Goodbooks EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goodbooks EDA\n",
    "\n",
    "((In progress!))\n",
    "\n",
    "EDA of the [Goodbooks](https://github.com/zygmuntz/goodbooks-10k) dataset, which includes 10,000 books and 6 million user ratings from Goodreads. \n",
    "\n",
    "Future plans:\n",
    "- More visualizations\n",
    "- Exploring the other included datasets\n",
    "- Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "# Set pandas float precision to 2, for readability\n",
    "pd.set_option('display.precision', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "books_df = pd.read_csv('goodbooks-10k-master/books.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>book_id</th>\n",
       "      <th>goodreads_book_id</th>\n",
       "      <th>best_book_id</th>\n",
       "      <th>work_id</th>\n",
       "      <th>books_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>authors</th>\n",
       "      <th>original_publication_year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_ratings_count</th>\n",
       "      <th>work_text_reviews_count</th>\n",
       "      <th>ratings_1</th>\n",
       "      <th>ratings_2</th>\n",
       "      <th>ratings_3</th>\n",
       "      <th>ratings_4</th>\n",
       "      <th>ratings_5</th>\n",
       "      <th>image_url</th>\n",
       "      <th>small_image_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2792775</td>\n",
       "      <td>272</td>\n",
       "      <td>439023483</td>\n",
       "      <td>9.78e+12</td>\n",
       "      <td>Suzanne Collins</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>The Hunger Games</td>\n",
       "      <td>...</td>\n",
       "      <td>4780653</td>\n",
       "      <td>4942365</td>\n",
       "      <td>155254</td>\n",
       "      <td>66715</td>\n",
       "      <td>127936</td>\n",
       "      <td>560092</td>\n",
       "      <td>1481305</td>\n",
       "      <td>2706317</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4640799</td>\n",
       "      <td>491</td>\n",
       "      <td>439554934</td>\n",
       "      <td>9.78e+12</td>\n",
       "      <td>J.K. Rowling, Mary GrandPré</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "      <td>...</td>\n",
       "      <td>4602479</td>\n",
       "      <td>4800065</td>\n",
       "      <td>75867</td>\n",
       "      <td>75504</td>\n",
       "      <td>101676</td>\n",
       "      <td>455024</td>\n",
       "      <td>1156318</td>\n",
       "      <td>3011543</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>41865</td>\n",
       "      <td>41865</td>\n",
       "      <td>3212258</td>\n",
       "      <td>226</td>\n",
       "      <td>316015849</td>\n",
       "      <td>9.78e+12</td>\n",
       "      <td>Stephenie Meyer</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Twilight</td>\n",
       "      <td>...</td>\n",
       "      <td>3866839</td>\n",
       "      <td>3916824</td>\n",
       "      <td>95009</td>\n",
       "      <td>456191</td>\n",
       "      <td>436802</td>\n",
       "      <td>793319</td>\n",
       "      <td>875073</td>\n",
       "      <td>1355439</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2657</td>\n",
       "      <td>2657</td>\n",
       "      <td>3275794</td>\n",
       "      <td>487</td>\n",
       "      <td>61120081</td>\n",
       "      <td>9.78e+12</td>\n",
       "      <td>Harper Lee</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "      <td>...</td>\n",
       "      <td>3198671</td>\n",
       "      <td>3340896</td>\n",
       "      <td>72586</td>\n",
       "      <td>60427</td>\n",
       "      <td>117415</td>\n",
       "      <td>446835</td>\n",
       "      <td>1001952</td>\n",
       "      <td>1714267</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4671</td>\n",
       "      <td>4671</td>\n",
       "      <td>245494</td>\n",
       "      <td>1356</td>\n",
       "      <td>743273567</td>\n",
       "      <td>9.78e+12</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>The Great Gatsby</td>\n",
       "      <td>...</td>\n",
       "      <td>2683664</td>\n",
       "      <td>2773745</td>\n",
       "      <td>51992</td>\n",
       "      <td>86236</td>\n",
       "      <td>197621</td>\n",
       "      <td>606158</td>\n",
       "      <td>936012</td>\n",
       "      <td>947718</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  goodreads_book_id  best_book_id  work_id  books_count       isbn  \\\n",
       "0        1            2767052       2767052  2792775          272  439023483   \n",
       "1        2                  3             3  4640799          491  439554934   \n",
       "2        3              41865         41865  3212258          226  316015849   \n",
       "3        4               2657          2657  3275794          487   61120081   \n",
       "4        5               4671          4671   245494         1356  743273567   \n",
       "\n",
       "     isbn13                      authors  original_publication_year  \\\n",
       "0  9.78e+12              Suzanne Collins                     2008.0   \n",
       "1  9.78e+12  J.K. Rowling, Mary GrandPré                     1997.0   \n",
       "2  9.78e+12              Stephenie Meyer                     2005.0   \n",
       "3  9.78e+12                   Harper Lee                     1960.0   \n",
       "4  9.78e+12          F. Scott Fitzgerald                     1925.0   \n",
       "\n",
       "                             original_title  \\\n",
       "0                          The Hunger Games   \n",
       "1  Harry Potter and the Philosopher's Stone   \n",
       "2                                  Twilight   \n",
       "3                     To Kill a Mockingbird   \n",
       "4                          The Great Gatsby   \n",
       "\n",
       "                         ...                         ratings_count  \\\n",
       "0                        ...                               4780653   \n",
       "1                        ...                               4602479   \n",
       "2                        ...                               3866839   \n",
       "3                        ...                               3198671   \n",
       "4                        ...                               2683664   \n",
       "\n",
       "  work_ratings_count  work_text_reviews_count  ratings_1  ratings_2  \\\n",
       "0            4942365                   155254      66715     127936   \n",
       "1            4800065                    75867      75504     101676   \n",
       "2            3916824                    95009     456191     436802   \n",
       "3            3340896                    72586      60427     117415   \n",
       "4            2773745                    51992      86236     197621   \n",
       "\n",
       "   ratings_3  ratings_4  ratings_5  \\\n",
       "0     560092    1481305    2706317   \n",
       "1     455024    1156318    3011543   \n",
       "2     793319     875073    1355439   \n",
       "3     446835    1001952    1714267   \n",
       "4     606158     936012     947718   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  https://images.gr-assets.com/books/1447303603m...   \n",
       "1  https://images.gr-assets.com/books/1474154022m...   \n",
       "2  https://images.gr-assets.com/books/1361039443m...   \n",
       "3  https://images.gr-assets.com/books/1361975680m...   \n",
       "4  https://images.gr-assets.com/books/1490528560m...   \n",
       "\n",
       "                                     small_image_url  \n",
       "0  https://images.gr-assets.com/books/1447303603s...  \n",
       "1  https://images.gr-assets.com/books/1474154022s...  \n",
       "2  https://images.gr-assets.com/books/1361039443s...  \n",
       "3  https://images.gr-assets.com/books/1361975680s...  \n",
       "4  https://images.gr-assets.com/books/1490528560s...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "book_id\n",
      "goodreads_book_id\n",
      "best_book_id\n",
      "work_id\n",
      "books_count\n",
      "isbn\n",
      "isbn13\n",
      "authors\n",
      "original_publication_year\n",
      "original_title\n",
      "title\n",
      "language_code\n",
      "average_rating\n",
      "ratings_count\n",
      "work_ratings_count\n",
      "work_text_reviews_count\n",
      "ratings_1\n",
      "ratings_2\n",
      "ratings_3\n",
      "ratings_4\n",
      "ratings_5\n",
      "image_url\n",
      "small_image_url\n"
     ]
    }
   ],
   "source": [
    "# Pretty-print the column names\n",
    "for column in books_df.columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these columns may seem redundant or odd. In particular, why does every book have several different IDs? Each ID serves a different purpose; according to the README from the git repo: \n",
    "\n",
    "*\"Each book may have many editions. goodreads_book_id and best_book_id generally point to the most popular edition of a given book, while goodreads work_id refers to the book in the abstract sense.\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.34\n",
       "1    4.44\n",
       "2    3.57\n",
       "3    4.25\n",
       "4    3.89\n",
       "5    4.26\n",
       "6    4.25\n",
       "7    3.79\n",
       "8    3.85\n",
       "9    4.24\n",
       "Name: average_rating, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_df['average_rating'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLSIidcDnUDh16hQlJSW0atXKjgsLC+PVV1/lyJEjGGNYvnw5vXr1omXLloSGhrJz504AsrOziY2NrX31IiLiVz6fPjp69ChRUVFVxjVr1oz09HTGjBlDeXk5Xbp04YknngAgIyOD1NRUiouL6dixI8OGDatd5SIi4ndXFApbt261/7/jjjvIysqqNo/L5cLlclUb3759+yqnlkREpP7R3WMi16mImxoRFqq3sPiX9iiR61RtH38tcjF69pGIiFgKBRERsRQKIiJiKRRERMRSKIiIiKVQEBERS6EgIiKWQkFERCyFgoiIWAoFERGxFAoiImIpFERExFIoiIiIpVAQERFLoSAiIpZCQURELIWCiIhYNQ6F4uJi4uPjOXr0KABTpkyhd+/eJCQkkJCQwJYtWwDIzc3F7XbTu3dvXn/9ddt+3759JCYm4nK5mDp1KhUVFX7eFBERqa0ahcKuXbsYPHgweXl5dtzevXtZtmwZ2dnZZGdn06tXL0pKSkhJSWHhwoVs2LCBvXv3sm3bNgAmTpzItGnT2LRpE8YYsrKy6mSDRETEdzUKhaysLNLS0oiMjATg3Llz5Ofnk5KSgtvtJjMzE6/Xy+7du4mOjqZVq1YEBQXhdrvJycnh2LFjlJSU0LlzZwASExPJycmpu60SERGfBNVkppdffrnK8MmTJ+nevTtpaWlERESQnJzMqlWraNy4MU6n084XGRlJQUEBJ06cqDLe6XRSUFDgp00QERF/qVEo/FirVq1YsGCBHR46dChr167F5XLhcDjseGMMDocDr9d70fFXonnzcF9KvSinM8Jvy7peqQ/UB9dSfen7+lJHfeJTKOzfv5+8vDxcLhdw/iAfFBREVFQUHo/HzufxeIiMjKw2/uTJk/ZUVE0VFhbj9Rpfyq3C6YzA4ymq9XKuZ+qDhtEH1/MBrT70fUPYBy4lIMDh04dpn36Saoxh1qxZnD59mvLyclauXEmvXr3o1KkTBw8e5NChQ1RWVrJ+/XpiY2Np2bIloaGh7Ny5E4Ds7GxiY2N9WbWIiNQhn74ptG/fntGjRzN48GAqKiro3bs38fHxAMyZM4dnnnmG0tJS4uLi6NOnDwAZGRmkpqZSXFxMx44dGTZsmP+2QkRE/OKKQmHr1q32/0lJSSQlJVWbJyYmhnXr1lUb3759e1atWuVDiSIicrXojmYREbEUCiIiYikURETEUiiIiIilUBAREUuhICIilkJBREQshYKIiFgKBRERsRQKIiJiKRRERMRSKIiIiKVQEBERy6dHZ4uIf0Tc1IiwUL0Npf7Q3ihyDYWFBuF+Ptunth+9luDnakR0+khERC6gUBAREUuhICIilkJBRESsGodCcXEx8fHxHD16FICVK1cSHx+P2+1mypQplJWVATB//nx69uxJQkICCQkJLF++HIB9+/aRmJiIy+Vi6tSpVFRU1MHmiIhIbdQoFHbt2sXgwYPJy8sD4ODBgyxZsoQVK1awbt06vF4vf/jDHwDYu3cv8+bNIzs7m+zsbJKSkgCYOHEi06ZNY9OmTRhjyMrKqpstEhERn9UoFLKyskhLSyMyMhKAkJAQ0tLSCA8Px+Fw0K5dO/Lz84HzobB48WLcbjfp6emUlpZy7NgxSkpK6Ny5MwCJiYnk5OTU0SaJiIivahQKL7/8MnfffbcdbtmyJffddx8Ap06dYvny5Tz44IOcPXuWDh06MHHiRNasWcOZM2dYuHAhJ06cwOl02vZOp5OCggI/b4qIiNRWrW5eKygoYNSoUfz617+mW7duALz99tt2+siRI0lJSSE2NhaHw2HHG2OqDNdE8+bhtSm1Cqczwm/Lul6pD9QH11J96fv6Ukd94nMofPPNN4waNYqhQ4cycuRIAPLz88nNzWXgwIHA+YN/UFAQUVFReDwe2/bkyZP2VFRNFRYW4/UaX8u1nM4IPJ6iWi/neqY+qD99cKMelOpL39eHOupKQIDDpw/TPv0ktbi4mCeffJJnn33WBgJAWFgYr776KkeOHMEYw/Lly+nVqxctW7YkNDSUnTt3ApCdnU1sbKwvqxYRkTrk0zeFVatWcfLkSd59913effddAB544AGeffZZ0tPTGTNmDOXl5XTp0oUnnngCgIyMDFJTUykuLqZjx44MGzbMf1shIiJ+cUWhsHXrVgBGjBjBiBEjLjqPy+XC5XJVG9++fXtWrVp15RWKiMhVozuaRUTEUiiIiIilUBAREUuhICIilkJBREQshYKIiFgKBRERsRQKIiJiKRRERMRSKIiIiFWrR2eLiFypsvJKn58OW1JaQdGZc36uSC6kUBCRqyokOBD389k+tf3otQQa7sOu6wedPhIREUuhICIilkJBREQshYKIiFgKBRERsRQKIiJiKRRERMRSKIiIiFWjUCguLiY+Pp6jR48CkJubi9vtpnfv3rz++ut2vn379pGYmIjL5WLq1KlUVFQAkJ+fT1JSEn369GHMmDGcPXu2DjZFRERq67KhsGvXLgYPHkxeXh4AJSUlpKSksHDhQjZs2MDevXvZtm0bABMnTmTatGls2rQJYwxZWVkAzJgxgyFDhpCTk8Ntt93GwoUL626LRETEZ5cNhaysLNLS0oiMjARg9+7dREdH06pVK4KCgnC73eTk5HDs2DFKSkro3LkzAImJieTk5FBeXs4XX3yBy+WqMl5EROqfyz776OWXX64yfOLECZxOpx2OjIykoKCg2nin00lBQQHffvst4eHhBAUFVRl/pZo3D7/iNj/F14dxNSTqA/XB9cqfr5v2gequ+IF4Xq8Xh8Nhh40xOByOnxz/w78X+vFwTRQWFuP1mitu92NOZwQez439SC31Qf3pAx2Urpy/Xrf6sg/UlYAAh08fpq/410dRUVF4PB477PF4iIyMrDb+5MmTREZG0qxZM4qKiqisrKwyv4iI1D9XHAqdOnXi4MGDHDp0iMrKStavX09sbCwtW7YkNDSUnTt3ApCdnU1sbCzBwcHcfffdbNiwAYC1a9cSGxvr360QERG/uOLTR6GhocyZM4dnnnmG0tJS4uLi6NOnDwAZGRmkpqZSXFxMx44dGTZsGABpaWlMnjyZRYsW0aJFC+bNm+ffrRAREb+ocShs3brV/j8mJoZ169ZVm6d9+/asWrWq2viWLVuydOlSH0sUEZGrRXc0i4iIpVAQERFLoSAiIpZCQURELIWCiIhYCgUREbEUCiIiYikURETEUiiIiIilUBAREUuhICIilkJBREQshYKIiFgKBRERsRQKIiJiKRRERMS64r+8JiJVRdzUiLBQvZWkYdCeLFJLYaFBuJ/P9qntR68l+LkakdrR6SMREbF8/qbwwQcfsGzZMjt89OhREhISOHfuHDt37qRRo0YAjB07ll69epGbm8vs2bMpLS2lb9++TJgwofbVi4iIX/kcCo888giPPPIIAAcOHODpp59m7NixDB8+nGXLlhEZGWnnLSkpISUlhaVLl9KiRQuSk5PZtm0bcXFxtd8CERHxG7+cPpo+fToTJkygUaNG5Ofnk5KSgtvtJjMzE6/Xy+7du4mOjqZVq1YEBQXhdrvJycnxx6pFRMSPan2hOTc3l5KSEvr27cuRI0fo3r07aWlpREREkJyczKpVq2jcuDFOp9O2iYyMpKCgoLarFhERP6t1KKxYsYInnngCgFatWrFgwQI7bejQoaxduxaXy4XD4bDjjTFVhmuiefPw2pZqOZ0RflvW9Up9oD64XvnzddM+UF2tQqGsrIwvvviCOXPmALB//37y8vJwuVzA+YN/UFAQUVFReDwe287j8VS55lAThYXFeL2mNuUC53cCj6eo1su5nqkP/NsHOrBcXf583Rry+yAgwOHTh+laXVPYv38/rVu3pnHjxsD5EJg1axanT5+mvLyclStX0qtXLzp16sTBgwc5dOgQlZWVrF+/ntjY2NqsWkRE6kCtvikcOXKEqKgoO9y+fXtGjx7N4MGDqaiooHfv3sTHxwMwZ84cnnnmGUpLS4mLi6NPnz61q1xERPyuVqHQr18/+vXrV2VcUlISSUlJ1eaNiYlh3bp1tVmdiIjUMd3RLCIilkJBREQshYKIiFgKBRERsRQKIiJiKRRERMRSKIiIiKVQEBERS6EgIiKWQkFERCyFgoiIWAoFERGxFAoiImIpFERExFIoiIiIpVAQERFLoSAiIpZCQURELIWCiIhYtfobzUOHDuXUqVMEBZ1fTHp6OocPH2bRokVUVFQwfPhw+/eac3NzmT17NqWlpfTt25cJEybUvnoREfErn0PBGENeXh7//d//bUOhoKCACRMmsHr1akJCQhg0aBDdunXj3/7t30hJSWHp0qW0aNGC5ORktm3bRlxcnN82REREas/nUPjHP/4BwMiRI/nuu+949NFHadKkCd27d6dp06YAuFwucnJy6Nq1K9HR0bRq1QoAt9tNTk6OQkFEpJ7x+ZrCmTNniImJYcGCBbz33nusWLGC/Px8nE6nnScyMpKCggJOnDhx0fEiIlK/+PxN4c477+TOO++0wwMHDmT27NmMGTPGjjPG4HA48Hq9OByOauOvRPPm4b6WWo3TGeG3ZV2v1Afqg+uVP1837QPV+RwKf/nLXygvLycmJgY4f6Bv2bIlHo/HzuPxeIiMjCQqKuqi469EYWExXq/xtVzL6YzA4ymq9XKuZ+oD//aBDixXlz9ft4b8PggIcPj0Ydrn00dFRUXMnTuX0tJSiouLWbNmDa+++io7duzg1KlTnDt3js2bNxMbG0unTp04ePAghw4dorKykvXr1xMbG+vrqkVEpI74/E2hZ8+e7Nq1iwEDBuD1ehkyZAh33XUXEyZMYNiwYZSXlzNw4EDuuOMOAObMmcMzzzxDaWkpcXFx9OnTx28bISIi/lGr+xTGjx/P+PHjq4xzu9243e5q88bExLBu3brarE5EROqY7mgWERFLoSAiIpZCQURELIWCiIhYCgUREbFq9esjkYYg4qZGhIXqrXA9KCuv9PlmwZLSCorOnPNzRQ2P3glywwsLDcL9fLbP7T96LcGP1cilhAQH+vxaffRaAg33/mX/0ekjERGxFAoiImIpFERExFIoiIiIpVAQERFLoSAiIpZCQURELIWCiIhYCgUREbEUCiIiYikURETEUiiIiIhVqwfizZ8/n40bNwIQFxfHpEmTmDJlCjt37qRRo0YAjB07ll69epGbm8vs2bMpLS2lb9++TJgwofbVi4iIX/kcCrm5uWzfvp01a9bgcDgYNWoUW7ZsYe/evSxbtozIyEg7b0lJCSkpKSxdupQWLVqQnJzMtm3biIuL88tGiIiIf/h8+sjpdDJ58mRCQkIIDg6mbdu25Ofnk5+fT0pKCm63m8zMTLxeL7t37yY6OppWrVoRFBSE2+0mJyfHn9shIiJ+4PM3hVtuucX+Py8vj40bN7J8+XI+//xz0tLSiIiIIDk5mVWrVtG4cWOcTqedPzIykoKCgtpVLiIiflfrP7Jz4MABkpOTmTRpEr/4xS9YsGCBnTZ06FDWrl2Ly+XC4XDY8caYKsM10bx5eG1LtXz9y00NifpAfXAj+vFrrn2gulqFws6dOxk3bhwpKSn079+f/fv3k5eXh8vlAs4f/IOCgoiKisLj8dh2Ho+nyjWHmigsLMbrNbUpFzi/E3g8N/bfX1IfVO0DHRhuHBfu9w39fRAQ4PDpw7TP1xSOHz/O008/TUZGBv379wfOh8CsWbM4ffo05eXlrFy5kl69etGpUycOHjzIoUOHqKysZP369cTGxvq6ahERqSM+f1NYsmQJpaWlzJkzx44bNGgQo0ePZvDgwVRUVNC7d2/i4+MBmDNnDs888wylpaXExcXRp0+f2lcvIiJ+5XMopKamkpqaetFpSUlJ1cbFxMSwbt06X1cnckkRNzUiLPTKdmedNhKprtYXmkXqg7DQINzPZ/vU9qPXEvxcjcj1S4+5EBERS6EgIiKWQkFERCyFgoiIWAoFERGx9OsjEbkhlJVX+vyYi5LSCorOnKuLsuodhYKI3BBCggNr9bPlhvtAjKp0+khERCyFgoiIWAoFERGxFAoiImIpFERExNKvj6Te8OVJpyLiX3oHSr2hJ52KXHs6fSQiIpZCQURELJ0+Er/SdQH1e5Y5AAAGPElEQVSR65veveJXui4gcn1TKIiIXMbFHqZ3Ja6nB+pd1VD46KOPWLRoERUVFQwfPpykpKSruXqpIZ0CEqmqNg/Tg+vrgXpX7Z1fUFDA66+/zurVqwkJCWHQoEF069aNX/7yl1erBKkhnQIS8a/afNO42t8yrloo5Obm0r17d5o2bQqAy+UiJyeHsWPH1qh9QIDDb7X4c1n1UXh4GKGX+aR/uR008meNfF7/jdb2Wq77emx7Ldd9rdqGBAfy5EubfWq7JLU3Z304Zvl6nHMYY4xPLa/Q4sWL+f7775kwYQIAH3zwAbt372bmzJlXY/UiIlIDV+0+Ba/Xi8Px/5PLGFNlWERErr2rFgpRUVF4PB477PF4iIyMvFqrFxGRGrhqoXDvvfeyY8cOTp06xblz59i8eTOxsbFXa/UiIlIDV+1C880338yECRMYNmwY5eXlDBw4kDvuuONqrV5ERGrgql1oFhGR+k8PxBMREUuhICIilkJBREQshYKIiFgN9qln8+fPZ+PGjQDExcUxadKkatM//PBDbrrpJgAeffTRBveAvjfeeINNmzbhcDgYOHAgTzzxRJXp+/btY+rUqZw9e5a7776bGTNmEBTUsHaJy/XBjbAfALzyyit8++23zJkzp8r4/Px8Jk6cSGFhIW3atCEjI4MmTZpcoyrr1k/1wZo1a3jttddo3rw5APfff7998sINyTRAn376qXnsscdMaWmpKSsrM8OGDTObN2+uMk9ycrL58ssvr1GFde+zzz4zgwYNMuXl5ebcuXOmZ8+e5ptvvqkyT//+/c1f//pXY4wxU6ZMMcuXL78WpdaZmvRBQ98PjDEmNzfXdOvWzbz44ovVpo0ePdqsX7/eGGPM/Pnzzdy5c692eVfFpfogPT3dfPTRR9egqvqpQZ4+cjqdTJ48mZCQEIKDg2nbti35+flV5tm7dy+LFy/G7XaTnp5OaWnpNaq2bnTt2pX333+foKAgCgsLqayspHHjxnb6sWPHKCkpoXPnzgAkJiaSk5NzrcqtE5frA2j4+8F3333H66+/zm9+85tq08rLy/niiy9wuVxAw9wH4NJ9ALBnzx7WrFmD2+3mhRde4PTp01e5wvqlQYbCLbfcYg92eXl5bNy4kbi4ODv97NmzdOjQgYkTJ7JmzRrOnDnDwoULr1W5dSY4OJjMzEz69+9PTEwMN998s5124sQJnE6nHXY6nRQUFFyLMuvUpfrgRtgPpk2bxoQJE+zpsQt9++23hIeH21OGDXUfuFQfwPntfuqpp1i3bh0tWrQgPT39KldYvzTIUPjBgQMHGDlyJJMmTaJ169Z2fJMmTXj77bdp27YtQUFBjBw5km3btl27QuvQuHHj2LFjB8ePHycrK8uOv5EeUPhTfdDQ94MPPviAFi1aEBMTc9HpF3vNG9o+cLk+AFiwYAF33XUXDoeDUaNG8ac//ekqVlj/NNhQ2LlzJyNGjOD555/n4YcfrjItPz+fVatW2WFjTIO7wPrNN9+wb98+ABo1akTv3r3Zv3+/nf7jBxSePHmywT2g8HJ90ND3gw0bNvDpp5+SkJBAZmYmW7duZdasWXZ6s2bNKCoqorKyEmiYD6m8XB8UFRXx3nvv2WFjDIGBgdeg0vqjQYbC8ePHefrpp8nIyKB///7VpoeFhfHqq69y5MgRjDEsX76cXr16XYNK687Ro0dJTU2lrKyMsrIyPv74Y+666y47vWXLloSGhrJz504AsrOzG9wDCi/XBw19P3j33XdZv3492dnZjBs3jgceeICUlBQ7PTg4mLvvvpsNGzYAsHbt2ga3D1yuDxo3bsw777zDrl27AFi2bFmD2gd80XA+Fl1gyZIllJaWVvnp2aBBg9i6dSvjxo3j9ttvJz09nTFjxlBeXk6XLl2q/VTxehcXF8fu3bsZMGAAgYGB9O7dm/79+/Of//mftg8yMjJITU2luLiYjh07MmzYsGtdtl/VpA8a+n5wMVOnTuWBBx7gwQcfJC0tjcmTJ7No0SJatGjBvHnzrnV5V8WFffC73/2O6dOnU1JSQuvWrZk7d+61Lu+a0gPxRETEapCnj0RExDcKBRERsRQKIiJiKRRERMRSKIiIiKVQEBERS6EgIiKWQkFERKz/B2X0+wQ3TsPhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "books_df.average_rating.hist(bins = 20)\n",
    "plt.title('Distribution of Goodbooks Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's interesting to see that this distribution resembles a normal distribution, centered around a rather high rating (4 out of 5). \n",
    "\n",
    "Having spent a decent amount of time looking at various book ratings on the actual Goodreads site, I have a few potential explanations for this: \n",
    "1. These books are a nonrandom sampling of the books on Goodreads, possibly the most \"popular\" 10,000 books on Goodreads. \n",
    "2. Goodreads users don't use the 1-5 rating system as it is meant to be used. According to Goodreads, a 3-star rating is sufficient to express that a user \"liked\" the book. (See below.) However, there are few ratings in this dataset that fell below a 3.5. And the most frequent average rating, roughly a 4, means \"*really* liked it.\"\n",
    "3. Users don't feel comfortable rating a book they didn't finish, so there is selection bias in rated books. Books that were rated were mostly books that readers decided to finish, which generally means the reader thought the book was worth their time.\n",
    "\n",
    "This could be explored further by looking into the within-user distribution of ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Goodreads Rating System](https://i.imgur.com/7bZN4lr.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.00\n",
       "mean         4.00\n",
       "std          0.25\n",
       "min          2.47\n",
       "25%          3.85\n",
       "50%          4.02\n",
       "75%          4.18\n",
       "max          4.82\n",
       "Name: average_rating, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_df.average_rating.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popular Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in tags.csv and book_tags.csv\n",
    "book_tags_df_tmp = pd.read_csv('goodbooks-10k-master/book_tags.csv')\n",
    "tags_df = pd.read_csv('goodbooks-10k-master/tags.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>tag_id</th>\n",
       "      <th>tag_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>--1-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>--10-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>--12-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>--122-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tag_id tag_name\n",
       "0       0        -\n",
       "1       1     --1-\n",
       "2       2    --10-\n",
       "3       3    --12-\n",
       "4       4   --122-"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>tag_id</th>\n",
       "      <th>tag_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34247</th>\n",
       "      <td>34247</td>\n",
       "      <td>Ｃhildrens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34248</th>\n",
       "      <td>34248</td>\n",
       "      <td>Ｆａｖｏｒｉｔｅｓ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34249</th>\n",
       "      <td>34249</td>\n",
       "      <td>Ｍａｎｇａ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34250</th>\n",
       "      <td>34250</td>\n",
       "      <td>ＳＥＲＩＥＳ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34251</th>\n",
       "      <td>34251</td>\n",
       "      <td>ｆａｖｏｕｒｉｔｅｓ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tag_id    tag_name\n",
       "34247   34247   Ｃhildrens\n",
       "34248   34248   Ｆａｖｏｒｉｔｅｓ\n",
       "34249   34249       Ｍａｎｇａ\n",
       "34250   34250      ＳＥＲＩＥＳ\n",
       "34251   34251  ｆａｖｏｕｒｉｔｅｓ"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the tag names look a little eccentric, presumably because any user can create a tag, but the most popular tags will float to the top when we sort by count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>goodreads_book_id</th>\n",
       "      <th>tag_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30574</td>\n",
       "      <td>167697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11305</td>\n",
       "      <td>37174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11557</td>\n",
       "      <td>34173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8717</td>\n",
       "      <td>12986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>33114</td>\n",
       "      <td>12716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   goodreads_book_id  tag_id   count\n",
       "0                  1   30574  167697\n",
       "1                  1   11305   37174\n",
       "2                  1   11557   34173\n",
       "3                  1    8717   12986\n",
       "4                  1   33114   12716"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags_df_tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_tags_df = pd.merge(book_tags_df_tmp, tags_df, on = 'tag_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>goodreads_book_id</th>\n",
       "      <th>tag_id</th>\n",
       "      <th>count</th>\n",
       "      <th>tag_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>865</td>\n",
       "      <td>30574</td>\n",
       "      <td>596234</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6140</th>\n",
       "      <td>2429135</td>\n",
       "      <td>30574</td>\n",
       "      <td>586235</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9108</th>\n",
       "      <td>18143977</td>\n",
       "      <td>30574</td>\n",
       "      <td>505884</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>30574</td>\n",
       "      <td>496107</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>24280</td>\n",
       "      <td>30574</td>\n",
       "      <td>488469</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7846</th>\n",
       "      <td>11235712</td>\n",
       "      <td>30574</td>\n",
       "      <td>474954</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4110</th>\n",
       "      <td>168668</td>\n",
       "      <td>30574</td>\n",
       "      <td>465640</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>18405</td>\n",
       "      <td>30574</td>\n",
       "      <td>454565</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>4981</td>\n",
       "      <td>30574</td>\n",
       "      <td>443942</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>930</td>\n",
       "      <td>30574</td>\n",
       "      <td>436097</td>\n",
       "      <td>to-read</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      goodreads_book_id  tag_id   count tag_name\n",
       "84                  865   30574  596234  to-read\n",
       "6140            2429135   30574  586235  to-read\n",
       "9108           18143977   30574  505884  to-read\n",
       "2                     3   30574  496107  to-read\n",
       "1671              24280   30574  488469  to-read\n",
       "7846           11235712   30574  474954  to-read\n",
       "4110             168668   30574  465640  to-read\n",
       "1438              18405   30574  454565  to-read\n",
       "383                4981   30574  443942  to-read\n",
       "91                  930   30574  436097  to-read"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_tags_df.sort_values(by='count', ascending = False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 10 tags by count are all \"to-read,\" which is not surprising. In order to tag a book as \"to-read,\" Goodreads users only need to click a button which puts the book on their \"to-read\" shelf. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tag_id_to_name = zip(book_tags_df['tag_id'], book_tags_df['tag_name'])\n",
    "tag_id_to_name = dict(tag_id_to_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30574 : to-read\n",
      "11305 : fantasy\n",
      "11557 : favorites\n",
      "8717 : currently-reading\n",
      "33114 : young-adult\n",
      "11743 : fiction\n",
      "14017 : harry-potter\n",
      "5207 : books-i-own\n",
      "22743 : owned\n",
      "32989 : ya\n",
      "27199 : series\n",
      "11590 : favourites\n",
      "18886 : magic\n",
      "6953 : childrens\n",
      "22753 : owned-books\n",
      "24960 : re-read\n",
      "1691 : adventure\n",
      "6857 : children\n",
      "15965 : j-k-rowling\n",
      "6888 : children-s\n",
      "26785 : sci-fi-fantasy\n",
      "6828 : childhood\n",
      "2104 : all-time-favorites\n",
      "9221 : default\n",
      "20774 : my-books\n"
     ]
    }
   ],
   "source": [
    "for k in list(tag_id_to_name)[:25]:\n",
    "    print(k, \":\", tag_id_to_name[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7L23kHL+cYwd5xa/KSOdhakaJHb9MJL9Vq1bRoEEDgoODCQoKYuPGjWhpaTF16lR27NhB37596du3LzNmzEBLS4sDBw6wadMmQkJCMDExYdeuXTx48IABAwbQsGFDABITE9mzZw86Ojr4+PgA2XWDGzZsYMuWLTx48IBFixaxfv16jIyM2LhxI0FBQXz55ZdERkaye/dunj17xvTp00lPT0dfX/+t47m1fCyq1HtF8lkJgiCUBv+aGQaI5FcoatWqxbRp09iyZQvx8fGcO3cOCwsLKlWqRMOGDYmJiUFXV5fatWtTpUoVoqOjmTdvHgCVKlXCwcGB2NhYDA0Nady48WtLFc6fP8/du3cZPnw4kF0WYWRkRLVq1dDX12fQoEHY2dkxZcqUAiU+QRAEuXj5TFcUub+jS5cuMXnyZEaMGEH37t3R0tLS1N05OzuzZ88edHV1cXJyAuDltT6SJKFWZ9eavFjvlxe1Wk2rVq1YtWoVAOnp6Tx58gQdHR22bNlCbGwskZGRDBo0iA0bNlC7du3CDlcQBOGD9uICF7Hg5T2cPn0aKysrBg8ezMOHDzl27BjdumUXjDs4OBAcHIwkSUyZMgXIruPbunUrSqWSBw8ecPjwYUJCQoiLi8v3GNra2qhUKpo3b45SqSQ+Pp7atWvz7bffkpiYyPDhw/H392fDhg20b9+ey5cva7Z5WxZeq97vgxAEQSjlVBnpJXr8MpX8evXqhZeXl+bMrmnTpiQkJADZZ3KtWrUiIyND80DrcePG4evri5OTE2q1mrFjx9KkSZPXJj8HBwecnZ0JDw9n3rx5fP3112RlZVGtWjUWLlyIiYkJLVq0wNHRkfLly9OqVasCP+ZMlDqI5d5yJOfYQcRf3MpMnV9ZIpKffL8A5By/nGMHEX9xX/YUndyLUExMDO7u7iU9DUEQBOElZeqyZ1kh6vzkU+uUFznHL+fYQV7xP8/MIC2l5O77ySr5rVq1ih07dqCtrU3Hjh25ceMGgwcPxtbWlsWLF3P58mXWrl1LUlISn332GatWrcLLy4t69epx5coVKleuzLJlyzA2Ns63wP2XX35h/vz56Ovrv/MKz3E7Z3Lv6YNCjl4QBKH02Oy2kjRKLvnJ5rLn8ePHOXLkCGFhYWzbto2bN29iY2NDdHQ0AL/++ivXr19HrVZz4sQJbG1tAbh69SojR45k165dVKxYkZ07d2oK3L///nsiIiLo1KkTQUFBZGRk4OPjQ3BwMOHh4W8slxAEQRBKhmzO/KKjo+nduzfly5cHwNXVlXXr1pGWlsbjx48BaNCgAb///juRkZGae3WVK1emcePGANSrV4/U1NR8C9zj4uKoWrWqppuDi4sLy5YtK+5QBUEQPgiiyL0YvNzQFqBChQqkpqZy4MABWrVqhampKdHR0fz++++0bNmSu3fv5no6S06z2vwK3O/cuZOrcF5bW7voAxMEQfhAiSL3YmBtbc3KlStxc3NDR0eHsLAwrK2tSU5OZuXKlcyaNYuqVavi6emJlZXVaxNXfgXu/v7+JCcnc/XqVRo2bMju3bvfaa4rnALfNUxBEIQPwvPMknuuJ8go+dnZ2XHlyhVcXV1RqVR06tSJYcOGce7cOX744Qdat26NgYEBmZmZ2NnZvXasKlWq5Fngrqury+LFi/H29kZHR0dzubSgRJ2fqHWSIznHDiL+4iaK3Eshkfzk+wUg5/jlHDuI+GVV5F5YReDu7u7ExMQUeL/ExEQ8PT3f+/iCIAjCh0U2lz3zUq1aNdasWVPS03iFKHKXT6FvXuQcv5xjB3nEr3qezsO0kr3fB6Ug+T18+JBRo0aRlJSEpaUlc+bM4eTJkyxdupSsrCxq1qyJn58fpqamnDt3jsDAQNLT0zExMcHPz49atWppxrp//z4eHh58/fXXNG3alClTpvD06VO0tLRQKpW0aNEi17ETEhIYPnw4R44ceWVeeR3r6tWr7N27l6VLlxIfH0+PHj04efIkpqamjBo1igkTJrBw4UKaNWvGmTNnePDgAUqlUlMz+LZ+9RxLepJoZisIQtnTcXsYiOSXnYCWL19OrVq1mDhxIqtXr2bTpk2EhoZibm7O2rVr8fPzIygoiEmTJrF06VIsLS3Zu3cvkyZNIiwsDIC0tDTGjBmDl5cXXbt2Zfny5XTp0oXRo0cTGRnJmTNnXkl++cnIyMjzWOvWrSMgIABJkoiOjqZy5crExsZib29PfHw8zZo1AyAzM5NNmzZx5MgRli1bVuDkJwiCUJbld4Yrqzq/Nm3a8MknnwDg5OSEj48PVlZWmJubA+Dm5sbq1au5ceMGFStWxNLSEoCePXsye/Zs0tKyb5DOmTMHU1NTTf++9u3bM378eK5cuYKtrS3Dhg176znldyxJkqhduzZxcXFER0fj4eHB6dOnqVChAtbW1igUCgBsbGyA7KL4lJSU9/+QBEEQypC8FrbIrs5PR+d/U5AkSZNAXnxNpVLlWaT+Yud1T09Pjh8/TmhoKEOHDqV169bs3r2bY8eOsWfPHrZt28akSZNQKpVAdq+/L774QjOWs7Oz5t8LFizI91hdunTh5MmTXL9+HV9fX4YPH46Wllau8oicwviXY3lbbdaIZraCIJRNqucl28Q2R4knvzNnznDnzh2qV69OREQEo0ePZv369SQkJGBubs6mTZto164d//rXv0hJSeHChQtYWlqyZ88ezMzMMDY2BqBRo0Z06dKFwYMH07VrV9atW0e1atXw8PCgXbt2uLi40KxZM7Zv3645dk6jWyDX6xkZGfkey9bWlrFjx9K0aVNMTEzQ1dXl6NGjTJo0qdA+E1HqIJZ7y5GcYwcRf3Er8eRXt25dZsyYwb1797C2tmbUqFHUrVsXLy8vMjMzMTMzIzAwED09PZYsWYK/vz/Pnj3DyMiIJUuW5Brrk08+YejQofj5+aFUKpk8eTLh4eFoa2vneTaXn9cdq06dOkiShJWVFQBWVlZcu3ZN0x1eEARBKP1KdZF7cHAwHTp0oE2bNri7u+Pl5UW7du2KfR7h4eHExsbyzTff4OnpSUBAANWqVSuy44kzP/n+9Svn+OUcO4j4ZVXk/ianT5/W3NMrLdasWVOkiU8QBEEoem+87ClJEkFBQRw6dAhtbW3c3Nw4dOiQ5izsxVo5Hx8fUlJSuHnzJt7e3gQEBGBpacmVK1f4+eefOXHiBOvWrSMrK4smTZowZ84c9PX16dSpE927d+fMmTNoa2uzdOlSzpw5w6VLl1AqlSxfvlwzH29vb9q2bcvAgQOB7Ke7TJkyhebNm+eat7W1NU2bNuXevXts3bqVH374gb1796JWq+nUqRPe3t4oFAqWLFlCVFQUqampVK1alSVLlmBqakpERAQrV67E0NCQGjVqYGBgAIC9vT3r168nNjaWEydOkJqayu3bt+nYsSO+vr4ALFq0iP3792NiYkKVKlWwt7enX79+b/1LEUXuZb/Q93XkHL+cY4eyH39GuorUR89KehrAWyS/ffv28dtvv7Fz504yMzMZMmQI6en5r9YxNjbWtPoJCAigc+fOLF26lGvXrrF582Y2btyIvr6+phnsl19+yb1792jfvj2zZs3im2++4aeffsLHx4ewsDC8vLxo0KCBZnxXV1dCQkIYOHAgf//9Nw8ePHgl8UF28bynpyft2rUjMjKSS5cusXXrVhQKBd7e3uzYsYMWLVpw/fp1Nm7ciJaWFlOnTmXHjh307t2boKAgIiIiMDY25vPPP9ckvxedPXuWXbt2oa2tTY8ePRg8eDB///03Z86cYdeuXTx79gwXFxfs7e3f6peRY1nAYVIflo7/gwiCIBSW2YscS3oKGm9MfqdPn6Znz57o6emhp6fH9u3bX/s8zpzauBw5iSkmJoabN29qztgyMzNzdT14sTbu119/zXf8du3aMWvWLBISEti+fXuuEoWX5Rw7KiqKCxcuaM6+nj9/jpmZGc7OzkybNo0tW7YQHx/PuXPnsLCw4OzZs7Rs2RJTU1Mgu/4wp+P7i1q2bImhYfZZWs2aNUlNTeXUqVO5Pq+uXbvmOz9BEAS5ed3ZbakqctfR0clVr5ZTHpCzTkalUuXavly5crl+zql5U6vV9OzZU1Nn9+TJk1z3816sjXvdGhyFQkHfvn3ZvXs3e/fu5fvvvycxMZExY8YAULVqVc3zOnPmolar8fDwYOTIkQA8evQIbW1tLl26xOTJkxkxYgTdu3dHS0tLU2v44hxerEXMK7YX562lpZVnTaIgCIKQd4E7lMIi97Zt27J+/XoGDRqESqVi9OjR1KlThz///BNra2sOHTr0Vgdq164d//nPf/jiiy+oVKkSvr6+WFhYMH78+Hz30dbWznPBS79+/RgyZAh169bVLD55sU7vZdbW1gQHBzNw4ED09fUZN24cLi4upKamYmVlxeDBg3n48CHHjh2jW7dutG7dGn9/fxITE6lSpQp79uyhYsWKbxVnhw4dWLNmDYMHDyYjI4Njx47RqFGjt9o3xwSlQ4G2FwRB+BBkpKvevFExeWPy+/TTT7l06RL9+vUjKyuL4cOH07RpU809OQeHt/uibtiwIV5eXnh4eJCVlUWjRo00Z2v5sbGxYc6cOa/U6H388cd8/PHHuLi4vNWx7e3tuXr1KgMHDkStVmNjY4OLiwtJSUl4eXnh5OQEZD/1JSEhAVNTU5RKJSNGjKB8+fLUrVv3rY4D0KVLF86ePYuLiwtGRkZUrVo11xni2xClDmK5txzJOXYQ8Re3Ul3nlxdJkkhKSsLd3Z1du3ahp6dX0lPK5ezZs9y4cQMXFxcyMzNxc3Nj3rx5NGzY8K3HEMlPvl8Aco5fzrGDiF/U+b3B/v37cXZ2ZtKkSe+c+HKa6M6cOZOLFy8WWlNdgNq1a7Nr1y769OlDv3796N27d4ESnyAIglD0SvzxZgXVo0cPevToUShjBQYGArxTF/j8GBsb8/333xfaeIIgCELh++CSX2HKeWQa5N1UV09P75Vi+blz53Lt2jWSk5Np0KABixcvJjk5mdGjR2NiYkK5cuVIT09n3LhxdOzYEUmS6N69Oxs2bHjrJ8OIIveyXej7JnKOX86xw4cZf0Z6OqmPSr45bUHJOvm96OWmuqGhoXh4eOQqlj99+jS6urps2rSJrKwsPDw8OH78OE2aNCE+Pp61a9dibm5OWFgY27dvp2PHjvz6669YWFgU6JFo6xZMIS3lfhFGKwiCUDi85v8AiOT3wXq5qW54eDgeHh7A/4rl27Zti7GxMT/99BPXr1/nxo0bPH36FIDKlStrGvD27NmTJUuW8PTpU7Zt21agR5sJgiB8aArrjLVUFbnLxctNdV/8OadY/vDhwwQHBzN8+HD69evHw4cPNcXwLxb3GxgY0LlzZ/bv3090dDRz5swppigEQRCKX2GsUi11Re5y8XJT3ZzHrb0oKiqKnj174urqyu3bt4mJiaF9+/Z5jufq6srEiROxs7MrcJ2fx7Sgd4pBEAShuGW85lnPpZlIfv/v5aa6/fv3f2WbAQMGMGXKFHbv3o2uri6tWrXK1Q3+Ra1bt0ahUODq6lrguYg6P1HrJEdyjh1E/MXtgyty/xBIksQff/zBtGnTiIiIKPD+IvnJ9wtAzvHLOXYQ8Ysi90LyusL1F1skvY+QkBBCQkJeeX3dunWMGjWKWbNmFcpxBEEQhMIlLnsWgREjRjBixIh33l/U+X14tU6FSc7xyzl2KL3xqzJUPEwtWz1Giy35rVq1ih07dqCtrU3Hjh25ceMGgwcPxtbWlsWLF3P58mXWrl1LUlISn332GatWrcLLy4t69epx5coVKleuzLJlyzA2NiYyMpLg4GBUKhXm5ub4+/tjYmLCL7/8wvz589HX16d27dqvnc+sWbO4cOECJiYmzJs3DzMzM+Lj45k9ezYpKSkYGBgwc+ZMLC0tSU5OZubMmdy5cwcdHR0mTpxI586dNWOp1WomTpyIubk5EydOZMaMGVy7dg2AIUOGaHoYvq341adRPfowbyILglD21JvSqaSnUOiKJfkdP36cI0eOEBYWhq6uLuPHj8fGxobo6GhsbW359ddf+eeff1Cr1Zw4cQJbW1sArl69yrx582jcuDHjx49n586d9O7dm0XCS4AMAAAgAElEQVSLFrF+/XqMjIzYuHEjQUFBzJkzBx8fH9atW0edOnWYOXPma+fUtm1b/P39+emnnwgMDGTFihV4e3szZswYunXrxrlz55gwYQL79+/H398fa2trRo4cye3btxk8eLDmXp4kSSiVSqpXr87UqVOJjY0lNTWViIgIEhMTWbRoUYGTnyAIQmlTHGelZa7OLzo6mt69e1O+fHkguwxg3bp1pKWl8fjxYyD7Ptzvv/9OZGSk5l5d5cqVNd3e69WrR2pqKufPn+fu3bsMHz4cgKysLIyMjIiLi6Nq1arUqVMHABcXF5YtW5bnfMqVK0efPn0AcHZ2ZunSpTx58oRbt27RrVs3AFq0aIGRkRHXr18nOjqagIAAILtje/PmzTl//jwAGzduJC0tjcOHD2vmGR8fz6hRo+jcuTNTp04t3A9TEAShBBT1YpwyWeeXV2fzChUqkJqayoEDB2jVqhWmpqZER0fz+++/07JlS+7evZtnp3S1Wk2rVq1YtWoVAOnp6Tx58oQ7d+7k6r6ura0NkGeXdy2t/63zySloz2vRa87xXn4v53WAli1b0rhxYwICAggODsbExITdu3dz8uRJjh8/jouLC7t3737rZriCIAhC0SuW5Gdtbc3KlStxc3NDR0eHsLAwrK2tSU5OZuXKlcyaNYuqVavi6emJlZWVJnHlpXnz5iiVSuLj46lduzbffvstiYmJ+Pv7k5yczNWrV2nYsCG7d+8GoFq1aq90eX/69CmHDx/GwcGBsLAwOnTogKGhIebm5hw4cEBz2TM5OZl69ephbW3N1q1bNZc9f/vtN3x9fYmLi6Nhw4Z4enri7OzMkSNHkCSJHTt2sHTpUmxsbIiKiuLu3bsFSn61x7R9tw9aEAShCKgySk8H9sJSLMnPzs6OK1eu4OrqikqlolOnTgwbNoxz587xww8/0Lp1awwMDMjMzMTOzu61Y1WpUoV58+bx9ddfk5WVRbVq1Vi4cCG6urosXrwYb29vdHR0NJdL81KxYkUOHTrEsmXLqFatGvPnzwdg4cKF+Pr6EhISgq6uLiEhIejp6TFz5kxmz55NeHg4AAEBAVStWlUznp6eHr6+vvj4+LBz504OHDhA79690dfXp0+fPgUurRB1fqLWSY7kHDuI+IubbIvcp0+fTmxsLAkJCcTFxeW7nbu7Oxs2bACy7w++fBZZFETyk+8XgJzjl3PsIOIvk/f8SqNt27Zx4cKFN3aDj42N1fy7OBKfIAiCUPRkmfzGjh2LJEl06NCBzMxMzp8/z99//8306dN58OAB5cqVIyAggK1btwLZz/TcsmULDRo0IC4ujmfPnqFUKomLi0OhUDBq1Cj69u1LeHg4J06cIDU1ldu3b9OxY0d8fX0LPD9R5F46C32Li5zjl3PsUHriz8jIJDX1eUlPo0jJMvmtWrWKBg0aEBERoSmZmDt3Lt27d2fo0KEcP36clStXsmzZMjZs2MCWLVty7R8SEoKJiQm7du3iwYMHDBgwgIYNGwJw9uxZdu3ahba2Nj169GDw4MEFvue3Zs0aHj16VDjBCoIgFNDkyZMBkfxk4fTp0yxevBgAW1tbTaF9XqKjo5k3bx4AlSpVwsHBgdjYWAwNDWnZsiWGhtlnbjVr1iQ1NbXoJy8IglDISuIstMwVuX8IXm5m+9dff1G3bt08t31d3V9etYmCIAgfmuJefCMWvJSQNm3asHv3btzc3Dh16hTLly8nNDQUbW1tVCpVruSYU/enVCp58OABhw8fJiQk5LWrRgvC09OzUMYRBEF4FxkZmSU9hSInkt//mz17Nkqlkp9//pny5ctrHmfm4OCAs7OzpsYPYNy4cfj6+uLk5IRarWbs2LE0adKk0JKfKHUQy73lSM6xg4i/uMm2zq80E8lPvl8Aco5fzrGDiF80sy1iaWlpjBs37q23f11TXEEQBOHDJLvkl5qaypUrV0p6GoIgCEIJkt09v4CAAJKSkhg3bhz29vb88MMPKBQKmjRpwqxZs6hQocIr+zx8+JBRo0aRlJSEpaUlc+bMQU9PT1P0DhAeHk5sbCzOzs4sW7aMjRs3al4/f/48c+fOfes5iiL30lHoW1LkHL+cY4eiiV+VmcHDFNEc+2WyS35KpZLhw4fz1Vdf4eXlxebNmzExMWHu3LksX76cadOmvbJPQkICy5cvp1atWkycOJHQ0FA8PDzyHN/a2hqlUsmtW7ewsLAgIiLi/wtG397FyHlkPH/4TvEJgiC8qHW3hYBIfi+TXfLLcfr0aezs7DAxMQHAzc2N6dOn57ltmzZt+OSTTwBwcnIiPDw83+SnUChwcXFhx44d9OvXj/v379O8efMiiUEQBOFtfChn1KLIvRi83GBXkiRUKhWHDx8mODgYAHt7e6ytrV8pgH/5Z4VCgUr1v35XLi4ujB49Gj09PZydnYs4EkEQhNf7EFaRiiL3Iqajo4NKpcLKyor169fz5ZdfYmxszObNm2nXrh0ODg44ODhoto+JieHMmTPcuXOH6tWrExERgY2NDQAmJiZcu3aNevXqceTIEYyNjQGoUaMG1atXZ+PGjYSGhhZ4js06zyicYAVBkD1VZkZJT6FUkl3yq1y5MmZmZgQGBvL555/j7u5OZmYmTZo0yXdRSt26dZkxYwb37t3D2tqa/v37A9kPfx07diympqa0bt2ahw//d5+uV69eHDhwgGrVqhV4jqLOr/T/lVpU5By/nGMHEX9xE0XuRUClUjF16lR69OhBt27dCry/SH7y/QKQc/xyjh1E/KLIvZjkVbw+c+ZMLl68+NZjhISEEBISAqBpWyRJEjY2NigUCrp27Vp4ExYEQRAKjewue75OYGDge4+hUCiIiop6rzFEnd+HsTKtqMg5fjnHDm8Xf3qmmkcpT4thNmWbSH7AunXrOHToEM+ePcPb2xuA7777jnLlyvHXX3/RoEEDgoKC0NPTY+3atZrawIoVK2JpaZlrrCdPnuDn58e1a9dQq9V4enri6OhYoPn4HL3E/WfiJrUgCK9a06tVSU+hTJB98gsPD+fAgQOsXr2asWPHal4/e/Yse/fupWrVqgwcOJBffvmFKlWqEBYWxrZt21AoFLi5ub2S/FauXEmTJk1YsGABjx8/ZtCgQTRv3pyaNWsWd2iCIJRRZfUMWdT5FZM//viDWbNmsXjx4lcea1avXj2qV68OQJ06dUhNTSU+Ph5bW1vNtj169HilXvDUqVM8f/6csLAwAJ4+fcq1a9dE8hMEodCUxYUxos6vGFWoUIH58+cTGBioqd3LkVdH9pc7s+vo6JCRkfvyZFZWFgsXLqRJkyYAJCcnY2RkVIRRCIIgCAUl6+RXo0YN7O3tOXjwoOapLq/Tvn17JkyYgJeXF3p6ehw8eBBbW9tc21hbWxMaGqp5gHbfvn3ZuHEjFhYWbz2vb+yaFjgWQRDkIT1TXdJTKBNknfxyTJ06FUdHR549e/ba7Ro1aoSHhwf9+/enYsWKmJmZvbKNl5cXvr6+ODo6olar8fb2LlDiA1HnVxYv6bwtOccv59hBxF/cRJF7KSSSn3y/AOQcv5xjBxG/KHIvBj4+PoSHh+f7/ubNm7GxsWHBggXvNbboAC8IglA6icueedi1axfz58+nU6dO7zVObGzsO+0nitzL5jLutyXn+OUcO7w+/ufpKtIevf7WjPD2ZJH8JEnim2++4dixY1StWhW1Wo2VlRURERGsW7eOrKwsmjRpwpw5c1izZg0XL15k7ty5KJVKnj59yg8//MDz58/JyMhg3rx5tGrVCnd3d7y8vGjXrh0JCQkMHz6cI0eOaI4ZEBAAwIABA9iyZUuB5jsq4ABJD8X/yQVB+J+di5yR70XRwieL5Ld//34uX77Mrl27SEtLo0+fPjx79ozdu3ezceNG9PX1WbRoEd9//z1eXl7ExMTg5eVF27ZtGTlyJKtWraJSpUps3bqV1atXs2rVqjceU6lUsmHDhgInPkEQhPyU9TNjUeReyGJjY+nWrRu6urpUqlSJzp07I0kSN2/eZODAgQBkZmbSuHHjXPtpaWmxYsUKjhw5Qnx8PLGxsWhpyfI2qSAIpUBZXhAjityLQF7F6Wq1mp49e6JUKoHsZ3Kq1bnrZ548eUL//v3p06cPbdu2pUGDBvz000+a93PGfLGLe2H4XlnwNkiCIJRtz9ML93tG7mSR/Nq3b8/333/PoEGDePbsGSdOnKBOnTocPHiQL774gkqVKuHr64uFhQXjx4/X7Hfjxg0UCgVjx45FkiSmTp2qSZAmJib8+eefWFtbc+jQoTyPq62tjUqlQkenYB+zKHUou3/dvomc45dz7CDiL26yuIbXtWtXrKyscHR05IsvvqBOnTp89NFHeHl54eHhQe/evcnKymLMmDG59mvYsCGNGjWiZ8+e9O7dGxMTE+7cuQPA6NGj+fnnn3FxceH58+d5HtfBwQFnZ2fS09OLPEZBEATh7Yki9/+X14rNd+Xj44OVlRX9+vV7p/3FmZ98//qVc/xyjh1E/KLIXRAEQRCKmCzu+eVl9erV7N27F7VaTadOnRg8eDDp6elMmDCB+Ph4LCwsCAwMxMjIiHPnzhEYGEh6ejomJib4+flRq1Yt4uPjmT17NikpKRgYGDBz5sxc/f2ePXvGZ599hqOjI0OHDn3ruYki97K9nPtN5By/nGOH7PhFMXvxkGXyi4yM5NKlS2zduhWFQoG3tzc7d+7k/v37uLu706ZNGxYsWMCKFSuYMmUKkyZNYunSpVhaWrJ3714mTZpEWFgY3t7ejBkzhm7dunHu3DkmTJjA/v37gezSCS8vL7p3716gxAeiyF0Q5EwUsxcPWSa/qKgoLly4oLkn9/z5cyRJonbt2rRp0wYAZ2dnfHx8uHHjBhUrVtSc0fXs2ZPZs2eTlpbGrVu36NYtuyyhRYsWGBkZcf36dQCWLVuGlpYWy5cvL4EIBUH4kMn1DFgUuRcxtVqNh4cHI0eOBODRo0f8888/TJ48WbONJEno6Oi80qk95720tFf/NpMkSVMK0bt3b54+fUpwcDDTpk0rokgEQSiL5LjwRRS5FwNra2uCg4MZOHAg+vr6jBs3DhcXF/766y8uX75M48aNCQsLo0OHDvzrX/8iJSWFCxcuYGlpyZ49ezAzM8PMzAxzc3MOHDigueyZnJxMvXr1gOzef/b29jg6OtKnTx8aNWr01vMTRe6CIF+imL14yDL52dvbc/XqVQYOHIharcbGxoa2bdtiYWHBihUruHXrFvXr12fixIno6emxZMkS/P39efbsGUZGRixZsgSAhQsX4uvrS0hICLq6uoSEhKCnp6c5jrGxMZMnT0apVLJ582a0tbXfan6i1EF+f/XmkHP8co4dRPzFTdT5lUIi+cn3C0DO8cs5dhDxizq/YuDu7k5MTMx7jxMTEyMa1gqCIHyAZHnZs7QTdX7yXOmWQ87xl4XYVRnpPEzNKOlpCG/wQSW/mJgYVq1aha6uLgkJCdjb22NgYKB5sHSPHj2Ii4tj0aJFAISEhKCvr//KMzsBNm/ezPz58wGYPn067dq149mzZyiVSuLi4lAoFIwaNYq+ffuSlZXFvHnziIqKQqFQ0KdPn1fGXLduHYcOHWL16tVs3LiRbdu2oaWlhaWlJX5+fgWK89bysahS773LRyQIQgn718wwQCS/0u6DSn4A58+fZ/fu3RgbG9OhQwemTZtGeHg406dPR19fn6ioKB4/foyhoSG7du1i/fr1eY5jYGBAREQEV69eZcyYMRw6dIiQkBBMTEzYtWsXDx48YMCAATRs2JAzZ85w9+5dduzYQUZGBu7u7tSvX5/y5csDEB4ezoEDB1i9ejV6enp89913nDhxAm1tbWbOnEliYiLVqlUrzo9JEARBeI0PLvnVr1+fjz/+GMhuK9S+fXsAzMzMePToEba2thw8eJCaNWtSs2bNfJNO//79gezODZUrV+b69etER0czb948ACpVqoSDgwOxsbH8+uuvuLi4oK2tTfny5XFyciIqKgp7e3v++OMPZs2axeLFi6lQoQIALVu2pH///jg4ODBy5EiR+ARBZt718m1ZuOz7PkSR+2vo6urm+vnl8gFXV1dWrlyJubm55gkunp6eJCUlAdnP9Hx5v6ysLHR0dHh54WtO0frLhe4vFrNXqFCB+fPnExgYiI2NDQYGBnz77becO3eOyMhIRo8eTVBQEFZWVoUQvSAIH4J3WbUoVnuKIvf30qZNG/755x/+/vtvZs6cCcCaNWte2W7nzp00bdqUixcv8uTJE2rVqoW1tTVbt25FqVTy4MEDDh8+rKnhi4iIwM7OjoyMDHbu3MnYsWMBqFGjBvb29hw8eJDg4GDGjBnD0KFD2bp1Ky1btuSff/4hLi6uQMnPwmtV4XwYgiAUO1WG6N/5IShzyQ/g008/JSUlJVfB+cuePn1K37590dLSYtGiRejq6jJu3Dh8fX1xcnJCrVYzduxYmjRpQv369blx4wbOzs5kZmbi5OTEp59+mqtcYurUqTg6OuLk5ISbmxv9+/enfPny1K5dG1dX1wLNX9T5ib9+5UjOsQvFr0wVuUuSRGZmJiNHjmTGjBk0adKkpKf0TkTyk+8XoJzjl3PsIOIXRe7v4d69e3Ts2JHmzZvTpEkTwsPD8fHxAbLv+yUmJua7b2EWrBdWEb0gCIJQNMrUZc+qVaty+vTpPN/L675faSWK3MWKN7kqzbE/z8wgLUXczysrSn3yU6lU+Pr6cu3aNZKTk2nQoAGTJ0/G09OTI0eOANnF7ADjx48nIiKClStXYmhoSI0aNTAwMACyH2a9fv16zMzM8i1Yf/jwIaNGjSIpKQlLS0vmzJmDnp4eR48eZenSpWRlZVGzZk38/PwwNTXNt8N7jvv37+Ph4cHXX39N165d3zrmcTtncu/pg8L6CAVBKASb3VaShkh+ZUWpT35nz55FV1eXTZs2kZWVhYeHB8ePH89z28TERIKCgoiIiMDY2JjPP/9ck/xyhIaG5luwnpCQwPLly6lVqxYTJ04kNDQUR0dHZs+eTWhoKObm5qxduxY/Pz+CgoLy7fAOkJaWxpgxY/Dy8ipQ4hMEofQq6jPT0nzmWxxEnd8L2rZti7GxMT/99BPXr1/nxo0bPH36NM9tz549S8uWLTE1NQXAycmJ6OjoXNvExMTkW7Depk0bPvnkE82+4eHhWFhYYGlpibm5OQBubm6sXr36tR3eAebMmYOpqamm07sgCB++olyQIha8iDq/XA4fPkxwcDDDhw+nX79+PHz4ECBXQbpKpUJHRweFQpHrdR2dV8N7XcH6i9vn18ldkiRUKlW+Hd5zxvL09OT48eOEhoYydOjQAsW8wimwQNsLglD0nmeK53WWJaU++UVFRdGzZ09cXV25ffs2MTExNG/enJSUFB48eIChoSEnTpzAzs6O1q1b4+/vT2JiIlWqVGHPnj1UrFgx13jW1tb5FqyfOXOGO3fuUL16dSIiIrCxsaF58+bMmTOHhIQEzM3N2bRpE+3atcu3w7uxsTGQ3cm9S5cuDB48mK5duxboEWei1EH89StHco5dKH6lPvkNGDCAKVOmsHv3bnR1dWnVqhUPHjxg9OjR9O/fn+rVq9OsWTMATE1NUSqVjBgxgvLly1O3bt1XxnNzc8u3YL1u3brMmDGDe/fuYW1tTf/+/dHW1sbPzw8vLy8yMzMxMzMjMDDwtR3ec3zyyScMHToUPz8/VqxYUSyflyAIgvBmZarI/W0kJCQwfPhwzUrR9+Hj44OVlZXmGaKFRZz5yfevfznHL+fYQcQvitwFQRAEoYiV+sue72v16tXs3bsXtVpNp06dGDx4MOnp6UyYMIH4+HgsLCwIDAzEyMgo37q9+Ph4Zs+eTUpKCgYGBsycOVOzyhPg2bNnfPbZZzg6OuLs7MykSZNITk4GYNy4cTg4OBRozqLIXSz3lqvSGrvqeToP08SCl7KkTCe/yMhILl26xNatW1EoFHh7e7Nz507u37+Pu7s7bdq0YcGCBaxYsYIpU6bkW7fn7e3NmDFj6NatG+fOnWPChAns378fgMzMTLy8vOjevTtDhw5l27Zt1KhRg9WrV3PlyhV27NhR4OT3q+dY0pNEJ3dBKC06bg8DkfzKlDKd/KKiorhw4YLmntzz58+RJInatWvTpk0bAJydnfHx8Xlt3d6tW7c09XotWrTAyMiI69evA7Bs2TK0tLRYvnw5kN3IdvHixSQmJtKlSxfGjRtX3GELglAEiuOstLSe+RYXUeReSNRqNR4eHowcORKAR48e8c8//zB58mTNNvnV8+W8l1O0/vLrOfV8vXv35unTpwQHBzNt2jQ++eQT9u7dy4kTJzh69Cj/+c9/2LNnD1pa4vaqIHzIinoxiljwIorcC421tTXBwcEMHDgQfX19xo0bh4uLC3/99ReXL1+mcePGhIWF0aFDh3zr9szMzDA3N+fAgQOay57JycnUq1cPyK7ns7e3x9HRkT59+nDmzBlu377N9OnT6dy5M3Z2djx+/PiVesPXabNGNLMVhNJE9Vw807OsKdPJz97enqtXrzJw4EDUajU2Nja0bdsWCwsLVqxYwa1bt6hfvz4TJ058bd3ewoUL8fX11XR1DwkJydUo19jYmMmTJ6NUKvnxxx+ZPHkyTk5OaGtr4+3tXaDEB6LUQfz1K8/45Ry7UPxkV+f3IRDJT75fgHKOX86xg4hf1PkVkYsXLzJz5sx833+x8e3rhISEaFooOTs7v3bbwmqOKwiCIBSuMn3Z80XNmjXTPAatsGzfvv2178fGxr7TuKLOT6x4k6vSGntGuorUR89KehpCISpTyc/JyYmlS5dSp04dJk+ejKGhIXPnzuXs2bOMHDmSZs2asWHDBn744Qe2bduGlpYWlpaW+Pn5AXDz5k3c3d25c+cO7du3JyAg4LXHa9CgAXFxcURFRbFw4UIAjIyMWLRoEd9++y2Q/WzSLVu2FCiOZQGHSX0o/kMThNJi9iLHkp6CUMjK1GVPW1tboqKiAPjjjz/47bffADhx4gRTp04FsssfvvvuO8LCwggPDyczM5PExEQA7t69S0hICHv37iUyMpJr16691XG//fZbfH19CQ8Pp0OHDly+fBmlUglQ4MQnCIIgFL0ydeZna2vLjz/+iLW1NXXr1uX69evcv3+fyMhIhg0bBoC2tjYtW7akf//+ODg4MHLkSE27oTZt2mhaEllYWGh6B76Jg4ODpmO7g4MDHTt2LJoABUEoMaLIveiJIvd31LJlS3x8fDh16hRWVlZUrlyZffv2oVKp+PjjjzXbffvtt5w7d47IyEhGjx5NUFAQkLuZbU5j3GXLlmk6QHz11Vd5HnfEiBHY2dlx9OhRFi5cyIULF/jiiy+KMFJBEIqbKHIvWqLI/T3o6OhgaWnJhg0bWLVqFVWqVGHu3Lm5Wg49ePCAoUOHsnXrVlq2bMk///xDXFwcFSpUyHPMCRMmMGHCBM3Ply9ffmWbAQMGMHfuXEaMGIGxsTGHDx8Gss8yc7rMF8QEZcGeBSoIQtHKSFeV9BSEQlamkh9kX/o8ffo0derUoUqVKty/f58uXbqQkZH9UNpKlSrh5uZG//79KV++PLVr18bV1ZV9+/a98zEnTZqEj48POjo6GBgYaBbKODg44OzsTHh4OPr6+m89nqjzE3/9ypGcYxeKnyhyL4VE8pPvF6Cc45dz7CDiF0XuRczd3Z2YmJhcRe85rwmCIAjyUOYue76toih6LyyiyF2seJOrkow9Iz2d1EeiZ59cfDDJLyYmhlWrVqGrq0tCQgL29vYYGBhw6NAhAHr06EFcXByLFi0Csh9Dpq+vz5gxY/Idb/ny5WzYsAGAzZs3M3/+fACmT59Ou3btCAkJ4dy5c9y9e5dhw4ZRt25dlixZwvPnz3n06BHTp0+na9eu+Pj4kJKSws2bN5k8eTJr165l48aNQPZj086fP8/cuXPfOtZ1C6aQlnL/nT8rQRAKzmv+D4BIfnLxwSQ/gPPnz7N7926MjY3p0KED06ZNIzw8nOnTp6Ovr09UVBSPHz/G0NCQXbt2sX79+rce28DAgIiICK5evcqYMWM0STUjI4M9e/YA2aUOAQEB1KlTh6ioKObNm0fXrl2B7M4Oq1atQpIk5s+fz61bt7CwsCAiIiJX/0BBEEqvkj7rLunjlzRR55eP+vXra+r1TExMaN++PQBmZmY8evQIW1tbDh48SM2aNalZs6ameP1t9O/fH4CGDRtSuXJlTaf2nM7ukN3a6OjRo+zbt4/z58/z5MkTzXs52ykUClxcXNixYwf9+vXj/v37NG/e/P0CFwShWJTkghOx4EXU+eVLV1c318/a2tq5fnZ1dWXlypWYm5travs8PT1JSkoCYPXq1fmO/eJYWVlZmtq8cuXKaV4fMmQI7dq1o127drRv354pU6Zo3ntxOxcXF0aPHo2ent4bOz8IgiAIxe+DSn5v0qZNG/755x/+/vtvzUrONWvWvNW+O3fupGnTply8eJEnT55Qq1atXO+npKRw48YNfv75Z/T09AgKCkKtVuc5Vo0aNahevTobN24kNDS0wHF4TAsq8D6CILyfjHTRrV1OylTyA/j0009JSUnJ1Wn9bTx9+pS+ffuipaXFokWLXjnLNDY2pn///vTu3RsdHR2sra15/vw5T58+zXO8Xr16ceDAgQJdes0h6vzEpR85knPsQvErE0Xu4eHhxMTE4O/vT8eOHVm8eDE2NjZ5bvvyKs/34e7ujpeXF+3atcv1ukqlYurUqfTo0YNu3boVeFyR/OT7BSjn+OUcO4j4RZH7O3r+/DkdO3ZkwIAB+Sa+4iBJEjY2NigUCs1KUEEQBKF0KTWXPVUqFb6+vly7do3k5GQaNGjA5MmT8fT01HRVCAkJAWD8+PFERESwcuVKDA0NqVGjBgYGBpw+fRp7e3uGDBmCmZkZ8+bNIyoqCuru3kQAAB1kSURBVIVCQZ8+fTQ1fw8fPmTUqFEkJSVhaWnJnDlz0NPT4+jRoyxdupSsrCxq1qyJn58fpqamnDt3jsDAQNLT0zExMcHPzy/XPcH79+/j4eHB119/TdOmTalTpw7x8fEMHDgQpVJJixYtCvRZiCJ3sdxbroozdlWGioepomm0XJWa5Hf27Fl0dXXZtGkTWVlZeHh4cPz48Ty3TUxMJCgoiIiICIyNjfn8888xMDDItU1oaCh3795lx44dZGRk4O7uTv369SlfvjwJCQksX76cWrVqMXHiREJDQ3F0dGT27NmEhoZibm7O2rVr8fPzIygoiEmTJrF06VIsLS3Zu3cvkyZNIiwsDIC0tDTGjBmj6ee3fPlyunTpwujRo4mMjOTMmTMFTn7xq0+jeiRuvgtCUao3pVNJT0EoQaUm+bVt2xZjY2N++uknrl+/zo0bN/JdTHL27FlatmyJqakpAE5OTkRHR+faJiYmBhcXF7S1tSlfvjxOTk5ERUVhb29PmzZt+OSTTzT7hoeHY2FhgaWlJebm5gC4ubmxevVqbty4QcWKFTV1fD179mT27NmkpWVfm54zZw6mpqaae3vt27dn/PjxXLlyBVtbW00TXUEQSp/SdpZd2uZT3GRZ5H748GGCg4MZPnw4/fr103RRf3E9Tk5vvJxGszny6peXlZWV62dJkjSlCS9uL0kSOjo6eW7/f+3de1wU9f748deygCKooECEmmmiKYp3QS0IzAsKqYihmZdTSXhErdRERc27FEki54hdHpYcJU0uipJpkonKRU2OleShvlKQihcEbyjLMr8/fLg/TSyx5Trv51+w7M583jPDvndmP+95l5WV3ff4H5c1efJkvv32W2JjYxk3bhw9e/Zk165d7N+/n+TkZBISEtiwYUNlN4cQohrUpgkmMuFFpUXuaWlpeHt7M2rUKPLy8sjIyKBr164UFRVRWFiIlZUVqampeHp60rNnT5YuXUpBQQF2dnYkJyfTpEmTe5bn5uZGYmIinp6elJaWkpSURFBQEADHjh3jzJkzODg4kJiYyLPPPkvXrl1ZtGgR+fn5tGzZki1btuDq6krbtm0pKirixIkTuLi4kJycjKOjI9bW1gB07NiR5557jrFjx/L888/z2Wef8dhjjzFx4kRcXV0ZOXJkpbdFm8Def3+DCiH+VFmpNKhVs1qT/EaPHs2sWbPYtWsXZmZm9OjRg8LCQl577TX8/f1xcHAwdGGwtbUlNDSUSZMmYWFhQbt27e5bXkBAALm5uQwfPhydToevry8DBw4kIyODdu3aMW/ePC5cuICbmxv+/v5otVqWLFlCcHAwOp0OR0dHli9fjrm5ORERESxdupSSkhKaNm1KRETEPet68sknGTduHEuWLCE0NJSZM2cSHx+PVqslLCys0ttCSh3k068aqTl2Uf3qRZ1ffSPJT71vgGqOX82xg8QvdX414O7GtkIIIeq/WnPZsybV5sa2QgghjE+SHw++5VlFRe87duzg0qVLzJ49m4MHDzJ9+nQyMzMxNTXF29ubmJgYXnzxRV544QUOHjxISUkJYWFhdO7c+aHHI0XuMt1braoi9tJSHcXFN42+XFG3SfJ7gEuXLlVY9D5jxgxmz54NQHp6Og0bNuTkyZPY2NjQuHFjQ+2htbU127ZtIyYmhvXr1xvuTvMwPvroI65cuVIlcQmhNrebSUvyE/eS5PcAd0ob/lj0HhkZybVr1yguLubo0aO89NJLZGZmYmFhgYeHh+H1d+4v6uTkxJ49e2okBiHEbXXlbLqujLOqqLLIvaadP3/e0HjW3t6eMWPG3PP3O0XvcDux7d27F41Gg5eXF2vWrEGj0TB9+nTD8xs0aADc7uwuhKhZdWEWpcz2VGmRe02zt7e/5zu/ixcvVlj0DuDh4cHSpUsZPHgwHTt25JdffkGr1dKpUyejjGXy5MlGWY4Q4vZ3fkL8kSS/B7C1ta2w6B3A1dWVCxcu0KdPHzQaDR07dsTGxsZo65Y6P/n0q0Zqjl1UPylyr4Uk+an3DVDN8as5dpD4pci9llu7du1fztz08vIiPz+fvLw85s2bV00jE0II8bDksmcVOnPmDHl5eZV+ndT5yYw3tTJm7GW6Ui4XSV9MUTFJfnepqJv86tWr+c9//sPWrVuxsbG5p7dfhw4dOHXqFADx8fFkZmayatUqw/KWLVtGfn4+ixcvZtGiRQ89ju8PrKD05mXjBieEyvQc9B4gyU9UTJLfXSrqJr9x40YSEhJISEhAo9EQEBBgSH5/JTQ0lKioqEolPiGE8dS1s+i6Nl5jkzq/GlJRN3lXV1c8PDywtLQEYMiQIRU2uBVC1D51aQKJTHiRCS81Zt++fcyaNYuGDRvi5+dH7969adSo0Z92jb/ztzsF8EIIIWo/OfO7S0Xd5Dt37sw333xDcHAw5ubm7N2713AbMxsbG3JycnByciIlJcXQ3f0OrVb7SEmxi7vMEBXi7yrTldb0EEQtJsnvLhV1ky8uLmbixIn4+/vTpEkTHB0dDc+fOXMmQUFB2Nra0rNnTy5fvneSylNPPcXVq1eZPXs277333kOPQ+r85NKPGqk5dlH9pMi9FpLkp943QDXHr+bYQeKX7/zqkPj4eEJCQoDb9+MsKCio4REJIYR4GHLZ00g++ugjoy1LitxlurdaGSP2Wzo9V4puGGE0oj5TffKrqLB95syZTJ48mZSUFADD7cymTZtGYmIi69atw8rKihYtWtCoUSPg9i3NNm7ciKOjIytWrCAtLQ2NRsMLL7xAYGBgpcYU8s0PXCqRL+uFeBQfDe1R00MQdYDqk19Fhe3ffvtthc8tKCggPDycxMRErK2tef311w3J747Y2FjOnj3Ljh07KC0tZfz48bRv357nnnuuGqIRQkDdPXuuq+M2Filyr0YVFbbfuFHxJZPjx4/TvXt3bG1tAfD19SU9Pf2e52RkZDBy5Ei0Wi0WFhb4+vqSlpYmyU+IalQXJ47IhBdpZlut9u3bR2RkJBMmTMDPz89QrnD3JNiysjJMTU3RaDR/WvAO3Hf3F0VR0Ov1lRrTKs/OlXq+EOL/u6Wr3P+bUCfVJ7+KCtu7du1KUVERhYWFWFlZkZqaiqenJz179mTp0qUUFBRgZ2dHcnIyTZo0uWd5bm5uJCYm4unpSWlpKUlJSQQFBVVqTFLqIJ9+1UjNsYvqp/rkV1Fhe2FhIa+99hr+/v44ODjQpUsX4HZ399DQUCZNmoSFhQXt2rW7b3kBAQHk5uYyfPhwdDodvr6+DBw4sLrDEkII8SfqXJH73LlzyczM5M0338THx6da1hkbGwvA2LFjmTt3LsHBwbRo0aLK1idnfur99K/m+NUcO0j88p3fX0hISODEiROYm5tX2zrHjh1r+DkjI4OpU6dW27qFEEIYX51KfkFBQSiKwujRo+nWrRvZ2dkUFxdjb29PREQEycnJ/PrrryxYsACAVatW4eDgQEBAAKGhoZw6dQqNRsOrr77KiBEj7mtAO378eIKDgwF47733KC8vx8nJiZYtWwLQoEEDzp8/T2BgIJs2bSIvL4+VK1dy8+ZNbGxsWLx4Ma1atWLDhg0kJCRgYmKCi4sLS5YsqVScUuQu073V6lFiv3mrjKtXSqpgNKI+q1PJLzo6mg4dOhAZGUl4eDiff/45JiYmvP322+zYsYMRI0YwYsQI5s2bh4mJCXv27GHLli2sXbsWGxsbdu7cSWFhIaNHj+bpp5/+03Xl5ubyzTff0LhxY0ORe2BgIJ9//jkffvghlpaWhIaGEh0djaOjI6mpqSxYsIBPPvmE9evXk5qailarZf78+RQUFPDYY489dJyvLtvD+cvyzyzEw0h6fzjqvVgoHlWdSn53tG7dmjlz5vDFF19w+vRpsrKyeOKJJ2jWrBlPP/00GRkZmJmZ0aZNG+zs7EhPT2fFihUANGvWjAEDBpCZmYmV1YPPsNq0aUPjxg/+FJqbm0teXh5TpkwxPHbt2jW0Wi3du3fH39+fAQMG8I9//KNSiU8IUXn15Wy5vsTxqKTI/S/88MMPzJw5k0mTJjF48GBMTEwM9XfDhw8nOTkZMzMzfH19gXtr9u78rtfr76vb0+l0hp8bNmz4p2MoLy+nZcuWbN++HQC9Xs/FixcB+Pe//01WVhYHDhzgtddeIzw8nD59+vz9wIUQFaoPE0VkwotMePlLR44coU+fPowdO5bLly+zf/9+Bg0aBMCAAQOIjIxEURRmzZoF3K6927ZtG6GhoRQWFrJv3z7Wrl3LhQsX+OWXX1AUhfz8fE6dOvWX69Zqtej1etq2bUtxcTFHjx6lV69exMXFkZSUxJo1axg3bhzbtm2je/funDt3jlOnTlUq+X0SOujRNowQKnTzVuUbRgtRJ5Pf0KFDCQ4ONpzZde7cmfz8fOD2GVuPHj0oLS3F0tISgKlTp/LOO+/g6+uLXq8nKCgIZ2dnSktLiYuLY8iQIbRp04aePXv+5bqfe+45AgMD+fjjj1mzZg3Lly/n1q1bWFlZERYWRrNmzQgICMDf3x8LCwvatGnDqFGjKhWflDrIp181UnPsovrVuTo/NZDkp943QDXHr+bYQeKXZrZ11N2NbYUQQtRudfKyZ30ndX4y460+kTo8URupKvllZGSwfv16GjZsyC+//EKHDh0IDw8nKSmJDRs2oNFocHZ2ZsGCBcTFxVVYMD969GiWLFlCTk4Oer2eyZMn33ebtbCwMA4dOoSJiQnPP/+8oXD+YUmdn6hPpA5P1Eaqu+x5/PhxFi5cyJdffsmZM2fYsmUL0dHRxMTEkJSUhIWFBVFRUfj4+LB37170ej2KorBnzx6GDRvGunXrcHZ2Jj4+nk2bNhEdHU1eXp5h+b///jsHDhxgx44dxMbG8vPPP3Pr1q0ajFgIIcQfqerMD8DJyQkHBwcAnnrqKYqLi/H09MTGxga43ZVh7ty5zJkzp8KC+cOHD3Pz5k3i4uIAuHHjBjk5OYblP/bYYzRo0IAxY8bg6enJrFmzaNCgQfUHKkQt8rCXcuvbJd/KkvilyL3K3J2INBoNTZo04cqVK4bHFEWhrOx23VBFBfPl5eW89957ODs7A3Dx4kWaNm1KUlIScLvB7RdffEFmZiYHDhxgzJgxxMTE0KZNm+oKUYha52Fm8clsR4lfityrWUpKCv/85z+xtrZm69atuLq6Ag8umI+NjWXZsmWcP3+eESNG8PnnnxuWdfLkSZYuXUpMTAx9+/bl5MmTnD59ulLJT4rcRX0iReiiNlJ98rOysuL1119n/Pjx6HQ6nJ2dWbx4MVBxwXxwcDDvvPMOPj4+6PV6Zs+ezRNPPMHRo0cB6NSpE926dcPHxwcLCwt69OiBu7t7pcYkdX7y6VcIUbWkyL0WkuSn3jd/Ncev5thB4pci9zru+++/Z/78+QBs3bqVnTt31vCIhBBC/JHqL3saW5cuXejSpQsA33333SN1c5Aid5nxVleVld7icnFpTQ9DiL+k2uQXHR3Njh070Gq19O/fn9zcXMaOHYuHhwerV6/m5MmTfPzxx5w/f55XXnmF6OhogoODcXJyIjs7m+bNm7NmzRqsra3vWW5GRgZRUVFMmTKFlJQU0tPTsbOz49lnn33osf0WFURZ8QVjhyxElWs7Pw6Q5CdqP1Umv2+//ZaUlBTi4uIwMzNj2rRpPPvss6Snp+Ph4cHRo0c5d+4cer2e1NRUPDw8APjpp59YsWIFnTp1Ytq0aSQlJTF+/PgK19GvXz+8vLzo06dPpRKfEHXd3zlzrctnvcYg8UudX5VKT09n2LBhWFhYADBq1Cg+++wzrl69yrVr1wDo0KEDP/74IwcOHDAkuObNm9OpUyfgdrF8cXFxzQQgRC32qJMWZMKHxC91flWsvLz8vscsLS0pLi5mz5499OjRA1tbW9LT0/nxxx/p3r07Z8+eva9AXlEU9u3bR2RkJABeXl64ubn97fE9ERz9t5chRE0oK5Vb+Ym6QZXJz83NjXXr1hEQEICpqSlxcXG4ublx8eJF1q1bx4IFC7C3t2fy5Mn06dMHrVb7wGUNGDCAAQMGGH7PyMgw/Hyn63tlSamDfPoVQlQtVSY/T09PsrOzGTVqFGVlZTzzzDO8/PLLZGVlsWHDBnr27EmjRo3Q6XR4eno+8nr69evH6tWrady4MUOGDDFiBEIIIf6OWl/kfuLECb766itmz55d00OpNnLmp94zHzXHr+bYQeKXIvc/+Pnnn7l06VJND0MIIUQ9UqWXPRVFITw8nK+//hqtVktAQADu7u4sXLiQoqIiGjVqxPz583FxcSEkJAQrKyt+/PFHCgoKmDp1KgMHDiQyMpIbN26wbt06AgMDeffdd8nMzESv1+Pn58ekSZPIyMggOjoaMzMz8vPz8fLyolGjRnz99dcAfPjhh9ja2tK3b18GDhzI8ePHsbS0JDw8nJYtW5KVlcXy5cu5desWNjY2LFmyhNatWzN+/HiaNm1KTk4OH3zwAceOHWP79u2UlJRgZmbG+++/T9u2bfHy8sLFxYXs7Gzc3NywtrbmzTffBCAkJAR3d3eGDh360NtNitxlundtdlNXytUimdgi6rYqTX67d+/mu+++IykpCZ1Ox0svvcTmzZuZOXMmgwYNIisrixkzZvDVV18BcO7cOTZv3sz//vc/JkyYwKhRo5g+fTqZmZlMmTKF2NhYABISEigtLeXVV1+lc+fOAPz3v/9l165dWFtb069fP+bMmUN8fDxz585l165dTJw4kcLCQrp3786SJUuIiYlh2bJlREZG8tZbb/HBBx/g4uLCl19+yVtvvWXo19ehQweioqK4du0aYWFhxMTE0LBhQ9asWcOmTZsMnd7d3d354IMPyMvLY+LEibzxxhvcvHmT9PR0w42yH9bUpPlcuFForN0ghFFtDVjHVST5ibqtSpPfkSNH8Pb2xtzcHHNzczZv3oynpyeDBt1u2dOtWzeaNm3K//3f/wHQv39/NBoN7du3p6io6L7lpaWlkZ2dTXp6OnC7keypU6do164d7du35/HHHwfAxsaGvn37AuDo6Gjo19egQQNGjBgBwMiRI1m9ejW5ubk0adIEFxcXALy9vVm4cCFXr96+9nzncSsrK95//3127dpFbm4uqampdOzY0TC2rl27AtCqVStatGjBkSNHOHPmDB4eHtLMVtQ7VXV2WtvPequaxF9PitxNTU3RaDSG3/Py8vjj/BpFUQzlAHeSxN2vududFkJ3kmdhYSGWlpZkZWVhZmZ2z3MrKk8wMTExLLu8vBytVlthzd/dY2rYsCEAZ8+eZfz48bz88su4u7tja2tLdna24TV3J7hRo0axc+dOzpw5w7Rp0yqMRYi6rComZsiED4m/3hS59+7dm40bNzJmzBjKysp444030Gg07Nmzx3DZ8+LFizg5OT1wGVqt1tBZ3c3Nja1bt+Lp6UlpaSkvvfRSpS4plpSUkJKSgpeXF/Hx8bi7u9O2bVuKioo4ceIELi4uJCcn4+joeN89O7///ntat27NpEmTuHnzJpGRkTg4OFS4niFDhvCvf/0LS0tLwxlhZfzLd3mlXyNEdbmpk3t3irqvSpPfwIED+eGHH/Dz86O8vJwJEybg6urKO++8w9q1azEzM2Pt2rWYm5s/cBkuLi5ERUURHh7OjBkz+PXXXxk5ciRlZWX4+fnh6up6T2H5X9m9ezcRERHY29sTFhaGubk5ERERLF26lJKSEpo2bUpERMR9r+vfvz+xsbEMHToURVHo3bs3OTk5Fa6jYcOGdOvWjfbt2z/0uO52+fJ11ZY6NG9uxaVL12p6GDWmrsRvYlLx1Znauty6QuJ/tPgf5XW1vs7PmDp06MCpU6eqdB2KonD9+nUCAgL49NNPsbOzq9L1CSGEqLxaX+dX13z//fd4eXnx4osvSuITQohaSlVnfkIIIQTImZ8QQggVkuQnhBBCdST5CSGEUB1JfkIIIVRHkp8QQgjVkeQnhBBCdST5CSGEUB1JfrVEUlISQ4cOZdCgQWzatKmmh2NUUVFRDBs2jGHDhvHuu+8CcPjwYXx9fRk0aNA9t5PLzs7Gz8+PwYMHM3/+fMN9Xc+cOcO4ceMYMmQIU6ZM4fr16zUSy6MKCwsjJCQEqHyMV65cITAwEG9vb8aNG8eFCxdqLI5HkZKSgp+fH97e3ixbtgxQ1/7fvn274fgPCwsD6v8xcO3aNXx8fMjPzweMt7+Nuh0UUePOnTuneHp6KpcvX1auX7+u+Pr6Kjk5OTU9LKM4dOiQEhAQoNy6dUspLS1VJkyYoCQlJSkeHh7Kb7/9puh0OuWVV15R9u/fryiKogwbNkw5fvy4oiiKMnfuXGXTpk2KoihKYGCgsnPnTkVRFCUqKkp59913ayagR3D48GHF1dVVmTNnjqIolY9x8eLFyvr16xVFUZSEhARlxowZ1R3CI/vtt9+UZ555Rjl79qxSWlqqjB07Vtm/f79q9v+NGzeU3r17K5cuXVJ0Op3i7++vHDp0qF4fA1lZWYqPj4/i7Oys5OXlKSUlJUbb38bcDnLmVwscPnzY0AG+UaNGDB48mN27d9f0sIzCzs6OkJAQzM3NMTMz46mnniI3N5fWrVvTqlUrTE1N8fX1Zffu3fz+++/cvHmTbt26AeDn58fu3bvR6XQcOXKEwYMH3/N4XVBUVERERARBQUEAjxTj/v378fX1BcDHx4cDBw6g0+lqIJrK27t3L0OHDsXBwQEzMzMiIiKwsLBQzf7X6/WUl5dTUlJCWVkZZWVlmJqa1utjYOvWrSxatAh7e3sATpw4YbT9bcztIMmvFjh//vw99wG1t7enoKCgBkdkPE5OToaDOzc3ly+//BKNRlNhvH/cDnZ2dhQUFHD58mWsrKwwNTW95/G6YOHChbz55ps0adIEuH9fP0yMd7/G1NQUKysrCgsLqzmSR/Prr7+i1+sJCgpi+PDhbN68+YHHe33c/1ZWVsyYMQNvb288PDxo0aIFZmZm9foYWL58Ob169TL8bsz9bcztIMmvFigvL7+nga+iKA9s6FtX5eTk8Morr/D222/TqlWrCuN90HaoaHvUhe3zxRdf8Pjjj9O3b1/DY8aIUVEUTEzqxr+uXq8nLS2NFStWsGXLFk6cOEFeXp4q9j/ATz/9RFxcHN988w2pqamYmJhw6NAhVR0DD9qvNf2/UKX9/MTDcXBw4OjRo4bfL1y4YLhkUB8cO3aM6dOnM2/ePIYNG0ZmZuY9X1TfidfBweGexy9evIi9vT3NmjXj6tWr6PV6tFptndk+ycnJXLhwgeHDh1NcXMyNGzfQaDSVjtHe3p6LFy/i4OBAWVkZ169fv6/Zcm1la2tL3759adasGQDPP/88u3fvRqvVGp5TX/c/wMGDB+nbty/NmzcHbl/C++STT1R1DPxxv/6d/W3M7VA3PjrUc/369SMtLY3CwkJKSkrYs2cP7u7uNT0sozh79ixTp04lPDycYcOGAdC1a1dOnz5tuCS2c+dO3N3dadGiBQ0aNODYsWPA7Vly7u7umJmZ0atXL5KTkwFITEysE9tnw4YN7Ny5k+3btzN9+nS8vLxYuXJlpWP08PAgMTERuJ1Qe/XqhZmZWc0EVUmenp4cPHiQK1euoNfrSU1NZciQIarY/wBPP/00hw8f5saNGyiKQkpKCn369FHVMWDM/3djbgdpaVRLJCUlsX79enQ6Hf7+/kyePLmmh2QUy5YtIy4ujieeeMLw2JgxY3jyySdZuXIlt27dwsPDg7lz56LRaPjpp58IDQ3l2rVrODs7s3LlSszNzfn9998JCQnh0qVLPP7446xevZqmTZvWYGSVEx8fT2ZmJqtWrap0jEVFRYSEhJCXl0fjxo0JDw+nZcuWNR3SQ9u2bRuffvopOp2O/v37ExoaSkZGhmr2/4cffkh8fDxmZmZ06dKFRYsWcfr06Xp/DHh5ebFx40ZatmxJWlqaUfa3MbeDJD8hhBCqI5c9hRBCqI4kPyGEEKojyU8IIYTqSPITQgihOpL8hBBCqI4kPyGEEKojyU8IIYTqSPITQgihOv8PgKpFbH9hc6oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "book_tags_df['tag_name'].value_counts()[:25].plot.barh(title = \"Most Frequently Used Tags\").invert_yaxis()\n",
    "# TODO: figure out how to plot this in Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon eyeballing this graph, it looks like there are a few common themes in the most frequently-used tags:\n",
    "- \"to-read\", \"to-buy\", \"wish-list\" express the intention of purchasing and reading the book\n",
    "- \"i-own\", \"owned\", \"books-i-own\", \"owned-books\", \"own-it\", \"library\", etc. all refer to already owning the book\n",
    "- \"favorites\", \"favourites\" are among a user's most-loved books\n",
    "\n",
    "These could all be useful from a business standpoint. For example, let's say the user's Goodreads account is linked up with her Amazon account. Amazon could include a feature whereby it pulls in data from the user's \"to-read,\" \"to-buy\", \"wish-list\" tags and creates an Amazon wishlist including these books. The wishlist could even be dynamically updated anytime she adds a new book, and Amazon can use the data to retarget her with these books all over Amazon and the rest of the web. \n",
    "\n",
    "\"i-own\" and similar tags could all be used to downweight recommendations of these books on the Amazon website and elsewhere, since presumably a user is not interested in buying a book she already owns, unless as a gift.\n",
    "\n",
    "\"favorites\" could be given special weight in recommending new books to the user, since she presumably most enjoys books with characteristics similar to this one. In this case, it may be useful to see if a book being tagged under \"favorites\" is correlated with higher ratings from the user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be continued!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}