{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8028fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from selenium.common.exceptions import TimeoutException, NoSuchElementException\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.chrome.options import Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f34cbe1",
   "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>Nombre</th>\n",
       "      <th>Enlace</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/kitchen/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comedor</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/dining/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Habitación infantil y juvenil</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/childrens-room/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Salón</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/living-room/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dormitorio</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/bedroom/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Baño</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/bathroom/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Espacios de trabajo, estudio y gaming</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/home-office/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Recibidor</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/hallway/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lavadero</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/laundry/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Trastero y garaje</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/garage/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>https://www.ikea.com/es/es/rooms/outdoor/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IKEA para Empresas</td>\n",
       "      <td>https://www.ikea.com/es/es/ikea-business/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Nombre  \\\n",
       "0                                  Cocina   \n",
       "1                                 Comedor   \n",
       "2           Habitación infantil y juvenil   \n",
       "3                                   Salón   \n",
       "4                              Dormitorio   \n",
       "5                                    Baño   \n",
       "6   Espacios de trabajo, estudio y gaming   \n",
       "7                               Recibidor   \n",
       "8                                Lavadero   \n",
       "9                       Trastero y garaje   \n",
       "10               Jardín, terraza y balcón   \n",
       "11                     IKEA para Empresas   \n",
       "\n",
       "                                              Enlace  \n",
       "0          https://www.ikea.com/es/es/rooms/kitchen/  \n",
       "1           https://www.ikea.com/es/es/rooms/dining/  \n",
       "2   https://www.ikea.com/es/es/rooms/childrens-room/  \n",
       "3      https://www.ikea.com/es/es/rooms/living-room/  \n",
       "4          https://www.ikea.com/es/es/rooms/bedroom/  \n",
       "5         https://www.ikea.com/es/es/rooms/bathroom/  \n",
       "6      https://www.ikea.com/es/es/rooms/home-office/  \n",
       "7          https://www.ikea.com/es/es/rooms/hallway/  \n",
       "8          https://www.ikea.com/es/es/rooms/laundry/  \n",
       "9           https://www.ikea.com/es/es/rooms/garage/  \n",
       "10         https://www.ikea.com/es/es/rooms/outdoor/  \n",
       "11         https://www.ikea.com/es/es/ikea-business/  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"estancias.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "767ef9cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando: Cocina\n",
      "Procesando: Comedor\n",
      "Procesando: Habitación infantil y juvenil\n",
      "Procesando: Salón\n",
      "Procesando: Dormitorio\n",
      "Procesando: Baño\n",
      "Procesando: Espacios de trabajo, estudio y gaming\n",
      "Procesando: Recibidor\n",
      "Procesando: Lavadero\n",
      "Procesando: Trastero y garaje\n",
      "No se encontraron items para Trastero y garaje\n",
      "Procesando: Jardín, terraza y balcón\n",
      "Procesando: IKEA para Empresas\n",
      "Proceso completado. CSV generado: estancias_items.csv\n"
     ]
    }
   ],
   "source": [
    "driver = webdriver.Chrome(service=service, options=options)\n",
    "try:\n",
    "    with open(\"estancias_items.csv\", \"w\", newline=\"\", encoding=\"utf-8\") as f_out:\n",
    "        writer = csv.writer(f_out)\n",
    "        writer.writerow([\"Estancia\", \"Mueble\", \"Enlace\"])\n",
    "        with open(\"estancias.csv\", newline=\"\", encoding=\"utf-8\") as f_in:\n",
    "            reader = csv.DictReader(f_in)\n",
    "            for row in reader:\n",
    "                estancia_nombre = row[\"Nombre\"]\n",
    "                estancia_url = row[\"Enlace\"]\n",
    "\n",
    "                print(f\"Procesando: {estancia_nombre}\")\n",
    "\n",
    "                driver.get(estancia_url)\n",
    "\n",
    "                # Cerrar pop-up de cookies si aparece\n",
    "                try:\n",
    "                    aceptar_cookies = WebDriverWait(driver, 3).until(\n",
    "                        EC.element_to_be_clickable((By.XPATH, \"//button[contains(text(),'Aceptar')]\"))\n",
    "                    )\n",
    "                    aceptar_cookies.click()\n",
    "                except TimeoutException:\n",
    "                    pass\n",
    "\n",
    "                # Esperar el carrusel de items\n",
    "                try:\n",
    "                    items = WebDriverWait(driver, 10).until(\n",
    "                        EC.presence_of_all_elements_located(\n",
    "                            (By.XPATH, \"//*[@id='hnf-carousel__tabs-navigation-rooms']/div[@role='listitem']/a\")\n",
    "                        )\n",
    "                    )\n",
    "                except TimeoutException:\n",
    "                    print(f\"No se encontraron items para {estancia_nombre}\")\n",
    "                    continue\n",
    "\n",
    "                # Guardar cada item en el CSV\n",
    "                for item in items:\n",
    "                    try:\n",
    "                        mueble_nombre = item.find_element(By.TAG_NAME, \"span\").text.strip()\n",
    "                        enlace_item = item.get_attribute(\"href\")\n",
    "                        writer.writerow([estancia_nombre, mueble_nombre, enlace_item])\n",
    "                    except NoSuchElementException:\n",
    "                        continue\n",
    "\n",
    "                time.sleep(1)  \n",
    "\n",
    "finally:\n",
    "    driver.quit()\n",
    "    print(\"Proceso completado. CSV generado: estancias_items.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc09108",
   "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>Estancia</th>\n",
       "      <th>Mueble</th>\n",
       "      <th>Enlace</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>Sistemas de cocinas</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/sistemas-cocina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>Electrodomésticos</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/electrodomestic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>Interiores de muebles de cocina y armarios</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/interiores-mueb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>Utensilios de cocina y repostería</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/utensilios-coci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cocina</td>\n",
       "      <td>Almacenaje de pared para cocina</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/almacenaje-pare...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>Mesas de exterior para jardín y terraza</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/mesas-exterior-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>Sombrillas, pérgolas y toldos</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/sombrillas-perg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>Barbacoas</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/barbacoas-24898/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>Pícnic y para llevar</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/picnic-para-lle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Jardín, terraza y balcón</td>\n",
       "      <td>Accesorios de barbacoa y grill</td>\n",
       "      <td>https://www.ikea.com/es/es/cat/cocinas-exterio...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Estancia                                      Mueble  \\\n",
       "0                      Cocina                         Sistemas de cocinas   \n",
       "1                      Cocina                           Electrodomésticos   \n",
       "2                      Cocina  Interiores de muebles de cocina y armarios   \n",
       "3                      Cocina           Utensilios de cocina y repostería   \n",
       "4                      Cocina             Almacenaje de pared para cocina   \n",
       "..                        ...                                         ...   \n",
       "124  Jardín, terraza y balcón     Mesas de exterior para jardín y terraza   \n",
       "125  Jardín, terraza y balcón               Sombrillas, pérgolas y toldos   \n",
       "126  Jardín, terraza y balcón                                   Barbacoas   \n",
       "127  Jardín, terraza y balcón                        Pícnic y para llevar   \n",
       "128  Jardín, terraza y balcón              Accesorios de barbacoa y grill   \n",
       "\n",
       "                                                Enlace  \n",
       "0    https://www.ikea.com/es/es/cat/sistemas-cocina...  \n",
       "1    https://www.ikea.com/es/es/cat/electrodomestic...  \n",
       "2    https://www.ikea.com/es/es/cat/interiores-mueb...  \n",
       "3    https://www.ikea.com/es/es/cat/utensilios-coci...  \n",
       "4    https://www.ikea.com/es/es/cat/almacenaje-pare...  \n",
       "..                                                 ...  \n",
       "124  https://www.ikea.com/es/es/cat/mesas-exterior-...  \n",
       "125  https://www.ikea.com/es/es/cat/sombrillas-perg...  \n",
       "126    https://www.ikea.com/es/es/cat/barbacoas-24898/  \n",
       "127  https://www.ikea.com/es/es/cat/picnic-para-lle...  \n",
       "128  https://www.ikea.com/es/es/cat/cocinas-exterio...  \n",
       "\n",
       "[129 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"estancias_items.csv\")\n",
    "df = df[df[\"Estancia\"] != \"IKEA para Empresas\"]\n",
    "df.to_csv(\"estancias_items.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ba9329",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"estancias_items.csv\", index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30667fb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando: Sistemas de cocinas\n",
      "Guardado: Cocinas METOD\n",
      "Guardado: Cocinas ENHET\n",
      "Guardado: Cocinas KNOXHULT\n",
      "Procesando: Electrodomésticos\n",
      "Guardado: Pequeños electrodomésticos de cocina\n",
      "Guardado: Neveras, frigoríficos y congeladores\n",
      "Guardado: Placas de cocina\n",
      "Guardado: Campanas extractoras\n",
      "Guardado: Hornos y hornos combi\n",
      "Guardado: Microondas y microondas combi\n",
      "Guardado: Lavavajillas\n",
      "Guardado: Accesorios para electrodomésticos\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Interiores de muebles de cocina y armarios\n",
      "Guardado: Organizadores de cajones y de armarios\n",
      "Guardado: Cuberteros y organizadores de cubiertos\n",
      "Guardado: Organizadores de productos de limpieza\n",
      "Guardado: Cubos de basura\n",
      "Procesando: Utensilios de cocina y repostería\n",
      "Guardado: Salvamanteles y tapas\n",
      "Guardado: Utensilios de cocina para mezclar y medir\n",
      "Guardado: Abrelatas, ralladores y peladores\n",
      "Guardado: Espátulas, cucharones y pinzas\n",
      "Guardado: Coladores y escurridores\n",
      "Guardado: Cubiteras y moldes para helados\n",
      "Guardado: Utensilios de repostería\n",
      "Procesando: Almacenaje de pared para cocina\n",
      "Guardado: Organizadores de pared para cocina\n",
      "Guardado: Baldas y estantes de pared para cocina\n",
      "Procesando: Envases y organizadores de alimentos\n",
      "Guardado: Táperes, tarteras y bolsas\n",
      "Guardado: Juegos de táperes\n",
      "Guardado: Organizadores para nevera\n",
      "Guardado: Organizadores para despensas y encimeras\n",
      "Guardado: Tarros, latas y botes\n",
      "Guardado: Especieros\n",
      "Guardado: Botellas de agua y termos\n",
      "Guardado: Bolsas isotérmicas\n",
      "Guardado: Botelleros\n",
      "Guardado: Mezcla y combina recipientes y tapas para alimentos\n",
      "Guardado: Accesorios para guardar alimentos y bolsas con cierre\n",
      "Procesando: Islas y carritos de cocina\n",
      "Guardado: Sistemas de cocinas\n",
      "Guardado: Interiores de muebles de cocina y armarios\n",
      "Guardado: Encimeras de cocina\n",
      "Guardado: Electrodomésticos\n",
      "Guardado: Estanterías para cocina, despensa y trastero\n",
      "Guardado: Almacenaje de pared para cocina\n",
      "Guardado: Mini cocinas\n",
      "Guardado: Salpicaderos de cocina y paneles de pared\n",
      "Guardado: Puertas y frentes de cocina\n",
      "Guardado: Fregaderos y grifos\n",
      "Guardado: Tiradores y pomos\n",
      "Guardado: Iluminación de cocina\n",
      "Guardado: Armarios de cocina\n",
      "Guardado: Baldas y cajones de cocina\n",
      "Procesando: Encimeras de cocina\n",
      "Guardado: Encimeras precortadas\n",
      "Guardado: Encimeras a medida\n",
      "Guardado: Copetes de encimera y accesorios\n",
      "Procesando: Menaje de cocina\n",
      "Guardado: Sartenes y woks\n",
      "Guardado: Ollas y cacerolas\n",
      "Guardado: Bandejas para horno\n",
      "Guardado: Salvamanteles y tapas\n",
      "Procesando: Fregaderos y grifos\n",
      "Guardado: Fregaderos\n",
      "Guardado: Grifos de cocina\n",
      "Guardado: Desagües y piezas de fregadero\n",
      "Procesando: Iluminación de cocina\n",
      "Guardado: Iluminación para armarios\n",
      "Guardado: Iluminación para vitrinas y librerías\n",
      "Guardado: Iluminación para armarios de baño\n",
      "Procesando: Puertas y frentes de cocina\n",
      "Guardado: Puertas y frentes de cajones METOD\n",
      "Guardado: Puertas y frentes de cajón ENHET\n",
      "Procesando: Armarios de cocina\n",
      "Guardado: Armarios de cocina METOD\n",
      "Guardado: Armarios de cocina KNOXHULT\n",
      "Guardado: Armarios altos METOD\n",
      "Guardado: Armarios de pared METOD\n",
      "Guardado: Armarios de cocina ENHET\n",
      "Guardado: Armarios para electrodomésticos METOD\n",
      "Guardado: Muebles de fregadero METOD\n",
      "Procesando: Conjuntos de comedor y cocina\n",
      "Guardado: Conjuntos de mesa y 2 sillas\n",
      "Guardado: Conjuntos de mesa y 4 sillas\n",
      "Guardado: Conjuntos de mesa y 6 sillas\n",
      "Guardado: Conjuntos de mesa y 8 sillas o más\n",
      "Procesando: Mesas de comedor y cocina\n",
      "Guardado: Mesas para 2 personas\n",
      "Guardado: Mesas para 4 personas\n",
      "Guardado: Mesas para 6 personas\n",
      "Guardado: Mesas para 8 personas\n",
      "Guardado: Mesas para 10 personas\n",
      "Guardado: Tableros y estructuras para mesas\n",
      "Procesando: Sillas de comedor, de cocina y plegables\n",
      "Guardado: Sillas de comedor y cocina\n",
      "Guardado: Sillas tapizadas\n",
      "Guardado: Fundas para silla\n",
      "Guardado: Estructuras de patas para silla y asientos\n",
      "Guardado: Cojines para silla\n",
      "Procesando: Aparadores y consolas\n",
      "Guardado: Aparadores\n",
      "Guardado: Mesas consola\n",
      "Procesando: Platos y vajillas\n",
      "Guardado: Vajillas\n",
      "Guardado: Platos planos\n",
      "Guardado: Cuencos y boles\n",
      "Guardado: Platos hondos\n",
      "Guardado: Platos de postre\n",
      "Procesando: Mesas altas y taburetes\n",
      "Guardado: Taburetes altos\n",
      "Guardado: Mesas altas\n",
      "Guardado: Conjuntos de mesas altas\n",
      "Procesando: Bancos de comedor\n",
      "Guardado: Bancos de recibidor\n",
      "Guardado: Pies de cama y bancos de dormitorio\n",
      "Guardado: Bancos con almacenaje\n",
      "Guardado: Bancos para jardín y exterior\n",
      "Guardado: Taburetes infantiles y bancos\n",
      "Procesando: Mantelería\n",
      "Guardado: Manteles y caminos de mesa\n",
      "Guardado: Posavasos y manteles individuales\n",
      "Guardado: Servilletas\n",
      "Procesando: Ensaladeras, fuentes, fruteros y soportes para tartas\n",
      "Guardado: Bandejas de servir\n",
      "Guardado: Ensaladeras y cuencos para servir\n",
      "Guardado: Soperas y fuentes\n",
      "Guardado: Soportes para tartas\n",
      "Guardado: Cubiertos de servir\n",
      "Procesando: Alfombras\n",
      "Guardado: Alfombras grandes y medianas\n",
      "Guardado: Alfombras de pasillo y pequeñas\n",
      "Guardado: Alfombras hechas a mano\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Felpudos\n",
      "Guardado: Alfombras de piel\n",
      "Guardado: Antideslizantes para alfombra\n",
      "Guardado: Alfombras redondas\n",
      "Guardado: Alfombras y cortinas infantiles\n",
      "Procesando: Iluminación\n",
      "Guardado: Lámparas, focos y apliques\n",
      "Guardado: Iluminación decorativa\n",
      "Guardado: Smart Lighting\n",
      "Guardado: Iluminación integrada\n",
      "Guardado: Luces y lámparas de baño\n",
      "Guardado: Bombillas LED\n",
      "Guardado: Iluminación de exterior\n",
      "Procesando: Flores y plantas\n",
      "Guardado: Plantas y flores artificiales\n",
      "Guardado: Plantas naturales\n",
      "Guardado: Flores secas y ramas decorativas\n",
      "Guardado: Herramientas de jardinería e invernaderos\n",
      "Procesando: Cortinas y estores\n",
      "Guardado: Cortinas\n",
      "Guardado: Estores\n",
      "Guardado: Barras de cortina y rieles\n",
      "Procesando: Juguetes y juegos\n",
      "Guardado: Almacenaje de juguetes\n",
      "Guardado: Material escolar y manualidades\n",
      "Guardado: Peluches\n",
      "Guardado: Juegos de imitación\n",
      "Guardado: Juguetes deportivos y juego activo\n",
      "Guardado: Juguetes de madera\n",
      "Guardado: Carpas infantiles y túneles\n",
      "Guardado: Juegos de cartas y en familia\n",
      "Procesando: Organizadores de juguetes y ropa\n",
      "Guardado: Sistema SMÅSTAD\n",
      "Guardado: Almacenaje de juguetes\n",
      "Guardado: Armarios infantiles\n",
      "Guardado: Cómodas infantiles\n",
      "Guardado: Cestas infantiles\n",
      "Guardado: Perchas infantiles y colgadores\n",
      "Guardado: Organizadores infantiles para armario\n",
      "Procesando: Mesas y sillas para niños y niñas\n",
      "Guardado: Sillas infantiles\n",
      "Guardado: Pupitres infantiles y mesas\n",
      "Guardado: Muebles infantiles de exterior y jardín\n",
      "Guardado: Taburetes infantiles y bancos\n",
      "Guardado: Sillones infantiles\n",
      "Guardado: Sillas altas para niños y niñas\n",
      "Procesando: Muebles de estudio y accesorios\n",
      "Guardado: Sistema RELATERA\n",
      "Guardado: Escritorios infantiles\n",
      "Guardado: Sillas de escritorio infantiles\n",
      "Guardado: Organizadores de escritorio infantiles\n",
      "Procesando: Organizadores de escritorio infantiles\n",
      "Guardado: Sistema RELATERA\n",
      "Guardado: Escritorios infantiles\n",
      "Guardado: Sillas de escritorio infantiles\n",
      "Procesando: Camas infantiles y juveniles\n",
      "Guardado: Camas altas\n",
      "Guardado: Camas juveniles y camas con almacenaje\n",
      "Guardado: Camas infantiles y camas extensibles\n",
      "Guardado: Literas\n",
      "Guardado: Doseles de cama infantiles y carpas\n",
      "Procesando: Colchones para niños y niñas\n",
      "Guardado: Juguetes y juegos\n",
      "Guardado: Organizadores de juguetes y ropa\n",
      "Guardado: Camas infantiles y juveniles\n",
      "Guardado: Mesas y sillas para niños y niñas\n",
      "Guardado: Muebles de estudio y accesorios\n",
      "Guardado: Lámparas infantiles\n",
      "Guardado: Vajilla infantil y cubiertos\n",
      "Guardado: Textiles para niños y niñas\n",
      "Guardado: Productos de seguridad infantil\n",
      "Procesando: Textiles para niños y niñas\n",
      "Guardado: Fundas nórdicas infantiles y sábanas\n",
      "Guardado: Edredones infantiles y almohadas\n",
      "Guardado: Cojines y mantas infantiles\n",
      "Guardado: Alfombras y cortinas infantiles\n",
      "Guardado: Doseles de cama infantiles y carpas\n",
      "Procesando: Juguetes para bebé\n",
      "Guardado: Juguetes de peluche para bebés\n",
      "Guardado: Juguetes de actividades para bebés\n",
      "Procesando: Muebles para bebé\n",
      "Guardado: Cambiadores para bebé\n",
      "Guardado: Cunas y minicunas\n",
      "Guardado: Armarios infantiles\n",
      "Guardado: Conjuntos de muebles para bebé\n",
      "Guardado: Cómodas infantiles\n",
      "Guardado: Tronas para bebé\n",
      "Guardado: Almacenaje de juguetes\n",
      "Procesando: Cunas y colchones para cuna\n",
      "Guardado: Cunas y minicunas\n",
      "Guardado: Colchones para cuna y minicuna\n",
      "Procesando: Textiles para bebé\n",
      "Guardado: Ropa de cuna\n",
      "Guardado: Toallas para bebé\n",
      "Guardado: Alfombras y cortinas para bebé\n",
      "Guardado: Edredones de cuna y almohadas\n",
      "Guardado: Colchas y mantas para bebé\n",
      "Procesando: Lámparas infantiles\n",
      "Guardado: Juguetes y juegos\n",
      "Guardado: Organizadores de juguetes y ropa\n",
      "Guardado: Camas infantiles y juveniles\n",
      "Guardado: Colchones para niños y niñas\n",
      "Guardado: Mesas y sillas para niños y niñas\n",
      "Guardado: Muebles de estudio y accesorios\n",
      "Guardado: Vajilla infantil y cubiertos\n",
      "Guardado: Textiles para niños y niñas\n",
      "Guardado: Productos de seguridad infantil\n",
      "Procesando: Cambiadores, bañeras y orinales\n",
      "Guardado: Cambiadores para bebé\n",
      "Guardado: Fundas para cambiador y accesorios\n",
      "Guardado: Toallas para bebé\n",
      "Guardado: Bañeras para bebé y orinales\n",
      "Procesando: Productos de seguridad infantil\n",
      "Guardado: Antideslizantes, reflectantes y prevención\n",
      "Procesando: Muebles de salón\n",
      "Guardado: Muebles de almacenaje para salón\n",
      "Guardado: Muebles TV\n",
      "Procesando: Estanterías y librerías\n",
      "Guardado: Librerías\n",
      "Guardado: Estanterías\n",
      "Guardado: Estantes de pared\n",
      "Guardado: Estanterías de cubos\n",
      "Guardado: Estanterías modulares\n",
      "Guardado: Estanterías para cocina, despensa y trastero\n",
      "Procesando: Sofás\n",
      "Guardado: Sofás de 2 plazas\n",
      "Guardado: Sofás de 3 plazas\n",
      "Guardado: Sofás con chaiselongue\n",
      "Guardado: Sofás rinconera\n",
      "Guardado: Módulos de sofá\n",
      "Procesando: Mesas bajas de salón, de centro y auxiliares\n",
      "Guardado: Mesas auxiliares\n",
      "Guardado: Mesas de centro\n",
      "Guardado: Mesas consola\n",
      "Guardado: Mesas nido\n",
      "Procesando: Armarios de salón y vitrinas\n",
      "Guardado: Muebles auxiliares\n",
      "Guardado: Aparadores\n",
      "Guardado: Vitrinas\n",
      "Procesando: Sillones\n",
      "Guardado: Sillones tapizados\n",
      "Guardado: Sillones de relax reclinables\n",
      "Guardado: Butacas\n",
      "Guardado: Sillones cama\n",
      "Guardado: Sillones de piel y piel sintética\n",
      "Guardado: Sillones infantiles\n",
      "Guardado: Sillones de mimbre y de ratán\n",
      "Procesando: Decoración y espejos\n",
      "Guardado: Decoración de pared\n",
      "Guardado: Velas y accesorios\n",
      "Guardado: Cajas y cestos\n",
      "Guardado: Espejos\n",
      "Guardado: Pizarras magnéticas y tablones\n",
      "Guardado: Jarrones y centros de mesa\n",
      "Guardado: Figuras decorativas y campanas de vidrio\n",
      "Guardado: Fragancias para el hogar\n",
      "Guardado: Macetas\n",
      "Guardado: Flores y plantas\n",
      "Guardado: Relojes\n",
      "Guardado: Papel de regalo, bolsas de regalo y accesorios\n",
      "Guardado: Adornos de Navidad\n",
      "Procesando: Alfombras\n",
      "Guardado: Alfombras grandes y medianas\n",
      "Guardado: Alfombras de pasillo y pequeñas\n",
      "Guardado: Alfombras hechas a mano\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Felpudos\n",
      "Guardado: Alfombras de piel\n",
      "Guardado: Antideslizantes para alfombra\n",
      "Guardado: Alfombras redondas\n",
      "Guardado: Alfombras y cortinas infantiles\n",
      "Procesando: Iluminación\n",
      "Guardado: Lámparas, focos y apliques\n",
      "Guardado: Iluminación decorativa\n",
      "Guardado: Smart Lighting\n",
      "Guardado: Iluminación integrada\n",
      "Guardado: Luces y lámparas de baño\n",
      "Guardado: Bombillas LED\n",
      "Guardado: Iluminación de exterior\n",
      "Procesando: Cojines\n",
      "Guardado: Fundas de cojín\n",
      "Guardado: Cojines con relleno\n",
      "Guardado: Cojines para jardín\n",
      "Guardado: Rellenos de cojín\n",
      "Guardado: Cojines y mantas infantiles\n",
      "Procesando: Mantas y plaids\n",
      "Guardado: Ropa de cama y almohadas\n",
      "Guardado: Cojines\n",
      "Guardado: Alfombras\n",
      "Guardado: Textiles de baño\n",
      "Guardado: Cojines de exterior\n",
      "Guardado: Mantelería\n",
      "Guardado: Textiles de cocina\n",
      "Guardado: Cojines para silla\n",
      "Guardado: Ropa y accesorios\n",
      "Guardado: Textiles para niños y niñas\n",
      "Guardado: Textiles para bebé\n",
      "Guardado: Cojines lumbares\n",
      "Guardado: Telas por metro y accesorios de costura\n",
      "Procesando: Cortinas y estores\n",
      "Guardado: Cortinas\n",
      "Guardado: Estores\n",
      "Guardado: Barras de cortina y rieles\n",
      "Procesando: Flores y plantas\n",
      "Guardado: Plantas y flores artificiales\n",
      "Guardado: Plantas naturales\n",
      "Guardado: Flores secas y ramas decorativas\n",
      "Guardado: Herramientas de jardinería e invernaderos\n",
      "Procesando: Camas\n",
      "Guardado: Camas de matrimonio o camas dobles\n",
      "Guardado: Canapés abatibles y camas con cajones\n",
      "Guardado: Bases de cama y camas continentales\n",
      "Guardado: Camas individuales\n",
      "Guardado: Camas supletorias y divanes\n",
      "Guardado: Camas tapizadas\n",
      "Guardado: Camas infantiles y juveniles\n",
      "Guardado: Literas y camas altas\n",
      "Guardado: Sofás cama y sillones cama\n",
      "Procesando: Colchones\n",
      "Guardado: Colchones viscoelásticos\n",
      "Guardado: Colchones de muelles y de muelles ensacados\n",
      "Guardado: Sobrecolchones y toppers\n",
      "Guardado: Fundas de colchón\n",
      "Guardado: Colchones para niños y niñas\n",
      "Guardado: Colchones para cuna y minicuna\n",
      "Guardado: Sábanas\n",
      "Procesando: Ropa de cama y almohadas\n",
      "Guardado: Fundas nórdicas, sábanas y fundas de almohada\n",
      "Guardado: Colchas y cubrecamas\n",
      "Guardado: Rellenos nórdicos y edredones\n",
      "Guardado: Almohadas\n",
      "Guardado: Protectores de almohada\n",
      "Guardado: Cojines\n",
      "Guardado: Fundas de colchón\n",
      "Guardado: Mantas y plaids\n",
      "Guardado: Fundas de somier\n",
      "Procesando: Armarios\n",
      "Guardado: Armarios modulares\n",
      "Guardado: Armarios de puertas abatibles\n",
      "Guardado: Armarios de puertas correderas\n",
      "Guardado: Armarios independientes\n",
      "Guardado: Armarios abiertos\n",
      "Guardado: Armarios para recibidor\n",
      "Guardado: Armarios con espejo\n",
      "Guardado: Armarios infantiles\n",
      "Guardado: Vestidores\n",
      "Guardado: Armarios esquineros\n",
      "Guardado: Estanterías armario compactas\n",
      "Guardado: Iluminación para armarios\n",
      "Guardado: Puertas correderas para sistema SKYTTA\n",
      "Guardado: Armarios para buhardillas y bajo escalera\n",
      "Guardado: Armarios empotrados\n",
      "Procesando: Cómodas\n",
      "Guardado: Mesitas de noche\n",
      "Guardado: Cómodas infantiles\n",
      "Guardado: Cajoneras con cestos\n",
      "Guardado: Tocadores\n",
      "Procesando: Mesitas de noche\n",
      "Guardado: Cómodas\n",
      "Guardado: Cómodas infantiles\n",
      "Guardado: Cajoneras con cestos\n",
      "Guardado: Tocadores\n",
      "Procesando: Organizadores de ropa y zapatos\n",
      "Guardado: Cajas para ropa\n",
      "Guardado: Perchas y colgadores\n",
      "Guardado: Organizadores infantiles para armario\n",
      "Guardado: Organizadores para colgar\n",
      "Guardado: Cajas y organizadores para zapatos\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Muebles zapateros\n",
      "Guardado: Percheros y burros\n",
      "Guardado: Bancos zapateros y estantes para zapatos\n",
      "Procesando: Dormitorios completos\n",
      "Guardado: Camas\n",
      "Guardado: Colchones\n",
      "Guardado: Ropa de cama y almohadas\n",
      "Guardado: Bases tapizadas\n",
      "Guardado: Cabeceros de cama\n",
      "Guardado: Mesitas de noche\n",
      "Guardado: Camas con colchón incluido\n",
      "Guardado: Cajones para cama\n",
      "Guardado: Somieres de láminas\n",
      "Guardado: Patas de somier y cama\n",
      "Guardado: Fundas de somier\n",
      "Procesando: Cortinas y estores\n",
      "Guardado: Cortinas\n",
      "Guardado: Estores\n",
      "Guardado: Barras de cortina y rieles\n",
      "Procesando: Alfombras\n",
      "Guardado: Alfombras grandes y medianas\n",
      "Guardado: Alfombras de pasillo y pequeñas\n",
      "Guardado: Alfombras hechas a mano\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Felpudos\n",
      "Guardado: Alfombras de piel\n",
      "Guardado: Antideslizantes para alfombra\n",
      "Guardado: Alfombras redondas\n",
      "Guardado: Alfombras y cortinas infantiles\n",
      "Procesando: Espejos\n",
      "Guardado: Espejos de pared\n",
      "Guardado: Espejos grandes\n",
      "Guardado: Espejos decorativos\n",
      "Guardado: Espejos redondos\n",
      "Guardado: Espejos de pie\n",
      "Guardado: Espejos para lavabo y tocador\n",
      "Guardado: Armarios de baño con espejo\n",
      "Guardado: Espejos para maquillaje y de aumento\n",
      "Guardado: Espejos con luz\n",
      "Procesando: Flores y plantas\n",
      "Guardado: Plantas y flores artificiales\n",
      "Guardado: Plantas naturales\n",
      "Guardado: Flores secas y ramas decorativas\n",
      "Guardado: Herramientas de jardinería e invernaderos\n",
      "Procesando: Relojes\n",
      "Guardado: Relojes de pared y de mesa\n",
      "Guardado: Despertadores\n",
      "Procesando: Sistemas de baño\n",
      "Guardado: Baños HAVBÄCK\n",
      "Guardado: Baños ÄNGSJÖN\n",
      "Guardado: Baños TÄNNFORSEN\n",
      "Guardado: Baños HEMNES\n",
      "Guardado: Baños ENHET\n",
      "Guardado: Baños NYSJÖN\n",
      "Procesando: Muebles de lavabo\n",
      "Guardado: Muebles de baño con lavabo\n",
      "Guardado: Muebles de baño sin lavabo\n",
      "Guardado: Muebles bajo el lavabo auxiliares\n",
      "Procesando: Duchas\n",
      "Guardado: Estantes y accesorios de ducha\n",
      "Guardado: Cortinas de baño\n",
      "Guardado: Columnas y sets de ducha\n",
      "Guardado: Alcachofas de ducha\n",
      "Guardado: Grifos de ducha\n",
      "Guardado: Platos de ducha y mamparas\n",
      "Procesando: Grifos de baño\n",
      "Guardado: Sistemas de baño\n",
      "Guardado: Muebles de lavabo\n",
      "Guardado: Estanterías para baño\n",
      "Guardado: Armarios de pared para baño\n",
      "Guardado: Cestas para baño, cajas y organizadores\n",
      "Guardado: Taburetes de baño y bancos\n",
      "Guardado: Carritos de baño\n",
      "Guardado: Baldas para baño y toalleros\n",
      "Guardado: Espejos de baño\n",
      "Guardado: Accesorios de baño\n",
      "Guardado: Lavadero en el baño\n",
      "Guardado: Textiles de baño\n",
      "Guardado: Luces y lámparas de baño\n",
      "Guardado: Encimeras de baño\n",
      "Guardado: Lavabos\n",
      "Guardado: Duchas\n",
      "Procesando: Espejos de baño\n",
      "Guardado: Espejos para lavabo y tocador\n",
      "Guardado: Armarios de baño con espejo\n",
      "Guardado: Espejos para maquillaje y de aumento\n",
      "Guardado: Espejos con luz\n",
      "Procesando: Lavabos\n",
      "Guardado: Sistemas de baño\n",
      "Guardado: Muebles de lavabo\n",
      "Guardado: Estanterías para baño\n",
      "Guardado: Armarios de pared para baño\n",
      "Guardado: Cestas para baño, cajas y organizadores\n",
      "Guardado: Taburetes de baño y bancos\n",
      "Guardado: Carritos de baño\n",
      "Guardado: Baldas para baño y toalleros\n",
      "Guardado: Espejos de baño\n",
      "Guardado: Accesorios de baño\n",
      "Guardado: Lavadero en el baño\n",
      "Guardado: Textiles de baño\n",
      "Guardado: Luces y lámparas de baño\n",
      "Guardado: Encimeras de baño\n",
      "Guardado: Grifos de baño\n",
      "Guardado: Duchas\n",
      "Procesando: Textiles de baño\n",
      "Guardado: Toallas\n",
      "Guardado: Alfombrillas de baño\n",
      "Guardado: Cortinas de baño\n",
      "Guardado: Albornoces, pantuflas y accesorios de spa\n",
      "Procesando: Dispensadores de jabón y jaboneras\n",
      "Guardado: Papeleras para baño\n",
      "Guardado: Estantes y accesorios de ducha\n",
      "Guardado: Escobillas de baño\n",
      "Guardado: Soportes para cepillos de dientes\n",
      "Guardado: Accesorios de baño sin taladro\n",
      "Procesando: Soportes para cepillos de dientes\n",
      "Guardado: Papeleras para baño\n",
      "Guardado: Estantes y accesorios de ducha\n",
      "Guardado: Dispensadores de jabón y jaboneras\n",
      "Guardado: Escobillas de baño\n",
      "Guardado: Accesorios de baño sin taladro\n",
      "Procesando: Escobillas de baño\n",
      "Guardado: Papeleras para baño\n",
      "Guardado: Estantes y accesorios de ducha\n",
      "Guardado: Dispensadores de jabón y jaboneras\n",
      "Guardado: Soportes para cepillos de dientes\n",
      "Guardado: Accesorios de baño sin taladro\n",
      "Procesando: Papeleras para baño\n",
      "Guardado: Estantes y accesorios de ducha\n",
      "Guardado: Dispensadores de jabón y jaboneras\n",
      "Guardado: Escobillas de baño\n",
      "Guardado: Soportes para cepillos de dientes\n",
      "Guardado: Accesorios de baño sin taladro\n",
      "Procesando: Lavadero en el baño\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Tendederos\n",
      "Guardado: Lavadoras y secadoras\n",
      "Guardado: Tablas de planchar\n",
      "Guardado: Muebles para lavadora\n",
      "Procesando: Luces y lámparas de baño\n",
      "Guardado: Iluminación para armarios de baño\n",
      "Guardado: Lámparas de techo para baño\n",
      "Guardado: Apliques de baño\n",
      "Guardado: Espejos con luz\n",
      "Procesando: Organizadores de escritorio y accesorios\n",
      "Guardado: Cajas y cestos\n",
      "Guardado: Organizadores de ropa y zapatos\n",
      "Guardado: Cubos de reciclaje\n",
      "Guardado: Organizadores para papeles y dispositivos\n",
      "Guardado: Cables, organizadores de cables y accesorios\n",
      "Guardado: Colgadores y baldas\n",
      "Guardado: Artículos de viaje, paraguas y bolsas\n",
      "Guardado: Cajas para mudanzas y bolsas\n",
      "Guardado: Accesorios de baño\n",
      "Guardado: Envases y organizadores de alimentos\n",
      "Procesando: Escritorios para el hogar\n",
      "Guardado: Sistemas de escritorios y mesas\n",
      "Guardado: Escritorios elevables\n",
      "Guardado: Escritorios de oficina\n",
      "Guardado: Escritorios infantiles\n",
      "Guardado: Escritorios de gaming\n",
      "Guardado: Mesas y soportes para ordenador portátil\n",
      "Guardado: Separadores de escritorio\n",
      "Procesando: Sillas de escritorio y sillas de confidente\n",
      "Guardado: Sillas de escritorio para el hogar\n",
      "Guardado: Sillas de oficina\n",
      "Guardado: Sillas de escritorio infantiles\n",
      "Procesando: Almacenaje para espacio de trabajo\n",
      "Guardado: Muebles para espacio de trabajo\n",
      "Guardado: Cajoneras para espacio de trabajo\n",
      "Procesando: Muebles de estudio y accesorios\n",
      "Guardado: Sistema RELATERA\n",
      "Guardado: Escritorios infantiles\n",
      "Guardado: Sillas de escritorio infantiles\n",
      "Guardado: Organizadores de escritorio infantiles\n",
      "Procesando: Tableros y patas para mesas y escritorios\n",
      "Guardado: Combinaciones de tablero y patas\n",
      "Guardado: Caballetes y patas para mesa\n",
      "Guardado: Tableros para mesa y escritorio\n",
      "Procesando: Muebles de gaming\n",
      "Guardado: Escritorios de gaming\n",
      "Guardado: Sillas de gaming\n",
      "Guardado: Accesorios de gaming\n",
      "Guardado: Conjuntos de escritorio y silla de gaming\n",
      "Procesando: Mesas y soportes para ordenador portátil\n",
      "Guardado: Sistemas de escritorios y mesas\n",
      "Guardado: Escritorios elevables\n",
      "Guardado: Escritorios para el hogar\n",
      "Guardado: Escritorios de oficina\n",
      "Guardado: Escritorios infantiles\n",
      "Guardado: Escritorios de gaming\n",
      "Guardado: Separadores de escritorio\n",
      "Procesando: Estanterías\n",
      "Guardado: Librerías\n",
      "Guardado: Estantes de pared\n",
      "Guardado: Estanterías de cubos\n",
      "Guardado: Estanterías modulares\n",
      "Guardado: Estanterías para cocina, despensa y trastero\n",
      "Procesando: Pilas, cables, cargadores y enchufes\n",
      "Guardado: Pilas y cargadores de pilas\n",
      "Guardado: Cargadores USB\n",
      "Guardado: Cargadores inalámbricos y accesorios\n",
      "Guardado: Cables, organizadores de cables y accesorios\n",
      "Procesando: Flexos y lámparas de escritorio LED\n",
      "Guardado: Lámparas de techo\n",
      "Guardado: Lámparas de mesa\n",
      "Guardado: Lámparas de pie\n",
      "Guardado: Lámparas de pared\n",
      "Guardado: Pies de lámpara, pantallas y cables\n",
      "Guardado: Focos\n",
      "Guardado: Lámparas infantiles\n",
      "Guardado: Lámparas inteligentes\n",
      "Guardado: Iluminación LED\n",
      "Guardado: Linternas y lámparas portátiles\n",
      "Procesando: Separadores de ambientes\n",
      "Guardado: Estanterías y librerías\n",
      "Guardado: Muebles de salón\n",
      "Guardado: Armarios\n",
      "Guardado: Sistemas de almacenaje\n",
      "Guardado: Cómodas, cajoneras y mesitas de noche\n",
      "Guardado: Armarios de salón y vitrinas\n",
      "Guardado: Almacenaje para el garaje\n",
      "Guardado: Aparadores y consolas\n",
      "Guardado: Carritos auxiliares\n",
      "Guardado: Armarios y estanterías de exterior\n",
      "Guardado: Conjuntos de muebles para recibidor\n",
      "Guardado: Almacenaje de juguetes\n",
      "Guardado: Almacenaje para espacio de trabajo\n",
      "Guardado: Muebles zapateros\n",
      "Procesando: Accesorios para móvil y tablet\n",
      "Guardado: Cargadores inalámbricos y accesorios\n",
      "Guardado: Soportes para móvil y tablet\n",
      "Guardado: Cargadores USB\n",
      "Procesando: Papeleras y cubos con pedal\n",
      "Guardado: Cubos de reciclaje\n",
      "Guardado: Papeleras para baño\n",
      "Guardado: Bolsas reutilizables\n",
      "Procesando: Organizadores para papeles y dispositivos\n",
      "Guardado: Cajas organizadoras\n",
      "Guardado: Pizarras magnéticas y tablones\n",
      "Guardado: Archivadores y carpetas\n",
      "Guardado: Revisteros\n",
      "Procesando: Armarios para recibidor\n",
      "Guardado: Armarios modulares\n",
      "Guardado: Armarios de puertas abatibles\n",
      "Guardado: Armarios de puertas correderas\n",
      "Guardado: Armarios independientes\n",
      "Guardado: Armarios abiertos\n",
      "Guardado: Armarios con espejo\n",
      "Guardado: Armarios infantiles\n",
      "Guardado: Vestidores\n",
      "Guardado: Armarios esquineros\n",
      "Guardado: Estanterías armario compactas\n",
      "Guardado: Iluminación para armarios\n",
      "Guardado: Puertas correderas para sistema SKYTTA\n",
      "Guardado: Armarios para buhardillas y bajo escalera\n",
      "Guardado: Armarios empotrados\n",
      "Procesando: Muebles zapateros\n",
      "Guardado: Cajas para ropa\n",
      "Guardado: Perchas y colgadores\n",
      "Guardado: Organizadores infantiles para armario\n",
      "Guardado: Organizadores para colgar\n",
      "Guardado: Cajas y organizadores para zapatos\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Percheros y burros\n",
      "Guardado: Bancos zapateros y estantes para zapatos\n",
      "Procesando: Bancos de recibidor\n",
      "Guardado: Bancos de comedor\n",
      "Guardado: Pies de cama y bancos de dormitorio\n",
      "Guardado: Bancos con almacenaje\n",
      "Guardado: Bancos para jardín y exterior\n",
      "Guardado: Taburetes infantiles y bancos\n",
      "Procesando: Bancos zapateros y estantes para zapatos\n",
      "Guardado: Cajas para ropa\n",
      "Guardado: Perchas y colgadores\n",
      "Guardado: Organizadores infantiles para armario\n",
      "Guardado: Organizadores para colgar\n",
      "Guardado: Cajas y organizadores para zapatos\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Muebles zapateros\n",
      "Guardado: Percheros y burros\n",
      "Procesando: Espejos\n",
      "Guardado: Espejos de pared\n",
      "Guardado: Espejos grandes\n",
      "Guardado: Espejos decorativos\n",
      "Guardado: Espejos redondos\n",
      "Guardado: Espejos de pie\n",
      "Guardado: Espejos para lavabo y tocador\n",
      "Guardado: Armarios de baño con espejo\n",
      "Guardado: Espejos para maquillaje y de aumento\n",
      "Guardado: Espejos con luz\n",
      "Procesando: Cajas y cestos\n",
      "Guardado: Cajas organizadoras\n",
      "Guardado: Cajas para ropa\n",
      "Guardado: Cestas infantiles\n",
      "Guardado: Cestas\n",
      "Guardado: Cajas de almacenaje\n",
      "Guardado: Cestas para baño, cajas y organizadores\n",
      "Procesando: Percheros, perchas y ganchos\n",
      "Guardado: Ganchos\n",
      "Guardado: Perchas y colgadores\n",
      "Procesando: Percheros y burros\n",
      "Guardado: Cajas para ropa\n",
      "Guardado: Perchas y colgadores\n",
      "Guardado: Organizadores infantiles para armario\n",
      "Guardado: Organizadores para colgar\n",
      "Guardado: Cajas y organizadores para zapatos\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Muebles zapateros\n",
      "Guardado: Bancos zapateros y estantes para zapatos\n",
      "Procesando: Centros de mesa\n",
      "Guardado: Floreros y jarrones decorativos\n",
      "Procesando: Paraguas, neceseres y accesorios de viaje\n",
      "Guardado: Bolsas y carritos\n",
      "Guardado: Estuches y neceseres\n",
      "Guardado: Bolsas isotérmicas\n",
      "Procesando: Muebles para lavadora\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Tendederos\n",
      "Guardado: Lavadoras y secadoras\n",
      "Guardado: Tablas de planchar\n",
      "Procesando: Cestos para ropa sucia y colada\n",
      "Guardado: Cubos de basura y bolsas\n",
      "Guardado: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Muebles para lavadora\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Accesorios de limpieza\n",
      "Guardado: Tendederos\n",
      "Guardado: Tablas de planchar\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Tendederos\n",
      "Guardado: Cubos de basura y bolsas\n",
      "Guardado: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Muebles para lavadora\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Accesorios de limpieza\n",
      "Guardado: Tablas de planchar\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Accesorios de limpieza\n",
      "Guardado: Cubos de basura y bolsas\n",
      "Guardado: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Muebles para lavadora\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Tendederos\n",
      "Guardado: Tablas de planchar\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Accesorios para la colada\n",
      "Guardado: Cubos de basura y bolsas\n",
      "Guardado: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Muebles para lavadora\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Accesorios de limpieza\n",
      "Guardado: Tendederos\n",
      "Guardado: Tablas de planchar\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Escurreplatos\n",
      "Guardado: Accesorios de fregadero\n",
      "Guardado: Paños, bayetas y estropajos\n",
      "Guardado: Dispensadores de jabón de cocina\n",
      "Procesando: Cubos de basura y bolsas\n",
      "Guardado: Cubos de reciclaje\n",
      "Guardado: Papeleras y cubos con pedal\n",
      "Guardado: Papeleras para baño\n",
      "Guardado: Bolsas reutilizables\n",
      "Procesando: Lavadoras y secadoras\n",
      "Guardado: Pequeños electrodomésticos de cocina\n",
      "Guardado: Neveras, frigoríficos y congeladores\n",
      "Guardado: Placas de cocina\n",
      "Guardado: Campanas extractoras\n",
      "Guardado: Hornos y hornos combi\n",
      "Guardado: Microondas y microondas combi\n",
      "Guardado: Lavavajillas\n",
      "Guardado: Accesorios para electrodomésticos\n",
      "Procesando: Perchas y colgadores\n",
      "Guardado: Ganchos\n",
      "Procesando: Tablas de planchar\n",
      "Guardado: Cubos de basura y bolsas\n",
      "Guardado: Escurreplatos y accesorios de fregadero\n",
      "Guardado: Muebles para lavadora\n",
      "Guardado: Cestos para ropa sucia y colada\n",
      "Guardado: Accesorios para la colada\n",
      "Guardado: Accesorios de limpieza\n",
      "Guardado: Tendederos\n",
      "Guardado: Lavadoras y secadoras\n",
      "Procesando: Muebles de exterior para jardín y terraza\n",
      "Guardado: Asientos de jardín y exterior\n",
      "Guardado: Comedor de exterior\n",
      "Guardado: Hamacas y tumbonas de jardín\n",
      "Guardado: Fundas para muebles de jardín\n",
      "Guardado: Muebles infantiles de exterior y jardín\n",
      "Guardado: Mesas de centro y auxiliares de exterior y jardín\n",
      "Guardado: Mantenimiento para muebles y tintes\n",
      "Procesando: Armarios y estanterías de exterior\n",
      "Guardado: Muebles de exterior para jardín y terraza\n",
      "Guardado: Suelos de exterior\n",
      "Guardado: Macetas de exterior y accesorios\n",
      "Guardado: Accesorios de exterior\n",
      "Guardado: Iluminación de exterior\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Sombrillas, pérgolas y toldos\n",
      "Guardado: Cocinas de exterior, barbacoas y accesorios\n",
      "Procesando: Suelos de exterior\n",
      "Guardado: Muebles de exterior para jardín y terraza\n",
      "Guardado: Armarios y estanterías de exterior\n",
      "Guardado: Macetas de exterior y accesorios\n",
      "Guardado: Accesorios de exterior\n",
      "Guardado: Iluminación de exterior\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Sombrillas, pérgolas y toldos\n",
      "Guardado: Cocinas de exterior, barbacoas y accesorios\n",
      "Procesando: Macetas de exterior y accesorios\n",
      "Guardado: Jardineras y maceteros de exterior\n",
      "Guardado: Plantas naturales\n",
      "Guardado: Herramientas de jardinería e invernaderos\n",
      "Guardado: Pulverizadores y regaderas\n",
      "Procesando: Accesorios de exterior\n",
      "Guardado: Cojines de exterior\n",
      "Guardado: Alfombras de exterior\n",
      "Guardado: Fundas para muebles de jardín\n",
      "Guardado: Pícnic y para llevar\n",
      "Guardado: Mantenimiento para muebles y tintes\n",
      "Procesando: Iluminación de exterior\n",
      "Guardado: Lámparas de pie de exterior\n",
      "Guardado: Lámparas de pared de exterior\n",
      "Guardado: Lámparas de mesa de exterior\n",
      "Guardado: Lámparas colgantes de exterior\n",
      "Guardado: Guirnaldas luminosas de exterior\n",
      "Guardado: Iluminación de caminos\n",
      "Guardado: Farolillos de exterior\n",
      "Procesando: Conjuntos de balcón\n",
      "Guardado: Sillas de comedor para exterior o jardín\n",
      "Guardado: Mesas de exterior para jardín y terraza\n",
      "Guardado: Conjuntos de mesas y sillas de jardín\n",
      "Procesando: Sofás de exterior, jardín y terraza\n",
      "Guardado: Conjuntos de sofás para exterior\n",
      "Guardado: Sofás modulares de exterior y jardín\n",
      "Guardado: Bancos para jardín y exterior\n",
      "Guardado: Sillas de exterior y jardín\n",
      "Guardado: Cojines para sillas de jardín y exterior\n",
      "Procesando: Sillas de exterior y jardín\n",
      "Guardado: Sillones y butacas de exterior y jardín\n",
      "Guardado: Sillas de comedor para exterior o jardín\n",
      "Procesando: Mesas de exterior para jardín y terraza\n",
      "Guardado: Mesas de comedor de jardín\n",
      "Guardado: Mesas de centro y auxiliares de exterior y jardín\n",
      "Procesando: Sombrillas, pérgolas y toldos\n",
      "Guardado: Parasoles y sombrillas\n",
      "Guardado: Toldos, paravientos y pantallas de privacidad\n",
      "Guardado: Pérgolas y cenadores\n",
      "Procesando: Barbacoas\n",
      "Guardado: Accesorios de barbacoa y grill\n",
      "Guardado: Cocinas al aire libre completas\n",
      "Guardado: Muebles y piezas de cocina para exteriores\n",
      "Procesando: Pícnic y para llevar\n",
      "Guardado: Accesorios para pícnic\n",
      "Guardado: Táperes, tarteras y bolsas\n",
      "Guardado: Botellas de agua y termos\n",
      "Guardado: Artículos de viaje, paraguas y bolsas\n",
      "Procesando: Accesorios de barbacoa y grill\n",
      "Guardado: Barbacoas\n",
      "Guardado: Cocinas al aire libre completas\n",
      "Guardado: Muebles y piezas de cocina para exteriores\n",
      "CSV generado: estancias_muebles.csv\n"
     ]
    }
   ],
   "source": [
    "driver = webdriver.Chrome(service=service, options=options)\n",
    "try:\n",
    "    with open(\"estancias_muebles.csv\", \"w\", newline=\"\", encoding=\"utf-8\") as f_out:\n",
    "        writer = csv.writer(f_out)\n",
    "        writer.writerow([\"Estancia\", \"Categoria\", \"Tipo de mueble\", \"Enlace\"])\n",
    "        f_out.flush()  # Forzar escritura inmediata del encabezado\n",
    "        \n",
    "        with open(\"estancias_muebles.csv\", newline=\"\", encoding=\"utf-8\") as f_in:\n",
    "            reader = csv.DictReader(f_in)\n",
    "            for row in reader:\n",
    "                estancia_nombre = row[\"Estancia\"]\n",
    "                estancia_mueble = row[\"Mueble\"]\n",
    "                estancia_url = row[\"Enlace\"]\n",
    "\n",
    "                print(f\"Procesando: {estancia_mueble}\")\n",
    "\n",
    "                driver.get(estancia_url)\n",
    "\n",
    "                # Cerrar pop-up de cookies\n",
    "                try:\n",
    "                    aceptar_cookies = WebDriverWait(driver, 3).until(\n",
    "                        EC.element_to_be_clickable((By.XPATH, \"//button[contains(text(),'Aceptar')]\"))\n",
    "                    )\n",
    "                    aceptar_cookies.click()\n",
    "                except TimeoutException:\n",
    "                    pass\n",
    "\n",
    "                # Esperar el carrusel de items\n",
    "                try:\n",
    "                    items = WebDriverWait(driver, 10).until(\n",
    "                        EC.presence_of_all_elements_located(\n",
    "                            (By.XPATH, \"//div[@id='hnf-carousel__tabs-navigation-products']/div[@role='listitem']/a\")\n",
    "                        )\n",
    "                    )\n",
    "                except TimeoutException:\n",
    "                    print(f\"No se encontraron items para {estancia_mueble}\")\n",
    "                    continue\n",
    "\n",
    "                # Guardar cada item en el CSV inmediatamente\n",
    "                for item in items:\n",
    "                    try:\n",
    "                        mueble_nombre = item.find_element(By.TAG_NAME, \"span\").text.strip()\n",
    "                        enlace_item = item.get_attribute(\"href\")\n",
    "                        writer.writerow([estancia_nombre, estancia_mueble, mueble_nombre, enlace_item])\n",
    "                        f_out.flush()  \n",
    "                        print(f\"Guardado: {mueble_nombre}\")\n",
    "                    except NoSuchElementException:\n",
    "                        continue\n",
    "\n",
    "                time.sleep(1)  \n",
    "\n",
    "finally:\n",
    "    driver.quit()\n",
    "    print(\"CSV generado: furniture.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bd6dd5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de filas antes de eliminar duplicados: 16760\n",
      "Número de enlaces de producto únicos: 7508\n",
      "Número de enlaces de producto duplicados: 9252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ry/n21nhjms3m1bwp1pbsdf_g3m0000gn/T/ipykernel_43431/3955596398.py:3: DtypeWarning: Columns (7,18,19,20,21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"furniture.csv\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"furniture.csv\")\n",
    "\n",
    "print(f\"Número de filas antes de eliminar duplicados: {len(df)}\")\n",
    "print(f\"Número de enlaces de producto únicos: {df['Enlace_producto'].nunique()}\")\n",
    "print(f\"Número de enlaces de producto duplicados: {df.duplicated(subset=['Enlace_producto']).sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1237fa71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Última categoría procesada: Armarios\n",
      "7508 URLs ya procesadas\n",
      "Saltando: Cocinas METOD (ya procesada)\n",
      "Saltando: Cocinas ENHET (ya procesada)\n",
      "Saltando: Cocinas KNOXHULT (ya procesada)\n",
      "Saltando: Pequeños electrodomésticos de cocina (ya procesada)\n",
      "Saltando: Neveras, frigoríficos y congeladores (ya procesada)\n",
      "Saltando: Placas de cocina (ya procesada)\n",
      "Saltando: Campanas extractoras (ya procesada)\n",
      "Saltando: Hornos y hornos combi (ya procesada)\n",
      "Saltando: Microondas y microondas combi (ya procesada)\n",
      "Saltando: Lavavajillas (ya procesada)\n",
      "Saltando: Accesorios para electrodomésticos (ya procesada)\n",
      "Saltando: Lavadoras y secadoras (ya procesada)\n",
      "Saltando: Organizadores de cajones y de armarios (ya procesada)\n",
      "Saltando: Cuberteros y organizadores de cubiertos (ya procesada)\n",
      "Saltando: Organizadores de productos de limpieza (ya procesada)\n",
      "Saltando: Cubos de basura (ya procesada)\n",
      "Saltando: Salvamanteles y tapas (ya procesada)\n",
      "Saltando: Utensilios de cocina para mezclar y medir (ya procesada)\n",
      "Saltando: Abrelatas, ralladores y peladores (ya procesada)\n",
      "Saltando: Espátulas, cucharones y pinzas (ya procesada)\n",
      "Saltando: Coladores y escurridores (ya procesada)\n",
      "Saltando: Cubiteras y moldes para helados (ya procesada)\n",
      "Saltando: Utensilios de repostería (ya procesada)\n",
      "Saltando: Organizadores de pared para cocina (ya procesada)\n",
      "Saltando: Baldas y estantes de pared para cocina (ya procesada)\n",
      "Saltando: Táperes, tarteras y bolsas (ya procesada)\n",
      "Saltando: Juegos de táperes (ya procesada)\n",
      "Saltando: Organizadores para nevera (ya procesada)\n",
      "Saltando: Organizadores para despensas y encimeras (ya procesada)\n",
      "Saltando: Tarros, latas y botes (ya procesada)\n",
      "Saltando: Especieros (ya procesada)\n",
      "Saltando: Botellas de agua y termos (ya procesada)\n",
      "Saltando: Bolsas isotérmicas (ya procesada)\n",
      "Saltando: Botelleros (ya procesada)\n",
      "Saltando: Mezcla y combina recipientes y tapas para alimentos (ya procesada)\n",
      "Saltando: Accesorios para guardar alimentos y bolsas con cierre (ya procesada)\n",
      "Saltando: Sistemas de cocinas (ya procesada)\n",
      "Saltando: Interiores de muebles de cocina y armarios (ya procesada)\n",
      "Saltando: Encimeras de cocina (ya procesada)\n",
      "Saltando: Electrodomésticos (ya procesada)\n",
      "Saltando: Estanterías para cocina, despensa y trastero (ya procesada)\n",
      "Saltando: Almacenaje de pared para cocina (ya procesada)\n",
      "Saltando: Mini cocinas (ya procesada)\n",
      "Saltando: Salpicaderos de cocina y paneles de pared (ya procesada)\n",
      "Saltando: Puertas y frentes de cocina (ya procesada)\n",
      "Saltando: Fregaderos y grifos (ya procesada)\n",
      "Saltando: Tiradores y pomos (ya procesada)\n",
      "Saltando: Iluminación de cocina (ya procesada)\n",
      "Saltando: Armarios de cocina (ya procesada)\n",
      "Saltando: Baldas y cajones de cocina (ya procesada)\n",
      "Saltando: Encimeras precortadas (ya procesada)\n",
      "Saltando: Encimeras a medida (ya procesada)\n",
      "Saltando: Copetes de encimera y accesorios (ya procesada)\n",
      "Saltando: Sartenes y woks (ya procesada)\n",
      "Saltando: Ollas y cacerolas (ya procesada)\n",
      "Saltando: Bandejas para horno (ya procesada)\n",
      "Saltando: Salvamanteles y tapas (ya procesada)\n",
      "Saltando: Fregaderos (ya procesada)\n",
      "Saltando: Grifos de cocina (ya procesada)\n",
      "Saltando: Desagües y piezas de fregadero (ya procesada)\n",
      "Saltando: Iluminación para armarios (ya procesada)\n",
      "Saltando: Iluminación para vitrinas y librerías (ya procesada)\n",
      "Saltando: Iluminación para armarios de baño (ya procesada)\n",
      "Saltando: Puertas y frentes de cajones METOD (ya procesada)\n",
      "Saltando: Puertas y frentes de cajón ENHET (ya procesada)\n",
      "Saltando: Armarios de cocina METOD (ya procesada)\n",
      "Saltando: Armarios de cocina KNOXHULT (ya procesada)\n",
      "Saltando: Armarios altos METOD (ya procesada)\n",
      "Saltando: Armarios de pared METOD (ya procesada)\n",
      "Saltando: Armarios de cocina ENHET (ya procesada)\n",
      "Saltando: Armarios para electrodomésticos METOD (ya procesada)\n",
      "Saltando: Muebles de fregadero METOD (ya procesada)\n",
      "Saltando: Conjuntos de mesa y 2 sillas (ya procesada)\n",
      "Saltando: Conjuntos de mesa y 4 sillas (ya procesada)\n",
      "Saltando: Conjuntos de mesa y 6 sillas (ya procesada)\n",
      "Saltando: Conjuntos de mesa y 8 sillas o más (ya procesada)\n",
      "Saltando: Mesas para 2 personas (ya procesada)\n",
      "Saltando: Mesas para 4 personas (ya procesada)\n",
      "Saltando: Mesas para 6 personas (ya procesada)\n",
      "Saltando: Mesas para 8 personas (ya procesada)\n",
      "Saltando: Mesas para 10 personas (ya procesada)\n",
      "Saltando: Tableros y estructuras para mesas (ya procesada)\n",
      "Saltando: Sillas de comedor y cocina (ya procesada)\n",
      "Saltando: Sillas tapizadas (ya procesada)\n",
      "Saltando: Fundas para silla (ya procesada)\n",
      "Saltando: Estructuras de patas para silla y asientos (ya procesada)\n",
      "Saltando: Cojines para silla (ya procesada)\n",
      "Saltando: Aparadores (ya procesada)\n",
      "Saltando: Mesas consola (ya procesada)\n",
      "Saltando: Vajillas (ya procesada)\n",
      "Saltando: Platos planos (ya procesada)\n",
      "Saltando: Cuencos y boles (ya procesada)\n",
      "Saltando: Platos hondos (ya procesada)\n",
      "Saltando: Platos de postre (ya procesada)\n",
      "Saltando: Taburetes altos (ya procesada)\n",
      "Saltando: Mesas altas (ya procesada)\n",
      "Saltando: Conjuntos de mesas altas (ya procesada)\n",
      "Saltando: Bancos de recibidor (ya procesada)\n",
      "Saltando: Pies de cama y bancos de dormitorio (ya procesada)\n",
      "Saltando: Bancos con almacenaje (ya procesada)\n",
      "Saltando: Bancos para jardín y exterior (ya procesada)\n",
      "Saltando: Taburetes infantiles y bancos (ya procesada)\n",
      "Saltando: Manteles y caminos de mesa (ya procesada)\n",
      "Saltando: Posavasos y manteles individuales (ya procesada)\n",
      "Saltando: Servilletas (ya procesada)\n",
      "Saltando: Bandejas de servir (ya procesada)\n",
      "Saltando: Ensaladeras y cuencos para servir (ya procesada)\n",
      "Saltando: Soperas y fuentes (ya procesada)\n",
      "Saltando: Soportes para tartas (ya procesada)\n",
      "Saltando: Cubiertos de servir (ya procesada)\n",
      "Saltando: Alfombras grandes y medianas (ya procesada)\n",
      "Saltando: Alfombras de pasillo y pequeñas (ya procesada)\n",
      "Saltando: Alfombras hechas a mano (ya procesada)\n",
      "Saltando: Alfombras de exterior (ya procesada)\n",
      "Saltando: Felpudos (ya procesada)\n",
      "Saltando: Alfombras de piel (ya procesada)\n",
      "Saltando: Antideslizantes para alfombra (ya procesada)\n",
      "Saltando: Alfombras redondas (ya procesada)\n",
      "Saltando: Alfombras y cortinas infantiles (ya procesada)\n",
      "Saltando: Lámparas, focos y apliques (ya procesada)\n",
      "Saltando: Iluminación decorativa (ya procesada)\n",
      "Saltando: Smart Lighting (ya procesada)\n",
      "Saltando: Iluminación integrada (ya procesada)\n",
      "Saltando: Luces y lámparas de baño (ya procesada)\n",
      "Saltando: Bombillas LED (ya procesada)\n",
      "Saltando: Iluminación de exterior (ya procesada)\n",
      "Saltando: Plantas y flores artificiales (ya procesada)\n",
      "Saltando: Plantas naturales (ya procesada)\n",
      "Saltando: Flores secas y ramas decorativas (ya procesada)\n",
      "Saltando: Herramientas de jardinería e invernaderos (ya procesada)\n",
      "Saltando: Cortinas (ya procesada)\n",
      "Saltando: Estores (ya procesada)\n",
      "Saltando: Barras de cortina y rieles (ya procesada)\n",
      "Saltando: Almacenaje de juguetes (ya procesada)\n",
      "Saltando: Material escolar y manualidades (ya procesada)\n",
      "Saltando: Peluches (ya procesada)\n",
      "Saltando: Juegos de imitación (ya procesada)\n",
      "Saltando: Juguetes deportivos y juego activo (ya procesada)\n",
      "Saltando: Juguetes de madera (ya procesada)\n",
      "Saltando: Carpas infantiles y túneles (ya procesada)\n",
      "Saltando: Juegos de cartas y en familia (ya procesada)\n",
      "Saltando: Sistema SMÅSTAD (ya procesada)\n",
      "Saltando: Almacenaje de juguetes (ya procesada)\n",
      "Saltando: Armarios infantiles (ya procesada)\n",
      "Saltando: Cómodas infantiles (ya procesada)\n",
      "Saltando: Cestas infantiles (ya procesada)\n",
      "Saltando: Perchas infantiles y colgadores (ya procesada)\n",
      "Saltando: Organizadores infantiles para armario (ya procesada)\n",
      "Saltando: Sillas infantiles (ya procesada)\n",
      "Saltando: Pupitres infantiles y mesas (ya procesada)\n",
      "Saltando: Muebles infantiles de exterior y jardín (ya procesada)\n",
      "Saltando: Taburetes infantiles y bancos (ya procesada)\n",
      "Saltando: Sillones infantiles (ya procesada)\n",
      "Saltando: Sillas altas para niños y niñas (ya procesada)\n",
      "Saltando: Sistema RELATERA (ya procesada)\n",
      "Saltando: Escritorios infantiles (ya procesada)\n",
      "Saltando: Sillas de escritorio infantiles (ya procesada)\n",
      "Saltando: Organizadores de escritorio infantiles (ya procesada)\n",
      "Saltando: Sistema RELATERA (ya procesada)\n",
      "Saltando: Escritorios infantiles (ya procesada)\n",
      "Saltando: Sillas de escritorio infantiles (ya procesada)\n",
      "Saltando: Camas altas (ya procesada)\n",
      "Saltando: Camas juveniles y camas con almacenaje (ya procesada)\n",
      "Saltando: Camas infantiles y camas extensibles (ya procesada)\n",
      "Saltando: Literas (ya procesada)\n",
      "Saltando: Doseles de cama infantiles y carpas (ya procesada)\n",
      "Saltando: Juguetes y juegos (ya procesada)\n",
      "Saltando: Organizadores de juguetes y ropa (ya procesada)\n",
      "Saltando: Camas infantiles y juveniles (ya procesada)\n",
      "Saltando: Mesas y sillas para niños y niñas (ya procesada)\n",
      "Saltando: Muebles de estudio y accesorios (ya procesada)\n",
      "Saltando: Lámparas infantiles (ya procesada)\n",
      "Saltando: Vajilla infantil y cubiertos (ya procesada)\n",
      "Saltando: Textiles para niños y niñas (ya procesada)\n",
      "Saltando: Productos de seguridad infantil (ya procesada)\n",
      "Saltando: Fundas nórdicas infantiles y sábanas (ya procesada)\n",
      "Saltando: Edredones infantiles y almohadas (ya procesada)\n",
      "Saltando: Cojines y mantas infantiles (ya procesada)\n",
      "Saltando: Alfombras y cortinas infantiles (ya procesada)\n",
      "Saltando: Doseles de cama infantiles y carpas (ya procesada)\n",
      "Saltando: Juguetes de peluche para bebés (ya procesada)\n",
      "Saltando: Juguetes de actividades para bebés (ya procesada)\n",
      "Saltando: Cambiadores para bebé (ya procesada)\n",
      "Saltando: Cunas y minicunas (ya procesada)\n",
      "Saltando: Armarios infantiles (ya procesada)\n",
      "Saltando: Conjuntos de muebles para bebé (ya procesada)\n",
      "Saltando: Cómodas infantiles (ya procesada)\n",
      "Saltando: Tronas para bebé (ya procesada)\n",
      "Saltando: Almacenaje de juguetes (ya procesada)\n",
      "Saltando: Cunas y minicunas (ya procesada)\n",
      "Saltando: Colchones para cuna y minicuna (ya procesada)\n",
      "Saltando: Ropa de cuna (ya procesada)\n",
      "Saltando: Toallas para bebé (ya procesada)\n",
      "Saltando: Alfombras y cortinas para bebé (ya procesada)\n",
      "Saltando: Edredones de cuna y almohadas (ya procesada)\n",
      "Saltando: Colchas y mantas para bebé (ya procesada)\n",
      "Saltando: Juguetes y juegos (ya procesada)\n",
      "Saltando: Organizadores de juguetes y ropa (ya procesada)\n",
      "Saltando: Camas infantiles y juveniles (ya procesada)\n",
      "Saltando: Colchones para niños y niñas (ya procesada)\n",
      "Saltando: Mesas y sillas para niños y niñas (ya procesada)\n",
      "Saltando: Muebles de estudio y accesorios (ya procesada)\n",
      "Saltando: Vajilla infantil y cubiertos (ya procesada)\n",
      "Saltando: Textiles para niños y niñas (ya procesada)\n",
      "Saltando: Productos de seguridad infantil (ya procesada)\n",
      "Saltando: Cambiadores para bebé (ya procesada)\n",
      "Saltando: Fundas para cambiador y accesorios (ya procesada)\n",
      "Saltando: Toallas para bebé (ya procesada)\n",
      "Saltando: Bañeras para bebé y orinales (ya procesada)\n",
      "Saltando: Antideslizantes, reflectantes y prevención (ya procesada)\n",
      "Saltando: Muebles de almacenaje para salón (ya procesada)\n",
      "Saltando: Muebles TV (ya procesada)\n",
      "Saltando: Librerías (ya procesada)\n",
      "Saltando: Estanterías (ya procesada)\n",
      "Saltando: Estantes de pared (ya procesada)\n",
      "Saltando: Estanterías de cubos (ya procesada)\n",
      "Saltando: Estanterías modulares (ya procesada)\n",
      "Saltando: Estanterías para cocina, despensa y trastero (ya procesada)\n",
      "Saltando: Sofás de 2 plazas (ya procesada)\n",
      "Saltando: Sofás de 3 plazas (ya procesada)\n",
      "Saltando: Sofás con chaiselongue (ya procesada)\n",
      "Saltando: Sofás rinconera (ya procesada)\n",
      "Saltando: Módulos de sofá (ya procesada)\n",
      "Saltando: Mesas auxiliares (ya procesada)\n",
      "Saltando: Mesas de centro (ya procesada)\n",
      "Saltando: Mesas consola (ya procesada)\n",
      "Saltando: Mesas nido (ya procesada)\n",
      "Saltando: Muebles auxiliares (ya procesada)\n",
      "Saltando: Aparadores (ya procesada)\n",
      "Saltando: Vitrinas (ya procesada)\n",
      "Saltando: Sillones tapizados (ya procesada)\n",
      "Saltando: Sillones de relax reclinables (ya procesada)\n",
      "Saltando: Butacas (ya procesada)\n",
      "Saltando: Sillones cama (ya procesada)\n",
      "Saltando: Sillones de piel y piel sintética (ya procesada)\n",
      "Saltando: Sillones infantiles (ya procesada)\n",
      "Saltando: Sillones de mimbre y de ratán (ya procesada)\n",
      "Saltando: Decoración de pared (ya procesada)\n",
      "Saltando: Velas y accesorios (ya procesada)\n",
      "Saltando: Cajas y cestos (ya procesada)\n",
      "Saltando: Espejos (ya procesada)\n",
      "Saltando: Pizarras magnéticas y tablones (ya procesada)\n",
      "Saltando: Jarrones y centros de mesa (ya procesada)\n",
      "Saltando: Figuras decorativas y campanas de vidrio (ya procesada)\n",
      "Saltando: Fragancias para el hogar (ya procesada)\n",
      "Saltando: Macetas (ya procesada)\n",
      "Saltando: Flores y plantas (ya procesada)\n",
      "Saltando: Relojes (ya procesada)\n",
      "Saltando: Papel de regalo, bolsas de regalo y accesorios (ya procesada)\n",
      "Saltando: Adornos de Navidad (ya procesada)\n",
      "Saltando: Alfombras grandes y medianas (ya procesada)\n",
      "Saltando: Alfombras de pasillo y pequeñas (ya procesada)\n",
      "Saltando: Alfombras hechas a mano (ya procesada)\n",
      "Saltando: Alfombras de exterior (ya procesada)\n",
      "Saltando: Felpudos (ya procesada)\n",
      "Saltando: Alfombras de piel (ya procesada)\n",
      "Saltando: Antideslizantes para alfombra (ya procesada)\n",
      "Saltando: Alfombras redondas (ya procesada)\n",
      "Saltando: Alfombras y cortinas infantiles (ya procesada)\n",
      "Saltando: Lámparas, focos y apliques (ya procesada)\n",
      "Saltando: Iluminación decorativa (ya procesada)\n",
      "Saltando: Smart Lighting (ya procesada)\n",
      "Saltando: Iluminación integrada (ya procesada)\n",
      "Saltando: Luces y lámparas de baño (ya procesada)\n",
      "Saltando: Bombillas LED (ya procesada)\n",
      "Saltando: Iluminación de exterior (ya procesada)\n",
      "Saltando: Fundas de cojín (ya procesada)\n",
      "Saltando: Cojines con relleno (ya procesada)\n",
      "Saltando: Cojines para jardín (ya procesada)\n",
      "Saltando: Rellenos de cojín (ya procesada)\n",
      "Saltando: Cojines y mantas infantiles (ya procesada)\n",
      "Saltando: Ropa de cama y almohadas (ya procesada)\n",
      "Saltando: Cojines (ya procesada)\n",
      "Saltando: Alfombras (ya procesada)\n",
      "Saltando: Textiles de baño (ya procesada)\n",
      "Saltando: Cojines de exterior (ya procesada)\n",
      "Saltando: Mantelería (ya procesada)\n",
      "Saltando: Textiles de cocina (ya procesada)\n",
      "Saltando: Cojines para silla (ya procesada)\n",
      "Saltando: Ropa y accesorios (ya procesada)\n",
      "Saltando: Textiles para niños y niñas (ya procesada)\n",
      "Saltando: Textiles para bebé (ya procesada)\n",
      "Saltando: Cojines lumbares (ya procesada)\n",
      "Saltando: Telas por metro y accesorios de costura (ya procesada)\n",
      "Saltando: Cortinas (ya procesada)\n",
      "Saltando: Estores (ya procesada)\n",
      "Saltando: Barras de cortina y rieles (ya procesada)\n",
      "Saltando: Plantas y flores artificiales (ya procesada)\n",
      "Saltando: Plantas naturales (ya procesada)\n",
      "Saltando: Flores secas y ramas decorativas (ya procesada)\n",
      "Saltando: Herramientas de jardinería e invernaderos (ya procesada)\n",
      "Saltando: Camas de matrimonio o camas dobles (ya procesada)\n",
      "Saltando: Canapés abatibles y camas con cajones (ya procesada)\n",
      "Saltando: Bases de cama y camas continentales (ya procesada)\n",
      "Saltando: Camas individuales (ya procesada)\n",
      "Saltando: Camas supletorias y divanes (ya procesada)\n",
      "Saltando: Camas tapizadas (ya procesada)\n",
      "Saltando: Camas infantiles y juveniles (ya procesada)\n",
      "Saltando: Literas y camas altas (ya procesada)\n",
      "Saltando: Sofás cama y sillones cama (ya procesada)\n",
      "Saltando: Colchones viscoelásticos (ya procesada)\n",
      "Saltando: Colchones de muelles y de muelles ensacados (ya procesada)\n",
      "Saltando: Sobrecolchones y toppers (ya procesada)\n",
      "Saltando: Fundas de colchón (ya procesada)\n",
      "Saltando: Colchones para niños y niñas (ya procesada)\n",
      "Saltando: Colchones para cuna y minicuna (ya procesada)\n",
      "Saltando: Sábanas (ya procesada)\n",
      "Saltando: Fundas nórdicas, sábanas y fundas de almohada (ya procesada)\n",
      "Saltando: Colchas y cubrecamas (ya procesada)\n",
      "Saltando: Rellenos nórdicos y edredones (ya procesada)\n",
      "Saltando: Almohadas (ya procesada)\n",
      "Saltando: Protectores de almohada (ya procesada)\n",
      "Saltando: Cojines (ya procesada)\n",
      "Saltando: Fundas de colchón (ya procesada)\n",
      "Saltando: Mantas y plaids (ya procesada)\n",
      "Saltando: Fundas de somier (ya procesada)\n",
      "Saltando: Armarios modulares (ya procesada)\n",
      "Saltando: Armarios de puertas abatibles (ya procesada)\n",
      "Saltando: Armarios de puertas correderas (ya procesada)\n",
      "Saltando: Armarios independientes (ya procesada)\n",
      "Saltando: Armarios abiertos (ya procesada)\n",
      "Saltando: Armarios para recibidor (ya procesada)\n",
      "Saltando: Armarios con espejo (ya procesada)\n",
      "Saltando: Armarios infantiles (ya procesada)\n",
      "Saltando: Vestidores (ya procesada)\n",
      "Saltando: Armarios esquineros (ya procesada)\n",
      "Saltando: Estanterías armario compactas (ya procesada)\n",
      "Saltando: Iluminación para armarios (ya procesada)\n",
      "Saltando: Puertas correderas para sistema SKYTTA (ya procesada)\n",
      "Saltando: Armarios para buhardillas y bajo escalera (ya procesada)\n",
      "Saltando: Armarios empotrados (ya procesada)\n",
      "Saltando: Mesitas de noche (ya procesada)\n",
      "Saltando: Cómodas infantiles (ya procesada)\n",
      "Saltando: Cajoneras con cestos (ya procesada)\n",
      "Saltando: Tocadores (ya procesada)\n",
      "Saltando: Cómodas (ya procesada)\n",
      "Saltando: Cómodas infantiles (ya procesada)\n",
      "Saltando: Cajoneras con cestos (ya procesada)\n",
      "Saltando: Tocadores (ya procesada)\n",
      "Saltando: Cajas para ropa (ya procesada)\n",
      "Saltando: Perchas y colgadores (ya procesada)\n",
      "Saltando: Organizadores infantiles para armario (ya procesada)\n",
      "Saltando: Organizadores para colgar (ya procesada)\n",
      "Saltando: Cajas y organizadores para zapatos (ya procesada)\n",
      "Saltando: Cestos para ropa sucia y colada (ya procesada)\n",
      "Saltando: Muebles zapateros (ya procesada)\n",
      "Saltando: Percheros y burros (ya procesada)\n",
      "Saltando: Bancos zapateros y estantes para zapatos (ya procesada)\n",
      "Saltando: Camas (ya procesada)\n",
      "Saltando: Colchones (ya procesada)\n",
      "Saltando: Ropa de cama y almohadas (ya procesada)\n",
      "Saltando: Bases tapizadas (ya procesada)\n",
      "Saltando: Cabeceros de cama (ya procesada)\n",
      "Saltando: Mesitas de noche (ya procesada)\n",
      "Saltando: Camas con colchón incluido (ya procesada)\n",
      "Saltando: Cajones para cama (ya procesada)\n",
      "Saltando: Somieres de láminas (ya procesada)\n",
      "Saltando: Patas de somier y cama (ya procesada)\n",
      "Saltando: Fundas de somier (ya procesada)\n",
      "Saltando: Cortinas (ya procesada)\n",
      "Saltando: Estores (ya procesada)\n",
      "Saltando: Barras de cortina y rieles (ya procesada)\n",
      "Saltando: Alfombras grandes y medianas (ya procesada)\n",
      "Saltando: Alfombras de pasillo y pequeñas (ya procesada)\n",
      "Saltando: Alfombras hechas a mano (ya procesada)\n",
      "Saltando: Alfombras de exterior (ya procesada)\n",
      "Saltando: Felpudos (ya procesada)\n",
      "Saltando: Alfombras de piel (ya procesada)\n",
      "Saltando: Antideslizantes para alfombra (ya procesada)\n",
      "Saltando: Alfombras redondas (ya procesada)\n",
      "Saltando: Alfombras y cortinas infantiles (ya procesada)\n",
      "Saltando: Espejos de pared (ya procesada)\n",
      "Saltando: Espejos grandes (ya procesada)\n",
      "Saltando: Espejos decorativos (ya procesada)\n",
      "Saltando: Espejos redondos (ya procesada)\n",
      "Saltando: Espejos de pie (ya procesada)\n",
      "Saltando: Espejos para lavabo y tocador (ya procesada)\n",
      "Saltando: Armarios de baño con espejo (ya procesada)\n",
      "Saltando: Espejos para maquillaje y de aumento (ya procesada)\n",
      "Saltando: Espejos con luz (ya procesada)\n",
      "Saltando: Plantas y flores artificiales (ya procesada)\n",
      "Saltando: Plantas naturales (ya procesada)\n",
      "Saltando: Flores secas y ramas decorativas (ya procesada)\n",
      "Saltando: Herramientas de jardinería e invernaderos (ya procesada)\n",
      "Saltando: Relojes de pared y de mesa (ya procesada)\n",
      "Saltando: Despertadores (ya procesada)\n",
      "Saltando: Baños HAVBÄCK (ya procesada)\n",
      "Saltando: Baños ÄNGSJÖN (ya procesada)\n",
      "Saltando: Baños TÄNNFORSEN (ya procesada)\n",
      "Saltando: Baños HEMNES (ya procesada)\n",
      "Saltando: Baños ENHET (ya procesada)\n",
      "Saltando: Baños NYSJÖN (ya procesada)\n",
      "Saltando: Muebles de baño con lavabo (ya procesada)\n",
      "Saltando: Muebles de baño sin lavabo (ya procesada)\n",
      "Saltando: Muebles bajo el lavabo auxiliares (ya procesada)\n",
      "Saltando: Estantes y accesorios de ducha (ya procesada)\n",
      "Saltando: Cortinas de baño (ya procesada)\n",
      "Saltando: Columnas y sets de ducha (ya procesada)\n",
      "Saltando: Alcachofas de ducha (ya procesada)\n",
      "Saltando: Grifos de ducha (ya procesada)\n",
      "Saltando: Platos de ducha y mamparas (ya procesada)\n",
      "Saltando: Sistemas de baño (ya procesada)\n",
      "Saltando: Muebles de lavabo (ya procesada)\n",
      "Saltando: Estanterías para baño (ya procesada)\n",
      "Saltando: Armarios de pared para baño (ya procesada)\n",
      "Saltando: Cestas para baño, cajas y organizadores (ya procesada)\n",
      "Saltando: Taburetes de baño y bancos (ya procesada)\n",
      "Saltando: Carritos de baño (ya procesada)\n",
      "Saltando: Baldas para baño y toalleros (ya procesada)\n",
      "Saltando: Espejos de baño (ya procesada)\n",
      "Saltando: Accesorios de baño (ya procesada)\n",
      "Saltando: Lavadero en el baño (ya procesada)\n",
      "Saltando: Textiles de baño (ya procesada)\n",
      "Saltando: Luces y lámparas de baño (ya procesada)\n",
      "Saltando: Encimeras de baño (ya procesada)\n",
      "Saltando: Lavabos (ya procesada)\n",
      "Saltando: Duchas (ya procesada)\n",
      "Saltando: Espejos para lavabo y tocador (ya procesada)\n",
      "Saltando: Armarios de baño con espejo (ya procesada)\n",
      "Saltando: Espejos para maquillaje y de aumento (ya procesada)\n",
      "Saltando: Espejos con luz (ya procesada)\n",
      "Saltando: Sistemas de baño (ya procesada)\n",
      "Saltando: Muebles de lavabo (ya procesada)\n",
      "Saltando: Estanterías para baño (ya procesada)\n",
      "Saltando: Armarios de pared para baño (ya procesada)\n",
      "Saltando: Cestas para baño, cajas y organizadores (ya procesada)\n",
      "Saltando: Taburetes de baño y bancos (ya procesada)\n",
      "Saltando: Carritos de baño (ya procesada)\n",
      "Saltando: Baldas para baño y toalleros (ya procesada)\n",
      "Saltando: Espejos de baño (ya procesada)\n",
      "Saltando: Accesorios de baño (ya procesada)\n",
      "Saltando: Lavadero en el baño (ya procesada)\n",
      "Saltando: Textiles de baño (ya procesada)\n",
      "Saltando: Luces y lámparas de baño (ya procesada)\n",
      "Saltando: Encimeras de baño (ya procesada)\n",
      "Saltando: Grifos de baño (ya procesada)\n",
      "Saltando: Duchas (ya procesada)\n",
      "Saltando: Toallas (ya procesada)\n",
      "Saltando: Alfombrillas de baño (ya procesada)\n",
      "Saltando: Cortinas de baño (ya procesada)\n",
      "Saltando: Albornoces, pantuflas y accesorios de spa (ya procesada)\n",
      "Saltando: Papeleras para baño (ya procesada)\n",
      "Saltando: Estantes y accesorios de ducha (ya procesada)\n",
      "Saltando: Escobillas de baño (ya procesada)\n",
      "Saltando: Soportes para cepillos de dientes (ya procesada)\n",
      "Saltando: Accesorios de baño sin taladro (ya procesada)\n",
      "Saltando: Papeleras para baño (ya procesada)\n",
      "Saltando: Estantes y accesorios de ducha (ya procesada)\n",
      "Saltando: Dispensadores de jabón y jaboneras (ya procesada)\n",
      "Saltando: Escobillas de baño (ya procesada)\n",
      "Saltando: Accesorios de baño sin taladro (ya procesada)\n",
      "Saltando: Papeleras para baño (ya procesada)\n",
      "Saltando: Estantes y accesorios de ducha (ya procesada)\n",
      "Saltando: Dispensadores de jabón y jaboneras (ya procesada)\n",
      "Saltando: Soportes para cepillos de dientes (ya procesada)\n",
      "Saltando: Accesorios de baño sin taladro (ya procesada)\n",
      "Saltando: Estantes y accesorios de ducha (ya procesada)\n",
      "Saltando: Dispensadores de jabón y jaboneras (ya procesada)\n",
      "Saltando: Escobillas de baño (ya procesada)\n",
      "Saltando: Soportes para cepillos de dientes (ya procesada)\n",
      "Saltando: Accesorios de baño sin taladro (ya procesada)\n",
      "Saltando: Accesorios para la colada (ya procesada)\n",
      "Saltando: Cestos para ropa sucia y colada (ya procesada)\n",
      "Saltando: Tendederos (ya procesada)\n",
      "Saltando: Lavadoras y secadoras (ya procesada)\n",
      "Saltando: Tablas de planchar (ya procesada)\n",
      "Saltando: Muebles para lavadora (ya procesada)\n",
      "Saltando: Iluminación para armarios de baño (ya procesada)\n",
      "Saltando: Lámparas de techo para baño (ya procesada)\n",
      "Saltando: Apliques de baño (ya procesada)\n",
      "Saltando: Espejos con luz (ya procesada)\n",
      "Saltando: Cajas y cestos (ya procesada)\n",
      "Saltando: Organizadores de ropa y zapatos (ya procesada)\n",
      "Saltando: Cubos de reciclaje (ya procesada)\n",
      "Saltando: Organizadores para papeles y dispositivos (ya procesada)\n",
      "Saltando: Cables, organizadores de cables y accesorios (ya procesada)\n",
      "Saltando: Colgadores y baldas (ya procesada)\n",
      "Saltando: Artículos de viaje, paraguas y bolsas (ya procesada)\n",
      "Saltando: Cajas para mudanzas y bolsas (ya procesada)\n",
      "Saltando: Accesorios de baño (ya procesada)\n",
      "Saltando: Envases y organizadores de alimentos (ya procesada)\n",
      "Saltando: Sistemas de escritorios y mesas (ya procesada)\n",
      "Saltando: Escritorios elevables (ya procesada)\n",
      "Saltando: Escritorios de oficina (ya procesada)\n",
      "Saltando: Escritorios infantiles (ya procesada)\n",
      "Saltando: Escritorios de gaming (ya procesada)\n",
      "Saltando: Mesas y soportes para ordenador portátil (ya procesada)\n",
      "Saltando: Separadores de escritorio (ya procesada)\n",
      "Saltando: Sillas de escritorio para el hogar (ya procesada)\n",
      "Saltando: Sillas de oficina (ya procesada)\n",
      "Saltando: Sillas de escritorio infantiles (ya procesada)\n",
      "Saltando: Muebles para espacio de trabajo (ya procesada)\n",
      "Saltando: Cajoneras para espacio de trabajo (ya procesada)\n",
      "Saltando: Sistema RELATERA (ya procesada)\n",
      "Saltando: Escritorios infantiles (ya procesada)\n",
      "Saltando: Sillas de escritorio infantiles (ya procesada)\n",
      "Saltando: Organizadores de escritorio infantiles (ya procesada)\n",
      "Saltando: Combinaciones de tablero y patas (ya procesada)\n",
      "Saltando: Caballetes y patas para mesa (ya procesada)\n",
      "Saltando: Tableros para mesa y escritorio (ya procesada)\n",
      "Saltando: Escritorios de gaming (ya procesada)\n",
      "Saltando: Sillas de gaming (ya procesada)\n",
      "Saltando: Accesorios de gaming (ya procesada)\n",
      "Saltando: Conjuntos de escritorio y silla de gaming (ya procesada)\n",
      "Saltando: Sistemas de escritorios y mesas (ya procesada)\n",
      "Saltando: Escritorios elevables (ya procesada)\n",
      "Saltando: Escritorios para el hogar (ya procesada)\n",
      "Saltando: Escritorios de oficina (ya procesada)\n",
      "Saltando: Escritorios infantiles (ya procesada)\n",
      "Saltando: Escritorios de gaming (ya procesada)\n",
      "Saltando: Separadores de escritorio (ya procesada)\n",
      "Saltando: Librerías (ya procesada)\n",
      "Saltando: Estantes de pared (ya procesada)\n",
      "Saltando: Estanterías de cubos (ya procesada)\n",
      "Saltando: Estanterías modulares (ya procesada)\n",
      "Saltando: Estanterías para cocina, despensa y trastero (ya procesada)\n",
      "Saltando: Pilas y cargadores de pilas (ya procesada)\n",
      "Saltando: Cargadores USB (ya procesada)\n",
      "Saltando: Cargadores inalámbricos y accesorios (ya procesada)\n",
      "Saltando: Cables, organizadores de cables y accesorios (ya procesada)\n",
      "Saltando: Lámparas de techo (ya procesada)\n",
      "Saltando: Lámparas de mesa (ya procesada)\n",
      "Saltando: Lámparas de pie (ya procesada)\n",
      "Saltando: Lámparas de pared (ya procesada)\n",
      "Saltando: Pies de lámpara, pantallas y cables (ya procesada)\n",
      "Saltando: Focos (ya procesada)\n",
      "Saltando: Lámparas infantiles (ya procesada)\n",
      "Saltando: Lámparas inteligentes (ya procesada)\n",
      "Saltando: Iluminación LED (ya procesada)\n",
      "Saltando: Linternas y lámparas portátiles (ya procesada)\n",
      "Saltando: Estanterías y librerías (ya procesada)\n",
      "Saltando: Muebles de salón (ya procesada)\n",
      "Reanudando desde la siguiente categoría después de: Armarios\n",
      "Procesando: Sistemas de almacenaje\n",
      "Encontrados 1261 productos\n",
      "1261 productos guardados para Sistemas de almacenaje\n",
      "\n",
      "Procesando: Cómodas, cajoneras y mesitas de noche\n",
      "Encontrados 175 productos\n",
      "175 productos guardados para Cómodas, cajoneras y mesitas de noche\n",
      "\n",
      "Procesando: Armarios de salón y vitrinas\n",
      "Encontrados 217 productos\n",
      "217 productos guardados para Armarios de salón y vitrinas\n",
      "\n",
      "Procesando: Almacenaje para el garaje\n",
      "Encontrados 250 productos\n",
      "250 productos guardados para Almacenaje para el garaje\n",
      "\n",
      "Procesando: Aparadores y consolas\n",
      "Encontrados 59 productos\n",
      "59 productos guardados para Aparadores y consolas\n",
      "\n",
      "Procesando: Carritos auxiliares\n",
      "Encontrados 26 productos\n",
      "26 productos guardados para Carritos auxiliares\n",
      "\n",
      "Procesando: Armarios y estanterías de exterior\n",
      "Encontrados 23 productos\n",
      "23 productos guardados para Armarios y estanterías de exterior\n",
      "\n",
      "Procesando: Conjuntos de muebles para recibidor\n",
      "Encontrados 10 productos\n",
      "10 productos guardados para Conjuntos de muebles para recibidor\n",
      "\n",
      "Procesando: Almacenaje de juguetes\n",
      "Encontrados 105 productos\n",
      "105 productos guardados para Almacenaje de juguetes\n",
      "\n",
      "Procesando: Almacenaje para espacio de trabajo\n",
      "Encontrados 40 productos\n",
      "40 productos guardados para Almacenaje para espacio de trabajo\n",
      "\n",
      "Procesando: Muebles zapateros\n",
      "Encontrados 31 productos\n",
      "31 productos guardados para Muebles zapateros\n",
      "\n",
      "Procesando: Cargadores inalámbricos y accesorios\n",
      "Encontrados 2 productos\n",
      "2 productos guardados para Cargadores inalámbricos y accesorios\n",
      "\n",
      "Procesando: Soportes para móvil y tablet\n",
      "Encontrados 14 productos\n",
      "14 productos guardados para Soportes para móvil y tablet\n",
      "\n",
      "Procesando: Cargadores USB\n",
      "Encontrados 19 productos\n",
      "19 productos guardados para Cargadores USB\n",
      "\n",
      "Procesando: Cubos de reciclaje\n",
      "Encontrados 10 productos\n",
      "10 productos guardados para Cubos de reciclaje\n",
      "\n",
      "Procesando: Papeleras para baño\n",
      "Encontrados 11 productos\n",
      "11 productos guardados para Papeleras para baño\n",
      "\n",
      "Procesando: Bolsas reutilizables\n",
      "Encontrados 11 productos\n",
      "11 productos guardados para Bolsas reutilizables\n",
      "\n",
      "Procesando: Cajas organizadoras\n",
      "Encontrados 88 productos\n",
      "88 productos guardados para Cajas organizadoras\n",
      "\n",
      "Procesando: Pizarras magnéticas y tablones\n",
      "Encontrados 29 productos\n",
      "29 productos guardados para Pizarras magnéticas y tablones\n",
      "\n",
      "Procesando: Archivadores y carpetas\n",
      "Encontrados 11 productos\n",
      "11 productos guardados para Archivadores y carpetas\n",
      "\n",
      "Procesando: Revisteros\n",
      "Encontrados 0 productos\n",
      "0 productos guardados para Revisteros\n",
      "\n",
      "Procesando: Armarios modulares\n",
      "Proceso completado\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 85\u001b[0m\n\u001b[1;32m     83\u001b[0m     btn \u001b[38;5;241m=\u001b[39m driver\u001b[38;5;241m.\u001b[39mfind_element(By\u001b[38;5;241m.\u001b[39mXPATH, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m//a[@aria-label=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMostrar más productos\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     84\u001b[0m     driver\u001b[38;5;241m.\u001b[39mexecute_script(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marguments[0].click();\u001b[39m\u001b[38;5;124m\"\u001b[39m, btn)\n\u001b[0;32m---> 85\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NoSuchElementException:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "service = Service(ChromeDriverManager().install())\n",
    "options = Options()\n",
    "driver = webdriver.Chrome(service=service, options=options)\n",
    "# VERIFICACIÓN PARA CONTINUAR DESDE LA ÚLTIMA CATEGORÍA\n",
    "last_processed_category = None\n",
    "try:\n",
    "    with open(\"furniture.csv\", \"r\", encoding=\"utf-8\") as f_check:\n",
    "        lines = f_check.readlines()\n",
    "        if len(lines) > 1:\n",
    "            last_line = lines[-1].strip()\n",
    "            last_estancia, last_categoria, last_tipo_mueble, _, _, _, _ = last_line.split(',', 6)\n",
    "            last_processed_category = last_tipo_mueble\n",
    "            print(f\"Última categoría procesada: {last_processed_category}\")\n",
    "except Exception as e:\n",
    "    print(f\"No se pudo leer la última categoría: {e}\")\n",
    "\n",
    "start_processing = (last_processed_category is None)\n",
    "\n",
    "try:\n",
    "    # Cargar URLs ya procesadas\n",
    "    try:\n",
    "        existing_df = pd.read_csv(\"furniture.csv\")\n",
    "        processed_urls = set(existing_df[\"Enlace_producto\"].dropna().tolist())\n",
    "        print(f\"{len(processed_urls)} URLs ya procesadas\")\n",
    "    except FileNotFoundError:\n",
    "        print(\"Creando nuevo archivo\")\n",
    "\n",
    "    with open(\"furniture.csv\", \"a\", newline=\"\", encoding=\"utf-8\") as f:\n",
    "        writer = csv.writer(f)\n",
    "        if f.tell() == 0:\n",
    "            writer.writerow([\n",
    "                \"Estancia\", \"Categoria\", \"Tipo_mueble\", \"Mas_vendido\", \n",
    "                \"Enlace_producto\", \"Nombre\", \"Descripcion\"\n",
    "            ])\n",
    "            f.flush()\n",
    "        \n",
    "        with open(\"estancias_muebles.csv\", newline=\"\", encoding=\"utf-8\") as f_in:\n",
    "            reader = csv.DictReader(f_in)\n",
    "            \n",
    "            for row in reader:\n",
    "                estancia_nombre = row[\"Estancia\"]\n",
    "                estancia_mueble = row[\"Categoria\"]\n",
    "                estancia_categoria = row[\"Tipo de mueble\"]\n",
    "                estancia_url = row[\"Enlace\"]\n",
    "                \n",
    "                # LÓGICA PARA CONTINUAR DESDE LA ÚLTIMA CATEGORÍA\n",
    "                if not start_processing:\n",
    "                    if estancia_categoria == last_processed_category:\n",
    "                        start_processing = True\n",
    "                        print(f\"Reanudando desde la siguiente categoría después de: {estancia_categoria}\")\n",
    "                        continue\n",
    "                    else:\n",
    "                        print(f\"Saltando: {estancia_categoria} (ya procesada)\")\n",
    "                        continue\n",
    "                \n",
    "                # El resto de tu código sigue exactamente igual\n",
    "                if estancia_url in processed_urls:\n",
    "                    print(f\"Saltando: {estancia_categoria}\")\n",
    "                    continue\n",
    "                \n",
    "                print(f\"Procesando: {estancia_categoria}\")\n",
    "                processed_urls.add(estancia_url)\n",
    "\n",
    "                driver.get(estancia_url)\n",
    "                time.sleep(3)\n",
    "\n",
    "                # Cerrar cookies\n",
    "                try:\n",
    "                    aceptar_cookies = WebDriverWait(driver, 3).until(\n",
    "                        EC.element_to_be_clickable((By.XPATH, \"//button[contains(text(),'Aceptar')]\"))\n",
    "                    )\n",
    "                    aceptar_cookies.click()\n",
    "                except:\n",
    "                    pass\n",
    "\n",
    "                # Cargar toda la página haciendo click en \"Mostrar más\"\n",
    "                while True:\n",
    "                    try:\n",
    "                        btn = driver.find_element(By.XPATH, \"//a[@aria-label='Mostrar más productos']\")\n",
    "                        driver.execute_script(\"arguments[0].click();\", btn)\n",
    "                        time.sleep(2)\n",
    "                    except NoSuchElementException:\n",
    "                        break\n",
    "\n",
    "                # Extraer productos\n",
    "                productos = driver.find_elements(By.CSS_SELECTOR, \".plp-fragment-wrapper .plp-mastercard\")\n",
    "                print(f\"Encontrados {len(productos)} productos\")\n",
    "                \n",
    "                # Procesar cada producto\n",
    "                nuevos = 0\n",
    "                for producto in productos:\n",
    "                    try:\n",
    "                        # Más vendido\n",
    "                        mas_vendido = 0\n",
    "                        try:\n",
    "                            badge = producto.find_element(By.CSS_SELECTOR, \".plp-product-badge--top-seller\")\n",
    "                            if badge and \"Más vendido\" in badge.text:\n",
    "                                mas_vendido = 1\n",
    "                        except:\n",
    "                            pass\n",
    "\n",
    "                        # Enlace del producto\n",
    "                        enlace = \"\"\n",
    "                        try:\n",
    "                            link = producto.find_element(By.CSS_SELECTOR, \"a.plp-price-link-wrapper\")\n",
    "                            enlace = link.get_attribute(\"href\")\n",
    "                        except:\n",
    "                            try:\n",
    "                                link = producto.find_element(By.CSS_SELECTOR, \"a.plp-link\")\n",
    "                                enlace = link.get_attribute(\"href\")\n",
    "                            except:\n",
    "                                pass\n",
    "\n",
    "                        # Nombre\n",
    "                        nombre = \"\"\n",
    "                        try:\n",
    "                            name_element = producto.find_element(By.CLASS_NAME, \"plp-price-module__product-name\")\n",
    "                            nombre = name_element.text.strip()\n",
    "                        except:\n",
    "                            pass\n",
    "\n",
    "                        # Descripción\n",
    "                        descripcion = \"\"\n",
    "                        try:\n",
    "                            desc_element = producto.find_element(By.CLASS_NAME, \"plp-price-module__description\")\n",
    "                            descripcion = desc_element.text.strip()\n",
    "                        except:\n",
    "                            pass\n",
    "\n",
    "                        # Solo guardar si existe enlace y nombre\n",
    "                        if enlace and nombre:\n",
    "                            writer.writerow([\n",
    "                                estancia_nombre,\n",
    "                                estancia_categoria,\n",
    "                                estancia_mueble,\n",
    "                                mas_vendido,\n",
    "                                enlace,\n",
    "                                nombre,\n",
    "                                descripcion\n",
    "                            ])\n",
    "                            f.flush()\n",
    "                            nuevos += 1\n",
    "                            \n",
    "                    except Exception as e:\n",
    "                        print(f\"Error procesando producto: {e}\")\n",
    "                        continue\n",
    "\n",
    "                print(f\"{nuevos} productos guardados para {estancia_categoria}\\n\")\n",
    "                time.sleep(1)\n",
    "finally:\n",
    "    driver.quit()\n",
    "    print(\"Proceso completado\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472e4ba2",
   "metadata": {},
   "source": [
    "# Limpieza e imputación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "731f5a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores únicos en 'Imagen_principal' (no nulos): 2653\n",
      "\n",
      "Conteo de valores vacíos en columnas relacionadas:\n",
      "Relacionado_1: 84 valores vacíos\n",
      "Relacionado_2: 84 valores vacíos\n",
      "Relacionado_3: 84 valores vacíos\n",
      "Relacionado_4: 84 valores vacíos\n",
      "Relacionado_5: 84 valores vacíos\n",
      "Relacionado_6: 84 valores vacíos\n",
      "Relacionado_7: 84 valores vacíos\n",
      "Relacionado_8: 84 valores vacíos\n",
      "Relacionado_9: 87 valores vacíos\n",
      "Relacionado_10: 106 valores vacíos\n",
      "Relacionado_11: 183 valores vacíos\n",
      "Relacionado_12: 535 valores vacíos\n",
      "\n",
      "Total de valores vacíos en todas las columnas relacionadas: 1583\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"furniture.csv\")\n",
    "\n",
    "# Filtrar filas donde 'Imagen_principal' tiene valores únicos no nulos\n",
    "filas_imagen_no_nula = df[df['Imagen_principal'].notna()]\n",
    "\n",
    "# Contar valores únicos en 'Imagen_principal' (excluyendo nulos)\n",
    "valores_unicos_imagen = filas_imagen_no_nula['Imagen_principal'].nunique()\n",
    "print(f\"Valores únicos en 'Imagen_principal' (no nulos): {valores_unicos_imagen}\")\n",
    "\n",
    "# Columnas relacionadas que queremos analizar\n",
    "columnas_relacionadas = [f'Relacionado_{i}' for i in range(1, 13)]\n",
    "\n",
    "# Contar valores vacíos en cada columna relacionada para estas filas\n",
    "print(\"\\nConteo de valores vacíos en columnas relacionadas:\")\n",
    "for columna in columnas_relacionadas:\n",
    "    if columna in df.columns:\n",
    "        # Contar valores nulos o vacíos (dependiendo de cómo estén representados los vacíos)\n",
    "        vacios = filas_imagen_no_nula[columna].isna().sum()\n",
    "        print(f\"{columna}: {vacios} valores vacíos\")\n",
    "    else:\n",
    "        print(f\"{columna}: Columna no encontrada en el DataFrame\")\n",
    "\n",
    "if all(col in df.columns for col in columnas_relacionadas):\n",
    "    total_vacios = sum(filas_imagen_no_nula[col].isna().sum() for col in columnas_relacionadas)\n",
    "    print(f\"\\nTotal de valores vacíos en todas las columnas relacionadas: {total_vacios}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8404de62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estancia\n",
       "Cocina                                   5998\n",
       "Espacios de trabajo, estudio y gaming    3915\n",
       "Salón                                    3051\n",
       "Comedor                                  3027\n",
       "Habitación infantil y juvenil            1759\n",
       "Dormitorio                                908\n",
       "Baño                                      494\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Estancia'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a0aae2bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitud del dataframe: 19152\n",
      "Valores únicos en 'Enlace_producto': 8215\n",
      "Filas con valor en 'Opiniones_5': 2678\n"
     ]
    }
   ],
   "source": [
    "print(f\"Longitud del dataframe: {len(df)}\")\n",
    "print(f\"Valores únicos en 'Enlace_producto': {df['Enlace_producto'].nunique()}\")\n",
    "print(f\"Filas con valor en 'Opiniones_5': {df['Opiniones_5'].notna().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7e09f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado = df[df['Imagen_principal'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "125dbae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estancia\n",
       "Salón    2671\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado['Estancia'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "55bb00a8",
   "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>Estancia</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Tipo_mueble</th>\n",
       "      <th>Mas_vendido</th>\n",
       "      <th>Enlace_producto</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Descripcion</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Opiniones_5</th>\n",
       "      <th>Opiniones_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Relacionado_3</th>\n",
       "      <th>Relacionado_4</th>\n",
       "      <th>Relacionado_5</th>\n",
       "      <th>Relacionado_6</th>\n",
       "      <th>Relacionado_7</th>\n",
       "      <th>Relacionado_8</th>\n",
       "      <th>Relacionado_9</th>\n",
       "      <th>Relacionado_10</th>\n",
       "      <th>Relacionado_11</th>\n",
       "      <th>Relacionado_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/brimnes-mueble-tv...</td>\n",
       "      <td>BRIMNES</td>\n",
       "      <td>Mueble TV con puertas de vidrio, blanco, 320x4...</td>\n",
       "      <td>397,99</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39477242.0</td>\n",
       "      <td>89184331.0</td>\n",
       "      <td>79602437.0</td>\n",
       "      <td>29278219.0</td>\n",
       "      <td>19521175.0</td>\n",
       "      <td>59602075.0</td>\n",
       "      <td>99602441.0</td>\n",
       "      <td>79398672.0</td>\n",
       "      <td>69398620.0</td>\n",
       "      <td>29398684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/kallax-lack-muebl...</td>\n",
       "      <td>KALLAX / LACK</td>\n",
       "      <td>Mueble TV con estantería, efecto roble tinte b...</td>\n",
       "      <td>242,97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9552172.0</td>\n",
       "      <td>89521073.0</td>\n",
       "      <td>49521112.0</td>\n",
       "      <td>69406286.0</td>\n",
       "      <td>59440610.0</td>\n",
       "      <td>39477242.0</td>\n",
       "      <td>29560675.0</td>\n",
       "      <td>69494607.0</td>\n",
       "      <td>79420300.0</td>\n",
       "      <td>19521175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/hemnes-mueble-tv-...</td>\n",
       "      <td>HEMNES</td>\n",
       "      <td>Mueble TV con estantería, tinte blanco/marrón ...</td>\n",
       "      <td>777</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39534817.0</td>\n",
       "      <td>50413526.0</td>\n",
       "      <td>19534818.0</td>\n",
       "      <td>19387902.0</td>\n",
       "      <td>59278232.0</td>\n",
       "      <td>99437333.0</td>\n",
       "      <td>9384051.0</td>\n",
       "      <td>59437330.0</td>\n",
       "      <td>39437331.0</td>\n",
       "      <td>59398673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/baggebo-mueble-tv...</td>\n",
       "      <td>BAGGEBO</td>\n",
       "      <td>Mueble TV con estantería, blanco, 174x35x160 cm</td>\n",
       "      <td>96,46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39477242.0</td>\n",
       "      <td>19521175.0</td>\n",
       "      <td>50409874.0</td>\n",
       "      <td>29560675.0</td>\n",
       "      <td>59398673.0</td>\n",
       "      <td>69494607.0</td>\n",
       "      <td>59184337.0</td>\n",
       "      <td>9503313.0</td>\n",
       "      <td>9552172.0</td>\n",
       "      <td>39184343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/besta-mueble-tv-c...</td>\n",
       "      <td>BESTÅ</td>\n",
       "      <td>Mueble TV con puertas de vidrio, negro-marrón/...</td>\n",
       "      <td>520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59406852.0</td>\n",
       "      <td>19406929.0</td>\n",
       "      <td>99406831.0</td>\n",
       "      <td>29575295.0</td>\n",
       "      <td>59488800.0</td>\n",
       "      <td>59507907.0</td>\n",
       "      <td>9488789.0</td>\n",
       "      <td>99406708.0</td>\n",
       "      <td>29406924.0</td>\n",
       "      <td>9412351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13825</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Textiles para bebé</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/len-saco-dormir-v...</td>\n",
       "      <td>LEN</td>\n",
       "      <td>Saco dormir, verde, 0-6 meses</td>\n",
       "      <td>13,99</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50457600.0</td>\n",
       "      <td>90542140.0</td>\n",
       "      <td>50573614.0</td>\n",
       "      <td>90542135.0</td>\n",
       "      <td>70572369.0</td>\n",
       "      <td>90573693.0</td>\n",
       "      <td>50113938.0</td>\n",
       "      <td>30526402.0</td>\n",
       "      <td>30530282.0</td>\n",
       "      <td>573616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13831</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Textiles para bebé</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/len-gasa-lunares-...</td>\n",
       "      <td>LEN</td>\n",
       "      <td>Gasa, lunares/Luna, 70x70 cm</td>\n",
       "      <td>5,99/2 unidad</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60572416.0</td>\n",
       "      <td>90542140.0</td>\n",
       "      <td>70572369.0</td>\n",
       "      <td>60453913.0</td>\n",
       "      <td>50573614.0</td>\n",
       "      <td>414137.0</td>\n",
       "      <td>10573649.0</td>\n",
       "      <td>50113938.0</td>\n",
       "      <td>489000.0</td>\n",
       "      <td>60526392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13832</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Cojines lumbares</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/bortberg-cojin-lu...</td>\n",
       "      <td>BORTBERG</td>\n",
       "      <td>Cojín lumbar, negro, 31x23 cm</td>\n",
       "      <td>12,99</td>\n",
       "      <td>218.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60569522.0</td>\n",
       "      <td>70586470.0</td>\n",
       "      <td>30473205.0</td>\n",
       "      <td>44881100.0</td>\n",
       "      <td>40507813.0</td>\n",
       "      <td>80412016.0</td>\n",
       "      <td>552020.0</td>\n",
       "      <td>10531942.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13833</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Cojines lumbares</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/raggarv-cojin-cue...</td>\n",
       "      <td>RAGGARV</td>\n",
       "      <td>Cojín cuello/lumbar</td>\n",
       "      <td>9,99</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40240989.0</td>\n",
       "      <td>80503436.0</td>\n",
       "      <td>10507584.0</td>\n",
       "      <td>89424468.0</td>\n",
       "      <td>80503441.0</td>\n",
       "      <td>20522032.0</td>\n",
       "      <td>80507608.0</td>\n",
       "      <td>30613055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13834</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Telas por metro y accesorios de costura</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.ikea.com/es/es/p/spjutsporre-tela-...</td>\n",
       "      <td>SPJUTSPORRE</td>\n",
       "      <td>Tela precortada, multicolor, 150x300 cm</td>\n",
       "      <td>19,99/3 m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60579955.0</td>\n",
       "      <td>50608147.0</td>\n",
       "      <td>70608146.0</td>\n",
       "      <td>30601137.0</td>\n",
       "      <td>80579822.0</td>\n",
       "      <td>90579826.0</td>\n",
       "      <td>20608144.0</td>\n",
       "      <td>608159.0</td>\n",
       "      <td>40579838.0</td>\n",
       "      <td>90608150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2671 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estancia                                Categoria       Tipo_mueble  \\\n",
       "10784    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10785    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10786    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10787    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10788    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "...        ...                                      ...               ...   \n",
       "13825    Salón                       Textiles para bebé   Mantas y plaids   \n",
       "13831    Salón                       Textiles para bebé   Mantas y plaids   \n",
       "13832    Salón                         Cojines lumbares   Mantas y plaids   \n",
       "13833    Salón                         Cojines lumbares   Mantas y plaids   \n",
       "13834    Salón  Telas por metro y accesorios de costura   Mantas y plaids   \n",
       "\n",
       "       Mas_vendido                                    Enlace_producto  \\\n",
       "10784            0  https://www.ikea.com/es/es/p/brimnes-mueble-tv...   \n",
       "10785            0  https://www.ikea.com/es/es/p/kallax-lack-muebl...   \n",
       "10786            0  https://www.ikea.com/es/es/p/hemnes-mueble-tv-...   \n",
       "10787            0  https://www.ikea.com/es/es/p/baggebo-mueble-tv...   \n",
       "10788            0  https://www.ikea.com/es/es/p/besta-mueble-tv-c...   \n",
       "...            ...                                                ...   \n",
       "13825            0  https://www.ikea.com/es/es/p/len-saco-dormir-v...   \n",
       "13831            0  https://www.ikea.com/es/es/p/len-gasa-lunares-...   \n",
       "13832            0  https://www.ikea.com/es/es/p/bortberg-cojin-lu...   \n",
       "13833            0  https://www.ikea.com/es/es/p/raggarv-cojin-cue...   \n",
       "13834            0  https://www.ikea.com/es/es/p/spjutsporre-tela-...   \n",
       "\n",
       "              Nombre                                        Descripcion  \\\n",
       "10784        BRIMNES  Mueble TV con puertas de vidrio, blanco, 320x4...   \n",
       "10785  KALLAX / LACK  Mueble TV con estantería, efecto roble tinte b...   \n",
       "10786         HEMNES  Mueble TV con estantería, tinte blanco/marrón ...   \n",
       "10787        BAGGEBO    Mueble TV con estantería, blanco, 174x35x160 cm   \n",
       "10788          BESTÅ  Mueble TV con puertas de vidrio, negro-marrón/...   \n",
       "...              ...                                                ...   \n",
       "13825            LEN                      Saco dormir, verde, 0-6 meses   \n",
       "13831            LEN                       Gasa, lunares/Luna, 70x70 cm   \n",
       "13832       BORTBERG                      Cojín lumbar, negro, 31x23 cm   \n",
       "13833        RAGGARV                                Cojín cuello/lumbar   \n",
       "13834    SPJUTSPORRE            Tela precortada, multicolor, 150x300 cm   \n",
       "\n",
       "              Precio  Opiniones_5  Opiniones_4  ...  Relacionado_3  \\\n",
       "10784         397,99         35.0          8.0  ...     39477242.0   \n",
       "10785         242,97          1.0          0.0  ...      9552172.0   \n",
       "10786            777         34.0         11.0  ...     39534817.0   \n",
       "10787          96,46          7.0          1.0  ...     39477242.0   \n",
       "10788            520          1.0          0.0  ...     59406852.0   \n",
       "...              ...          ...          ...  ...            ...   \n",
       "13825          13,99         11.0          4.0  ...     50457600.0   \n",
       "13831  5,99/2 unidad          3.0          5.0  ...     60572416.0   \n",
       "13832          12,99        218.0         75.0  ...     60569522.0   \n",
       "13833           9,99        143.0         55.0  ...     40240989.0   \n",
       "13834      19,99/3 m          0.0          1.0  ...     60579955.0   \n",
       "\n",
       "       Relacionado_4  Relacionado_5  Relacionado_6  Relacionado_7  \\\n",
       "10784     89184331.0     79602437.0     29278219.0     19521175.0   \n",
       "10785     89521073.0     49521112.0     69406286.0     59440610.0   \n",
       "10786     50413526.0     19534818.0     19387902.0     59278232.0   \n",
       "10787     19521175.0     50409874.0     29560675.0     59398673.0   \n",
       "10788     19406929.0     99406831.0     29575295.0     59488800.0   \n",
       "...              ...            ...            ...            ...   \n",
       "13825     90542140.0     50573614.0     90542135.0     70572369.0   \n",
       "13831     90542140.0     70572369.0     60453913.0     50573614.0   \n",
       "13832     70586470.0     30473205.0     44881100.0     40507813.0   \n",
       "13833     80503436.0     10507584.0     89424468.0     80503441.0   \n",
       "13834     50608147.0     70608146.0     30601137.0     80579822.0   \n",
       "\n",
       "       Relacionado_8  Relacionado_9  Relacionado_10 Relacionado_11  \\\n",
       "10784     59602075.0     99602441.0      79398672.0     69398620.0   \n",
       "10785     39477242.0     29560675.0      69494607.0     79420300.0   \n",
       "10786     99437333.0      9384051.0      59437330.0     39437331.0   \n",
       "10787     69494607.0     59184337.0       9503313.0      9552172.0   \n",
       "10788     59507907.0      9488789.0      99406708.0     29406924.0   \n",
       "...              ...            ...             ...            ...   \n",
       "13825     90573693.0     50113938.0      30526402.0     30530282.0   \n",
       "13831       414137.0     10573649.0      50113938.0       489000.0   \n",
       "13832     80412016.0       552020.0      10531942.0            NaN   \n",
       "13833     20522032.0     80507608.0      30613055.0            NaN   \n",
       "13834     90579826.0     20608144.0        608159.0     40579838.0   \n",
       "\n",
       "      Relacionado_12  \n",
       "10784     29398684.0  \n",
       "10785     19521175.0  \n",
       "10786     59398673.0  \n",
       "10787     39184343.0  \n",
       "10788      9412351.0  \n",
       "...              ...  \n",
       "13825       573616.0  \n",
       "13831     60526392.0  \n",
       "13832            NaN  \n",
       "13833            NaN  \n",
       "13834     90608150.0  \n",
       "\n",
       "[2671 rows x 34 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de2e3fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Estancia', 'Categoria', 'Tipo_mueble', 'Mas_vendido',\n",
       "       'Enlace_producto', 'Nombre', 'Descripcion', 'Precio', 'Opiniones_5',\n",
       "       'Opiniones_4', 'Opiniones_3', 'Opiniones_2', 'Opiniones_1',\n",
       "       'Valoracion_Diseno', 'Valoracion_Montaje', 'Valoracion_Calidad',\n",
       "       'Valoracion_Relacion', 'Valoracion_Funcionamiento', 'Medidas',\n",
       "       'Variantes_Nombres', 'Variantes_Enlaces', 'Imagen_principal',\n",
       "       'Relacionado_1', 'Relacionado_2', 'Relacionado_3', 'Relacionado_4',\n",
       "       'Relacionado_5', 'Relacionado_6', 'Relacionado_7', 'Relacionado_8',\n",
       "       'Relacionado_9', 'Relacionado_10', 'Relacionado_11', 'Relacionado_12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "df8c27af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estancia                      object\n",
       "Categoria                     object\n",
       "Tipo_mueble                   object\n",
       "Mas_vendido                    int64\n",
       "Enlace_producto               object\n",
       "Nombre                        object\n",
       "Descripcion                   object\n",
       "Precio                        object\n",
       "Opiniones_5                  float64\n",
       "Opiniones_4                  float64\n",
       "Opiniones_3                  float64\n",
       "Opiniones_2                  float64\n",
       "Opiniones_1                  float64\n",
       "Valoracion_Diseno            float64\n",
       "Valoracion_Montaje           float64\n",
       "Valoracion_Calidad           float64\n",
       "Valoracion_Relacion          float64\n",
       "Valoracion_Funcionamiento    float64\n",
       "Medidas                       object\n",
       "Variantes_Nombres             object\n",
       "Variantes_Enlaces             object\n",
       "Imagen_principal              object\n",
       "Relacionado_1                float64\n",
       "Relacionado_2                float64\n",
       "Relacionado_3                float64\n",
       "Relacionado_4                float64\n",
       "Relacionado_5                float64\n",
       "Relacionado_6                float64\n",
       "Relacionado_7                float64\n",
       "Relacionado_8                float64\n",
       "Relacionado_9                float64\n",
       "Relacionado_10               float64\n",
       "Relacionado_11               float64\n",
       "Relacionado_12               float64\n",
       "ID                            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado['ID'] = df_scrapeado['Enlace_producto'].str.extract(r'(\\d+)/?$')\n",
    "df_scrapeado.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7e5b5165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estancia                      object\n",
       "Categoria                     object\n",
       "Tipo_mueble                   object\n",
       "Mas_vendido                    int32\n",
       "Enlace_producto               object\n",
       "Nombre                        object\n",
       "Descripcion                   object\n",
       "Precio                       float64\n",
       "Opiniones_5                    int32\n",
       "Opiniones_4                    int32\n",
       "Opiniones_3                    int32\n",
       "Opiniones_2                    int32\n",
       "Opiniones_1                    int32\n",
       "Valoracion_Diseno            float32\n",
       "Valoracion_Montaje           float32\n",
       "Valoracion_Calidad           float32\n",
       "Valoracion_Relacion          float32\n",
       "Valoracion_Funcionamiento    float32\n",
       "Medidas                       object\n",
       "Variantes_Nombres             object\n",
       "Variantes_Enlaces             object\n",
       "Imagen_principal              object\n",
       "Relacionado_1                  Int32\n",
       "Relacionado_2                  Int32\n",
       "Relacionado_3                  Int32\n",
       "Relacionado_4                  Int32\n",
       "Relacionado_5                  Int32\n",
       "Relacionado_6                  Int32\n",
       "Relacionado_7                  Int32\n",
       "Relacionado_8                  Int32\n",
       "Relacionado_9                  Int32\n",
       "Relacionado_10                 Int32\n",
       "Relacionado_11                 Int32\n",
       "Relacionado_12                 Int32\n",
       "ID                             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cambiar las comas del precio por puntos\n",
    "df_scrapeado['Precio'] = df_scrapeado['Precio'].str.replace(',', '.')\n",
    "#Eliminar todo lo que viene después de la barra del precio, por ejemplo '5/2 unidad'\n",
    "df_scrapeado['Precio'] = df_scrapeado['Precio'].str.split('/').str[0]\n",
    "#Algunas caputarron el precio erróneo, porque han sido rebajados, pero quedó \n",
    "#el precio anterior. Precio 'viejo' que usaré. tengo que limpiarlo porque aparecen como 'Precio anterior 14.99'\n",
    "df_scrapeado['Precio'] = df_scrapeado['Precio'].str.replace('Precio anterior ', '')\n",
    "df_scrapeado['Precio'] = df_scrapeado['Precio'].astype('float64').round(2)\n",
    "\n",
    "df_scrapeado = df_scrapeado.astype({\n",
    "    'Mas_vendido' : 'int32',\n",
    "    'Opiniones_5' : 'int32',\n",
    "    'Opiniones_4': 'int32',\n",
    "    'Opiniones_3': 'int32',\n",
    "    'Opiniones_2': 'int32',\n",
    "    'Opiniones_1': 'int32',\n",
    "    'Valoracion_Diseno': 'float32',\n",
    "    'Valoracion_Montaje': 'float32',\n",
    "    'Valoracion_Calidad': 'float32',\n",
    "    'Valoracion_Relacion': 'float32',\n",
    "    'Valoracion_Funcionamiento': 'float32',\n",
    "    'Relacionado_1' : 'Int32',\n",
    "    'Relacionado_2' : 'Int32',\n",
    "    'Relacionado_3' : 'Int32',\n",
    "    'Relacionado_4' : 'Int32',\n",
    "    'Relacionado_5' : 'Int32',\n",
    "    'Relacionado_6' : 'Int32',\n",
    "    'Relacionado_7' : 'Int32',\n",
    "    'Relacionado_8' : 'Int32',\n",
    "    'Relacionado_9' : 'Int32',\n",
    "    'Relacionado_10' : 'Int32',\n",
    "    'Relacionado_11' : 'Int32',\n",
    "    'Relacionado_12' : 'Int32',\n",
    "    'ID' : 'int32'})\n",
    "df_scrapeado.dtypes\n",
    "\n",
    "    #Medidas\n",
    "    #Variantes_Nombres\n",
    "    #Variantes_Enlaces\n",
    "    #Imagen_principal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2d7389c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quitar el prefijo\n",
    "df_scrapeado['Enlace_producto'] = df_scrapeado['Enlace_producto'].str.replace('https://www.ikea.com/es/es/p/', '')\n",
    "\n",
    "# Quitar el ID final (incluyendo la 's' si existe)\n",
    "df_scrapeado['Enlace_producto'] = df_scrapeado['Enlace_producto'].str.replace(r'-s?\\d+/?$', '', regex=True)\n",
    "#Reconstruir enlace\n",
    "#df_scrapeado['Enlace_completo'] = 'https://www.ikea.com/es/es/p/' + df_scrapeado['Enlace_producto'] + df_scrapeado['ID'].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3668cbc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10784                                  brimnes-mueble-tv-con-puertas-vidrio-blanco\n",
      "10785               kallax-lack-mueble-tv-con-estanteria-efecto-roble-tinte-blanco\n",
      "10786    hemnes-mueble-tv-con-estanteria-tinte-blanco-marron-claro-vidrio-incoloro\n",
      "Name: Enlace_producto, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Ver el contenido REAL sin truncamiento\n",
    "with pd.option_context('display.max_colwidth', None):\n",
    "    print(df_scrapeado['Enlace_producto'].head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c00a3717",
   "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>Estancia</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Tipo_mueble</th>\n",
       "      <th>Mas_vendido</th>\n",
       "      <th>Enlace_producto</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Descripcion</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Opiniones_5</th>\n",
       "      <th>Opiniones_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Relacionado_6</th>\n",
       "      <th>Relacionado_7</th>\n",
       "      <th>Relacionado_8</th>\n",
       "      <th>Relacionado_9</th>\n",
       "      <th>Relacionado_10</th>\n",
       "      <th>Relacionado_11</th>\n",
       "      <th>Relacionado_12</th>\n",
       "      <th>ID</th>\n",
       "      <th>Resenas</th>\n",
       "      <th>Valoracion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>brimnes-mueble-tv-con-puertas-vidrio-blanco</td>\n",
       "      <td>BRIMNES</td>\n",
       "      <td>Mueble TV con puertas de vidrio, blanco, 320x4...</td>\n",
       "      <td>397.99</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>29278219</td>\n",
       "      <td>19521175</td>\n",
       "      <td>59602075</td>\n",
       "      <td>99602441</td>\n",
       "      <td>79398672</td>\n",
       "      <td>69398620</td>\n",
       "      <td>29398684</td>\n",
       "      <td>59278232</td>\n",
       "      <td>46</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>kallax-lack-mueble-tv-con-estanteria-efecto-ro...</td>\n",
       "      <td>KALLAX / LACK</td>\n",
       "      <td>Mueble TV con estantería, efecto roble tinte b...</td>\n",
       "      <td>242.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>69406286</td>\n",
       "      <td>59440610</td>\n",
       "      <td>39477242</td>\n",
       "      <td>29560675</td>\n",
       "      <td>69494607</td>\n",
       "      <td>79420300</td>\n",
       "      <td>19521175</td>\n",
       "      <td>29552171</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>hemnes-mueble-tv-con-estanteria-tinte-blanco-m...</td>\n",
       "      <td>HEMNES</td>\n",
       "      <td>Mueble TV con estantería, tinte blanco/marrón ...</td>\n",
       "      <td>777.00</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>19387902</td>\n",
       "      <td>59278232</td>\n",
       "      <td>99437333</td>\n",
       "      <td>9384051</td>\n",
       "      <td>59437330</td>\n",
       "      <td>39437331</td>\n",
       "      <td>59398673</td>\n",
       "      <td>19299571</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>baggebo-mueble-tv-con-estanteria-blanco</td>\n",
       "      <td>BAGGEBO</td>\n",
       "      <td>Mueble TV con estantería, blanco, 174x35x160 cm</td>\n",
       "      <td>96.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>29560675</td>\n",
       "      <td>59398673</td>\n",
       "      <td>69494607</td>\n",
       "      <td>59184337</td>\n",
       "      <td>9503313</td>\n",
       "      <td>9552172</td>\n",
       "      <td>39184343</td>\n",
       "      <td>29443653</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>besta-mueble-tv-con-puertas-vidrio-negro-marro...</td>\n",
       "      <td>BESTÅ</td>\n",
       "      <td>Mueble TV con puertas de vidrio, negro-marrón/...</td>\n",
       "      <td>520.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29575295</td>\n",
       "      <td>59488800</td>\n",
       "      <td>59507907</td>\n",
       "      <td>9488789</td>\n",
       "      <td>99406708</td>\n",
       "      <td>29406924</td>\n",
       "      <td>9412351</td>\n",
       "      <td>59406710</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estancia                         Categoria       Tipo_mueble  \\\n",
       "10784    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10785    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10786    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10787    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10788    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "\n",
       "       Mas_vendido                                    Enlace_producto  \\\n",
       "10784            0        brimnes-mueble-tv-con-puertas-vidrio-blanco   \n",
       "10785            0  kallax-lack-mueble-tv-con-estanteria-efecto-ro...   \n",
       "10786            0  hemnes-mueble-tv-con-estanteria-tinte-blanco-m...   \n",
       "10787            0            baggebo-mueble-tv-con-estanteria-blanco   \n",
       "10788            0  besta-mueble-tv-con-puertas-vidrio-negro-marro...   \n",
       "\n",
       "              Nombre                                        Descripcion  \\\n",
       "10784        BRIMNES  Mueble TV con puertas de vidrio, blanco, 320x4...   \n",
       "10785  KALLAX / LACK  Mueble TV con estantería, efecto roble tinte b...   \n",
       "10786         HEMNES  Mueble TV con estantería, tinte blanco/marrón ...   \n",
       "10787        BAGGEBO    Mueble TV con estantería, blanco, 174x35x160 cm   \n",
       "10788          BESTÅ  Mueble TV con puertas de vidrio, negro-marrón/...   \n",
       "\n",
       "       Precio  Opiniones_5  Opiniones_4  ...  Relacionado_6  Relacionado_7  \\\n",
       "10784  397.99           35            8  ...       29278219       19521175   \n",
       "10785  242.97            1            0  ...       69406286       59440610   \n",
       "10786  777.00           34           11  ...       19387902       59278232   \n",
       "10787   96.46            7            1  ...       29560675       59398673   \n",
       "10788  520.00            1            0  ...       29575295       59488800   \n",
       "\n",
       "       Relacionado_8  Relacionado_9  Relacionado_10  Relacionado_11  \\\n",
       "10784       59602075       99602441        79398672        69398620   \n",
       "10785       39477242       29560675        69494607        79420300   \n",
       "10786       99437333        9384051        59437330        39437331   \n",
       "10787       69494607       59184337         9503313         9552172   \n",
       "10788       59507907        9488789        99406708        29406924   \n",
       "\n",
       "       Relacionado_12        ID Resenas Valoracion  \n",
       "10784        29398684  59278232      46        4.7  \n",
       "10785        19521175  29552171       1        5.0  \n",
       "10786        59398673  19299571      51        4.5  \n",
       "10787        39184343  29443653       8        4.9  \n",
       "10788         9412351  59406710       1        5.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opiniones = [\"Opiniones_5\", \"Opiniones_4\", \"Opiniones_3\", \"Opiniones_2\", \"Opiniones_1\"]\n",
    "\n",
    "# Total de reseñas\n",
    "df_scrapeado[\"Resenas\"] = df_scrapeado[opiniones].sum(axis=1)\n",
    "\n",
    "# Valoración ponderada\n",
    "df_scrapeado[\"Valoracion\"] = ((df_scrapeado[\"Opiniones_5\"]*5 + df_scrapeado[\"Opiniones_4\"]*4 + df_scrapeado[\"Opiniones_3\"]*3 + df_scrapeado[\"Opiniones_2\"]*2 + df_scrapeado[\"Opiniones_1\"]*1) / df_scrapeado[\"Resenas\"]\n",
    ").round(1)\n",
    "\n",
    "df_scrapeado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9a1347b3",
   "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>Estancia</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Tipo_mueble</th>\n",
       "      <th>Mas_vendido</th>\n",
       "      <th>Enlace_producto</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Descripcion</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Opiniones_5</th>\n",
       "      <th>Opiniones_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Relacionado_7</th>\n",
       "      <th>Relacionado_8</th>\n",
       "      <th>Relacionado_9</th>\n",
       "      <th>Relacionado_10</th>\n",
       "      <th>Relacionado_11</th>\n",
       "      <th>Relacionado_12</th>\n",
       "      <th>ID</th>\n",
       "      <th>Resenas</th>\n",
       "      <th>Valoracion</th>\n",
       "      <th>Medidas_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>brimnes-mueble-tv-con-puertas-vidrio-blanco</td>\n",
       "      <td>BRIMNES</td>\n",
       "      <td>Mueble TV con puertas de vidrio, blanco,</td>\n",
       "      <td>397.99</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>19521175</td>\n",
       "      <td>59602075</td>\n",
       "      <td>99602441</td>\n",
       "      <td>79398672</td>\n",
       "      <td>69398620</td>\n",
       "      <td>29398684</td>\n",
       "      <td>59278232</td>\n",
       "      <td>46</td>\n",
       "      <td>4.7</td>\n",
       "      <td>320x41x190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>kallax-lack-mueble-tv-con-estanteria-efecto-ro...</td>\n",
       "      <td>KALLAX / LACK</td>\n",
       "      <td>Mueble TV con estantería, efecto roble tinte b...</td>\n",
       "      <td>242.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>59440610</td>\n",
       "      <td>39477242</td>\n",
       "      <td>29560675</td>\n",
       "      <td>69494607</td>\n",
       "      <td>79420300</td>\n",
       "      <td>19521175</td>\n",
       "      <td>29552171</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>224x39x147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>hemnes-mueble-tv-con-estanteria-tinte-blanco-m...</td>\n",
       "      <td>HEMNES</td>\n",
       "      <td>Mueble TV con estantería, tinte blanco/marrón ...</td>\n",
       "      <td>777.00</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>59278232</td>\n",
       "      <td>99437333</td>\n",
       "      <td>9384051</td>\n",
       "      <td>59437330</td>\n",
       "      <td>39437331</td>\n",
       "      <td>59398673</td>\n",
       "      <td>19299571</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>326x197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>baggebo-mueble-tv-con-estanteria-blanco</td>\n",
       "      <td>BAGGEBO</td>\n",
       "      <td>Mueble TV con estantería, blanco,</td>\n",
       "      <td>96.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>59398673</td>\n",
       "      <td>69494607</td>\n",
       "      <td>59184337</td>\n",
       "      <td>9503313</td>\n",
       "      <td>9552172</td>\n",
       "      <td>39184343</td>\n",
       "      <td>29443653</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>174x35x160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>besta-mueble-tv-con-puertas-vidrio-negro-marro...</td>\n",
       "      <td>BESTÅ</td>\n",
       "      <td>Mueble TV con puertas de vidrio, negro-marrón/...</td>\n",
       "      <td>520.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>59488800</td>\n",
       "      <td>59507907</td>\n",
       "      <td>9488789</td>\n",
       "      <td>99406708</td>\n",
       "      <td>29406924</td>\n",
       "      <td>9412351</td>\n",
       "      <td>59406710</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>300x42x211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estancia                         Categoria       Tipo_mueble  \\\n",
       "10784    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10785    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10786    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10787    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10788    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "\n",
       "       Mas_vendido                                    Enlace_producto  \\\n",
       "10784            0        brimnes-mueble-tv-con-puertas-vidrio-blanco   \n",
       "10785            0  kallax-lack-mueble-tv-con-estanteria-efecto-ro...   \n",
       "10786            0  hemnes-mueble-tv-con-estanteria-tinte-blanco-m...   \n",
       "10787            0            baggebo-mueble-tv-con-estanteria-blanco   \n",
       "10788            0  besta-mueble-tv-con-puertas-vidrio-negro-marro...   \n",
       "\n",
       "              Nombre                                        Descripcion  \\\n",
       "10784        BRIMNES          Mueble TV con puertas de vidrio, blanco,    \n",
       "10785  KALLAX / LACK  Mueble TV con estantería, efecto roble tinte b...   \n",
       "10786         HEMNES  Mueble TV con estantería, tinte blanco/marrón ...   \n",
       "10787        BAGGEBO                 Mueble TV con estantería, blanco,    \n",
       "10788          BESTÅ  Mueble TV con puertas de vidrio, negro-marrón/...   \n",
       "\n",
       "       Precio  Opiniones_5  Opiniones_4  ...  Relacionado_7  Relacionado_8  \\\n",
       "10784  397.99           35            8  ...       19521175       59602075   \n",
       "10785  242.97            1            0  ...       59440610       39477242   \n",
       "10786  777.00           34           11  ...       59278232       99437333   \n",
       "10787   96.46            7            1  ...       59398673       69494607   \n",
       "10788  520.00            1            0  ...       59488800       59507907   \n",
       "\n",
       "       Relacionado_9  Relacionado_10  Relacionado_11  Relacionado_12  \\\n",
       "10784       99602441        79398672        69398620        29398684   \n",
       "10785       29560675        69494607        79420300        19521175   \n",
       "10786        9384051        59437330        39437331        59398673   \n",
       "10787       59184337         9503313         9552172        39184343   \n",
       "10788        9488789        99406708        29406924         9412351   \n",
       "\n",
       "             ID  Resenas Valoracion   Medidas_1  \n",
       "10784  59278232       46        4.7  320x41x190  \n",
       "10785  29552171        1        5.0  224x39x147  \n",
       "10786  19299571       51        4.5     326x197  \n",
       "10787  29443653        8        4.9  174x35x160  \n",
       "10788  59406710        1        5.0  300x42x211  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Separar las medidas de la descripcion para luego compararlas con las capturadas\n",
    "df_scrapeado['Medidas_1'] = df_scrapeado['Descripcion'].str.extract(r'([\\d\\.]+(?:\\s?x\\s?[\\d\\.]+)+)\\s*cm', expand=False)\n",
    "df_scrapeado['Descripcion'] = df_scrapeado['Descripcion'].str.replace(r'[\\d\\.]+(?:\\s?x\\s?[\\d\\.]+)*\\s*cm', '', regex=True)\n",
    "df_scrapeado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9fa19705",
   "metadata": {},
   "outputs": [],
   "source": [
    "#limpiar residuos de la descripcion\n",
    "df_scrapeado['Descripcion'] = df_scrapeado['Descripcion'].str.replace(r',\\s*$', '', regex=True)\n",
    "df_scrapeado['Descripcion'] = df_scrapeado['Descripcion'].str.replace(\n",
    "    r',\\s*[\\d\\/\\.]+(?:\\s?[x×]\\s?[\\d\\/\\.]+)*\\s*[kgcmKGCM\\/]*\\s*$', \n",
    "    '', \n",
    "    regex=True\n",
    ").str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "836f0c5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descripcion\n",
      "0      96\n",
      "1    2341\n",
      "2     234\n",
      "Name: count, dtype: int64\n",
      "\n",
      "--- 0 comas ---\n",
      "['Mueble TV con estantería', 'Mueble TV con estantería', 'Combinación de mueble para TV']\n",
      "\n",
      "--- 1 coma ---\n",
      "['Mueble TV con puertas de vidrio, blanco', 'Mueble TV con estantería, efecto roble tinte blanco', 'Mueble TV con estantería, tinte blanco/marrón claro vidrio incoloro']\n",
      "\n",
      "--- 2 comas ---\n",
      "['Armario TV, almacenaje y puertas, chapa roble/vidrio incoloro', 'Armario TV, almacenaje y puertas, hueso/vidrio incoloro', 'Secciones con baldas, pino, 139x50x124-']\n",
      "\n",
      "--- 3+ comas ---\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# Contar comas en la descripción limpia\n",
    "conteo_comas = df_scrapeado['Descripcion'].str.count(',')\n",
    "\n",
    "# Ver la distribución\n",
    "print(conteo_comas.value_counts().sort_index())\n",
    "\n",
    "# Ver ejemplos de cada caso\n",
    "print(\"\\n--- 0 comas ---\")\n",
    "print(df_scrapeado[conteo_comas == 0]['Descripcion'].head(3).tolist())\n",
    "\n",
    "print(\"\\n--- 1 coma ---\") \n",
    "print(df_scrapeado[conteo_comas == 1]['Descripcion'].head(3).tolist())\n",
    "\n",
    "print(\"\\n--- 2 comas ---\")\n",
    "print(df_scrapeado[conteo_comas == 2]['Descripcion'].head(3).tolist())\n",
    "\n",
    "print(\"\\n--- 3+ comas ---\")\n",
    "print(df_scrapeado[conteo_comas >= 3]['Descripcion'].head(3).tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "96f4461f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear máscara para las filas que contienen patrón de medidas (no solo las que terminan en - o cm)\n",
    "mask = df_scrapeado['Descripcion'].str.contains(r'[\\d\\.]+(?:\\s?[x×]\\s?[\\d\\.]+)+[^\\w]?\\s*$', na=False)\n",
    "\n",
    "# Solo aplicar a esas filas\n",
    "df_scrapeado.loc[mask, 'Medidas_1'] = df_scrapeado.loc[mask, 'Descripcion'].str.extract(r'([\\d\\.]+(?:\\s?[x×]\\s?[\\d\\.]+)+)', expand=False)\n",
    "df_scrapeado.loc[mask, 'Descripcion'] = df_scrapeado.loc[mask, 'Descripcion'].str.replace(r',?\\s*[\\d\\.]+(?:\\s?[x×]\\s?[\\d\\.]+)+[^\\w]?\\s*$', '', regex=True).str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ddd2f667",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertir comas decimales a puntos para que no separe descripcion ahí en casos como 'Sofá con chaiselongue de 2,5 plazas'\n",
    "df_scrapeado['Descripcion'] = df_scrapeado['Descripcion'].str.replace(r'(\\d),(\\d)', r'\\1.\\2', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "59f5e4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conteo de comas en descripcion\n",
    "conteo_comas = df_scrapeado['Descripcion'].str.count(',')\n",
    "\n",
    "df_scrapeado['Color'] = ''\n",
    "\n",
    "# Si hay una coma, separar el color\n",
    "mask_1_coma = conteo_comas == 1\n",
    "split_1_coma = df_scrapeado.loc[mask_1_coma, 'Descripcion'].str.split(',', n=1, expand=True)\n",
    "df_scrapeado.loc[mask_1_coma, 'Descripcion'] = split_1_coma[0].str.strip()\n",
    "df_scrapeado.loc[mask_1_coma, 'Color'] = split_1_coma[1].str.strip()\n",
    "\n",
    "# Si hay dos comas, asignar la última a color y eliminar lo del medio\n",
    "mask_2_mas_comas = conteo_comas >= 2\n",
    "split_2_mas = df_scrapeado.loc[mask_2_mas_comas, 'Descripcion'].str.split(',', n=2, expand=True)\n",
    "df_scrapeado.loc[mask_2_mas_comas, 'Descripcion'] = split_2_mas[0].str.strip()\n",
    "df_scrapeado.loc[mask_2_mas_comas, 'Color'] = split_2_mas[2].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2810fad0",
   "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>Estancia</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Tipo_mueble</th>\n",
       "      <th>Mas_vendido</th>\n",
       "      <th>Enlace_producto</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Descripcion</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Opiniones_5</th>\n",
       "      <th>Opiniones_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Relacionado_8</th>\n",
       "      <th>Relacionado_9</th>\n",
       "      <th>Relacionado_10</th>\n",
       "      <th>Relacionado_11</th>\n",
       "      <th>Relacionado_12</th>\n",
       "      <th>ID</th>\n",
       "      <th>Resenas</th>\n",
       "      <th>Valoracion</th>\n",
       "      <th>Medidas_1</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>brimnes-mueble-tv-con-puertas-vidrio-blanco</td>\n",
       "      <td>BRIMNES</td>\n",
       "      <td>Mueble TV con puertas de vidrio</td>\n",
       "      <td>397.99</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>59602075</td>\n",
       "      <td>99602441</td>\n",
       "      <td>79398672</td>\n",
       "      <td>69398620</td>\n",
       "      <td>29398684</td>\n",
       "      <td>59278232</td>\n",
       "      <td>46</td>\n",
       "      <td>4.7</td>\n",
       "      <td>320x41x190</td>\n",
       "      <td>blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>kallax-lack-mueble-tv-con-estanteria-efecto-ro...</td>\n",
       "      <td>KALLAX / LACK</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>242.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>39477242</td>\n",
       "      <td>29560675</td>\n",
       "      <td>69494607</td>\n",
       "      <td>79420300</td>\n",
       "      <td>19521175</td>\n",
       "      <td>29552171</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>224x39x147</td>\n",
       "      <td>efecto roble tinte blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>hemnes-mueble-tv-con-estanteria-tinte-blanco-m...</td>\n",
       "      <td>HEMNES</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>777.00</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>99437333</td>\n",
       "      <td>9384051</td>\n",
       "      <td>59437330</td>\n",
       "      <td>39437331</td>\n",
       "      <td>59398673</td>\n",
       "      <td>19299571</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>326x197</td>\n",
       "      <td>tinte blanco/marrón claro vidrio incoloro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>baggebo-mueble-tv-con-estanteria-blanco</td>\n",
       "      <td>BAGGEBO</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>96.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>69494607</td>\n",
       "      <td>59184337</td>\n",
       "      <td>9503313</td>\n",
       "      <td>9552172</td>\n",
       "      <td>39184343</td>\n",
       "      <td>29443653</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>174x35x160</td>\n",
       "      <td>blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>besta-mueble-tv-con-puertas-vidrio-negro-marro...</td>\n",
       "      <td>BESTÅ</td>\n",
       "      <td>Mueble TV con puertas de vidrio</td>\n",
       "      <td>520.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>59507907</td>\n",
       "      <td>9488789</td>\n",
       "      <td>99406708</td>\n",
       "      <td>29406924</td>\n",
       "      <td>9412351</td>\n",
       "      <td>59406710</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>300x42x211</td>\n",
       "      <td>negro-marrón/Lappviken vidrio transparente neg...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estancia                         Categoria       Tipo_mueble  \\\n",
       "10784    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10785    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10786    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10787    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "10788    Salón  Muebles de almacenaje para salón  Muebles de salón   \n",
       "\n",
       "       Mas_vendido                                    Enlace_producto  \\\n",
       "10784            0        brimnes-mueble-tv-con-puertas-vidrio-blanco   \n",
       "10785            0  kallax-lack-mueble-tv-con-estanteria-efecto-ro...   \n",
       "10786            0  hemnes-mueble-tv-con-estanteria-tinte-blanco-m...   \n",
       "10787            0            baggebo-mueble-tv-con-estanteria-blanco   \n",
       "10788            0  besta-mueble-tv-con-puertas-vidrio-negro-marro...   \n",
       "\n",
       "              Nombre                      Descripcion  Precio  Opiniones_5  \\\n",
       "10784        BRIMNES  Mueble TV con puertas de vidrio  397.99           35   \n",
       "10785  KALLAX / LACK         Mueble TV con estantería  242.97            1   \n",
       "10786         HEMNES         Mueble TV con estantería  777.00           34   \n",
       "10787        BAGGEBO         Mueble TV con estantería   96.46            7   \n",
       "10788          BESTÅ  Mueble TV con puertas de vidrio  520.00            1   \n",
       "\n",
       "       Opiniones_4  ...  Relacionado_8  Relacionado_9  Relacionado_10  \\\n",
       "10784            8  ...       59602075       99602441        79398672   \n",
       "10785            0  ...       39477242       29560675        69494607   \n",
       "10786           11  ...       99437333        9384051        59437330   \n",
       "10787            1  ...       69494607       59184337         9503313   \n",
       "10788            0  ...       59507907        9488789        99406708   \n",
       "\n",
       "       Relacionado_11  Relacionado_12        ID  Resenas  Valoracion  \\\n",
       "10784        69398620        29398684  59278232       46         4.7   \n",
       "10785        79420300        19521175  29552171        1         5.0   \n",
       "10786        39437331        59398673  19299571       51         4.5   \n",
       "10787         9552172        39184343  29443653        8         4.9   \n",
       "10788        29406924         9412351  59406710        1         5.0   \n",
       "\n",
       "        Medidas_1                                              Color  \n",
       "10784  320x41x190                                             blanco  \n",
       "10785  224x39x147                          efecto roble tinte blanco  \n",
       "10786     326x197          tinte blanco/marrón claro vidrio incoloro  \n",
       "10787  174x35x160                                             blanco  \n",
       "10788  300x42x211  negro-marrón/Lappviken vidrio transparente neg...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificar resultados\n",
    "df_scrapeado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9269b51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10784        Ancho: 320 cm | Fondo: 41 cm | Altura: 190 cm\n",
       "10785    Ancho: 224 cm | Fondo: 39 cm | Peso máximo/bal...\n",
       "10786    Ancho: 326 cm | Altura: 197 cm | profundidad m...\n",
       "10787        Fondo: 35 cm | Altura: 160 cm | Ancho: 174 cm\n",
       "10788    Ancho: 300 cm | Fondo: 42 cm | Altura: 211 cm ...\n",
       "                               ...                        \n",
       "13822    Longitud: 120 cm | Ancho: 60 cm | Unidades: 2 ...\n",
       "13823     Ancho: 70 cm | Longitud: 140 cm | Peso: 405 g/m²\n",
       "13831    Longitud: 70 cm | Unidades: 2 unidad | Ancho: ...\n",
       "13832        Longitud: 31 cm | Ancho: 23 cm | Grosor: 9 cm\n",
       "13834    Superficie: 4.50 m² | Longitud: 300 cm | Peso:...\n",
       "Name: Medidas, Length: 1414, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_scrapeado[['Medidas', 'Medidas_1']].head(20)\n",
    "#df_scrapeado[df_scrapeado['Medidas_1'].notna()][['Medidas', 'Medidas_1']].tail(20)\n",
    "df_scrapeado[df_scrapeado['Medidas_1'].notna()]['Medidas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "11fdbef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medidas con 3 dimensiones procesadas: 715\n",
      "Medidas_1 no nulas restantes: 699\n",
      "      Medidas_1 Ancho Largo Altura\n",
      "10784      None   320    41    190\n",
      "10785      None   224    39    147\n",
      "10786   326x197   NaN   NaN    NaN\n",
      "10787      None   174    35    160\n",
      "10788      None   300    42    211\n",
      "10789      None   338    41    190\n",
      "10790      None   147    39     60\n",
      "10791      None   285    37    140\n",
      "10792      None   300    42    231\n",
      "10793      None   200    41     95\n"
     ]
    }
   ],
   "source": [
    "# Crear máscara para medidas con 3 dimensiones (2 'x')\n",
    "mask_3d = df_scrapeado['Medidas_1'].str.count('x') == 2\n",
    "\n",
    "# Separar solo las medidas con 3 dimensiones\n",
    "medidas_split = df_scrapeado.loc[mask_3d, 'Medidas_1'].str.split('x', expand=True)\n",
    "\n",
    "# Asignar a las nuevas columnas solo donde hay 3 dimensiones\n",
    "df_scrapeado.loc[mask_3d, 'Ancho'] = medidas_split[0]\n",
    "df_scrapeado.loc[mask_3d, 'Largo'] = medidas_split[1]\n",
    "df_scrapeado.loc[mask_3d, 'Altura'] = medidas_split[2]\n",
    "\n",
    "# BORRAR Medidas_1 donde teníamos 3 dimensiones\n",
    "df_scrapeado.loc[mask_3d, 'Medidas_1'] = None\n",
    "\n",
    "# Verificar\n",
    "print(f\"Medidas con 3 dimensiones procesadas: {mask_3d.sum()}\")\n",
    "print(f\"Medidas_1 no nulas restantes: {df_scrapeado['Medidas_1'].notna().sum()}\")\n",
    "print(df_scrapeado[['Medidas_1', 'Ancho', 'Largo', 'Altura']].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0cfc5149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10784    False\n",
       "10785    False\n",
       "10786     True\n",
       "10787    False\n",
       "10788    False\n",
       "Name: Medidas_1, dtype: bool"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado['Medidas_1'].notna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "52dd81ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# objeto de texto a float\n",
    "df_scrapeado['Ancho'] = pd.to_numeric(df_scrapeado['Ancho'], errors='coerce').astype('float32')\n",
    "df_scrapeado['Largo'] = pd.to_numeric(df_scrapeado['Largo'], errors='coerce').astype('float32')\n",
    "df_scrapeado['Altura'] = pd.to_numeric(df_scrapeado['Altura'], errors='coerce').astype('float32')\n",
    "\n",
    "# Redondear\n",
    "df_scrapeado['Ancho'] = df_scrapeado['Ancho'].round(1)\n",
    "df_scrapeado['Largo'] = df_scrapeado['Largo'].round(1)\n",
    "df_scrapeado['Altura'] = df_scrapeado['Altura'].round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "79cae765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10784    Ancho: 320 cm  |  Fondo: 41 cm  |  Altura: 190 cm\n",
       "10785    Ancho: 224 cm  |  Fondo: 39 cm  |  Altura: 147...\n",
       "10786    Ancho: 326 cm  |  Altura: 197 cm  |  profundid...\n",
       "10787    Fondo: 35 cm  |  Altura: 160 cm  |  Ancho: 174 cm\n",
       "10788    Ancho: 300 cm  |  Fondo: 42 cm  |  Altura: 211...\n",
       "                               ...                        \n",
       "13825                                                  NaN\n",
       "13831                     Longitud: 70 cm  |  Ancho: 70 cm\n",
       "13832    Longitud: 31 cm  |  Ancho: 23 cm  |  Grosor: 9 cm\n",
       "13833                  Grosor: 11.5 cm  |  Longitud: 28 cm\n",
       "13834                   Longitud: 300 cm  |  Ancho: 150 cm\n",
       "Name: Medidas, Length: 2671, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrar solo elementos que terminan exactamente con \"cm\" (no cm², kg, etc.)\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.findall(r'[^|]*\\bcm\\b[^|]*').str.join(' | ')\n",
    "df_scrapeado['Medidas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3d8ca9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medidas\n",
       "Altura                         1935\n",
       "Ancho                          1581\n",
       "Fondo                          1151\n",
       "Longitud                        407\n",
       "diámetro                        342\n",
       "                               ... \n",
       "Altura, posición horizontal       1\n",
       "Altura, posición vertical         1\n",
       "Ancho, posición horizontal        1\n",
       "Ancho, posición vertical          1\n",
       "Anchura inferior                  1\n",
       "Name: count, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado['Medidas'].str.split('|').explode().str.split(':', n=1).str[0].str.strip().value_counts()\n",
    "#.str.split('|') - Divide por cada \"|\"\n",
    "#.explode() - Convierte cada elemento de las listas en filas independientes\n",
    "#.str.split(':', n=1) - Divide por el primer \":\" (limitando a 2 partes)\n",
    "#.str[0] - Toma la parte antes de \":\" (el nombre de la dimensión)\n",
    "#.str.strip() - Elimina espacios sobrantes\n",
    "#.value_counts() - Cuenta la frecuencia de cada valor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eaa89494",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Extraer Altura a nueva columna\n",
    "df_scrapeado['Altura_1'] = df_scrapeado['Medidas'].str.extract(r'Altura[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Altura de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'Altura[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'Altura[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5696e3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraer Ancho a nueva columna\n",
    "df_scrapeado['Ancho_1'] = df_scrapeado['Medidas'].str.extract(r'Ancho[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Ancho de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'Ancho[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'Ancho[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()\n",
    "\n",
    "# Extraer Fondo a nueva columna\n",
    "df_scrapeado['Largo_1'] = df_scrapeado['Medidas'].str.extract(r'Fondo[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Fondo de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'Fondo[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'Fondo[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()\n",
    "\n",
    "# Extraer Longitud a nueva columna\n",
    "df_scrapeado['Largo_1'] = df_scrapeado['Medidas'].str.extract(r'Fondo[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Fondo de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'Fondo[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'Fondo[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()\n",
    "\n",
    "# Extraer Longitud a nueva columna\n",
    "df_scrapeado['Diametro'] = df_scrapeado['Medidas'].str.extract(r'diámetro[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Fondo de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'diámetro[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'diámetro[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68c9da8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraer Altura a nueva columna\n",
    "df_scrapeado['Longitud_1'] = df_scrapeado['Medidas'].str.extract(r'Longitud[^:]*:\\s*([^|]*)', flags=re.IGNORECASE)\n",
    "\n",
    "# Eliminar solo la parte de Altura de Medidas, manteniendo el resto\n",
    "df_scrapeado['Medidas'] = df_scrapeado['Medidas'].str.replace(r'Longitud[^|]*\\s*\\|\\s*', '', case=False, regex=True).str.replace(r'Longitud[^|]*', '', case=False, regex=True).str.replace(r'\\s*\\|\\s*$', '', regex=True).str.replace(r'^\\|\\s*', '', regex=True).str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a4eef70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado = df_scrapeado.drop('Medidas', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b69b417",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado[['Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1']] = df_scrapeado[['Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1']].replace(' cm', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "054c28a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado[['Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1']] = df_scrapeado[['Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1']].apply(pd.to_numeric, errors='coerce').round().astype('Int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b8b3ac62",
   "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>Medidas_1</th>\n",
       "      <th>Altura_1</th>\n",
       "      <th>Ancho_1</th>\n",
       "      <th>Largo_1</th>\n",
       "      <th>Diametro</th>\n",
       "      <th>Longitud_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>326x197</td>\n",
       "      <td>197</td>\n",
       "      <td>326</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10850</th>\n",
       "      <td>203x185</td>\n",
       "      <td>185</td>\n",
       "      <td>203</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10854</th>\n",
       "      <td>181x42</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>181</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10857</th>\n",
       "      <td>121x42</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>121</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10867</th>\n",
       "      <td>147x60</td>\n",
       "      <td>60</td>\n",
       "      <td>147</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13822</th>\n",
       "      <td>60x120</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>60</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823</th>\n",
       "      <td>70x140</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13831</th>\n",
       "      <td>70x70</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13832</th>\n",
       "      <td>31x23</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>23</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13834</th>\n",
       "      <td>150x300</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>150</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Medidas_1  Altura_1  Ancho_1  Largo_1  Diametro  Longitud_1\n",
       "10786   326x197       197      326     <NA>      <NA>        <NA>\n",
       "10850   203x185       185      203     <NA>      <NA>        <NA>\n",
       "10854    181x42      <NA>      181     <NA>      <NA>        <NA>\n",
       "10857    121x42      <NA>      121     <NA>      <NA>        <NA>\n",
       "10867    147x60        60      147     <NA>      <NA>        <NA>\n",
       "...         ...       ...      ...      ...       ...         ...\n",
       "13822    60x120      <NA>       60     <NA>      <NA>         120\n",
       "13823    70x140      <NA>       70     <NA>      <NA>         140\n",
       "13831     70x70      <NA>       70     <NA>      <NA>          70\n",
       "13832     31x23      <NA>       23     <NA>      <NA>          31\n",
       "13834   150x300      <NA>      150     <NA>      <NA>         300\n",
       "\n",
       "[699 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado[df_scrapeado['Medidas_1'].notna()][['Medidas_1','Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "161771d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado[['Medidas_a', 'Medidas_b']] = df_scrapeado['Medidas_1'].str.split('x', expand=True)\n",
    "df_scrapeado = df_scrapeado.drop('Medidas_1', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cc79724e",
   "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>Altura_1</th>\n",
       "      <th>Ancho_1</th>\n",
       "      <th>Largo_1</th>\n",
       "      <th>Diametro</th>\n",
       "      <th>Longitud_1</th>\n",
       "      <th>Medidas_a</th>\n",
       "      <th>Medidas_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>197</td>\n",
       "      <td>326</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>326</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10850</th>\n",
       "      <td>185</td>\n",
       "      <td>203</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>203</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10854</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>181</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>181</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10857</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>121</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>121</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10867</th>\n",
       "      <td>60</td>\n",
       "      <td>147</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>147</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13822</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>60</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>140</td>\n",
       "      <td>70</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13831</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13832</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>23</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13834</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>150</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>300</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Altura_1  Ancho_1  Largo_1  Diametro  Longitud_1 Medidas_a Medidas_b\n",
       "10786       197      326     <NA>      <NA>        <NA>       326       197\n",
       "10850       185      203     <NA>      <NA>        <NA>       203       185\n",
       "10854      <NA>      181     <NA>      <NA>        <NA>       181        42\n",
       "10857      <NA>      121     <NA>      <NA>        <NA>       121        42\n",
       "10867        60      147     <NA>      <NA>        <NA>       147        60\n",
       "...         ...      ...      ...       ...         ...       ...       ...\n",
       "13822      <NA>       60     <NA>      <NA>         120        60       120\n",
       "13823      <NA>       70     <NA>      <NA>         140        70       140\n",
       "13831      <NA>       70     <NA>      <NA>          70        70        70\n",
       "13832      <NA>       23     <NA>      <NA>          31        31        23\n",
       "13834      <NA>      150     <NA>      <NA>         300       150       300\n",
       "\n",
       "[699 rows x 7 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado[df_scrapeado['Medidas_b'].notna()][['Altura_1', 'Ancho_1', 'Largo_1', 'Diametro', 'Longitud_1','Medidas_a', 'Medidas_b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "373eecd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Poner el valor de diametro en largo y ancho si están vacíos ambos, y borrarla\n",
    "df_scrapeado.loc[df_scrapeado['Ancho_1'].isna() & df_scrapeado['Largo_1'].isna(), ['Ancho_1', 'Largo_1']] = df_scrapeado['Diametro']\n",
    "df_scrapeado = df_scrapeado.drop('Diametro', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "97a50ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Altura_1     478\n",
       "Ancho_1      525\n",
       "Largo_1     1718\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Coincidencia de medidas\n",
    "df_scrapeado[['Altura_1', 'Ancho_1', 'Largo_1']].astype(str).eq(df_scrapeado['Longitud_1'].astype(str), axis=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e5a3ba4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Si Largo está vacío, reemplazarlo por Longitud\n",
    "\n",
    "df_scrapeado.loc[df_scrapeado['Largo_1'].isna(), 'Largo_1'] = df_scrapeado['Longitud_1']\n",
    "df_scrapeado = df_scrapeado.drop('Longitud_1', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cec56459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ancho     24\n",
       "Largo     40\n",
       "Altura     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Coincidencia de medidas\n",
    "df_scrapeado[['Ancho', 'Largo', 'Altura']].astype('float64').eq(df_scrapeado['Largo_1'].astype('float64'), axis=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9125d7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado.loc[df_scrapeado['Ancho_1'].isna(), 'Ancho_1'] = df_scrapeado['Medidas_a']\n",
    "df_scrapeado.loc[df_scrapeado['Altura_1'].isna(), 'Altura_1'] = df_scrapeado['Medidas_b']\n",
    "df_scrapeado = df_scrapeado.drop(['Medidas_a', 'Medidas_b'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d2d8d7",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c58faa4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Enlace_producto</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>brimnes-mueble-tv-con-puertas-vidrio-blanco</td>\n",
       "      <td>blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>kallax-lack-mueble-tv-con-estanteria-efecto-ro...</td>\n",
       "      <td>efecto roble tinte blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>hemnes-mueble-tv-con-estanteria-tinte-blanco-m...</td>\n",
       "      <td>tinte blanco/marrón claro vidrio incoloro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>baggebo-mueble-tv-con-estanteria-blanco</td>\n",
       "      <td>blanco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>besta-mueble-tv-con-puertas-vidrio-negro-marro...</td>\n",
       "      <td>negro-marrón/Lappviken vidrio transparente neg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13825</th>\n",
       "      <td>len-saco-dormir-verde</td>\n",
       "      <td>0-6 meses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13831</th>\n",
       "      <td>len-gasa-lunares-luna</td>\n",
       "      <td>lunares/Luna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13832</th>\n",
       "      <td>bortberg-cojin-lumbar-negro</td>\n",
       "      <td>negro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13833</th>\n",
       "      <td>raggarv-cojin-cuello-lumbar</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13834</th>\n",
       "      <td>spjutsporre-tela-precortada-multicolor</td>\n",
       "      <td>multicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2671 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Enlace_producto  \\\n",
       "10784        brimnes-mueble-tv-con-puertas-vidrio-blanco   \n",
       "10785  kallax-lack-mueble-tv-con-estanteria-efecto-ro...   \n",
       "10786  hemnes-mueble-tv-con-estanteria-tinte-blanco-m...   \n",
       "10787            baggebo-mueble-tv-con-estanteria-blanco   \n",
       "10788  besta-mueble-tv-con-puertas-vidrio-negro-marro...   \n",
       "...                                                  ...   \n",
       "13825                              len-saco-dormir-verde   \n",
       "13831                              len-gasa-lunares-luna   \n",
       "13832                        bortberg-cojin-lumbar-negro   \n",
       "13833                        raggarv-cojin-cuello-lumbar   \n",
       "13834             spjutsporre-tela-precortada-multicolor   \n",
       "\n",
       "                                                   Color  \n",
       "10784                                             blanco  \n",
       "10785                          efecto roble tinte blanco  \n",
       "10786          tinte blanco/marrón claro vidrio incoloro  \n",
       "10787                                             blanco  \n",
       "10788  negro-marrón/Lappviken vidrio transparente neg...  \n",
       "...                                                  ...  \n",
       "13825                                          0-6 meses  \n",
       "13831                                       lunares/Luna  \n",
       "13832                                              negro  \n",
       "13833                                                     \n",
       "13834                                         multicolor  \n",
       "\n",
       "[2671 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado[['Enlace_producto', 'Color']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "40b94de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ancho     24\n",
       "Largo     40\n",
       "Altura     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Coincidencia de medidas\n",
    "df_scrapeado[['Ancho', 'Largo', 'Altura']].astype('float64').eq(df_scrapeado['Largo_1'].astype('float64'), axis=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "539161f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ancho     650\n",
       "Largo      46\n",
       "Altura     46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado[['Ancho', 'Largo', 'Altura']].astype('float64').eq(df_scrapeado['Ancho_1'].astype('float64'), axis=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9fccecc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ancho      42\n",
       "Largo      29\n",
       "Altura    654\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado[['Ancho', 'Largo', 'Altura']].astype('float64').eq(df_scrapeado['Altura_1'].astype('float64'), axis=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4e41f037",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado['Largo'] = df_scrapeado['Largo'].fillna(df_scrapeado['Largo_1'])\n",
    "df_scrapeado['Ancho'] = df_scrapeado['Ancho'].fillna(df_scrapeado['Ancho_1'])\n",
    "df_scrapeado['Altura'] = df_scrapeado['Altura'].fillna(df_scrapeado['Altura_1'])\n",
    "df_scrapeado = df_scrapeado.drop(['Largo_1','Altura_1', 'Ancho_1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9efccf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estancia</th>\n",
       "      <th>Categoria</th>\n",
       "      <th>Tipo_mueble</th>\n",
       "      <th>Mas_vendido</th>\n",
       "      <th>Enlace_producto</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Descripcion</th>\n",
       "      <th>Precio</th>\n",
       "      <th>Opiniones_5</th>\n",
       "      <th>Opiniones_4</th>\n",
       "      <th>Opiniones_3</th>\n",
       "      <th>Opiniones_2</th>\n",
       "      <th>Opiniones_1</th>\n",
       "      <th>Valoracion_Diseno</th>\n",
       "      <th>Valoracion_Montaje</th>\n",
       "      <th>Valoracion_Calidad</th>\n",
       "      <th>Valoracion_Relacion</th>\n",
       "      <th>Valoracion_Funcionamiento</th>\n",
       "      <th>Variantes_Nombres</th>\n",
       "      <th>Variantes_Enlaces</th>\n",
       "      <th>Imagen_principal</th>\n",
       "      <th>Relacionado_1</th>\n",
       "      <th>Relacionado_2</th>\n",
       "      <th>Relacionado_3</th>\n",
       "      <th>Relacionado_4</th>\n",
       "      <th>Relacionado_5</th>\n",
       "      <th>Relacionado_6</th>\n",
       "      <th>Relacionado_7</th>\n",
       "      <th>Relacionado_8</th>\n",
       "      <th>Relacionado_9</th>\n",
       "      <th>Relacionado_10</th>\n",
       "      <th>Relacionado_11</th>\n",
       "      <th>Relacionado_12</th>\n",
       "      <th>ID</th>\n",
       "      <th>Resenas</th>\n",
       "      <th>Valoracion</th>\n",
       "      <th>Color</th>\n",
       "      <th>Ancho</th>\n",
       "      <th>Largo</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10784</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>brimnes-mueble-tv-con-puertas-vidrio-blanco</td>\n",
       "      <td>BRIMNES</td>\n",
       "      <td>Mueble TV con puertas de vidrio</td>\n",
       "      <td>397.99</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/bri...</td>\n",
       "      <td>59398673</td>\n",
       "      <td>39602439</td>\n",
       "      <td>39477242</td>\n",
       "      <td>89184331</td>\n",
       "      <td>79602437</td>\n",
       "      <td>29278219</td>\n",
       "      <td>19521175</td>\n",
       "      <td>59602075</td>\n",
       "      <td>99602441</td>\n",
       "      <td>79398672</td>\n",
       "      <td>69398620</td>\n",
       "      <td>29398684</td>\n",
       "      <td>59278232</td>\n",
       "      <td>46</td>\n",
       "      <td>4.7</td>\n",
       "      <td>blanco</td>\n",
       "      <td>320.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>kallax-lack-mueble-tv-con-estanteria-efecto-ro...</td>\n",
       "      <td>KALLAX / LACK</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>242.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Blanco, Negro-marrón</td>\n",
       "      <td>s09552172, s89552173</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/kal...</td>\n",
       "      <td>89552173</td>\n",
       "      <td>59560674</td>\n",
       "      <td>9552172</td>\n",
       "      <td>89521073</td>\n",
       "      <td>49521112</td>\n",
       "      <td>69406286</td>\n",
       "      <td>59440610</td>\n",
       "      <td>39477242</td>\n",
       "      <td>29560675</td>\n",
       "      <td>69494607</td>\n",
       "      <td>79420300</td>\n",
       "      <td>19521175</td>\n",
       "      <td>29552171</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>efecto roble tinte blanco</td>\n",
       "      <td>224.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10786</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>hemnes-mueble-tv-con-estanteria-tinte-blanco-m...</td>\n",
       "      <td>HEMNES</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>777.00</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Negro-marrón/marrón claro vidrio incoloro</td>\n",
       "      <td>s09336569</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/hem...</td>\n",
       "      <td>9336569</td>\n",
       "      <td>19437332</td>\n",
       "      <td>39534817</td>\n",
       "      <td>50413526</td>\n",
       "      <td>19534818</td>\n",
       "      <td>19387902</td>\n",
       "      <td>59278232</td>\n",
       "      <td>99437333</td>\n",
       "      <td>9384051</td>\n",
       "      <td>59437330</td>\n",
       "      <td>39437331</td>\n",
       "      <td>59398673</td>\n",
       "      <td>19299571</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>tinte blanco/marrón claro vidrio incoloro</td>\n",
       "      <td>326.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10787</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>baggebo-mueble-tv-con-estanteria-blanco</td>\n",
       "      <td>BAGGEBO</td>\n",
       "      <td>Mueble TV con estantería</td>\n",
       "      <td>96.46</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/bag...</td>\n",
       "      <td>59278232</td>\n",
       "      <td>59278213</td>\n",
       "      <td>39477242</td>\n",
       "      <td>19521175</td>\n",
       "      <td>50409874</td>\n",
       "      <td>29560675</td>\n",
       "      <td>59398673</td>\n",
       "      <td>69494607</td>\n",
       "      <td>59184337</td>\n",
       "      <td>9503313</td>\n",
       "      <td>9552172</td>\n",
       "      <td>39184343</td>\n",
       "      <td>29443653</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>blanco</td>\n",
       "      <td>174.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10788</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Muebles de almacenaje para salón</td>\n",
       "      <td>Muebles de salón</td>\n",
       "      <td>0</td>\n",
       "      <td>besta-mueble-tv-con-puertas-vidrio-negro-marro...</td>\n",
       "      <td>BESTÅ</td>\n",
       "      <td>Mueble TV con puertas de vidrio</td>\n",
       "      <td>520.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Blanco/Lappviken vidrio transparente blanco, E...</td>\n",
       "      <td>s99406831, s59406833, s19556075, s29406924, s1...</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/bes...</td>\n",
       "      <td>19556075</td>\n",
       "      <td>59406833</td>\n",
       "      <td>59406852</td>\n",
       "      <td>19406929</td>\n",
       "      <td>99406831</td>\n",
       "      <td>29575295</td>\n",
       "      <td>59488800</td>\n",
       "      <td>59507907</td>\n",
       "      <td>9488789</td>\n",
       "      <td>99406708</td>\n",
       "      <td>29406924</td>\n",
       "      <td>9412351</td>\n",
       "      <td>59406710</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>negro-marrón/Lappviken vidrio transparente neg...</td>\n",
       "      <td>300.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13825</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Textiles para bebé</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>len-saco-dormir-verde</td>\n",
       "      <td>LEN</td>\n",
       "      <td>Saco dormir</td>\n",
       "      <td>13.99</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/len...</td>\n",
       "      <td>90588543</td>\n",
       "      <td>60453913</td>\n",
       "      <td>50457600</td>\n",
       "      <td>90542140</td>\n",
       "      <td>50573614</td>\n",
       "      <td>90542135</td>\n",
       "      <td>70572369</td>\n",
       "      <td>90573693</td>\n",
       "      <td>50113938</td>\n",
       "      <td>30526402</td>\n",
       "      <td>30530282</td>\n",
       "      <td>573616</td>\n",
       "      <td>542130</td>\n",
       "      <td>25</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0-6 meses</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13831</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Textiles para bebé</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>len-gasa-lunares-luna</td>\n",
       "      <td>LEN</td>\n",
       "      <td>Gasa</td>\n",
       "      <td>5.99</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/len...</td>\n",
       "      <td>90588543</td>\n",
       "      <td>60496432</td>\n",
       "      <td>60572416</td>\n",
       "      <td>90542140</td>\n",
       "      <td>70572369</td>\n",
       "      <td>60453913</td>\n",
       "      <td>50573614</td>\n",
       "      <td>414137</td>\n",
       "      <td>10573649</td>\n",
       "      <td>50113938</td>\n",
       "      <td>489000</td>\n",
       "      <td>60526392</td>\n",
       "      <td>90489208</td>\n",
       "      <td>9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>lunares/Luna</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13832</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Cojines lumbares</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>bortberg-cojin-lumbar-negro</td>\n",
       "      <td>BORTBERG</td>\n",
       "      <td>Cojín lumbar</td>\n",
       "      <td>12.99</td>\n",
       "      <td>218</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/bor...</td>\n",
       "      <td>60530351</td>\n",
       "      <td>40240989</td>\n",
       "      <td>60569522</td>\n",
       "      <td>70586470</td>\n",
       "      <td>30473205</td>\n",
       "      <td>44881100</td>\n",
       "      <td>40507813</td>\n",
       "      <td>80412016</td>\n",
       "      <td>552020</td>\n",
       "      <td>10531942</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>10461010</td>\n",
       "      <td>343</td>\n",
       "      <td>4.4</td>\n",
       "      <td>negro</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13833</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Cojines lumbares</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>raggarv-cojin-cuello-lumbar</td>\n",
       "      <td>RAGGARV</td>\n",
       "      <td>Cojín cuello/lumbar</td>\n",
       "      <td>9.99</td>\n",
       "      <td>143</td>\n",
       "      <td>55</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/rag...</td>\n",
       "      <td>10461010</td>\n",
       "      <td>44881100</td>\n",
       "      <td>40240989</td>\n",
       "      <td>80503436</td>\n",
       "      <td>10507584</td>\n",
       "      <td>89424468</td>\n",
       "      <td>80503441</td>\n",
       "      <td>20522032</td>\n",
       "      <td>80507608</td>\n",
       "      <td>30613055</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>60530351</td>\n",
       "      <td>240</td>\n",
       "      <td>4.3</td>\n",
       "      <td></td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>28.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13834</th>\n",
       "      <td>Salón</td>\n",
       "      <td>Telas por metro y accesorios de costura</td>\n",
       "      <td>Mantas y plaids</td>\n",
       "      <td>0</td>\n",
       "      <td>spjutsporre-tela-precortada-multicolor</td>\n",
       "      <td>SPJUTSPORRE</td>\n",
       "      <td>Tela precortada</td>\n",
       "      <td>19.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.ikea.com/es/es/images/products/spj...</td>\n",
       "      <td>579816</td>\n",
       "      <td>60579818</td>\n",
       "      <td>60579955</td>\n",
       "      <td>50608147</td>\n",
       "      <td>70608146</td>\n",
       "      <td>30601137</td>\n",
       "      <td>80579822</td>\n",
       "      <td>90579826</td>\n",
       "      <td>20608144</td>\n",
       "      <td>608159</td>\n",
       "      <td>40579838</td>\n",
       "      <td>90608150</td>\n",
       "      <td>579840</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>multicolor</td>\n",
       "      <td>150.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2671 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estancia                                Categoria       Tipo_mueble  \\\n",
       "10784    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10785    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10786    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10787    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "10788    Salón         Muebles de almacenaje para salón  Muebles de salón   \n",
       "...        ...                                      ...               ...   \n",
       "13825    Salón                       Textiles para bebé   Mantas y plaids   \n",
       "13831    Salón                       Textiles para bebé   Mantas y plaids   \n",
       "13832    Salón                         Cojines lumbares   Mantas y plaids   \n",
       "13833    Salón                         Cojines lumbares   Mantas y plaids   \n",
       "13834    Salón  Telas por metro y accesorios de costura   Mantas y plaids   \n",
       "\n",
       "       Mas_vendido                                    Enlace_producto  \\\n",
       "10784            0        brimnes-mueble-tv-con-puertas-vidrio-blanco   \n",
       "10785            0  kallax-lack-mueble-tv-con-estanteria-efecto-ro...   \n",
       "10786            0  hemnes-mueble-tv-con-estanteria-tinte-blanco-m...   \n",
       "10787            0            baggebo-mueble-tv-con-estanteria-blanco   \n",
       "10788            0  besta-mueble-tv-con-puertas-vidrio-negro-marro...   \n",
       "...            ...                                                ...   \n",
       "13825            0                              len-saco-dormir-verde   \n",
       "13831            0                              len-gasa-lunares-luna   \n",
       "13832            0                        bortberg-cojin-lumbar-negro   \n",
       "13833            0                        raggarv-cojin-cuello-lumbar   \n",
       "13834            0             spjutsporre-tela-precortada-multicolor   \n",
       "\n",
       "              Nombre                      Descripcion  Precio  Opiniones_5  \\\n",
       "10784        BRIMNES  Mueble TV con puertas de vidrio  397.99           35   \n",
       "10785  KALLAX / LACK         Mueble TV con estantería  242.97            1   \n",
       "10786         HEMNES         Mueble TV con estantería  777.00           34   \n",
       "10787        BAGGEBO         Mueble TV con estantería   96.46            7   \n",
       "10788          BESTÅ  Mueble TV con puertas de vidrio  520.00            1   \n",
       "...              ...                              ...     ...          ...   \n",
       "13825            LEN                      Saco dormir   13.99           11   \n",
       "13831            LEN                             Gasa    5.99            3   \n",
       "13832       BORTBERG                     Cojín lumbar   12.99          218   \n",
       "13833        RAGGARV              Cojín cuello/lumbar    9.99          143   \n",
       "13834    SPJUTSPORRE                  Tela precortada   19.99            0   \n",
       "\n",
       "       Opiniones_4  Opiniones_3  Opiniones_2  Opiniones_1  Valoracion_Diseno  \\\n",
       "10784            8            2            1            0                4.8   \n",
       "10785            0            0            0            0                4.0   \n",
       "10786           11            3            3            0                4.7   \n",
       "10787            1            0            0            0                4.6   \n",
       "10788            0            0            0            0                5.0   \n",
       "...            ...          ...          ...          ...                ...   \n",
       "13825            4            2            1            7                3.9   \n",
       "13831            5            0            1            0                4.3   \n",
       "13832           75           25           12           13                4.5   \n",
       "13833           55           23            7           12                4.3   \n",
       "13834            1            0            0            0                4.0   \n",
       "\n",
       "       Valoracion_Montaje  Valoracion_Calidad  Valoracion_Relacion  \\\n",
       "10784                 4.4                 4.4                  4.7   \n",
       "10785                 5.0                 5.0                  5.0   \n",
       "10786                 4.3                 4.1                  4.4   \n",
       "10787                 4.9                 4.5                  4.9   \n",
       "10788                 5.0                 5.0                  5.0   \n",
       "...                   ...                 ...                  ...   \n",
       "13825                 3.8                 3.8                  3.8   \n",
       "13831                 5.0                 3.9                  4.0   \n",
       "13832                 4.8                 4.4                  4.3   \n",
       "13833                 4.6                 4.3                  4.3   \n",
       "13834                 4.0                 5.0                  5.0   \n",
       "\n",
       "       Valoracion_Funcionamiento  \\\n",
       "10784                        4.8   \n",
       "10785                        5.0   \n",
       "10786                        4.6   \n",
       "10787                        4.5   \n",
       "10788                        5.0   \n",
       "...                          ...   \n",
       "13825                        3.4   \n",
       "13831                        4.1   \n",
       "13832                        4.4   \n",
       "13833                        4.2   \n",
       "13834                        4.0   \n",
       "\n",
       "                                       Variantes_Nombres  \\\n",
       "10784                                                NaN   \n",
       "10785                               Blanco, Negro-marrón   \n",
       "10786          Negro-marrón/marrón claro vidrio incoloro   \n",
       "10787                                                NaN   \n",
       "10788  Blanco/Lappviken vidrio transparente blanco, E...   \n",
       "...                                                  ...   \n",
       "13825                                                NaN   \n",
       "13831                                                NaN   \n",
       "13832                                                NaN   \n",
       "13833                                                NaN   \n",
       "13834                                                NaN   \n",
       "\n",
       "                                       Variantes_Enlaces  \\\n",
       "10784                                                NaN   \n",
       "10785                               s09552172, s89552173   \n",
       "10786                                          s09336569   \n",
       "10787                                                NaN   \n",
       "10788  s99406831, s59406833, s19556075, s29406924, s1...   \n",
       "...                                                  ...   \n",
       "13825                                                NaN   \n",
       "13831                                                NaN   \n",
       "13832                                                NaN   \n",
       "13833                                                NaN   \n",
       "13834                                                NaN   \n",
       "\n",
       "                                        Imagen_principal  Relacionado_1  \\\n",
       "10784  https://www.ikea.com/es/es/images/products/bri...       59398673   \n",
       "10785  https://www.ikea.com/es/es/images/products/kal...       89552173   \n",
       "10786  https://www.ikea.com/es/es/images/products/hem...        9336569   \n",
       "10787  https://www.ikea.com/es/es/images/products/bag...       59278232   \n",
       "10788  https://www.ikea.com/es/es/images/products/bes...       19556075   \n",
       "...                                                  ...            ...   \n",
       "13825  https://www.ikea.com/es/es/images/products/len...       90588543   \n",
       "13831  https://www.ikea.com/es/es/images/products/len...       90588543   \n",
       "13832  https://www.ikea.com/es/es/images/products/bor...       60530351   \n",
       "13833  https://www.ikea.com/es/es/images/products/rag...       10461010   \n",
       "13834  https://www.ikea.com/es/es/images/products/spj...         579816   \n",
       "\n",
       "       Relacionado_2  Relacionado_3  Relacionado_4  Relacionado_5  \\\n",
       "10784       39602439       39477242       89184331       79602437   \n",
       "10785       59560674        9552172       89521073       49521112   \n",
       "10786       19437332       39534817       50413526       19534818   \n",
       "10787       59278213       39477242       19521175       50409874   \n",
       "10788       59406833       59406852       19406929       99406831   \n",
       "...              ...            ...            ...            ...   \n",
       "13825       60453913       50457600       90542140       50573614   \n",
       "13831       60496432       60572416       90542140       70572369   \n",
       "13832       40240989       60569522       70586470       30473205   \n",
       "13833       44881100       40240989       80503436       10507584   \n",
       "13834       60579818       60579955       50608147       70608146   \n",
       "\n",
       "       Relacionado_6  Relacionado_7  Relacionado_8  Relacionado_9  \\\n",
       "10784       29278219       19521175       59602075       99602441   \n",
       "10785       69406286       59440610       39477242       29560675   \n",
       "10786       19387902       59278232       99437333        9384051   \n",
       "10787       29560675       59398673       69494607       59184337   \n",
       "10788       29575295       59488800       59507907        9488789   \n",
       "...              ...            ...            ...            ...   \n",
       "13825       90542135       70572369       90573693       50113938   \n",
       "13831       60453913       50573614         414137       10573649   \n",
       "13832       44881100       40507813       80412016         552020   \n",
       "13833       89424468       80503441       20522032       80507608   \n",
       "13834       30601137       80579822       90579826       20608144   \n",
       "\n",
       "       Relacionado_10  Relacionado_11  Relacionado_12        ID  Resenas  \\\n",
       "10784        79398672        69398620        29398684  59278232       46   \n",
       "10785        69494607        79420300        19521175  29552171        1   \n",
       "10786        59437330        39437331        59398673  19299571       51   \n",
       "10787         9503313         9552172        39184343  29443653        8   \n",
       "10788        99406708        29406924         9412351  59406710        1   \n",
       "...               ...             ...             ...       ...      ...   \n",
       "13825        30526402        30530282          573616    542130       25   \n",
       "13831        50113938          489000        60526392  90489208        9   \n",
       "13832        10531942            <NA>            <NA>  10461010      343   \n",
       "13833        30613055            <NA>            <NA>  60530351      240   \n",
       "13834          608159        40579838        90608150    579840        1   \n",
       "\n",
       "       Valoracion                                              Color  Ancho  \\\n",
       "10784         4.7                                             blanco  320.0   \n",
       "10785         5.0                          efecto roble tinte blanco  224.0   \n",
       "10786         4.5          tinte blanco/marrón claro vidrio incoloro  326.0   \n",
       "10787         4.9                                             blanco  174.0   \n",
       "10788         5.0  negro-marrón/Lappviken vidrio transparente neg...  300.0   \n",
       "...           ...                                                ...    ...   \n",
       "13825         3.4                                          0-6 meses   <NA>   \n",
       "13831         4.1                                       lunares/Luna   70.0   \n",
       "13832         4.4                                              negro   23.0   \n",
       "13833         4.3                                                      <NA>   \n",
       "13834         4.0                                         multicolor  150.0   \n",
       "\n",
       "       Largo  Altura  \n",
       "10784   41.0   190.0  \n",
       "10785   39.0   147.0  \n",
       "10786   <NA>   197.0  \n",
       "10787   35.0   160.0  \n",
       "10788   42.0   211.0  \n",
       "...      ...     ...  \n",
       "13825   <NA>    <NA>  \n",
       "13831   70.0    70.0  \n",
       "13832   31.0    23.0  \n",
       "13833   28.0    <NA>  \n",
       "13834  300.0   300.0  \n",
       "\n",
       "[2671 rows x 40 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df_scrapeado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b149ce03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recoger todas las columnas de relacionados en una lista\n",
    "columnas_rel = [f'Relacionado_{i}' for i in range(1, 13)]\n",
    "df_scrapeado['relacionados_lista'] = df_scrapeado[columnas_rel].values.tolist()\n",
    "\n",
    "# Limpiar valores vacíos\n",
    "df_scrapeado['relacionados_lista'] = df_scrapeado['relacionados_lista'].apply(\n",
    "    lambda x: [item for item in x if pd.notna(item) and item != '']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "962c68ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminar las columnas individuales de relacionados\n",
    "columnas_rel = [f'Relacionado_{i}' for i in range(1, 13)]\n",
    "df_scrapeado = df_scrapeado.drop(columns=columnas_rel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "052fa764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpiar y crear listas de las variantes de colores\n",
    "df_scrapeado['variantes_nombres_lista'] = df_scrapeado['Variantes_Nombres'].str.split(', ')\n",
    "df_scrapeado['variantes_enlaces_lista'] = df_scrapeado['Variantes_Enlaces'].str.replace('s', '').str.split(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d9149341",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminar las columnas 'Variantes_Nombres', 'Variantes_Enlaces'\n",
    "df_scrapeado = df_scrapeado.drop(columns=['Variantes_Nombres', 'Variantes_Enlaces'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "140efca6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aplicando limpieza de colores...\n",
      "\n",
      "==================================================\n",
      "     COMPROBACIÓN DE RESULTADOS (Primeras 15 Filas)\n",
      "==================================================\n",
      "                                                   Color Color_Limpio  \\\n",
      "10784                                             blanco       Blanco   \n",
      "10785                          efecto roble tinte blanco       Marrón   \n",
      "10786          tinte blanco/marrón claro vidrio incoloro       Blanco   \n",
      "10787                                             blanco       Blanco   \n",
      "10788  negro-marrón/Lappviken vidrio transparente neg...        Negro   \n",
      "10789                                             blanco       Blanco   \n",
      "10790                                             blanco       Blanco   \n",
      "10791                                             blanco       Blanco   \n",
      "10792        blanco/Lappviken vidrio transparente blanco       Blanco   \n",
      "10793                                             blanco       Blanco   \n",
      "10794  chapa roble tinte blanco Lappviken/Sindvik vid...       Marrón   \n",
      "10795                   blanco/efecto roble tinte blanco       Marrón   \n",
      "10796                            blanco/Lappviken blanco       Blanco   \n",
      "10797                                             blanco       Blanco   \n",
      "10798                                             blanco       Blanco   \n",
      "\n",
      "                                 variantes_nombres_lista  \\\n",
      "10784                                                NaN   \n",
      "10785                             [Blanco, Negro-marrón]   \n",
      "10786        [Negro-marrón/marrón claro vidrio incoloro]   \n",
      "10787                                                NaN   \n",
      "10788  [Blanco/Lappviken vidrio transparente blanco, ...   \n",
      "10789                                                NaN   \n",
      "10790          [Efecto roble tinte blanco, Negro-marrón]   \n",
      "10791                                                NaN   \n",
      "10792  [Blanco Lappviken/efecto nogal vidrio incoloro...   \n",
      "10793                                                NaN   \n",
      "10794                                                NaN   \n",
      "10795                                                NaN   \n",
      "10796              [Blanco/Selsviken alto brillo/blanco]   \n",
      "10797                                                NaN   \n",
      "10798                                                NaN   \n",
      "\n",
      "                                       Variantes_colores  \n",
      "10784                                                 []  \n",
      "10785                                    [Blanco, Negro]  \n",
      "10786                                            [Negro]  \n",
      "10787                                                 []  \n",
      "10788  [Blanco, Marrón, Gris Oscuro, Blanco, Negro, G...  \n",
      "10789                                                 []  \n",
      "10790                                    [Marrón, Negro]  \n",
      "10791                                                 []  \n",
      "10792  [Marrón, Blanco, Marrón, Marrón, Gris Oscuro, ...  \n",
      "10793                                                 []  \n",
      "10794                                                 []  \n",
      "10795                                                 []  \n",
      "10796                                           [Blanco]  \n",
      "10797                                                 []  \n",
      "10798                                                 []  \n",
      "\n",
      "==================================================\n",
      "     Distribución 'Color_Limpio' (Top 10)\n",
      "==================================================\n",
      "Color_Limpio\n",
      "Blanco         760\n",
      "Negro          199\n",
      "Marrón         170\n",
      "Gris           159\n",
      "Beige          107\n",
      "Verde           84\n",
      "Rojo            62\n",
      "Amarillo        49\n",
      "Azul            45\n",
      "Gris Oscuro     42\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "def extraer_color_mejorado(texto):\n",
    "    if pd.isna(texto):\n",
    "        return None\n",
    "    \n",
    "    texto = str(texto).lower()\n",
    "    \n",
    "    # Mapear variantes y sinónimos\n",
    "    mapeo_colores = {\n",
    "        'beis': 'beige',\n",
    "        'nogal': 'marrón', \n",
    "        'roble': 'marrón',\n",
    "        'turquesa': 'azul',\n",
    "        'verdoso': 'verde',\n",
    "        'grisáceo': 'gris',\n",
    "        'rojizo': 'rojo',\n",
    "        'anaranjado': 'naranja',\n",
    "        'dorado': 'amarillo'\n",
    "    }\n",
    "    \n",
    "    # Lista de colores en español\n",
    "    colores_espanol = ['blanco', 'negro', 'gris', 'marrón', 'rojo', 'azul', 'verde', \n",
    "                      'amarillo', 'rosa', 'morado', 'naranja', 'beige', 'antracita',\n",
    "                      'dorado', 'plateado', 'vainilla', 'hueso', 'natural', 'madera']\n",
    "    \n",
    "    if texto.startswith('+'):\n",
    "        return None\n",
    "    \n",
    "    for variante, color_base in mapeo_colores.items():\n",
    "        if variante in texto:\n",
    "            return color_base.title()\n",
    "    \n",
    "    for color in colores_espanol:\n",
    "        if color in texto:\n",
    "            palabras = texto.split()\n",
    "            if color in palabras:\n",
    "                idx = palabras.index(color)\n",
    "                if idx + 1 < len(palabras) and palabras[idx + 1] in ['claro', 'oscuro', 'medio', 'vivo']:\n",
    "                    return f\"{color} {palabras[idx + 1]}\".title()\n",
    "            return color.title()\n",
    "    \n",
    "    return None\n",
    "print(\"Aplicando limpieza de colores...\")\n",
    "\n",
    "df_scrapeado['Color_Limpio'] = df_scrapeado['Color'].apply(extraer_color_mejorado)\n",
    "\n",
    "df_scrapeado['Variantes_colores'] = df_scrapeado['variantes_nombres_lista'].apply(\n",
    "    lambda x: [extraer_color_mejorado(item) for item in x if extraer_color_mejorado(item) is not None] \n",
    "    if isinstance(x, list) else []\n",
    ")\n",
    "\n",
    "\n",
    "# Comprobación\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"     COMPROBACIÓN DE RESULTADOS (Primeras 15 Filas)\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "print(df_scrapeado[['Color', 'Color_Limpio', 'variantes_nombres_lista', 'Variantes_colores']].head(15))\n",
    "\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"     Distribución 'Color_Limpio' (Top 10)\")\n",
    "print(\"=\"*50)\n",
    "print(df_scrapeado['Color_Limpio'].value_counts().head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4f0812b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado = df_scrapeado.drop(columns=['variantes_nombres_lista', 'Color'])\n",
    "df_scrapeado = df_scrapeado.rename(columns={\n",
    "    'variantes_enlaces_lista': 'Variantes_ID',\n",
    "    'Color_Limpio': 'Color'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "304c04f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado.to_csv('furniture_scrapeado.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7758fed0",
   "metadata": {},
   "source": [
    "# Crear variable score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6fc3bd7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado = pd.read_csv('furniture_scrapeado.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6ffe6f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPUTAR VALORACIONES CON LA MEDIA\n",
    "rating_cols = ['Valoracion_Diseno', 'Valoracion_Montaje', \n",
    "               'Valoracion_Calidad', 'Valoracion_Relacion', \n",
    "               'Valoracion_Funcionamiento', 'Valoracion']\n",
    "\n",
    "for col in rating_cols:\n",
    "    df_scrapeado[col] = df_scrapeado[col].fillna(df_scrapeado[col].mean())\n",
    "\n",
    "# IMPUTAR DIMENSIONES\n",
    "dimension_cols = ['Ancho', 'Largo', 'Altura']\n",
    "df_scrapeado[dimension_cols] = df_scrapeado[dimension_cols].replace(-1, np.nan)\n",
    "\n",
    "# Imputar por 'Descripcion'\n",
    "for col in dimension_cols:\n",
    "    df_scrapeado[col] = df_scrapeado[col].fillna(\n",
    "        df_scrapeado.groupby('Descripcion')[col].transform('mean')\n",
    "    )\n",
    "\n",
    "# Imputar por 'Tipo_mueble'\n",
    "for col in dimension_cols:\n",
    "    df_scrapeado[col] = df_scrapeado[col].fillna(\n",
    "        df_scrapeado.groupby('Tipo_mueble')[col].transform('mean')\n",
    "    )\n",
    "\n",
    "# Media global por si queda algún caso aislado\n",
    "for col in dimension_cols:\n",
    "    df_scrapeado[col] = df_scrapeado[col].fillna(df_scrapeado[col].mean())\n",
    "\n",
    "# OTRAS IMPUTACIONES\n",
    "df_scrapeado['Variantes_ID'] = df_scrapeado['Variantes_ID'].fillna('Sin_variacion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ad015c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estancia                       0\n",
       "Categoria                      0\n",
       "Tipo_mueble                    0\n",
       "Mas_vendido                    0\n",
       "Enlace_producto                0\n",
       "Nombre                         0\n",
       "Descripcion                    0\n",
       "Precio                         0\n",
       "Opiniones_5                    0\n",
       "Opiniones_4                    0\n",
       "Opiniones_3                    0\n",
       "Opiniones_2                    0\n",
       "Opiniones_1                    0\n",
       "Valoracion_Diseno              0\n",
       "Valoracion_Montaje             0\n",
       "Valoracion_Calidad             0\n",
       "Valoracion_Relacion            0\n",
       "Valoracion_Funcionamiento      0\n",
       "Imagen_principal               0\n",
       "ID                             0\n",
       "Resenas                        0\n",
       "Valoracion                     0\n",
       "Ancho                          0\n",
       "Largo                          0\n",
       "Altura                         0\n",
       "relacionados_lista             0\n",
       "Variantes_ID                   0\n",
       "Color                        716\n",
       "Variantes_colores              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scrapeado.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7fb93736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nulos en 'Color' ANTES del parche: 716\n",
      "Nulos en 'Color' DESPUÉS del parche: 557\n",
      "Nulos en 'Color' DESPUÉS del relleno final: 0\n",
      "\n",
      "==================================================\n",
      "     Distribución 'Color' (Top 15)\n",
      "==================================================\n",
      "Color\n",
      "Blanco         779\n",
      "Desconocido    557\n",
      "Negro          217\n",
      "Gris           175\n",
      "Marrón         173\n",
      "Beige          128\n",
      "Verde          105\n",
      "Rojo            78\n",
      "Amarillo        59\n",
      "Azul            53\n",
      "Rosa            48\n",
      "Natural         46\n",
      "Gris Oscuro     42\n",
      "Hueso           38\n",
      "Antracita       34\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    extraer_color_mejorado\n",
    "except NameError:\n",
    "    print(\"ERROR: 'extraer_color_mejorado' no está definida.\")\n",
    "\n",
    "print(f\"Nulos en 'Color' ANTES del parche: {df_scrapeado['Color'].isnull().sum()}\")\n",
    "\n",
    "colores_del_enlace = df_scrapeado['Enlace_producto'].apply(extraer_color_mejorado)\n",
    "\n",
    "df_scrapeado['Color'] = df_scrapeado['Color'].fillna(colores_del_enlace)\n",
    "\n",
    "print(f\"Nulos en 'Color' DESPUÉS del parche: {df_scrapeado['Color'].isnull().sum()}\")\n",
    "\n",
    "df_scrapeado['Color'] = df_scrapeado['Color'].fillna('Desconocido')\n",
    "\n",
    "print(f\"Nulos en 'Color' DESPUÉS del relleno final: {df_scrapeado['Color'].isnull().sum()}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"     Distribución 'Color' (Top 15)\")\n",
    "print(\"=\"*50)\n",
    "print(df_scrapeado['Color'].value_counts().head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f3b3563b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DISTRIBUCIÓN DEL SCORE ===\n",
      "count    2671.000000\n",
      "mean        0.648073\n",
      "std         0.136992\n",
      "min         0.000000\n",
      "25%         0.571744\n",
      "50%         0.648521\n",
      "75%         0.730503\n",
      "max         1.000000\n",
      "Name: Score, dtype: float64\n",
      "\n",
      "=== PERCENTILES ===\n",
      "Percentil 0%: 0.000000\n",
      "Percentil 10%: 0.464287\n",
      "Percentil 25%: 0.571744\n",
      "Percentil 50%: 0.648521\n",
      "Percentil 75%: 0.730503\n",
      "Percentil 90%: 0.812082\n",
      "Percentil 95%: 0.884448\n",
      "Percentil 99%: 0.996325\n",
      "Percentil 100%: 1.000000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats.mstats import winsorize\n",
    "\n",
    "def calculadora_score_robusta(df):\n",
    "    # Agrupación por descripción\n",
    "    descripcion_counts = df['Descripcion'].value_counts()\n",
    "    #Si la descripción es menor que 10 en el value_counts, la agrupa por la Categoría\n",
    "    grupo_mapeo = {}\n",
    "    for descripcion in df['Descripcion'].unique():\n",
    "        if descripcion_counts[descripcion] < 10:\n",
    "            moda_categoria = df[df['Descripcion'] == descripcion]['Categoria'].mode()\n",
    "            grupo_mapeo[descripcion] = f\"CATEGORIA_{moda_categoria.iloc[0]}\" if len(moda_categoria) > 0 else \"OTROS\"\n",
    "        else:\n",
    "            grupo_mapeo[descripcion] = descripcion\n",
    "    \n",
    "    df['grupo_agrupacion'] = df['Descripcion'].map(grupo_mapeo)\n",
    "    \n",
    "    # Calcular precios medios por grupo\n",
    "    precios_medios = df.groupby('grupo_agrupacion')['Precio'].transform('mean')\n",
    "    \n",
    "    # Cálculo de factores \n",
    "    \n",
    "    # Factor de Popularidad (30%)\n",
    "    popularidad = (\n",
    "        (df['Mas_vendido'] * 2) + \n",
    "        (np.log(df['Resenas'] + 1) * 0.5) +\n",
    "        ((df['Opiniones_5'] / (df['Resenas'] + 1)) * 0.5)\n",
    "    )\n",
    "    \n",
    "    # Factor de Calidad (40%)\n",
    "    calidad = (\n",
    "        (df['Valoracion'].fillna(0) * 0.4) +\n",
    "        (df['Valoracion_Diseno'].fillna(0) * 0.15) +\n",
    "        (df['Valoracion_Calidad'].fillna(0) * 0.25) +\n",
    "        (df['Valoracion_Funcionamiento'].fillna(0) * 0.2)\n",
    "    )\n",
    "    \n",
    "    # Factor de Relación Calidad-Precio (30%)\n",
    "    # limitar ratio y excluir productos muy  para evitar outliers\n",
    "    precio_minimo_considerar = 10  # Productos más baratos se tratan diferente\n",
    "    \n",
    "    # Para productos con precio >= 10€: cálculo normal con límites\n",
    "    mascara_precio_normal = df['Precio'] >= precio_minimo_considerar\n",
    "    ratio_precio_normal = df['Precio'] / precios_medios\n",
    "    ratio_precio_normal = np.clip(ratio_precio_normal, 0.2, 5)\n",
    "    # Para productos muy baratos (<10€): usar valor fijo conservador\n",
    "    # Esto evita que dominen por ser extremadamente baratos\n",
    "    ratio_precio_baratos = np.ones(len(df)) * 2.0 \n",
    "    \n",
    "    # Combinar ambos\n",
    "    ratio_precio_final = np.where(mascara_precio_normal, \n",
    "                                 ratio_precio_normal, \n",
    "                                 ratio_precio_baratos)\n",
    "    \n",
    "    relacion_calidad_precio = (\n",
    "        ((1 / ratio_precio_final) * 0.6) + # Inverso del ratio precio\n",
    "        (df['Valoracion_Relacion'].fillna(0) * 0.4) # Valoración explícita de los clientes\n",
    "    )\n",
    "    \n",
    "    # Combinar factores\n",
    "    score_final = (\n",
    "        (popularidad * 0.30) +\n",
    "        (calidad * 0.40) +\n",
    "        (relacion_calidad_precio * 0.30)\n",
    "    )\n",
    "    \n",
    "    # Aplicar winsorization (1% superior)\n",
    "    score_final = winsorize(score_final, limits=[0, 0.01])\n",
    "    \n",
    "    # Normalizar\n",
    "    score_normalizado = (score_final - score_final.min()) / (score_final.max() - score_final.min())\n",
    "    \n",
    "    return score_normalizado\n",
    "\n",
    "df_scrapeado['Score'] = calculadora_score_robusta(df_scrapeado)\n",
    "\n",
    "# resultados\n",
    "print(\"=== DISTRIBUCIÓN DEL SCORE ===\")\n",
    "print(df_scrapeado['Score'].describe())\n",
    "\n",
    "print(\"\\n=== PERCENTILES ===\")\n",
    "for p in [0, 10, 25, 50, 75, 90, 95, 99, 100]:\n",
    "    valor = df_scrapeado['Score'].quantile(p/100)\n",
    "    print(f\"Percentil {p}%: {valor:.6f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c4496c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "df_scrapeado['Score'].hist(bins=50, alpha=0.7, edgecolor='black')\n",
    "plt.title('Distribución de Scores de Optimalidad')\n",
    "plt.xlabel('Score (0-1)')\n",
    "plt.ylabel('Número de Productos')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.axvline(df_scrapeado['Score'].mean(), color='red', linestyle='--', label=f'Media: {df_scrapeado[\"Score\"].mean():.3f}')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "56f79be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "df_scrapeado['Score'].plot(kind='box')\n",
    "plt.title('Boxplot del Score - Detección de Outliers')\n",
    "plt.ylabel('Score')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a2f231cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scrapeado.to_csv('furniture_score.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b4e32f",
   "metadata": {},
   "source": [
    "# Modelado"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a46727",
   "metadata": {},
   "source": [
    "# Sistema Híbrido de Recomendación de Muebles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "29632b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import re \n",
    "from typing import List, Dict, Optional, Any, Tuple\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.model_selection import train_test_split \n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "pd.set_option('display.max_colwidth', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6cd921",
   "metadata": {},
   "source": [
    "## Generación del Dataset para Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4ff60222",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset cargado: 2671 productos.\n",
      "Columnas 'Variantes_ID_parsed' y 'relacionados_lista_parsed' creadas.\n"
     ]
    }
   ],
   "source": [
    "def parse_id_list(s: str) -> List[int]:\n",
    "    \"\"\"\n",
    "    Convierte un string como \"[123, 456]\" o \"Sin_variacion\" en una lista de IDs.\n",
    "    \"\"\"\n",
    "    if not isinstance(s, str) or '[' not in s:\n",
    "        return []\n",
    "    \n",
    "    # Usamos regex para encontrar todos los números\n",
    "    ids = re.findall(r'\\d+', s)\n",
    "    return [int(id) for id in ids]\n",
    "\n",
    "# Cargar el dataset\n",
    "try:\n",
    "    df = pd.read_csv(\"furniture_score.csv\")\n",
    "    print(f\"Dataset cargado: {df.shape[0]} productos.\")\n",
    "\n",
    "    # Convertir las columnas de listas-string a listas reales\n",
    "    df['Variantes_ID_parsed'] = df['Variantes_ID'].apply(parse_id_list)\n",
    "    df['relacionados_lista_parsed'] = df['relacionados_lista'].apply(parse_id_list)\n",
    "    \n",
    "    # Crear un set de todos los IDs para muestreo rápido\n",
    "    all_product_ids = set(df['ID'])\n",
    "\n",
    "    print(\"Columnas 'Variantes_ID_parsed' y 'relacionados_lista_parsed' creadas.\")\n",
    "    \n",
    "except FileNotFoundError:\n",
    "    print(\"ERROR: No se encontró 'furniture_score.csv'.\")\n",
    "    df = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "da0f78f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se generaron 31141 tripletes de datos.\n",
      " - Tamaño del Training Set: 24912 tripletes\n",
      " - Tamaño del Test Set:    6229 tripletes\n"
     ]
    }
   ],
   "source": [
    "if df is not None:\n",
    "    triplets = []\n",
    "    \n",
    "    # Iteramos por cada producto (Ancla)\n",
    "    for _, row in df.iterrows():\n",
    "        anchor_id = row['ID']\n",
    "        \n",
    "        # Crear Positivos desde Variantes\n",
    "        positive_ids = set(row['Variantes_ID_parsed'])\n",
    "        # Crear Positivos desde Relacionados\n",
    "        positive_ids.update(row['relacionados_lista_parsed'])\n",
    "\n",
    "        if not positive_ids:\n",
    "            continue # Si no hay positivos, no podemos crear un triplete\n",
    "\n",
    "        # IDs que NO son positivos (Ancla + Positivos)\n",
    "        non_positive_ids = all_product_ids - positive_ids - {anchor_id}\n",
    "        if not non_positive_ids:\n",
    "            continue # Caso raro: un item está relacionado con todo\n",
    "\n",
    "        # Crear un triplete por cada positivo encontrado\n",
    "        for pos_id in positive_ids:\n",
    "            # Seleccionar un negativo aleatorio\n",
    "            neg_id = np.random.choice(list(non_positive_ids))\n",
    "            triplets.append((anchor_id, pos_id, neg_id))\n",
    "\n",
    "    print(f\"Se generaron {len(triplets)} tripletes de datos.\")\n",
    "\n",
    "    if triplets:\n",
    "        train_triplets, test_triplets = train_test_split(triplets, test_size=0.2, random_state=42)\n",
    "\n",
    "        print(f\" - Tamaño del Training Set: {len(train_triplets)} tripletes\")\n",
    "        print(f\" - Tamaño del Test Set:    {len(test_triplets)} tripletes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "12e3398b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if df is not None:\n",
    "    #Preprocesar Texto: Combinar columnas relevantes y limpiar NaNs\n",
    "    text_cols = ['Nombre', 'Descripcion', 'Categoria', 'Tipo_mueble', 'grupo_agrupacion']\n",
    "    for col in text_cols:\n",
    "        if col in df.columns:\n",
    "            df[col] = df[col].fillna('')\n",
    "        \n",
    "    df['text_data'] = df['Nombre'] + ' ' + df['Descripcion'] + ' ' + df['Categoria'] + ' ' + df['Tipo_mueble'] + ' ' + df['grupo_agrupacion']\n",
    "\n",
    "    # Vectorizar con TF-IDF\n",
    "    tfidf_vec = TfidfVectorizer(max_features=5000)\n",
    "    tfidf_matrix = tfidf_vec.fit_transform(df['text_data'])\n",
    "\n",
    "    # Reducir Dimensionalidad con SVD (Crear los \"embeddings\")\n",
    "    svd = TruncatedSVD(n_components=64, random_state=42) # 64 dimensiones\n",
    "    vector_estilo = svd.fit_transform(tfidf_matrix)\n",
    "\n",
    "    # Añadir vectores al DataFrame\n",
    "    df['vector_estilo'] = list(vector_estilo)\n",
    "    df[['Nombre', 'vector_estilo']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee289fd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Definida la arquitectura del modelo\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import BertModel, BertTokenizer\n",
    "\n",
    "\n",
    "class StyleEncoder(nn.Module):\n",
    "    def __init__(self, n_dims=128):\n",
    "        super(StyleEncoder, self).__init__()\n",
    "        \n",
    "        # modelo BERT pre-entrenado.\n",
    "        self.bert = BertModel.from_pretrained('bert-base-multilingual-cased')\n",
    "        self.fc = nn.Linear(self.bert.config.hidden_size, n_dims)\n",
    "        \n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        #  Pasa los tokens por BERT\n",
    "        bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
    "        pooler_output = bert_output[1] \n",
    "        \n",
    "        # Pasa la salida de BERT por la capa final\n",
    "        vector_estilo = self.fc(pooler_output)\n",
    "        \n",
    "        return vector_estilo\n",
    "\n",
    "print(\"Definida la arquitectura del modelo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ed01ce43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Definida la clase 'TripletFurnitureDataset'\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from transformers import BertTokenizer\n",
    "\n",
    "class TripletFurnitureDataset(Dataset):\n",
    "    def __init__(self, triplets, df_lookup, tokenizer):\n",
    "        self.triplets = triplets\n",
    "        self.df_lookup = df_lookup # El DataFrame con ID como índice\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_len = 64 # Longitud máxima de tokens (ej. 64 palabras)\n",
    "\n",
    "    def __len__(self):\n",
    "        # Devuelve el número total de tripletes\n",
    "        return len(self.triplets)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # Obtener los IDs del triplete\n",
    "        anchor_id, pos_id, neg_id = self.triplets[idx]\n",
    "        \n",
    "        # Buscar el texto de cada ID en el DataFrame\n",
    "        anchor_text = self.df_lookup.get(anchor_id, {}).get('text_data', '')\n",
    "        pos_text = self.df_lookup.get(pos_id, {}).get('text_data', '')\n",
    "        neg_text = self.df_lookup.get(neg_id, {}).get('text_data', '')\n",
    "        \n",
    "        # Tokenizar\n",
    "        anchor_tokens = self.tokenizer(\n",
    "            anchor_text, \n",
    "            padding='max_length',   \n",
    "            truncation=True,       \n",
    "            max_length=self.max_len, \n",
    "            return_tensors='pt'      \n",
    "        )\n",
    "        \n",
    "        pos_tokens = self.tokenizer(\n",
    "            pos_text, \n",
    "            padding='max_length', \n",
    "            truncation=True, \n",
    "            max_length=self.max_len, \n",
    "            return_tensors='pt'\n",
    "        )\n",
    "        \n",
    "        neg_tokens = self.tokenizer(\n",
    "            neg_text, \n",
    "            padding='max_length', \n",
    "            truncation=True, \n",
    "            max_length=self.max_len, \n",
    "            return_tensors='pt'\n",
    "        )\n",
    "        \n",
    "        # Devolvemos los 3 grupos de tokens\n",
    "        return anchor_tokens, pos_tokens, neg_tokens\n",
    "\n",
    "print(\"Definida la clase 'TripletFurnitureDataset'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "832d3f09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iniciando configuración del entrenamiento...\n",
      "Usando dispositivo: cpu\n",
      "Creando DataLoaders...\n",
      "Iniciando Entrenamiento por 3 épocas\n",
      "con Early Stopping: Paciencia=3 chequeos\n",
      "  [Época 1/3, Lote 50/1557] Loss: 0.6967\n",
      "    -> Mejora! (Nuevo mejor loss: 0.6967. Reiniciando paciencia.)\n",
      "  [Época 1/3, Lote 100/1557] Loss: 0.7759\n",
      "    -> No hay mejora. (Chequeo 1/3 sin mejora)\n",
      "  [Época 1/3, Lote 150/1557] Loss: 0.1995\n",
      "    -> Mejora! (Nuevo mejor loss: 0.1995. Reiniciando paciencia.)\n",
      "  [Época 1/3, Lote 200/1557] Loss: 0.3397\n",
      "    -> No hay mejora. (Chequeo 1/3 sin mejora)\n",
      "  [Época 1/3, Lote 250/1557] Loss: 0.3699\n",
      "    -> No hay mejora. (Chequeo 2/3 sin mejora)\n",
      "  [Época 1/3, Lote 300/1557] Loss: 0.5312\n",
      "    -> No hay mejora. (Chequeo 3/3 sin mejora)\n",
      "\n",
      "--- DETENCIÓN TEMPRANA: El loss no ha mejorado en 3 chequeos. ---\n",
      "--- Fin Época 1, Loss Promedio: 0.4585 ---\n",
      "¡Entrenamiento completado (o detenido tempranamente)!\n",
      "Cargado el 'best_model.pth' con el loss más bajo.\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "import numpy as np \n",
    "import os\n",
    "\n",
    "# Entrenamiento con Early Stopping\n",
    "\n",
    "if 'train_triplets' in locals() and df is not None:\n",
    "    \n",
    "\n",
    "    print(\"Iniciando configuración del entrenamiento...\")\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    print(f\"Usando dispositivo: {device}\")\n",
    "\n",
    "    tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
    "    model = StyleEncoder(n_dims=128).to(device)\n",
    "\n",
    "    df['text_data'] = df['Nombre'] + ' ' + df['Descripcion']\n",
    "    df_lookup = df.set_index('ID').to_dict('index')\n",
    "\n",
    "    print(\"Creando DataLoaders...\")\n",
    "    train_dataset = TripletFurnitureDataset(train_triplets, df_lookup, tokenizer)\n",
    "    train_loader = DataLoader(\n",
    "        train_dataset, \n",
    "        batch_size=16,\n",
    "        shuffle=True\n",
    "    )\n",
    "\n",
    "    #  Función de Pérdida y Optimizador\n",
    "    loss_fn = nn.TripletMarginLoss(margin=1.0)\n",
    "    optimizer = optim.Adam(model.parameters(), lr=1e-5)\n",
    "    \n",
    "    NUM_EPOCHS = 3\n",
    "    \n",
    "\n",
    "    patience = 3 \n",
    "    checks_without_improvement = 0\n",
    "    best_loss_at_check = float('inf') \n",
    "    \n",
    "    print(f\"Iniciando Entrenamiento por {NUM_EPOCHS} épocas\")\n",
    "    print(f\"con Early Stopping: Paciencia={patience} chequeos\")\n",
    "    \n",
    "\n",
    "    model.train()\n",
    "    \n",
    "    stop_training = False \n",
    "    \n",
    "    for epoch in range(NUM_EPOCHS):\n",
    "        total_loss = 0\n",
    "        \n",
    "        for i, (anchor_tokens, pos_tokens, neg_tokens) in enumerate(train_loader):\n",
    "            \n",
    "            anchor_input_ids = anchor_tokens['input_ids'].squeeze(1).to(device)\n",
    "            anchor_attention_mask = anchor_tokens['attention_mask'].squeeze(1).to(device)\n",
    "            pos_input_ids = pos_tokens['input_ids'].squeeze(1).to(device)\n",
    "            pos_attention_mask = pos_tokens['attention_mask'].squeeze(1).to(device)\n",
    "            neg_input_ids = neg_tokens['input_ids'].squeeze(1).to(device)\n",
    "            neg_attention_mask = neg_tokens['attention_mask'].squeeze(1).to(device)\n",
    "\n",
    "            vec_anchor = model(anchor_input_ids, anchor_attention_mask)\n",
    "            vec_pos = model(pos_input_ids, pos_attention_mask)\n",
    "            vec_neg = model(neg_input_ids, neg_attention_mask)\n",
    "            \n",
    "            loss = loss_fn(vec_anchor, vec_pos, vec_neg)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            total_loss += loss.item()\n",
    "            \n",
    "            if (i + 1) % 50 == 0:\n",
    "                current_loss = loss.item()\n",
    "                print(f\"  [Época {epoch+1}/{NUM_EPOCHS}, Lote {i+1}/{len(train_loader)}] Loss: {current_loss:.4f}\")\n",
    "\n",
    "                if current_loss < best_loss_at_check:\n",
    "                    print(f\"    -> Mejora! (Nuevo mejor loss: {current_loss:.4f}. Reiniciando paciencia.)\")\n",
    "                    best_loss_at_check = current_loss\n",
    "                    checks_without_improvement = 0\n",
    "                    torch.save(model.state_dict(), 'best_model.pth')\n",
    "                else:\n",
    "                    checks_without_improvement += 1\n",
    "                    print(f\"    -> No hay mejora. (Chequeo {checks_without_improvement}/{patience} sin mejora)\")\n",
    "                \n",
    "                if checks_without_improvement >= patience:\n",
    "                    print(f\"\\n--- DETENCIÓN TEMPRANA: El loss no ha mejorado en {patience} chequeos. ---\")\n",
    "                    stop_training = True\n",
    "                    break \n",
    "\n",
    "        print(f\"--- Fin Época {epoch+1}, Loss Promedio: {total_loss / (i+1):.4f} ---\")\n",
    "        \n",
    "        if stop_training:\n",
    "            break \n",
    "\n",
    "    print(\"¡Entrenamiento completado (o detenido tempranamente)!\")\n",
    "\n",
    "    if os.path.exists('best_model.pth'):\n",
    "        model.load_state_dict(torch.load('best_model.pth'))\n",
    "        print(\"Cargado el 'best_model.pth' con el loss más bajo.\")\n",
    "    else:\n",
    "        print(\"Entrenamiento finalizado. Se usará el modelo final (no se guardó un 'best_model').\")\n",
    "\n",
    "else:\n",
    "    print(\"Error: Ejecuta primero las celdas para generar 'df' y 'train_triplets'.\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "861f4344",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cargando el modelo entrenado...\n",
      "Modelo cargado y listo en el dispositivo: cpu\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from transformers import BertTokenizer\n",
    "\n",
    "\n",
    "\n",
    "print(\"Cargando el modelo entrenado...\")\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "try:\n",
    "    model = StyleEncoder(n_dims=128).to(device)\n",
    "except NameError:\n",
    "    print(\"ERROR: La clase 'StyleEncoder' no está definida.\")\n",
    "\n",
    "if os.path.exists('best_model.pth'):\n",
    "    model.load_state_dict(torch.load('best_model.pth', map_location=device))\n",
    "else:\n",
    "    print(\"No se encontró 'best_model.pth'.\")\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
    "\n",
    "print(f\"Modelo cargado y listo en el dispositivo: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ae03662f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generando vectores de estilo para todos los productos...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generando Vectores: 100%|██████████| 2671/2671 [01:46<00:00, 25.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- ¡Completado! ---\n",
      "Se han generado 2671 vectores inteligentes.\n",
      "El DataFrame 'df_scrapeado' está 100% listo.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "if 'model' in locals() and 'df_scrapeado' in locals() and 'tokenizer' in locals():\n",
    "    print(\"Generando vectores de estilo para todos los productos...\")\n",
    "\n",
    "    model.eval() \n",
    "    \n",
    "    df_scrapeado['text_data'] = df_scrapeado['Nombre'].fillna('') + ' ' + df_scrapeado['Descripcion'].fillna('')\n",
    "    \n",
    "    generated_vectors = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for text in tqdm(df_scrapeado['text_data'], desc=\"Generando Vectores\"):\n",
    "            \n",
    "            tokens = tokenizer(\n",
    "                text, \n",
    "                padding='max_length', \n",
    "                truncation=True, \n",
    "                max_length=64,\n",
    "                return_tensors='pt'\n",
    "            )\n",
    "            \n",
    "            input_ids = tokens['input_ids'].to(device)\n",
    "            attention_mask = tokens['attention_mask'].to(device)\n",
    "            \n",
    "            vector_estilo = model(input_ids, attention_mask)\n",
    "            \n",
    "            generated_vectors.append(vector_estilo.cpu().detach().numpy().flatten())\n",
    "\n",
    "    df_scrapeado['vector_estilo'] = generated_vectors\n",
    "    \n",
    "    print(\"\\n--- ¡Completado! ---\")\n",
    "    print(f\"Se han generado {len(generated_vectors)} vectores inteligentes.\")\n",
    "    print(\"El DataFrame 'df_scrapeado' está 100% listo.\")\n",
    "\n",
    "else:\n",
    "    print(\"Error: No se encontró 'model', 'df_scrapeado' o 'tokenizer'.\")\n",
    "    print(\"Asegúrate de haber ejecutado las celdas anteriores.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9da614",
   "metadata": {},
   "source": [
    "## Optimización (Knapsack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2e76171c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from typing import List, Dict, Optional, Any\n",
    "from IPython.display import display, Image\n",
    "\n",
    "def calcular_coherencia_media(combinacion_vectores: List[np.ndarray]) -> float:\n",
    "    if len(combinacion_vectores) < 2: return 1.0\n",
    "    vectores = np.array(combinacion_vectores)\n",
    "    sim_matrix = cosine_similarity(vectores)\n",
    "    indices_triu = np.triu_indices_from(sim_matrix, k=1)\n",
    "    return float(np.mean(sim_matrix[indices_triu])) if indices_triu[0].size > 0 else 1.0\n",
    "\n",
    "def recomendar_combinacion(df_completo: pd.DataFrame, \n",
    "                           tipos_mueble_con_opciones: List[Dict[str, str]],\n",
    "                           presupuesto: float, \n",
    "                           pesos: Dict[str, float] = {'base': 0.5, 'coherencia': 0.5},\n",
    "                           top_n: int = 5) -> List[Dict[str, Any]]:\n",
    "    \n",
    "    print(f\"\\nBuscando {top_n} mejores combinaciones para: {tipos_mueble_con_opciones} (Presupuesto: {presupuesto:.2f}€)\")\n",
    "    \n",
    "    listas_candidatos = []\n",
    "    \n",
    "    for item_request in tipos_mueble_con_opciones:\n",
    "        tipo = item_request['tipo']\n",
    "        color = item_request.get('color', None)\n",
    "\n",
    "        candidatos_pool = df_completo[df_completo['Tipo_mueble'] == tipo]\n",
    "        items_encontrados_pre_precio = len(candidatos_pool)\n",
    "\n",
    "        if color:\n",
    "            print(f\"  - Filtrando '{tipo}' por color '{color}'...\")\n",
    "            candidatos_pool = candidatos_pool[\n",
    "                candidatos_pool['Color'].str.contains(color, case=False, na=False)\n",
    "            ]\n",
    "            items_encontrados_pre_precio = len(candidatos_pool)\n",
    "            print(f\"  - Encontrados {items_encontrados_pre_precio} items pre-grupo tras filtro de color.\")\n",
    "        \n",
    "        candidatos_meta = candidatos_pool[\n",
    "            candidatos_pool['Precio'] <= presupuesto\n",
    "        ].sort_values('Score', ascending=False).drop_duplicates(subset=['grupo_agrupacion'])\n",
    "        \n",
    "        candidatos = candidatos_meta.to_dict('records')\n",
    "        \n",
    "        if not candidatos:\n",
    "            if items_encontrados_pre_precio > 0:\n",
    "                print(f\"  -> Fracaso: Se encontraron {items_encontrados_pre_precio} items para '{tipo}' (Color: {color or 'Cualquiera'}),\")\n",
    "                print(f\"              PERO todos superan el presupuesto de {presupuesto}€.\")\n",
    "            else:\n",
    "                print(f\"  -> Fracaso: No se encontró ningún producto para '{tipo}' (Color: {color or 'Cualquiera'}).\")\n",
    "            return []\n",
    "            \n",
    "        listas_candidatos.append(candidatos)\n",
    "        print(f\"  - Encontrados {len(candidatos)} candidatos meta para '{tipo}' (Color: {color or 'Cualquiera'})\")\n",
    "\n",
    "    combinaciones_validas = []\n",
    "    combinaciones_probadas = 0\n",
    "    \n",
    "    for combo_tupla in itertools.product(*listas_candidatos):\n",
    "        combinaciones_probadas += 1\n",
    "        precio_total = sum(item['Precio'] for item in combo_tupla)\n",
    "        \n",
    "        if precio_total <= presupuesto:\n",
    "            score_base = np.mean([item['Score'] for item in combo_tupla])\n",
    "            vectores = [item['vector_estilo'] for item in combo_tupla]\n",
    "            score_coherencia = calcular_coherencia_media(vectores)\n",
    "            puntuacion_final = (pesos['base'] * score_base) + (pesos['coherencia'] * score_coherencia)\n",
    "            \n",
    "            combinaciones_validas.append({\n",
    "                'items': combo_tupla, 'precio_total': precio_total,\n",
    "                'puntuacion_final': puntuacion_final, 'score_base_avg': score_base,\n",
    "                'score_coherencia_avg': score_coherencia, 'tipos_incluidos': tipos_mueble_con_opciones\n",
    "            })\n",
    "\n",
    "    if not combinaciones_validas:\n",
    "        if combinaciones_probadas > 0:\n",
    "            print(f\"  -> Fracaso: Se probaron {combinaciones_probadas} combinaciones, pero todas superan el presupuesto.\")\n",
    "        return []\n",
    "    \n",
    "    combinaciones_validas.sort(key=lambda x: x['puntuacion_final'], reverse=True)\n",
    "    print(f\"  -> Éxito: Encontradas {len(combinaciones_validas)} combinaciones válidas. Devolviendo las {top_n} mejores.\")\n",
    "    return combinaciones_validas[:top_n]\n",
    "\n",
    "\n",
    "def buscar_mejor_opcion_knapsack(df_completo: pd.DataFrame, \n",
    "                                 tipos_deseados_priorizados: List[Dict[str, str]],\n",
    "                                 presupuesto: float):\n",
    "    \n",
    "    print(f\"INICIANDO BÚSQUEDA KNAPSACK (Presupuesto: {presupuesto:.2f}€)\")\n",
    "    print(f\"Lista de deseos (priorizada): {tipos_deseados_priorizados}\")\n",
    "\n",
    "    lista_actual = list(tipos_deseados_priorizados)\n",
    "    \n",
    "    while len(lista_actual) > 0:\n",
    "        recomendaciones = recomendar_combinacion(df_completo, lista_actual, presupuesto, top_n=5)\n",
    "        \n",
    "        if recomendaciones: \n",
    "            mejor_opcion = recomendaciones[0]\n",
    "            \n",
    "            print(f\" ¡RECOMENDACIÓN PRINCIPAL \")\n",
    "            print(f\"Puntuación Final: {mejor_opcion['puntuacion_final']:.4f}\")\n",
    "            \n",
    "            print(\"\\nDetalle de productos (Imágenes y Enlaces):\")\n",
    "            \n",
    "            lista_de_precios = []\n",
    "            \n",
    "            for item in mejor_opcion['items']:\n",
    "                print(f\"  ---------------------------------\")\n",
    "                \n",
    "                try:\n",
    "                    display(Image(url=item['Imagen_principal'], width=150))\n",
    "                except Exception as e:\n",
    "                    print(f\"  (No se pudo cargar la imagen: {item['Imagen_principal']})\")\n",
    "\n",
    "                print(f\"  > {item['Nombre']} (ID: {item['ID']})\")\n",
    "                print(f\"  > Ver producto: https://www.ikea.com/es/es/p/{item['Enlace_producto']}-{item['ID']}\")\n",
    "                \n",
    "                lista_de_precios.append((item['Nombre'], item['Precio']))\n",
    "\n",
    "            print(\"\\n\" + \"=\"*52)\n",
    "            print(f\"{'RESUMEN DE PRECIOS':^52}\")\n",
    "            print(\"=\"*52)\n",
    "            for nombre, precio in lista_de_precios:\n",
    "                print(f\"  {nombre:<40} {precio:>10.2f}€\")\n",
    "            print(\"-\" * 52)\n",
    "            print(f\"  {'TOTAL:':<40} {mejor_opcion['precio_total']:>10.2f}€\")\n",
    "            print(\"=\"*52)\n",
    "\n",
    "            if len(recomendaciones) > 1:\n",
    "                print(\"\\n--- PLAN B (Otras opciones óptimas) ---\")\n",
    "                for i, opcion in enumerate(recomendaciones[1:], start=2):\n",
    "                    print(f\"\\n  Opción {i} (Puntuación: {opcion['puntuacion_final']:.4f}, Precio: {opcion['precio_total']:.2f}€):\")\n",
    "                    for item in opcion['items']:\n",
    "                        print(f\"    > {item['Nombre']} (Ver: https://www.ikea.com/es/es/p/{item['Enlace_producto']}-{item['ID']})\")\n",
    "            \n",
    "            print(\"=======================================================\")\n",
    "            \n",
    "            if 'generar_visualizacion_isometrica' in globals():\n",
    "                print(\"\\nGenerando visualización isométrica...\")\n",
    "                generar_visualizacion_isometrica(mejor_opcion['items'])\n",
    "            \n",
    "            return recomendaciones\n",
    "        \n",
    "        item_eliminado = lista_actual.pop()\n",
    "        print(f\"\\nNo se encontró combinación. Quitamos '{item_eliminado}' y reintentamos...\")\n",
    "        \n",
    "    print(f\"NO SE ENCONTRÓ COMBINACIÓN OPTIMA   \")\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893cb9a2",
   "metadata": {},
   "source": [
    "## Ejecución de Pruebas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7ab2d329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INICIANDO BÚSQUEDA KNAPSACK (Presupuesto: 3000.00€)\n",
      "Lista de deseos (priorizada): [{'tipo': 'Sofás'}, {'tipo': 'Sofás', 'color': 'blanco'}, {'tipo': 'Mesas bajas de salón, de centro y auxiliares'}]\n",
      "\n",
      "Buscando 5 mejores combinaciones para: [{'tipo': 'Sofás'}, {'tipo': 'Sofás', 'color': 'blanco'}, {'tipo': 'Mesas bajas de salón, de centro y auxiliares'}] (Presupuesto: 3000.00€)\n",
      "  - Encontrados 11 candidatos meta para 'Sofás' (Color: Cualquiera)\n",
      "  - Filtrando 'Sofás' por color 'blanco'...\n",
      "  - Encontrados 5 items pre-grupo tras filtro de color.\n",
      "  - Encontrados 3 candidatos meta para 'Sofás' (Color: blanco)\n",
      "  - Encontrados 7 candidatos meta para 'Mesas bajas de salón, de centro y auxiliares' (Color: Cualquiera)\n",
      "  -> Éxito: Encontradas 174 combinaciones válidas. Devolviendo las 5 mejores.\n",
      " ¡RECOMENDACIÓN PRINCIPAL \n",
      "Puntuación Final: 0.7304\n",
      "\n",
      "Detalle de productos (Imágenes y Enlaces):\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/glostad-sofa-2-plazas-knisa-gris-oscuro__0950864_pe800736_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > GLOSTAD (ID: 50489012)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/glostad-sofa-2-plazas-knisa-gris-oscuro-50489012\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/sorvallen-sofa-3-plazas-con-chaiselongue-dcha-tallmyra-negro-blanco__1069222_pe853805_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > SÖRVALLEN (ID: 69432445)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/sorvallen-sofa-3-plazas-con-chaiselongue-dcha-tallmyra-negro-blanco-69432445\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/gladom-mesa-bandeja-negro__0567223_pe664991_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > GLADOM (ID: 50411990)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990\n",
      "\n",
      "====================================================\n",
      "                 RESUMEN DE PRECIOS                 \n",
      "====================================================\n",
      "  GLOSTAD                                      119.00€\n",
      "  SÖRVALLEN                                   1549.00€\n",
      "  GLADOM                                        19.99€\n",
      "----------------------------------------------------\n",
      "  TOTAL:                                      1687.99€\n",
      "====================================================\n",
      "\n",
      "--- PLAN B (Otras opciones óptimas) ---\n",
      "\n",
      "  Opción 2 (Puntuación: 0.7185, Precio: 2287.99€):\n",
      "    > GLOSTAD (Ver: https://www.ikea.com/es/es/p/glostad-sofa-2-plazas-knisa-gris-oscuro-50489012)\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-4-plazas-con-chaiselongues-tallmyra-negro-blanco-9432448)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "\n",
      "  Opción 3 (Puntuación: 0.7172, Precio: 1087.99€):\n",
      "    > GLOSTAD (Ver: https://www.ikea.com/es/es/p/glostad-sofa-2-plazas-knisa-gris-oscuro-50489012)\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "\n",
      "  Opción 4 (Puntuación: 0.6982, Precio: 2067.99€):\n",
      "    > KIVIK (Ver: https://www.ikea.com/es/es/p/kivik-sofa-3-plazas-tibbleby-beis-gris-49440597)\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-3-plazas-con-chaiselongue-dcha-tallmyra-negro-blanco-69432445)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "\n",
      "  Opción 5 (Puntuación: 0.6866, Precio: 2667.99€):\n",
      "    > KIVIK (Ver: https://www.ikea.com/es/es/p/kivik-sofa-3-plazas-tibbleby-beis-gris-49440597)\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-4-plazas-con-chaiselongues-tallmyra-negro-blanco-9432448)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "=======================================================\n",
      "\n",
      "Generando visualización isométrica...\n",
      "Visualización 3D guardada en 'muebles_isometrica.png' ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lista_deseada_priorizada = [\n",
    "    {'tipo': 'Sofás'},\n",
    "    {'tipo': 'Sofás', 'color': 'blanco'},\n",
    "    {'tipo': 'Mesas bajas de salón, de centro y auxiliares'}\n",
    "]\n",
    "presupuesto_personalizado = 3000.00\n",
    "\n",
    "if 'df_scrapeado' in locals() and 'buscar_mejor_opcion_knapsack' in locals():\n",
    "    buscar_mejor_opcion_knapsack(\n",
    "        df_scrapeado,\n",
    "        lista_deseada_priorizada, \n",
    "        presupuesto_personalizado\n",
    "    )\n",
    "else:\n",
    "    print(\"Error: 'df_scrapeado' o 'buscar_mejor_opcion_knapsack' no están definidos.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8ad68d",
   "metadata": {},
   "source": [
    "# Visualización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "408dafc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "def generar_visualizacion_isometrica(items, filename='muebles_isometrica.png'):    \n",
    "    fig = plt.figure(figsize=(12, 9))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    # Listas para guardar las posiciones y dimensiones\n",
    "    x_pos = []\n",
    "    y_pos = []\n",
    "    z_pos = []\n",
    "    dx = [] # Ancho\n",
    "    dy = [] # Largo\n",
    "    dz = [] # Altura\n",
    "    labels = []\n",
    "    \n",
    "    current_x_offset = 0  # Para poner los muebles uno al lado del otro\n",
    "    \n",
    "    for item in items:\n",
    "        # Obtener dimensiones. Rellenar -1 o NaN con un valor por defecto (ej. 40cm)\n",
    "        ancho = float(item.get('Ancho', 40))\n",
    "        largo = float(item.get('Largo', 40))\n",
    "        altura = float(item.get('Altura', 40))\n",
    "        \n",
    "        # Corregir valores nulos o negativos\n",
    "        if ancho <= 0: ancho = 40\n",
    "        if largo <= 0: largo = 40\n",
    "        if altura <= 0: altura = 40\n",
    "            \n",
    "        # 2. Añadir el mueble a la lista de dibujo\n",
    "        x_pos.append(current_x_offset)\n",
    "        y_pos.append(0) # Todos alineados en el eje Y\n",
    "        z_pos.append(0) # Todos en el \"suelo\" (z=0)\n",
    "        \n",
    "        dx.append(ancho)\n",
    "        dy.append(largo)\n",
    "        dz.append(altura)\n",
    "        \n",
    "        labels.append(f\"{item['Nombre']}\\n({ancho}x{largo}x{altura})\")\n",
    "        \n",
    "        # Actualizar el \"offset\" para el siguiente mueble\n",
    "        #    Le añadimos el ancho del mueble actual + 50cm de espacio\n",
    "        current_x_offset += ancho + 50\n",
    "\n",
    "    # Dibujar todas las \"cajas\" (paralelepípedos) a la vez\n",
    "    colors = plt.cm.viridis(np.linspace(0, 1, len(items))) # Un color para cada mueble\n",
    "    ax.bar3d(x_pos, y_pos, z_pos, dx, dy, dz, color=colors, alpha=0.7)\n",
    "\n",
    "    # Configurar la vista\n",
    "    ax.set_xlabel('Ancho (cm)')\n",
    "    ax.set_ylabel('Largo (cm)')\n",
    "    ax.set_zlabel('Altura (cm)')\n",
    "    ax.set_xticks(np.array(x_pos) + np.array(dx) / 2)\n",
    "    ax.set_xticklabels(labels, rotation=15, ha='right', fontsize=9)\n",
    "    ax.view_init(elev=30, azim=-45)\n",
    "    \n",
    "    ax.set_title('Muebles Recomendados')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(filename)\n",
    "    print(f\"Visualización 3D guardada en '{filename}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "fc2abee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INICIANDO BÚSQUEDA KNAPSACK (Presupuesto: 1000.00€)\n",
      "Lista de deseos (priorizada): [{'tipo': 'Sofás', 'color': 'blanco'}, {'tipo': 'Mesas bajas de salón, de centro y auxiliares'}, {'tipo': 'Estanterías y librerías'}]\n",
      "\n",
      "Buscando 5 mejores combinaciones para: [{'tipo': 'Sofás', 'color': 'blanco'}, {'tipo': 'Mesas bajas de salón, de centro y auxiliares'}, {'tipo': 'Estanterías y librerías'}] (Presupuesto: 1000.00€)\n",
      "  - Filtrando 'Sofás' por color 'blanco'...\n",
      "  - Encontrados 5 items pre-grupo tras filtro de color.\n",
      "  - Encontrados 1 candidatos meta para 'Sofás' (Color: blanco)\n",
      "  - Encontrados 7 candidatos meta para 'Mesas bajas de salón, de centro y auxiliares' (Color: Cualquiera)\n",
      "  - Encontrados 25 candidatos meta para 'Estanterías y librerías' (Color: Cualquiera)\n",
      "  -> Éxito: Encontradas 41 combinaciones válidas. Devolviendo las 5 mejores.\n",
      " ¡RECOMENDACIÓN PRINCIPAL \n",
      "Puntuación Final: 0.7320\n",
      "\n",
      "Detalle de productos (Imágenes y Enlaces):\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/sorvallen-sofa-2-plazas-tallmyra-negro-blanco__1069135_pe853769_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > SÖRVALLEN (ID: 39432442)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/gladom-mesa-bandeja-negro__0567223_pe664991_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > GLADOM (ID: 50411990)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990\n",
      "  ---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ikea.com/es/es/images/products/kallax-accesorio-con-2-cajones-blanco__0640671_pe699976_s5.jpg?f=s\" width=\"150\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > KALLAX (ID: 70286645)\n",
      "  > Ver producto: https://www.ikea.com/es/es/p/kallax-accesorio-con-2-cajones-blanco-70286645\n",
      "\n",
      "====================================================\n",
      "                 RESUMEN DE PRECIOS                 \n",
      "====================================================\n",
      "  SÖRVALLEN                                    949.00€\n",
      "  GLADOM                                        19.99€\n",
      "  KALLAX                                        20.00€\n",
      "----------------------------------------------------\n",
      "  TOTAL:                                       988.99€\n",
      "====================================================\n",
      "\n",
      "--- PLAN B (Otras opciones óptimas) ---\n",
      "\n",
      "  Opción 2 (Puntuación: 0.7289, Precio: 988.99€):\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "    > EKET (Ver: https://www.ikea.com/es/es/p/eket-mueble-almacenaje-pared-blanco-39285808)\n",
      "\n",
      "  Opción 3 (Puntuación: 0.7231, Precio: 983.99€):\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442)\n",
      "    > GLADOM (Ver: https://www.ikea.com/es/es/p/gladom-mesa-bandeja-negro-50411990)\n",
      "    > EKET (Ver: https://www.ikea.com/es/es/p/eket-mueble-almacenaje-azul-grisaceo-claro-20556215)\n",
      "\n",
      "  Opción 4 (Puntuación: 0.6892, Precio: 988.99€):\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442)\n",
      "    > LACK (Ver: https://www.ikea.com/es/es/p/lack-mesa-centro-blanco-90449905)\n",
      "    > KALLAX (Ver: https://www.ikea.com/es/es/p/kallax-accesorio-con-2-cajones-blanco-70286645)\n",
      "\n",
      "  Opción 5 (Puntuación: 0.6816, Precio: 988.99€):\n",
      "    > SÖRVALLEN (Ver: https://www.ikea.com/es/es/p/sorvallen-sofa-2-plazas-tallmyra-negro-blanco-39432442)\n",
      "    > LACK (Ver: https://www.ikea.com/es/es/p/lack-mesa-centro-blanco-90449905)\n",
      "    > EKET (Ver: https://www.ikea.com/es/es/p/eket-mueble-almacenaje-pared-blanco-39285808)\n",
      "=======================================================\n",
      "\n",
      "Generando visualización isométrica...\n",
      "Visualización 3D guardada en 'muebles_isometrica.png'\n",
      "Visualización 3D guardada en 'muebles_isometrica.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RESUPUESTO\n",
    "lista_deseada_priorizada = [\n",
    "    {'tipo': 'Sofás', 'color': 'blanco'},\n",
    "    {'tipo': 'Mesas bajas de salón, de centro y auxiliares'},\n",
    "    {'tipo': 'Estanterías y librerías'}\n",
    "]\n",
    "presupuesto_personalizado = 1000.00\n",
    "\n",
    "# RECOMENDADOR\n",
    "\n",
    "if 'df_scrapeado' in locals() and 'buscar_mejor_opcion_knapsack' in locals():\n",
    "    \n",
    "    recomendaciones = buscar_mejor_opcion_knapsack(\n",
    "        df_scrapeado, \n",
    "        lista_deseada_priorizada, \n",
    "        presupuesto_personalizado\n",
    "    )\n",
    "    \n",
    "    if recomendaciones:\n",
    "        mejor_opcion_items = recomendaciones[0]['items']\n",
    "        generar_visualizacion_isometrica(mejor_opcion_items)\n",
    "\n",
    "else:\n",
    "    print(\"Error\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tfm_env",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}