goodreads.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import json\n",
    "import re\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = './data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to load zipped datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(file_name, head = 500):\n",
    "    count = 0\n",
    "    data = []\n",
    "    with gzip.open(file_name) as fin:\n",
    "        for l in fin:\n",
    "            d = json.loads(l)\n",
    "            count += 1\n",
    "            data.append(d)\n",
    "            \n",
    "            # break if reaches the 100th line\n",
    "            if (head is not None) and (count > head):\n",
    "                break\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and display sample records of books ~/authors/works/series~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "books = load_data(os.path.join(DIR, 'goodreads_books_mystery_thriller_crime.json.gz'), 5000)\n",
    "reviews = load_data(os.path.join(DIR, 'goodreads_reviews_mystery_thriller_crime.json.gz'), 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " == sample record (books) ==\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'isbn': '0671047582',\n",
       " 'text_reviews_count': '6',\n",
       " 'series': ['147483'],\n",
       " 'country_code': 'US',\n",
       " 'language_code': '',\n",
       " 'popular_shelves': [{'count': '116', 'name': 'to-read'},\n",
       "  {'count': '13', 'name': 'hardy-boys'},\n",
       "  {'count': '10', 'name': 'mystery'},\n",
       "  {'count': '3', 'name': 'fiction'},\n",
       "  {'count': '2', 'name': 'default'},\n",
       "  {'count': '2', 'name': 'young-adult'},\n",
       "  {'count': '2', 'name': 'childrens'},\n",
       "  {'count': '2', 'name': 'children'},\n",
       "  {'count': '1', 'name': 'interlibrary-loan'},\n",
       "  {'count': '1', 'name': 'over-100-pages'},\n",
       "  {'count': '1', 'name': 'childrens-book'},\n",
       "  {'count': '1', 'name': 'c'},\n",
       "  {'count': '1', 'name': 'soft-cover'},\n",
       "  {'count': '1', 'name': 'read-in-high-school'},\n",
       "  {'count': '1', 'name': 'lead-male'},\n",
       "  {'count': '1', 'name': 'amateur-sleuths'},\n",
       "  {'count': '1', 'name': 'read-by-andrecito'},\n",
       "  {'count': '1', 'name': 'need-hardy-boys'},\n",
       "  {'count': '1', 'name': 'my-library'},\n",
       "  {'count': '1', 'name': 'summer-2016-5776'},\n",
       "  {'count': '1', 'name': 'my-shelf'},\n",
       "  {'count': '1', 'name': 'detective'},\n",
       "  {'count': '1', 'name': 'brothers'},\n",
       "  {'count': '1', 'name': 'children-hardy-boys'},\n",
       "  {'count': '1', 'name': 'hardy-boys-nancy-drew'},\n",
       "  {'count': '1', 'name': 'nice-book-but-will-sacrifice-it-if'},\n",
       "  {'count': '1', 'name': 'middle-grade-fantasy'},\n",
       "  {'count': '1', 'name': 'series-hardy-boys-digest'},\n",
       "  {'count': '1', 'name': 'unread'},\n",
       "  {'count': '1', 'name': 'owned'},\n",
       "  {'count': '1', 'name': 'ebooks-i-own'},\n",
       "  {'count': '1', 'name': 'to-read-hardy-boys'},\n",
       "  {'count': '1', 'name': 'middle-grade'},\n",
       "  {'count': '1', 'name': 'series-hardy-boys'},\n",
       "  {'count': '1', 'name': 'children-s-fiction'},\n",
       "  {'count': '1', 'name': 'to-read-nancy-drew-hardy-boys'},\n",
       "  {'count': '1', 'name': 'crime'},\n",
       "  {'count': '1', 'name': 'my-books'},\n",
       "  {'count': '1', 'name': 'favorites'},\n",
       "  {'count': '1', 'name': 'children-general'},\n",
       "  {'count': '1', 'name': 'paperback'},\n",
       "  {'count': '1', 'name': 'series-books'},\n",
       "  {'count': '1', 'name': 'for-ned'},\n",
       "  {'count': '1', 'name': 'action-adventure'},\n",
       "  {'count': '1', 'name': 'read-during-streak'},\n",
       "  {'count': '1', 'name': 'childhood-read'},\n",
       "  {'count': '1', 'name': 'the-hardy-boy-mystery-stories'},\n",
       "  {'count': '1', 'name': 'digests'},\n",
       "  {'count': '1', 'name': 'boys'},\n",
       "  {'count': '1', 'name': 'hardy'},\n",
       "  {'count': '1', 'name': 'opl'},\n",
       "  {'count': '1', 'name': 'fun-read'}],\n",
       " 'asin': '',\n",
       " 'is_ebook': 'false',\n",
       " 'average_rating': '3.85',\n",
       " 'kindle_asin': 'B007Z4RXN6',\n",
       " 'similar_books': [],\n",
       " 'description': '',\n",
       " 'format': '',\n",
       " 'link': 'https://www.goodreads.com/book/show/278642.Training_for_Trouble',\n",
       " 'authors': [{'author_id': '1879', 'role': ''}],\n",
       " 'publisher': '',\n",
       " 'num_pages': '',\n",
       " 'publication_day': '',\n",
       " 'isbn13': '9780671047580',\n",
       " 'publication_month': '',\n",
       " 'edition_information': '',\n",
       " 'publication_year': '',\n",
       " 'url': 'https://www.goodreads.com/book/show/278642.Training_for_Trouble',\n",
       " 'image_url': 'https://s.gr-assets.com/assets/nophoto/book/111x148-bcc042a9c91a29c1d680899eff700a03.png',\n",
       " 'book_id': '278642',\n",
       " 'ratings_count': '94',\n",
       " 'work_id': '270257',\n",
       " 'title': 'Training for Trouble (Hardy Boys, #161)',\n",
       " 'title_without_series': 'Training for Trouble (Hardy Boys, #161)'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(' == sample record (books) ==')\n",
    "np.random.choice(books)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> {'count': '1', 'name': 'لمعرض-الكتاب-2018'},\n",
    "> \n",
    "> 'title': 'Човекът, който знаеше всичко',\n",
    "\n",
    "auch nicht lateinische Buchstaben! Übersetzen? löschen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'isbn': '184737297X',\n",
       " 'text_reviews_count': '15',\n",
       " 'series': ['169353'],\n",
       " 'country_code': 'US',\n",
       " 'language_code': '',\n",
       " 'popular_shelves': [{'count': '159', 'name': 'to-read'},\n",
       "  {'count': '12', 'name': 'historical-fiction'},\n",
       "  {'count': '11', 'name': 'mystery'},\n",
       "  {'count': '10', 'name': 'historical-mystery'},\n",
       "  {'count': '7', 'name': 'medieval'},\n",
       "  {'count': '6', 'name': 'historical'},\n",
       "  {'count': '5', 'name': 'crime'},\n",
       "  {'count': '3', 'name': 'series'},\n",
       "  {'count': '3', 'name': 'ebook'},\n",
       "  {'count': '3', 'name': 'fiction'},\n",
       "  {'count': '3', 'name': 'crowner-john'},\n",
       "  {'count': '2', 'name': 'default'},\n",
       "  {'count': '2', 'name': 'books'},\n",
       "  {'count': '2', 'name': 'owned'},\n",
       "  {'count': '2', 'name': 'mystery-thrillers'},\n",
       "  {'count': '2', 'name': 'c'},\n",
       "  {'count': '2', 'name': 'library'},\n",
       "  {'count': '2', 'name': 'wish-list'},\n",
       "  {'count': '2', 'name': 'england'},\n",
       "  {'count': '2', 'name': 'medieval-england'},\n",
       "  {'count': '2', 'name': 'mystery-historical'},\n",
       "  {'count': '2', 'name': 'audiobook'},\n",
       "  {'count': '1', 'name': 'recs-not-interested'},\n",
       "  {'count': '1', 'name': 'available'},\n",
       "  {'count': '1', 'name': 'public-library'},\n",
       "  {'count': '1', 'name': 'boulder'},\n",
       "  {'count': '1', 'name': 'fiction-historical'},\n",
       "  {'count': '1', 'name': 'fiction-crime'},\n",
       "  {'count': '1', 'name': 'downloadable-audio'},\n",
       "  {'count': '1', 'name': 'detectives'},\n",
       "  {'count': '1', 'name': 'need-to-obtain'},\n",
       "  {'count': '1', 'name': 'read-2017'},\n",
       "  {'count': '1', 'name': 'r-f'},\n",
       "  {'count': '1', 'name': 'hist-myst'},\n",
       "  {'count': '1', 'name': 'currently-reading'},\n",
       "  {'count': '1', 'name': 'historic'},\n",
       "  {'count': '1', 'name': 'e-reader-suspense'},\n",
       "  {'count': '1', 'name': 'historical-murder-mystery'},\n",
       "  {'count': '1', 'name': 'historic-fiction'},\n",
       "  {'count': '1', 'name': 'crowner-john-mystery'},\n",
       "  {'count': '1', 'name': 'mideaval-mystery'},\n",
       "  {'count': '1', 'name': 'crime-historical'},\n",
       "  {'count': '1', 'name': 'on-my-shelf'},\n",
       "  {'count': '1', 'name': 'need-to-buy'},\n",
       "  {'count': '1', 'name': 'zenigma-crowner-john'},\n",
       "  {'count': '1', 'name': 'warehouse-sinc'},\n",
       "  {'count': '1', 'name': 'a-missing-piece'},\n",
       "  {'count': '1', 'name': 'library-audiobooks'},\n",
       "  {'count': '1', 'name': 'physical-currently-owned'},\n",
       "  {'count': '1', 'name': 'no-loan'},\n",
       "  {'count': '1', 'name': 'tbr-busting-2015'},\n",
       "  {'count': '1', 'name': 'spring-2015'},\n",
       "  {'count': '1', 'name': 'plantagenet-1154-1485'},\n",
       "  {'count': '1', 'name': 'medieval5c-16c'},\n",
       "  {'count': '1', 'name': 'forensics'},\n",
       "  {'count': '1', 'name': 'zscribd'},\n",
       "  {'count': '1', 'name': 'tbr-2014'},\n",
       "  {'count': '1', 'name': 'pub-simon-schuster'},\n",
       "  {'count': '1', 'name': 'pc-400-499'},\n",
       "  {'count': '1', 'name': 'e-book'},\n",
       "  {'count': '1', 'name': 'c-green'},\n",
       "  {'count': '1', 'name': 'author-k'},\n",
       "  {'count': '1', 'name': 'murder-mystery'},\n",
       "  {'count': '1', 'name': 'knight-b'},\n",
       "  {'count': '1', 'name': 'british'},\n",
       "  {'count': '1', 'name': 'tbr-busting-2014'},\n",
       "  {'count': '1', 'name': 'fraudio'},\n",
       "  {'count': '1', 'name': 'under-500-ratings'},\n",
       "  {'count': '1', 'name': 'winter-20142015'},\n",
       "  {'count': '1', 'name': 'published-2009'},\n",
       "  {'count': '1', 'name': 'historcal-mysteries'},\n",
       "  {'count': '1', 'name': 'in-library'},\n",
       "  {'count': '1', 'name': 'tbr'},\n",
       "  {'count': '1', 'name': 'one-click-dig'},\n",
       "  {'count': '1', 'name': 'dropped'},\n",
       "  {'count': '1', 'name': 'e-library'},\n",
       "  {'count': '1', 'name': 'library-book'},\n",
       "  {'count': '1', 'name': 'read-prior-to-2014'},\n",
       "  {'count': '1', 'name': 'historical-crime-fiction'},\n",
       "  {'count': '1', 'name': 'historical-who-dunnit'},\n",
       "  {'count': '1', 'name': 'lcpl-eaudio'},\n",
       "  {'count': '1', 'name': 'pinossa'},\n",
       "  {'count': '1', 'name': 'b-cher-2012'},\n",
       "  {'count': '1', 'name': 'purchasesaug2013'},\n",
       "  {'count': '1', 'name': 'histmyst'},\n",
       "  {'count': '1', 'name': 'books-i-own'},\n",
       "  {'count': '1', 'name': 'historical-novels'},\n",
       "  {'count': '1', 'name': 'should-read'},\n",
       "  {'count': '1', 'name': 'mystery-historical-all'},\n",
       "  {'count': '1', 'name': 'peter'},\n",
       "  {'count': '1', 'name': 'books-to-get-list'},\n",
       "  {'count': '1', 'name': 'knight-bernard'},\n",
       "  {'count': '1', 'name': 'mp3'},\n",
       "  {'count': '1', 'name': 'needed-mysteries'},\n",
       "  {'count': '1', 'name': 'historical-hoodunits'},\n",
       "  {'count': '1', 'name': 'detective-crowner-john'},\n",
       "  {'count': '1', 'name': 'books-in-english'},\n",
       "  {'count': '1', 'name': 'to'},\n",
       "  {'count': '1', 'name': 'need'},\n",
       "  {'count': '1', 'name': '1100s-ad'}],\n",
       " 'asin': '',\n",
       " 'is_ebook': 'false',\n",
       " 'average_rating': '3.93',\n",
       " 'kindle_asin': 'B007YLTG5I',\n",
       " 'similar_books': ['439108',\n",
       "  '522621',\n",
       "  '116770',\n",
       "  '1275927',\n",
       "  '6202655',\n",
       "  '8409638',\n",
       "  '386777',\n",
       "  '11284834',\n",
       "  '1041348'],\n",
       " 'description': \"London, 1196. At the command of Richard the Lionheart, Sir John de Wolfe has left his beloved West Country for the Palace of Westminster, where he has been appointed Coroner of the Verge. But with the king overseas, embroiled in a costly war against King Philip of France, Sir John is dismayed to discover that the English court is a hotbed of greed, corruption and petty in-fighting.\\nThe murder of one of the palace clerks, stabbed in broad daylight and thrown into the River Thames, leads John to suspect that there's a conspiracy underway to overthrow King Richard. And with the visit of the dowager Queen Eleanor fast approaching, the new Coroner must risk his life to prove his suspicions are right, root out the traitors within and prevent a national catastrophe.\",\n",
       " 'format': 'Hardcover',\n",
       " 'link': 'https://www.goodreads.com/book/show/6066814-crowner-royal',\n",
       " 'authors': [{'author_id': '37778', 'role': ''}],\n",
       " 'publisher': 'Simon & Schuster UK',\n",
       " 'num_pages': '400',\n",
       " 'publication_day': '6',\n",
       " 'isbn13': '9781847372970',\n",
       " 'publication_month': '4',\n",
       " 'edition_information': '',\n",
       " 'publication_year': '2009',\n",
       " 'url': 'https://www.goodreads.com/book/show/6066814-crowner-royal',\n",
       " 'image_url': 'https://images.gr-assets.com/books/1328724803m/6066814.jpg',\n",
       " 'book_id': '6066814',\n",
       " 'ratings_count': '186',\n",
       " 'work_id': '6243149',\n",
       " 'title': 'Crowner Royal (Crowner John Mystery, #13)',\n",
       " 'title_without_series': 'Crowner Royal (Crowner John Mystery, #13)'}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(type(books[0]))\n",
    "display(len(books[0]))\n",
    "display(books[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5001"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books = pd.DataFrame(books)\n",
    "df_reviews = pd.DataFrame(reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>isbn</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>series</th>\n",
       "      <th>country_code</th>\n",
       "      <th>language_code</th>\n",
       "      <th>popular_shelves</th>\n",
       "      <th>asin</th>\n",
       "      <th>is_ebook</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>kindle_asin</th>\n",
       "      <th>...</th>\n",
       "      <th>publication_month</th>\n",
       "      <th>edition_information</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>url</th>\n",
       "      <th>image_url</th>\n",
       "      <th>book_id</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_id</th>\n",
       "      <th>title</th>\n",
       "      <th>title_without_series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184737297X</td>\n",
       "      <td>15</td>\n",
       "      <td>[169353]</td>\n",
       "      <td>US</td>\n",
       "      <td></td>\n",
       "      <td>[{'count': '159', 'name': 'to-read'}, {'count'...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.93</td>\n",
       "      <td>B007YLTG5I</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>2009</td>\n",
       "      <td>https://www.goodreads.com/book/show/6066814-cr...</td>\n",
       "      <td>https://images.gr-assets.com/books/1328724803m...</td>\n",
       "      <td>6066814</td>\n",
       "      <td>186</td>\n",
       "      <td>6243149</td>\n",
       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>60</td>\n",
       "      <td>[1052227]</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>[{'count': '54', 'name': 'currently-reading'},...</td>\n",
       "      <td>B01NCIKAQX</td>\n",
       "      <td>true</td>\n",
       "      <td>4.33</td>\n",
       "      <td>B01NCIKAQX</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>https://www.goodreads.com/book/show/33394837-t...</td>\n",
       "      <td>https://images.gr-assets.com/books/1493114742m...</td>\n",
       "      <td>33394837</td>\n",
       "      <td>269</td>\n",
       "      <td>54143148</td>\n",
       "      <td>The House of Memory (Pluto's Snitch #2)</td>\n",
       "      <td>The House of Memory (Pluto's Snitch #2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>23</td>\n",
       "      <td>[953679]</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>[{'count': '90', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td>B01ALOWJN0</td>\n",
       "      <td>true</td>\n",
       "      <td>3.49</td>\n",
       "      <td>B01ALOWJN0</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>https://www.goodreads.com/book/show/29074697-t...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>29074697</td>\n",
       "      <td>192</td>\n",
       "      <td>49305010</td>\n",
       "      <td>The Slaughtered Virgin of Zenopolis (Inspector...</td>\n",
       "      <td>The Slaughtered Virgin of Zenopolis (Inspector...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0854563903</td>\n",
       "      <td>8</td>\n",
       "      <td>[408775]</td>\n",
       "      <td>US</td>\n",
       "      <td></td>\n",
       "      <td>[{'count': '51', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.30</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>Large Print</td>\n",
       "      <td>1975</td>\n",
       "      <td>https://www.goodreads.com/book/show/1902202.De...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>1902202</td>\n",
       "      <td>52</td>\n",
       "      <td>1903897</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8838920931</td>\n",
       "      <td>3</td>\n",
       "      <td>[274410]</td>\n",
       "      <td>US</td>\n",
       "      <td>ita</td>\n",
       "      <td>[{'count': '48', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.54</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.goodreads.com/book/show/9671977-ar...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474788304m...</td>\n",
       "      <td>9671977</td>\n",
       "      <td>22</td>\n",
       "      <td>2152906</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         isbn text_reviews_count     series country_code language_code  \\\n",
       "0  184737297X                 15   [169353]           US                 \n",
       "1                             60  [1052227]           US           eng   \n",
       "2                             23   [953679]           US           eng   \n",
       "3  0854563903                  8   [408775]           US                 \n",
       "4  8838920931                  3   [274410]           US           ita   \n",
       "\n",
       "                                     popular_shelves        asin is_ebook  \\\n",
       "0  [{'count': '159', 'name': 'to-read'}, {'count'...                false   \n",
       "1  [{'count': '54', 'name': 'currently-reading'},...  B01NCIKAQX     true   \n",
       "2  [{'count': '90', 'name': 'to-read'}, {'count':...  B01ALOWJN0     true   \n",
       "3  [{'count': '51', 'name': 'to-read'}, {'count':...                false   \n",
       "4  [{'count': '48', 'name': 'to-read'}, {'count':...                false   \n",
       "\n",
       "  average_rating kindle_asin  ... publication_month edition_information  \\\n",
       "0           3.93  B007YLTG5I  ...                 4                       \n",
       "1           4.33  B01NCIKAQX  ...                                         \n",
       "2           3.49  B01ALOWJN0  ...                                         \n",
       "3           3.30              ...                12         Large Print   \n",
       "4           3.54              ...                                         \n",
       "\n",
       "  publication_year                                                url  \\\n",
       "0             2009  https://www.goodreads.com/book/show/6066814-cr...   \n",
       "1                   https://www.goodreads.com/book/show/33394837-t...   \n",
       "2                   https://www.goodreads.com/book/show/29074697-t...   \n",
       "3             1975  https://www.goodreads.com/book/show/1902202.De...   \n",
       "4             2006  https://www.goodreads.com/book/show/9671977-ar...   \n",
       "\n",
       "                                           image_url   book_id ratings_count  \\\n",
       "0  https://images.gr-assets.com/books/1328724803m...   6066814           186   \n",
       "1  https://images.gr-assets.com/books/1493114742m...  33394837           269   \n",
       "2  https://s.gr-assets.com/assets/nophoto/book/11...  29074697           192   \n",
       "3  https://s.gr-assets.com/assets/nophoto/book/11...   1902202            52   \n",
       "4  https://images.gr-assets.com/books/1474788304m...   9671977            22   \n",
       "\n",
       "    work_id                                              title  \\\n",
       "0   6243149          Crowner Royal (Crowner John Mystery, #13)   \n",
       "1  54143148            The House of Memory (Pluto's Snitch #2)   \n",
       "2  49305010  The Slaughtered Virgin of Zenopolis (Inspector...   \n",
       "3   1903897            Dead in the Morning (Patrick Grant, #1)   \n",
       "4   2152906                   Aristotele e i misteri di Eleusi   \n",
       "\n",
       "                                title_without_series  \n",
       "0          Crowner Royal (Crowner John Mystery, #13)  \n",
       "1            The House of Memory (Pluto's Snitch #2)  \n",
       "2  The Slaughtered Virgin of Zenopolis (Inspector...  \n",
       "3            Dead in the Morning (Patrick Grant, #1)  \n",
       "4                   Aristotele e i misteri di Eleusi  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5001 entries, 0 to 5000\n",
      "Data columns (total 29 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   isbn                  5001 non-null   object\n",
      " 1   text_reviews_count    5001 non-null   object\n",
      " 2   series                5001 non-null   object\n",
      " 3   country_code          5001 non-null   object\n",
      " 4   language_code         5001 non-null   object\n",
      " 5   popular_shelves       5001 non-null   object\n",
      " 6   asin                  5001 non-null   object\n",
      " 7   is_ebook              5001 non-null   object\n",
      " 8   average_rating        5001 non-null   object\n",
      " 9   kindle_asin           5001 non-null   object\n",
      " 10  similar_books         5001 non-null   object\n",
      " 11  description           5001 non-null   object\n",
      " 12  format                5001 non-null   object\n",
      " 13  link                  5001 non-null   object\n",
      " 14  authors               5001 non-null   object\n",
      " 15  publisher             5001 non-null   object\n",
      " 16  num_pages             5001 non-null   object\n",
      " 17  publication_day       5001 non-null   object\n",
      " 18  isbn13                5001 non-null   object\n",
      " 19  publication_month     5001 non-null   object\n",
      " 20  edition_information   5001 non-null   object\n",
      " 21  publication_year      5001 non-null   object\n",
      " 22  url                   5001 non-null   object\n",
      " 23  image_url             5001 non-null   object\n",
      " 24  book_id               5001 non-null   object\n",
      " 25  ratings_count         5001 non-null   object\n",
      " 26  work_id               5001 non-null   object\n",
      " 27  title                 5001 non-null   object\n",
      " 28  title_without_series  5001 non-null   object\n",
      "dtypes: object(29)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_books.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>A fun, fast paced science fiction thriller. I ...</td>\n",
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       "      <td>http://www.telegraph.co.uk/culture/10...</td>\n",
       "      <td>Tue Nov 01 11:09:18 -0700 2016</td>\n",
       "      <td>Tue Nov 01 11:09:44 -0700 2016</td>\n",
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       "      <td>3</td>\n",
       "      <td>The Name of the Rose is a thrilling Dan Brown-...</td>\n",
       "      <td>Wed Sep 08 01:22:27 -0700 2010</td>\n",
       "      <td>Wed Dec 14 12:30:43 -0800 2016</td>\n",
       "      <td>Mon Aug 10 00:00:00 -0700 2015</td>\n",
       "      <td>Mon Jul 20 00:00:00 -0700 2015</td>\n",
       "      <td>17</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "                            user_id   book_id  \\\n",
       "0  8842281e1d1347389f2ab93d60773d4d   6392944   \n",
       "1  8842281e1d1347389f2ab93d60773d4d  28684704   \n",
       "2  8842281e1d1347389f2ab93d60773d4d  32283133   \n",
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       "4  8842281e1d1347389f2ab93d60773d4d   8694005   \n",
       "\n",
       "                          review_id  rating  \\\n",
       "0  5e212a62bced17b4dbe41150e5bb9037       3   \n",
       "1  2ede853b14dc4583f96cf5d120af636f       3   \n",
       "2  8e4d61801907e591018bdc3442a9cf2b       0   \n",
       "3  022bb6daffa49adc27f6b20b6ebeb37d       4   \n",
       "4  0e317947e1fd341f573192111bb2921d       3   \n",
       "\n",
       "                                         review_text  \\\n",
       "0  I haven't read a fun mystery book in a while a...   \n",
       "1  A fun, fast paced science fiction thriller. I ...   \n",
       "2           http://www.telegraph.co.uk/culture/10...   \n",
       "3  An amazing and unique creation: JJ Abrams and ...   \n",
       "4  The Name of the Rose is a thrilling Dan Brown-...   \n",
       "\n",
       "                       date_added                    date_updated  \\\n",
       "0  Mon Jul 24 02:48:17 -0700 2017  Sun Jul 30 09:28:03 -0700 2017   \n",
       "1  Tue Nov 15 11:29:22 -0800 2016  Mon Mar 20 23:40:27 -0700 2017   \n",
       "2  Tue Nov 01 11:09:18 -0700 2016  Tue Nov 01 11:09:44 -0700 2016   \n",
       "3  Wed Mar 26 13:51:30 -0700 2014  Tue Sep 23 01:44:36 -0700 2014   \n",
       "4  Wed Sep 08 01:22:27 -0700 2010  Wed Dec 14 12:30:43 -0800 2016   \n",
       "\n",
       "                          read_at                      started_at  n_votes  \\\n",
       "0  Tue Jul 25 00:00:00 -0700 2017  Mon Jul 24 00:00:00 -0700 2017        6   \n",
       "1  Sat Mar 18 23:22:42 -0700 2017  Fri Mar 17 23:45:40 -0700 2017       22   \n",
       "2                                                                        9   \n",
       "3  Sun Sep 21 00:00:00 -0700 2014  Sat Jul 26 00:00:00 -0700 2014        7   \n",
       "4  Mon Aug 10 00:00:00 -0700 2015  Mon Jul 20 00:00:00 -0700 2015       17   \n",
       "\n",
       "   n_comments  \n",
       "0           0  \n",
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       "4           6  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>https://www.goodreads.com/book/show/26708400-a...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>2588538</td>\n",
       "      <td>1</td>\n",
       "      <td>3311984</td>\n",
       "      <td>The Woman in White</td>\n",
       "      <td>The Woman in White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>806</td>\n",
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       "<p>4 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "        isbn text_reviews_count series country_code language_code  \\\n",
       "count   5001               5001   5001         5001          5001   \n",
       "unique  3017                320   2825            1            51   \n",
       "top                           1     []           US           eng   \n",
       "freq    1985                806   1938         5001          1903   \n",
       "\n",
       "                                          popular_shelves  asin is_ebook  \\\n",
       "count                                                5001  5001     5001   \n",
       "unique                                               4659  1148        2   \n",
       "top     [{'count': '107382', 'name': 'to-read'}, {'cou...          false   \n",
       "freq                                                    7  3854     3479   \n",
       "\n",
       "       average_rating kindle_asin  ... publication_month edition_information  \\\n",
       "count            5001        5001  ...              5001                5001   \n",
       "unique            219        2664  ...                13                 250   \n",
       "top              3.91              ...                                         \n",
       "freq               89        2252  ...              1785                4476   \n",
       "\n",
       "       publication_year                                                url  \\\n",
       "count              5001                                               5001   \n",
       "unique               87                                               5001   \n",
       "top                      https://www.goodreads.com/book/show/26708400-a...   \n",
       "freq               1258                                                  1   \n",
       "\n",
       "                                                image_url  book_id  \\\n",
       "count                                                5001     5001   \n",
       "unique                                               3073     5001   \n",
       "top     https://s.gr-assets.com/assets/nophoto/book/11...  2588538   \n",
       "freq                                                 1929        1   \n",
       "\n",
       "       ratings_count  work_id               title title_without_series  \n",
       "count           5001     5001                5001                 5001  \n",
       "unique           909     4624                4850                 4850  \n",
       "top                1  3311984  The Woman in White   The Woman in White  \n",
       "freq             161        7                   7                    7  \n",
       "\n",
       "[4 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nur 275 unique ISBNs, 123 ASIN... hm. ISBN sollte eigentlich schon überall gesetzt sein.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21749e2fdf0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_books[\"average_rating\"].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>isbn</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>series</th>\n",
       "      <th>country_code</th>\n",
       "      <th>language_code</th>\n",
       "      <th>popular_shelves</th>\n",
       "      <th>asin</th>\n",
       "      <th>is_ebook</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>kindle_asin</th>\n",
       "      <th>...</th>\n",
       "      <th>publication_month</th>\n",
       "      <th>edition_information</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>url</th>\n",
       "      <th>image_url</th>\n",
       "      <th>book_id</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_id</th>\n",
       "      <th>title</th>\n",
       "      <th>title_without_series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184737297X</td>\n",
       "      <td>15</td>\n",
       "      <td>[169353]</td>\n",
       "      <td>US</td>\n",
       "      <td></td>\n",
       "      <td>[{'count': '159', 'name': 'to-read'}, {'count'...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.93</td>\n",
       "      <td>B007YLTG5I</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>2009</td>\n",
       "      <td>https://www.goodreads.com/book/show/6066814-cr...</td>\n",
       "      <td>https://images.gr-assets.com/books/1328724803m...</td>\n",
       "      <td>6066814</td>\n",
       "      <td>186</td>\n",
       "      <td>6243149</td>\n",
       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>60</td>\n",
       "      <td>[1052227]</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>[{'count': '54', 'name': 'currently-reading'},...</td>\n",
       "      <td>B01NCIKAQX</td>\n",
       "      <td>true</td>\n",
       "      <td>4.33</td>\n",
       "      <td>B01NCIKAQX</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>https://www.goodreads.com/book/show/33394837-t...</td>\n",
       "      <td>https://images.gr-assets.com/books/1493114742m...</td>\n",
       "      <td>33394837</td>\n",
       "      <td>269</td>\n",
       "      <td>54143148</td>\n",
       "      <td>The House of Memory (Pluto's Snitch #2)</td>\n",
       "      <td>The House of Memory (Pluto's Snitch #2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>23</td>\n",
       "      <td>[953679]</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>[{'count': '90', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td>B01ALOWJN0</td>\n",
       "      <td>true</td>\n",
       "      <td>3.49</td>\n",
       "      <td>B01ALOWJN0</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>https://www.goodreads.com/book/show/29074697-t...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>29074697</td>\n",
       "      <td>192</td>\n",
       "      <td>49305010</td>\n",
       "      <td>The Slaughtered Virgin of Zenopolis (Inspector...</td>\n",
       "      <td>The Slaughtered Virgin of Zenopolis (Inspector...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0854563903</td>\n",
       "      <td>8</td>\n",
       "      <td>[408775]</td>\n",
       "      <td>US</td>\n",
       "      <td></td>\n",
       "      <td>[{'count': '51', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.30</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>Large Print</td>\n",
       "      <td>1975</td>\n",
       "      <td>https://www.goodreads.com/book/show/1902202.De...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>1902202</td>\n",
       "      <td>52</td>\n",
       "      <td>1903897</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8838920931</td>\n",
       "      <td>3</td>\n",
       "      <td>[274410]</td>\n",
       "      <td>US</td>\n",
       "      <td>ita</td>\n",
       "      <td>[{'count': '48', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.54</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.goodreads.com/book/show/9671977-ar...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474788304m...</td>\n",
       "      <td>9671977</td>\n",
       "      <td>22</td>\n",
       "      <td>2152906</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         isbn text_reviews_count     series country_code language_code  \\\n",
       "0  184737297X                 15   [169353]           US                 \n",
       "1                             60  [1052227]           US           eng   \n",
       "2                             23   [953679]           US           eng   \n",
       "3  0854563903                  8   [408775]           US                 \n",
       "4  8838920931                  3   [274410]           US           ita   \n",
       "\n",
       "                                     popular_shelves        asin is_ebook  \\\n",
       "0  [{'count': '159', 'name': 'to-read'}, {'count'...                false   \n",
       "1  [{'count': '54', 'name': 'currently-reading'},...  B01NCIKAQX     true   \n",
       "2  [{'count': '90', 'name': 'to-read'}, {'count':...  B01ALOWJN0     true   \n",
       "3  [{'count': '51', 'name': 'to-read'}, {'count':...                false   \n",
       "4  [{'count': '48', 'name': 'to-read'}, {'count':...                false   \n",
       "\n",
       "  average_rating kindle_asin  ... publication_month edition_information  \\\n",
       "0           3.93  B007YLTG5I  ...                 4                       \n",
       "1           4.33  B01NCIKAQX  ...                                         \n",
       "2           3.49  B01ALOWJN0  ...                                         \n",
       "3           3.30              ...                12         Large Print   \n",
       "4           3.54              ...                                         \n",
       "\n",
       "  publication_year                                                url  \\\n",
       "0             2009  https://www.goodreads.com/book/show/6066814-cr...   \n",
       "1                   https://www.goodreads.com/book/show/33394837-t...   \n",
       "2                   https://www.goodreads.com/book/show/29074697-t...   \n",
       "3             1975  https://www.goodreads.com/book/show/1902202.De...   \n",
       "4             2006  https://www.goodreads.com/book/show/9671977-ar...   \n",
       "\n",
       "                                           image_url   book_id ratings_count  \\\n",
       "0  https://images.gr-assets.com/books/1328724803m...   6066814           186   \n",
       "1  https://images.gr-assets.com/books/1493114742m...  33394837           269   \n",
       "2  https://s.gr-assets.com/assets/nophoto/book/11...  29074697           192   \n",
       "3  https://s.gr-assets.com/assets/nophoto/book/11...   1902202            52   \n",
       "4  https://images.gr-assets.com/books/1474788304m...   9671977            22   \n",
       "\n",
       "    work_id                                              title  \\\n",
       "0   6243149          Crowner Royal (Crowner John Mystery, #13)   \n",
       "1  54143148            The House of Memory (Pluto's Snitch #2)   \n",
       "2  49305010  The Slaughtered Virgin of Zenopolis (Inspector...   \n",
       "3   1903897            Dead in the Morning (Patrick Grant, #1)   \n",
       "4   2152906                   Aristotele e i misteri di Eleusi   \n",
       "\n",
       "                                title_without_series  \n",
       "0          Crowner Royal (Crowner John Mystery, #13)  \n",
       "1            The House of Memory (Pluto's Snitch #2)  \n",
       "2  The Slaughtered Virgin of Zenopolis (Inspector...  \n",
       "3            Dead in the Morning (Patrick Grant, #1)  \n",
       "4                   Aristotele e i misteri di Eleusi  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.style.highlight_max(axis=1)\n",
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# b = a['popular_shelves'].apply(pd.Series).merge(a, left_index = True, right_index = True).drop(['popular_shelves',0], axis = 1)\n",
    "# b.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# b.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "duplicates definitely need to be removed beforehand.\n",
    "- how to find all of them?\n",
    "- [ ] same title\n",
    "- [ ] same kindle_asin\n",
    "- [ ] very close title (how?)\n",
    "- [ ] how to combine them? reviews? create an extra index? so the review don't get lost?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.concat(g for _, g in df_books.groupby(\"title\") if len(g) > 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books.set_index(\"book_id\", inplace=True)\n",
    "df_reviews.set_index(\"review_id\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>An amazing and unique creation: JJ Abrams and ...</td>\n",
       "      <td>Wed Mar 26 13:51:30 -0700 2014</td>\n",
       "      <td>Tue Sep 23 01:44:36 -0700 2014</td>\n",
       "      <td>Sun Sep 21 00:00:00 -0700 2014</td>\n",
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       "      <td>Mon Aug 10 00:00:00 -0700 2015</td>\n",
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       "      <td>In This Grave Hour, #13 in the Maisie Dobbs my...</td>\n",
       "      <td>Sun Mar 12 20:21:26 -0700 2017</td>\n",
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       "      <td>I read this for a book club assignment. I have...</td>\n",
       "      <td>Wed Feb 01 23:25:00 -0800 2017</td>\n",
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       "      <td>Fri Jan 27 10:54:30 -0800 2017</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           user_id   book_id  \\\n",
       "review_id                                                                      \n",
       "5e212a62bced17b4dbe41150e5bb9037  8842281e1d1347389f2ab93d60773d4d   6392944   \n",
       "2ede853b14dc4583f96cf5d120af636f  8842281e1d1347389f2ab93d60773d4d  28684704   \n",
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       "0e317947e1fd341f573192111bb2921d  8842281e1d1347389f2ab93d60773d4d   8694005   \n",
       "...                                                            ...       ...   \n",
       "da3afde4e35be07556d25941be8e7594  5da7f0c9d042ac1bd51a7f1e08bbf6a3  30653727   \n",
       "9f48ba1335588f5e2b02d5e4427ff611  5da7f0c9d042ac1bd51a7f1e08bbf6a3   2634424   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe  5da7f0c9d042ac1bd51a7f1e08bbf6a3  30508247   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6  5da7f0c9d042ac1bd51a7f1e08bbf6a3    188230   \n",
       "c296dca609072c43eba74cb87c8edad9  5da7f0c9d042ac1bd51a7f1e08bbf6a3  28532419   \n",
       "\n",
       "                                  rating  \\\n",
       "review_id                                  \n",
       "5e212a62bced17b4dbe41150e5bb9037       3   \n",
       "2ede853b14dc4583f96cf5d120af636f       3   \n",
       "8e4d61801907e591018bdc3442a9cf2b       0   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d       4   \n",
       "0e317947e1fd341f573192111bb2921d       3   \n",
       "...                                  ...   \n",
       "da3afde4e35be07556d25941be8e7594       5   \n",
       "9f48ba1335588f5e2b02d5e4427ff611       3   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe       4   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6       4   \n",
       "c296dca609072c43eba74cb87c8edad9       4   \n",
       "\n",
       "                                                                        review_text  \\\n",
       "review_id                                                                             \n",
       "5e212a62bced17b4dbe41150e5bb9037  I haven't read a fun mystery book in a while a...   \n",
       "2ede853b14dc4583f96cf5d120af636f  A fun, fast paced science fiction thriller. I ...   \n",
       "8e4d61801907e591018bdc3442a9cf2b           http://www.telegraph.co.uk/culture/10...   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  An amazing and unique creation: JJ Abrams and ...   \n",
       "0e317947e1fd341f573192111bb2921d  The Name of the Rose is a thrilling Dan Brown-...   \n",
       "...                                                                             ...   \n",
       "da3afde4e35be07556d25941be8e7594  In This Grave Hour, #13 in the Maisie Dobbs my...   \n",
       "9f48ba1335588f5e2b02d5e4427ff611  I read this for a book club assignment. I have...   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe  I love the Laurie King's Mary Russell characte...   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6  Crocodile on the Sandbank is the first in a lo...   \n",
       "c296dca609072c43eba74cb87c8edad9  Stella Lavender works for the North Carolina S...   \n",
       "\n",
       "                                                      date_added  \\\n",
       "review_id                                                          \n",
       "5e212a62bced17b4dbe41150e5bb9037  Mon Jul 24 02:48:17 -0700 2017   \n",
       "2ede853b14dc4583f96cf5d120af636f  Tue Nov 15 11:29:22 -0800 2016   \n",
       "8e4d61801907e591018bdc3442a9cf2b  Tue Nov 01 11:09:18 -0700 2016   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  Wed Mar 26 13:51:30 -0700 2014   \n",
       "0e317947e1fd341f573192111bb2921d  Wed Sep 08 01:22:27 -0700 2010   \n",
       "...                                                          ...   \n",
       "da3afde4e35be07556d25941be8e7594  Sun Mar 12 20:21:26 -0700 2017   \n",
       "9f48ba1335588f5e2b02d5e4427ff611  Wed Feb 01 23:25:00 -0800 2017   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe  Fri Jan 27 10:54:30 -0800 2017   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6  Wed Jan 25 14:37:45 -0800 2017   \n",
       "c296dca609072c43eba74cb87c8edad9  Wed Jan 18 11:36:28 -0800 2017   \n",
       "\n",
       "                                                    date_updated  \\\n",
       "review_id                                                          \n",
       "5e212a62bced17b4dbe41150e5bb9037  Sun Jul 30 09:28:03 -0700 2017   \n",
       "2ede853b14dc4583f96cf5d120af636f  Mon Mar 20 23:40:27 -0700 2017   \n",
       "8e4d61801907e591018bdc3442a9cf2b  Tue Nov 01 11:09:44 -0700 2016   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  Tue Sep 23 01:44:36 -0700 2014   \n",
       "0e317947e1fd341f573192111bb2921d  Wed Dec 14 12:30:43 -0800 2016   \n",
       "...                                                          ...   \n",
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       "c296dca609072c43eba74cb87c8edad9  Tue May 30 16:03:59 -0700 2017   \n",
       "\n",
       "                                                         read_at  \\\n",
       "review_id                                                          \n",
       "5e212a62bced17b4dbe41150e5bb9037  Tue Jul 25 00:00:00 -0700 2017   \n",
       "2ede853b14dc4583f96cf5d120af636f  Sat Mar 18 23:22:42 -0700 2017   \n",
       "8e4d61801907e591018bdc3442a9cf2b                                   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  Sun Sep 21 00:00:00 -0700 2014   \n",
       "0e317947e1fd341f573192111bb2921d  Mon Aug 10 00:00:00 -0700 2015   \n",
       "...                                                          ...   \n",
       "da3afde4e35be07556d25941be8e7594  Mon Jul 17 00:00:00 -0700 2017   \n",
       "9f48ba1335588f5e2b02d5e4427ff611  Wed Feb 01 00:00:00 -0800 2017   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe  Thu Jan 26 00:00:00 -0800 2017   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6  Tue Jan 24 00:00:00 -0800 2017   \n",
       "c296dca609072c43eba74cb87c8edad9  Fri Mar 17 00:00:00 -0700 2017   \n",
       "\n",
       "                                                      started_at  n_votes  \\\n",
       "review_id                                                                   \n",
       "5e212a62bced17b4dbe41150e5bb9037  Mon Jul 24 00:00:00 -0700 2017        6   \n",
       "2ede853b14dc4583f96cf5d120af636f  Fri Mar 17 23:45:40 -0700 2017       22   \n",
       "8e4d61801907e591018bdc3442a9cf2b                                        9   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  Sat Jul 26 00:00:00 -0700 2014        7   \n",
       "0e317947e1fd341f573192111bb2921d  Mon Jul 20 00:00:00 -0700 2015       17   \n",
       "...                                                          ...      ...   \n",
       "da3afde4e35be07556d25941be8e7594                                        0   \n",
       "9f48ba1335588f5e2b02d5e4427ff611                                        0   \n",
       "3e339a97bcd6fbb35b90a33df5a525fe                                        0   \n",
       "2c267a9b68bfd1f0f39e6b6397d472a6                                        0   \n",
       "c296dca609072c43eba74cb87c8edad9                                        1   \n",
       "\n",
       "                                  n_comments  \n",
       "review_id                                     \n",
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       "\n",
       "[5001 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "Empty DataFrame\n",
       "Columns: [user_id, book_id, rating, review_text, date_added, date_updated, read_at, started_at, n_votes, n_comments]\n",
       "Index: []"
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     "metadata": {},
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   "source": [
    "df_reviews.groupby(level=0).filter(lambda x: len(x) > 1).head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews.drop(columns=['review_text', 'date_added', 'date_updated', 'read_at', 'started_at', 'n_votes', 'n_comments'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "                                                           user_id   book_id  \\\n",
       "review_id                                                                      \n",
       "5e212a62bced17b4dbe41150e5bb9037  8842281e1d1347389f2ab93d60773d4d   6392944   \n",
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       "\n",
       "                                  rating  \n",
       "review_id                                 \n",
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5001 entries, 6066814 to 26114327\n",
      "Data columns (total 28 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   isbn                  5001 non-null   object\n",
      " 1   text_reviews_count    5001 non-null   object\n",
      " 2   series                5001 non-null   object\n",
      " 3   country_code          5001 non-null   object\n",
      " 4   language_code         5001 non-null   object\n",
      " 5   popular_shelves       5001 non-null   object\n",
      " 6   asin                  5001 non-null   object\n",
      " 7   is_ebook              5001 non-null   object\n",
      " 8   average_rating        5001 non-null   object\n",
      " 9   kindle_asin           5001 non-null   object\n",
      " 10  similar_books         5001 non-null   object\n",
      " 11  description           5001 non-null   object\n",
      " 12  format                5001 non-null   object\n",
      " 13  link                  5001 non-null   object\n",
      " 14  authors               5001 non-null   object\n",
      " 15  publisher             5001 non-null   object\n",
      " 16  num_pages             5001 non-null   object\n",
      " 17  publication_day       5001 non-null   object\n",
      " 18  isbn13                5001 non-null   object\n",
      " 19  publication_month     5001 non-null   object\n",
      " 20  edition_information   5001 non-null   object\n",
      " 21  publication_year      5001 non-null   object\n",
      " 22  url                   5001 non-null   object\n",
      " 23  image_url             5001 non-null   object\n",
      " 24  ratings_count         5001 non-null   object\n",
      " 25  work_id               5001 non-null   object\n",
      " 26  title                 5001 non-null   object\n",
      " 27  title_without_series  5001 non-null   object\n",
      "dtypes: object(28)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_books.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>[408775]</td>\n",
       "      <td>US</td>\n",
       "      <td></td>\n",
       "      <td>[{'count': '51', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.30</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>9780854563906</td>\n",
       "      <td>12</td>\n",
       "      <td>Large Print</td>\n",
       "      <td>1975</td>\n",
       "      <td>https://www.goodreads.com/book/show/1902202.De...</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>52</td>\n",
       "      <td>1903897</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "      <td>Dead in the Morning (Patrick Grant, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9671977</th>\n",
       "      <td>8838920931</td>\n",
       "      <td>3</td>\n",
       "      <td>[274410]</td>\n",
       "      <td>US</td>\n",
       "      <td>ita</td>\n",
       "      <td>[{'count': '48', 'name': 'to-read'}, {'count':...</td>\n",
       "      <td></td>\n",
       "      <td>false</td>\n",
       "      <td>3.54</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>9788838920936</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.goodreads.com/book/show/9671977-ar...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474788304m...</td>\n",
       "      <td>22</td>\n",
       "      <td>2152906</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "      <td>Aristotele e i misteri di Eleusi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                isbn text_reviews_count     series country_code language_code  \\\n",
       "book_id                                                                         \n",
       "6066814   184737297X                 15   [169353]           US                 \n",
       "33394837                             60  [1052227]           US           eng   \n",
       "29074697                             23   [953679]           US           eng   \n",
       "1902202   0854563903                  8   [408775]           US                 \n",
       "9671977   8838920931                  3   [274410]           US           ita   \n",
       "\n",
       "                                            popular_shelves        asin  \\\n",
       "book_id                                                                   \n",
       "6066814   [{'count': '159', 'name': 'to-read'}, {'count'...               \n",
       "33394837  [{'count': '54', 'name': 'currently-reading'},...  B01NCIKAQX   \n",
       "29074697  [{'count': '90', 'name': 'to-read'}, {'count':...  B01ALOWJN0   \n",
       "1902202   [{'count': '51', 'name': 'to-read'}, {'count':...               \n",
       "9671977   [{'count': '48', 'name': 'to-read'}, {'count':...               \n",
       "\n",
       "         is_ebook average_rating kindle_asin  ...         isbn13  \\\n",
       "book_id                                       ...                  \n",
       "6066814     false           3.93  B007YLTG5I  ...  9781847372970   \n",
       "33394837     true           4.33  B01NCIKAQX  ...                  \n",
       "29074697     true           3.49  B01ALOWJN0  ...                  \n",
       "1902202     false           3.30              ...  9780854563906   \n",
       "9671977     false           3.54              ...  9788838920936   \n",
       "\n",
       "         publication_month edition_information publication_year  \\\n",
       "book_id                                                           \n",
       "6066814                  4                                 2009   \n",
       "33394837                                                          \n",
       "29074697                                                          \n",
       "1902202                 12         Large Print             1975   \n",
       "9671977                                                    2006   \n",
       "\n",
       "                                                        url  \\\n",
       "book_id                                                       \n",
       "6066814   https://www.goodreads.com/book/show/6066814-cr...   \n",
       "33394837  https://www.goodreads.com/book/show/33394837-t...   \n",
       "29074697  https://www.goodreads.com/book/show/29074697-t...   \n",
       "1902202   https://www.goodreads.com/book/show/1902202.De...   \n",
       "9671977   https://www.goodreads.com/book/show/9671977-ar...   \n",
       "\n",
       "                                                  image_url ratings_count  \\\n",
       "book_id                                                                     \n",
       "6066814   https://images.gr-assets.com/books/1328724803m...           186   \n",
       "33394837  https://images.gr-assets.com/books/1493114742m...           269   \n",
       "29074697  https://s.gr-assets.com/assets/nophoto/book/11...           192   \n",
       "1902202   https://s.gr-assets.com/assets/nophoto/book/11...            52   \n",
       "9671977   https://images.gr-assets.com/books/1474788304m...            22   \n",
       "\n",
       "           work_id                                              title  \\\n",
       "book_id                                                                 \n",
       "6066814    6243149          Crowner Royal (Crowner John Mystery, #13)   \n",
       "33394837  54143148            The House of Memory (Pluto's Snitch #2)   \n",
       "29074697  49305010  The Slaughtered Virgin of Zenopolis (Inspector...   \n",
       "1902202    1903897            Dead in the Morning (Patrick Grant, #1)   \n",
       "9671977    2152906                   Aristotele e i misteri di Eleusi   \n",
       "\n",
       "                                       title_without_series  \n",
       "book_id                                                      \n",
       "6066814           Crowner Royal (Crowner John Mystery, #13)  \n",
       "33394837            The House of Memory (Pluto's Snitch #2)  \n",
       "29074697  The Slaughtered Virgin of Zenopolis (Inspector...  \n",
       "1902202             Dead in the Morning (Patrick Grant, #1)  \n",
       "9671977                    Aristotele e i misteri di Eleusi  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [isbn, text_reviews_count, series, country_code, language_code, popular_shelves, asin, is_ebook, average_rating, kindle_asin, similar_books, description, format, link, authors, publisher, num_pages, publication_day, isbn13, publication_month, edition_information, publication_year, url, image_url, ratings_count, work_id, title, title_without_series]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 28 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books[df_books['country_code'].notna() == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                      title  \\\n",
       "book_id                                                       \n",
       "6066814           Crowner Royal (Crowner John Mystery, #13)   \n",
       "33394837            The House of Memory (Pluto's Snitch #2)   \n",
       "29074697  The Slaughtered Virgin of Zenopolis (Inspector...   \n",
       "1902202             Dead in the Morning (Patrick Grant, #1)   \n",
       "9671977                    Aristotele e i misteri di Eleusi   \n",
       "\n",
       "         text_reviews_count  \\\n",
       "book_id                       \n",
       "6066814                  15   \n",
       "33394837                 60   \n",
       "29074697                 23   \n",
       "1902202                   8   \n",
       "9671977                   3   \n",
       "\n",
       "                                            popular_shelves average_rating  \n",
       "book_id                                                                     \n",
       "6066814   [{'count': '159', 'name': 'to-read'}, {'count'...           3.93  \n",
       "33394837  [{'count': '54', 'name': 'currently-reading'},...           4.33  \n",
       "29074697  [{'count': '90', 'name': 'to-read'}, {'count':...           3.49  \n",
       "1902202   [{'count': '51', 'name': 'to-read'}, {'count':...           3.30  \n",
       "9671977   [{'count': '48', 'name': 'to-read'}, {'count':...           3.54  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books = df_books[['title', 'text_reviews_count', 'popular_shelves', 'average_rating']]\n",
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books = df_books.astype({\"title\": 'string', \"text_reviews_count\": np.uint, 'average_rating': np.float64})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>33394837</th>\n",
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      ],
      "text/plain": [
       "                                                      title  \\\n",
       "book_id                                                       \n",
       "6066814           Crowner Royal (Crowner John Mystery, #13)   \n",
       "33394837            The House of Memory (Pluto's Snitch #2)   \n",
       "29074697  The Slaughtered Virgin of Zenopolis (Inspector...   \n",
       "1902202             Dead in the Morning (Patrick Grant, #1)   \n",
       "9671977                    Aristotele e i misteri di Eleusi   \n",
       "\n",
       "          text_reviews_count  \\\n",
       "book_id                        \n",
       "6066814                   15   \n",
       "33394837                  60   \n",
       "29074697                  23   \n",
       "1902202                    8   \n",
       "9671977                    3   \n",
       "\n",
       "                                            popular_shelves  average_rating  \n",
       "book_id                                                                      \n",
       "6066814   [{'count': '159', 'name': 'to-read'}, {'count'...            3.93  \n",
       "33394837  [{'count': '54', 'name': 'currently-reading'},...            4.33  \n",
       "29074697  [{'count': '90', 'name': 'to-read'}, {'count':...            3.49  \n",
       "1902202   [{'count': '51', 'name': 'to-read'}, {'count':...            3.30  \n",
       "9671977   [{'count': '48', 'name': 'to-read'}, {'count':...            3.54  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                  string\n",
       "text_reviews_count     uint32\n",
       "popular_shelves        object\n",
       "average_rating        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "todo: für weitere untersuchungen (Genres) _popular shelves_ noch weiter exploden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "      <td>15</td>\n",
       "      <td>{'count': '7', 'name': 'medieval'}</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26114327</th>\n",
       "      <td>Obsession Falls (Virtue Falls, #2)</td>\n",
       "      <td>11</td>\n",
       "      <td>{'count': '1', 'name': 'mysteries-surprise-end...</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26114327</th>\n",
       "      <td>Obsession Falls (Virtue Falls, #2)</td>\n",
       "      <td>11</td>\n",
       "      <td>{'count': '1', 'name': 'murder-mysteries'}</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26114327</th>\n",
       "      <td>Obsession Falls (Virtue Falls, #2)</td>\n",
       "      <td>11</td>\n",
       "      <td>{'count': '1', 'name': 'aaa-books-2017'}</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26114327</th>\n",
       "      <td>Obsession Falls (Virtue Falls, #2)</td>\n",
       "      <td>11</td>\n",
       "      <td>{'count': '1', 'name': '2017-summer-list'}</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26114327</th>\n",
       "      <td>Obsession Falls (Virtue Falls, #2)</td>\n",
       "      <td>11</td>\n",
       "      <td>{'count': '1', 'name': 'tower-teams'}</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392242 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title  text_reviews_count  \\\n",
       "book_id                                                                   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "...                                             ...                 ...   \n",
       "26114327         Obsession Falls (Virtue Falls, #2)                  11   \n",
       "26114327         Obsession Falls (Virtue Falls, #2)                  11   \n",
       "26114327         Obsession Falls (Virtue Falls, #2)                  11   \n",
       "26114327         Obsession Falls (Virtue Falls, #2)                  11   \n",
       "26114327         Obsession Falls (Virtue Falls, #2)                  11   \n",
       "\n",
       "                                            popular_shelves  average_rating  \n",
       "book_id                                                                      \n",
       "6066814                 {'count': '159', 'name': 'to-read'}            3.93  \n",
       "6066814       {'count': '12', 'name': 'historical-fiction'}            3.93  \n",
       "6066814                  {'count': '11', 'name': 'mystery'}            3.93  \n",
       "6066814       {'count': '10', 'name': 'historical-mystery'}            3.93  \n",
       "6066814                  {'count': '7', 'name': 'medieval'}            3.93  \n",
       "...                                                     ...             ...  \n",
       "26114327  {'count': '1', 'name': 'mysteries-surprise-end...            3.77  \n",
       "26114327         {'count': '1', 'name': 'murder-mysteries'}            3.77  \n",
       "26114327           {'count': '1', 'name': 'aaa-books-2017'}            3.77  \n",
       "26114327         {'count': '1', 'name': '2017-summer-list'}            3.77  \n",
       "26114327              {'count': '1', 'name': 'tower-teams'}            3.77  \n",
       "\n",
       "[392242 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.explode('popular_shelves')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility to split the data into training and test sets.\n",
    "def split_dataframe(df, holdout_fraction=0.2):\n",
    "  \"\"\"Splits a DataFrame into training and test sets.\n",
    "  Args:\n",
    "    df: a dataframe.\n",
    "    holdout_fraction: fraction of dataframe rows to use in the test set.\n",
    "  Returns:\n",
    "    train: dataframe for training\n",
    "    test: dataframe for testing\n",
    "  \"\"\"\n",
    "  test = df.sample(frac=holdout_fraction, replace=False)\n",
    "  train = df[~df.index.isin(test.index)]\n",
    "  return train, test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_recommenders as tfrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ratings:\n",
    "`<MapDataset shapes: {movie_title: (), user_id: ()}, types: {movie_title: tf.string, user_id: tf.string}>`\n",
    "\n",
    "`{'movie_title': <tf.Tensor: shape=(), dtype=string, numpy=b\"One Flew Over the Cuckoo's Nest (1975)\">, 'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'138'>}`\n",
    "\n",
    "movies:\n",
    "`<MapDataset shapes: (), types: tf.string>`\n",
    "\n",
    "`tf.Tensor(b'You So Crazy (1994)', shape=(), dtype=string)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sprich: \n",
    "user (ratings): 1:1 Titel <-> UserID\n",
    "item: nur Titel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, Text\n",
    "\n",
    "class GoodReadsModel(tfrs.Model):\n",
    "  # We derive from a custom base class to help reduce boilerplate. Under the hood,\n",
    "  # these are still plain Keras Models.\n",
    "\n",
    "  def __init__(\n",
    "      self,\n",
    "      user_model: tf.keras.Model,\n",
    "      book_model: tf.keras.Model,\n",
    "      task: tfrs.tasks.Retrieval):\n",
    "    super().__init__()\n",
    "\n",
    "    # Set up user and movie representations.\n",
    "    self.user_model = user_model\n",
    "    self.book_model = book_model\n",
    "\n",
    "    # Set up a retrieval task.\n",
    "    self.task = task\n",
    "\n",
    "  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
    "    # Define how the loss is computed.\n",
    "\n",
    "    user_embeddings = self.user_model(features['user_id'])\n",
    "    movie_embeddings = self.book_model(features['book_id'])\n",
    "\n",
    "    return self.task(user_embeddings, movie_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build vocabularies to convert user ids and movie titles into integer indices for embedding layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>title</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>popular_shelves</th>\n",
       "      <th>average_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6066814</th>\n",
       "      <td>Crowner Royal (Crowner John Mystery, #13)</td>\n",
       "      <td>15</td>\n",
       "      <td>[{'count': '159', 'name': 'to-read'}, {'count'...</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33394837</th>\n",
       "      <td>The House of Memory (Pluto's Snitch #2)</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'count': '54', 'name': 'currently-reading'},...</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title  text_reviews_count  \\\n",
       "book_id                                                                   \n",
       "6066814   Crowner Royal (Crowner John Mystery, #13)                  15   \n",
       "33394837    The House of Memory (Pluto's Snitch #2)                  60   \n",
       "\n",
       "                                            popular_shelves  average_rating  \n",
       "book_id                                                                      \n",
       "6066814   [{'count': '159', 'name': 'to-read'}, {'count'...            3.93  \n",
       "33394837  [{'count': '54', 'name': 'currently-reading'},...            4.33  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['6066814', '33394837', '29074697', '1902202', '9671977', '16158998',\n",
       "       '25162836', '2805495', '22722787', '16158996',\n",
       "       ...\n",
       "       '17978149', '17978144', '17667189', '3429795', '606991', '18669319',\n",
       "       '13599705', '6530667', '6530669', '26114327'],\n",
       "      dtype='object', name='book_id', length=5001)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_ids = df_books.index\n",
    "book_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id\n",
       "5e212a62bced17b4dbe41150e5bb9037    8842281e1d1347389f2ab93d60773d4d\n",
       "2ede853b14dc4583f96cf5d120af636f    8842281e1d1347389f2ab93d60773d4d\n",
       "8e4d61801907e591018bdc3442a9cf2b    8842281e1d1347389f2ab93d60773d4d\n",
       "022bb6daffa49adc27f6b20b6ebeb37d    8842281e1d1347389f2ab93d60773d4d\n",
       "0e317947e1fd341f573192111bb2921d    8842281e1d1347389f2ab93d60773d4d\n",
       "                                                  ...               \n",
       "da3afde4e35be07556d25941be8e7594    5da7f0c9d042ac1bd51a7f1e08bbf6a3\n",
       "9f48ba1335588f5e2b02d5e4427ff611    5da7f0c9d042ac1bd51a7f1e08bbf6a3\n",
       "3e339a97bcd6fbb35b90a33df5a525fe    5da7f0c9d042ac1bd51a7f1e08bbf6a3\n",
       "2c267a9b68bfd1f0f39e6b6397d472a6    5da7f0c9d042ac1bd51a7f1e08bbf6a3\n",
       "c296dca609072c43eba74cb87c8edad9    5da7f0c9d042ac1bd51a7f1e08bbf6a3\n",
       "Name: user_id, Length: 5001, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ids = df_reviews['user_id']\n",
    "user_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_id</th>\n",
       "      <th></th>\n",
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       "      <td>8842281e1d1347389f2ab93d60773d4d</td>\n",
       "      <td>6392944</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ede853b14dc4583f96cf5d120af636f</th>\n",
       "      <td>8842281e1d1347389f2ab93d60773d4d</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>8e4d61801907e591018bdc3442a9cf2b</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>022bb6daffa49adc27f6b20b6ebeb37d</th>\n",
       "      <td>8842281e1d1347389f2ab93d60773d4d</td>\n",
       "      <td>17860739</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0e317947e1fd341f573192111bb2921d</th>\n",
       "      <td>8842281e1d1347389f2ab93d60773d4d</td>\n",
       "      <td>8694005</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           user_id   book_id  \\\n",
       "review_id                                                                      \n",
       "5e212a62bced17b4dbe41150e5bb9037  8842281e1d1347389f2ab93d60773d4d   6392944   \n",
       "2ede853b14dc4583f96cf5d120af636f  8842281e1d1347389f2ab93d60773d4d  28684704   \n",
       "8e4d61801907e591018bdc3442a9cf2b  8842281e1d1347389f2ab93d60773d4d  32283133   \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  8842281e1d1347389f2ab93d60773d4d  17860739   \n",
       "0e317947e1fd341f573192111bb2921d  8842281e1d1347389f2ab93d60773d4d   8694005   \n",
       "\n",
       "                                  rating  \n",
       "review_id                                 \n",
       "5e212a62bced17b4dbe41150e5bb9037       3  \n",
       "2ede853b14dc4583f96cf5d120af636f       3  \n",
       "8e4d61801907e591018bdc3442a9cf2b       0  \n",
       "022bb6daffa49adc27f6b20b6ebeb37d       4  \n",
       "0e317947e1fd341f573192111bb2921d       3  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ids.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kann ich mir sparen wenn ich gleich mit den IDs arbeite:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_book_ids = tf.convert_to_tensor(book_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.string>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_book_ds = tf.data.Dataset.from_tensor_slices(tf_book_ids)\n",
    "tf_book_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "useridtensor= tf.convert_to_tensor(df_reviews['user_id'])\n",
    "bookidtensor= tf.convert_to_tensor(df_reviews['book_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: {user_id: (), book_id: ()}, types: {user_id: tf.string, book_id: tf.string}>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_ds = tf.data.Dataset.from_tensor_slices({\"user_id\": useridtensor, \"book_id\": bookidtensor})\n",
    "review_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_reviews = tf.data.Dataset.from_tensors([tf.convert_to_tensor(df_reviews['user_id']), tf.convert_to_tensor(df_reviews['book_id'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorDataset shapes: (2, 5001), types: tf.string>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'6392944'>}\n",
      "--\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'28684704'>}\n",
      "--\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'32283133'>}\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "for line in review_ds.take(3):\n",
    "    print(line)\n",
    "    print(\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_ids_vocabulary = tf.keras.layers.StringLookup(mask_token=None)\n",
    "user_ids_vocabulary.adapt(tf_reviews.map(lambda x: x[0]))\n",
    "\n",
    "book_titles_vocabulary = tf.keras.layers.StringLookup(mask_token=None)\n",
    "book_titles_vocabulary.adapt(tf_book_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "493"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_ids_vocabulary.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       " '012515e5802b2e0f42915118c90fa04b']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ids_vocabulary.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5002\n"
     ]
    },
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       " '464088',\n",
       " ...]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(book_titles_vocabulary.get_vocabulary()))\n",
    "book_titles_vocabulary.get_vocabulary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define user and movie models.\n",
    "user_model = tf.keras.Sequential([\n",
    "    user_ids_vocabulary,\n",
    "    tf.keras.layers.Embedding(user_ids_vocabulary.vocabulary_size(), 64)\n",
    "])\n",
    "book_model = tf.keras.Sequential([\n",
    "    book_titles_vocabulary,\n",
    "    tf.keras.layers.Embedding(book_titles_vocabulary.vocabulary_size(), 64)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'6066814', shape=(), dtype=string)\n",
      "tf.Tensor(b'33394837', shape=(), dtype=string)\n",
      "tf.Tensor(b'29074697', shape=(), dtype=string)\n",
      "---\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'6392944'>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'28684704'>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'32283133'>}\n"
     ]
    }
   ],
   "source": [
    "for e in tf_book_ds.take(3):\n",
    "    print(e)\n",
    "print(\"---\")\n",
    "for e in review_ds.take(3):\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your objectives.\n",
    "task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(\n",
    "    tf_book_ds.batch(32).map(book_model)\n",
    "  )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and evaluate it.\n",
    "\n",
    "Create the model, train it, and generate predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'6392944'>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'28684704'>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'8842281e1d1347389f2ab93d60773d4d'>, 'book_id': <tf.Tensor: shape=(), dtype=string, numpy=b'32283133'>}\n"
     ]
    }
   ],
   "source": [
    "for e in review_ds.take(3):\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "157/157 [==============================] - 19s 108ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0552 - factorized_top_k/top_5_categorical_accuracy: 0.1560 - factorized_top_k/top_10_categorical_accuracy: 0.2098 - factorized_top_k/top_50_categorical_accuracy: 0.2533 - factorized_top_k/top_100_categorical_accuracy: 0.2667 - loss: 110.0767 - regularization_loss: 0.0000e+00 - total_loss: 110.0767\n",
      "Epoch 2/20\n",
      "157/157 [==============================] - 16s 103ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0060 - factorized_top_k/top_5_categorical_accuracy: 0.0958 - factorized_top_k/top_10_categorical_accuracy: 0.2797 - factorized_top_k/top_50_categorical_accuracy: 0.5373 - factorized_top_k/top_100_categorical_accuracy: 0.5469 - loss: 111.8378 - regularization_loss: 0.0000e+00 - total_loss: 111.8378\n",
      "Epoch 3/20\n",
      "157/157 [==============================] - 17s 106ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0332 - factorized_top_k/top_5_categorical_accuracy: 0.1808 - factorized_top_k/top_10_categorical_accuracy: 0.3237 - factorized_top_k/top_50_categorical_accuracy: 0.6231 - factorized_top_k/top_100_categorical_accuracy: 0.6347 - loss: 113.5795 - regularization_loss: 0.0000e+00 - total_loss: 113.5795\n",
      "Epoch 4/20\n",
      "157/157 [==============================] - 16s 105ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0052 - factorized_top_k/top_5_categorical_accuracy: 0.1352 - factorized_top_k/top_10_categorical_accuracy: 0.2943 - factorized_top_k/top_50_categorical_accuracy: 0.7522 - factorized_top_k/top_100_categorical_accuracy: 0.7864 - loss: 115.5443 - regularization_loss: 0.0000e+00 - total_loss: 115.5443\n",
      "Epoch 5/20\n",
      "157/157 [==============================] - 18s 117ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0240 - factorized_top_k/top_5_categorical_accuracy: 0.2146 - factorized_top_k/top_10_categorical_accuracy: 0.3087 - factorized_top_k/top_50_categorical_accuracy: 0.7455 - factorized_top_k/top_100_categorical_accuracy: 0.7942 - loss: 111.8110 - regularization_loss: 0.0000e+00 - total_loss: 111.8110\n",
      "Epoch 6/20\n",
      "157/157 [==============================] - 18s 115ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0488 - factorized_top_k/top_5_categorical_accuracy: 0.1978 - factorized_top_k/top_10_categorical_accuracy: 0.4067 - factorized_top_k/top_50_categorical_accuracy: 0.8280 - factorized_top_k/top_100_categorical_accuracy: 0.8508 - loss: 112.8686 - regularization_loss: 0.0000e+00 - total_loss: 112.8686\n",
      "Epoch 7/20\n",
      "157/157 [==============================] - 16s 105ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0034 - factorized_top_k/top_5_categorical_accuracy: 0.0446 - factorized_top_k/top_10_categorical_accuracy: 0.1068 - factorized_top_k/top_50_categorical_accuracy: 0.8098 - factorized_top_k/top_100_categorical_accuracy: 0.8248 - loss: 111.7243 - regularization_loss: 0.0000e+00 - total_loss: 111.7243\n",
      "Epoch 8/20\n",
      "157/157 [==============================] - 17s 106ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0356 - factorized_top_k/top_5_categorical_accuracy: 0.1860 - factorized_top_k/top_10_categorical_accuracy: 0.3595 - factorized_top_k/top_50_categorical_accuracy: 0.8358 - factorized_top_k/top_100_categorical_accuracy: 0.8492 - loss: 110.5264 - regularization_loss: 0.0000e+00 - total_loss: 110.5264\n",
      "Epoch 9/20\n",
      "157/157 [==============================] - 18s 116ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0802 - factorized_top_k/top_5_categorical_accuracy: 0.2547 - factorized_top_k/top_10_categorical_accuracy: 0.4281 - factorized_top_k/top_50_categorical_accuracy: 0.8218 - factorized_top_k/top_100_categorical_accuracy: 0.8340 - loss: 109.8054 - regularization_loss: 0.0000e+00 - total_loss: 109.8054\n",
      "Epoch 10/20\n",
      "157/157 [==============================] - 21s 135ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0242 - factorized_top_k/top_5_categorical_accuracy: 0.1604 - factorized_top_k/top_10_categorical_accuracy: 0.2719 - factorized_top_k/top_50_categorical_accuracy: 0.8148 - factorized_top_k/top_100_categorical_accuracy: 0.8306 - loss: 109.5100 - regularization_loss: 0.0000e+00 - total_loss: 109.5100\n",
      "Epoch 11/20\n",
      "157/157 [==============================] - 17s 109ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0030 - factorized_top_k/top_5_categorical_accuracy: 0.1102 - factorized_top_k/top_10_categorical_accuracy: 0.2158 - factorized_top_k/top_50_categorical_accuracy: 0.8036 - factorized_top_k/top_100_categorical_accuracy: 0.8270 - loss: 109.2218 - regularization_loss: 0.0000e+00 - total_loss: 109.2218\n",
      "Epoch 12/20\n",
      "157/157 [==============================] - 17s 111ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0690 - factorized_top_k/top_10_categorical_accuracy: 0.1814 - factorized_top_k/top_50_categorical_accuracy: 0.7974 - factorized_top_k/top_100_categorical_accuracy: 0.8128 - loss: 109.0707 - regularization_loss: 0.0000e+00 - total_loss: 109.0707\n",
      "Epoch 13/20\n",
      "157/157 [==============================] - 19s 121ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0024 - factorized_top_k/top_5_categorical_accuracy: 0.0548 - factorized_top_k/top_10_categorical_accuracy: 0.1648 - factorized_top_k/top_50_categorical_accuracy: 0.7748 - factorized_top_k/top_100_categorical_accuracy: 0.8148 - loss: 108.9303 - regularization_loss: 0.0000e+00 - total_loss: 108.9303\n",
      "Epoch 14/20\n",
      "157/157 [==============================] - 16s 103ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0030 - factorized_top_k/top_5_categorical_accuracy: 0.0572 - factorized_top_k/top_10_categorical_accuracy: 0.1546 - factorized_top_k/top_50_categorical_accuracy: 0.7824 - factorized_top_k/top_100_categorical_accuracy: 0.8188 - loss: 108.8731 - regularization_loss: 0.0000e+00 - total_loss: 108.8731\n",
      "Epoch 15/20\n",
      "157/157 [==============================] - 16s 102ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0022 - factorized_top_k/top_5_categorical_accuracy: 0.0526 - factorized_top_k/top_10_categorical_accuracy: 0.1588 - factorized_top_k/top_50_categorical_accuracy: 0.7880 - factorized_top_k/top_100_categorical_accuracy: 0.8186 - loss: 108.8246 - regularization_loss: 0.0000e+00 - total_loss: 108.8246\n",
      "Epoch 16/20\n",
      "157/157 [==============================] - 17s 108ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0030 - factorized_top_k/top_5_categorical_accuracy: 0.0718 - factorized_top_k/top_10_categorical_accuracy: 0.1660 - factorized_top_k/top_50_categorical_accuracy: 0.8084 - factorized_top_k/top_100_categorical_accuracy: 0.8174 - loss: 108.8047 - regularization_loss: 0.0000e+00 - total_loss: 108.8047\n",
      "Epoch 17/20\n",
      "157/157 [==============================] - 17s 106ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0024 - factorized_top_k/top_5_categorical_accuracy: 0.0754 - factorized_top_k/top_10_categorical_accuracy: 0.1616 - factorized_top_k/top_50_categorical_accuracy: 0.7950 - factorized_top_k/top_100_categorical_accuracy: 0.8196 - loss: 108.8035 - regularization_loss: 0.0000e+00 - total_loss: 108.8035\n",
      "Epoch 18/20\n",
      "157/157 [==============================] - 17s 105ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0026 - factorized_top_k/top_5_categorical_accuracy: 0.0848 - factorized_top_k/top_10_categorical_accuracy: 0.1708 - factorized_top_k/top_50_categorical_accuracy: 0.8084 - factorized_top_k/top_100_categorical_accuracy: 0.8174 - loss: 108.7879 - regularization_loss: 0.0000e+00 - total_loss: 108.7879\n",
      "Epoch 19/20\n",
      "157/157 [==============================] - 17s 108ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0026 - factorized_top_k/top_5_categorical_accuracy: 0.0838 - factorized_top_k/top_10_categorical_accuracy: 0.1800 - factorized_top_k/top_50_categorical_accuracy: 0.7876 - factorized_top_k/top_100_categorical_accuracy: 0.8146 - loss: 108.7844 - regularization_loss: 0.0000e+00 - total_loss: 108.7844\n",
      "Epoch 20/20\n",
      "157/157 [==============================] - 16s 103ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0034 - factorized_top_k/top_5_categorical_accuracy: 0.0900 - factorized_top_k/top_10_categorical_accuracy: 0.1832 - factorized_top_k/top_50_categorical_accuracy: 0.7982 - factorized_top_k/top_100_categorical_accuracy: 0.8138 - loss: 108.7793 - regularization_loss: 0.0000e+00 - total_loss: 108.7793\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_recommenders.layers.factorized_top_k.BruteForce at 0x217562efdc0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a retrieval model.\n",
    "model = GoodReadsModel(user_model, book_model, task)\n",
    "model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))\n",
    "\n",
    "# Train for 3 epochs.\n",
    "model.fit(review_ds.batch(32), epochs=20)\n",
    "\n",
    "# Use brute-force search to set up retrieval using the trained representations.\n",
    "index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n",
    "index.index_from_dataset(\n",
    "    tf_book_ds.batch(32).map(lambda title: (title, model.book_model(title))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 recommendations for user 42: [b'13418181' b'22754100' b'3239884']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get some recommendations.\n",
    "values, titles = index(np.array([\"42\"]))\n",
    "print(f\"Top 3 recommendations for user 42: {titles[0, :3]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow_recommenders.layers.factorized_top_k.BruteForce at 0x217562efdc0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=\n",
       "array([[0.18735203, 0.12515944, 0.11364166, 0.11310186, 0.10056313,\n",
       "        0.09756363, 0.09151186, 0.08761363, 0.07602005, 0.07572394]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['13418181', '22754100', '3239884', '20810581', '3039522']\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>popular_shelves</th>\n",
       "      <th>average_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13418181</th>\n",
       "      <td>Proof (Bloodline Trilogy, #1)</td>\n",
       "      <td>155</td>\n",
       "      <td>[{'count': '1047', 'name': 'to-read'}, {'count...</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239884</th>\n",
       "      <td>Dead Heat</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'count': '2407', 'name': 'to-read'}, {'count...</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20810581</th>\n",
       "      <td>Kuolema Niilillä</td>\n",
       "      <td>2</td>\n",
       "      <td>[{'count': '2943', 'name': 'mystery'}, {'count...</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22754100</th>\n",
       "      <td>The Girl on the Train</td>\n",
       "      <td>6043</td>\n",
       "      <td>[{'count': '626050', 'name': 'to-read'}, {'cou...</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3039522</th>\n",
       "      <td>Enkelitanssi (Inspector Winter, #1)</td>\n",
       "      <td>4</td>\n",
       "      <td>[{'count': '2375', 'name': 'to-read'}, {'count...</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        title  text_reviews_count  \\\n",
       "book_id                                                             \n",
       "13418181        Proof (Bloodline Trilogy, #1)                 155   \n",
       "3239884                             Dead Heat                   1   \n",
       "20810581                     Kuolema Niilillä                   2   \n",
       "22754100                The Girl on the Train                6043   \n",
       "3039522   Enkelitanssi (Inspector Winter, #1)                   4   \n",
       "\n",
       "                                            popular_shelves  average_rating  \n",
       "book_id                                                                      \n",
       "13418181  [{'count': '1047', 'name': 'to-read'}, {'count...            4.15  \n",
       "3239884   [{'count': '2407', 'name': 'to-read'}, {'count...            4.36  \n",
       "20810581  [{'count': '2943', 'name': 'mystery'}, {'count...            4.07  \n",
       "22754100  [{'count': '626050', 'name': 'to-read'}, {'cou...            3.88  \n",
       "3039522   [{'count': '2375', 'name': 'to-read'}, {'count...            3.57  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = [str(title.numpy()).split(\"'\")[1] for title in titles[0, :5]]\n",
    "print(ids)\n",
    "df_books[df_books.index.isin(ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    06316bec7a49286f1f98d5acce24f923\n",
       "book_id                              457930\n",
       "rating                                    1\n",
       "Name: eb1ea982ea68a99ba372164913236f2c, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.iloc[42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1007501', '457930']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_review_book_ids = df_reviews[df_reviews['user_id'] == '06316bec7a49286f1f98d5acce24f923']['book_id'].tolist()\n",
    "user_review_book_ids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.GoodReadsModel object at 0x00000217549C3F70>, because it is not built.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.GoodReadsModel object at 0x00000217549C3F70>, because it is not built.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Model <__main__.GoodReadsModel object at 0x00000217549C3F70> cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined when calling `.fit()` or `.predict()`. To manually set the shapes, call `model.build(input_shape)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-101-b988f1f65d5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'saved_model/goodreads_model_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%Y%m%d-%H%M%S\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mkdir -p saved_model'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\saving\\saving_utils.py\u001b[0m in \u001b[0;36mraise_model_input_error\u001b[1;34m(model)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mraise_model_input_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m   raise ValueError(\n\u001b[0m\u001b[0;32m     85\u001b[0m       \u001b[1;34mf'Model {model} cannot be saved because the input shapes have not '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m       \u001b[1;34mf'been set. Usually, input shapes are automatically determined when '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Model <__main__.GoodReadsModel object at 0x00000217549C3F70> cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined when calling `.fit()` or `.predict()`. To manually set the shapes, call `model.build(input_shape)"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "fn = 'saved_model/goodreads_model_' + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "!mkdir -p saved_model\n",
    "model.save(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\simon\\AppData\\Local\\Temp\\tmpcwpwq_7c\\bruteforce\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\simon\\AppData\\Local\\Temp\\tmpcwpwq_7c\\bruteforce\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.GoodReadsModel object at 0x00000217549C3F70>, because it is not built.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.GoodReadsModel object at 0x00000217549C3F70>, because it is not built.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow_recommenders.metrics.factorized_top_k.FactorizedTopK object at 0x000002175497B730>, because it is not built.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow_recommenders.metrics.factorized_top_k.FactorizedTopK object at 0x000002175497B730>, because it is not built.\n"
     ]
    },
    {
     "ename": "FailedPreconditionError",
     "evalue": "Failed to serialize the input pipeline graph: ResourceGather is stateful. [Op:DatasetToGraphV2]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-896a99c33fe2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTemporaryDirectory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m   \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"model\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m   tf.saved_model.save(\n\u001b[0m\u001b[0;32m     14\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m       \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, export_dir, signatures, options)\u001b[0m\n\u001b[0;32m   1278\u001b[0m   \u001b[1;31m# pylint: enable=line-too-long\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1279\u001b[0m   \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIncrementWriteApi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_SAVE_V2_LABEL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1280\u001b[1;33m   \u001b[0msave_and_return_nodes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexport_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1281\u001b[0m   \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIncrementWrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrite_version\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"2\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36msave_and_return_nodes\u001b[1;34m(obj, export_dir, signatures, options, experimental_skip_checkpoint)\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1314\u001b[0m   _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (\n\u001b[1;32m-> 1315\u001b[1;33m       _build_meta_graph(obj, signatures, options, meta_graph_def))\n\u001b[0m\u001b[0;32m   1316\u001b[0m   saved_model.saved_model_schema_version = (\n\u001b[0;32m   1317\u001b[0m       constants.SAVED_MODEL_SCHEMA_VERSION)\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36m_build_meta_graph\u001b[1;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[0;32m   1485\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1486\u001b[0m   \u001b[1;32mwith\u001b[0m \u001b[0msave_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1487\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_build_meta_graph_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_graph_def\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36m_build_meta_graph_impl\u001b[1;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[0;32m   1434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1435\u001b[0m   \u001b[1;31m# Use _SaveableView to provide a frozen listing of properties and functions.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1436\u001b[1;33m   saveable_view = _SaveableView(checkpoint_graph_view, options,\n\u001b[0m\u001b[0;32m   1437\u001b[0m                                 wrapped_functions)\n\u001b[0;32m   1438\u001b[0m   \u001b[0mobject_saver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTrackableSaver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcheckpoint_graph_view\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, checkpoint_view, options, wrapped_functions)\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[1;31m# Run through the nodes in the object graph first for side effects of\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m     \u001b[1;31m# creating variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 202\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_trace_all_concrete_functions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     (self._trackable_objects, self.node_paths, self._node_ids,\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\saved_model\\save.py\u001b[0m in \u001b[0;36m_trace_all_concrete_functions\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    304\u001b[0m       \u001b[1;32mfor\u001b[0m \u001b[0mfunction\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheckpoint_view\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlist_functions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdef_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFunction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m           \u001b[0mfunction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_list_all_concrete_functions_for_serialization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_list_all_concrete_functions_for_serialization\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1194\u001b[0m       \u001b[0mA\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0minstances\u001b[0m \u001b[0mof\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mConcreteFunction\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m     \"\"\"\n\u001b[1;32m-> 1196\u001b[1;33m     \u001b[0mconcrete_functions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_list_all_concrete_functions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1197\u001b[0m     \u001b[0mseen_signatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1198\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mconcrete_function\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconcrete_functions\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_list_all_concrete_functions\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1176\u001b[0m     \u001b[1;34m\"\"\"Returns all concrete functions.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1177\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_signature\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_concrete_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1179\u001b[0m     \u001b[0mconcrete_functions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1180\u001b[0m     \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1257\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mget_concrete_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1258\u001b[0m     \u001b[1;31m# Implements GenericFunction.get_concrete_function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1259\u001b[1;33m     \u001b[0mconcrete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_concrete_function_garbage_collected\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1260\u001b[0m     \u001b[0mconcrete\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1261\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mconcrete\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_get_concrete_function_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1237\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1238\u001b[0m         \u001b[0minitializers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1240\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize_uninitialized_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minitializers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    778\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_deleter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFunctionDeleter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lifted_initializer_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m--> 780\u001b[1;33m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m    781\u001b[0m             *args, **kwds))\n\u001b[0;32m    782\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3155\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3156\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3157\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3158\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3555\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3556\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3557\u001b[1;33m           \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3558\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3559\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3390\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3391\u001b[0m     graph_function = ConcreteFunction(\n\u001b[1;32m-> 3392\u001b[1;33m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[0;32m   3393\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3394\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)\u001b[0m\n\u001b[0;32m   1141\u001b[0m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1143\u001b[1;33m       \u001b[0mfunc_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1145\u001b[0m       \u001b[1;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    670\u001b[0m         \u001b[1;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    671\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompile_with_xla\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 672\u001b[1;33m           \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    673\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py\u001b[0m in \u001b[0;36m_creator\u001b[1;34m()\u001b[0m\n\u001b[0;32m    279\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mdef_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_signature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mautograph\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_creator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 281\u001b[1;33m       \u001b[0mresource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_resource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    282\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mresource\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    241\u001b[0m             \u001b[1;31m# to have a weak reference to the Dataset to avoid creating\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m             \u001b[1;31m# reference cycles and making work for the garbage collector.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 243\u001b[1;33m             lambda: weak_self._trace_variant_creation()()),  # pylint: disable=unnecessary-lambda,protected-access\n\u001b[0m\u001b[0;32m    244\u001b[0m         name=\"_variant_tracker\")\n\u001b[0;32m    245\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_attr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m_trace_variant_creation\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    363\u001b[0m       \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m       graph_def = graph_pb2.GraphDef().FromString(\n\u001b[1;32m--> 365\u001b[1;33m           self._as_serialized_graph(external_state_policy=options_lib\n\u001b[0m\u001b[0;32m    366\u001b[0m                                     .ExternalStatePolicy.FAIL).numpy())\n\u001b[0;32m    367\u001b[0m     \u001b[0moutput_node_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    550\u001b[0m                 \u001b[1;34m'in a future version'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'after %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m                 instructions)\n\u001b[1;32m--> 552\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m_as_serialized_graph\u001b[1;34m(self, allow_stateful, strip_device_assignment, external_state_policy)\u001b[0m\n\u001b[0;32m    302\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexternal_state_policy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m       \u001b[0mpolicy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexternal_state_policy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m       return gen_dataset_ops.dataset_to_graph_v2(\n\u001b[0m\u001b[0;32m    305\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_variant_tensor\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m           \u001b[0mexternal_state_policy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpolicy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\ops\\gen_dataset_ops.py\u001b[0m in \u001b[0;36mdataset_to_graph_v2\u001b[1;34m(input_dataset, external_state_policy, strip_device_assignment, name)\u001b[0m\n\u001b[0;32m   1157\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1158\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1159\u001b[1;33m       \u001b[0m_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1160\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1161\u001b[0m       \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[1;34m(e, name)\u001b[0m\n\u001b[0;32m   7105\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7106\u001b[0m   \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\" name: \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7107\u001b[1;33m   \u001b[1;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFailedPreconditionError\u001b[0m: Failed to serialize the input pipeline graph: ResourceGather is stateful. [Op:DatasetToGraphV2]"
     ]
    }
   ],
   "source": [
    "import tempfile\n",
    "\n",
    "with tempfile.TemporaryDirectory() as tmp:\n",
    "  path = os.path.join(tmp, \"bruteforce\")\n",
    "  tf.saved_model.save(\n",
    "      index,\n",
    "      path,\n",
    "      options=tf.saved_model.SaveOptions(namespace_whitelist=[\"BruteForce\"])\n",
    "  )\n",
    "\n",
    "with tempfile.TemporaryDirectory() as tmp:\n",
    "  path = os.path.join(tmp, \"model\")\n",
    "  tf.saved_model.save(\n",
    "      model,\n",
    "      path,\n",
    "  )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# tf.data.Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import pathlib\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': <tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=\n",
      "array([[[1.],\n",
      "        [2.]]], dtype=float32)>, 'y': <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'index', b'count'], dtype=object)>, 'z': <tf.Tensor: shape=(), dtype=float32, numpy=0.5>}\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_io as tfio\n",
    "\n",
    "# Test case is to resolve the issue where multiple dimension tensor\n",
    "# was not supported for decode_json.\n",
    "# The issue was initially raised in:\n",
    "# https://github.com/tensorflow/io/pull/695#issuecomment-683270751\n",
    "r = '{\"x\": [[[1.0], [2.0]]], \"y\": [\"index\", \"count\"], \"z\": 0.5}'\n",
    "\n",
    "@tf.function(autograph=False)\n",
    "def parse_json(json_text):\n",
    "    specs = {\n",
    "        \"x\": tf.TensorSpec(tf.TensorShape([1, 2, 1]), tf.float32),\n",
    "        \"y\": tf.TensorSpec(tf.TensorShape([2]), tf.string),\n",
    "        \"z\": tf.TensorSpec(tf.TensorShape([]), tf.float32),\n",
    "    }\n",
    "    parsed = tfio.experimental.serialization.decode_json(json_text, specs)\n",
    "    return parsed\n",
    "\n",
    "v = parse_json(r)\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": " not a valid JSON object\n\t [[{{node IO>DecodeJSON}}]] [Op:__inference_parse_json_book_184]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-669f578d5d49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mparsed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[0mparse_json_book\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     56\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     60\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m:  not a valid JSON object\n\t [[{{node IO>DecodeJSON}}]] [Op:__inference_parse_json_book_184]"
     ]
    }
   ],
   "source": [
    "json_string = str(books[0])\n",
    "@tf.function(autograph=False)\n",
    "def parse_json_book(json_text):\n",
    "    specs = {\n",
    "        \"isbn\": tf.TensorSpec(tf.TensorShape(1), tf.string),\n",
    "        \"text_reviews_count\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"series\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"country_code\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"language_code\": tf.TensorSpec(tf.TensorShape(1), tf.string),\n",
    "        \"popular_shelves\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"asin\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"is_ebook\": tf.TensorSpec(tf.TensorShape([]), tf.bool),\n",
    "        \"average_rating\": tf.TensorSpec(tf.TensorShape([]), tf.float32),\n",
    "        \"kindle_asin\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"similar_books\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"description\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"format\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"authors\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"publisher\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"num_pages\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"publication_day\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"isbn13\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"publication_month\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"edition_information\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"publication_year\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"url\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"book_id\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"ratings_count\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"work_id\": tf.TensorSpec(tf.TensorShape([]), tf.int32),\n",
    "        \"title\": tf.TensorSpec(tf.TensorShape([]), tf.string),\n",
    "        \"title_without_series\": tf.TensorSpec(tf.TensorShape([]), tf.string)\n",
    "    }\n",
    "    parsed = tfio.experimental.serialization.decode_json(json_text, specs)\n",
    "    return parsed\n",
    "\n",
    "parse_json_book(json_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# DASK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import dask.dataframe as dd\n",
    "import dask.array as da\n",
    "import dask.bag as db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-783c343c6021>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdd_books\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDIR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'goodreads_books_mystery_thriller_crime.json.gz'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdd_books\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\dataframe\\io\\json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(url_path, orient, lines, storage_options, blocksize, sample, encoding, errors, compression, meta, engine, **kwargs)\u001b[0m\n\u001b[0;32m    216\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfiles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m         ]\n\u001b[1;32m--> 218\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_delayed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\dataframe\\io\\io.py\u001b[0m in \u001b[0;36mfrom_delayed\u001b[1;34m(dfs, meta, divisions, prefix, verify_meta)\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmeta\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m         \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmake_meta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    594\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m         \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_meta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    281\u001b[0m         \u001b[0mdask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m         \"\"\"\n\u001b[1;32m--> 283\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    284\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    563\u001b[0m         \u001b[0mpostcomputes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 565\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    566\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\threaded.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m                 \u001b[0mpools\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthread\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpool\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     results = get_async(\n\u001b[0m\u001b[0;32m     77\u001b[0m         \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\local.py\u001b[0m in \u001b[0;36mget_async\u001b[1;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[0;32m    485\u001b[0m                         \u001b[0m_execute_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Re-execute locally\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m                         \u001b[0mraise_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    488\u001b[0m                 \u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mworker_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"cache\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\local.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(exc, tb)\u001b[0m\n\u001b[0;32m    315\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\local.py\u001b[0m in \u001b[0;36mexecute_task\u001b[1;34m(key, task_info, dumps, loads, get_id, pack_exception)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mtask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtask_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_execute_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m         \u001b[0mid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\core.py\u001b[0m in \u001b[0;36m_execute_task\u001b[1;34m(arg, cache, dsk)\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[1;31m# temporaries by their reference count and can execute certain\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# operations in-place.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_execute_task\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mishashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\dask\\dataframe\\io\\json.py\u001b[0m in \u001b[0;36mread_json_file\u001b[1;34m(f, orient, lines, engine, kwargs)\u001b[0m\n\u001b[0;32m    231\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mread_json_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    197\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    297\u001b[0m                 )\n\u001b[0;32m    298\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 299\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 563\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    690\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    691\u001b[0m                 \u001b[0mdata_lines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 692\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_lines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    693\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    694\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_combine_lines\u001b[1;34m(self, lines)\u001b[0m\n\u001b[0;32m    673\u001b[0m         \"\"\"\n\u001b[0;32m    674\u001b[0m         return (\n\u001b[1;32m--> 675\u001b[1;33m             \u001b[1;34mf'[{\",\".join((line for line in (line.strip() for line in lines) if line))}]'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    676\u001b[0m         )\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "dd_books = dd.read_json(os.path.join(DIR, 'goodreads_books_mystery_thriller_crime.json.gz'), lines=True)\n",
    "dd_books"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Pandas v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not reserve memory block",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-3b1b36ee2b45>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd_books\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDIR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'goodreads_books_mystery_thriller_crime.json.gz'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpd_books\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    197\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    297\u001b[0m                 )\n\u001b[0;32m    298\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 299\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 563\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    690\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    691\u001b[0m                 \u001b[0mdata_lines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 692\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_lines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    693\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    694\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    829\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    830\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 831\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    832\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    833\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1077\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1078\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m-> 1079\u001b[1;33m                 \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1080\u001b[0m             )\n\u001b[0;32m   1081\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Could not reserve memory block"
     ]
    }
   ],
   "source": [
    "pd_books = pd.read_json(os.path.join(DIR, 'goodreads_books_mystery_thriller_crime.json.gz'), lines=True)\n",
    "pd_books"
   ]
  }
 ],
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