boy        133608       33.795       2          1  \n",
       "sack         4698       44.100      14          1  \n",
       "cake         3175       53.659       6          1  \n",
       "bandit        279       55.800      40          2  \n",
       "ster        31005       47.543       8          1  \n",
       "bro         12267       52.280       6          1  \n",
       "rag          8266       43.636      20          1  \n",
       "trumpet      1290       54.917      43          2  \n",
       "nugget      10859       58.761      14          2  \n",
       "head       855756       53.107       3          1  \n",
       "bag       1347188       45.823       4          1  \n",
       "wad         84336       48.886       7          1  \n",
       "licker       9815       59.266      18          2  \n",
       "cunt        15170       61.179       5          1  \n",
       "knuckle      1723       61.912      32          2  \n",
       "splash        638       64.379      42          1  \n",
       "shit       863707       51.807       1          1  \n",
       "tard       302488       52.168       7          1  \n",
       "pirate        688       60.777      35          2  \n",
       "stick       59925       54.555       7          1  \n",
       "boi         49881       66.071       6          1  \n",
       "waffle      17379       67.093      17          2  \n",
       "twat         3296       67.320      17          1  \n",
       "nose         5890       68.688      15          1  \n",
       "bitch        6430       69.740       9          1  \n",
       "oid         25000       62.029      12          1  \n",
       "pile        42953       64.422      19          1  \n",
       "socket        641       73.848      42          2  \n",
       "stain       92128       76.589       9          1  \n",
       "nuts        20122       77.060      10          1  \n",
       "weed        25307       76.029      15          1  \n",
       "azoid        1999       80.964      38          2  \n",
       "burglar      1196       81.305      40          2  \n",
       "lord       300942       81.854       1          1  \n",
       "slut        34414       84.095      15          1  \n",
       "puffin        532       84.444      53          2  \n",
       "bird        34414       89.840      12          1  \n",
       "nazi         5617       92.889      36          2  \n",
       "nozzle      46107       94.476      33          2  \n",
       "sucker     142886       94.870      16          2  \n",
       "lib         21815       95.713      36          1  \n",
       "ass       3631048       96.717       4          1  \n",
       "clown       44220       97.472      22          1  \n",
       "canoe       25607       98.356      40          2  \n",
       "wit        228196       98.368      28          1  \n",
       "hat        618186       98.728      13          1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.sort_values(by='cp_rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "worthy-basketball",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pre\n",
       "ass        3.111e+00\n",
       "bastard    4.023e-03\n",
       "bird       9.164e-03\n",
       "bitch      8.739e-02\n",
       "bum        2.201e+01\n",
       "butt       1.136e+01\n",
       "cock       1.149e-01\n",
       "crap       2.414e-02\n",
       "creep      1.095e-02\n",
       "cum        3.867e-02\n",
       "dick       5.366e-01\n",
       "dip        4.023e-01\n",
       "dirt       7.376e-03\n",
       "dog        1.140e-01\n",
       "donkey     2.190e-02\n",
       "dork       2.682e-03\n",
       "douche     3.858e-01\n",
       "dumb       4.290e+01\n",
       "fag        2.995e-02\n",
       "faggot     1.296e-02\n",
       "fart       1.386e-02\n",
       "fem        3.129e-03\n",
       "fuck       8.752e-01\n",
       "gay        2.883e-02\n",
       "geek       6.705e-04\n",
       "harpy      0.000e+00\n",
       "homo       8.940e-03\n",
       "idiot      9.164e-03\n",
       "jerk       8.940e-03\n",
       "jizz       2.906e-03\n",
       "knob       2.459e-03\n",
       "lame       1.296e-02\n",
       "lard       8.493e-03\n",
       "lib        4.269e-02\n",
       "loser      7.599e-03\n",
       "monkey     5.610e-02\n",
       "moron      8.940e-04\n",
       "nazi       6.258e-03\n",
       "pecker     2.235e-04\n",
       "piss       4.984e-02\n",
       "poo        5.364e-03\n",
       "poop       1.565e-02\n",
       "prick      8.046e-03\n",
       "puke       8.940e-03\n",
       "pussy      1.207e-01\n",
       "queer      2.906e-03\n",
       "rat        3.905e+00\n",
       "right      2.012e-03\n",
       "scum       2.848e+00\n",
       "shit       3.473e+00\n",
       "skeeze     0.000e+00\n",
       "sleaze     2.906e-03\n",
       "slime      1.341e-02\n",
       "slut       1.654e-02\n",
       "snot       4.694e-03\n",
       "soy        6.705e-03\n",
       "spit       1.967e-01\n",
       "spunk      4.470e-04\n",
       "stink      4.470e-03\n",
       "tit        6.884e+00\n",
       "trump      8.649e-02\n",
       "turd       1.587e-02\n",
       "twat       3.330e-02\n",
       "wank       1.073e-02\n",
       "weasel     6.705e-03\n",
       "whore      7.152e-03\n",
       "Name: fuck, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcol = mat.loc[:, 'fuck']\n",
    "100 * fcol / (fcol.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "lyric-taxation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66 73 17\n"
     ]
    }
   ],
   "source": [
    "pset = set(pf.index.to_list())\n",
    "sset = set(sf.index.to_list())\n",
    "inter = set.intersection(pset, sset)\n",
    "\n",
    "print(\n",
    "    len(pset),\n",
    "    len(sset),\n",
    "    len(inter),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bottom-denial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ass',\n",
       " 'bird',\n",
       " 'bitch',\n",
       " 'cock',\n",
       " 'dick',\n",
       " 'dog',\n",
       " 'fart',\n",
       " 'fuck',\n",
       " 'lib',\n",
       " 'monkey',\n",
       " 'nazi',\n",
       " 'rat',\n",
       " 'shit',\n",
       " 'slut',\n",
       " 'twat',\n",
       " 'weasel',\n",
       " 'whore'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "valued-cylinder",
   "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>pre</th>\n",
       "      <th>suff</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>dumb</td>\n",
       "      <td>ass</td>\n",
       "      <td>3.631e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3140</th>\n",
       "      <td>scum</td>\n",
       "      <td>bag</td>\n",
       "      <td>1.347e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>douche</td>\n",
       "      <td>bag</td>\n",
       "      <td>1.167e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>dip</td>\n",
       "      <td>shit</td>\n",
       "      <td>8.637e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>dick</td>\n",
       "      <td>head</td>\n",
       "      <td>8.558e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>lard</td>\n",
       "      <td>nazi</td>\n",
       "      <td>0.000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3088</th>\n",
       "      <td>lard</td>\n",
       "      <td>pirate</td>\n",
       "      <td>0.000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3087</th>\n",
       "      <td>lard</td>\n",
       "      <td>slut</td>\n",
       "      <td>0.000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>lard</td>\n",
       "      <td>puffin</td>\n",
       "      <td>0.000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2635</th>\n",
       "      <td>dip</td>\n",
       "      <td>pile</td>\n",
       "      <td>0.000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4818 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pre    suff      count\n",
       "1948    dumb     ass  3.631e+06\n",
       "3140    scum     bag  1.347e+06\n",
       "2337  douche     bag  1.167e+06\n",
       "2696     dip    shit  8.637e+05\n",
       "1222    dick    head  8.558e+05\n",
       "...      ...     ...        ...\n",
       "3090    lard    nazi  0.000e+00\n",
       "3088    lard  pirate  0.000e+00\n",
       "3087    lard    slut  0.000e+00\n",
       "3080    lard  puffin  0.000e+00\n",
       "2635     dip    pile  0.000e+00\n",
       "\n",
       "[4818 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "champion-retirement",
   "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>pre</th>\n",
       "      <th>suff</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>whore</td>\n",
       "      <td>muncher</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>douche</td>\n",
       "      <td>azoid</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>dork</td>\n",
       "      <td>monkey</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>puke</td>\n",
       "      <td>bird</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>nazi</td>\n",
       "      <td>trumpet</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>geek</td>\n",
       "      <td>splash</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4745</th>\n",
       "      <td>faggot</td>\n",
       "      <td>wad</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>loser</td>\n",
       "      <td>slut</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>geek</td>\n",
       "      <td>fart</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>snot</td>\n",
       "      <td>rat</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pre     suff  count\n",
       "2936   whore  muncher    1.0\n",
       "2401  douche    azoid    1.0\n",
       "2491    dork   monkey    1.0\n",
       "377     puke     bird    1.0\n",
       "2746    nazi  trumpet    1.0\n",
       "2779    geek   splash    1.0\n",
       "4745  faggot      wad    1.0\n",
       "4620   loser     slut    1.0\n",
       "2843    geek     fart    1.0\n",
       "84      snot      rat    1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count'] == 1].sample(10, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "800bf0f0-d0f5-41f2-bc69-6c8e766f5845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.974169"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".987**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da4f709c-8f87-4f59-a12c-e598373b1164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dumbfuck: 42.9%, cp = 18.4%\n",
      "bumfuck: 22.0%, cp = 4.8%\n",
      "buttfuck: 11.4%, cp = 1.3%\n",
      "titfuck: 6.9%, cp = 0.5%\n",
      "ratfuck: 3.9%, cp = 0.2%\n"
     ]
    }
   ],
   "source": [
    "denom = df[df.suff=='fuck']['count'].sum()\n",
    "\n",
    "pred = dict(\n",
    "dumb = 191951,\n",
    "bum = 98471,\n",
    "    butt=50818,\n",
    "    tit=30802,\n",
    "    rat=17471,\n",
    ")\n",
    "\n",
    "for pref, count in pred.items():\n",
    "    proportion = count / denom\n",
    "    print(f\"{pref}fuck: {proportion*100:.1f}%, cp = {100 * proportion**2:.1f}%\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73976242-3e96-4e2b-a9ea-2f72a9b8ec8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "447425.77499999997"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.suff=='fuck']['count'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "180224b5-039b-4c47-ba4f-07140c5091c9",
   "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>pre</th>\n",
       "      <th>suff</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>dumb</td>\n",
       "      <td>fuck</td>\n",
       "      <td>191951.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>bum</td>\n",
       "      <td>fuck</td>\n",
       "      <td>98471.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>butt</td>\n",
       "      <td>fuck</td>\n",
       "      <td>50818.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>tit</td>\n",
       "      <td>fuck</td>\n",
       "      <td>30802.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3717</th>\n",
       "      <td>rat</td>\n",
       "      <td>fuck</td>\n",
       "      <td>17471.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>shit</td>\n",
       "      <td>fuck</td>\n",
       "      <td>15540.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>ass</td>\n",
       "      <td>fuck</td>\n",
       "      <td>13918.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3206</th>\n",
       "      <td>scum</td>\n",
       "      <td>fuck</td>\n",
       "      <td>12741.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1819</th>\n",
       "      <td>fuck</td>\n",
       "      <td>fuck</td>\n",
       "      <td>3916.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>dick</td>\n",
       "      <td>fuck</td>\n",
       "      <td>2401.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pre  suff       count\n",
       "1965  dumb  fuck  191951.050\n",
       "1162   bum  fuck   98471.400\n",
       "1016  butt  fuck   50818.450\n",
       "1746   tit  fuck   30802.750\n",
       "3717   rat  fuck   17471.125\n",
       "651   shit  fuck   15540.000\n",
       "1089   ass  fuck   13918.000\n",
       "3206  scum  fuck   12741.000\n",
       "1819  fuck  fuck    3916.000\n",
       "1235  dick  fuck    2401.000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.suff=='fuck'].sort_values(by='count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93416f31-76c8-4024-9efa-9556f36af1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2398.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.loc['poop', 'face']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e587bf26-4787-4459-a1a7-734a66baf118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           1054\n",
       "cp              45.7\n",
       "entropy        -4.05\n",
       "count_rank        56\n",
       "cp_rank           15\n",
       "entropy_rank       9\n",
       "topmatch         ass\n",
       "topcount         319\n",
       "topcountpct     30.3\n",
       "nzeros            22\n",
       "syllables          2\n",
       "Name: faggot, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.loc['faggot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "incorporated-render",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mat.loc[:, 'puffin']"
   ]
  }
 ],
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