02_need_efficiency_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "import regex as re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Processing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pile-sorting data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in Chinese & English raw sorting data; aggregate into a single dataframe (sorting_raw):\n",
    "\n",
    "\"Since the pile sorts correlated highly across groups (Malt et al., 1999), we aggregated pile-sorting responses from participants across the two groups, and took the similarity sim(i,j) of any two objects i and j to be the proportion of all participants who sorted those two objects into the same pile.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1001, 2)\n"
     ]
    }
   ],
   "source": [
    "sorting_raw = pd.DataFrame()\n",
    "\n",
    "\n",
    "# read in Chinese raw sorting data\n",
    "for filename in os.listdir('malt_data/sorting/chinese'):  # or use participants(ps) number range() with try/except since file numbering is not consecutive prob due to data cleaning\n",
    "    \n",
    "    indf = pd.read_csv('malt_data/sorting/chinese/' + filename, names = ['piles'])  # each pile: space separated str\n",
    "    indf['piles'] = indf.piles.apply(lambda x: [int(i) for i in x.split()])  # convert each space separated str in 'piles' into a lst of int\n",
    "    \n",
    "    num = re.compile(r'\\d+').findall(filename)[0]\n",
    "    indf['ps'] = 'c' + str(num)  # extract ps number from filename\n",
    "    \n",
    "    sorting_raw = sorting_raw.append(indf)\n",
    "\n",
    "    \n",
    "# same for English raw sorting data\n",
    "for filename in os.listdir('malt_data/sorting/english'):  \n",
    "    indf = pd.read_csv('malt_data/sorting/english/' + filename, names = ['piles'])\n",
    "    indf['piles'] = indf.piles.apply(lambda x: [int(i) for i in x.split()])\n",
    "    num = re.compile(r'\\d+').findall(filename)[0]\n",
    "    indf['ps'] = 'e' + str(num)\n",
    "    sorting_raw = sorting_raw.append(indf)\n",
    "\n",
    "\n",
    "# reindex to remove duplicated indices from merging multiple indf\n",
    "sorting_raw = sorting_raw.reset_index(drop = True)\n",
    "\n",
    "\n",
    "# pd.set_option('display.max_rows', None)  # display all rows in the dataframe\n",
    "print(np.shape(sorting_raw))\n",
    "# sorting_raw # sanity\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge piles created by each participant into an outerlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ps</th>\n",
       "      <th>piles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c1</td>\n",
       "      <td>[[42], [23, 24, 16, 35, 22], [57, 54, 43, 58, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c11</td>\n",
       "      <td>[[6, 52, 1, 8, 3, 2, 29], [33, 31, 42], [56, 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c12</td>\n",
       "      <td>[[20], [32], [46], [21, 35, 16, 24, 23, 26, 22...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c14</td>\n",
       "      <td>[[21, 51, 55, 20, 28, 33], [25, 56, 23, 24, 47...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c15</td>\n",
       "      <td>[[32, 51], [25, 55, 56], [35, 48, 39], [59], [...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>e8</td>\n",
       "      <td>[[44], [40, 28, 41, 49], [45], [18, 10, 11, 7,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>e80</td>\n",
       "      <td>[[10, 44, 11, 14, 9, 53], [49, 25, 55], [24, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>e82</td>\n",
       "      <td>[[59, 43, 54, 57, 58, 60], [20, 28, 55, 33, 51...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>e83</td>\n",
       "      <td>[[29, 17], [33, 28, 50], [58, 57, 54, 43, 60],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>e9</td>\n",
       "      <td>[[41, 40, 49], [45, 43, 54, 60, 59, 57, 58, 42...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ps                                              piles\n",
       "0    c1  [[42], [23, 24, 16, 35, 22], [57, 54, 43, 58, ...\n",
       "1   c11  [[6, 52, 1, 8, 3, 2, 29], [33, 31, 42], [56, 5...\n",
       "2   c12  [[20], [32], [46], [21, 35, 16, 24, 23, 26, 22...\n",
       "3   c14  [[21, 51, 55, 20, 28, 33], [25, 56, 23, 24, 47...\n",
       "4   c15  [[32, 51], [25, 55, 56], [35, 48, 39], [59], [...\n",
       "..  ...                                                ...\n",
       "85   e8  [[44], [40, 28, 41, 49], [45], [18, 10, 11, 7,...\n",
       "86  e80  [[10, 44, 11, 14, 9, 53], [49, 25, 55], [24, 4...\n",
       "87  e82  [[59, 43, 54, 57, 58, 60], [20, 28, 55, 33, 51...\n",
       "88  e83  [[29, 17], [33, 28, 50], [58, 57, 54, 43, 60],...\n",
       "89   e9  [[41, 40, 49], [45, 43, 54, 60, 59, 57, 58, 42...\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorting_merged = sorting_raw.groupby('ps').agg(lambda x: tuple(x)).applymap(list).reset_index()\n",
    "sorting_merged"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naming data (from Malt's study and our study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# English naming data from Malt's naming task (modal names without modifiers)\n",
    "modal_name_naming = pd.read_csv('malt_data/naming/naming_en_modal.csv')\n",
    "modal_name_naming.columns = ['obj', 'cat_en_malt']\n",
    "\n",
    "# Chinese naming data from Malt's study\n",
    "modal_name_naming['cat_ch_malt'] = pd.read_csv('malt_data/naming/naming_ch_modal.csv')['cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>cat_en_malt</th>\n",
       "      <th>cat_ch_malt</th>\n",
       "      <th>cat_en_ours1</th>\n",
       "      <th>cat_en_ours2</th>\n",
       "      <th>cat_ch_ours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>container</td>\n",
       "      <td>he2</td>\n",
       "      <td>container</td>\n",
       "      <td>container</td>\n",
       "      <td>he2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>container</td>\n",
       "      <td>he2</td>\n",
       "      <td>canister</td>\n",
       "      <td>canister</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>container</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>container</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>tube</td>\n",
       "      <td>guan3</td>\n",
       "      <td>tube</td>\n",
       "      <td>tube</td>\n",
       "      <td>guan3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>bottle</td>\n",
       "      <td>guan4</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>container</td>\n",
       "      <td>guan4</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>box</td>\n",
       "      <td>he2</td>\n",
       "      <td>box</td>\n",
       "      <td>box</td>\n",
       "      <td>he2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jar</td>\n",
       "      <td>jar</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>container</td>\n",
       "      <td>he2</td>\n",
       "      <td>tub</td>\n",
       "      <td>tub</td>\n",
       "      <td>he2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>can</td>\n",
       "      <td>guan4</td>\n",
       "      <td>can</td>\n",
       "      <td>can</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>bottle</td>\n",
       "      <td>guan4</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>he2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>container</td>\n",
       "      <td>guan4</td>\n",
       "      <td>container</td>\n",
       "      <td>container</td>\n",
       "      <td>guan4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>bottle</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>bottle</td>\n",
       "      <td>tong3</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>container</td>\n",
       "      <td>tong3</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>container</td>\n",
       "      <td>ping2</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>ping2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "      <td>jug</td>\n",
       "      <td>jug</td>\n",
       "      <td>tong3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj cat_en_malt cat_ch_malt cat_en_ours1 cat_en_ours2 cat_ch_ours\n",
       "0     1   container         he2    container    container         he2\n",
       "1     2   container         he2     canister     canister       ping2\n",
       "2     3      bottle       ping2       bottle       bottle       ping2\n",
       "3     4      bottle       ping2       bottle       bottle       ping2\n",
       "4     5      bottle       ping2       bottle       bottle       ping2\n",
       "5     6   container       ping2    container    container       ping2\n",
       "6     7         jar       ping2          jar          jar       ping2\n",
       "7     8      bottle       ping2       bottle       bottle       ping2\n",
       "8     9   container       ping2       bottle       bottle       ping2\n",
       "9    10         jar       ping2       bottle          jar       ping2\n",
       "10   11         jar       ping2          jar          jar       ping2\n",
       "11   12         jar       ping2          jar          jar       guan4\n",
       "12   13         jar       ping2          jar          jar       ping2\n",
       "13   14         jar       ping2          jar          jar       ping2\n",
       "14   15         jar       ping2          jar          jar       ping2\n",
       "15   16   container       guan4          can          can       ping2\n",
       "16   17         jar       ping2          jar          jar       ping2\n",
       "17   18         jar       ping2          jar          jar       ping2\n",
       "18   19         jar       ping2          jar          jar       guan4\n",
       "19   20        tube       guan3         tube         tube       guan3\n",
       "20   21      bottle       guan4       bottle       bottle       ping2\n",
       "21   22   container       guan4       bottle       bottle       ping2\n",
       "22   23         can       guan4          can          can       ping2\n",
       "23   24         can       guan4          can          can       ping2\n",
       "24   25   container       ping2       bottle       bottle       ping2\n",
       "25   26         can       guan4          can          can       guan4\n",
       "26   27         jar       ping2          jar          jar       ping2\n",
       "27   28      bottle       ping2       bottle       bottle       ping2\n",
       "28   29         jar       ping2          jar          jar       guan4\n",
       "29   30         jar       ping2          jar          jar       ping2\n",
       "30   31         jar       ping2          jar          jar       ping2\n",
       "31   32         box         he2          box          box         he2\n",
       "32   33      bottle       ping2       bottle       bottle       ping2\n",
       "33   34         jar       ping2          jar          jar       ping2\n",
       "34   35   container       ping2       bottle       bottle       ping2\n",
       "35   36         jar       ping2          jar          jar       ping2\n",
       "36   37         jar       ping2          jar          jar       guan4\n",
       "37   38         jar       ping2          jar          jar       guan4\n",
       "38   39   container       ping2       bottle       bottle       ping2\n",
       "39   40      bottle       ping2       bottle       bottle       ping2\n",
       "40   41      bottle       ping2       bottle       bottle       ping2\n",
       "41   42      bottle       ping2       bottle       bottle       ping2\n",
       "42   43         jug       tong3          jug          jug       tong3\n",
       "43   44         jar       ping2          jar          jar       ping2\n",
       "44   45   container         he2          tub          tub         he2\n",
       "45   46         can       guan4          can          can       guan4\n",
       "46   47         can       guan4          can          can       ping2\n",
       "47   48   container       ping2       bottle       bottle       ping2\n",
       "48   49      bottle       ping2       bottle       bottle       ping2\n",
       "49   50      bottle       ping2       bottle       bottle       ping2\n",
       "50   51      bottle       guan4       bottle       bottle         he2\n",
       "51   52   container       ping2       bottle       bottle       ping2\n",
       "52   53   container       guan4    container    container       guan4\n",
       "53   54         jug       tong3          jug          jug       tong3\n",
       "54   55      bottle       ping2       bottle       bottle       ping2\n",
       "55   56      bottle       ping2       bottle       bottle       ping2\n",
       "56   57      bottle       tong3          jug          jug       tong3\n",
       "57   58   container       tong3          jug          jug       tong3\n",
       "58   59   container       ping2          jug          jug       ping2\n",
       "59   60         jug       tong3          jug          jug       tong3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# English naming data from our study\n",
    "modal_name_naming['cat_en_ours1'] = pd.read_csv('survey_data/naming/naming_survey_en_cat1.csv')['cat']\n",
    "modal_name_naming['cat_en_ours2'] = pd.read_csv('survey_data/naming/naming_survey_en_cat2.csv')['cat']\n",
    "\n",
    "# Chinese\n",
    "modal_name_naming['cat_ch_ours'] = pd.read_csv('survey_data/naming/naming_survey_ch_cat.csv')['cat']\n",
    "\n",
    "modal_name_naming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'modal_name_naming' (DataFrame)\n"
     ]
    }
   ],
   "source": [
    "%store modal_name_naming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "modal_name_naming.to_csv(\"naming_all.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat_en_malt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bottle</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>box</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>can</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>container</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jar</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jug</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tube</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ct\n",
       "cat_en_malt    \n",
       "bottle       16\n",
       "box           1\n",
       "can           5\n",
       "container    15\n",
       "jar          19\n",
       "jug           3\n",
       "tube          1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Malt English naming data freq by cat\n",
    "cat_freq_en_malt = modal_name_naming.groupby('cat_en_malt').count()[['obj']]\n",
    "cat_freq_en_malt.columns = ['ct']\n",
    "\n",
    "cat_freq_en_malt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat_en_ours1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bottle</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>box</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>can</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canister</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>container</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jar</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jug</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tub</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tube</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ct\n",
       "cat_en_ours1    \n",
       "bottle        23\n",
       "box            1\n",
       "can            6\n",
       "canister       1\n",
       "container      3\n",
       "jar           18\n",
       "jug            6\n",
       "tub            1\n",
       "tube           1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our English naming data freq by cat 1\n",
    "cat_freq_en_ours1 = modal_name_naming.groupby('cat_en_ours1').count()[['obj']]\n",
    "cat_freq_en_ours1.columns = ['ct']\n",
    "\n",
    "cat_freq_en_ours1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat_en_ours2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bottle</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>box</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>can</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canister</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>container</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jar</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jug</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tub</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tube</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ct\n",
       "cat_en_ours2    \n",
       "bottle        22\n",
       "box            1\n",
       "can            6\n",
       "canister       1\n",
       "container      3\n",
       "jar           19\n",
       "jug            6\n",
       "tub            1\n",
       "tube           1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our English naming data freq by cat 2\n",
    "cat_freq_en_ours2 = modal_name_naming.groupby('cat_en_ours2').count()[['obj']]\n",
    "cat_freq_en_ours2.columns = ['ct']\n",
    "\n",
    "cat_freq_en_ours2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat_ch_malt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>guan3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guan4</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he2</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ping2</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tong3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ct\n",
       "cat_ch_malt    \n",
       "guan3         1\n",
       "guan4        10\n",
       "he2           4\n",
       "ping2        40\n",
       "tong3         5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Malt's Chinese naming data freq by cat\n",
    "cat_freq_ch_malt = modal_name_naming.groupby('cat_ch_malt').count()[['obj']]\n",
    "cat_freq_ch_malt.columns = ['ct']\n",
    "\n",
    "cat_freq_ch_malt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat_ch_ours</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>guan3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guan4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he2</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ping2</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tong3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ct\n",
       "cat_ch_ours    \n",
       "guan3         1\n",
       "guan4         8\n",
       "he2           4\n",
       "ping2        42\n",
       "tong3         5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our Chinese naming data freq by cat\n",
    "cat_freq_ch_ours = modal_name_naming.groupby('cat_ch_ours').count()[['obj']]\n",
    "cat_freq_ch_ours.columns = ['ct']\n",
    "\n",
    "cat_freq_ch_ours"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Need probability data (from Ngram and our freq rating task)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google Ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  \n",
       "0                   7.084100e-06  \n",
       "1                   2.965000e-07  \n",
       "2                   6.930000e-08  \n",
       "3                   2.600000e-07  \n",
       "4                   3.206800e-06  \n",
       "5                   2.693620e-05  \n",
       "6                   4.263660e-05  \n",
       "7                   2.471000e-07  \n",
       "8                   2.693620e-05  \n",
       "9                   5.931000e-07  \n",
       "10                  5.766000e-07  \n",
       "11                  5.894000e-07  \n",
       "12                  1.466200e-06  \n",
       "13                  4.263660e-05  \n",
       "14                  1.650000e-08  \n",
       "15                  2.693620e-05  \n",
       "16                  1.466200e-06  \n",
       "17                  4.263660e-05  \n",
       "18                  4.263660e-05  \n",
       "19                  1.483000e-07  \n",
       "20                  1.967080e-05  \n",
       "21                  2.693620e-05  \n",
       "22                  7.644300e-06  \n",
       "23                  7.644300e-06  \n",
       "24                  0.000000e+00  \n",
       "25                  3.813000e-07  \n",
       "26                  2.075800e-06  \n",
       "27                  6.359200e-06  \n",
       "28                  1.466200e-06  \n",
       "29                  4.263660e-05  \n",
       "30                  4.263660e-05  \n",
       "31                  6.079200e-06  \n",
       "32                  6.359200e-06  \n",
       "33                  4.263660e-05  \n",
       "34                  2.693620e-05  \n",
       "35                  1.768100e-06  \n",
       "36                  4.263660e-05  \n",
       "37                  4.263660e-05  \n",
       "38                  2.693620e-05  \n",
       "39                  5.848500e-06  \n",
       "40                  5.848500e-06  \n",
       "41                  2.810590e-05  \n",
       "42                  2.405300e-06  \n",
       "43                  1.153000e-07  \n",
       "44                  6.590000e-08  \n",
       "45                  2.560000e-07  \n",
       "46                  7.644300e-06  \n",
       "47                  2.693620e-05  \n",
       "48                  6.359200e-06  \n",
       "49                  2.810590e-05  \n",
       "50                  6.359200e-06  \n",
       "51                  0.000000e+00  \n",
       "52                  1.812000e-07  \n",
       "53                  7.627000e-07  \n",
       "54                  6.359200e-06  \n",
       "55                  2.283400e-05  \n",
       "56                  2.306000e-07  \n",
       "57                  2.693620e-05  \n",
       "58                  2.693620e-05  \n",
       "59                  2.773400e-06  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "need_prob = pd.read_csv('ngram_data/need_prob_ae_nonlemmatized_all.csv') # non-lemmatized corpus data\n",
    "# need_prob = pd.read_csv('need_prob_ae_lemmatized_all.csv') # lemmatized corpus data\n",
    "\n",
    "need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "      <th>en_ngram_1999_undivided</th>\n",
       "      <th>en_ngram_1999_divided</th>\n",
       "      <th>en_ngram_2019_undivided</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.038558</td>\n",
       "      <td>0.008578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.003883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.003970</td>\n",
       "      <td>0.000698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "      <td>0.031032</td>\n",
       "      <td>0.127120</td>\n",
       "      <td>0.023818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.002513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.007361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.002141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.002912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>0.000924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.027648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.003358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  en_ngram_1999_undivided  \\\n",
       "0                   7.084100e-06                 0.009413   \n",
       "1                   2.965000e-07                 0.001046   \n",
       "2                   6.930000e-08                 0.000020   \n",
       "3                   2.600000e-07                 0.000837   \n",
       "4                   3.206800e-06                 0.005368   \n",
       "5                   2.693620e-05                 0.038974   \n",
       "6                   4.263660e-05                 0.042062   \n",
       "7                   2.471000e-07                 0.000310   \n",
       "8                   2.693620e-05                 0.038974   \n",
       "9                   5.931000e-07                 0.000697   \n",
       "10                  5.766000e-07                 0.000969   \n",
       "11                  5.894000e-07                 0.002118   \n",
       "12                  1.466200e-06                 0.003383   \n",
       "13                  4.263660e-05                 0.042062   \n",
       "14                  1.650000e-08                 0.000136   \n",
       "15                  2.693620e-05                 0.038974   \n",
       "16                  1.466200e-06                 0.003383   \n",
       "17                  4.263660e-05                 0.042062   \n",
       "18                  4.263660e-05                 0.042062   \n",
       "19                  1.483000e-07                 0.000503   \n",
       "20                  1.967080e-05                 0.031032   \n",
       "21                  2.693620e-05                 0.038974   \n",
       "22                  7.644300e-06                 0.011932   \n",
       "23                  7.644300e-06                 0.011932   \n",
       "24                  0.000000e+00                 0.000019   \n",
       "25                  3.813000e-07                 0.000122   \n",
       "26                  2.075800e-06                 0.003177   \n",
       "27                  6.359200e-06                 0.009339   \n",
       "28                  1.466200e-06                 0.003383   \n",
       "29                  4.263660e-05                 0.042062   \n",
       "30                  4.263660e-05                 0.042062   \n",
       "31                  6.079200e-06                 0.002221   \n",
       "32                  6.359200e-06                 0.009339   \n",
       "33                  4.263660e-05                 0.042062   \n",
       "34                  2.693620e-05                 0.038974   \n",
       "35                  1.768100e-06                 0.001941   \n",
       "36                  4.263660e-05                 0.042062   \n",
       "37                  4.263660e-05                 0.042062   \n",
       "38                  2.693620e-05                 0.038974   \n",
       "39                  5.848500e-06                 0.011545   \n",
       "40                  5.848500e-06                 0.011545   \n",
       "41                  2.810590e-05                 0.027796   \n",
       "42                  2.405300e-06                 0.003280   \n",
       "43                  1.153000e-07                 0.000078   \n",
       "44                  6.590000e-08                 0.000175   \n",
       "45                  2.560000e-07                 0.000409   \n",
       "46                  7.644300e-06                 0.011932   \n",
       "47                  2.693620e-05                 0.038974   \n",
       "48                  6.359200e-06                 0.009339   \n",
       "49                  2.810590e-05                 0.027796   \n",
       "50                  6.359200e-06                 0.009339   \n",
       "51                  0.000000e+00                 0.000019   \n",
       "52                  1.812000e-07                 0.000251   \n",
       "53                  7.627000e-07                 0.001407   \n",
       "54                  6.359200e-06                 0.009339   \n",
       "55                  2.283400e-05                 0.030002   \n",
       "56                  2.306000e-07                 0.001007   \n",
       "57                  2.693620e-05                 0.038974   \n",
       "58                  2.693620e-05                 0.038974   \n",
       "59                  2.773400e-06                 0.002794   \n",
       "\n",
       "    en_ngram_1999_divided  en_ngram_2019_undivided  \n",
       "0                0.038558                 0.008578  \n",
       "1                0.004284                 0.000359  \n",
       "2                0.000084                 0.000084  \n",
       "3                0.003428                 0.000315  \n",
       "4                0.021990                 0.003883  \n",
       "5                0.017712                 0.032615  \n",
       "6                0.019098                 0.051626  \n",
       "7                0.001271                 0.000299  \n",
       "8                0.017712                 0.032615  \n",
       "9                0.002856                 0.000718  \n",
       "10               0.003970                 0.000698  \n",
       "11               0.008675                 0.000714  \n",
       "12               0.004627                 0.001775  \n",
       "13               0.019098                 0.051626  \n",
       "14               0.000555                 0.000020  \n",
       "15               0.017712                 0.032615  \n",
       "16               0.004627                 0.001775  \n",
       "17               0.019098                 0.051626  \n",
       "18               0.019098                 0.051626  \n",
       "19               0.002060                 0.000180  \n",
       "20               0.127120                 0.023818  \n",
       "21               0.017712                 0.032615  \n",
       "22               0.016267                 0.009256  \n",
       "23               0.016267                 0.009256  \n",
       "24               0.000040                 0.000000  \n",
       "25               0.000501                 0.000462  \n",
       "26               0.013013                 0.002513  \n",
       "27               0.007651                 0.007700  \n",
       "28               0.004627                 0.001775  \n",
       "29               0.019098                 0.051626  \n",
       "30               0.019098                 0.051626  \n",
       "31               0.009097                 0.007361  \n",
       "32               0.007651                 0.007700  \n",
       "33               0.019098                 0.051626  \n",
       "34               0.017712                 0.032615  \n",
       "35               0.007953                 0.002141  \n",
       "36               0.019098                 0.051626  \n",
       "37               0.019098                 0.051626  \n",
       "38               0.017712                 0.032615  \n",
       "39               0.023617                 0.007082  \n",
       "40               0.023617                 0.007082  \n",
       "41               0.056812                 0.034032  \n",
       "42               0.013435                 0.002912  \n",
       "43               0.000317                 0.000140  \n",
       "44               0.000717                 0.000080  \n",
       "45               0.001675                 0.000310  \n",
       "46               0.016267                 0.009256  \n",
       "47               0.017712                 0.032615  \n",
       "48               0.007651                 0.007700  \n",
       "49               0.056812                 0.034032  \n",
       "50               0.007651                 0.007700  \n",
       "51               0.000040                 0.000000  \n",
       "52               0.001030                 0.000219  \n",
       "53               0.005766                 0.000924  \n",
       "54               0.007651                 0.007700  \n",
       "55               0.122903                 0.027648  \n",
       "56               0.004127                 0.000279  \n",
       "57               0.017712                 0.032615  \n",
       "58               0.017712                 0.032615  \n",
       "59               0.011447                 0.003358  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalizing need_prob['en']:\n",
    "total_en_1999_undivided = sum(need_prob['en_ngram_freq_1999_undivided'])\n",
    "need_prob['en_ngram_1999_undivided'] = need_prob['en_ngram_freq_1999_undivided'].apply(lambda x: x / total_en_1999_undivided)\n",
    "\n",
    "total_en_1999_divided = sum(need_prob['en_ngram_freq_1999_divided'])\n",
    "need_prob['en_ngram_1999_divided'] = need_prob['en_ngram_freq_1999_divided'].apply(lambda x: x / total_en_1999_divided)\n",
    "\n",
    "total_en_2019_undivided = sum(need_prob['en_ngram_freq_2019_undivided'])\n",
    "need_prob['en_ngram_2019_undivided'] = need_prob['en_ngram_freq_2019_undivided'].apply(lambda x: x / total_en_2019_undivided)\n",
    "\n",
    "# total_en_2019_divided = sum(need_prob['en_ngram_freq_2019_divided'])\n",
    "# need_prob['en_ngram_2019_divided'] = need_prob['en_ngram_freq_2019_divided'].apply(lambda x: x / total_en_2019_divided)\n",
    "\n",
    "need_prob"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our survey's freq rating task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %store -r need_prob_rating_en\n",
    "%store -r need_prob_rating_en_excluded\n",
    "# %store -r need_prob_rating_ch\n",
    "%store -r need_prob_rating_ch_excluded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "      <th>en_ngram_1999_undivided</th>\n",
       "      <th>en_ngram_1999_divided</th>\n",
       "      <th>en_ngram_2019_undivided</th>\n",
       "      <th>en_survey_nontrans</th>\n",
       "      <th>ch_survey_nontrans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.038558</td>\n",
       "      <td>0.008578</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.017703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.013206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.017703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.015303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.015851</td>\n",
       "      <td>0.013503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.015006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.003970</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.015006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.014709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.016206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.015613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.015303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.013806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.022206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "      <td>0.031032</td>\n",
       "      <td>0.127120</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.015006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.013806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>0.010806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.019213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.014794</td>\n",
       "      <td>0.015303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.017703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>0.018903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.023109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.016503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.018903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.012903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.018013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.017406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.013503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.014709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.017406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.017703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.021613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.013737</td>\n",
       "      <td>0.016206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>0.012006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.022206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.016813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.013503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.016203</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.015909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.027648</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.019806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.018309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.014709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.024657</td>\n",
       "      <td>0.021613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  en_ngram_1999_undivided  \\\n",
       "0                   7.084100e-06                 0.009413   \n",
       "1                   2.965000e-07                 0.001046   \n",
       "2                   6.930000e-08                 0.000020   \n",
       "3                   2.600000e-07                 0.000837   \n",
       "4                   3.206800e-06                 0.005368   \n",
       "5                   2.693620e-05                 0.038974   \n",
       "6                   4.263660e-05                 0.042062   \n",
       "7                   2.471000e-07                 0.000310   \n",
       "8                   2.693620e-05                 0.038974   \n",
       "9                   5.931000e-07                 0.000697   \n",
       "10                  5.766000e-07                 0.000969   \n",
       "11                  5.894000e-07                 0.002118   \n",
       "12                  1.466200e-06                 0.003383   \n",
       "13                  4.263660e-05                 0.042062   \n",
       "14                  1.650000e-08                 0.000136   \n",
       "15                  2.693620e-05                 0.038974   \n",
       "16                  1.466200e-06                 0.003383   \n",
       "17                  4.263660e-05                 0.042062   \n",
       "18                  4.263660e-05                 0.042062   \n",
       "19                  1.483000e-07                 0.000503   \n",
       "20                  1.967080e-05                 0.031032   \n",
       "21                  2.693620e-05                 0.038974   \n",
       "22                  7.644300e-06                 0.011932   \n",
       "23                  7.644300e-06                 0.011932   \n",
       "24                  0.000000e+00                 0.000019   \n",
       "25                  3.813000e-07                 0.000122   \n",
       "26                  2.075800e-06                 0.003177   \n",
       "27                  6.359200e-06                 0.009339   \n",
       "28                  1.466200e-06                 0.003383   \n",
       "29                  4.263660e-05                 0.042062   \n",
       "30                  4.263660e-05                 0.042062   \n",
       "31                  6.079200e-06                 0.002221   \n",
       "32                  6.359200e-06                 0.009339   \n",
       "33                  4.263660e-05                 0.042062   \n",
       "34                  2.693620e-05                 0.038974   \n",
       "35                  1.768100e-06                 0.001941   \n",
       "36                  4.263660e-05                 0.042062   \n",
       "37                  4.263660e-05                 0.042062   \n",
       "38                  2.693620e-05                 0.038974   \n",
       "39                  5.848500e-06                 0.011545   \n",
       "40                  5.848500e-06                 0.011545   \n",
       "41                  2.810590e-05                 0.027796   \n",
       "42                  2.405300e-06                 0.003280   \n",
       "43                  1.153000e-07                 0.000078   \n",
       "44                  6.590000e-08                 0.000175   \n",
       "45                  2.560000e-07                 0.000409   \n",
       "46                  7.644300e-06                 0.011932   \n",
       "47                  2.693620e-05                 0.038974   \n",
       "48                  6.359200e-06                 0.009339   \n",
       "49                  2.810590e-05                 0.027796   \n",
       "50                  6.359200e-06                 0.009339   \n",
       "51                  0.000000e+00                 0.000019   \n",
       "52                  1.812000e-07                 0.000251   \n",
       "53                  7.627000e-07                 0.001407   \n",
       "54                  6.359200e-06                 0.009339   \n",
       "55                  2.283400e-05                 0.030002   \n",
       "56                  2.306000e-07                 0.001007   \n",
       "57                  2.693620e-05                 0.038974   \n",
       "58                  2.693620e-05                 0.038974   \n",
       "59                  2.773400e-06                 0.002794   \n",
       "\n",
       "    en_ngram_1999_divided  en_ngram_2019_undivided  en_survey_nontrans  \\\n",
       "0                0.038558                 0.008578            0.014089   \n",
       "1                0.004284                 0.000359            0.007749   \n",
       "2                0.000084                 0.000084            0.016555   \n",
       "3                0.003428                 0.000315            0.017964   \n",
       "4                0.021990                 0.003883            0.020782   \n",
       "5                0.017712                 0.032615            0.018669   \n",
       "6                0.019098                 0.051626            0.018316   \n",
       "7                0.001271                 0.000299            0.015851   \n",
       "8                0.017712                 0.032615            0.021839   \n",
       "9                0.002856                 0.000718            0.018669   \n",
       "10               0.003970                 0.000698            0.017260   \n",
       "11               0.008675                 0.000714            0.011976   \n",
       "12               0.004627                 0.001775            0.017964   \n",
       "13               0.019098                 0.051626            0.017964   \n",
       "14               0.000555                 0.000020            0.012328   \n",
       "15               0.017712                 0.032615            0.016907   \n",
       "16               0.004627                 0.001775            0.016555   \n",
       "17               0.019098                 0.051626            0.013385   \n",
       "18               0.019098                 0.051626            0.021839   \n",
       "19               0.002060                 0.000180            0.020077   \n",
       "20               0.127120                 0.023818            0.010567   \n",
       "21               0.017712                 0.032615            0.010919   \n",
       "22               0.016267                 0.009256            0.014089   \n",
       "23               0.016267                 0.009256            0.016555   \n",
       "24               0.000040                 0.000000            0.023600   \n",
       "25               0.000501                 0.000462            0.008806   \n",
       "26               0.013013                 0.002513            0.021134   \n",
       "27               0.007651                 0.007700            0.014794   \n",
       "28               0.004627                 0.001775            0.021134   \n",
       "29               0.019098                 0.051626            0.019373   \n",
       "30               0.019098                 0.051626            0.017260   \n",
       "31               0.009097                 0.007361            0.013385   \n",
       "32               0.007651                 0.007700            0.018669   \n",
       "33               0.019098                 0.051626            0.023600   \n",
       "34               0.017712                 0.032615            0.012328   \n",
       "35               0.007953                 0.002141            0.020782   \n",
       "36               0.019098                 0.051626            0.016555   \n",
       "37               0.019098                 0.051626            0.021839   \n",
       "38               0.017712                 0.032615            0.011976   \n",
       "39               0.023617                 0.007082            0.010567   \n",
       "40               0.023617                 0.007082            0.010215   \n",
       "41               0.056812                 0.034032            0.015498   \n",
       "42               0.013435                 0.002912            0.019021   \n",
       "43               0.000317                 0.000140            0.018316   \n",
       "44               0.000717                 0.000080            0.017612   \n",
       "45               0.001675                 0.000310            0.014089   \n",
       "46               0.016267                 0.009256            0.013737   \n",
       "47               0.017712                 0.032615            0.020430   \n",
       "48               0.007651                 0.007700            0.021486   \n",
       "49               0.056812                 0.034032            0.019021   \n",
       "50               0.007651                 0.007700            0.010215   \n",
       "51               0.000040                 0.000000            0.010567   \n",
       "52               0.001030                 0.000219            0.016203   \n",
       "53               0.005766                 0.000924            0.015498   \n",
       "54               0.007651                 0.007700            0.023600   \n",
       "55               0.122903                 0.027648            0.023600   \n",
       "56               0.004127                 0.000279            0.019725   \n",
       "57               0.017712                 0.032615            0.010919   \n",
       "58               0.017712                 0.032615            0.010919   \n",
       "59               0.011447                 0.003358            0.024657   \n",
       "\n",
       "    ch_survey_nontrans  \n",
       "0             0.017703  \n",
       "1             0.013206  \n",
       "2             0.017703  \n",
       "3             0.015303  \n",
       "4             0.018903  \n",
       "5             0.013206  \n",
       "6             0.015909  \n",
       "7             0.013503  \n",
       "8             0.015006  \n",
       "9             0.013503  \n",
       "10            0.018309  \n",
       "11            0.015006  \n",
       "12            0.014709  \n",
       "13            0.016206  \n",
       "14            0.015613  \n",
       "15            0.015303  \n",
       "16            0.015909  \n",
       "17            0.013806  \n",
       "18            0.019806  \n",
       "19            0.022206  \n",
       "20            0.012606  \n",
       "21            0.015909  \n",
       "22            0.015006  \n",
       "23            0.013806  \n",
       "24            0.019806  \n",
       "25            0.010806  \n",
       "26            0.019213  \n",
       "27            0.015303  \n",
       "28            0.017703  \n",
       "29            0.018903  \n",
       "30            0.015909  \n",
       "31            0.023109  \n",
       "32            0.016503  \n",
       "33            0.018903  \n",
       "34            0.012903  \n",
       "35            0.018606  \n",
       "36            0.018013  \n",
       "37            0.019213  \n",
       "38            0.017406  \n",
       "39            0.012903  \n",
       "40            0.013503  \n",
       "41            0.014709  \n",
       "42            0.017406  \n",
       "43            0.017703  \n",
       "44            0.018309  \n",
       "45            0.021613  \n",
       "46            0.016206  \n",
       "47            0.018309  \n",
       "48            0.012006  \n",
       "49            0.022206  \n",
       "50            0.016813  \n",
       "51            0.013503  \n",
       "52            0.015909  \n",
       "53            0.018309  \n",
       "54            0.015909  \n",
       "55            0.019806  \n",
       "56            0.019806  \n",
       "57            0.018309  \n",
       "58            0.014709  \n",
       "59            0.021613  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use normalized data:\n",
    "need_prob['en_survey_nontrans'] = need_prob_rating_en_excluded[[\"need_prob\"]]\n",
    "need_prob['ch_survey_nontrans'] = need_prob_rating_ch_excluded[[\"need_prob\"]]\n",
    "\n",
    "need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "      <th>en_ngram_1999_undivided</th>\n",
       "      <th>en_ngram_1999_divided</th>\n",
       "      <th>en_ngram_2019_undivided</th>\n",
       "      <th>en_survey_nontrans</th>\n",
       "      <th>ch_survey_nontrans</th>\n",
       "      <th>en_survey_raw</th>\n",
       "      <th>ch_survey_raw</th>\n",
       "      <th>en_ch_survey_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.038558</td>\n",
       "      <td>0.008578</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>-0.064453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>-0.538086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.373047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.970703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>1.399414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.945312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.015851</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>0.856445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>1.701172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>1.356445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.003970</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.410156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>-0.048828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>1.056641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.839844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.015613</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>2.261719</td>\n",
       "      <td>-0.074219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.783203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.632812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.005859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.5625</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.345703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "      <td>0.031032</td>\n",
       "      <td>0.127120</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012606</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.826172</td>\n",
       "      <td>0.048828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>-0.367188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>0.326172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>0.010806</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.565430</td>\n",
       "      <td>-0.002930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>0.966797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.014794</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.408203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>1.185547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.4375</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.699219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.757812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.023109</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>3.347656</td>\n",
       "      <td>-0.972656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>2.390625</td>\n",
       "      <td>0.921875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>1.449219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.318359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.695312</td>\n",
       "      <td>0.992188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.018013</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.609375</td>\n",
       "      <td>0.328125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>1.091797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>-0.396484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.005859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.143555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>0.619141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>0.853516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.685547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.472656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>-0.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.013737</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>2.4375</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.089844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.972656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>1.739258</td>\n",
       "      <td>2.074219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.158203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>2.435547</td>\n",
       "      <td>-0.623047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.081055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.016203</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.570312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.097656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>1.882812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.027648</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>0.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>-0.714844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>-0.193359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.024657</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>4.3750</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>1.244141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  en_ngram_1999_undivided  \\\n",
       "0                   7.084100e-06                 0.009413   \n",
       "1                   2.965000e-07                 0.001046   \n",
       "2                   6.930000e-08                 0.000020   \n",
       "3                   2.600000e-07                 0.000837   \n",
       "4                   3.206800e-06                 0.005368   \n",
       "5                   2.693620e-05                 0.038974   \n",
       "6                   4.263660e-05                 0.042062   \n",
       "7                   2.471000e-07                 0.000310   \n",
       "8                   2.693620e-05                 0.038974   \n",
       "9                   5.931000e-07                 0.000697   \n",
       "10                  5.766000e-07                 0.000969   \n",
       "11                  5.894000e-07                 0.002118   \n",
       "12                  1.466200e-06                 0.003383   \n",
       "13                  4.263660e-05                 0.042062   \n",
       "14                  1.650000e-08                 0.000136   \n",
       "15                  2.693620e-05                 0.038974   \n",
       "16                  1.466200e-06                 0.003383   \n",
       "17                  4.263660e-05                 0.042062   \n",
       "18                  4.263660e-05                 0.042062   \n",
       "19                  1.483000e-07                 0.000503   \n",
       "20                  1.967080e-05                 0.031032   \n",
       "21                  2.693620e-05                 0.038974   \n",
       "22                  7.644300e-06                 0.011932   \n",
       "23                  7.644300e-06                 0.011932   \n",
       "24                  0.000000e+00                 0.000019   \n",
       "25                  3.813000e-07                 0.000122   \n",
       "26                  2.075800e-06                 0.003177   \n",
       "27                  6.359200e-06                 0.009339   \n",
       "28                  1.466200e-06                 0.003383   \n",
       "29                  4.263660e-05                 0.042062   \n",
       "30                  4.263660e-05                 0.042062   \n",
       "31                  6.079200e-06                 0.002221   \n",
       "32                  6.359200e-06                 0.009339   \n",
       "33                  4.263660e-05                 0.042062   \n",
       "34                  2.693620e-05                 0.038974   \n",
       "35                  1.768100e-06                 0.001941   \n",
       "36                  4.263660e-05                 0.042062   \n",
       "37                  4.263660e-05                 0.042062   \n",
       "38                  2.693620e-05                 0.038974   \n",
       "39                  5.848500e-06                 0.011545   \n",
       "40                  5.848500e-06                 0.011545   \n",
       "41                  2.810590e-05                 0.027796   \n",
       "42                  2.405300e-06                 0.003280   \n",
       "43                  1.153000e-07                 0.000078   \n",
       "44                  6.590000e-08                 0.000175   \n",
       "45                  2.560000e-07                 0.000409   \n",
       "46                  7.644300e-06                 0.011932   \n",
       "47                  2.693620e-05                 0.038974   \n",
       "48                  6.359200e-06                 0.009339   \n",
       "49                  2.810590e-05                 0.027796   \n",
       "50                  6.359200e-06                 0.009339   \n",
       "51                  0.000000e+00                 0.000019   \n",
       "52                  1.812000e-07                 0.000251   \n",
       "53                  7.627000e-07                 0.001407   \n",
       "54                  6.359200e-06                 0.009339   \n",
       "55                  2.283400e-05                 0.030002   \n",
       "56                  2.306000e-07                 0.001007   \n",
       "57                  2.693620e-05                 0.038974   \n",
       "58                  2.693620e-05                 0.038974   \n",
       "59                  2.773400e-06                 0.002794   \n",
       "\n",
       "    en_ngram_1999_divided  en_ngram_2019_undivided  en_survey_nontrans  \\\n",
       "0                0.038558                 0.008578            0.014089   \n",
       "1                0.004284                 0.000359            0.007749   \n",
       "2                0.000084                 0.000084            0.016555   \n",
       "3                0.003428                 0.000315            0.017964   \n",
       "4                0.021990                 0.003883            0.020782   \n",
       "5                0.017712                 0.032615            0.018669   \n",
       "6                0.019098                 0.051626            0.018316   \n",
       "7                0.001271                 0.000299            0.015851   \n",
       "8                0.017712                 0.032615            0.021839   \n",
       "9                0.002856                 0.000718            0.018669   \n",
       "10               0.003970                 0.000698            0.017260   \n",
       "11               0.008675                 0.000714            0.011976   \n",
       "12               0.004627                 0.001775            0.017964   \n",
       "13               0.019098                 0.051626            0.017964   \n",
       "14               0.000555                 0.000020            0.012328   \n",
       "15               0.017712                 0.032615            0.016907   \n",
       "16               0.004627                 0.001775            0.016555   \n",
       "17               0.019098                 0.051626            0.013385   \n",
       "18               0.019098                 0.051626            0.021839   \n",
       "19               0.002060                 0.000180            0.020077   \n",
       "20               0.127120                 0.023818            0.010567   \n",
       "21               0.017712                 0.032615            0.010919   \n",
       "22               0.016267                 0.009256            0.014089   \n",
       "23               0.016267                 0.009256            0.016555   \n",
       "24               0.000040                 0.000000            0.023600   \n",
       "25               0.000501                 0.000462            0.008806   \n",
       "26               0.013013                 0.002513            0.021134   \n",
       "27               0.007651                 0.007700            0.014794   \n",
       "28               0.004627                 0.001775            0.021134   \n",
       "29               0.019098                 0.051626            0.019373   \n",
       "30               0.019098                 0.051626            0.017260   \n",
       "31               0.009097                 0.007361            0.013385   \n",
       "32               0.007651                 0.007700            0.018669   \n",
       "33               0.019098                 0.051626            0.023600   \n",
       "34               0.017712                 0.032615            0.012328   \n",
       "35               0.007953                 0.002141            0.020782   \n",
       "36               0.019098                 0.051626            0.016555   \n",
       "37               0.019098                 0.051626            0.021839   \n",
       "38               0.017712                 0.032615            0.011976   \n",
       "39               0.023617                 0.007082            0.010567   \n",
       "40               0.023617                 0.007082            0.010215   \n",
       "41               0.056812                 0.034032            0.015498   \n",
       "42               0.013435                 0.002912            0.019021   \n",
       "43               0.000317                 0.000140            0.018316   \n",
       "44               0.000717                 0.000080            0.017612   \n",
       "45               0.001675                 0.000310            0.014089   \n",
       "46               0.016267                 0.009256            0.013737   \n",
       "47               0.017712                 0.032615            0.020430   \n",
       "48               0.007651                 0.007700            0.021486   \n",
       "49               0.056812                 0.034032            0.019021   \n",
       "50               0.007651                 0.007700            0.010215   \n",
       "51               0.000040                 0.000000            0.010567   \n",
       "52               0.001030                 0.000219            0.016203   \n",
       "53               0.005766                 0.000924            0.015498   \n",
       "54               0.007651                 0.007700            0.023600   \n",
       "55               0.122903                 0.027648            0.023600   \n",
       "56               0.004127                 0.000279            0.019725   \n",
       "57               0.017712                 0.032615            0.010919   \n",
       "58               0.017712                 0.032615            0.010919   \n",
       "59               0.011447                 0.003358            0.024657   \n",
       "\n",
       "    ch_survey_nontrans  en_survey_raw  ch_survey_raw  en_ch_survey_raw  \n",
       "0             0.017703         2.5000       2.564453         -0.064453  \n",
       "1             0.013206         1.3750       1.913086         -0.538086  \n",
       "2             0.017703         2.9375       2.564453          0.373047  \n",
       "3             0.015303         3.1875       2.216797          0.970703  \n",
       "4             0.018903         3.6875       2.738281          0.949219  \n",
       "5             0.013206         3.3125       1.913086          1.399414  \n",
       "6             0.015909         3.2500       2.304688          0.945312  \n",
       "7             0.013503         2.8125       1.956055          0.856445  \n",
       "8             0.015006         3.8750       2.173828          1.701172  \n",
       "9             0.013503         3.3125       1.956055          1.356445  \n",
       "10            0.018309         3.0625       2.652344          0.410156  \n",
       "11            0.015006         2.1250       2.173828         -0.048828  \n",
       "12            0.014709         3.1875       2.130859          1.056641  \n",
       "13            0.016206         3.1875       2.347656          0.839844  \n",
       "14            0.015613         2.1875       2.261719         -0.074219  \n",
       "15            0.015303         3.0000       2.216797          0.783203  \n",
       "16            0.015909         2.9375       2.304688          0.632812  \n",
       "17            0.013806         2.3750       2.000000          0.375000  \n",
       "18            0.019806         3.8750       2.869141          1.005859  \n",
       "19            0.022206         3.5625       3.216797          0.345703  \n",
       "20            0.012606         1.8750       1.826172          0.048828  \n",
       "21            0.015909         1.9375       2.304688         -0.367188  \n",
       "22            0.015006         2.5000       2.173828          0.326172  \n",
       "23            0.013806         2.9375       2.000000          0.937500  \n",
       "24            0.019806         4.1875       2.869141          1.318359  \n",
       "25            0.010806         1.5625       1.565430         -0.002930  \n",
       "26            0.019213         3.7500       2.783203          0.966797  \n",
       "27            0.015303         2.6250       2.216797          0.408203  \n",
       "28            0.017703         3.7500       2.564453          1.185547  \n",
       "29            0.018903         3.4375       2.738281          0.699219  \n",
       "30            0.015909         3.0625       2.304688          0.757812  \n",
       "31            0.023109         2.3750       3.347656         -0.972656  \n",
       "32            0.016503         3.3125       2.390625          0.921875  \n",
       "33            0.018903         4.1875       2.738281          1.449219  \n",
       "34            0.012903         2.1875       1.869141          0.318359  \n",
       "35            0.018606         3.6875       2.695312          0.992188  \n",
       "36            0.018013         2.9375       2.609375          0.328125  \n",
       "37            0.019213         3.8750       2.783203          1.091797  \n",
       "38            0.017406         2.1250       2.521484         -0.396484  \n",
       "39            0.012903         1.8750       1.869141          0.005859  \n",
       "40            0.013503         1.8125       1.956055         -0.143555  \n",
       "41            0.014709         2.7500       2.130859          0.619141  \n",
       "42            0.017406         3.3750       2.521484          0.853516  \n",
       "43            0.017703         3.2500       2.564453          0.685547  \n",
       "44            0.018309         3.1250       2.652344          0.472656  \n",
       "45            0.021613         2.5000       3.130859         -0.630859  \n",
       "46            0.016206         2.4375       2.347656          0.089844  \n",
       "47            0.018309         3.6250       2.652344          0.972656  \n",
       "48            0.012006         3.8125       1.739258          2.074219  \n",
       "49            0.022206         3.3750       3.216797          0.158203  \n",
       "50            0.016813         1.8125       2.435547         -0.623047  \n",
       "51            0.013503         1.8750       1.956055         -0.081055  \n",
       "52            0.015909         2.8750       2.304688          0.570312  \n",
       "53            0.018309         2.7500       2.652344          0.097656  \n",
       "54            0.015909         4.1875       2.304688          1.882812  \n",
       "55            0.019806         4.1875       2.869141          1.318359  \n",
       "56            0.019806         3.5000       2.869141          0.630859  \n",
       "57            0.018309         1.9375       2.652344         -0.714844  \n",
       "58            0.014709         1.9375       2.130859         -0.193359  \n",
       "59            0.021613         4.3750       3.130859          1.244141  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add non-normalized data for data-driven approach:\n",
    "need_prob['en_survey_raw'] = need_prob_rating_en_excluded[[\"mean\"]]\n",
    "need_prob['ch_survey_raw'] = need_prob_rating_ch_excluded[[\"mean\"]]\n",
    "need_prob['en_ch_survey_raw'] = need_prob['en_survey_raw'] - need_prob['ch_survey_raw']\n",
    "\n",
    "need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "      <th>en_ngram_1999_undivided</th>\n",
       "      <th>en_ngram_1999_divided</th>\n",
       "      <th>en_ngram_2019_undivided</th>\n",
       "      <th>en_survey_nontrans</th>\n",
       "      <th>ch_survey_nontrans</th>\n",
       "      <th>en_survey_raw</th>\n",
       "      <th>ch_survey_raw</th>\n",
       "      <th>en_ch_survey_raw</th>\n",
       "      <th>en_zipf</th>\n",
       "      <th>ch_zipf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.038558</td>\n",
       "      <td>0.008578</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>-0.064453</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>-0.538086</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>0.005766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.373047</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.970703</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.007462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.949219</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.021144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>1.399414</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.005766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.945312</td>\n",
       "      <td>0.009961</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.015851</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>0.856445</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>0.006041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>1.701172</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.007048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>1.356445</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.006041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.003970</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>0.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>-0.048828</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.007048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>1.056641</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.006677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.839844</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.009062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.015613</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>2.261719</td>\n",
       "      <td>-0.074219</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.007929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.783203</td>\n",
       "      <td>0.007618</td>\n",
       "      <td>0.007462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.632812</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.006343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.005859</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.031716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.5625</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.345703</td>\n",
       "      <td>0.016187</td>\n",
       "      <td>0.063431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "      <td>0.031032</td>\n",
       "      <td>0.127120</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012606</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.826172</td>\n",
       "      <td>0.048828</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.005286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>-0.367188</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>0.326172</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.007048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.006343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.031716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>0.010806</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.565430</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.004879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>0.966797</td>\n",
       "      <td>0.025900</td>\n",
       "      <td>0.025372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.014794</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.408203</td>\n",
       "      <td>0.005886</td>\n",
       "      <td>0.007462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>1.185547</td>\n",
       "      <td>0.025900</td>\n",
       "      <td>0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.4375</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.699219</td>\n",
       "      <td>0.012950</td>\n",
       "      <td>0.021144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.757812</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.023109</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>3.347656</td>\n",
       "      <td>-0.972656</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.126862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>2.390625</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.009759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>1.449219</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.021144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.318359</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.005516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.695312</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.018123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.018013</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.609375</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.014096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>1.091797</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.025372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>-0.396484</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.011533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.005516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.143555</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.006041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>0.619141</td>\n",
       "      <td>0.006167</td>\n",
       "      <td>0.006677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>0.853516</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.011533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.685547</td>\n",
       "      <td>0.009961</td>\n",
       "      <td>0.012686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.472656</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>-0.630859</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.042287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.013737</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>2.4375</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.089844</td>\n",
       "      <td>0.005396</td>\n",
       "      <td>0.009062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.972656</td>\n",
       "      <td>0.018500</td>\n",
       "      <td>0.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>1.739258</td>\n",
       "      <td>2.074219</td>\n",
       "      <td>0.032374</td>\n",
       "      <td>0.005074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.158203</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.063431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>2.435547</td>\n",
       "      <td>-0.623047</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.010572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.081055</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.006041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.016203</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.006816</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>0.006167</td>\n",
       "      <td>0.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>1.882812</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.027648</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.031716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>0.630859</td>\n",
       "      <td>0.014389</td>\n",
       "      <td>0.031716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>-0.714844</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.015858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>-0.193359</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.006677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.024657</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>4.3750</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>1.244141</td>\n",
       "      <td>0.129498</td>\n",
       "      <td>0.042287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  en_ngram_1999_undivided  \\\n",
       "0                   7.084100e-06                 0.009413   \n",
       "1                   2.965000e-07                 0.001046   \n",
       "2                   6.930000e-08                 0.000020   \n",
       "3                   2.600000e-07                 0.000837   \n",
       "4                   3.206800e-06                 0.005368   \n",
       "5                   2.693620e-05                 0.038974   \n",
       "6                   4.263660e-05                 0.042062   \n",
       "7                   2.471000e-07                 0.000310   \n",
       "8                   2.693620e-05                 0.038974   \n",
       "9                   5.931000e-07                 0.000697   \n",
       "10                  5.766000e-07                 0.000969   \n",
       "11                  5.894000e-07                 0.002118   \n",
       "12                  1.466200e-06                 0.003383   \n",
       "13                  4.263660e-05                 0.042062   \n",
       "14                  1.650000e-08                 0.000136   \n",
       "15                  2.693620e-05                 0.038974   \n",
       "16                  1.466200e-06                 0.003383   \n",
       "17                  4.263660e-05                 0.042062   \n",
       "18                  4.263660e-05                 0.042062   \n",
       "19                  1.483000e-07                 0.000503   \n",
       "20                  1.967080e-05                 0.031032   \n",
       "21                  2.693620e-05                 0.038974   \n",
       "22                  7.644300e-06                 0.011932   \n",
       "23                  7.644300e-06                 0.011932   \n",
       "24                  0.000000e+00                 0.000019   \n",
       "25                  3.813000e-07                 0.000122   \n",
       "26                  2.075800e-06                 0.003177   \n",
       "27                  6.359200e-06                 0.009339   \n",
       "28                  1.466200e-06                 0.003383   \n",
       "29                  4.263660e-05                 0.042062   \n",
       "30                  4.263660e-05                 0.042062   \n",
       "31                  6.079200e-06                 0.002221   \n",
       "32                  6.359200e-06                 0.009339   \n",
       "33                  4.263660e-05                 0.042062   \n",
       "34                  2.693620e-05                 0.038974   \n",
       "35                  1.768100e-06                 0.001941   \n",
       "36                  4.263660e-05                 0.042062   \n",
       "37                  4.263660e-05                 0.042062   \n",
       "38                  2.693620e-05                 0.038974   \n",
       "39                  5.848500e-06                 0.011545   \n",
       "40                  5.848500e-06                 0.011545   \n",
       "41                  2.810590e-05                 0.027796   \n",
       "42                  2.405300e-06                 0.003280   \n",
       "43                  1.153000e-07                 0.000078   \n",
       "44                  6.590000e-08                 0.000175   \n",
       "45                  2.560000e-07                 0.000409   \n",
       "46                  7.644300e-06                 0.011932   \n",
       "47                  2.693620e-05                 0.038974   \n",
       "48                  6.359200e-06                 0.009339   \n",
       "49                  2.810590e-05                 0.027796   \n",
       "50                  6.359200e-06                 0.009339   \n",
       "51                  0.000000e+00                 0.000019   \n",
       "52                  1.812000e-07                 0.000251   \n",
       "53                  7.627000e-07                 0.001407   \n",
       "54                  6.359200e-06                 0.009339   \n",
       "55                  2.283400e-05                 0.030002   \n",
       "56                  2.306000e-07                 0.001007   \n",
       "57                  2.693620e-05                 0.038974   \n",
       "58                  2.693620e-05                 0.038974   \n",
       "59                  2.773400e-06                 0.002794   \n",
       "\n",
       "    en_ngram_1999_divided  en_ngram_2019_undivided  en_survey_nontrans  \\\n",
       "0                0.038558                 0.008578            0.014089   \n",
       "1                0.004284                 0.000359            0.007749   \n",
       "2                0.000084                 0.000084            0.016555   \n",
       "3                0.003428                 0.000315            0.017964   \n",
       "4                0.021990                 0.003883            0.020782   \n",
       "5                0.017712                 0.032615            0.018669   \n",
       "6                0.019098                 0.051626            0.018316   \n",
       "7                0.001271                 0.000299            0.015851   \n",
       "8                0.017712                 0.032615            0.021839   \n",
       "9                0.002856                 0.000718            0.018669   \n",
       "10               0.003970                 0.000698            0.017260   \n",
       "11               0.008675                 0.000714            0.011976   \n",
       "12               0.004627                 0.001775            0.017964   \n",
       "13               0.019098                 0.051626            0.017964   \n",
       "14               0.000555                 0.000020            0.012328   \n",
       "15               0.017712                 0.032615            0.016907   \n",
       "16               0.004627                 0.001775            0.016555   \n",
       "17               0.019098                 0.051626            0.013385   \n",
       "18               0.019098                 0.051626            0.021839   \n",
       "19               0.002060                 0.000180            0.020077   \n",
       "20               0.127120                 0.023818            0.010567   \n",
       "21               0.017712                 0.032615            0.010919   \n",
       "22               0.016267                 0.009256            0.014089   \n",
       "23               0.016267                 0.009256            0.016555   \n",
       "24               0.000040                 0.000000            0.023600   \n",
       "25               0.000501                 0.000462            0.008806   \n",
       "26               0.013013                 0.002513            0.021134   \n",
       "27               0.007651                 0.007700            0.014794   \n",
       "28               0.004627                 0.001775            0.021134   \n",
       "29               0.019098                 0.051626            0.019373   \n",
       "30               0.019098                 0.051626            0.017260   \n",
       "31               0.009097                 0.007361            0.013385   \n",
       "32               0.007651                 0.007700            0.018669   \n",
       "33               0.019098                 0.051626            0.023600   \n",
       "34               0.017712                 0.032615            0.012328   \n",
       "35               0.007953                 0.002141            0.020782   \n",
       "36               0.019098                 0.051626            0.016555   \n",
       "37               0.019098                 0.051626            0.021839   \n",
       "38               0.017712                 0.032615            0.011976   \n",
       "39               0.023617                 0.007082            0.010567   \n",
       "40               0.023617                 0.007082            0.010215   \n",
       "41               0.056812                 0.034032            0.015498   \n",
       "42               0.013435                 0.002912            0.019021   \n",
       "43               0.000317                 0.000140            0.018316   \n",
       "44               0.000717                 0.000080            0.017612   \n",
       "45               0.001675                 0.000310            0.014089   \n",
       "46               0.016267                 0.009256            0.013737   \n",
       "47               0.017712                 0.032615            0.020430   \n",
       "48               0.007651                 0.007700            0.021486   \n",
       "49               0.056812                 0.034032            0.019021   \n",
       "50               0.007651                 0.007700            0.010215   \n",
       "51               0.000040                 0.000000            0.010567   \n",
       "52               0.001030                 0.000219            0.016203   \n",
       "53               0.005766                 0.000924            0.015498   \n",
       "54               0.007651                 0.007700            0.023600   \n",
       "55               0.122903                 0.027648            0.023600   \n",
       "56               0.004127                 0.000279            0.019725   \n",
       "57               0.017712                 0.032615            0.010919   \n",
       "58               0.017712                 0.032615            0.010919   \n",
       "59               0.011447                 0.003358            0.024657   \n",
       "\n",
       "    ch_survey_nontrans  en_survey_raw  ch_survey_raw  en_ch_survey_raw  \\\n",
       "0             0.017703         2.5000       2.564453         -0.064453   \n",
       "1             0.013206         1.3750       1.913086         -0.538086   \n",
       "2             0.017703         2.9375       2.564453          0.373047   \n",
       "3             0.015303         3.1875       2.216797          0.970703   \n",
       "4             0.018903         3.6875       2.738281          0.949219   \n",
       "5             0.013206         3.3125       1.913086          1.399414   \n",
       "6             0.015909         3.2500       2.304688          0.945312   \n",
       "7             0.013503         2.8125       1.956055          0.856445   \n",
       "8             0.015006         3.8750       2.173828          1.701172   \n",
       "9             0.013503         3.3125       1.956055          1.356445   \n",
       "10            0.018309         3.0625       2.652344          0.410156   \n",
       "11            0.015006         2.1250       2.173828         -0.048828   \n",
       "12            0.014709         3.1875       2.130859          1.056641   \n",
       "13            0.016206         3.1875       2.347656          0.839844   \n",
       "14            0.015613         2.1875       2.261719         -0.074219   \n",
       "15            0.015303         3.0000       2.216797          0.783203   \n",
       "16            0.015909         2.9375       2.304688          0.632812   \n",
       "17            0.013806         2.3750       2.000000          0.375000   \n",
       "18            0.019806         3.8750       2.869141          1.005859   \n",
       "19            0.022206         3.5625       3.216797          0.345703   \n",
       "20            0.012606         1.8750       1.826172          0.048828   \n",
       "21            0.015909         1.9375       2.304688         -0.367188   \n",
       "22            0.015006         2.5000       2.173828          0.326172   \n",
       "23            0.013806         2.9375       2.000000          0.937500   \n",
       "24            0.019806         4.1875       2.869141          1.318359   \n",
       "25            0.010806         1.5625       1.565430         -0.002930   \n",
       "26            0.019213         3.7500       2.783203          0.966797   \n",
       "27            0.015303         2.6250       2.216797          0.408203   \n",
       "28            0.017703         3.7500       2.564453          1.185547   \n",
       "29            0.018903         3.4375       2.738281          0.699219   \n",
       "30            0.015909         3.0625       2.304688          0.757812   \n",
       "31            0.023109         2.3750       3.347656         -0.972656   \n",
       "32            0.016503         3.3125       2.390625          0.921875   \n",
       "33            0.018903         4.1875       2.738281          1.449219   \n",
       "34            0.012903         2.1875       1.869141          0.318359   \n",
       "35            0.018606         3.6875       2.695312          0.992188   \n",
       "36            0.018013         2.9375       2.609375          0.328125   \n",
       "37            0.019213         3.8750       2.783203          1.091797   \n",
       "38            0.017406         2.1250       2.521484         -0.396484   \n",
       "39            0.012903         1.8750       1.869141          0.005859   \n",
       "40            0.013503         1.8125       1.956055         -0.143555   \n",
       "41            0.014709         2.7500       2.130859          0.619141   \n",
       "42            0.017406         3.3750       2.521484          0.853516   \n",
       "43            0.017703         3.2500       2.564453          0.685547   \n",
       "44            0.018309         3.1250       2.652344          0.472656   \n",
       "45            0.021613         2.5000       3.130859         -0.630859   \n",
       "46            0.016206         2.4375       2.347656          0.089844   \n",
       "47            0.018309         3.6250       2.652344          0.972656   \n",
       "48            0.012006         3.8125       1.739258          2.074219   \n",
       "49            0.022206         3.3750       3.216797          0.158203   \n",
       "50            0.016813         1.8125       2.435547         -0.623047   \n",
       "51            0.013503         1.8750       1.956055         -0.081055   \n",
       "52            0.015909         2.8750       2.304688          0.570312   \n",
       "53            0.018309         2.7500       2.652344          0.097656   \n",
       "54            0.015909         4.1875       2.304688          1.882812   \n",
       "55            0.019806         4.1875       2.869141          1.318359   \n",
       "56            0.019806         3.5000       2.869141          0.630859   \n",
       "57            0.018309         1.9375       2.652344         -0.714844   \n",
       "58            0.014709         1.9375       2.130859         -0.193359   \n",
       "59            0.021613         4.3750       3.130859          1.244141   \n",
       "\n",
       "     en_zipf   ch_zipf  \n",
       "0   0.005630  0.012686  \n",
       "1   0.004047  0.005766  \n",
       "2   0.007194  0.012686  \n",
       "3   0.009250  0.007462  \n",
       "4   0.021583  0.021144  \n",
       "5   0.010791  0.005766  \n",
       "6   0.009961  0.008457  \n",
       "7   0.006475  0.006041  \n",
       "8   0.043166  0.007048  \n",
       "9   0.010791  0.006041  \n",
       "10  0.008094  0.015858  \n",
       "11  0.004796  0.007048  \n",
       "12  0.009250  0.006677  \n",
       "13  0.009250  0.009062  \n",
       "14  0.004981  0.007929  \n",
       "15  0.007618  0.007462  \n",
       "16  0.007194  0.008457  \n",
       "17  0.005180  0.006343  \n",
       "18  0.043166  0.031716  \n",
       "19  0.016187  0.063431  \n",
       "20  0.004465  0.005286  \n",
       "21  0.004625  0.008457  \n",
       "22  0.005630  0.007048  \n",
       "23  0.007194  0.006343  \n",
       "24  0.064749  0.031716  \n",
       "25  0.004177  0.004879  \n",
       "26  0.025900  0.025372  \n",
       "27  0.005886  0.007462  \n",
       "28  0.025900  0.012686  \n",
       "29  0.012950  0.021144  \n",
       "30  0.008094  0.008457  \n",
       "31  0.005180  0.126862  \n",
       "32  0.010791  0.009759  \n",
       "33  0.064749  0.021144  \n",
       "34  0.004981  0.005516  \n",
       "35  0.021583  0.018123  \n",
       "36  0.007194  0.014096  \n",
       "37  0.043166  0.025372  \n",
       "38  0.004796  0.011533  \n",
       "39  0.004465  0.005516  \n",
       "40  0.004317  0.006041  \n",
       "41  0.006167  0.006677  \n",
       "42  0.011773  0.011533  \n",
       "43  0.009961  0.012686  \n",
       "44  0.008633  0.015858  \n",
       "45  0.005630  0.042287  \n",
       "46  0.005396  0.009062  \n",
       "47  0.018500  0.015858  \n",
       "48  0.032374  0.005074  \n",
       "49  0.011773  0.063431  \n",
       "50  0.004317  0.010572  \n",
       "51  0.004465  0.006041  \n",
       "52  0.006816  0.008457  \n",
       "53  0.006167  0.015858  \n",
       "54  0.064749  0.008457  \n",
       "55  0.064749  0.031716  \n",
       "56  0.014389  0.031716  \n",
       "57  0.004625  0.015858  \n",
       "58  0.004625  0.006677  \n",
       "59  0.129498  0.042287  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# zipf need\n",
    "need_prob['en_zipf'] = pd.read_csv('survey_data/need_prob/need_prob_zipf.csv')[['en']]\n",
    "need_prob['ch_zipf'] = pd.read_csv('survey_data/need_prob/need_prob_zipf.csv')[['ch']]\n",
    "need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>en_ngram_freq_1999_undivided</th>\n",
       "      <th>en_ngram_freq_1999_divided</th>\n",
       "      <th>en_ngram_freq_2019_undivided</th>\n",
       "      <th>en_ngram_1999_undivided</th>\n",
       "      <th>en_ngram_1999_divided</th>\n",
       "      <th>en_ngram_2019_undivided</th>\n",
       "      <th>en_survey_nontrans</th>\n",
       "      <th>ch_survey_nontrans</th>\n",
       "      <th>en_survey_raw</th>\n",
       "      <th>ch_survey_raw</th>\n",
       "      <th>en_ch_survey_raw</th>\n",
       "      <th>en_zipf</th>\n",
       "      <th>ch_zipf</th>\n",
       "      <th>en_survey_raw_sort</th>\n",
       "      <th>ch_survey_raw_sort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>6.400000e-06</td>\n",
       "      <td>7.084100e-06</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.038558</td>\n",
       "      <td>0.008578</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>-0.064453</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.012686</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>7.110000e-07</td>\n",
       "      <td>2.965000e-07</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.004284</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>1.3750</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>-0.538086</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>1.390000e-08</td>\n",
       "      <td>6.930000e-08</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.373047</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.012686</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>5.690000e-07</td>\n",
       "      <td>2.600000e-07</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.970703</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.007462</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.650000e-06</td>\n",
       "      <td>3.206800e-06</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.949219</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.021144</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.913086</td>\n",
       "      <td>1.399414</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.945312</td>\n",
       "      <td>0.009961</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.110000e-07</td>\n",
       "      <td>2.471000e-07</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.015851</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>0.856445</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>1.701172</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>4.740000e-07</td>\n",
       "      <td>5.931000e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>1.356445</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>6.590000e-07</td>\n",
       "      <td>5.766000e-07</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.003970</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>0.015858</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>1.440000e-06</td>\n",
       "      <td>5.894000e-07</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>-0.048828</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>1.056641</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.839844</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>9.220000e-08</td>\n",
       "      <td>1.650000e-08</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.015613</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>2.261719</td>\n",
       "      <td>-0.074219</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.007929</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.783203</td>\n",
       "      <td>0.007618</td>\n",
       "      <td>0.007462</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.632812</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.005859</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.031716</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>3.420000e-07</td>\n",
       "      <td>1.483000e-07</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.5625</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.345703</td>\n",
       "      <td>0.016187</td>\n",
       "      <td>0.063431</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>2.110000e-05</td>\n",
       "      <td>1.967080e-05</td>\n",
       "      <td>0.031032</td>\n",
       "      <td>0.127120</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012606</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.826172</td>\n",
       "      <td>0.048828</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.005286</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>-0.367188</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.015006</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>2.173828</td>\n",
       "      <td>0.326172</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.031716</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>8.320000e-08</td>\n",
       "      <td>3.813000e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>0.010806</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.565430</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.004879</td>\n",
       "      <td>31.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.160000e-06</td>\n",
       "      <td>2.075800e-06</td>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>0.966797</td>\n",
       "      <td>0.025900</td>\n",
       "      <td>0.025372</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.014794</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>2.216797</td>\n",
       "      <td>0.408203</td>\n",
       "      <td>0.005886</td>\n",
       "      <td>0.007462</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2.300000e-06</td>\n",
       "      <td>7.680000e-07</td>\n",
       "      <td>1.466200e-06</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.021134</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>1.185547</td>\n",
       "      <td>0.025900</td>\n",
       "      <td>0.012686</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>3.4375</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>0.699219</td>\n",
       "      <td>0.012950</td>\n",
       "      <td>0.021144</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.757812</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>1.510000e-06</td>\n",
       "      <td>6.079200e-06</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>0.013385</td>\n",
       "      <td>0.023109</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>3.347656</td>\n",
       "      <td>-0.972656</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.126862</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>2.390625</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.738281</td>\n",
       "      <td>1.449219</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.021144</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.318359</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.005516</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.320000e-06</td>\n",
       "      <td>1.768100e-06</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.020782</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>2.695312</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.018123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.016555</td>\n",
       "      <td>0.018013</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>2.609375</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.007194</td>\n",
       "      <td>0.014096</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>2.860000e-05</td>\n",
       "      <td>3.170000e-06</td>\n",
       "      <td>4.263660e-05</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.021839</td>\n",
       "      <td>0.019213</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>2.783203</td>\n",
       "      <td>1.091797</td>\n",
       "      <td>0.043166</td>\n",
       "      <td>0.025372</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>-0.396484</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.011533</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.869141</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.005516</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>7.850000e-06</td>\n",
       "      <td>3.920000e-06</td>\n",
       "      <td>5.848500e-06</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.143555</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>0.619141</td>\n",
       "      <td>0.006167</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.230000e-06</td>\n",
       "      <td>2.405300e-06</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.017406</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>0.853516</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.011533</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>5.270000e-08</td>\n",
       "      <td>1.153000e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>2.564453</td>\n",
       "      <td>0.685547</td>\n",
       "      <td>0.009961</td>\n",
       "      <td>0.012686</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>1.190000e-07</td>\n",
       "      <td>6.590000e-08</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.472656</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.015858</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.780000e-07</td>\n",
       "      <td>2.560000e-07</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>-0.630859</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.042287</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>8.113000e-06</td>\n",
       "      <td>2.700000e-06</td>\n",
       "      <td>7.644300e-06</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.013737</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>2.4375</td>\n",
       "      <td>2.347656</td>\n",
       "      <td>0.089844</td>\n",
       "      <td>0.005396</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.972656</td>\n",
       "      <td>0.018500</td>\n",
       "      <td>0.015858</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>1.739258</td>\n",
       "      <td>2.074219</td>\n",
       "      <td>0.032374</td>\n",
       "      <td>0.005074</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1.890000e-05</td>\n",
       "      <td>9.430000e-06</td>\n",
       "      <td>2.810590e-05</td>\n",
       "      <td>0.027796</td>\n",
       "      <td>0.056812</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>0.019021</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>3.216797</td>\n",
       "      <td>0.158203</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.063431</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.010215</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>1.8125</td>\n",
       "      <td>2.435547</td>\n",
       "      <td>-0.623047</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.010572</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1.320000e-08</td>\n",
       "      <td>6.600000e-09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.013503</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>1.956055</td>\n",
       "      <td>-0.081055</td>\n",
       "      <td>0.004465</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.710000e-07</td>\n",
       "      <td>1.812000e-07</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.016203</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>0.570312</td>\n",
       "      <td>0.006816</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>9.570000e-07</td>\n",
       "      <td>7.627000e-07</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>0.006167</td>\n",
       "      <td>0.015858</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.350000e-06</td>\n",
       "      <td>1.270000e-06</td>\n",
       "      <td>6.359200e-06</td>\n",
       "      <td>0.009339</td>\n",
       "      <td>0.007651</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.304688</td>\n",
       "      <td>1.882812</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.008457</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.040000e-05</td>\n",
       "      <td>2.283400e-05</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.027648</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>4.1875</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>1.318359</td>\n",
       "      <td>0.064749</td>\n",
       "      <td>0.031716</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>6.850000e-07</td>\n",
       "      <td>2.306000e-07</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>2.869141</td>\n",
       "      <td>0.630859</td>\n",
       "      <td>0.014389</td>\n",
       "      <td>0.031716</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.018309</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.652344</td>\n",
       "      <td>-0.714844</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.015858</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>2.650000e-05</td>\n",
       "      <td>2.940000e-06</td>\n",
       "      <td>2.693620e-05</td>\n",
       "      <td>0.038974</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>0.032615</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>0.014709</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.130859</td>\n",
       "      <td>-0.193359</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>1.900000e-06</td>\n",
       "      <td>2.773400e-06</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.024657</td>\n",
       "      <td>0.021613</td>\n",
       "      <td>4.3750</td>\n",
       "      <td>3.130859</td>\n",
       "      <td>1.244141</td>\n",
       "      <td>0.129498</td>\n",
       "      <td>0.042287</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj  en_ngram_freq_1999_undivided  en_ngram_freq_1999_divided  \\\n",
       "0     1                  6.400000e-06                6.400000e-06   \n",
       "1     2                  7.110000e-07                7.110000e-07   \n",
       "2     3                  1.390000e-08                1.390000e-08   \n",
       "3     4                  5.690000e-07                5.690000e-07   \n",
       "4     5                  3.650000e-06                3.650000e-06   \n",
       "5     6                  2.650000e-05                2.940000e-06   \n",
       "6     7                  2.860000e-05                3.170000e-06   \n",
       "7     8                  2.110000e-07                2.110000e-07   \n",
       "8     9                  2.650000e-05                2.940000e-06   \n",
       "9    10                  4.740000e-07                4.740000e-07   \n",
       "10   11                  6.590000e-07                6.590000e-07   \n",
       "11   12                  1.440000e-06                1.440000e-06   \n",
       "12   13                  2.300000e-06                7.680000e-07   \n",
       "13   14                  2.860000e-05                3.170000e-06   \n",
       "14   15                  9.220000e-08                9.220000e-08   \n",
       "15   16                  2.650000e-05                2.940000e-06   \n",
       "16   17                  2.300000e-06                7.680000e-07   \n",
       "17   18                  2.860000e-05                3.170000e-06   \n",
       "18   19                  2.860000e-05                3.170000e-06   \n",
       "19   20                  3.420000e-07                3.420000e-07   \n",
       "20   21                  2.110000e-05                2.110000e-05   \n",
       "21   22                  2.650000e-05                2.940000e-06   \n",
       "22   23                  8.113000e-06                2.700000e-06   \n",
       "23   24                  8.113000e-06                2.700000e-06   \n",
       "24   25                  1.320000e-08                6.600000e-09   \n",
       "25   26                  8.320000e-08                8.320000e-08   \n",
       "26   27                  2.160000e-06                2.160000e-06   \n",
       "27   28                  6.350000e-06                1.270000e-06   \n",
       "28   29                  2.300000e-06                7.680000e-07   \n",
       "29   30                  2.860000e-05                3.170000e-06   \n",
       "30   31                  2.860000e-05                3.170000e-06   \n",
       "31   32                  1.510000e-06                1.510000e-06   \n",
       "32   33                  6.350000e-06                1.270000e-06   \n",
       "33   34                  2.860000e-05                3.170000e-06   \n",
       "34   35                  2.650000e-05                2.940000e-06   \n",
       "35   36                  1.320000e-06                1.320000e-06   \n",
       "36   37                  2.860000e-05                3.170000e-06   \n",
       "37   38                  2.860000e-05                3.170000e-06   \n",
       "38   39                  2.650000e-05                2.940000e-06   \n",
       "39   40                  7.850000e-06                3.920000e-06   \n",
       "40   41                  7.850000e-06                3.920000e-06   \n",
       "41   42                  1.890000e-05                9.430000e-06   \n",
       "42   43                  2.230000e-06                2.230000e-06   \n",
       "43   44                  5.270000e-08                5.270000e-08   \n",
       "44   45                  1.190000e-07                1.190000e-07   \n",
       "45   46                  2.780000e-07                2.780000e-07   \n",
       "46   47                  8.113000e-06                2.700000e-06   \n",
       "47   48                  2.650000e-05                2.940000e-06   \n",
       "48   49                  6.350000e-06                1.270000e-06   \n",
       "49   50                  1.890000e-05                9.430000e-06   \n",
       "50   51                  6.350000e-06                1.270000e-06   \n",
       "51   52                  1.320000e-08                6.600000e-09   \n",
       "52   53                  1.710000e-07                1.710000e-07   \n",
       "53   54                  9.570000e-07                9.570000e-07   \n",
       "54   55                  6.350000e-06                1.270000e-06   \n",
       "55   56                  2.040000e-05                2.040000e-05   \n",
       "56   57                  6.850000e-07                6.850000e-07   \n",
       "57   58                  2.650000e-05                2.940000e-06   \n",
       "58   59                  2.650000e-05                2.940000e-06   \n",
       "59   60                  1.900000e-06                1.900000e-06   \n",
       "\n",
       "    en_ngram_freq_2019_undivided  en_ngram_1999_undivided  \\\n",
       "0                   7.084100e-06                 0.009413   \n",
       "1                   2.965000e-07                 0.001046   \n",
       "2                   6.930000e-08                 0.000020   \n",
       "3                   2.600000e-07                 0.000837   \n",
       "4                   3.206800e-06                 0.005368   \n",
       "5                   2.693620e-05                 0.038974   \n",
       "6                   4.263660e-05                 0.042062   \n",
       "7                   2.471000e-07                 0.000310   \n",
       "8                   2.693620e-05                 0.038974   \n",
       "9                   5.931000e-07                 0.000697   \n",
       "10                  5.766000e-07                 0.000969   \n",
       "11                  5.894000e-07                 0.002118   \n",
       "12                  1.466200e-06                 0.003383   \n",
       "13                  4.263660e-05                 0.042062   \n",
       "14                  1.650000e-08                 0.000136   \n",
       "15                  2.693620e-05                 0.038974   \n",
       "16                  1.466200e-06                 0.003383   \n",
       "17                  4.263660e-05                 0.042062   \n",
       "18                  4.263660e-05                 0.042062   \n",
       "19                  1.483000e-07                 0.000503   \n",
       "20                  1.967080e-05                 0.031032   \n",
       "21                  2.693620e-05                 0.038974   \n",
       "22                  7.644300e-06                 0.011932   \n",
       "23                  7.644300e-06                 0.011932   \n",
       "24                  0.000000e+00                 0.000019   \n",
       "25                  3.813000e-07                 0.000122   \n",
       "26                  2.075800e-06                 0.003177   \n",
       "27                  6.359200e-06                 0.009339   \n",
       "28                  1.466200e-06                 0.003383   \n",
       "29                  4.263660e-05                 0.042062   \n",
       "30                  4.263660e-05                 0.042062   \n",
       "31                  6.079200e-06                 0.002221   \n",
       "32                  6.359200e-06                 0.009339   \n",
       "33                  4.263660e-05                 0.042062   \n",
       "34                  2.693620e-05                 0.038974   \n",
       "35                  1.768100e-06                 0.001941   \n",
       "36                  4.263660e-05                 0.042062   \n",
       "37                  4.263660e-05                 0.042062   \n",
       "38                  2.693620e-05                 0.038974   \n",
       "39                  5.848500e-06                 0.011545   \n",
       "40                  5.848500e-06                 0.011545   \n",
       "41                  2.810590e-05                 0.027796   \n",
       "42                  2.405300e-06                 0.003280   \n",
       "43                  1.153000e-07                 0.000078   \n",
       "44                  6.590000e-08                 0.000175   \n",
       "45                  2.560000e-07                 0.000409   \n",
       "46                  7.644300e-06                 0.011932   \n",
       "47                  2.693620e-05                 0.038974   \n",
       "48                  6.359200e-06                 0.009339   \n",
       "49                  2.810590e-05                 0.027796   \n",
       "50                  6.359200e-06                 0.009339   \n",
       "51                  0.000000e+00                 0.000019   \n",
       "52                  1.812000e-07                 0.000251   \n",
       "53                  7.627000e-07                 0.001407   \n",
       "54                  6.359200e-06                 0.009339   \n",
       "55                  2.283400e-05                 0.030002   \n",
       "56                  2.306000e-07                 0.001007   \n",
       "57                  2.693620e-05                 0.038974   \n",
       "58                  2.693620e-05                 0.038974   \n",
       "59                  2.773400e-06                 0.002794   \n",
       "\n",
       "    en_ngram_1999_divided  en_ngram_2019_undivided  en_survey_nontrans  \\\n",
       "0                0.038558                 0.008578            0.014089   \n",
       "1                0.004284                 0.000359            0.007749   \n",
       "2                0.000084                 0.000084            0.016555   \n",
       "3                0.003428                 0.000315            0.017964   \n",
       "4                0.021990                 0.003883            0.020782   \n",
       "5                0.017712                 0.032615            0.018669   \n",
       "6                0.019098                 0.051626            0.018316   \n",
       "7                0.001271                 0.000299            0.015851   \n",
       "8                0.017712                 0.032615            0.021839   \n",
       "9                0.002856                 0.000718            0.018669   \n",
       "10               0.003970                 0.000698            0.017260   \n",
       "11               0.008675                 0.000714            0.011976   \n",
       "12               0.004627                 0.001775            0.017964   \n",
       "13               0.019098                 0.051626            0.017964   \n",
       "14               0.000555                 0.000020            0.012328   \n",
       "15               0.017712                 0.032615            0.016907   \n",
       "16               0.004627                 0.001775            0.016555   \n",
       "17               0.019098                 0.051626            0.013385   \n",
       "18               0.019098                 0.051626            0.021839   \n",
       "19               0.002060                 0.000180            0.020077   \n",
       "20               0.127120                 0.023818            0.010567   \n",
       "21               0.017712                 0.032615            0.010919   \n",
       "22               0.016267                 0.009256            0.014089   \n",
       "23               0.016267                 0.009256            0.016555   \n",
       "24               0.000040                 0.000000            0.023600   \n",
       "25               0.000501                 0.000462            0.008806   \n",
       "26               0.013013                 0.002513            0.021134   \n",
       "27               0.007651                 0.007700            0.014794   \n",
       "28               0.004627                 0.001775            0.021134   \n",
       "29               0.019098                 0.051626            0.019373   \n",
       "30               0.019098                 0.051626            0.017260   \n",
       "31               0.009097                 0.007361            0.013385   \n",
       "32               0.007651                 0.007700            0.018669   \n",
       "33               0.019098                 0.051626            0.023600   \n",
       "34               0.017712                 0.032615            0.012328   \n",
       "35               0.007953                 0.002141            0.020782   \n",
       "36               0.019098                 0.051626            0.016555   \n",
       "37               0.019098                 0.051626            0.021839   \n",
       "38               0.017712                 0.032615            0.011976   \n",
       "39               0.023617                 0.007082            0.010567   \n",
       "40               0.023617                 0.007082            0.010215   \n",
       "41               0.056812                 0.034032            0.015498   \n",
       "42               0.013435                 0.002912            0.019021   \n",
       "43               0.000317                 0.000140            0.018316   \n",
       "44               0.000717                 0.000080            0.017612   \n",
       "45               0.001675                 0.000310            0.014089   \n",
       "46               0.016267                 0.009256            0.013737   \n",
       "47               0.017712                 0.032615            0.020430   \n",
       "48               0.007651                 0.007700            0.021486   \n",
       "49               0.056812                 0.034032            0.019021   \n",
       "50               0.007651                 0.007700            0.010215   \n",
       "51               0.000040                 0.000000            0.010567   \n",
       "52               0.001030                 0.000219            0.016203   \n",
       "53               0.005766                 0.000924            0.015498   \n",
       "54               0.007651                 0.007700            0.023600   \n",
       "55               0.122903                 0.027648            0.023600   \n",
       "56               0.004127                 0.000279            0.019725   \n",
       "57               0.017712                 0.032615            0.010919   \n",
       "58               0.017712                 0.032615            0.010919   \n",
       "59               0.011447                 0.003358            0.024657   \n",
       "\n",
       "    ch_survey_nontrans  en_survey_raw  ch_survey_raw  en_ch_survey_raw  \\\n",
       "0             0.017703         2.5000       2.564453         -0.064453   \n",
       "1             0.013206         1.3750       1.913086         -0.538086   \n",
       "2             0.017703         2.9375       2.564453          0.373047   \n",
       "3             0.015303         3.1875       2.216797          0.970703   \n",
       "4             0.018903         3.6875       2.738281          0.949219   \n",
       "5             0.013206         3.3125       1.913086          1.399414   \n",
       "6             0.015909         3.2500       2.304688          0.945312   \n",
       "7             0.013503         2.8125       1.956055          0.856445   \n",
       "8             0.015006         3.8750       2.173828          1.701172   \n",
       "9             0.013503         3.3125       1.956055          1.356445   \n",
       "10            0.018309         3.0625       2.652344          0.410156   \n",
       "11            0.015006         2.1250       2.173828         -0.048828   \n",
       "12            0.014709         3.1875       2.130859          1.056641   \n",
       "13            0.016206         3.1875       2.347656          0.839844   \n",
       "14            0.015613         2.1875       2.261719         -0.074219   \n",
       "15            0.015303         3.0000       2.216797          0.783203   \n",
       "16            0.015909         2.9375       2.304688          0.632812   \n",
       "17            0.013806         2.3750       2.000000          0.375000   \n",
       "18            0.019806         3.8750       2.869141          1.005859   \n",
       "19            0.022206         3.5625       3.216797          0.345703   \n",
       "20            0.012606         1.8750       1.826172          0.048828   \n",
       "21            0.015909         1.9375       2.304688         -0.367188   \n",
       "22            0.015006         2.5000       2.173828          0.326172   \n",
       "23            0.013806         2.9375       2.000000          0.937500   \n",
       "24            0.019806         4.1875       2.869141          1.318359   \n",
       "25            0.010806         1.5625       1.565430         -0.002930   \n",
       "26            0.019213         3.7500       2.783203          0.966797   \n",
       "27            0.015303         2.6250       2.216797          0.408203   \n",
       "28            0.017703         3.7500       2.564453          1.185547   \n",
       "29            0.018903         3.4375       2.738281          0.699219   \n",
       "30            0.015909         3.0625       2.304688          0.757812   \n",
       "31            0.023109         2.3750       3.347656         -0.972656   \n",
       "32            0.016503         3.3125       2.390625          0.921875   \n",
       "33            0.018903         4.1875       2.738281          1.449219   \n",
       "34            0.012903         2.1875       1.869141          0.318359   \n",
       "35            0.018606         3.6875       2.695312          0.992188   \n",
       "36            0.018013         2.9375       2.609375          0.328125   \n",
       "37            0.019213         3.8750       2.783203          1.091797   \n",
       "38            0.017406         2.1250       2.521484         -0.396484   \n",
       "39            0.012903         1.8750       1.869141          0.005859   \n",
       "40            0.013503         1.8125       1.956055         -0.143555   \n",
       "41            0.014709         2.7500       2.130859          0.619141   \n",
       "42            0.017406         3.3750       2.521484          0.853516   \n",
       "43            0.017703         3.2500       2.564453          0.685547   \n",
       "44            0.018309         3.1250       2.652344          0.472656   \n",
       "45            0.021613         2.5000       3.130859         -0.630859   \n",
       "46            0.016206         2.4375       2.347656          0.089844   \n",
       "47            0.018309         3.6250       2.652344          0.972656   \n",
       "48            0.012006         3.8125       1.739258          2.074219   \n",
       "49            0.022206         3.3750       3.216797          0.158203   \n",
       "50            0.016813         1.8125       2.435547         -0.623047   \n",
       "51            0.013503         1.8750       1.956055         -0.081055   \n",
       "52            0.015909         2.8750       2.304688          0.570312   \n",
       "53            0.018309         2.7500       2.652344          0.097656   \n",
       "54            0.015909         4.1875       2.304688          1.882812   \n",
       "55            0.019806         4.1875       2.869141          1.318359   \n",
       "56            0.019806         3.5000       2.869141          0.630859   \n",
       "57            0.018309         1.9375       2.652344         -0.714844   \n",
       "58            0.014709         1.9375       2.130859         -0.193359   \n",
       "59            0.021613         4.3750       3.130859          1.244141   \n",
       "\n",
       "     en_zipf   ch_zipf  en_survey_raw_sort  ch_survey_raw_sort  \n",
       "0   0.005630  0.012686                23.0                10.0  \n",
       "1   0.004047  0.005766                32.0                22.0  \n",
       "2   0.007194  0.012686                18.0                10.0  \n",
       "3   0.009250  0.007462                14.0                17.0  \n",
       "4   0.021583  0.021144                 6.0                 6.0  \n",
       "5   0.010791  0.005766                12.0                22.0  \n",
       "6   0.009961  0.008457                13.0                15.0  \n",
       "7   0.006475  0.006041                20.0                21.0  \n",
       "8   0.043166  0.007048                 3.0                18.0  \n",
       "9   0.010791  0.006041                12.0                21.0  \n",
       "10  0.008094  0.015858                16.0                 8.0  \n",
       "11  0.004796  0.007048                27.0                18.0  \n",
       "12  0.009250  0.006677                14.0                19.0  \n",
       "13  0.009250  0.009062                14.0                14.0  \n",
       "14  0.004981  0.007929                26.0                16.0  \n",
       "15  0.007618  0.007462                17.0                17.0  \n",
       "16  0.007194  0.008457                18.0                15.0  \n",
       "17  0.005180  0.006343                25.0                20.0  \n",
       "18  0.043166  0.031716                 3.0                 4.0  \n",
       "19  0.016187  0.063431                 8.0                 2.0  \n",
       "20  0.004465  0.005286                29.0                24.0  \n",
       "21  0.004625  0.008457                28.0                15.0  \n",
       "22  0.005630  0.007048                23.0                18.0  \n",
       "23  0.007194  0.006343                18.0                20.0  \n",
       "24  0.064749  0.031716                 2.0                 4.0  \n",
       "25  0.004177  0.004879                31.0                26.0  \n",
       "26  0.025900  0.025372                 5.0                 5.0  \n",
       "27  0.005886  0.007462                22.0                17.0  \n",
       "28  0.025900  0.012686                 5.0                10.0  \n",
       "29  0.012950  0.021144                10.0                 6.0  \n",
       "30  0.008094  0.008457                16.0                15.0  \n",
       "31  0.005180  0.126862                25.0                 1.0  \n",
       "32  0.010791  0.009759                12.0                13.0  \n",
       "33  0.064749  0.021144                 2.0                 6.0  \n",
       "34  0.004981  0.005516                26.0                23.0  \n",
       "35  0.021583  0.018123                 6.0                 7.0  \n",
       "36  0.007194  0.014096                18.0                 9.0  \n",
       "37  0.043166  0.025372                 3.0                 5.0  \n",
       "38  0.004796  0.011533                27.0                11.0  \n",
       "39  0.004465  0.005516                29.0                23.0  \n",
       "40  0.004317  0.006041                30.0                21.0  \n",
       "41  0.006167  0.006677                21.0                19.0  \n",
       "42  0.011773  0.011533                11.0                11.0  \n",
       "43  0.009961  0.012686                13.0                10.0  \n",
       "44  0.008633  0.015858                15.0                 8.0  \n",
       "45  0.005630  0.042287                23.0                 3.0  \n",
       "46  0.005396  0.009062                24.0                14.0  \n",
       "47  0.018500  0.015858                 7.0                 8.0  \n",
       "48  0.032374  0.005074                 4.0                25.0  \n",
       "49  0.011773  0.063431                11.0                 2.0  \n",
       "50  0.004317  0.010572                30.0                12.0  \n",
       "51  0.004465  0.006041                29.0                21.0  \n",
       "52  0.006816  0.008457                19.0                15.0  \n",
       "53  0.006167  0.015858                21.0                 8.0  \n",
       "54  0.064749  0.008457                 2.0                15.0  \n",
       "55  0.064749  0.031716                 2.0                 4.0  \n",
       "56  0.014389  0.031716                 9.0                 4.0  \n",
       "57  0.004625  0.015858                28.0                 8.0  \n",
       "58  0.004625  0.006677                28.0                19.0  \n",
       "59  0.129498  0.042287                 1.0                 3.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add ranking\n",
    "need_prob[\"en_survey_raw_sort\"] = need_prob_rating_en_excluded['rank']\n",
    "need_prob[\"ch_survey_raw_sort\"] = need_prob_rating_ch_excluded['rank']\n",
    "need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'need_prob' (DataFrame)\n"
     ]
    }
   ],
   "source": [
    "%store need_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need_prob.sort_values('en_ch_survey_raw').to_csv(\"need_prob_all.csv\")\n",
    "need_prob.to_csv(\"need_prob_all.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Need Prob Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r need_prob\n",
    "%store -r modal_name_naming"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corpus vs subjective estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ngram need prob freq (1999 non-lemmatized undivided) vs our EN need ratings non-transformed (both not normalized)\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.regplot(x = need_prob[\"en_survey_raw\"], y = need_prob[\"en_ngram_freq_1999_undivided\"], truncate=False)\n",
    "\n",
    "ax.set_xlabel(\"EN need estimate\", fontsize=12)\n",
    "ax.set_ylabel(\"EN ngram frequency 1999 \\n\", fontsize=12)  \n",
    "ax.set_title(\"Corpus vs. estimates\", fontsize=20, pad=20)\n",
    "plt.xlim([1.2, 4.5])\n",
    "plt.xticks(np.arange(1.5, 4.6, 0.5))\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_corpus_vs_survey.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ngram vs en survey regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "X = need_prob[\"en_survey_raw\"].values.reshape((-1, 1))\n",
    "y = need_prob[\"en_ngram_freq_1999_undivided\"]\n",
    "reg = LinearRegression().fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept: 9.290425e-06\n",
      "slope: [6.8800693e-07]\n"
     ]
    }
   ],
   "source": [
    "# coefficients\n",
    "print('intercept:', reg.intercept_)\n",
    "print('slope:', reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                 \n",
      "========================================================================================\n",
      "Dep. Variable:     en_ngram_freq_1999_undivided   R-squared:                       0.002\n",
      "Model:                                      OLS   Adj. R-squared:                 -0.015\n",
      "Method:                           Least Squares   F-statistic:                    0.1154\n",
      "Date:                          Sat, 04 Jun 2022   Prob (F-statistic):              0.735\n",
      "Time:                                  19:03:09   Log-Likelihood:                 596.45\n",
      "No. Observations:                            60   AIC:                            -1189.\n",
      "Df Residuals:                                58   BIC:                            -1185.\n",
      "Df Model:                                     1                                         \n",
      "Covariance Type:                      nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       9.295e-06   6.19e-06      1.502      0.139    -3.1e-06    2.17e-05\n",
      "x1           6.89e-07   2.03e-06      0.340      0.735   -3.37e-06    4.75e-06\n",
      "==============================================================================\n",
      "Omnibus:                       81.658   Durbin-Watson:                   1.854\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                8.466\n",
      "Skew:                           0.502   Prob(JB):                       0.0145\n",
      "Kurtosis:                       1.458   Cond. No.                         13.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# another way of getting linear regression results \n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "X = need_prob[\"en_survey_raw\"].values.reshape((-1, 1))\n",
    "y = need_prob[\"en_ngram_freq_1999_undivided\"]\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EN Corpus 2019 vs 1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EN ngram need prob freq 1999 vs 2019 without transform (both not normalized)\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.regplot(x = need_prob[\"en_ngram_freq_2019_undivided\"], y = need_prob[\"en_ngram_freq_1999_undivided\"], truncate=False)\n",
    "\n",
    "ax.set_xlabel(\"EN ngram frequency 2019\", fontsize=12)\n",
    "ax.set_ylabel(\"EN ngram frequency 1999 \\n\", fontsize=12)  \n",
    "ax.set_title(\"Corpus 1999 vs. 2019\", fontsize=20, pad=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_corpus_1999_vs_2019.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                 \n",
      "========================================================================================\n",
      "Dep. Variable:     en_ngram_freq_1999_undivided   R-squared:                       0.936\n",
      "Model:                                      OLS   Adj. R-squared:                  0.935\n",
      "Method:                           Least Squares   F-statistic:                     850.2\n",
      "Date:                          Sat, 04 Jun 2022   Prob (F-statistic):           2.43e-36\n",
      "Time:                                  19:09:10   Log-Likelihood:                 678.92\n",
      "No. Observations:                            60   AIC:                            -1354.\n",
      "Df Residuals:                                58   BIC:                            -1350.\n",
      "Df Model:                                     1                                         \n",
      "Covariance Type:                      nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.366e-06   5.16e-07      2.645      0.010    3.32e-07     2.4e-06\n",
      "x1             0.7240      0.025     29.158      0.000       0.674       0.774\n",
      "==============================================================================\n",
      "Omnibus:                        6.409   Durbin-Watson:                   1.757\n",
      "Prob(Omnibus):                  0.041   Jarque-Bera (JB):                6.568\n",
      "Skew:                           0.794   Prob(JB):                       0.0375\n",
      "Kurtosis:                       2.676   Cond. No.                     6.41e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.41e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# linear regression\n",
    "\n",
    "X = need_prob[\"en_ngram_freq_2019_undivided\"].values.reshape((-1, 1))\n",
    "# print(X)\n",
    "y = need_prob[\"en_ngram_freq_1999_undivided\"]\n",
    "# print(y)\n",
    "X = sm.add_constant(X)\n",
    "# print(X)\n",
    "\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())\n",
    "\n",
    "\n",
    "# save output as txt\n",
    "with open('summary_EN_nonlemmatized_1999_2019.txt', 'w') as fh:\n",
    "    fh.write(results.summary().as_text())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subjective estimates CH vs EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# raw (non-normalized, non-transformed) EN vs CH need ratings from our study\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.regplot(x = need_prob[\"en_survey_raw\"], y = need_prob[\"ch_survey_raw\"], truncate=False)\n",
    "# plt.plot([0, 4.5], [0, 4.5], color=\"black\", linewidth=0.5)\n",
    "\n",
    "ax.set_xlabel(\"English\", fontsize=12)\n",
    "ax.set_ylabel(\"Chinese \\n\", fontsize=12)  \n",
    "ax.set_title(\"Estimates\", fontsize=20, pad=20)\n",
    "plt.xlim([1.2, 4.5])\n",
    "plt.ylim([1.2, 4.5])\n",
    "plt.xticks(np.arange(1.5, 4.6, 0.5))\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_ENraw_vs_CHraw.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          ch_survey_raw   R-squared:                       0.239\n",
      "Model:                            OLS   Adj. R-squared:                  0.225\n",
      "Method:                 Least Squares   F-statistic:                     18.17\n",
      "Date:                Sat, 04 Jun 2022   Prob (F-statistic):           7.53e-05\n",
      "Time:                        19:10:33   Log-Likelihood:                -21.836\n",
      "No. Observations:                  60   AIC:                             47.67\n",
      "Df Residuals:                      58   BIC:                             51.86\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.6506      0.185      8.927      0.000       1.280       2.021\n",
      "x1             0.2583      0.061      4.263      0.000       0.137       0.380\n",
      "==============================================================================\n",
      "Omnibus:                        4.950   Durbin-Watson:                   2.105\n",
      "Prob(Omnibus):                  0.084   Jarque-Bera (JB):                4.308\n",
      "Skew:                           0.426   Prob(JB):                        0.116\n",
      "Kurtosis:                       3.999   Cond. No.                         13.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# get linear regression results\n",
    "\n",
    "X = need_prob[\"en_survey_raw\"].values.reshape((-1, 1))\n",
    "y = need_prob[\"ch_survey_raw\"]\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EN subjective estimates sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EN raw rating sorted\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.regplot(x = need_prob[\"en_survey_raw_sort\"], y = need_prob[\"en_survey_raw\"], truncate=False, ci=None)\n",
    "\n",
    "ax.set_ylabel(\"Need estimate\", fontsize=15)  \n",
    "ax.set_title(\"EN estimates, sorted\", fontsize=20, pad=20)\n",
    "ax.xaxis.set_ticks([])\n",
    "# ax.set_xlabel(\"English\", fontsize=15)\n",
    "ax.set_xlabel(\"\")\n",
    "plt.ylim([1.2, 4.5])\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_survey_sorted_EN.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CH subjective estimates sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CH raw rating sorted\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "ax = sns.regplot(x = need_prob[\"ch_survey_raw_sort\"], y = need_prob[\"ch_survey_raw\"], truncate=False, ci=None)\n",
    "\n",
    "ax.set_ylabel(\"Need estimate\", fontsize=15)  \n",
    "ax.set_title(\"CH estimates, sorted\", fontsize=20, pad=20)\n",
    "ax.xaxis.set_ticks([])\n",
    "# ax.set_xlabel(\"Chinese\", fontsize=15)\n",
    "ax.set_xlabel(\"\")\n",
    "plt.ylim([1.2, 4.5])\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_survey_sorted_CH.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zipf CH vs EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed and normalized EN vs CH need ratings from our study\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax = sns.regplot(x = need_prob[\"en_zipf\"], y = need_prob[\"ch_zipf\"])\n",
    "\n",
    "ax.set_xlabel(\"English\", fontsize=15)\n",
    "ax.set_ylabel(\"Chinese\", fontsize=15)  \n",
    "ax.set_title(\"Zipf-transformed Need Probability\", fontsize=20, pad=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('need_ENzipf_vs_CHzipf.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity Space"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Since the pile sorts correlated highly across groups (Malt et al., 1999), we aggregated pile-sorting responses from participants across the two groups, and took the similarity sim(i,j) of any two objects i and j to be the proportion of all participants who sorted those two objects into the same pile.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for finding outerlist index given an element in an innerlist of the outerlist\n",
    "def find_outerlst_index(elm, outerlst):\n",
    "    for innerlst in outerlst:\n",
    "        if elm in innerlst:\n",
    "            return outerlst.index(innerlst)\n",
    "\n",
    "# [find_outerlst_index(1, piles) for piles in sorting_merged['piles']]  # sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# similarity space based on sorting results\n",
    "sim = {}  # key: tuple (i, j); value: similarity score\n",
    "num_ps = len(sorting_merged.index)  # total number of participants in the sorting task\n",
    "\n",
    "for i in range(1, 61):\n",
    "    for j in range (1, 61):\n",
    "        num_samepile = 0\n",
    "        for piles in sorting_merged['piles']:\n",
    "            if find_outerlst_index(i, piles) == find_outerlst_index(j, piles):\n",
    "                num_samepile += 1\n",
    "        sim[(i, j)] = num_samepile / num_ps\n",
    "\n",
    "# len(sim)  # sanity check\n",
    "# sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # sanity: print sim\n",
    "# for i in sim:\n",
    "#     print(i, sim[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %store sim"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulated Chaining in Similarity Space"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yang's Approach\n",
    "start with all exemplars unallocated to categories.  \n",
    "repeat until all exemplars allocated:\n",
    "- probabilistically select a cat C to either start or expand, where the probability of a cat being selected is proportional to the number of remaining unallocated exemplars it has.\n",
    "- start or expand C as follows:\n",
    "    - if you have not yet allocated any exemplars to C:\n",
    "        - randomly pick an unallocated exemplar and label it C.\n",
    "    - else (i.e. you do already have one or more exemplars allocated to C, and we will need to chain from them):\n",
    "        - choose an exemplar E from C [Yang's approach: this will be the exemplar that is closest to some non-allocated exemplar]\n",
    "        - chain from E, as follows:\n",
    "            - select a new exemplar by sampling as-yet-unallocated exemplars in proportion to their similarity with E.\n",
    "            - label that new exemplar with label C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### add columns orig, orig_sq, sq to naming_en_lsdf to track chaining sequence #####\n",
    "\n",
    "def chaining_yang(sim, cat_freq_df):\n",
    "    \n",
    "    # creating and pre-populating a 5-column df (columns = ['obj', 'name', 'cat', 'orig', 'orig_sq', 'sq']):\n",
    "    d = {'obj': [i for i in range(1, 60 + 1)], 'cat': ['NA' for i in range(60)], 'orig': [0 for i in range(60)], 'orig_sq': [0 for i in range(60)], 'sq': [0 for i in range(60)]}\n",
    "    naming_lsdf = pd.DataFrame(data = d)    \n",
    "    \n",
    "    \n",
    "    cat_tracker = cat_freq_df.copy()  # tracking remaining capacity of each cat\n",
    "    remaining_lsdf = naming_lsdf[naming_lsdf['cat'] == 'NA']  # tracking unassigned exemplars\n",
    "    \n",
    "    while 'NA' in naming_lsdf['cat'].tolist():\n",
    "        \n",
    "        # probabilistically select a cat exp_cat to either start or expand:\n",
    "        p_cat_ratio = np.array(cat_tracker.ct.tolist())  # convert lst of raw prob to array\n",
    "        p_cat = p_cat_ratio / p_cat_ratio.sum()  # normalize. otherwise \"probability does not sum to 1\" error with np.random.choice\n",
    "        exp_cat = np.random.choice(cat_tracker.index, p = p_cat)  # the cat to be expanded\n",
    "        \n",
    "        # start expanding:\n",
    "        if exp_cat not in naming_lsdf['cat'].tolist():  # if not yet allocated any exemplars to exp_cat\n",
    "            ex = np.random.choice(remaining_lsdf['obj'])  # randomly pick an unallocated exemplar ex and label it exp_cat\n",
    "            naming_lsdf.at[naming_lsdf[naming_lsdf.obj == ex].index[0], 'cat'] = exp_cat  # assign cat\n",
    "            naming_lsdf.at[naming_lsdf[naming_lsdf.obj == ex].index[0], 'sq'] = 1 # mark sequence of cat assignment\n",
    "        else:\n",
    "            same_cat_all = naming_lsdf[naming_lsdf.cat == exp_cat].obj.tolist()  # list of allocated exp in exp_cat\n",
    "            # choose the exemplar in exp_cat that is closest to some non-allocated exemplar to expand from:\n",
    "            max_sim = 0  # for tracking the max similarity between allocated exemplar and non-allocated exemplar\n",
    "            init_ex = []  # for tracking selected exemplar(s) to expand from\n",
    "            for i in same_cat_all:\n",
    "                i_max_sim = max([sim[(i, j)] for j in remaining_lsdf['obj'].tolist()])  # allocated exemplar i's max similarity to some non-allocated exemplar\n",
    "                if i_max_sim > max_sim:\n",
    "                    max_sim = i_max_sim\n",
    "                    init_ex = [i]\n",
    "                if i_max_sim == max_sim: \n",
    "                    init_ex.append(i)\n",
    "            init_ex = np.random.choice(init_ex)  # the chosen exemplar to expand from\n",
    "            sq_last = naming_lsdf[naming_lsdf.obj == init_ex].sq.item()\n",
    "            # select a new exemplar by sampling as-yet-unallocated exemplars in proportion to their similarity with init_ex. label that new exemplar exp_cat: \n",
    "            p_ratio = [sim[(init_ex, j)] for j in remaining_lsdf['obj'].tolist()]\n",
    "            p_ratio = np.array(p_ratio)  # convert lst to array\n",
    "#             display(p_ratio)  # sanity check for debugging\n",
    "            p = p_ratio / p_ratio.sum() if p_ratio.sum() != 0 else np.array([0]) # normalize. otherwise \"probability does not sum to 1\" error with np.random.choice\n",
    "################# attention: this part used to produce error occasionally due to 0 in denominator (fixed) ################          \n",
    "            if not np.array_equal(p, np.array([0])):  # check if p equals to np.array([0]). `if p != [0]` does not work: `The truth value of an array with more than one element is ambiguous`\n",
    "                exp_ex = np.random.choice(remaining_lsdf['obj'], p = p)  # pick an exemplar to assign the exp_cat label\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'cat'] = exp_cat\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'orig'] = init_ex\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'orig_sq'] = sq_last\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'sq'] = sq_last + 1\n",
    "            else:\n",
    "                exp_ex = np.random.choice(remaining_lsdf['obj'])  # pick an exemplar to assign the exp_cat label\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'cat'] = exp_cat\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'orig'] = init_ex\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'orig_sq'] = sq_last\n",
    "                naming_lsdf.at[naming_lsdf[naming_lsdf.obj == exp_ex].index[0], 'sq'] = sq_last + 1\n",
    "##################       \n",
    "        cat_tracker.at[exp_cat, 'ct'] -= 1  # update cat tracker\n",
    "        remaining_lsdf = naming_lsdf[naming_lsdf['cat'] == 'NA']  # tracking unassigned exemplars\n",
    " \n",
    "    \n",
    "    return naming_lsdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>cat</th>\n",
       "      <th>orig</th>\n",
       "      <th>orig_sq</th>\n",
       "      <th>sq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bottle</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>container</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>container</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>jug</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>bottle</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>bottle</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>jar</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>container</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>container</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>box</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>jar</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>tube</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>jar</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>container</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>jar</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>jar</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>container</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>can</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>jar</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>bottle</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>bottle</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>container</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>bottle</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>bottle</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>container</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>container</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>container</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>container</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>bottle</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>jar</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>container</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>jug</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>bottle</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>bottle</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>jug</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>container</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>jar</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>container</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>bottle</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>bottle</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>container</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>bottle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>bottle</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>can</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>can</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>bottle</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>can</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>jar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>jar</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>can</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>jar</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obj        cat  orig  orig_sq  sq\n",
       "0     1     bottle    23        2   3\n",
       "1     2     bottle     1        3   4\n",
       "2     3        jar    57        2   3\n",
       "3     4  container    10        3   4\n",
       "4     5  container     4        4   5\n",
       "5     6        jug     0        0   1\n",
       "6     7     bottle    31        5   6\n",
       "7     8     bottle    31        5   6\n",
       "8     9        jar    18        4   5\n",
       "9    10  container    30        2   3\n",
       "10   11  container    34        3   4\n",
       "11   12        box     0        0   1\n",
       "12   13        jar    18        4   5\n",
       "13   14       tube     0        0   1\n",
       "14   15        jar    13        5   6\n",
       "15   16  container    24        4   5\n",
       "16   17        jar    13        5   6\n",
       "17   18        jar    50        3   4\n",
       "18   19  container    34        3   4\n",
       "19   20        can    59        1   2\n",
       "20   21        jar    56        1   2\n",
       "21   22     bottle    47        1   2\n",
       "22   23     bottle    47        1   2\n",
       "23   24  container    34        3   4\n",
       "24   25     bottle    48        3   4\n",
       "25   26     bottle    23        2   3\n",
       "26   27  container    34        3   4\n",
       "27   28  container    34        3   4\n",
       "28   29  container    34        3   4\n",
       "29   30  container    39        1   2\n",
       "30   31     bottle    36        4   5\n",
       "31   32        jar    57        2   3\n",
       "32   33        jar    57        2   3\n",
       "33   34  container    30        2   3\n",
       "34   35        jug     6        1   2\n",
       "35   36     bottle    44        3   4\n",
       "36   37     bottle    31        5   6\n",
       "37   38        jug     6        1   2\n",
       "38   39  container     0        0   1\n",
       "39   40        jar    41        3   4\n",
       "40   41        jar    57        2   3\n",
       "41   42  container    10        3   4\n",
       "42   43        jar    57        2   3\n",
       "43   44     bottle    23        2   3\n",
       "44   45     bottle     1        3   4\n",
       "45   46  container    24        4   5\n",
       "46   47     bottle     0        0   1\n",
       "47   48     bottle    23        2   3\n",
       "48   49        jar    57        2   3\n",
       "49   50        jar    57        2   3\n",
       "50   51        can    55        2   3\n",
       "51   52        can    51        3   4\n",
       "52   53     bottle    22        2   3\n",
       "53   54        jar    57        2   3\n",
       "54   55        can    59        1   2\n",
       "55   56        jar     0        0   1\n",
       "56   57        jar    56        1   2\n",
       "57   58        jar    57        2   3\n",
       "58   59        can     0        0   1\n",
       "59   60        jar    57        2   3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "naming_en_ysdf1 = chaining_yang(sim, cat_freq_en_malt)\n",
    "naming_en_ysdf1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nMDS of Similarity Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html?highlight=multidimensional%20scaling\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "\n",
    "# generate and pre-populate a 60*60 np 2d array\n",
    "simtrx = np.zeros(3600).reshape((60, 60))\n",
    "# similarity matrix\n",
    "for i in range(60):\n",
    "    for j in range(60):\n",
    "        simtrx[i, j] = sim[(i+1, j+1)]\n",
    "\n",
    "# transform similarity matrix to distance matrix\n",
    "converter = lambda x: 1 - x\n",
    "vfunc = np.vectorize(converter)\n",
    "dismtrx = vfunc(simtrx)\n",
    "\n",
    "# get coordinates of the 60 objs through MDS\n",
    "mds = MDS(n_components=2, metric=True, n_init=50, max_iter=300, eps=0.001, dissimilarity=\"precomputed\", n_jobs=1)\n",
    "pos = mds.fit(dismtrx).embedding_\n",
    "nmds = MDS(n_components=2, metric=False, n_init=1, max_iter=1000, eps=0.001, dissimilarity=\"precomputed\", n_jobs=1)\n",
    "npos = nmds.fit_transform(dismtrx, init=pos)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naming EN vs CH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "\n",
    "cdict = {\"bottle\": 'red', \"container\": 'blue', \"jug\": \"gray\", \"can\": \"darkviolet\", \"jar\": \"green\", \"box\": \"pink\", \"tube\": \"cyan\",\n",
    "        \"canister\": \"gold\", \"tub\": \"orange\",\n",
    "        \"ping2\": \"red\", \"tong3\": \"gray\", \"guan4\": \"darkviolet\", \"he2\": \"pink\", \"guan3\": \"cyan\"}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "# plot EN 1999\n",
    "for cat in np.unique(modal_name_naming[\"cat_en_malt\"]):\n",
    "    ix = np.where(modal_name_naming[\"cat_en_malt\"]==cat)\n",
    "    ax.scatter(npos[:, 0][ix], npos[:, 1][ix], fc=mcolors.to_rgba(cdict[cat],0.5), ec=mcolors.to_rgba(\"black\",1), label=cat, s = 100)\n",
    "ax.set_title('1999 naming', fontsize=20, pad=20)\n",
    "ax.set_ylabel('EN', fontsize=15)\n",
    "\n",
    "# Shrink current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.6, box.height * 0.6])\n",
    "# Put a legend to the right of the current axis, and manually set legend texts\n",
    "ax.legend([\"bottle (16)\", \"box (1)\", \"can (5)\", \"container (15)\", \"jar (19)\", \"jug (3)\", \"tube (1)\"], loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "# remove axis ticks\n",
    "ax.xaxis.set_ticks([])\n",
    "ax.yaxis.set_ticks([])\n",
    "\n",
    "# plt.savefig('nmds_EN_1999.pdf', bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "\n",
    "cdict = {\"bottle\": 'red', \"container\": 'blue', \"jug\": \"gray\", \"can\": \"darkviolet\", \"jar\": \"green\", \"box\": \"pink\", \"tube\": \"cyan\",\n",
    "        \"canister\": \"gold\", \"tub\": \"orange\",\n",
    "        \"ping2\": \"red\", \"tong3\": \"gray\", \"guan4\": \"darkviolet\", \"he2\": \"pink\", \"guan3\": \"cyan\"}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "# plot EN 2021\n",
    "for cat in np.unique(modal_name_naming[\"cat_en_ours1\"]):\n",
    "    ix = np.where(modal_name_naming[\"cat_en_ours1\"]==cat)\n",
    "    ax.scatter(npos[:, 0][ix], npos[:, 1][ix], fc=mcolors.to_rgba(cdict[cat],0.5), ec=mcolors.to_rgba(\"black\",1), label=cat, s = 100)\n",
    "ax.set_title('English', fontsize=20, pad=20)\n",
    "# ax.set_ylabel('EN', fontsize=15)\n",
    "\n",
    "# Shrink current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.6, box.height * 0.6])\n",
    "# Put a legend to the right of the current axis, and manually set legend texts\n",
    "ax.legend([\"bottle (23)\", \"box (1)\", \"can (6)\", \"canister (1)\", \"container (3)\", \"jar (18)\", \"jug (6)\", \"tub (1)\", \"tube (1)\"], loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 14})\n",
    "\n",
    "# remove axis ticks\n",
    "ax.xaxis.set_ticks([])\n",
    "ax.yaxis.set_ticks([])\n",
    "\n",
    "# plt.savefig('nmds_EN_2021.pdf', bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "\n",
    "cdict = {\"bottle\": 'red', \"container\": 'blue', \"jug\": \"gray\", \"can\": \"darkviolet\", \"jar\": \"green\", \"box\": \"pink\", \"tube\": \"cyan\",\n",
    "        \"canister\": \"gold\", \"tub\": \"orange\",\n",
    "        \"ping2\": \"red\", \"tong3\": \"gray\", \"guan4\": \"darkviolet\", \"he2\": \"pink\", \"guan3\": \"cyan\"}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "# plot CH 1999\n",
    "for cat in np.unique(modal_name_naming[\"cat_ch_malt\"]):\n",
    "    ix = np.where(modal_name_naming[\"cat_ch_malt\"]==cat)\n",
    "    ax.scatter(npos[:, 0][ix], npos[:, 1][ix], fc=mcolors.to_rgba(cdict[cat],0.5), ec=mcolors.to_rgba(\"black\",1), label=cat, s = 100)\n",
    "ax.set_title('1999 naming', fontsize=20, pad=20)\n",
    "ax.set_ylabel('CH', fontsize=15)\n",
    "\n",
    "# Shrink current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.6, box.height * 0.6])\n",
    "# Put a legend to the right of the current axis, and manually set legend texts\n",
    "ax.legend([\"guǎn (1)\", \"guan4 (10)\", \"he2 (4)\", \"ping2 (40)\", \"tong3 (5)\"], loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "# remove axis ticks\n",
    "ax.xaxis.set_ticks([])\n",
    "ax.yaxis.set_ticks([])\n",
    "\n",
    "# plt.savefig('nmds_CH_1999.pdf', bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "\n",
    "cdict = {\"bottle\": 'red', \"container\": 'blue', \"jug\": \"gray\", \"can\": \"darkviolet\", \"jar\": \"green\", \"box\": \"pink\", \"tube\": \"cyan\",\n",
    "        \"canister\": \"gold\", \"tub\": \"orange\",\n",
    "        \"ping2\": \"red\", \"tong3\": \"gray\", \"guan4\": \"darkviolet\", \"he2\": \"pink\", \"guan3\": \"cyan\"}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "# plot CH 2021\n",
    "for cat in np.unique(modal_name_naming[\"cat_ch_ours\"]):\n",
    "    ix = np.where(modal_name_naming[\"cat_ch_ours\"]==cat)\n",
    "    ax.scatter(npos[:, 0][ix], npos[:, 1][ix], fc=mcolors.to_rgba(cdict[cat],0.5), ec=mcolors.to_rgba(\"black\",1), label=cat, s = 100)\n",
    "ax.set_title('Chinese', fontsize=20, pad=20)\n",
    "# ax.set_ylabel('CH', fontsize=15)\n",
    "\n",
    "# Shrink current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.6, box.height * 0.6])\n",
    "# Put a legend to the right of the current axis, and manually set legend texts\n",
    "ax.legend([\"guǎn (1)\", \"guàn (8)\", \"hé (4)\", \"píng (42)\", \"tǒng (5)\"], loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 14})\n",
    "\n",
    "# remove axis ticks\n",
    "ax.xaxis.set_ticks([])\n",
    "ax.yaxis.set_ticks([])\n",
    "\n",
    "# plt.savefig('nmds_CH_2021.pdf', bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Communicative Cost Calculation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "listener distribution > unit communicative cost of target object i > overall communicative cost in the domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_listener_distribs(naming_df, cattype, sim):  #returns a dict of dicts: ldis[cat][obj], s.t. list[cat] is the listener distrib for cat, over objects.\n",
    "    ldis = {}  # dict to hold listener distribs\n",
    "    all_cats = sorted(naming_df[cattype].unique())\n",
    "    all_objs = sorted(naming_df.obj.unique())\n",
    "    for c in all_cats:\n",
    "        objs_in_cat = naming_df[naming_df[cattype]==c].obj.values\n",
    "        # create initially non-normalized listener distrib for this cat\n",
    "        ldis[c] = {}\n",
    "        denom = 0\n",
    "        for i in all_objs:\n",
    "            ldis[c][i] = sum([sim[(i,j)] for j in objs_in_cat])\n",
    "            denom += ldis[c][i]\n",
    "        # normalize\n",
    "        ldis[c] = {k:v/denom for k,v in ldis[c].items()}\n",
    "    return(ldis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_ldis_ucc_cost(naming_df, cattype, ldis, need_prob, lg):\n",
    "\n",
    "    # add listener distribution ('ldis') and unit communicative cost ('ucc') to naming_df for each obj\n",
    "    for i in range(1, len(naming_df)+1):  # i: obj\n",
    "        cat_i = naming_df[naming_df.obj==i][cattype].values[0]\n",
    "        ldis_i = ldis[cat_i][i]\n",
    "        naming_df.at[i-1, cattype+'_ldis'] = ldis_i\n",
    "        naming_df.at[i-1, cattype+'_ucc'] = math.log((1 / ldis_i), 2)\n",
    "    \n",
    "    # overall communicative cost\n",
    "    return np.sum([naming_df[naming_df['obj'] == obj][cattype+'_ucc'].values[0]\n",
    "                   * need_prob[need_prob['obj'] == obj][lg].values[0] \n",
    "                   for obj in naming_df['obj']])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicating Fig 2B for Sanity Check"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "indexing the 8 objects: index(coordinates)  \n",
    "1(0,1) 2(1,1) 3(2,1) 4(3,1)   \n",
    "5(0,0) 6(1,0) 7(2,0) 8(3,0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up indices of the 8 objects\n",
    "matrix_coord = {\n",
    "    1:(0,1), 2:(1,1), 3:(2,1), 4:(3,1),\n",
    "    5:(0,0), 6:(1,0), 7:(2,0), 8:(3,0)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up similarity space of the 8 objects\n",
    "sim_matrix = {}\n",
    "for i in range(1,9):\n",
    "    for j in range(1,9):\n",
    "        sim_matrix[(i,j)] = math.exp(-np.linalg.norm(np.asarray(matrix_coord[i]) - np.asarray(matrix_coord[j])))   # sim(i,j) = exp(-distance(i,j))\n",
    "\n",
    "# sim_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up need_prob df with uniform need prob distribution of the 8 objects\n",
    "d = {'obj': list(range(1,9)), 'matrix': [1/8]*8}\n",
    "need_prob_matrix = pd.DataFrame(data=d)\n",
    "# need_prob_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj</th>\n",
       "      <th>matrix_nonchained</th>\n",
       "      <th>matrix_chained</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obj matrix_nonchained matrix_chained\n",
       "0    1                 A              A\n",
       "1    2                 A              A\n",
       "2    3                 B              A\n",
       "3    4                 B              A\n",
       "4    5                 A              B\n",
       "5    6                 A              B\n",
       "6    7                 B              B\n",
       "7    8                 B              B"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up \"naming\" df for the two types of partitions in Fig 2A\n",
    "d = {'obj': list(range(1,9)), 'matrix_nonchained': ['A','A','B','B','A','A','B','B'], 'matrix_chained': ['A','A','A','A','B','B','B','B']}\n",
    "naming_matrix = pd.DataFrame(data=d)\n",
    "naming_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.378571491232675"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for non-chained partition\n",
    "l = create_listener_distribs(naming_matrix, \"matrix_nonchained\", sim_matrix)\n",
    "cc_matrix_nonchained = add_ldis_ucc_cost(naming_matrix, \"matrix_nonchained\", l, need_prob_matrix, 'matrix')\n",
    "cc_matrix_nonchained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.593781594479587"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for chained partition\n",
    "l = create_listener_distribs(naming_matrix, \"matrix_chained\", sim_matrix)\n",
    "cc_matrix_chained = add_ldis_ucc_cost(naming_matrix, \"matrix_chained\", l, need_prob_matrix, 'matrix')\n",
    "cc_matrix_chained"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All possible partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1, 2, 3, 4], [5, 6, 7, 8]],\n",
       " [[2, 3, 4, 5], [1, 6, 7, 8]],\n",
       " [[1, 3, 4, 5], [2, 6, 7, 8]],\n",
       " [[1, 2, 4, 5], [3, 6, 7, 8]],\n",
       " [[1, 2, 3, 5], [4, 6, 7, 8]],\n",
       " [[3, 4, 5, 6], [1, 2, 7, 8]],\n",
       " [[2, 4, 5, 6], [1, 3, 7, 8]],\n",
       " [[1, 4, 5, 6], [2, 3, 7, 8]],\n",
       " [[2, 3, 5, 6], [1, 4, 7, 8]],\n",
       " [[1, 3, 5, 6], [2, 4, 7, 8]],\n",
       " [[1, 2, 5, 6], [3, 4, 7, 8]],\n",
       " [[2, 3, 4, 6], [1, 5, 7, 8]],\n",
       " [[1, 3, 4, 6], [2, 5, 7, 8]],\n",
       " [[1, 2, 4, 6], [3, 5, 7, 8]],\n",
       " [[1, 2, 3, 6], [4, 5, 7, 8]],\n",
       " [[4, 5, 6, 7], [1, 2, 3, 8]],\n",
       " [[3, 5, 6, 7], [1, 2, 4, 8]],\n",
       " [[2, 5, 6, 7], [1, 3, 4, 8]],\n",
       " [[1, 5, 6, 7], [2, 3, 4, 8]],\n",
       " [[3, 4, 6, 7], [1, 2, 5, 8]],\n",
       " [[2, 4, 6, 7], [1, 3, 5, 8]],\n",
       " [[1, 4, 6, 7], [2, 3, 5, 8]],\n",
       " [[2, 3, 6, 7], [1, 4, 5, 8]],\n",
       " [[1, 3, 6, 7], [2, 4, 5, 8]],\n",
       " [[1, 2, 6, 7], [3, 4, 5, 8]],\n",
       " [[3, 4, 5, 7], [1, 2, 6, 8]],\n",
       " [[2, 4, 5, 7], [1, 3, 6, 8]],\n",
       " [[1, 4, 5, 7], [2, 3, 6, 8]],\n",
       " [[2, 3, 5, 7], [1, 4, 6, 8]],\n",
       " [[1, 3, 5, 7], [2, 4, 6, 8]],\n",
       " [[1, 2, 5, 7], [3, 4, 6, 8]],\n",
       " [[2, 3, 4, 7], [1, 5, 6, 8]],\n",
       " [[1, 3, 4, 7], [2, 5, 6, 8]],\n",
       " [[1, 2, 4, 7], [3, 5, 6, 8]],\n",
       " [[1, 2, 3, 7], [4, 5, 6, 8]]]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all possible partitions of 8 objects into two groups of 4\n",
    "from more_itertools import set_partitions\n",
    "partitions = [x for x in set_partitions(list(range(1,9)), 2) if len(x[0])==4]  # Yield the set partitions of [1, 2, 3, 4, 5, 6, 7, 8] into 2 parts (Set partitions are not order-preserving); and keep only the partitions into two groups of 4.\n",
    "partitions\n",
    "#len(partitions)  # sanity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the communicative costs of all possible partitions\n",
    "\n",
    "cc_matrix_all = []\n",
    "\n",
    "d = {'obj': list(range(1,9))}\n",
    "naming_matrix_iter = pd.DataFrame(data=d)\n",
    "\n",
    "for partition in partitions:\n",
    "    # assign cat A or B to the 8 objects according to the current way of partition:\n",
    "    naming_matrix_iter['matrix'] = np.where(naming_matrix_iter['obj'].isin(partition[0]), 'A', 'B') \n",
    "    # calculate communicative cost:\n",
    "    l = create_listener_distribs(naming_matrix_iter, \"matrix\", sim_matrix)\n",
    "    cc_matrix_all.append(add_ldis_ucc_cost(naming_matrix_iter, \"matrix\", l, need_prob_matrix, 'matrix'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fc783b73cd0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "\n",
    "figure, axis = plt.subplots()\n",
    "\n",
    "axis.hist(cc_matrix_all, bins=30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\")\n",
    "axis.set_ylabel(\"freq\")  \n",
    "axis.set_title(\"Fig 2B replication\")\n",
    "\n",
    "plt.axvline(cc_matrix_nonchained, color='b', linewidth=2)\n",
    "plt.axvline(cc_matrix_chained, color='k', linestyle='--', linewidth=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation (Yang's Chaining) and Plotting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### English"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "English survey naming data, EN Zipf need probability"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survey v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.7258245744515115"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for English survey naming 1 using zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_ours1\", sim)\n",
    "# display(l)\n",
    "cc_en_survey1_zipf = add_ldis_ucc_cost(modal_name_naming, \"cat_en_ours1\", l, need_prob, 'en_zipf')\n",
    "cc_en_survey1_zipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_en_ysmu_survey1_zipf = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_ours1)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_survey1_zipf.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'en_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_en_ysmu_survey1_zipf, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"English\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for English \\n(Malt's naming data, Yang's chaining, ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_en_survey1_zipf, color='k', linewidth=1)\n",
    "plt.savefig('cost_EN_ours_EN_zipf_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00248"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_en_ysmu_survey1_zipf < cc_en_survey1_zipf) / np.size(cc_en_ysmu_survey1_zipf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survey v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.703297104469895"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for English survey naming 2 using zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_ours2\", sim)\n",
    "# display(l)\n",
    "cc_en_survey2_zipf = add_ldis_ucc_cost(modal_name_naming, \"cat_en_ours2\", l, need_prob, 'en_zipf')\n",
    "cc_en_survey2_zipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_en_ysmu_survey2_zipf = []\n",
    "\n",
    "for _ in range(1000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_ours2)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_survey2_zipf.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'en_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fefcc5b74f0>"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots()\n",
    "\n",
    "axis.hist(cc_en_ysmu_survey2_zipf, bins = 50)\n",
    "axis.set_xlabel(\"communicative cost\")\n",
    "axis.set_ylabel(\"freq\")  \n",
    "axis.set_title(\"Simulation Result for English \\n(survey naming data 2, Yang's chaining, zipf need probability)\")\n",
    "\n",
    "plt.axvline(cc_en_survey2_zipf, color='k', linewidth=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "English survey naming data, **CH** Zipf need probability"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survey v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2597469783555715"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for English survey naming 1 using CH zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_ours1\", sim)\n",
    "# display(l)\n",
    "cc_en_survey1_chzipf = add_ldis_ucc_cost(modal_name_naming, \"cat_en_ours1\", l, need_prob, 'ch_zipf')\n",
    "cc_en_survey1_chzipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_en_ysmu_survey1_chzipf = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_ours1)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_survey1_chzipf.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'ch_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_en_ysmu_survey1_chzipf, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"English\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for English \\n(Malt's naming data, Yang's chaining, ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_en_survey1_chzipf, color='k', linewidth=1)\n",
    "plt.savefig('cost_EN_ours_CH_zipf_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_en_ysmu_survey1_chzipf < cc_en_survey1_chzipf) / np.size(cc_en_ysmu_survey1_chzipf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survey v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.246702507251091"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for English survey naming 2 using CH zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_ours2\", sim)\n",
    "# display(l)\n",
    "cc_en_survey2_chzipf = add_ldis_ucc_cost(modal_name_naming, \"cat_en_ours2\", l, need_prob, 'ch_zipf')\n",
    "cc_en_survey2_chzipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_en_ysmu_survey2_chzipf = []\n",
    "\n",
    "for _ in range(1000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_ours2)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_survey2_chzipf.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'ch_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fefcb9b2c40>"
      ]
     },
     "execution_count": 679,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots()\n",
    "\n",
    "axis.hist(cc_en_ysmu_survey2_chzipf, bins = 50)\n",
    "axis.set_xlabel(\"communicative cost\")\n",
    "axis.set_ylabel(\"freq\")  \n",
    "axis.set_title(\"Simulation Result for English \\n(survey naming data 2, Yang's chaining, CH zipf need probability)\")\n",
    "\n",
    "plt.axvline(cc_en_survey2_chzipf, color='k', linewidth=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "English Malt's naming data, En Ngram 1999 need probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9403898674400475"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for English Malt's naming using ngram need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_malt\", sim)\n",
    "# display(l)\n",
    "cc_en_malt_ngram = add_ldis_ucc_cost(modal_name_naming, \"cat_en_malt\", l, need_prob, 'en_ngram_1999_undivided')\n",
    "cc_en_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_en_ysmu_malt_ngram = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_malt)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_malt_ngram.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'en_ngram_1999_undivided'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'cc_en_ysmu_malt_ngram' (list)\n",
      "Stored 'cc_en_malt_ngram' (float64)\n"
     ]
    }
   ],
   "source": [
    "%store cc_en_ysmu_malt_ngram\n",
    "%store cc_en_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r cc_en_ysmu_malt_ngram\n",
    "%store -r cc_en_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_en_ysmu_malt_ngram, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"English\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for English \\n(Malt's naming data, Yang's chaining, ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_en_malt_ngram, color='k', linewidth=1)\n",
    "plt.savefig('cost_EN_Malt_EN_Ngram_1999_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00354"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_en_ysmu_malt_ngram <= cc_en_malt_ngram) / np.size(cc_en_ysmu_malt_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## divided\n",
    "\n",
    "\n",
    "# cost for English Malt's naming using ngram need prob info 1999 divided\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_en_malt\", sim)\n",
    "# display(l)\n",
    "cc_en_malt_ngram_divided = add_ldis_ucc_cost(modal_name_naming, \"cat_en_malt\", l, need_prob, 'en_ngram_1999_divided')\n",
    "cc_en_malt_ngram_divided\n",
    "\n",
    "\n",
    "cc_en_ysmu_malt_ngram_divided = []\n",
    "\n",
    "for _ in range(10000):\n",
    "    naming_en_ysdf = chaining_yang(sim, cat_freq_en_malt)\n",
    "    l = create_listener_distribs(naming_en_ysdf, \"cat\", sim)\n",
    "    cc_en_ysmu_malt_ngram_divided.append(add_ldis_ucc_cost(naming_en_ysdf, \"cat\", l, need_prob, 'en_ngram_1999_divided'))\n",
    "\n",
    "    \n",
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_en_ysmu_malt_ngram_divided, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"English\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for English \\n(Malt's naming data, Yang's chaining, ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_en_malt_ngram, color='k', linewidth=1)\n",
    "plt.savefig('cost_EN_Malt_EN_Ngram_1999_divided_50k.pdf')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chinese"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chinese survey naming, **En** zipf need probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.267601338389932"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for Chinese survey naming using EN zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_ch_ours\", sim)\n",
    "# display(l)\n",
    "cc_ch_survey_enzipf = add_ldis_ucc_cost(modal_name_naming, \"cat_ch_ours\", l, need_prob, 'en_zipf')\n",
    "cc_ch_survey_enzipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_ch_ysmu_survey_enzipf = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_ch_ysdf = chaining_yang(sim, cat_freq_ch_ours)\n",
    "    l = create_listener_distribs(naming_ch_ysdf, \"cat\", sim)\n",
    "    cc_ch_ysmu_survey_enzipf.append(add_ldis_ucc_cost(naming_ch_ysdf, \"cat\", l, need_prob, 'en_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_ch_ysmu_survey_enzipf, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"Chinese\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for Chinese \\n(survey naming data, Yang's chaining, EN zipf need prob)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_ch_survey_enzipf, color='k', linewidth=1)\n",
    "plt.savefig('cost_CH_ours_EN_zipf_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07242"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_ch_ysmu_survey_enzipf < cc_ch_survey_enzipf) / np.size(cc_ch_ysmu_survey_enzipf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chinese survey naming, Ch zipf need probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.007830171236951"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for Chinese survey naming using CH zipf need prob info\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_ch_ours\", sim)\n",
    "# display(l)\n",
    "cc_ch_survey_zipf = add_ldis_ucc_cost(modal_name_naming, \"cat_ch_ours\", l, need_prob, 'ch_zipf')\n",
    "cc_ch_survey_zipf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_ch_ysmu_survey_zipf = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_ch_ysdf = chaining_yang(sim, cat_freq_ch_ours)\n",
    "    l = create_listener_distribs(naming_ch_ysdf, \"cat\", sim)\n",
    "    cc_ch_ysmu_survey_zipf.append(add_ldis_ucc_cost(naming_ch_ysdf, \"cat\", l, need_prob, 'ch_zipf'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_ch_ysmu_survey_zipf, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"Chinese\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for Chinese \\n(survey naming data, Yang's chaining, CH zipf need prob)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_ch_survey_zipf, color='k', linewidth=1)\n",
    "plt.savefig('cost_CH_ours_CH_zipf_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00212"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_ch_ysmu_survey_zipf < cc_ch_survey_zipf) / np.size(cc_ch_ysmu_survey_zipf)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chinese Malt's naming, **En** Ngram need probability 1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.336080487854696"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cost for Chinese Malt's naming using en need prob\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_ch_malt\", sim)\n",
    "# display(l)\n",
    "cc_ch_malt_ngram = add_ldis_ucc_cost(modal_name_naming, \"cat_ch_malt\", l, need_prob, 'en_ngram_1999_undivided')\n",
    "cc_ch_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_ch_ysmu_malt_ngram = []\n",
    "\n",
    "for _ in range(50000):\n",
    "    naming_ch_ysdf = chaining_yang(sim, cat_freq_ch_malt)\n",
    "    l = create_listener_distribs(naming_ch_ysdf, \"cat\", sim)\n",
    "    cc_ch_ysmu_malt_ngram.append(add_ldis_ucc_cost(naming_ch_ysdf, \"cat\", l, need_prob, 'en_ngram_1999_undivided'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'cc_ch_ysmu_malt_ngram' (list)\n",
      "Stored 'cc_ch_malt_ngram' (float64)\n"
     ]
    }
   ],
   "source": [
    "%store cc_ch_ysmu_malt_ngram\n",
    "%store cc_ch_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r cc_ch_ysmu_malt_ngram\n",
    "%store -r cc_ch_malt_ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_ch_ysmu_malt_ngram, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"Chinese\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for Chinese \\n(Malt's naming, Yang's chaining, En Ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_ch_malt_ngram, color='k', linewidth=1)\n",
    "plt.savefig('cost_CH_Malt_EN_Ngram_1999_50k.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1295"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(cc_ch_ysmu_malt_ngram <= cc_ch_malt_ngram) / np.size(cc_ch_ysmu_malt_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## divided\n",
    "\n",
    "\n",
    "# cost for Chinese Malt's naming using en need prob info 1999 divided\n",
    "l = create_listener_distribs(modal_name_naming, \"cat_ch_malt\", sim)\n",
    "# display(l)\n",
    "cc_ch_malt_ngram_divided = add_ldis_ucc_cost(modal_name_naming, \"cat_ch_malt\", l, need_prob, 'en_ngram_1999_divided')\n",
    "cc_ch_malt_ngram_divided\n",
    "\n",
    "\n",
    "cc_ch_ysmu_malt_ngram_divided = []\n",
    "\n",
    "for _ in range(10000):\n",
    "    naming_ch_ysdf = chaining_yang(sim, cat_freq_ch_malt)\n",
    "    l = create_listener_distribs(naming_ch_ysdf, \"cat\", sim)\n",
    "    cc_ch_ysmu_malt_ngram_divided.append(add_ldis_ucc_cost(naming_ch_ysdf, \"cat\", l, need_prob, 'en_ngram_1999_divided'))\n",
    "\n",
    "    \n",
    "figure, axis = plt.subplots(figsize=(4,4))\n",
    "\n",
    "axis.hist(cc_ch_ysmu_malt_ngram_divided, bins = 30, color = \"white\", ec=\"black\")\n",
    "axis.set_xlabel(\"communicative cost\", fontsize=15)\n",
    "axis.set_ylabel(\"frequency\", fontsize=15)  \n",
    "axis.set_title(\"Chinese\", fontsize=20, pad=20)\n",
    "# axis.set_title(\"Simulation Result for Chinese \\n(Malt's naming, Yang's chaining, En Ngram need probability)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.axvline(cc_ch_malt_ngram, color='k', linewidth=1)\n",
    "plt.savefig('cost_CH_Malt_EN_Ngram_1999_divided_50k.pdf')\n"
   ]
  }
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