convert_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.utils import shuffle\n",
    "from brand_lists import *\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "#converte i risultati da {-1,0,1} a {0, 0.5, 1}\n",
    "def convert_value(val):\n",
    "    if val == 1: return 1\n",
    "    if val == -1: return 0\n",
    "    if val == 0: return 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>baby</th>\n",
       "      <th>car</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>clothing</th>\n",
       "      <th>coffee</th>\n",
       "      <th>computer</th>\n",
       "      <th>fashion</th>\n",
       "      <th>home</th>\n",
       "      <th>household appliances</th>\n",
       "      <th>italian</th>\n",
       "      <th>licorice</th>\n",
       "      <th>office</th>\n",
       "      <th>pasta</th>\n",
       "      <th>scent</th>\n",
       "      <th>sporty</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>werewolfdonut</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>wilhelmdonkor27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>xkj1985x</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  baby  car  chocolate  clothing  coffee  computer  \\\n",
       "0           007maybe     1    1          0         0       0         0   \n",
       "1            21Phylk     0    0          0         1       0         0   \n",
       "2          ABO_mondo     0    1          0         0       0         0   \n",
       "3            ACE2276     0    1          0         0       0         0   \n",
       "4          AGnomblea     1    0          0         0       0         0   \n",
       "..               ...   ...  ...        ...       ...     ...       ...   \n",
       "493    werewolfdonut     0    0          0         0       0         0   \n",
       "494  wilhelmdonkor27     0    0          0         0       0         0   \n",
       "495     wittystwitty     0    0          0         0       0         0   \n",
       "496         xkj1985x     0    1          0         0       0         0   \n",
       "497     ziopaolo2015     0    0          0         0       0         0   \n",
       "\n",
       "     fashion  home  household appliances  italian  licorice  office  pasta  \\\n",
       "0          0    -1                     0        0         0       0      0   \n",
       "1          1     0                     0        0         0       0      0   \n",
       "2          1     1                     0        1         0       0      1   \n",
       "3          0     0                     0        0         0       0      0   \n",
       "4          0     0                     0        0         0       0      0   \n",
       "..       ...   ...                   ...      ...       ...     ...    ...   \n",
       "493        1     0                     0        0         0       0      0   \n",
       "494        0     0                     0        0         0       0      0   \n",
       "495        0     0                     0        0         0       0      0   \n",
       "496        0     0                     0       -1         0       0      0   \n",
       "497        0     0                     0        0         0       0      0   \n",
       "\n",
       "     scent  sporty  technology  \n",
       "0        0       1           0  \n",
       "1        0       0           0  \n",
       "2        0       0           1  \n",
       "3        0       0           0  \n",
       "4        0       0           0  \n",
       "..     ...     ...         ...  \n",
       "493      0       0           0  \n",
       "494      0       0           0  \n",
       "495      0       0           0  \n",
       "496      0       1           0  \n",
       "497      0       0           0  \n",
       "\n",
       "[498 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Caricamento tabella UTENTI-TAGS\n",
    "users_tags_df = pd.read_excel('sentiment_results/users_tags_table_vader.xlsx', index_col=0)\n",
    "users_tags_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>User</th>\n",
       "      <th>baby</th>\n",
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       "      <th>pasta</th>\n",
       "      <th>scent</th>\n",
       "      <th>sporty</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
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       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>werewolfdonut</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>wilhelmdonkor27</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>xkj1985x</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  baby  car  chocolate  clothing  coffee  computer  \\\n",
       "0           007maybe   1.0  1.0        0.5       0.5     0.5       0.5   \n",
       "1            21Phylk   0.5  0.5        0.5       1.0     0.5       0.5   \n",
       "2          ABO_mondo   0.5  1.0        0.5       0.5     0.5       0.5   \n",
       "3            ACE2276   0.5  1.0        0.5       0.5     0.5       0.5   \n",
       "4          AGnomblea   1.0  0.5        0.5       0.5     0.5       0.5   \n",
       "..               ...   ...  ...        ...       ...     ...       ...   \n",
       "493    werewolfdonut   0.5  0.5        0.5       0.5     0.5       0.5   \n",
       "494  wilhelmdonkor27   0.5  0.5        0.5       0.5     0.5       0.5   \n",
       "495     wittystwitty   0.5  0.5        0.5       0.5     0.5       0.5   \n",
       "496         xkj1985x   0.5  1.0        0.5       0.5     0.5       0.5   \n",
       "497     ziopaolo2015   0.5  0.5        0.5       0.5     0.5       0.5   \n",
       "\n",
       "     fashion  home  household appliances  italian  licorice  office  pasta  \\\n",
       "0        0.5   0.0                   0.5      0.5       0.5     0.5    0.5   \n",
       "1        1.0   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "2        1.0   1.0                   0.5      1.0       0.5     0.5    1.0   \n",
       "3        0.5   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "4        0.5   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "..       ...   ...                   ...      ...       ...     ...    ...   \n",
       "493      1.0   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "494      0.5   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "495      0.5   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "496      0.5   0.5                   0.5      0.0       0.5     0.5    0.5   \n",
       "497      0.5   0.5                   0.5      0.5       0.5     0.5    0.5   \n",
       "\n",
       "     scent  sporty  technology  \n",
       "0      0.5     1.0         0.5  \n",
       "1      0.5     0.5         0.5  \n",
       "2      0.5     0.5         1.0  \n",
       "3      0.5     0.5         0.5  \n",
       "4      0.5     0.5         0.5  \n",
       "..     ...     ...         ...  \n",
       "493    0.5     0.5         0.5  \n",
       "494    0.5     0.5         0.5  \n",
       "495    0.5     0.5         0.5  \n",
       "496    0.5     1.0         0.5  \n",
       "497    0.5     0.5         0.5  \n",
       "\n",
       "[498 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conversione della scala dei valori da {-1,0,1} a {0, 0.5, 1}\n",
    "for tag in all_brands_tags:\n",
    "    users_tags_df[tag] = users_tags_df[tag].apply(lambda x: convert_value(x))\n",
    "\n",
    "users_tags_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Liste di utenti e brands:\n",
    "all_users_list = list(np.unique(users_tags_df[\"User\"])) #lista di utenti #lista di utenti\n",
    "all_brands_list = brands_dict.keys()\n",
    "\n",
    "#Inizializzazione della tabella users-brands: (Valori di default= 0.5 [Neutrali])\n",
    "users_brands_df = pd.DataFrame(all_users_list, columns=['User'])    #creazione del dataframe\n",
    "for brand in all_brands_list:\n",
    "    users_brands_df[brand] = 0.5  \n",
    "\n",
    "\n",
    "#computazione degli indici di compatibilità degli utenti rispetto ai brand:\n",
    "#ESEMPIO:\n",
    "# Mario ha ottenuto polarità -1, 0, 0 per i tre tag di Amarelli. \n",
    "# Diventeranno 0, 0.5, 0.5 e l’indice di compatibilità tra Mario e Amarelli avrà valore (0 + 0.5 + 0.5) / 3 = 0.33.\n",
    "\n",
    "#per tutti gli utenti\n",
    "for username in all_users_list:\n",
    "    #per tutti i brands\n",
    "    for brand in all_brands_list:\n",
    "        #lista dei tags associati al brand\n",
    "        brand_tags = brands_dict[brand]\n",
    "        total = 0\n",
    "        count_tag = 0\n",
    "        for tag in brand_tags:\n",
    "            #copia valore di sentiment sulla tabella users_tags:\n",
    "            sentiment_value = users_tags_df.loc[users_tags_df[\"User\"] == username , tag].values[0]\n",
    "            total = total + sentiment_value\n",
    "            count_tag = count_tag +1\n",
    "        #Il sentiment dell'utente rispetto il brand sarà la media dei sentiment diviso il numero di tags (3)\n",
    "        users_brands_df.loc[ users_brands_df[\"User\"] == username, brand] = total/count_tag\n",
    "\n",
    "\n",
    "\n",
    "#Funzione che salva in formato CSV un dataframe\n",
    "if not os.path.exists('final_results'):\n",
    "    os.makedirs('final_results')\n",
    "users_tags_df.to_csv(\"final_results/users_tags_values.csv\", index=False) #salvare il dataframe in un CSV\n",
    "users_brands_df.to_csv(\"final_results/users_brands_values.csv\", index=False) #salvare il dataframe in un CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>User</th>\n",
       "      <th>alfaromeo</th>\n",
       "      <th>samsung</th>\n",
       "      <th>lavazza</th>\n",
       "      <th>armani</th>\n",
       "      <th>adidas</th>\n",
       "      <th>microsoft</th>\n",
       "      <th>barilla</th>\n",
       "      <th>ferrero</th>\n",
       "      <th>delonghi</th>\n",
       "      <th>amarelli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>werewolfdonut</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>wilhelmdonkor27</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>xkj1985x</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  alfaromeo   samsung   lavazza    armani    adidas  \\\n",
       "0           007maybe   0.833333  0.500000  0.500000  0.500000  0.666667   \n",
       "1            21Phylk   0.500000  0.500000  0.500000  0.833333  0.833333   \n",
       "2          ABO_mondo   0.833333  0.666667  0.666667  0.833333  0.666667   \n",
       "3            ACE2276   0.666667  0.500000  0.500000  0.500000  0.500000   \n",
       "4          AGnomblea   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "..               ...        ...       ...       ...       ...       ...   \n",
       "493    werewolfdonut   0.500000  0.500000  0.500000  0.666667  0.666667   \n",
       "494  wilhelmdonkor27   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "495     wittystwitty   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "496         xkj1985x   0.666667  0.500000  0.333333  0.333333  0.666667   \n",
       "497     ziopaolo2015   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "\n",
       "     microsoft   barilla   ferrero  delonghi  amarelli  \n",
       "0     0.500000  0.333333  0.666667  0.333333  0.666667  \n",
       "1     0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "2     0.666667  1.000000  0.666667  0.666667  0.500000  \n",
       "3     0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "4     0.500000  0.500000  0.666667  0.500000  0.666667  \n",
       "..         ...       ...       ...       ...       ...  \n",
       "493   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "494   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "495   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "496   0.500000  0.333333  0.333333  0.500000  0.500000  \n",
       "497   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "\n",
       "[498 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_brands_df = pd.read_csv('final_results/users_brands_values.csv')\n",
    "users_brands_df"
   ]
  }
 ],
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