{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW5 -- Artificial Artificial Intelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "neg = pd.read_csv('AMT_neg.csv')\n",
    "pos = pd.read_csv('AMT_pos.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>HITTypeId</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Reward</th>\n",
       "      <th>CreationTime</th>\n",
       "      <th>MaxAssignments</th>\n",
       "      <th>RequesterAnnotation</th>\n",
       "      <th>AssignmentDurationInSeconds</th>\n",
       "      <th>...</th>\n",
       "      <th>RejectionTime</th>\n",
       "      <th>RequesterFeedback</th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>LifetimeApprovalRate</th>\n",
       "      <th>Last30DaysApprovalRate</th>\n",
       "      <th>Last7DaysApprovalRate</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3IQ9O0AYW6ZI3GD740H32KGG2SWITJ</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:08:17 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821423;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Missed Opportunity\\nI had been very excited to...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3IQ9O0AYW6ZI3GD740H32KGG2SWITJ</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:08:17 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821423;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Missed Opportunity\\nI had been very excited to...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3IQ9O0AYW6ZI3GD740H32KGG2SWITJ</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:08:17 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821423;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>449</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Missed Opportunity\\nI had been very excited to...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId                       HITTypeId  \\\n",
       "0  3IQ9O0AYW6ZI3GD740H32KGG2SWITJ  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "1  3IQ9O0AYW6ZI3GD740H32KGG2SWITJ  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "2  3IQ9O0AYW6ZI3GD740H32KGG2SWITJ  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "\n",
       "                Title         Description         Keywords Reward  \\\n",
       "0  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "1  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "2  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "\n",
       "                   CreationTime  MaxAssignments  \\\n",
       "0  Fri Nov 01 12:08:17 PDT 2019               3   \n",
       "1  Fri Nov 01 12:08:17 PDT 2019               3   \n",
       "2  Fri Nov 01 12:08:17 PDT 2019               3   \n",
       "\n",
       "                                RequesterAnnotation  \\\n",
       "0  BatchId:3821423;OriginalHitTemplateId:928390909;   \n",
       "1  BatchId:3821423;OriginalHitTemplateId:928390909;   \n",
       "2  BatchId:3821423;OriginalHitTemplateId:928390909;   \n",
       "\n",
       "   AssignmentDurationInSeconds  ...  RejectionTime RequesterFeedback  \\\n",
       "0                        10800  ...            NaN               NaN   \n",
       "1                        10800  ...            NaN               NaN   \n",
       "2                        10800  ...            NaN               NaN   \n",
       "\n",
       "   WorkTimeInSeconds  LifetimeApprovalRate Last30DaysApprovalRate  \\\n",
       "0                 44              0% (0/0)               0% (0/0)   \n",
       "1                  7              0% (0/0)               0% (0/0)   \n",
       "2                449              0% (0/0)               0% (0/0)   \n",
       "\n",
       "  Last7DaysApprovalRate                                         Input.text  \\\n",
       "0              0% (0/0)  Missed Opportunity\\nI had been very excited to...   \n",
       "1              0% (0/0)  Missed Opportunity\\nI had been very excited to...   \n",
       "2              0% (0/0)  Missed Opportunity\\nI had been very excited to...   \n",
       "\n",
       "  Answer.sentiment.label Approve Reject  \n",
       "0                Neutral     NaN    NaN  \n",
       "1               Negative     NaN    NaN  \n",
       "2               Positive     NaN    NaN  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>HITTypeId</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Reward</th>\n",
       "      <th>CreationTime</th>\n",
       "      <th>MaxAssignments</th>\n",
       "      <th>RequesterAnnotation</th>\n",
       "      <th>AssignmentDurationInSeconds</th>\n",
       "      <th>...</th>\n",
       "      <th>RejectionTime</th>\n",
       "      <th>RequesterFeedback</th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>LifetimeApprovalRate</th>\n",
       "      <th>Last30DaysApprovalRate</th>\n",
       "      <th>Last7DaysApprovalRate</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>487</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3VMV5CHJZ8F47P7CECH0H830NF4GTP</td>\n",
       "      <td>3N0K7CX2I27L2NR2L8D93MF8LIRA5J</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>Sentiment analysis</td>\n",
       "      <td>sentiment, text</td>\n",
       "      <td>$0.02</td>\n",
       "      <td>Fri Nov 01 12:11:19 PDT 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>BatchId:3821427;OriginalHitTemplateId:928390909;</td>\n",
       "      <td>10800</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1052</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>funny like a clown\\nGreetings again from the d...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            HITId                       HITTypeId  \\\n",
       "0  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "1  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "2  3VMV5CHJZ8F47P7CECH0H830NF4GTP  3N0K7CX2I27L2NR2L8D93MF8LIRA5J   \n",
       "\n",
       "                Title         Description         Keywords Reward  \\\n",
       "0  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "1  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "2  Sentiment analysis  Sentiment analysis  sentiment, text  $0.02   \n",
       "\n",
       "                   CreationTime  MaxAssignments  \\\n",
       "0  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "1  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "2  Fri Nov 01 12:11:19 PDT 2019               3   \n",
       "\n",
       "                                RequesterAnnotation  \\\n",
       "0  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "1  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "2  BatchId:3821427;OriginalHitTemplateId:928390909;   \n",
       "\n",
       "   AssignmentDurationInSeconds  ...  RejectionTime RequesterFeedback  \\\n",
       "0                        10800  ...            NaN               NaN   \n",
       "1                        10800  ...            NaN               NaN   \n",
       "2                        10800  ...            NaN               NaN   \n",
       "\n",
       "   WorkTimeInSeconds  LifetimeApprovalRate Last30DaysApprovalRate  \\\n",
       "0                355              0% (0/0)               0% (0/0)   \n",
       "1                487              0% (0/0)               0% (0/0)   \n",
       "2               1052              0% (0/0)               0% (0/0)   \n",
       "\n",
       "  Last7DaysApprovalRate                                         Input.text  \\\n",
       "0              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "1              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "2              0% (0/0)  funny like a clown\\nGreetings again from the d...   \n",
       "\n",
       "  Answer.sentiment.label Approve Reject  \n",
       "0               Positive     NaN    NaN  \n",
       "1                Neutral     NaN    NaN  \n",
       "2               Positive     NaN    NaN  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HITId',\n",
       " 'HITTypeId',\n",
       " 'Title',\n",
       " 'Description',\n",
       " 'Keywords',\n",
       " 'Reward',\n",
       " 'CreationTime',\n",
       " 'MaxAssignments',\n",
       " 'RequesterAnnotation',\n",
       " 'AssignmentDurationInSeconds',\n",
       " 'AutoApprovalDelayInSeconds',\n",
       " 'Expiration',\n",
       " 'NumberOfSimilarHITs',\n",
       " 'LifetimeInSeconds',\n",
       " 'AssignmentId',\n",
       " 'WorkerId',\n",
       " 'AssignmentStatus',\n",
       " 'AcceptTime',\n",
       " 'SubmitTime',\n",
       " 'AutoApprovalTime',\n",
       " 'ApprovalTime',\n",
       " 'RejectionTime',\n",
       " 'RequesterFeedback',\n",
       " 'WorkTimeInSeconds',\n",
       " 'LifetimeApprovalRate',\n",
       " 'Last30DaysApprovalRate',\n",
       " 'Last7DaysApprovalRate',\n",
       " 'Input.text',\n",
       " 'Answer.sentiment.label',\n",
       " 'Approve',\n",
       " 'Reject']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many unique turkers worked on each dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53 Turkers worked on NEG batch\n",
      "38 Turkers worked on POS batch\n"
     ]
    }
   ],
   "source": [
    "def get_unique(df, column):\n",
    "    unique = np.unique(df[column], return_counts=True)\n",
    "    df = pd.DataFrame(zip(unique[0], unique[1]))\n",
    "    return len(unique[0]), unique, df\n",
    "\n",
    "num_neg, unique_neg, u_neg_df = get_unique(neg, 'WorkerId')    \n",
    "num_pos, unique_pos, u_pos_df = get_unique(pos, 'WorkerId')\n",
    "\n",
    "print(num_neg, 'Turkers worked on NEG batch')\n",
    "print(num_pos, 'Turkers worked on POS batch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many HITS did each unique turker do?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aa920b8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_neg_df.plot(kind='bar',x=0,y=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c0be898>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_pos_df.plot(kind='bar',x=0,y=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the `max` and `min` HIT for unique turkers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For neg, the min was: 1 and the max was: 37\n",
      "For pos, the min was: 1 and the max was: 40\n"
     ]
    }
   ],
   "source": [
    "print('For {}, the min was: {} and the max was: {}'.format('neg', unique_neg[1].min(), unique_neg[1].max())) \n",
    "print('For {}, the min was: {} and the max was: {}'.format('pos', unique_pos[1].min(), unique_pos[1].max())) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did a specitic Sentiment take longer for turkers to assess? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Negative')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.catplot(x=\"Answer.sentiment.label\", \n",
    "            y=\"WorkTimeInSeconds\", \n",
    "            kind=\"bar\", \n",
    "            order=['Negative', 'Neutral', 'Positive'], \n",
    "            data=neg);\n",
    "plt.title('Negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Positive')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Answer.sentiment.label\", \n",
    "            y=\"WorkTimeInSeconds\", \n",
    "            kind=\"bar\", \n",
    "            order=['Negative', 'Neutral', 'Positive'], \n",
    "            data=pos)\n",
    "plt.title('Positive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many turkers had less than 10 second response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "response_time = neg[neg['WorkTimeInSeconds'] < 10]\n",
    "response_time_check = neg[neg['WorkTimeInSeconds'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(response_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(response_time_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for potential bots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Did anyone have a consistent average low response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>HITId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A13CLN8L5HFT46</td>\n",
       "      <td>7.230769</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A18WFPSLFV4FKY</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1IQV3QUWRA8G1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1N1ULK71RHVMM</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1S2MN0E9BHPVA</td>\n",
       "      <td>173.444444</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WorkTimeInSeconds  HITId\n",
       "WorkerId                                \n",
       "A13CLN8L5HFT46           7.230769   13.0\n",
       "A18WFPSLFV4FKY          47.000000    2.0\n",
       "A1IQV3QUWRA8G1          22.000000    1.0\n",
       "A1N1ULK71RHVMM          10.000000    3.0\n",
       "A1S2MN0E9BHPVA         173.444444   27.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pos.groupby(['WorkerId'])['HITId'].count()\n",
    "work_time = pos.groupby(['WorkerId'])['WorkTimeInSeconds'].mean()\n",
    "new_df = pd.DataFrame([work_time, count]).T\n",
    "new_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Did anyone have a consistent average high response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WorkTimeInSeconds</th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkTimeInMin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A13CLN8L5HFT46</td>\n",
       "      <td>7.230769</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.120513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A18WFPSLFV4FKY</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1IQV3QUWRA8G1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1N1ULK71RHVMM</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1S2MN0E9BHPVA</td>\n",
       "      <td>173.444444</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.890741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WorkTimeInSeconds  HITId  WorkTimeInMin\n",
       "WorkerId                                               \n",
       "A13CLN8L5HFT46           7.230769   13.0       0.120513\n",
       "A18WFPSLFV4FKY          47.000000    2.0       0.783333\n",
       "A1IQV3QUWRA8G1          22.000000    1.0       0.366667\n",
       "A1N1ULK71RHVMM          10.000000    3.0       0.166667\n",
       "A1S2MN0E9BHPVA         173.444444   27.0       2.890741"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['WorkTimeInMin'] = new_df['WorkTimeInSeconds']/60\n",
    "new_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WorkerId        Answer.sentiment.label\n",
       "A13CLN8L5HFT46  Neutral                    2\n",
       "                Positive                  11\n",
       "A18WFPSLFV4FKY  Positive                   2\n",
       "A1IQV3QUWRA8G1  Positive                   1\n",
       "A1N1ULK71RHVMM  Negative                   1\n",
       "                                          ..\n",
       "AMC42JMQA8A5U   Positive                   1\n",
       "AO2WNSGOXAX52   Neutral                    3\n",
       "                Positive                   1\n",
       "AOMFEAWQHU3D8   Neutral                    1\n",
       "                Positive                   6\n",
       "Name: Answer.sentiment.label, Length: 74, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pos.groupby(['WorkerId', 'Answer.sentiment.label'])['Answer.sentiment.label'].count()\n",
    "# count = pos.groupby(['WorkerId'])['Answer.sentiment.label'].count()\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Did anyone answer ONLY pos/neg/neutral?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Neutral</th>\n",
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       "      <th>Negative</th>\n",
       "      <th>Total</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A13CLN8L5HFT46</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A18WFPSLFV4FKY</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>A1IQV3QUWRA8G1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>A1N1ULK71RHVMM</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1S2MN0E9BHPVA</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neutral  Positive  Negative  Total\n",
       "WorkerId                                          \n",
       "A13CLN8L5HFT46        2        11         0     13\n",
       "A18WFPSLFV4FKY        0         2         0      2\n",
       "A1IQV3QUWRA8G1        0         1         0      1\n",
       "A1N1ULK71RHVMM        0         2         1      3\n",
       "A1S2MN0E9BHPVA        2        21         4     27"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnn = pd.DataFrame()\n",
    "pnn['Neutral'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Neutral').sum())\n",
    "pnn['Positive'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Positive').sum())\n",
    "pnn['Negative'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Negative').sum())\n",
    "pnn['Total'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: x.count())\n",
    "pnn[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is getting a little confusing, let's just look at our top performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "top = pnn.sort_values(by=['Total'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>A681XM15AN28F</td>\n",
       "      <td>13</td>\n",
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       "      <td>40</td>\n",
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       "      <td>A1Y66T7FKJ8PJA</td>\n",
       "      <td>5</td>\n",
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       "      <td>A37L5E8MHHQGZM</td>\n",
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       "    <tr>\n",
       "      <td>AE03LUY7RH400</td>\n",
       "      <td>4</td>\n",
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       "      <td>21</td>\n",
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       "      <td>A2G44A4ZPWRPXU</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1YK1IKACUJMV4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A3AW887GI0NLKF</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A3HAEQW13YPT6A</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neutral  Positive  Negative  Total\n",
       "WorkerId                                          \n",
       "A681XM15AN28F        13        20         7     40\n",
       "A1Y66T7FKJ8PJA        5        23         7     35\n",
       "A33ENZVC1XB4BA        0        34         0     34\n",
       "A1S2MN0E9BHPVA        2        21         4     27\n",
       "A37L5E8MHHQGZM        6        13         3     22\n",
       "AE03LUY7RH400         4        10         7     21\n",
       "A2G44A4ZPWRPXU        4        12         2     18\n",
       "A1YK1IKACUJMV4        0        15         0     15\n",
       "A3AW887GI0NLKF        3        10         2     15\n",
       "A3HAEQW13YPT6A        0        14         0     14"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting!! Looking from here, we have three workers who ONLY chose positive. \n",
    "\n",
    "Let's look at their response time to see if we can determine if they are a bot!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "top['Avg_WorkTimeInSeconds'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.mean())\n",
    "top['Avg_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.mean()/60)\n",
    "top['Min_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.min()/60)\n",
    "top['Max_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.max()/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                Neutral  Positive  Negative  Total  Avg_WorkTimeInSeconds  \\\n",
       "WorkerId                                                                    \n",
       "A681XM15AN28F        13        20         7     40              13.575000   \n",
       "A1Y66T7FKJ8PJA        5        23         7     35             695.857143   \n",
       "A33ENZVC1XB4BA        0        34         0     34             366.647059   \n",
       "A1S2MN0E9BHPVA        2        21         4     27             173.444444   \n",
       "A37L5E8MHHQGZM        6        13         3     22             346.272727   \n",
       "AE03LUY7RH400         4        10         7     21             102.238095   \n",
       "A2G44A4ZPWRPXU        4        12         2     18             221.277778   \n",
       "A1YK1IKACUJMV4        0        15         0     15             593.600000   \n",
       "A3AW887GI0NLKF        3        10         2     15             269.400000   \n",
       "A3HAEQW13YPT6A        0        14         0     14             442.928571   \n",
       "\n",
       "                Avg_WorkTimeInMin  Min_WorkTimeInMin  Max_WorkTimeInMin  \n",
       "WorkerId                                                                 \n",
       "A681XM15AN28F            0.226250           0.100000           0.833333  \n",
       "A1Y66T7FKJ8PJA          11.597619           0.216667          22.000000  \n",
       "A33ENZVC1XB4BA           6.110784           0.616667           9.916667  \n",
       "A1S2MN0E9BHPVA           2.890741           0.400000           4.983333  \n",
       "A37L5E8MHHQGZM           5.771212           2.150000           8.283333  \n",
       "AE03LUY7RH400            1.703968           0.100000           3.433333  \n",
       "A2G44A4ZPWRPXU           3.687963           0.383333           7.383333  \n",
       "A1YK1IKACUJMV4           9.893333           1.716667          11.000000  \n",
       "A3AW887GI0NLKF           4.490000           1.616667           7.216667  \n",
       "A3HAEQW13YPT6A           7.382143           0.866667          11.100000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even more interesting! These two don't appear to be bots, based on our current metric which is time variability.\n",
    "\n",
    "HOWEVER, worker `A681XM15AN28F` appears to only work for an average of 13 seconds per review which doesn't seem like enough time to read and judge a review..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 2: Second submission to AMT\n",
    "\n",
    "TOO MANY REVIEWERS!\n",
    "\n",
    "Here is when we realized that doing a kappa score with over 30 individual reviewers would be tricky, so we rusubmitted to AMT and required the turkers to be 'Master' in the hopes that this additional barrier-to-entry would help reduce the amount of turkers working on the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2 = pd.read_csv('HW5_amt_v2.csv')\n",
    "v2[:5]\n",
    "len(v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, I didn't separate the df into pos and neg before submitting to AMT, so we have to reimport the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.read_csv('all_JK_extremes_labeled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops! That's right, we replicated each review * 3 so three separate people could look at each review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels2 = labels.append([labels] * 2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>PoN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>272</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227</td>\n",
       "      <td>lose of both time and money\\nThis was one of ...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>lose of both time and money\\nThis was one of ...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>poor plot\\nPoor plot. i find no reason for jo...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>poor plot\\nPoor plot. i find no reason for jo...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>poor plot\\nPoor plot. i find no reason for jo...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0 PoN\n",
       "76    #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "174   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "272   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "116   A 'Triumph of the Will' for Nihilists\\n'Joker...   N\n",
       "18    A 'Triumph of the Will' for Nihilists\\n'Joker...   N\n",
       "..                                                 ...  ..\n",
       "227   lose of both time and money\\nThis was one of ...   N\n",
       "31    lose of both time and money\\nThis was one of ...   N\n",
       "207   poor plot\\nPoor plot. i find no reason for jo...   N\n",
       "11    poor plot\\nPoor plot. i find no reason for jo...   N\n",
       "109   poor plot\\nPoor plot. i find no reason for jo...   N\n",
       "\n",
       "[294 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels2.sort_values(by='0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shoot! I realized I had to delete some emojis for the csv to be accepted by AMT, so the reviews themselves won't actually be matching... solution: Create two 'for-matching' columns made up of the first 5 words of each review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "v2['for_matching'] = v2.apply(lambda x: x['Input.text'].split()[:5], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels2['for_matching'] = labels2.apply(lambda x: x['0'].split()[:5], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annnnnd why did I do that when I could just sort the df and apply the PoN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>for_matching</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>272</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0 PoN  \\\n",
       "76    #LetRottenTomatoesRotSquad\\nI am a simple guy...   P   \n",
       "174   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P   \n",
       "272   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P   \n",
       "116   A 'Triumph of the Will' for Nihilists\\n'Joker...   N   \n",
       "18    A 'Triumph of the Will' for Nihilists\\n'Joker...   N   \n",
       "214   A 'Triumph of the Will' for Nihilists\\n'Joker...   N   \n",
       "\n",
       "                                       for_matching  \n",
       "76   [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "174  [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "272  [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "116                   [A, 'Triumph, of, the, Will']  \n",
       "18                    [A, 'Triumph, of, the, Will']  \n",
       "214                   [A, 'Triumph, of, the, Will']  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_labels = labels2.sort_values(by='0')\n",
    "sorted_labels[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "      <th>for_matching</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>229</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>228</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[#LetRottenTomatoesRotSquad, I, am, a, simple]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[A, 'Triumph, of, the, Will']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Input.text Answer.sentiment.label  \\\n",
       "229   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "228   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "227   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "53    A 'Triumph of the Will' for Nihilists\\n'Joker...                Neutral   \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...               Negative   \n",
       "54    A 'Triumph of the Will' for Nihilists\\n'Joker...               Negative   \n",
       "\n",
       "     Approve  Reject                                    for_matching  \n",
       "229      NaN     NaN  [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "228      NaN     NaN  [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "227      NaN     NaN  [#LetRottenTomatoesRotSquad, I, am, a, simple]  \n",
       "53       NaN     NaN                   [A, 'Triumph, of, the, Will']  \n",
       "55       NaN     NaN                   [A, 'Triumph, of, the, Will']  \n",
       "54       NaN     NaN                   [A, 'Triumph, of, the, Will']  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_v2 = v2.sort_values(by='Input.text')\n",
    "sorted_v2[sorted_v2.columns[-5:]][:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df = sorted_v2.copy()\n",
    "# all_df['PoN'] = sorted_labels['PoN'].tolist()\n",
    "# THIS DIDN'T WORK BECAUSE I DIDN'T WAIT UNTIL ALL WERE DONE FROM AMT. RESEARCHER ERROR BUT OMG I HATE MYSELF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.66666666666667"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "293/3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirming that YEP. 293 isn't divisible by 3, meaning I didn't wait until the last turker finished. omg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reuploading now -- WITH BETTER CODE AND BETTER VARIABLE NAMES!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "294\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>Last7DaysApprovalRate</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0% (0/0)</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Last7DaysApprovalRate                                         Input.text  \\\n",
       "0              0% (0/0)   Everyone praised an overrated movie.\\nOverrat...   \n",
       "1              0% (0/0)   Everyone praised an overrated movie.\\nOverrat...   \n",
       "2              0% (0/0)   Everyone praised an overrated movie.\\nOverrat...   \n",
       "3              0% (0/0)   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "4              0% (0/0)   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "\n",
       "  Answer.sentiment.label  Approve  Reject  \n",
       "0               Negative      NaN     NaN  \n",
       "1               Negative      NaN     NaN  \n",
       "2               Negative      NaN     NaN  \n",
       "3               Negative      NaN     NaN  \n",
       "4               Negative      NaN     NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker = pd.read_csv('HW5_amt_294.csv')\n",
    "print(len(turker))\n",
    "turker[turker.columns[-5:]][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "294\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>PoN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Terrible\\nThe only thing good about this movi...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Watch Taxi Driver instead\\nThis is a poor att...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>I learned one thing.\\nIt borrows a lot of ele...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0 PoN\n",
       "0   Everyone praised an overrated movie.\\nOverrat...   N\n",
       "1   What idiotic FIlm\\nI can say that Phoenix is ...   N\n",
       "2   Terrible\\nThe only thing good about this movi...   N\n",
       "3   Watch Taxi Driver instead\\nThis is a poor att...   N\n",
       "4   I learned one thing.\\nIt borrows a lot of ele...   N"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting labels...\n",
    "labels = pd.read_csv('all_JK_extremes_labeled.csv')\n",
    "# X3\n",
    "labels = labels.append([labels] * 2, ignore_index=True)\n",
    "print(len(labels))\n",
    "labels[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOW, TO SORT!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_labels = labels.sort_values(by=['0'])\n",
    "sorted_turker = turker.sort_values(by=['Input.text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
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       "      <th>0</th>\n",
       "      <th>PoN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>272</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0 PoN\n",
       "76    #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "174   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "272   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P\n",
       "116   A 'Triumph of the Will' for Nihilists\\n'Joker...   N\n",
       "18    A 'Triumph of the Will' for Nihilists\\n'Joker...   N"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228     #LetRottenTomatoesRotSquad\\nI am a simple guy...\n",
       "229     #LetRottenTomatoesRotSquad\\nI am a simple guy...\n",
       "230     #LetRottenTomatoesRotSquad\\nI am a simple guy...\n",
       "56      A 'Triumph of the Will' for Nihilists\\n'Joker...\n",
       "55      A 'Triumph of the Will' for Nihilists\\n'Joker...\n",
       "Name: Input.text, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_turker['Input.text'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OMG HOORAY HOORAY HOORAY!!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: FUN FACT!! I can type here and then hit the `esc` key to turn this cell into markdown!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Input.text</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Approve</th>\n",
       "      <th>Reject</th>\n",
       "      <th>PoN</th>\n",
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       "    <tr>\n",
       "      <td>228</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>229</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Input.text Answer.sentiment.label  \\\n",
       "228   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "229   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "230   #LetRottenTomatoesRotSquad\\nI am a simple guy...               Positive   \n",
       "56    A 'Triumph of the Will' for Nihilists\\n'Joker...               Negative   \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...               Negative   \n",
       "\n",
       "     Approve  Reject PoN  \n",
       "228      NaN     NaN   P  \n",
       "229      NaN     NaN   P  \n",
       "230      NaN     NaN   P  \n",
       "56       NaN     NaN   N  \n",
       "55       NaN     NaN   N  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YUCK THIS IS SO AGGRIVATING!! This line below doens't work because it still uses indexes.\n",
    "# So the P and N didn't match up \n",
    "# sorted_turker['PoN'] = sorted_labels['PoN']\n",
    "sorted_turker['PoN'] = sorted_labels['PoN'].tolist()\n",
    "sorted_turker[sorted_turker.columns[-5:]][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 3: ANALYZE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's clean ALL the things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df = sorted_turker[['Input.text', 'WorkerId', 'Answer.sentiment.label', 'PoN']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Input.text</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>PoN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>228</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Positive</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>229</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Positive</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>AURYD2FH3FUOQ</td>\n",
       "      <td>Positive</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Input.text        WorkerId  \\\n",
       "228   #LetRottenTomatoesRotSquad\\nI am a simple guy...   A681XM15AN28F   \n",
       "229   #LetRottenTomatoesRotSquad\\nI am a simple guy...  A2XFO0X6RCS98M   \n",
       "230   #LetRottenTomatoesRotSquad\\nI am a simple guy...   AURYD2FH3FUOQ   \n",
       "56    A 'Triumph of the Will' for Nihilists\\n'Joker...  A1T79J0XQXDDGC   \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...  A2XFO0X6RCS98M   \n",
       "\n",
       "    Answer.sentiment.label PoN  \n",
       "228               Positive   P  \n",
       "229               Positive   P  \n",
       "230               Positive   P  \n",
       "56                Negative   N  \n",
       "55                Negative   N  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df_all = all_df.copy()\n",
    "all_df_all['APoN'] = all_df_all.apply(lambda x: x['Answer.sentiment.label'][0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>228</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>A681XM15AN28F</td>\n",
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       "      <td>P</td>\n",
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       "    <tr>\n",
       "      <td>229</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Positive</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>AURYD2FH3FUOQ</td>\n",
       "      <td>Positive</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>265</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>266</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>ASB8T0H7L99RF</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Input.text        WorkerId  \\\n",
       "228   #LetRottenTomatoesRotSquad\\nI am a simple guy...   A681XM15AN28F   \n",
       "229   #LetRottenTomatoesRotSquad\\nI am a simple guy...  A2XFO0X6RCS98M   \n",
       "230   #LetRottenTomatoesRotSquad\\nI am a simple guy...   AURYD2FH3FUOQ   \n",
       "56    A 'Triumph of the Will' for Nihilists\\n'Joker...  A1T79J0XQXDDGC   \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...  A2XFO0X6RCS98M   \n",
       "..                                                 ...             ...   \n",
       "265  Venice 76 review\\nI have just watched the Joke...   ARLGZWN6W91WD   \n",
       "266  Venice 76 review\\nI have just watched the Joke...  A38DC3BG1ZCVZ2   \n",
       "93   lose of both time and money\\nThis was one of t...  A2XFO0X6RCS98M   \n",
       "94   lose of both time and money\\nThis was one of t...  A3EZ0H07TSDAPW   \n",
       "95   lose of both time and money\\nThis was one of t...   ASB8T0H7L99RF   \n",
       "\n",
       "    Answer.sentiment.label PoN APoN  \n",
       "228               Positive   P    P  \n",
       "229               Positive   P    P  \n",
       "230               Positive   P    P  \n",
       "56                Negative   N    N  \n",
       "55                Negative   N    N  \n",
       "..                     ...  ..  ...  \n",
       "265               Positive   N    P  \n",
       "266               Positive   N    P  \n",
       "93                Negative   N    N  \n",
       "94                Negative   N    N  \n",
       "95                Negative   N    N  \n",
       "\n",
       "[294 rows x 5 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df_all['agree'] = all_df_all.apply(lambda x: x['PoN'] == x['APoN'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>This is extremely bad...\\nThis whole film make...</td>\n",
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       "      <td>216</td>\n",
       "      <td>Took my 65 year old mother to see it.\\nI saw t...</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>Took my 65 year old mother to see it.\\nI saw t...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>Took my 65 year old mother to see it.\\nI saw t...</td>\n",
       "      <td>AKSJ3C5O3V9RB</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>264</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>265</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>266</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>ASB8T0H7L99RF</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Input.text        WorkerId  \\\n",
       "38   This is extremely bad...\\nThis whole film make...  A3EZ0H07TSDAPW   \n",
       "216  Took my 65 year old mother to see it.\\nI saw t...  A3EZ0H07TSDAPW   \n",
       "217  Took my 65 year old mother to see it.\\nI saw t...  A2XFO0X6RCS98M   \n",
       "218  Took my 65 year old mother to see it.\\nI saw t...   AKSJ3C5O3V9RB   \n",
       "264  Venice 76 review\\nI have just watched the Joke...  A3EZ0H07TSDAPW   \n",
       "265  Venice 76 review\\nI have just watched the Joke...   ARLGZWN6W91WD   \n",
       "266  Venice 76 review\\nI have just watched the Joke...  A38DC3BG1ZCVZ2   \n",
       "93   lose of both time and money\\nThis was one of t...  A2XFO0X6RCS98M   \n",
       "94   lose of both time and money\\nThis was one of t...  A3EZ0H07TSDAPW   \n",
       "95   lose of both time and money\\nThis was one of t...   ASB8T0H7L99RF   \n",
       "\n",
       "    Answer.sentiment.label PoN APoN  agree  \n",
       "38                Negative   N    N   True  \n",
       "216               Positive   N    P  False  \n",
       "217               Positive   N    P  False  \n",
       "218               Positive   N    P  False  \n",
       "264               Positive   N    P  False  \n",
       "265               Positive   N    P  False  \n",
       "266               Positive   N    P  False  \n",
       "93                Negative   N    N   True  \n",
       "94                Negative   N    N   True  \n",
       "95                Negative   N    N   True  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df_all[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets see how many agree!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>Input.text</th>\n",
       "      <th>PoN</th>\n",
       "      <th>agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>A Breath of Fresh Cinema\\nBursting with emoti...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>A MASTERPIECE\\nJoaquin Phoenix's performance ...</td>\n",
       "      <td>N</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>A brilliant movie\\nThis movie is slow but nev...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Input.text PoN     agree\n",
       "0   #LetRottenTomatoesRotSquad\\nI am a simple guy...   P  1.000000\n",
       "1   A 'Triumph of the Will' for Nihilists\\n'Joker...   N  1.000000\n",
       "2   A Breath of Fresh Cinema\\nBursting with emoti...   P  1.000000\n",
       "3   A MASTERPIECE\\nJoaquin Phoenix's performance ...   N  0.333333\n",
       "4   A brilliant movie\\nThis movie is slow but nev...   P  1.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agree_df = pd.DataFrame(all_df_all.groupby(['Input.text','PoN'])['agree'].mean())\n",
    "agree_df = agree_df.reset_index()\n",
    "agree_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK so this actually gave us something we want...\n",
    "BUT PLEASE TELL ME THE BETTER WAY!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Input.text</th>\n",
       "      <th>PoN</th>\n",
       "      <th>agree</th>\n",
       "      <th>agree_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>A Breath of Fresh Cinema\\nBursting with emoti...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>A MASTERPIECE\\nJoaquin Phoenix's performance ...</td>\n",
       "      <td>N</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>disparity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>A brilliant movie\\nThis movie is slow but nev...</td>\n",
       "      <td>P</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>The mirror of society\\nActing 10/10\\nActors 10...</td>\n",
       "      <td>N</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>agree_wrong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>This is extremely bad...\\nThis whole film make...</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>Took my 65 year old mother to see it.\\nI saw t...</td>\n",
       "      <td>N</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>agree_wrong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>N</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>agree_wrong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Input.text PoN     agree  \\\n",
       "0    #LetRottenTomatoesRotSquad\\nI am a simple guy...   P  1.000000   \n",
       "1    A 'Triumph of the Will' for Nihilists\\n'Joker...   N  1.000000   \n",
       "2    A Breath of Fresh Cinema\\nBursting with emoti...   P  1.000000   \n",
       "3    A MASTERPIECE\\nJoaquin Phoenix's performance ...   N  0.333333   \n",
       "4    A brilliant movie\\nThis movie is slow but nev...   P  1.000000   \n",
       "..                                                ...  ..       ...   \n",
       "93  The mirror of society\\nActing 10/10\\nActors 10...   N  0.000000   \n",
       "94  This is extremely bad...\\nThis whole film make...   N  1.000000   \n",
       "95  Took my 65 year old mother to see it.\\nI saw t...   N  0.000000   \n",
       "96  Venice 76 review\\nI have just watched the Joke...   N  0.000000   \n",
       "97  lose of both time and money\\nThis was one of t...   N  1.000000   \n",
       "\n",
       "   agree_factor  \n",
       "0         agree  \n",
       "1         agree  \n",
       "2         agree  \n",
       "3     disparity  \n",
       "4         agree  \n",
       "..          ...  \n",
       "93  agree_wrong  \n",
       "94        agree  \n",
       "95  agree_wrong  \n",
       "96  agree_wrong  \n",
       "97        agree  \n",
       "\n",
       "[98 rows x 4 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def return_agreement(num):\n",
    "    if num == 0:\n",
    "        return 'agree_wrong'\n",
    "    if num == 1:\n",
    "        return 'agree'\n",
    "    if (num/1) !=0:\n",
    "        return 'disparity'\n",
    "\n",
    "agree_df['agree_factor'] = agree_df.apply(lambda x: return_agreement(x['agree']), axis=1)\n",
    "agree_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gdf_forplot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-120-63f275846f4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#             data=gdp_forplot);\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# plt.title('By Polarity')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mgdf_forplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgdf_forplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'gdf_forplot' is not defined"
     ]
    }
   ],
   "source": [
    "# sns.catplot(x=\"Answer.sentiment.label\", \n",
    "#             y=\"WorkTimeInSeconds\", \n",
    "#             kind=\"bar\", \n",
    "#             order=['Negative', 'Neutral', 'Positive'], \n",
    "#             data=gdp_forplot);\n",
    "# plt.title('By Polarity')\n",
    "gdf_forplot = gdf_forplot.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gdf_forplot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-121-34ec1b542aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgdf_forplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'agree'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'agree'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'gdf_forplot' is not defined"
     ]
    }
   ],
   "source": [
    "gdf_forplot.groupby(['agree'])['agree'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agree     PoN\n",
       "0.000000  N      14\n",
       "          P      17\n",
       "0.333333  N      10\n",
       "          P       7\n",
       "0.666667  N       9\n",
       "          P       8\n",
       "1.000000  N      15\n",
       "          P      18\n",
       "Name: agree, dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_forplot.groupby(['agree','PoN'])['agree'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agree_factor</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>PoN</th>\n",
       "      <th>agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>agree</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>agree_wrong</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>disparity</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agree_factor  Input.text  PoN  agree\n",
       "0        agree          33   33     33\n",
       "1  agree_wrong          31   31     31\n",
       "2    disparity          34   34     34"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = agree_df.groupby(['agree_factor']).count()\n",
    "df1.reset_index(inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'How many turkers agreed on sentiment?')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=['Agreed', 'Disagreed'],\n",
    "           y= [64,34],\n",
    "           data = df1);\n",
    "plt.title('How many turkers agreed on sentiment?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'How many turkers agreed on sentiment, but were wrong?')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"agree_factor\", y=\"agree\", data=df1);\n",
    "plt.title('How many turkers agreed on sentiment, but were wrong?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = agree_df.groupby(['agree_factor', 'PoN']).count()\n",
    "df2.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'What was the pos/neg split for the turkers?')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"agree_factor\",\n",
    "           y=\"agree\",\n",
    "           hue=\"PoN\",\n",
    "           data=df2);\n",
    "plt.title(\"What was the pos/neg split for the turkers?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What was the kappa score for the turkers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333337"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example code\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "y1 = [0,1,2,3,4,0,1,2,3,4,0,1,2,3,4]\n",
    "y2 = [0,1,2,2,4,1,2,3,0,0,0,2,2,4,4]\n",
    "cohen_kappa_score(y1,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>A1Y66T7FKJ8PJA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN</td>\n",
       "      <td>A33ENZVC1XB4BA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK</td>\n",
       "      <td>A1IQV3QUWRA8G1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AE03LUY7RH400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>369 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               HITTypeId  Title  Description  \\\n",
       "HITId                          WorkerId                                        \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU          1      1            1   \n",
       "                               A33ENZVC1XB4BA          1      1            1   \n",
       "                               A3RA9555K7Z7GN          1      1            1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA          1      1            1   \n",
       "                               A1Y66T7FKJ8PJA          1      1            1   \n",
       "...                                                  ...    ...          ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA          1      1            1   \n",
       "                               A681XM15AN28F           1      1            1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1          1      1            1   \n",
       "                               A681XM15AN28F           1      1            1   \n",
       "                               AE03LUY7RH400           1      1            1   \n",
       "\n",
       "                                               Keywords  Reward  CreationTime  \\\n",
       "HITId                          WorkerId                                         \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU         1       1             1   \n",
       "                               A33ENZVC1XB4BA         1       1             1   \n",
       "                               A3RA9555K7Z7GN         1       1             1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA         1       1             1   \n",
       "                               A1Y66T7FKJ8PJA         1       1             1   \n",
       "...                                                 ...     ...           ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA         1       1             1   \n",
       "                               A681XM15AN28F          1       1             1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1         1       1             1   \n",
       "                               A681XM15AN28F          1       1             1   \n",
       "                               AE03LUY7RH400          1       1             1   \n",
       "\n",
       "                                               MaxAssignments  \\\n",
       "HITId                          WorkerId                         \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU               1   \n",
       "                               A33ENZVC1XB4BA               1   \n",
       "                               A3RA9555K7Z7GN               1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA               1   \n",
       "                               A1Y66T7FKJ8PJA               1   \n",
       "...                                                       ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA               1   \n",
       "                               A681XM15AN28F                1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1               1   \n",
       "                               A681XM15AN28F                1   \n",
       "                               AE03LUY7RH400                1   \n",
       "\n",
       "                                               RequesterAnnotation  \\\n",
       "HITId                          WorkerId                              \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                    1   \n",
       "                               A33ENZVC1XB4BA                    1   \n",
       "                               A3RA9555K7Z7GN                    1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                    1   \n",
       "                               A1Y66T7FKJ8PJA                    1   \n",
       "...                                                            ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                    1   \n",
       "                               A681XM15AN28F                     1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                    1   \n",
       "                               A681XM15AN28F                     1   \n",
       "                               AE03LUY7RH400                     1   \n",
       "\n",
       "                                               AssignmentDurationInSeconds  \\\n",
       "HITId                          WorkerId                                      \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                            1   \n",
       "                               A33ENZVC1XB4BA                            1   \n",
       "                               A3RA9555K7Z7GN                            1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                            1   \n",
       "                               A1Y66T7FKJ8PJA                            1   \n",
       "...                                                                    ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                            1   \n",
       "                               A681XM15AN28F                             1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                            1   \n",
       "                               A681XM15AN28F                             1   \n",
       "                               AE03LUY7RH400                             1   \n",
       "\n",
       "                                               AutoApprovalDelayInSeconds  \\\n",
       "HITId                          WorkerId                                     \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                           1   \n",
       "                               A33ENZVC1XB4BA                           1   \n",
       "                               A3RA9555K7Z7GN                           1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                           1   \n",
       "                               A1Y66T7FKJ8PJA                           1   \n",
       "...                                                                   ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                           1   \n",
       "                               A681XM15AN28F                            1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                           1   \n",
       "                               A681XM15AN28F                            1   \n",
       "                               AE03LUY7RH400                            1   \n",
       "\n",
       "                                               ...  RejectionTime  \\\n",
       "HITId                          WorkerId        ...                  \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU  ...              0   \n",
       "                               A33ENZVC1XB4BA  ...              0   \n",
       "                               A3RA9555K7Z7GN  ...              0   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA  ...              0   \n",
       "                               A1Y66T7FKJ8PJA  ...              0   \n",
       "...                                            ...            ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA  ...              0   \n",
       "                               A681XM15AN28F   ...              0   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1  ...              0   \n",
       "                               A681XM15AN28F   ...              0   \n",
       "                               AE03LUY7RH400   ...              0   \n",
       "\n",
       "                                               RequesterFeedback  \\\n",
       "HITId                          WorkerId                            \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                  0   \n",
       "                               A33ENZVC1XB4BA                  0   \n",
       "                               A3RA9555K7Z7GN                  0   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                  0   \n",
       "                               A1Y66T7FKJ8PJA                  0   \n",
       "...                                                          ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                  0   \n",
       "                               A681XM15AN28F                   0   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                  0   \n",
       "                               A681XM15AN28F                   0   \n",
       "                               AE03LUY7RH400                   0   \n",
       "\n",
       "                                               WorkTimeInSeconds  \\\n",
       "HITId                          WorkerId                            \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                  1   \n",
       "                               A33ENZVC1XB4BA                  1   \n",
       "                               A3RA9555K7Z7GN                  1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                  1   \n",
       "                               A1Y66T7FKJ8PJA                  1   \n",
       "...                                                          ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                  1   \n",
       "                               A681XM15AN28F                   1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                  1   \n",
       "                               A681XM15AN28F                   1   \n",
       "                               AE03LUY7RH400                   1   \n",
       "\n",
       "                                               LifetimeApprovalRate  \\\n",
       "HITId                          WorkerId                               \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                     1   \n",
       "                               A33ENZVC1XB4BA                     1   \n",
       "                               A3RA9555K7Z7GN                     1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                     1   \n",
       "                               A1Y66T7FKJ8PJA                     1   \n",
       "...                                                             ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                     1   \n",
       "                               A681XM15AN28F                      1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                     1   \n",
       "                               A681XM15AN28F                      1   \n",
       "                               AE03LUY7RH400                      1   \n",
       "\n",
       "                                               Last30DaysApprovalRate  \\\n",
       "HITId                          WorkerId                                 \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                       1   \n",
       "                               A33ENZVC1XB4BA                       1   \n",
       "                               A3RA9555K7Z7GN                       1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                       1   \n",
       "                               A1Y66T7FKJ8PJA                       1   \n",
       "...                                                               ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                       1   \n",
       "                               A681XM15AN28F                        1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                       1   \n",
       "                               A681XM15AN28F                        1   \n",
       "                               AE03LUY7RH400                        1   \n",
       "\n",
       "                                               Last7DaysApprovalRate  \\\n",
       "HITId                          WorkerId                                \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                      1   \n",
       "                               A33ENZVC1XB4BA                      1   \n",
       "                               A3RA9555K7Z7GN                      1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                      1   \n",
       "                               A1Y66T7FKJ8PJA                      1   \n",
       "...                                                              ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                      1   \n",
       "                               A681XM15AN28F                       1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                      1   \n",
       "                               A681XM15AN28F                       1   \n",
       "                               AE03LUY7RH400                       1   \n",
       "\n",
       "                                               Input.text  \\\n",
       "HITId                          WorkerId                     \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU           1   \n",
       "                               A33ENZVC1XB4BA           1   \n",
       "                               A3RA9555K7Z7GN           1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA           1   \n",
       "                               A1Y66T7FKJ8PJA           1   \n",
       "...                                                   ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA           1   \n",
       "                               A681XM15AN28F            1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1           1   \n",
       "                               A681XM15AN28F            1   \n",
       "                               AE03LUY7RH400            1   \n",
       "\n",
       "                                               Answer.sentiment.label  \\\n",
       "HITId                          WorkerId                                 \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU                       1   \n",
       "                               A33ENZVC1XB4BA                       1   \n",
       "                               A3RA9555K7Z7GN                       1   \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA                       1   \n",
       "                               A1Y66T7FKJ8PJA                       1   \n",
       "...                                                               ...   \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA                       1   \n",
       "                               A681XM15AN28F                        1   \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1                       1   \n",
       "                               A681XM15AN28F                        1   \n",
       "                               AE03LUY7RH400                        1   \n",
       "\n",
       "                                               Approve  Reject  \n",
       "HITId                          WorkerId                         \n",
       "301KG0KX9CLONM8AF3FM1L6B224H2X A2739JVQYMPMOU        0       0  \n",
       "                               A33ENZVC1XB4BA        0       0  \n",
       "                               A3RA9555K7Z7GN        0       0  \n",
       "30F94FBDNRK8G8Z1YQPMGXC372CBT3 A1S2MN0E9BHPVA        0       0  \n",
       "                               A1Y66T7FKJ8PJA        0       0  \n",
       "...                                                ...     ...  \n",
       "3ZVPAMTJWN3RRAUKANC5HT2IZU7RGN A33ENZVC1XB4BA        0       0  \n",
       "                               A681XM15AN28F         0       0  \n",
       "3ZZAYRN1I6RSZ2OA2VU8MHC2E1VOTK A1IQV3QUWRA8G1        0       0  \n",
       "                               A681XM15AN28F         0       0  \n",
       "                               AE03LUY7RH400         0       0  \n",
       "\n",
       "[369 rows x 29 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.groupby(['HITId', 'WorkerId']).count()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Oh boy! This will be super fun. First, I'm going to brainstorm \"out loud\" how I'm going to do this when AMT doesn't "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnn = pd.DataFrame()\n",
    "# pnn['Neutral'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Neutral').sum())\n",
    "# pnn['Positive'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Positive').sum())\n",
    "# pnn['Negative'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Negative').sum())\n",
    "# pnn['Total'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: x.count())\n",
    "# pnn[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Positive</td>\n",
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       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "      <td>AURYD2FH3FUOQ</td>\n",
       "      <td>Positive</td>\n",
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       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <td>266</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
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       "      <td>93</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
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       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
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       "      <td>95</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
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       "      <td>Negative</td>\n",
       "      <td>N</td>\n",
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       "<p>294 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                                            Input.text        WorkerId  \\\n",
       "228   #LetRottenTomatoesRotSquad\\nI am a simple guy...   A681XM15AN28F   \n",
       "229   #LetRottenTomatoesRotSquad\\nI am a simple guy...  A2XFO0X6RCS98M   \n",
       "230   #LetRottenTomatoesRotSquad\\nI am a simple guy...   AURYD2FH3FUOQ   \n",
       "56    A 'Triumph of the Will' for Nihilists\\n'Joker...  A1T79J0XQXDDGC   \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...  A2XFO0X6RCS98M   \n",
       "..                                                 ...             ...   \n",
       "265  Venice 76 review\\nI have just watched the Joke...   ARLGZWN6W91WD   \n",
       "266  Venice 76 review\\nI have just watched the Joke...  A38DC3BG1ZCVZ2   \n",
       "93   lose of both time and money\\nThis was one of t...  A2XFO0X6RCS98M   \n",
       "94   lose of both time and money\\nThis was one of t...  A3EZ0H07TSDAPW   \n",
       "95   lose of both time and money\\nThis was one of t...   ASB8T0H7L99RF   \n",
       "\n",
       "    Answer.sentiment.label PoN  \n",
       "228               Positive   P  \n",
       "229               Positive   P  \n",
       "230               Positive   P  \n",
       "56                Negative   N  \n",
       "55                Negative   N  \n",
       "..                     ...  ..  \n",
       "265               Positive   N  \n",
       "266               Positive   N  \n",
       "93                Negative   N  \n",
       "94                Negative   N  \n",
       "95                Negative   N  \n",
       "\n",
       "[294 rows x 4 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                Neutral  Positive  Negative  Total  Avg_WorkTimeInSeconds  \\\n",
       "WorkerId                                                                    \n",
       "A681XM15AN28F        13        20         7     40              13.575000   \n",
       "A1Y66T7FKJ8PJA        5        23         7     35             695.857143   \n",
       "A33ENZVC1XB4BA        0        34         0     34             366.647059   \n",
       "A1S2MN0E9BHPVA        2        21         4     27             173.444444   \n",
       "A37L5E8MHHQGZM        6        13         3     22             346.272727   \n",
       "AE03LUY7RH400         4        10         7     21             102.238095   \n",
       "A2G44A4ZPWRPXU        4        12         2     18             221.277778   \n",
       "A1YK1IKACUJMV4        0        15         0     15             593.600000   \n",
       "A3AW887GI0NLKF        3        10         2     15             269.400000   \n",
       "A3HAEQW13YPT6A        0        14         0     14             442.928571   \n",
       "\n",
       "                Avg_WorkTimeInMin  Min_WorkTimeInMin  Max_WorkTimeInMin  \n",
       "WorkerId                                                                 \n",
       "A681XM15AN28F            0.226250           0.100000           0.833333  \n",
       "A1Y66T7FKJ8PJA          11.597619           0.216667          22.000000  \n",
       "A33ENZVC1XB4BA           6.110784           0.616667           9.916667  \n",
       "A1S2MN0E9BHPVA           2.890741           0.400000           4.983333  \n",
       "A37L5E8MHHQGZM           5.771212           2.150000           8.283333  \n",
       "AE03LUY7RH400            1.703968           0.100000           3.433333  \n",
       "A2G44A4ZPWRPXU           3.687963           0.383333           7.383333  \n",
       "A1YK1IKACUJMV4           9.893333           1.716667          11.000000  \n",
       "A3AW887GI0NLKF           4.490000           1.616667           7.216667  \n",
       "A3HAEQW13YPT6A           7.382143           0.866667          11.100000  "
      ]
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     "execution_count": 138,
     "metadata": {},
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   "source": [
    "top[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf = pd.DataFrame(turker.groupby(['HITId', 'WorkerId']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>HITId            ...</td>\n",
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       "                                                    0  \\\n",
       "0    (302OLP89DZ7MBHSY6QU0WCST11GACJ, A1T79J0XQXDDGC)   \n",
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       "4    (3087LXLJ6MGXDGEQ5QN8FC1JPSW0FT, A1T79J0XQXDDGC)   \n",
       "..                                                ...   \n",
       "289  (3ZLW647WALV9TE1B0IQKXR51J0B327, A38DC3BG1ZCVZ2)   \n",
       "290   (3ZLW647WALV9TE1B0IQKXR51J0B327, ARLGZWN6W91WD)   \n",
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       "293   (3ZRKL6Z1E833SPUXPCCA737ELZESG6, A681XM15AN28F)   \n",
       "\n",
       "                                                     1  \n",
       "0                                 HITId            ...  \n",
       "1                                 HITId            ...  \n",
       "2                                 HITId            ...  \n",
       "3                                  HITId           ...  \n",
       "4                                  HITId           ...  \n",
       "..                                                 ...  \n",
       "289                                HITId           ...  \n",
       "290                                HITId           ...  \n",
       "291                               HITId            ...  \n",
       "292                               HITId            ...  \n",
       "293                               HITId            ...  \n",
       "\n",
       "[294 rows x 2 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HITId', 'HITTypeId', 'Title', 'Description', 'Keywords', 'Reward',\n",
       "       'CreationTime', 'MaxAssignments', 'RequesterAnnotation',\n",
       "       'AssignmentDurationInSeconds', 'AutoApprovalDelayInSeconds',\n",
       "       'Expiration', 'NumberOfSimilarHITs', 'LifetimeInSeconds',\n",
       "       'AssignmentId', 'WorkerId', 'AssignmentStatus', 'AcceptTime',\n",
       "       'SubmitTime', 'AutoApprovalTime', 'ApprovalTime', 'RejectionTime',\n",
       "       'RequesterFeedback', 'WorkTimeInSeconds', 'LifetimeApprovalRate',\n",
       "       'Last30DaysApprovalRate', 'Last7DaysApprovalRate', 'Input.text',\n",
       "       'Answer.sentiment.label', 'Approve', 'Reject'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "turker_clean = turker[['HITId', 'WorkerId', 'Answer.sentiment.label', 'Input.text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
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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>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Input.text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>AH5A86OLRZWCS</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>A2HGRSPR50ENHL</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>AKSJ3C5O3V9RB</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>37MQ8Z1JQEWA9HYZP3JANL1ES162YC</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>37MQ8Z1JQEWA9HYZP3JANL1ES162YC</td>\n",
       "      <td>AKSJ3C5O3V9RB</td>\n",
       "      <td>Negative</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>289</td>\n",
       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A194R45ACMQEOR</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>292</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1L8RL58MYU4NC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              HITId        WorkerId Answer.sentiment.label  \\\n",
       "0    338GLSUI43BXEPY2ES6SPI72KKESF7   AH5A86OLRZWCS               Negative   \n",
       "1    338GLSUI43BXEPY2ES6SPI72KKESF7  A2HGRSPR50ENHL               Negative   \n",
       "2    338GLSUI43BXEPY2ES6SPI72KKESF7   AKSJ3C5O3V9RB               Negative   \n",
       "3    37MQ8Z1JQEWA9HYZP3JANL1ES162YC   ARLGZWN6W91WD               Negative   \n",
       "4    37MQ8Z1JQEWA9HYZP3JANL1ES162YC   AKSJ3C5O3V9RB               Negative   \n",
       "..                              ...             ...                    ...   \n",
       "289  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A3EZ0H07TSDAPW               Negative   \n",
       "290  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A38DC3BG1ZCVZ2               Positive   \n",
       "291  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A194R45ACMQEOR               Positive   \n",
       "292  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1L8RL58MYU4NC               Positive   \n",
       "293  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1T79J0XQXDDGC               Positive   \n",
       "\n",
       "                                            Input.text  \n",
       "0     Everyone praised an overrated movie.\\nOverrat...  \n",
       "1     Everyone praised an overrated movie.\\nOverrat...  \n",
       "2     Everyone praised an overrated movie.\\nOverrat...  \n",
       "3     What idiotic FIlm\\nI can say that Phoenix is ...  \n",
       "4     What idiotic FIlm\\nI can say that Phoenix is ...  \n",
       "..                                                 ...  \n",
       "289   Oscar for Phoenix\\nI will stop watching movie...  \n",
       "290   Oscar for Phoenix\\nI will stop watching movie...  \n",
       "291   Joker > Endgame\\nNeed I say more? Everything ...  \n",
       "292   Joker > Endgame\\nNeed I say more? Everything ...  \n",
       "293   Joker > Endgame\\nNeed I say more? Everything ...  \n",
       "\n",
       "[294 rows x 4 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turker_clean.groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARLGZWN6W91WD     46\n",
       "A681XM15AN28F     37\n",
       "A1T79J0XQXDDGC    34\n",
       "A2XFO0X6RCS98M    33\n",
       "A3EZ0H07TSDAPW    33\n",
       "A1L8RL58MYU4NC    28\n",
       "A38DC3BG1ZCVZ2    22\n",
       "AKSJ3C5O3V9RB     21\n",
       "ASB8T0H7L99RF     10\n",
       "AE03LUY7RH400      6\n",
       "A37JENVKZQ56U6     5\n",
       "A194R45ACMQEOR     5\n",
       "AH5A86OLRZWCS      4\n",
       "A2HG1N3BVQO6I      4\n",
       "AURYD2FH3FUOQ      2\n",
       "AMC42JMQA8A5U      2\n",
       "ATHS9GUME1XCA      1\n",
       "A2HGRSPR50ENHL     1\n",
       "Name: WorkerId, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker_clean.WorkerId.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "turker1 = turker_clean[turker_clean['WorkerId'] == 'ARLGZWN6W91WD']\n",
    "turker2 = turker_clean[turker_clean['WorkerId'] == 'A681XM15AN28F']\n",
    "turker3 = turker_clean[turker_clean['WorkerId'] == 'A1T79J0XQXDDGC']\n",
    "turker4 = turker_clean[turker_clean['WorkerId'] == 'A2XFO0X6RCS98M']\n",
    "turker5 = turker_clean[turker_clean['WorkerId'] == 'A3EZ0H07TSDAPW']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "turker1.reset_index(drop=True, inplace=True)\n",
    "turker2.reset_index(drop=True, inplace=True)\n",
    "turker3.reset_index(drop=True, inplace=True)\n",
    "turker4.reset_index(drop=True, inplace=True)\n",
    "turker5.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.concat([turker1, turker2, turker3, turker4, turker5], axis=0, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Input.text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Positive</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Positive</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Negative</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>3J9UN9O9J3SDII0MOGETUATBIZD0JW</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Took my 65 year old mother to see it.\\nI saw t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>31ODACBENUFU5EOBS8HM1HBGRMNSQ1</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>31ODACBENUFU5EOBS8HM1HBGRMNSQ1</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>3M93N4X8HKNDJRKYXIXD4GZUDRVSJA</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>3M93N4X8HKNDJRKYXIXD4GZUDRVSJA</td>\n",
       "      <td>A2XFO0X6RCS98M</td>\n",
       "      <td>Negative</td>\n",
       "      <td>lose of both time and money\\nThis was one of t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              HITId        WorkerId Answer.sentiment.label  \\\n",
       "79   3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG   A681XM15AN28F               Positive   \n",
       "142  3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG  A2XFO0X6RCS98M               Positive   \n",
       "122  3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5  A2XFO0X6RCS98M               Negative   \n",
       "55   3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5   A681XM15AN28F                Neutral   \n",
       "87   3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5  A1T79J0XQXDDGC               Negative   \n",
       "..                              ...             ...                    ...   \n",
       "175  3J9UN9O9J3SDII0MOGETUATBIZD0JW  A3EZ0H07TSDAPW               Positive   \n",
       "43   31ODACBENUFU5EOBS8HM1HBGRMNSQ1   ARLGZWN6W91WD               Positive   \n",
       "180  31ODACBENUFU5EOBS8HM1HBGRMNSQ1  A3EZ0H07TSDAPW               Positive   \n",
       "162  3M93N4X8HKNDJRKYXIXD4GZUDRVSJA  A3EZ0H07TSDAPW               Negative   \n",
       "127  3M93N4X8HKNDJRKYXIXD4GZUDRVSJA  A2XFO0X6RCS98M               Negative   \n",
       "\n",
       "                                            Input.text  \n",
       "79    #LetRottenTomatoesRotSquad\\nI am a simple guy...  \n",
       "142   #LetRottenTomatoesRotSquad\\nI am a simple guy...  \n",
       "122   A 'Triumph of the Will' for Nihilists\\n'Joker...  \n",
       "55    A 'Triumph of the Will' for Nihilists\\n'Joker...  \n",
       "87    A 'Triumph of the Will' for Nihilists\\n'Joker...  \n",
       "..                                                 ...  \n",
       "175  Took my 65 year old mother to see it.\\nI saw t...  \n",
       "43   Venice 76 review\\nI have just watched the Joke...  \n",
       "180  Venice 76 review\\nI have just watched the Joke...  \n",
       "162  lose of both time and money\\nThis was one of t...  \n",
       "127  lose of both time and money\\nThis was one of t...  \n",
       "\n",
       "[183 rows x 4 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.sort_values(by='Input.text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2 = pd.concat([turker1, turker2], axis=0, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Input.text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Positive</td>\n",
       "      <td>#LetRottenTomatoesRotSquad\\nI am a simple guy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>A 'Triumph of the Will' for Nihilists\\n'Joker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>39O0SQZVJN78YHJJHK8BBGPP0UD7RV</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>A Breath of Fresh Cinema\\nBursting with emoti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>334ZEL5JX6FRK2BVDVPICCGGCL5SOT</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Positive</td>\n",
       "      <td>A brilliant movie\\nThis movie is slow but nev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>3DWGDA5POF4MG2LY1OWCB3NFIEPV1E</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>A clean masterpiece!\\nWhat I loved the most a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3D17ECOUOEV24TJFHEQ6S8VWRUX31Q</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Overhyped and not everyone joker performance i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3G3AJKPCXLSKCVDMTH2YG0YCCF1Y43</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Ridiculous well acted Trash\\nSaw the movie Jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>3JAOYN9IHL2YEWXU4I4PG1ATPEB33I</td>\n",
       "      <td>A681XM15AN28F</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>The king has no clothes\\nRead the reviews- the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>3J5XXLQDHMBIQ5ZDOSAVZW2CGY3V36</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>The mirror of society\\nActing 10/10\\nActors 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>31ODACBENUFU5EOBS8HM1HBGRMNSQ1</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Venice 76 review\\nI have just watched the Joke...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             HITId       WorkerId Answer.sentiment.label  \\\n",
       "33  3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG  A681XM15AN28F               Positive   \n",
       "9   3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5  A681XM15AN28F                Neutral   \n",
       "36  39O0SQZVJN78YHJJHK8BBGPP0UD7RV  ARLGZWN6W91WD               Positive   \n",
       "30  334ZEL5JX6FRK2BVDVPICCGGCL5SOT  A681XM15AN28F               Positive   \n",
       "31  3DWGDA5POF4MG2LY1OWCB3NFIEPV1E  ARLGZWN6W91WD               Positive   \n",
       "..                             ...            ...                    ...   \n",
       "7   3D17ECOUOEV24TJFHEQ6S8VWRUX31Q  ARLGZWN6W91WD               Negative   \n",
       "6   3G3AJKPCXLSKCVDMTH2YG0YCCF1Y43  A681XM15AN28F                Neutral   \n",
       "17  3JAOYN9IHL2YEWXU4I4PG1ATPEB33I  A681XM15AN28F                Neutral   \n",
       "38  3J5XXLQDHMBIQ5ZDOSAVZW2CGY3V36  ARLGZWN6W91WD               Positive   \n",
       "43  31ODACBENUFU5EOBS8HM1HBGRMNSQ1  ARLGZWN6W91WD               Positive   \n",
       "\n",
       "                                           Input.text  \n",
       "33   #LetRottenTomatoesRotSquad\\nI am a simple guy...  \n",
       "9    A 'Triumph of the Will' for Nihilists\\n'Joker...  \n",
       "36   A Breath of Fresh Cinema\\nBursting with emoti...  \n",
       "30   A brilliant movie\\nThis movie is slow but nev...  \n",
       "31   A clean masterpiece!\\nWhat I loved the most a...  \n",
       "..                                                ...  \n",
       "7   Overhyped and not everyone joker performance i...  \n",
       "6   Ridiculous well acted Trash\\nSaw the movie Jok...  \n",
       "17  The king has no clothes\\nRead the reviews- the...  \n",
       "38  The mirror of society\\nActing 10/10\\nActors 10...  \n",
       "43  Venice 76 review\\nI have just watched the Joke...  \n",
       "\n",
       "[83 rows x 4 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df2.sort_values(by='Input.text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_df2['Input.text'].value_counts()\n",
    "# df = pd.DataFrame(merged_df2.groupby('HITId'))\n",
    "# df.set_index([turker1, turker2]).unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>37MQ8Z1JQEWA9HYZP3JANL1ES162YC</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3I7SHAD35MWH116RCCCUPHVFU7E7M7</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3XUSYT70IT10FW0UEKSIRCYYDFG0DI</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3SD15I2WD2UXBFKCNK2NN4MDZ5D63R</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3P7QK0GJ3TLAE784LPLT1SAGYVA2Z3</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>39KV3A5D187KZWJWW98G1QULMWW7SJ</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>35F6NGNVM8JLEWWBL9D6BVQ7OFA7T8</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>31ODACBENUFU5EOBS8HM1HBGRMNSQ1</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>3PN6H8C9R4QWG9YC6MPBGIABM1SDAM</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              HITId        WorkerId Answer.sentiment.label\n",
       "0    37MQ8Z1JQEWA9HYZP3JANL1ES162YC   ARLGZWN6W91WD               Negative\n",
       "1    3I7SHAD35MWH116RCCCUPHVFU7E7M7   ARLGZWN6W91WD               Negative\n",
       "2    3XUSYT70IT10FW0UEKSIRCYYDFG0DI   ARLGZWN6W91WD               Negative\n",
       "3    3SD15I2WD2UXBFKCNK2NN4MDZ5D63R   ARLGZWN6W91WD               Negative\n",
       "4    3P7QK0GJ3TLAE784LPLT1SAGYVA2Z3   ARLGZWN6W91WD               Negative\n",
       "..                              ...             ...                    ...\n",
       "178  39KV3A5D187KZWJWW98G1QULMWW7SJ  A3EZ0H07TSDAPW                Neutral\n",
       "179  35F6NGNVM8JLEWWBL9D6BVQ7OFA7T8  A3EZ0H07TSDAPW               Positive\n",
       "180  31ODACBENUFU5EOBS8HM1HBGRMNSQ1  A3EZ0H07TSDAPW               Positive\n",
       "181  3PN6H8C9R4QWG9YC6MPBGIABM1SDAM  A3EZ0H07TSDAPW                Neutral\n",
       "182  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A3EZ0H07TSDAPW               Negative\n",
       "\n",
       "[183 rows x 3 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouped = turker_clean.groupby(['HITId','WorkerId'])\n",
    "# grouped.set_index(['HITId', 'WorkerId']).mean().unstack(level=0)\n",
    "df = merged_df.drop('Input.text', axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 REVIEW1   REVIEW2   REVIEW3   REVIEW4   REVIEW5   REVIEW6  \\\n",
      "Turker                                                                       \n",
      "A1T79J0XQXDDGC  Positive  Negative  Positive  Positive  Negative  Negative   \n",
      "A2XFO0X6RCS98M  Negative  Negative  Negative  Negative  Positive  Negative   \n",
      "A3EZ0H07TSDAPW  Positive   Neutral  Positive  Negative  Negative  Positive   \n",
      "A681XM15AN28F   Negative  Positive  Positive  Positive  Positive  Negative   \n",
      "ARLGZWN6W91WD   Negative  Negative  Negative  Negative  Negative  Negative   \n",
      "\n",
      "                 REVIEW7   REVIEW8   REVIEW9  REVIEW10  ...  REVIEW38  \\\n",
      "Turker                                                  ...             \n",
      "A1T79J0XQXDDGC  Negative  Positive  Negative  Negative  ...       NaN   \n",
      "A2XFO0X6RCS98M  Negative  Negative  Negative  Negative  ...       NaN   \n",
      "A3EZ0H07TSDAPW  Negative  Positive  Positive  Negative  ...       NaN   \n",
      "A681XM15AN28F    Neutral   Neutral   Neutral   Neutral  ...       NaN   \n",
      "ARLGZWN6W91WD   Negative  Negative  Negative  Negative  ...  Positive   \n",
      "\n",
      "                REVIEW39  REVIEW40  REVIEW41  REVIEW42  REVIEW43  REVIEW44  \\\n",
      "Turker                                                                       \n",
      "A1T79J0XQXDDGC       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "A2XFO0X6RCS98M       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "A3EZ0H07TSDAPW       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "A681XM15AN28F        NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "ARLGZWN6W91WD   Positive  Positive  Positive  Negative  Positive  Positive   \n",
      "\n",
      "                REVIEW45  REVIEW46  \\\n",
      "Turker                               \n",
      "A1T79J0XQXDDGC       NaN       NaN   \n",
      "A2XFO0X6RCS98M       NaN       NaN   \n",
      "A3EZ0H07TSDAPW       NaN       NaN   \n",
      "A681XM15AN28F        NaN       NaN   \n",
      "ARLGZWN6W91WD   Positive  Positive   \n",
      "\n",
      "                                                        SENTIMENT  \n",
      "Turker                                                             \n",
      "A1T79J0XQXDDGC  302OLP89DZ7MBHSY6QU0WCST11GACJ32LAQ1JNT9PNC787...  \n",
      "A2XFO0X6RCS98M  3I7SHAD35MWH116RCCCUPHVFU7E7M73XUSYT70IT10FW0U...  \n",
      "A3EZ0H07TSDAPW  38O9DZ0A62N8QXOTJKOI4UHLTRD62G3I7SHAD35MWH116R...  \n",
      "A681XM15AN28F   3SD15I2WD2UXBFKCNK2NN4MDZ5D63R302OLP89DZ7MBHSY...  \n",
      "ARLGZWN6W91WD   37MQ8Z1JQEWA9HYZP3JANL1ES162YC3I7SHAD35MWH116R...  \n",
      "\n",
      "[5 rows x 47 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Turker': merged_df['WorkerId'].tolist(),\n",
    "                   'REVIEW': merged_df['Answer.sentiment.label'].tolist(),\n",
    "                   'SENTIMENT': merged_df['HITId'].tolist() })\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "values = grouped['SENTIMENT'].agg('sum')\n",
    "id_df = grouped['REVIEW'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "result = pd.concat([id_df, values], axis=1)\n",
    "result_df = pd.DataFrame(result)\n",
    "print(result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turker                                          A1T79J0XQXDDGC  \\\n",
      "SENTIMENT1                                            Positive   \n",
      "SENTIMENT2                                            Negative   \n",
      "SENTIMENT3                                            Positive   \n",
      "SENTIMENT4                                            Positive   \n",
      "SENTIMENT5                                            Negative   \n",
      "SENTIMENT6                                            Negative   \n",
      "SENTIMENT7                                            Negative   \n",
      "SENTIMENT8                                            Positive   \n",
      "SENTIMENT9                                            Negative   \n",
      "SENTIMENT10                                           Negative   \n",
      "SENTIMENT11                                           Negative   \n",
      "SENTIMENT12                                           Negative   \n",
      "SENTIMENT13                                           Negative   \n",
      "SENTIMENT14                                           Negative   \n",
      "SENTIMENT15                                           Positive   \n",
      "SENTIMENT16                                           Positive   \n",
      "SENTIMENT17                                           Positive   \n",
      "SENTIMENT18                                           Positive   \n",
      "SENTIMENT19                                           Positive   \n",
      "SENTIMENT20                                           Positive   \n",
      "SENTIMENT21                                           Positive   \n",
      "SENTIMENT22                                           Positive   \n",
      "SENTIMENT23                                           Positive   \n",
      "SENTIMENT24                                           Positive   \n",
      "SENTIMENT25                                           Positive   \n",
      "SENTIMENT26                                           Positive   \n",
      "SENTIMENT27                                           Positive   \n",
      "SENTIMENT28                                           Positive   \n",
      "SENTIMENT29                                           Positive   \n",
      "SENTIMENT30                                           Negative   \n",
      "SENTIMENT31                                           Positive   \n",
      "SENTIMENT32                                           Positive   \n",
      "SENTIMENT33                                           Positive   \n",
      "SENTIMENT34                                           Positive   \n",
      "SENTIMENT35                                                NaN   \n",
      "SENTIMENT36                                                NaN   \n",
      "SENTIMENT37                                                NaN   \n",
      "SENTIMENT38                                                NaN   \n",
      "SENTIMENT39                                                NaN   \n",
      "SENTIMENT40                                                NaN   \n",
      "SENTIMENT41                                                NaN   \n",
      "SENTIMENT42                                                NaN   \n",
      "SENTIMENT43                                                NaN   \n",
      "SENTIMENT44                                                NaN   \n",
      "SENTIMENT45                                                NaN   \n",
      "SENTIMENT46                                                NaN   \n",
      "REVIEW       302OLP89DZ7MBHSY6QU0WCST11GACJ32LAQ1JNT9PNC787...   \n",
      "\n",
      "Turker                                          A2XFO0X6RCS98M  \\\n",
      "SENTIMENT1                                            Negative   \n",
      "SENTIMENT2                                            Negative   \n",
      "SENTIMENT3                                            Negative   \n",
      "SENTIMENT4                                            Negative   \n",
      "SENTIMENT5                                            Positive   \n",
      "SENTIMENT6                                            Negative   \n",
      "SENTIMENT7                                            Negative   \n",
      "SENTIMENT8                                            Negative   \n",
      "SENTIMENT9                                            Negative   \n",
      "SENTIMENT10                                           Negative   \n",
      "SENTIMENT11                                           Negative   \n",
      "SENTIMENT12                                           Negative   \n",
      "SENTIMENT13                                           Negative   \n",
      "SENTIMENT14                                           Negative   \n",
      "SENTIMENT15                                           Positive   \n",
      "SENTIMENT16                                           Negative   \n",
      "SENTIMENT17                                           Negative   \n",
      "SENTIMENT18                                           Positive   \n",
      "SENTIMENT19                                           Positive   \n",
      "SENTIMENT20                                           Positive   \n",
      "SENTIMENT21                                           Positive   \n",
      "SENTIMENT22                                           Positive   \n",
      "SENTIMENT23                                           Positive   \n",
      "SENTIMENT24                                           Positive   \n",
      "SENTIMENT25                                           Positive   \n",
      "SENTIMENT26                                           Positive   \n",
      "SENTIMENT27                                           Positive   \n",
      "SENTIMENT28                                           Positive   \n",
      "SENTIMENT29                                           Positive   \n",
      "SENTIMENT30                                           Positive   \n",
      "SENTIMENT31                                           Positive   \n",
      "SENTIMENT32                                           Positive   \n",
      "SENTIMENT33                                           Positive   \n",
      "SENTIMENT34                                                NaN   \n",
      "SENTIMENT35                                                NaN   \n",
      "SENTIMENT36                                                NaN   \n",
      "SENTIMENT37                                                NaN   \n",
      "SENTIMENT38                                                NaN   \n",
      "SENTIMENT39                                                NaN   \n",
      "SENTIMENT40                                                NaN   \n",
      "SENTIMENT41                                                NaN   \n",
      "SENTIMENT42                                                NaN   \n",
      "SENTIMENT43                                                NaN   \n",
      "SENTIMENT44                                                NaN   \n",
      "SENTIMENT45                                                NaN   \n",
      "SENTIMENT46                                                NaN   \n",
      "REVIEW       3I7SHAD35MWH116RCCCUPHVFU7E7M73XUSYT70IT10FW0U...   \n",
      "\n",
      "Turker                                          A3EZ0H07TSDAPW  \\\n",
      "SENTIMENT1                                            Positive   \n",
      "SENTIMENT2                                             Neutral   \n",
      "SENTIMENT3                                            Positive   \n",
      "SENTIMENT4                                            Negative   \n",
      "SENTIMENT5                                            Negative   \n",
      "SENTIMENT6                                            Positive   \n",
      "SENTIMENT7                                            Negative   \n",
      "SENTIMENT8                                            Positive   \n",
      "SENTIMENT9                                            Positive   \n",
      "SENTIMENT10                                           Negative   \n",
      "SENTIMENT11                                            Neutral   \n",
      "SENTIMENT12                                           Negative   \n",
      "SENTIMENT13                                           Negative   \n",
      "SENTIMENT14                                            Neutral   \n",
      "SENTIMENT15                                            Neutral   \n",
      "SENTIMENT16                                           Positive   \n",
      "SENTIMENT17                                           Negative   \n",
      "SENTIMENT18                                           Negative   \n",
      "SENTIMENT19                                            Neutral   \n",
      "SENTIMENT20                                            Neutral   \n",
      "SENTIMENT21                                            Neutral   \n",
      "SENTIMENT22                                           Positive   \n",
      "SENTIMENT23                                           Positive   \n",
      "SENTIMENT24                                            Neutral   \n",
      "SENTIMENT25                                           Positive   \n",
      "SENTIMENT26                                           Positive   \n",
      "SENTIMENT27                                           Positive   \n",
      "SENTIMENT28                                           Positive   \n",
      "SENTIMENT29                                            Neutral   \n",
      "SENTIMENT30                                           Positive   \n",
      "SENTIMENT31                                           Positive   \n",
      "SENTIMENT32                                            Neutral   \n",
      "SENTIMENT33                                           Negative   \n",
      "SENTIMENT34                                                NaN   \n",
      "SENTIMENT35                                                NaN   \n",
      "SENTIMENT36                                                NaN   \n",
      "SENTIMENT37                                                NaN   \n",
      "SENTIMENT38                                                NaN   \n",
      "SENTIMENT39                                                NaN   \n",
      "SENTIMENT40                                                NaN   \n",
      "SENTIMENT41                                                NaN   \n",
      "SENTIMENT42                                                NaN   \n",
      "SENTIMENT43                                                NaN   \n",
      "SENTIMENT44                                                NaN   \n",
      "SENTIMENT45                                                NaN   \n",
      "SENTIMENT46                                                NaN   \n",
      "REVIEW       38O9DZ0A62N8QXOTJKOI4UHLTRD62G3I7SHAD35MWH116R...   \n",
      "\n",
      "Turker                                           A681XM15AN28F  \\\n",
      "SENTIMENT1                                            Negative   \n",
      "SENTIMENT2                                            Positive   \n",
      "SENTIMENT3                                            Positive   \n",
      "SENTIMENT4                                            Positive   \n",
      "SENTIMENT5                                            Positive   \n",
      "SENTIMENT6                                            Negative   \n",
      "SENTIMENT7                                             Neutral   \n",
      "SENTIMENT8                                             Neutral   \n",
      "SENTIMENT9                                             Neutral   \n",
      "SENTIMENT10                                            Neutral   \n",
      "SENTIMENT11                                           Positive   \n",
      "SENTIMENT12                                           Positive   \n",
      "SENTIMENT13                                           Negative   \n",
      "SENTIMENT14                                           Positive   \n",
      "SENTIMENT15                                            Neutral   \n",
      "SENTIMENT16                                            Neutral   \n",
      "SENTIMENT17                                            Neutral   \n",
      "SENTIMENT18                                            Neutral   \n",
      "SENTIMENT19                                           Positive   \n",
      "SENTIMENT20                                           Negative   \n",
      "SENTIMENT21                                            Neutral   \n",
      "SENTIMENT22                                           Positive   \n",
      "SENTIMENT23                                            Neutral   \n",
      "SENTIMENT24                                            Neutral   \n",
      "SENTIMENT25                                           Negative   \n",
      "SENTIMENT26                                            Neutral   \n",
      "SENTIMENT27                                           Negative   \n",
      "SENTIMENT28                                           Positive   \n",
      "SENTIMENT29                                           Negative   \n",
      "SENTIMENT30                                            Neutral   \n",
      "SENTIMENT31                                           Positive   \n",
      "SENTIMENT32                                           Negative   \n",
      "SENTIMENT33                                           Positive   \n",
      "SENTIMENT34                                           Positive   \n",
      "SENTIMENT35                                           Negative   \n",
      "SENTIMENT36                                            Neutral   \n",
      "SENTIMENT37                                           Positive   \n",
      "SENTIMENT38                                                NaN   \n",
      "SENTIMENT39                                                NaN   \n",
      "SENTIMENT40                                                NaN   \n",
      "SENTIMENT41                                                NaN   \n",
      "SENTIMENT42                                                NaN   \n",
      "SENTIMENT43                                                NaN   \n",
      "SENTIMENT44                                                NaN   \n",
      "SENTIMENT45                                                NaN   \n",
      "SENTIMENT46                                                NaN   \n",
      "REVIEW       3SD15I2WD2UXBFKCNK2NN4MDZ5D63R302OLP89DZ7MBHSY...   \n",
      "\n",
      "Turker                                           ARLGZWN6W91WD  \n",
      "SENTIMENT1                                            Negative  \n",
      "SENTIMENT2                                            Negative  \n",
      "SENTIMENT3                                            Negative  \n",
      "SENTIMENT4                                            Negative  \n",
      "SENTIMENT5                                            Negative  \n",
      "SENTIMENT6                                            Negative  \n",
      "SENTIMENT7                                            Negative  \n",
      "SENTIMENT8                                            Negative  \n",
      "SENTIMENT9                                            Negative  \n",
      "SENTIMENT10                                           Negative  \n",
      "SENTIMENT11                                           Negative  \n",
      "SENTIMENT12                                           Negative  \n",
      "SENTIMENT13                                           Negative  \n",
      "SENTIMENT14                                           Negative  \n",
      "SENTIMENT15                                           Negative  \n",
      "SENTIMENT16                                           Negative  \n",
      "SENTIMENT17                                           Negative  \n",
      "SENTIMENT18                                           Negative  \n",
      "SENTIMENT19                                            Neutral  \n",
      "SENTIMENT20                                           Negative  \n",
      "SENTIMENT21                                           Negative  \n",
      "SENTIMENT22                                           Negative  \n",
      "SENTIMENT23                                           Positive  \n",
      "SENTIMENT24                                           Positive  \n",
      "SENTIMENT25                                           Positive  \n",
      "SENTIMENT26                                           Positive  \n",
      "SENTIMENT27                                           Positive  \n",
      "SENTIMENT28                                           Positive  \n",
      "SENTIMENT29                                           Positive  \n",
      "SENTIMENT30                                           Positive  \n",
      "SENTIMENT31                                           Positive  \n",
      "SENTIMENT32                                           Positive  \n",
      "SENTIMENT33                                           Positive  \n",
      "SENTIMENT34                                           Positive  \n",
      "SENTIMENT35                                           Positive  \n",
      "SENTIMENT36                                           Positive  \n",
      "SENTIMENT37                                           Positive  \n",
      "SENTIMENT38                                           Positive  \n",
      "SENTIMENT39                                           Positive  \n",
      "SENTIMENT40                                           Positive  \n",
      "SENTIMENT41                                           Positive  \n",
      "SENTIMENT42                                           Negative  \n",
      "SENTIMENT43                                           Positive  \n",
      "SENTIMENT44                                           Positive  \n",
      "SENTIMENT45                                           Positive  \n",
      "SENTIMENT46                                           Positive  \n",
      "REVIEW       37MQ8Z1JQEWA9HYZP3JANL1ES162YC3I7SHAD35MWH116R...  \n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Turker': merged_df['WorkerId'].tolist(),\n",
    "                   'SENTIMENT': merged_df['Answer.sentiment.label'].tolist(),\n",
    "                   'REVIEW': merged_df['HITId'].tolist() })\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "values = grouped['REVIEW'].agg('sum')\n",
    "id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "id_df = id_df.rename(columns={i: 'SENTIMENT{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "result = pd.concat([id_df, values], axis=1)\n",
    "result_df = pd.DataFrame(result)\n",
    "print(result_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = result_df.T['A3EZ0H07TSDAPW'].tolist()\n",
    "len(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2 = result_df.T['A2XFO0X6RCS98M'].tolist()\n",
    "len(t2)\n",
    "t3 = result_df.T['A681XM15AN28F'].tolist()\n",
    "len(t3)\n",
    "t4 = result_df.T['ARLGZWN6W91WD'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Positive',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Negative',\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Negative',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Negative',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
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
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43974358974358974"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "y1 = t1[:-1]\n",
    "y2 = t2[:-1]\n",
    "cohen_kappa_score(y1,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07585335018963324"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "y3 = t3[:-1]\n",
    "y4 = t4[:-1]\n",
    "cohen_kappa_score(y3,y4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turker_clean\n",
    "turker_clean_test = turker_clean.copy()\n",
    "turker_clean_test.reset_index(inplace=True)\n",
    "\n",
    "id_dict = {}\n",
    "id_num = 1\n",
    "def return_new_id(old_id,):\n",
    "    if old_id in id_dict.keys():\n",
    "        return id_dict[old_id]\n",
    "    else:\n",
    "        id_num = id_num + 1\n",
    "        id_dict.update({ old_id: id_num })\n",
    "        return num\n",
    "\n",
    "# turker_clean_test['ReviewID'] = turker_clean_test.apply(lambda x: return_new_id(x['HITId']), axis=1)\n",
    "# turker_clean_test\n",
    "turker_clean_test\n",
    "\n",
    "# import Counter \n",
    "# Counter(K)\n",
    "\n",
    "new_ids = pd.factorize(turker_clean_test['HITId'].tolist())\n",
    "new_ids[0]\n",
    "turker_clean_test['ReviewID'] = new_ids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>HITId</th>\n",
       "      <th>WorkerId</th>\n",
       "      <th>Answer.sentiment.label</th>\n",
       "      <th>Input.text</th>\n",
       "      <th>ReviewID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>AH5A86OLRZWCS</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>A2HGRSPR50ENHL</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>338GLSUI43BXEPY2ES6SPI72KKESF7</td>\n",
       "      <td>AKSJ3C5O3V9RB</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Everyone praised an overrated movie.\\nOverrat...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>37MQ8Z1JQEWA9HYZP3JANL1ES162YC</td>\n",
       "      <td>ARLGZWN6W91WD</td>\n",
       "      <td>Negative</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>37MQ8Z1JQEWA9HYZP3JANL1ES162YC</td>\n",
       "      <td>AKSJ3C5O3V9RB</td>\n",
       "      <td>Negative</td>\n",
       "      <td>What idiotic FIlm\\nI can say that Phoenix is ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>290</td>\n",
       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A194R45ACMQEOR</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>292</td>\n",
       "      <td>292</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1L8RL58MYU4NC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>293</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                           HITId        WorkerId  \\\n",
       "0        0  338GLSUI43BXEPY2ES6SPI72KKESF7   AH5A86OLRZWCS   \n",
       "1        1  338GLSUI43BXEPY2ES6SPI72KKESF7  A2HGRSPR50ENHL   \n",
       "2        2  338GLSUI43BXEPY2ES6SPI72KKESF7   AKSJ3C5O3V9RB   \n",
       "3        3  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   ARLGZWN6W91WD   \n",
       "4        4  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   AKSJ3C5O3V9RB   \n",
       "..     ...                             ...             ...   \n",
       "289    289  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A3EZ0H07TSDAPW   \n",
       "290    290  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A38DC3BG1ZCVZ2   \n",
       "291    291  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A194R45ACMQEOR   \n",
       "292    292  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1L8RL58MYU4NC   \n",
       "293    293  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1T79J0XQXDDGC   \n",
       "\n",
       "    Answer.sentiment.label                                         Input.text  \\\n",
       "0                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "1                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "2                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "3                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "4                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "..                     ...                                                ...   \n",
       "289               Negative   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "290               Positive   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "291               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "292               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "293               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "\n",
       "     ReviewID  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           1  \n",
       "..        ...  \n",
       "289        96  \n",
       "290        96  \n",
       "291        97  \n",
       "292        97  \n",
       "293        97  \n",
       "\n",
       "[294 rows x 6 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker_clean_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_turker_ids = pd.factorize(turker_clean_test['WorkerId'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_ids = ['T_' + str(id) for id in new_turker_ids[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T_0',\n",
       " 'T_1',\n",
       " 'T_2',\n",
       " 'T_3',\n",
       " 'T_2',\n",
       " 'T_4',\n",
       " 'T_5',\n",
       " 'T_6',\n",
       " 'T_7',\n",
       " 'T_8',\n",
       " 'T_5',\n",
       " 'T_3',\n",
       " 'T_5',\n",
       " 'T_3',\n",
       " 'T_8',\n",
       " 'T_5',\n",
       " 'T_3',\n",
       " 'T_9',\n",
       " 'T_10',\n",
       " 'T_9',\n",
       " 'T_8',\n",
       " 'T_3',\n",
       " 'T_9',\n",
       " 'T_4',\n",
       " 'T_7',\n",
       " 'T_10',\n",
       " 'T_2',\n",
       " 'T_9',\n",
       " 'T_7',\n",
       " 'T_4',\n",
       " 'T_9',\n",
       " 'T_3',\n",
       " 'T_2',\n",
       " 'T_11',\n",
       " 'T_0',\n",
       " 'T_9',\n",
       " 'T_4',\n",
       " 'T_8',\n",
       " 'T_5',\n",
       " 'T_5',\n",
       " 'T_9',\n",
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       " 'T_10']"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "      <td>96</td>\n",
       "      <td>T_5</td>\n",
       "    </tr>\n",
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       "      <td>290</td>\n",
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       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "      <td>96</td>\n",
       "      <td>T_7</td>\n",
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       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A194R45ACMQEOR</td>\n",
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       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
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       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
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       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
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      ],
      "text/plain": [
       "     index                           HITId        WorkerId  \\\n",
       "0        0  338GLSUI43BXEPY2ES6SPI72KKESF7   AH5A86OLRZWCS   \n",
       "1        1  338GLSUI43BXEPY2ES6SPI72KKESF7  A2HGRSPR50ENHL   \n",
       "2        2  338GLSUI43BXEPY2ES6SPI72KKESF7   AKSJ3C5O3V9RB   \n",
       "3        3  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   ARLGZWN6W91WD   \n",
       "4        4  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   AKSJ3C5O3V9RB   \n",
       "..     ...                             ...             ...   \n",
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       "291    291  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A194R45ACMQEOR   \n",
       "292    292  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1L8RL58MYU4NC   \n",
       "293    293  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1T79J0XQXDDGC   \n",
       "\n",
       "    Answer.sentiment.label                                         Input.text  \\\n",
       "0                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "1                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "2                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "3                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "4                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "..                     ...                                                ...   \n",
       "289               Negative   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "290               Positive   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "291               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "292               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "293               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "\n",
       "     ReviewID  T_ID  \n",
       "0           0   T_0  \n",
       "1           0   T_1  \n",
       "2           0   T_2  \n",
       "3           1   T_3  \n",
       "4           1   T_2  \n",
       "..        ...   ...  \n",
       "289        96   T_5  \n",
       "290        96   T_7  \n",
       "291        97  T_13  \n",
       "292        97   T_4  \n",
       "293        97  T_10  \n",
       "\n",
       "[294 rows x 7 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker_clean_test['T_ID'] = t_ids\n",
    "turker_clean_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "turker_clean_test['sentiment'] = turker_clean_test.apply(lambda x: x['Answer.sentiment.label'][0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A3EZ0H07TSDAPW</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH</td>\n",
       "      <td>A38DC3BG1ZCVZ2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Oscar for Phoenix\\nI will stop watching movie...</td>\n",
       "      <td>96</td>\n",
       "      <td>T_7</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A194R45ACMQEOR</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "      <td>T_13</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>292</td>\n",
       "      <td>292</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1L8RL58MYU4NC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "      <td>T_4</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>293</td>\n",
       "      <td>3FO95NVK5C0UHF3B5N6M67LLN8PSR2</td>\n",
       "      <td>A1T79J0XQXDDGC</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Joker &gt; Endgame\\nNeed I say more? Everything ...</td>\n",
       "      <td>97</td>\n",
       "      <td>T_10</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                           HITId        WorkerId  \\\n",
       "0        0  338GLSUI43BXEPY2ES6SPI72KKESF7   AH5A86OLRZWCS   \n",
       "1        1  338GLSUI43BXEPY2ES6SPI72KKESF7  A2HGRSPR50ENHL   \n",
       "2        2  338GLSUI43BXEPY2ES6SPI72KKESF7   AKSJ3C5O3V9RB   \n",
       "3        3  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   ARLGZWN6W91WD   \n",
       "4        4  37MQ8Z1JQEWA9HYZP3JANL1ES162YC   AKSJ3C5O3V9RB   \n",
       "..     ...                             ...             ...   \n",
       "289    289  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A3EZ0H07TSDAPW   \n",
       "290    290  3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH  A38DC3BG1ZCVZ2   \n",
       "291    291  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A194R45ACMQEOR   \n",
       "292    292  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1L8RL58MYU4NC   \n",
       "293    293  3FO95NVK5C0UHF3B5N6M67LLN8PSR2  A1T79J0XQXDDGC   \n",
       "\n",
       "    Answer.sentiment.label                                         Input.text  \\\n",
       "0                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "1                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "2                 Negative   Everyone praised an overrated movie.\\nOverrat...   \n",
       "3                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "4                 Negative   What idiotic FIlm\\nI can say that Phoenix is ...   \n",
       "..                     ...                                                ...   \n",
       "289               Negative   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "290               Positive   Oscar for Phoenix\\nI will stop watching movie...   \n",
       "291               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "292               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "293               Positive   Joker > Endgame\\nNeed I say more? Everything ...   \n",
       "\n",
       "     ReviewID  T_ID sentiment  \n",
       "0           0   T_0         N  \n",
       "1           0   T_1         N  \n",
       "2           0   T_2         N  \n",
       "3           1   T_3         N  \n",
       "4           1   T_2         N  \n",
       "..        ...   ...       ...  \n",
       "289        96   T_5         N  \n",
       "290        96   T_7         P  \n",
       "291        97  T_13         P  \n",
       "292        97   T_4         P  \n",
       "293        97  T_10         P  \n",
       "\n",
       "[294 rows x 8 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turker_clean_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "even_cleaner_df = turker_clean_test[['ReviewID', 'T_ID', 'sentiment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
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       "..  ..                                                ...\n",
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       "[98 rows x 2 columns]"
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     "execution_count": 301,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turker    T_0  T_1  T_10 T_11 T_12 T_13 T_14 T_15 T_16 T_17  T_2   T_3   T_4  \\\n",
      "REVIEW1     N    N     P    N    N    N    N    N    N    P    N     N     N   \n",
      "REVIEW2     N  NaN     N    N    N    N    P    N    P  NaN    N     N     N   \n",
      "REVIEW3     N  NaN     P    P    N    N  NaN  NaN    P  NaN    N     N     N   \n",
      "REVIEW4     N  NaN     P    P    N    P  NaN  NaN    P  NaN    N     N     N   \n",
      "REVIEW5   NaN  NaN     N    N    P    P  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW6   NaN  NaN     N  NaN    P  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW7   NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW8   NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     P   \n",
      "REVIEW9   NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW10  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW11  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW12  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
      "REVIEW13  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
      "REVIEW14  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
      "REVIEW15  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
      "REVIEW16  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
      "REVIEW17  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
      "REVIEW18  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
      "REVIEW19  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     P   \n",
      "REVIEW20  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
      "REVIEW21  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
      "REVIEW22  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     N     P   \n",
      "REVIEW23  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW24  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW25  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW26  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW27  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW28  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
      "REVIEW29  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW30  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW31  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW32  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW33  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW34  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW35  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW36  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW37  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW38  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW39  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW40  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW41  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW42  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     N   NaN   \n",
      "REVIEW43  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW44  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW45  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW46  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
      "REVIEW     99    0  1797  304  254  255  104  121  261   56  954  2177  1342   \n",
      "\n",
      "Turker     T_5  T_6   T_7   T_8   T_9  \n",
      "REVIEW1      P    N     N     N     N  \n",
      "REVIEW2      N    N     N     N     P  \n",
      "REVIEW3      P    P     N     N     P  \n",
      "REVIEW4      N    P     N     N     P  \n",
      "REVIEW5      N    P     N     P     P  \n",
      "REVIEW6      P    P     N     N     N  \n",
      "REVIEW7      N    P     N     N     N  \n",
      "REVIEW8      P    P     P     N     N  \n",
      "REVIEW9      P    P     P     N     N  \n",
      "REVIEW10     N    P     P     N     N  \n",
      "REVIEW11     N  NaN     P     N     P  \n",
      "REVIEW12     N  NaN     P     N     P  \n",
      "REVIEW13     N  NaN     P     N     N  \n",
      "REVIEW14     N  NaN     P     N     P  \n",
      "REVIEW15     N  NaN     P     P     N  \n",
      "REVIEW16     P  NaN     P     N     N  \n",
      "REVIEW17     N  NaN     P     N     N  \n",
      "REVIEW18     N  NaN     P     P     N  \n",
      "REVIEW19     N  NaN     P     P     P  \n",
      "REVIEW20     N  NaN     P     P     N  \n",
      "REVIEW21     N  NaN     P     P     N  \n",
      "REVIEW22     P  NaN     P     P     P  \n",
      "REVIEW23     P  NaN   NaN     P     N  \n",
      "REVIEW24     N  NaN   NaN     P     N  \n",
      "REVIEW25     P  NaN   NaN     P     N  \n",
      "REVIEW26     P  NaN   NaN     P     N  \n",
      "REVIEW27     P  NaN   NaN     P     N  \n",
      "REVIEW28     P  NaN   NaN     P     P  \n",
      "REVIEW29     N  NaN   NaN     P     N  \n",
      "REVIEW30     P  NaN   NaN     P     N  \n",
      "REVIEW31     P  NaN   NaN     P     P  \n",
      "REVIEW32     N  NaN   NaN     P     N  \n",
      "REVIEW33     N  NaN   NaN     P     P  \n",
      "REVIEW34   NaN  NaN   NaN   NaN     P  \n",
      "REVIEW35   NaN  NaN   NaN   NaN     N  \n",
      "REVIEW36   NaN  NaN   NaN   NaN     N  \n",
      "REVIEW37   NaN  NaN   NaN   NaN     P  \n",
      "REVIEW38   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW39   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW40   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW41   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW42   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW43   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW44   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW45   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW46   NaN  NaN   NaN   NaN   NaN  \n",
      "REVIEW    1458  597  1339  1605  1536  \n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),\n",
    "                   'SENTIMENT': even_cleaner_df['sentiment'].tolist(),\n",
    "                   'REVIEW': even_cleaner_df['ReviewID'].tolist() })\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "values = grouped['REVIEW'].agg('sum')\n",
    "id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "result = pd.concat([id_df, values], axis=1)\n",
    "result_df = pd.DataFrame(result)\n",
    "print(result_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(result_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Turker</th>\n",
       "      <th>T_0</th>\n",
       "      <th>T_1</th>\n",
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       "      <th>T_11</th>\n",
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       "      <th>T_8</th>\n",
       "      <th>T_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>REVIEW1</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW2</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW3</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW4</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>1797</td>\n",
       "      <td>304</td>\n",
       "      <td>254</td>\n",
       "      <td>255</td>\n",
       "      <td>104</td>\n",
       "      <td>121</td>\n",
       "      <td>261</td>\n",
       "      <td>56</td>\n",
       "      <td>954</td>\n",
       "      <td>2177</td>\n",
       "      <td>1342</td>\n",
       "      <td>1458</td>\n",
       "      <td>597</td>\n",
       "      <td>1339</td>\n",
       "      <td>1605</td>\n",
       "      <td>1536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Turker    T_0  T_1  T_10 T_11 T_12 T_13 T_14 T_15 T_16 T_17  T_2   T_3   T_4  \\\n",
       "REVIEW1     N    N     P    N    N    N    N    N    N    P    N     N     N   \n",
       "REVIEW2     N  NaN     N    N    N    N    P    N    P  NaN    N     N     N   \n",
       "REVIEW3     N  NaN     P    P    N    N  NaN  NaN    P  NaN    N     N     N   \n",
       "REVIEW4     N  NaN     P    P    N    P  NaN  NaN    P  NaN    N     N     N   \n",
       "REVIEW5   NaN  NaN     N    N    P    P  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW6   NaN  NaN     N  NaN    P  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW7   NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW8   NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     P   \n",
       "REVIEW9   NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW10  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW11  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW12  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     N   \n",
       "REVIEW13  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
       "REVIEW14  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
       "REVIEW15  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     N   \n",
       "REVIEW16  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
       "REVIEW17  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
       "REVIEW18  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
       "REVIEW19  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    N     N     P   \n",
       "REVIEW20  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
       "REVIEW21  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN    P     N     P   \n",
       "REVIEW22  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     N     P   \n",
       "REVIEW23  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW24  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW25  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW26  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW27  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW28  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P     P   \n",
       "REVIEW29  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW30  NaN  NaN     N  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW31  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW32  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW33  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW34  NaN  NaN     P  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW35  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW36  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW37  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW38  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW39  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW40  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW41  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW42  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     N   NaN   \n",
       "REVIEW43  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW44  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW45  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW46  NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     P   NaN   \n",
       "REVIEW     99    0  1797  304  254  255  104  121  261   56  954  2177  1342   \n",
       "\n",
       "Turker     T_5  T_6   T_7   T_8   T_9  \n",
       "REVIEW1      P    N     N     N     N  \n",
       "REVIEW2      N    N     N     N     P  \n",
       "REVIEW3      P    P     N     N     P  \n",
       "REVIEW4      N    P     N     N     P  \n",
       "REVIEW5      N    P     N     P     P  \n",
       "REVIEW6      P    P     N     N     N  \n",
       "REVIEW7      N    P     N     N     N  \n",
       "REVIEW8      P    P     P     N     N  \n",
       "REVIEW9      P    P     P     N     N  \n",
       "REVIEW10     N    P     P     N     N  \n",
       "REVIEW11     N  NaN     P     N     P  \n",
       "REVIEW12     N  NaN     P     N     P  \n",
       "REVIEW13     N  NaN     P     N     N  \n",
       "REVIEW14     N  NaN     P     N     P  \n",
       "REVIEW15     N  NaN     P     P     N  \n",
       "REVIEW16     P  NaN     P     N     N  \n",
       "REVIEW17     N  NaN     P     N     N  \n",
       "REVIEW18     N  NaN     P     P     N  \n",
       "REVIEW19     N  NaN     P     P     P  \n",
       "REVIEW20     N  NaN     P     P     N  \n",
       "REVIEW21     N  NaN     P     P     N  \n",
       "REVIEW22     P  NaN     P     P     P  \n",
       "REVIEW23     P  NaN   NaN     P     N  \n",
       "REVIEW24     N  NaN   NaN     P     N  \n",
       "REVIEW25     P  NaN   NaN     P     N  \n",
       "REVIEW26     P  NaN   NaN     P     N  \n",
       "REVIEW27     P  NaN   NaN     P     N  \n",
       "REVIEW28     P  NaN   NaN     P     P  \n",
       "REVIEW29     N  NaN   NaN     P     N  \n",
       "REVIEW30     P  NaN   NaN     P     N  \n",
       "REVIEW31     P  NaN   NaN     P     P  \n",
       "REVIEW32     N  NaN   NaN     P     N  \n",
       "REVIEW33     N  NaN   NaN     P     P  \n",
       "REVIEW34   NaN  NaN   NaN   NaN     P  \n",
       "REVIEW35   NaN  NaN   NaN   NaN     N  \n",
       "REVIEW36   NaN  NaN   NaN   NaN     N  \n",
       "REVIEW37   NaN  NaN   NaN   NaN     P  \n",
       "REVIEW38   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW39   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW40   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW41   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW42   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW43   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW44   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW45   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW46   NaN  NaN   NaN   NaN   NaN  \n",
       "REVIEW    1458  597  1339  1605  1536  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## That is obviously wrong because only THREE people commented on Review1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turker     T_0  T_1                                T_10   T_11    T_12   T_13  \\\n",
      "REVIEW1      0    0                                   6     11      13     15   \n",
      "REVIEW2     11  NaN                                   8     47      25     19   \n",
      "REVIEW3     42  NaN                                  14     55      29     44   \n",
      "REVIEW4     46  NaN                                  15     95      57     80   \n",
      "REVIEW5    NaN  NaN                                  18     96      59     97   \n",
      "REVIEW6    NaN  NaN                                  20    NaN      71    NaN   \n",
      "REVIEW7    NaN  NaN                                  21    NaN     NaN    NaN   \n",
      "REVIEW8    NaN  NaN                                  22    NaN     NaN    NaN   \n",
      "REVIEW9    NaN  NaN                                  23    NaN     NaN    NaN   \n",
      "REVIEW10   NaN  NaN                                  26    NaN     NaN    NaN   \n",
      "REVIEW11   NaN  NaN                                  33    NaN     NaN    NaN   \n",
      "REVIEW12   NaN  NaN                                  35    NaN     NaN    NaN   \n",
      "REVIEW13   NaN  NaN                                  41    NaN     NaN    NaN   \n",
      "REVIEW14   NaN  NaN                                  42    NaN     NaN    NaN   \n",
      "REVIEW15   NaN  NaN                                  51    NaN     NaN    NaN   \n",
      "REVIEW16   NaN  NaN                                  52    NaN     NaN    NaN   \n",
      "REVIEW17   NaN  NaN                                  53    NaN     NaN    NaN   \n",
      "REVIEW18   NaN  NaN                                  59    NaN     NaN    NaN   \n",
      "REVIEW19   NaN  NaN                                  60    NaN     NaN    NaN   \n",
      "REVIEW20   NaN  NaN                                  62    NaN     NaN    NaN   \n",
      "REVIEW21   NaN  NaN                                  67    NaN     NaN    NaN   \n",
      "REVIEW22   NaN  NaN                                  68    NaN     NaN    NaN   \n",
      "REVIEW23   NaN  NaN                                  69    NaN     NaN    NaN   \n",
      "REVIEW24   NaN  NaN                                  74    NaN     NaN    NaN   \n",
      "REVIEW25   NaN  NaN                                  77    NaN     NaN    NaN   \n",
      "REVIEW26   NaN  NaN                                  79    NaN     NaN    NaN   \n",
      "REVIEW27   NaN  NaN                                  80    NaN     NaN    NaN   \n",
      "REVIEW28   NaN  NaN                                  81    NaN     NaN    NaN   \n",
      "REVIEW29   NaN  NaN                                  82    NaN     NaN    NaN   \n",
      "REVIEW30   NaN  NaN                                  87    NaN     NaN    NaN   \n",
      "REVIEW31   NaN  NaN                                  90    NaN     NaN    NaN   \n",
      "REVIEW32   NaN  NaN                                  91    NaN     NaN    NaN   \n",
      "REVIEW33   NaN  NaN                                  94    NaN     NaN    NaN   \n",
      "REVIEW34   NaN  NaN                                  97    NaN     NaN    NaN   \n",
      "REVIEW35   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW36   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW37   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW38   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW39   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW40   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW41   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW42   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW43   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW44   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW45   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW46   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
      "REVIEW    NNNN    N  PNPPNNNPNNNNNNPPPPPPPPPPPPPPPNPPPP  NNPPN  NNNNPP  NNNPP   \n",
      "\n",
      "Turker   T_14 T_15  T_16 T_17                    T_2  \\\n",
      "REVIEW1    28   29    39   56                      0   \n",
      "REVIEW2    76   92    58  NaN                      1   \n",
      "REVIEW3   NaN  NaN    70  NaN                      8   \n",
      "REVIEW4   NaN  NaN    94  NaN                     10   \n",
      "REVIEW5   NaN  NaN   NaN  NaN                     16   \n",
      "REVIEW6   NaN  NaN   NaN  NaN                     21   \n",
      "REVIEW7   NaN  NaN   NaN  NaN                     27   \n",
      "REVIEW8   NaN  NaN   NaN  NaN                     32   \n",
      "REVIEW9   NaN  NaN   NaN  NaN                     36   \n",
      "REVIEW10  NaN  NaN   NaN  NaN                     37   \n",
      "REVIEW11  NaN  NaN   NaN  NaN                     45   \n",
      "REVIEW12  NaN  NaN   NaN  NaN                     47   \n",
      "REVIEW13  NaN  NaN   NaN  NaN                     50   \n",
      "REVIEW14  NaN  NaN   NaN  NaN                     64   \n",
      "REVIEW15  NaN  NaN   NaN  NaN                     66   \n",
      "REVIEW16  NaN  NaN   NaN  NaN                     72   \n",
      "REVIEW17  NaN  NaN   NaN  NaN                     75   \n",
      "REVIEW18  NaN  NaN   NaN  NaN                     83   \n",
      "REVIEW19  NaN  NaN   NaN  NaN                     85   \n",
      "REVIEW20  NaN  NaN   NaN  NaN                     86   \n",
      "REVIEW21  NaN  NaN   NaN  NaN                     93   \n",
      "REVIEW22  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW23  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW24  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW25  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW26  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW27  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW28  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW29  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW30  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW31  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW32  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW33  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW34  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW35  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW36  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW37  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW38  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW39  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW40  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW41  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW42  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW43  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW44  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW45  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW46  NaN  NaN   NaN  NaN                    NaN   \n",
      "REVIEW     NP   NN  NPPP    P  NNNNNNNNNNNNPPPPPPNPP   \n",
      "\n",
      "Turker                                               T_3  \\\n",
      "REVIEW1                                                1   \n",
      "REVIEW2                                                3   \n",
      "REVIEW3                                                4   \n",
      "REVIEW4                                                5   \n",
      "REVIEW5                                                7   \n",
      "REVIEW6                                               10   \n",
      "REVIEW7                                               14   \n",
      "REVIEW8                                               16   \n",
      "REVIEW9                                               17   \n",
      "REVIEW10                                              19   \n",
      "REVIEW11                                              22   \n",
      "REVIEW12                                              23   \n",
      "REVIEW13                                              26   \n",
      "REVIEW14                                              28   \n",
      "REVIEW15                                              30   \n",
      "REVIEW16                                              34   \n",
      "REVIEW17                                              35   \n",
      "REVIEW18                                              36   \n",
      "REVIEW19                                              38   \n",
      "REVIEW20                                              40   \n",
      "REVIEW21                                              44   \n",
      "REVIEW22                                              46   \n",
      "REVIEW23                                              48   \n",
      "REVIEW24                                              49   \n",
      "REVIEW25                                              54   \n",
      "REVIEW26                                              55   \n",
      "REVIEW27                                              57   \n",
      "REVIEW28                                              58   \n",
      "REVIEW29                                              60   \n",
      "REVIEW30                                              61   \n",
      "REVIEW31                                              63   \n",
      "REVIEW32                                              64   \n",
      "REVIEW33                                              65   \n",
      "REVIEW34                                              70   \n",
      "REVIEW35                                              71   \n",
      "REVIEW36                                              73   \n",
      "REVIEW37                                              74   \n",
      "REVIEW38                                              75   \n",
      "REVIEW39                                              77   \n",
      "REVIEW40                                              79   \n",
      "REVIEW41                                              82   \n",
      "REVIEW42                                              85   \n",
      "REVIEW43                                              86   \n",
      "REVIEW44                                              88   \n",
      "REVIEW45                                              90   \n",
      "REVIEW46                                              95   \n",
      "REVIEW    NNNNNNNNNNNNNNNNNNNNNNPPPPPPPPPPPPPPPPPPPNPPPP   \n",
      "\n",
      "Turker                             T_4                                T_5  \\\n",
      "REVIEW1                              1                                  2   \n",
      "REVIEW2                              7                                  3   \n",
      "REVIEW3                              9                                  4   \n",
      "REVIEW4                             12                                  5   \n",
      "REVIEW5                             17                                 12   \n",
      "REVIEW6                             19                                 13   \n",
      "REVIEW7                             27                                 14   \n",
      "REVIEW8                             29                                 20   \n",
      "REVIEW9                             30                                 22   \n",
      "REVIEW10                            33                                 24   \n",
      "REVIEW11                            40                                 26   \n",
      "REVIEW12                            41                                 28   \n",
      "REVIEW13                            42                                 31   \n",
      "REVIEW14                            45                                 36   \n",
      "REVIEW15                            47                                 39   \n",
      "REVIEW16                            48                                 43   \n",
      "REVIEW17                            50                                 45   \n",
      "REVIEW18                            55                                 46   \n",
      "REVIEW19                            61                                 48   \n",
      "REVIEW20                            65                                 49   \n",
      "REVIEW21                            69                                 52   \n",
      "REVIEW22                            73                                 53   \n",
      "REVIEW23                            77                                 54   \n",
      "REVIEW24                            78                                 56   \n",
      "REVIEW25                            87                                 58   \n",
      "REVIEW26                            90                                 72   \n",
      "REVIEW27                            93                                 75   \n",
      "REVIEW28                            97                                 78   \n",
      "REVIEW29                           NaN                                 85   \n",
      "REVIEW30                           NaN                                 87   \n",
      "REVIEW31                           NaN                                 88   \n",
      "REVIEW32                           NaN                                 94   \n",
      "REVIEW33                           NaN                                 96   \n",
      "REVIEW34                           NaN                                NaN   \n",
      "REVIEW35                           NaN                                NaN   \n",
      "REVIEW36                           NaN                                NaN   \n",
      "REVIEW37                           NaN                                NaN   \n",
      "REVIEW38                           NaN                                NaN   \n",
      "REVIEW39                           NaN                                NaN   \n",
      "REVIEW40                           NaN                                NaN   \n",
      "REVIEW41                           NaN                                NaN   \n",
      "REVIEW42                           NaN                                NaN   \n",
      "REVIEW43                           NaN                                NaN   \n",
      "REVIEW44                           NaN                                NaN   \n",
      "REVIEW45                           NaN                                NaN   \n",
      "REVIEW46                           NaN                                NaN   \n",
      "REVIEW    NNNNNNNPNNNNNNNPPPPPPPPPPPPP  PNPNNPNPPNNNNNNPNNNNNPPNPPPPNPPNN   \n",
      "\n",
      "Turker           T_6                     T_7  \\\n",
      "REVIEW1            2                       2   \n",
      "REVIEW2           31                       8   \n",
      "REVIEW3           50                       9   \n",
      "REVIEW4           51                      24   \n",
      "REVIEW5           62                      34   \n",
      "REVIEW6           63                      39   \n",
      "REVIEW7           74                      43   \n",
      "REVIEW8           84                      51   \n",
      "REVIEW9           89                      56   \n",
      "REVIEW10          91                      60   \n",
      "REVIEW11         NaN                      63   \n",
      "REVIEW12         NaN                      64   \n",
      "REVIEW13         NaN                      82   \n",
      "REVIEW14         NaN                      83   \n",
      "REVIEW15         NaN                      84   \n",
      "REVIEW16         NaN                      86   \n",
      "REVIEW17         NaN                      88   \n",
      "REVIEW18         NaN                      89   \n",
      "REVIEW19         NaN                      91   \n",
      "REVIEW20         NaN                      92   \n",
      "REVIEW21         NaN                      95   \n",
      "REVIEW22         NaN                      96   \n",
      "REVIEW23         NaN                     NaN   \n",
      "REVIEW24         NaN                     NaN   \n",
      "REVIEW25         NaN                     NaN   \n",
      "REVIEW26         NaN                     NaN   \n",
      "REVIEW27         NaN                     NaN   \n",
      "REVIEW28         NaN                     NaN   \n",
      "REVIEW29         NaN                     NaN   \n",
      "REVIEW30         NaN                     NaN   \n",
      "REVIEW31         NaN                     NaN   \n",
      "REVIEW32         NaN                     NaN   \n",
      "REVIEW33         NaN                     NaN   \n",
      "REVIEW34         NaN                     NaN   \n",
      "REVIEW35         NaN                     NaN   \n",
      "REVIEW36         NaN                     NaN   \n",
      "REVIEW37         NaN                     NaN   \n",
      "REVIEW38         NaN                     NaN   \n",
      "REVIEW39         NaN                     NaN   \n",
      "REVIEW40         NaN                     NaN   \n",
      "REVIEW41         NaN                     NaN   \n",
      "REVIEW42         NaN                     NaN   \n",
      "REVIEW43         NaN                     NaN   \n",
      "REVIEW44         NaN                     NaN   \n",
      "REVIEW45         NaN                     NaN   \n",
      "REVIEW46         NaN                     NaN   \n",
      "REVIEW    NNPPPPPPPP  NNNNNNNPPPPPPPPPPPPPPP   \n",
      "\n",
      "Turker                                  T_8  \\\n",
      "REVIEW1                                   3   \n",
      "REVIEW2                                   4   \n",
      "REVIEW3                                   6   \n",
      "REVIEW4                                  12   \n",
      "REVIEW5                                  15   \n",
      "REVIEW6                                  18   \n",
      "REVIEW7                                  20   \n",
      "REVIEW8                                  24   \n",
      "REVIEW9                                  25   \n",
      "REVIEW10                                 27   \n",
      "REVIEW11                                 31   \n",
      "REVIEW12                                 32   \n",
      "REVIEW13                                 34   \n",
      "REVIEW14                                 37   \n",
      "REVIEW15                                 38   \n",
      "REVIEW16                                 40   \n",
      "REVIEW17                                 43   \n",
      "REVIEW18                                 59   \n",
      "REVIEW19                                 61   \n",
      "REVIEW20                                 62   \n",
      "REVIEW21                                 66   \n",
      "REVIEW22                                 67   \n",
      "REVIEW23                                 68   \n",
      "REVIEW24                                 72   \n",
      "REVIEW25                                 73   \n",
      "REVIEW26                                 76   \n",
      "REVIEW27                                 78   \n",
      "REVIEW28                                 79   \n",
      "REVIEW29                                 80   \n",
      "REVIEW30                                 81   \n",
      "REVIEW31                                 89   \n",
      "REVIEW32                                 92   \n",
      "REVIEW33                                 93   \n",
      "REVIEW34                                NaN   \n",
      "REVIEW35                                NaN   \n",
      "REVIEW36                                NaN   \n",
      "REVIEW37                                NaN   \n",
      "REVIEW38                                NaN   \n",
      "REVIEW39                                NaN   \n",
      "REVIEW40                                NaN   \n",
      "REVIEW41                                NaN   \n",
      "REVIEW42                                NaN   \n",
      "REVIEW43                                NaN   \n",
      "REVIEW44                                NaN   \n",
      "REVIEW45                                NaN   \n",
      "REVIEW46                                NaN   \n",
      "REVIEW    NNNNPNNNNNNNNNPNNPPPPPPPPPPPPPPPP   \n",
      "\n",
      "Turker                                      T_9  \n",
      "REVIEW1                                       5  \n",
      "REVIEW2                                       6  \n",
      "REVIEW3                                       7  \n",
      "REVIEW4                                       9  \n",
      "REVIEW5                                      10  \n",
      "REVIEW6                                      11  \n",
      "REVIEW7                                      13  \n",
      "REVIEW8                                      16  \n",
      "REVIEW9                                      17  \n",
      "REVIEW10                                     18  \n",
      "REVIEW11                                     21  \n",
      "REVIEW12                                     23  \n",
      "REVIEW13                                     25  \n",
      "REVIEW14                                     30  \n",
      "REVIEW15                                     32  \n",
      "REVIEW16                                     33  \n",
      "REVIEW17                                     35  \n",
      "REVIEW18                                     37  \n",
      "REVIEW19                                     38  \n",
      "REVIEW20                                     41  \n",
      "REVIEW21                                     44  \n",
      "REVIEW22                                     49  \n",
      "REVIEW23                                     52  \n",
      "REVIEW24                                     53  \n",
      "REVIEW25                                     54  \n",
      "REVIEW26                                     57  \n",
      "REVIEW27                                     65  \n",
      "REVIEW28                                     66  \n",
      "REVIEW29                                     67  \n",
      "REVIEW30                                     68  \n",
      "REVIEW31                                     69  \n",
      "REVIEW32                                     70  \n",
      "REVIEW33                                     71  \n",
      "REVIEW34                                     76  \n",
      "REVIEW35                                     81  \n",
      "REVIEW36                                     83  \n",
      "REVIEW37                                     84  \n",
      "REVIEW38                                    NaN  \n",
      "REVIEW39                                    NaN  \n",
      "REVIEW40                                    NaN  \n",
      "REVIEW41                                    NaN  \n",
      "REVIEW42                                    NaN  \n",
      "REVIEW43                                    NaN  \n",
      "REVIEW44                                    NaN  \n",
      "REVIEW45                                    NaN  \n",
      "REVIEW46                                    NaN  \n",
      "REVIEW    NPPPPNNNNNPPNPNNNNPNNPNNNNNPNNPNPPNNP  \n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),\n",
    "                   'SENTIMENT': even_cleaner_df['ReviewID'].tolist(),\n",
    "                   'REVIEW': even_cleaner_df['sentiment'].tolist() })\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "values = grouped['REVIEW'].agg('sum')\n",
    "id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "result = pd.concat([id_df, values], axis=1)\n",
    "result_df = pd.DataFrame(result)\n",
    "print(result_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(result_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Turker</th>\n",
       "      <th>T_0</th>\n",
       "      <th>T_1</th>\n",
       "      <th>T_10</th>\n",
       "      <th>T_11</th>\n",
       "      <th>T_12</th>\n",
       "      <th>T_13</th>\n",
       "      <th>T_14</th>\n",
       "      <th>T_15</th>\n",
       "      <th>T_16</th>\n",
       "      <th>T_17</th>\n",
       "      <th>T_2</th>\n",
       "      <th>T_3</th>\n",
       "      <th>T_4</th>\n",
       "      <th>T_5</th>\n",
       "      <th>T_6</th>\n",
       "      <th>T_7</th>\n",
       "      <th>T_8</th>\n",
       "      <th>T_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>REVIEW1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW2</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>76</td>\n",
       "      <td>92</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW3</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>29</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW4</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>95</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>96</td>\n",
       "      <td>59</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>63</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "      <td>74</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>84</td>\n",
       "      <td>51</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>89</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>91</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>22</td>\n",
       "      <td>40</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>26</td>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "      <td>45</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>47</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>36</td>\n",
       "      <td>55</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>59</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>66</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>73</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>87</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>93</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>97</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>REVIEW</td>\n",
       "      <td>NNNN</td>\n",
       "      <td>N</td>\n",
       "      <td>PNPPNNNPNNNNNNPPPPPPPPPPPPPPPNPPPP</td>\n",
       "      <td>NNPPN</td>\n",
       "      <td>NNNNPP</td>\n",
       "      <td>NNNPP</td>\n",
       "      <td>NP</td>\n",
       "      <td>NN</td>\n",
       "      <td>NPPP</td>\n",
       "      <td>P</td>\n",
       "      <td>NNNNNNNNNNNNPPPPPPNPP</td>\n",
       "      <td>NNNNNNNNNNNNNNNNNNNNNNPPPPPPPPPPPPPPPPPPPNPPPP</td>\n",
       "      <td>NNNNNNNPNNNNNNNPPPPPPPPPPPPP</td>\n",
       "      <td>PNPNNPNPPNNNNNNPNNNNNPPNPPPPNPPNN</td>\n",
       "      <td>NNPPPPPPPP</td>\n",
       "      <td>NNNNNNNPPPPPPPPPPPPPPP</td>\n",
       "      <td>NNNNPNNNNNNNNNPNNPPPPPPPPPPPPPPPP</td>\n",
       "      <td>NPPPPNNNNNPPNPNNNNPNNPNNNNNPNNPNPPNNP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Turker     T_0  T_1                                T_10   T_11    T_12   T_13  \\\n",
       "REVIEW1      0    0                                   6     11      13     15   \n",
       "REVIEW2     11  NaN                                   8     47      25     19   \n",
       "REVIEW3     42  NaN                                  14     55      29     44   \n",
       "REVIEW4     46  NaN                                  15     95      57     80   \n",
       "REVIEW5    NaN  NaN                                  18     96      59     97   \n",
       "REVIEW6    NaN  NaN                                  20    NaN      71    NaN   \n",
       "REVIEW7    NaN  NaN                                  21    NaN     NaN    NaN   \n",
       "REVIEW8    NaN  NaN                                  22    NaN     NaN    NaN   \n",
       "REVIEW9    NaN  NaN                                  23    NaN     NaN    NaN   \n",
       "REVIEW10   NaN  NaN                                  26    NaN     NaN    NaN   \n",
       "REVIEW11   NaN  NaN                                  33    NaN     NaN    NaN   \n",
       "REVIEW12   NaN  NaN                                  35    NaN     NaN    NaN   \n",
       "REVIEW13   NaN  NaN                                  41    NaN     NaN    NaN   \n",
       "REVIEW14   NaN  NaN                                  42    NaN     NaN    NaN   \n",
       "REVIEW15   NaN  NaN                                  51    NaN     NaN    NaN   \n",
       "REVIEW16   NaN  NaN                                  52    NaN     NaN    NaN   \n",
       "REVIEW17   NaN  NaN                                  53    NaN     NaN    NaN   \n",
       "REVIEW18   NaN  NaN                                  59    NaN     NaN    NaN   \n",
       "REVIEW19   NaN  NaN                                  60    NaN     NaN    NaN   \n",
       "REVIEW20   NaN  NaN                                  62    NaN     NaN    NaN   \n",
       "REVIEW21   NaN  NaN                                  67    NaN     NaN    NaN   \n",
       "REVIEW22   NaN  NaN                                  68    NaN     NaN    NaN   \n",
       "REVIEW23   NaN  NaN                                  69    NaN     NaN    NaN   \n",
       "REVIEW24   NaN  NaN                                  74    NaN     NaN    NaN   \n",
       "REVIEW25   NaN  NaN                                  77    NaN     NaN    NaN   \n",
       "REVIEW26   NaN  NaN                                  79    NaN     NaN    NaN   \n",
       "REVIEW27   NaN  NaN                                  80    NaN     NaN    NaN   \n",
       "REVIEW28   NaN  NaN                                  81    NaN     NaN    NaN   \n",
       "REVIEW29   NaN  NaN                                  82    NaN     NaN    NaN   \n",
       "REVIEW30   NaN  NaN                                  87    NaN     NaN    NaN   \n",
       "REVIEW31   NaN  NaN                                  90    NaN     NaN    NaN   \n",
       "REVIEW32   NaN  NaN                                  91    NaN     NaN    NaN   \n",
       "REVIEW33   NaN  NaN                                  94    NaN     NaN    NaN   \n",
       "REVIEW34   NaN  NaN                                  97    NaN     NaN    NaN   \n",
       "REVIEW35   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW36   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW37   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW38   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW39   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW40   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW41   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW42   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW43   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW44   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW45   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW46   NaN  NaN                                 NaN    NaN     NaN    NaN   \n",
       "REVIEW    NNNN    N  PNPPNNNPNNNNNNPPPPPPPPPPPPPPPNPPPP  NNPPN  NNNNPP  NNNPP   \n",
       "\n",
       "Turker   T_14 T_15  T_16 T_17                    T_2  \\\n",
       "REVIEW1    28   29    39   56                      0   \n",
       "REVIEW2    76   92    58  NaN                      1   \n",
       "REVIEW3   NaN  NaN    70  NaN                      8   \n",
       "REVIEW4   NaN  NaN    94  NaN                     10   \n",
       "REVIEW5   NaN  NaN   NaN  NaN                     16   \n",
       "REVIEW6   NaN  NaN   NaN  NaN                     21   \n",
       "REVIEW7   NaN  NaN   NaN  NaN                     27   \n",
       "REVIEW8   NaN  NaN   NaN  NaN                     32   \n",
       "REVIEW9   NaN  NaN   NaN  NaN                     36   \n",
       "REVIEW10  NaN  NaN   NaN  NaN                     37   \n",
       "REVIEW11  NaN  NaN   NaN  NaN                     45   \n",
       "REVIEW12  NaN  NaN   NaN  NaN                     47   \n",
       "REVIEW13  NaN  NaN   NaN  NaN                     50   \n",
       "REVIEW14  NaN  NaN   NaN  NaN                     64   \n",
       "REVIEW15  NaN  NaN   NaN  NaN                     66   \n",
       "REVIEW16  NaN  NaN   NaN  NaN                     72   \n",
       "REVIEW17  NaN  NaN   NaN  NaN                     75   \n",
       "REVIEW18  NaN  NaN   NaN  NaN                     83   \n",
       "REVIEW19  NaN  NaN   NaN  NaN                     85   \n",
       "REVIEW20  NaN  NaN   NaN  NaN                     86   \n",
       "REVIEW21  NaN  NaN   NaN  NaN                     93   \n",
       "REVIEW22  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW23  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW24  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW25  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW26  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW27  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW28  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW29  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW30  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW31  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW32  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW33  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW34  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW35  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW36  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW37  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW38  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW39  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW40  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW41  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW42  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW43  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW44  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW45  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW46  NaN  NaN   NaN  NaN                    NaN   \n",
       "REVIEW     NP   NN  NPPP    P  NNNNNNNNNNNNPPPPPPNPP   \n",
       "\n",
       "Turker                                               T_3  \\\n",
       "REVIEW1                                                1   \n",
       "REVIEW2                                                3   \n",
       "REVIEW3                                                4   \n",
       "REVIEW4                                                5   \n",
       "REVIEW5                                                7   \n",
       "REVIEW6                                               10   \n",
       "REVIEW7                                               14   \n",
       "REVIEW8                                               16   \n",
       "REVIEW9                                               17   \n",
       "REVIEW10                                              19   \n",
       "REVIEW11                                              22   \n",
       "REVIEW12                                              23   \n",
       "REVIEW13                                              26   \n",
       "REVIEW14                                              28   \n",
       "REVIEW15                                              30   \n",
       "REVIEW16                                              34   \n",
       "REVIEW17                                              35   \n",
       "REVIEW18                                              36   \n",
       "REVIEW19                                              38   \n",
       "REVIEW20                                              40   \n",
       "REVIEW21                                              44   \n",
       "REVIEW22                                              46   \n",
       "REVIEW23                                              48   \n",
       "REVIEW24                                              49   \n",
       "REVIEW25                                              54   \n",
       "REVIEW26                                              55   \n",
       "REVIEW27                                              57   \n",
       "REVIEW28                                              58   \n",
       "REVIEW29                                              60   \n",
       "REVIEW30                                              61   \n",
       "REVIEW31                                              63   \n",
       "REVIEW32                                              64   \n",
       "REVIEW33                                              65   \n",
       "REVIEW34                                              70   \n",
       "REVIEW35                                              71   \n",
       "REVIEW36                                              73   \n",
       "REVIEW37                                              74   \n",
       "REVIEW38                                              75   \n",
       "REVIEW39                                              77   \n",
       "REVIEW40                                              79   \n",
       "REVIEW41                                              82   \n",
       "REVIEW42                                              85   \n",
       "REVIEW43                                              86   \n",
       "REVIEW44                                              88   \n",
       "REVIEW45                                              90   \n",
       "REVIEW46                                              95   \n",
       "REVIEW    NNNNNNNNNNNNNNNNNNNNNNPPPPPPPPPPPPPPPPPPPNPPPP   \n",
       "\n",
       "Turker                             T_4                                T_5  \\\n",
       "REVIEW1                              1                                  2   \n",
       "REVIEW2                              7                                  3   \n",
       "REVIEW3                              9                                  4   \n",
       "REVIEW4                             12                                  5   \n",
       "REVIEW5                             17                                 12   \n",
       "REVIEW6                             19                                 13   \n",
       "REVIEW7                             27                                 14   \n",
       "REVIEW8                             29                                 20   \n",
       "REVIEW9                             30                                 22   \n",
       "REVIEW10                            33                                 24   \n",
       "REVIEW11                            40                                 26   \n",
       "REVIEW12                            41                                 28   \n",
       "REVIEW13                            42                                 31   \n",
       "REVIEW14                            45                                 36   \n",
       "REVIEW15                            47                                 39   \n",
       "REVIEW16                            48                                 43   \n",
       "REVIEW17                            50                                 45   \n",
       "REVIEW18                            55                                 46   \n",
       "REVIEW19                            61                                 48   \n",
       "REVIEW20                            65                                 49   \n",
       "REVIEW21                            69                                 52   \n",
       "REVIEW22                            73                                 53   \n",
       "REVIEW23                            77                                 54   \n",
       "REVIEW24                            78                                 56   \n",
       "REVIEW25                            87                                 58   \n",
       "REVIEW26                            90                                 72   \n",
       "REVIEW27                            93                                 75   \n",
       "REVIEW28                            97                                 78   \n",
       "REVIEW29                           NaN                                 85   \n",
       "REVIEW30                           NaN                                 87   \n",
       "REVIEW31                           NaN                                 88   \n",
       "REVIEW32                           NaN                                 94   \n",
       "REVIEW33                           NaN                                 96   \n",
       "REVIEW34                           NaN                                NaN   \n",
       "REVIEW35                           NaN                                NaN   \n",
       "REVIEW36                           NaN                                NaN   \n",
       "REVIEW37                           NaN                                NaN   \n",
       "REVIEW38                           NaN                                NaN   \n",
       "REVIEW39                           NaN                                NaN   \n",
       "REVIEW40                           NaN                                NaN   \n",
       "REVIEW41                           NaN                                NaN   \n",
       "REVIEW42                           NaN                                NaN   \n",
       "REVIEW43                           NaN                                NaN   \n",
       "REVIEW44                           NaN                                NaN   \n",
       "REVIEW45                           NaN                                NaN   \n",
       "REVIEW46                           NaN                                NaN   \n",
       "REVIEW    NNNNNNNPNNNNNNNPPPPPPPPPPPPP  PNPNNPNPPNNNNNNPNNNNNPPNPPPPNPPNN   \n",
       "\n",
       "Turker           T_6                     T_7  \\\n",
       "REVIEW1            2                       2   \n",
       "REVIEW2           31                       8   \n",
       "REVIEW3           50                       9   \n",
       "REVIEW4           51                      24   \n",
       "REVIEW5           62                      34   \n",
       "REVIEW6           63                      39   \n",
       "REVIEW7           74                      43   \n",
       "REVIEW8           84                      51   \n",
       "REVIEW9           89                      56   \n",
       "REVIEW10          91                      60   \n",
       "REVIEW11         NaN                      63   \n",
       "REVIEW12         NaN                      64   \n",
       "REVIEW13         NaN                      82   \n",
       "REVIEW14         NaN                      83   \n",
       "REVIEW15         NaN                      84   \n",
       "REVIEW16         NaN                      86   \n",
       "REVIEW17         NaN                      88   \n",
       "REVIEW18         NaN                      89   \n",
       "REVIEW19         NaN                      91   \n",
       "REVIEW20         NaN                      92   \n",
       "REVIEW21         NaN                      95   \n",
       "REVIEW22         NaN                      96   \n",
       "REVIEW23         NaN                     NaN   \n",
       "REVIEW24         NaN                     NaN   \n",
       "REVIEW25         NaN                     NaN   \n",
       "REVIEW26         NaN                     NaN   \n",
       "REVIEW27         NaN                     NaN   \n",
       "REVIEW28         NaN                     NaN   \n",
       "REVIEW29         NaN                     NaN   \n",
       "REVIEW30         NaN                     NaN   \n",
       "REVIEW31         NaN                     NaN   \n",
       "REVIEW32         NaN                     NaN   \n",
       "REVIEW33         NaN                     NaN   \n",
       "REVIEW34         NaN                     NaN   \n",
       "REVIEW35         NaN                     NaN   \n",
       "REVIEW36         NaN                     NaN   \n",
       "REVIEW37         NaN                     NaN   \n",
       "REVIEW38         NaN                     NaN   \n",
       "REVIEW39         NaN                     NaN   \n",
       "REVIEW40         NaN                     NaN   \n",
       "REVIEW41         NaN                     NaN   \n",
       "REVIEW42         NaN                     NaN   \n",
       "REVIEW43         NaN                     NaN   \n",
       "REVIEW44         NaN                     NaN   \n",
       "REVIEW45         NaN                     NaN   \n",
       "REVIEW46         NaN                     NaN   \n",
       "REVIEW    NNPPPPPPPP  NNNNNNNPPPPPPPPPPPPPPP   \n",
       "\n",
       "Turker                                  T_8  \\\n",
       "REVIEW1                                   3   \n",
       "REVIEW2                                   4   \n",
       "REVIEW3                                   6   \n",
       "REVIEW4                                  12   \n",
       "REVIEW5                                  15   \n",
       "REVIEW6                                  18   \n",
       "REVIEW7                                  20   \n",
       "REVIEW8                                  24   \n",
       "REVIEW9                                  25   \n",
       "REVIEW10                                 27   \n",
       "REVIEW11                                 31   \n",
       "REVIEW12                                 32   \n",
       "REVIEW13                                 34   \n",
       "REVIEW14                                 37   \n",
       "REVIEW15                                 38   \n",
       "REVIEW16                                 40   \n",
       "REVIEW17                                 43   \n",
       "REVIEW18                                 59   \n",
       "REVIEW19                                 61   \n",
       "REVIEW20                                 62   \n",
       "REVIEW21                                 66   \n",
       "REVIEW22                                 67   \n",
       "REVIEW23                                 68   \n",
       "REVIEW24                                 72   \n",
       "REVIEW25                                 73   \n",
       "REVIEW26                                 76   \n",
       "REVIEW27                                 78   \n",
       "REVIEW28                                 79   \n",
       "REVIEW29                                 80   \n",
       "REVIEW30                                 81   \n",
       "REVIEW31                                 89   \n",
       "REVIEW32                                 92   \n",
       "REVIEW33                                 93   \n",
       "REVIEW34                                NaN   \n",
       "REVIEW35                                NaN   \n",
       "REVIEW36                                NaN   \n",
       "REVIEW37                                NaN   \n",
       "REVIEW38                                NaN   \n",
       "REVIEW39                                NaN   \n",
       "REVIEW40                                NaN   \n",
       "REVIEW41                                NaN   \n",
       "REVIEW42                                NaN   \n",
       "REVIEW43                                NaN   \n",
       "REVIEW44                                NaN   \n",
       "REVIEW45                                NaN   \n",
       "REVIEW46                                NaN   \n",
       "REVIEW    NNNNPNNNNNNNNNPNNPPPPPPPPPPPPPPPP   \n",
       "\n",
       "Turker                                      T_9  \n",
       "REVIEW1                                       5  \n",
       "REVIEW2                                       6  \n",
       "REVIEW3                                       7  \n",
       "REVIEW4                                       9  \n",
       "REVIEW5                                      10  \n",
       "REVIEW6                                      11  \n",
       "REVIEW7                                      13  \n",
       "REVIEW8                                      16  \n",
       "REVIEW9                                      17  \n",
       "REVIEW10                                     18  \n",
       "REVIEW11                                     21  \n",
       "REVIEW12                                     23  \n",
       "REVIEW13                                     25  \n",
       "REVIEW14                                     30  \n",
       "REVIEW15                                     32  \n",
       "REVIEW16                                     33  \n",
       "REVIEW17                                     35  \n",
       "REVIEW18                                     37  \n",
       "REVIEW19                                     38  \n",
       "REVIEW20                                     41  \n",
       "REVIEW21                                     44  \n",
       "REVIEW22                                     49  \n",
       "REVIEW23                                     52  \n",
       "REVIEW24                                     53  \n",
       "REVIEW25                                     54  \n",
       "REVIEW26                                     57  \n",
       "REVIEW27                                     65  \n",
       "REVIEW28                                     66  \n",
       "REVIEW29                                     67  \n",
       "REVIEW30                                     68  \n",
       "REVIEW31                                     69  \n",
       "REVIEW32                                     70  \n",
       "REVIEW33                                     71  \n",
       "REVIEW34                                     76  \n",
       "REVIEW35                                     81  \n",
       "REVIEW36                                     83  \n",
       "REVIEW37                                     84  \n",
       "REVIEW38                                    NaN  \n",
       "REVIEW39                                    NaN  \n",
       "REVIEW40                                    NaN  \n",
       "REVIEW41                                    NaN  \n",
       "REVIEW42                                    NaN  \n",
       "REVIEW43                                    NaN  \n",
       "REVIEW44                                    NaN  \n",
       "REVIEW45                                    NaN  \n",
       "REVIEW46                                    NaN  \n",
       "REVIEW    NPPPPNNNNNPPNPNNNNPNNPNNNNNPNNPNPPNNP  "
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrameGroupBy' object has no attribute 'tolist'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-317-1bc38d4e9879>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Turker'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m# values = grouped['REVIEW'].agg('sum')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         raise AttributeError(\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0;34m\"%r object has no attribute %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m         )\n\u001b[1;32m    568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrameGroupBy' object has no attribute 'tolist'"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),\n",
    "                   'SENTIMENT': even_cleaner_df['ReviewID'].tolist(),\n",
    "                   'REVIEW': even_cleaner_df['sentiment'].tolist() })\n",
    "\n",
    "grouped = df.groupby('Turker')\n",
    "print(grouped.tolist())\n",
    "# values = grouped['REVIEW'].agg('sum')\n",
    "# id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()\n",
    "# id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})\n",
    "# result = pd.concat([id_df, values], axis=1)\n",
    "# result_df = pd.DataFrame(result)\n",
    "# print(result_df.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want every review on the left side and I want all 46 turkers on the top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({ 'review': even_cleaner_df['ReviewID']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 11, 42, 46]\n",
      "[0]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[11, 47, 55, 95, 96]\n",
      "[0, 11, 42, 46]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[15, 19, 44, 80, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[15, 19, 44, 80, 97]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[28, 76]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[29, 92]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[39, 58, 70, 94]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[0, 11, 42, 46]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[15, 19, 44, 80, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[0, 11, 42, 46]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[11, 47, 55, 95, 96]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[11, 47, 55, 95, 96]\n",
      "[56]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[39, 58, 70, 94]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[39, 58, 70, 94]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[13, 25, 29, 57, 59, 71]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[28, 76]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[15, 19, 44, 80, 97]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[2, 31, 50, 51, 62, 63, 74, 84, 89, 91]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[29, 92]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93]\n",
      "[3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[39, 58, 70, 94]\n",
      "[1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95]\n",
      "[11, 47, 55, 95, 96]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[11, 47, 55, 95, 96]\n",
      "[2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96]\n",
      "[2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96]\n",
      "[15, 19, 44, 80, 97]\n",
      "[1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97]\n",
      "[6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]\n"
     ]
    }
   ],
   "source": [
    "def get_array_of_reviews(turker, df):\n",
    "    a = ['nan']*98\n",
    "    df = even_cleaner_df[even_cleaner_df['T_ID'] == turker] \n",
    "    t_reviews = df['ReviewID'].tolist()\n",
    "    t_sentiment = df['sentiment'].tolist()\n",
    "    for index,review in enumerate(t_reviews):\n",
    "        a[review] = t_sentiment[index]\n",
    "    print(t_reviews)\n",
    "\n",
    "    return a\n",
    "\n",
    "sparse_df = even_cleaner_df.copy()\n",
    "sparse_df['big_array'] = sparse_df.apply(lambda x: get_array_of_reviews(x['T_ID'], even_cleaner_df), axis=1)\n",
    "# t0 = even_cleaner_df[even_cleaner_df['T_ID'] == 'T_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReviewID</th>\n",
       "      <th>T_ID</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>big_array</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>N</td>\n",
       "      <td>[N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...</td>\n",
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       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>T_0</td>\n",
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       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>42</td>\n",
       "      <td>T_0</td>\n",
       "      <td>N</td>\n",
       "      <td>[N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>46</td>\n",
       "      <td>T_0</td>\n",
       "      <td>N</td>\n",
       "      <td>[N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ReviewID T_ID sentiment  \\\n",
       "0           0  T_0         N   \n",
       "34         11  T_0         N   \n",
       "126        42  T_0         N   \n",
       "140        46  T_0         N   \n",
       "\n",
       "                                             big_array  \n",
       "0    [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...  \n",
       "34   [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...  \n",
       "126  [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...  \n",
       "140  [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReviewID</th>\n",
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       "      <th>sentiment</th>\n",
       "      <th>big_array</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>N</td>\n",
       "      <td>[N, nan, nan, nan, nan, nan, nan, nan, nan, na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>T_1</td>\n",
       "      <td>N</td>\n",
       "      <td>[N, nan, nan, nan, nan, nan, nan, nan, nan, na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>T_2</td>\n",
       "      <td>N</td>\n",
       "      <td>[N, N, nan, nan, nan, nan, nan, nan, N, nan, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>T_3</td>\n",
       "      <td>N</td>\n",
       "      <td>[nan, N, nan, N, N, N, nan, N, nan, nan, N, na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>T_2</td>\n",
       "      <td>N</td>\n",
       "      <td>[N, N, nan, nan, nan, nan, nan, nan, N, nan, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>289</td>\n",
       "      <td>96</td>\n",
       "      <td>T_5</td>\n",
       "      <td>N</td>\n",
       "      <td>[nan, nan, P, N, P, N, nan, nan, nan, nan, nan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>96</td>\n",
       "      <td>T_7</td>\n",
       "      <td>P</td>\n",
       "      <td>[nan, nan, N, nan, nan, nan, nan, nan, N, N, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>97</td>\n",
       "      <td>T_13</td>\n",
       "      <td>P</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>292</td>\n",
       "      <td>97</td>\n",
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       "      <td>[nan, N, nan, nan, nan, nan, nan, N, nan, N, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>97</td>\n",
       "      <td>T_10</td>\n",
       "      <td>P</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, P, nan, N, nan,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ReviewID  T_ID sentiment  \\\n",
       "0           0   T_0         N   \n",
       "1           0   T_1         N   \n",
       "2           0   T_2         N   \n",
       "3           1   T_3         N   \n",
       "4           1   T_2         N   \n",
       "..        ...   ...       ...   \n",
       "289        96   T_5         N   \n",
       "290        96   T_7         P   \n",
       "291        97  T_13         P   \n",
       "292        97   T_4         P   \n",
       "293        97  T_10         P   \n",
       "\n",
       "                                             big_array  \n",
       "0    [N, nan, nan, nan, nan, nan, nan, nan, nan, na...  \n",
       "1    [N, nan, nan, nan, nan, nan, nan, nan, nan, na...  \n",
       "2    [N, N, nan, nan, nan, nan, nan, nan, N, nan, N...  \n",
       "3    [nan, N, nan, N, N, N, nan, N, nan, nan, N, na...  \n",
       "4    [N, N, nan, nan, nan, nan, nan, nan, N, nan, N...  \n",
       "..                                                 ...  \n",
       "289  [nan, nan, P, N, P, N, nan, nan, nan, nan, nan...  \n",
       "290  [nan, nan, N, nan, nan, nan, nan, nan, N, N, n...  \n",
       "291  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
       "292  [nan, N, nan, nan, nan, nan, nan, N, nan, N, n...  \n",
       "293  [nan, nan, nan, nan, nan, nan, P, nan, N, nan,...  \n",
       "\n",
       "[294 rows x 4 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = sparse_df[sparse_df['T_ID'] == 'T_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     ReviewID T_ID sentiment  \\\n",
       "0           0  T_0         N   \n",
       "34         11  T_0         N   \n",
       "126        42  T_0         N   \n",
       "140        46  T_0         N   \n",
       "\n",
       "                                             big_array  \n",
       "0    [N, nan, nan, nan, nan, nan, nan, nan, nan, na...  \n",
       "34   [N, nan, nan, nan, nan, nan, nan, nan, nan, na...  \n",
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       "140  [N, nan, nan, nan, nan, nan, nan, nan, nan, na...  "
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     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-364-49ad7af36cba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msparse_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'big_array'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1986\u001b[0m         \u001b[0mCategories\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1987\u001b[0m         \"\"\"\n\u001b[0;32m-> 1988\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1989\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1403\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithms\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1405\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    406\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0muniques\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.unique\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable._unique\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "sparse_df['big_array'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['T_0', 'T_1', 'T_2', 'T_3', 'T_4', 'T_5', 'T_6', 'T_7', 'T_8',\n",
       "       'T_9', 'T_10', 'T_11', 'T_12', 'T_13', 'T_14', 'T_15', 'T_16',\n",
       "       'T_17'], dtype=object)"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "even_cleaner_df['T_ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
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       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
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       " 'nan',\n",
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       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
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       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'N',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'P',\n",
       " 'nan',\n",
       " 'nan',\n",
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       " 'nan',\n",
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       " 'nan',\n",
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       " 'nan',\n",
       " 'P',\n",
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       " 'P',\n",
       " 'nan',\n",
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       " 'N',\n",
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       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
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       " 'nan',\n",
       " 'nan',\n",
       " 'P',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan',\n",
       " 'nan']"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_df['big_array'][sparse_df['T_ID'] == 'T_2'].tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39004149377593356"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "y1 = sparse_df['big_array'][sparse_df['T_ID'] == 'T_0'].tolist()[0]\n",
    "y2 = sparse_df['big_array'][sparse_df['T_ID'] == 'T_1'].tolist()[0]\n",
    "cohen_kappa_score(y1,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_kappa(num):\n",
    "    y1 = sparse_df['big_array'][sparse_df['T_ID'] == 'T_'+str(num)].tolist()[0]\n",
    "    y2 = sparse_df['big_array'][sparse_df['T_ID'] == 'T_'+str(num + 1)].tolist()[0]\n",
    "    return cohen_kappa_score(y1,y2)\n",
    "\n",
    "kappas = [calculate_kappa(num) for num in range(16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.39004149377593356,\n",
       " 0.07634307257304429,\n",
       " 0.023255813953488413,\n",
       " 0.11578947368421055,\n",
       " -0.10975609756097549,\n",
       " -0.04981253347616499,\n",
       " 0.29547088425593093,\n",
       " -0.02821170435999054,\n",
       " -0.01071003570011908,\n",
       " 0.005658536585365748,\n",
       " -0.06968933669185562,\n",
       " -0.04457364341085279,\n",
       " -0.04457364341085279,\n",
       " -0.02235469448584193,\n",
       " -0.015544041450777257,\n",
       " -0.01730103806228378]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kappas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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