2-batch_analysing(task2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = pd.read_csv(r'C:\\Users\\wasee\\OneDrive\\שולחן העבודה\\Big data in behavioral science\\task2\\Batch_4631161_batch_results_33.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <th>Input.Position</th>\n",
       "      <th>Answer.Gender</th>\n",
       "      <th>Answer.age</th>\n",
       "      <th>Answer.carbon</th>\n",
       "      <th>Answer.environment</th>\n",
       "      <th>Answer.following</th>\n",
       "      <th>Answer.impact</th>\n",
       "      <th>Answer.wellbeing</th>\n",
       "      <th>Approve</th>\n",
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       "      <td>answer questions regarding an attached reddit ...</td>\n",
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       "      <td>Reddit , motive</td>\n",
       "      <td>$0.13</td>\n",
       "      <td>Tue Dec 14 03:47:38 PST 2021</td>\n",
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       "      <td>answer questions regarding an attached reddit ...</td>\n",
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       "      <td>Reddit , motive</td>\n",
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       "      <td>5</td>\n",
       "      <td>BatchId:4631161;OriginalHitTemplateId:929448123;</td>\n",
       "      <td>3600</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>30NHI4EO3WZV4YOYY328KNK70PDTB7</td>\n",
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       "      <td>act as if you are the writer of the comment an...</td>\n",
       "      <td>Reddit , motive</td>\n",
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       "      <td>Tue Dec 14 03:47:38 PST 2021</td>\n",
       "      <td>5</td>\n",
       "      <td>BatchId:4631161;OriginalHitTemplateId:929448123;</td>\n",
       "      <td>3600</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>30NHI4EO3WZV4YOYY328KNK70PDTB7</td>\n",
       "      <td>answer questions regarding an attached reddit ...</td>\n",
       "      <td>act as if you are the writer of the comment an...</td>\n",
       "      <td>Reddit , motive</td>\n",
       "      <td>$0.13</td>\n",
       "      <td>Tue Dec 14 03:47:38 PST 2021</td>\n",
       "      <td>5</td>\n",
       "      <td>BatchId:4631161;OriginalHitTemplateId:929448123;</td>\n",
       "      <td>3600</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      ],
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       "                            HITId                       HITTypeId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  30NHI4EO3WZV4YOYY328KNK70PDTB7   \n",
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       "\n",
       "                                               Title  \\\n",
       "0  answer questions regarding an attached reddit ...   \n",
       "1  answer questions regarding an attached reddit ...   \n",
       "2  answer questions regarding an attached reddit ...   \n",
       "3  answer questions regarding an attached reddit ...   \n",
       "4  answer questions regarding an attached reddit ...   \n",
       "\n",
       "                                         Description         Keywords Reward  \\\n",
       "0  act as if you are the writer of the comment an...  Reddit , motive  $0.13   \n",
       "1  act as if you are the writer of the comment an...  Reddit , motive  $0.13   \n",
       "2  act as if you are the writer of the comment an...  Reddit , motive  $0.13   \n",
       "3  act as if you are the writer of the comment an...  Reddit , motive  $0.13   \n",
       "4  act as if you are the writer of the comment an...  Reddit , motive  $0.13   \n",
       "\n",
       "                   CreationTime  MaxAssignments  \\\n",
       "0  Tue Dec 14 03:47:38 PST 2021               5   \n",
       "1  Tue Dec 14 03:47:38 PST 2021               5   \n",
       "2  Tue Dec 14 03:47:38 PST 2021               5   \n",
       "3  Tue Dec 14 03:47:38 PST 2021               5   \n",
       "4  Tue Dec 14 03:47:38 PST 2021               5   \n",
       "\n",
       "                                RequesterAnnotation  \\\n",
       "0  BatchId:4631161;OriginalHitTemplateId:929448123;   \n",
       "1  BatchId:4631161;OriginalHitTemplateId:929448123;   \n",
       "2  BatchId:4631161;OriginalHitTemplateId:929448123;   \n",
       "3  BatchId:4631161;OriginalHitTemplateId:929448123;   \n",
       "4  BatchId:4631161;OriginalHitTemplateId:929448123;   \n",
       "\n",
       "   AssignmentDurationInSeconds  ...  Input.Position Answer.Gender  Answer.age  \\\n",
       "0                         3600  ...              15        Female          59   \n",
       "1                         3600  ...              15          Male          44   \n",
       "2                         3600  ...              15          Male          41   \n",
       "3                         3600  ...              15          Male          52   \n",
       "4                         3600  ...              21          Male          41   \n",
       "\n",
       "   Answer.carbon Answer.environment Answer.following Answer.impact  \\\n",
       "0              5                  5           banana             5   \n",
       "1              5                  5           banana             4   \n",
       "2              5                  5           banana             2   \n",
       "3              5                  5           banana             5   \n",
       "4              2                  1           banana             5   \n",
       "\n",
       "  Answer.wellbeing Approve Reject  \n",
       "0                5     NaN    NaN  \n",
       "1                4     NaN    NaN  \n",
       "2                2     NaN    NaN  \n",
       "3                4     NaN    NaN  \n",
       "4                5     NaN    NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_res = batch[['HITId','WorkerId','Input.Post_text','Input.Subreddit','Input.our_classification','Answer.carbon','Answer.environment','Answer.following','Answer.impact','Answer.wellbeing']]\n",
    "batch_info = batch[['HITId','WorkerId','AcceptTime','WorkTimeInSeconds','Input.Subreddit','Answer.Gender','Answer.age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>A1IU5OP7BBZHZ7</td>\n",
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       "      <td>vegan</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>vegan</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>vegan</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A14W0AXTJ3R19V</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>vegan</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>banana</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>7</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>vegan</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
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       "      <th>8</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A2F0X4LN9N4O4C</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>vegan</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>A14W0AXTJ3R19V</td>\n",
       "      <td>My stress levels go up seeing clutter everywhe...</td>\n",
       "      <td>minimalism</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
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      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "5  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A1IU5OP7BBZHZ7   \n",
       "6  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A14W0AXTJ3R19V   \n",
       "7  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A2LRFPJOIC51KY   \n",
       "8  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A2F0X4LN9N4O4C   \n",
       "9  31KSVEGZ35NQMU0YVCOTN74LNSIWR5  A14W0AXTJ3R19V   \n",
       "\n",
       "                                     Input.Post_text Input.Subreddit  \\\n",
       "0  A combination of respect and commitment to the...           vegan   \n",
       "1  A combination of respect and commitment to the...           vegan   \n",
       "2  A combination of respect and commitment to the...           vegan   \n",
       "3  A combination of respect and commitment to the...           vegan   \n",
       "4  health reasons made me switch but the continue...           vegan   \n",
       "5  health reasons made me switch but the continue...           vegan   \n",
       "6  health reasons made me switch but the continue...           vegan   \n",
       "7  health reasons made me switch but the continue...           vegan   \n",
       "8  health reasons made me switch but the continue...           vegan   \n",
       "9  My stress levels go up seeing clutter everywhe...      minimalism   \n",
       "\n",
       "  Input.our_classification  Answer.carbon  Answer.environment  \\\n",
       "0               biospheric              5                   5   \n",
       "1               biospheric              5                   5   \n",
       "2               biospheric              5                   5   \n",
       "3               biospheric              5                   5   \n",
       "4               egocentric              2                   1   \n",
       "5               egocentric              4                   1   \n",
       "6               egocentric              2                   3   \n",
       "7               egocentric              1                   1   \n",
       "8               egocentric              3                   1   \n",
       "9               egocentric              2                   1   \n",
       "\n",
       "  Answer.following  Answer.impact  Answer.wellbeing  \n",
       "0           banana              5                 5  \n",
       "1           banana              4                 4  \n",
       "2           banana              2                 2  \n",
       "3           banana              5                 4  \n",
       "4           banana              5                 5  \n",
       "5           banana              5                 3  \n",
       "6           banana              2                 2  \n",
       "7           banana              1                 1  \n",
       "8           banana              5                 4  \n",
       "9           banana              3                 2  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_res = batch_res.rename(columns={'Input.our_classification': 'our_classification', 'Answer.carbon': 'help_generations', 'Answer.environment': 'positive_impact', 'Answer.following': 'attention', 'Answer.impact': 'protect_environment', 'Answer.wellbeing': 'beneficial_wellbeing'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Input.Post_text</th>\n",
       "      <th>our_classification</th>\n",
       "      <th>help_generations</th>\n",
       "      <th>positive_impact</th>\n",
       "      <th>attention</th>\n",
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       "      <td>biospheric</td>\n",
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       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "\n",
       "                                     Input.Post_text our_classification  \\\n",
       "0  A combination of respect and commitment to the...         biospheric   \n",
       "1  A combination of respect and commitment to the...         biospheric   \n",
       "2  A combination of respect and commitment to the...         biospheric   \n",
       "3  A combination of respect and commitment to the...         biospheric   \n",
       "4  health reasons made me switch but the continue...         egocentric   \n",
       "\n",
       "   help_generations  positive_impact attention  protect_environment  \\\n",
       "0                 5                5    banana                    5   \n",
       "1                 5                5    banana                    4   \n",
       "2                 5                5    banana                    2   \n",
       "3                 5                5    banana                    5   \n",
       "4                 2                1    banana                    5   \n",
       "\n",
       "   beneficial_wellbeing  \n",
       "0                     5  \n",
       "1                     4  \n",
       "2                     2  \n",
       "3                     4  \n",
       "4                     5  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>AcceptTime</th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>Input.Subreddit</th>\n",
       "      <th>Answer.Gender</th>\n",
       "      <th>Answer.age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>Tue Dec 14 22:58:41 PST 2021</td>\n",
       "      <td>151</td>\n",
       "      <td>vegan</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>Tue Dec 14 04:17:08 PST 2021</td>\n",
       "      <td>38</td>\n",
       "      <td>vegan</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>Tue Dec 14 07:16:19 PST 2021</td>\n",
       "      <td>62</td>\n",
       "      <td>vegan</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>Tue Dec 14 06:29:23 PST 2021</td>\n",
       "      <td>84</td>\n",
       "      <td>vegan</td>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>Tue Dec 14 07:13:50 PST 2021</td>\n",
       "      <td>44</td>\n",
       "      <td>vegan</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "\n",
       "                     AcceptTime  WorkTimeInSeconds Input.Subreddit  \\\n",
       "0  Tue Dec 14 22:58:41 PST 2021                151           vegan   \n",
       "1  Tue Dec 14 04:17:08 PST 2021                 38           vegan   \n",
       "2  Tue Dec 14 07:16:19 PST 2021                 62           vegan   \n",
       "3  Tue Dec 14 06:29:23 PST 2021                 84           vegan   \n",
       "4  Tue Dec 14 07:13:50 PST 2021                 44           vegan   \n",
       "\n",
       "  Answer.Gender  Answer.age  \n",
       "0        Female          59  \n",
       "1          Male          44  \n",
       "2          Male          41  \n",
       "3          Male          52  \n",
       "4          Male          41  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_info.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of Worker's worktime and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wasee\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Answer.age', ylabel='Density'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XXniBnTt38uCDD7JixQoKCwttXZsQQggH0KSgUBQFFxcXDh48SExMDAC1tbU2LUwIIYRjaFJQ9OjRg/nz51NQUMDIkSP53e9+x4ABA2xdmxBCCAfQpLGeVqxYwc6dO4mOjsbd3Z0RI0YwY8YMG5cmhBDCETQpKHx8fBgxYgTl5eV8++23DBkyhAsXLhAREWHr+oQQbVRtvZGyyjqq9QZMZgVXFxW+3u4EdPTCw12eMdOWNCko1qxZw//8z/8QGBhomadSqdi9e7fNChNCtC2KolCoq+JCYTl52krKq+obfW0Xfy96d/UnPKwzfj4erViluB1NCoqUlBR27Nghw3gIIW5gNJk5ebGEY2eKqaypx81VRai6AwPDAgjs5E0Hb3fcXF0wmMxU1xjQldWQr63k65NXyci5Sv8enRkVEUwHCQyH1aSgCAkJkZAQQjRgNJk5ca6Yb87o0NcZCQn0JSYymF5dO+HudvPvyaj9vQnr2pG7BgVTUV3P8bM6vr1QwrmCcmIigxnStwsqlaqVeyJupUlBMXr0aF5//XXuu+8+vLy8LPNvdY1i27ZtrF27FqPRyLx585gzZ06D9pycHBYvXkx1dTUjRoxg2bJluLl9X9LJkyf56U9/avWh30KI1nfpSgUHjhVSUV1PN00H7hoYRFd1h2ato6OvB3dHhTK0Xxf2f1PIV8cvk6+t5P6RPfH0kGsYjqRJQfHZZ58BkJqaapl3q2sUWq3W8ghVDw8PZs2axahRo+jbt6/lNQsXLuSll14iKiqKRYsWsWnTJmbPng2AXq/nxRdfxGAw3FbHhBAtr1pvYP83hVy4XE5nP0+mx/Wmm8bvjtbZ0deTqWN6kX2+hK+OX+Zf/znLtLG96Ojr2UJVizvVpKDYs2dPs1eclpZGTEwM/v7+AMTHx5OamspTTz0FQGFhIbW1tZbnWiQmJvLmm29aguLVV19l3rx5HD16tNnbFkK0vHMFZew9WoDJZCYmMpio/mpcXZp0K9YtqVQqBvftQkBHL75Mv8SWveeZPq4P/h0kLBxBk97l6upqli9fzrx58ygrK2Pp0qVUV1dbXaaoqAi1Wm2Z1mg0aLXaRtvVarWlfffu3dTW1jJp0qRmdUYI0fLqDSZ2H8lj+6FcOvl68PCEAUSHB7VYSPxQqKYDM8b1wWgys3XvOSqqG//mlGg9TTqieOmll9BoNJSUlODp6UlVVRVLly7lL3/5S6PLmM3mBhelFEVpMN1Yu06nY+3atXz44Ye30Z3rbHVNIzMz0ybrdTRttZ9u3v7k5hU0a5ncvFwbVXNzIZ1M5F8sa9VtQtPf0x/vw8oaExnnqqmuNdOvqyf9unpQfu0q5ddsVel1I/v7kJ5TxWf/Oc2YgR3wcP8+lKztw7b6u9tcrd3PJgVFTk4OK1asYN++fXh7e7Ny5UqmTZtmdZng4GAyMjIs0zqdDo1G06Bdp9NZpouLi9FoNOzdu5eysrIGF76nT5/O+vXr6dChaRfLIiMj8fRs2UPWzMxMoqOjW3Sdjqgt97OotIae5U2/CJqbl0vPHj1tWNGNQoI1aAL6tOo2m/Oe/nAfnsm7xsGcAtzdXJgxrhehzbxYfacCA6tIOXCB7HwT0+PCcHG5/sGysX3Yln93m8MW/ayrq7P6AbtJx44uPzrENJlMN8z7sdjYWNLT0yktLUWv17Njxw7i4uIs7aGhoXh6elqSMSUlhbi4OH7yk5+wa9cuUlJSSElJsbQ1NSSEEHfGZDaz/5sCdn6dh9rfm4cn9G/1kADoqu7AvdHduFxcTdqJy62+ffG9Jh1R3HXXXfz5z3+mtraWAwcO8PHHHzNq1CirywQFBZGcnExSUhIGg4GZM2cyZMgQ5s+fz4IFCxg8eDArV65kyZIlVFVVERERQVJSUot0Sghxe65V1LJ173multYQ1U9NzOAQXF3sd1/DgJ4BaK/pOX62mK5dOtA7tJPdamnPmhQUzz77LP/93/+Nn58fq1evZuzYsfzqV7+65XIJCQkkJCQ0mLdu3TrLz+Hh4WzevNnqOk6fPt2UEoUQd+jE+WJe/fsR9HVG4mN60rebv71LAmDMkBC0JdXsycxH09nb3uW0S7c89bRz507mzp3Le++9R0FBAX5+fgwfPrzFrwEIIexDURS27jvPkr+m4ePlxsx7+zlMSAC4urhw/8iemEwKezLyURTF3iW1O1aPKL788ktWrVrFggULCA8PR6VSceLECV5++WXq6uqYOHFia9UphLABfZ2RtzYd48CxQkYPDmFOfDg5l0rtXdYN/P08iR0cwv5jhRzKvsr0ca37hYD2zmpQfPTRR3z44Yd07drVMq9Pnz4MHTqURYsWSVAI8QN19SauVdZSW29CURQ8PVzp5OuJj5ebQ45fdFlXxcsffk2BtpJ5Uwfx0Pi+6K7p7V1WoyL6BHI67xqf7DrNPdHd6CQ347Uaq0FRXV3dICS+06tXL+rq6mxWlBBtRWVNPScvlHDxSgUl5Td/PLC3pxs9gv0IC+nIwLCAVq7w5vZ/U8A7m4/j6uLCC/NHM2yA5tYL2ZmLSsX46G58uvssH3z+Lb+ZNdzeJbUbVoPC1bXx76TLeULRntXUGjiUfZVTuaWgQFf19ZFTAzp64e3photKhb7eSHlVHVdLarh0uYLTudfYfSSPsVGhTB3TiwE9Orf6kUZNrYG/bTnBnox8BvTszMJHRxAU4NOqNdyJwE7exMf05Iu0S4yP7s7QfupbLyTuWJO+9SSE+N7Z/GvsPVqA0agwuE8XhvZT09G38WcpDOkLZrPC1ZJqyqvqSc++wt7MAnqHdiJhbG/GDQ/F3c32o6VmndPx9qbjaEurefj+/jxy/wBcXVt+GA5bSxjbm29O61j7ryzeXjgetzbYh7bGalCcPn2a4cNvPLxTFIX6ehmDRbQvZrPCgWOFZF8oITjAh/vu6oG/X9POk7u4qOiq7sC0sRp+mTiYfd8U8vlXF1iz8Rv+/sVJpo7pxeTRYTY5715Va+KNDZn8J7OA4EAfXvnVWCJ6B956QQfl4e7K4w9E8PIHX7PjcC5TYnvZuySnZzUodu7c2Vp1COHQDEYzqYcukXe1kmH91cREhliGlGguHy93Jo8OY1JMT46d0ZGy/zzrU0/x6a4zjB/RnQfu7k2P4I53XHNNrYH/PXiRTbuuYjTBTyf056cT+uPpBM+rHhURTETvQP65/TT3DO+Gj5e7vUtyalaDIjQ0tLXqEMJhGU1mvki7SGFRFfcM79Zin8ZVKhXDBmgYNkBDvraSlP3n+U9GPtsP5TJ8gIapY3sxrL+62aelyirrSD10iX/vP09ljYG+IZ4kPxrbIuHjKFQqFY8nRPC7Nfv57D/neHTyQHuX5NTkGoUQVpjNCqnplygoqmLCXd0Z0NM231rqHuTHUz+JYu7kgaQeusT/fnWRF98/jLenGyMGBjFiYBDhYZ0JDvC94UjGZFYoKKrk5IUSvj6p5ZvTRZjMCiMHBfPw/f2p1F1wqpD4Tv8enbk7KpQt+84zOTbM3uU4NQkKIRqhKAr7jxWSe7WSccO72SwkfqhTB08enjCAxHv6kXVOR/qJKxzOvsqBY4UAeLi50MXfGx8vNxSgRm+kuFyPwWgGQBPgwwNxfZhwV3dLOGTqGtta25c0ZSDpJ66wPvUUY/re+vXi9khQCNGIby+U8O2FEoYP0BDZyhd/3d1ciA4PIjo8iP96SCHvagVn8q5RqKumuExPTa0BlUpFaBc3Rg8OoWeIH/17dCZU3cEhb+6zleBAX6aMCePzry4SHhRk73KclgSFEDdRXKbnq+OX6RHkR0xksF1rcXVR0atrJ3p1lZFTb2bm+H6kpl3iwLcVTLzH3tU4J/kCshA/Um8wsf1QLl4erkwY2aNdfUJvizp39GJSbBjHL9Zwpdj6I5rF7ZGgEOIHFEVh3zcFlFfVcf+onnh7ykF3W/DQ+H64usCmXWfsXYpTkqAQ4gfO5pdxJq+MuwYF2+WpbuL2BHT0IrpPB/Zk5nO1RI4qWppNg2Lbtm1MmTKFiRMnsn79+hvac3JySExMJD4+nsWLF2M0GgHIyMggMTGRhIQEnnzyScrLy21ZphDA9SG3DxwrRNPZh+iBjj9InmhozCA/XF1UclRhAzYLCq1Wy6pVq9iwYQNbt25l48aNnDt3rsFrFi5cyNKlS9m+fTuKorBp0yYA/vCHP/D666+zbds2+vbty/vvv2+rMoWwOJh1mXqDiXtHdMNFrku0OR19XIkf1ZM9GfkUlznucOltkc2CIi0tjZiYGPz9/fHx8SE+Pp7U1FRLe2FhIbW1tURFRQGQmJhoaf/iiy/o27cvBoMBrVZLx47Od7OQcCz52kpO515j+AANgZ3kcZtt1Yx7+qIAKfvP27sUp2KzoCgqKkKt/n4IYI1Gg1arbbRdrVZb2t3d3Tl9+jTjxo3j8OHDTJ061VZlCoHBaGLv0QL8/TyJHijfxW/LggJ8iIsKZfuhS1TVyMClLcVmX+kwm80NvlaoKEqD6Vu1DxgwgLS0ND755BOSk5P55JNPmrzt7OzsO6z+5jIzM22yXkfTVvvp5u1Pbl5Bs5bJzcvlVIGeiup6Rod3oLAw30bVXRfSyUT+xTKbbuNmmvqe3s4+bG3W9mFmZibhQfXsPWpi3eaDxEU459mI1v4btVlQBAcHk5GRYZnW6XRoNJoG7Trd92MLFBcXo9FoqKur48CBA0yYMAGABx54gNdee61Z246MjMTTs2WHa87MzCQ6OrpF1+mI2nI/i0pr6Fne9AH0cvNy6RwYwsWMU/Tv4c/wyJ42rO66kGANmoDWfd5zc97T5u5De2hsH/6wn19fTOfo+XJ+9UiUU4yW+0O2+Butq6uz+gHbZqeeYmNjSU9Pp7S0FL1ez44dO4iLi7O0h4aG4unpaUnGlJQU4uLicHNzY9myZZaiv/zyy5s+E0OIlpCWdRmVSsXoyBB7lyJa0Mzx/SirqmPPkTx7l+IUbHZEERQURHJyMklJSRgMBmbOnMmQIUOYP38+CxYsYPDgwaxcuZIlS5ZQVVVFREQESUlJuLq6smrVKpYuXYrJZCIoKIiXX37ZVmWKdqykwsj5wipGDgqmg0/jT6gTbU9kn0D6dfdny97zTIwJw/U2nx0irrPpbacJCQkkJCQ0mLdu3TrLz+Hh4WzevPmG5UaMGMFnn31my9JEO2dWFL7Nq6GDjzvDBshzl52NSqUicXxfXvsogyMnrxIjR4x3RO7MFu3SqUulVNSYiR3cVZ657KRGR4ag7uzNv/dfsHcpbZ78hYh2x2gy8/VJLf6+rvTtJiOyOitXVxemjenNifPFXCiU0R3uhASFaHdOnCumWm9gYHdvGRnWyU2M6YmXh6vcgHeHJChEu1JXbyLzVBE9gvwI7Cgjwzq7Dt7uTLirB/u/KeBaRa29y2mzJChEu/LNmSLqDCZiBsvFzfYi4e7emMwK/5t20d6ltFkSFKLdqNYbOH5WR7/u/qj9ZTyn9qKrugN3DQzmy7RL1BlM9i6nTZKgEO1GRo4Ws1lhVIR9H20qWt/0cb2pqK5n31HHHp7EUUlQiHahrKqOkxdLGNQ7kE4dWnZ4F+H4BvfpQq+uHUnZfx5FUexdTpsjQSHaha+/vYqLiwsjZHTYdkmlUvHA3X3Iu1rJsTO6Wy8gGpCgEE5Pd62Gs/llDO3XBV8vd3uXI+xk3PBQ/P08+fcBuQGvuSQohNM7lH0VTw9Xhg2Qx5u2Z+5urkyJ7UVGjpZ8baW9y2lTJCiEUysoqiJPW0l0uMbphpsWzTd5dBjubi5sk6OKZpGgEE5LURTST1yhg7c7g/t0sXc5wgH4+3lyz/Bu7M7Ip6JanoDXVBIUwmldvFxB0bUa7hoUJAP/CYvpcX2oN5jYfuiSvUtpM+SvRzgls1nhUPYVOvt5Et4zwN7lCAfSM6Qjw/qr+fyrCxiMZnuX0yZIUAindCq3lGuVdYyKDMZFHlojfmTGuL6UVtRx4FihvUtpE2waFNu2bWPKlClMnDiR9evX39Cek5NDYmIi8fHxLF68GKPRCFx/JuzMmTOZPn068+bNo7BQ3kzRdAajicPfXiU40IfeXWUYcXGjYQPUdA/yY+u+c3IDXhPYLCi0Wi2rVq1iw4YNbN26lY0bN3Lu3LkGr1m4cCFLly5l+/btKIrCpk2bLPNfeuklUlJSSEhI4KWXXrJVmcIJHT9bTE2tkdjBXWUYcXFTKpWKGeP6cPFyBVnniu1djsOzWVCkpaURExODv78/Pj4+xMfHk5qaamkvLCyktraWqKgoABITE0lNTaW+vp5nnnmG8PBwAAYMGMCVK1dsVaZwMvo6I0dPF9Gra0dCuvjauxzhwO4Z3o1OHTzYuk+eVXErNguKoqIi1Orvn0Ws0WjQarWNtqvVarRaLR4eHkyfPh0As9nM22+/zYQJE2xVpnAyR3K0GE1mRsszksUteLi7MlVuwGsSmz25xWw2NzjsVxSlwfSt2uvr63n++ecxGo388pe/bNa2s7Oz76DyxmVmZtpkvY6mrfaz3OBN9vliunfxoKJMS0XZrZfJzcu1eV0/FNLJRP7FslbdJjT9PXXz9ic3z7FHWLW2D5v7u9u1gwlXF3j/s8MkjOzcAtW1jtb+G7VZUAQHB5ORkWGZ1ul0aDSaBu063feDcxUXF1vaq6ur+a//+i/8/f1Zu3Yt7u7NG58nMjIST8+WHSE0MzOT6OjoFl2nI2rL/Vz+/iFcXVy4d2RffL1v/TuTm5dLzx49W6Gy74UEa9AE9GnVbTbnPS0qraFnuWPfwd7YPrzd392swmPszcwnee7dbWJkYVv8jdbV1Vn9gG2zU0+xsbGkp6dTWlqKXq9nx44dxMXFWdpDQ0Px9PS0JGNKSoqlfeHChfTs2ZPVq1fj4eFhqxKFEzmTd40jJ7VE9Vc3KSSE+M70uN7UG82kpl+ydykOy2ZBERQURHJyMklJScyYMYNp06YxZMgQ5s+fz4kTJwBYuXIlK1asYNKkSdTU1JCUlMTJkyfZvXs3R48e5cEHH2T69OnMnz/fVmUKJ6AoCu//Oxs/H3eG9VffegEhfqBHcEeiwzV8fvAiBqM8Ae9mbPp0+YSEBBISEhrMW7duneXn8PBwNm/e3KB90KBBnD592pZlCSez72gBJy+WMm/KQDxk4D9xG2aM68Mf/5bOvqOFTBjZw97lOBy5M1u0aTW1Bj74/Fv6dvdnbFSovcsRbdTQfmrCQuQJeI2RoBBt2qZdZyitqOPJBwfjIjfXidukUqmYHteHS1cqyDxVZO9yHI4EhWizCnVVpOw/z313dWeADPwn7tC44d3o0smLzXvO2rsUhyNBIdokRVH462dZeLi7Mm/qIHuXI5yAu5sLD47vy7cXSvj2Qom9y3EoEhSiTdqTkc+xMzrmTh5IZz8ve5cjnMTEUT3p6OvBp7vP2LsUhyJBIdqcaxW1vJeSzcCwAKbE9rJ3OcKJeHm4MT2uD5mnijhfUGbvchyGBIVoc/625QR1BhMLHo6SZ02IFjdlTC98vNz4VK5VWEhQiDYl/cRlDmZd5pGJA+im8bN3OcIJdfB2Z+qYXqRlXZbBAv+fBIVoM0rK9byz+Ti9u3biwXv62rsc4cSmx/XB092VT3bIzb8gQSHaCJNZ4Y0NR6mtN/Hso9G4ucqvrrCdTh08Sbi7N/uPFXLxcrm9y7E7+WsTbcLmPWfIOlfML2cMpnuQnHIStpd4T198vdzYsP2UvUuxOwkK4fC+vVDChtRTxA0LlXF4RKvp4OPB9HF9OZR9lXP5ZfYux64kKIRDKy7T8/o/MggK8OXXM4fKM7BFq5oe1xs/H3c+Ts2xdyl2JUEhHFZNrYFl7x1CX2dk0WMj8fGS50yI1uXj5U7i+H5knipq13drS1AIh2QymXn9HxnkaSt5ft5dhIV0tHdJop2aNrYXgZ28eO/f2ZjN7XNkWQkK4XAUReGvW06QeaqIXz00hOEDNLdeSAgb8fJwI2nKIM7ll7H3qGM/T9xWbBoU27ZtY8qUKUycOJH169ff0J6Tk0NiYiLx8fEsXrwYo9HYoH316tW89dZbtixROBizWeGdzcdJTb/EzHv7ER8TZu+ShOCe4d3o292fj744SW2d8dYLOBmbBYVWq2XVqlVs2LCBrVu3snHjRs6dO9fgNQsXLmTp0qVs374dRVHYtGkTAJWVlSxatIgPPvjAVuUJB2QymVmz8Ru2H8rlJ/f1I2nKQHuXJAQALi4qnnggkpLyWrbsPXfrBZyMzYIiLS2NmJgY/P398fHxIT4+ntTUVEt7YWEhtbW1REVFAZCYmGhp3717N2FhYTz22GO2Kk84mDqDiT+vz2RPRj6PTgonacog+YaTcCgRvQMZM7Qr/9p7juIyvb3LaVU2C4qioiLU6u8fdK/RaNBqtY22q9VqS/uMGTP4xS9+gaurPP+4PSi6VsPz73xFWtZlfv5ABA/fP8DeJQlxUz+bOgjFrPDfW0/Yu5RW5WarFZvN5gafCBVFaTB9q/Y7kZ2d3SLr+bHMzEybrNfRtFY/FUUh61INX2SUoSjw8N2BdO9Qftvbd/P2JzeveRcbc/Nyb2tbtyukk4n8i2Wtuk1o+nt6O/uwtVnbh63xu3t3RAd2H7/Cx1u/YmB3b5tv72Za+98imwVFcHAwGRkZlmmdTodGo2nQrtPpLNPFxcUN2u9EZGQknp6eLbKu72RmZhIdHd2i63RErdXPaxW1rP0si/QT1xjUK4DkR4YTHOh7R+ssKq2hZ3nTj0Jz83Lp2aPnHW2zuUKCNWgC+rTqNpvznjZ3H9pDY/uwtX53h0aZuaDbx87j1TwYPwpf79a9v8cW/ayrq7P6Adtmp55iY2NJT0+ntLQUvV7Pjh07iIuLs7SHhobi6elpScaUlJQG7cI5VesN/OPLHH6xYhdHTmp5bNogXvnV2DsOCSFai5urC0/9JIqyylo++uKkvctpFTY7oggKCiI5OZmkpCQMBgMzZ85kyJAhzJ8/nwULFjB48GBWrlzJkiVLqKqqIiIigqSkJFuVI+ysqLSGHV/n8r9fXaRKbyAuKpQ5k8Lpqu5g79KEaLb+PToz7e7ebDtwgbhh3YjoHWjvkmzKZkEBkJCQQEJCQoN569ats/wcHh7O5s2bG13+6aeftlltonkqa+rR1zbv++NVegPZ54tJP3HFMvzBkH5dmB7Xh57B1++0LiqtabEa6wymFluXELfy6KSBfP3tVf6yIZM3f3sPHXw87F2Szdg0KITz0NcaOXq6qNF2RVGorjWiu1aD7pqeAl0VV4urUQBfb3eiBwYxMCyAjr4elJTXUlJe2+I1DujZucXXKURjvD3dWPjoCJ576wBvfXqM55PuctqvdEtQiGZTFIXKmnp01/ToyvSW/+v//45VFRDo7030wCDCQjqi6ezttH9Aon3r36MzSVMG8sHnJ0k9lMvk0WH2LskmJCiEVYqicKW4msxTRRw5eZWia3qKy/SW0zwuKujc0YueIX6o/X1Q+3vTxd8LdzfH/uaMEC1lxri+HD9bzHtbTzAwLMApB7CUoBANGIwKWed0nLp0jVO5pZzOvUZFdT1wfRiDLp286NOtE5rO10MhoJOXPJZUtGsuLip+88gwfvPGXl78n8OsXHA3nf287F1Wi5KgaOcURaGgqIrMU0V8c7qIrHNFGE2FAHTTdGDkoGDCwzrTpZM3V0trcHWRU0hC/FhnPy+WPD6K5985yMv/8zUv/2oMnu7Oc1QtQdEOKYrC2fwyDhwrJC3rMkXXro9b003TgRF9OzBxbAThYQH4/eBbHEWlNeja2fg2QjRHv+6deXbOcFb8/Qir/nmU5x4dgYuTfLCSoGhHLl4uZ9/RAg4cv0xRaQ1uriqi+mv4yX39GT5AgybA5/pdn4OC7V2qEG3S6MFd+dnUCD74/FvW+Z3gFzMGO8UXOSQonFyV3sC+owXs+jqXcwXluLqoiOqvZvbEAYyKDKFDKw8/IISze/CePpRW1JKy/zxms8IvHxzS5o8sJCickNmskH2hmJ2H80jLuky90Uyvrh35xYzBjBvejY6+zntjkBD2plKp+PkDEbi6qPhs7zlMZoVfPTS0TYeFBIUTKSnXs+tIHru+zuNqSQ2+Xm7cN7IHE0f2pE+3Tk5xCCxEW6BSqfjZtEG4uqr4dPdZqvUGnpk1DC+PtvlPbtusWliYTGYycrRsP5xLZo4WswKD+3Rhdnw4sUO6OtU3L4RoS1QqFXMnD8TXy52/f3GSy7pqFj02kqAAH3uX1mwSFG3U5eIqdn2dx+4jeZRW1NHZz5OH7u3HhJE96NpFBtoTwhGoVCoeurcfPUM6svLjDJJX7ePZOdEMD2+ZRyq0FgmKNkRfZ+Rw9hV2fp1H1rliXFQwYmAwE0f1YMTAIFzlxjchHNKIgUG88ZtxvPTB1/xpXTr3j+zB4wkRbWYgQQkKB2cwmvnmTBH7jhZw+Nur1NWbCArwYe7kgdx3V3cCO9nnCVtCiObpqu7A6uRxfLLzNP/6zzkycrT8/IFI7o4KdfgL3RIUDkhfZ+TYmSK+/lbL4W+vUFljwM/HnXujuzNueDcGhgU4/C+WEOJGHu6uJE0ZxJghXXlz0zFWrs9k856zzI4fQExkiMN+4USCwgF8N4xG1rlijpy8Sta5YgxGM75ebowYGMy44aFE9dfg7ianloRwBn26+fPGb8bx1bFC/rnjFK98eIQewX5MigljfHQ3hzslJUFhB0aTmXxtJScvlpJ9vpjsCyWUVdYBEBLoy5TYXoyMCGJQr0AZcE8IJ+XqomLc8G6MHdqVvUcL+PzgRf576wk+/PxbYiJDGBUZzPDwIIe4KdamQbFt2zbWrl2L0Whk3rx5zJkzp0F7Tk4Oixcvprq6mhEjRrBs2TLc3Ny4fPkyCxcupKSkhF69erFy5Up8fdveM5XNZoXiMj1XiqspKKrkfGE5Fy6Xk3ulEqPJDECXTl5E9VcT2bsLg/sEEtLF12EPP4UQLc/V1YX77urBfXf14HxBGdsP55KWdZn9xwpxdVERHhbAoF4BhIcFMKCHfR7OZbOg0Gq1rFq1is8++wwPDw9mzZrFqFGj6Nu3r+U1Cxcu5KWXXiIqKopFixaxadMmZs+ezbJly5g9ezZTp07lnXfe4d1332XhwoW2KrXZDEYTVTUGqvQGqvUGKmrqKS2vpbTi+n8l5bVoS6u5WlKDwWi2LOfn40Gf0E5Mj+tN79BO9O/RmaAAHwkGIQRw/ZTUr7r588sHh3Am9xqHv71C1rliPvvP9Tu8AXw8XehzuJZQTQe6+Huh9vcmsJM3an9vm33QtFlQpKWlERMTg7+/PwDx8fGkpqby1FNPAVBYWEhtbS1RUVEAJCYm8uabb/KTn/yEI0eO8M4771jmP/roo00KCkW5viPr6+ubXa++zkjKvvOUV9djMJgwmMzUG80YjSYMRjOV1bV8sHsHNbUmDMZGns2sgo4+HnTq4EnfUD9iIzVoOvsQFOhDUGcf/P08b3gTb6dWW6urq7thntFQjwuO/Uxqk9HQrBo93FSt3iejoZ66uta/CfJm7+nNtIX32do+bGo/24LeXX3p3bUvj9zflzqDidzLFVy6WkH2mXxqTWZOnL1Ktd7QYJk5k8IZH9292dv67t+h7/4N/TGbBUVRURFqtdoyrdFoyMrKarRdrVaj1Wq5du0aHTp0wM3NrcH8pjAYru+0M2fO3FbNkV3h+oM8b7Zb/G5jjfVAPcbKMgorofC2qmp92dnZN50f6FjX125QritrVo2BYb5Ama3KuakrBWVcKWjVTQKNv6c34+jvs7V92Jx+tkVdfaHrMP//n7rZHd7lZGeX3/b6DQYDXl43PnTJZkFhNpsbfHpWFKXBdGPtP34d0ORDKV9fX/r374+7u7uczhFCiCZSFAWDwdDotWCbBUVwcDAZGRmWaZ1Oh0ajadCu0+ks08XFxWg0GgICAqisrMRkMuHq6nrDcta4uLjg53c7n/yFEKJ9u9mRxHds9t3L2NhY0tPTKS0tRa/Xs2PHDuLi4iztoaGheHp6kpmZCUBKSgpxcXG4u7szYsQIvvjiCwC2bt3aYDkhhBCtS6U0dvWiBWzbto2//e1vGAwGZs6cyfz585k/fz4LFixg8ODBnDp1iiVLllBVVUVERAQrVqzAw8ODwsJCnn/+eUpKSggJCeGNN96gU6dOtipTCCGEFTYNCiGEEG2f3PYrhBDCKgkKIYQQVklQCCGEsEqCQgghhFUyeuxNvP3223z55ZcAjBs3jueee460tDRWrFhBXV0dkydPJjk52c5Vtow1a9awfft2VCoVM2fO5LHHHnPavgK89tprXLt2jVdffdUp+zl37lxKS0stIxssX76c6upqp+vnnj17ePvtt9Hr9YwZM4YlS5Y45fv56aef8vHHH1umCwoKmD59OhMmTGjdviqigYMHDyoPP/ywUldXp9TX1ytJSUnKtm3blHHjxil5eXmKwWBQHn/8cWXv3r32LvWOHT58WJk1a5ZiMBgUvV6vjB8/XsnJyXHKviqKoqSlpSmjRo1Sfv/73yt6vd7p+mk2m5WxY8cqBoPBMs8Z+5mXl6eMHTtWuXLlilJfX6888sgjyt69e52unz925swZ5f7771cuX77c6n2VU08/olaref755/Hw8MDd3Z0+ffpw6dIlevbsSffu3XFzcyMhIYHU1FR7l3rHRo4cyUcffYSbmxslJSWYTCYqKiqcsq9lZWWsWrWKJ598EoCsrCyn6+eFCxcAePzxx3nggQf4+OOPnbKfO3fuZMqUKQQHB+Pu7s6qVavw9vZ2un7+2AsvvEBycjL5+fmt3lcJih/p16+fZUTbS5cu8eWXX6JSqW4Y4LCpAxU6Ond3d958802mTp3K6NGjbzqYozP0denSpSQnJ9OxY0fg5oNWtvV+VlRUMHr0aN555x0+/PBDPvnkEy5fvux0/czNzcVkMvHkk08yffp0NmzY4JTv5w+lpaVRW1vL5MmT7dJXCYpGnD17lscff5znnnuO7t27Wx3gsK1bsGAB6enpXLlyhUuXLjldXz/99FNCQkIYPXq0Zd6tBq1si4YNG8brr7+On58fAQEBzJw5kzfffNPp+mkymUhPT+eVV15h48aNZGVlkZ+f73T9/KFPPvmExx57DLDP765czL6JzMxMFixYwKJFi5g6dSpff/11gwEMmzNQoSM7f/489fX1DBw4EG9vbyZOnEhqaiqurt+P9e8Mff3iiy/Q6XRMnz6d8vJyampqKCwsdLp+ZmRkYDAYLIGoKAqhoaFO97vbpUsXRo8eTUBAAAATJkxwyt/b79TX13PkyBFeffVV4MYBVVujr3JE8SNXrlzh17/+NStXrmTq1KkADB06lIsXL1oOeT///HOnGKiwoKCAJUuWUF9fT319Pbt372bWrFlO19cPPviAzz//nJSUFBYsWMC9997Le++953T9rKys5PXXX6euro6qqiq2bNnCb3/7W6fr5/jx4/nqq6+oqKjAZDJx4MABJk2a5HT9/M7p06cJCwvDx+f68yfs8e+RHFH8yPvvv09dXZ0lvQFmzZrFq6++ytNPP01dXR3jxo1j0qRJdqyyZYwbN46srCxmzJiBq6srEydOZOrUqQQEBDhdX3/M09PT6d7T8ePHc/z4cWbMmIHZbGb27NkMGzbM6fo5dOhQnnjiCWbPno3BYGDMmDE88sgj9O7d26n6+Z38/HyCg4Mt0/b43ZVBAYUQQlglp56EEEJYJUEhhBDCKgkKIYQQVklQCCGEsEqCQgghhFUSFKLdMhgMjB07lieeeMLepQjh0CQoRLu1c+dOwsPDyc7O5vz58/YuRwiHJfdRiHZr7ty5TJkyhbNnz2I0Glm+fDmHDx9m1apVdO/e3TJ/2bJlREdHk5GRwauvvorZbAbgl7/8JT169ODJJ59k3759APz85z+nS5cuvPbaa9TX13P33Xeza9cuioqKePnllykrK8NkMjF37lxmzpzJ4cOHefnll/Hx8aG6upp//etfeHh4AFBcXMzSpUspKSlBp9MRGhrK6tWrCQwMJCsrixdeeAGDwUCPHj24fPkyzz//PKNGjWLPnj2sXbsWg8GAl5cXv//97xk2bJjd9rNwAjYdxFwIB3X27FklIiJCKS0tVY4fP64MGTJEKS0tVQ4dOqQMHDhQOXnypKIoivL+++8rc+bMURRFUZKSkpTPP/9cURRFycnJUV544QVFURTl3nvvVU6fPq3o9XrlnnvuUeLi4hRFUZS9e/cqTzzxhGIwGJQpU6Yo2dnZiqIoSkVFhTJ58mTlm2++UQ4dOqSEh4crBQUFN9T44YcfKn/7298URbn+rIknnnhCef/99xWDwaDExcVZnkGQnp6uDBgwQDl06JBy8eJFZdq0aUppaamiKNefYTBmzBilurraVrtStAMyhIdol/75z38yfvx4OnfuTOfOnenWrRubNm0iKiqKrl27MnDgQAAGDRrEli1bAJg8eTLLly9nz549xMbG8tvf/haA+++/n/3799OvXz9iYmI4ffo0Z8+eZffu3UycOJFLly6Rl5fHokWLLNuvra3l5MmT9OnTh5CQEEJDQ2+ocd68eWRkZPDBBx9w6dIlzp49y9ChQzlz5gxwfQgWgJiYGPr16wfAwYMHKSoq4mc/+5llPSqViry8PMLDw1t+R4p2QYJCtDs1NTWkpKTg4eHBvffeC0BVVRUff/wxkZGReHl5WV6rUqlQ/v/s7KxZsxg/fjwHDx7kwIEDvP3226SmpjJhwgTWrFlDUVERY8aMITAwkK+++or9+/eTnJxMUVERfn5+pKSkWNZbXFyMn58fx44dswz29mN//vOfycrK4qGHHmLUqFEYjUYURcHV1dVS03e+GznVbDYzevRoVq9ebWm7cuWK04ykKuxDLmaLdmfbtm34+/tz4MAB9uzZw549e9i1axc1NTWUlpY2utysWbPIyckhMTGRF198kYqKCnQ6HcOHDyc/P5+9e/cSGxvLmDFj+Pvf/05YWBidO3emV69eeHl5WYLiypUrTJs2jezsbKt1fvXVV8ybN48ZM2YQGBhIWloaJpOJPn364OHhwf79+4HrT+s7c+YMKpWK0aNHc/DgQcvF+X379vHAAw9QW1vbQntPtEdyRCHanX/+85889thjDZ5f0LFjR+bOncuHH37Y6HLPPvssr7zyCqtXr0alUvHUU0/RrVs3AOLi4jhx4gQBAQFER0dTXl7OxIkTAfDw8ODdd9/l5Zdf5r333sNoNPLMM88QHR3N4cOHb6gtOzubl19+mV//+te8/vrrrFmzBnd3d4YPH05eXh5ubm689dZb/OlPf+KNN94gLCyMLl264OXlRd++fVm+fDm//e1vURQFNzc31q5di6+vb8vvSNFuyLeehGiDXnvtNcs3rK5cucL06dPZtWuX5VGvQrQkOaIQog0KDQ3lZz/7GW5ubiiKwksvvSQhIWxGjiiEEEJYJRezhRBCWCVBIYQQwioJCiGEEFZJUAghhLBKgkIIIYRVEhRCCCGs+j85xWGNvD/aOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(batch_info['Answer.age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Answer.age', ylabel='count'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j6AHAcgQ9AFiOoAcAyxH0AGA5gh4ALEfQA4DlCHoAsBxBDwCWI+gBwHIEPQBYjqAHAMsR9ABgOYIeACznaNB//PHHSkhIUHx8vN5//30nhwIA1MCxWwkWFRVp7ty5+vDDDxUVFaXBgwera9euuvXWW50aEgBQDceCPiMjQ926ddO1114rSXrggQe0du1ajR079oLHGWMkSeXl5ef97Nroei/TUT6fr1b709/lxeb+atubZHd/4dSbVH1/32Xmdxn6fS5T3dZ6sGDBApWWlmrChAmSpOXLlysnJ0evvPLKBY87efKkcnNznSgJAKzXrl07NW3a9Jxtjs3oq6qq5HK5Ao+NMec8rkmTJk3Url07RUZGBrU/AOBsxvr9fjVp0uS8nzkW9Ndff72ysrICj48cOaLrrrvuosddddVV5/1rBAC4uIYNG1a73bFP3dxzzz3avn27SkpKVFZWps8++0y9evVyajgAQA0cm9G3atVKEyZM0LBhw+T3+5WSkqKOHTs6NRwAoAaOnYwFAFwe+GYsAFiOoAcAyxH0AGA5gh4ALOfYp24uV/Pnz9enn34ql8ullJQUjRw5UpLk9/uVmpqqZ555Rl27dg1xlXU3e/Zsffvtt5o1a5Yku/r7YW+StGTJEn366adavHhxCCurm6FDh6qkpEQREWf/Ws6YMUN79uzR4sWL5XK55Ha79fLLLysqKirElV6aH/bXt29frVu3LvDzoqIiderUSQsWLAhViXVS3euXn5+vd999Vw0aNFDXrl01adKkwM9/VOYKsmPHDjN48GDj9/tNWVmZiYuLM/n5+SY/P98MGjTIdOjQwWRmZoa6zDrLyMgwXbt2NS+99JIxxljV3w97M8aYvLw807NnT/PYY4+FsLK6qaqqMj169DB+vz+wbd++faZv377m5MmTpqqqykycONEsXLgwdEXWQXX9fd/hw4fN/fffb7766qsft7B6Ul1/+fn5pmfPnqaoqMgYY8y0adPMn//855DUd0Ut3dx999167733FBERoaNHj6qyslKNGzfW3//+d6WmpqpTp06hLrHOjh07prlz52r06NGBbbb0V11v5eXlmjp1qsaNGxfCyupu3759kqRRo0bpoYce0pIlSxQVFaVp06bp6quvlsvlUrt27XTo0KEQV3ppquvv++bMmaPBgwerTZs2Iaiu7qrrb+/evbrrrrsCVwSIi4vT+vXrQ1LfFRX0khQZGak33nhDiYmJ8ng8atWqlSZOnKg+ffqEurR6MXXqVE2YMEHXXHNNYJst/VXX2+9//3sNHDhQN910Uwgrq7sTJ07I4/Horbfe0qJFi/TBBx+ooKBA3bt3lySVlJTo/fff1/333x/iSi9Ndf1t27ZNklRQUKCdO3dq2LBhIa7y0lXX3/Hjx7Vr1y4VFhaqsrJSa9euVXFxcUjqu+KCXpLGjRun7du3q7CwUMuWLQt1OfVm+fLlat26tTweT6hLqXfV9bZt2zYVFhZq4MCBIaysfnTu3Flz5sxR06ZN1bx5c6WkpGjTpk2Szq5dDx8+XAMHDgzb8ysX6u9vf/ubhgwZErbnHqTq+9u3b59eeOEFPf3003r00Ud1++23KzIyMiT1XVEnY/Pz81VeXq727durUaNGio+P1969e0NdVr1Zs2aNjhw5ogEDBuj48eMqLS3Va6+9psmTJ4e6tDqrrjeXy6W8vDwNGDBApaWlKi4u1nPPPad58+aFutxay8rKkt/vD/xDZoxRRESE8vPzlZqaqqFDh2rUqFEhrvLS1dSfJG3YsEFpaWmhLK/OauqvY8eO+uijjyRJn3zySej+5xmSMwMhsnHjRpOcnGx8Pp/x+Xxm5MiR5h//+Efg54899ljYn6z8zooVK845YWmMPf1V11tmZmZYn4xNT083SUlJ5syZM+bkyZPmwQcfNF988YXp3bu3WblyZajLq7Pq+vvnP/9pjh49anr27Bnq8uqsuv6ysrJM7969zcmTJ43P5zNDhgwxq1evDkl9V9SMvnfv3srJyVFSUpIaNGig+Ph4JSYmhrosQHFxcdq1a5eSkpJUVVWlIUOGyOv1qri4WAsXLtTChQslSffdd5/Gjx8f4mprr7r+OnfurJycHF1//fWhLq/OqusvNjZWY8aM0aBBg1RRUaH+/fvrwQcfDEl9XNQMACx3RZ6MBYArCUEPAJYj6AHAcgQ9AFiOoAcAyxH0CGt+v189evRQampqqEsBLlsEPcLaunXrFBMTI6/Xq/z8/FCXA1yW+Bw9wtrQoUOVkJCgvLw8VVRUaMaMGdqxY4fmzp2rm266KbD95ZdfVmxsrLKysjRr1ixVVVVJkp566indfPPNGj16dODaK48//rhatmyp2bNnq7y8XD179tT69et1+PBhzZw5U8eOHVNlZaWGDh2qlJQU7dixQzNnzlTjxo11+vRprVixInDdluLiYk2dOlVHjx7VkSNHdMMNN2jevHlq0aKFcnJyNH36dPn9ft188806dOiQJk2apK5duyo9PV3vvPOO/H6/GjZsqJdeekmdO3cO2fOMMBeS7+MC9SAvL8/ceeedpqSkxOzatct07NjRlJSUmMzMTNO+fXuze/duY4wxaWlp5tFHHzXGGDNs2LDAZS++/PJLM336dGOMMffdd5/Zu3evKSsrM/fee6/p1auXMebsZTNSU1ON3+83CQkJxuv1GmOMOXHihOnXr5/517/+ZTIzM01MTIw5ePDgeTUuWrTILFiwwBhz9prlqampJi0tzfj9ftOrVy+zceNGY4wx27dvN7fffrvJzMw0X331lenfv78pKSkxxhiTm5trunfvbk6fPu3UUwnLXVGXQIBdli5dqri4ODVr1kzNmjXTjTfeqGXLlumuu+7Sz372M7Vv316SdMcdd2jlypWSpH79+mnGjBlKT0/XPffco+eff17S2bsdbd68Wbfddpu6deumvXv3Ki8vTxs2bFB8fLwKCgq0f//+cy4Qd+bMGe3evVtt27ZV69atdcMNN5xX4/Dhw5WVlaWFCxeqoKBAeXl56tSpk3JzcyWdvSyHJHXr1k233XabpLNX5Tx8+LBGjBgR+D0ul0v79+9XTExM/T+RsB5Bj7BUWlqqVatWKSoqSvfdd58k6dSpU1qyZIncbrcaNmwY2Nflcsn8vxXKwYMHKy4uTtu2bdOWLVv05ptvau3aterTp4/mz5+vw4cPq3v37mrRooW2bt2qzZs3a8KECTp8+LCaNm2qVatWBX5vcXGxmjZtqn//+99q3LhxtXX+7ne/U05OTuASwxUVFTLGqEGDBoGavtOgQQNJUlVVlTwezzlX4SwsLAzcwAKoLU7GIix9/PHHuvbaa7Vlyxalp6crPT1d69evV2lpqUpKSmo8bvDgwfryyy+VnJysV155RSdOnNCRI0fUpUsXHThwQBs3btQ999yj7t276y9/+YvatGmjZs2a6ZZbblHDhg0DQV9YWKj+/fvL6/VesM6tW7dq+PDhSkpKUosWLZSRkaHKykq1bdtWUVFR2rx5syQpJydHubm5crlc8ng82rZtW+Dk8qZNm/TQQw/pzJkz9fTs4UrDjB5haenSpRo5cmRgFixJ11xzjYYOHapFixbVeNyLL76o1157TfPmzZPL5dLYsWN14403SpJ69eql//znP2revLliY2N1/PhxxcfHS5KioqL09ttva+bMmXr33XdVUVGh8ePHKzY2Vjt27DivNq/Xq5kzZ2rMmDGaM2eO5s+fr8jISHXp0kX79+9XRESE/vjHP2ratGn6wx/+oDZt2qhly5Zq2LChbr31Vs2YMUPPP/984Lrm77zzjpo0aVL/TySuCHzqBgiR2bNnBz7hU1hYqAEDBmj9+vXn3CoRqA/M6IEQueGGGzRixAhFRETIGKNXX32VkIcjmNEDgOU4GQsAliPoAcByBD0AWI6gBwDLEfQAYDmCHgAs93+EGkohpGun+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = batch_info['Answer.age'], color = '#3498DB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24520f87c40>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(batch_info['Answer.age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x245245f1910>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "exec_time_seconds = batch_info['WorkTimeInSeconds']\n",
    "sns.displot(exec_time_seconds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wasee\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='WorkTimeInSeconds', ylabel='Density'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(exec_time_seconds)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on Workers worktime , age and gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     33.000000\n",
       "mean      62.636364\n",
       "std       31.585022\n",
       "min       21.000000\n",
       "25%       38.000000\n",
       "50%       58.000000\n",
       "75%       81.000000\n",
       "max      151.000000\n",
       "Name: WorkTimeInSeconds, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_info['WorkTimeInSeconds'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    33.000000\n",
       "mean     49.545455\n",
       "std       8.143598\n",
       "min      31.000000\n",
       "25%      44.000000\n",
       "50%      52.000000\n",
       "75%      57.000000\n",
       "max      59.000000\n",
       "Name: Answer.age, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_info['Answer.age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       33\n",
       "unique       2\n",
       "top       Male\n",
       "freq        20\n",
       "Name: Answer.Gender, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_info['Answer.Gender'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_info['Answer.Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_res.to_csv('batch_res.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Input.Post_text</th>\n",
       "      <th>our_classification</th>\n",
       "      <th>help_generations</th>\n",
       "      <th>positive_impact</th>\n",
       "      <th>attention</th>\n",
       "      <th>protect_environment</th>\n",
       "      <th>beneficial_wellbeing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "\n",
       "                                     Input.Post_text our_classification  \\\n",
       "0  A combination of respect and commitment to the...         biospheric   \n",
       "1  A combination of respect and commitment to the...         biospheric   \n",
       "2  A combination of respect and commitment to the...         biospheric   \n",
       "3  A combination of respect and commitment to the...         biospheric   \n",
       "4  health reasons made me switch but the continue...         egocentric   \n",
       "\n",
       "   help_generations  positive_impact attention  protect_environment  \\\n",
       "0                 5                5    banana                    5   \n",
       "1                 5                5    banana                    4   \n",
       "2                 5                5    banana                    2   \n",
       "3                 5                5    banana                    5   \n",
       "4                 2                1    banana                    5   \n",
       "\n",
       "   beneficial_wellbeing  \n",
       "0                     5  \n",
       "1                     4  \n",
       "2                     2  \n",
       "3                     4  \n",
       "4                     5  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Catching cheaters : banana test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         33\n",
       "unique         1\n",
       "top       banana\n",
       "freq          33\n",
       "Name: attention, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res['attention'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enough with bananas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Input.Post_text</th>\n",
       "      <th>our_classification</th>\n",
       "      <th>help_generations</th>\n",
       "      <th>positive_impact</th>\n",
       "      <th>protect_environment</th>\n",
       "      <th>beneficial_wellbeing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "\n",
       "                                     Input.Post_text our_classification  \\\n",
       "0  A combination of respect and commitment to the...         biospheric   \n",
       "1  A combination of respect and commitment to the...         biospheric   \n",
       "2  A combination of respect and commitment to the...         biospheric   \n",
       "3  A combination of respect and commitment to the...         biospheric   \n",
       "4  health reasons made me switch but the continue...         egocentric   \n",
       "\n",
       "   help_generations  positive_impact  protect_environment  \\\n",
       "0                 5                5                    5   \n",
       "1                 5                5                    4   \n",
       "2                 5                5                    2   \n",
       "3                 5                5                    5   \n",
       "4                 2                1                    5   \n",
       "\n",
       "   beneficial_wellbeing  \n",
       "0                     5  \n",
       "1                     4  \n",
       "2                     2  \n",
       "3                     4  \n",
       "4                     5  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res = batch_res[['HITId','WorkerId','Input.Post_text','our_classification','help_generations','positive_impact','protect_environment','beneficial_wellbeing']]\n",
    "batch_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring workers per post "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of posts: 8\n",
      "Number of raters per post:\n",
      "5    3\n",
      "4    3\n",
      "3    2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of posts: {batch_res['HITId'].nunique()}\")\n",
    "\n",
    "raters = batch_res.groupby('HITId').size()\n",
    "print(f\"Number of raters per post:\\n{raters.value_counts()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HITId\n",
       "30U1YOGZGBR3E30KT6J8LTKW2EWDS2    4\n",
       "30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE    5\n",
       "31KSVEGZ35NQMU0YVCOTN74LNSIWR5    4\n",
       "3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA    5\n",
       "3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO    3\n",
       "3MYASTQBG86R1NL712U3QT5VKQBDQD    5\n",
       "3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ    3\n",
       "3TD33TP5DMX6ZAG6YA6B532QDNDBAX    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Input.Post_text\n",
       "A combination of respect and commitment to the Animals, and a real passion for not letting this planet die out in the next two generations. A lot of people have said \"oh but isn't it really restrictive and inconvenient\" and my response is always :what would be really inconvenient is not having a planet to live on in 50 years because we were too selfish to make sacrifices for the future.                                                                                                                                                                                                                                                                                                                                          4\n",
       "I have a degree in Environmental Science, and I worked in a state's Solid waste handling offices.  I have seen first hand the amount of garbage and dangerous pollutants we produce thanks to our one use, cradle to grave style of living.  Permanent cysts built into the shallow crust, oozing toxins, radiation, and biological waste.    \\n  \\nGoogle superfund sites and your home town, find out how close your local waste pit is.  Find out how many of your local parks are old landfills or topped over brownfields. Look around and realize how much our society revels in its own decrepit love for the next shiny thing with zero consideration to its true cost.    \\n  \\nAnd don't even get me started on water use.\\n  \\n     3\n",
       "I was at the lowest point in my life, I'd had an attempt at ending it.  I had nothing to live for and no value.\\n\\nIn that darkness I sat and took stock, what did I care about? Where can I matter?\\n\\nI went through a number of answers before landing on animals, to become the voice of the voiceless, to stand up to all injustices transcending gender, race, sexuality, religion, age, disability or species.\\n\\nIt gave me a reason to keep on living, and I won't waste it.                                                                                                                                                                                                                                                          5\n",
       "My stress levels go up seeing clutter everywhere. Having clear surfaces helps me stay calm. It's visually peaceful, and helps my internal peace as well. \\n\\nCleaning also gives me a sense of control over my life. I may not be able to control the world around me, but I can make sure my personal space is comfortable and safe.                                                                                                                                                                                                                                                                                                                                                                                                          4\n",
       "Not wanting or needing to harm and kill animals is a pretty big deterrent for me.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              4\n",
       "Realizing that all of the labor and resources wasted producing and distributing this useless crap could be used to help people.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                3\n",
       "So many reasons...\\n\\nTo save money. To clear mental space. Because there are hoarders in my family and I've seen the damage it does. To give myself more room for creativity. To encourage me to spend more time with my loved ones rather than my things. Because it gives me freedom to move with the tides in life if I have to. Because the more I clear away, the more honest I become with myself.                                                                                                                                                                                                                                                                                                                                      5\n",
       "health reasons made me switch but the continued motivation is minimising suffering and associated bad karma                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raters_post = batch_res.groupby('Input.Post_text').size()\n",
    "raters_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cronbach's alpha = 0.852\n"
     ]
    }
   ],
   "source": [
    "questions_df = batch_res[['help_generations','positive_impact','protect_environment','beneficial_wellbeing']]\n",
    "\n",
    "alpha = pg.cronbach_alpha(data=questions_df) # Add nan_policy='listwise' for deletion of missing values (remove the entire rows)\n",
    "print(f\"cronbach's alpha = {alpha[0]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Input.Post_text</th>\n",
       "      <th>our_classification</th>\n",
       "      <th>help_generations</th>\n",
       "      <th>positive_impact</th>\n",
       "      <th>protect_environment</th>\n",
       "      <th>beneficial_wellbeing</th>\n",
       "      <th>scale_score</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId        WorkerId  \\\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3  30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "\n",
       "                                     Input.Post_text our_classification  \\\n",
       "0  A combination of respect and commitment to the...         biospheric   \n",
       "1  A combination of respect and commitment to the...         biospheric   \n",
       "2  A combination of respect and commitment to the...         biospheric   \n",
       "3  A combination of respect and commitment to the...         biospheric   \n",
       "4  health reasons made me switch but the continue...         egocentric   \n",
       "\n",
       "   help_generations  positive_impact  protect_environment  \\\n",
       "0                 5                5                    5   \n",
       "1                 5                5                    4   \n",
       "2                 5                5                    2   \n",
       "3                 5                5                    5   \n",
       "4                 2                1                    5   \n",
       "\n",
       "   beneficial_wellbeing  scale_score  \n",
       "0                     5         5.00  \n",
       "1                     4         4.50  \n",
       "2                     2         3.50  \n",
       "3                     4         4.75  \n",
       "4                     5         3.25  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_score = questions_df.mean(axis = 1) # Compute mean score for each row\n",
    "batch_res['scale_score'] = scale_score\n",
    "batch_res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: for the next function to work, the data needs to be sorted by post ID.\n",
    "\n",
    "def add_rater_id(df, post_id_column):\n",
    "    rater_id = []\n",
    "    current_post_id = -1\n",
    "    for post_id in df[post_id_column]:\n",
    "        if post_id != current_post_id:  # If current index has changed\n",
    "            # Set rater index to 1\n",
    "            rater_index = 1\n",
    "            # Save the current input index\n",
    "            current_post_id = post_id\n",
    "        # Add the rater id to the list and increment it for the next row\n",
    "        rater_id.append(rater_index)\n",
    "        rater_index += 1\n",
    "    return rater_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Input.Post_text</th>\n",
       "      <th>our_classification</th>\n",
       "      <th>help_generations</th>\n",
       "      <th>positive_impact</th>\n",
       "      <th>protect_environment</th>\n",
       "      <th>beneficial_wellbeing</th>\n",
       "      <th>scale_score</th>\n",
       "      <th>rater_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>A combination of respect and commitment to the...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A14W0AXTJ3R19V</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>A2F0X4LN9N4O4C</td>\n",
       "      <td>health reasons made me switch but the continue...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>A14W0AXTJ3R19V</td>\n",
       "      <td>My stress levels go up seeing clutter everywhe...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>My stress levels go up seeing clutter everywhe...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>My stress levels go up seeing clutter everywhe...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>My stress levels go up seeing clutter everywhe...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>So many reasons...\\n\\nTo save money. To clear ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>So many reasons...\\n\\nTo save money. To clear ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>So many reasons...\\n\\nTo save money. To clear ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>A2F0X4LN9N4O4C</td>\n",
       "      <td>So many reasons...\\n\\nTo save money. To clear ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>So many reasons...\\n\\nTo save money. To clear ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>Realizing that all of the labor and resources ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>Realizing that all of the labor and resources ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>Realizing that all of the labor and resources ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3MYASTQBG86R1NL712U3QT5VKQBDQD</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>I was at the lowest point in my life, I'd had ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3MYASTQBG86R1NL712U3QT5VKQBDQD</td>\n",
       "      <td>A2F0X4LN9N4O4C</td>\n",
       "      <td>I was at the lowest point in my life, I'd had ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3MYASTQBG86R1NL712U3QT5VKQBDQD</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>I was at the lowest point in my life, I'd had ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3MYASTQBG86R1NL712U3QT5VKQBDQD</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>I was at the lowest point in my life, I'd had ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3MYASTQBG86R1NL712U3QT5VKQBDQD</td>\n",
       "      <td>A3V5IIPZ95YVKH</td>\n",
       "      <td>I was at the lowest point in my life, I'd had ...</td>\n",
       "      <td>egocentric</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>I have a degree in Environmental Science, and ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>I have a degree in Environmental Science, and ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>I have a degree in Environmental Science, and ...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3TD33TP5DMX6ZAG6YA6B532QDNDBAX</td>\n",
       "      <td>A1IU5OP7BBZHZ7</td>\n",
       "      <td>Not wanting or needing to harm and kill animal...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3TD33TP5DMX6ZAG6YA6B532QDNDBAX</td>\n",
       "      <td>A2F0X4LN9N4O4C</td>\n",
       "      <td>Not wanting or needing to harm and kill animal...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3TD33TP5DMX6ZAG6YA6B532QDNDBAX</td>\n",
       "      <td>A2LRFPJOIC51KY</td>\n",
       "      <td>Not wanting or needing to harm and kill animal...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3TD33TP5DMX6ZAG6YA6B532QDNDBAX</td>\n",
       "      <td>A3E26Y02PGX10Y</td>\n",
       "      <td>Not wanting or needing to harm and kill animal...</td>\n",
       "      <td>biospheric</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             HITId        WorkerId  \\\n",
       "0   30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3E26Y02PGX10Y   \n",
       "1   30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A2LRFPJOIC51KY   \n",
       "2   30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A3V5IIPZ95YVKH   \n",
       "3   30U1YOGZGBR3E30KT6J8LTKW2EWDS2  A1IU5OP7BBZHZ7   \n",
       "4   30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A3V5IIPZ95YVKH   \n",
       "5   30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A1IU5OP7BBZHZ7   \n",
       "6   30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A14W0AXTJ3R19V   \n",
       "7   30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A2LRFPJOIC51KY   \n",
       "8   30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE  A2F0X4LN9N4O4C   \n",
       "9   31KSVEGZ35NQMU0YVCOTN74LNSIWR5  A14W0AXTJ3R19V   \n",
       "10  31KSVEGZ35NQMU0YVCOTN74LNSIWR5  A3E26Y02PGX10Y   \n",
       "11  31KSVEGZ35NQMU0YVCOTN74LNSIWR5  A1IU5OP7BBZHZ7   \n",
       "12  31KSVEGZ35NQMU0YVCOTN74LNSIWR5  A2LRFPJOIC51KY   \n",
       "13  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA  A3E26Y02PGX10Y   \n",
       "14  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA  A1IU5OP7BBZHZ7   \n",
       "15  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA  A3V5IIPZ95YVKH   \n",
       "16  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA  A2F0X4LN9N4O4C   \n",
       "17  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA  A2LRFPJOIC51KY   \n",
       "18  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO  A1IU5OP7BBZHZ7   \n",
       "19  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO  A3E26Y02PGX10Y   \n",
       "20  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO  A2LRFPJOIC51KY   \n",
       "21  3MYASTQBG86R1NL712U3QT5VKQBDQD  A1IU5OP7BBZHZ7   \n",
       "22  3MYASTQBG86R1NL712U3QT5VKQBDQD  A2F0X4LN9N4O4C   \n",
       "23  3MYASTQBG86R1NL712U3QT5VKQBDQD  A3E26Y02PGX10Y   \n",
       "24  3MYASTQBG86R1NL712U3QT5VKQBDQD  A2LRFPJOIC51KY   \n",
       "25  3MYASTQBG86R1NL712U3QT5VKQBDQD  A3V5IIPZ95YVKH   \n",
       "26  3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ  A3E26Y02PGX10Y   \n",
       "27  3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ  A2LRFPJOIC51KY   \n",
       "28  3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ  A1IU5OP7BBZHZ7   \n",
       "29  3TD33TP5DMX6ZAG6YA6B532QDNDBAX  A1IU5OP7BBZHZ7   \n",
       "30  3TD33TP5DMX6ZAG6YA6B532QDNDBAX  A2F0X4LN9N4O4C   \n",
       "31  3TD33TP5DMX6ZAG6YA6B532QDNDBAX  A2LRFPJOIC51KY   \n",
       "32  3TD33TP5DMX6ZAG6YA6B532QDNDBAX  A3E26Y02PGX10Y   \n",
       "\n",
       "                                      Input.Post_text our_classification  \\\n",
       "0   A combination of respect and commitment to the...         biospheric   \n",
       "1   A combination of respect and commitment to the...         biospheric   \n",
       "2   A combination of respect and commitment to the...         biospheric   \n",
       "3   A combination of respect and commitment to the...         biospheric   \n",
       "4   health reasons made me switch but the continue...         egocentric   \n",
       "5   health reasons made me switch but the continue...         egocentric   \n",
       "6   health reasons made me switch but the continue...         egocentric   \n",
       "7   health reasons made me switch but the continue...         egocentric   \n",
       "8   health reasons made me switch but the continue...         egocentric   \n",
       "9   My stress levels go up seeing clutter everywhe...         egocentric   \n",
       "10  My stress levels go up seeing clutter everywhe...         egocentric   \n",
       "11  My stress levels go up seeing clutter everywhe...         egocentric   \n",
       "12  My stress levels go up seeing clutter everywhe...         egocentric   \n",
       "13  So many reasons...\\n\\nTo save money. To clear ...         egocentric   \n",
       "14  So many reasons...\\n\\nTo save money. To clear ...         egocentric   \n",
       "15  So many reasons...\\n\\nTo save money. To clear ...         egocentric   \n",
       "16  So many reasons...\\n\\nTo save money. To clear ...         egocentric   \n",
       "17  So many reasons...\\n\\nTo save money. To clear ...         egocentric   \n",
       "18  Realizing that all of the labor and resources ...         biospheric   \n",
       "19  Realizing that all of the labor and resources ...         biospheric   \n",
       "20  Realizing that all of the labor and resources ...         biospheric   \n",
       "21  I was at the lowest point in my life, I'd had ...         egocentric   \n",
       "22  I was at the lowest point in my life, I'd had ...         egocentric   \n",
       "23  I was at the lowest point in my life, I'd had ...         egocentric   \n",
       "24  I was at the lowest point in my life, I'd had ...         egocentric   \n",
       "25  I was at the lowest point in my life, I'd had ...         egocentric   \n",
       "26  I have a degree in Environmental Science, and ...         biospheric   \n",
       "27  I have a degree in Environmental Science, and ...         biospheric   \n",
       "28  I have a degree in Environmental Science, and ...         biospheric   \n",
       "29  Not wanting or needing to harm and kill animal...         biospheric   \n",
       "30  Not wanting or needing to harm and kill animal...         biospheric   \n",
       "31  Not wanting or needing to harm and kill animal...         biospheric   \n",
       "32  Not wanting or needing to harm and kill animal...         biospheric   \n",
       "\n",
       "    help_generations  positive_impact  protect_environment  \\\n",
       "0                  5                5                    5   \n",
       "1                  5                5                    4   \n",
       "2                  5                5                    2   \n",
       "3                  5                5                    5   \n",
       "4                  2                1                    5   \n",
       "5                  4                1                    5   \n",
       "6                  2                3                    2   \n",
       "7                  1                1                    1   \n",
       "8                  3                1                    5   \n",
       "9                  2                1                    3   \n",
       "10                 3                2                    4   \n",
       "11                 3                5                    5   \n",
       "12                 1                1                    2   \n",
       "13                 2                1                    4   \n",
       "14                 3                1                    3   \n",
       "15                 2                2                    5   \n",
       "16                 4                4                    5   \n",
       "17                 1                1                    1   \n",
       "18                 3                4                    4   \n",
       "19                 4                4                    3   \n",
       "20                 4                2                    2   \n",
       "21                 4                5                    4   \n",
       "22                 4                2                    5   \n",
       "23                 5                5                    5   \n",
       "24                 1                2                    1   \n",
       "25                 5                2                    4   \n",
       "26                 4                4                    3   \n",
       "27                 5                5                    5   \n",
       "28                 5                5                    4   \n",
       "29                 5                5                    4   \n",
       "30                 1                1                    1   \n",
       "31                 1                2                    1   \n",
       "32                 4                4                    4   \n",
       "\n",
       "    beneficial_wellbeing  scale_score  rater_id  \n",
       "0                      5         5.00         1  \n",
       "1                      4         4.50         2  \n",
       "2                      2         3.50         3  \n",
       "3                      4         4.75         4  \n",
       "4                      5         3.25         1  \n",
       "5                      3         3.25         2  \n",
       "6                      2         2.25         3  \n",
       "7                      1         1.00         4  \n",
       "8                      4         3.25         5  \n",
       "9                      2         2.00         1  \n",
       "10                     4         3.25         2  \n",
       "11                     5         4.50         3  \n",
       "12                     2         1.50         4  \n",
       "13                     4         2.75         1  \n",
       "14                     3         2.50         2  \n",
       "15                     5         3.50         3  \n",
       "16                     5         4.50         4  \n",
       "17                     1         1.00         5  \n",
       "18                     3         3.50         1  \n",
       "19                     3         3.50         2  \n",
       "20                     2         2.50         3  \n",
       "21                     3         4.00         1  \n",
       "22                     5         4.00         2  \n",
       "23                     5         5.00         3  \n",
       "24                     1         1.25         4  \n",
       "25                     5         4.00         5  \n",
       "26                     3         3.50         1  \n",
       "27                     3         4.50         2  \n",
       "28                     5         4.75         3  \n",
       "29                     4         4.50         1  \n",
       "30                     1         1.00         2  \n",
       "31                     1         1.25         3  \n",
       "32                     4         4.00         4  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_res[\"rater_id\"] =  add_rater_id(batch_res, 'HITId')\n",
    "batch_res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import agreement\n",
    "\n",
    "# We will also need to change the format to [coder, instance, rate] to fit the agreement function\n",
    "formatted_rates = df_binary[['rater_id', 'Input.Index', 'IsGuilty']].values.tolist()\n",
    "\n",
    "ratingtask = agreement.AnnotationTask(data=formatted_rates)\n",
    "print(f\"Fleiss' Kappa: {ratingtask.multi_kappa():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Description</th>\n",
       "      <th>ICC</th>\n",
       "      <th>F</th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
       "      <th>CI95%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</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>ICC1</th>\n",
       "      <td>Single raters absolute</td>\n",
       "      <td>0.145</td>\n",
       "      <td>1.849</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[-0.15, 0.93]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2</th>\n",
       "      <td>Single random raters</td>\n",
       "      <td>0.117</td>\n",
       "      <td>1.556</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.269</td>\n",
       "      <td>[-0.21, 0.93]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3</th>\n",
       "      <td>Single fixed raters</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1.556</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.269</td>\n",
       "      <td>[-0.17, 0.92]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC1k</th>\n",
       "      <td>Average raters absolute</td>\n",
       "      <td>0.459</td>\n",
       "      <td>1.849</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[-1.76, 0.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2k</th>\n",
       "      <td>Average random raters</td>\n",
       "      <td>0.398</td>\n",
       "      <td>1.556</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.269</td>\n",
       "      <td>[-6.23, 0.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3k</th>\n",
       "      <td>Average fixed raters</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1.556</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.269</td>\n",
       "      <td>[-2.9, 0.98]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Description    ICC      F  df1  df2   pval          CI95%\n",
       "Type                                                                        \n",
       "ICC1    Single raters absolute  0.145  1.849    2   12  0.200  [-0.15, 0.93]\n",
       "ICC2      Single random raters  0.117  1.556    2    8  0.269  [-0.21, 0.93]\n",
       "ICC3       Single fixed raters  0.100  1.556    2    8  0.269  [-0.17, 0.92]\n",
       "ICC1k  Average raters absolute  0.459  1.849    2   12  0.200  [-1.76, 0.99]\n",
       "ICC2k    Average random raters  0.398  1.556    2    8  0.269  [-6.23, 0.99]\n",
       "ICC3k     Average fixed raters  0.357  1.556    2    8  0.269   [-2.9, 0.98]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icc = pg.intraclass_corr(data=batch_res, targets='HITId', raters = \"rater_id\", \n",
    "                         ratings='help_generations', nan_policy = 'omit').round(3) \n",
    "\n",
    "# ‘omit’ performs the calculations after deleting target(s) with one or more missing values (= listwise deletion) \n",
    "\n",
    "icc.set_index(\"Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>HITId</th>\n",
       "      <th>help_generations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30U1YOGZGBR3E30KT6J8LTKW2EWDS2</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31KSVEGZ35NQMU0YVCOTN74LNSIWR5</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>3.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId  help_generations\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2          5.000000\n",
       "1  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE          2.400000\n",
       "2  31KSVEGZ35NQMU0YVCOTN74LNSIWR5          2.250000\n",
       "3  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA          2.400000\n",
       "4  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO          3.666667"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "help_generations_agg = batch_res.groupby('HITId', as_index=False)[['help_generations']].mean()\n",
    "help_generations_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Description</th>\n",
       "      <th>ICC</th>\n",
       "      <th>F</th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
       "      <th>CI95%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</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>ICC1</th>\n",
       "      <td>Single raters absolute</td>\n",
       "      <td>0.240</td>\n",
       "      <td>2.577</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.117</td>\n",
       "      <td>[-0.11, 0.95]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2</th>\n",
       "      <td>Single random raters</td>\n",
       "      <td>0.252</td>\n",
       "      <td>2.882</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.114</td>\n",
       "      <td>[-0.09, 0.95]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3</th>\n",
       "      <td>Single fixed raters</td>\n",
       "      <td>0.273</td>\n",
       "      <td>2.882</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.114</td>\n",
       "      <td>[-0.12, 0.96]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC1k</th>\n",
       "      <td>Average raters absolute</td>\n",
       "      <td>0.612</td>\n",
       "      <td>2.577</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.117</td>\n",
       "      <td>[-0.98, 0.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2k</th>\n",
       "      <td>Average random raters</td>\n",
       "      <td>0.627</td>\n",
       "      <td>2.882</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.114</td>\n",
       "      <td>[-0.65, 0.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3k</th>\n",
       "      <td>Average fixed raters</td>\n",
       "      <td>0.653</td>\n",
       "      <td>2.882</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.114</td>\n",
       "      <td>[-1.1, 0.99]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Description    ICC      F  df1  df2   pval          CI95%\n",
       "Type                                                                        \n",
       "ICC1    Single raters absolute  0.240  2.577    2   12  0.117  [-0.11, 0.95]\n",
       "ICC2      Single random raters  0.252  2.882    2    8  0.114  [-0.09, 0.95]\n",
       "ICC3       Single fixed raters  0.273  2.882    2    8  0.114  [-0.12, 0.96]\n",
       "ICC1k  Average raters absolute  0.612  2.577    2   12  0.117  [-0.98, 0.99]\n",
       "ICC2k    Average random raters  0.627  2.882    2    8  0.114  [-0.65, 0.99]\n",
       "ICC3k     Average fixed raters  0.653  2.882    2    8  0.114   [-1.1, 0.99]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icc = pg.intraclass_corr(data=batch_res, targets='HITId', raters = \"rater_id\", \n",
    "                         ratings='positive_impact', nan_policy = 'omit').round(3) \n",
    "\n",
    "# ‘omit’ performs the calculations after deleting target(s) with one or more missing values (= listwise deletion) \n",
    "\n",
    "icc.set_index(\"Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <td>1.800000</td>\n",
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       "      <th>4</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId  positive_impact\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2         5.000000\n",
       "1  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE         1.400000\n",
       "2  31KSVEGZ35NQMU0YVCOTN74LNSIWR5         2.250000\n",
       "3  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA         1.800000\n",
       "4  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO         3.333333"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_impact_agg = batch_res.groupby('HITId', as_index=False)[['positive_impact']].mean()\n",
    "positive_impact_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>ICC</th>\n",
       "      <th>F</th>\n",
       "      <th>df1</th>\n",
       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
       "      <th>CI95%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICC1</th>\n",
       "      <td>Single raters absolute</td>\n",
       "      <td>-0.243</td>\n",
       "      <td>0.022</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.979</td>\n",
       "      <td>[-0.25, -0.03]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2</th>\n",
       "      <td>Single random raters</td>\n",
       "      <td>-0.292</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.982</td>\n",
       "      <td>[-0.3, -0.26]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3</th>\n",
       "      <td>Single fixed raters</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.982</td>\n",
       "      <td>[-0.25, -0.06]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC1k</th>\n",
       "      <td>Average raters absolute</td>\n",
       "      <td>-45.500</td>\n",
       "      <td>0.022</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.979</td>\n",
       "      <td>[-235.96, -0.18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2k</th>\n",
       "      <td>Average random raters</td>\n",
       "      <td>8.750</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.982</td>\n",
       "      <td>[7.64, 59.15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3k</th>\n",
       "      <td>Average fixed raters</td>\n",
       "      <td>-52.500</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.982</td>\n",
       "      <td>[-323.18, -0.36]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Description     ICC      F  df1  df2   pval  \\\n",
       "Type                                                             \n",
       "ICC1    Single raters absolute  -0.243  0.022    2   12  0.979   \n",
       "ICC2      Single random raters  -0.292  0.019    2    8  0.982   \n",
       "ICC3       Single fixed raters  -0.244  0.019    2    8  0.982   \n",
       "ICC1k  Average raters absolute -45.500  0.022    2   12  0.979   \n",
       "ICC2k    Average random raters   8.750  0.019    2    8  0.982   \n",
       "ICC3k     Average fixed raters -52.500  0.019    2    8  0.982   \n",
       "\n",
       "                  CI95%  \n",
       "Type                     \n",
       "ICC1     [-0.25, -0.03]  \n",
       "ICC2      [-0.3, -0.26]  \n",
       "ICC3     [-0.25, -0.06]  \n",
       "ICC1k  [-235.96, -0.18]  \n",
       "ICC2k     [7.64, 59.15]  \n",
       "ICC3k  [-323.18, -0.36]  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icc = pg.intraclass_corr(data=batch_res, targets='HITId', raters = \"rater_id\", \n",
    "                         ratings='protect_environment', nan_policy = 'omit').round(3) \n",
    "\n",
    "# ‘omit’ performs the calculations after deleting target(s) with one or more missing values (= listwise deletion) \n",
    "\n",
    "icc.set_index(\"Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>protect_environment</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>4.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId  protect_environment\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2                  4.0\n",
       "1  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE                  3.6\n",
       "2  31KSVEGZ35NQMU0YVCOTN74LNSIWR5                  3.5\n",
       "3  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA                  3.6\n",
       "4  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO                  3.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protect_environment_agg = batch_res.groupby('HITId', as_index=False)[['protect_environment']].mean()\n",
    "protect_environment_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>df2</th>\n",
       "      <th>pval</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICC1</th>\n",
       "      <td>Single raters absolute</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>0.306</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.742</td>\n",
       "      <td>[-0.23, 0.69]</td>\n",
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       "    <tr>\n",
       "      <th>ICC2</th>\n",
       "      <td>Single random raters</td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.788</td>\n",
       "      <td>[-0.33, 0.68]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3</th>\n",
       "      <td>Single fixed raters</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.788</td>\n",
       "      <td>[-0.24, 0.63]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC1k</th>\n",
       "      <td>Average raters absolute</td>\n",
       "      <td>-2.269</td>\n",
       "      <td>0.306</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.742</td>\n",
       "      <td>[-15.66, 0.92]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC2k</th>\n",
       "      <td>Average random raters</td>\n",
       "      <td>-16.000</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.788</td>\n",
       "      <td>[5.35, 0.91]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICC3k</th>\n",
       "      <td>Average fixed raters</td>\n",
       "      <td>-3.077</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.788</td>\n",
       "      <td>[-23.7, 0.9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Description     ICC      F  df1  df2   pval           CI95%\n",
       "Type                                                                          \n",
       "ICC1    Single raters absolute  -0.161  0.306    2   12  0.742   [-0.23, 0.69]\n",
       "ICC2      Single random raters  -0.232  0.245    2    8  0.788   [-0.33, 0.68]\n",
       "ICC3       Single fixed raters  -0.178  0.245    2    8  0.788   [-0.24, 0.63]\n",
       "ICC1k  Average raters absolute  -2.269  0.306    2   12  0.742  [-15.66, 0.92]\n",
       "ICC2k    Average random raters -16.000  0.245    2    8  0.788    [5.35, 0.91]\n",
       "ICC3k     Average fixed raters  -3.077  0.245    2    8  0.788    [-23.7, 0.9]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icc = pg.intraclass_corr(data=batch_res, targets='HITId', raters = \"rater_id\", \n",
    "                         ratings='beneficial_wellbeing', nan_policy = 'omit').round(3) \n",
    "\n",
    "# ‘omit’ performs the calculations after deleting target(s) with one or more missing values (= listwise deletion) \n",
    "\n",
    "icc.set_index(\"Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>beneficial_wellbeing</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>3.250000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>3.600000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId  beneficial_wellbeing\n",
       "0  30U1YOGZGBR3E30KT6J8LTKW2EWDS2              3.750000\n",
       "1  30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE              3.000000\n",
       "2  31KSVEGZ35NQMU0YVCOTN74LNSIWR5              3.250000\n",
       "3  3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA              3.600000\n",
       "4  3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO              2.666667"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beneficial_wellbeing_agg = batch_res.groupby('HITId', as_index=False)[['beneficial_wellbeing']].mean()\n",
    "beneficial_wellbeing_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>positive_impact</th>\n",
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       "      <th>HITId</th>\n",
       "      <th></th>\n",
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       "      <th>3MYASTQBG86R1NL712U3QT5VKQBDQD</th>\n",
       "      <td>3.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ</th>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3TD33TP5DMX6ZAG6YA6B532QDNDBAX</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                positive_impact\n",
       "HITId                                          \n",
       "30U1YOGZGBR3E30KT6J8LTKW2EWDS2         5.000000\n",
       "30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE         1.400000\n",
       "31KSVEGZ35NQMU0YVCOTN74LNSIWR5         2.250000\n",
       "3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA         1.800000\n",
       "3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO         3.333333\n",
       "3MYASTQBG86R1NL712U3QT5VKQBDQD         3.200000\n",
       "3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ         4.666667\n",
       "3TD33TP5DMX6ZAG6YA6B532QDNDBAX         3.000000"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_df = batch_res.groupby('HITId')[['positive_impact']].mean()\n",
    "percent_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inter-rater percent agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_fig = sns.histplot(percent_df['positive_impact'])\n",
    "percent_fig.set(xlabel = \"Percent of raters that tagged the post as 'positive_impact'\", ylabel='Number of posts'); # setting axes labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HITId\n",
       "30U1YOGZGBR3E30KT6J8LTKW2EWDS2    5.000000\n",
       "30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE    3.600000\n",
       "31KSVEGZ35NQMU0YVCOTN74LNSIWR5    2.750000\n",
       "3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA    3.200000\n",
       "3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO    3.333333\n",
       "3MYASTQBG86R1NL712U3QT5VKQBDQD    3.200000\n",
       "3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ    4.666667\n",
       "3TD33TP5DMX6ZAG6YA6B532QDNDBAX    3.000000\n",
       "Name: positive_impact, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agreement = percent_df['positive_impact'].apply(lambda i: i if i > 2.5 else 5 - i)\n",
    "agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HITId\n",
       "30U1YOGZGBR3E30KT6J8LTKW2EWDS2    5.000000\n",
       "30Y6N4AHYQRRDO07EHJ3VJF0GCDDRE    3.600000\n",
       "31KSVEGZ35NQMU0YVCOTN74LNSIWR5    2.750000\n",
       "3GMLHYZ0LFSMRRNFE71MDFCL5ZMYUA    3.200000\n",
       "3KTCJ4SCVHW6OD73TG3R8IKHLSB1MO    3.333333\n",
       "3MYASTQBG86R1NL712U3QT5VKQBDQD    3.200000\n",
       "3R16PJFTS4MSQO4JVM9DR3ZG87Q4KZ    4.666667\n",
       "3TD33TP5DMX6ZAG6YA6B532QDNDBAX    3.000000\n",
       "Name: positive_impact, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Agreement'), Text(0, 0.5, 'Number of posts')]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agree_fig = sns.countplot(x = agreement, color = '#3498DB')\n",
    "agree_fig.set(xlabel = \"Agreement\", ylabel='Number of posts') # setting axes labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean rater agreement across posts = 0.719\n"
     ]
    }
   ],
   "source": [
    "mean_agreement = agreement.mean()/5\n",
    "print(f\"Mean rater agreement across posts = {mean_agreement:.3f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}