CrossLingual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross lingual similarity \n",
    "The measure is defined to quatnify the typological similarity across languages. It's based on typological (language-specific) features annotated in [WALS](https://wals.info/). \n",
    "\n",
    "The code was initally used for the work: [\"Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes'\"](https://aclanthology.org/2021.emnlp-main.376.pdf)\n",
    "\n",
    "Author: Tomasz Limisiewicz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib\n",
    "from matplotlib import rc\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "sns.set(style='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download WALS Data\n",
    "\n",
    "Used the data from https://github.com/cldf-datasets/cldf-datasets @commit 12a1b516fdf2010c9856ac77ce873b44475ac52e\n",
    "\n",
    "Access: 21.03.2022\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-03-21 16:15:58--  https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/languages.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 391679 (382K) [text/plain]\n",
      "Saving to: ‘data/WALS/languages.csv’\n",
      "\n",
      "data/WALS/languages 100%[===================>] 382.50K  1.84MB/s    in 0.2s    \n",
      "\n",
      "2022-03-21 16:15:59 (1.84 MB/s) - ‘data/WALS/languages.csv’ saved [391679/391679]\n",
      "\n",
      "--2022-03-21 16:15:59--  https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/values.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4641862 (4.4M) [text/plain]\n",
      "Saving to: ‘data/WALS/values.csv’\n",
      "\n",
      "data/WALS/values.cs 100%[===================>]   4.43M  10.5MB/s    in 0.4s    \n",
      "\n",
      "2022-03-21 16:16:00 (10.5 MB/s) - ‘data/WALS/values.csv’ saved [4641862/4641862]\n",
      "\n",
      "--2022-03-21 16:16:00--  https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/parameters.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7981 (7.8K) [text/plain]\n",
      "Saving to: ‘data/WALS/parameters.csv’\n",
      "\n",
      "data/WALS/parameter 100%[===================>]   7.79K  --.-KB/s    in 0.002s  \n",
      "\n",
      "2022-03-21 16:16:00 (4.69 MB/s) - ‘data/WALS/parameters.csv’ saved [7981/7981]\n",
      "\n",
      "--2022-03-21 16:16:01--  https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/chapters.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 41682 (41K) [text/plain]\n",
      "Saving to: ‘data/WALS/chapters.csv’\n",
      "\n",
      "data/WALS/chapters. 100%[===================>]  40.71K  --.-KB/s    in 0.06s   \n",
      "\n",
      "2022-03-21 16:16:01 (633 KB/s) - ‘data/WALS/chapters.csv’ saved [41682/41682]\n",
      "\n",
      "--2022-03-21 16:16:01--  https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/areas.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 443 [text/plain]\n",
      "Saving to: ‘data/WALS/areas.csv’\n",
      "\n",
      "data/WALS/areas.csv 100%[===================>]     443  --.-KB/s    in 0s      \n",
      "\n",
      "2022-03-21 16:16:02 (15.2 MB/s) - ‘data/WALS/areas.csv’ saved [443/443]\n",
      "\n",
      "--2022-03-21 16:16:02--  http://based/\n",
      "Resolving based (based)... failed: Name or service not known.\n",
      "wget: unable to resolve host address ‘based’\n",
      "FINISHED --2022-03-21 16:16:02--\n",
      "Total wall clock time: 0.3s\n",
      "Downloaded: 1 files, 443 in 0s (15.2 MB/s)\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/WALS/languages.csv https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/languages.csv\n",
    "!wget -O data/WALS/values.csv https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/values.csv\n",
    "\n",
    "!wget -O data/WALS/parameters.csv https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/parameters.csv\n",
    "!wget -O data/WALS/chapters.csv https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/chapters.csv\n",
    "!wget -O data/WALS/areas.csv https://raw.githubusercontent.com/cldf-datasets/wals/master/cldf/areas.csv    based"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtaining feature -> area mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature 2 area mapping\n",
    "parameters_df = pd.read_csv('data/WALS/parameters.csv', index_col='ID')\n",
    "chapters_df = pd.read_csv('data/WALS/chapters.csv', index_col='Number')\n",
    "areas_df = pd.read_csv('data/WALS/areas.csv', index_col='ID')\n",
    "\n",
    "parameters_areas_df = parameters_df.join(chapters_df, how='left', on=\"Chapter_ID\", rsuffix=\"_chapter\")\\\n",
    "             .join(areas_df, how='left', on=\"Area_ID\", rsuffix=\"_area\")[[\"Name\", \"Name_area\"]]\n",
    "\n",
    "parameters_areas_df.columns = [\"Name\", \"Area\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtaining cross table with languages and featues:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abv</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abw</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abz</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ace</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aci</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acm</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aco</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adi</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adk</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yzv</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zag</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zai</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zaj</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zan</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zap</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zaq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zar</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zay</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zaz</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zch</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zen</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zfl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zhn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zim</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zno</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zpi</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zpr</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zqc</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zqo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zqr</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zqs</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zso</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zsq</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zte</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zul</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zun</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zya</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2662 rows × 192 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Parameter_ID  100A  101A  102A  103A  104A  105A  106A  107A  108A  108B ...   \\\n",
       "Language_ID                                                              ...    \n",
       "aab            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "aar            NaN   2.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "aba            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abb            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abd            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abe            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abh            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abi            2.0   NaN   5.0   3.0   2.0   NaN   NaN   2.0   NaN   NaN ...    \n",
       "abk            3.0   2.0   5.0   2.0   3.0   1.0   2.0   2.0   3.0   4.0 ...    \n",
       "abm            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abn            1.0   NaN   1.0   1.0   1.0   NaN   NaN   2.0   NaN   NaN ...    \n",
       "abo            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abu            1.0   1.0   1.0   1.0   1.0   1.0   NaN   2.0   NaN   NaN ...    \n",
       "abv            NaN   4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abw            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "abz            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "ace            4.0   NaN   5.0   5.0   2.0   4.0   NaN   2.0   NaN   NaN ...    \n",
       "acg            NaN   2.0   NaN   NaN   NaN   NaN   2.0   NaN   NaN   NaN ...    \n",
       "ach            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "aci            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "acl            NaN   2.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "acm            2.0   NaN   5.0   2.0   5.0   NaN   NaN   2.0   NaN   NaN ...    \n",
       "acn            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "aco            4.0   2.0   5.0   2.0   5.0   NaN   NaN   2.0   1.0   NaN ...    \n",
       "acu            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "ada            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "adg            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "adi            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "adk            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "adn            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "...            ...   ...   ...   ...   ...   ...   ...   ...   ...   ... ...    \n",
       "yzv            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zag            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zai            NaN   2.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zaj            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zam            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zan            2.0   NaN   5.0   2.0   2.0   NaN   NaN   1.0   NaN   NaN ...    \n",
       "zap            NaN   2.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zaq            NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN ...    \n",
       "zar            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zay            NaN   2.0   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN ...    \n",
       "zaz            NaN   NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN ...    \n",
       "zch            NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN ...    \n",
       "zen            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zfl            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zhn            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zim            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zno            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zpi            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zpr            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zqc            2.0   2.0   5.0   2.0   5.0   NaN   NaN   2.0   1.0   2.0 ...    \n",
       "zqo            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zqr            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zqs            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zso            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zsq            2.0   NaN   5.0   2.0   2.0   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zte            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zul            2.0   2.0   5.0   2.0   2.0   2.0   2.0   1.0   3.0   4.0 ...    \n",
       "zun            1.0   NaN   1.0   1.0   1.0   NaN   NaN   1.0   NaN   NaN ...    \n",
       "zya            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "zzo            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN ...    \n",
       "\n",
       "Parameter_ID  91A  92A  93A  94A  95A  96A  97A  98A  99A   9A  \n",
       "Language_ID                                                     \n",
       "aab           NaN  2.0  2.0  NaN  NaN  NaN  4.0  NaN  NaN  NaN  \n",
       "aar           NaN  6.0  NaN  NaN  1.0  2.0  2.0  NaN  NaN  NaN  \n",
       "aba           NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  \n",
       "abb           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "abd           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "abe           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "abh           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "abi           1.0  NaN  1.0  1.0  4.0  4.0  5.0  1.0  1.0  3.0  \n",
       "abk           1.0  6.0  2.0  4.0  1.0  1.0  2.0  1.0  1.0  3.0  \n",
       "abm           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  3.0  \n",
       "abn           NaN  NaN  NaN  NaN  NaN  NaN  2.0  NaN  NaN  NaN  \n",
       "abo           NaN  NaN  NaN  NaN  NaN  2.0  2.0  NaN  NaN  NaN  \n",
       "abu           2.0  2.0  2.0  5.0  4.0  4.0  4.0  NaN  NaN  NaN  \n",
       "abv           NaN  NaN  2.0  NaN  NaN  2.0  2.0  NaN  NaN  NaN  \n",
       "abw           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "abz           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  3.0  \n",
       "ace           3.0  5.0  3.0  1.0  5.0  5.0  5.0  NaN  NaN  NaN  \n",
       "acg           NaN  NaN  1.0  NaN  3.0  4.0  4.0  NaN  NaN  NaN  \n",
       "ach           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "aci           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "acl           NaN  NaN  2.0  1.0  4.0  4.0  4.0  NaN  NaN  NaN  \n",
       "acm           NaN  NaN  NaN  NaN  4.0  NaN  NaN  NaN  NaN  NaN  \n",
       "acn           NaN  2.0  2.0  NaN  1.0  1.0  5.0  NaN  NaN  NaN  \n",
       "aco           NaN  NaN  NaN  1.0  5.0  5.0  5.0  1.0  NaN  3.0  \n",
       "acu           NaN  6.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "ada           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "adg           3.0  NaN  2.0  NaN  1.0  NaN  2.0  NaN  NaN  NaN  \n",
       "adi           2.0  2.0  2.0  5.0  3.0  4.0  4.0  NaN  NaN  NaN  \n",
       "adk           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  3.0  \n",
       "adn           NaN  NaN  NaN  1.0  5.0  5.0  5.0  NaN  NaN  NaN  \n",
       "...           ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "yzv           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zag           NaN  6.0  NaN  2.0  1.0  2.0  2.0  NaN  NaN  NaN  \n",
       "zai           1.0  2.0  1.0  1.0  4.0  4.0  4.0  NaN  NaN  NaN  \n",
       "zaj           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zam           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zan           NaN  NaN  2.0  NaN  4.0  NaN  3.0  NaN  NaN  NaN  \n",
       "zap           NaN  1.0  1.0  5.0  4.0  NaN  4.0  NaN  NaN  NaN  \n",
       "zaq           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zar           NaN  NaN  NaN  NaN  5.0  5.0  5.0  NaN  NaN  NaN  \n",
       "zay           NaN  6.0  1.0  NaN  NaN  1.0  1.0  NaN  NaN  NaN  \n",
       "zaz           NaN  6.0  2.0  NaN  1.0  NaN  2.0  NaN  NaN  NaN  \n",
       "zch           1.0  6.0  1.0  NaN  3.0  4.0  3.0  NaN  NaN  NaN  \n",
       "zen           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zfl           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zhn           NaN  2.0  2.0  5.0  4.0  4.0  NaN  NaN  NaN  NaN  \n",
       "zim           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zno           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  \n",
       "zpi           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zpr           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zqc           1.0  6.0  NaN  5.0  NaN  NaN  3.0  4.0  4.0  2.0  \n",
       "zqo           NaN  NaN  1.0  1.0  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zqr           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zqs           NaN  NaN  NaN  NaN  4.0  NaN  NaN  NaN  NaN  NaN  \n",
       "zso           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  3.0  \n",
       "zsq           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zte           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "zul           NaN  2.0  2.0  1.0  4.0  4.0  4.0  1.0  1.0  NaN  \n",
       "zun           NaN  NaN  1.0  NaN  1.0  NaN  2.0  NaN  NaN  3.0  \n",
       "zya           NaN  1.0  1.0  5.0  4.0  NaN  4.0  NaN  NaN  NaN  \n",
       "zzo           NaN  6.0  2.0  1.0  NaN  4.0  4.0  NaN  NaN  NaN  \n",
       "\n",
       "[2662 rows x 192 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# languages values pivot\n",
    "languages_df =  pd.read_csv('data/WALS/languages.csv', index_col='ID')\n",
    "values_df = pd.read_csv('data/WALS/values.csv', index_col='ID')\n",
    "\n",
    "languages_df.index = languages_df.index.fillna('nan')\n",
    "values_df.Language_ID = values_df.Language_ID.fillna('nan')\n",
    "\n",
    "\n",
    "values_languages_df = values_df.join(languages_df, how='right', on='Language_ID', rsuffix='_language')[['Language_ID','Parameter_ID', 'Value']]\n",
    "\n",
    "\n",
    "languages_x_features_df = values_languages_df.pivot(index='Language_ID', columns='Parameter_ID', values='Value').drop(labels=np.nan, axis=1)\n",
    "\n",
    "languages_x_features_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watch out: \n",
    "1. at the time, there is now features annontated for: Fiote `fio` and Jiarong `jia` \n",
    "2. Nandi `nan` is treated as `NaN` value, fixed above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process WALS Data\n",
    "\n",
    "Selecting the languages used in the analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "languages = ['eng', 'spa', 'slo', 'ind', 'mnd', 'fin', 'arg', 'fre', 'bsq']\n",
    "\n",
    "wals2iso = {'eng': 'EN', 'spa': 'ES', 'slo': 'SL', 'ind': 'ID', 'mnd': 'ZH', 'fin': 'FI', 'arg': 'AR', 'fre': 'FR', 'bsq': 'EU'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the syntactinc and lexical features (specified by the areas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dep_areas = {\"Nominal Syntax\", \"Word Order\", \"Simple Clauses\", \"Complex Sentences\"}\n",
    "lex_areas = {\"Nominal Categories\", \"Verbal Categories\", \"Lexicon\"}\n",
    "\n",
    "\n",
    "# *_feaures stores list of selected features \n",
    "dep_features = parameters_areas_df[parameters_areas_df.Area.isin(dep_areas)].index\n",
    "lex_features = parameters_areas_df[parameters_areas_df.Area.isin(lex_areas)].index\n",
    "\n",
    "all_features = parameters_areas_df.index\n",
    "\n",
    "languages_dep_features = languages_x_features_df[dep_features].loc[languages]\n",
    "languages_lex_features = languages_x_features_df[lex_features].loc[languages]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few syntactic features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58A</th>\n",
       "      <td>Obligatory Possessive Inflection</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58B</th>\n",
       "      <td>Number of Possessive Nouns</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59A</th>\n",
       "      <td>Possessive Classification</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60A</th>\n",
       "      <td>Genitives, Adjectives and Relative Clauses</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61A</th>\n",
       "      <td>Adjectives without Nouns</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62A</th>\n",
       "      <td>Action Nominal Constructions</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63A</th>\n",
       "      <td>Noun Phrase Conjunction</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64A</th>\n",
       "      <td>Nominal and Verbal Conjunction</td>\n",
       "      <td>Nominal Syntax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81A</th>\n",
       "      <td>Order of Subject, Object and Verb</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81B</th>\n",
       "      <td>Languages with two Dominant Orders of Subject,...</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82A</th>\n",
       "      <td>Order of Subject and Verb</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83A</th>\n",
       "      <td>Order of Object and Verb</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84A</th>\n",
       "      <td>Order of Object, Oblique, and Verb</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85A</th>\n",
       "      <td>Order of Adposition and Noun Phrase</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86A</th>\n",
       "      <td>Order of Genitive and Noun</td>\n",
       "      <td>Word Order</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name            Area\n",
       "ID                                                                    \n",
       "58A                   Obligatory Possessive Inflection  Nominal Syntax\n",
       "58B                         Number of Possessive Nouns  Nominal Syntax\n",
       "59A                          Possessive Classification  Nominal Syntax\n",
       "60A         Genitives, Adjectives and Relative Clauses  Nominal Syntax\n",
       "61A                           Adjectives without Nouns  Nominal Syntax\n",
       "62A                       Action Nominal Constructions  Nominal Syntax\n",
       "63A                            Noun Phrase Conjunction  Nominal Syntax\n",
       "64A                     Nominal and Verbal Conjunction  Nominal Syntax\n",
       "81A                  Order of Subject, Object and Verb      Word Order\n",
       "81B  Languages with two Dominant Orders of Subject,...      Word Order\n",
       "82A                          Order of Subject and Verb      Word Order\n",
       "83A                           Order of Object and Verb      Word Order\n",
       "84A                 Order of Object, Oblique, and Verb      Word Order\n",
       "85A                Order of Adposition and Noun Phrase      Word Order\n",
       "86A                         Order of Genitive and Noun      Word Order"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters_areas_df.loc[dep_features].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few lexical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30A</th>\n",
       "      <td>Number of Genders</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31A</th>\n",
       "      <td>Sex-based and Non-sex-based Gender Systems</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32A</th>\n",
       "      <td>Systems of Gender Assignment</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33A</th>\n",
       "      <td>Coding of Nominal Plurality</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34A</th>\n",
       "      <td>Occurrence of Nominal Plurality</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35A</th>\n",
       "      <td>Plurality in Independent Personal Pronouns</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36A</th>\n",
       "      <td>The Associative Plural</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37A</th>\n",
       "      <td>Definite Articles</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38A</th>\n",
       "      <td>Indefinite Articles</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39A</th>\n",
       "      <td>Inclusive/Exclusive Distinction in Independent...</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39B</th>\n",
       "      <td>Inclusive/Exclusive Forms in Pama-Nyungan</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40A</th>\n",
       "      <td>Inclusive/Exclusive Distinction in Verbal Infl...</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41A</th>\n",
       "      <td>Distance Contrasts in Demonstratives</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42A</th>\n",
       "      <td>Pronominal and Adnominal Demonstratives</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43A</th>\n",
       "      <td>Third Person Pronouns and Demonstratives</td>\n",
       "      <td>Nominal Categories</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name                Area\n",
       "ID                                                                        \n",
       "30A                                  Number of Genders  Nominal Categories\n",
       "31A         Sex-based and Non-sex-based Gender Systems  Nominal Categories\n",
       "32A                       Systems of Gender Assignment  Nominal Categories\n",
       "33A                        Coding of Nominal Plurality  Nominal Categories\n",
       "34A                    Occurrence of Nominal Plurality  Nominal Categories\n",
       "35A         Plurality in Independent Personal Pronouns  Nominal Categories\n",
       "36A                             The Associative Plural  Nominal Categories\n",
       "37A                                  Definite Articles  Nominal Categories\n",
       "38A                                Indefinite Articles  Nominal Categories\n",
       "39A  Inclusive/Exclusive Distinction in Independent...  Nominal Categories\n",
       "39B          Inclusive/Exclusive Forms in Pama-Nyungan  Nominal Categories\n",
       "40A  Inclusive/Exclusive Distinction in Verbal Infl...  Nominal Categories\n",
       "41A               Distance Contrasts in Demonstratives  Nominal Categories\n",
       "42A            Pronominal and Adnominal Demonstratives  Nominal Categories\n",
       "43A           Third Person Pronouns and Demonstratives  Nominal Categories"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters_areas_df.loc[lex_features].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute distances and coverage\n",
    "\n",
    "Hamming similarity (a.k.a. string similarity) between rows in langugage x feature cross-table.\n",
    "\n",
    "A features is comparable for pair of languages <=> it is non-NaN for both of them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hamming_similarity(rowX, rowY):\n",
    "    comparable_feats = 0\n",
    "    same_feats = 0\n",
    "    for fX, fY in zip(rowX, rowY):\n",
    "        if not pd.isnull(fX) and not pd.isnull(fY):\n",
    "            comparable_feats += 1\n",
    "            if fX == fY:\n",
    "                same_feats += 1\n",
    "                \n",
    "    if not comparable_feats:\n",
    "        return 0.\n",
    "    else:\n",
    "        return same_feats / comparable_feats\n",
    "    \n",
    "def comparable_features(rowX, rowY):\n",
    "    comparable_feats = 0\n",
    "    for fX, fY in zip(rowX, rowY):\n",
    "        if not pd.isnull(fX) and not pd.isnull(fY):\n",
    "            comparable_feats += 1\n",
    "    \n",
    "    return comparable_feats\n",
    "                \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coverage\n",
    "\n",
    "Number of comparable (non-NaN for both) features for pairs of languges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparable_dep_vector = pdist(languages_dep_features, metric=comparable_features)\n",
    "\n",
    "comparable_dep_table = pd.DataFrame(\n",
    "    squareform(comparable_dep_vector),\n",
    "    columns = languages_dep_features.index,\n",
    "    index = languages_dep_features.index\n",
    ")\n",
    "\n",
    "comparable_lex_vector = pdist(languages_lex_features, metric=comparable_features)\n",
    "\n",
    "comparable_lex_table = pd.DataFrame(\n",
    "    squareform(comparable_lex_vector),\n",
    "    columns = languages_lex_features.index,\n",
    "    index = languages_lex_features.index\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Language_ID</th>\n",
       "      <th>eng</th>\n",
       "      <th>spa</th>\n",
       "      <th>slo</th>\n",
       "      <th>ind</th>\n",
       "      <th>mnd</th>\n",
       "      <th>fin</th>\n",
       "      <th>arg</th>\n",
       "      <th>fre</th>\n",
       "      <th>bsq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language_ID</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>eng</th>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spa</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slo</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <td>53.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnd</th>\n",
       "      <td>55.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>53.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arg</th>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fre</th>\n",
       "      <td>57.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bsq</th>\n",
       "      <td>53.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Language_ID   eng   spa   slo   ind   mnd   fin   arg   fre   bsq\n",
       "Language_ID                                                      \n",
       "eng           0.0  54.0  14.0  53.0  55.0  53.0  21.0  57.0  53.0\n",
       "spa          54.0   0.0  14.0  50.0  52.0  50.0  20.0  54.0  50.0\n",
       "slo          14.0  14.0   0.0  14.0  14.0  14.0   8.0  14.0  14.0\n",
       "ind          53.0  50.0  14.0   0.0  51.0  53.0  21.0  53.0  53.0\n",
       "mnd          55.0  52.0  14.0  51.0   0.0  51.0  19.0  55.0  51.0\n",
       "fin          53.0  50.0  14.0  53.0  51.0   0.0  21.0  53.0  53.0\n",
       "arg          21.0  20.0   8.0  21.0  19.0  21.0   0.0  21.0  21.0\n",
       "fre          57.0  54.0  14.0  53.0  55.0  53.0  21.0   0.0  53.0\n",
       "bsq          53.0  50.0  14.0  53.0  51.0  53.0  21.0  53.0   0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparable_lex_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Language_ID</th>\n",
       "      <th>eng</th>\n",
       "      <th>spa</th>\n",
       "      <th>slo</th>\n",
       "      <th>ind</th>\n",
       "      <th>mnd</th>\n",
       "      <th>fin</th>\n",
       "      <th>arg</th>\n",
       "      <th>fre</th>\n",
       "      <th>bsq</th>\n",
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       "    <tr>\n",
       "      <th>Language_ID</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>eng</th>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spa</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slo</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <td>69.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnd</th>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arg</th>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fre</th>\n",
       "      <td>68.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bsq</th>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Language_ID   eng   spa   slo   ind   mnd   fin   arg   fre   bsq\n",
       "Language_ID                                                      \n",
       "eng           0.0  69.0  24.0  69.0  67.0  70.0  37.0  68.0  63.0\n",
       "spa          69.0   0.0  24.0  68.0  66.0  69.0  37.0  67.0  62.0\n",
       "slo          24.0  24.0   0.0  24.0  24.0  24.0  24.0  24.0  19.0\n",
       "ind          69.0  68.0  24.0   0.0  66.0  69.0  36.0  67.0  62.0\n",
       "mnd          67.0  66.0  24.0  66.0   0.0  67.0  35.0  66.0  62.0\n",
       "fin          70.0  69.0  24.0  69.0  67.0   0.0  37.0  68.0  63.0\n",
       "arg          37.0  37.0  24.0  36.0  35.0  37.0   0.0  37.0  31.0\n",
       "fre          68.0  67.0  24.0  67.0  66.0  68.0  37.0   0.0  61.0\n",
       "bsq          63.0  62.0  19.0  62.0  62.0  63.0  31.0  61.0   0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparable_dep_table "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairwise_dep_vector = pdist(languages_dep_features, metric=hamming_similarity)\n",
    "\n",
    "pairwise_dep_table = pd.DataFrame(\n",
    "    squareform(pairwise_dep_vector),\n",
    "    columns = languages_dep_features.index,\n",
    "    index = languages_dep_features.index\n",
    ")\n",
    "\n",
    "pairwise_lex_vector = pdist(languages_lex_features, metric=hamming_similarity)\n",
    "\n",
    "pairwise_lex_table = pd.DataFrame(\n",
    "    squareform(pairwise_lex_vector),\n",
    "    columns = languages_lex_features.index,\n",
    "    index = languages_lex_features.index\n",
    ")\n",
    "\n",
    "np.fill_diagonal(pairwise_dep_table.values, 1.0)\n",
    "np.fill_diagonal(pairwise_lex_table.values, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Language_ID</th>\n",
       "      <th>eng</th>\n",
       "      <th>spa</th>\n",
       "      <th>slo</th>\n",
       "      <th>ind</th>\n",
       "      <th>mnd</th>\n",
       "      <th>fin</th>\n",
       "      <th>arg</th>\n",
       "      <th>fre</th>\n",
       "      <th>bsq</th>\n",
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       "    <tr>\n",
       "      <th>Language_ID</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>eng</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.710145</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.594203</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.460317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spa</th>\n",
       "      <td>0.710145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.439394</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.516129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slo</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <td>0.594203</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.621212</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.537313</td>\n",
       "      <td>0.370968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnd</th>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.439394</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.621212</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522388</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>0.522388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.507937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arg</th>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fre</th>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.537313</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.426230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bsq</th>\n",
       "      <td>0.460317</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.507937</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.426230</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Language_ID       eng       spa       slo       ind       mnd       fin  \\\n",
       "Language_ID                                                               \n",
       "eng          1.000000  0.710145  0.916667  0.594203  0.552239  0.742857   \n",
       "spa          0.710145  1.000000  0.791667  0.588235  0.439394  0.652174   \n",
       "slo          0.916667  0.791667  1.000000  0.750000  0.833333  0.791667   \n",
       "ind          0.594203  0.588235  0.750000  1.000000  0.621212  0.507246   \n",
       "mnd          0.552239  0.439394  0.833333  0.621212  1.000000  0.522388   \n",
       "fin          0.742857  0.652174  0.791667  0.507246  0.522388  1.000000   \n",
       "arg          0.756757  0.837838  0.791667  0.722222  0.514286  0.675676   \n",
       "fre          0.691176  0.761194  0.541667  0.537313  0.363636  0.647059   \n",
       "bsq          0.460317  0.516129  0.526316  0.370968  0.419355  0.507937   \n",
       "\n",
       "Language_ID       arg       fre       bsq  \n",
       "Language_ID                                \n",
       "eng          0.756757  0.691176  0.460317  \n",
       "spa          0.837838  0.761194  0.516129  \n",
       "slo          0.791667  0.541667  0.526316  \n",
       "ind          0.722222  0.537313  0.370968  \n",
       "mnd          0.514286  0.363636  0.419355  \n",
       "fin          0.675676  0.647059  0.507937  \n",
       "arg          1.000000  0.621622  0.451613  \n",
       "fre          0.621622  1.000000  0.426230  \n",
       "bsq          0.451613  0.426230  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairwise_dep_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Language_ID</th>\n",
       "      <th>eng</th>\n",
       "      <th>spa</th>\n",
       "      <th>slo</th>\n",
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       "      <th>eng</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.642857</td>\n",
       "      <td>0.471698</td>\n",
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       "      <td>0.631579</td>\n",
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       "      <td>0.360000</td>\n",
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       "      <td>0.580000</td>\n",
       "      <td>0.350000</td>\n",
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       "      <td>0.460000</td>\n",
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       "      <td>0.642857</td>\n",
       "      <td>0.642857</td>\n",
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       "      <td>0.357143</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.571429</td>\n",
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       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <td>0.471698</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.358491</td>\n",
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       "      <th>mnd</th>\n",
       "      <td>0.545455</td>\n",
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       "      <td>0.666667</td>\n",
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       "      <td>0.368421</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.431373</td>\n",
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       "      <th>fin</th>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.490566</td>\n",
       "      <td>0.452830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arg</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fre</th>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.454545</td>\n",
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       "    <tr>\n",
       "      <th>bsq</th>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.358491</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "Language_ID       eng       spa       slo       ind       mnd       fin  \\\n",
       "Language_ID                                                               \n",
       "eng          1.000000  0.648148  0.642857  0.471698  0.545455  0.528302   \n",
       "spa          0.648148  1.000000  0.642857  0.360000  0.500000  0.580000   \n",
       "slo          0.642857  0.642857  1.000000  0.357143  0.571429  0.571429   \n",
       "ind          0.471698  0.360000  0.357143  1.000000  0.666667  0.433962   \n",
       "mnd          0.545455  0.500000  0.571429  0.666667  1.000000  0.490196   \n",
       "fin          0.528302  0.580000  0.571429  0.433962  0.490196  1.000000   \n",
       "arg          0.285714  0.350000  0.250000  0.238095  0.368421  0.428571   \n",
       "fre          0.631579  0.759259  0.714286  0.301887  0.454545  0.490566   \n",
       "bsq          0.415094  0.460000  0.571429  0.358491  0.431373  0.452830   \n",
       "\n",
       "Language_ID       arg       fre       bsq  \n",
       "Language_ID                                \n",
       "eng          0.285714  0.631579  0.415094  \n",
       "spa          0.350000  0.759259  0.460000  \n",
       "slo          0.250000  0.714286  0.571429  \n",
       "ind          0.238095  0.301887  0.358491  \n",
       "mnd          0.368421  0.454545  0.431373  \n",
       "fin          0.428571  0.490566  0.452830  \n",
       "arg          1.000000  0.333333  0.238095  \n",
       "fre          0.333333  1.000000  0.471698  \n",
       "bsq          0.238095  0.471698  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairwise_lex_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising similarity\n",
    "\n",
    "The results in two prevous cells presented in a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7.5,7.5))\n",
    "\n",
    "\n",
    "matrix_triu = np.triu(pairwise_lex_table)\n",
    "sns.heatmap(pairwise_lex_table, annot = True, mask=matrix_triu, fmt='.2f', square=True, cbar=False, cmap=\"rocket_r\", vmin=0.2, vmax=1)\n",
    "\n",
    "matrix_tril = np.tril(pairwise_dep_table)\n",
    "g = sns.heatmap(pairwise_dep_table, annot = True, mask=matrix_tril, fmt='.2f', square=True, cbar=False, cmap=\"rocket_r\", vmin=0.2, vmax=1)\n",
    "\n",
    "g.set_xlabel('LEXICAL')\n",
    "g.xaxis.set_label_position(\"bottom\")\n",
    "g.set_xticklabels(['EN', 'ES', 'SL', 'ID', 'ZH', 'FI', 'AR', 'FR', 'EU'])\n",
    "g.xaxis.tick_top()\n",
    "\n",
    "g.set_ylabel('DEPENDENCY')\n",
    "g.yaxis.set_label_position(\"right\")\n",
    "g.set_yticklabels(['EN', 'ES', 'SL', 'ID', 'ZH', 'FI', 'AR', 'FR', 'EU'])\n",
    "\n",
    "g.tick_params(axis='both', which='both', length=0)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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