lesmiz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b984568-f159-4b71-85d4-4ccb9aee455b",
   "metadata": {},
   "source": [
    "## Les Miz Sentiment Analysis: Death of Gavroche, in English and French  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8983392b-8b64-4620-a7f3-7fade6bc20e2",
   "metadata": {},
   "source": [
    "## the plan: \n",
    "1. reformat English and French version into coherent word groups \n",
    "2. send english DF and French DF through \"cardiffnlp/twitter-xlm-roberta-base-sentiment\" model\n",
    "3. compare specific phrases and overall results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0cf7c36-33d9-484d-ba23-f553097d9cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import pipeline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59c96efc-3074-4609-bcf0-aa79cc8e852c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>It was a charming and terrible sight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gavroche, though shot at, was teasing the fusillade.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>He had the air of being greatly diverted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>It was the sparrow pecking at the sportsmen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To each discharge he retorted</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [It was a charming and terrible sight,  Gavroche, though shot at, was teasing the fusillade. ,  He had the air of being greatly diverted,  It was the sparrow pecking at the sportsmen,  To each discharge he retorted]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf = pd.read_csv(\"books/english.csv\", index_col = 0)\n",
    "edf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01f9d512-1a13-4f98-99f3-c2e2d2ef1b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model:\n",
    "# list = []\n",
    "# for row in ebx: \n",
    "#     td = row.select(\"td\")\n",
    "#     dict = {}\n",
    "#     dict[\"station\"] = td[0].text.strip()\n",
    "#     dict[\"address\"] = td[1].text.strip()\n",
    "#     list.append(dict)\n",
    "# print(list)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6269f2e8-adb0-4e5e-b73d-2559434d9d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is INCOMPLETE: \n",
    "# results = []\n",
    "# # for sentences in df:\n",
    "# for index, row in edf.iterrows():\n",
    "#     dict = {}\n",
    "#     sentiment_pipeline = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-xlm-roberta-base-sentiment\")\n",
    "#     data = index \n",
    "#     rating = sentiment_pipeline(data)\n",
    "#     print(index)\n",
    "#     print(rating)\n",
    "#     #df['score'] = rating\n",
    "#     # print(df['score'])\n",
    "#     dict['label'] = rating[0]['label']\n",
    "#     dict['score'] = rating[0]['score']\n",
    "#     results.append(dict)\n",
    "# print(results) "
   ]
  },
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     "text": [
      "It was a charming and terrible sight\n",
      "[{'label': 'negative', 'score': 0.9529926180839539}]\n",
      " Gavroche, though shot at, was teasing the fusillade. \n",
      "[{'label': 'negative', 'score': 0.4210546612739563}]\n",
      " He had the air of being greatly diverted\n",
      "[{'label': 'positive', 'score': 0.48061373829841614}]\n",
      " It was the sparrow pecking at the sportsmen\n",
      "[{'label': 'negative', 'score': 0.5273667573928833}]\n",
      " To each discharge he retorted\n",
      "[{'label': 'neutral', 'score': 0.43959590792655945}]\n",
      "with a couplet\n",
      "[{'label': 'neutral', 'score': 0.4968397617340088}]\n",
      " They aimed at him constantly\n",
      "[{'label': 'negative', 'score': 0.6753860712051392}]\n",
      " and always missed him\n",
      "[{'label': 'negative', 'score': 0.3560687005519867}]\n",
      "The National Guardsmen and the soldiers laughed as they took aim at him.\n",
      "[{'label': 'positive', 'score': 0.5773722529411316}]\n",
      " He lay down\n",
      "[{'label': 'neutral', 'score': 0.549929141998291}]\n",
      " sprang to his feet\n",
      "[{'label': 'neutral', 'score': 0.5386978387832642}]\n",
      " hid in the corner of a doorway\n",
      "[{'label': 'neutral', 'score': 0.5266876220703125}]\n",
      "then made a bound\n",
      "[{'label': 'neutral', 'score': 0.5571023225784302}]\n",
      " disappeared\n",
      "[{'label': 'negative', 'score': 0.43254566192626953}]\n",
      " reappeared\n",
      "[{'label': 'neutral', 'score': 0.3789016604423523}]\n",
      " scampered away\n",
      "[{'label': 'negative', 'score': 0.6002640724182129}]\n",
      " returned\n",
      "[{'label': 'neutral', 'score': 0.38802292943000793}]\n",
      "replied to the grape-shot with his thumb at his nose\n",
      "[{'label': 'neutral', 'score': 0.6055102944374084}]\n",
      " and all the while went on pillaging the cartouches\n",
      "[{'label': 'negative', 'score': 0.6896965503692627}]\n",
      " emptying the cartridge-boxes\n",
      "[{'label': 'neutral', 'score': 0.5393020510673523}]\n",
      "and filling his basket\n",
      "[{'label': 'neutral', 'score': 0.5524231791496277}]\n",
      " The insurgents, panting with anxiety\n",
      "[{'label': 'negative', 'score': 0.902625322341919}]\n",
      " followed him with their eyes\n",
      "[{'label': 'neutral', 'score': 0.5836014151573181}]\n",
      " The barricade trembled\n",
      "[{'label': 'negative', 'score': 0.8814650177955627}]\n",
      " He sang\n",
      "[{'label': 'neutral', 'score': 0.43523508310317993}]\n",
      "He was not a  child\n",
      "[{'label': 'neutral', 'score': 0.5093202590942383}]\n",
      " he was not a man;\n",
      "[{'label': 'neutral', 'score': 0.5572024583816528}]\n",
      "he was a strange gamin-fairy\n",
      "[{'label': 'negative', 'score': 0.7257220149040222}]\n",
      " He might have been called the invulnerable dwarf of the fray\n",
      "[{'label': 'negative', 'score': 0.8699467182159424}]\n",
      " The bullets flew after him\n",
      "[{'label': 'negative', 'score': 0.6512694954872131}]\n",
      " he was more nimble than they\n",
      "[{'label': 'negative', 'score': 0.40426281094551086}]\n",
      " He played a fearful game of hide and seek with death\n",
      "[{'label': 'negative', 'score': 0.5758906602859497}]\n",
      "every time that the flat-nosed face of the spectre approached\n",
      "[{'label': 'neutral', 'score': 0.46692466735839844}]\n",
      " the urchin administered to it a fillip\n",
      "[{'label': 'neutral', 'score': 0.48515111207962036}]\n",
      "One bullet however\n",
      "[{'label': 'neutral', 'score': 0.4478260576725006}]\n",
      "better aimed or more treacherous than the rest\n",
      "[{'label': 'negative', 'score': 0.9133269786834717}]\n",
      "finally struck the will-o’-the-wisp of a child\n",
      "[{'label': 'positive', 'score': 0.6571321487426758}]\n",
      " Gavroche was seen to stagger\n",
      "[{'label': 'negative', 'score': 0.5701798796653748}]\n",
      " then he sank to the earth\n",
      "[{'label': 'negative', 'score': 0.4721197187900543}]\n",
      " The whole barricade gave vent to a cry\n",
      "[{'label': 'negative', 'score': 0.6677193641662598}]\n",
      "but there was something of Antæus in that pygmy;\n",
      "[{'label': 'neutral', 'score': 0.4963710308074951}]\n",
      "for the gamin to touch the pavement\n",
      "[{'label': 'neutral', 'score': 0.41208720207214355}]\n",
      "is the same as for the giant to touch the earth;\n",
      "[{'label': 'neutral', 'score': 0.5483947992324829}]\n",
      "Gavroche had fallen only to rise again;\n",
      "[{'label': 'negative', 'score': 0.4193210005760193}]\n",
      "he remained in a sitting posture\n",
      "[{'label': 'negative', 'score': 0.5129579305648804}]\n",
      " a long thread of blood streaked his face\n",
      "[{'label': 'negative', 'score': 0.5885011553764343}]\n",
      " he raised both arms in the air\n",
      "[{'label': 'neutral', 'score': 0.7149354815483093}]\n",
      " glanced in the direction whence the shot had come\n",
      "[{'label': 'neutral', 'score': 0.6545478105545044}]\n",
      "And began to sing:\n",
      "[{'label': 'neutral', 'score': 0.4657364785671234}]\n",
      "“I have fallen to the earth\n",
      "[{'label': 'negative', 'score': 0.7456450462341309}]\n",
      "’Tis the fault of Voltaire;\n",
      "[{'label': 'negative', 'score': 0.8034857511520386}]\n",
      "With my nose in the gutter\n",
      "[{'label': 'negative', 'score': 0.803199052810669}]\n",
      "’Tis the fault of \n",
      "[{'label': 'negative', 'score': 0.8430755138397217}]\n",
      "He did not finish\n",
      "[{'label': 'negative', 'score': 0.8346465229988098}]\n",
      " A second bullet from the same marksman stopped him short\n",
      "[{'label': 'negative', 'score': 0.7098829746246338}]\n",
      " This time he fell face downward on the pavement\n",
      "[{'label': 'neutral', 'score': 0.5199488997459412}]\n",
      " and moved no more\n",
      "[{'label': 'neutral', 'score': 0.5417121052742004}]\n",
      " This grand little soul had taken its flight\n",
      "[{'label': 'positive', 'score': 0.4738297462463379}]\n",
      "[{'text': 'It was a charming and terrible sight', 'label': 'negative', 'score': 0.9529926180839539}, {'text': ' Gavroche, though shot at, was teasing the fusillade. ', 'label': 'negative', 'score': 0.4210546612739563}, {'text': ' He had the air of being greatly diverted', 'label': 'positive', 'score': 0.48061373829841614}, {'text': ' It was the sparrow pecking at the sportsmen', 'label': 'negative', 'score': 0.5273667573928833}, {'text': ' To each discharge he retorted', 'label': 'neutral', 'score': 0.43959590792655945}, {'text': 'with a couplet', 'label': 'neutral', 'score': 0.4968397617340088}, {'text': ' They aimed at him constantly', 'label': 'negative', 'score': 0.6753860712051392}, {'text': ' and always missed him', 'label': 'negative', 'score': 0.3560687005519867}, {'text': 'The National Guardsmen and the soldiers laughed as they took aim at him.', 'label': 'positive', 'score': 0.5773722529411316}, {'text': ' He lay down', 'label': 'neutral', 'score': 0.549929141998291}, {'text': ' sprang to his feet', 'label': 'neutral', 'score': 0.5386978387832642}, {'text': ' hid in the corner of a doorway', 'label': 'neutral', 'score': 0.5266876220703125}, {'text': 'then made a bound', 'label': 'neutral', 'score': 0.5571023225784302}, {'text': ' disappeared', 'label': 'negative', 'score': 0.43254566192626953}, {'text': ' reappeared', 'label': 'neutral', 'score': 0.3789016604423523}, {'text': ' scampered away', 'label': 'negative', 'score': 0.6002640724182129}, {'text': ' returned', 'label': 'neutral', 'score': 0.38802292943000793}, {'text': 'replied to the grape-shot with his thumb at his nose', 'label': 'neutral', 'score': 0.6055102944374084}, {'text': ' and all the while went on pillaging the cartouches', 'label': 'negative', 'score': 0.6896965503692627}, {'text': ' emptying the cartridge-boxes', 'label': 'neutral', 'score': 0.5393020510673523}, {'text': 'and filling his basket', 'label': 'neutral', 'score': 0.5524231791496277}, {'text': ' The insurgents, panting with anxiety', 'label': 'negative', 'score': 0.902625322341919}, {'text': ' followed him with their eyes', 'label': 'neutral', 'score': 0.5836014151573181}, {'text': ' The barricade trembled', 'label': 'negative', 'score': 0.8814650177955627}, {'text': ' He sang', 'label': 'neutral', 'score': 0.43523508310317993}, {'text': 'He was not a  child', 'label': 'neutral', 'score': 0.5093202590942383}, {'text': ' he was not a man;', 'label': 'neutral', 'score': 0.5572024583816528}, {'text': 'he was a strange gamin-fairy', 'label': 'negative', 'score': 0.7257220149040222}, {'text': ' He might have been called the invulnerable dwarf of the fray', 'label': 'negative', 'score': 0.8699467182159424}, {'text': ' The bullets flew after him', 'label': 'negative', 'score': 0.6512694954872131}, {'text': ' he was more nimble than they', 'label': 'negative', 'score': 0.40426281094551086}, {'text': ' He played a fearful game of hide and seek with death', 'label': 'negative', 'score': 0.5758906602859497}, {'text': 'every time that the flat-nosed face of the spectre approached', 'label': 'neutral', 'score': 0.46692466735839844}, {'text': ' the urchin administered to it a fillip', 'label': 'neutral', 'score': 0.48515111207962036}, {'text': 'One bullet however', 'label': 'neutral', 'score': 0.4478260576725006}, {'text': 'better aimed or more treacherous than the rest', 'label': 'negative', 'score': 0.9133269786834717}, {'text': 'finally struck the will-o’-the-wisp of a child', 'label': 'positive', 'score': 0.6571321487426758}, {'text': ' Gavroche was seen to stagger', 'label': 'negative', 'score': 0.5701798796653748}, {'text': ' then he sank to the earth', 'label': 'negative', 'score': 0.4721197187900543}, {'text': ' The whole barricade gave vent to a cry', 'label': 'negative', 'score': 0.6677193641662598}, {'text': 'but there was something of Antæus in that pygmy;', 'label': 'neutral', 'score': 0.4963710308074951}, {'text': 'for the gamin to touch the pavement', 'label': 'neutral', 'score': 0.41208720207214355}, {'text': 'is the same as for the giant to touch the earth;', 'label': 'neutral', 'score': 0.5483947992324829}, {'text': 'Gavroche had fallen only to rise again;', 'label': 'negative', 'score': 0.4193210005760193}, {'text': 'he remained in a sitting posture', 'label': 'negative', 'score': 0.5129579305648804}, {'text': ' a long thread of blood streaked his face', 'label': 'negative', 'score': 0.5885011553764343}, {'text': ' he raised both arms in the air', 'label': 'neutral', 'score': 0.7149354815483093}, {'text': ' glanced in the direction whence the shot had come', 'label': 'neutral', 'score': 0.6545478105545044}, {'text': 'And began to sing:', 'label': 'neutral', 'score': 0.4657364785671234}, {'text': '“I have fallen to the earth', 'label': 'negative', 'score': 0.7456450462341309}, {'text': '’Tis the fault of Voltaire;', 'label': 'negative', 'score': 0.8034857511520386}, {'text': 'With my nose in the gutter', 'label': 'negative', 'score': 0.803199052810669}, {'text': '’Tis the fault of ', 'label': 'negative', 'score': 0.8430755138397217}, {'text': 'He did not finish', 'label': 'negative', 'score': 0.8346465229988098}, {'text': ' A second bullet from the same marksman stopped him short', 'label': 'negative', 'score': 0.7098829746246338}, {'text': ' This time he fell face downward on the pavement', 'label': 'neutral', 'score': 0.5199488997459412}, {'text': ' and moved no more', 'label': 'neutral', 'score': 0.5417121052742004}, {'text': ' This grand little soul had taken its flight', 'label': 'positive', 'score': 0.4738297462463379}]\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "# for sentences in df:\n",
    "for index, row in edf.iterrows():\n",
    "    dict = {}\n",
    "    sentiment_pipeline = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-xlm-roberta-base-sentiment\")\n",
    "    data = index \n",
    "    rating = sentiment_pipeline(data)\n",
    "    print(index)\n",
    "    print(rating)\n",
    "    #df['score'] = rating\n",
    "    # print(df['score'])\n",
    "    dict['text'] = index\n",
    "    dict['label'] = rating[0]['label']\n",
    "    dict['score'] = rating[0]['score']\n",
    "    results.append(dict)\n",
    "print(results) "
   ]
  },
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   "execution_count": 38,
   "id": "ea53c0dc-cfa3-4c76-b730-d43a770ff6fb",
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       "      <td>It was a charming and terrible sight</td>\n",
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       "      <th>1</th>\n",
       "      <td>Gavroche, though shot at, was teasing the fus...</td>\n",
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       "      <td>He had the air of being greatly diverted</td>\n",
       "      <td>positive</td>\n",
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       "      <th>3</th>\n",
       "      <td>It was the sparrow pecking at the sportsmen</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.527367</td>\n",
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       "      <th>4</th>\n",
       "      <td>To each discharge he retorted</td>\n",
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       "      <td>0.439596</td>\n",
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       "      <th>5</th>\n",
       "      <td>with a couplet</td>\n",
       "      <td>neutral</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>They aimed at him constantly</td>\n",
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       "      <th>7</th>\n",
       "      <td>and always missed him</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.356069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The National Guardsmen and the soldiers laughe...</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.577372</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>He lay down</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.549929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sprang to his feet</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.538698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hid in the corner of a doorway</td>\n",
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       "      <td>0.526688</td>\n",
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       "      <th>12</th>\n",
       "      <td>then made a bound</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.557102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>disappeared</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.432546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>reappeared</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.378902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>scampered away</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.600264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>returned</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.388023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>replied to the grape-shot with his thumb at hi...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.605510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>and all the while went on pillaging the carto...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.689697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>emptying the cartridge-boxes</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.539302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>and filling his basket</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.552423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The insurgents, panting with anxiety</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.902625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>followed him with their eyes</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.583601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>The barricade trembled</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.881465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>He sang</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.435235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>He was not a  child</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.509320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>he was not a man;</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.557202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>he was a strange gamin-fairy</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.725722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>He might have been called the invulnerable dw...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.869947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>The bullets flew after him</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.651269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>he was more nimble than they</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.404263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>He played a fearful game of hide and seek wit...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.575891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>every time that the flat-nosed face of the spe...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.466925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>the urchin administered to it a fillip</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.485151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>One bullet however</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.447826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>better aimed or more treacherous than the rest</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.913327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>finally struck the will-o’-the-wisp of a child</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.657132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Gavroche was seen to stagger</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.570180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>then he sank to the earth</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.472120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>The whole barricade gave vent to a cry</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.667719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>but there was something of Antæus in that pygmy;</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.496371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>for the gamin to touch the pavement</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.412087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>is the same as for the giant to touch the earth;</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.548395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Gavroche had fallen only to rise again;</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.419321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>he remained in a sitting posture</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.512958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>a long thread of blood streaked his face</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.588501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>he raised both arms in the air</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.714935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>glanced in the direction whence the shot had ...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.654548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>And began to sing:</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>“I have fallen to the earth</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.745645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>’Tis the fault of Voltaire;</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.803486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>With my nose in the gutter</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.803199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>’Tis the fault of</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.843076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>He did not finish</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.834647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>A second bullet from the same marksman stoppe...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.709883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>This time he fell face downward on the pavement</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.519949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>and moved no more</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.541712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>This grand little soul had taken its flight</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.473830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text     label     score\n",
       "0                It was a charming and terrible sight  negative  0.952993\n",
       "1    Gavroche, though shot at, was teasing the fus...  negative  0.421055\n",
       "2            He had the air of being greatly diverted  positive  0.480614\n",
       "3         It was the sparrow pecking at the sportsmen  negative  0.527367\n",
       "4                       To each discharge he retorted   neutral  0.439596\n",
       "5                                      with a couplet   neutral  0.496840\n",
       "6                        They aimed at him constantly  negative  0.675386\n",
       "7                               and always missed him  negative  0.356069\n",
       "8   The National Guardsmen and the soldiers laughe...  positive  0.577372\n",
       "9                                         He lay down   neutral  0.549929\n",
       "10                                 sprang to his feet   neutral  0.538698\n",
       "11                     hid in the corner of a doorway   neutral  0.526688\n",
       "12                                  then made a bound   neutral  0.557102\n",
       "13                                        disappeared  negative  0.432546\n",
       "14                                         reappeared   neutral  0.378902\n",
       "15                                     scampered away  negative  0.600264\n",
       "16                                           returned   neutral  0.388023\n",
       "17  replied to the grape-shot with his thumb at hi...   neutral  0.605510\n",
       "18   and all the while went on pillaging the carto...  negative  0.689697\n",
       "19                       emptying the cartridge-boxes   neutral  0.539302\n",
       "20                             and filling his basket   neutral  0.552423\n",
       "21               The insurgents, panting with anxiety  negative  0.902625\n",
       "22                       followed him with their eyes   neutral  0.583601\n",
       "23                             The barricade trembled  negative  0.881465\n",
       "24                                            He sang   neutral  0.435235\n",
       "25                                He was not a  child   neutral  0.509320\n",
       "26                                  he was not a man;   neutral  0.557202\n",
       "27                       he was a strange gamin-fairy  negative  0.725722\n",
       "28   He might have been called the invulnerable dw...  negative  0.869947\n",
       "29                         The bullets flew after him  negative  0.651269\n",
       "30                       he was more nimble than they  negative  0.404263\n",
       "31   He played a fearful game of hide and seek wit...  negative  0.575891\n",
       "32  every time that the flat-nosed face of the spe...   neutral  0.466925\n",
       "33             the urchin administered to it a fillip   neutral  0.485151\n",
       "34                                 One bullet however   neutral  0.447826\n",
       "35     better aimed or more treacherous than the rest  negative  0.913327\n",
       "36     finally struck the will-o’-the-wisp of a child  positive  0.657132\n",
       "37                       Gavroche was seen to stagger  negative  0.570180\n",
       "38                          then he sank to the earth  negative  0.472120\n",
       "39             The whole barricade gave vent to a cry  negative  0.667719\n",
       "40   but there was something of Antæus in that pygmy;   neutral  0.496371\n",
       "41                for the gamin to touch the pavement   neutral  0.412087\n",
       "42   is the same as for the giant to touch the earth;   neutral  0.548395\n",
       "43            Gavroche had fallen only to rise again;  negative  0.419321\n",
       "44                   he remained in a sitting posture  negative  0.512958\n",
       "45           a long thread of blood streaked his face  negative  0.588501\n",
       "46                     he raised both arms in the air   neutral  0.714935\n",
       "47   glanced in the direction whence the shot had ...   neutral  0.654548\n",
       "48                                 And began to sing:   neutral  0.465736\n",
       "49                        “I have fallen to the earth  negative  0.745645\n",
       "50                        ’Tis the fault of Voltaire;  negative  0.803486\n",
       "51                         With my nose in the gutter  negative  0.803199\n",
       "52                                 ’Tis the fault of   negative  0.843076\n",
       "53                                  He did not finish  negative  0.834647\n",
       "54   A second bullet from the same marksman stoppe...  negative  0.709883\n",
       "55    This time he fell face downward on the pavement   neutral  0.519949\n",
       "56                                  and moved no more   neutral  0.541712\n",
       "57        This grand little soul had taken its flight  positive  0.473830"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf = pd.DataFrame(results)\n",
    "engdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d31397d-5c02-4d8f-8968-d49887c716fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive\n"
     ]
    }
   ],
   "source": [
    "# print(engdf[2]['label'])\n",
    "print(engdf.loc[2].at[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "76c67daa-fafb-42a7-894e-bf5b1e2a8993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(engdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cf8ee828-1dce-4411-acad-de16a73dd0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5888202323995787"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf2 = engdf['score'].mean()\n",
    "engdf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a50d4b-5882-442d-bce0-8bddc79398d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# france_array = []\n",
    "# england_array = []\n",
    "# germany_array = []\n",
    "# others_array = []\n",
    "# total_array = []\n",
    "\n",
    "# for q in consorts[\"places\"]:\n",
    "#     places = str(consorts['places'])\n",
    "#     total +=1\n",
    "#     if q == \"France\":\n",
    "#         france += 1     \n",
    "#     elif q == \"England\":\n",
    "#         england += 1\n",
    "        \n",
    "#     elif q == \"Germany\":\n",
    "#         germany += 1\n",
    "#     else:\n",
    "#         others += 1\n",
    "    \n",
    "#     total_array.append(total)\n",
    "#     france_array.append(france)\n",
    "#     england_array.append(england) \n",
    "#     germany_array.append(germany)\n",
    "#     others_array.append(others)\n",
    "\n",
    "#     consorts[\"France\"] = france_array\n",
    "#     consorts[\"Germany\"] = germany_array\n",
    "#     consorts[\"England\"] = england_array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ad1ec2e4-08bd-4d30-b02f-88690e192970",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[51], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m neuscore \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m engdf:\n\u001b[0;32m----> 9\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpositive\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m     10\u001b[0m         positives \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     11\u001b[0m         poscore \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "#DO NOT USE!! STUPIDLY COMPLEX! \n",
    "positives = 0\n",
    "poscore = 0\n",
    "negatives = 0\n",
    "negscore = 0\n",
    "neutrals = 0\n",
    "neuscore = 0\n",
    "\n",
    "for row in engdf:\n",
    "    if row['label']== 'positive':\n",
    "        positives +=1\n",
    "        poscore = int(row['score'])\n",
    "    elif row['label']== 'negative':\n",
    "        negatives +=1\n",
    "        negscore = int(row['score'])\n",
    "    elif row['label']== 'neutral':\n",
    "        neutrals +=1\n",
    "        neuscore = int(row['label'])\n",
    "print(positives, poscore)\n",
    "print(negatives, negscore)\n",
    "print(neutrals, neuscore)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f854223d-3760-4cad-8100-09aeeaaa6bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    28\n",
       "neutral     26\n",
       "positive     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4ef75a2f-a415-4e58-8f19-107ea5790035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    0.662522\n",
       "neutral     0.515846\n",
       "positive    0.547237\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf.groupby(['label'])['score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f940e28-cd99-4f51-9cec-438b06a392ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "326d44a6-2943-48da-8bb5-03ed617d611f",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Le spectacle était épouvantable et charmant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gavroche, fusillé, taquinait la fusillade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Il avait l'air de s'amuser beaucoup</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C'était le moineau becquetant les chasseurs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Il répondait à chaque décharge</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Le spectacle était épouvantable et charmant,  Gavroche, fusillé, taquinait la fusillade,  Il avait l'air de s'amuser beaucoup,  C'était le moineau becquetant les chasseurs,  Il répondait à chaque décharge]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf = pd.read_csv(\"books/french.csv\", index_col = 0)\n",
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "976199be-924b-41dc-a079-4d5d4f237b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le spectacle était épouvantable et charmant\n",
      "[{'label': 'positive', 'score': 0.9434570670127869}]\n",
      " Gavroche, fusillé, taquinait la fusillade\n",
      "[{'label': 'negative', 'score': 0.8871347904205322}]\n",
      " Il avait l'air de s'amuser beaucoup\n",
      "[{'label': 'positive', 'score': 0.9017786383628845}]\n",
      " C'était le moineau becquetant les chasseurs\n",
      "[{'label': 'negative', 'score': 0.795271635055542}]\n",
      " Il répondait à chaque décharge\n",
      "[{'label': 'neutral', 'score': 0.5504280924797058}]\n",
      "par un couplet\n",
      "[{'label': 'neutral', 'score': 0.5416300296783447}]\n",
      " On le visait sans cesse\n",
      "[{'label': 'positive', 'score': 0.413178950548172}]\n",
      " on le manquait toujours\n",
      "[{'label': 'negative', 'score': 0.6138972043991089}]\n",
      " Les gardes nationaux et les soldats riaient en l'ajustant\n",
      "[{'label': 'negative', 'score': 0.7059093713760376}]\n",
      " Il se couchait\n",
      "[{'label': 'negative', 'score': 0.7937528491020203}]\n",
      " puis se redressait\n",
      "[{'label': 'negative', 'score': 0.42073869705200195}]\n",
      " s'effaçait dans un coin de porte\n",
      "[{'label': 'negative', 'score': 0.7228686213493347}]\n",
      " puis bondissait\n",
      "[{'label': 'neutral', 'score': 0.41561421751976013}]\n",
      "disparaissait\n",
      "[{'label': 'negative', 'score': 0.47840720415115356}]\n",
      " reparaissait\n",
      "[{'label': 'negative', 'score': 0.35358038544654846}]\n",
      " se sauvait\n",
      "[{'label': 'positive', 'score': 0.35002902150154114}]\n",
      " revenait\n",
      "[{'label': 'neutral', 'score': 0.3778737485408783}]\n",
      " ripostait à la mitraille par des pieds de nez\n",
      "[{'label': 'negative', 'score': 0.49018725752830505}]\n",
      " et cependant pillait les cartouches\n",
      "[{'label': 'negative', 'score': 0.753360390663147}]\n",
      "vidait les gibernes\n",
      "[{'label': 'negative', 'score': 0.40373480319976807}]\n",
      "et remplissait son panier\n",
      "[{'label': 'neutral', 'score': 0.4249899387359619}]\n",
      " Les insurgés, haletants d'anxiété\n",
      "[{'label': 'negative', 'score': 0.9350647330284119}]\n",
      " le suivaient des yeux\n",
      "[{'label': 'negative', 'score': 0.5427963137626648}]\n",
      " La barricade tremblait;\n",
      "[{'label': 'negative', 'score': 0.8943210244178772}]\n",
      "Lui, il chantait\n",
      "[{'label': 'neutral', 'score': 0.5099213719367981}]\n",
      " Ce n'était pas un enfant\n",
      "[{'label': 'negative', 'score': 0.5122464895248413}]\n",
      " ce n'était pas un homme;\n",
      "[{'label': 'neutral', 'score': 0.4958876371383667}]\n",
      "c'était un étrange gamin fée\n",
      "[{'label': 'negative', 'score': 0.8780734539031982}]\n",
      " On eût dit le nain invulnérable de la mêlée\n",
      "[{'label': 'negative', 'score': 0.5914828181266785}]\n",
      " Les balles couraient après lui\n",
      "[{'label': 'negative', 'score': 0.5598840117454529}]\n",
      " il était plus leste qu'elles\n",
      "[{'label': 'negative', 'score': 0.683789074420929}]\n",
      " Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\n",
      "[{'label': 'negative', 'score': 0.9013264179229736}]\n",
      "chaque fois que la face camarde du spectre s'approchait\n",
      "[{'label': 'negative', 'score': 0.38270097970962524}]\n",
      " le gamin lui donnait une pichenette\n",
      "[{'label': 'neutral', 'score': 0.41927069425582886}]\n",
      "Une balle pourtant\n",
      "[{'label': 'negative', 'score': 0.41555628180503845}]\n",
      " mieux ajustée ou plus traître que les autres\n",
      "[{'label': 'negative', 'score': 0.4451725482940674}]\n",
      " finit par atteindre l'enfant feu follet\n",
      "[{'label': 'negative', 'score': 0.6089064478874207}]\n",
      " On vit Gavroche chanceler\n",
      "[{'label': 'positive', 'score': 0.41579997539520264}]\n",
      " puis il s’affaissa\n",
      "[{'label': 'negative', 'score': 0.7884112596511841}]\n",
      " Toute la barricade poussa un cri;\n",
      "[{'label': 'negative', 'score': 0.851386308670044}]\n",
      "mais il y avait de l'Antée dans ce pygmée;\n",
      "[{'label': 'negative', 'score': 0.5079840421676636}]\n",
      " pour le gamin toucher le pavé\n",
      "[{'label': 'negative', 'score': 0.4075724184513092}]\n",
      "c'est comme pour le géant toucher la terre;\n",
      "[{'label': 'neutral', 'score': 0.4176287353038788}]\n",
      "Gavroche n'était tombé que pour se redresser; il\n",
      "[{'label': 'negative', 'score': 0.4331025779247284}]\n",
      "resta assis sur son séant\n",
      "[{'label': 'negative', 'score': 0.41186630725860596}]\n",
      " un long filet de sang rayait son visage\n",
      "[{'label': 'negative', 'score': 0.41465386748313904}]\n",
      " il éleva ses deux bras en l’air\n",
      "[{'label': 'neutral', 'score': 0.47212541103363037}]\n",
      " regarda du côté d'où était venu le coup\n",
      "[{'label': 'neutral', 'score': 0.49433600902557373}]\n",
      "et se mit à chanter\n",
      "[{'label': 'neutral', 'score': 0.37322676181793213}]\n",
      "Je suis tombé par terre\n",
      "[{'label': 'negative', 'score': 0.8766723871231079}]\n",
      "C'est la faute à Voltaire\n",
      "[{'label': 'negative', 'score': 0.9520866870880127}]\n",
      "Le nez dans le ruisseau\n",
      "[{'label': 'negative', 'score': 0.4588301479816437}]\n",
      "C'est la faute à\n",
      "[{'label': 'negative', 'score': 0.9563672542572021}]\n",
      "Il n'acheva point\n",
      "[{'label': 'negative', 'score': 0.8897107839584351}]\n",
      " Une seconde balle du même tireur l'arrêta court\n",
      "[{'label': 'negative', 'score': 0.43102195858955383}]\n",
      "Cette fois il s'abattit la face contre le pavé\n",
      "[{'label': 'negative', 'score': 0.5315203070640564}]\n",
      " et ne remua plus\n",
      "[{'label': 'negative', 'score': 0.47912436723709106}]\n",
      " Cette petite grande âme venait de s'envoler\n",
      "[{'label': 'negative', 'score': 0.6855504512786865}]\n"
     ]
    }
   ],
   "source": [
    "fresults = []\n",
    "# for sentences in df:\n",
    "for index, row in fdf.iterrows():\n",
    "    dict = {}\n",
    "    sentiment_pipeline = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-xlm-roberta-base-sentiment\")\n",
    "    data = index \n",
    "    rating = sentiment_pipeline(data)\n",
    "    print(index)\n",
    "    print(rating)\n",
    "    #df['score'] = rating\n",
    "    # print(df['score'])\n",
    "    dict['text'] = index\n",
    "    dict['label'] = rating[0]['label']\n",
    "    dict['score'] = rating[0]['score']\n",
    "    fresults.append(dict)\n",
    "# print(f-results) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3e16bb94-2d25-41ee-8416-89bde8c492de",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le spectacle était épouvantable et charmant</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.943457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavroche, fusillé, taquinait la fusillade</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.887135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Il avait l'air de s'amuser beaucoup</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.901779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C'était le moineau becquetant les chasseurs</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.795272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Il répondait à chaque décharge</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.550428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>par un couplet</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.541630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>On le visait sans cesse</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.413179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>on le manquait toujours</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.613897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Les gardes nationaux et les soldats riaient e...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.705909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Il se couchait</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.793753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>puis se redressait</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.420739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s'effaçait dans un coin de porte</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.722869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>puis bondissait</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.415614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>disparaissait</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.478407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>reparaissait</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.353580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>se sauvait</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.350029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>revenait</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.377874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ripostait à la mitraille par des pieds de nez</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.490187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>et cependant pillait les cartouches</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.753360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>vidait les gibernes</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.403735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>et remplissait son panier</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.424990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Les insurgés, haletants d'anxiété</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.935065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>le suivaient des yeux</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.542796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>La barricade tremblait;</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.894321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lui, il chantait</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.509921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ce n'était pas un enfant</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.512246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ce n'était pas un homme;</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.495888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>c'était un étrange gamin fée</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.878073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>On eût dit le nain invulnérable de la mêlée</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.591483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Les balles couraient après lui</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.559884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>il était plus leste qu'elles</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.683789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Il jouait on ne sait quel effrayant jeu de ca...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.901326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>chaque fois que la face camarde du spectre s'a...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.382701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>le gamin lui donnait une pichenette</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.419271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Une balle pourtant</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.415556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mieux ajustée ou plus traître que les autres</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.445173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>finit par atteindre l'enfant feu follet</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.608906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>On vit Gavroche chanceler</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.415800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>puis il s’affaissa</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.788411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Toute la barricade poussa un cri;</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.851386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mais il y avait de l'Antée dans ce pygmée;</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.507984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>pour le gamin toucher le pavé</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.407572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>c'est comme pour le géant toucher la terre;</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.417629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Gavroche n'était tombé que pour se redresser; il</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.433103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>resta assis sur son séant</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.411866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>un long filet de sang rayait son visage</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.414654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>il éleva ses deux bras en l’air</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.472125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>regarda du côté d'où était venu le coup</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.494336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>et se mit à chanter</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.373227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Je suis tombé par terre</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.876672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>C'est la faute à Voltaire</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.952087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Le nez dans le ruisseau</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.458830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>C'est la faute à</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.956367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Il n'acheva point</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.889711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Une seconde balle du même tireur l'arrêta court</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.431022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Cette fois il s'abattit la face contre le pavé</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.531520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>et ne remua plus</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.479124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Cette petite grande âme venait de s'envoler</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.685550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text     label     score\n",
       "0         Le spectacle était épouvantable et charmant  positive  0.943457\n",
       "1           Gavroche, fusillé, taquinait la fusillade  negative  0.887135\n",
       "2                 Il avait l'air de s'amuser beaucoup  positive  0.901779\n",
       "3         C'était le moineau becquetant les chasseurs  negative  0.795272\n",
       "4                      Il répondait à chaque décharge   neutral  0.550428\n",
       "5                                      par un couplet   neutral  0.541630\n",
       "6                             On le visait sans cesse  positive  0.413179\n",
       "7                             on le manquait toujours  negative  0.613897\n",
       "8    Les gardes nationaux et les soldats riaient e...  negative  0.705909\n",
       "9                                      Il se couchait  negative  0.793753\n",
       "10                                 puis se redressait  negative  0.420739\n",
       "11                   s'effaçait dans un coin de porte  negative  0.722869\n",
       "12                                    puis bondissait   neutral  0.415614\n",
       "13                                      disparaissait  negative  0.478407\n",
       "14                                       reparaissait  negative  0.353580\n",
       "15                                         se sauvait  positive  0.350029\n",
       "16                                           revenait   neutral  0.377874\n",
       "17      ripostait à la mitraille par des pieds de nez  negative  0.490187\n",
       "18                et cependant pillait les cartouches  negative  0.753360\n",
       "19                                vidait les gibernes  negative  0.403735\n",
       "20                          et remplissait son panier   neutral  0.424990\n",
       "21                  Les insurgés, haletants d'anxiété  negative  0.935065\n",
       "22                              le suivaient des yeux  negative  0.542796\n",
       "23                            La barricade tremblait;  negative  0.894321\n",
       "24                                   Lui, il chantait   neutral  0.509921\n",
       "25                           Ce n'était pas un enfant  negative  0.512246\n",
       "26                           ce n'était pas un homme;   neutral  0.495888\n",
       "27                       c'était un étrange gamin fée  negative  0.878073\n",
       "28        On eût dit le nain invulnérable de la mêlée  negative  0.591483\n",
       "29                     Les balles couraient après lui  negative  0.559884\n",
       "30                       il était plus leste qu'elles  negative  0.683789\n",
       "31   Il jouait on ne sait quel effrayant jeu de ca...  negative  0.901326\n",
       "32  chaque fois que la face camarde du spectre s'a...  negative  0.382701\n",
       "33                le gamin lui donnait une pichenette   neutral  0.419271\n",
       "34                                 Une balle pourtant  negative  0.415556\n",
       "35       mieux ajustée ou plus traître que les autres  negative  0.445173\n",
       "36            finit par atteindre l'enfant feu follet  negative  0.608906\n",
       "37                          On vit Gavroche chanceler  positive  0.415800\n",
       "38                                 puis il s’affaissa  negative  0.788411\n",
       "39                  Toute la barricade poussa un cri;  negative  0.851386\n",
       "40         mais il y avait de l'Antée dans ce pygmée;  negative  0.507984\n",
       "41                      pour le gamin toucher le pavé  negative  0.407572\n",
       "42        c'est comme pour le géant toucher la terre;   neutral  0.417629\n",
       "43   Gavroche n'était tombé que pour se redresser; il  negative  0.433103\n",
       "44                          resta assis sur son séant  negative  0.411866\n",
       "45            un long filet de sang rayait son visage  negative  0.414654\n",
       "46                    il éleva ses deux bras en l’air   neutral  0.472125\n",
       "47            regarda du côté d'où était venu le coup   neutral  0.494336\n",
       "48                                et se mit à chanter   neutral  0.373227\n",
       "49                            Je suis tombé par terre  negative  0.876672\n",
       "50                          C'est la faute à Voltaire  negative  0.952087\n",
       "51                            Le nez dans le ruisseau  negative  0.458830\n",
       "52                                   C'est la faute à  negative  0.956367\n",
       "53                                  Il n'acheva point  negative  0.889711\n",
       "54    Une seconde balle du même tireur l'arrêta court  negative  0.431022\n",
       "55     Cette fois il s'abattit la face contre le pavé  negative  0.531520\n",
       "56                                   et ne remua plus  negative  0.479124\n",
       "57        Cette petite grande âme venait de s'envoler  negative  0.685550"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frdf = pd.DataFrame(fresults)\n",
    "frdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "84305458-479d-427d-a58a-903cc73aca1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5924689867373171"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frdf2 = frdf['score'].mean()\n",
    "frdf2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2962957e-b0c5-468c-aab7-a7aeceb6a6e7",
   "metadata": {},
   "source": [
    "## The French original is overwhelmingly negative -- 41 out of 58 labels; only 12 neutrals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fb4af4f7-7bfd-4f59-a665-5b1d2ffdaaea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    41\n",
       "neutral     12\n",
       "positive     5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frdf['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5538dfb5-c896-4ea7-8e09-0e24e237f20f",
   "metadata": {},
   "source": [
    "## The English translation has almost the same number of negatives (28) and neutrals (26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c76b209a-a6ae-4df3-a478-244cfd7c1fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    28\n",
       "neutral     26\n",
       "positive     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5f532a-87bb-4c53-90ea-21b3e37c5967",
   "metadata": {},
   "source": [
    "### French labels and scores: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "364823b6-2528-4be2-bdbf-b5f339ab6d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    0.630391\n",
       "neutral     0.457744\n",
       "positive    0.604849\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frdf.groupby(['label'])['score'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58fe0978-d600-441d-869c-f7a524a6e72b",
   "metadata": {},
   "source": [
    "### English labels and scores: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "67b30806-43c4-4930-a98a-c1dc743b54db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "negative    0.662522\n",
       "neutral     0.515846\n",
       "positive    0.547237\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engdf.groupby(['label'])['score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95fe712-e260-446b-a4b7-71b459463269",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cceaa5d4-0957-47b7-8ba0-589d6c6611a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "frdf.to_csv(\"hugo_fr_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e5e19df6-51bd-40a1-b161-4fe60b269621",
   "metadata": {},
   "outputs": [],
   "source": [
    "engdf.to_csv(\"hugo_en_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fbcbf6c-7041-4b5e-b42b-b5992d88d4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d55fa8a-af44-42b9-95e8-4c507a9ba6bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a206d155-637e-4147-b15d-7de03d8e2094",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "for items in french_df: \n",
    "        \n",
    "texts = [xxxx]\n",
    "\n",
    "places = ['France', 'England', 'England', 'England', 'Spain', \n",
    "          'England', 'England', 'Germany', 'England', 'England',\n",
    "          'Denmark', 'France']\n",
    "\n",
    "# ones = [1] * len(names)  \n",
    "\n",
    "df = pd.DataFrame({'Name': names, 'places': places, 'ones': ones})\n",
    "\n",
    "custom_colors = {'France': '#0068ff',\n",
    "                 'Spain': '#008838',\n",
    "                 'England': '#ff59c4',\n",
    "                 'Germany': '#fa8c00',\n",
    "                 'Denmark': '#b67ac9'}\n",
    "\n",
    "color_strentgh = {\"score\":\n",
    "    \n",
    "}\n",
    "\n",
    "ax = sns.barplot(x='Name', y='ones', data=df, hue='places', palette=custom_colors, color_strength)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1,1), loc=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e00c57a7-f4ee-401b-abf5-76249b10828e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c20aa3af-e9ab-4abd-a16f-15a06f347303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2db4161a0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "\n",
    "# Map labels to colors\n",
    "colors = []\n",
    "for label in df['label']:\n",
    "  if label == 'negative':\n",
    "    colors.append('red') \n",
    "  elif label == 'neutral':\n",
    "    colors.append('yellow')\n",
    "  else:\n",
    "    colors.append('blue')\n",
    "\n",
    "\n",
    "# Convert to RGBA values\n",
    "rgba_colors = [mcolors.to_rgba(c) for c in colors]\n",
    "\n",
    "# Plot heatmap \n",
    "plt.imshow([rgba_colors], aspect='auto')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a140bede-83f9-4111-aa19-803494dc82ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2c5bc9b70>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "# Map labels to colors\n",
    "colors = []\n",
    "for label in df['label']:\n",
    "  if label == 'negative':\n",
    "    colors.append('red') \n",
    "  elif label == 'neutral':\n",
    "    colors.append('yellow')\n",
    "  else:\n",
    "    colors.append('blue')\n",
    "\n",
    "rgba_colors = []\n",
    "for color, score in zip(colors, df['score']):\n",
    "  # rgba = mcolors.to_rgba(color)\n",
    "  # rgba[3] = score # Set alpha channel to score\n",
    "  # rgba_colors.append(rgba)\n",
    "    r, g, b = mcolors.to_rgb(color)\n",
    "    rgba_color = [r, g, b, score]\n",
    "    rgba_colors.append(rgba_color)\n",
    "\n",
    "plt.imshow([rgba_colors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "865dfea4-51b6-48d3-beee-3d2e9c390b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "# Create figure \n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "y_pos = range(len(df)) \n",
    "\n",
    "for i, row in df.iterrows():\n",
    "\n",
    "    #tuple error:\n",
    "  # color = colors[i] \n",
    "  # rgba = mcolors.to_rgba(color)\n",
    "  # rgba[3] = row['score']  \n",
    "      \n",
    "# Get RGB values  \n",
    "  r, g, b = mcolors.to_rgb(colors[i])\n",
    "  \n",
    "  # Build RGBA array\n",
    "  rgba = [r, g, b, row['score']] \n",
    "\n",
    "  # Add horizontal bar\n",
    "  ax.barh(y=i, width=1, height=0.5, color=rgba, align='center')\n",
    "\n",
    "  # Add text\n",
    "  ax.text(1.1, i, row['text'], va='center')\n",
    "\n",
    "plt.yticks(y_pos)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d77cdb50-802c-4510-87d6-201b4d95fce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "# Create figure \n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "y_pos = range(len(df)) \n",
    "\n",
    "bar_height = 0.5 # Set larger height\n",
    "\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "      \n",
    "# Get RGB values  \n",
    "  r, g, b = mcolors.to_rgb(colors[i])  \n",
    "# Build RGBA array\n",
    "  rgba = [r, g, b, row['score']] \n",
    "\n",
    "\n",
    "  # Draw background bar\n",
    "#  ax.barh(y=i, width=1, height=0.5, color=rgba, align='center')\n",
    "    \n",
    "    # Draw TALLER  bar \n",
    "  ax.barh(y=i, width=1, height=bar_height, color=rgba, align='center')  \n",
    "\n",
    "\n",
    "  \n",
    "  #the lines below generated a typeerror \n",
    "# Overlay text\n",
    "  # ax.text(0.5, i, row['text'], fontsize=14, va='center', ha='right',\n",
    "  #         bbox=dict(boxstyle=\"square\", ec=rgba, fc=rgba)) \n",
    "\n",
    "#new attempt, also unworking: \n",
    "# Define bbox dict\n",
    "# bbox_dict = dict(boxstyle=\"square\", ec=rgba, fc=rgba)\n",
    "# # Pass to text\n",
    "# ax.text(0.5, i, row['text'], fontsize=14, va='center', ha='right',\n",
    "#         bbox=bbox_dict)\n",
    "\n",
    "#First we need to define a boxstyle. This can just be a string:\n",
    "boxstyle = \"square\"\n",
    "#Then we build the bbox dict using that boxstyle, and the RGBA color:\n",
    "bbox_dict = {'boxstyle': boxstyle, 'ec': rgba, 'fc': rgba}\n",
    "#Now bbox_dict is a proper dictionary that can be passed to ax.text:\n",
    "#ax.text(0.5, i, row['text'], fontsize=14, va='center', ha='right', bbox=bbox_dict)\n",
    "\n",
    "  # Display text\n",
    "ax.text(0.5, i, row['text'], fontsize=14, verticalalignment='center', \n",
    "         horizontalalignment='right', bbox=bbox_dict)\n",
    "\n",
    "# Set larger y-axis ticks   \n",
    "plt.yticks(y_pos, fontsize=14) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0567deae-5867-4055-852b-2eba89fe52c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODEL: \n",
    "# import altair as alt\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "\n",
    "# # Compute x^2 + y^2 across a 2D grid\n",
    "# x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n",
    "# z = x ** 2 + y ** 2\n",
    "\n",
    "# # Convert this grid to columnar data expected by Altair\n",
    "# source = pd.DataFrame({'x': x.ravel(),\n",
    "#                      'y': y.ravel(),\n",
    "#                      'z': z.ravel()})\n",
    "\n",
    "# alt.Chart(source).mark_rect().encode(\n",
    "#     x='x:O',\n",
    "#     y='y:O',\n",
    "#     color='z:Q'\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1f7615a4-d2b9-47fb-9779-967e1cd9da3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div id=\"altair-viz-503190a7b54b42de90c2e44865aa3dbd\"></div>\n",
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       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.8.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
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       "    function maybeLoadScript(lib, version) {\n",
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       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
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       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
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       "      maybeLoadScript(\"vega\", \"5\")\n",
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{\"data-3ec4487d306fa7c7e56dce6c7b565521\": [{\"text\": \"Le spectacle \\u00e9tait \\u00e9pouvantable et charmant\", \"label\": \"positive\", \"score\": 0.943457}, {\"text\": \"Gavroche, fusill\\u00e9, taquinait la fusillade\", \"label\": \"negative\", \"score\": 0.887135}, {\"text\": \"Il avait l'air de s'amuser beaucoup\", \"label\": \"positive\", \"score\": 0.901779}, {\"text\": \"C'\\u00e9tait le moineau becquetant les chasseurs\", \"label\": \"negative\", \"score\": 0.795272}, {\"text\": \"Il r\\u00e9pondait \\u00e0 chaque d\\u00e9charge\", \"label\": \"neutral\", \"score\": 0.550428}, {\"text\": \"par un couplet\", \"label\": \"neutral\", \"score\": 0.54163}, {\"text\": \"On le visait sans cesse\", \"label\": \"positive\", \"score\": 0.413179}, {\"text\": \"on le manquait toujours\", \"label\": \"negative\", \"score\": 0.613897}, {\"text\": \"Les gardes nationaux et les soldats riaient en l'ajustant\", \"label\": \"negative\", \"score\": 0.705909}, {\"text\": \"Il se couchait\", \"label\": \"negative\", \"score\": 0.793753}, {\"text\": \"puis se redressait\", \"label\": \"negative\", \"score\": 0.420739}, {\"text\": \"s'effa\\u00e7ait dans un coin de porte\", \"label\": \"negative\", \"score\": 0.722869}, {\"text\": \"puis bondissait\", \"label\": \"neutral\", \"score\": 0.415614}, {\"text\": \"disparaissait\", \"label\": \"negative\", \"score\": 0.478407}, {\"text\": \"reparaissait\", \"label\": \"negative\", \"score\": 0.35358}, {\"text\": \"se sauvait\", \"label\": \"positive\", \"score\": 0.350029}, {\"text\": \"revenait\", \"label\": \"neutral\", \"score\": 0.377874}, {\"text\": \"ripostait \\u00e0 la mitraille par des pieds de nez\", \"label\": \"negative\", \"score\": 0.490187}, {\"text\": \"et cependant pillait les cartouches\", \"label\": \"negative\", \"score\": 0.75336}, {\"text\": \"vidait les gibernes\", \"label\": \"negative\", \"score\": 0.403735}, {\"text\": \"et remplissait son panier\", \"label\": \"neutral\", \"score\": 0.42499}, {\"text\": \"Les insurg\\u00e9s, haletants d'anxi\\u00e9t\\u00e9\", \"label\": \"negative\", \"score\": 0.935065}, {\"text\": \"le suivaient des yeux\", \"label\": \"negative\", \"score\": 0.542796}, {\"text\": \"La barricade tremblait;\", \"label\": \"negative\", \"score\": 0.894321}, {\"text\": \"Lui, il chantait\", \"label\": \"negative\", \"score\": 0.509921}, {\"text\": \"Ce n'\\u00e9tait pas un enfant\", \"label\": \"neutral\", \"score\": 0.512246}, {\"text\": \"ce n'\\u00e9tait pas un homme;\", \"label\": \"negative\", \"score\": 0.495888}, {\"text\": \"c'\\u00e9tait un \\u00e9trange gamin f\\u00e9e\", \"label\": \"neutral\", \"score\": 0.878073}, {\"text\": \"On e\\u00fbt dit le nain invuln\\u00e9rable de la m\\u00eal\\u00e9e\", \"label\": \"negative\", \"score\": 0.591483}, {\"text\": \"Les balles couraient apr\\u00e8s lui\", \"label\": \"negative\", \"score\": 0.559884}, {\"text\": \"il \\u00e9tait plus leste qu'elles\", \"label\": \"negative\", \"score\": 0.683789}, {\"text\": \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\", \"label\": \"negative\", \"score\": 0.901326}, {\"text\": \"chaque fois que la face camarde du spectre s'approchait\", \"label\": \"negative\", \"score\": 0.382701}, {\"text\": \"le gamin lui donnait une pichenette\", \"label\": \"neutral\", \"score\": 0.419271}, {\"text\": \"Une balle pourtant\", \"label\": \"negative\", \"score\": 0.415556}, {\"text\": \"mieux ajust\\u00e9e ou plus tra\\u00eetre que les autres\", \"label\": \"negative\", \"score\": 0.445173}, {\"text\": \"finit par atteindre l'enfant feu follet\", \"label\": \"negative\", \"score\": 0.608906}, {\"text\": \"On vit Gavroche chanceler\", \"label\": \"positive\", \"score\": 0.4158}, {\"text\": \"puis il s\\u2019affaissa\", \"label\": \"negative\", \"score\": 0.788411}, {\"text\": \"Toute la barricade poussa un cri;\", \"label\": \"negative\", \"score\": 0.851386}, {\"text\": \"mais il y avait de l'Ant\\u00e9e dans ce pygm\\u00e9e;\", \"label\": \"negative\", \"score\": 0.507984}, {\"text\": \"pour le gamin toucher le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.407572}, {\"text\": \"c'est comme pour le g\\u00e9ant toucher la terre;\", \"label\": \"neutral\", \"score\": 0.417629}, {\"text\": \"Gavroche n'\\u00e9tait tomb\\u00e9 que pour se redresser; il\", \"label\": \"negative\", \"score\": 0.433103}, {\"text\": \"resta assis sur son s\\u00e9ant\", \"label\": \"neutral\", \"score\": 0.411866}, {\"text\": \"un long filet de sang rayait son visage\", \"label\": \"neutral\", \"score\": 0.414654}, {\"text\": \"il \\u00e9leva ses deux bras en l\\u2019air\", \"label\": \"negative\", \"score\": 0.472125}, {\"text\": \"regarda du c\\u00f4t\\u00e9 d'o\\u00f9 \\u00e9tait venu le coup\", \"label\": \"negative\", \"score\": 0.494336}, {\"text\": \"et se mit \\u00e0 chanter\", \"label\": \"negative\", \"score\": 0.373227}, {\"text\": \"Je suis tomb\\u00e9 par terre\", \"label\": \"negative\", \"score\": 0.876672}, {\"text\": \"C'est la faute \\u00e0 Voltaire\", \"label\": \"negative\", \"score\": 0.952087}, {\"text\": \"Le nez dans le ruisseau\", \"label\": \"neutral\", \"score\": 0.45883}, {\"text\": \"C'est la faute \\u00e0\", \"label\": \"neutral\", \"score\": 0.956367}, {\"text\": \"Il n'acheva point\", \"label\": \"negative\", \"score\": 0.889711}, {\"text\": \"Une seconde balle du m\\u00eame tireur l'arr\\u00eata court\", \"label\": \"negative\", \"score\": 0.431022}, {\"text\": \"Cette fois il s'abattit la face contre le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.53152}, {\"text\": \"et ne remua plus\", \"label\": \"negative\", \"score\": 0.479124}, {\"text\": \"Cette petite grande \\u00e2me venait de s'envoler\", \"label\": \"negative\", \"score\": 0.68555}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mcolors\n",
    "import plotly.graph_objects as go\n",
    "import altair as alt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "\n",
    "# Define color mappings\n",
    "color_scale = alt.Scale(domain=['negative', 'neutral', 'positive'],\n",
    "                        range=['red', 'yellow', 'blue'])\n",
    "opacity_scale = alt.Scale(domain=[0, 1], range=[0.2, 1])\n",
    "\n",
    "# Create the Altair chart\n",
    "heatmap = alt.Chart(df).mark_bar().encode(\n",
    "    y=alt.Y('text:N', title='Text'),\n",
    "    x=alt.X('label:N', title='Label', sort=['negative', 'neutral', 'positive']),\n",
    "    color=alt.Color('label:N', scale=color_scale),\n",
    "    opacity=alt.Opacity('score:Q', scale=opacity_scale),\n",
    "    tooltip=['text', 'label', 'score']\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=600\n",
    ")\n",
    "\n",
    "heatmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cf4c77b4-180a-4709-b7cf-8e8d1ad9c2fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "Requirement already satisfied: altair in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (5.0.1)\n",
      "Requirement already satisfied: jinja2 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (3.1.2)\n",
      "Requirement already satisfied: jsonschema>=3.0 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (4.18.4)\n",
      "Requirement already satisfied: numpy in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (1.24.3)\n",
      "Requirement already satisfied: pandas>=0.18 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (2.0.3)\n",
      "Requirement already satisfied: toolz in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (0.12.0)\n",
      "Requirement already satisfied: typing-extensions>=4.0.1 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from altair) (4.5.0)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from jsonschema>=3.0->altair) (23.1.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from jsonschema>=3.0->altair) (2023.7.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from jsonschema>=3.0->altair) (0.30.0)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from jsonschema>=3.0->altair) (0.9.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from pandas>=0.18->altair) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from pandas>=0.18->altair) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from pandas>=0.18->altair) (2023.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from jinja2->altair) (2.1.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/mfhan/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=0.18->altair) (1.16.0)\n",
      "\u001b[33mDEPRECATION: dexplot 0.1.4 has a non-standard dependency specifier scipy>=1.0matplotlib>=3.1. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of dexplot or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade altair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "025a1576-e9a5-4939-b0bf-dbc41b11c658",
   "metadata": {},
   "outputs": [
    {
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\"width\": 150}, \"transform\": [{\"calculate\": \"'All'\", \"as\": \"dummy\"}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"datasets\": {\"data-3ec4487d306fa7c7e56dce6c7b565521\": [{\"text\": \"Le spectacle \\u00e9tait \\u00e9pouvantable et charmant\", \"label\": \"positive\", \"score\": 0.943457}, {\"text\": \"Gavroche, fusill\\u00e9, taquinait la fusillade\", \"label\": \"negative\", \"score\": 0.887135}, {\"text\": \"Il avait l'air de s'amuser beaucoup\", \"label\": \"positive\", \"score\": 0.901779}, {\"text\": \"C'\\u00e9tait le moineau becquetant les chasseurs\", \"label\": \"negative\", \"score\": 0.795272}, {\"text\": \"Il r\\u00e9pondait \\u00e0 chaque d\\u00e9charge\", \"label\": \"neutral\", \"score\": 0.550428}, {\"text\": \"par un couplet\", \"label\": \"neutral\", \"score\": 0.54163}, {\"text\": \"On le visait sans cesse\", \"label\": \"positive\", \"score\": 0.413179}, {\"text\": \"on le manquait toujours\", \"label\": \"negative\", \"score\": 0.613897}, {\"text\": \"Les gardes nationaux et les soldats riaient en l'ajustant\", \"label\": \"negative\", \"score\": 0.705909}, {\"text\": \"Il se couchait\", \"label\": \"negative\", \"score\": 0.793753}, {\"text\": \"puis se redressait\", \"label\": \"negative\", \"score\": 0.420739}, {\"text\": \"s'effa\\u00e7ait dans un coin de porte\", \"label\": \"negative\", \"score\": 0.722869}, {\"text\": \"puis bondissait\", \"label\": \"neutral\", \"score\": 0.415614}, {\"text\": \"disparaissait\", \"label\": \"negative\", \"score\": 0.478407}, {\"text\": \"reparaissait\", \"label\": \"negative\", \"score\": 0.35358}, {\"text\": \"se sauvait\", \"label\": \"positive\", \"score\": 0.350029}, {\"text\": \"revenait\", \"label\": \"neutral\", \"score\": 0.377874}, {\"text\": \"ripostait \\u00e0 la mitraille par des pieds de nez\", \"label\": \"negative\", \"score\": 0.490187}, {\"text\": \"et cependant pillait les cartouches\", \"label\": \"negative\", \"score\": 0.75336}, {\"text\": \"vidait les gibernes\", \"label\": \"negative\", \"score\": 0.403735}, {\"text\": \"et remplissait son panier\", \"label\": \"neutral\", \"score\": 0.42499}, {\"text\": \"Les insurg\\u00e9s, haletants d'anxi\\u00e9t\\u00e9\", \"label\": \"negative\", \"score\": 0.935065}, {\"text\": \"le suivaient des yeux\", \"label\": \"negative\", \"score\": 0.542796}, {\"text\": \"La barricade tremblait;\", \"label\": \"negative\", \"score\": 0.894321}, {\"text\": \"Lui, il chantait\", \"label\": \"negative\", \"score\": 0.509921}, {\"text\": \"Ce n'\\u00e9tait pas un enfant\", \"label\": \"neutral\", \"score\": 0.512246}, {\"text\": \"ce n'\\u00e9tait pas un homme;\", \"label\": \"negative\", \"score\": 0.495888}, {\"text\": \"c'\\u00e9tait un \\u00e9trange gamin f\\u00e9e\", \"label\": \"neutral\", \"score\": 0.878073}, {\"text\": \"On e\\u00fbt dit le nain invuln\\u00e9rable de la m\\u00eal\\u00e9e\", \"label\": \"negative\", \"score\": 0.591483}, {\"text\": \"Les balles couraient apr\\u00e8s lui\", \"label\": \"negative\", \"score\": 0.559884}, {\"text\": \"il \\u00e9tait plus leste qu'elles\", \"label\": \"negative\", \"score\": 0.683789}, {\"text\": \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\", \"label\": \"negative\", \"score\": 0.901326}, {\"text\": \"chaque fois que la face camarde du spectre s'approchait\", \"label\": \"negative\", \"score\": 0.382701}, {\"text\": \"le gamin lui donnait une pichenette\", \"label\": \"neutral\", \"score\": 0.419271}, {\"text\": \"Une balle pourtant\", \"label\": \"negative\", \"score\": 0.415556}, {\"text\": \"mieux ajust\\u00e9e ou plus tra\\u00eetre que les autres\", \"label\": \"negative\", \"score\": 0.445173}, {\"text\": \"finit par atteindre l'enfant feu follet\", \"label\": \"negative\", \"score\": 0.608906}, {\"text\": \"On vit Gavroche chanceler\", \"label\": \"positive\", \"score\": 0.4158}, {\"text\": \"puis il s\\u2019affaissa\", \"label\": \"negative\", \"score\": 0.788411}, {\"text\": \"Toute la barricade poussa un cri;\", \"label\": \"negative\", \"score\": 0.851386}, {\"text\": \"mais il y avait de l'Ant\\u00e9e dans ce pygm\\u00e9e;\", \"label\": \"negative\", \"score\": 0.507984}, {\"text\": \"pour le gamin toucher le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.407572}, {\"text\": \"c'est comme pour le g\\u00e9ant toucher la terre;\", \"label\": \"neutral\", \"score\": 0.417629}, {\"text\": \"Gavroche n'\\u00e9tait tomb\\u00e9 que pour se redresser; il\", \"label\": \"negative\", \"score\": 0.433103}, {\"text\": \"resta assis sur son s\\u00e9ant\", \"label\": \"neutral\", \"score\": 0.411866}, {\"text\": \"un long filet de sang rayait son visage\", \"label\": \"neutral\", \"score\": 0.414654}, {\"text\": \"il \\u00e9leva ses deux bras en l\\u2019air\", \"label\": \"negative\", \"score\": 0.472125}, {\"text\": \"regarda du c\\u00f4t\\u00e9 d'o\\u00f9 \\u00e9tait venu le coup\", \"label\": \"negative\", \"score\": 0.494336}, {\"text\": \"et se mit \\u00e0 chanter\", \"label\": \"negative\", \"score\": 0.373227}, {\"text\": \"Je suis tomb\\u00e9 par terre\", \"label\": \"negative\", \"score\": 0.876672}, {\"text\": \"C'est la faute \\u00e0 Voltaire\", \"label\": \"negative\", \"score\": 0.952087}, {\"text\": \"Le nez dans le ruisseau\", \"label\": \"neutral\", \"score\": 0.45883}, {\"text\": \"C'est la faute \\u00e0\", \"label\": \"neutral\", \"score\": 0.956367}, {\"text\": \"Il n'acheva point\", \"label\": \"negative\", \"score\": 0.889711}, {\"text\": \"Une seconde balle du m\\u00eame tireur l'arr\\u00eata court\", \"label\": \"negative\", \"score\": 0.431022}, {\"text\": \"Cette fois il s'abattit la face contre le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.53152}, {\"text\": \"et ne remua plus\", \"label\": \"negative\", \"score\": 0.479124}, {\"text\": \"Cette petite grande \\u00e2me venait de s'envoler\", \"label\": \"negative\", \"score\": 0.68555}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "\n",
    "# # Define color scale for positive values (blue shades)\n",
    "# blue_scale = alt.Scale(domain=(0, 1), range=[\"#f0f9e8\", \"#a1d76a\", \"#31a354\", \"#006d2c\"])\n",
    "\n",
    "# # Define color scale for negative values (red shades)\n",
    "# red_scale = alt.Scale(domain=(0, 1), range=[\"#fef0d9\", \"#fdcc8a\", \"#fc8d59\", \"#d7301f\"])\n",
    "\n",
    "# # Define color scale for neutral values (yellow shades)\n",
    "# yellow_scale = alt.Scale(domain=(0, 1), range=[\"#ffffcc\", \"#ffeda0\", \"#fed976\", \"#feb24c\"])\n",
    "\n",
    "# # Create the Altair chart\n",
    "# heatmap = alt.Chart(df).mark_bar().encode(\n",
    "#     y=alt.Y('text:N', title='Text', sort=None),\n",
    "#     x=alt.X('label:N', title='Label', sort=['negative', 'neutral', 'positive']),\n",
    "#     color=alt.Color('label:N', scale=alt.Scale(domain=['negative', 'neutral', 'positive'], range=['red', 'yellow', 'blue'])),\n",
    "#     opacity=alt.Opacity('score:Q', scale=alt.Scale(scheme='viridis')),\n",
    "#     tooltip=['text', 'label', 'score']\n",
    "# ).properties(\n",
    "#     width=600,\n",
    "#     height=600\n",
    "# )\n",
    "\n",
    "#this resulted in an all-orange affair: \n",
    "# # Create a quantile scale for color to get natural breaks\n",
    "# color_scale = alt.Scale(\n",
    "#     domain=[0, 0.2, 0.4, 0.6, 0.8, 1],\n",
    "#     range=['#f0f8ff', '#faebd7', '#cd5c5c', '#ff4500', '#8b0000', '#800000']\n",
    "# )\n",
    "\n",
    "# # Create the Altair chart\n",
    "# heatmap = alt.Chart(df).mark_bar().encode(\n",
    "#     y=alt.Y('text:N', title='Text', sort=None),\n",
    "#     x=alt.X('label:N', title='Label', sort=['negative', 'neutral', 'positive']),\n",
    "#     color=alt.Color('score:Q', scale=color_scale),\n",
    "#     tooltip=['text', 'label', 'score']\n",
    "# ).properties(\n",
    "#     width=600,\n",
    "#     height=600\n",
    "# )\n",
    "\n",
    "\n",
    "\n",
    "# Define color mappings\n",
    "color_scale = alt.Scale(domain=['negative', 'neutral', 'positive'],\n",
    "                        range=['red', 'yellow', 'blue'])\n",
    "opacity_scale = alt.Scale(domain=[0, 1], range=[0.8, 1])\n",
    "\n",
    "# Create the Altair chart\n",
    "heatmap = alt.Chart(df).mark_bar().encode(\n",
    "    y=alt.Y('text:N', title='Text', sort=None),\n",
    "    x=alt.X('label:N', title='Label', sort=['negative', 'neutral', 'positive']),\n",
    "    color=alt.Color('label:N', scale=color_scale),\n",
    "    opacity=alt.Opacity('score:Q', scale=opacity_scale),\n",
    "    tooltip=['text', 'label', 'score']\n",
    ").properties(\n",
    "    width=150,\n",
    "    height=600\n",
    ").facet(\n",
    "    column=alt.Column('dummy:N', header=None),  # Create a dummy column to align all bars in a single column\n",
    ").transform_calculate(\n",
    "    dummy=\"'All'\"  # A dummy value to create a single column\n",
    ")\n",
    "\n",
    "heatmap.configure_view(strokeOpacity=0)  # Remove gridlines and axis ticks\n",
    "\n",
    "heatmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "685b8394-c554-4c91-91a4-2787b416f226",
   "metadata": {},
   "outputs": [
    {
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       "        .then(() => displayChart(vegaEmbed));\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-c32579d48b0a56abf7c984e73fd573ce\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"color\": {\"field\": \"color\", \"scale\": null, \"type\": \"nominal\"}, \"opacity\": {\"field\": \"score\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"text\", \"type\": \"nominal\"}, {\"field\": \"label\", \"type\": \"ordinal\"}, {\"field\": \"score\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"text\", \"type\": \"ordinal\"}}, \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"datasets\": {\"data-c32579d48b0a56abf7c984e73fd573ce\": [{\"text\": \"Le spectacle \\u00e9tait \\u00e9pouvantable et charmant\", \"label\": \"positive\", \"score\": 0.943457, \"color\": \"blue\"}, {\"text\": \"Gavroche, fusill\\u00e9, taquinait la fusillade\", \"label\": \"negative\", \"score\": 0.887135, \"color\": \"red\"}, {\"text\": \"Il avait l'air de s'amuser beaucoup\", \"label\": \"positive\", \"score\": 0.901779, \"color\": \"blue\"}, {\"text\": \"C'\\u00e9tait le moineau becquetant les chasseurs\", \"label\": \"negative\", \"score\": 0.795272, \"color\": \"red\"}, {\"text\": \"Il r\\u00e9pondait \\u00e0 chaque d\\u00e9charge\", \"label\": \"neutral\", \"score\": 0.550428, \"color\": \"yellow\"}, {\"text\": \"par un couplet\", \"label\": \"neutral\", \"score\": 0.54163, \"color\": \"yellow\"}, {\"text\": \"On le visait sans cesse\", \"label\": \"positive\", \"score\": 0.413179, \"color\": \"blue\"}, {\"text\": \"on le manquait toujours\", \"label\": \"negative\", \"score\": 0.613897, \"color\": \"red\"}, {\"text\": \"Les gardes nationaux et les soldats riaient en l'ajustant\", \"label\": \"negative\", \"score\": 0.705909, \"color\": \"red\"}, {\"text\": \"Il se couchait\", \"label\": \"negative\", \"score\": 0.793753, \"color\": \"red\"}, {\"text\": \"puis se redressait\", \"label\": \"negative\", \"score\": 0.420739, \"color\": \"red\"}, {\"text\": \"s'effa\\u00e7ait dans un coin de porte\", \"label\": \"negative\", \"score\": 0.722869, \"color\": \"red\"}, {\"text\": \"puis bondissait\", \"label\": \"neutral\", \"score\": 0.415614, \"color\": \"yellow\"}, {\"text\": \"disparaissait\", \"label\": \"negative\", \"score\": 0.478407, \"color\": \"red\"}, {\"text\": \"reparaissait\", \"label\": \"negative\", \"score\": 0.35358, \"color\": \"red\"}, {\"text\": \"se sauvait\", \"label\": \"positive\", \"score\": 0.350029, \"color\": \"blue\"}, {\"text\": \"revenait\", \"label\": \"neutral\", \"score\": 0.377874, \"color\": \"yellow\"}, {\"text\": \"ripostait \\u00e0 la mitraille par des pieds de nez\", \"label\": \"negative\", \"score\": 0.490187, \"color\": \"red\"}, {\"text\": \"et cependant pillait les cartouches\", \"label\": \"negative\", \"score\": 0.75336, \"color\": \"red\"}, {\"text\": \"vidait les gibernes\", \"label\": \"negative\", \"score\": 0.403735, \"color\": \"red\"}, {\"text\": \"et remplissait son panier\", \"label\": \"neutral\", \"score\": 0.42499, \"color\": \"yellow\"}, {\"text\": \"Les insurg\\u00e9s, haletants d'anxi\\u00e9t\\u00e9\", \"label\": \"negative\", \"score\": 0.935065, \"color\": \"red\"}, {\"text\": \"le suivaient des yeux\", \"label\": \"negative\", \"score\": 0.542796, \"color\": \"red\"}, {\"text\": \"La barricade tremblait;\", \"label\": \"negative\", \"score\": 0.894321, \"color\": \"red\"}, {\"text\": \"Lui, il chantait\", \"label\": \"negative\", \"score\": 0.509921, \"color\": \"red\"}, {\"text\": \"Ce n'\\u00e9tait pas un enfant\", \"label\": \"neutral\", \"score\": 0.512246, \"color\": \"yellow\"}, {\"text\": \"ce n'\\u00e9tait pas un homme;\", \"label\": \"negative\", \"score\": 0.495888, \"color\": \"red\"}, {\"text\": \"c'\\u00e9tait un \\u00e9trange gamin f\\u00e9e\", \"label\": \"neutral\", \"score\": 0.878073, \"color\": \"yellow\"}, {\"text\": \"On e\\u00fbt dit le nain invuln\\u00e9rable de la m\\u00eal\\u00e9e\", \"label\": \"negative\", \"score\": 0.591483, \"color\": \"red\"}, {\"text\": \"Les balles couraient apr\\u00e8s lui\", \"label\": \"negative\", \"score\": 0.559884, \"color\": \"red\"}, {\"text\": \"il \\u00e9tait plus leste qu'elles\", \"label\": \"negative\", \"score\": 0.683789, \"color\": \"red\"}, {\"text\": \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\", \"label\": \"negative\", \"score\": 0.901326, \"color\": \"red\"}, {\"text\": \"chaque fois que la face camarde du spectre s'approchait\", \"label\": \"negative\", \"score\": 0.382701, \"color\": \"red\"}, {\"text\": \"le gamin lui donnait une pichenette\", \"label\": \"neutral\", \"score\": 0.419271, \"color\": \"yellow\"}, {\"text\": \"Une balle pourtant\", \"label\": \"negative\", \"score\": 0.415556, \"color\": \"red\"}, {\"text\": \"mieux ajust\\u00e9e ou plus tra\\u00eetre que les autres\", \"label\": \"negative\", \"score\": 0.445173, \"color\": \"red\"}, {\"text\": \"finit par atteindre l'enfant feu follet\", \"label\": \"negative\", \"score\": 0.608906, \"color\": \"red\"}, {\"text\": \"On vit Gavroche chanceler\", \"label\": \"positive\", \"score\": 0.4158, \"color\": \"blue\"}, {\"text\": \"puis il s\\u2019affaissa\", \"label\": \"negative\", \"score\": 0.788411, \"color\": \"red\"}, {\"text\": \"Toute la barricade poussa un cri;\", \"label\": \"negative\", \"score\": 0.851386, \"color\": \"red\"}, {\"text\": \"mais il y avait de l'Ant\\u00e9e dans ce pygm\\u00e9e;\", \"label\": \"negative\", \"score\": 0.507984, \"color\": \"red\"}, {\"text\": \"pour le gamin toucher le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.407572, \"color\": \"red\"}, {\"text\": \"c'est comme pour le g\\u00e9ant toucher la terre;\", \"label\": \"neutral\", \"score\": 0.417629, \"color\": \"yellow\"}, {\"text\": \"Gavroche n'\\u00e9tait tomb\\u00e9 que pour se redresser; il\", \"label\": \"negative\", \"score\": 0.433103, \"color\": \"red\"}, {\"text\": \"resta assis sur son s\\u00e9ant\", \"label\": \"neutral\", \"score\": 0.411866, \"color\": \"yellow\"}, {\"text\": \"un long filet de sang rayait son visage\", \"label\": \"neutral\", \"score\": 0.414654, \"color\": \"yellow\"}, {\"text\": \"il \\u00e9leva ses deux bras en l\\u2019air\", \"label\": \"negative\", \"score\": 0.472125, \"color\": \"red\"}, {\"text\": \"regarda du c\\u00f4t\\u00e9 d'o\\u00f9 \\u00e9tait venu le coup\", \"label\": \"negative\", \"score\": 0.494336, \"color\": \"red\"}, {\"text\": \"et se mit \\u00e0 chanter\", \"label\": \"negative\", \"score\": 0.373227, \"color\": \"red\"}, {\"text\": \"Je suis tomb\\u00e9 par terre\", \"label\": \"negative\", \"score\": 0.876672, \"color\": \"red\"}, {\"text\": \"C'est la faute \\u00e0 Voltaire\", \"label\": \"negative\", \"score\": 0.952087, \"color\": \"red\"}, {\"text\": \"Le nez dans le ruisseau\", \"label\": \"neutral\", \"score\": 0.45883, \"color\": \"yellow\"}, {\"text\": \"C'est la faute \\u00e0\", \"label\": \"neutral\", \"score\": 0.956367, \"color\": \"yellow\"}, {\"text\": \"Il n'acheva point\", \"label\": \"negative\", \"score\": 0.889711, \"color\": \"red\"}, {\"text\": \"Une seconde balle du m\\u00eame tireur l'arr\\u00eata court\", \"label\": \"negative\", \"score\": 0.431022, \"color\": \"red\"}, {\"text\": \"Cette fois il s'abattit la face contre le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.53152, \"color\": \"red\"}, {\"text\": \"et ne remua plus\", \"label\": \"negative\", \"score\": 0.479124, \"color\": \"red\"}, {\"text\": \"Cette petite grande \\u00e2me venait de s'envoler\", \"label\": \"negative\", \"score\": 0.68555, \"color\": \"red\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import altair as alt \n",
    "\n",
    "\n",
    "df = pd.DataFrame({\n",
    "  'text': [\"Le spectacle était épouvantable et charmant\", \"Gavroche, fusillé, taquinait la fusillade\",  \n",
    "           \"Il avait l'air de s'amuser beaucoup\", \"C'était le moineau becquetant les chasseurs\",\n",
    "           \"Il répondait à chaque décharge\", \"par un couplet\", \"On le visait sans cesse\",\n",
    "           \"on le manquait toujours\", \"Les gardes nationaux et les soldats riaient en l'ajustant\", \n",
    "           \"Il se couchait\", \"puis se redressait\", \"s'effaçait dans un coin de porte\",\n",
    "           \"puis bondissait\", \"disparaissait\", \"reparaissait\", \"se sauvait\", \"revenait\",\n",
    "           \"ripostait à la mitraille par des pieds de nez\", \"et cependant pillait les cartouches\",\n",
    "           \"vidait les gibernes\", \"et remplissait son panier\", \"Les insurgés, haletants d'anxiété\",\n",
    "           \"le suivaient des yeux\", \"La barricade tremblait;\", \"Lui, il chantait\",  \n",
    "           \"Ce n'était pas un enfant\", \"ce n'était pas un homme;\", \"c'était un étrange gamin fée\",\n",
    "           \"On eût dit le nain invulnérable de la mêlée\", \"Les balles couraient après lui\",\n",
    "           \"il était plus leste qu'elles\", \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\n",
    "           \"chaque fois que la face camarde du spectre s'approchait\",\n",
    "           \"le gamin lui donnait une pichenette\", \"Une balle pourtant\",\n",
    "           \"mieux ajustée ou plus traître que les autres\", \"finit par atteindre l'enfant feu follet\",\n",
    "           \"On vit Gavroche chanceler\", \"puis il s’affaissa\", \"Toute la barricade poussa un cri;\",\n",
    "           \"mais il y avait de l'Antée dans ce pygmée;\", \"pour le gamin toucher le pavé\",\n",
    "           \"c'est comme pour le géant toucher la terre;\", \"Gavroche n'était tombé que pour se redresser; il\", \n",
    "           \"resta assis sur son séant\", \"un long filet de sang rayait son visage\",\n",
    "           \"il éleva ses deux bras en l’air\", \"regarda du côté d'où était venu le coup\",\n",
    "           \"et se mit à chanter\", \"Je suis tombé par terre\", \"C'est la faute à Voltaire\",\n",
    "           \"Le nez dans le ruisseau\", \"C'est la faute à\", \"Il n'acheva point\",\n",
    "           \"Une seconde balle du même tireur l'arrêta court\",\n",
    "           \"Cette fois il s'abattit la face contre le pavé\", \"et ne remua plus\",\n",
    "           \"Cette petite grande âme venait de s'envoler\"],\n",
    "           \n",
    "  'label': ['positive', 'negative', 'positive', 'negative', 'neutral', 'neutral', \n",
    "            'positive', 'negative', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'negative', 'positive', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'neutral', 'negative', 'negative', 'negative', \n",
    "            'negative', 'neutral', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'positive', 'negative', 'negative', 'negative', 'negative', \n",
    "            'neutral', 'negative', 'neutral', 'neutral', 'negative', 'negative', \n",
    "            'negative', 'negative', 'negative', 'neutral', 'neutral', 'negative', \n",
    "            'negative', 'negative', 'negative', 'negative'],\n",
    "            \n",
    "  'score': [0.943457, 0.887135, 0.901779, 0.795272, 0.550428, 0.541630, 0.413179, \n",
    "            0.613897, 0.705909, 0.793753, 0.420739, 0.722869, 0.415614, 0.478407, \n",
    "            0.353580, 0.350029, 0.377874, 0.490187, 0.753360, 0.403735, 0.424990, \n",
    "            0.935065, 0.542796, 0.894321, 0.509921, 0.512246, 0.495888, 0.878073, \n",
    "            0.591483, 0.559884, 0.683789, 0.901326, 0.382701, 0.419271, 0.415556, \n",
    "            0.445173, 0.608906, 0.415800, 0.788411, 0.851386, 0.507984, 0.407572, \n",
    "            0.417629, 0.433103, 0.411866, 0.414654, 0.472125, 0.494336, 0.373227, \n",
    "            0.876672, 0.952087, 0.458830, 0.956367, 0.889711, 0.431022, 0.531520, \n",
    "            0.479124, 0.685550]}\n",
    ")\n",
    "# Convert 'label' column to category type\n",
    "df['label'] = df['label'].astype('category')\n",
    "\n",
    "# Assign specific colors to the categories in 'label'\n",
    "df['label'] = df['label'].cat.set_categories(['positive', 'neutral', 'negative'], ordered=True)\n",
    "df['color'] = df['label'].map({'positive': 'blue', 'neutral': 'yellow', 'negative': 'red'})\n",
    "\n",
    "# Create Altair chart\n",
    "chart = alt.Chart(df).mark_bar().encode(\n",
    "    x='text:O',\n",
    "    color=alt.Color('color:N', scale=None),\n",
    "    opacity='score:Q',\n",
    "    tooltip=['text', 'label', 'score']\n",
    ").properties(width=800)\n",
    "\n",
    "chart.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8cc869af-ae55-4bf2-957f-5834d14dff04",
   "metadata": {},
   "outputs": [
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       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-6150b65fb15442a3958d48a5cad5ec4b\"></div>\n",
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       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-bb6add9ccf50eaeba9d6488345a80f7d\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"color\": {\"field\": \"color\", \"scale\": null, \"type\": \"nominal\"}, \"opacity\": {\"field\": \"score\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"text\", \"type\": \"nominal\"}, {\"field\": \"label\", \"type\": \"ordinal\"}, {\"field\": \"score\", \"type\": \"quantitative\"}], \"y\": {\"field\": \"text\", \"sort\": \"-x\", \"type\": \"ordinal\"}}, \"height\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"datasets\": {\"data-bb6add9ccf50eaeba9d6488345a80f7d\": [{\"text\": \"Le spectacle \\u00e9tait \\u00e9pouvantable et charmant\", \"label\": \"positive\", \"score\": 0.943457, \"color\": \"#084285\"}, {\"text\": \"Gavroche, fusill\\u00e9, taquinait la fusillade\", \"label\": \"negative\", \"score\": 0.887135, \"color\": \"#a50f15\"}, {\"text\": \"Il avait l'air de s'amuser beaucoup\", \"label\": \"positive\", \"score\": 0.901779, \"color\": \"#084c95\"}, {\"text\": \"C'\\u00e9tait le moineau becquetant les chasseurs\", \"label\": \"negative\", \"score\": 0.795272, \"color\": \"#c1161b\"}, {\"text\": \"Il r\\u00e9pondait \\u00e0 chaque d\\u00e9charge\", \"label\": \"neutral\", \"score\": 0.550428, \"color\": \"#f88a21\"}, {\"text\": \"par un couplet\", \"label\": \"neutral\", \"score\": 0.54163, \"color\": \"#f98e23\"}, {\"text\": \"On le visait sans cesse\", \"label\": \"positive\", \"score\": 0.413179, \"color\": \"#92c4de\"}, {\"text\": \"on le manquait toujours\", \"label\": \"negative\", \"score\": 0.613897, \"color\": \"#f14331\"}, {\"text\": \"Les gardes nationaux et les soldats riaient en l'ajustant\", \"label\": \"negative\", \"score\": 0.705909, \"color\": \"#db2824\"}, {\"text\": \"Il se couchait\", \"label\": \"negative\", \"score\": 0.793753, \"color\": \"#c1161b\"}, {\"text\": \"puis se redressait\", \"label\": \"negative\", \"score\": 0.420739, \"color\": \"#fc8565\"}, {\"text\": \"s'effa\\u00e7ait dans un coin de porte\", \"label\": \"negative\", \"score\": 0.722869, \"color\": \"#d52221\"}, {\"text\": \"puis bondissait\", \"label\": \"neutral\", \"score\": 0.415614, \"color\": \"#feba46\"}, {\"text\": \"disparaissait\", \"label\": \"negative\", \"score\": 0.478407, \"color\": \"#fb7555\"}, {\"text\": \"reparaissait\", \"label\": \"negative\", \"score\": 0.35358, \"color\": \"#fc9c7d\"}, {\"text\": \"se sauvait\", \"label\": \"positive\", \"score\": 0.350029, \"color\": \"#a8cee4\"}, {\"text\": \"revenait\", \"label\": \"neutral\", \"score\": 0.377874, \"color\": \"#fec754\"}, {\"text\": \"ripostait \\u00e0 la mitraille par des pieds de nez\", \"label\": \"negative\", \"score\": 0.490187, \"color\": \"#fb6e4e\"}, {\"text\": \"et cependant pillait les cartouches\", \"label\": \"negative\", \"score\": 0.75336, \"color\": \"#cc191e\"}, {\"text\": \"vidait les gibernes\", \"label\": \"negative\", \"score\": 0.403735, \"color\": \"#fc8b6b\"}, {\"text\": \"et remplissait son panier\", \"label\": \"neutral\", \"score\": 0.42499, \"color\": \"#feb643\"}, {\"text\": \"Les insurg\\u00e9s, haletants d'anxi\\u00e9t\\u00e9\", \"label\": \"negative\", \"score\": 0.935065, \"color\": \"#8e0912\"}, {\"text\": \"le suivaient des yeux\", \"label\": \"negative\", \"score\": 0.542796, \"color\": \"#f85d42\"}, {\"text\": \"La barricade tremblait;\", \"label\": \"negative\", \"score\": 0.894321, \"color\": \"#a10e15\"}, {\"text\": \"Lui, il chantait\", \"label\": \"negative\", \"score\": 0.509921, \"color\": \"#fb6c4c\"}, {\"text\": \"Ce n'\\u00e9tait pas un enfant\", \"label\": \"neutral\", \"score\": 0.512246, \"color\": \"#fd9728\"}, {\"text\": \"ce n'\\u00e9tait pas un homme;\", \"label\": \"negative\", \"score\": 0.495888, \"color\": \"#fb6e4e\"}, {\"text\": \"c'\\u00e9tait un \\u00e9trange gamin f\\u00e9e\", \"label\": \"neutral\", \"score\": 0.878073, \"color\": \"#9e3604\"}, {\"text\": \"On e\\u00fbt dit le nain invuln\\u00e9rable de la m\\u00eal\\u00e9e\", \"label\": \"negative\", \"score\": 0.591483, \"color\": \"#f34a36\"}, {\"text\": \"Les balles couraient apr\\u00e8s lui\", \"label\": \"negative\", \"score\": 0.559884, \"color\": \"#f7593f\"}, {\"text\": \"il \\u00e9tait plus leste qu'elles\", \"label\": \"negative\", \"score\": 0.683789, \"color\": \"#e12d26\"}, {\"text\": \"Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\", \"label\": \"negative\", \"score\": 0.901326, \"color\": \"#9c0d14\"}, {\"text\": \"chaque fois que la face camarde du spectre s'approchait\", \"label\": \"negative\", \"score\": 0.382701, \"color\": \"#fc9272\"}, {\"text\": \"le gamin lui donnait une pichenette\", \"label\": \"neutral\", \"score\": 0.419271, \"color\": \"#feba46\"}, {\"text\": \"Une balle pourtant\", \"label\": \"negative\", \"score\": 0.415556, \"color\": \"#fc8969\"}, {\"text\": \"mieux ajust\\u00e9e ou plus tra\\u00eetre que les autres\", \"label\": \"negative\", \"score\": 0.445173, \"color\": \"#fc7f5f\"}, {\"text\": \"finit par atteindre l'enfant feu follet\", \"label\": \"negative\", \"score\": 0.608906, \"color\": \"#f24633\"}, {\"text\": \"On vit Gavroche chanceler\", \"label\": \"positive\", \"score\": 0.4158, \"color\": \"#92c4de\"}, {\"text\": \"puis il s\\u2019affaissa\", \"label\": \"negative\", \"score\": 0.788411, \"color\": \"#c4161c\"}, {\"text\": \"Toute la barricade poussa un cri;\", \"label\": \"negative\", \"score\": 0.851386, \"color\": \"#af1117\"}, {\"text\": \"mais il y avait de l'Ant\\u00e9e dans ce pygm\\u00e9e;\", \"label\": \"negative\", \"score\": 0.507984, \"color\": \"#fb6c4c\"}, {\"text\": \"pour le gamin toucher le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.407572, \"color\": \"#fc8b6b\"}, {\"text\": \"c'est comme pour le g\\u00e9ant toucher la terre;\", \"label\": \"neutral\", \"score\": 0.417629, \"color\": \"#feba46\"}, {\"text\": \"Gavroche n'\\u00e9tait tomb\\u00e9 que pour se redresser; il\", \"label\": \"negative\", \"score\": 0.433103, \"color\": \"#fc8262\"}, {\"text\": \"resta assis sur son s\\u00e9ant\", \"label\": \"neutral\", \"score\": 0.411866, \"color\": \"#feba46\"}, {\"text\": \"un long filet de sang rayait son visage\", \"label\": \"neutral\", \"score\": 0.414654, \"color\": \"#feba46\"}, {\"text\": \"il \\u00e9leva ses deux bras en l\\u2019air\", \"label\": \"negative\", \"score\": 0.472125, \"color\": \"#fb7555\"}, {\"text\": \"regarda du c\\u00f4t\\u00e9 d'o\\u00f9 \\u00e9tait venu le coup\", \"label\": \"negative\", \"score\": 0.494336, \"color\": \"#fb6e4e\"}, {\"text\": \"et se mit \\u00e0 chanter\", \"label\": \"negative\", \"score\": 0.373227, \"color\": \"#fc9576\"}, {\"text\": \"Je suis tomb\\u00e9 par terre\", \"label\": \"negative\", \"score\": 0.876672, \"color\": \"#a91016\"}, {\"text\": \"C'est la faute \\u00e0 Voltaire\", \"label\": \"negative\", \"score\": 0.952087, \"color\": \"#840711\"}, {\"text\": \"Le nez dans le ruisseau\", \"label\": \"neutral\", \"score\": 0.45883, \"color\": \"#feab39\"}, {\"text\": \"C'est la faute \\u00e0\", \"label\": \"neutral\", \"score\": 0.956367, \"color\": \"#7e2c05\"}, {\"text\": \"Il n'acheva point\", \"label\": \"negative\", \"score\": 0.889711, \"color\": \"#a50f15\"}, {\"text\": \"Une seconde balle du m\\u00eame tireur l'arr\\u00eata court\", \"label\": \"negative\", \"score\": 0.431022, \"color\": \"#fc8262\"}, {\"text\": \"Cette fois il s'abattit la face contre le pav\\u00e9\", \"label\": \"negative\", \"score\": 0.53152, \"color\": \"#f96044\"}, {\"text\": \"et ne remua plus\", \"label\": \"negative\", \"score\": 0.479124, \"color\": \"#fb7555\"}, {\"text\": \"Cette petite grande \\u00e2me venait de s'envoler\", \"label\": \"negative\", \"score\": 0.68555, \"color\": \"#e12d26\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Convert 'label' column to category type\n",
    "df['label'] = df['label'].astype('category')\n",
    "\n",
    "# Assign specific colors to the categories in 'label'\n",
    "df['label'] = df['label'].cat.set_categories(['positive', 'neutral', 'negative'], ordered=True)\n",
    "\n",
    "# Define color palettes\n",
    "positive_palette = sns.color_palette(\"Blues\", 100).as_hex() \n",
    "neutral_palette = sns.color_palette(\"YlOrBr\", 100).as_hex() # Beige/Tan color palette\n",
    "negative_palette = sns.color_palette(\"Reds\", 100).as_hex() # Dark Crimson color palette\n",
    "\n",
    "# Assign colors based on score and label\n",
    "df['color'] = df.apply(lambda row: positive_palette[int(row['score']*100-1)] if row['label']=='positive'\n",
    "                       else (neutral_palette[int(row['score']*100-1)] if row['label']=='neutral'\n",
    "                       else negative_palette[int(row['score']*100-1)]), axis=1)\n",
    "\n",
    "# Create Altair chart\n",
    "chart = alt.Chart(df).mark_bar().encode(\n",
    "    y=alt.Y('text:O', sort='-x'),\n",
    "    color=alt.Color('color:N', scale=None),\n",
    "    opacity='score:Q',\n",
    "    tooltip=['text', 'label', 'score']\n",
    ").properties(height=800)\n",
    "\n",
    "chart.display()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4926ecb0-99ab-43c7-97f5-137075fd3899",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1601f911-4785-46f2-b5e9-98a6e25c3b60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "726c0680-1a89-45b6-863e-7ecec352ce4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15a6faeb-7861-4eb6-98ae-a7f5b2ee8f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL:\n",
    "# dict['text'] = index\n",
    "#     dict['label'] = rating[0]['label']\n",
    "#     dict['score'] = rating[0]['score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "019959d6-9998-4629-91f8-2369cf528473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'text': 'Le spectacle était épouvantable et charmant', 'label': 'positive', 'score': 0.9434570670127869, 'categ_score': 2}, {'text': ' Gavroche, fusillé, taquinait la fusillade', 'label': 'negative', 'score': 0.8871347904205322, 'categ_score': -2}, {'text': \" Il avait l'air de s'amuser beaucoup\", 'label': 'positive', 'score': 0.9017786383628845, 'categ_score': 2}, {'text': \" C'était le moineau becquetant les chasseurs\", 'label': 'negative', 'score': 0.795271635055542, 'categ_score': -2}, {'text': ' Il répondait à chaque décharge', 'label': 'neutral', 'score': 0.5504280924797058, 'categ_score': 0}, {'text': 'par un couplet', 'label': 'neutral', 'score': 0.5416300296783447, 'categ_score': 0}, {'text': ' On le visait sans cesse', 'label': 'positive', 'score': 0.413178950548172, 'categ_score': 1}, {'text': ' on le manquait toujours', 'label': 'negative', 'score': 0.6138972043991089, 'categ_score': -2}, {'text': \" Les gardes nationaux et les soldats riaient en l'ajustant\", 'label': 'negative', 'score': 0.7059093713760376, 'categ_score': -2}, {'text': ' Il se couchait', 'label': 'negative', 'score': 0.7937528491020203, 'categ_score': -2}, {'text': ' puis se redressait', 'label': 'negative', 'score': 0.42073869705200195, 'categ_score': -1}, {'text': \" s'effaçait dans un coin de porte\", 'label': 'negative', 'score': 0.7228686213493347, 'categ_score': -2}, {'text': ' puis bondissait', 'label': 'neutral', 'score': 0.41561421751976013, 'categ_score': 0}, {'text': 'disparaissait', 'label': 'negative', 'score': 0.47840720415115356, 'categ_score': -1}, {'text': ' reparaissait', 'label': 'negative', 'score': 0.35358038544654846, 'categ_score': -1}, {'text': ' se sauvait', 'label': 'positive', 'score': 0.35002902150154114, 'categ_score': 1}, {'text': ' revenait', 'label': 'neutral', 'score': 0.3778737485408783, 'categ_score': 0}, {'text': ' ripostait à la mitraille par des pieds de nez', 'label': 'negative', 'score': 0.49018725752830505, 'categ_score': -1}, {'text': ' et cependant pillait les cartouches', 'label': 'negative', 'score': 0.753360390663147, 'categ_score': -2}, {'text': 'vidait les gibernes', 'label': 'negative', 'score': 0.40373480319976807, 'categ_score': -1}, {'text': 'et remplissait son panier', 'label': 'neutral', 'score': 0.4249899387359619, 'categ_score': 0}, {'text': \" Les insurgés, haletants d'anxiété\", 'label': 'negative', 'score': 0.9350647330284119, 'categ_score': -2}, {'text': ' le suivaient des yeux', 'label': 'negative', 'score': 0.5427963137626648, 'categ_score': -2}, {'text': ' La barricade tremblait;', 'label': 'negative', 'score': 0.8943210244178772, 'categ_score': -2}, {'text': 'Lui, il chantait', 'label': 'neutral', 'score': 0.5099213719367981, 'categ_score': 0}, {'text': \" Ce n'était pas un enfant\", 'label': 'negative', 'score': 0.5122464895248413, 'categ_score': -2}, {'text': \" ce n'était pas un homme;\", 'label': 'neutral', 'score': 0.4958876371383667, 'categ_score': 0}, {'text': \"c'était un étrange gamin fée\", 'label': 'negative', 'score': 0.8780734539031982, 'categ_score': -2}, {'text': ' On eût dit le nain invulnérable de la mêlée', 'label': 'negative', 'score': 0.5914828181266785, 'categ_score': -2}, {'text': ' Les balles couraient après lui', 'label': 'negative', 'score': 0.5598840117454529, 'categ_score': -2}, {'text': \" il était plus leste qu'elles\", 'label': 'negative', 'score': 0.683789074420929, 'categ_score': -2}, {'text': ' Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;', 'label': 'negative', 'score': 0.9013264179229736, 'categ_score': -2}, {'text': \"chaque fois que la face camarde du spectre s'approchait\", 'label': 'negative', 'score': 0.38270097970962524, 'categ_score': -1}, {'text': ' le gamin lui donnait une pichenette', 'label': 'neutral', 'score': 0.41927069425582886, 'categ_score': 0}, {'text': 'Une balle pourtant', 'label': 'negative', 'score': 0.41555628180503845, 'categ_score': -1}, {'text': ' mieux ajustée ou plus traître que les autres', 'label': 'negative', 'score': 0.4451725482940674, 'categ_score': -1}, {'text': \" finit par atteindre l'enfant feu follet\", 'label': 'negative', 'score': 0.6089064478874207, 'categ_score': -2}, {'text': ' On vit Gavroche chanceler', 'label': 'positive', 'score': 0.41579997539520264, 'categ_score': 1}, {'text': ' puis il s’affaissa', 'label': 'negative', 'score': 0.7884112596511841, 'categ_score': -2}, {'text': ' Toute la barricade poussa un cri;', 'label': 'negative', 'score': 0.851386308670044, 'categ_score': -2}, {'text': \"mais il y avait de l'Antée dans ce pygmée;\", 'label': 'negative', 'score': 0.5079840421676636, 'categ_score': -2}, {'text': ' pour le gamin toucher le pavé', 'label': 'negative', 'score': 0.4075724184513092, 'categ_score': -1}, {'text': \"c'est comme pour le géant toucher la terre;\", 'label': 'neutral', 'score': 0.4176287353038788, 'categ_score': 0}, {'text': \"Gavroche n'était tombé que pour se redresser; il\", 'label': 'negative', 'score': 0.4331025779247284, 'categ_score': -1}, {'text': 'resta assis sur son séant', 'label': 'negative', 'score': 0.41186630725860596, 'categ_score': -1}, {'text': ' un long filet de sang rayait son visage', 'label': 'negative', 'score': 0.41465386748313904, 'categ_score': -1}, {'text': ' il éleva ses deux bras en l’air', 'label': 'neutral', 'score': 0.47212541103363037, 'categ_score': 0}, {'text': \" regarda du côté d'où était venu le coup\", 'label': 'neutral', 'score': 0.49433600902557373, 'categ_score': 0}, {'text': 'et se mit à chanter', 'label': 'neutral', 'score': 0.37322676181793213, 'categ_score': 0}, {'text': 'Je suis tombé par terre', 'label': 'negative', 'score': 0.8766723871231079, 'categ_score': -2}, {'text': \"C'est la faute à Voltaire\", 'label': 'negative', 'score': 0.9520866870880127, 'categ_score': -2}, {'text': 'Le nez dans le ruisseau', 'label': 'negative', 'score': 0.4588301479816437, 'categ_score': -1}, {'text': \"C'est la faute à\", 'label': 'negative', 'score': 0.9563672542572021, 'categ_score': -2}, {'text': \"Il n'acheva point\", 'label': 'negative', 'score': 0.8897107839584351, 'categ_score': -2}, {'text': \" Une seconde balle du même tireur l'arrêta court\", 'label': 'negative', 'score': 0.43102195858955383, 'categ_score': -1}, {'text': \"Cette fois il s'abattit la face contre le pavé\", 'label': 'negative', 'score': 0.5315203070640564, 'categ_score': -2}, {'text': ' et ne remua plus', 'label': 'negative', 'score': 0.47912436723709106, 'categ_score': -1}, {'text': \" Cette petite grande âme venait de s'envoler\", 'label': 'negative', 'score': 0.6855504512786865, 'categ_score': -2}]\n"
     ]
    }
   ],
   "source": [
    "for row in fresults: \n",
    "    if row['label'] == 'negative':\n",
    "        if row['score'] > 0.5:\n",
    "            row['categ_score'] = -2\n",
    "        else: \n",
    "            row['categ_score'] = -1\n",
    "    elif row['label'] == 'positive':\n",
    "        if row['score'] > 0.5:\n",
    "            row['categ_score'] = 2\n",
    "        else:\n",
    "            row['categ_score'] = 1\n",
    "    else:\n",
    "        row['categ_score'] = 0\n",
    "print(fresults)            \n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "11bcbc86-e147-4453-b0cc-0ecb4b0db41c",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "      <th>score</th>\n",
       "      <th>categ_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le spectacle était épouvantable et charmant</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.943457</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavroche, fusillé, taquinait la fusillade</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.887135</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Il avait l'air de s'amuser beaucoup</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.901779</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C'était le moineau becquetant les chasseurs</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.795272</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Il répondait à chaque décharge</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.550428</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           text     label     score  \\\n",
       "0   Le spectacle était épouvantable et charmant  positive  0.943457   \n",
       "1     Gavroche, fusillé, taquinait la fusillade  negative  0.887135   \n",
       "2           Il avait l'air de s'amuser beaucoup  positive  0.901779   \n",
       "3   C'était le moineau becquetant les chasseurs  negative  0.795272   \n",
       "4                Il répondait à chaque décharge   neutral  0.550428   \n",
       "\n",
       "   categ_score  \n",
       "0            2  \n",
       "1           -2  \n",
       "2            2  \n",
       "3           -2  \n",
       "4            0  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_categ = pd.DataFrame(fresults)\n",
    "fr_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "70288664-8bbf-49b8-88d4-ce616f44df0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_categ.to_csv(\"fr_categ.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e203bf98-db0e-4f88-a04a-c8a9549185d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'text': 'It was a charming and terrible sight', 'label': 'negative', 'score': 0.9529926180839539, 'categ_score': -2}\n"
     ]
    }
   ],
   "source": [
    "for row in results: \n",
    "    if row['label'] == 'negative':\n",
    "        if row['score'] > 0.5:\n",
    "            row['categ_score'] = -2\n",
    "        else: \n",
    "            row['categ_score'] = -1\n",
    "    elif row['label'] == 'positive':\n",
    "        if row['score'] > 0.5:\n",
    "            row['categ_score'] = 2\n",
    "        else:\n",
    "            row['categ_score'] = 1\n",
    "    else:\n",
    "        row['categ_score'] = 0\n",
    "print(results[0])      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8df7520d-6e95-44c5-b3f0-b2f1668c5c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>text</th>\n",
       "      <th>label</th>\n",
       "      <th>score</th>\n",
       "      <th>categ_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>It was a charming and terrible sight</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.952993</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavroche, though shot at, was teasing the fus...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.421055</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>He had the air of being greatly diverted</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.480614</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>It was the sparrow pecking at the sportsmen</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.527367</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>To each discharge he retorted</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.439596</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label     score  \\\n",
       "0               It was a charming and terrible sight  negative  0.952993   \n",
       "1   Gavroche, though shot at, was teasing the fus...  negative  0.421055   \n",
       "2           He had the air of being greatly diverted  positive  0.480614   \n",
       "3        It was the sparrow pecking at the sportsmen  negative  0.527367   \n",
       "4                      To each discharge he retorted   neutral  0.439596   \n",
       "\n",
       "   categ_score  \n",
       "0           -2  \n",
       "1           -1  \n",
       "2            1  \n",
       "3           -2  \n",
       "4            0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miz_eng_categ = pd.DataFrame(results)\n",
    "miz_eng_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc394e35-26d1-4123-841a-4dcfa6038562",
   "metadata": {},
   "outputs": [],
   "source": [
    "miz_eng_categ.to_csv(\"miz_eng_categ.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60aead8a-c0be-48d6-8241-4a7374a31410",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a language column and set to english -- same for french"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ca0d749-d50a-44af-80d1-7c25e6659758",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fr_categ' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mtype\u001b[39m(\u001b[43mfr_categ\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fr_categ' is not defined"
     ]
    }
   ],
   "source": [
    "type(fr_categ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d142465f-9285-4d4b-a38b-f1bfdfa16515",
   "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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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>score</th>\n",
       "      <th>categ_score</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>It was a charming and terrible sight</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.952993</td>\n",
       "      <td>-2</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavroche, though shot at, was teasing the fus...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.421055</td>\n",
       "      <td>-1</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>He had the air of being greatly diverted</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.480614</td>\n",
       "      <td>1</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>It was the sparrow pecking at the sportsmen</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.527367</td>\n",
       "      <td>-2</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>To each discharge he retorted</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.439596</td>\n",
       "      <td>0</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label     score  \\\n",
       "0               It was a charming and terrible sight  negative  0.952993   \n",
       "1   Gavroche, though shot at, was teasing the fus...  negative  0.421055   \n",
       "2           He had the air of being greatly diverted  positive  0.480614   \n",
       "3        It was the sparrow pecking at the sportsmen  negative  0.527367   \n",
       "4                      To each discharge he retorted   neutral  0.439596   \n",
       "\n",
       "   categ_score language  \n",
       "0           -2  english  \n",
       "1           -1  english  \n",
       "2            1  english  \n",
       "3           -2  english  \n",
       "4            0  english  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miz_eng_categ[\"language\"] = \"english\"\n",
    "miz_eng_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47b82e4f-ab05-427a-901a-9700685a48d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f55b7a9c-35ec-4f96-9e1d-c2f2d88430d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "miz_eng_categ[\"line_num\"] = np.arange(len(miz_eng_categ))+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "898597f6-9459-448e-a8d8-55fa12c9b50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gavroche, though shot at, was teasing the fus...</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.421055</td>\n",
       "      <td>-1</td>\n",
       "      <td>english</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>He had the air of being greatly diverted</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.480614</td>\n",
       "      <td>1</td>\n",
       "      <td>english</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>It was the sparrow pecking at the sportsmen</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.527367</td>\n",
       "      <td>-2</td>\n",
       "      <td>english</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>To each discharge he retorted</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.439596</td>\n",
       "      <td>0</td>\n",
       "      <td>english</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label     score  \\\n",
       "0               It was a charming and terrible sight  negative  0.952993   \n",
       "1   Gavroche, though shot at, was teasing the fus...  negative  0.421055   \n",
       "2           He had the air of being greatly diverted  positive  0.480614   \n",
       "3        It was the sparrow pecking at the sportsmen  negative  0.527367   \n",
       "4                      To each discharge he retorted   neutral  0.439596   \n",
       "\n",
       "   categ_score language  line_num  \n",
       "0           -2  english         1  \n",
       "1           -1  english         2  \n",
       "2            1  english         3  \n",
       "3           -2  english         4  \n",
       "4            0  english         5  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miz_eng_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "419ef8a6-cd5a-42d6-840e-aa6d3faeaf84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>categ_score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Le spectacle était épouvantable et charmant</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.943457</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Gavroche, fusillé, taquinait la fusillade</td>\n",
       "      <td>negative</td>\n",
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       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Il avait l'air de s'amuser beaucoup</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.901779</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>C'était le moineau becquetant les chasseurs</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.795272</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Il répondait à chaque décharge</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.550428</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                          text     label  \\\n",
       "0           0   Le spectacle était épouvantable et charmant  positive   \n",
       "1           1     Gavroche, fusillé, taquinait la fusillade  negative   \n",
       "2           2           Il avait l'air de s'amuser beaucoup  positive   \n",
       "3           3   C'était le moineau becquetant les chasseurs  negative   \n",
       "4           4                Il répondait à chaque décharge   neutral   \n",
       "\n",
       "      score  categ_score  \n",
       "0  0.943457            2  \n",
       "1  0.887135           -2  \n",
       "2  0.901779            2  \n",
       "3  0.795272           -2  \n",
       "4  0.550428            0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = px.data.consorts()\n",
    "fr_categ = pd.read_csv('hugo/miz_fr_categ.csv')\n",
    "fr_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ae336b2-35f0-46dc-9aaf-6ac952c3edff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Le spectacle était épouvantable et charmant</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.943457</td>\n",
       "      <td>2</td>\n",
       "      <td>french</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Gavroche, fusillé, taquinait la fusillade</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.887135</td>\n",
       "      <td>-2</td>\n",
       "      <td>french</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Il avait l'air de s'amuser beaucoup</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.901779</td>\n",
       "      <td>2</td>\n",
       "      <td>french</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>C'était le moineau becquetant les chasseurs</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.795272</td>\n",
       "      <td>-2</td>\n",
       "      <td>french</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Il répondait à chaque décharge</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.550428</td>\n",
       "      <td>0</td>\n",
       "      <td>french</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                          text     label  \\\n",
       "0           0   Le spectacle était épouvantable et charmant  positive   \n",
       "1           1     Gavroche, fusillé, taquinait la fusillade  negative   \n",
       "2           2           Il avait l'air de s'amuser beaucoup  positive   \n",
       "3           3   C'était le moineau becquetant les chasseurs  negative   \n",
       "4           4                Il répondait à chaque décharge   neutral   \n",
       "\n",
       "      score  categ_score language  \n",
       "0  0.943457            2   french  \n",
       "1  0.887135           -2   french  \n",
       "2  0.901779            2   french  \n",
       "3  0.795272           -2   french  \n",
       "4  0.550428            0   french  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_categ[\"language\"] = \"french\"\n",
    "fr_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d99e821e-3ea6-4178-8e86-a7b23f5aa27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_categ[\"line_num\"] = np.arange(len(fr_categ))+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "157cf5a1-9064-487c-90b2-21e88ad0c4a4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>french</td>\n",
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       "   Unnamed: 0                                          text     label  \\\n",
       "0           0   Le spectacle était épouvantable et charmant  positive   \n",
       "1           1     Gavroche, fusillé, taquinait la fusillade  negative   \n",
       "2           2           Il avait l'air de s'amuser beaucoup  positive   \n",
       "3           3   C'était le moineau becquetant les chasseurs  negative   \n",
       "4           4                Il répondait à chaque décharge   neutral   \n",
       "\n",
       "      score  categ_score language  line_num  \n",
       "0  0.943457            2   french         1  \n",
       "1  0.887135           -2   french         2  \n",
       "2  0.901779            2   french         3  \n",
       "3  0.795272           -2   french         4  \n",
       "4  0.550428            0   french         5  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a175cf8e-c1e4-4cf6-a7f6-ba585749fe31",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                           text     label     score  \\\n",
       "0   Le spectacle était épouvantable et charmant  positive  0.943457   \n",
       "1     Gavroche, fusillé, taquinait la fusillade  negative  0.887135   \n",
       "2           Il avait l'air de s'amuser beaucoup  positive  0.901779   \n",
       "3   C'était le moineau becquetant les chasseurs  negative  0.795272   \n",
       "4                Il répondait à chaque décharge   neutral  0.550428   \n",
       "\n",
       "   categ_score language  line_num  \n",
       "0            2   french         1  \n",
       "1           -2   french         2  \n",
       "2            2   french         3  \n",
       "3           -2   french         4  \n",
       "4            0   french         5  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_categ = fr_categ.drop('Unnamed: 0', axis=1)\n",
    "fr_categ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89cf21aa-6573-4861-95fd-f7c3e6beb347",
   "metadata": {},
   "outputs": [
    {
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       "      <td>It was the sparrow pecking at the sportsmen</td>\n",
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      "text/plain": [
       "                                                text     label     score  \\\n",
       "0               It was a charming and terrible sight  negative  0.952993   \n",
       "1   Gavroche, though shot at, was teasing the fus...  negative  0.421055   \n",
       "2           He had the air of being greatly diverted  positive  0.480614   \n",
       "3        It was the sparrow pecking at the sportsmen  negative  0.527367   \n",
       "4                      To each discharge he retorted   neutral  0.439596   \n",
       "\n",
       "   categ_score language  line_num  \n",
       "0           -2  english         1  \n",
       "1           -1  english         2  \n",
       "2            1  english         3  \n",
       "3           -2  english         4  \n",
       "4            0  english         5  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "miz_eng_categ.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4b56c08-e55b-4719-ba72-fd0f46fe2caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "combo = pd.concat([miz_eng_categ, fr_categ], ignore_index=True, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "89a3b64b-4a68-43a7-ae99-e01378ddb699",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                text     label     score  \\\n",
       "0               It was a charming and terrible sight  negative  0.952993   \n",
       "1   Gavroche, though shot at, was teasing the fus...  negative  0.421055   \n",
       "2           He had the air of being greatly diverted  positive  0.480614   \n",
       "3        It was the sparrow pecking at the sportsmen  negative  0.527367   \n",
       "4                      To each discharge he retorted   neutral  0.439596   \n",
       "\n",
       "   categ_score language  line_num  \n",
       "0           -2  english         1  \n",
       "1           -1  english         2  \n",
       "2            1  english         3  \n",
       "3           -2  english         4  \n",
       "4            0  english         5  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5728b93c-808d-4e85-985c-3ccd1ecfd6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>112</th>\n",
       "      <td>Une seconde balle du même tireur l'arrêta court</td>\n",
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       "      <td>0.431022</td>\n",
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       "      <td>french</td>\n",
       "      <td>55</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Cette fois il s'abattit la face contre le pavé</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.531520</td>\n",
       "      <td>-2</td>\n",
       "      <td>french</td>\n",
       "      <td>56</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>et ne remua plus</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.479124</td>\n",
       "      <td>-1</td>\n",
       "      <td>french</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Cette petite grande âme venait de s'envoler</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.685550</td>\n",
       "      <td>-2</td>\n",
       "      <td>french</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text     label     score  \\\n",
       "111                                 Il n'acheva point  negative  0.889711   \n",
       "112   Une seconde balle du même tireur l'arrêta court  negative  0.431022   \n",
       "113    Cette fois il s'abattit la face contre le pavé  negative  0.531520   \n",
       "114                                  et ne remua plus  negative  0.479124   \n",
       "115       Cette petite grande âme venait de s'envoler  negative  0.685550   \n",
       "\n",
       "     categ_score language  line_num  \n",
       "111           -2   french        54  \n",
       "112           -1   french        55  \n",
       "113           -2   french        56  \n",
       "114           -1   french        57  \n",
       "115           -2   french        58  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c296a21-2423-4a15-89af-db096d24d500",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51330af4-742f-4c91-9c94-f73e57ce5a4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ce1b4d60-4991-4985-a95d-b6e477e4017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3fbadc98-a372-4d4e-b68d-45cca9c3d5f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "combo.to_csv(\"hugo/combo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f748c7-0c26-447e-a941-96dcd8b6977a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "71f3e49b-0b8f-4721-bcb4-9e33f8389e09",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'to_json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcombo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_json\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcombo.json\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'to_json'"
     ]
    }
   ],
   "source": [
    "combo.to_json(\"combo.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59c2c0d6-6633-457f-8490-f9be3c8dbbfd",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Object of type DataFrame is not JSON serializable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[43], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhugo/combo.json\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m outfile:\n\u001b[0;32m----> 2\u001b[0m     \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcombo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutfile\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.11/lib/python3.10/json/__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    173\u001b[0m     iterable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(skipkeys\u001b[38;5;241m=\u001b[39mskipkeys, ensure_ascii\u001b[38;5;241m=\u001b[39mensure_ascii,\n\u001b[1;32m    174\u001b[0m         check_circular\u001b[38;5;241m=\u001b[39mcheck_circular, allow_nan\u001b[38;5;241m=\u001b[39mallow_nan, indent\u001b[38;5;241m=\u001b[39mindent,\n\u001b[1;32m    175\u001b[0m         separators\u001b[38;5;241m=\u001b[39mseparators,\n\u001b[1;32m    176\u001b[0m         default\u001b[38;5;241m=\u001b[39mdefault, sort_keys\u001b[38;5;241m=\u001b[39msort_keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\u001b[38;5;241m.\u001b[39miterencode(obj)\n\u001b[1;32m    177\u001b[0m \u001b[38;5;66;03m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;66;03m# a debuggability cost\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m    180\u001b[0m     fp\u001b[38;5;241m.\u001b[39mwrite(chunk)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.11/lib/python3.10/json/encoder.py:438\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    436\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCircular reference detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    437\u001b[0m     markers[markerid] \u001b[38;5;241m=\u001b[39m o\n\u001b[0;32m--> 438\u001b[0m o \u001b[38;5;241m=\u001b[39m \u001b[43m_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    439\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m markers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.11/lib/python3.10/json/encoder.py:179\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault\u001b[39m(\u001b[38;5;28mself\u001b[39m, o):\n\u001b[1;32m    161\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;124;03m    a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;124;03m    (to raise a ``TypeError``).\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    177\u001b[0m \n\u001b[1;32m    178\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mObject of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    180\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not JSON serializable\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: Object of type DataFrame is not JSON serializable"
     ]
    }
   ],
   "source": [
    "with open(\"hugo/combo.json\", \"w\") as outfile:\n",
    "    json.dump(combo, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4944c3e7-40d8-4030-903f-d3b35b1a8b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"text\":\"It was a charming and terrible sight\",\"label\":\"negative\",\"score\":0.9529926181,\"categ_score\":-2,\"language\":\"english\",\"line_num\":1},{\"text\":\" Gavroche, though shot at, was teasing the fusillade. \",\"label\":\"negative\",\"score\":0.4210546613,\"categ_score\":-1,\"language\":\"english\",\"line_num\":2},{\"text\":\" He had the air of being greatly diverted\",\"label\":\"positive\",\"score\":0.4806137383,\"categ_score\":1,\"language\":\"english\",\"line_num\":3},{\"text\":\" It was the sparrow pecking at the sportsmen\",\"label\":\"negative\",\"score\":0.5273667574,\"categ_score\":-2,\"language\":\"english\",\"line_num\":4},{\"text\":\" To each discharge he retorted\",\"label\":\"neutral\",\"score\":0.4395959079,\"categ_score\":0,\"language\":\"english\",\"line_num\":5},{\"text\":\"with a couplet\",\"label\":\"neutral\",\"score\":0.4968397617,\"categ_score\":0,\"language\":\"english\",\"line_num\":6},{\"text\":\" They aimed at him constantly\",\"label\":\"negative\",\"score\":0.6753860712,\"categ_score\":-2,\"language\":\"english\",\"line_num\":7},{\"text\":\" and always missed him\",\"label\":\"negative\",\"score\":0.3560687006,\"categ_score\":-1,\"language\":\"english\",\"line_num\":8},{\"text\":\"The National Guardsmen and the soldiers laughed as they took aim at him.\",\"label\":\"positive\",\"score\":0.5773722529,\"categ_score\":2,\"language\":\"english\",\"line_num\":9},{\"text\":\" He lay down\",\"label\":\"neutral\",\"score\":0.549929142,\"categ_score\":0,\"language\":\"english\",\"line_num\":10},{\"text\":\" sprang to his feet\",\"label\":\"neutral\",\"score\":0.5386978388,\"categ_score\":0,\"language\":\"english\",\"line_num\":11},{\"text\":\" hid in the corner of a doorway\",\"label\":\"neutral\",\"score\":0.5266876221,\"categ_score\":0,\"language\":\"english\",\"line_num\":12},{\"text\":\"then made a bound\",\"label\":\"neutral\",\"score\":0.5571023226,\"categ_score\":0,\"language\":\"english\",\"line_num\":13},{\"text\":\" disappeared\",\"label\":\"negative\",\"score\":0.4325456619,\"categ_score\":-1,\"language\":\"english\",\"line_num\":14},{\"text\":\" reappeared\",\"label\":\"neutral\",\"score\":0.3789016604,\"categ_score\":0,\"language\":\"english\",\"line_num\":15},{\"text\":\" scampered away\",\"label\":\"negative\",\"score\":0.6002640724,\"categ_score\":-2,\"language\":\"english\",\"line_num\":16},{\"text\":\" returned\",\"label\":\"neutral\",\"score\":0.3880229294,\"categ_score\":0,\"language\":\"english\",\"line_num\":17},{\"text\":\"replied to the grape-shot with his thumb at his nose\",\"label\":\"neutral\",\"score\":0.6055102944,\"categ_score\":0,\"language\":\"english\",\"line_num\":18},{\"text\":\" and all the while went on pillaging the cartouches\",\"label\":\"negative\",\"score\":0.6896965504,\"categ_score\":-2,\"language\":\"english\",\"line_num\":19},{\"text\":\" emptying the cartridge-boxes\",\"label\":\"neutral\",\"score\":0.5393020511,\"categ_score\":0,\"language\":\"english\",\"line_num\":20},{\"text\":\"and filling his basket\",\"label\":\"neutral\",\"score\":0.5524231791,\"categ_score\":0,\"language\":\"english\",\"line_num\":21},{\"text\":\" The insurgents, panting with anxiety\",\"label\":\"negative\",\"score\":0.9026253223,\"categ_score\":-2,\"language\":\"english\",\"line_num\":22},{\"text\":\" followed him with their eyes\",\"label\":\"neutral\",\"score\":0.5836014152,\"categ_score\":0,\"language\":\"english\",\"line_num\":23},{\"text\":\" The barricade trembled\",\"label\":\"negative\",\"score\":0.8814650178,\"categ_score\":-2,\"language\":\"english\",\"line_num\":24},{\"text\":\" He sang\",\"label\":\"neutral\",\"score\":0.4352350831,\"categ_score\":0,\"language\":\"english\",\"line_num\":25},{\"text\":\"He was not a  child\",\"label\":\"neutral\",\"score\":0.5093202591,\"categ_score\":0,\"language\":\"english\",\"line_num\":26},{\"text\":\" he was not a man;\",\"label\":\"neutral\",\"score\":0.5572024584,\"categ_score\":0,\"language\":\"english\",\"line_num\":27},{\"text\":\"he was a strange gamin-fairy\",\"label\":\"negative\",\"score\":0.7257220149,\"categ_score\":-2,\"language\":\"english\",\"line_num\":28},{\"text\":\" He might have been called the invulnerable dwarf of the fray\",\"label\":\"negative\",\"score\":0.8699467182,\"categ_score\":-2,\"language\":\"english\",\"line_num\":29},{\"text\":\" The bullets flew after him\",\"label\":\"negative\",\"score\":0.6512694955,\"categ_score\":-2,\"language\":\"english\",\"line_num\":30},{\"text\":\" he was more nimble than they\",\"label\":\"negative\",\"score\":0.4042628109,\"categ_score\":-1,\"language\":\"english\",\"line_num\":31},{\"text\":\" He played a fearful game of hide and seek with death\",\"label\":\"negative\",\"score\":0.5758906603,\"categ_score\":-2,\"language\":\"english\",\"line_num\":32},{\"text\":\"every time that the flat-nosed face of the spectre approached\",\"label\":\"neutral\",\"score\":0.4669246674,\"categ_score\":0,\"language\":\"english\",\"line_num\":33},{\"text\":\" the urchin administered to it a fillip\",\"label\":\"neutral\",\"score\":0.4851511121,\"categ_score\":0,\"language\":\"english\",\"line_num\":34},{\"text\":\"One bullet however\",\"label\":\"neutral\",\"score\":0.4478260577,\"categ_score\":0,\"language\":\"english\",\"line_num\":35},{\"text\":\"better aimed or more treacherous than the rest\",\"label\":\"negative\",\"score\":0.9133269787,\"categ_score\":-2,\"language\":\"english\",\"line_num\":36},{\"text\":\"finally struck the will-o\\u2019-the-wisp of a child\",\"label\":\"positive\",\"score\":0.6571321487,\"categ_score\":2,\"language\":\"english\",\"line_num\":37},{\"text\":\" Gavroche was seen to stagger\",\"label\":\"negative\",\"score\":0.5701798797,\"categ_score\":-2,\"language\":\"english\",\"line_num\":38},{\"text\":\" then he sank to the earth\",\"label\":\"negative\",\"score\":0.4721197188,\"categ_score\":-1,\"language\":\"english\",\"line_num\":39},{\"text\":\" The whole barricade gave vent to a cry\",\"label\":\"negative\",\"score\":0.6677193642,\"categ_score\":-2,\"language\":\"english\",\"line_num\":40},{\"text\":\"but there was something of Ant\\u00e6us in that pygmy;\",\"label\":\"neutral\",\"score\":0.4963710308,\"categ_score\":0,\"language\":\"english\",\"line_num\":41},{\"text\":\"for the gamin to touch the pavement\",\"label\":\"neutral\",\"score\":0.4120872021,\"categ_score\":0,\"language\":\"english\",\"line_num\":42},{\"text\":\"is the same as for the giant to touch the earth;\",\"label\":\"neutral\",\"score\":0.5483947992,\"categ_score\":0,\"language\":\"english\",\"line_num\":43},{\"text\":\"Gavroche had fallen only to rise again;\",\"label\":\"negative\",\"score\":0.4193210006,\"categ_score\":-1,\"language\":\"english\",\"line_num\":44},{\"text\":\"he remained in a sitting posture\",\"label\":\"negative\",\"score\":0.5129579306,\"categ_score\":-2,\"language\":\"english\",\"line_num\":45},{\"text\":\" a long thread of blood streaked his face\",\"label\":\"negative\",\"score\":0.5885011554,\"categ_score\":-2,\"language\":\"english\",\"line_num\":46},{\"text\":\" he raised both arms in the air\",\"label\":\"neutral\",\"score\":0.7149354815,\"categ_score\":0,\"language\":\"english\",\"line_num\":47},{\"text\":\" glanced in the direction whence the shot had come\",\"label\":\"neutral\",\"score\":0.6545478106,\"categ_score\":0,\"language\":\"english\",\"line_num\":48},{\"text\":\"And began to sing:\",\"label\":\"neutral\",\"score\":0.4657364786,\"categ_score\":0,\"language\":\"english\",\"line_num\":49},{\"text\":\"\\u201cI have fallen to the earth\",\"label\":\"negative\",\"score\":0.7456450462,\"categ_score\":-2,\"language\":\"english\",\"line_num\":50},{\"text\":\"\\u2019Tis the fault of Voltaire;\",\"label\":\"negative\",\"score\":0.8034857512,\"categ_score\":-2,\"language\":\"english\",\"line_num\":51},{\"text\":\"With my nose in the gutter\",\"label\":\"negative\",\"score\":0.8031990528,\"categ_score\":-2,\"language\":\"english\",\"line_num\":52},{\"text\":\"\\u2019Tis the fault of \",\"label\":\"negative\",\"score\":0.8430755138,\"categ_score\":-2,\"language\":\"english\",\"line_num\":53},{\"text\":\"He did not finish\",\"label\":\"negative\",\"score\":0.834646523,\"categ_score\":-2,\"language\":\"english\",\"line_num\":54},{\"text\":\" A second bullet from the same marksman stopped him short\",\"label\":\"negative\",\"score\":0.7098829746,\"categ_score\":-2,\"language\":\"english\",\"line_num\":55},{\"text\":\" This time he fell face downward on the pavement\",\"label\":\"neutral\",\"score\":0.5199488997,\"categ_score\":0,\"language\":\"english\",\"line_num\":56},{\"text\":\" and moved no more\",\"label\":\"neutral\",\"score\":0.5417121053,\"categ_score\":0,\"language\":\"english\",\"line_num\":57},{\"text\":\" This grand little soul had taken its flight\",\"label\":\"positive\",\"score\":0.4738297462,\"categ_score\":1,\"language\":\"english\",\"line_num\":58},{\"text\":\"Le spectacle \\u00e9tait \\u00e9pouvantable et charmant\",\"label\":\"positive\",\"score\":0.943457067,\"categ_score\":2,\"language\":\"french\",\"line_num\":1},{\"text\":\" Gavroche, fusill\\u00e9, taquinait la fusillade\",\"label\":\"negative\",\"score\":0.8871347904,\"categ_score\":-2,\"language\":\"french\",\"line_num\":2},{\"text\":\" Il avait l'air de s'amuser beaucoup\",\"label\":\"positive\",\"score\":0.9017786384,\"categ_score\":2,\"language\":\"french\",\"line_num\":3},{\"text\":\" C'\\u00e9tait le moineau becquetant les chasseurs\",\"label\":\"negative\",\"score\":0.7952716351,\"categ_score\":-2,\"language\":\"french\",\"line_num\":4},{\"text\":\" Il r\\u00e9pondait \\u00e0 chaque d\\u00e9charge\",\"label\":\"neutral\",\"score\":0.5504280925,\"categ_score\":0,\"language\":\"french\",\"line_num\":5},{\"text\":\"par un couplet\",\"label\":\"neutral\",\"score\":0.5416300297,\"categ_score\":0,\"language\":\"french\",\"line_num\":6},{\"text\":\" On le visait sans cesse\",\"label\":\"positive\",\"score\":0.4131789505,\"categ_score\":1,\"language\":\"french\",\"line_num\":7},{\"text\":\" on le manquait toujours\",\"label\":\"negative\",\"score\":0.6138972044,\"categ_score\":-2,\"language\":\"french\",\"line_num\":8},{\"text\":\" Les gardes nationaux et les soldats riaient en l'ajustant\",\"label\":\"negative\",\"score\":0.7059093714,\"categ_score\":-2,\"language\":\"french\",\"line_num\":9},{\"text\":\" Il se couchait\",\"label\":\"negative\",\"score\":0.7937528491,\"categ_score\":-2,\"language\":\"french\",\"line_num\":10},{\"text\":\" puis se redressait\",\"label\":\"negative\",\"score\":0.4207386971,\"categ_score\":-1,\"language\":\"french\",\"line_num\":11},{\"text\":\" s'effa\\u00e7ait dans un coin de porte\",\"label\":\"negative\",\"score\":0.7228686213,\"categ_score\":-2,\"language\":\"french\",\"line_num\":12},{\"text\":\" puis bondissait\",\"label\":\"neutral\",\"score\":0.4156142175,\"categ_score\":0,\"language\":\"french\",\"line_num\":13},{\"text\":\"disparaissait\",\"label\":\"negative\",\"score\":0.4784072042,\"categ_score\":-1,\"language\":\"french\",\"line_num\":14},{\"text\":\" reparaissait\",\"label\":\"negative\",\"score\":0.3535803854,\"categ_score\":-1,\"language\":\"french\",\"line_num\":15},{\"text\":\" se sauvait\",\"label\":\"positive\",\"score\":0.3500290215,\"categ_score\":1,\"language\":\"french\",\"line_num\":16},{\"text\":\" revenait\",\"label\":\"neutral\",\"score\":0.3778737485,\"categ_score\":0,\"language\":\"french\",\"line_num\":17},{\"text\":\" ripostait \\u00e0 la mitraille par des pieds de nez\",\"label\":\"negative\",\"score\":0.4901872575,\"categ_score\":-1,\"language\":\"french\",\"line_num\":18},{\"text\":\" et cependant pillait les cartouches\",\"label\":\"negative\",\"score\":0.7533603907,\"categ_score\":-2,\"language\":\"french\",\"line_num\":19},{\"text\":\"vidait les gibernes\",\"label\":\"negative\",\"score\":0.4037348032,\"categ_score\":-1,\"language\":\"french\",\"line_num\":20},{\"text\":\"et remplissait son panier\",\"label\":\"neutral\",\"score\":0.4249899387,\"categ_score\":0,\"language\":\"french\",\"line_num\":21},{\"text\":\" Les insurg\\u00e9s, haletants d'anxi\\u00e9t\\u00e9\",\"label\":\"negative\",\"score\":0.935064733,\"categ_score\":-2,\"language\":\"french\",\"line_num\":22},{\"text\":\" le suivaient des yeux\",\"label\":\"negative\",\"score\":0.5427963138,\"categ_score\":-2,\"language\":\"french\",\"line_num\":23},{\"text\":\" La barricade tremblait;\",\"label\":\"negative\",\"score\":0.8943210244,\"categ_score\":-2,\"language\":\"french\",\"line_num\":24},{\"text\":\"Lui, il chantait\",\"label\":\"neutral\",\"score\":0.5099213719,\"categ_score\":0,\"language\":\"french\",\"line_num\":25},{\"text\":\" Ce n'\\u00e9tait pas un enfant\",\"label\":\"negative\",\"score\":0.5122464895,\"categ_score\":-2,\"language\":\"french\",\"line_num\":26},{\"text\":\" ce n'\\u00e9tait pas un homme;\",\"label\":\"neutral\",\"score\":0.4958876371,\"categ_score\":0,\"language\":\"french\",\"line_num\":27},{\"text\":\"c'\\u00e9tait un \\u00e9trange gamin f\\u00e9e\",\"label\":\"negative\",\"score\":0.8780734539,\"categ_score\":-2,\"language\":\"french\",\"line_num\":28},{\"text\":\" On e\\u00fbt dit le nain invuln\\u00e9rable de la m\\u00eal\\u00e9e\",\"label\":\"negative\",\"score\":0.5914828181,\"categ_score\":-2,\"language\":\"french\",\"line_num\":29},{\"text\":\" Les balles couraient apr\\u00e8s lui\",\"label\":\"negative\",\"score\":0.5598840117,\"categ_score\":-2,\"language\":\"french\",\"line_num\":30},{\"text\":\" il \\u00e9tait plus leste qu'elles\",\"label\":\"negative\",\"score\":0.6837890744,\"categ_score\":-2,\"language\":\"french\",\"line_num\":31},{\"text\":\" Il jouait on ne sait quel effrayant jeu de cache-cache avec la mort;\",\"label\":\"negative\",\"score\":0.9013264179,\"categ_score\":-2,\"language\":\"french\",\"line_num\":32},{\"text\":\"chaque fois que la face camarde du spectre s'approchait\",\"label\":\"negative\",\"score\":0.3827009797,\"categ_score\":-1,\"language\":\"french\",\"line_num\":33},{\"text\":\" le gamin lui donnait une pichenette\",\"label\":\"neutral\",\"score\":0.4192706943,\"categ_score\":0,\"language\":\"french\",\"line_num\":34},{\"text\":\"Une balle pourtant\",\"label\":\"negative\",\"score\":0.4155562818,\"categ_score\":-1,\"language\":\"french\",\"line_num\":35},{\"text\":\" mieux ajust\\u00e9e ou plus tra\\u00eetre que les autres\",\"label\":\"negative\",\"score\":0.4451725483,\"categ_score\":-1,\"language\":\"french\",\"line_num\":36},{\"text\":\" finit par atteindre l'enfant feu follet\",\"label\":\"negative\",\"score\":0.6089064479,\"categ_score\":-2,\"language\":\"french\",\"line_num\":37},{\"text\":\" On vit Gavroche chanceler\",\"label\":\"positive\",\"score\":0.4157999754,\"categ_score\":1,\"language\":\"french\",\"line_num\":38},{\"text\":\" puis il s\\u2019affaissa\",\"label\":\"negative\",\"score\":0.7884112597,\"categ_score\":-2,\"language\":\"french\",\"line_num\":39},{\"text\":\" Toute la barricade poussa un cri;\",\"label\":\"negative\",\"score\":0.8513863087,\"categ_score\":-2,\"language\":\"french\",\"line_num\":40},{\"text\":\"mais il y avait de l'Ant\\u00e9e dans ce pygm\\u00e9e;\",\"label\":\"negative\",\"score\":0.5079840422,\"categ_score\":-2,\"language\":\"french\",\"line_num\":41},{\"text\":\" pour le gamin toucher le pav\\u00e9\",\"label\":\"negative\",\"score\":0.4075724185,\"categ_score\":-1,\"language\":\"french\",\"line_num\":42},{\"text\":\"c'est comme pour le g\\u00e9ant toucher la terre;\",\"label\":\"neutral\",\"score\":0.4176287353,\"categ_score\":0,\"language\":\"french\",\"line_num\":43},{\"text\":\"Gavroche n'\\u00e9tait tomb\\u00e9 que pour se redresser; il\",\"label\":\"negative\",\"score\":0.4331025779,\"categ_score\":-1,\"language\":\"french\",\"line_num\":44},{\"text\":\"resta assis sur son s\\u00e9ant\",\"label\":\"negative\",\"score\":0.4118663073,\"categ_score\":-1,\"language\":\"french\",\"line_num\":45},{\"text\":\" un long filet de sang rayait son visage\",\"label\":\"negative\",\"score\":0.4146538675,\"categ_score\":-1,\"language\":\"french\",\"line_num\":46},{\"text\":\" il \\u00e9leva ses deux bras en l\\u2019air\",\"label\":\"neutral\",\"score\":0.472125411,\"categ_score\":0,\"language\":\"french\",\"line_num\":47},{\"text\":\" regarda du c\\u00f4t\\u00e9 d'o\\u00f9 \\u00e9tait venu le coup\",\"label\":\"neutral\",\"score\":0.494336009,\"categ_score\":0,\"language\":\"french\",\"line_num\":48},{\"text\":\"et se mit \\u00e0 chanter\",\"label\":\"neutral\",\"score\":0.3732267618,\"categ_score\":0,\"language\":\"french\",\"line_num\":49},{\"text\":\"Je suis tomb\\u00e9 par terre\",\"label\":\"negative\",\"score\":0.8766723871,\"categ_score\":-2,\"language\":\"french\",\"line_num\":50},{\"text\":\"C'est la faute \\u00e0 Voltaire\",\"label\":\"negative\",\"score\":0.9520866871,\"categ_score\":-2,\"language\":\"french\",\"line_num\":51},{\"text\":\"Le nez dans le ruisseau\",\"label\":\"negative\",\"score\":0.458830148,\"categ_score\":-1,\"language\":\"french\",\"line_num\":52},{\"text\":\"C'est la faute \\u00e0\",\"label\":\"negative\",\"score\":0.9563672543,\"categ_score\":-2,\"language\":\"french\",\"line_num\":53},{\"text\":\"Il n'acheva point\",\"label\":\"negative\",\"score\":0.889710784,\"categ_score\":-2,\"language\":\"french\",\"line_num\":54},{\"text\":\" Une seconde balle du m\\u00eame tireur l'arr\\u00eata court\",\"label\":\"negative\",\"score\":0.4310219586,\"categ_score\":-1,\"language\":\"french\",\"line_num\":55},{\"text\":\"Cette fois il s'abattit la face contre le pav\\u00e9\",\"label\":\"negative\",\"score\":0.5315203071,\"categ_score\":-2,\"language\":\"french\",\"line_num\":56},{\"text\":\" et ne remua plus\",\"label\":\"negative\",\"score\":0.4791243672,\"categ_score\":-1,\"language\":\"french\",\"line_num\":57},{\"text\":\" Cette petite grande \\u00e2me venait de s'envoler\",\"label\":\"negative\",\"score\":0.6855504513,\"categ_score\":-2,\"language\":\"french\",\"line_num\":58}]\n"
     ]
    }
   ],
   "source": [
    "df2 = combo.to_json(orient = 'records')\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78a6ba81-456d-40db-9323-1c84078283ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba1df482-2d19-4452-9936-29d05f3daabe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44b64a01-c9bb-4af1-a72c-7d172477a9d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
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