{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78729"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"https://www.science.org/doi/10.1126/science.1199295\"\"\"\n",
    "\n",
    "import http.cookiejar\n",
    "import io\n",
    "import pathlib\n",
    "import re\n",
    "import shutil\n",
    "import subprocess\n",
    "import urllib.parse\n",
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "DOI = '10.1126/science.1199295'\n",
    "\n",
    "PDF = pathlib.Path('1199295-atkinson-som.pdf')\n",
    "\n",
    "TXT = PDF.with_suffix('.txt')\n",
    "\n",
    "URL = 'https://www.science.org/action/downloadSupplement'\n",
    "\n",
    "URL = (urllib.parse.urlparse(URL)\n",
    "       ._replace(query=urllib.parse.urlencode({'doi': DOI,\n",
    "                                               'file': PDF})))\n",
    "\n",
    "HEADERS = {'User-Agent': ('Mozilla/5.0 (X11; U; Linux i686)'\n",
    "                          ' Gecko/20071127 Firefox/2.0.0.11')}\n",
    "\n",
    "ENCODING = 'utf-8'\n",
    "\n",
    "if not PDF.exists():\n",
    "    cookies = http.cookiejar.CookieJar()\n",
    "    processor = urllib.request.HTTPCookieProcessor(cookies)\n",
    "    opener = urllib.request.build_opener(processor)\n",
    "\n",
    "    request = urllib.request.Request(URL.geturl(), headers=HEADERS)\n",
    "    with opener.open(request) as u, PDF.open('wb') as f:\n",
    "        shutil.copyfileobj(u, f)\n",
    "\n",
    "if not TXT.exists():  # requires one of popper-utils, miktex-poppler-bin, xpdf\n",
    "    cmd = ['pdftotext', '-f', '21', '-l', '33', '-layout', '-nopgbrk', PDF, TXT]\n",
    "    subprocess.run(cmd, check=True)\n",
    "\n",
    "(_, _, S1) = TXT.read_text(encoding=ENCODING).strip().partition('\\n\\n\\n')\n",
    "\n",
    "assert ',' not in S1\n",
    "\n",
    "len(S1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 504)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S1_HEAD = ['Language Name',\n",
    "           'WALS code',\n",
    "           'Family',\n",
    "           'Latitude',\n",
    "           'Longitude',\n",
    "           'Normalized Vowel Diversity',\n",
    "           'Normalized Consonant Diversity',\n",
    "           'Normalized Tone Diversity',\n",
    "           'Total Normalized Phoneme Diversity',\n",
    "           'ISO codes',\n",
    "           'Estimated Speaker Pop. Size',\n",
    "           'Distance from best fit origin']\n",
    "\n",
    "S1_FEAT = [h for h in S1_HEAD if h.startswith('Normalized ')]\n",
    "S1_OUTC = S1_HEAD[-2:]\n",
    "\n",
    "S1_DATA = S1[S1.index('\\nAbkhaz '):].strip()\n",
    "\n",
    "S1_DATA = re.sub(r'(\\))([a-z]{3} )', r'\\1 \\2', S1_DATA)  # fix missing space\n",
    "S1_DATA = re.sub(r'\\n +([a-z]{3})\\n(.+?)  ', r'\\n\\2 \\1 ', S1_DATA)  # fix splitted lines\n",
    "S1_DATA = S1_DATA.replace('(San Mateo del Mar)', '(San Mateo Del Mar)')  # simplify iso detection\n",
    "\n",
    "S1_DATA = [l.strip() for l in S1_DATA.splitlines()]\n",
    "\n",
    "\n",
    "len(S1_HEAD), len(S1_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Language Name,WALS code,Family,Latitude,Longitude,Normalized Vowel Diversity,Normalized Consonant Diversity,Normalized Tone Diversity,Total Normalized Phoneme Diversity,ISO codes,Estimated Speaker Pop. Size,Distance from best fit origin\\nAbkhaz,abk,Northwest Caucasian,43.08,41,-1.2345266,-1.5544112,-0.7687792,-1.185905651,abk,105952,5856.362\\nAcoma,aco,Keresan,34.92,-107.58,-0.4846364,-0.7169629,1.86204304,0.220147906,kjq,3391,18601.19\\nAndoke,adk,Andoke,-0.67,-72,1.3900889,-1.5544112,0.54663194,0.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROW = re.compile(r'''\n",
    "^\n",
    "(.+?)\\ +\n",
    "([a-z]{2,3})\\ +\n",
    "(.+?)\\ +\n",
    "(-?\\d+(?:\\.\\d+)?)\\ +\n",
    "(-?\\d+(?:\\.\\d+)?)\\ +\n",
    "(-?\\d+\\.\\d+)\\ +\n",
    "(-?\\d+\\.\\d+)\\ +\n",
    "(-?\\d+\\.\\d+)\\ +\n",
    "(-?\\d+\\.\\d+)\\ +\n",
    "(.+?)\\ +\n",
    "(\\d+)\\ +\n",
    "(\\d+(?:\\.\\d+)?)\n",
    "$\n",
    "'''.strip(), flags=re.VERBOSE)\n",
    "\n",
    "csv = '\\n'.join(','.join(r) for r in [S1_HEAD] + [ROW.match(l).groups() for l in S1_DATA])\n",
    "\n",
    "csv[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 504 entries, abk to zun\n",
      "Data columns (total 11 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   Language Name                       504 non-null    object \n",
      " 1   Family                              504 non-null    object \n",
      " 2   Latitude                            504 non-null    float64\n",
      " 3   Longitude                           504 non-null    float64\n",
      " 4   Normalized Vowel Diversity          504 non-null    float64\n",
      " 5   Normalized Consonant Diversity      504 non-null    float64\n",
      " 6   Normalized Tone Diversity           504 non-null    float64\n",
      " 7   Total Normalized Phoneme Diversity  504 non-null    float64\n",
      " 8   ISO codes                           504 non-null    object \n",
      " 9   Estimated Speaker Pop. Size         504 non-null    int64  \n",
      " 10  Distance from best fit origin       504 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(3)\n",
      "memory usage: 47.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language Name</th>\n",
       "      <th>Family</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Normalized Vowel Diversity</th>\n",
       "      <th>Normalized Consonant Diversity</th>\n",
       "      <th>Normalized Tone Diversity</th>\n",
       "      <th>Total Normalized Phoneme Diversity</th>\n",
       "      <th>ISO codes</th>\n",
       "      <th>Estimated Speaker Pop. Size</th>\n",
       "      <th>Distance from best fit origin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALS code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abk</th>\n",
       "      <td>Abkhaz</td>\n",
       "      <td>Northwest Caucasian</td>\n",
       "      <td>43.08</td>\n",
       "      <td>41.00</td>\n",
       "      <td>-1.234527</td>\n",
       "      <td>-1.554411</td>\n",
       "      <td>-0.768779</td>\n",
       "      <td>-1.185906</td>\n",
       "      <td>abk</td>\n",
       "      <td>105952</td>\n",
       "      <td>5856.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aco</th>\n",
       "      <td>Acoma</td>\n",
       "      <td>Keresan</td>\n",
       "      <td>34.92</td>\n",
       "      <td>-107.58</td>\n",
       "      <td>-0.484636</td>\n",
       "      <td>-0.716963</td>\n",
       "      <td>1.862043</td>\n",
       "      <td>0.220148</td>\n",
       "      <td>kjq</td>\n",
       "      <td>3391</td>\n",
       "      <td>18601.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adk</th>\n",
       "      <td>Andoke</td>\n",
       "      <td>Andoke</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-72.00</td>\n",
       "      <td>1.390089</td>\n",
       "      <td>-1.554411</td>\n",
       "      <td>0.546632</td>\n",
       "      <td>0.127437</td>\n",
       "      <td>ano</td>\n",
       "      <td>619</td>\n",
       "      <td>23780.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aea</th>\n",
       "      <td>Aleut (Eastern)</td>\n",
       "      <td>Eskimo-Aleut</td>\n",
       "      <td>54.75</td>\n",
       "      <td>-164.00</td>\n",
       "      <td>-1.234527</td>\n",
       "      <td>0.957934</td>\n",
       "      <td>-0.768779</td>\n",
       "      <td>-0.348457</td>\n",
       "      <td>ale</td>\n",
       "      <td>490</td>\n",
       "      <td>14629.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aeg</th>\n",
       "      <td>Arabic (Egyptian)</td>\n",
       "      <td>Afro-Asiatic</td>\n",
       "      <td>30.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>-0.484636</td>\n",
       "      <td>0.120485</td>\n",
       "      <td>-0.768779</td>\n",
       "      <td>-0.377643</td>\n",
       "      <td>arz</td>\n",
       "      <td>46321000</td>\n",
       "      <td>4153.443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Language Name               Family  Latitude  Longitude  \\\n",
       "WALS code                                                                \n",
       "abk                   Abkhaz  Northwest Caucasian     43.08      41.00   \n",
       "aco                    Acoma              Keresan     34.92    -107.58   \n",
       "adk                   Andoke               Andoke     -0.67     -72.00   \n",
       "aea          Aleut (Eastern)         Eskimo-Aleut     54.75    -164.00   \n",
       "aeg        Arabic (Egyptian)         Afro-Asiatic     30.00      31.00   \n",
       "\n",
       "           Normalized Vowel Diversity  Normalized Consonant Diversity  \\\n",
       "WALS code                                                               \n",
       "abk                         -1.234527                       -1.554411   \n",
       "aco                         -0.484636                       -0.716963   \n",
       "adk                          1.390089                       -1.554411   \n",
       "aea                         -1.234527                        0.957934   \n",
       "aeg                         -0.484636                        0.120485   \n",
       "\n",
       "           Normalized Tone Diversity  Total Normalized Phoneme Diversity  \\\n",
       "WALS code                                                                  \n",
       "abk                        -0.768779                           -1.185906   \n",
       "aco                         1.862043                            0.220148   \n",
       "adk                         0.546632                            0.127437   \n",
       "aea                        -0.768779                           -0.348457   \n",
       "aeg                        -0.768779                           -0.377643   \n",
       "\n",
       "          ISO codes  Estimated Speaker Pop. Size  \\\n",
       "WALS code                                          \n",
       "abk             abk                       105952   \n",
       "aco             kjq                         3391   \n",
       "adk             ano                          619   \n",
       "aea             ale                          490   \n",
       "aeg             arz                     46321000   \n",
       "\n",
       "           Distance from best fit origin  \n",
       "WALS code                                 \n",
       "abk                             5856.362  \n",
       "aco                            18601.190  \n",
       "adk                            23780.160  \n",
       "aea                            14629.420  \n",
       "aeg                             4153.443  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import itertools\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "\n",
    "def pearsonr(df, /, left, right, *, func=scipy.stats.pearsonr) -> pd.Series:\n",
    "    df = df[[left, right]].dropna()\n",
    "    name = '%s & %s' % (left, right)\n",
    "    return pd.Series(func(df[left], df[right]), index=('r', 'p'), name=name)\n",
    "\n",
    "with io.StringIO(csv) as f:\n",
    "    s1 = pd.read_csv(f, na_values='', keep_default_na=False, index_col='WALS code')\n",
    "\n",
    "s1.info()\n",
    "assert s1.index.is_unique\n",
    "assert s1.index.is_monotonic_increasing\n",
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Estimated Speaker Pop. Size &amp; Normalized Vowel Diversity</th>\n",
       "      <td>0.03153</td>\n",
       "      <td>0.48006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimated Speaker Pop. Size &amp; Normalized Consonant Diversity</th>\n",
       "      <td>0.08969</td>\n",
       "      <td>0.04415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimated Speaker Pop. Size &amp; Normalized Tone Diversity</th>\n",
       "      <td>0.03651</td>\n",
       "      <td>0.41345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimated Speaker Pop. Size &amp; Total Normalized Phoneme Diversity</th>\n",
       "      <td>0.08219</td>\n",
       "      <td>0.06523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance from best fit origin &amp; Normalized Vowel Diversity</th>\n",
       "      <td>-0.39421</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance from best fit origin &amp; Normalized Consonant Diversity</th>\n",
       "      <td>-0.26014</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance from best fit origin &amp; Normalized Tone Diversity</th>\n",
       "      <td>-0.39063</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance from best fit origin &amp; Total Normalized Phoneme Diversity</th>\n",
       "      <td>-0.54466</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          r        p\n",
       "Estimated Speaker Pop. Size & Normalized Vowel ...  0.03153  0.48006\n",
       "Estimated Speaker Pop. Size & Normalized Conson...  0.08969  0.04415\n",
       "Estimated Speaker Pop. Size & Normalized Tone D...  0.03651  0.41345\n",
       "Estimated Speaker Pop. Size & Total Normalized ...  0.08219  0.06523\n",
       "Distance from best fit origin & Normalized Vowe... -0.39421  0.00000\n",
       "Distance from best fit origin & Normalized Cons... -0.26014  0.00000\n",
       "Distance from best fit origin & Normalized Tone... -0.39063  0.00000\n",
       "Distance from best fit origin & Total Normalize... -0.54466  0.00000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PAIRS = list(itertools.product(S1_OUTC, S1_FEAT + ['Total Normalized Phoneme Diversity']))\n",
    "\n",
    "pd.DataFrame([pearsonr(s1, x, y) for x, y in PAIRS]).round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Population &amp; Total Normalized Phoneme Diversity</th>\n",
       "      <td>0.38454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       r    p\n",
       "Population & Total Normalized Phoneme Diversity  0.38454  0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1.plot.scatter(x='Estimated Speaker Pop. Size', y='Total Normalized Phoneme Diversity', logx=True);\n",
    "\n",
    "(pearsonr(s1.assign(Population=s1['Estimated Speaker Pop. Size'].apply(np.log10)),\n",
    "         'Population', 'Total Normalized Phoneme Diversity')\n",
    " .to_frame().T.round(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance &amp; Total Normalized Phoneme Diversity</th>\n",
       "      <td>-0.54466</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     r    p\n",
       "Distance & Total Normalized Phoneme Diversity -0.54466  0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1.plot.scatter(x='Distance from best fit origin', y='Total Normalized Phoneme Diversity');\n",
    "\n",
    "(pearsonr(s1.assign(Distance=s1['Distance from best fit origin']),\n",
    "         'Distance', 'Total Normalized Phoneme Diversity')\n",
    " .to_frame().T.round(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2679 entries, aab to zzo\n",
      "Data columns (total 7 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Name                          2679 non-null   object \n",
      " 1   latitude                      2679 non-null   float64\n",
      " 2   longitude                     2679 non-null   float64\n",
      " 3   family                        2679 non-null   object \n",
      " 4   1A Consonant Inventories      563 non-null    object \n",
      " 5   2A Vowel Quality Inventories  564 non-null    object \n",
      " 6   13A Tone                      527 non-null    object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 167.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>family</th>\n",
       "      <th>1A Consonant Inventories</th>\n",
       "      <th>2A Vowel Quality Inventories</th>\n",
       "      <th>13A Tone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wals_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aab</th>\n",
       "      <td>Arapesh (Abu)</td>\n",
       "      <td>-3.450000</td>\n",
       "      <td>142.950000</td>\n",
       "      <td>Torricelli</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aar</th>\n",
       "      <td>Aari</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>36.583333</td>\n",
       "      <td>Afro-Asiatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aba</th>\n",
       "      <td>Abau</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>141.250000</td>\n",
       "      <td>Sepik</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abb</th>\n",
       "      <td>Arabic (Chadian)</td>\n",
       "      <td>13.833333</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>Afro-Asiatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abd</th>\n",
       "      <td>Abidji</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>-4.583333</td>\n",
       "      <td>Niger-Congo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name   latitude   longitude        family  \\\n",
       "wals_code                                                          \n",
       "aab           Arapesh (Abu)  -3.450000  142.950000    Torricelli   \n",
       "aar                    Aari   6.000000   36.583333  Afro-Asiatic   \n",
       "aba                    Abau  -4.000000  141.250000         Sepik   \n",
       "abb        Arabic (Chadian)  13.833333   20.833333  Afro-Asiatic   \n",
       "abd                  Abidji   5.666667   -4.583333   Niger-Congo   \n",
       "\n",
       "          1A Consonant Inventories 2A Vowel Quality Inventories 13A Tone  \n",
       "wals_code                                                                 \n",
       "aab                            NaN                          NaN      NaN  \n",
       "aar                            NaN                          NaN      NaN  \n",
       "aba                            NaN                          NaN      NaN  \n",
       "abb                            NaN                          NaN      NaN  \n",
       "abd                            NaN                          NaN      NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "URL = 'http://cdstar.shh.mpg.de/bitstreams/EAEA0-7269-77E5-3E10-0/wals_language.csv.zip'\n",
    "\n",
    "ARCHIVE = pathlib.Path(URL.rpartition('/')[2])\n",
    "\n",
    "EXTRACT = 'language.csv'\n",
    "\n",
    "INFO = ['wals_code', 'Name', 'family', 'latitude', 'longitude']\n",
    "\n",
    "FEAT = ['2A Vowel Quality Inventories', '1A Consonant Inventories', '13A Tone']\n",
    "\n",
    "if not ARCHIVE.exists():\n",
    "    urllib.request.urlretrieve(URL, ARCHIVE)\n",
    "\n",
    "with zipfile.ZipFile(ARCHIVE) as archive:\n",
    "    wf = pd.read_csv(archive.open(EXTRACT), encoding=ENCODING,\n",
    "                     na_values='', keep_default_na=False,\n",
    "                     index_col=INFO[0], usecols=INFO + FEAT)\n",
    "\n",
    "wf.info()\n",
    "assert wf.index.is_unique\n",
    "wf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 567 entries, abi to zun\n",
      "Data columns (total 7 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Name                          567 non-null    object \n",
      " 1   latitude                      567 non-null    float64\n",
      " 2   longitude                     567 non-null    float64\n",
      " 3   family                        567 non-null    object \n",
      " 4   1A Consonant Inventories      563 non-null    object \n",
      " 5   2A Vowel Quality Inventories  564 non-null    object \n",
      " 6   13A Tone                      527 non-null    object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 35.4+ KB\n"
     ]
    }
   ],
   "source": [
    "wf = wf.dropna(how='all', subset=FEAT)\n",
    "\n",
    "wf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 504 entries, abk to zun\n",
      "Data columns (total 18 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   Language Name                       504 non-null    object \n",
      " 1   Family                              504 non-null    object \n",
      " 2   Latitude                            504 non-null    float64\n",
      " 3   Longitude                           504 non-null    float64\n",
      " 4   Normalized Vowel Diversity          504 non-null    float64\n",
      " 5   Normalized Consonant Diversity      504 non-null    float64\n",
      " 6   Normalized Tone Diversity           504 non-null    float64\n",
      " 7   Total Normalized Phoneme Diversity  504 non-null    float64\n",
      " 8   ISO codes                           504 non-null    object \n",
      " 9   Estimated Speaker Pop. Size         504 non-null    int64  \n",
      " 10  Distance from best fit origin       504 non-null    float64\n",
      " 11  Name                                504 non-null    object \n",
      " 12  latitude                            504 non-null    float64\n",
      " 13  longitude                           504 non-null    float64\n",
      " 14  family                              504 non-null    object \n",
      " 15  1A Consonant Inventories            504 non-null    object \n",
      " 16  2A Vowel Quality Inventories        504 non-null    object \n",
      " 17  13A Tone                            504 non-null    object \n",
      "dtypes: float64(9), int64(1), object(8)\n",
      "memory usage: 91.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = s1.join(wf)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>family</th>\n",
       "      <th>1A Consonant Inventories</th>\n",
       "      <th>2A Vowel Quality Inventories</th>\n",
       "      <th>13A Tone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abi</th>\n",
       "      <td>Abipón</td>\n",
       "      <td>-29.000000</td>\n",
       "      <td>-61.000000</td>\n",
       "      <td>Guaicuruan</td>\n",
       "      <td>2 Moderately small</td>\n",
       "      <td>2 Average (5-6)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abm</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>32.333333</td>\n",
       "      <td>-87.416667</td>\n",
       "      <td>Muskogean</td>\n",
       "      <td>1 Small</td>\n",
       "      <td>1 Small (2-4)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ach</th>\n",
       "      <td>Aché</td>\n",
       "      <td>-25.250000</td>\n",
       "      <td>-55.166667</td>\n",
       "      <td>Tupian</td>\n",
       "      <td>1 Small</td>\n",
       "      <td>2 Average (5-6)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acm</th>\n",
       "      <td>Achumawi</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>-121.000000</td>\n",
       "      <td>Hokan</td>\n",
       "      <td>2 Moderately small</td>\n",
       "      <td>2 Average (5-6)</td>\n",
       "      <td>2 Simple tone system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adz</th>\n",
       "      <td>Adzera</td>\n",
       "      <td>-6.250000</td>\n",
       "      <td>146.250000</td>\n",
       "      <td>Austronesian</td>\n",
       "      <td>2 Moderately small</td>\n",
       "      <td>1 Small (2-4)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name   latitude   longitude        family 1A Consonant Inventories  \\\n",
       "abi    Abipón -29.000000  -61.000000    Guaicuruan       2 Moderately small   \n",
       "abm   Alabama  32.333333  -87.416667     Muskogean                  1 Small   \n",
       "ach      Aché -25.250000  -55.166667        Tupian                  1 Small   \n",
       "acm  Achumawi  41.500000 -121.000000         Hokan       2 Moderately small   \n",
       "adz    Adzera  -6.250000  146.250000  Austronesian       2 Moderately small   \n",
       "\n",
       "    2A Vowel Quality Inventories              13A Tone  \n",
       "abi              2 Average (5-6)                   NaN  \n",
       "abm                1 Small (2-4)                   NaN  \n",
       "ach              2 Average (5-6)                   NaN  \n",
       "acm              2 Average (5-6)  2 Simple tone system  \n",
       "adz                1 Small (2-4)                   NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.loc[wf.index.difference(s1.index)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>mismatches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2A Vowel Quality Inventories</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1A Consonant Inventories</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13A Tone</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mismatches\n",
       "2A Vowel Quality Inventories           1\n",
       "1A Consonant Inventories              86\n",
       "13A Tone                               0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COLS = list(itertools.chain.from_iterable(zip(S1_FEAT, FEAT)))\n",
    "\n",
    "MAP = dict(zip(S1_FEAT, FEAT))\n",
    "\n",
    "df[COLS] = (df[COLS]\n",
    "            .apply(lambda x: x.astype(pd.api.types.CategoricalDtype(ordered=True)).cat.codes))\n",
    "\n",
    "mism = df[S1_FEAT].rename(columns=MAP) != df[FEAT]\n",
    "\n",
    "mism.sum().to_frame('mismatches')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Normalized Vowel Diversity</th>\n",
       "      <th>2A Vowel Quality Inventories</th>\n",
       "      <th>Normalized Consonant Diversity</th>\n",
       "      <th>1A Consonant Inventories</th>\n",
       "      <th>Normalized Tone Diversity</th>\n",
       "      <th>13A Tone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALS code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abk</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aco</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aea</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aeg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agh</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Normalized Vowel Diversity  2A Vowel Quality Inventories  \\\n",
       "WALS code                                                             \n",
       "abk                                 0                             0   \n",
       "aco                                 1                             1   \n",
       "aea                                 0                             0   \n",
       "aeg                                 1                             1   \n",
       "agh                                 2                             2   \n",
       "\n",
       "           Normalized Consonant Diversity  1A Consonant Inventories  \\\n",
       "WALS code                                                             \n",
       "abk                                     0                         4   \n",
       "aco                                     1                         4   \n",
       "aea                                     3                         2   \n",
       "aeg                                     2                         3   \n",
       "agh                                     3                         2   \n",
       "\n",
       "           Normalized Tone Diversity  13A Tone  \n",
       "WALS code                                       \n",
       "abk                                0         0  \n",
       "aco                                2         2  \n",
       "aea                                0         0  \n",
       "aeg                                0         0  \n",
       "agh                                1         1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mism.any(axis=1), COLS].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4      1\n",
       "-3      7\n",
       "-2     10\n",
       "-1     21\n",
       " 0    418\n",
       " 1     33\n",
       " 2      9\n",
       " 3      4\n",
       " 4      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = df['Normalized Consonant Diversity'] - df['1A Consonant Inventories']\n",
    "\n",
    "diff.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nf = df[diff != 0].assign(diff=diff)\n",
    "\n",
    "nf['diff'].sort_values().plot.bar(figsize=(15, 3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diff &amp; Estimated Speaker Pop. Size</th>\n",
       "      <td>0.076365</td>\n",
       "      <td>0.484652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diff &amp; Distance from best fit origin</th>\n",
       "      <td>0.417043</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             r         p\n",
       "diff & Estimated Speaker Pop. Size    0.076365  0.484652\n",
       "diff & Distance from best fit origin  0.417043  0.000065"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(nf, 'diff', x) for x in\n",
    "              ['Estimated Speaker Pop. Size', 'Distance from best fit origin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nf.plot.scatter(x='Distance from best fit origin', y='diff');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Diversity &amp; Estimated Speaker Pop. Size</th>\n",
       "      <td>0.06618</td>\n",
       "      <td>0.1379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diversity &amp; Distance from best fit origin</th>\n",
       "      <td>-0.58884</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 r       p\n",
       "Diversity & Estimated Speaker Pop. Size    0.06618  0.1379\n",
       "Diversity & Distance from best fit origin -0.58884  0.0000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf = df[FEAT + S1_OUTC + ['Total Normalized Phoneme Diversity']].copy()\n",
    "\n",
    "nf[FEAT] = nf[FEAT].apply(scipy.stats.zscore)\n",
    "\n",
    "nf['Diversity'] = nf[FEAT].mean(axis=1)\n",
    "\n",
    "pd.DataFrame([pearsonr(nf, 'Diversity', x) for x in\n",
    "              ['Estimated Speaker Pop. Size', 'Distance from best fit origin']]).round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Population &amp; Diversity</th>\n",
       "      <td>0.41379</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              r    p\n",
       "Population & Diversity  0.41379  0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pearsonr(nf.assign(Population=s1['Estimated Speaker Pop. Size'].apply(np.log10)),\n",
    "          'Population', 'Diversity')\n",
    " .to_frame().T.round(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance &amp; Diversity</th>\n",
       "      <td>-0.58884</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            r    p\n",
       "Distance & Diversity -0.58884  0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pearsonr(nf.assign(Distance=s1['Distance from best fit origin']),\n",
    "          'Distance', 'Diversity')\n",
    " .to_frame().T.round(5))"
   ]
  }
 ],
 "metadata": {
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