love_languages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = sorted(['Skylar Lee', 'Alex Zirbel', 'Ben Dong', \n",
    "                   'Bogdan Kyruliuk', 'Cathy Chen', 'Chongyuan Xiang', \n",
    "                   'Daniel Zhang', 'David Liao', 'David Lundgren', \n",
    "                  'Ella Kim', 'Jiwoo Song', 'Josh Wang',\n",
    "                  'Jules Landry-Simard', 'Lucy Wang', 'Rachid Belaid',\n",
    "                  'Rahul Raina', 'Sheng Wu', 'Varun Ballari', \n",
    "                  'Vicky Zhang', 'Jon Chu', 'Vignesh Kanth', \"Jeff Zhou\", 'Kevin Wu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "presence = dict([(name, True) for name in names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_male = {'Alex Zirbel': True,\n",
    " 'Ben Dong': True,\n",
    " 'Bogdan Kyruliuk': True,\n",
    " 'Cathy Chen': False,\n",
    " 'Chongyuan Xiang': True,\n",
    " 'Daniel Zhang': True,\n",
    " 'David Liao': True,\n",
    " 'David Lundgren': True,\n",
    " 'Ella Kim': False,\n",
    " 'Jiwoo Song': True,\n",
    " 'Jon Chu': True,\n",
    " 'Josh Wang': True,\n",
    " 'Jules Landry-Simard': True,\n",
    " 'Lucy Wang': False,\n",
    " 'Rachid Belaid': True,\n",
    " 'Rahul Raina': True,\n",
    " 'Sheng Wu': True,\n",
    " 'Skylar Lee': False,\n",
    " 'Varun Ballari': True,\n",
    " 'Vicky Zhang': False,\n",
    " 'Vignesh Kanth': True,\n",
    "          'Jeff Zhou': True,\n",
    "          'Kevin Wu': True}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fix presence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alex Zirbel': True,\n",
       " 'Ben Dong': True,\n",
       " 'Bogdan Kyruliuk': True,\n",
       " 'Cathy Chen': True,\n",
       " 'Chongyuan Xiang': True,\n",
       " 'Daniel Zhang': True,\n",
       " 'David Liao': True,\n",
       " 'David Lundgren': True,\n",
       " 'Ella Kim': True,\n",
       " 'Jeff Zhou': True,\n",
       " 'Jiwoo Song': True,\n",
       " 'Jon Chu': True,\n",
       " 'Josh Wang': True,\n",
       " 'Jules Landry-Simard': True,\n",
       " 'Kevin Wu': True,\n",
       " 'Lucy Wang': True,\n",
       " 'Rachid Belaid': True,\n",
       " 'Rahul Raina': True,\n",
       " 'Sheng Wu': True,\n",
       " 'Skylar Lee': True,\n",
       " 'Varun Ballari': True,\n",
       " 'Vicky Zhang': True,\n",
       " 'Vignesh Kanth': True}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "presence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = {0: ['Skylar Lee'],\n",
    "          1: ['Ella Kim', 'Jiwoo Song', 'Lucy Wang'],\n",
    "          2: ['Jules Landry-Simard', 'David Liao'],\n",
    "          3: ['Chongyuan Xiang', 'Varun Ballari', 'Jon Chu'],\n",
    "          4: ['David Lundgren', 'Josh Wang', 'Bogdan Kyruliuk'],\n",
    "          5: ['Ben Dong','Alex Zirbel',  'Cathy Chen',  'Daniel Zhang', 'Rahul Raina'],\n",
    "          6: ['Vicky Zhang', 'Sheng Wu', 'Rachid Belaid'],\n",
    "          7: ['Vignesh Kanth', \"Jeff Zhou\", 'Kevin Wu']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups_table = defaultdict(list)\n",
    "for group, l in groups.items():\n",
    "    for name in l:\n",
    "        groups_table['name'].append(name)\n",
    "        groups_table['group'].append(group)\n",
    "        groups_table['present'].append(presence[name])\n",
    "        groups_table['is_male'].append(is_male[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_df = pd.DataFrame(groups_table).query('present')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame.from_csv('/Users/skylarlee/Downloads/Party Invite (Responses) - Form Responses 1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.reset_index().rename(\n",
    "    columns={\n",
    "        'What is your name?': 'name',\n",
    "        'What is your score for Words of Affirmation?': 'words_of_affirmation',\n",
    "        'What is your score for Quality Time?': 'quality_time',\n",
    "        'What is your score for Receiving Gifts?': 'receiving_gifts',\n",
    "        'What is your score for Acts of Service?': 'acts_of_service',\n",
    "        'What is your score for Physical Touch?': 'physical_touch'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>name</th>\n",
       "      <th>words_of_affirmation</th>\n",
       "      <th>quality_time</th>\n",
       "      <th>receiving_gifts</th>\n",
       "      <th>acts_of_service</th>\n",
       "      <th>physical_touch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-08 00:42:53</td>\n",
       "      <td>Skylar Lee</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-11 20:59:17</td>\n",
       "      <td>Cathy Chen</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-12 06:06:33</td>\n",
       "      <td>Vicky Zhang</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-12 07:06:25</td>\n",
       "      <td>Daniel Zhang</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-13 03:11:13</td>\n",
       "      <td>Sheng Wu</td>\n",
       "      <td>9.47</td>\n",
       "      <td>12.63</td>\n",
       "      <td>1.58</td>\n",
       "      <td>4.74</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-13 03:12:19</td>\n",
       "      <td>Alex Zirbel</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-13 03:15:26</td>\n",
       "      <td>Ben Dong</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-13 03:21:34</td>\n",
       "      <td>Jeff Zhou</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-13 03:25:39</td>\n",
       "      <td>Lucy Wang</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-13 03:32:30</td>\n",
       "      <td>Varun Ballari</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-07-13 03:35:29</td>\n",
       "      <td>Jules Landry-Simard</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-07-13 03:37:18</td>\n",
       "      <td>David Liao</td>\n",
       "      <td>2.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-07-13 03:52:07</td>\n",
       "      <td>David Lundgren</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-07-13 04:12:57</td>\n",
       "      <td>Rachid Belaid</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-07-13 05:18:29</td>\n",
       "      <td>Kevin Wu</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Timestamp                 name  words_of_affirmation  \\\n",
       "0  2019-07-08 00:42:53           Skylar Lee                  3.00   \n",
       "1  2019-07-11 20:59:17           Cathy Chen                  0.00   \n",
       "2  2019-07-12 06:06:33          Vicky Zhang                  5.00   \n",
       "3  2019-07-12 07:06:25         Daniel Zhang                  8.00   \n",
       "4  2019-07-13 03:11:13             Sheng Wu                  9.47   \n",
       "5  2019-07-13 03:12:19          Alex Zirbel                  4.00   \n",
       "6  2019-07-13 03:15:26             Ben Dong                  6.00   \n",
       "7  2019-07-13 03:21:34            Jeff Zhou                  4.00   \n",
       "8  2019-07-13 03:25:39            Lucy Wang                  5.00   \n",
       "9  2019-07-13 03:32:30        Varun Ballari                  7.00   \n",
       "10 2019-07-13 03:35:29  Jules Landry-Simard                  6.00   \n",
       "11 2019-07-13 03:37:18           David Liao                  2.00   \n",
       "12 2019-07-13 03:52:07       David Lundgren                  7.00   \n",
       "13 2019-07-13 04:12:57        Rachid Belaid                 11.00   \n",
       "14 2019-07-13 05:18:29             Kevin Wu                  3.00   \n",
       "\n",
       "    quality_time  receiving_gifts  acts_of_service  physical_touch  \n",
       "0           7.00             2.00            11.00            7.00  \n",
       "1           8.00             6.00            12.00            4.00  \n",
       "2          12.00             2.00             8.00            2.00  \n",
       "3           8.00             1.00             5.00            8.00  \n",
       "4          12.63             1.58             4.74            1.58  \n",
       "5           5.00            10.00             1.00           10.00  \n",
       "6           6.00             4.00            10.00            4.00  \n",
       "7           9.00             2.00             7.00            8.00  \n",
       "8           7.00             2.00            12.00            3.00  \n",
       "9          11.00             4.00             2.00            6.00  \n",
       "10          6.00             5.00             7.00            6.00  \n",
       "11         10.00             8.00             7.00            3.00  \n",
       "12         10.00             1.00             2.00           10.00  \n",
       "13          4.00             8.00             7.00            0.00  \n",
       "14          9.00             0.00             8.00            9.00  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = base_df.merge(data_df, on=['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## group multiplication factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "GROUP_FACTOR = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['group'] = data_df['group'] * GROUP_FACTOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import squareform, pdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words_of_affirmation</th>\n",
       "      <th>quality_time</th>\n",
       "      <th>receiving_gifts</th>\n",
       "      <th>acts_of_service</th>\n",
       "      <th>physical_touch</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.47</td>\n",
       "      <td>12.63</td>\n",
       "      <td>1.58</td>\n",
       "      <td>4.74</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    words_of_affirmation  quality_time  receiving_gifts  acts_of_service  \\\n",
       "0                   3.00          7.00             2.00            11.00   \n",
       "1                   5.00          7.00             2.00            12.00   \n",
       "2                   6.00          6.00             5.00             7.00   \n",
       "3                   2.00         10.00             8.00             7.00   \n",
       "4                   7.00         11.00             4.00             2.00   \n",
       "5                   7.00         10.00             1.00             2.00   \n",
       "6                   6.00          6.00             4.00            10.00   \n",
       "7                   4.00          5.00            10.00             1.00   \n",
       "8                   0.00          8.00             6.00            12.00   \n",
       "9                   8.00          8.00             1.00             5.00   \n",
       "10                  5.00         12.00             2.00             8.00   \n",
       "11                  9.47         12.63             1.58             4.74   \n",
       "12                 11.00          4.00             8.00             7.00   \n",
       "13                  4.00          9.00             2.00             7.00   \n",
       "14                  3.00          9.00             0.00             8.00   \n",
       "\n",
       "    physical_touch  group  \n",
       "0             7.00      0  \n",
       "1             3.00      0  \n",
       "2             6.00      0  \n",
       "3             3.00      0  \n",
       "4             6.00      0  \n",
       "5            10.00      0  \n",
       "6             4.00      0  \n",
       "7            10.00      0  \n",
       "8             4.00      0  \n",
       "9             8.00      0  \n",
       "10            2.00      0  \n",
       "11            1.58      0  \n",
       "12            0.00      0  \n",
       "13            8.00      0  \n",
       "14            9.00      0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.loc[:, ['words_of_affirmation', 'quality_time',\n",
    "       'receiving_gifts', 'acts_of_service', 'physical_touch', 'group']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_df = pd.DataFrame(squareform(pdist(data_df.loc[:, ['words_of_affirmation', 'quality_time',\n",
    "       'receiving_gifts', 'acts_of_service', 'physical_touch', 'group']])), columns=data_df.name.unique(), index=data_df.name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Skylar Lee</th>\n",
       "      <th>Lucy Wang</th>\n",
       "      <th>Jules Landry-Simard</th>\n",
       "      <th>David Liao</th>\n",
       "      <th>Varun Ballari</th>\n",
       "      <th>David Lundgren</th>\n",
       "      <th>Ben Dong</th>\n",
       "      <th>Alex Zirbel</th>\n",
       "      <th>Cathy Chen</th>\n",
       "      <th>Daniel Zhang</th>\n",
       "      <th>Vicky Zhang</th>\n",
       "      <th>Sheng Wu</th>\n",
       "      <th>Rachid Belaid</th>\n",
       "      <th>Jeff Zhou</th>\n",
       "      <th>Kevin Wu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Skylar Lee</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>6.000000</td>\n",
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       "      <td>4.690416</td>\n",
       "      <td>4.582576</td>\n",
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       "    <tr>\n",
       "      <th>Lucy Wang</th>\n",
       "      <td>4.582576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.708204</td>\n",
       "      <td>8.888194</td>\n",
       "      <td>11.532563</td>\n",
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       "      <td>15.459625</td>\n",
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       "      <td>10.323672</td>\n",
       "      <td>10.723805</td>\n",
       "      <td>7.416198</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jules Landry-Simard</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.708204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>7.211103</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>3.741657</td>\n",
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       "      <td>7.937254</td>\n",
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       "      <td>5.099020</td>\n",
       "      <td>7.280110</td>\n",
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       "      <th>David Liao</th>\n",
       "      <td>8.831761</td>\n",
       "      <td>8.888194</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.717798</td>\n",
       "      <td>12.165525</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>10.862780</td>\n",
       "      <td>6.164414</td>\n",
       "      <td>10.862780</td>\n",
       "      <td>7.141428</td>\n",
       "      <td>10.538415</td>\n",
       "      <td>11.224972</td>\n",
       "      <td>8.124038</td>\n",
       "      <td>10.148892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Varun Ballari</th>\n",
       "      <td>10.862780</td>\n",
       "      <td>11.532563</td>\n",
       "      <td>7.211103</td>\n",
       "      <td>8.717798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>9.695360</td>\n",
       "      <td>9.899495</td>\n",
       "      <td>12.884099</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>7.810250</td>\n",
       "      <td>6.454316</td>\n",
       "      <td>11.916375</td>\n",
       "      <td>6.782330</td>\n",
       "      <td>9.000000</td>\n",
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       "    <tr>\n",
       "      <th>David Lundgren</th>\n",
       "      <td>10.770330</td>\n",
       "      <td>12.767145</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>12.165525</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.224972</td>\n",
       "      <td>10.770330</td>\n",
       "      <td>14.628739</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>10.440307</td>\n",
       "      <td>9.579050</td>\n",
       "      <td>15.033296</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>7.416198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Dong</th>\n",
       "      <td>4.898979</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>3.741657</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>9.695360</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>6.480741</td>\n",
       "      <td>7.937254</td>\n",
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       "    <tr>\n",
       "      <th>Alex Zirbel</th>\n",
       "      <td>13.341664</td>\n",
       "      <td>15.459625</td>\n",
       "      <td>9.055385</td>\n",
       "      <td>10.862780</td>\n",
       "      <td>9.899495</td>\n",
       "      <td>10.770330</td>\n",
       "      <td>12.569805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.071247</td>\n",
       "      <td>11.224972</td>\n",
       "      <td>15.066519</td>\n",
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       "      <td>13.784049</td>\n",
       "      <td>10.954451</td>\n",
       "      <td>12.922848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cathy Chen</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.557439</td>\n",
       "      <td>8.366600</td>\n",
       "      <td>6.164414</td>\n",
       "      <td>12.884099</td>\n",
       "      <td>14.628739</td>\n",
       "      <td>6.928203</td>\n",
       "      <td>14.071247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.409674</td>\n",
       "      <td>8.774964</td>\n",
       "      <td>13.755661</td>\n",
       "      <td>13.490738</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>9.327379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Zhang</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.219544</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>10.862780</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>11.224972</td>\n",
       "      <td>12.409674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.426150</td>\n",
       "      <td>8.075779</td>\n",
       "      <td>11.916375</td>\n",
       "      <td>4.690416</td>\n",
       "      <td>6.082763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vicky Zhang</th>\n",
       "      <td>7.937254</td>\n",
       "      <td>6.480741</td>\n",
       "      <td>7.937254</td>\n",
       "      <td>7.141428</td>\n",
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       "      <td>7.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>6.855655</td>\n",
       "      <td>8.124038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheng Wu</th>\n",
       "      <td>11.928881</td>\n",
       "      <td>10.323672</td>\n",
       "      <td>9.609277</td>\n",
       "      <td>10.538415</td>\n",
       "      <td>6.454316</td>\n",
       "      <td>9.579050</td>\n",
       "      <td>9.766176</td>\n",
       "      <td>15.617881</td>\n",
       "      <td>13.755661</td>\n",
       "      <td>8.075779</td>\n",
       "      <td>5.599839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.208845</td>\n",
       "      <td>9.465633</td>\n",
       "      <td>11.100369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rachid Belaid</th>\n",
       "      <td>13.190906</td>\n",
       "      <td>10.723805</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>11.224972</td>\n",
       "      <td>11.916375</td>\n",
       "      <td>15.033296</td>\n",
       "      <td>8.366600</td>\n",
       "      <td>13.784049</td>\n",
       "      <td>13.490738</td>\n",
       "      <td>11.916375</td>\n",
       "      <td>11.874342</td>\n",
       "      <td>11.208845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.190906</td>\n",
       "      <td>15.329710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeff Zhou</th>\n",
       "      <td>4.690416</td>\n",
       "      <td>7.416198</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>8.124038</td>\n",
       "      <td>6.782330</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.480741</td>\n",
       "      <td>10.954451</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>4.690416</td>\n",
       "      <td>6.855655</td>\n",
       "      <td>9.465633</td>\n",
       "      <td>13.190906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.645751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Wu</th>\n",
       "      <td>4.582576</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.280110</td>\n",
       "      <td>10.148892</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.416198</td>\n",
       "      <td>7.937254</td>\n",
       "      <td>12.922848</td>\n",
       "      <td>9.327379</td>\n",
       "      <td>6.082763</td>\n",
       "      <td>8.124038</td>\n",
       "      <td>11.100369</td>\n",
       "      <td>15.329710</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Skylar Lee  Lucy Wang  Jules Landry-Simard  David Liao  \\\n",
       "Skylar Lee             0.000000   4.582576             6.000000    8.831761   \n",
       "Lucy Wang              4.582576   0.000000             6.708204    8.888194   \n",
       "Jules Landry-Simard    6.000000   6.708204             0.000000    7.071068   \n",
       "David Liao             8.831761   8.888194             7.071068    0.000000   \n",
       "Varun Ballari         10.862780  11.532563             7.211103    8.717798   \n",
       "David Lundgren        10.770330  12.767145             8.602325   12.165525   \n",
       "Ben Dong               4.898979   3.316625             3.741657    7.615773   \n",
       "Alex Zirbel           13.341664  15.459625             9.055385   10.862780   \n",
       "Cathy Chen             6.000000   6.557439             8.366600    6.164414   \n",
       "Daniel Zhang           8.000000   9.219544             5.656854   10.862780   \n",
       "Vicky Zhang            7.937254   6.480741             7.937254    7.141428   \n",
       "Sheng Wu              11.928881  10.323672             9.609277   10.538415   \n",
       "Rachid Belaid         13.190906  10.723805             8.602325   11.224972   \n",
       "Jeff Zhou              4.690416   7.416198             5.099020    8.124038   \n",
       "Kevin Wu               4.582576   8.000000             7.280110   10.148892   \n",
       "\n",
       "                     Varun Ballari  David Lundgren   Ben Dong  Alex Zirbel  \\\n",
       "Skylar Lee               10.862780       10.770330   4.898979    13.341664   \n",
       "Lucy Wang                11.532563       12.767145   3.316625    15.459625   \n",
       "Jules Landry-Simard       7.211103        8.602325   3.741657     9.055385   \n",
       "David Liao                8.717798       12.165525   7.615773    10.862780   \n",
       "Varun Ballari             0.000000        5.099020   9.695360     9.899495   \n",
       "David Lundgren            5.099020        0.000000  11.224972    10.770330   \n",
       "Ben Dong                  9.695360       11.224972   0.000000    12.569805   \n",
       "Alex Zirbel               9.899495       10.770330  12.569805     0.000000   \n",
       "Cathy Chen               12.884099       14.628739   6.928203    14.071247   \n",
       "Daniel Zhang              5.656854        4.242641   7.615773    11.224972   \n",
       "Vicky Zhang               7.810250       10.440307   7.000000    15.066519   \n",
       "Sheng Wu                  6.454316        9.579050   9.766176    15.617881   \n",
       "Rachid Belaid            11.916375       15.033296   8.366600    13.784049   \n",
       "Jeff Zhou                 6.782330        6.324555   6.480741    10.954451   \n",
       "Kevin Wu                  9.000000        7.416198   7.937254    12.922848   \n",
       "\n",
       "                     Cathy Chen  Daniel Zhang  Vicky Zhang   Sheng Wu  \\\n",
       "Skylar Lee             6.000000      8.000000     7.937254  11.928881   \n",
       "Lucy Wang              6.557439      9.219544     6.480741  10.323672   \n",
       "Jules Landry-Simard    8.366600      5.656854     7.937254   9.609277   \n",
       "David Liao             6.164414     10.862780     7.141428  10.538415   \n",
       "Varun Ballari         12.884099      5.656854     7.810250   6.454316   \n",
       "David Lundgren        14.628739      4.242641    10.440307   9.579050   \n",
       "Ben Dong               6.928203      7.615773     7.000000   9.766176   \n",
       "Alex Zirbel           14.071247     11.224972    15.066519  15.617881   \n",
       "Cathy Chen             0.000000     12.409674     8.774964  13.755661   \n",
       "Daniel Zhang          12.409674      0.000000     8.426150   8.075779   \n",
       "Vicky Zhang            8.774964      8.426150     0.000000   5.599839   \n",
       "Sheng Wu              13.755661      8.075779     5.599839   0.000000   \n",
       "Rachid Belaid         13.490738     11.916375    11.874342  11.208845   \n",
       "Jeff Zhou              8.602325      4.690416     6.855655   9.465633   \n",
       "Kevin Wu               9.327379      6.082763     8.124038  11.100369   \n",
       "\n",
       "                     Rachid Belaid  Jeff Zhou   Kevin Wu  \n",
       "Skylar Lee               13.190906   4.690416   4.582576  \n",
       "Lucy Wang                10.723805   7.416198   8.000000  \n",
       "Jules Landry-Simard       8.602325   5.099020   7.280110  \n",
       "David Liao               11.224972   8.124038  10.148892  \n",
       "Varun Ballari            11.916375   6.782330   9.000000  \n",
       "David Lundgren           15.033296   6.324555   7.416198  \n",
       "Ben Dong                  8.366600   6.480741   7.937254  \n",
       "Alex Zirbel              13.784049  10.954451  12.922848  \n",
       "Cathy Chen               13.490738   8.602325   9.327379  \n",
       "Daniel Zhang             11.916375   4.690416   6.082763  \n",
       "Vicky Zhang              11.874342   6.855655   8.124038  \n",
       "Sheng Wu                 11.208845   9.465633  11.100369  \n",
       "Rachid Belaid             0.000000  13.190906  15.329710  \n",
       "Jeff Zhou                13.190906   0.000000   2.645751  \n",
       "Kevin Wu                 15.329710   2.645751   0.000000  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brute force assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "shuffled_names = list(distance_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lucy Wang',\n",
       " 'Ben Dong',\n",
       " 'David Liao',\n",
       " 'Vicky Zhang',\n",
       " 'Alex Zirbel',\n",
       " 'Skylar Lee',\n",
       " 'David Lundgren',\n",
       " 'Sheng Wu',\n",
       " 'Rachid Belaid',\n",
       " 'Jeff Zhou',\n",
       " 'Daniel Zhang',\n",
       " 'Cathy Chen',\n",
       " 'Jules Landry-Simard',\n",
       " 'Varun Ballari']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffle(shuffled_names)\n",
    "shuffled_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "assigned = set()\n",
    "pairs = []\n",
    "for subj in shuffled_names:\n",
    "    ds = distance_df.to_dict()[subj]\n",
    "    cur_max = 0\n",
    "    candidate = subj\n",
    "    if subj in assigned:\n",
    "        continue\n",
    "        \n",
    "    for friend, score in ds.items():\n",
    "        if friend in assigned:\n",
    "            continue\n",
    "            \n",
    "        if score > cur_max:\n",
    "            candidate = friend\n",
    "    \n",
    "    assigned.add(subj)\n",
    "    assigned.add(candidate)\n",
    "    pairs.append((subj, candidate))            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Lucy Wang', 'Jeff Zhou'),\n",
       " ('Ben Dong', 'Rachid Belaid'),\n",
       " ('David Liao', 'Sheng Wu'),\n",
       " ('Vicky Zhang', 'Daniel Zhang'),\n",
       " ('Alex Zirbel', 'Cathy Chen'),\n",
       " ('Skylar Lee', 'David Lundgren'),\n",
       " ('Jules Landry-Simard', 'Varun Ballari')]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### most popular and least popular love language "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group                                                                   0\n",
       "is_male                                                                10\n",
       "name                    Skylar LeeLucy WangJules Landry-SimardDavid Li...\n",
       "present                                                                14\n",
       "words_of_affirmation                                                77.47\n",
       "quality_time                                                       115.63\n",
       "receiving_gifts                                                     56.58\n",
       "acts_of_service                                                     95.74\n",
       "physical_touch                                                      72.58\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>present</th>\n",
       "      <th>words_of_affirmation</th>\n",
       "      <th>quality_time</th>\n",
       "      <th>receiving_gifts</th>\n",
       "      <th>acts_of_service</th>\n",
       "      <th>physical_touch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>3.250</td>\n",
       "      <td>8.500</td>\n",
       "      <td>3.000</td>\n",
       "      <td>10.750</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.447</td>\n",
       "      <td>8.163</td>\n",
       "      <td>4.458</td>\n",
       "      <td>5.274</td>\n",
       "      <td>5.658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group  present  words_of_affirmation  quality_time  receiving_gifts  \\\n",
       "is_male                                                                        \n",
       "False        0     True                 3.250         8.500            3.000   \n",
       "True         0     True                 6.447         8.163            4.458   \n",
       "\n",
       "         acts_of_service  physical_touch  \n",
       "is_male                                   \n",
       "False             10.750           4.000  \n",
       "True               5.274           5.658  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.groupby('is_male').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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