PreliminarAnalysis.ipynb

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   "id": "647466cc-37f3-4ec9-b93b-489d5c6eb4e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acf67d16-8ded-4e0f-9a97-eeae1b9a26e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = 'Results_ALL.xlsx'\n",
    "results_df = pd.read_excel(results_path, dtype = str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78db16c3-e4e5-4b7a-a320-7e70bfceb1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = results_df.rename(columns={\n",
    "    'Hora de inicio': 'start_date',\n",
    "    'Hora de finalización': 'end_date',\n",
    "    'Selecciona la asignatura':'subject',\n",
    "    'Selecciona la pregunta': 'question',\n",
    "    'Conversación con ChatGPT': 'conversation_ChatGPT',\n",
    "    '¿Cuántas preguntas has realizado a ChatGPT para obtener la mejor respuesta?': 'numTries_ChatGPT',\n",
    "    'Conversación con Gemini': 'conversation_Gemini',\n",
    "    '¿Cuántas preguntas has realizado a Gemini para obtener la mejor respuesta?': 'numTries_Gemini',\n",
    "    'Conversación con Copilot': 'conversation_Copilot',\n",
    "    '¿Cuántas preguntas has realizado a Copilot para obtener la mejor respuesta?': 'numTries_Copilot',\n",
    "    '¿Te ha ayudado ChatGPT a responder a la pregunta?': 'rating_ChatGPT',\n",
    "    '¿Te ha ayudado Gemini a responder a la pregunta?': 'rating_Gemini',\n",
    "    '¿Te ha ayudado Copilot a responder a la pregunta?': 'rating_Copilot',\n",
    "    'Indica tu respuesta a la pregunta':'final_answer'\n",
    "}).drop(columns=results_df.columns, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "628e88b7-1f25-426d-b174-03ec3ca54b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Lenguajes de Programación - Haskell',\n",
       "       'Desarrollo de Aplicaciones Web',\n",
       "       'Desarrollo de Aplicaciones Distribuidas',\n",
       "       'Estructura de datos II', 'Lenguajes de Programación - Python',\n",
       "       'Ampliación de Ingeniería del Software'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df['date'] = pd.to_datetime(results_df['start_date'], errors='coerce')\n",
    "results_df.loc[(results_df[\"date\"]>\"2024-03-15\") & (results_df[\"date\"]<\"2024-04-15\"), \"subject\"] = \"Lenguajes de Programación - Haskell\"\n",
    "results_df.loc[results_df[\"subject\"]==\"Lenguajes de Programación\", \"subject\"] = \"Lenguajes de Programación - Python\"\n",
    "results_df['subject'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6778357d-b879-48d1-be93-3dcb32457e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First attempt\n",
    "# results_df = results_df[results_df['subject'].isin([\"Ampliación de Ingeniería del Software\",\"Desarrollo de Aplicaciones Distribuidas\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0c6827f-093e-4435-b527-8229cde8d457",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final attempt\n",
    "results_df = results_df[results_df['subject'].isin([\n",
    "       'Lenguajes de Programación - Haskell',\n",
    "       'Desarrollo de Aplicaciones Web',\n",
    "       'Desarrollo de Aplicaciones Distribuidas',\n",
    "       'Estructura de datos II',\n",
    "       'Ampliación de Ingeniería del Software'\n",
    "])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7916bad9-fbf0-43e0-a76f-608ffb3bf6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df['numTries_ChatGPT'] = results_df['numTries_ChatGPT'].astype(int)\n",
    "results_df['numTries_Gemini'] = results_df['numTries_Gemini'].astype(int)\n",
    "results_df['numTries_Copilot'] = results_df['numTries_Copilot'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77557964-8d45-4cde-8d65-482c114da9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_questions = [\"Pregunta corta\", \"Pregunta de explicar código\", \"Pregunta de escribir código\"]\n",
    "grouped_questions = [\"Short Questions\", \"Interpreting source code questions\", \"Code writing questions\"]\n",
    "def group_questions(question):\n",
    "    if question in [\"Pregunta 1\", \"Pregunta 2\"]: return grouped_questions[0]\n",
    "    if question in [\"Pregunta 3\", \"Pregunta 4\"]: return grouped_questions[1]\n",
    "    if question in [\"Pregunta 5\", \"Pregunta 6\"]: return grouped_questions[2]\n",
    "results_df['question_grouped'] = results_df['question'].apply(group_questions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41c882a5-931c-488a-91a5-14d184573373",
   "metadata": {},
   "outputs": [],
   "source": [
    "response_map = {\n",
    " 'Si. Me ha dado la mejor respuesta' : 'Mejor respuesta',\n",
    " 'Si. Me ha dado una respuesta correcta, pero menos completa que otras IAs': 'Correcta, pero no tan completa',\n",
    " 'No. Me ha contestado correctamente, pero no responde a la pregunta que le he hecho': 'No responde a la pregunta',\n",
    " 'No. Me ha contestado de forma errónea': 'Responde incorrectamente'\n",
    "}\n",
    "results_df['rating_ChatGPT_reduced'] = results_df['rating_ChatGPT'].apply(lambda rating: response_map[rating])\n",
    "results_df['rating_Gemini_reduced'] = results_df['rating_Gemini'].apply(lambda rating: response_map[rating])\n",
    "results_df['rating_Copilot_reduced'] = results_df['rating_Copilot'].apply(lambda rating: response_map[rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b1285c8-21de-43a0-8bdf-ef11879d6873",
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       "      <th>0</th>\n",
       "      <td>2024-03-15 13:26:03</td>\n",
       "      <td>2024-03-15 13:31:23</td>\n",
       "      <td>Lenguajes de Programación - Haskell</td>\n",
       "      <td>Pregunta 1</td>\n",
       "      <td>Pregunta 1 (Pregunta de teoría con respuesta d...</td>\n",
       "      <td>3</td>\n",
       "      <td>Pregunta 1 (Pregunta de teoría con respuesta d...</td>\n",
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       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
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       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
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       "      <td>Pregunta 1</td>\n",
       "      <td>User ¿Qué es la aplicación de una función en...</td>\n",
       "      <td>1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskel...</td>\n",
       "      <td>1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskel...</td>\n",
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       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>Si. Me ha dado la mejor respuesta</td>\n",
       "      <td>2024-03-15 13:19:25</td>\n",
       "      <td>Short Questions</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Mejor respuesta</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
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       "      <th>2</th>\n",
       "      <td>2024-03-15 13:25:56</td>\n",
       "      <td>2024-03-15 13:33:42</td>\n",
       "      <td>Lenguajes de Programación - Haskell</td>\n",
       "      <td>Pregunta 1</td>\n",
       "      <td>En Haskell, la aplicación de una función se re...</td>\n",
       "      <td>1</td>\n",
       "      <td>En Haskell, la aplicación de una función se re...</td>\n",
       "      <td>1</td>\n",
       "      <td>La **aplicación de una función** en **Haskell*...</td>\n",
       "      <td>1</td>\n",
       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>Si. Me ha dado la mejor respuesta</td>\n",
       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>2024-03-15 13:25:56</td>\n",
       "      <td>Short Questions</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Mejor respuesta</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-03-15 13:31:31</td>\n",
       "      <td>2024-03-15 13:33:54</td>\n",
       "      <td>Lenguajes de Programación - Haskell</td>\n",
       "      <td>Pregunta 1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskel...</td>\n",
       "      <td>1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskel...</td>\n",
       "      <td>1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskel...</td>\n",
       "      <td>1</td>\n",
       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>Si. Me ha dado una respuesta correcta, pero me...</td>\n",
       "      <td>Si. Me ha dado la mejor respuesta</td>\n",
       "      <td>2024-03-15 13:31:31</td>\n",
       "      <td>Short Questions</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Mejor respuesta</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
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       "      <th>4</th>\n",
       "      <td>2024-03-15 13:27:36</td>\n",
       "      <td>2024-03-15 13:34:03</td>\n",
       "      <td>Lenguajes de Programación - Haskell</td>\n",
       "      <td>Pregunta 1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskell?</td>\n",
       "      <td>1</td>\n",
       "      <td>¿Qué es la aplicación de una función en Haskell?</td>\n",
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       "      <td>¿Qué es la aplicación de una función en Haskell?</td>\n",
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       "      <td>Si. Me ha dado la mejor respuesta</td>\n",
       "      <td>Si. Me ha dado la mejor respuesta</td>\n",
       "      <td>2024-03-15 13:27:36</td>\n",
       "      <td>Short Questions</td>\n",
       "      <td>Correcta, pero no tan completa</td>\n",
       "      <td>Mejor respuesta</td>\n",
       "      <td>Mejor respuesta</td>\n",
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      "text/plain": [
       "            start_date             end_date  \\\n",
       "0  2024-03-15 13:26:03  2024-03-15 13:31:23   \n",
       "1  2024-03-15 13:19:25  2024-03-15 13:32:30   \n",
       "2  2024-03-15 13:25:56  2024-03-15 13:33:42   \n",
       "3  2024-03-15 13:31:31  2024-03-15 13:33:54   \n",
       "4  2024-03-15 13:27:36  2024-03-15 13:34:03   \n",
       "\n",
       "                               subject    question  \\\n",
       "0  Lenguajes de Programación - Haskell  Pregunta 1   \n",
       "1  Lenguajes de Programación - Haskell  Pregunta 1   \n",
       "2  Lenguajes de Programación - Haskell  Pregunta 1   \n",
       "3  Lenguajes de Programación - Haskell  Pregunta 1   \n",
       "4  Lenguajes de Programación - Haskell  Pregunta 1   \n",
       "\n",
       "                                conversation_ChatGPT  numTries_ChatGPT  \\\n",
       "0  Pregunta 1 (Pregunta de teoría con respuesta d...                 3   \n",
       "1    User ¿Qué es la aplicación de una función en...                 1   \n",
       "2  En Haskell, la aplicación de una función se re...                 1   \n",
       "3  ¿Qué es la aplicación de una función en Haskel...                 1   \n",
       "4   ¿Qué es la aplicación de una función en Haskell?                 1   \n",
       "\n",
       "                                 conversation_Gemini  numTries_Gemini  \\\n",
       "0  Pregunta 1 (Pregunta de teoría con respuesta d...                1   \n",
       "1  ¿Qué es la aplicación de una función en Haskel...                1   \n",
       "2  En Haskell, la aplicación de una función se re...                1   \n",
       "3  ¿Qué es la aplicación de una función en Haskel...                1   \n",
       "4   ¿Qué es la aplicación de una función en Haskell?                1   \n",
       "\n",
       "                                conversation_Copilot  numTries_Copilot  \\\n",
       "0  Pregunta 1 (Pregunta de teoría con respuesta d...                 1   \n",
       "1  ¿Qué es la aplicación de una función en Haskel...                 3   \n",
       "2  La **aplicación de una función** en **Haskell*...                 1   \n",
       "3  ¿Qué es la aplicación de una función en Haskel...                 1   \n",
       "4   ¿Qué es la aplicación de una función en Haskell?                 1   \n",
       "\n",
       "                                      rating_ChatGPT  \\\n",
       "0                  Si. Me ha dado la mejor respuesta   \n",
       "1  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "2  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "3  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "4  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "\n",
       "                                      rating_Copilot  \\\n",
       "0  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "1  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "2                  Si. Me ha dado la mejor respuesta   \n",
       "3  Si. Me ha dado una respuesta correcta, pero me...   \n",
       "4                  Si. Me ha dado la mejor respuesta   \n",
       "\n",
       "                                       rating_Gemini                date  \\\n",
       "0  Si. Me ha dado una respuesta correcta, pero me... 2024-03-15 13:26:03   \n",
       "1                  Si. Me ha dado la mejor respuesta 2024-03-15 13:19:25   \n",
       "2  Si. Me ha dado una respuesta correcta, pero me... 2024-03-15 13:25:56   \n",
       "3                  Si. Me ha dado la mejor respuesta 2024-03-15 13:31:31   \n",
       "4                  Si. Me ha dado la mejor respuesta 2024-03-15 13:27:36   \n",
       "\n",
       "  question_grouped          rating_ChatGPT_reduced  \\\n",
       "0  Short Questions                 Mejor respuesta   \n",
       "1  Short Questions  Correcta, pero no tan completa   \n",
       "2  Short Questions  Correcta, pero no tan completa   \n",
       "3  Short Questions  Correcta, pero no tan completa   \n",
       "4  Short Questions  Correcta, pero no tan completa   \n",
       "\n",
       "            rating_Gemini_reduced          rating_Copilot_reduced  \n",
       "0  Correcta, pero no tan completa  Correcta, pero no tan completa  \n",
       "1                 Mejor respuesta  Correcta, pero no tan completa  \n",
       "2  Correcta, pero no tan completa                 Mejor respuesta  \n",
       "3                 Mejor respuesta  Correcta, pero no tan completa  \n",
       "4                 Mejor respuesta                 Mejor respuesta  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f04b6e9-f99b-4eef-acfa-e4eeba1262e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def numTries_perQuestion(question):\n",
    "    df = results_df\n",
    "    if question != 'All':\n",
    "        df = results_df[results_df['question_grouped']==question]\n",
    "    results = [\n",
    "        question,\n",
    "        df['numTries_ChatGPT'].mean(),\n",
    "        df[df['numTries_Gemini']>0]['numTries_Gemini'].mean(),\n",
    "        #df['numTries_Gemini'].mean(),\n",
    "        df['numTries_Copilot'].mean()\n",
    "    ]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5e2e0a-5f97-423b-9f68-e5701a534686",
   "metadata": {},
   "source": [
    "# Número de preguntas hechas a cada IA (media)\n",
    "\n",
    "Comentarios:\n",
    "- Hay 13 alumnos que en Gemini tienen un 0 porque tuvieron problemas con su cuenta de Google\n",
    "\n",
    "Observaciones preliminares:\n",
    "- En general, la media de nº de preguntas para todas las IAs es de ~1.26 intentos de pregunta\n",
    "- Las preguntas de escribir código (5 y 6) son las que menos preguntas de media han requerido (salvo para Copilot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a7cbe74-4f2f-45e3-a57e-95727e0db8fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results_df[results_df['numTries_Gemini']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f018e5e2-3204-41d4-8697-37c932db9091",
   "metadata": {},
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>Gemini</th>\n",
       "      <th>Copilot</th>\n",
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       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>1.237</td>\n",
       "      <td>1.201</td>\n",
       "      <td>1.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Short Questions</td>\n",
       "      <td>1.201</td>\n",
       "      <td>1.190</td>\n",
       "      <td>1.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interpreting source code questions</td>\n",
       "      <td>1.209</td>\n",
       "      <td>1.210</td>\n",
       "      <td>1.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Code writing questions</td>\n",
       "      <td>1.322</td>\n",
       "      <td>1.206</td>\n",
       "      <td>1.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Question  ChatGPT  Gemini  Copilot\n",
       "0                                 All    1.237   1.201    1.209\n",
       "1                     Short Questions    1.201   1.190    1.240\n",
       "2  Interpreting source code questions    1.209   1.210    1.147\n",
       "3              Code writing questions    1.322   1.206    1.240"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = [numTries_perQuestion(\"All\")] + [numTries_perQuestion(q) for q in grouped_questions]\n",
    "pd.DataFrame(rows,columns=['Question','ChatGPT','Gemini','Copilot']).round(decimals=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "453f1a27-2e2a-4c93-8bd9-de2a2aeb2b87",
   "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>numTries_ChatGPT</th>\n",
       "      <th>numTries_Gemini</th>\n",
       "      <th>numTries_Copilot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.237192</td>\n",
       "      <td>1.064516</td>\n",
       "      <td>1.208729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.746794</td>\n",
       "      <td>0.634566</td>\n",
       "      <td>0.599493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       numTries_ChatGPT  numTries_Gemini  numTries_Copilot\n",
       "count        527.000000       527.000000        527.000000\n",
       "mean           1.237192         1.064516          1.208729\n",
       "std            0.746794         0.634566          0.599493\n",
       "min            0.000000         0.000000          0.000000\n",
       "25%            1.000000         1.000000          1.000000\n",
       "50%            1.000000         1.000000          1.000000\n",
       "75%            1.000000         1.000000          1.000000\n",
       "max           10.000000         6.000000          6.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[['numTries_ChatGPT','numTries_Gemini','numTries_Copilot']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46e3380d-6fd2-4f8b-919d-16fc5661220b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numTries_ChatGPT 0.003795066413662239\n",
      "numTries_Gemini 0.11385199240986717\n",
      "numTries_Copilot 0.022770398481973434\n"
     ]
    }
   ],
   "source": [
    "for ia in ['numTries_ChatGPT','numTries_Gemini','numTries_Copilot']:\n",
    "    one_question_per = (results_df[ia][results_df[ia]==0].count() \n",
    "         / results_df[ia].count())\n",
    "    print(ia, one_question_per)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afea87b6-93d1-4d3e-b923-9199ed27d5b7",
   "metadata": {},
   "source": [
    "# Cuánto ha ayudado cada IA en cada pregunta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9f82a7c-cc6c-4db1-9d54-395a324fc3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = {\n",
    "    'Mejor respuesta':  'limegreen',\n",
    "    'Correcta, pero no tan completa': 'yellow', \n",
    "    'No responde a la pregunta': 'orange', \n",
    "    'Responde incorrectamente': 'orangered'\n",
    "}\n",
    "\n",
    "def ia_rating_pieChart(ia, question, ax):\n",
    "    df = results_df[results_df['question_grouped']==question].groupby(['rating_'+ia+'_reduced']).count()\n",
    "    title = ia if question == grouped_questions[0] else \"\"\n",
    "    ylabel = question if ia == \"ChatGPT\" else \"\"\n",
    "    showLegend = question == grouped_questions[2] and ia == \"Copilot\"\n",
    "    df.plot.pie(\n",
    "        y='question_grouped', ylabel=ylabel, autopct='%1.1f%%', labels=None,#labeldistance=None,\n",
    "        title=title,\n",
    "        ax=ax, legend=showLegend, colors=[colors[name] for name in df.index]\n",
    "    )\n",
    "    if showLegend: \n",
    "        # SPANISH ax.legend(df.index,loc='lower center', bbox_to_anchor=(0.4, 3.2, 0, 0))\n",
    "        ax.legend([\n",
    "            \"Yes, it gave a correct answer, but less complete than other AIs\", \n",
    "            \"Yes, it gave the best answer\", \n",
    "            \"No, the answer is correct, but not to the question asked.\",\n",
    "            \"No, it has given the wrong answer\"\n",
    "        ],loc='lower center', bbox_to_anchor=(0.4, 3.2, 0, 0))\n",
    "    return df\n",
    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(10,10))\n",
    "fig.subplots_adjust(hspace=0, wspace=0.05)\n",
    "\n",
    "row,col = 0,0\n",
    "for question in grouped_questions:\n",
    "    for ia in ['ChatGPT','Gemini','Copilot']:\n",
    "        ia_rating_pieChart(ia, question, axes[row,col])\n",
    "        if col == 2:\n",
    "            col = 0\n",
    "            row = row + 1\n",
    "        else:\n",
    "            col+=1\n",
    "plt.savefig('IA_comparative_per_question_type.png', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}