codigo_principal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las librerias que vamos a utilizar\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tabulate import tabulate\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Importamos las diferentes funciones definidas que estan en la carpeta 'modulos'\n",
    "from modulos.extraccion_campo_busq import extraer_campo\n",
    "from modulos.extraccion_consulta import extraer_sintaxis\n",
    "from modulos.validacion_campos_busq import verificar_error_campo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Abrimos el archivo 'Estadisticas consultas catálogo-reportresults.csv' que contiene los datos de la busqueda de los usuarios en el sistema Koha\n",
    "#desde septiembre de 2018, hasta la primera semana de septiembre de 2024\n",
    "datos = pd.read_csv(\"archivos/Estadísticas consultas catálogo-reportresults.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DESCRIPCIÓN DE LAS COLUMNAS DEL ARCHIVO:\n",
    "\n",
    "* `sessionid`    ---> Identificador del inicio de sesión.\n",
    "* `query_desc`   ---> Campos utilizados en la búsqueda.\n",
    "* `type`         ---> ('biblio', 'authority').\n",
    "* `total`        ---> Cantidad de registros que devuelve el sistema con los términos ingresados.\n",
    "* `time`         ---> Fecha y hora de la búsqueda.\n",
    "* `branchcode`   ---> Identificador de la Facultad de Ingeniería ('FING').\n",
    "* `categorycode` ---> Identificadores de la persona que hace la búsqueda, si es docente, de biblioteca, egresado, invitado de otra facultad, alumno de intercambio, etc. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploramos los datos crudos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>sessionid</th>\n",
       "      <th>query_desc</th>\n",
       "      <th>type</th>\n",
       "      <th>total</th>\n",
       "      <th>time</th>\n",
       "      <th>branchcode</th>\n",
       "      <th>categorycode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8b973f78de404b6eb6548e4a229cb725</td>\n",
       "      <td>au,wrdl: bronzino</td>\n",
       "      <td>biblio</td>\n",
       "      <td>16</td>\n",
       "      <td>2018-10-17 15:27:49</td>\n",
       "      <td>FING</td>\n",
       "      <td>DOCENTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8b973f78de404b6eb6548e4a229cb725</td>\n",
       "      <td>au,wrdl: bronzino</td>\n",
       "      <td>biblio</td>\n",
       "      <td>16</td>\n",
       "      <td>2018-10-17 15:28:25</td>\n",
       "      <td>FING</td>\n",
       "      <td>DOCENTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8b973f78de404b6eb6548e4a229cb725</td>\n",
       "      <td>au,wrdl: webster</td>\n",
       "      <td>biblio</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-17 15:29:51</td>\n",
       "      <td>FING</td>\n",
       "      <td>DOCENTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8b973f78de404b6eb6548e4a229cb725</td>\n",
       "      <td>au,wrdl: webster</td>\n",
       "      <td>biblio</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-17 15:30:02</td>\n",
       "      <td>FING</td>\n",
       "      <td>DOCENTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8b973f78de404b6eb6548e4a229cb725</td>\n",
       "      <td>au,wrdl: webster</td>\n",
       "      <td>biblio</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-17 15:30:13</td>\n",
       "      <td>FING</td>\n",
       "      <td>DOCENTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          sessionid         query_desc    type  total  \\\n",
       "0  8b973f78de404b6eb6548e4a229cb725  au,wrdl: bronzino  biblio     16   \n",
       "1  8b973f78de404b6eb6548e4a229cb725  au,wrdl: bronzino  biblio     16   \n",
       "2  8b973f78de404b6eb6548e4a229cb725   au,wrdl: webster  biblio     10   \n",
       "3  8b973f78de404b6eb6548e4a229cb725   au,wrdl: webster  biblio     10   \n",
       "4  8b973f78de404b6eb6548e4a229cb725   au,wrdl: webster  biblio     10   \n",
       "\n",
       "                  time branchcode categorycode  \n",
       "0  2018-10-17 15:27:49       FING      DOCENTE  \n",
       "1  2018-10-17 15:28:25       FING      DOCENTE  \n",
       "2  2018-10-17 15:29:51       FING      DOCENTE  \n",
       "3  2018-10-17 15:30:02       FING      DOCENTE  \n",
       "4  2018-10-17 15:30:13       FING      DOCENTE  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostramos las primeras filas del archivo\n",
    "datos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sessionid       object\n",
       "query_desc      object\n",
       "type            object\n",
       "total            int64\n",
       "time            object\n",
       "branchcode      object\n",
       "categorycode    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostramos los tipos de datos de las columnas\n",
    "datos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2535.000000\n",
       "mean       190.157791\n",
       "std        820.385073\n",
       "min          0.000000\n",
       "25%          2.000000\n",
       "50%          7.000000\n",
       "75%         33.000000\n",
       "max      11525.000000\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostramos algunas de las estadisticas de la columna 'total'\n",
    "datos['total'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Las búsquedas totales que no tuvieron ningun resultado son: 317\n",
      "Esto representa un porcentaje del 12.5 %\n"
     ]
    }
   ],
   "source": [
    "# Cantidad de busquedas que no arrojaron resultados\n",
    "cero_resultados = datos[datos['total'] == 0]\n",
    "\n",
    "print(f\"Las búsquedas totales que no tuvieron ningun resultado son: {len(cero_resultados)}\")\n",
    "print(f\"Esto representa un porcentaje del {round((len(cero_resultados) / len(datos))*100, 2)} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Máximo de resultados que encontró el sistema: 11525\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>sessionid</th>\n",
       "      <th>query_desc</th>\n",
       "      <th>type</th>\n",
       "      <th>total</th>\n",
       "      <th>time</th>\n",
       "      <th>branchcode</th>\n",
       "      <th>categorycode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>7a7a070b4c157d901bc30c69555b7dfa</td>\n",
       "      <td>kw,wrdl: c++,</td>\n",
       "      <td>biblio</td>\n",
       "      <td>11525</td>\n",
       "      <td>2022-11-01 11:30:25</td>\n",
       "      <td>FING</td>\n",
       "      <td>BIBLIOTECA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             sessionid      query_desc    type  total  \\\n",
       "1394  7a7a070b4c157d901bc30c69555b7dfa  kw,wrdl: c++,   biblio  11525   \n",
       "\n",
       "                     time branchcode categorycode  \n",
       "1394  2022-11-01 11:30:25       FING   BIBLIOTECA  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buscamos la consulta que obtuvo el mayor numero de resultados encontrado\n",
    "max_resultado = datos['total'].max()\n",
    "print(f\"Máximo de resultados que encontró el sistema: {max_resultado}\")\n",
    "\n",
    "# Encontramos el usuario que realizo esta busqueda\n",
    "datos[datos['total'] == max_resultado]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentaje de búsquedas con resultados menores a 100: 86.27%\n"
     ]
    }
   ],
   "source": [
    "# Definimos los rangos de las barras: de 1000 en 1000\n",
    "bins = range(0, int(datos['total'].max()) + 1000, 1000)\n",
    "\n",
    "# Histograma de la columna 'total'\n",
    "plt.hist(datos['total'], bins=bins, edgecolor='black', color='red')\n",
    "plt.title('Distribución de resultados obtenidos por los usuarios')\n",
    "plt.xlabel('Número de resultados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.show()\n",
    "\n",
    "#=======================================================================================\n",
    "# Histograma de la columna 'total' centrada en los resultados menores a 1000\n",
    "# Definimos los rangos de las barras: de 100 en 100\n",
    "bins = range(0, int(datos['total'].max()) + 100, 100)\n",
    "plt.hist(datos['total'], bins=bins, edgecolor='black', color='blue')\n",
    "plt.title('Distribución de resultados obtenidos por los usuarios')\n",
    "plt.xlabel('Número de resultados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.xlim(0, 1000)\n",
    "plt.show()\n",
    "\n",
    "# Porcentaje de busquedas con resultados menores a 100\n",
    "print(f\"Porcentaje de búsquedas con resultados menores a 100: {round(len(datos[datos['total'] <= 100]) / len(datos)*100, 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identificación de las variables más importantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtramos el Data Frame: nos quedaremos con las variables que resolverán los objetivos planteados\n",
    "df_filtrado = datos[['sessionid', 'query_desc', 'total', 'time', 'categorycode']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Etapa de limpieza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de filas duplicadas eliminadas: 65\n",
      "Representando un 2.56% del total de filas\n"
     ]
    }
   ],
   "source": [
    "# Borramos las filas duplicadas en el Data Frame\n",
    "df_filtrado.drop_duplicates()\n",
    "\n",
    "print(\"Cantidad de filas duplicadas eliminadas:\", len(df_filtrado) - len(df_filtrado.drop_duplicates()))\n",
    "print(f\"Representando un {((len(df_filtrado) - len(df_filtrado.drop_duplicates())) / len(df_filtrado)) * 100:.2f}% del total de filas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2535 entries, 0 to 2534\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   sessionid     2535 non-null   object\n",
      " 1   query_desc    2535 non-null   object\n",
      " 2   total         2535 non-null   int64 \n",
      " 3   time          2535 non-null   object\n",
      " 4   categorycode  2535 non-null   object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 99.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Nos fijamos si las columnas tienen algún valor nulo\n",
    "df_filtrado.info()\n",
    "\n",
    "# Podemos observar que de las 2535 filas ninguna posee valores nulos en sus variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos el DataFrame filtrado en un archivo CSV\n",
    "df_filtrado.to_csv('archivos/df_filtrado.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hay busquedas que fueron realizadas para una practica academica, estas busquedas como contienen informacion que no es relevante para el analisis, las eliminaremos\n",
    "df_filtrado = df_filtrado.drop(df_filtrado[df_filtrado['sessionid'] == 'e03def7e497ec6c9bb60f290d5247dc8'].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparación de los datos para el análisis estadístico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DEFINICIÓN DE LOS CAMPOS DE BÚSQUEDA UTILIZADOS POR LOS USUARIOS Y QUE BRINDA EL SISTEMA:\n",
    "\n",
    "* `kw`        ----> Palabra clave. Palabra que tiene que estar presente en el material, puede ser en cualquier campo.\n",
    "* `au/AU`     ----> Nombre o apellido del autor. Todo el material que está cargado para ese autor.\n",
    "* `mc-itype`  ----> Tipo del material, ya sea libro, revista, tesis, folleto, apuntes, sean impresas u otro formato.\n",
    "* `ti`        ----> Título del material.\n",
    "* `an`        ----> Número de la autoridad registrada en la biblioteca. Asocia ese número con X autor/materia.\n",
    "* `su`        ----> Materia/s asociada/s al contenido del material. Descriptores y palabras claves asignados por el personal.\n",
    "* `BC/bc`     ----> Equivalente al código de barras de un producto. Número del inventario.\n",
    "* `su-to`     ----> Escritura del sistema cuando en el catálogo en línea, la sintaxis involucra un elemento inicial y le agrega un tema.\n",
    "* `callnum`   ----> Número del estante (en biblioteca) donde está el material.\n",
    "* `I-format`  ----> El formato (soporte del material). Excluye impreso.\n",
    "* `se`        ----> Identifica una serie/colección.\n",
    "* `In`        ----> Es el idioma del material.\n",
    "* `Provider`  ----> Editorial del libro.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos una lista de los campos de búsqueda que estan disponibles en el sistema\n",
    "campos_busqueda = ['kw', 'au', 'mc-itype', 'ti', 'an', 'itype', 'su', 'BC', 'bc', 'callnum', \n",
    "    'I-format', 'AU', 'an', 'se', 'In', 'bx', 'se', 'su-to', 'mc-itype,phr', 'l-format', 'Provider']\n",
    "\n",
    "# Creamos una expresión regular para encontrar los campos\n",
    "patrones = r'\\b(?:' + '|'.join(campos_busqueda) + r')\\b'\n",
    "\n",
    "# Buscamos las búsquedas que no tienen un campo ingresado\n",
    "filas_sin_query = df_filtrado[~ df_filtrado['query_desc'].str.contains(patrones, na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputamos las filas que no tienen campos ingresados\n",
    "filas_sin_query.loc[filas_sin_query.index == 750, 'query_desc'] = filas_sin_query.loc[750, 'query_desc'].replace('robot', 'kw: robot')\n",
    "filas_sin_query.loc[filas_sin_query.index == 752, 'query_desc'] = filas_sin_query.loc[752, 'query_desc'].replace('robot', 'kw: robot')\n",
    "filas_sin_query.loc[filas_sin_query.index == 754, 'query_desc'] = filas_sin_query.loc[754, 'query_desc'].replace('da vinci', 'kw,wrdl: da vinci')\n",
    "filas_sin_query.loc[filas_sin_query.index == 756, 'query_desc'] = filas_sin_query.loc[756, 'query_desc'].replace('robotica', 'kw: robotica')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actualizamos los valores de los campos de busqueda en el data frame principal\n",
    "df_filtrado.update(filas_sin_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplicar la función para crear una nueva columna 'campos_utilzado'\n",
    "df_filtrado.loc[:, 'campo_utilizado'] = df_filtrado['query_desc'].apply(extraer_campo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pero como no se guardan todos los campos que utilizo el usuario, tendremos que realizar otra imputacion para estos casos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Buscamos las filas que tienen más de un : en la búsqueda. Esto nos indica que el usuario ingresó más de un campo de búsqueda\n",
    "df_filtrado['contador'] = df_filtrado['query_desc'].str.count(':')\n",
    "\n",
    "# Filtramos las filas que tienen más de un : en la búsqueda\n",
    "dos_puntos = df_filtrado[df_filtrado['contador'] > 1]\n",
    "\n",
    "# Borramos la columna 'contador' ya que no es necesaria\n",
    "df_filtrado.drop('contador', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputación de los campos utilizados por los usuarios para hacer la busqueda\n",
    "df_filtrado.loc[5, 'campo_utilizado'] = (df_filtrado.loc[5, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[6, 'campo_utilizado'] = (df_filtrado.loc[6, 'campo_utilizado'] + ', su-to' + ', su-to')\n",
    "df_filtrado.loc[7, 'campo_utilizado'] = (df_filtrado.loc[7, 'campo_utilizado'] + ', su-to' + ', su-to' + ', su-to')\n",
    "df_filtrado.loc[8, 'campo_utilizado'] = (df_filtrado.loc[8, 'campo_utilizado'] + ', su-to' + ', su-to')\n",
    "df_filtrado.loc[9, 'campo_utilizado'] = (df_filtrado.loc[9, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[10, 'campo_utilizado'] = (df_filtrado.loc[10, 'campo_utilizado'] + ', su-to' + ', su-to')\n",
    "df_filtrado.loc[11, 'campo_utilizado'] = (df_filtrado.loc[11, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[29, 'campo_utilizado'] = (df_filtrado.loc[29, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[33, 'campo_utilizado'] = (df_filtrado.loc[33, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[35, 'campo_utilizado'] = (df_filtrado.loc[35, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[48, 'campo_utilizado'] = (df_filtrado.loc[48, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[49, 'campo_utilizado'] = (df_filtrado.loc[49, 'campo_utilizado'] + ', mc-itype' + ', mc-itype')\n",
    "df_filtrado.loc[111, 'campo_utilizado'] = (df_filtrado.loc[111, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[252, 'campo_utilizado'] = (df_filtrado.loc[252, 'campo_utilizado'] + ', l-format')\n",
    "df_filtrado.loc[256, 'campo_utilizado'] = (df_filtrado.loc[256, 'campo_utilizado'] + ', l-format')\n",
    "df_filtrado.loc[277, 'campo_utilizado'] = (df_filtrado.loc[277, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[288, 'campo_utilizado'] = (df_filtrado.loc[288, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[358, 'campo_utilizado'] = (df_filtrado.loc[358, 'campo_utilizado'] + ', ti'+ ', not au')\n",
    "df_filtrado.loc[368, 'campo_utilizado'] = (df_filtrado.loc[368, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[374, 'campo_utilizado'] = (df_filtrado.loc[374, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[395, 'campo_utilizado'] = (df_filtrado.loc[395, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[397, 'campo_utilizado'] = (df_filtrado.loc[397, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[429, 'campo_utilizado'] = (df_filtrado.loc[429, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[431, 'campo_utilizado'] = (df_filtrado.loc[431, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[432, 'campo_utilizado'] = (df_filtrado.loc[432, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[444, 'campo_utilizado'] = (df_filtrado.loc[444, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[453, 'campo_utilizado'] = (df_filtrado.loc[453, 'campo_utilizado'] + ', su')\n",
    "df_filtrado.loc[628, 'campo_utilizado'] = (df_filtrado.loc[628, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[636, 'campo_utilizado'] = (df_filtrado.loc[636, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[637, 'campo_utilizado'] = (df_filtrado.loc[637, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[649, 'campo_utilizado'] = (df_filtrado.loc[649, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[746, 'campo_utilizado'] = (df_filtrado.loc[746, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[747, 'campo_utilizado'] = (df_filtrado.loc[747, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[749, 'campo_utilizado'] = (df_filtrado.loc[749, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[768, 'campo_utilizado'] = (df_filtrado.loc[768, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[773, 'campo_utilizado'] = (df_filtrado.loc[773, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[774, 'campo_utilizado'] = (df_filtrado.loc[774, 'campo_utilizado'] + ', se')\n",
    "df_filtrado.loc[775, 'campo_utilizado'] = (df_filtrado.loc[775, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[776, 'campo_utilizado'] = (df_filtrado.loc[776, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[779, 'campo_utilizado'] = (df_filtrado.loc[779, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[780, 'campo_utilizado'] = (df_filtrado.loc[780, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[862, 'campo_utilizado'] = (df_filtrado.loc[862, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[868, 'campo_utilizado'] = (df_filtrado.loc[868, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[869, 'campo_utilizado'] = (df_filtrado.loc[869, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[884, 'campo_utilizado'] = (df_filtrado.loc[884, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[964, 'campo_utilizado'] = (df_filtrado.loc[964, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[974, 'campo_utilizado'] = (df_filtrado.loc[974, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[975, 'campo_utilizado'] = (df_filtrado.loc[975, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1117, 'campo_utilizado'] = (df_filtrado.loc[1117, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1119, 'campo_utilizado'] = (df_filtrado.loc[1119, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1131, 'campo_utilizado'] = (df_filtrado.loc[1131, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1187, 'campo_utilizado'] = (df_filtrado.loc[1187, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1188, 'campo_utilizado'] = (df_filtrado.loc[1188, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1189, 'campo_utilizado'] = (df_filtrado.loc[1189, 'campo_utilizado'] + ', au' + ', mc-itype' + ', mc-itype')\n",
    "df_filtrado.loc[1196, 'campo_utilizado'] = (df_filtrado.loc[1196, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1223, 'campo_utilizado'] = (df_filtrado.loc[1223, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1273, 'campo_utilizado'] = (df_filtrado.loc[1273, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1275, 'campo_utilizado'] = (df_filtrado.loc[1275, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1302, 'campo_utilizado'] = (df_filtrado.loc[1302, 'campo_utilizado'] + ', kw' + ', mc-itype')\n",
    "df_filtrado.loc[1303, 'campo_utilizado'] = (df_filtrado.loc[1303, 'campo_utilizado'] + ', kw' + ', mc-itype')\n",
    "df_filtrado.loc[1304, 'campo_utilizado'] = (df_filtrado.loc[1304, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1305, 'campo_utilizado'] = (df_filtrado.loc[1305, 'campo_utilizado'] + ', ti')\n",
    "df_filtrado.loc[1306, 'campo_utilizado'] = (df_filtrado.loc[1306, 'campo_utilizado'] + ', ti' + ', mc-itype' + ', mc-itype')\n",
    "df_filtrado.loc[1307, 'campo_utilizado'] = (df_filtrado.loc[1307, 'campo_utilizado'] + ', ti' + ', In')\n",
    "df_filtrado.loc[1384, 'campo_utilizado'] = (df_filtrado.loc[1384, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1470, 'campo_utilizado'] = (df_filtrado.loc[1470, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1471, 'campo_utilizado'] = (df_filtrado.loc[1471, 'campo_utilizado'] + ', mc-itype' + ', su-to')\n",
    "df_filtrado.loc[1472, 'campo_utilizado'] = (df_filtrado.loc[1472, 'campo_utilizado'] + ', mc-itype' + ', su-to')\n",
    "df_filtrado.loc[1473, 'campo_utilizado'] = (df_filtrado.loc[1473, 'campo_utilizado'] + ', mc-itype' + ', su-to')\n",
    "df_filtrado.loc[1474, 'campo_utilizado'] = (df_filtrado.loc[1474, 'campo_utilizado'] + ', mc-itype' + ', su-to')\n",
    "df_filtrado.loc[1475, 'campo_utilizado'] = (df_filtrado.loc[1475, 'campo_utilizado'] + ', mc-itype' + ', su-to' + ', au')\n",
    "df_filtrado.loc[1494, 'campo_utilizado'] = (df_filtrado.loc[1494, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[1593, 'campo_utilizado'] = (df_filtrado.loc[1593, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1594, 'campo_utilizado'] = (df_filtrado.loc[1594, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1596, 'campo_utilizado'] = (df_filtrado.loc[1596, 'campo_utilizado'] + ', kw')\n",
    "df_filtrado.loc[1597, 'campo_utilizado'] = (df_filtrado.loc[1597, 'campo_utilizado'] + ', kw' + ', mc-itype')\n",
    "df_filtrado.loc[1658, 'campo_utilizado'] = (df_filtrado.loc[1658, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1731, 'campo_utilizado'] = (df_filtrado.loc[1731, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1769, 'campo_utilizado'] = (df_filtrado.loc[1769, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1782, 'campo_utilizado'] = (df_filtrado.loc[1782, 'campo_utilizado'] + ', su-to')\n",
    "df_filtrado.loc[1841, 'campo_utilizado'] = (df_filtrado.loc[1841, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1842, 'campo_utilizado'] = (df_filtrado.loc[1842, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1845, 'campo_utilizado'] = (df_filtrado.loc[1845, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1880, 'campo_utilizado'] = (df_filtrado.loc[1880, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1961, 'campo_utilizado'] = (df_filtrado.loc[1961, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1962, 'campo_utilizado'] = (df_filtrado.loc[1962, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1968, 'campo_utilizado'] = (df_filtrado.loc[1968, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1969, 'campo_utilizado'] = (df_filtrado.loc[1969, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[1978, 'campo_utilizado'] = (df_filtrado.loc[1978, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2045, 'campo_utilizado'] = (df_filtrado.loc[2045, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[2202, 'campo_utilizado'] = (df_filtrado.loc[2202, 'campo_utilizado'] + ', l-format')\n",
    "df_filtrado.loc[2206, 'campo_utilizado'] = (df_filtrado.loc[2206, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2217, 'campo_utilizado'] = (df_filtrado.loc[2217, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2219, 'campo_utilizado'] = (df_filtrado.loc[2219, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2239, 'campo_utilizado'] = (df_filtrado.loc[2239, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[2323, 'campo_utilizado'] = (df_filtrado.loc[2323, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[2365, 'campo_utilizado'] = (df_filtrado.loc[2365, 'campo_utilizado'] + ', mc-itype' + ', mc-itype')\n",
    "df_filtrado.loc[2379, 'campo_utilizado'] = (df_filtrado.loc[2379, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2427, 'campo_utilizado'] = (df_filtrado.loc[2427, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2474, 'campo_utilizado'] = (df_filtrado.loc[2474, 'campo_utilizado'] + ', au')\n",
    "df_filtrado.loc[2490, 'campo_utilizado'] = (df_filtrado.loc[2490, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2491, 'campo_utilizado'] = (df_filtrado.loc[2491, 'campo_utilizado'] + ', mc-itype')\n",
    "df_filtrado.loc[2533, 'campo_utilizado'] = (df_filtrado.loc[2533, 'campo_utilizado'] + ', au')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agregamos una nueva columna que contabilice la cantidad de campos utilizados por el usuario\n",
    "df_filtrado['cant_campos_utilizados'] = df_filtrado['campo_utilizado'].apply(lambda x: 0 if pd.isna(x) else x.count(',') + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pasamos todos los campos utilizados a minúsculas\n",
    "df_filtrado['campo_utilizado'] = df_filtrado['campo_utilizado'].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representaciones graficas de los primeros analisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico de barras para ver la cantidad de campos de busquedas utilizados a la vez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cant_campos_utilizados\n",
      "0       1\n",
      "1    2382\n",
      "2      85\n",
      "3      14\n",
      "4       4\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contamos la cantidad de veces que se utiliza cada cantidad de campos\n",
    "cant_campos = df_filtrado['cant_campos_utilizados'].value_counts().sort_index()\n",
    "print(cant_campos)\n",
    "\n",
    "# Gráfico de barras para la cantidad de campos utilizados por los usuarios\n",
    "plt.figure(figsize=(10, 6))\n",
    "cant_campos.plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Cantidad de campos utilizados')\n",
    "plt.ylabel('Número de usuarios')\n",
    "plt.title('Cantidad de campos utilizados por los usuarios')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico de barras para visualizar los campos de busqueda mas utilizados por los usuarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convertimos los campos a minúsculas y sumamos las frecuencias\n",
    "frecuencia_campos = df_filtrado['campo_utilizado'].str.lower().str.split(', ', expand=True).stack().value_counts()\n",
    "\n",
    "# Gráfico de las frecuencias de los campos utilizados por los usuarios\n",
    "plt.figure(figsize=(12, 8))\n",
    "frecuencia_campos.plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Campos utilizados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Frecuencia de campos utilizados por los usuarios')\n",
    "plt.show()\n",
    "\n",
    "#===========================================================================================================================\n",
    "\n",
    "# Gráfico de las frecuencias de los campos utilizados por los usuarios sin 'kw'\n",
    "# Filtramos la serie para excluir el campo 'kw'\n",
    "frecuencia_campos_sin_kw = frecuencia_campos.drop('kw')\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "frecuencia_campos_sin_kw.plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Campos utilizados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Frecuencia de campos utilizados por los usuarios (sin \"kw\")')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tabla de frecuencias de las combinaciones de campos que utilizaron los usuarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Combinacion de Campos         |   Frecuencia |   Porcentaje |\n",
      "|:------------------------------|-------------:|-------------:|\n",
      "| kw, mc-itype                  |           35 |        33.98 |\n",
      "| au, kw                        |           12 |        11.65 |\n",
      "| kw, su-to                     |            9 |         8.74 |\n",
      "| au, mc-itype                  |            5 |         4.85 |\n",
      "| au, ti                        |            4 |         3.88 |\n",
      "| mc-itype, mc-itype, su-to     |            4 |         3.88 |\n",
      "| au, su-to                     |            3 |         2.91 |\n",
      "| mc-itype, su-to               |            3 |         2.91 |\n",
      "| mc-itype, mc-itype            |            3 |         2.91 |\n",
      "| l-format, mc-itype            |            3 |         2.91 |\n",
      "| au, su-to, su-to              |            3 |         2.91 |\n",
      "| au, mc-itype, mc-itype, ti    |            2 |         1.94 |\n",
      "| kw, kw, mc-itype              |            2 |         1.94 |\n",
      "| mc-itype, su                  |            2 |         1.94 |\n",
      "| au, mc-itype, mc-itype, su-to |            1 |         0.97 |\n",
      "| kw, su                        |            1 |         0.97 |\n",
      "| au, in, ti                    |            1 |         0.97 |\n",
      "| mc-itype, ti                  |            1 |         0.97 |\n",
      "| kw, mc-itype, su              |            1 |         0.97 |\n",
      "| su, ti                        |            1 |         0.97 |\n",
      "| kw, se                        |            1 |         0.97 |\n",
      "| kw, not au, ti                |            1 |         0.97 |\n",
      "| au, au                        |            1 |         0.97 |\n",
      "| su-to, ti                     |            1 |         0.97 |\n",
      "| mc-itype, mc-itype, mc-itype  |            1 |         0.97 |\n",
      "| au, su-to, su-to, su-to       |            1 |         0.97 |\n",
      "| kw, mc-itype, mc-itype        |            1 |         0.97 |\n"
     ]
    }
   ],
   "source": [
    "# Ordenar los términos en cada combinación para estandarizar\n",
    "df_filtrado['campo_utilizado'] = df_filtrado['campo_utilizado'].fillna('').apply(lambda x: ', '.join(sorted(x.split(', '))))\n",
    "\n",
    "# Filtramos las filas con más de un campo utilizado\n",
    "combinaciones_campos = df_filtrado[df_filtrado['cant_campos_utilizados'] > 1]\n",
    "\n",
    "# Contamos las combinaciones de campos utilizados\n",
    "combinaciones_frecuencia = combinaciones_campos['campo_utilizado'].value_counts()\n",
    "combinaciones_frecuencia_df = combinaciones_frecuencia.reset_index()\n",
    "combinaciones_frecuencia_df.columns = ['Combinacion de Campos', 'Frecuencia']\n",
    "combinaciones_frecuencia_df['Porcentaje'] = round((combinaciones_frecuencia_df['Frecuencia'] / combinaciones_frecuencia_df['Frecuencia'].sum()) * 100,2)\n",
    "\n",
    "# Mostramos la tabla de frecuencia\n",
    "print(tabulate(combinaciones_frecuencia_df, headers='keys', tablefmt='pipe', showindex=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analizar los porcentajes de busquedas según la categoría del usuario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | categorycode   |   count |\n",
      "|---:|:---------------|--------:|\n",
      "|  0 | AGRO           |       6 |\n",
      "|  1 | BIBLIOTECA     |    2048 |\n",
      "|  2 | BIOINF         |       6 |\n",
      "|  3 | BIOINGENIE     |      29 |\n",
      "|  4 | DOCENTE        |     164 |\n",
      "|  5 | HIST           |      34 |\n",
      "|  6 | INTERALUM      |       2 |\n",
      "|  7 | INVI           |      54 |\n",
      "|  8 | S              |     121 |\n",
      "|  9 | TEC_MED_N      |      21 |\n",
      "| 10 | TEC_PROD_M     |       1 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extraemos las categorias de los usuarios y contamos sus frecuencias\n",
    "total_categorias = df_filtrado['categorycode'].value_counts().sort_index()\n",
    "total_categorias_df = total_categorias.reset_index()\n",
    "total_categorias_df.columns = ['categorycode', 'count']\n",
    "print(tabulate(total_categorias_df, headers='keys', tablefmt='pipe', showindex=True))\n",
    "\n",
    "# Grafico de torta para total_categorias sin generalizar\n",
    "colors = plt.cm.tab20.colors\n",
    "plt.figure(figsize=(10, 6))\n",
    "total_categorias.plot(kind='pie', autopct='%1.1f%%', colors=colors, startangle=90)\n",
    "plt.title('Distribución de categorías de usuarios')\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorycode_generalizado\n",
      "Dentro de la Biblioteca    2048\n",
      "Fuera de la Biblioteca      438\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xt21v0KCBrXUnyw8//EBmZiZDhgxxeL2qVatGw4YNs71eQUFB3H333bbLPj4+dOjQgYMHD9q2ff/99zRt2pQmTZo43GfPnj0Bst1nbu9x+9ckNjaWCxcu0KVLlwL97Obl4YcfZvv27cybN49q1arl+HgXLlzg7NmzdOvWjYMHD3LhwgUAli5dSmpqKo8++qjDZ80TTzyR7XF+++03OnToYOt2BevzN3r0aKKjo9m1a5fD/lmv49mzZ4t1fO5C4aiEJSQkOASRK/3rX/+iU6dOjBo1iqpVqzJ06FDmzJlTqKBUo0aNQg2+vnIdGovFQoMGDYo0OyoqKorGjRsXauDnmTNnSEpKonHjxtmua9q0KZmZmdnGF1wZOrJ+cPMaD5QVnHJad+fKx96/fz8XLlygSpUqhIeHO3wnJCQQExOT6+NkfRC3aNEi133AOmPvlVdesY3tqVy5MuHh4cTFxdk+7PKSFSB69uyZrcbFixfbasw67gYNGmS7jyu3HT58mOrVq2d7j2Z1410ZPuvWrZtvnYU51ldffZW4uDgaNWpEy5YtefbZZ7PN8MzJ4cOHC/y6Qv7PWXF169aN2267jfHjx1O5cmVuvvlmZsyYkW1MS0GdP3+exx9/nKpVq+Lv7094eLjtuc/pvVKQ1+Xw4cN4eHhkew9Uq1aN0NBQ22td3GMxrujWi4mJITk5uUDvx/3792MYBg0bNsz2eu3evTvb61WzZs1sf4SFhYU5fC7s37+fnTt3Zru/Ro0a2eqzl9tzuWDBAq699lr8/PyoWLEi4eHhTJ8+vUA/u7n56KOPmDFjBtOmTbOF3yxr1qyhd+/eBAYGEhoaSnh4uG08adZj5vYZFx4eni2kHj58ONfPXPv7ypL1OpqxVpYz0pijEnTs2DEuXLiQ44dCFn9/f/7880+WL1/Or7/+yu+//853331Hz549Wbx4MZ6envk+TmHHCRVEXq0+BamppOX2mFd+EBdVZmYmVapU4auvvsrx+pKYov7oo48yY8YMnnjiCTp27EhISAgWi4WhQ4cWKAxn7TN79myHvzCzlMXMpIK+1wp6rF27diUqKoqffvqJxYsX8+mnn/L222/z4YcfMmrUqGLXW9znLK+fgyv3mzt3LmvXruWXX35h0aJF3Hfffbz11lusXbuWoKCgAt8XwJAhQ/jrr7949tlnad26NUFBQWRmZtKvX78c3yuF+QzI75ddQY4lN5UqVSrWzMXMzEwsFgsLFy7M8Wf+yscuyOdCZmYmLVu2ZMqUKTnuW6tWLYfLOT2Xq1at4qabbqJr16588MEHRERE4O3tzYwZM/j666/zPa6crFu3jscff5xRo0YxevRoh+uioqLo1asXTZo0YcqUKdSqVQsfHx9+++033n777SL3MhRG1utYuXLlUn8sV6BwVIKyBt317ds3z/08PDzo1asXvXr1YsqUKUyaNImXX36Z5cuX07t37xJP7ld2YRiGwYEDB7jqqqts28LCwoiLi8t228OHDzt0hdWvX59//vmHtLS0PAcs2wsPDycgICDH00zs2bMHDw+PbB9YRVGnTh0g+/EC2R67fv36LF26lE6dOhU6bGY9H/mtKDt37lyGDRvmMM350qVL2Z7n3F7vrMHtVapUoXfv3rk+TtZxHzhwINt1V26rU6cOS5cuJT4+3qH1aM+ePQ73VVgFPVaAihUrMmLECEaMGEFCQgJdu3Zl3LhxeYajOnXqFPh1hfyfs9xk/fUdFxfnMBA4t+7ca6+9lmuvvZaJEyfy9ddfc9ddd/Htt98yatQoh/uyd+V9xcbG8scffzB+/HiHhfgK2/V4pTp16pCZmcn+/fttrQVgHagbFxeX7bXO61hy06RJEw4dOuSwrUqVKvj5+RXo/Vi/fn0Mw6Bu3bq2lp3iql+/Plu3bqVXr15F/iydN28efn5+LFq0CF9fX9v2GTNmFOn+zpw5w+DBg2ndujXvv/9+tut/+eUXUlJS+Pnnnx1aza/sArT/jLP/XD5z5ky2kFqnTp1cP3Pt7yvLoUOH8PDwKLHXwdWpW62ELFu2jNdee426detmGwdh7/z589m2tW7dGsDWjB0YGAhk/1Atqi+++MJhHNTcuXM5efKkQ/9//fr1Wbt2rcOMhwULFmTr7rrttts4e/Ys7733XrbHya1Vx9PTk+uvv56ffvrJoSvv9OnTfP3113Tu3LlEZkdERETQunVrZs2a5dD0vWTJkmz960OGDCEjI4PXXnst2/2kp6fn+dyHh4fTtWtXPv/8c44cOeJwnf1z4Onpme05mTZtWraWg9xe7759+1KhQgUmTZqU41iQrKUNqlevTosWLfjiiy9ISEiwXb9y5Uq2b9/ucJv+/fuTkZGR7fV7++23sVgs2caEFFRBj/XKqelBQUE0aNAg3y6c/v37s3btWtatW2fbdubMmWwtfwV9znKTFa7+/PNP27bExERmzZrlsF9sbGy2473y57hOnTp4eno63BfABx984HA5qzXkyvt755138qw1P/3798/xfrJaVLJmXBXkWHLTsWNHduzY4bCfp6cnvXv3Zv78+Zw4ccK2/cCBA9nGtN166614enoyfvz4bDUYhpHt/VIQQ4YM4fjx43zyySfZrktOTiYxMTHf+/D09MRisTi8f6Ojo7PNdCuIjIwMhg4dSmpqKvPmzctxSERO74ELFy5kC2O9e/fG29ubadOmOeyb03ulf//+rFu3jr///tu2LTExkY8//pjIyMhs46w2btxI8+bNtZjnZWo5KoKFCxeyZ88e0tPTOX36NMuWLWPJkiXUqVOHn3/+Oc8FwF599VX+/PNPBgwYQJ06dYiJieGDDz6gZs2atoFz9evXJzQ0lA8//JDg4GACAwO55pprCjz+40oVK1akc+fOjBgxgtOnT/POO+/QoEEDh+UGRo0axdy5c+nXrx9DhgwhKiqKL7/8MtvU/HvvvZcvvviCp556inXr1tGlSxcSExNZunQpDz/8MDfffHOONUyYMMG2vtPDDz+Ml5cXH330ESkpKTmuU1JUkydPZsCAAXTu3Jn77ruP8+fPM23aNJo3b+4QHLp168YDDzzA5MmT2bJlC9dffz3e3t7s37+f77//nqlTpzJ48OBcH+fdd9+lc+fOtGnThtGjR1O3bl2io6P59ddfbSubDxw4kNmzZxMSEkKzZs34+++/Wbp0KZUqVXK4r9atW+Pp6ckbb7zBhQsX8PX1ta13Mn36dO655x7atGnD0KFDCQ8P58iRI/z666906tTJFnImTZrEzTffTKdOnRgxYgSxsbG89957tGjRwuG4b7zxRnr06MHLL79MdHQ0rVq1YvHixfz000888cQTuS7FkJ+CHmuzZs3o3r07bdu2pWLFimzYsIG5c+fme+qJ5557jtmzZ9OvXz8ef/xx21T+OnXqOIxZqlChQoGfs5xcf/311K5dm5EjR/Lss8/i6enJ559/bruPLLNmzeKDDz7glltuoX79+sTHx/PJJ59QoUIFWygJCQnh9ttvZ9q0aVgsFurXr8+CBQuyjXmpUKECXbt25T//+Q9paWnUqFGDxYsXZ2uRKaxWrVoxbNgwPv74Y+Li4ujWrRvr1q1j1qxZDBo0iB49ehT4WHJz880389prr7Fy5Uquv/562/Zx48axePFiOnXqxEMPPWQL5C1atHBY+b9+/fpMmDCBF198kejoaAYNGkRwcDCHDh3ixx9/ZPTo0TzzzDOFOu577rmHOXPm8OCDD7J8+XI6depERkYGe/bsYc6cOSxatMhhEd+cDBgwgClTptCvXz/uvPNOYmJieP/992nQoEGBxsjZ+/DDD1m2bJmtHntVq1alT58+XH/99fj4+HDjjTfywAMPkJCQwCeffEKVKlU4efKkbf/w8HCeeeYZJk+ezMCBA+nfvz+bN29m4cKF2brDXnjhBb755htuuOEGHnvsMSpWrMisWbM4dOgQ8+bNczjtT1paGitXruThhx8u1LG5tTKeHefSsqbzZn37+PgY1apVM/r06WNMnTrVYbp8liun1/7xxx/GzTffbFSvXt3w8fExqlevbtxxxx3Gvn37HG73008/Gc2aNbNNfc2a9tutWzejefPmOdaX21T+b775xnjxxReNKlWqGP7+/saAAQOyTUE3DMN46623jBo1ahi+vr5Gp06djA0bNmS7T8OwTjl9+eWXjbp16xre3t5GtWrVjMGDBztM0+eKqd6GYRibNm0y+vbtawQFBRkBAQFGjx49jL/++ivH5/jKacX5TY+1N2/ePKNp06aGr6+v0axZM+OHH34whg0b5jClPcvHH39stG3b1vD39zeCg4ONli1bGs8995xx4sSJfB9nx44dxi233GKEhoYafn5+RuPGjY2xY8faro+NjTVGjBhhVK5c2QgKCjL69u1r7NmzJ8dlEz755BOjXr16hqenZ7bjXL58udG3b18jJCTE8PPzM+rXr28MHz7c2LBhg8N9fPvtt0aTJk0MX19fo0WLFsbPP/9s3HbbbUaTJk0c9ouPjzeefPJJo3r16oa3t7fRsGFD47///a/D1GDDsL6GjzzySI7HfuXrW9BjnTBhgtGhQwcjNDTU8Pf3N5o0aWJMnDjRSE1Nzff53rZtm9GtWzfDz8/PqFGjhvHaa68Zn332mcMU+8I+ZznZuHGjcc011xg+Pj5G7dq1jSlTpmSbyr9p0ybjjjvuMGrXrm34+voaVapUMQYOHJjt/s+cOWPcdtttRkBAgBEWFmY88MADxo4dO7JN5T927JjtvRQSEmLcfvvtxokTJ7I9z1mfJzktBZLTMgRpaWnG+PHjbT+rtWrVMl588UXj0qVLtn0Keiy5ueqqq4yRI0dm2/7HH38YV199teHj42PUr1/f+PTTT42nn37a8PPzy7bvvHnzjM6dOxuBgYFGYGCg0aRJE+ORRx4x9u7da9snt8++nH62U1NTjTfeeMNo3ry54evra4SFhRlt27Y1xo8fb1y4cMG2X17v8c8++8xo2LCh4evrazRp0sSYMWNGjs9xflP5s26T07f9Z+vPP/9sXHXVVYafn58RGRlpvPHGG7alU+zf3xkZGcb48eONiIgIw9/f3+jevbuxY8eOHD9XoqKijMGDB9s+ozp06GAsWLAg27EuXLjQAIz9+/fn+FyURxbDKKERriLidFq3bk14eHiBVqAWKYrZs2fzyCOPcOTIkXxPJD1o0KAiLeUgpWvQoEFYLJZczyZQHmnMkYgbSEtLIz093WHbihUr2Lp1a46nlBEpKXfddRe1a9fONtD4ypPg7t+/n99++03vRyeze/duFixYkOP4y/JMLUcibiA6OprevXtz9913U716dfbs2cOHH35ISEgIO3bsyDb2R6S0RURE2M4Xd/jwYaZPn05KSgqbN2/Occ0qEWeiAdkibiAsLIy2bdvy6aefcubMGQIDAxkwYACvv/66gpGYol+/fnzzzTecOnUKX19fOnbsyKRJkxSMxCWo5UhERETEjsYciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGx42V2ASJSPqSkQ3wqJFz+vpQOmYb1O8MA4/K/mZf/b7FkUClyJ5bLXx542P6fddkHH3ztvvzww0N/84lIMSkciUihGQYkpVlDjn3giU+F+JQctqdAWmbhHiPIP53GkWsLXZs33g6BKacvP/wIJpgKVMAHn0I/hoi4N4UjEcnVhUtwMgFOxv/v35hEuJhqbeFxRmmXvxJIKND+fvhR4fJXCCEO//fDr5SrFRFnpHAkIpxPhhPxcMo+CCVYW4fc3aXLXzHEZLvOBx9bWMoKTCGEUJGKanEScWMKRyLlhGHA2SQ4YReATsXDqUTr+B/JLpVUzl7+smfBQiihhBNOFaoQTjiVqKTxTiJuQuFIxE1lGnDsIuw7Z/0+cB4Sy0FLUFkwMIi9/LWPfQB44kklKtnCUhWqEEKIyZWKSFEoHIm4iUwDjlyA/edg33lrGCoP3WLOIoMMYi5/ZfHFl3C7r6pUxR9/E6sUkYJQOBJxURmZ1jC0zy4MqXvMuaSQwrHLX1kqUYla1KI2talCFXXFiTgh/VSKuIiMTIiKhd8PwNR/4MlF8Poa+GEP7IhRMHIV5zjHFrbwMz/zBV+whCXsYQ+JJJpdmpSgcePG0bp162LdR/fu3XniiSdKpJ68rFixAovFQlxcHAAzZ84kNDQ0z9tceXzDhw9n0KBBpVZjWVPLkYgTS0iFLaes3/vOQUqG2RVJSUollUOXvwAqUpFal7+qUa1ctyoNHz6cWbNmZdu+f/9+GjRoYEJFrql79+6sXLnSdrlKlSp07dqVN998kzp16gBw3XXXcfLkSUJCij5GburUqRhGwdf3WLFiBT169CA2NjbfIGYGhSMRJxObDJtPWb8PnHfe9YSk5J2//LWVrXjjTQ1q2MJSEEFml1fm+vXrx4wZMxy2hYeHl8pjGYZBRkYGXl7u92vx/vvv59VXX8UwDA4fPswTTzzB3XffzapVqwDw8fGhWrVqxXqM4gQrZ1R+/ywRcSIxidbussmr4cU/4Lud1pYiBaPyK400oolmFav4mq/5kR/ZwQ6SSTa7tDLj6+tLtWrVHL49PT1z7MJ54okn6N69u+1yZmYmkydPpm7duvj7+9OqVSvmzp1ruz6rK2nhwoW0bdsWX19fVq9eTVRUFDfffDNVq1YlKCiI9u3bs3Tp0nxrff3116latSrBwcGMHDmSS5cuZdvn008/pWnTpvj5+dGkSRM++OCDQj0fs2fPpl27dgQHB1OtWjXuvPNOYmKyr891pYCAAKpVq0ZERATXXnstY8aMYdOmTdmei6xutSzz58+nYcOG+Pn50bdvX44ePZrrY1z5mqSkpPDYY49RpUoV/Pz86Ny5M+vXrwcgOjqaHj16ABAWFobFYmH48OFA/q8bwM6dOxk4cCAVKlQgODiYLl26EBUVBcD69evp06cPlStXJiQkhG7dujkca0EpHImYJDYZlkTBxFUwdjn8uAei40B5SHJyhjP8xV98yZcsZCEHOEA6GmiWm8mTJ/PFF1/w4YcfsnPnTp588knuvvtuhy4mgBdeeIHXX3+d3bt3c9VVV5GQkED//v35448/2Lx5M/369ePGG2/kyJEjuT7WnDlzGDduHJMmTWLDhg1ERERkCz5fffUVr7zyChMnTmT37t1MmjSJsWPH5th1mJu0tDRee+01tm7dyvz584mOjraFioI6f/48c+bM4Zprrslzv6SkJCZOnMgXX3zBmjVriIuLY+jQoQV+nOeee4558+Yxa9YsNm3aRIMGDejbty/nz5+nVq1azJs3D4C9e/dy8uRJpk6dCuT/uh0/fpyuXbvi6+vLsmXL2LhxI/fddx/p6dafhfj4eIYNG8bq1atZu3YtDRs2pH///sTHxxfqeXK/9kMRJ5aYChtPwvrjsP+8gpAUnoHB0ctf3nhTl7o0oAE1qIEFi9nllagFCxYQFPS/7sQbbriB77//Pt/bpaSkMGnSJJYuXUrHjh0BqFevHqtXr+ajjz6iW7dutn1fffVV+vTpY7tcsWJFWrVqZbv82muv8eOPP/Lzzz8zZsyYHB/vnXfeYeTIkYwcORKACRMmsHTpUofWo3//+9+89dZb3HrrrQDUrVuXXbt28dFHHzFs2LCCPB3cd999tv/Xq1ePd999l/bt25OQkODwPF3pgw8+4NNPP8UwDJKSkmjUqBGLFi3K87HS0tJ47733bCFq1qxZNG3alHXr1tGhQ4c8b5uYmMj06dOZOXMmN9xwAwCffPIJS5Ys4bPPPuPZZ5+lYsWKgHUMVNaYo4K8bu+//z4hISF8++23eHt7A9CoUSPbY/fs2dOhlo8//pjQ0FBWrlzJwIED86zbnsKRSClLy7AOqF53AnadgfRCnoBVJDdppLHv8lcggdSnPg1pSCUqmV1aiejRowfTp0+3XQ4MDCzQ7Q4cOEBSUpJD6AFITU3l6quvdtjWrl07h8sJCQmMGzeOX3/9lZMnT5Kenk5ycnKeLUe7d+/mwQcfdNjWsWNHli9fDljDQlRUFCNHjuT++++37ZOenl6osTobN25k3LhxbN26ldjYWDIzrR8mR44coVmzZrne7q677uLll18G4PTp00yaNInrr7+ejRs3EhwcnONtvLy8aN++ve1ykyZNCA0NZffu3fmGo6ioKNLS0ujUqZNtm7e3Nx06dGD37t253q4gr9uWLVvo0qWLLRhd6fTp0/zf//0fK1asICYmhoyMDJKSkvJ8/XKicCRSSs4nw4poWH1EK1NL6UskkW2XvypSkQaXv1x5IHdgYGCOM9M8PDyyzYxKS/vfD1lCgvWkw7/++is1atRw2M/X1zfbY9h75plnWLJkCW+++SYNGjTA39+fwYMHk5qaWuTjyKrnk08+ydad5enpWaD7SExMpG/fvvTt25evvvqK8PBwjhw5Qt++ffOtLSQkxPY8NmjQgM8++4yIiAi+++47Ro0aVYQjKh0Fed38/fNeRHXYsGGcO3eOqVOnUqdOHXx9fenYsWOhXz+FI5EStu8cLD8EW05rQLWY4zznWcc61rOemtSkBS2oRS2zyyox4eHh7Nixw2Hbli1bbK0JzZo1w9fXlyNHjjh0oRXEmjVrGD58OLfccgtg/YUdHR2d522aNm3KP//8w7333mvbtnbtWtv/q1atSvXq1Tl48CB33XVXoerJsmfPHs6dO8frr79OrVrW13LDhg1Fuq+sQJacnPvg/vT0dDZs2GBrJdq7dy9xcXE0bdo03/uvX78+Pj4+rFmzxrZcQFpaGuvXr7et2+TjYz1xc0bG/9YnKcjrdtVVVzFr1izS0tJybD1as2YNH3zwAf379wfg6NGjnD17Ntt++VE4EikBqRmw7rg1FB0r3Lg/kVJjPz4pjDBa0IKGNMTLxT/6e/bsyX//+1+++OILOnbsyJdffsmOHTtsXS/BwcE888wzPPnkk2RmZtK5c2cuXLjAmjVrqFChQp5jfBo2bMgPP/zAjTfeiMViYezYsbbuq9w8/vjjDB8+nHbt2tGpUye++uordu7cSb169Wz7jB8/nscee4yQkBD69etHSkoKGzZsIDY2lqeeeirfY65duzY+Pj5MmzaNBx98kB07dvDaa68V6PlKSkri1KlTgLXb6bXXXsPPz4/rr78+19t4e3vz6KOP8u677+Ll5cWYMWO49tpr8+1SA2tr3EMPPWQbW1S7dm3+85//kJSUZBuXVadOHSwWCwsWLKB///74+/sX6HUbM2YM06ZNY+jQobz44ouEhISwdu1aOnToQOPGjWnYsKFtVt/Fixd59tln821tyolmq4kUw/lk+GE3vLAUZm9TMBLnFUusbVmA9awniSSzSyqyvn37MnbsWJ577jnat29PfHy8Q6sNWAdSjx07lsmTJ9O0aVP69evHr7/+St26dfO87ylTphAWFsZ1113HjTfeSN++fWnTpk2et/nXv/5lq6dt27YcPnyYhx56yGGfUaNG8emnnzJjxgxatmxJt27dmDlzZr71ZAkPD2fmzJl8//33NGvWjNdff50333yzQLf95JNPiIiIICIigh49enD27Fl+++03GjdunOttAgICeP7557nzzjvp1KkTQUFBfPfddwV6PLAubXDbbbdxzz330KZNGw4cOMCiRYsICwsDoEaNGowfP54XXniBqlWr2ga75/e6VapUiWXLlpGQkEC3bt1o27Ytn3zyia0V6bPPPiM2NpY2bdpwzz332JYTKCyLUZglLUUEgL1nYXk0bFXXWakJ8k+hca+CT3OWwvHAg/rUpyUtqUxls8sRcSqu3bYqUobUdSbuJJNM9l/+iiCClrSkDnXcbjkAkaJQOBLJR2wyLIuGNZp1Jm7q5OWvClSgOc1pTGN88DG7LBHTKByJ5CI+BRYegJWHtTaRlA8Xucjf/M1GNtKc5rSilUKSlEsKRyJXSE6DJQfhj0NwSWdnkHIolVQ2s5ld7KI1rWlOc5ef4SZSGHq3i1yWlmFdtHHhAXWfiQCkkMI//MN2ttOGNjShCR6a5CzlgMKRlHsZmfDXUfh1P8RmP5G2SLmXRBKrWc02ttGWtjSggQZui1tTOJJyyzBgw0n4ZS+cTjS7GhHnd5GLLGc5W9lKO9oRSaTZJYmUCoUjKZd2xMD8PXD0otmViLie85xnMYupSlXa057qVDe7JJESpXAk5cqB8/DjHuu/IlI8pznNAhZQgxp0oAPhhJtdkkiJUDiScuHoRfhpD2yPMbsSEfdznOP8yI/Uox7Xci1BBJldkkixKByJW0tOs7YU/XkYdJYPkdJ1kIMc4QhtaMNVXKWZbeKyFI7EbW05Bd/sgDjNQBMpM+mks4517GMfnehEDWqYXZJIoSkciduJu2QNRVtOmV2JSPkVRxy/8iv1qEdHOhJIoNkliRSYwpG4DcOwdp/9sEcrW4s4i4Mc5ChHaUtbWtBCXW3iEhSOxC2cjIfZ2yAq1uxKRORKaaSxlrVEEUUXulCZymaXJJInhSNxaemZ8Nt+WBSlk8OKOLsznOFHfqQlLWlHO52vTZyW3pnisg6ct7YWnUowuxIRKSgDg21s4xCH6EIXalLT7JJEslE4EpeTnAbzdsPqI5qeL+Kq4onnN36jEY24juvwwcfskkRsFI7EpWw6Cd/ugAspZlciIiVhH/s4xSl60pMqVDG7HBFA4UhcxMUU+GobbDltdiUiUtIucpGf+Zm2tKU1rbFgMbskKecUjsTp7T4Ln2+2BiQRcU+ZZLKe9RzjGD3ooVOQiKm04IQ4rYxM66k/pq5VMBIpL05yknnM4xCHzC5FyjG1HIlTOpcEn23WukUi5VEKKSxhCU1ownVcpyn/Uub0jhOns+mkdYp+UprZlYiImfawxzZYWwtHSllSt5o4jbQM+Ho7fLRRwUhErOKIYz7z2cY2DC3eIWVELUfiFM4mWUPRkQtmVyIiziaTTNaylmMcozvdCSDA7JLEzanlSEy3IwYmrVIwEpG8HeMY85jHKU6ZXYq4OYUjMU2mAb/shffWQaK60USkAJJJZgEL2Mtes0sRN6ZuNTFFYqp17aIdZ8yuRERcTSaZrGQlscRyDddo0UgpcQpHUuYOx1nHF51LNrsSEXFl29hGLLH0opfOzSYlSt1qUqbWn4D//qVgJCIl4yhH+YmfuMhFs0sRN6JwJGVm6UH4bBOkZZpdiYi4k1hi+ZEfOcEJs0sRN6FwJKXOMOD7XdZvrVIiIqUhhRR+4zd2s9vsUsQNKBxJqUrPtJ4GZOlBsysREXeXSSarWMUa1pCJmqil6DQgW0pNchpM3wB7z5ldiYiUJzvZSRxx9KY3vviaXY64ILUcSamIuwRv/qVgJCLmOM5x5jOfC2h1WSk8hSMpcSfi4Y01cCze7EpEpDy7wAV+4ifOob/SpHAUjqREHThvnap/XlP1RcQJXOISC1hADDFmlyIuROFISsymk/DOWkjSqUBExImkkMKv/Kqp/lJgCkdSIpYfgo83ag0jEXFOaaSxkIUc4YjZpYgLUDiSYjEM+GE3fLtTaxiJiHPLIIPFLOYQh8wuRZycwpEUWUYmzNwCi6LMrkREpGAyyWQpS9nHPrNLESemdY6kSDIN+GQTbD5ldiUiIoVjYLCCFaSTTjOamV2OOCG1HEmhZRowY4uCkYi4ttWsZhvbzC5DnJDCkRTa19th3XGzqxARKb61rGUDG8wuQ5yMwpEUypydsEqTPUTEjWxiE2tZa3YZ4kQUjqTAftoLf2iSh4i4oW1s4y/+MrsMcRIKR1Igvx+A3/abXYWISOnZwQ42s9nsMsQJKBxJvpZHw497zK5CRKT0rWc9e9AHXnmncCR5WnMUvtthdhUiImVnFau0UGQ5p3AkuVp/AmZv1crXIlK+GBgsY5nOxVaOKRxJjraehhmbFYxEpHzKIINFLOIsZ80uRUygcCTZ7D5jPYlshpKRiJRjWServchFs0uRMqZwJA4OnIcPNkB6ptmViIiYL5lkfuVXkkgyuxQpQwpHYnPkAkxbB6kZZlciIuI84olnIQtJJdXsUqSMKBwJABcuwfvr4FK62ZWIiDifc5xjEYtIRx+S5YHCkZCWAdM3QFyK2ZWIiDivk5xkGcvIROMO3J3CkfD1DjgUZ3YVIiLOL5po1rDG7DKklCkclXPLDsFfR82uQkTEdey+/CXuS+GoHNtzFr7fZXYVIiKuZw1riCHG7DKklCgclVNnk6xrGWVqLSMRkULLJJMlLNEUfzelcFQOpaTD9PWQmGZ2JSIiriuRRJayVAO03ZDCUTk0cyscize7ChER13eKU/zN32aXISVM4aic+W0/bDppdhUiIu5jJzvZxz6zy5ASpHBUjmw9DT/vNbsKERH3s4pVOkmtG1E4KidOxsPnm0Hjr0VESl4GGSxmMZe4ZHYpUgIUjsqBpDTryWR1ahARkdKTQAJ/8IcGaLsBhSM3l2nAp5sgJtHsSkRE3N9xjrOOdWaXIcWkcOTmftoLO8+YXYWISPmxjW0c4IDZZUgxKBy5sb1nYZF+PkVEytyf/MkFLphdhhSRwpGbSkqDGVs0AFtExAzppLOc5Rp/5KIUjtzU19shVpMmRERME0MMW9hidhlSBApHbuifY7D+hNlViIjIJjZp/SMXpHDkZs4lwTc7zK5CRETAeoLa5SwngwyzS5FCUDhyI5mGdZxRstYzEhFxGrHEsp71ZpchhaBw5EYWR8H+82ZXISIiV9rOdk6iE1u6CoUjN3H8Ivyi8x6KiDglA4MVrCCVVLNLkQJQOHIDGZkwcyuka8aoiIjTiieev/nb7DKkABSO3MCiKDiitcZERJzeXvZymMNmlyH5UDhycSfi4df9ZlchIiIF9Sd/cgktROfMFI5cWKYBs7aoO01ExJUkk8wqVpldhuRB4ciFLYqCaHWniYi4nEMcYj9q9ndWXmYXIEVzIh4WaHaaiNPb9+c+Fv93MUc2HuHCyQs89ONDtB7U2nb9A5YHcrzdrf+5lb7P9s31fmOPx/LD8z+wc+FOUpNSCW8QzrAZw4hsFwnA4jcXs/g/iwHo+3xf+jzdx3bbQ/8c4uuHv+aFf17A08uz+AcpRbKWtdShDj74mF2KXEHhyEV9uU3daSKuIDUxlZqtatLpvk58eOuH2a7/z8n/OFzesXAHs0fOps1tbXK9z8TYRP7b6b806tGIRxc+SnB4MDH7YwgMCwTg2LZj/PzKz4xZMAbDMHh/4Ps0u74ZNVrWICM9g68e/Iq7P75bwchkySSzkY10pKPZpcgVFI5c0PrjEBVrdhUiUhAtbmhBixta5Hp9SLUQh8tbf9pKox6NCK8XnuttFr2xiLBaYQyfMdy2rXLdyrb/n9pzippX1aRJzyYA1LiqBqf2nKJGyxos/u9iGnZtSGT7yKIdkJSoHeygCU0II8zsUsSOxhy5mLQM+GGP2VWISGm4ePoi23/dTueRnfPcb9vP26jTrg4f3f4Rz1R5hglXT2DVJ/8b4FujZQ1O7zvN+SPnOXf4HDH7Yqjeojpnos7w14y/uHnCzaV9KFJABgZ/8ZfZZcgV1HLkYhYfhPPJZlchIqXh71l/4xfsx9W3Xp3nfmcOnmHl9JX0fqo3N7x0A9Hro/nuse/w8vGi47CORDSNYNCkQbzT5x0ABk0eRETTCN7u/Ta3/ec2di7ayYJxC/D09mTI1CE06tqoDI5OcnOc4xzkIPWoZ3YpcpnCkQuJuwSLDphdhYiUljWfr6HDXR3w9vPOcz8j06BOuzrcMukWAGpfXZsTO06w8sOVdBxmHb/S7cFudHuwm+02WcGrXsd6vNL4FV5c/yJxx+L4dOinTDw0EW/fvB9TStda1lKb2njp17JTULeaC/lxD6RkmF2FiJSG/av2c3rvaTqPyrtLDSAkIoSIZhEO2yKaRhB7JOfBiAlnE1gwfgFDpw3l0D+HqNqoKlUbVqVxj8ZkpGUQsy+mRI5Bii6BBLawxewy5DKFIxcRHQf/HDO7ChEpLWs+W0PttrWp1apWvvvW71Sf03tPO2w7ve80FetUzHH/OU/OodeTvQirGUZmRiYZaf/7KyszPZPMDE19dQZb2cpFLppdhqBw5DLm7ATD7CJEpNAuJVzi6JajHN1yFICzh85ydMtRzh85b9sn+WIyG7/fmGur0ZReU1j+3nLb5d5P9ubg2oP8Nuk3Yg7EsO7rdaz6eBXdH+me7ba7luzi9L7Ttusi20dyas8pdizcwZ8f/4nF00LVxlVL7oClyDLI0IlpnYQ6N12Apu6LuK7DGw4zpccU2+Xvn/oegI7DOjJ85nAA1n+7HsMw6HBHhxzv42zUWRLOJtguR7aP5KEfH+LHF3/k11d/pXLdygx5ZwjX3HWNw+1Sk1P5dsy33P/d/Xh4WP8WDqsZxtBpQ5k1YhZevl6MmDUCH38tQugsDnOYoxylFvm3IErpsRiGoQYJJ5aaAf9eoRlqUv4E+afQuNcss8sQKXMhhHA7t+Ohzh3T6Jl3ckuiFIxERMqTC1xgO9vNLqNcUzhyYnGXrCeXFRGR8mUTm0giyewyyi2FIyemqfsiIuVTGmlsYpPZZZRbCkdOSlP3RUTKtz3sIZ54s8solxSOnJSm7ouIlG+ZZKr1yCQKR05o4wlN3RcREdjHPi5wwewyyh2FIyf0m86fJiIigIHBRjaaXUa5o3DkZHbEwDGtHi8iIpdFEUUs6k4oSwpHTuZ3tRqJiIgdi2Fhc/wps8soV3T6ECcSdR72n89/PxERcX8ehgfBCY3Yse1qNsQG06QbVA82u6ryQeHIifyuBR9FRMo9+1AUE/u/NLTwAIy82sTCyhGFIydxIh62nza7ChERMUtuoSjLhhNwUyMIDzShuHJG4chJLDqgdY1ERMqj/EJRlkzDOi71nlZlWFw5pXDkBM4nw7oTZlchIiJlqaChyN7a4zCgEVT0L+XiyjmFIyewOMr6F4GIiLi/ooSiLOmZ1hOS39GilIoTQOHIdAmpsOao2VWIiEhpK04osrfmCAxoCBV8S7A4caBwZLJlhyA1w+wqRESktJRUKMqSlglLD8KtTUugOMmRwpGJLqXDimizqxARkdJQ0qHI3l9H4abG4KWlnEuFwpGJVh2BxDSzqxARkZJUmqEoS3wqbD4J7WuUyt2XewpHJkm/3CwqIiLuoSxCkb0/jygclRaFI5P8cwziLpldhYiIFFdZh6Is+87BqQSoFlRmD1luKByZZIlajUREXJqH4UGFy6HodBmGInurjsDtzUx5aLemcGSCA+fhZILZVYiISFE4QyjK8vdRGNQYvD1NLcPtKByZ4G+tayQi4nKcKRRlSUyDTSfhmppmV+JeFI7KWGoGbDhpdhUiIlJQzhiK7P15ROGopCkclbEtp6zrG4mIiHNz9lCU5cB5OBEP1Z23RJejcFTG/lKXmoiIU3OVUGRv1WH4l863VmIUjspQbDLsOWt2FSIikhNXDEVZ1h6HW5qCjwZmlwiFozK09jgYZhchIiIOXDkUZUlKg40noGMtsytxDwpHZUiz1EREnIc7hCJ7fx5ROCopCkdl5GAsnE40uwoREXG3UJTlYCwcvwg1KphdietTOCojajUSETGXu4Yie38ehjtaml2F61M4KgNpWttIRMQ0HoYHFRIbsnPb1Zw6797NKv8ch9uaaWB2cSkclYEtp62D5UREpOyUp1CUJTkdtp+GttXNrsS1KRyVAXWpiYiUnfIYiuxtOaVwVFwKR6Us7hLs1tpGIiKlrryHoiw7zkBGJnh6mF2J61I4KmX/HINMLW4kIlJqFIocJaXBvnPQNNzsSlyXwlEp+/uY2RWIiLgni2EhJLGRQlEOtpxSOCoOhaNSdOwinEwwuwoREfdiH4o2KBTlaOtpTekvDoWjUrTttNkViIi4D4Wigou9BIfjoE6o2ZW4JoWjUqRwJCJSfApFRbPltMJRUSkclZKLKRAdZ3YVIiKuS6GoeLacgpsbm12Fa1I4KiU7YkCT1ERECk+hqGSciIcziRAeaHYlrkfhqJSoS01EpHAUikrellPQp77ZVbgehaNSkJ6phR9FRApKoaj0bDmtcFQUCkelYN85uJRudhUiIs5Noaj0HYyF+BQI9jW7EteicFQK1KUmIpI7haKyk2lYfyd1qm12Ja5F4agU7DpjdgUiIs5HocgcWxSOCk3hqISdT4bTiWZXISLiPBSKzLX7DKSkg69+4xeYnqoStlutRiIigEKRs0jLtPZoXB1hdiWuQ+GohGmWmoiUdwpFzmffOYWjwlA4KkGGAXsUjkSknFIocl5RsWZX4FoUjkrQ0YsQn2p2FSIiZUuhyPkduwipGeDjaXYlrkHhqARpvJGIlCcKRa4jw7Ce77NRJbMrcQ0KRyVI441EpDxQKHJNB2MVjgpK4aiEZBpw4LzZVYiIlB5rKGrIzm1tFIpc0EGNOyowhaMScjLeOl1SRMTdKBS5B4WjglM4KiGHL5hdgYhIyVIoci/xqRCTCFUCza7E+SkclRCFIxFxFwpF7isqVuGoIBSOSsgRhSMRcXEKRe7vYCx0rGl2Fc5P4agEZBrWNSRERFyRQlH5cVAThwpE4agEnEqwLq4lIuJKFIrKn+PxcCkd/PTbP096ekqAxhuJiCtRKCq/DOBQLDQNN7sS56ZwVAKOxJldgYhI/hSKBKyDshWO8qZwVAKOaLyRiDgxhSKxp/WO8qdwVEyZBhxVt5qIOCGFIsnJoTgwDLBYzK7EeSkcFdPpBEjRYGwRcSIKRZKXpDQ4mQDVg82uxHkpHBWTBmOLiLOwGBZCkhqya1sbNpxTKJLcHbmgcJQXhaNi0uKPImI2hSIprFMJZlfg3BSOiknhSETMolAkRXU60ewKnJvCUTEYBhzVTDURKWMKRVJcajnKm8JRMZxOtK40KiJSFhSKpKScSbTOtvbQjLUcKRwVg1qNRKQsKBRJSUvLhHNJEB5odiXOSeGoGM4lmV2BiLgzhSIpTacSFI5yo3BUDOeTza5ARNyRQpGUhdOJ0NLsIpyUwlExKByJSElSKJKydFqDsnOlcFQMsZfMrkBE3IFCkZjhjIaG5ErhqBjUciQixaFQJGY6q3CUK4WjIrqUbj0/jYhIYf0vFF3NhnMhZpcj5dT5ZE3nz43CURHFqtVIRApJoUicSYYBcZegor/ZlTgfhaMiUpeaiBSUQpE4q7NJCkc5UTgqIg3GFpH8KBSJszuXBFQyuwrno3BURGo5EpHcKBSJqzir32U5UjgqIoUjEbmSQpG4Gp3pIWcKR0WkcCQiWbJC0W6FInEx5/S7LEcKR0WkMUciolAkri4x1ewKnJPCURFpKr9I+aVQJO4iOd3sCpyTwlERXEyBtEyzqxCRsqZQJO4mWYsZ50jhqAjUaiRSvigUibu6lA6GARatku1A4agINBhbpHxQKBJ3Z2ANSP7eZlfiXBSOiiBBA9hE3Jo1FDVg97Y2CkXi9pIVjrJROCoCjTcScU8KRVIeJacBOoWIA4WjIkjNMLsCESlJCkVSnmnGWnYKR0WQpnAk4hYUikQ0Yy0nCkdFkKpuNRGXplAk8j9JCkfZKBwVQbpajkRckkKRSHbqVstO4agI1HIk4loUikRyp3CUncJREWjMkYhrUCgSyZ/GHGWncFQECkcizk2hSKTgFI6yUzgqAnWriTgnhSKRwlO3WnYKR0WgliMR56JQJFJ0ajnKTuGoCLRCtohzsIWi7W3YcFahSKQo1HKUncJREajlSMRcCkUiJUdnfchO4agI9EYSMYdCkUjJ87CYXYHzUTgqgnR1q4mUKYUikdLj5WF2Bc5H4agI1HIkUjYUikRKn6dajrJROCoCDcgWKX0pqV6c+GeIQpFIKfNUy1E2ekqKQAOyRUpfWoYnJxSMREqdwlF2ekqKQIPXRETEXXjpd1o2CkdF4O1pdgUiIiIlQy1H2ekpKQJvPWsiIuImFI6y01NSBGo5EhERd6HZatkpHBWBWo5ERMRdaJ2j7PSUFIFajkRExF2o5Sg7haMiUMuRiIi4C405yk5PSRGo5UhERNyFwlF2ekqKQC1HIiLiLjTmKDs9JUXgr5OuiIiIm9CYo+wUjorA39vsCkREREqGWo6y01NSBApHIiLiLtRylJ3CURGoW01ERNxFgP7gz0bhqAj0RhIREXcR4md2Bc5H4agI1HIkIiLuIkzhKBuFoyLQmCMREXEXajnKTuGoCBSORETEHXh5QJCP2VU4H4WjIghSOBIRETcQqlajHCkcFUGYv9kViIiIFF+Ir9kVOCeFoyLw8YQKekOJiIiLU8tRzhSOiqhygNkViIiIFI8GY+dM4aiIKqtrTUREXJxajnKmcFREldRyJCIiLi5UQ0RypHBUROpWExERV6eWo5wpHBWRWo5ERMTVKRzlTOGoiDTmSEREXJ0GZOdM4aiIKvqDh8XsKkRERIrGz8v6LdkpHBWRp4dO1iciIq5LXWq5UzgqBo07EhERV6XVsXOncFQMlTTuSEREXJROhZU7haNi0HR+ERFxVRFBZlfgvBSOikHhSEREXFX1YLMrcF4KR8WgcCQiIq5K4Sh3CkfFoDFHIiLiinw99TssLwpHxRDqB156BkVExMVEBINFa/XlSr/ai8Fi0YA2ERFxPTXUpZYnhaNiqhNqdgUiIiKFo/FGeVM4Kqa6oWZXICIiUjgKR3lTOCqmyFCzKxARESkcdavlTeGomKoHW0f9i4iIuIJQXwjRedXypHBUTB4WqB1idhUiIiIFUzvU7Aqcn8JRCVDXmoiIuIo6+oM+XwpHJUCDskVExFUoHOVP4agEqOVIRERchYaC5E/hqARUCoAKvmZXISIikjcNxi4YhaMSotYjERFxdhqMXTAKRyVE4UhERJydutQKRuGohGhQtoiIOLsGFc2uwDUoHJWQOiGgExyLiIiz8vaABmFmV+EaFI5KSKAPhAeaXYWIiEjOGlYCb53RoUAUjkqQxh2JiIizalbZ7Apch8JRCdK4IxERcVZNw82uwHUoHJUgDXQTERFnVMEXagSbXYXrUDgqQbUqQIgWgxQRESfTtDJYNGuowBSOSpDFAi2rmF2FiIiII3WpFY7CUQlrWdXsCkRERBw11WDsQlE4KmFNK1vXkhAREXEG1YMhVOdTKxT9Gi9hvl7QqJLZVYiIiFip1ajwFI5KgbrWRETEWTTTeKNCUzgqBVdpULaIiDgBLw/1ZhSFwlEpqBSg9SRERMR89cLAR6cMKTSFo1KirjURETGbutSKRuGolKhrTUREzKbB2EWjcFRK6oZBkI/ZVYiISHkV6A21Q8yuwjUpHJUSDwu0UHOmiIiY5Kqq1t9FUngKR6VI445ERMQs19QwuwLXpXBUipqHg6dSu4iIlLFQX2is8UZFpnBUivy9oUFFs6sQEZHypn0NdakVh8JRKbtKXWsiIlLG1KVWPApHpax1NVB4FxGRslI9GGppllqxKByVssoBUF9dayIiUkbUalR8Ckdl4LqaZlcgIiLlgQXooHBUbApHZaBtdZ3bRkRESl/DilDR3+wqXJ/CURnw84Krq5ldhYiIuLtr1FNRIhSOykhHvWFFRKQUeXlA2wizq3APCkdlpHFlCPMzuwoREXFXV1Wxrq8nxadwVEY8LHCtWo9ERKSUqEut5CgclSF1rYmISGkI9IYWVcyuwn0oHJWhqkHQqJLZVYiIiLtpG2EdcyQlQ09lGeta2+wKRETE3ahLrWQpHJWxqyMg2MfsKkRExF1UDoD6YWZX4V4UjsqYlwd0rGV2FSIi4i56RIJFJ/EsUQpHJuhaWyejFRGR4gvwhs4arlHiFI5MEB4ITSqbXYWIiLi6zrWtZ2GQkqVwZJKudcyuQEREXJmnBXrVNbsK96RwZJJWVSHE1+wqRETEVbWvAaE680KpUDgyiacH9FTiFxGRIrq+ntkVuC+FIxN1j7SuaioiIlIYzcKhRgWzq3BfCkcm8vOCHmo9EhGRQuqjVqNSpXBksl51NdNAREQKrmYFa8uRlB6FI5MFeFu710RERApCrUalT+HICfSpBz6eZlchIiLOLswP2lc3uwr3p3DkBIJ8oItWOBURkXz0rGud7SylS0+xk+hb33reNRERkZz4eekP6bKiX8dOIsQPOumEtCIikosutcFfy7+UCYUjJ9K3vnU5eBEREXs6VUjZUjhyIpUC4JqaZlchIiLOpl11CPM3u4ryQ+HIyfSrDx5qPRIRkcs8LDCgodlVlC8KR06mahC0jTC7ChERcRada1l/N0jZUThyQv0bghqPRETE1xMGNjK7ivJH4cgJVQ+GVtXMrkJERMzWs651NrOULYUjJzVArUciIuVaoLd1FrOUPYUjJ1U7BK6pYXYVIiJilv4Nta6RWRSOnNitTa0rooqISPlSyR+61TG7ivJL4ciJhfhp+qazyszIYP2XY/lmZF0+u82fb+6vz6ZvX8MwDOv16Wn8M/N5vh/Tks8HB/LlsOosn3IviedOFPgxtnz/Oh/faOGvT55w2P73p08x646KfDWiFvtXfOVw3cHV3/P7qzcW+/hExFw3NQZvnZDcNGqXcHK96sLqI3A60exKxN7WeW+w67fp9HhyFmG1m3PmwAZWTh2BT0AILW56jPSUJM5GbaLNv8ZSqW4rUhJi+euTx1k04SZufXtDvvcfs289u3//iIqRVzlsP7zuFw6s/Jr+ry7mwon9rHz3Pmpd3Re/kMqkJl5g/eyXGfDa0tI6bBEpA5EaVmE6tRw5OU8P+Fdzs6uQK53e/ReR195M7fYDCK4aSb1Og6nR+npi9q8DwCcwhAGvLaF+lyGE1mxM1SbX0umB9zh7YCMJMUfyvO+05ASWv3UXXR79BN+gMIfrYo/uJqJld8IbtqNBtzvwCajAxdOHAFg74zma3vAQQVV0ZkoRVzakOVg0I8dUCkcuoHkVaFXV7CrEXtWm13F86x/EHd8HwLlDWzm9ezW12t6Q621Sky6AxYJPUGie9736w0eo1W4ANVv3znZdpbqtOHtgAykJsZw5sJH0lGRCqjfg1M7VnIvaRIsbHyvWcYmIudpXh/oVza5C1K3mIoY0h11nIC3T7EoEoPXgF0hNusich5pg8fDEyMyg/T0Tadj9rhz3T0+9xLqZz9Ogq7W1JzcH/vyWs1GbuGXK+hyvr9WmLw26382PT7XH08ef7k/Owss3kFXTH6L7EzPZtXA6OxdMw69CZbo88jEV66jZUcRVeHtYJ+KI+RSOXETlAOhTH37bb3YlAhC1eg4HVn5Fz2e+pmLt5pw9uIW/P32CwIrVadRrmMO+melpLH1jCIZh0Pnh6bneZ8KZo/z9yeP0f3UJXj65r/rW7s5xtLtznO3yxm/GU6NVbzw8vdn83QQGv7edI+sXsOLte7n1nY3FPlYRKRvX14eKOrmsU1A4ciE3NIC1x+B8stmVyD8znqX14Bdo0HUoABUjW5Jw5jCbv5/sEI6yglFCzGEGTlyWZ6vR2QMbSY6L4Ycn2ti2GZkZnNz5JzsXvMfIH1Lw8HScvhJ3dA/7l3/JbVM3s3fJ50Q074p/SDj1Og9h5dT7SE2KxycguISPXkRKWqifFnx0JgpHLsTHEwY3hY83mV2JpKckYbE4DtmzeHiC8b9+z6xgdOHEfgZOWo5fhUp53mf1Vr0Y/N52h20r3xlBSM0mtB78fLZgZBgGq95/gI6jpuDtH0RmZgaZGWm2xwZruBIR53dLE/DVb2SnoZfCxbStDo0Pw95zZldSvtVpfyOb50wkKLw2YbWbc/bgZrbPn0LjPvcB1nCy5PXBnI3aRL9XFmBkZpAUewoA36CKeHr7ALDg5V5EdryFFgPH4BMQTMU6LRwex8svEL8KlbJtB9iz+FP8QsKp08G6rlG1Zp3Y+M04Tu9Zy9GNCwmr1QzffAZ/i4j5GlTU1H1no3Dkgv7VHCasgkzD7ErKr+semMaGr8ayevrDJF+IIaBidZr2e4A2Q18BIPHccQ7/8zMA8x5r7XDbgZOWU71ldwAunori0sWzhX78pNjTbJ4zkZv/85dtW5VGHbhq0NP8/uoA/EOq0P3JWUU7OBEpM94ecO9VmrrvbCxG1pK+4lK+2wHLos2uQkREiuP2ZtC7ntlVyJW0zpGLurExBPuYXYWIiBRVg4rQs67ZVUhOFI5cVIA3DGpidhUiIlIUWd1pHupOc0oKRy6sUy1oUtnsKkREpLAGNYGqQWZXIblROHJhFgsMb2VtRRIREdeg7jTnp3Dk4sL84a6WZlchIiIFoe4016Bw5AbaVdcaGSIirkDdaa5B4chN3NECKumcPCIiTqt+mLrTXIXCkZvw94YRrUEttSIizsfbA4a1Uneaq1A4ciMNK+nEhSIizuhmdae5FIUjN3NTY6gdYnYVIiKSpX4Y9FJ3mktROHIznh4w8mprE66IiJhL3WmuSb9C3VC1ILitmdlViIiIutNck8KRm+oRCS3Cza5CRKT8ah6u7jRXpXDkxoa11slpRUTMUDnAOsRB3WmuSeHIjVXwhbuvMrsKEZHyxccTHmwHgfrj1GUpHLm51tWgcy2zqxARKT/ubgm1KphdhRSHwlE5MKS5dZC2iIiUrp6RcE1Ns6uQ4lI4Kgd8veDhduDvZXYlIiLuq2FFGKyZwm5B4aicqBpkHRyosYEiIiUv1BdGt7WuNSeuTy9jOdKyqnXNDRERKTleHvBAO+skGHEPCkflzA0NoG2E2VWIiLiPIc2hXpjZVUhJUjgqh4a3hpqaSSEiUmydakG3OmZXISVN4agc8vG0DtAO0hocIiJFVicE7mhhdhVSGhSOyqlKAfBAW/DUCG0RkUIL9rEu9OjtaXYlUhoUjsqxRpW0graISGF5WGBUG6job3YlUloUjsq562pZB2mLiEjBDGoCTSqbXYWUJoUj4ebGmsEmIlIQ3epA3/pmVyGlTeFIsFhgRGtNRRURyUv76jBUA7DLBYUjAayDCh9uB5UDzK5ERMT5tKxi/SPSQ5NYygWFI7EJ9oUx7SHA2+xKREScR4OKOjVIeaOXWhxEBFtbkHw1PVVEhFoVrH80+ugzsVxROJJsGlaCR9qDt94dIlKOVQ2Ex64Bf7Wmlzv69Sc5alwZHlZAEpFyKswPnrhWJ5Mtr/SrT3LVLNy6AqyX3iUiUo4E+ViDkRZ5LL/0a0/y1KLK5YGImqEhIuWAnxc81gGqBZldiZhJ4Ujy1aqqdal8TWEVEXfm7WGdkFIn1OxKxGwKR1IgbSJg5NUKSCLinjwscH8b63hLEYUjKbB21WF4K1A+EhF3YgGGtYJW1cyuRJyFwpEUyjU1rR8iCkgi4i6GNIdra5pdhTgThSMptI614O6rFJBExLVZsJ4rrWddsysRZ+NldgHimjrXhoxM+HqH2ZWIiBSeh8XaCq4WI8mJwpEUWbdIyDDgu51mVyIiUnBeHtbB1601xkhyoXAkxdKzrjUgzd1ldiUiIvnz9YSH2kNTzUqTPFgMwzDMLkJc39pjMHsbpGeaXYmISM4CvOHRDlAvzOxKxNkpHEmJ2XsWPtwISWlmVyIi4qiCLzx+DdSsYHYl4goUjqREnYiH99bBuWSzKxERsaoSaD0lSHig2ZWIq1A4khJ3McUakA5fMLsSESnvIkNhTHsI9jW7EnElCkdSKlIz4NNNsPW02ZWISHnV8vKJs308za5EXI3CkZSazMvT/FdEm12JiJQ3nWvDXS11PkgpGoUjKXVLD1qn+uuNJiJl4cZGMLCR2VWIK1M4kjKx6SR8vhnSNNVfREqJhwXubgmdaptdibg6hSMpM4di4f31EJ9qdiUi4m5CfK3jixpUNLsScQcKR1KmziTCtHVwOtHsSkTEXTSsaD0dSIif2ZWIu1A4kjKXmAofbIAD582uRERcXZ96cEsT8PQwuxJxJwpHYoq0DPh2B6w+anYlIuKK/LxgWCtoE2F2JeKOFI7EVOuPw5fb4VK62ZWIiKuoHgwPtoWqQWZXIu5K4UhMdyYRPtmkFbVFJH8dqsPdV4Gvl9mViDtTOBKnkJEJP+yBPw5qPSQRyc7TArc3gx51za5EygOFI3Eq20/DzK2QoOn+InJZmJ91mn69MLMrkfJC4UicTtwl+Gwz7DtndiUiYrYmlWHU1TpxrJQthSNxSpkG/Loffttv/b+IlC8WoF8DuKmxzo8mZU/hSJzavnPWVqS4S2ZXIiJlJdDbOk2/VTWzK5HySuFInF5CKszcAttjzK5EREpbmwi4owVUUDeamEjhSFzG0oPw4x5I18lrRdxOBV9rKNKijuIMFI7EpRyOg083QUyS2ZWISEnpWNM6TT/Qx+xKRKwUjsTlpGbAL3th6SEN1hZxZWF+1gUdW1QxuxIRRwpH4rKOXIDZ26z/iojrsABd6sBtTa3nSBNxNgpH4tIyDeuq2j/vs7YoiYhzqxIA97SCRpXMrkQkdwpH4hbOJsFX22HXGbMrEZGcWIDe9azrFvl4ml2NSN4UjsStrD0Gc3dBvE4/IuI0IoKs6xbV1ek/xEUoHInbSUqDn/bCn4c1YFvETJ4W6yrX/RuCl4fZ1YgUnMKRuK2jF+Gb7RAVa3YlIuVP40pwe3OoVcHsSkQKT+FI3JphWLva5u1WV5tIWagRDLc21fR8cW0KR1IuJF/ualuprjaRUhHmBzc3hmtq6kSx4voUjqRcOREPP++FLadAb3yR4gvwhn71oWdd8NYsNHETCkdSLh25YA1JOpmtSNF4eUCPSLihgU77Ie5H4UjKtUOx1gUktT6SSMFYgGtqWNcrqhRgdjUipUPhSAQ4cN7akrT3nNmViDivZuHWwdaagSbuTuFIxM7es9aB25r+L/I/tSpYz4PWNNzsSkTKhsKRSA52xlhbkqJ1UlspxyoHWGegta8OFs1Ak3JE4UgkD1tPwy97rQtKipQXtUPg+nrQtrqm5Uv5pAXdrxAZGck777xT5NtHR0djsVjYsmVLidWUm+HDhzNo0CDb5e7du/PEE0/keZsrj89isTB//vxSqc8dtKoKL3eBB9pC9WCzqxEpXc3C4Ylrre/59jUUjKT8KlQ4Gj58OBaLBYvFgre3N1WrVqVPnz58/vnnZGZmlnhxBfll746yAlbWt4+PDw0aNGDChAnYN/RNnTqVmTNnFuuxTp48yQ033FDg/a8MZOWBxQJtImBsV3iwLTSpbJ2xI+IOPCzQoQb8X1d4/BpoWtnsikTM51XYG/Tr148ZM2aQkZHB6dOn+f3333n88ceZO3cuP//8M15ehb7LYjEMg4yMjDJ/3LKwdOlSmjdvTkpKCqtXr2bUqFFEREQwcuRIAEJCQor9GNWqVSv2fZQXHha4OsL6fTrButr238esJ7oVcTX+XnBdLehdDyr6m12NiHMpdLear68v1apVo0aNGrRp04aXXnqJn376iYULFzq0YsTFxTFq1CjCw8OpUKECPXv2ZOvWrbbrx40bR+vWrZk9ezaRkZGEhIQwdOhQ4uPjAWsLxcqVK5k6daqtBSU6OpoVK1ZgsVhYuHAhbdu2xdfXl9WrV5OSksJjjz1GlSpV8PPzo3Pnzqxfvz7PY4mJieHGG2/E39+funXr8tVXX2XbJ7/jyE9GRgYjR46kbt26+Pv707hxY6ZOnVqg21aqVIlq1apRp04d7rrrLjp16sSmTZts1+fUipOens6YMWMICQmhcuXKjB07lryGlV3ZrbZ9+3Z69uyJv78/lSpVYvTo0SQkJADW12zWrFn89NNPttdkxYoVABw9epQhQ4YQGhpKxYoVufnmm4mOjnZ4rM8//5zmzZvj6+tLREQEY8aMsV03ZcoUWrZsSWBgILVq1eLhhx+2Pa4zqhoEQ5rDG73hnqusYzREXEFEENzZAl7vbX0PKxiJZFciY4569uxJq1at+OGHH2zbbr/9dmJiYli4cCEbN26kTZs29OrVi/Pnz9v2iYqKYv78+SxYsIAFCxawcuVKXn/9dcDaZdSxY0fuv/9+Tp48ycmTJ6lVq5btti+88AKvv/46u3fv5qqrruK5555j3rx5zJo1i02bNtGgQQP69u3r8HhXGj58OEePHmX58uXMnTuXDz74gJgYxyWTC3IcecnMzKRmzZp8//337Nq1i1deeYWXXnqJOXPmFOj2WTZs2MDGjRu55ppr8txv1qxZeHl5sW7dOqZOncqUKVP49NNPC/QYiYmJ9O3bl7CwMNavX8/333/P0qVLbSHmmWeeYciQIfTr18/2mlx33XWkpaXRt29fgoODWbVqFWvWrCEoKIh+/fqRmmo92+v06dN55JFHGD16NNu3b+fnn3+mQYMGtsf28PDg3XffZefOncyaNYtly5bx3HPPFeo5MoOPJ3SubR2j8UIn6FgTvDWST5yMBev4uSeugXHdoVsk+LlfY7tIiSmxH48mTZqwbds2AFavXs26deuIiYnB19cXgDfffJP58+czd+5cRo8eDViDw8yZMwkOto50veeee/jjjz+YOHEiISEh+Pj4EBAQkGPXz6uvvkqfPn0A6y/16dOnM3PmTNv4mU8++YQlS5bw2Wef8eyzz2a7/b59+1i4cCHr1q2jffv2AHz22Wc0bdrUtk9BjyMv3t7ejB8/3na5bt26/P3338yZM4chQ4bkedvrrrsODw8PUlNTSUtLY/To0dx777153qZWrVq8/fbbWCwWGjduzPbt23n77be5//77863166+/5tKlS3zxxRcEBgYC8N5773HjjTfyxhtvULVqVfz9/UlJSXF4Tb788ksyMzP59NNPsVye7ztjxgxCQ0NZsWIF119/PRMmTODpp5/m8ccft90u63kHHMaWRUZGMmHCBB588EE++OCDfOt2FnXDrN+3N4M1R+HPw3AmyeyqpDwL8IZOtaB7pHVavogUTImFI8MwbL8Yt27dSkJCApUqVXLYJzk5maioKNvlyMhIWzACiIiIyNZyk5t27drZ/h8VFUVaWhqdOnWybfP29qZDhw7s3r07x9vv3r0bLy8v2rZta9vWpEkTQkNDbZcLehz5ef/99/n88885cuQIycnJpKam0rp163xv991339G0aVPS0tLYsWMHjz76KGFhYbbWtZxce+21ttcBoGPHjrz11ltkZGTg6Zn3WSF3795Nq1atbMEIoFOnTmRmZrJ3716qVq2a4+22bt3KgQMHHF5LgEuXLhEVFUVMTAwnTpygV69euT720qVLmTx5Mnv27OHixYukp6dz6dIlkpKSCAhwrU/1QB+4vj70qWc9LcnKw9ZzuGVq0QwpA14e0KIKdKgOLataWzdFpHBKLBzt3r2bunXrApCQkEBERIRtPIo9+/Dh7e3tcJ3FYinwrDf7X+ClpaDHkZdvv/2WZ555hrfeeouOHTsSHBzMf//7X/755598b1urVi1b11PTpk2Jiopi7NixjBs3Dj8/v8IcSqlKSEigbdu2OY7ZCg8Px8Mj736m6OhoBg4cyEMPPcTEiROpWLEiq1evZuTIkaSmprpcOMpisUDzKtbv88nWlqQ1R+FiitmVibvxsEDjStZZZ1dXA3/v/G8jIrkrkXC0bNkytm/fzpNPPglAmzZtOHXqFF5eXkRGRhb5fn18fMjIyMh3v/r16+Pj48OaNWuoU6cOAGlpaaxfvz7XpQCaNGlCeno6GzdutHXv7N27l7i4ONs+JXEca9as4brrruPhhx+2bStMq5M9T09P0tPTSU1NzTUcXRm61q5dS8OGDfNtNQJrAJs5cyaJiYm28LlmzRo8PDxo3LgxkPNr0qZNG7777juqVKlChQo5n3QpMjKSP/74gx49emS7buPGjWRmZvLWW2/ZglRhx2Q5u4r+MKgJ3NgI9p2HzSdh6ymIU1CSYqgXZl29ul11qOBrdjUi7qPQQ0dTUlI4deoUx48fZ9OmTUyaNImbb76ZgQMH2sbD9O7dm44dOzJo0CAWL15MdHQ0f/31Fy+//DIbNmwo8GNFRkbyzz//EB0dzdmzZ3NtVQoMDOShhx7i2Wef5ffff2fXrl3cf//9JCUl2aa9X6lx48b069ePBx54gH/++YeNGzcyatQo/P3/N3WjJI6jYcOGbNiwgUWLFrFv3z7Gjh2b7yy6LOfOnePUqVMcO3aMhQsXMnXqVHr06JFrAAE4cuQITz31FHv37uWbb75h2rRpDuN88nLXXXfh5+fHsGHD2LFjB8uXL+fRRx/lnnvusXWpRUZGsm3bNvbu3cvZs2dJS0vjrrvuonLlytx8882sWrWKQ4cOsWLFCh577DGOHTsGWGe6vfXWW7z77rvs37+fTZs2MW3aNAAaNGhAWloa06ZN4+DBg8yePZsPP/ywQDW7Gk8P6zoyd7a0zhZ67jpr95vGg0hBVQ+2Bu2JPeH5TtCzroKRSEkrdMvR77//TkREBF5eXoSFhdGqVSveffddhg0bZvur32Kx8Ntvv/Hyyy8zYsQIzpw5Q7Vq1ejatWuu41Zy8swzzzBs2DCaNWtGcnIyhw4dynXf119/nczMTO655x7i4+Np164dixYtIiwsLNfbzJgxg1GjRtGtWzeqVq3KhAkTGDt2rO36kjiOBx54gM2bN/Ovf/0Li8XCHXfcwcMPP8zChQvzvW3v3r0Ba4tRREQE/fv3Z+LEiXne5t577yU5OZkOHTrg6enJ448/XqCB4wABAQEsWrSIxx9/nPbt2xMQEMBtt93GlClTbPvcf//9rFixgnbt2pGQkMDy5cvp3r07f/75J88//zy33nor8fHx1KhRg169etmC3LBhw7h06RJvv/02zzzzDJUrV2bw4MEAtGrViilTpvDGG2/w4osv0rVrVyZPnpzv4HNXZ7FA/YrW78HN4OgF2HTK2qp00nlXMRATVA6wthC1rw41cv/bSERKiM6tJuKETiXA5lOw6SQc0clvy6VQX+uCox1qWLvPRKTsKByJOLnzydaQtPkURJ0H/cC6pwBvaFTJenqaJpUgQufyEzGNwpGIC7mYAttOw96z1oHdcZfMrkiKyscTGlS0zjJrUtm6yrpO9CriHBSORFxYTCLsOwd7z8H+cxCrsOS0PCxQN/Ryy1Bla1eZl1ZTF3FKCkcibuTM5bB0IBYOxlpPkKsfcHNYsA6ezuoma1hJp+wQcRUKRyJuLDEVDsXBoVg4GAfRcZCUZnJRbsgChAdCjeDL3xWgYUUI1hR7EZekcCRSjhiGdSbcoTg4EQ+nE62tS2eTIEOfBAUS7GMNP/ZBqHqwTtMh4k4UjkSEjExrQIpJhFOJEJPwv+BUXlfx9vGE6kH/Cz9ZgUgLLoq4P4UjEcnTpXRraDptF5hOJ1q/L6WbXV3ReVgg1M/6HWb3b6UAawgKD9TsMZHySuFIRIosMdU6hikx7fK/V15Og6TUKy6nQWr+p0wsEl9PCPLJ+TvYxzoGKCsEBfsq/IhIzhSORKTMpWU4hqVMwxpULBbwtPzv/x5Yz0dnwbotv29vjfsRkRKgcCQiIiJiR0uQiYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2FI5ERERE7CgciYiIiNhROBIRERGxo3AkIiIiYkfhSERERMSOwpGIiIiIHYUjERERETsKRyIiIiJ2/h/SlUr0TcQWWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definimos un diccionario para mapear las categorías originales a las categorías generalizadas\n",
    "diccionario_categorias = {\n",
    "    'AGRO': 'Fuera de la Biblioteca',\n",
    "    'BIBLIOTECA': 'Dentro de la Biblioteca',\n",
    "    'BIOINF': 'Fuera de la Biblioteca',\n",
    "    'BIOINGENIE': 'Fuera de la Biblioteca',\n",
    "    'DOCENTE': 'Fuera de la Biblioteca',\n",
    "    'HIST': 'Fuera de la Biblioteca',\n",
    "    'INTERALUM': 'Fuera de la Biblioteca',\n",
    "    'INVI': 'Fuera de la Biblioteca',\n",
    "    'S': 'Fuera de la Biblioteca',\n",
    "    'TEC_MED_N': 'Fuera de la Biblioteca',\n",
    "    'TEC_PROD_M': 'Fuera de la Biblioteca'\n",
    "}\n",
    "\n",
    "# Aplicamos el mapeo a la columna 'categorycode'\n",
    "df_filtrado['categorycode_generalizado'] = df_filtrado['categorycode'].map(diccionario_categorias)\n",
    "\n",
    "# Grafico de torta para total_categorias generalizado\n",
    "colores = ['#66b3ff','#99ff99']\n",
    "total_categorias_generalizado = df_filtrado['categorycode_generalizado'].value_counts().sort_index()\n",
    "print(total_categorias_generalizado)\n",
    "plt.figure(figsize=(10, 6))\n",
    "total_categorias_generalizado.plot(kind='pie', autopct='%1.1f%%', colors=colores, startangle=90)\n",
    "plt.title('Distribución de categorías de usuarios (generalizado)')\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis de los datos que no obtuvieron resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extraemos las sintaxis de los usuarios cuando el sistema no encuentra ningun resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "317\n"
     ]
    }
   ],
   "source": [
    "# Filtramos otra vez las búsquedas que tengan como resultado cero, pero ahora con las nuevas columnas creadas\n",
    "cero_resultados = df_filtrado[df_filtrado['total'] == 0].copy()\n",
    "\n",
    "# Vemos cuántas búsquedas tienen cero resultados\n",
    "print(len(cero_resultados))\n",
    "\n",
    "# Aplicamos la función para extraer la sintaxis de las búsquedas que tienen cero resultados\n",
    "cero_resultados.loc[:, 'sintaxis'] = cero_resultados['query_desc'].apply(extraer_sintaxis)\n",
    "\n",
    "# Reordenamos las columnas\n",
    "cero_resultados = cero_resultados[['sessionid', 'query_desc', 'total', 'time', 'categorycode', 'campo_utilizado', 'sintaxis', 'cant_campos_utilizados']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos el DataFrame con los resultados son iguales a cero en un archivo CSV (esto fue solicitado por la coordinadora)\n",
    "cero_resultados.to_csv('archivos/df_cero_resultados.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las búsquedas que no arrojaron resultados en el sistema fueron categorizadas para facilitar su análisis. \n",
    "Las categorías son las siguientes:\n",
    "\n",
    "1. **Error de tipeo**: Esta categoría contiene las búsquedas en las que el usuario ingresó términos con errores (ortográficos, tipográficos, etc.). Esto provocó que el sistema no encontrara resultados.\n",
    "\n",
    "2. **No hay resultados**: Búsquedas en las que el usuario ingresó correctamente la consulta, pero el sistema no tiene el material registrado.\n",
    "\n",
    "3. **Campo incorrecto**: Se refiere a búsquedas en las que el usuario ingresó la consulta en un campo equivocado.\n",
    "\n",
    "4. **No había resultados en el tiempo de la consulta**: Estas son búsquedas en las que, al momento de la consulta, el material no estaba registrado en el sistema. Sin embargo, si la misma consulta se realizara en la actualidad, el sistema sí devolvería resultados.\n",
    "\n",
    "5. **Combinación de campos incorrecta**: Consultas en las que el usuario combinó múltiples campos de búsqueda, pero la combinación de términos no coincidió con ningún material en el sistema.\n",
    "\n",
    "6. **Lugar incorrecto de consulta**: Se refiere a búsquedas realizadas en un sistema que no tiene acceso a la información deseada.\n",
    "\n",
    "7. **Usuario no ingresó nada**: Son búsquedas donde el usuario no ingresó ningún término de búsqueda, lo que resultó en una consulta sin contenido.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplicar la función al DataFrame 'cero_resultados' y crear una nueva columna 'error_campo'\n",
    "cero_resultados['problema_detectado'] = cero_resultados.apply(verificar_error_campo, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clasificación de los errores en las búsquedas que no tienen resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vemos cual es la falla en el/los campo/s utilizado/s en las búsquedas que tienen cero resultados\n",
    "cero_resultados.loc[50, 'problema_detectado'] = (cero_resultados.loc[50, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[2236, 'problema_detectado'] = (cero_resultados.loc[2236, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2237, 'problema_detectado'] = (cero_resultados.loc[2237, 'problema_detectado'] + 'no hay resultados') \n",
    "cero_resultados.loc[47, 'problema_detectado'] = (cero_resultados.loc[47, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[874, 'problema_detectado'] = (cero_resultados.loc[874, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1704, 'problema_detectado'] = (cero_resultados.loc[1704, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1803, 'problema_detectado'] = (cero_resultados.loc[1803, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[873, 'problema_detectado'] = (cero_resultados.loc[873, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1804, 'problema_detectado'] = (cero_resultados.loc[1804, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1805, 'problema_detectado'] = (cero_resultados.loc[1805, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1806, 'problema_detectado'] = (cero_resultados.loc[1806, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1807, 'problema_detectado'] = (cero_resultados.loc[1807, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[129, 'problema_detectado'] = (cero_resultados.loc[129, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[130, 'problema_detectado'] = (cero_resultados.loc[130, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1654, 'problema_detectado'] = (cero_resultados.loc[1654, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[131, 'problema_detectado'] = (cero_resultados.loc[131, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[450, 'problema_detectado'] = (cero_resultados.loc[450, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[449, 'problema_detectado'] = (cero_resultados.loc[449, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[446, 'problema_detectado'] = (cero_resultados.loc[446, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2108, 'problema_detectado'] = (cero_resultados.loc[2108, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[408, 'problema_detectado'] = (cero_resultados.loc[408, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2111, 'problema_detectado'] = (cero_resultados.loc[2111, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[241, 'problema_detectado'] = (cero_resultados.loc[241, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[242, 'problema_detectado'] = (cero_resultados.loc[242, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[386, 'problema_detectado'] = (cero_resultados.loc[386, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1588, 'problema_detectado'] = (cero_resultados.loc[1588, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[79, 'problema_detectado'] = (cero_resultados.loc[79, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[19, 'problema_detectado'] = (cero_resultados.loc[19, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[24, 'problema_detectado'] = (cero_resultados.loc[24, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[54, 'problema_detectado'] = (cero_resultados.loc[54, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[55, 'problema_detectado'] = (cero_resultados.loc[55, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[61, 'problema_detectado'] = (cero_resultados.loc[61, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[62, 'problema_detectado'] = (cero_resultados.loc[62, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[63, 'problema_detectado'] = (cero_resultados.loc[63, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[64, 'problema_detectado'] = (cero_resultados.loc[64, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[66, 'problema_detectado'] = (cero_resultados.loc[66, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[68, 'problema_detectado'] = (cero_resultados.loc[68, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[69, 'problema_detectado'] = (cero_resultados.loc[69, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[70, 'problema_detectado'] = (cero_resultados.loc[70, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[72, 'problema_detectado'] = (cero_resultados.loc[72, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[73, 'problema_detectado'] = (cero_resultados.loc[73, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[74, 'problema_detectado'] = (cero_resultados.loc[74, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[75, 'problema_detectado'] = (cero_resultados.loc[75, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[76, 'problema_detectado'] = (cero_resultados.loc[76, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[77, 'problema_detectado'] = (cero_resultados.loc[77, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[78, 'problema_detectado'] = (cero_resultados.loc[78, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[84, 'problema_detectado'] = (cero_resultados.loc[84, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[89, 'problema_detectado'] = (cero_resultados.loc[89, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[90, 'problema_detectado'] = (cero_resultados.loc[90, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[95, 'problema_detectado'] = (cero_resultados.loc[95, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[113, 'problema_detectado'] = (cero_resultados.loc[113, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[114, 'problema_detectado'] = (cero_resultados.loc[114, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[115, 'problema_detectado'] = (cero_resultados.loc[115, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[116, 'problema_detectado'] = (cero_resultados.loc[116, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[117, 'problema_detectado'] = (cero_resultados.loc[117, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[118, 'problema_detectado'] = (cero_resultados.loc[118, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[128, 'problema_detectado'] = (cero_resultados.loc[128, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[138, 'problema_detectado'] = (cero_resultados.loc[138, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[140, 'problema_detectado'] = (cero_resultados.loc[140, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[151, 'problema_detectado'] = (cero_resultados.loc[151, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[158, 'problema_detectado'] = (cero_resultados.loc[158, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[159, 'problema_detectado'] = (cero_resultados.loc[159, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[185, 'problema_detectado'] = (cero_resultados.loc[185, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[219, 'problema_detectado'] = (cero_resultados.loc[219, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[220, 'problema_detectado'] = (cero_resultados.loc[220, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[221, 'problema_detectado'] = (cero_resultados.loc[221, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[222, 'problema_detectado'] = (cero_resultados.loc[222, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[243, 'problema_detectado'] = (cero_resultados.loc[243, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[271, 'problema_detectado'] = (cero_resultados.loc[271, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[274, 'problema_detectado'] = (cero_resultados.loc[274, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[278, 'problema_detectado'] = (cero_resultados.loc[278, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[293, 'problema_detectado'] = (cero_resultados.loc[293, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[294, 'problema_detectado'] = (cero_resultados.loc[294, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[298, 'problema_detectado'] = (cero_resultados.loc[298, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[323, 'problema_detectado'] = (cero_resultados.loc[323, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[327, 'problema_detectado'] = (cero_resultados.loc[327, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[339, 'problema_detectado'] = (cero_resultados.loc[339, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[346, 'problema_detectado'] = (cero_resultados.loc[346, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[371, 'problema_detectado'] = (cero_resultados.loc[371, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[372, 'problema_detectado'] = (cero_resultados.loc[372, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[389, 'problema_detectado'] = (cero_resultados.loc[389, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[390, 'problema_detectado'] = (cero_resultados.loc[390, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[402, 'problema_detectado'] = (cero_resultados.loc[402, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[415, 'problema_detectado'] = (cero_resultados.loc[415, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[483, 'problema_detectado'] = (cero_resultados.loc[483, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[484, 'problema_detectado'] = (cero_resultados.loc[484, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[485, 'problema_detectado'] = (cero_resultados.loc[485, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[493, 'problema_detectado'] = (cero_resultados.loc[493, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[495, 'problema_detectado'] = (cero_resultados.loc[495, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[498, 'problema_detectado'] = (cero_resultados.loc[498, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[512, 'problema_detectado'] = (cero_resultados.loc[512, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[513, 'problema_detectado'] = (cero_resultados.loc[513, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[520, 'problema_detectado'] = (cero_resultados.loc[520, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[522, 'problema_detectado'] = (cero_resultados.loc[522, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[535, 'problema_detectado'] = (cero_resultados.loc[535, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[552, 'problema_detectado'] = (cero_resultados.loc[552, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[583, 'problema_detectado'] = (cero_resultados.loc[583, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[586, 'problema_detectado'] = (cero_resultados.loc[586, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[592, 'problema_detectado'] = (cero_resultados.loc[592, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[655, 'problema_detectado'] = (cero_resultados.loc[655, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[699, 'problema_detectado'] = (cero_resultados.loc[699, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[738, 'problema_detectado'] = (cero_resultados.loc[738, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[754, 'problema_detectado'] = (cero_resultados.loc[754, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[766, 'problema_detectado'] = (cero_resultados.loc[766, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[770, 'problema_detectado'] = (cero_resultados.loc[770, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[771, 'problema_detectado'] = (cero_resultados.loc[771, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[776, 'problema_detectado'] = (cero_resultados.loc[776, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[793, 'problema_detectado'] = (cero_resultados.loc[793, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[802, 'problema_detectado'] = (cero_resultados.loc[802, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[809, 'problema_detectado'] = (cero_resultados.loc[809, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[810, 'problema_detectado'] = (cero_resultados.loc[810, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[816, 'problema_detectado'] = (cero_resultados.loc[816, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[817, 'problema_detectado'] = (cero_resultados.loc[817, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[820, 'problema_detectado'] = (cero_resultados.loc[820, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[837, 'problema_detectado'] = (cero_resultados.loc[837, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[839, 'problema_detectado'] = (cero_resultados.loc[839, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[843, 'problema_detectado'] = (cero_resultados.loc[843, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[848, 'problema_detectado'] = (cero_resultados.loc[848, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[852, 'problema_detectado'] = (cero_resultados.loc[852, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[865, 'problema_detectado'] = (cero_resultados.loc[865, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[871, 'problema_detectado'] = (cero_resultados.loc[871, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[872, 'problema_detectado'] = (cero_resultados.loc[872, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[885, 'problema_detectado'] = (cero_resultados.loc[885, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[891, 'problema_detectado'] = (cero_resultados.loc[891, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[892, 'problema_detectado'] = (cero_resultados.loc[892, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[894, 'problema_detectado'] = (cero_resultados.loc[894, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[895, 'problema_detectado'] = (cero_resultados.loc[895, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[897, 'problema_detectado'] = (cero_resultados.loc[897, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[938, 'problema_detectado'] = (cero_resultados.loc[938, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[939, 'problema_detectado'] = (cero_resultados.loc[939, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[944, 'problema_detectado'] = (cero_resultados.loc[944, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[946, 'problema_detectado'] = (cero_resultados.loc[946, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[981, 'problema_detectado'] = (cero_resultados.loc[981, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[982, 'problema_detectado'] = (cero_resultados.loc[982, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[983, 'problema_detectado'] = (cero_resultados.loc[983, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[986, 'problema_detectado'] = (cero_resultados.loc[986, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[987, 'problema_detectado'] = (cero_resultados.loc[987, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1001, 'problema_detectado'] = (cero_resultados.loc[1001, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1006, 'problema_detectado'] = (cero_resultados.loc[1006, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1023, 'problema_detectado'] = (cero_resultados.loc[1023, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1024, 'problema_detectado'] = (cero_resultados.loc[1024, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1025, 'problema_detectado'] = (cero_resultados.loc[1025, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1055, 'problema_detectado'] = (cero_resultados.loc[1055, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1075, 'problema_detectado'] = (cero_resultados.loc[1075, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1076, 'problema_detectado'] = (cero_resultados.loc[1076, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1086, 'problema_detectado'] = (cero_resultados.loc[1086, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1087, 'problema_detectado'] = (cero_resultados.loc[1087, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1088, 'problema_detectado'] = (cero_resultados.loc[1088, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1143, 'problema_detectado'] = (cero_resultados.loc[1143, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1145, 'problema_detectado'] = (cero_resultados.loc[1145, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1146, 'problema_detectado'] = (cero_resultados.loc[1146, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1147, 'problema_detectado'] = (cero_resultados.loc[1147, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1150, 'problema_detectado'] = (cero_resultados.loc[1150, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1151, 'problema_detectado'] = (cero_resultados.loc[1151, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1152, 'problema_detectado'] = (cero_resultados.loc[1152, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1153, 'problema_detectado'] = (cero_resultados.loc[1153, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1154, 'problema_detectado'] = (cero_resultados.loc[1154, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1155, 'problema_detectado'] = (cero_resultados.loc[1155, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1188, 'problema_detectado'] = (cero_resultados.loc[1188, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[1189, 'problema_detectado'] = (cero_resultados.loc[1189, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[1207, 'problema_detectado'] = (cero_resultados.loc[1207, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1208, 'problema_detectado'] = (cero_resultados.loc[1208, 'problema_detectado'] + 'campo incorrecto')\n",
    "cero_resultados.loc[1227, 'problema_detectado'] = (cero_resultados.loc[1227, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1234, 'problema_detectado'] = (cero_resultados.loc[1234, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1235, 'problema_detectado'] = (cero_resultados.loc[1235, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1238, 'problema_detectado'] = (cero_resultados.loc[1238, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1239, 'problema_detectado'] = (cero_resultados.loc[1239, 'problema_detectado'] + 'no había resultados en el tiempo de la consulta')\n",
    "cero_resultados.loc[1253, 'problema_detectado'] = (cero_resultados.loc[1253, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1258, 'problema_detectado'] = (cero_resultados.loc[1258, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1270, 'problema_detectado'] = (cero_resultados.loc[1270, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1278, 'problema_detectado'] = (cero_resultados.loc[1278, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1281, 'problema_detectado'] = (cero_resultados.loc[1281, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1286, 'problema_detectado'] = (cero_resultados.loc[1286, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1292, 'problema_detectado'] = (cero_resultados.loc[1292, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1301, 'problema_detectado'] = (cero_resultados.loc[1301, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1302, 'problema_detectado'] = (cero_resultados.loc[1302, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[1303, 'problema_detectado'] = (cero_resultados.loc[1303, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[1312, 'problema_detectado'] = (cero_resultados.loc[1312, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1332, 'problema_detectado'] = (cero_resultados.loc[1332, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1333, 'problema_detectado'] = (cero_resultados.loc[1333, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1334, 'problema_detectado'] = (cero_resultados.loc[1334, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1343, 'problema_detectado'] = (cero_resultados.loc[1343, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1344, 'problema_detectado'] = (cero_resultados.loc[1344, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1345, 'problema_detectado'] = (cero_resultados.loc[1345, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1346, 'problema_detectado'] = (cero_resultados.loc[1346, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1361, 'problema_detectado'] = (cero_resultados.loc[1361, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1411, 'problema_detectado'] = (cero_resultados.loc[1411, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1435, 'problema_detectado'] = (cero_resultados.loc[1435, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1442, 'problema_detectado'] = (cero_resultados.loc[1442, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1448, 'problema_detectado'] = (cero_resultados.loc[1448, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1455, 'problema_detectado'] = (cero_resultados.loc[1455, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1480, 'problema_detectado'] = (cero_resultados.loc[1480, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1483, 'problema_detectado'] = (cero_resultados.loc[1483, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1506, 'problema_detectado'] = (cero_resultados.loc[1506, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1507, 'problema_detectado'] = (cero_resultados.loc[1507, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1532, 'problema_detectado'] = (cero_resultados.loc[1532, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1550, 'problema_detectado'] = (cero_resultados.loc[1550, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1556, 'problema_detectado'] = (cero_resultados.loc[1556, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1566, 'problema_detectado'] = (cero_resultados.loc[1566, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1587, 'problema_detectado'] = (cero_resultados.loc[1587, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1596, 'problema_detectado'] = (cero_resultados.loc[1596, 'problema_detectado'] + 'combinacion de campos incorrecta')\n",
    "cero_resultados.loc[1614, 'problema_detectado'] = (cero_resultados.loc[1614, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1656, 'problema_detectado'] = (cero_resultados.loc[1656, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1664, 'problema_detectado'] = (cero_resultados.loc[1664, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1665, 'problema_detectado'] = (cero_resultados.loc[1665, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1673, 'problema_detectado'] = (cero_resultados.loc[1673, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1682, 'problema_detectado'] = (cero_resultados.loc[1682, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1684, 'problema_detectado'] = (cero_resultados.loc[1684, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1724, 'problema_detectado'] = (cero_resultados.loc[1724, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1727, 'problema_detectado'] = (cero_resultados.loc[1727, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1752, 'problema_detectado'] = (cero_resultados.loc[1752, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1822, 'problema_detectado'] = (cero_resultados.loc[1822, 'problema_detectado'] + 'lugar incorrecto de consulta')\n",
    "cero_resultados.loc[1823, 'problema_detectado'] = (cero_resultados.loc[1823, 'problema_detectado'] + 'lugar incorrecto de consulta')\n",
    "cero_resultados.loc[1825, 'problema_detectado'] = (cero_resultados.loc[1825, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1826, 'problema_detectado'] = (cero_resultados.loc[1826, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1829, 'problema_detectado'] = (cero_resultados.loc[1829, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1835, 'problema_detectado'] = (cero_resultados.loc[1835, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1843, 'problema_detectado'] = (cero_resultados.loc[1843, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1847, 'problema_detectado'] = (cero_resultados.loc[1847, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1883, 'problema_detectado'] = (cero_resultados.loc[1883, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1888, 'problema_detectado'] = (cero_resultados.loc[1888, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1897, 'problema_detectado'] = (cero_resultados.loc[1897, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1898, 'problema_detectado'] = (cero_resultados.loc[1898, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1899, 'problema_detectado'] = (cero_resultados.loc[1899, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1900, 'problema_detectado'] = (cero_resultados.loc[1900, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1951, 'problema_detectado'] = (cero_resultados.loc[1951, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1954, 'problema_detectado'] = (cero_resultados.loc[1954, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[1955, 'problema_detectado'] = (cero_resultados.loc[1955, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[1986, 'problema_detectado'] = (cero_resultados.loc[1986, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2015, 'problema_detectado'] = (cero_resultados.loc[2015, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2017, 'problema_detectado'] = (cero_resultados.loc[2017, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2019, 'problema_detectado'] = (cero_resultados.loc[2019, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2020, 'problema_detectado'] = (cero_resultados.loc[2020, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2024, 'problema_detectado'] = (cero_resultados.loc[2024, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2030, 'problema_detectado'] = (cero_resultados.loc[2030, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2043, 'problema_detectado'] = (cero_resultados.loc[2043, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2049, 'problema_detectado'] = (cero_resultados.loc[2049, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2051, 'problema_detectado'] = (cero_resultados.loc[2051, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2058, 'problema_detectado'] = (cero_resultados.loc[2058, 'problema_detectado'] + 'lugar incorrecto de consulta')\n",
    "cero_resultados.loc[2078, 'problema_detectado'] = (cero_resultados.loc[2078, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2086, 'problema_detectado'] = (cero_resultados.loc[2086, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2088, 'problema_detectado'] = (cero_resultados.loc[2088, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2110, 'problema_detectado'] = (cero_resultados.loc[2110, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2138, 'problema_detectado'] = (cero_resultados.loc[2138, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2167, 'problema_detectado'] = (cero_resultados.loc[2167, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2168, 'problema_detectado'] = (cero_resultados.loc[2168, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2169, 'problema_detectado'] = (cero_resultados.loc[2169, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2177, 'problema_detectado'] = (cero_resultados.loc[2177, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2180, 'problema_detectado'] = (cero_resultados.loc[2180, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2183, 'problema_detectado'] = (cero_resultados.loc[2183, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2185, 'problema_detectado'] = (cero_resultados.loc[2185, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2189, 'problema_detectado'] = (cero_resultados.loc[2189, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2194, 'problema_detectado'] = (cero_resultados.loc[2194, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2195, 'problema_detectado'] = (cero_resultados.loc[2195, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2223, 'problema_detectado'] = (cero_resultados.loc[2223, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2268, 'problema_detectado'] = (cero_resultados.loc[2268, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2286, 'problema_detectado'] = (cero_resultados.loc[2286, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2287, 'problema_detectado'] = (cero_resultados.loc[2287, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2288, 'problema_detectado'] = (cero_resultados.loc[2288, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2289, 'problema_detectado'] = (cero_resultados.loc[2289, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2290, 'problema_detectado'] = (cero_resultados.loc[2290, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2291, 'problema_detectado'] = (cero_resultados.loc[2291, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2324, 'problema_detectado'] = (cero_resultados.loc[2324, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2326, 'problema_detectado'] = (cero_resultados.loc[2326, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2328, 'problema_detectado'] = (cero_resultados.loc[2328, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2329, 'problema_detectado'] = (cero_resultados.loc[2329, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2334, 'problema_detectado'] = (cero_resultados.loc[2334, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2353, 'problema_detectado'] = (cero_resultados.loc[2353, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2355, 'problema_detectado'] = (cero_resultados.loc[2355, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2375, 'problema_detectado'] = (cero_resultados.loc[2375, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2385, 'problema_detectado'] = (cero_resultados.loc[2385, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2399, 'problema_detectado'] = (cero_resultados.loc[2399, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2448, 'problema_detectado'] = (cero_resultados.loc[2448, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2449, 'problema_detectado'] = (cero_resultados.loc[2449, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2450, 'problema_detectado'] = (cero_resultados.loc[2450, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2451, 'problema_detectado'] = (cero_resultados.loc[2451, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2454, 'problema_detectado'] = (cero_resultados.loc[2454, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2459, 'problema_detectado'] = (cero_resultados.loc[2459, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2460, 'problema_detectado'] = (cero_resultados.loc[2460, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2470, 'problema_detectado'] = (cero_resultados.loc[2470, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2471, 'problema_detectado'] = (cero_resultados.loc[2471, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2475, 'problema_detectado'] = (cero_resultados.loc[2475, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2480, 'problema_detectado'] = (cero_resultados.loc[2480, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2484, 'problema_detectado'] = (cero_resultados.loc[2484, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2486, 'problema_detectado'] = (cero_resultados.loc[2486, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2493, 'problema_detectado'] = (cero_resultados.loc[2493, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2494, 'problema_detectado'] = (cero_resultados.loc[2494, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2495, 'problema_detectado'] = (cero_resultados.loc[2495, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2506, 'problema_detectado'] = (cero_resultados.loc[2506, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2507, 'problema_detectado'] = (cero_resultados.loc[2507, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2508, 'problema_detectado'] = (cero_resultados.loc[2508, 'problema_detectado'] + 'error de tipeo')\n",
    "cero_resultados.loc[2512, 'problema_detectado'] = (cero_resultados.loc[2512, 'problema_detectado'] + 'no hay resultados')\n",
    "cero_resultados.loc[2531, 'problema_detectado'] = (cero_resultados.loc[2531, 'problema_detectado'] + 'error de tipeo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos el DataFrame resultante de las búsquedas con cero resultados, con los errores ya categorizados\n",
    "cero_resultados.to_csv('archivos/busquedas_cero_resultados.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico de torta para ver los porcentajes de las causas de los ceros en los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Problema                                        |   Frecuencia |\n",
      "|:------------------------------------------------|-------------:|\n",
      "| error de tipeo                                  |          169 |\n",
      "| no hay resultados                               |           81 |\n",
      "| campo incorrecto                                |           40 |\n",
      "| no había resultados en el tiempo de la consulta |           17 |\n",
      "| combinacion de campos incorrecta                |            6 |\n",
      "| lugar incorrecto de consulta                    |            3 |\n",
      "| usuario no ingreso nada                         |            1 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "problemas_df = cero_resultados['problema_detectado'].value_counts().reset_index()\n",
    "problemas_df.columns = ['Problema', 'Frecuencia']\n",
    "print(tabulate(problemas_df, headers='keys', tablefmt='pipe', showindex=False))\n",
    "cero_resultados['problema_detectado'].value_counts().plot.pie(autopct='%1.1f%%', figsize=(10, 10))\n",
    "plt.title('Problemas en la búsqueda de material')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis a los datos con al menos un resultado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Medidas descriptivas sobre las filas que tienen al menos un resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2169\n"
     ]
    }
   ],
   "source": [
    "# Filtramos las búsquedas que tengan al menos un resultado\n",
    "busq_con_resultados = df_filtrado[df_filtrado['total'] > 0].copy()\n",
    "\n",
    "# Vemos cuántas búsquedas tienen al menos un resultado\n",
    "print(len(busq_con_resultados))\n",
    "\n",
    "# Aplicamos la función para extraer la sintaxis de las búsquedas que tienen al menos un resultado\n",
    "busq_con_resultados.loc[:, 'sintaxis'] = busq_con_resultados['query_desc'].apply(extraer_sintaxis)\n",
    "\n",
    "# Reordenamos las columnas\n",
    "busq_con_resultados = busq_con_resultados[['sessionid', 'query_desc', 'total', 'time', 'categorycode', 'campo_utilizado', 'sintaxis', 'cant_campos_utilizados']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El porcentaje de busquedas que tengan 10 o menos resultados son: 50.76%\n",
      "El porcentaje de busquedas que tengan 50 o menos resultados son: 77.5%\n",
      "El porcentaje de busquedas que tengan 100 o mas resultados son: 16.04%\n"
     ]
    }
   ],
   "source": [
    "# Queremos saber el porcentaje de búsquedas que tengan 10 o menos resultados\n",
    "print(f\"El porcentaje de busquedas que tengan 10 o menos resultados son: {round(len(busq_con_resultados[busq_con_resultados['total'] <= 10]) / len(busq_con_resultados)*100, 2)}%\")\n",
    "\n",
    "# Lo mismo, pero esta vez para las búsquedas que tengan 50 o menos resultados\n",
    "print(f\"El porcentaje de busquedas que tengan 50 o menos resultados son: {round(len(busq_con_resultados[busq_con_resultados['total'] <= 50]) / len(busq_con_resultados)*100, 2)}%\")\n",
    "\n",
    "# Lo mismo, pero ahora las busquedas con resultados mayores a 100\n",
    "print(f\"El porcentaje de busquedas que tengan 100 o mas resultados son: {round(len(busq_con_resultados[busq_con_resultados['total'] > 100]) / len(busq_con_resultados)*100, 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráficos y tablas de frecuencias para saber la cantidad campos y qué campos son los más utilizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Campo    |   Frecuencia |\n",
      "|:---------|-------------:|\n",
      "| kw       |         1595 |\n",
      "| au       |          210 |\n",
      "| mc-itype |          197 |\n",
      "| ti       |          108 |\n",
      "| an       |           64 |\n",
      "| su       |           56 |\n",
      "| su-to    |           30 |\n",
      "| bc       |           14 |\n",
      "| l-format |            5 |\n",
      "| se       |            2 |\n",
      "| not au   |            1 |\n",
      "| provider |            1 |\n",
      "| in       |            1 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convertimos los campos a minúsculas y sumamos las frecuencias\n",
    "frecuencia_campos = busq_con_resultados['campo_utilizado'].str.lower().str.split(', ', expand=True).stack().value_counts()\n",
    "frecuencia_campos_df = frecuencia_campos.reset_index()\n",
    "frecuencia_campos_df.columns = ['Campo', 'Frecuencia']\n",
    "print(tabulate(frecuencia_campos_df, headers='keys', tablefmt='pipe', showindex=False))\n",
    "\n",
    "# Gráfico de las frecuencias de los campos utilizados por los usuarios\n",
    "plt.figure(figsize=(12, 8))\n",
    "frecuencia_campos.plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Campos utilizados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Frecuencia de campos utilizados por los usuarios donde obtuvieron resultados')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   cant_campos_utilizados |   count |\n",
      "|-------------------------:|--------:|\n",
      "|                        1 |    2072 |\n",
      "|                        2 |      82 |\n",
      "|                        3 |      12 |\n",
      "|                        4 |       3 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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qKSlJ8fHxl+37zTffKDMzUwsXLtTjjz+udu3aqVWrVv/qj4DL/QH1T6GhoUpKSsrzh0hiYqLT/M8//6xffvlFb7/9toYNG6ZOnTqpVatWCg4OzrM9Sfk+RnPhNi9WT37rHz9+PM+doOrVq+v06dNq1apVvlN+dwH/ua6kS4bigh5zYbrwuE+fPq2UlBTHi/u55+fCc5mVlaUDBw44lueKiIjQK6+8orVr1+r777/Xb7/9pqlTp16yhvLly+cZ/U5SnjsrrlxnBfXLL7+oZ8+e6ty5c76j682bN08eHh5aunSpevfurbZt2zruZP5TaGiocnJyHHcwc1143ipXrixPT898r809e/bIzc1NISEhjrYKFSroscce02effabDhw+rXr16+Y64l5/C/mz/jdzzc2FNR48eVVpaWp593XHHHXrjjTe0adMmzZo1Szt37tTnn39+yX3kdx1lZWUpJSUlT9/LndeCXpMFvT5ccTWvEaCkI2wBKJAXXnhB5cqVU9++fXX06NE8y5OSkjRx4kRJ//+uxz/DTnp6uqZPn37F+899vyW/P04u1K5dOx05csRpaOSzZ8/m+cLl/Oo0xjiOI1dQUJAaNGigmTNnOj22ExcXp127dl22nlatWqlMmTJ69913nfaV38iCDz30kOLj47V06dI8y9LS0nT+/PmL7icqKkre3t6KjY3VX3/95bQsd78FPebC9MEHHzi9GzNlyhSdP39ebdu2lfT3+XF3d9ekSZOc6vr444+Vnp7uGG0tIyMjz/FHRETIzc3tso+AVa9eXevXr3ca/XHRokWOR6VyuXKdFcTp06d133336YYbbtDMmTPzDXOlSpWSzWZzuqNx8OBBLViwwKlf7vmaNGmSU/uF11GpUqUUFRWlr7/+2vE4n/R36Jg9e7buvvtux6PBJ06ccFrXy8tLNWrUKPAjdYX12RaGdu3aScp7PsaPHy9Jjn2dPHkyzz/E5H6BdUGuowvfm/zggw/y3I0qyHmtXr269uzZo+PHjzvatm3bph9//NFp3YJeH664mtcIUNIx9DuAAqlevbpmz56t//znP6pdu7Z69OihunXrKisrS+vWrdPcuXMd3zMTFRUld3d3dejQQY8//rhOnz6tDz/8UP7+/vn+C3BBNGjQQKVKldJbb72l9PR02e12x/d4Xahfv35677331KNHD23evFlBQUH63//+l+fLUWvVqqXq1avr+eef12+//SYfHx/Nmzcv3/eOYmNj1b59e919993q3bu3/vjjD7377ruqU6fOZR89qly5sp5//nnFxsbq3nvvVbt27bR161Z99913qlSpklPfoUOHauHChbr33nsdw9+fOXNGP//8s7788ksdPHgwzzq5fHx89M4776hv37667bbb1LVrV5UvX17btm3T2bNnNXPmTJeOubBkZWWpZcuWeuihh5SYmKj3339fd999tzp27Og4PzExMRo1apTatGmjjh07Ovrddttt6t69u6S/h7QeNGiQHnzwQd188806f/68/ve//6lUqVKXfbyyb9+++vLLL9WmTRs99NBDSkpK0qeffprn/afq1avLz89PU6dOlbe3t8qVK6fGjRsX6D2Y/IwaNUq7du3SK6+8oq+//jrPviIjI9W+fXuNHz9ebdq0UdeuXXXs2DFNnjxZNWrUcHo3pkGDBnrkkUf0/vvvKz09XXfeeadWrFihffv25dnv66+/rri4ON1999168sknVbp0af33v/9VZmamxo4d6+gXHh6u5s2bq2HDhqpQoYI2bdqkL7/8UoMGDSrQ8RXWZ1sY6tevr549e+qDDz5QWlqamjVrpg0bNmjmzJnq3LmzWrRoIUmaOXOm3n//fd13332qXr26Tp06pQ8//FA+Pj6OwHYxffv21RNPPKEuXbqodevW2rZtm5YuXZrnd7Ig57V3794aP368oqOj1adPHx07dkxTp05VnTp1HINTSCrw9eGqq3WNACXeVR//EECx9ssvv5h+/fqZsLAw4+7ubry9vc1dd91l3n33XfPXX385+i1cuNDUq1fPeHh4mLCwMPPWW2+ZadOm5RkWOzQ0NN9hhfMbFvnDDz801apVM6VKlXIawj2/vocOHTIdO3Y0np6eplKlSuaZZ54xS5YsyTP0+65du0yrVq2Ml5eXqVSpkunXr5/Ztm1bvsN/z5s3z9SuXdvY7XYTHh5uvvrqq3yHac5Pdna2GTVqlAkKCjJly5Y1zZs3Nzt27Mh3SPJTp06ZmJgYU6NGDePu7m4qVapk7rzzTvN///d/TsNsX8zChQvNnXfeacqWLWt8fHzM7bffbj777DOXj7lnz56mXLlyebbfrFkzU6dOnTztF36WucODr1mzxvTv39+UL1/eeHl5mW7dupkTJ07kWf+9994ztWrVMmXKlDEBAQFmwIAB5uTJk47l+/fvN7179zbVq1c3Hh4epkKFCqZFixZm+fLllz0nxhjz9ttvmxtuuMHY7XZz1113mU2bNuV77Xz99dcmPDzclC5d2umcXMnQ77nD5+c3/fNz//jjj81NN91k7Ha7qVWrlpk+fXqeoc2NMebPP/80Tz/9tKlYsaIpV66c6dChgzl8+HC+Q5Jv2bLFREdHGy8vL+Pp6WlatGhh1q1b59Tn9ddfN7fffrvx8/MzZcuWNbVq1TJvvPHGZa+zwv5sjbn4dXUx+Z2fc+fOmVGjRpmqVauaMmXKmJCQEBMTE+P036YtW7aYRx55xFSpUsXY7Xbj7+9v7r33XrNp06bL7jM7O9sMGzbMVKpUyXh6epro6Gizb9++PL/HBT2vn376qalWrZpxd3c3DRo0MEuXLs33Oivo9SHJDBw4MN/ar/Y1AuBvNmMsfvsWAFAizZgxQ4899pg2btyoRo0aFXU5KER8tgBQMLyzBQAAAAAWIGwBAAAAgAUIWwAAAABgAd7ZAgAAAAALcGcLAAAAACxA2AIAAAAAC/ClxgWQk5OjI0eOyNvbWzabrajLAQAAAFBEjDE6deqUgoOD5eZ26XtXhK0COHLkiEJCQoq6DAAAAADXiMOHD+vGG2+8ZB/CVgF4e3tL+vuE+vj4FHE1AAAAAIpKRkaGQkJCHBnhUghbBZD76KCPjw9hCwAAAECBXi9igAwAAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALlC7qAnD1hL24uKhLKPEOjmlf1CUAAADgKuHOFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFijSsBUbG6vbbrtN3t7e8vf3V+fOnZWYmOjU56+//tLAgQNVsWJFeXl5qUuXLjp69KhTn+TkZLVv316enp7y9/fX0KFDdf78eac+q1ev1q233iq73a4aNWpoxowZVh8eAAAAgBKsSMPWmjVrNHDgQK1fv15xcXE6d+6coqKidObMGUefwYMH65tvvtHcuXO1Zs0aHTlyRPfff79jeXZ2ttq3b6+srCytW7dOM2fO1IwZMzR8+HBHnwMHDqh9+/Zq0aKFEhIS9Oyzz6pv375aunTpVT1eAAAAACWHzRhjirqIXMePH5e/v7/WrFmjpk2bKj09XZUrV9bs2bP1wAMPSJL27Nmj2rVrKz4+XnfccYe+++473XvvvTpy5IgCAgIkSVOnTtWwYcN0/Phxubu7a9iwYVq8eLF27Njh2NfDDz+stLQ0LVmy5LJ1ZWRkyNfXV+np6fLx8bHm4K+CsBcXF3UJJd7BMe2LugQAAAD8C65kg2vqna309HRJUoUKFSRJmzdv1rlz59SqVStHn1q1aqlKlSqKj4+XJMXHxysiIsIRtCQpOjpaGRkZ2rlzp6PPP7eR2yd3GxfKzMxURkaG0wQAAAAArrhmwlZOTo6effZZ3XXXXapbt64kKTU1Ve7u7vLz83PqGxAQoNTUVEeffwat3OW5yy7VJyMjQ3/++WeeWmJjY+Xr6+uYQkJCCuUYAQAAAJQc10zYGjhwoHbs2KHPP/+8qEtRTEyM0tPTHdPhw4eLuiQAAAAAxUzpoi5AkgYNGqRFixZp7dq1uvHGGx3tgYGBysrKUlpamtPdraNHjyowMNDRZ8OGDU7byx2t8J99LhzB8OjRo/Lx8VHZsmXz1GO322W32wvl2AAAAACUTEV6Z8sYo0GDBmn+/PlauXKlqlat6rS8YcOGKlOmjFasWOFoS0xMVHJysiIjIyVJkZGR+vnnn3Xs2DFHn7i4OPn4+Cg8PNzR55/byO2Tuw0AAAAAKGxFemdr4MCBmj17tr7++mt5e3s73rHy9fVV2bJl5evrqz59+mjIkCGqUKGCfHx89NRTTykyMlJ33HGHJCkqKkrh4eF69NFHNXbsWKWmpuqVV17RwIEDHXennnjiCb333nt64YUX1Lt3b61cuVJffPGFFi9mdD4AAAAA1ijSO1tTpkxRenq6mjdvrqCgIMc0Z84cR5933nlH9957r7p06aKmTZsqMDBQX331lWN5qVKltGjRIpUqVUqRkZHq3r27evToodGjRzv6VK1aVYsXL1ZcXJzq16+vt99+Wx999JGio6Ov6vECAAAAKDmuqe/ZulbxPVsoLHzPFgAAQPFWbL9nCwAAAACuF4QtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwQJGGrbVr16pDhw4KDg6WzWbTggULnJbbbLZ8p3Hjxjn6hIWF5Vk+ZswYp+1s375dTZo0kYeHh0JCQjR27NircXgAAAAASrAiDVtnzpxR/fr1NXny5HyXp6SkOE3Tpk2TzWZTly5dnPqNHj3aqd9TTz3lWJaRkaGoqCiFhoZq8+bNGjdunEaOHKkPPvjA0mMDAAAAULKVLsqdt23bVm3btr3o8sDAQKf5r7/+Wi1atFC1atWc2r29vfP0zTVr1ixlZWVp2rRpcnd3V506dZSQkKDx48erf//+//4gAAAAACAfxeadraNHj2rx4sXq06dPnmVjxoxRxYoVdcstt2jcuHE6f/68Y1l8fLyaNm0qd3d3R1t0dLQSExN18uTJfPeVmZmpjIwMpwkAAAAAXFGkd7ZcMXPmTHl7e+v+++93an/66ad16623qkKFClq3bp1iYmKUkpKi8ePHS5JSU1NVtWpVp3UCAgIcy8qXL59nX7GxsRo1apRFRwIAAACgJCg2YWvatGnq1q2bPDw8nNqHDBni+LlevXpyd3fX448/rtjYWNnt9ivaV0xMjNN2MzIyFBIScmWFAwAAACiRikXY+v7775WYmKg5c+Zctm/jxo11/vx5HTx4UDVr1lRgYKCOHj3q1Cd3/mLvednt9isOagAAAAAgFZN3tj7++GM1bNhQ9evXv2zfhIQEubm5yd/fX5IUGRmptWvX6ty5c44+cXFxqlmzZr6PEAIAAABAYSjSsHX69GklJCQoISFBknTgwAElJCQoOTnZ0ScjI0Nz585V375986wfHx+vCRMmaNu2bdq/f79mzZqlwYMHq3v37o4g1bVrV7m7u6tPnz7auXOn5syZo4kTJzo9JggAAAAAha1IHyPctGmTWrRo4ZjPDUA9e/bUjBkzJEmff/65jDF65JFH8qxvt9v1+eefa+TIkcrMzFTVqlU1ePBgpyDl6+urZcuWaeDAgWrYsKEqVaqk4cOHM+w7AAAAAEvZjDGmqIu41mVkZMjX11fp6eny8fEp6nKuWNiLi4u6hBLv4Jj2RV0CAAAA/gVXskGxeGcLAAAAAIobwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFigSMPW2rVr1aFDBwUHB8tms2nBggVOy3v16iWbzeY0tWnTxqnPH3/8oW7dusnHx0d+fn7q06ePTp8+7dRn+/btatKkiTw8PBQSEqKxY8dafWgAAAAASrgiDVtnzpxR/fr1NXny5Iv2adOmjVJSUhzTZ5995rS8W7du2rlzp+Li4rRo0SKtXbtW/fv3dyzPyMhQVFSUQkNDtXnzZo0bN04jR47UBx98YNlxAQAAAEDpotx527Zt1bZt20v2sdvtCgwMzHfZ7t27tWTJEm3cuFGNGjWSJL377rtq166d/u///k/BwcGaNWuWsrKyNG3aNLm7u6tOnTpKSEjQ+PHjnUIZAAAAABSma/6drdWrV8vf3181a9bUgAEDdOLECcey+Ph4+fn5OYKWJLVq1Upubm766aefHH2aNm0qd3d3R5/o6GglJibq5MmT+e4zMzNTGRkZThMAAAAAuOKaDltt2rTRJ598ohUrVuitt97SmjVr1LZtW2VnZ0uSUlNT5e/v77RO6dKlVaFCBaWmpjr6BAQEOPXJnc/tc6HY2Fj5+vo6ppCQkMI+NAAAAADXuSJ9jPByHn74YcfPERERqlevnqpXr67Vq1erZcuWlu03JiZGQ4YMccxnZGQQuAAAAAC45Jq+s3WhatWqqVKlStq3b58kKTAwUMeOHXPqc/78ef3xxx+O97wCAwN19OhRpz658xd7F8xut8vHx8dpAgAAAABXFKuw9euvv+rEiRMKCgqSJEVGRiotLU2bN2929Fm5cqVycnLUuHFjR5+1a9fq3Llzjj5xcXGqWbOmypcvf3UPAAAAAECJUaRh6/Tp00pISFBCQoIk6cCBA0pISFBycrJOnz6toUOHav369Tp48KBWrFihTp06qUaNGoqOjpYk1a5dW23atFG/fv20YcMG/fjjjxo0aJAefvhhBQcHS5K6du0qd3d39enTRzt37tScOXM0ceJEp8cEAQAAAKCwFWnY2rRpk2655RbdcsstkqQhQ4bolltu0fDhw1WqVClt375dHTt21M0336w+ffqoYcOG+v7772W32x3bmDVrlmrVqqWWLVuqXbt2uvvuu52+Q8vX11fLli3TgQMH1LBhQz333HMaPnw4w74DAAAAsJTNGGOKuohrXUZGhnx9fZWenl6s398Ke3FxUZdQ4h0c076oSwAAAMC/4Eo2KFbvbAEAAABAcUHYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALBAoYSttLS0wtgMAAAAAFw3XA5bb731lubMmeOYf+ihh1SxYkXdcMMN2rZtW6EWBwAAAADFlctha+rUqQoJCZEkxcXFKS4uTt99953atm2roUOHFnqBAAAAAFAclXZ1hdTUVEfYWrRokR566CFFRUUpLCxMjRs3LvQCAQAAAKA4cvnOVvny5XX48GFJ0pIlS9SqVStJkjFG2dnZhVsdAAAAABRTLt/Zuv/++9W1a1fddNNNOnHihNq2bStJ2rp1q2rUqFHoBQIAAABAceRy2HrnnXcUFhamw4cPa+zYsfLy8pIkpaSk6Mknnyz0AgEAAACgOHI5bJUpU0bPP/98nvbBgwcXSkEAAAAAcD1wOWxJUlJSkiZMmKDdu3dLksLDw/Xss8+qWrVqhVocAAAAABRXLg+QsXTpUoWHh2vDhg2qV6+e6tWrp59++knh4eGKi4uzokYAAAAAKHZcvrP14osvavDgwRozZkye9mHDhql169aFVhwAAAAAFFcu39navXu3+vTpk6e9d+/e2rVrV6EUBQAAAADFncthq3LlykpISMjTnpCQIH9//8KoCQAAAACKPZcfI+zXr5/69++v/fv3684775Qk/fjjj3rrrbc0ZMiQQi8QAAAAAIojl8PWq6++Km9vb7399tuKiYmRJAUHB2vkyJF6+umnC71AAAAAACiOXA5bNptNgwcP1uDBg3Xq1ClJkre3d6EXBgAAAADF2RV9z1YuQhYAAAAA5K9AYevWW2/VihUrVL58ed1yyy2y2WwX7btly5ZCKw4AAAAAiqsCha1OnTrJbrdLkjp37mxlPQAAAABwXShQ2BoxYoQkKTs7Wy1atFC9evXk5+dnZV0AAAAAUKy59D1bpUqVUlRUlE6ePGlVPQAAAABwXXD5S43r1q2r/fv3W1ELAAAAAFw3XA5br7/+up5//nktWrRIKSkpysjIcJoAAAAAAFcw9Hu7du0kSR07dnQaldAYI5vNpuzs7MKrDgAAAACKKZfD1qpVq6yoAwAAAACuKy6HrWbNmllRBwAAAABcV1wOW7nOnj2r5ORkZWVlObXXq1fvXxcFAAAAAMWdy2Hr+PHjeuyxx/Tdd9/lu5x3tgAAAADgCkYjfPbZZ5WWlqaffvpJZcuW1ZIlSzRz5kzddNNNWrhwoRU1AgAAAECx4/KdrZUrV+rrr79Wo0aN5ObmptDQULVu3Vo+Pj6KjY1V+/btragTAAAAAIoVl+9snTlzRv7+/pKk8uXL6/jx45KkiIgIbdmyxaVtrV27Vh06dFBwcLBsNpsWLFjgWHbu3DkNGzZMERERKleunIKDg9WjRw8dOXLEaRthYWGy2WxO05gxY5z6bN++XU2aNJGHh4dCQkI0duxYVw8bAAAAAFzictiqWbOmEhMTJUn169fXf//7X/3222+aOnWqgoKCXNrWmTNnVL9+fU2ePDnPsrNnz2rLli169dVXtWXLFn311VdKTExUx44d8/QdPXq0UlJSHNNTTz3lWJaRkaGoqCiFhoZq8+bNGjdunEaOHKkPPvjAxSMHAAAAgIJz+THCZ555RikpKZKkESNGqE2bNpo1a5bc3d01Y8YMl7bVtm1btW3bNt9lvr6+iouLc2p77733dPvttys5OVlVqlRxtHt7eyswMDDf7cyaNUtZWVmaNm2a3N3dVadOHSUkJGj8+PHq37+/S/UCAAAAQEG5fGere/fu6tWrlySpYcOGOnTokDZu3KjDhw/rP//5T2HX5yQ9PV02m01+fn5O7WPGjFHFihV1yy23aNy4cTp//rxjWXx8vJo2bSp3d3dHW3R0tBITE3Xy5Ml895OZmamMjAynCQAAAABcccXfs5XL09NTt956a2HUckl//fWXhg0bpkceeUQ+Pj6O9qefflq33nqrKlSooHXr1ikmJkYpKSkaP368JCk1NVVVq1Z12lZAQIBjWfny5fPsKzY2VqNGjbLwaAAAAABc71wOW717977k8mnTpl1xMRdz7tw5PfTQQzLGaMqUKU7LhgwZ4vi5Xr16cnd31+OPP67Y2FjZ7fYr2l9MTIzTdjMyMhQSEnJlxQMAAAAokVwOWxc+enfu3Dnt2LFDaWlpuueeewqtsH9u/6GHHtKhQ4e0cuVKp7ta+WncuLHOnz+vgwcPqmbNmgoMDNTRo0ed+uTOX+w9L7vdfsVBDQAAAACkKwhb8+fPz9OWk5OjAQMGqHr16oVSVK7coLV3716tWrVKFStWvOw6CQkJcnNzcwxPHxkZqZdfflnnzp1TmTJlJElxcXGqWbNmvo8QAgAAAEBhcHmAjHw34uamIUOG6J133nFpvdOnTyshIUEJCQmSpAMHDighIUHJyck6d+6cHnjgAW3atEmzZs1Sdna2UlNTlZqaqqysLEl/D34xYcIEbdu2Tfv379esWbM0ePBgde/e3RGkunbtKnd3d/Xp00c7d+7UnDlzNHHiRKfHBAEAAACgsP3rATJyJSUlOY0CWBCbNm1SixYtHPO5Aahnz54aOXKkFi5cKElq0KCB03qrVq1S8+bNZbfb9fnnn2vkyJHKzMxU1apVNXjwYKcg5evrq2XLlmngwIFq2LChKlWqpOHDhzPsOwAAAABLuRy2LrwjZIxRSkqKFi9erJ49e7q0rebNm8sYc9Hll1omSbfeeqvWr19/2f3Uq1dP33//vUu1AQAAAMC/4XLY2rp1q9O8m5ubKleurLfffvuyIxUCAAAAQEnhcthatWqVFXUAAAAAwHXF5QEy/vzzT509e9Yxf+jQIU2YMEHLli0r1MIAAAAAoDhzOWx16tRJn3zyiSQpLS1Nt99+u95++2116tQpzxcOAwAAAEBJ5XLY2rJli5o0aSJJ+vLLLxUYGKhDhw7pk08+0aRJkwq9QAAAAAAojlwOW2fPnpW3t7ckadmyZbr//vvl5uamO+64Q4cOHSr0AgEAAACgOHI5bNWoUUMLFizQ4cOHtXTpUkVFRUmSjh07Jh8fn0IvEAAAAACKI5fD1vDhw/X8888rLCxMjRs3VmRkpKS/73LdcssthV4gAAAAABRHLg/9/sADD+juu+9WSkqK6tev72hv2bKl7rvvvkItDgAAAACKK5fDliQFBgYqMDDQqe32228vlIIAAAAA4Hrgcthq0aKFbDbbRZevXLnyXxUEAAAAANcDl8NWgwYNnObPnTunhIQE7dixQz179iysugAAAACgWHM5bL3zzjv5to8cOVKnT5/+1wUBAAAAwPXA5dEIL6Z79+6aNm1aYW0OAAAAAIq1Qgtb8fHx8vDwKKzNAQAAAECx5vJjhPfff7/TvDFGKSkp2rRpk1599dVCKwwAAAAAijOXw5avr6/TvJubm2rWrKnRo0crKiqq0AoDAAAAgOLM5bA1ffp0K+oAAAAAgOtKob2zBQAAAAD4/whbAAAAAGABwhYAAAAAWICwBQAAAAAWuOKwlZWVpcTERJ0/f74w6wEAAACA64LLYevs2bPq06ePPD09VadOHSUnJ0uSnnrqKY0ZM6bQCwQAAACA4sjlsBUTE6Nt27Zp9erV8vDwcLS3atVKc+bMKdTiAAAAAKC4cvl7thYsWKA5c+bojjvukM1mc7TXqVNHSUlJhVocAAAAABRXLt/ZOn78uPz9/fO0nzlzxil8AQAAAEBJ5nLYatSokRYvXuyYzw1YH330kSIjIwuvMgAAAAAoxlx+jPDNN99U27ZttWvXLp0/f14TJ07Url27tG7dOq1Zs8aKGgEAAACg2HH5ztbdd9+thIQEnT9/XhEREVq2bJn8/f0VHx+vhg0bWlEjAAAAABQ7Lt/ZkqTq1avrww8/LOxaAAAAAOC6UaCwlZGRUeAN+vj4XHExAAAAAHC9KFDY8vPzK/BIg9nZ2f+qIAAAAAC4HhQobK1atcrx88GDB/Xiiy+qV69ejtEH4+PjNXPmTMXGxlpTJQAAAAAUMwUKW82aNXP8PHr0aI0fP16PPPKIo61jx46KiIjQBx98oJ49exZ+lQAAAABQzLg8GmF8fLwaNWqUp71Ro0basGFDoRQFAAAAAMWdy2ErJCQk35EIP/roI4WEhBRKUQAAAABQ3Lk89Ps777yjLl266LvvvlPjxo0lSRs2bNDevXs1b968Qi8QAAAAAIojl+9stWvXTnv37lXHjh31xx9/6I8//lCHDh30yy+/qF27dlbUCAAAAADFzhV9qfGNN96oN954o7BrAQAAAIDrhst3tgAAAAAAl0fYAgAAAAALFGnYWrt2rTp06KDg4GDZbDYtWLDAabkxRsOHD1dQUJDKli2rVq1aae/evU59/vjjD3Xr1k0+Pj7y8/NTnz59dPr0aac+27dvV5MmTeTh4aGQkBCNHTvW6kMDAAAAUMIVadg6c+aM6tevr8mTJ+e7fOzYsZo0aZKmTp2qn376SeXKlVN0dLT++usvR59u3bpp586diouL06JFi7R27Vr179/fsTwjI0NRUVEKDQ3V5s2bNW7cOI0cOVIffPCB5ccHAAAAoOSyGWPMlax4/PhxJSYmSpJq1qypypUr/7tCbDbNnz9fnTt3lvT3Xa3g4GA999xzev755yVJ6enpCggI0IwZM/Twww9r9+7dCg8P18aNGx1ftLxkyRK1a9dOv/76q4KDgzVlyhS9/PLLSk1Nlbu7uyTpxRdf1IIFC7Rnz54C1ZaRkSFfX1+lp6fLx8fnXx1nUQp7cXFRl1DiHRzTvqhLAAAAwL/gSjZw+c7WmTNn1Lt3bwUHB6tp06Zq2rSpgoOD1adPH509e/aKi77QgQMHlJqaqlatWjnafH191bhxY8XHx0uS4uPj5efn5whaktSqVSu5ubnpp59+cvRp2rSpI2hJUnR0tBITE3Xy5Ml8952ZmamMjAynCQAAAABc4XLYGjJkiNasWaOFCxcqLS1NaWlp+vrrr7VmzRo999xzhVZYamqqJCkgIMCpPSAgwLEsNTVV/v7+TstLly6tChUqOPXJbxv/3MeFYmNj5evr65hCQkL+/QEBAAAAKFFcDlvz5s3Txx9/rLZt28rHx0c+Pj5q166dPvzwQ3355ZdW1HjVxcTEKD093TEdPny4qEsCAAAAUMy4HLbOnj2b506RJPn7+xfqY4SBgYGSpKNHjzq1Hz161LEsMDBQx44dc1p+/vx5/fHHH0598tvGP/dxIbvd7giSuRMAAAAAuMLlsBUZGakRI0Y4jQj4559/atSoUYqMjCy0wqpWrarAwECtWLHC0ZaRkaGffvrJsZ/IyEilpaVp8+bNjj4rV65UTk6OGjdu7Oizdu1anTt3ztEnLi5ONWvWVPny5QutXgAAAAD4p9KurjBhwgS1adNGN954o+rXry9J2rZtmzw8PLR06VKXtnX69Gnt27fPMX/gwAElJCSoQoUKqlKlip599lm9/vrruummm1S1alW9+uqrCg4OdoxYWLt2bbVp00b9+vXT1KlTde7cOQ0aNEgPP/ywgoODJUldu3bVqFGj1KdPHw0bNkw7duzQxIkT9c4777h66AAAAABQYC6HrYiICO3du1ezZs1yDJ3+yCOPqFu3bipbtqxL29q0aZNatGjhmB8yZIgkqWfPnpoxY4ZeeOEFnTlzRv3791daWpruvvtuLVmyRB4eHo51Zs2apUGDBqlly5Zyc3NTly5dNGnSJMdyX19fLVu2TAMHDlTDhg1VqVIlDR8+3Om7uAAAAACgsLn0PVvnzp1TrVq1tGjRItWuXdvKuq4pfM8WCgvfswUAAFC8WfY9W2XKlHF6VwsAAAAAkD+XB8gYOHCg3nrrLZ0/f96KegAAAADguuDyO1sbN27UihUrtGzZMkVERKhcuXJOy7/66qtCKw4AAAAAiiuXw5afn5+6dOliRS0AAAAAcN1wOWxNnz7dijoAAAAA4Lri8jtbknT+/HktX75c//3vf3Xq1ClJ0pEjR3T69OlCLQ4AAAAAiiuX72wdOnRIbdq0UXJysjIzM9W6dWt5e3vrrbfeUmZmpqZOnWpFnQAAAABQrLh8Z+uZZ55Ro0aNdPLkSacvMb7vvvu0YsWKQi0OAAAAAIorl+9sff/991q3bp3c3d2d2sPCwvTbb78VWmEAAAAAUJy5fGcrJydH2dnZedp//fVXeXt7F0pRAAAAAFDcuRy2oqKiNGHCBMe8zWbT6dOnNWLECLVr164wawMAAACAYsvlxwjffvttRUdHKzw8XH/99Ze6du2qvXv3qlKlSvrss8+sqBEAAAAAih2Xw9aNN96obdu26fPPP9f27dt1+vRp9enTR926dXMaMAMAAAAASjKXw5YklS5dWt27dy/sWgAAAADgunFFYevIkSP64YcfdOzYMeXk5Dgte/rppwulMAAAAAAozlwOWzNmzNDjjz8ud3d3VaxYUTabzbHMZrMRtgAAAABAVxC2Xn31VQ0fPlwxMTFyc3N5MEMAAAAAKBFcTktnz57Vww8/TNACAAAAgEtwOTH16dNHc+fOtaIWAAAAALhuuPwYYWxsrO69914tWbJEERERKlOmjNPy8ePHF1pxAAAAAFBcXVHYWrp0qWrWrClJeQbIAAAAAABcQdh6++23NW3aNPXq1cuCcgAAAADg+uDyO1t2u1133XWXFbUAAAAAwHXD5bD1zDPP6N1337WiFgAAAAC4brj8GOGGDRu0cuVKLVq0SHXq1MkzQMZXX31VaMUBAAAAQHHlctjy8/PT/fffb0UtAAAAAHDdcDlsTZ8+3Yo6AAAAAOC64vI7WwAAAACAy3P5zlbVqlUv+X1a+/fv/1cFAQAAAMD14LJh68svv9Qdd9yhG2+8UZL07LPPOi0/d+6ctm7dqiVLlmjo0KGWFAkAAAAAxc1lw1bp0qXVpEkTLViwQPXr19czzzyTb7/Jkydr06ZNhV4gAAAAABRHl31nq3PnzpozZ4569ux5yX5t27bVvHnzCq0wAAAAACjOCjRAxu233661a9dess+XX36pChUqFEpRAAAAAFDcFXiADB8fH0nSLbfc4jRAhjFGqampOn78uN5///3CrxAAAAAAiiGXRyPs3Lmz07ybm5sqV66s5s2bq1atWoVVFwAAAAAUay6HrREjRlhRBwAAAABcV/hSYwAAAACwQIHvbLm5uV3yy4wlyWaz6fz58/+6KAAAAAAo7goctubPn3/RZfHx8Zo0aZJycnIKpSgAAAAAKO4KHLY6deqUpy0xMVEvvviivvnmG3Xr1k2jR48u1OIAAAAAoLi6one2jhw5on79+ikiIkLnz59XQkKCZs6cqdDQ0MKuDwAAAACKJZfCVnp6uoYNG6YaNWpo586dWrFihb755hvVrVvXqvoAAAAAoFgq8GOEY8eO1VtvvaXAwEB99tln+T5WCAAAAAD4W4HvbL344ov666+/VKNGDc2cOVP3339/vlNhCwsLk81myzMNHDhQktS8efM8y5544gmnbSQnJ6t9+/by9PSUv7+/hg4dyqiJAAAAACxV4DtbPXr0uOzQ71bYuHGjsrOzHfM7duxQ69at9eCDDzra+vXr5zQ4h6enp+Pn7OxstW/fXoGBgVq3bp1SUlLUo0cPlSlTRm+++ebVOQgAAAAAJU6Bw9aMGTMsLOPiKleu7DQ/ZswYVa9eXc2aNXO0eXp6KjAwMN/1ly1bpl27dmn58uUKCAhQgwYN9Nprr2nYsGEaOXKk3N3dLa0fAAAAQMl0RaMRFpWsrCx9+umn6t27t9NdtlmzZqlSpUqqW7euYmJidPbsWcey+Ph4RUREKCAgwNEWHR2tjIwM7dy5M9/9ZGZmKiMjw2kCAAAAAFcU+M7WtWDBggVKS0tTr169HG1du3ZVaGiogoODtX37dg0bNkyJiYn66quvJEmpqalOQUuSYz41NTXf/cTGxmrUqFHWHAQAAACAEqFYha2PP/5Ybdu2VXBwsKOtf//+jp8jIiIUFBSkli1bKikpSdWrV7+i/cTExGjIkCGO+YyMDIWEhFx54QAAAABKnGITtg4dOqTly5c77lhdTOPGjSVJ+/btU/Xq1RUYGKgNGzY49Tl69KgkXfQ9L7vdLrvdXghVAwAAACipis07W9OnT5e/v7/at29/yX4JCQmSpKCgIElSZGSkfv75Zx07dszRJy4uTj4+PgoPD7esXgAAAAAlW7G4s5WTk6Pp06erZ8+eKl36/5eclJSk2bNnq127dqpYsaK2b9+uwYMHq2nTpqpXr54kKSoqSuHh4Xr00Uc1duxYpaam6pVXXtHAgQO5ewUAAADAMsUibC1fvlzJycnq3bu3U7u7u7uWL1+uCRMm6MyZMwoJCVGXLl30yiuvOPqUKlVKixYt0oABAxQZGaly5cqpZ8+eTt/LBQAAAACFrViEraioKBlj8rSHhIRozZo1l10/NDRU3377rRWlAQAAAEC+is07WwAAAABQnBC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAtd02Bo5cqRsNpvTVKtWLcfyv/76SwMHDlTFihXl5eWlLl266OjRo07bSE5OVvv27eXp6Sl/f38NHTpU58+fv9qHAgAAAKCEKV3UBVxOnTp1tHz5csd86dL/v+TBgwdr8eLFmjt3rnx9fTVo0CDdf//9+vHHHyVJ2dnZat++vQIDA7Vu3TqlpKSoR48eKlOmjN58882rfiwAAAAASo5rPmyVLl1agYGBedrT09P18ccfa/bs2brnnnskSdOnT1ft2rW1fv163XHHHVq2bJl27dql5cuXKyAgQA0aNNBrr72mYcOGaeTIkXJ3d7/ahwMAAACghLimHyOUpL179yo4OFjVqlVTt27dlJycLEnavHmzzp07p1atWjn61qpVS1WqVFF8fLwkKT4+XhEREQoICHD0iY6OVkZGhnbu3HnRfWZmZiojI8NpAgAAAABXXNNhq3HjxpoxY4aWLFmiKVOm6MCBA2rSpIlOnTql1NRUubu7y8/Pz2mdgIAApaamSpJSU1Odglbu8txlFxMbGytfX1/HFBISUrgHBgAAAOC6d00/Rti2bVvHz/Xq1VPjxo0VGhqqL774QmXLlrVsvzExMRoyZIhjPiMjg8AFAAAAwCXX9J2tC/n5+enmm2/Wvn37FBgYqKysLKWlpTn1OXr0qOMdr8DAwDyjE+bO5/ceWC673S4fHx+nCQAAAABcUazC1unTp5WUlKSgoCA1bNhQZcqU0YoVKxzLExMTlZycrMjISElSZGSkfv75Zx07dszRJy4uTj4+PgoPD7/q9QMAAAAoOa7pxwiff/55dejQQaGhoTpy5IhGjBihUqVK6ZFHHpGvr6/69OmjIUOGqEKFCvLx8dFTTz2lyMhI3XHHHZKkqKgohYeH69FHH9XYsWOVmpqqV155RQMHDpTdbi/iowMAAABwPbumw9avv/6qRx55RCdOnFDlypV19913a/369apcubIk6Z133pGbm5u6dOmizMxMRUdH6/3333esX6pUKS1atEgDBgxQZGSkypUrp549e2r06NFFdUgAAAAASgibMcYUdRHXuoyMDPn6+io9Pb1Yv78V9uLioi6hxDs4pn1RlwAAAIB/wZVsUKze2QIAAACA4oKwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFrimw1ZsbKxuu+02eXt7y9/fX507d1ZiYqJTn+bNm8tmszlNTzzxhFOf5ORktW/fXp6envL399fQoUN1/vz5q3koAAAAAEqY0kVdwKWsWbNGAwcO1G233abz58/rpZdeUlRUlHbt2qVy5co5+vXr10+jR492zHt6ejp+zs7OVvv27RUYGKh169YpJSVFPXr0UJkyZfTmm29e1eMBAAAAUHJc02FryZIlTvMzZsyQv7+/Nm/erKZNmzraPT09FRgYmO82li1bpl27dmn58uUKCAhQgwYN9Nprr2nYsGEaOXKk3N3dLT0GAAAAACXTNf0Y4YXS09MlSRUqVHBqnzVrlipVqqS6desqJiZGZ8+edSyLj49XRESEAgICHG3R0dHKyMjQzp07891PZmamMjIynCYAAAAAcMU1fWfrn3JycvTss8/qrrvuUt26dR3tXbt2VWhoqIKDg7V9+3YNGzZMiYmJ+uqrryRJqampTkFLkmM+NTU1333FxsZq1KhRFh0JAAAAgJKg2IStgQMHaseOHfrhhx+c2vv37+/4OSIiQkFBQWrZsqWSkpJUvXr1K9pXTEyMhgwZ4pjPyMhQSEjIlRUOAAAAoEQqFo8RDho0SIsWLdKqVat04403XrJv48aNJUn79u2TJAUGBuro0aNOfXLnL/ael91ul4+Pj9MEAAAAAK64psOWMUaDBg3S/PnztXLlSlWtWvWy6yQkJEiSgoKCJEmRkZH6+eefdezYMUefuLg4+fj4KDw83JK6AQAAAOCafoxw4MCBmj17tr7++mt5e3s73rHy9fVV2bJllZSUpNmzZ6tdu3aqWLGitm/frsGDB6tp06aqV6+eJCkqKkrh4eF69NFHNXbsWKWmpuqVV17RwIEDZbfbi/LwAAAAAFzHruk7W1OmTFF6erqaN2+uoKAgxzRnzhxJkru7u5YvX66oqCjVqlVLzz33nLp06aJvvvnGsY1SpUpp0aJFKlWqlCIjI9W9e3f16NHD6Xu5AAAAAKCwXdN3towxl1weEhKiNWvWXHY7oaGh+vbbbwurLAAAAAC4rGv6zhYAAAAAFFeELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAAC5Qu6gIA4Gqav/5oUZdQot13R0BRlwAAwFXDnS0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsECJCluTJ09WWFiYPDw81LhxY23YsKGoSwIAAABwnSpd1AVcLXPmzNGQIUM0depUNW7cWBMmTFB0dLQSExPl7+9f1OUBAHB1jPQt6gowMr2oKwBwlZSYO1vjx49Xv3799Nhjjyk8PFxTp06Vp6enpk2bVtSlAQAAALgOlYg7W1lZWdq8ebNiYmIcbW5ubmrVqpXi4+Pz9M/MzFRmZqZjPj3973+BysjIsL5YC+Vkni3qEkq84n4NXQ/OnjlV1CWUaBkZZYu6BGSaoq4A/H9B0Vs1sqgrQIuRRV3BFcv9e86Yy//3tESErd9//13Z2dkKCAhwag8ICNCePXvy9I+NjdWoUaPytIeEhFhWI0oG3wlFXQEAoMiN4VFOQHqnqAv4106dOiVf30v/PpeIsOWqmJgYDRkyxDGfk5OjP/74QxUrVpTNZivCykqujIwMhYSE6PDhw/Lx8SnqcoAiwe8BwO8BIPF7UNSMMTp16pSCg4Mv27dEhK1KlSqpVKlSOnr0qFP70aNHFRgYmKe/3W6X3W53avPz87OyRBSQj48P/1FBicfvAcDvASDxe1CULndHK1eJGCDD3d1dDRs21IoVKxxtOTk5WrFihSIjI4uwMgAAAADXqxJxZ0uShgwZop49e6pRo0a6/fbbNWHCBJ05c0aPPfZYUZcGAAAA4DpUYsLWf/7zHx0/flzDhw9XamqqGjRooCVLluQZNAPXJrvdrhEjRuR5vBMoSfg9APg9ACR+D4oTmynImIUAAAAAAJeUiHe2AAAAAOBqI2wBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAUI4xvV3wQtgAAAIBixG63a/fu3UVdBgqgxHzPFq4vhw8f1ogRIzRt2rSiLgWwzJ9//qnNmzerQoUKCg8Pd1r2119/6YsvvlCPHj2KqDrg6ti9e7fWr1+vyMhI1apVS3v27NHEiROVmZmp7t2765577inqEgHLDBkyJN/27OxsjRkzRhUrVpQkjR8//mqWBRfwPVsolrZt26Zbb71V2dnZRV0KYIlffvlFUVFRSk5Ols1m0913363PP/9cQUFBkqSjR48qODiY3wFc15YsWaJOnTrJy8tLZ8+e1fz589WjRw/Vr19fOTk5WrNmjZYtW0bgwnXLzc1N9evXl5+fn1P7mjVr1KhRI5UrV042m00rV64smgJxWYQtXJMWLlx4yeX79+/Xc889xx+auG7dd999OnfunGbMmKG0tDQ9++yz2rVrl1avXq0qVaoQtlAi3Hnnnbrnnnv0+uuv6/PPP9eTTz6pAQMG6I033pAkxcTEaPPmzVq2bFkRVwpYY8yYMfrggw/00UcfOf2jQpkyZbRt27Y8Tz3g2kPYwjXJzc1NNpvtki+A2mw2/tDEdSsgIEDLly9XRESEpL9fhn7yySf17bffatWqVSpXrhxhC9c9X19fbd68WTVq1FBOTo7sdrs2bNigW265RZK0Y8cOtWrVSqmpqUVcKWCdjRs3qnv37urQoYNiY2NVpkwZwlYxwgAZuCYFBQXpq6++Uk5OTr7Tli1birpEwFJ//vmnSpf+/6/V2mw2TZkyRR06dFCzZs30yy+/FGF1wNVjs9kk/f2PcB4eHvL19XUs8/b2Vnp6elGVBlwVt912mzZv3qzjx4+rUaNG2rFjh+P3Atc+whauSQ0bNtTmzZsvuvxyd72A4q5WrVratGlTnvb33ntPnTp1UseOHYugKuDqCgsL0969ex3z8fHxqlKlimM+OTnZ8R4jcD3z8vLSzJkzFRMTo1atWvFUQzFC2MI1aejQobrzzjsvurxGjRpatWrVVawIuLruu+8+ffbZZ/kue++99/TII4/wDw647g0YMMDpj8q6des63fH97rvvGBwDJcrDDz+sTZs26auvvlJoaGhRl4MC4J0tAAAAALAAd7YAAAAAwAKELQAAAACwAGELAAAAACxA2AIASJIOHjwom82mhISEi/ZZvXq1bDab0tLS/tW+mjdvrmeffdaldUaOHKkGDRr8q/3i8sLCwjRhwgTHvM1m04IFCyQV7Br5twrrGgOAawFhCwCuIampqXrqqadUrVo12e12hYSEqEOHDlqxYkWh7qdXr17q3LmzU1tISIhSUlJUt27dQt0Xrk0zZsyQn59fnvaNGzeqf//++a7DNQIAril9+S4AgKvh4MGDuuuuu+Tn56dx48YpIiJC586d09KlSzVw4EDt2bPH0v2XKlVKgYGBlu4D177KlStfdBnXCAC4hjtbAHCNePLJJ2Wz2bRhwwZ16dJFN998s+rUqaMhQ4Zo/fr1jn7jx49XRESEypUrp5CQED355JM6ffq0Y3nuHYulS5eqdu3a8vLyUps2bZSSkiLp78fxZs6cqa+//lo2m002m02rV6/O9xGxb7/9VjfffLPKli2rFi1a6ODBg041nzhxQo888ohuuOEGeXp6KiIiIs/3g505c0Y9evSQl5eXgoKC9PbbbxfofIwZM0YBAQHy9vZWnz599Ndff+Xp89FHH6l27dry8PBQrVq19P77719ymzk5ORo7dqxq1Kghu92uKlWq6I033nAsHzZsmG6++WZ5enqqWrVqevXVV3Xu3DnH8txHGadNm6YqVarIy8tLTz75pLKzszV27FgFBgbK39/faZvS34/iTZkyRW3btlXZsmVVrVo1ffnll059fv75Z91zzz0qW7asKlasqP79+zt9rqtXr9btt9+ucuXKyc/PT3fddZcOHTqU73Hm9yheQkKCbDabDh48qNWrV+uxxx5Tenq64xoYOXKkpLyPEf7ThddIr169HOv/c1q9erUk6X//+58aNWokb29vBQYGqmvXrjp27JjTNi93jUnSvHnzVKdOHdntdoWFheW5ht5//33ddNNN8vDwUEBAgB544IF86weAq84AAIrciRMnjM1mM2+++eZl+77zzjtm5cqV5sCBA2bFihWmZs2aZsCAAY7l06dPN2XKlDGtWrUyGzduNJs3bza1a9c2Xbt2NcYYc+rUKfPQQw+ZNm3amJSUFJOSkmIyMzPNgQMHjCSzdetWY4wxycnJxm63myFDhpg9e/aYTz/91AQEBBhJ5uTJk8YYY3799Vczbtw4s3XrVpOUlGQmTZpkSpUqZX766SdHPQMGDDBVqlQxy5cvN9u3bzf33nuv8fb2Ns8888xFj3HOnDnGbrebjz76yOzZs8e8/PLLxtvb29SvX9/R59NPPzVBQUFm3rx5Zv/+/WbevHmmQoUKZsaMGRfd7gsvvGDKly9vZsyYYfbt22e+//578+GHHzqWv/baa+bHH380Bw4cMAsXLjQBAQHmrbfeciwfMWKE8fLyMg888IDZuXOnWbhwoXF3dzfR0dHmqaeeMnv27DHTpk0zksz69esd60kyFStWNB9++KFJTEw0r7zyiilVqpTZtWuXMcaY06dPm6CgIHP//febn3/+2axYscJUrVrV9OzZ0xhjzLlz54yvr695/vnnzb59+8yuXbvMjBkzzKFDh/I9zlWrVjl9TsYYs3XrViPJHDhwwGRmZpoJEyYYHx8fxzVw6tQpY4wxoaGh5p133nGqff78+cYYk+caSUtLc6yfkpJinnnmGePv729SUlKMMcZ8/PHH5ttvvzVJSUkmPj7eREZGmrZt2zq2XZBrbNOmTcbNzc2MHj3aJCYmmunTp5uyZcua6dOnG2OM2bhxoylVqpSZPXu2OXjwoNmyZYuZOHHiRa8BALiaCFsAcA346aefjCTz1Vdfubzu3LlzTcWKFR3z06dPN5LMvn37HG2TJ082AQEBjvmePXuaTp06OW3nwj+kY2JiTHh4uFOfYcOG5fkj/kLt27c3zz33nDHm72Dn7u5uvvjiC8fyEydOmLJly14ybEVGRponn3zSqa1x48ZOYat69epm9uzZTn1ee+01ExkZme82MzIyjN1udwpXlzNu3DjTsGFDx/yIESOMp6enycjIcLRFR0ebsLAwk52d7WirWbOmiY2NdcxLMk888USe48kNyR988IEpX768OX36tGP54sWLjZubm0lNTTUnTpwwkszq1asLVPflwpYxf18nvr6+edZ1JWz907x584yHh4f54YcfLlrXxo0bjSRHsCvINda1a1fTunVrpz5Dhw51rDdv3jzj4+Pj9JkAwLWCxwgB4BpgjClw3+XLl6tly5a64YYb5O3trUcffVQnTpzQ2bNnHX08PT1VvXp1x3xQUFCex7cuZ/fu3WrcuLFTW2RkpNN8dna2XnvtNUVERKhChQry8vLS0qVLlZycLElKSkpSVlaW03YqVKigmjVr/qt9nzlzRklJSerTp4+8vLwc0+uvv66kpKSLbjMzM1MtW7a86H7nzJmju+66S4GBgfLy8tIrr7ziOJZcYWFh8vb2dswHBAQoPDxcbm5uTm0Xnu8Lz11kZKR2797tqK1+/foqV66cY/ldd92lnJwcJSYmqkKFCurVq5eio6PVoUMHTZw40fFY6LVg69atevTRR/Xee+/prrvucrRv3rxZHTp0UJUqVeTt7a1mzZpJkuOcFuQa2717t9M2pb/Pzd69e5Wdna3WrVsrNDRU1apV06OPPqpZs2Y5/S4AQFEibAHANeCmm26SzWa77CAYBw8e1L333qt69epp3rx52rx5syZPnixJysrKcvQrU6aM03o2m82lQFdQ48aN08SJEzVs2DCtWrVKCQkJio6OdqrFCrnvMn344YdKSEhwTDt27HB6v+2fypYte8ltxsfHq1u3bmrXrp0WLVqkrVu36uWXX85zLPmd2/zacnJyXD2sS5o+fbri4+N15513as6cObr55psveqy5we+fn/k/3z0rTKmpqerYsaP69u2rPn36ONrPnDmj6Oho+fj4aNasWdq4caPmz58vSYV6fXh7e2vLli367LPPFBQUpOHDh6t+/foMHQ/gmkDYAoBrQIUKFRQdHa3JkyfrzJkzeZbn/uG4efNm5eTk6O2339Ydd9yhm2++WUeOHHF5f+7u7srOzr5kn9q1a2vDhg1ObRf+cf/jjz+qU6dO6t69u+rXr69q1arpl19+cSyvXr26ypQpo59++snRdvLkSac+F9v3P9e5cN8BAQEKDg7W/v37VaNGDaepatWq+W7zpptuUtmyZS86jP66desUGhqql19+WY0aNdJNN9100QEorsSF5279+vWqXbu2pL+Pd9u2bU6f/Y8//ig3Nzenu4C33HKLYmJitG7dOtWtW1ezZ8/Od1+5Iwr+8+7Xhd+NVZBr4HL++usvderUSbVq1dL48eOdlu3Zs0cnTpzQmDFj1KRJE9WqVSvP3b6CXGO1a9fWjz/+6NT2448/6uabb1apUqUkSaVLl1arVq00duxYbd++XQcPHtTKlSv/1bEBQGEgbAHANWLy5MnKzs7W7bffrnnz5mnv3r3avXu3Jk2a5Hi0qkaNGjp37pzeffdd7d+/X//73/80depUl/cVFham7du3KzExUb///nu+dz2eeOIJ7d27V0OHDlViYqJmz56tGTNmOPW56aabFBcXp3Xr1mn37t16/PHHdfToUcdyLy8v9enTR0OHDtXKlSu1Y8cO9erVy+mRu/w888wzmjZtmqZPn65ffvlFI0aM0M6dO536jBo1SrGxsZo0aZJ++eUX/fzzz5o+fXqeP/pzeXh4aNiwYXrhhRf0ySefKCkpSevXr9fHH3/sOJbk5GR9/vnnSkpK0qRJkxx3YgrD3LlzNW3aNMfxbNiwQYMGDZIkdevWTR4eHurZs6d27NihVatW6amnntKjjz6qgIAAHThwQDExMYqPj9ehQ4e0bNky7d271xHWLlSjRg2FhIRo5MiR2rt3rxYvXpxnBL+wsDCdPn1aK1as0O+//35Fj949/vjjOnz4sCZNmqTjx48rNTVVqampysrKUpUqVeTu7u64VhcuXKjXXnvNaf2CXGPPPfecVqxYoddee02//PKLZs6cqffee0/PP/+8JGnRokWaNGmSEhISdOjQIX3yySfKycm57KOqAHBVFPE7YwCAfzhy5IgZOHCgCQ0NNe7u7uaGG24wHTt2NKtWrXL0GT9+vAkKCjJly5Y10dHR5pNPPnEaUCC/gQ/mz59v/vmf/GPHjpnWrVsbLy8vI8msWrUq38EPvvnmG1OjRg1jt9tNkyZNHCPt5e7rxIkTplOnTsbLy8v4+/ubV155xfTo0cNp8I1Tp06Z7t27G09PTxMQEGDGjh1rmjVrdskBMowx5o033jCVKlUyXl5epmfPnuaFF15wGiDDGGNmzZplGjRoYNzd3U358uVN06ZNLznISHZ2tnn99ddNaGioKVOmjKlSpYrTCJBDhw41FStWNF5eXuY///mPeeedd5zO5YgRI/LUkN9gIxcenyQzefJk07p1a2O3201YWJiZM2eO0zrbt283LVq0MB4eHqZChQqmX79+joEkUlNTTefOnU1QUJBxd3c3oaGhZvjw4U6Dclzohx9+MBEREcbDw8M0adLEzJ0712mADGOMeeKJJ0zFihWNJDNixAhjjGsDZISGhhpJeabc63X27NkmLCzM2O12ExkZaRYuXOjyNWaMMV9++aUJDw93fGbjxo1zLPv+++9Ns2bNTPny5U3ZsmVNvXr18pxbACgqNmMseIgfAAA42Gw2zZ8/X507dy7qUgAAVxGPEQIAAACABQhbAAAAAGCB0kVdAAAA1zue2AeAkok7WwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGCB/wfe7E1F2RevewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contamos la cantidad de veces que se utiliza cada cantidad de campos\n",
    "cant_campos = busq_con_resultados['cant_campos_utilizados'].value_counts().sort_index()\n",
    "print(tabulate(cant_campos.reset_index(), headers='keys', tablefmt='pipe', showindex=False))\n",
    "\n",
    "# Gráfico de barras para la cantidad de campos utilizados por los usuarios\n",
    "plt.figure(figsize=(10, 6))\n",
    "cant_campos.plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Cantidad de campos utilizados')\n",
    "plt.ylabel('Número de usuarios')\n",
    "plt.title('Cantidad de campos utilizados por los usuarios')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vemos si al realizar la mismas consultas, el sistema devuelve la misma cantidad de resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>query_desc</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>diferencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>, mc-itype:LIB</td>\n",
       "      <td>3654.966292</td>\n",
       "      <td>43.227319</td>\n",
       "      <td>3608</td>\n",
       "      <td>3777</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>, mc-itype:RL</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>31.759513</td>\n",
       "      <td>92</td>\n",
       "      <td>163</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>kw,wrdl: calculo,</td>\n",
       "      <td>192.750000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>150</td>\n",
       "      <td>207</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>su:\"DIVULGACION CIENTIFICA\",</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>38.890873</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>, mc-itype:TESINA</td>\n",
       "      <td>800.400000</td>\n",
       "      <td>16.994117</td>\n",
       "      <td>788</td>\n",
       "      <td>820</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>, mc-itype,phr:FICCION</td>\n",
       "      <td>268.500000</td>\n",
       "      <td>9.192388</td>\n",
       "      <td>262</td>\n",
       "      <td>275</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        query_desc         mean        std   min   max  \\\n",
       "3                   , mc-itype:LIB  3654.966292  43.227319  3608  3777   \n",
       "6                    , mc-itype:RL   118.000000  31.759513    92   163   \n",
       "158             kw,wrdl: calculo,    192.750000  28.500000   150   207   \n",
       "322  su:\"DIVULGACION CIENTIFICA\",     33.500000  38.890873     6    61   \n",
       "8                , mc-itype:TESINA   800.400000  16.994117   788   820   \n",
       "1           , mc-itype,phr:FICCION   268.500000   9.192388   262   275   \n",
       "\n",
       "     diferencia  \n",
       "3           169  \n",
       "6            71  \n",
       "158          57  \n",
       "322          55  \n",
       "8            32  \n",
       "1            13  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificar las consultas repetidas, agrupando por query_desc\n",
    "consultas_repetidas = busq_con_resultados.groupby(['query_desc']).size().reset_index(name='repeticiones')\n",
    "consultas_repetidas = consultas_repetidas[consultas_repetidas['repeticiones'] > 1]\n",
    "\n",
    "# Filtrar las búsquedas repetidas\n",
    "variacion_resultados = busq_con_resultados[busq_con_resultados['query_desc'].isin(consultas_repetidas['query_desc'])]\n",
    "variacion_resultados = variacion_resultados.groupby(['query_desc'])['total'].agg(['mean', 'std', 'min', 'max']).reset_index()\n",
    "\n",
    "# Si la desviación estándar es 0, significa que los resultados son iguales. Por eso nos vamos a quedar con aquellas consultas que tengan una desviación estándar mayor a 0\n",
    "variacion_resultados = variacion_resultados[variacion_resultados['std'] > 0]\n",
    "\n",
    "#calculamos la diferencia entre el máximo y el mínimo\n",
    "variacion_resultados['diferencia'] = variacion_resultados['max'] - variacion_resultados['min']\n",
    "\n",
    "# Filtramos las consultas con una desviación estándar mayor a 10\n",
    "variacion_resultados_filtrados = variacion_resultados[variacion_resultados['std'] > 9]\n",
    "\n",
    "# Ordenamos de mayor a menor la diferencia\n",
    "variacion_resultados_filtrados = variacion_resultados_filtrados.sort_values(by='diferencia', ascending=False)\n",
    "\n",
    "# Mostramos los resultados obtenidos\n",
    "variacion_resultados_filtrados\n",
    "\n",
    "# Estos resultados son interesantes analizarlos en profundidad ya que nos puede dar información sobre la evolución de los registros a lo largo del tiempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear un boxplot para cada consulta por separado\n",
    "consultas = variacion_resultados_filtrados['query_desc'].unique()\n",
    "num_consultas = len(consultas)\n",
    "colors = plt.cm.tab20.colors  # Usamos una paleta de colores\n",
    "\n",
    "plt.figure(figsize=(15, 18))\n",
    "\n",
    "for i, (consulta, color) in enumerate(zip(consultas, colors), 1):\n",
    "    plt.subplot(3, 2, i)\n",
    "    sns.boxplot(x='query_desc', y='total', data=busq_con_resultados[busq_con_resultados['query_desc'] == consulta], color=color)\n",
    "    plt.xticks(rotation=0)\n",
    "    plt.xlabel('Consulta')\n",
    "    plt.ylabel('Número de resultados')\n",
    "    plt.title(f'Distribución de resultados para la consulta: {consulta}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparación de las busquedas que utilizan un solo campos, con las busquedas que hacen una combinacion de campos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Medidas descriptivas sobre los dos grupos de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tamaño del dataframe con búsquedas de un solo campo es: 2072\n",
      "El tamaño del dataframe con búsquedas de más de un campo es: 97\n",
      "\n",
      "\n",
      "El porcentaje de búsquedas de un solo campo es: 95.53%\n",
      "El porcentaje de búsquedas de más de un campo es: 4.47%\n",
      "\n",
      "\n",
      "El promedio de resultados obtenidos en las búsquedas de un solo campo es: 228.56563706563708\n",
      "El promedio de resultados obtenidos en las búsquedas de más de un campo es: 86.73195876288659\n",
      "\n",
      "\n",
      "El máximo de resultados obtenidos en las búsquedas de un solo campo es: 11525\n",
      "El máximo de resultados obtenidos en las búsquedas de más de un campo es: 3634\n",
      "\n",
      "\n",
      "La mediana de resultados obtenidos en las búsquedas de un solo campo es: 10.0\n",
      "La mediana de resultados obtenidos en las búsquedas de más de un campo es: 7.0\n"
     ]
    }
   ],
   "source": [
    "# Separamos las búsquedas que se realizaron con un solo campo y las que se realizaron con más de un campo\n",
    "df_1campo = busq_con_resultados[busq_con_resultados['cant_campos_utilizados'] == 1].copy()\n",
    "df_multi_campos = busq_con_resultados[busq_con_resultados['cant_campos_utilizados'] > 1].copy()\n",
    "\n",
    "# Tamaño de cada dataframe\n",
    "print(f\"El tamaño del dataframe con búsquedas de un solo campo es: {len(df_1campo)}\")\n",
    "print(f\"El tamaño del dataframe con búsquedas de más de un campo es: {len(df_multi_campos)}\")\n",
    "print(\"\\n\")\n",
    "\n",
    "# Representando en porcentaje\n",
    "print(f\"El porcentaje de búsquedas de un solo campo es: {round(len(df_1campo) / len(busq_con_resultados)*100, 2)}%\")\n",
    "print(f\"El porcentaje de búsquedas de más de un campo es: {round(len(df_multi_campos) / len(busq_con_resultados)*100, 2)}%\")\n",
    "print(\"\\n\")\n",
    "\n",
    "# Vamos a ver si hay alguna diferencia en la cantidad de resultados obtenidos entre las búsquedas de un solo campo y las de más de un campo\n",
    "print(f\"El promedio de resultados obtenidos en las búsquedas de un solo campo es: {df_1campo['total'].mean()}\")\n",
    "print(f\"El promedio de resultados obtenidos en las búsquedas de más de un campo es: {df_multi_campos['total'].mean()}\")\n",
    "print(\"\\n\")\n",
    "print(f\"El máximo de resultados obtenidos en las búsquedas de un solo campo es: {df_1campo['total'].max()}\")\n",
    "print(f\"El máximo de resultados obtenidos en las búsquedas de más de un campo es: {df_multi_campos['total'].max()}\")\n",
    "print(\"\\n\")\n",
    "print(f\"La mediana de resultados obtenidos en las búsquedas de un solo campo es: {df_1campo['total'].median()}\")\n",
    "print(f\"La mediana de resultados obtenidos en las búsquedas de más de un campo es: {df_multi_campos['total'].median()}\")\n",
    "\n",
    "# Notamos una gran diferencia en los resultados obtenidos entre las búsquedas de un solo campo y las de más de un campo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La tasa de error para las búsquedas de un solo campo es: 97.79%\n",
      "La tasa de error para las búsquedas de más de un campo es: 1.89%\n"
     ]
    }
   ],
   "source": [
    "# Calculamos la tasa de error para las búsquedas de un solo campo\n",
    "tasa_1campo = len(cero_resultados[cero_resultados['cant_campos_utilizados'] == 1]) / len(cero_resultados) * 100\n",
    "print(f\"La tasa de error para las búsquedas de un solo campo es: {round(tasa_1campo, 2)}%\")\n",
    "\n",
    "# Calculamos la tasa de error para las búsquedas de más de un campo\n",
    "tasa_multicampos = len(cero_resultados[cero_resultados['cant_campos_utilizados'] > 1]) / len(cero_resultados) * 100\n",
    "print(f\"La tasa de error para las búsquedas de más de un campo es: {round(tasa_multicampos, 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogramas de la distribuciones de los resultados según la cantidad de campos utilizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma para búsquedas de un solo campo\n",
    "bins = range(0, int(df_1campo['total'].max()) + 500, 500)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df_1campo['total'], kde=True, color='blue', label='Un solo campo', bins=bins)\n",
    "plt.axvline(df_1campo['total'].mean(), color='red', linestyle='dashed', linewidth=1, label=f'Media: {df_1campo[\"total\"].mean():.2f}')\n",
    "plt.xlabel('Número de resultados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Distribución de resultados: un solo campo')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#==================================================================================================================================================\n",
    "\n",
    "# Histograma para búsquedas de múltiples campos\n",
    "bins = range(0, int(df_multi_campos['total'].max()) + 100, 100)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df_multi_campos['total'], kde=True, color='orange', label='Múltiples campos', bins=bins)\n",
    "plt.axvline(df_multi_campos['total'].mean(), color='red', linestyle='dashed', linewidth=1, label=f'Media: {df_multi_campos[\"total\"].mean():.2f}')\n",
    "plt.xlabel('Número de resultados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Distribución de resultados: múltiples campos')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unificación de los dos histogramas anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqoYOHSoXl7//k6V///4KCAjQkiVLJMn6mfz000+5Dm8bO3asDMOwe28nSXrmmWdsnjdt2lTnzp3Lcv5dLy91W75LN85fNmLECEmyvgc5vTY8PFzdu3e3LnN3d9dzzz2ny5cvZzukzGLFihX666+/9NJLL2WZV+3678/1n3lycrLOnj2rf/zjHzIMQzt37syy3X79+lkfBwUFqWrVqvL19VXXrl2ty6tWraqgoCD99ttvWV4/YMAAmx5LAwcOlJubm/V9yus5k5PHH39cp0+fthmG+s033ygzM1OPP/649Zg9PDy0du3aPA2fzc7tvg8FpVOnTrrjjjusz++55x41atQo19/47777TpmZmeratavNb1V4eLgqV65s/a3K6/f2Rjt37tSxY8c0dOjQLFcvzencvHbtms6ePat7771Xkmx+jyyu/0xcXV119913yzAM9e3b17rc8lll95nc7N/Dbdu26fTp03r22WdtvlMdOnRQtWrVrOemPc4vAChsCJ4AoIi4fPmyzX/U3ujxxx/Xfffdp379+iksLEzdunXT119/fUsh1B133HFLE4lXrlzZ5rnJZFKlSpXyNQ/F0aNHVbVq1VuaMP3MmTO6cuWKqlatmmVd9erVlZmZmWWOjRuveFeqVClJyvU/8C2h1I3HKynLvo8cOaJLly4pNDRUISEhNrfLly/r9OnTNz0uNze3LEP48rrd5s2bq0uXLho3bpyCg4PVsWNHzZ49O0/zAd2uq1evasyYMYqMjJSnp6eCg4MVEhKiixcv2szZcuLEiTy9l5b3/cblHh4euvPOO63ro6OjNXz4cH388ccKDg5WTEyM3n///TzNE2Mv+Tmv8lL3iRMn5OLiokqVKtm8Njw8XEFBQbkGppb3+foARjJ/Nyzrc2KZV80ytCwn8fHxio2NVenSpeXn56eQkBDrMLUb338vLy+FhITYLAsMDFS5cuWyhMuBgYHZvnc3njd+fn6KiIiw/ubk9ZzJSdu2bRUYGGgz9Hb+/PmqW7euqlSpIskc0P/rX//S0qVLFRYWZh0yl5CQkOu2LezxPhSU7L6nVapUyfU3/siRIzIMQ5UrV87yW3XgwAHrb1V+v7d5PTfPnz+v559/XmFhYfL29lZISIh1yGJ2+7jxOxwYGCgvLy8FBwdnWZ6Xc/PGfw9zOjclqVq1atb1t3t+AUBhxBxPAFAE/P7777p06VKWPz6v5+3trfXr12vNmjVasmSJli1bpvnz5+v+++/X8uXL5erqetP93Oq8THmRW2+lvNRkbznt0zAMu2w/MzNToaGhmjdvXrbrb/yDMzuenp5ZwoK8btdkMumbb77Rpk2b9N///lc//fST+vTpo7feekubNm2Sn59frp/J7RgyZIhmz56toUOHqnHjxgoMDJTJZFK3bt0cfqn2t956S7Gxsfr++++1fPlyPffcc5o0aZI2bdqU7TxcucnP+5Pf8yqvdedUkzNlZGTogQce0Pnz5zVq1ChVq1ZNvr6++uOPPxQbG5vlM8/pPXL0d/JWeHp6qlOnTlq4cKE++OADJSYmasOGDZo4caJNu6FDh+qhhx7SokWL9NNPP+m1117TpEmTtHr1atWrVy/XfdzO++Co7649ZWZmymQyaenSpdkek5+fn/WxPb+3N+ratat++eUXjRw5UnXr1pWfn58yMzPVtm3bbH+PsqvVWefm7ZxfAFAY0eMJAIqAzz77TJIUExOTazsXFxe1atVK06ZN0/79+/XGG29o9erV1qEN9v7j9ciRIzbPDcPQr7/+ajP5aalSpbK9mtSNPQ8qVqyoQ4cO3dLwsJCQEPn4+OjQoUNZ1h08eFAuLi6KjIzM8/ZyEhUVJSnr8UrKsu+KFSvq3Llzuu+++9S6desstzp16uSrhlvd7r333qs33nhD27Zt07x587Rv3z599dVXkv7ujXPj53Kz3iAWOZ1H33zzjXr16qW33npLjz76qB544AE1adIky36ioqLy9F5a3vcbl6empurYsWPW9Ra1atXSq6++qvXr1+vnn3/WH3/8oY8++ihPx3S9231/blVudUdFRSkzMzPL+5WYmKiLFy9meQ+uZ3mfb/wj++DBg9b1ObFMGJ/bFcz27Nmjw4cP66233tKoUaPUsWNHtW7dWmXLls39gG/Dje/D5cuXderUKetvzq2eM9l5/PHHdfbsWa1atUoLFiyQYRjWYXbXq1ixokaMGKHly5dr7969Sk1NzXLFM3u7nXMzP7//2X1PDx8+nOsE1xUrVpRhGIqOjs72t8oy3M3iVr+3eTk3L1y4oFWrVumll17SuHHj9Mgjj+iBBx6wGZJtbzf79zCnc9Oy7MZz0xnnFwA4CsETABRyq1ev1oQJExQdHZ1lDpzrnT9/PsuyunXrSpJ1mJWvr6+krH+05JflikcW33zzjU6dOqV27dpZl1WsWFGbNm1SamqqddnixYuzDIHr0qWLzp49q/feey/LfnL6v8uurq5q06aNvv/+e5uhH4mJifriiy/UpEkTBQQE5PfwrCIiIlS3bl3NnTvXZojGihUrtH//fpu2Xbt2VUZGhiZMmJBlO+np6fl+7/O63QsXLmR5v248D6KiouTq6qr169fbtPvggw/yVIuvr2+2x+Hq6ppl3++++26W3hjt27fXpk2btGXLFuuyM2fOZOnN1bp1a3l4eOidd96x2e4nn3yiS5cuqUOHDpKkpKQkpaen27y2Vq1acnFxsRliePbsWR08ePCm88kEBAQoODg43+9PXuWl7vbt20uSpk+fbtNu2rRpkmR9D7LTvn17JSQk2AwbS09P17vvvis/P78sV267Xps2beTv769Jkybp2rVrNussn4WlN8j1n41hGDaXtLe3mTNn2oTTH374odLT062/OXk9Z3LTunVrlS5dWvPnz9f8+fN1zz332FxV7sqVK1nek4oVK8rf39/hQ1otocv152ZycrLmzp1709fm5/d/0aJF+uOPP6zPt2zZos2bN9v8xt+oc+fOcnV11bhx47L8HhiGoXPnzknK+/f2RvXr11d0dLSmT5+e5VhyOzelrN8je7rZv4d33323QkND9dFHH9kc39KlS3XgwAHruenM8wsAHIWhdgBQiCxdulQHDx5Uenq6EhMTtXr1aq1YsUJRUVH64Ycfskzye73x48dr/fr16tChg6KionT69Gl98MEHKleunJo0aSLJ/B+vQUFB+uijj+Tv7y9fX181atQo35fqLl26tJo0aaLevXsrMTFR06dPV6VKldS/f39rm379+umbb75R27Zt1bVrVx09elSff/659Q8oi549e+rTTz/V8OHDtWXLFjVt2lTJyclauXKlnn32WXXs2DHbGl5//XWtWLFCTZo00bPPPis3Nzf9+9//VkpKiqZMmZKv48rOpEmT1KFDBzVp0kR9+vTR+fPn9e6776pmzZq6fPmytV3z5s319NNPa9KkSYqLi1ObNm3k7u6uI0eOaMGCBZoxY4YeffTRW95/Xrc7d+5cffDBB3rkkUdUsWJF/fXXX/rPf/6jgIAAa4gRGBioxx57TO+++65MJpMqVqyoxYsX52n+KUlq0KCBVq5cqWnTpqls2bKKjo5Wo0aN9OCDD+qzzz5TYGCgatSooY0bN2rlypUqU6aMzetffPFFffbZZ2rbtq2ef/55+fr6aubMmYqKitLu3but7UJCQjR69GiNGzdObdu21cMPP6xDhw7pgw8+UMOGDdWjRw9J5nB28ODBeuyxx1SlShWlp6frs88+k6urq7p06WLd3nvvvadx48ZpzZo1N51gvF+/fpo8ebL69eunu+++W+vXr9fhw4fz9P7kVV7qrlOnjnr16qWZM2fq4sWLat68ubZs2aK5c+eqU6dOatmyZY7bHzBggP79738rNjZW27dvV4UKFfTNN99ow4YNmj59eq5zxgUEBOjtt99Wv3791LBhQz3xxBMqVaqUdu3apStXrmju3LmqVq2aKlasqBdeeEF//PGHAgIC9O233zp0TqLU1FS1atVKXbt2tZ4LTZo00cMPPywp7+dMbtzd3dW5c2d99dVXSk5O1ptvvmmz/vDhw9YaatSoITc3Ny1cuFCJiYnq1q2bQ47bok2bNipfvrz69u2rkSNHytXVVbNmzVJISIji4+NzfW1+fv8rVaqkJk2aaODAgUpJSdH06dNVpkwZvfjii7nu5/XXX9fo0aN1/PhxderUSf7+/jp27JgWLlyoAQMG6IUXXsjz9/ZGLi4u+vDDD/XQQw+pbt266t27tyIiInTw4EHt27dPP/30kwICAqxzI6WlpemOO+7Q8uXLdezYsZu/yfl0s38P3d3d9a9//Uu9e/dW8+bN1b17dyUmJmrGjBmqUKGChg0bJsm55xcAOEyBXT8PAJAjy6WwLTcPDw8jPDzceOCBB4wZM2Zke9nzGy9hvWrVKqNjx45G2bJlDQ8PD6Ns2bJG9+7djcOHD9u87vvvvzdq1KhhuLm52Vxau3nz5kbNmjWzre/Gy3JbLh/95ZdfGqNHjzZCQ0MNb29vo0OHDjaXULd46623jDvuuMPw9PQ07rvvPmPbtm1ZtmkY5stfv/LKK0Z0dLTh7u5uhIeHG48++qhx9OhRaxvdcHlxwzCMHTt2GDExMYafn5/h4+NjtGzZ0vjll1+yfY+3bt1qs9xyLGvWrMn22K/37bffGtWrVzc8PT2NGjVqGN99912Wy5BbzJw502jQoIHh7e1t+Pv7G7Vq1TJefPFF488//8x1H7169TJ8fX1zXH+z7e7YscPo3r27Ub58ecPT09MIDQ01HnzwQWPbtm022zlz5ozRpUsXw8fHxyhVqpTx9NNPG3v37s1yufXsLt9+8OBBo1mzZoa3t7chyXpp8QsXLhi9e/c2goODDT8/PyMmJsY4ePCgERUVZXP5ccMwjN27dxvNmzc3vLy8jDvuuMOYMGGC8cknn2S5JLxhGMZ7771nVKtWzXB3dzfCwsKMgQMH2lxG/bfffjP69OljVKxY0fD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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparar la distribución de los resultados entre ambos dataframes\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Distribución de resultados para búsquedas de un solo campo\n",
    "sns.histplot(df_1campo['total'], kde=True, color='blue', label='Un solo campo', bins=30)\n",
    "# Distribución de resultados para búsquedas de múltiples campos\n",
    "sns.histplot(df_multi_campos['total'], kde=True, color='orange', label='Múltiples campos', bins=30)\n",
    "\n",
    "plt.xlabel('Número de resultados')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Distribución de resultados: un solo campo vs múltiples campos')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis sobre las diferentes terminos y campos de busqueda utilizados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conteo de las distintas consultas que se hicieron en el sistema, para una posterior graficación de los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminos_busq = busq_con_resultados\n",
    "terminos_busq.loc[:, 'sintaxis'] = terminos_busq['query_desc'].apply(extraer_sintaxis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| sintaxis    |   count |\n",
      "|:------------|--------:|\n",
      "| serway      |      90 |\n",
      "| lib         |      90 |\n",
      "| cooper      |      74 |\n",
      "| cengel      |      43 |\n",
      "| prometheus  |      43 |\n",
      "| silverthorn |      34 |\n",
      "| lodish      |      29 |\n",
      "| mezquita    |      25 |\n",
      "| ross        |      21 |\n",
      "| churchill   |      21 |\n",
      "| stewart     |      20 |\n",
      "| tortora     |      19 |\n",
      "| curtis      |      19 |\n",
      "| chang       |      18 |\n",
      "| alberts     |      18 |\n",
      "| rhoades     |      15 |\n",
      "| boylestad   |      15 |\n",
      "| rev         |      14 |\n",
      "| tipler      |      13 |\n",
      "| frumento    |      13 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contar la frecuencia de cada término, posterior al pasado de los terminos a minusculas\n",
    "frecuencia_terminos = terminos_busq['sintaxis'].str.lower().value_counts()\n",
    "\n",
    "# Mostrar los términos más frecuentes\n",
    "print(tabulate(frecuencia_terminos.head(20).reset_index(), headers='keys', tablefmt='pipe', showindex=False))\n",
    "\n",
    "# Graficar la frecuencia de los términos más frecuentes\n",
    "plt.figure(figsize=(12, 8))\n",
    "frecuencia_terminos.head(20).plot(kind='bar', color=plt.cm.tab20.colors)\n",
    "plt.xlabel('Términos de búsqueda')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.title('Términos de búsqueda más frecuentes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico de sectores que representa los porcentajes de las busquedas tuvieron un resultado, o no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular la tasa de respuesta\n",
    "total_busquedas = len(df_filtrado)\n",
    "busquedas_con_resultados = len(df_filtrado[df_filtrado['total'] > 0])\n",
    "tasa_respuesta = busquedas_con_resultados / total_busquedas\n",
    "\n",
    "# Gráfico de pastel para la tasa de respuesta general\n",
    "labels = ['Con resultados', 'Sin resultados']\n",
    "sizes = [busquedas_con_resultados, total_busquedas - busquedas_con_resultados]\n",
    "plt.pie(sizes, labels= labels, autopct='%1.1f%%', startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.title('Tasa de respuesta')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tasas de respuesta según cada campo o combinación de los mismos ingresados en el sistema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laura\\AppData\\Local\\Temp\\ipykernel_3048\\1468788901.py:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  tasa_respuesta_por_termino = df_filtrado.groupby('campo_utilizado').apply(lambda x: len(x[x['total'] > 0]) / len(x)).reset_index(name='tasa_respuesta')\n"
     ]
    }
   ],
   "source": [
    "# Ordenar los términos en cada combinación para estandarizar\n",
    "df_filtrado['campo_utilizado'] = df_filtrado['campo_utilizado'].fillna('').apply(lambda x: ', '.join(sorted(x.split(', '))))\n",
    "\n",
    "# Calcular la tasa de respuesta por tipo de campo de búsqueda utilizado\n",
    "tasa_respuesta_por_termino = df_filtrado.groupby('campo_utilizado').apply(lambda x: len(x[x['total'] > 0]) / len(x)).reset_index(name='tasa_respuesta')\n",
    "\n",
    "# Calcular la tasa de error\n",
    "tasa_respuesta_por_termino['tasa_error'] = 1 - tasa_respuesta_por_termino['tasa_respuesta']\n",
    "\n",
    "# Dividir los porcentajes obtenidos\n",
    "tasa_resp1 = tasa_respuesta_por_termino[tasa_respuesta_por_termino['tasa_respuesta'] == 1]\n",
    "tasa_resp0 = tasa_respuesta_por_termino[tasa_respuesta_por_termino['tasa_respuesta'] < 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>campo_utilizado</th>\n",
       "      <th>tasa_respuesta</th>\n",
       "      <th>tasa_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>au</td>\n",
       "      <td>0.805430</td>\n",
       "      <td>0.194570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>au, mc-itype, mc-itype, ti</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>au, ti</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bc</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>callnum</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>kw</td>\n",
       "      <td>0.881239</td>\n",
       "      <td>0.118761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kw, kw, mc-itype</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kw, mc-itype</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kw, su</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>mc-itype</td>\n",
       "      <td>0.968992</td>\n",
       "      <td>0.031008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>se</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>su</td>\n",
       "      <td>0.881356</td>\n",
       "      <td>0.118644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ti</td>\n",
       "      <td>0.804878</td>\n",
       "      <td>0.195122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               campo_utilizado  tasa_respuesta  tasa_error\n",
       "0                                     0.000000    1.000000\n",
       "2                           au        0.805430    0.194570\n",
       "8   au, mc-itype, mc-itype, ti        0.500000    0.500000\n",
       "12                      au, ti        0.750000    0.250000\n",
       "13                          bc        0.933333    0.066667\n",
       "14                     callnum        0.000000    1.000000\n",
       "15                          kw        0.881239    0.118761\n",
       "16            kw, kw, mc-itype        0.000000    1.000000\n",
       "17                kw, mc-itype        0.971429    0.028571\n",
       "22                      kw, su        0.000000    1.000000\n",
       "26                    mc-itype        0.968992    0.031008\n",
       "34                          se        0.500000    0.500000\n",
       "35                          su        0.881356    0.118644\n",
       "38                          ti        0.804878    0.195122"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos cuales campos tienen un porcentaje de respuestas menor a 1\n",
    "tasa_resp0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>campo_utilizado</th>\n",
       "      <th>tasa_respuesta</th>\n",
       "      <th>tasa_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>an</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>au, au</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>au, in, ti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>au, kw</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>au, mc-itype</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>au, mc-itype, mc-itype, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>au, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>au, su-to, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>au, su-to, su-to, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>kw, mc-itype, mc-itype</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>kw, mc-itype, su</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>kw, not au, ti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>kw, se</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>kw, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>l-format</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>l-format, mc-itype</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>mc-itype, mc-itype</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mc-itype, mc-itype, mc-itype</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>mc-itype, mc-itype, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>mc-itype, su</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>mc-itype, su-to</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>mc-itype, ti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>provider</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>su, ti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>su-to, ti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  campo_utilizado  tasa_respuesta  tasa_error\n",
       "1                              an             1.0         0.0\n",
       "3                          au, au             1.0         0.0\n",
       "4                      au, in, ti             1.0         0.0\n",
       "5                          au, kw             1.0         0.0\n",
       "6                    au, mc-itype             1.0         0.0\n",
       "7   au, mc-itype, mc-itype, su-to             1.0         0.0\n",
       "9                       au, su-to             1.0         0.0\n",
       "10               au, su-to, su-to             1.0         0.0\n",
       "11        au, su-to, su-to, su-to             1.0         0.0\n",
       "18         kw, mc-itype, mc-itype             1.0         0.0\n",
       "19               kw, mc-itype, su             1.0         0.0\n",
       "20                 kw, not au, ti             1.0         0.0\n",
       "21                         kw, se             1.0         0.0\n",
       "23                      kw, su-to             1.0         0.0\n",
       "24                       l-format             1.0         0.0\n",
       "25             l-format, mc-itype             1.0         0.0\n",
       "27             mc-itype, mc-itype             1.0         0.0\n",
       "28   mc-itype, mc-itype, mc-itype             1.0         0.0\n",
       "29      mc-itype, mc-itype, su-to             1.0         0.0\n",
       "30                   mc-itype, su             1.0         0.0\n",
       "31                mc-itype, su-to             1.0         0.0\n",
       "32                   mc-itype, ti             1.0         0.0\n",
       "33                       provider             1.0         0.0\n",
       "36                         su, ti             1.0         0.0\n",
       "37                      su-to, ti             1.0         0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos los porcentajes que tuvieron el 100% de efectividad\n",
    "tasa_resp1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semana 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasas de respuesta para los dos grupos de datos: con las búsquedas con un campo y con las búsquedas que tienen una combinación de campos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Porcentajes de encontrar una respuesta según la cantidad de camps utilizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   cant_campos_utilizados  tasa_respuesta  tasa_error\n",
      "0                       1        0.731269    0.268731\n",
      "1                       2        0.920089    0.079911\n",
      "2                       3        0.875000    0.125000\n",
      "3                       4        0.833333    0.166667\n"
     ]
    }
   ],
   "source": [
    "tasa_resp0.loc[:, 'cant_campos_utilizados'] = tasa_resp0['campo_utilizado'].apply(lambda x: 0 if pd.isna(x) else x.count(',') + 1)\n",
    "tasa_resp1.loc[:, 'cant_campos_utilizados'] = tasa_resp1['campo_utilizado'].apply(lambda x: 0 if pd.isna(x) else x.count(',') + 1)\n",
    "\n",
    "# Concatenamos los dataframes\n",
    "tasas = pd.concat([tasa_resp0, tasa_resp1])\n",
    "\n",
    "# Calculamos la tasa de respuesta para cada cantidad de campos utilizados\n",
    "tasa_respuesta_por_cant_campos = tasas.groupby('cant_campos_utilizados')['tasa_respuesta'].mean().reset_index()\n",
    "\n",
    "# Calculamos la tasa de error\n",
    "tasa_respuesta_por_cant_campos['tasa_error'] = 1 - tasa_respuesta_por_cant_campos['tasa_respuesta']\n",
    "\n",
    "# Mostramos los resultados\n",
    "print(tasa_respuesta_por_cant_campos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot de la distribución de resultados devueltos por el sistema según la cantidad de campos utilizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear boxplot para comparar búsquedas simples y combinadas\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='cant_campos_utilizados', y='total', data=df_filtrado, palette='Set3', hue='cant_campos_utilizados', legend=False)\n",
    "plt.xlabel(\"Tipo de Búsqueda (0=Sin campos, 1=Simple, >1=Combinada)\")\n",
    "plt.ylabel(\"Número de resultados obtenidos\")\n",
    "plt.title(\"Comparación de resultados obtenidos entre búsquedas simples y combinadas\")\n",
    "# El limitador del eje Y esta configurado para que se vea mejor la distribución de los datos\n",
    "plt.ylim(0, 500)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}