cleanup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load CSVs\n",
    "books_path = Path('Resources/books.csv')\n",
    "ratings_path = Path('Resources/ratings.csv')\n",
    "users_path = Path('Resources/users.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 6452: expected 8 fields, saw 9\\nSkipping line 43667: expected 8 fields, saw 10\\nSkipping line 51751: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 92038: expected 8 fields, saw 9\\nSkipping line 104319: expected 8 fields, saw 9\\nSkipping line 121768: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 144058: expected 8 fields, saw 9\\nSkipping line 150789: expected 8 fields, saw 9\\nSkipping line 157128: expected 8 fields, saw 9\\nSkipping line 180189: expected 8 fields, saw 9\\nSkipping line 185738: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 209388: expected 8 fields, saw 9\\nSkipping line 220626: expected 8 fields, saw 9\\nSkipping line 227933: expected 8 fields, saw 11\\nSkipping line 228957: expected 8 fields, saw 10\\nSkipping line 245933: expected 8 fields, saw 9\\nSkipping line 251296: expected 8 fields, saw 9\\nSkipping line 259941: expected 8 fields, saw 9\\nSkipping line 261529: expected 8 fields, saw 9\\n'\n"
     ]
    }
   ],
   "source": [
    "# Read CSV with latin-1 encoding due to variety of latin characters - \n",
    "# And assign data types to string for consistency\n",
    "# Columns ISBN and Year-Of-Publication cannot be read in as int data types. For example, ISBN contains alphanumeric values. Year-Of-Publication column contains alphabetical values and not exclusively number values. This is managed below.\n",
    "\n",
    "books_df = pd.read_csv(\n",
    "    books_path,\n",
    "    sep=';',\n",
    "    on_bad_lines='warn',\n",
    "    encoding='latin-1', \n",
    "    dtype={\n",
    "        'ISBN': str,\n",
    "        'Book-Title': str,\n",
    "        'Book-Author': str,\n",
    "        'Year-Of-Publication': str,\n",
    "        'Publisher': str,\n",
    "        'Image-URL-S': str,\n",
    "        'Image-URL-M': str,\n",
    "        'Image-URL-L': str,\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: The warning above indicates rows with inconsistent columns to the rest of the books_df dataset and will be omitted from the cleaned DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV\n",
    "ratings_df = pd.read_csv(ratings_path, sep=';', on_bad_lines='warn', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV\n",
    "users_df = pd.read_csv(users_path, sep=';', on_bad_lines='warn', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleanup\n",
    "- Check nulls\n",
    "- Check duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
       "      <th>Image-URL-M</th>\n",
       "      <th>Image-URL-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ISBN                                         Book-Title  \\\n",
       "0  0195153448                                Classical Mythology   \n",
       "1  0002005018                                       Clara Callan   \n",
       "2  0060973129                               Decision in Normandy   \n",
       "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4  0393045218                             The Mummies of Urumchi   \n",
       "\n",
       "            Book-Author Year-Of-Publication                   Publisher  \\\n",
       "0    Mark P. O. Morford                2002     Oxford University Press   \n",
       "1  Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
       "2          Carlo D'Este                1991             HarperPerennial   \n",
       "3      Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
       "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
       "\n",
       "                                         Image-URL-S  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-M  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-L  \n",
       "0  http://images.amazon.com/images/P/0195153448.0...  \n",
       "1  http://images.amazon.com/images/P/0002005018.0...  \n",
       "2  http://images.amazon.com/images/P/0060973129.0...  \n",
       "3  http://images.amazon.com/images/P/0374157065.0...  \n",
       "4  http://images.amazon.com/images/P/0393045218.0...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check head\n",
    "books_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication', 'Publisher',\n",
       "       'Image-URL-S', 'Image-URL-M', 'Image-URL-L'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check columns\n",
    "books_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276725</td>\n",
       "      <td>034545104X</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>276726</td>\n",
       "      <td>0155061224</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>276727</td>\n",
       "      <td>0446520802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>276729</td>\n",
       "      <td>052165615X</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>276729</td>\n",
       "      <td>0521795028</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  Book-Rating\n",
       "0   276725  034545104X            0\n",
       "1   276726  0155061224            5\n",
       "2   276727  0446520802            0\n",
       "3   276729  052165615X            3\n",
       "4   276729  0521795028            6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check head\n",
    "ratings_df.head()"
   ]
  },
  {
   "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>User-ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nyc, new york, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>moscow, yukon territory, russia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>porto, v.n.gaia, portugal</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>farnborough, hants, united kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID                            Location   Age\n",
       "0        1                  nyc, new york, usa   NaN\n",
       "1        2           stockton, california, usa  18.0\n",
       "2        3     moscow, yukon territory, russia   NaN\n",
       "3        4           porto, v.n.gaia, portugal  17.0\n",
       "4        5  farnborough, hants, united kingdom   NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check head\n",
    "users_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "books_df (271360, 8)\n",
      "ratings_df (1149780, 3)\n",
      "users_df (278858, 3)\n"
     ]
    }
   ],
   "source": [
    "# Check shape\n",
    "print('books_df', books_df.shape)\n",
    "print('ratings_df', ratings_df.shape)\n",
    "print('users_df', users_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                   0\n",
       "Book-Title             0\n",
       "Book-Author            1\n",
       "Year-Of-Publication    0\n",
       "Publisher              2\n",
       "Image-URL-S            0\n",
       "Image-URL-M            0\n",
       "Image-URL-L            3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check nulls\n",
    "books_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID        0\n",
       "ISBN           0\n",
       "Book-Rating    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check nulls\n",
    "ratings_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID          0\n",
       "Location         0\n",
       "Age         110762\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check nulls\n",
    "users_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check duplicates\n",
    "books_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check duplicates\n",
    "ratings_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check duplicates\n",
    "users_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Locate null values in Books DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to locate index of values - Source: https://www.geeksforgeeks.org/find-location-of-an-element-in-pandas-dataframe-in-python/\n",
    "def get_indexes(object, value):\n",
    "     \n",
    "    # Empty list\n",
    "    position_list = []\n",
    "     \n",
    "    # isin() method will return a DataFrame with\n",
    "    # boolean values, True at the positions   \n",
    "    # where element exists\n",
    "    result = object.isin([value])\n",
    "     \n",
    "    # any() method will return\n",
    "    # a boolean series\n",
    "    series_object = result.any()\n",
    " \n",
    "    # Get list of column names where\n",
    "    # element exists\n",
    "    column_names = list(series_object[series_object == True].index)\n",
    "    \n",
    "    # Iterate over the list of columns and\n",
    "    # extract the row index where element exists\n",
    "    for col in column_names:\n",
    "        rows = list(result[col][result[col] == True].index)\n",
    " \n",
    "        for row in rows:\n",
    "            position_list.append((row, col))\n",
    "             \n",
    "    # This list contains a list tuples with\n",
    "    # the index of element in the dataframe\n",
    "    return position_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of NaN in books_df: \n",
      "(187689, 'Book-Author')\n",
      "(128890, 'Publisher')\n",
      "(129037, 'Publisher')\n",
      "(209538, 'Image-URL-L')\n",
      "(220731, 'Image-URL-L')\n",
      "(221678, 'Image-URL-L')\n"
     ]
    }
   ],
   "source": [
    "# Apply get_indexes function to locate NaN values\n",
    "positions = get_indexes(books_df, np.nan)\n",
    "print('Index positions of NaN in books_df: ')\n",
    "for i in range (len(positions)):\n",
    "    print(positions[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manage Null Values\n",
    "\n",
    "## Books DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          9627982032\n",
       "Book-Title             The Credit Suisse Guide to Managing Your Perso...\n",
       "Book-Author                                                          NaN\n",
       "Year-Of-Publication                                                 1995\n",
       "Publisher                                 Edinburgh Financial Publishing\n",
       "Image-URL-S            http://images.amazon.com/images/P/9627982032.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/9627982032.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/9627982032.0...\n",
       "Name: 187689, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[187689]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          193169656X\n",
       "Book-Title                                                   Tyrant Moon\n",
       "Book-Author                                              Elaine Corvidae\n",
       "Year-Of-Publication                                                 2002\n",
       "Publisher                                                            NaN\n",
       "Image-URL-S            http://images.amazon.com/images/P/193169656X.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/193169656X.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/193169656X.0...\n",
       "Name: 128890, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[128890]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          1931696993\n",
       "Book-Title                                               Finders Keepers\n",
       "Book-Author                                              Linnea Sinclair\n",
       "Year-Of-Publication                                                 2001\n",
       "Publisher                                                            NaN\n",
       "Image-URL-S            http://images.amazon.com/images/P/1931696993.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/1931696993.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/1931696993.0...\n",
       "Name: 129037, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[129037]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          078946697X\n",
       "Book-Title             DK Readers: Creating the X-Men, How It All Beg...\n",
       "Book-Author                                                         2000\n",
       "Year-Of-Publication                                    DK Publishing Inc\n",
       "Publisher              http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-S            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-L                                                          NaN\n",
       "Name: 209538, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[209538]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          2070426769\n",
       "Book-Title             Peuple du ciel, suivi de 'Les Bergers\\\";Jean-M...\n",
       "Book-Author                                                         2003\n",
       "Year-Of-Publication                                            Gallimard\n",
       "Publisher              http://images.amazon.com/images/P/2070426769.0...\n",
       "Image-URL-S            http://images.amazon.com/images/P/2070426769.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/2070426769.0...\n",
       "Image-URL-L                                                          NaN\n",
       "Name: 220731, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[220731]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          0789466953\n",
       "Book-Title             DK Readers: Creating the X-Men, How Comic Book...\n",
       "Book-Author                                                         2000\n",
       "Year-Of-Publication                                    DK Publishing Inc\n",
       "Publisher              http://images.amazon.com/images/P/0789466953.0...\n",
       "Image-URL-S            http://images.amazon.com/images/P/0789466953.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/0789466953.0...\n",
       "Image-URL-L                                                          NaN\n",
       "Name: 221678, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing values\n",
    "books_df.iloc[221678]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add missing author and publisher values as per research on Amazon and bookfinder.com via ISBN search\n",
    "books_df.at[187689, 'Book-Author'] = 'Larissa Anne Downes, Editor'\n",
    "books_df.at[128890, 'Publisher'] = 'CreateSpace Independent Publishing Platform'\n",
    "books_df.at[129037, 'Publisher'] = 'NovelBooks Inc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          9627982032\n",
       "Book-Title             The Credit Suisse Guide to Managing Your Perso...\n",
       "Book-Author                                  Larissa Anne Downes, Editor\n",
       "Year-Of-Publication                                                 1995\n",
       "Publisher                                 Edinburgh Financial Publishing\n",
       "Image-URL-S            http://images.amazon.com/images/P/9627982032.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/9627982032.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/9627982032.0...\n",
       "Name: 187689, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check corrected value\n",
    "books_df.iloc[187689]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          0307121259\n",
       "Book-Title                             I Am a Bunny (Golden Sturdy Book)\n",
       "Book-Author                                                     O. Risom\n",
       "Year-Of-Publication                                                 1963\n",
       "Publisher                                                   Golden Books\n",
       "Image-URL-S            http://images.amazon.com/images/P/0307121259.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/0307121259.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/0307121259.0...\n",
       "Name: 6452, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check corrected value\n",
    "books_df.iloc[6452]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          078946697X\n",
       "Book-Title             DK Readers: Creating the X-Men, How It All Beg...\n",
       "Book-Author                                                         2000\n",
       "Year-Of-Publication                                    DK Publishing Inc\n",
       "Publisher              http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-S            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-L                                                          NaN\n",
       "Name: 209538, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN to correct missing value\n",
    "books_df.iloc[209538]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          078946697X\n",
       "Book-Title             DK Readers: The Story of the X-Men, How It All...\n",
       "Book-Author                                           Michael Teitelbaum\n",
       "Year-Of-Publication                                                 2000\n",
       "Publisher                                              DK Publishing Inc\n",
       "Image-URL-S            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/078946697X.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/078946697X.0...\n",
       "Name: 209538, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shift and adjust values as per research on Amazon and bookfinder.com via ISBN search\n",
    "books_df.iloc[209538] = books_df.iloc[209538].shift()\n",
    "books_df.at[209538, 'ISBN'] = '078946697X'\n",
    "books_df.at[209538, 'Book-Title'] = 'DK Readers: The Story of the X-Men, How It All Began (Level 4: Proficient Readers)'\n",
    "books_df.at[209538, 'Book-Author'] = 'Michael Teitelbaum'\n",
    "books_df.at[209538, 'Publisher'] = 'DK Publishing Inc'\n",
    "books_df.iloc[209538]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          2070426769\n",
       "Book-Title                           Peuple du ciel suivi de les Bergers\n",
       "Book-Author                                           Jean-Marie Gustave\n",
       "Year-Of-Publication                                                 2003\n",
       "Publisher                                                      Gallimard\n",
       "Image-URL-S            http://images.amazon.com/images/P/2070426769.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/2070426769.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/2070426769.0...\n",
       "Name: 220731, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shift and adjust values as per research on Amazon and bookfinder.com via ISBN search\n",
    "books_df.iloc[220731] = books_df.iloc[220731].shift()\n",
    "books_df.at[220731, 'ISBN'] = '2070426769'\n",
    "books_df.at[220731, 'Book-Title'] = 'Peuple du ciel suivi de les Bergers'\n",
    "books_df.at[220731, 'Book-Author'] = 'Jean-Marie Gustave'\n",
    "books_df.at[220731, 'Publisher'] = 'Gallimard'\n",
    "books_df.iloc[220731]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ISBN                                                          2070426769\n",
       "Book-Title             DK Readers: Creating the X-Men, How Comic Book...\n",
       "Book-Author                                                James Buckley\n",
       "Year-Of-Publication                                                 2000\n",
       "Publisher                                              DK Publishing Inc\n",
       "Image-URL-S            http://images.amazon.com/images/P/0789466953.0...\n",
       "Image-URL-M            http://images.amazon.com/images/P/0789466953.0...\n",
       "Image-URL-L            http://images.amazon.com/images/P/0789466953.0...\n",
       "Name: 221678, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shift and adjust values as per research on Amazon and bookfinder.com via ISBN search\n",
    "books_df.iloc[221678] = books_df.iloc[221678].shift()\n",
    "books_df.at[221678, 'ISBN'] = '2070426769'\n",
    "books_df.at[221678, 'Book-Title'] = 'DK Readers: Creating the X-Men, How Comic Books Come to Life (Level 4: Proficient Readers)'\n",
    "books_df.at[221678, 'Book-Author'] = 'James Buckley'\n",
    "books_df.at[221678, 'Publisher'] = 'DK Publishing Inc'\n",
    "books_df.iloc[221678]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271360"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check books_df row count. This count is for when I drop rows due to Year-Of-Publication errors below - \n",
    "# I want to ensure only the expected rows are dropped.\n",
    "books_row_count = len(books_df)\n",
    "books_row_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check ISBN alphanemuric count to determine if column should remain as string data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to detect alphanumeric values\n",
    "# Source - https://www.geeksforgeeks.org/how-to-check-string-is-alphanumeric-or-not-using-regular-expression/\n",
    "\n",
    "# Function to check string\n",
    "# is alphanumeric or not\n",
    "def isAlphaNumeric(str):\n",
    "\n",
    "\t# Regex to check string is\n",
    "\t# alphanumeric or not.\n",
    "\tregex = \"^(?=.*[a-zA-Z])(?=.*[0-9])[A-Za-z0-9]+$\"\n",
    "\n",
    "\t# Compile the ReGex\n",
    "\tp = re.compile(regex)\n",
    "\n",
    "\t# If the string is empty\n",
    "\t# return false\n",
    "\tif(str == None):\n",
    "\t\treturn False\n",
    "\n",
    "\t# Return if the string\n",
    "\t# matched the ReGex\n",
    "\tif(re.search(p, str)):\n",
    "\t\treturn True\n",
    "\telse:\n",
    "\t\treturn False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store ISBN values to list\n",
    "ISBN_list = books_df['ISBN'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List comprehension for Alphanumeric check on ISBN_List\n",
    "alphanumeric_list = [isAlphaNumeric(i) for i in ISBN_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22330"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count for True\n",
    "alphanumeric_list.count(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249030"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count for False\n",
    "alphanumeric_list.count(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given the results, the ISBN column will remain as string data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Unique values for Year-of-Publication column\n",
    "\n",
    "- There are some Year-of-Publication values set into the future and some at zero. We will remove these from the dataset.\n",
    "\n",
    "- NOTE: Future opportunity to programmatically impute values via a matching algorithm. For example, there are a number of duplicate books with different publishers and publication year. A matching algorithm can help manage this issue further by identifying these duplicates and consolidating the books into a single book title listing in the dataset. If duplicate title doesn't exist, then an assessment on how to manage these could be done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '1376', '1378', '1806', '1897', '1900', '1901', '1902', '1904', '1906', '1908', '1909', '1910', '1911', '1914', '1917', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2008', '2010', '2011', '2012', '2020', '2021', '2024', '2026', '2030', '2037', '2038', '2050']\n"
     ]
    }
   ],
   "source": [
    "# Check unique Year-of-Publication values\n",
    "print(sorted(books_df['Year-Of-Publication'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Locate Year-of-Publication errors, count and drop from Books DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 0 in books_df: \n",
      "(176, 'Year-Of-Publication')\n",
      "(188, 'Year-Of-Publication')\n",
      "(288, 'Year-Of-Publication')\n",
      "(351, 'Year-Of-Publication')\n",
      "(542, 'Year-Of-Publication')\n",
      "(641, 'Year-Of-Publication')\n",
      "(646, 'Year-Of-Publication')\n",
      "(728, 'Year-Of-Publication')\n",
      "(732, 'Year-Of-Publication')\n",
      "(735, 'Year-Of-Publication')\n",
      "(797, 'Year-Of-Publication')\n",
      "(810, 'Year-Of-Publication')\n",
      "(820, 'Year-Of-Publication')\n",
      "(924, 'Year-Of-Publication')\n",
      "(1058, 'Year-Of-Publication')\n",
      "(1095, 'Year-Of-Publication')\n",
      "(1165, 'Year-Of-Publication')\n",
      "(1227, 'Year-Of-Publication')\n",
      "(1253, 'Year-Of-Publication')\n",
      "(1261, 'Year-Of-Publication')\n",
      "(1263, 'Year-Of-Publication')\n",
      "(1381, 'Year-Of-Publication')\n",
      "(1390, 'Year-Of-Publication')\n",
      "(1448, 'Year-Of-Publication')\n",
      "(1623, 'Year-Of-Publication')\n",
      "(1631, 'Year-Of-Publication')\n",
      "(1637, 'Year-Of-Publication')\n",
      "(1734, 'Year-Of-Publication')\n",
      "(1743, 'Year-Of-Publication')\n",
      "(1814, 'Year-Of-Publication')\n",
      "(1870, 'Year-Of-Publication')\n",
      "(1905, 'Year-Of-Publication')\n",
      "(1913, 'Year-Of-Publication')\n",
      "(1991, 'Year-Of-Publication')\n",
      "(2004, 'Year-Of-Publication')\n",
      "(2177, 'Year-Of-Publication')\n",
      "(2185, 'Year-Of-Publication')\n",
      "(2190, 'Year-Of-Publication')\n",
      "(2194, 'Year-Of-Publication')\n",
      "(2269, 'Year-Of-Publication')\n",
      "(2270, 'Year-Of-Publication')\n",
      "(2307, 'Year-Of-Publication')\n",
      "(2314, 'Year-Of-Publication')\n",
      "(2399, 'Year-Of-Publication')\n",
      "(2473, 'Year-Of-Publication')\n",
      "(2587, 'Year-Of-Publication')\n",
      "(2612, 'Year-Of-Publication')\n",
      "(2776, 'Year-Of-Publication')\n",
      "(3004, 'Year-Of-Publication')\n",
      "(3010, 'Year-Of-Publication')\n",
      "(3078, 'Year-Of-Publication')\n",
      "(3114, 'Year-Of-Publication')\n",
      "(3173, 'Year-Of-Publication')\n",
      "(3265, 'Year-Of-Publication')\n",
      "(3272, 'Year-Of-Publication')\n",
      "(3514, 'Year-Of-Publication')\n",
      "(3543, 'Year-Of-Publication')\n",
      "(3548, 'Year-Of-Publication')\n",
      "(3589, 'Year-Of-Publication')\n",
      "(3637, 'Year-Of-Publication')\n",
      "(3654, 'Year-Of-Publication')\n",
      "(3689, 'Year-Of-Publication')\n",
      "(3698, 'Year-Of-Publication')\n",
      "(3736, 'Year-Of-Publication')\n",
      "(3768, 'Year-Of-Publication')\n",
      "(3781, 'Year-Of-Publication')\n",
      "(3796, 'Year-Of-Publication')\n",
      "(3988, 'Year-Of-Publication')\n",
      "(4135, 'Year-Of-Publication')\n",
      "(4330, 'Year-Of-Publication')\n",
      "(4352, 'Year-Of-Publication')\n",
      "(4476, 'Year-Of-Publication')\n",
      "(4477, 'Year-Of-Publication')\n",
      "(4571, 'Year-Of-Publication')\n",
      "(4586, 'Year-Of-Publication')\n",
      "(4613, 'Year-Of-Publication')\n",
      "(4635, 'Year-Of-Publication')\n",
      "(4805, 'Year-Of-Publication')\n",
      "(4898, 'Year-Of-Publication')\n",
      "(4899, 'Year-Of-Publication')\n",
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      "(250191, 'Year-Of-Publication')\n",
      "(250236, 'Year-Of-Publication')\n",
      "(250278, 'Year-Of-Publication')\n",
      "(250295, 'Year-Of-Publication')\n",
      "(250304, 'Year-Of-Publication')\n",
      "(250313, 'Year-Of-Publication')\n",
      "(250314, 'Year-Of-Publication')\n",
      "(250329, 'Year-Of-Publication')\n",
      "(250468, 'Year-Of-Publication')\n",
      "(250540, 'Year-Of-Publication')\n",
      "(250584, 'Year-Of-Publication')\n",
      "(250762, 'Year-Of-Publication')\n",
      "(250782, 'Year-Of-Publication')\n",
      "(250934, 'Year-Of-Publication')\n",
      "(250950, 'Year-Of-Publication')\n",
      "(251003, 'Year-Of-Publication')\n",
      "(251097, 'Year-Of-Publication')\n",
      "(251128, 'Year-Of-Publication')\n",
      "(251179, 'Year-Of-Publication')\n",
      "(251245, 'Year-Of-Publication')\n",
      "(251397, 'Year-Of-Publication')\n",
      "(251641, 'Year-Of-Publication')\n",
      "(251704, 'Year-Of-Publication')\n",
      "(251768, 'Year-Of-Publication')\n",
      "(251804, 'Year-Of-Publication')\n",
      "(251807, 'Year-Of-Publication')\n",
      "(251856, 'Year-Of-Publication')\n",
      "(251862, 'Year-Of-Publication')\n",
      "(252066, 'Year-Of-Publication')\n",
      "(252155, 'Year-Of-Publication')\n",
      "(252157, 'Year-Of-Publication')\n",
      "(252208, 'Year-Of-Publication')\n",
      "(252220, 'Year-Of-Publication')\n",
      "(252301, 'Year-Of-Publication')\n",
      "(252401, 'Year-Of-Publication')\n",
      "(252427, 'Year-Of-Publication')\n",
      "(252442, 'Year-Of-Publication')\n",
      "(252576, 'Year-Of-Publication')\n",
      "(252755, 'Year-Of-Publication')\n",
      "(252818, 'Year-Of-Publication')\n",
      "(252923, 'Year-Of-Publication')\n",
      "(252948, 'Year-Of-Publication')\n",
      "(252962, 'Year-Of-Publication')\n",
      "(252992, 'Year-Of-Publication')\n",
      "(253201, 'Year-Of-Publication')\n",
      "(253307, 'Year-Of-Publication')\n",
      "(253437, 'Year-Of-Publication')\n",
      "(253465, 'Year-Of-Publication')\n",
      "(253531, 'Year-Of-Publication')\n",
      "(253605, 'Year-Of-Publication')\n",
      "(253606, 'Year-Of-Publication')\n",
      "(253679, 'Year-Of-Publication')\n",
      "(253752, 'Year-Of-Publication')\n",
      "(253877, 'Year-Of-Publication')\n",
      "(253982, 'Year-Of-Publication')\n",
      "(254040, 'Year-Of-Publication')\n",
      "(254051, 'Year-Of-Publication')\n",
      "(254255, 'Year-Of-Publication')\n",
      "(254300, 'Year-Of-Publication')\n",
      "(254481, 'Year-Of-Publication')\n",
      "(254612, 'Year-Of-Publication')\n",
      "(254703, 'Year-Of-Publication')\n",
      "(254836, 'Year-Of-Publication')\n",
      "(255041, 'Year-Of-Publication')\n",
      "(255074, 'Year-Of-Publication')\n",
      "(255132, 'Year-Of-Publication')\n",
      "(255152, 'Year-Of-Publication')\n",
      "(255170, 'Year-Of-Publication')\n",
      "(255188, 'Year-Of-Publication')\n",
      "(255382, 'Year-Of-Publication')\n",
      "(255449, 'Year-Of-Publication')\n",
      "(255511, 'Year-Of-Publication')\n",
      "(255583, 'Year-Of-Publication')\n",
      "(255632, 'Year-Of-Publication')\n",
      "(255634, 'Year-Of-Publication')\n",
      "(255636, 'Year-Of-Publication')\n",
      "(255719, 'Year-Of-Publication')\n",
      "(255855, 'Year-Of-Publication')\n",
      "(255915, 'Year-Of-Publication')\n",
      "(255916, 'Year-Of-Publication')\n",
      "(255918, 'Year-Of-Publication')\n",
      "(255922, 'Year-Of-Publication')\n",
      "(255985, 'Year-Of-Publication')\n",
      "(256001, 'Year-Of-Publication')\n",
      "(256121, 'Year-Of-Publication')\n",
      "(256122, 'Year-Of-Publication')\n",
      "(256123, 'Year-Of-Publication')\n",
      "(256124, 'Year-Of-Publication')\n",
      "(256127, 'Year-Of-Publication')\n",
      "(256128, 'Year-Of-Publication')\n",
      "(256153, 'Year-Of-Publication')\n",
      "(256167, 'Year-Of-Publication')\n",
      "(256212, 'Year-Of-Publication')\n",
      "(256456, 'Year-Of-Publication')\n",
      "(256564, 'Year-Of-Publication')\n",
      "(256661, 'Year-Of-Publication')\n",
      "(256697, 'Year-Of-Publication')\n",
      "(256883, 'Year-Of-Publication')\n",
      "(256889, 'Year-Of-Publication')\n",
      "(256895, 'Year-Of-Publication')\n",
      "(256951, 'Year-Of-Publication')\n",
      "(256967, 'Year-Of-Publication')\n",
      "(256985, 'Year-Of-Publication')\n",
      "(257007, 'Year-Of-Publication')\n",
      "(257008, 'Year-Of-Publication')\n",
      "(257039, 'Year-Of-Publication')\n",
      "(257055, 'Year-Of-Publication')\n",
      "(257087, 'Year-Of-Publication')\n",
      "(257165, 'Year-Of-Publication')\n",
      "(257235, 'Year-Of-Publication')\n",
      "(257239, 'Year-Of-Publication')\n",
      "(257407, 'Year-Of-Publication')\n",
      "(257582, 'Year-Of-Publication')\n",
      "(257746, 'Year-Of-Publication')\n",
      "(257776, 'Year-Of-Publication')\n",
      "(257807, 'Year-Of-Publication')\n",
      "(257817, 'Year-Of-Publication')\n",
      "(257828, 'Year-Of-Publication')\n",
      "(257829, 'Year-Of-Publication')\n",
      "(257830, 'Year-Of-Publication')\n",
      "(257831, 'Year-Of-Publication')\n",
      "(257833, 'Year-Of-Publication')\n",
      "(257876, 'Year-Of-Publication')\n",
      "(257935, 'Year-Of-Publication')\n",
      "(257944, 'Year-Of-Publication')\n",
      "(257995, 'Year-Of-Publication')\n",
      "(258026, 'Year-Of-Publication')\n",
      "(258135, 'Year-Of-Publication')\n",
      "(258202, 'Year-Of-Publication')\n",
      "(258298, 'Year-Of-Publication')\n",
      "(258334, 'Year-Of-Publication')\n",
      "(258584, 'Year-Of-Publication')\n",
      "(258700, 'Year-Of-Publication')\n",
      "(258703, 'Year-Of-Publication')\n",
      "(258741, 'Year-Of-Publication')\n",
      "(258795, 'Year-Of-Publication')\n",
      "(258852, 'Year-Of-Publication')\n",
      "(258863, 'Year-Of-Publication')\n",
      "(258872, 'Year-Of-Publication')\n",
      "(258884, 'Year-Of-Publication')\n",
      "(259318, 'Year-Of-Publication')\n",
      "(259406, 'Year-Of-Publication')\n",
      "(259600, 'Year-Of-Publication')\n",
      "(259601, 'Year-Of-Publication')\n",
      "(259602, 'Year-Of-Publication')\n",
      "(259603, 'Year-Of-Publication')\n",
      "(259617, 'Year-Of-Publication')\n",
      "(259799, 'Year-Of-Publication')\n",
      "(260897, 'Year-Of-Publication')\n",
      "(261167, 'Year-Of-Publication')\n",
      "(261197, 'Year-Of-Publication')\n",
      "(261226, 'Year-Of-Publication')\n",
      "(261291, 'Year-Of-Publication')\n",
      "(261318, 'Year-Of-Publication')\n",
      "(261437, 'Year-Of-Publication')\n",
      "(261461, 'Year-Of-Publication')\n",
      "(261524, 'Year-Of-Publication')\n",
      "(261590, 'Year-Of-Publication')\n",
      "(261823, 'Year-Of-Publication')\n",
      "(261929, 'Year-Of-Publication')\n",
      "(261930, 'Year-Of-Publication')\n",
      "(261931, 'Year-Of-Publication')\n",
      "(262120, 'Year-Of-Publication')\n",
      "(262121, 'Year-Of-Publication')\n",
      "(262146, 'Year-Of-Publication')\n",
      "(262220, 'Year-Of-Publication')\n",
      "(262287, 'Year-Of-Publication')\n",
      "(262371, 'Year-Of-Publication')\n",
      "(262414, 'Year-Of-Publication')\n",
      "(262436, 'Year-Of-Publication')\n",
      "(262449, 'Year-Of-Publication')\n",
      "(262455, 'Year-Of-Publication')\n",
      "(262461, 'Year-Of-Publication')\n",
      "(262471, 'Year-Of-Publication')\n",
      "(262473, 'Year-Of-Publication')\n",
      "(262530, 'Year-Of-Publication')\n",
      "(262553, 'Year-Of-Publication')\n",
      "(262555, 'Year-Of-Publication')\n",
      "(262609, 'Year-Of-Publication')\n",
      "(262663, 'Year-Of-Publication')\n",
      "(262664, 'Year-Of-Publication')\n",
      "(262679, 'Year-Of-Publication')\n",
      "(262682, 'Year-Of-Publication')\n",
      "(262687, 'Year-Of-Publication')\n",
      "(262807, 'Year-Of-Publication')\n",
      "(262857, 'Year-Of-Publication')\n",
      "(262859, 'Year-Of-Publication')\n",
      "(262872, 'Year-Of-Publication')\n",
      "(262873, 'Year-Of-Publication')\n",
      "(262878, 'Year-Of-Publication')\n",
      "(262961, 'Year-Of-Publication')\n",
      "(262992, 'Year-Of-Publication')\n",
      "(263039, 'Year-Of-Publication')\n",
      "(263063, 'Year-Of-Publication')\n",
      "(263075, 'Year-Of-Publication')\n",
      "(263076, 'Year-Of-Publication')\n",
      "(263083, 'Year-Of-Publication')\n",
      "(263126, 'Year-Of-Publication')\n",
      "(263339, 'Year-Of-Publication')\n",
      "(263383, 'Year-Of-Publication')\n",
      "(263414, 'Year-Of-Publication')\n",
      "(263443, 'Year-Of-Publication')\n",
      "(263543, 'Year-Of-Publication')\n",
      "(263618, 'Year-Of-Publication')\n",
      "(263648, 'Year-Of-Publication')\n",
      "(263699, 'Year-Of-Publication')\n",
      "(263702, 'Year-Of-Publication')\n",
      "(263782, 'Year-Of-Publication')\n",
      "(263833, 'Year-Of-Publication')\n",
      "(263871, 'Year-Of-Publication')\n",
      "(263927, 'Year-Of-Publication')\n",
      "(263936, 'Year-Of-Publication')\n",
      "(264015, 'Year-Of-Publication')\n",
      "(264016, 'Year-Of-Publication')\n",
      "(264021, 'Year-Of-Publication')\n",
      "(264039, 'Year-Of-Publication')\n",
      "(264056, 'Year-Of-Publication')\n",
      "(264067, 'Year-Of-Publication')\n",
      "(264224, 'Year-Of-Publication')\n",
      "(264256, 'Year-Of-Publication')\n",
      "(264433, 'Year-Of-Publication')\n",
      "(264677, 'Year-Of-Publication')\n",
      "(264697, 'Year-Of-Publication')\n",
      "(264712, 'Year-Of-Publication')\n",
      "(264717, 'Year-Of-Publication')\n",
      "(264720, 'Year-Of-Publication')\n",
      "(264723, 'Year-Of-Publication')\n",
      "(265005, 'Year-Of-Publication')\n",
      "(265069, 'Year-Of-Publication')\n",
      "(265078, 'Year-Of-Publication')\n",
      "(265123, 'Year-Of-Publication')\n",
      "(265233, 'Year-Of-Publication')\n",
      "(265235, 'Year-Of-Publication')\n",
      "(265255, 'Year-Of-Publication')\n",
      "(265403, 'Year-Of-Publication')\n",
      "(265418, 'Year-Of-Publication')\n",
      "(265476, 'Year-Of-Publication')\n",
      "(265531, 'Year-Of-Publication')\n",
      "(265534, 'Year-Of-Publication')\n",
      "(265553, 'Year-Of-Publication')\n",
      "(265607, 'Year-Of-Publication')\n",
      "(265681, 'Year-Of-Publication')\n",
      "(265682, 'Year-Of-Publication')\n",
      "(265773, 'Year-Of-Publication')\n",
      "(265852, 'Year-Of-Publication')\n",
      "(265896, 'Year-Of-Publication')\n",
      "(266008, 'Year-Of-Publication')\n",
      "(266156, 'Year-Of-Publication')\n",
      "(266176, 'Year-Of-Publication')\n",
      "(266220, 'Year-Of-Publication')\n",
      "(266295, 'Year-Of-Publication')\n",
      "(266359, 'Year-Of-Publication')\n",
      "(266403, 'Year-Of-Publication')\n",
      "(266554, 'Year-Of-Publication')\n",
      "(266591, 'Year-Of-Publication')\n",
      "(266660, 'Year-Of-Publication')\n",
      "(266869, 'Year-Of-Publication')\n",
      "(266952, 'Year-Of-Publication')\n",
      "(267051, 'Year-Of-Publication')\n",
      "(267172, 'Year-Of-Publication')\n",
      "(267284, 'Year-Of-Publication')\n",
      "(267306, 'Year-Of-Publication')\n",
      "(267380, 'Year-Of-Publication')\n",
      "(267468, 'Year-Of-Publication')\n",
      "(267507, 'Year-Of-Publication')\n",
      "(267617, 'Year-Of-Publication')\n",
      "(267931, 'Year-Of-Publication')\n",
      "(268041, 'Year-Of-Publication')\n",
      "(268283, 'Year-Of-Publication')\n",
      "(268289, 'Year-Of-Publication')\n",
      "(268294, 'Year-Of-Publication')\n",
      "(268320, 'Year-Of-Publication')\n",
      "(268322, 'Year-Of-Publication')\n",
      "(268342, 'Year-Of-Publication')\n",
      "(268507, 'Year-Of-Publication')\n",
      "(268572, 'Year-Of-Publication')\n",
      "(268576, 'Year-Of-Publication')\n",
      "(268592, 'Year-Of-Publication')\n",
      "(268604, 'Year-Of-Publication')\n",
      "(268641, 'Year-Of-Publication')\n",
      "(268656, 'Year-Of-Publication')\n",
      "(268727, 'Year-Of-Publication')\n",
      "(268752, 'Year-Of-Publication')\n",
      "(268762, 'Year-Of-Publication')\n",
      "(268763, 'Year-Of-Publication')\n",
      "(268819, 'Year-Of-Publication')\n",
      "(268820, 'Year-Of-Publication')\n",
      "(268822, 'Year-Of-Publication')\n",
      "(268824, 'Year-Of-Publication')\n",
      "(269023, 'Year-Of-Publication')\n",
      "(269031, 'Year-Of-Publication')\n",
      "(269386, 'Year-Of-Publication')\n",
      "(269663, 'Year-Of-Publication')\n",
      "(269834, 'Year-Of-Publication')\n",
      "(269840, 'Year-Of-Publication')\n",
      "(269841, 'Year-Of-Publication')\n",
      "(269962, 'Year-Of-Publication')\n",
      "(270247, 'Year-Of-Publication')\n",
      "(270312, 'Year-Of-Publication')\n",
      "(270320, 'Year-Of-Publication')\n",
      "(270358, 'Year-Of-Publication')\n",
      "(270359, 'Year-Of-Publication')\n",
      "(270360, 'Year-Of-Publication')\n",
      "(270374, 'Year-Of-Publication')\n",
      "(270397, 'Year-Of-Publication')\n",
      "(270484, 'Year-Of-Publication')\n",
      "(270524, 'Year-Of-Publication')\n",
      "(270637, 'Year-Of-Publication')\n",
      "(270679, 'Year-Of-Publication')\n",
      "(270777, 'Year-Of-Publication')\n",
      "(270794, 'Year-Of-Publication')\n",
      "(270913, 'Year-Of-Publication')\n",
      "(271094, 'Year-Of-Publication')\n",
      "(271182, 'Year-Of-Publication')\n",
      "(271196, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of 0 value in Year-of-Publication\n",
    "year_zero_positions = get_indexes(books_df, '0')\n",
    "print('Index positions of 0 in books_df: ')\n",
    "for i in range (len(year_zero_positions)):\n",
    "    print(year_zero_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4618"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication with value of 0\n",
    "year_zero_error = len(year_zero_positions)\n",
    "year_zero_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2050 in books_df: \n",
      "(80264, 'Year-Of-Publication')\n",
      "(97826, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2050_positions = get_indexes(books_df, '2050')\n",
    "print('Index positions of 2050 in books_df: ')\n",
    "for i in range (len(year_2050_positions)):\n",
    "    print(year_2050_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors\n",
    "year_2050_error = len(year_2050_positions)\n",
    "year_2050_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2038 in books_df: \n",
      "(116053, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2038_positions = get_indexes(books_df, '2038')\n",
    "print('Index positions of 2038 in books_df: ')\n",
    "for i in range (len(year_2038_positions)):\n",
    "    print(year_2038_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors\n",
    "year_2038_error = len(year_2038_positions)\n",
    "year_2038_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2037 in books_df: \n",
      "(255409, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2037_positions = get_indexes(books_df, '2037')\n",
    "print('Index positions of 2037 in books_df: ')\n",
    "for i in range (len(year_2037_positions)):\n",
    "    print(year_2037_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors\n",
    "year_2037_error = len(year_2037_positions)\n",
    "year_2037_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2030 in books_df: \n",
      "(37487, 'Year-Of-Publication')\n",
      "(55676, 'Year-Of-Publication')\n",
      "(78168, 'Year-Of-Publication')\n",
      "(192993, 'Year-Of-Publication')\n",
      "(228173, 'Year-Of-Publication')\n",
      "(240169, 'Year-Of-Publication')\n",
      "(260974, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2030_positions = get_indexes(books_df, '2030')\n",
    "print('Index positions of 2030 in books_df: ')\n",
    "for i in range (len(year_2030_positions)):\n",
    "    print(year_2030_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors\n",
    "year_2030_error = len(year_2030_positions)\n",
    "year_2030_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2026 in books_df: \n",
      "(118294, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2026_positions = get_indexes(books_df, '2026')\n",
    "print('Index positions of 2026 in books_df: ')\n",
    "for i in range (len(year_2026_positions)):\n",
    "    print(year_2026_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors\n",
    "year_2026_error = len(year_2026_positions)\n",
    "year_2026_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of 2024 in books_df: \n",
      "(142716, 'Book-Title')\n",
      "(246842, 'Year-Of-Publication')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of x value in Year-of-Publication\n",
    "year_2024_positions = get_indexes(books_df, '2024')\n",
    "print('Index positions of 2024 in books_df: ')\n",
    "for i in range (len(year_2024_positions)):\n",
    "    print(year_2024_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of Year-of-Publication errors, exlcuding (142716, 'Book-Title')\n",
    "year_2024_error = len(year_2024_positions) - 1\n",
    "year_2024_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total Year-of-Publication error count is 4631.\n"
     ]
    }
   ],
   "source": [
    "# Total count of Year-of-Publication data errors\n",
    "sum_year_errors = year_zero_error + year_2050_error + year_2038_error + year_2037_error + year_2030_error + year_2026_error + year_2024_error\n",
    "print(f'The total Year-of-Publication error count is {sum_year_errors}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop all entries with incorrect Year-of-Publication dates\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '0']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2050']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2038']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2037']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2030']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2026']\n",
    "books_df = books_df[books_df['Year-Of-Publication'] != '2024']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1376', '1378', '1806', '1897', '1900', '1901', '1902', '1904', '1906', '1908', '1909', '1910', '1911', '1914', '1917', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2008', '2010', '2011', '2012', '2020', '2021']\n"
     ]
    }
   ],
   "source": [
    "# Check unique Year-of-Publication values\n",
    "print(sorted(books_df['Year-Of-Publication'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "266729"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check books_df row count after Year-of-Publication row drop.\n",
    "books_row_count2 = len(books_df)\n",
    "books_row_count2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subtract books_row_count with books_rows_count2 and sum_year_errors. This is to ensure only expected rows have been dropped.\n",
    "books_df_difference = books_row_count - books_row_count2 - sum_year_errors\n",
    "books_df_difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert string to int data type\n",
    "books_df['Year-Of-Publication'] = books_df['Year-Of-Publication'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Image-URL columns since they do not add value for recommendation system\n",
    "# books_df.drop(books_df.iloc[:, 5:8], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write cleaned DataFrame to csv\n",
    "# books_df.to_csv('Resources/books_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Users DataFrame \n",
    "- Manage Nulls \n",
    "- Check Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 113.0, 114.0, 115.0, 116.0, 118.0, 119.0, 123.0, 124.0, 127.0, 128.0, 132.0, 133.0, 136.0, 137.0, 138.0, 140.0, 141.0, 143.0, 146.0, 147.0, 148.0, 151.0, 152.0, 156.0, 157.0, 159.0, 162.0, 168.0, 172.0, 175.0, 183.0, 186.0, 189.0, 199.0, 200.0, 201.0, 204.0, 207.0, 208.0, 209.0, 210.0, 212.0, 219.0, 220.0, 223.0, 226.0, 228.0, 229.0, 230.0, 231.0, 237.0, 239.0, 244.0]\n"
     ]
    }
   ],
   "source": [
    "# Check unique age values\n",
    "print(sorted(users_df['Age'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278858"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check users_df row count\n",
    "len(users_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute null values with average age\n",
    "- Replace age values < 5 and > 90 with nulls\n",
    "- Impute values with mean aggregate function\n",
    "\n",
    "NOTE: It doesn't make sense to have age values that are < 5 and > 90. It is unlikely anyone under age 5 or over age 90 would be rating books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110762"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN values\n",
    "users_df['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace Age values below 5 and above 90 with NaN values source - The idea to manage these values this way originates from https://www.kaggle.com/code/saurabhbagchi/recommender-system-for-books\n",
    "users_df.loc[(users_df['Age'] > 90) | (users_df['Age'] < 5), 'Age'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112074"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN values\n",
    "users_df['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.72384041634689"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Age mean\n",
    "users_df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Age column NaN values with mean\n",
    "users_df['Age'].fillna(users_df['Age'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Age column data type to int\n",
    "users_df['Age'] = users_df['Age'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]\n"
     ]
    }
   ],
   "source": [
    "# Check unique age values\n",
    "print(sorted(users_df['Age'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Check nulls\n",
    "users_df['Location'].isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Though there are no nulls, there are values that are string 'n/a, n/a, n/a' values in the Location column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index positions of n/a, n/a, n/a in users_df: \n",
      "(3095, 'Location')\n",
      "(10646, 'Location')\n",
      "(11675, 'Location')\n",
      "(61592, 'Location')\n",
      "(70062, 'Location')\n",
      "(86150, 'Location')\n",
      "(119402, 'Location')\n",
      "(123570, 'Location')\n",
      "(184324, 'Location')\n",
      "(238301, 'Location')\n",
      "(271681, 'Location')\n"
     ]
    }
   ],
   "source": [
    "# Get indexes of n/a, n/a, n/a in users_df\n",
    "na_positions = get_indexes(users_df, 'n/a, n/a, n/a')\n",
    "print('Index positions of n/a, n/a, n/a in users_df: ')\n",
    "for i in range (len(na_positions)):\n",
    "    print(na_positions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of n/a entries\n",
    "location_na_count = len(na_positions)\n",
    "location_na_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278858"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_row_count = len(users_df)\n",
    "users_row_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop entries with n/a listed for location\n",
    "users_df = users_df[users_df['Location'] != 'n/a, n/a, n/a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate new users_df row count\n",
    "users_row_count2 = len(users_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm difference betwen users_row_count prior to dropping n/a, users_row_count2 after dropping n/a and and location_na_count = 0\n",
    "users_row_count - users_row_count2 - location_na_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write cleaned DataFrame to csv\n",
    "#users_df.to_csv('Resources/users_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Ratings DataFrame for unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "# Check Book-Rating values\n",
    "print(sorted(ratings_df['Book-Rating'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1149780"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check ratings_df row count\n",
    "ratings_row_count = len(ratings_df)\n",
    "ratings_row_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID        716109\n",
       "ISBN           716109\n",
       "Book-Rating    716109\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check count of Book-Rating 0\n",
    "ratings_df[ratings_df['Book-Rating'] == 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rating score of 0 since they add no value to recommendation system\n",
    "ratings_df = ratings_df[ratings_df['Book-Rating'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "433671"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check ratings_df row count after 0s are dropped\n",
    "ratings_row_count2 = len(ratings_df)\n",
    "ratings_row_count2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check remaining row count\n",
    "ratings_row_count - ratings_row_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "# Check Book-Rating values\n",
    "print(sorted(ratings_df['Book-Rating'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write cleaned DataFrame to csv\n",
    "#ratings_df.to_csv('Resources/ratings_cleaned.csv')"
   ]
  }
 ],
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