to_make_csv_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:12.066524Z",
     "start_time": "2024-11-11T03:40:12.020494Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.025673Z",
     "start_time": "2024-11-11T03:40:12.094306Z"
    }
   },
   "cell_type": "code",
   "source": "book_filter_df=pd.read_csv('book_filter_df.csv')",
   "id": "d2e8da8b6e6859f6",
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'book_filter_df.csv'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[2], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m book_filter_df\u001B[38;5;241m=\u001B[39m\u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mbook_filter_df.csv\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ml_env\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001B[0m, in \u001B[0;36mread_csv\u001B[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[0m\n\u001B[0;32m   1013\u001B[0m kwds_defaults \u001B[38;5;241m=\u001B[39m _refine_defaults_read(\n\u001B[0;32m   1014\u001B[0m     dialect,\n\u001B[0;32m   1015\u001B[0m     delimiter,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1022\u001B[0m     dtype_backend\u001B[38;5;241m=\u001B[39mdtype_backend,\n\u001B[0;32m   1023\u001B[0m )\n\u001B[0;32m   1024\u001B[0m kwds\u001B[38;5;241m.\u001B[39mupdate(kwds_defaults)\n\u001B[1;32m-> 1026\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ml_env\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001B[0m, in \u001B[0;36m_read\u001B[1;34m(filepath_or_buffer, kwds)\u001B[0m\n\u001B[0;32m    617\u001B[0m _validate_names(kwds\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnames\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[0;32m    619\u001B[0m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[1;32m--> 620\u001B[0m parser \u001B[38;5;241m=\u001B[39m \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    622\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[0;32m    623\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m parser\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ml_env\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001B[0m, in \u001B[0;36mTextFileReader.__init__\u001B[1;34m(self, f, engine, **kwds)\u001B[0m\n\u001B[0;32m   1617\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moptions[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m kwds[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m   1619\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles: IOHandles \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m-> 1620\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ml_env\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001B[0m, in \u001B[0;36mTextFileReader._make_engine\u001B[1;34m(self, f, engine)\u001B[0m\n\u001B[0;32m   1878\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[0;32m   1879\u001B[0m         mode \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m-> 1880\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;241m=\u001B[39m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1881\u001B[0m \u001B[43m    \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1882\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1883\u001B[0m \u001B[43m    \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1884\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcompression\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1885\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmemory_map\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmemory_map\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1886\u001B[0m \u001B[43m    \u001B[49m\u001B[43mis_text\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mis_text\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1887\u001B[0m \u001B[43m    \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding_errors\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstrict\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1888\u001B[0m \u001B[43m    \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstorage_options\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1889\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1890\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m   1891\u001B[0m f \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles\u001B[38;5;241m.\u001B[39mhandle\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ml_env\\Lib\\site-packages\\pandas\\io\\common.py:873\u001B[0m, in \u001B[0;36mget_handle\u001B[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[0m\n\u001B[0;32m    868\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(handle, \u001B[38;5;28mstr\u001B[39m):\n\u001B[0;32m    869\u001B[0m     \u001B[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001B[39;00m\n\u001B[0;32m    870\u001B[0m     \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[0;32m    871\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mencoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mmode:\n\u001B[0;32m    872\u001B[0m         \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[1;32m--> 873\u001B[0m         handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m    874\u001B[0m \u001B[43m            \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    875\u001B[0m \u001B[43m            \u001B[49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    876\u001B[0m \u001B[43m            \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    877\u001B[0m \u001B[43m            \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    878\u001B[0m \u001B[43m            \u001B[49m\u001B[43mnewline\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m    879\u001B[0m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    880\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    881\u001B[0m         \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[0;32m    882\u001B[0m         handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mopen\u001B[39m(handle, ioargs\u001B[38;5;241m.\u001B[39mmode)\n",
      "\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'book_filter_df.csv'"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### user-book 평점 dataframe\n",
    "- user_book_df.csv"
   ],
   "id": "9e240d4c93668c34"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.052550400Z",
     "start_time": "2024-11-10T18:12:07.084916Z"
    }
   },
   "cell_type": "code",
   "source": [
    "user_book_df=book_filter_df.pivot_table(index='user_id',columns='book_title',values='rating',fill_value=0)\n",
    "book_user_df=user_book_df.T"
   ],
   "id": "27a4ecd611c9f240",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1924, 3374)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.057506200Z",
     "start_time": "2024-11-10T18:12:07.252178Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "book_similarity = cosine_similarity(book_user_df, book_user_df)\n",
    "book_similarity_df = pd.DataFrame(book_similarity, index=user_book_df.columns, columns=user_book_df.columns)"
   ],
   "id": "eddbd32fc67a9cf3",
   "outputs": [],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.059432300Z",
     "start_time": "2024-11-10T18:12:07.936552Z"
    }
   },
   "cell_type": "code",
   "source": "book_similarity_df.to_csv(\"book_similarity_df.csv\")",
   "id": "7d497cb8c5438493",
   "outputs": [],
   "execution_count": 93
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.059432300Z",
     "start_time": "2024-11-10T18:12:14.798444Z"
    }
   },
   "cell_type": "code",
   "source": "user_book_df.to_csv(\"user_book_df.csv\")",
   "id": "2624b05408bb070d",
   "outputs": [],
   "execution_count": 94
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### 연령별 유사도 dataframe \n",
    "- age_similarity_df.csv\n",
    "- "
   ],
   "id": "fc30f1dca0d21f9e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.059432300Z",
     "start_time": "2024-11-10T18:12:18.110033Z"
    }
   },
   "cell_type": "code",
   "source": "ages_book_=book_filter_df.groupby(['ages','book_title']).size().reset_index(name='count')",
   "id": "c3157ad7d03f7a60",
   "outputs": [],
   "execution_count": 99
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.061064700Z",
     "start_time": "2024-11-10T18:12:18.220395Z"
    }
   },
   "cell_type": "code",
   "source": [
    "ages_book_df=ages_book_.pivot_table(index='ages',columns='book_title',values='count',fill_value=0)\n",
    "book_ages_df=ages_book_df.T"
   ],
   "id": "3ba5858d68532ae5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ages                       10    20    30   40   50   60   70\n",
       "book_title                                                   \n",
       "'salem's lot              0.0   0.0   4.0  1.0  0.0  0.0  0.0\n",
       "10 lb. penalty            0.0   1.0   4.0  3.0  1.0  1.0  0.0\n",
       "101 dalmatians            0.0   2.0   0.0  1.0  0.0  0.0  0.0\n",
       "16 lighthouse road        1.0   0.0   2.0  3.0  2.0  0.0  0.0\n",
       "1984                      0.0  18.0  13.0  6.0  1.0  0.0  0.0\n",
       "...                       ...   ...   ...  ...  ...  ...  ...\n",
       "a suitable vengeance      0.0   0.0   3.0  1.0  1.0  0.0  0.0\n",
       "a superior death          0.0   0.0   3.0  1.0  3.0  0.0  0.0\n",
       "a swiftly tilting planet  1.0   3.0   5.0  0.0  0.0  0.0  0.0\n",
       "a tale of two cities      0.0   2.0   2.0  1.0  0.0  0.0  0.0\n",
       "a tangled web             0.0   1.0   0.0  0.0  0.0  0.0  0.0\n",
       "\n",
       "[100 rows x 7 columns]"
      ],
      "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>ages</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'salem's lot</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 lb. penalty</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 dalmatians</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 lighthouse road</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a suitable vengeance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a superior death</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a swiftly tilting planet</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a tale of two cities</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a tangled web</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 100
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-11T03:40:13.067450400Z",
     "start_time": "2024-11-10T18:12:18.390065Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "ages_similarity=cosine_similarity(book_ages_df,book_ages_df)\n",
    "ages_similarity_df=pd.DataFrame(ages_similarity,index=ages_book_df.columns, columns=ages_book_df.columns)\n",
    "ages_similarity_df.to_csv(\"age_similarity_df.csv\",index=True)"
   ],
   "id": "6987c525daaeae04",
   "outputs": [],
   "execution_count": 101
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### 작가유사도 dataframe\n",
    "- author_ratings_pred_df.csv\n",
    "- author_similarity_df.csv"
   ],
   "id": "9157df0d64b798a2"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "user_author_rating_df = book_filter_df.groupby(['user_id', 'book_author'])['rating'].mean().reset_index()\n",
    "user_author_df = user_author_rating_df.pivot_table(index=\"user_id\", columns=\"book_author\", values=\"rating\",fill_value=0)\n",
    "author_user_df = user_author_df.T"
   ],
   "id": "5b274e27f19d6ce8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "user_author_sim = cosine_similarity(author_user_df, author_user_df)\n",
    "user_author_sim_df = pd.DataFrame(user_author_sim, index=author_user_df.index, columns=author_user_df.index)"
   ],
   "id": "3f413763f9245ac"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "user_author_sim_df.to_csv(\"author_similarity_df.csv\")",
   "id": "349af7ce2111404"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def predict_ratings(author_user_df, user_author_sim):\n",
    "    ratings_pred = np.dot(user_author_sim, author_user_df) / np.abs(user_author_sim).sum(axis=1)[:, np.newaxis]\n",
    "    return ratings_pred\n",
    "\n",
    "ratings_pred = predict_ratings(author_user_df, user_author_sim)\n",
    "ratings_pred = ratings_pred.T\n",
    "ratings_pred_df = pd.DataFrame(ratings_pred, index=user_author_df.index, columns=user_author_df.columns)\n"
   ],
   "id": "90d7709552fac1ba"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "ratings_pred_df.to_csv(\"author_rating_pred_df.csv\")",
   "id": "8086a8dd2b1d7477"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### 책 평점별 유사도 dataframe\n",
    "- book_ratings_pred_df.csv\n",
    "- book_review_data.csv\n",
    "- -book_similarity_df.csv"
   ],
   "id": "23328b2b8b9969a7"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "book_sim = cosine_similarity(book_user_df, book_user_df)\n",
    "print(book_sim.shape)\n",
    "book_sim_df = pd.DataFrame(book_sim, index=user_book_df.columns, columns=user_book_df.columns)"
   ],
   "id": "3f54a4cbdb639bac"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "book_sim.df.to_csv(\"book_similarity_df.csv\")",
   "id": "452cab5ba16b1db4"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def predict_ratings(user_book_df, book_sim_df):\n",
    "    return user_book_df.dot(book_sim_df) / np.abs(book_sim_df).sum(axis=1)\n",
    "ratings_pred_df = predict_ratings(user_book_df, book_sim_df)\n",
    "\n",
    "print(ratings_pred_df.shape)"
   ],
   "id": "8f7f1e69dcb51067"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "ratings_pred_df.to_csv(\"book_ratings_pred_df.csv\")",
   "id": "455aa93cad619db1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### category별 추천\n",
    "- top_book_by_category"
   ],
   "id": "26c5f4c2c22dafc0"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "user_category_df = pd.DataFrame(book_filter_df[['user_id', 'book_title', 'L_Category']])\n",
    "group_by_category_df = user_category_df.groupby('user_id')['L_Category'].value_counts()\n",
    "group_by_category_df = group_by_category_df.reset_index()\n",
    "group_by_df = user_category_df.groupby([\"user_id\", \"L_Category\", 'book_title']).size().reset_index(name='count')\n",
    "\n",
    "# 피벗테이블로 만들기\n",
    "user_category_count_df = group_by_category_df.pivot_table(index='user_id', columns='L_Category', values='count',fill_value=0)\n",
    "top_category = user_category_count_df.idxmax(axis=1)\n",
    "\n",
    "top_category_by_user_df = top_category.reset_index(name='top_L_category')\n",
    "\n",
    "top_book_by_category = book_filter_df[['L_Category', 'book_title']].drop_duplicates()\n",
    "book_ratings = book_filter_df.groupby(['L_Category', 'book_title'])[\"rating\"].mean().reset_index()\n",
    "book_filter_df['L_Category'].unique()\n",
    "\n",
    "top_book_by_category = pd.DataFrame(index=range(1, 11), columns=book_filter_df[\"L_Category\"].drop_duplicates().values)\n",
    "\n",
    "columns = book_filter_df[\"L_Category\"].drop_duplicates().values\n",
    "for col in columns:\n",
    "    book = book_ratings[book_ratings[\"L_Category\"] == col].sort_values(by='rating', ascending=False)[:10]['book_title']\n",
    "    top_book_by_category[col] = book.values\n"
   ],
   "id": "f4a35d3b6e4bd310"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "top_book_by_category.to_csv(\"top_book_by_category.csv\")",
   "id": "8e13c4ce24618ccd"
  }
 ],
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