movielens_movies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "PATH_MOVIES  = 'Material/introducao-a-data-science-aula0/aula0/ml-latest-small/movies.csv'\n",
    "PATH_RATINGS = 'Material/introducao-a-data-science-aula0/aula0/ml-latest-small/ratings.csv'\n",
    "\n",
    "df_movies    = pd.read_csv(PATH_MOVIES)\n",
    "df_ratings   = pd.read_csv(PATH_RATINGS)\n",
    "\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9209302325581397"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Toy Story (movieId == 1) rating mean\n",
    "df_ratings.query('movieId==1').rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4318181818181817"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# movieId == 2 mean\n",
    "df_ratings.query('movieId==2').rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>userId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>306.530233</td>\n",
       "      <td>3.920930</td>\n",
       "      <td>1.129835e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>329.554545</td>\n",
       "      <td>3.431818</td>\n",
       "      <td>1.135805e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>283.596154</td>\n",
       "      <td>3.259615</td>\n",
       "      <td>1.005110e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>219.857143</td>\n",
       "      <td>2.357143</td>\n",
       "      <td>8.985789e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>299.571429</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>9.926643e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userId    rating     timestamp\n",
       "movieId                                    \n",
       "1        306.530233  3.920930  1.129835e+09\n",
       "2        329.554545  3.431818  1.135805e+09\n",
       "3        283.596154  3.259615  1.005110e+09\n",
       "4        219.857143  2.357143  8.985789e+08\n",
       "5        299.571429  3.071429  9.926643e+08"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All means grouped by movieId\n",
    "all_means = df_ratings.groupby('movieId').mean()\n",
    "all_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    3.920930\n",
       "2    3.431818\n",
       "3    3.259615\n",
       "4    2.357143\n",
       "5    3.071429\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All movies mean (only column 'rating')\n",
    "rating_means = df_ratings.groupby('movieId').mean()['rating']\n",
    "rating_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rating_means.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='rating'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.boxplot(x=rating_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9724.000000\n",
       "mean        3.262448\n",
       "std         0.869874\n",
       "min         0.500000\n",
       "25%         2.800000\n",
       "50%         3.416667\n",
       "75%         3.911765\n",
       "max         5.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_means.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jeanl\\AppData\\Local\\Temp\\ipykernel_8380\\3397267953.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(rating_means)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='rating', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERESbrpfL5YiJiUFSUpLpmMFgQFJSkqkl6M9GjBiBzMxMGAwG07GMjAz4+/sz2BAREREAM8NNTU0NFixYgOHDhyMsLAy9evVq9mqrxMRErF69Gp9//jnOnj2LJ554AjU1NZg9ezYAYMaMGZg/f77p/CeeeALl5eV4+umnkZGRgW3btuHNN9/E3LlzzfkyiIiISITM6pZ65JFH8Ouvv+Khhx6Cv7+/aVuG9po6dSpKSkqwcOFCqNVqREdHY8eOHaZBxrm5uc1ahoKCgrBz5048++yziIyMRI8ePfD000/j+eefN+v5REREJD5mhZsff/wR27Ztw4gRIzpcwLx58zBv3rwW39uzZ881x+Lj43Hw4MEOP5eIiIjEyaxuKQ8PD3h6elq6FiIiIqIOMyvcvPbaa1i4cGGz/aWIiIiIrIFZ3VJLlizBhQsXoFKpEBISAnt7+2bvp6amWqQ4IiIiovYyK9xMmTLFwmUQERERWYZZ4aa11YCJiEhYukYD7GUSs2exEomBWeEGACoqKrBp0yZcuHAB//jHP+Dp6YnU1FSoVCr06NHDkjUSEdF1XK7VYccpNfLKa1FR1wBfVwWmDg2Cv9JR6NKIBGFWuDlx4gQSEhKgVCqRnZ2NOXPmwNPTE5s3b0Zubi7WrVtn6TqJiKgFl2t0+Gz/RZRW60zHiqu0WLnnAu6MCkBsCGe2Uvdj1mypxMREzJo1C+fPn4eDg4Pp+KRJk7B3716LFUdERK2rb9Dj0S+OorRaB3dHezw8MhR/v60P+qhc0GgwYvOxfGQUVQldJlGXMyvcHDlyBI899tg1x3v06AG1Wt3hooiI6MZe33YGR7Ivw8FeipnDQ9DbxwVeLgrMiA/B0CstNlvS8lGjbRS4UqKuZVa3lEKhgEajueZ4RkYGfHx8OlwUEYnP+kO5NzxnelxwF1QiDjllNfj6cB4AYNrQYKjcfm9Fl0okmDTID+eLq1BR24AluzKwcHJ/oUol6nJmtdzceeed+Ne//oWGhgYAgEQiQW5uLp5//nnce++9Fi2QiIiu9eHPmdAbjLiljw/CVa7XvK+wk2FKdNPkjs8OXMSJSxVdXCGRcMwKN0uWLEF1dTV8fHxQV1eHW265BWFhYXB1dcUbb7xh6RqJiOgPcspqsPlYPgDg6YTwVs/ro3JFVKASRiPwftL5riqPSHBmdUsplUrs3r0b+/fvx/Hjx1FdXY0hQ4YgISHB0vUREdGf/LHVZkiwB84Vtj5oeGyECifyK/HT2WJkFFWhTwutPERi0+5wYzAYsHbtWmzevBnZ2dmQSCQIDQ2Fn58fjEYjF44iIupEpdVafNeGVpurvF0VmDDADz+eUmPVrxew9IHoTq6QSHjt6pYyGo2488478cgjjyA/Px+DBg3CgAEDkJOTg1mzZuHuu+/urDqJiAjAlmP5aDQYERXkjiHBHm265vFbegMAtqYVIL+irjPLI7IK7Qo3a9euxd69e5GUlIRjx47h66+/xoYNG3D8+HH89NNP+Pnnn7mAHxFRJzEajdiUcgkAcF9MYJuviwpyx/DeXmg0GPHpbxc7qzwiq9GucPP111/jxRdfxJgxY65579Zbb8ULL7yAr776ymLFERHR704XaHBOXQW5nRR3Rga069pHb+4FAPg2JQ+1uq5b9+ZQVhmW7s7AB0nn8c3RPDTqDV32bOq+2jXm5sSJE3jnnXdafX/ixIl4//33O1wUERFd69ujTevajOuvgtLJvl3X3hzug2BPJ+SW1+KH44V4YGhQZ5TYzBcHc7Dw+1MwGn8/NiDADdOGBUPK8ZnUidrVclNeXg6VStXq+yqVCpcvX+5wUURE1Jy2UY/vjxcAAO6PbX8wkUolpkUSvzqUY9HaWvLhz+exYEtTsBkb4YupsUGQy6Q4XaDBrtNcyZ46V7vCjV6vh51d6409MpkMjY1c5puIyNJ+TS9BRW0DVG4KjAzzNuse98cEQi6T4vilSpy8VGnhCn+345Qa/96VAQD4261h+GRmLN6+LxLv3BcJANh7vhSpOfwgTJ2nXd1SRqMRs2bNgkKhaPF9rVZrkaKIiKi5H081tXbcPigAMql5XTpeLgpMHOSH79MK8NWhHLwVGGnJEgEAVfUNWLT1FICmcT6J4/qa3psyuAe+TyvAL+nF+OlsEaKD3dk9RZ2iXS03M2fOhK+vL5RKZYsvX19fzJgxo7NqJSLqlnSNBvx0tggAMGmQX4fu9WBcTwDA92kF0NQ3dLi2P3t3ZzqKNFqEeDkh8bY+17w/uq8PHO1lqKhruO7ig0Qd0a6Wm88++6yz6iAiolbsv1CKqvpG+Loq2ry2TWuGhnigj8oFGUXV+C41HzOHh1imSABpeRX44mDTeJ437h4EB3vZNefYy6QYGuKBvedLcfBiGfoHuFns+URXmbW3FBERdZ0fTxYCACYM9IPUzC6pqyQSian15qtDOTD+cSpTBxiNRry5/SyMRuCewT0w4jrjguJCvSABkFlcjZIqDmcgy2O4ISKyYo16A3afaeqSmjCwY11SV909pAcc7WXIKKrGkWzLDOz97XwpDl8sh9xOin9M6Hvdcz2c5ejr17TH1cGLZRZ5PtEfMdwQEVmxQxfLcbm2AZ7OcgwL8bTIPd0c7HFXdNMigJaYFm40GvHuznQAwIybesJf6XjDa+J7eQEAjuVeht5gmdYjoqsYboiIrNj2K11S4weoYCez3I/sq11TP55Uo7S6Y11DO0+rcTK/Es5yGZ4Y3btN1/T2dYGTXIb6BgNyy2s79HyiP2O4ISKyUnqDETtPX+2S8rfovQcFKhEVqIROb8AXyea33ugNRtOaNg+PDIWXS8tLhfyZVCJBuK8LACCjiLOmyLIYboiIrNTR7HKUVmuhdLTH8N5eFr//nCv7Ta1LzkadTm/WPbYcy0dmcTWUjvZ45Mr92qqPqmnczXmGG7IwhhsiIit1deG+hH4q2FuwS+qqCQP8EOTpiMu1DdiUktfu63WNBvznp6ZWmydG94abQ/v2uwq/Em4KKutR1Qlr7lD3xXBDRGSFDAYjdl7Zg6mjC/e1xk4mxSMjm1pbPtl3sd0DezceycWly3XwcVVgZnxIu5/vorBDD/emwcfni6rbfT1RaxhuiIisUNqlChRW1sNFYYeR4ebtJdUW98cGwt3JHjlltabBy22hqW/Ae0mZAJr2j3KUX7tgX1v0UTWNu0ln1xRZEMMNEdENXK7VIelsEZbsSseSXek4dLEMukZDpz5zx5UuqbH9fKGwMy84tIWT3A6zh4cCAN7ecQ71DW0be7N0VwZKq7UI9XbG1KHBZj//6ribzOJqGCy0oCARww0R0XXklNXgP7szkHSuGGU1OpTV6PB9WgHGLt3TaQNhjUYjtp1oakWZaKGF+65nzs2hCFA64NLlOnz0a9YNzz9xqQKfJ2cDAF67ayDkdub/Kgn0cIKDvRR1DXrkX64z+z5Ef8RwQ0TUilptIzYcyUOjwYhAD0c8EBuIOyL94aqwQ155HR7+/Cgu1+gs/tzU3ArkV9TBRWGH0X19LX7/P3OS2+HF2/sBAFbsycSly62vO9OoN+Cl707BaASmRAd0uMtMJpUgxMsZALjeDVkMww0RUQuMRiM2pV5CZV0DvJzleHhEKKKDPDC8tzf+NjYcQZ6OyC2vxZNfpaJBb9kuqv8dLwAAjOuvanHzyc5w+yB/xPfygrbRgH9uOgFt47XdU0ajEQu+P4WT+ZVwc7DDS7f3t8izgz2dADDckOUw3BARtSAl5zLOqatgJ5Vg2rBgKP4QMpwVdvhkxlA4y2VIzirDkiuL2FmC3mDED1e6pCZHBVjsvjcikUjw6l0D4CSX4cCFMsz96tg1oW3p7gx8fTgPUgnwzn1R8HFt24J9NxLEcEMWxnBDRPQnBoMRe8+XAGhaYybA/dq9kvr6uWLJA1EAgE9+y7LYKrsHs8pQWq2Fu5P9dXfW7gx9VK74ZEYs5HZS/HS2CLM/O4LtJwtxILMUj31xFB/83DQ76rUpAy22iScABHo4QgKgsq4BlXVc74Y6zk7oAojIfOsP5d7wnOlx5s9k6a72ZBSjtFoHB3sp4nq1vlnlhIH+GNdfhV1nivDyllPY+OhNkEgkHXr21S6piQP9OzRQ11zDw7zx0f/F4NEvjmJfZin2ZZY2e/8f4/ua9qWyFIWdDH5KBxRW1rP1hiyCLTdERH/y6b6LAIDYnp43nIa9cHJ/ONhLcfhiObak5XfoudpGvWlV4slRlt1Lqj3GRPji+7kj8cjIUPT0coKLwg7T44Kx85mbMXdMWKc88+q4mzyGG7IAttwQEf3B2UIN9meWQQIgvg37OQV6OOGpW8Px7s50vLn9HG7r7wcXhXk/Wn88qUZlXQP8lQ6IC7X8XlLt0T/ADf0D+uPlOywzaPhGgj2dcOhiOVtuyCLYckNE9Adr92cDAAYEuMHDSd6ma+aM6oUQLyeUVGnx0a8XzH72lwebdueeNiwYMmnHurdszdWWm/yKuhZnahG1B8MNEdEV2ka9aQuCm9qxC7fcTooXJkYAAFb/loWCivYvRne2UIOjOZdhJ5XgL0OD2n29rfN0lsNJLoPeYMTpAo3Q5ZCNY7ghIrpif2YpqrSN8HVVmBaWa6vxA/wwLMQT9Q0G/HtneruffbXVZvwAP/i6ObT7elsnkUhMrTepOZcFroZsHcMNEdEV2082DeadONAP0nbOepJIJHj5jqZVfjcfy0dKO35BV2sbseVY02DkB2/qvrPbAj2aptyz5YY6iuGGiAhAg96A3WeKAAATB5k3Uyky0B33xQQCAF767mSbVy5eu/8ianR69PZxRnwvYQcSC+nqekKn8isFroRsHWdLEREBSL5Qhsq6Bni7yDE0xBNZJTVm3efFSf3w09kinFNXYe3+bMy5udd1zy+uqseKPU2DkP82NrzD6+SYyxrWTLoabi6UVKNOp4ejvGu2niDxYbghIgLw46mmgcTjB/h1aKaSp7McL07sh3/+9wTe2XkOukYDPJxbn3V14lIFanV6RAW5484u3G7BGrk52MNVYYcqbSPOqjUYEuwhdElko9gtRUTdnt5gxM7TTV1Sk8zskvqj+2ICMTTEAw16I748lNPq1ObCyjpsPJoHAFhwez/BWm2sib9702Dq0+yaog5guCGibu9kfiXKa3RwdbBDXGjr2y20lVQqwX+mRsNZYYfCynpsPJIHg9HY7JzLtTp8eTAHRmPTjtyxIR1/rhhc7ZrioGLqCIYbIur29l/ZP2l4by/YySzzYzHQwwkP3dQTdlIJzqmr8OXBHJRWawEApdVafLrvIi7XNiDU2xmL7uyaVYBtQYDyyqDiArbckPmsItwsX74cISEhcHBwQFxcHA4fPtym6zZs2ACJRIIpU6Z0boFEJGq/XdkBfGS4j0XvG+zphPtiAiEBcE5dhWU/ZeCtH89i6e4MlNfo4Oksx9dzboKva/db16Y1V1tu0tVV0DW2bbYZ0Z8JHm42btyIxMRELFq0CKmpqYiKisL48eNRXFx83euys7Px3HPPYdSoUV1UKRGJUa2uEak5FQCAkWHeFr9/ZKA7nro1HH1VrjAYAU19IyQAeno54ZGRofBTMtj8kYeTPdwc7NCgN+J8cZXQ5ZCNEny21NKlSzFnzhzMnj0bALBq1Sps27YNa9aswQsvvNDiNXq9Hg8++CBeffVV/Pbbb6ioqGj1/lqtFlqt1vR3jYb9uET0uyPZl6HTG9DD3REhXk6d8gw/pQNmDg9BQUUdtI0GBLg73HC38e5KIpFgYA8lDlwow+l8DQYEKIUuiWyQoC03Op0OKSkpSEhIMB2TSqVISEhAcnJyq9f961//gq+vLx5++OEbPmPx4sVQKpWmV1BQ99uzhYhat+9ql1SYd6fPVgpwd0SotzODzQ0MCHADwHE3ZD5Bw01paSn0ej1UKlWz4yqVCmq1usVr9u3bh08//RSrV69u0zPmz5+PyspK0ysvL6/DdROReOzLLAMAjAi3fJcUmWdgj6bWGs6YInMJ3i3VHlVVVXjooYewevVqeHu37QeRQqGAQqHo5MqIyBaVVGlxtrDpF+jwduwCTp2rn39Ty825Qg0MBiOkHVhUkbonQcONt7c3ZDIZioqKmh0vKiqCn5/fNedfuHAB2dnZmDx5sumYwdA0mt7Ozg7p6eno3bt35xZNRKJx4ELTFPD+/m7wduGHIGvRy9sZcjspanR65F2uRc927tBOJGi3lFwuR0xMDJKSkkzHDAYDkpKSEB8ff835EREROHnyJNLS0kyvO++8E2PGjEFaWhrH0xBRu1xd32Yku6Ssip1Mij4qFwAwtawRtYfg3VKJiYmYOXMmYmNjMWzYMCxbtgw1NTWm2VMzZsxAjx49sHjxYjg4OGDgwIHNrnd3dweAa44TEV2P0WjEvvNXwk0nTAGnjunn54ZT+RqcKazChIEd3xKDuhfBw83UqVNRUlKChQsXQq1WIzo6Gjt27DANMs7NzYVUKvhyPEQkMhdLa1BQWQ+5TIqh3PrA6lwdd8OWGzKH4OEGAObNm4d58+a1+N6ePXuue+3atWstXxARid6+K11SMT094Cjn1GxrE+HvCkDYcLP+UO4Nz5keF9wFlVB7sUmEiLolU5cUx9tYpf5XWm4uXa6Dpr5B4GrI1jDcEFG306g3IDmraX0bjrexTu5Ocvhf2ZoiXc1tGKh9GG6IqNs5kV+JqvpGKB3tTQvGkfXhuBsyF8MNEXU7+690SQ3v7QUZF4izWv2sYNwN2SaGGyLqdn67Mph4BLukrNrVlpszheyWovZhuCGibqVG24hjuZcBAKM4mNiqXQ036WoN9AajwNWQLWG4IaJu5fDFcjTojQj0cESwp5PQ5dB1hHg5w8FeivoGA7LLaoQuh2yIVaxzQ0TUVa6ubzMq3BsSSfcYb9OW9VqskUwqQV+VK45fqsTZQg16+7gIXRLZCLbcEFG3sp/jbWwKZ0yRORhuiKjbKK6qxzl1FSQSYHhvhhtb8Hu44aBiajuGGyLqNg5kNi3cNyDADZ7OcoGroba4Gm7OseWG2oHhhoi6jd/Os0vK1lzdY6qgsh4VtTqBqyFbwXBDRN2C0WjEvswSANxywZa4Odgj0MMRALumqO0YboioWzinrkKRRgtHexmGhngKXQ61Q4QfBxVT+zDcEFG3sCe9qdUmvrcXHOxlAldD7dGf2zBQOzHcEFG3sCe9GAAwuq+PwJVQe5lmTKkZbqhtuIgfEYleVX0DUnKatly4pQ/DjblutBjg9LjgTnnu1XCTUVSNRr0BdjJ+Lqfr4/8hRCR6+zPL0GgwItTbGT29nIUuh9op2NMJznIZdI0GZJVyGwa6MYYbIhK9XzOauqTYamObpFIJ+vo1jbs5XVApcDVkCxhuiEjUjEajaTAxx9vYroE9lACA0/kcd0M3xnBDRKJ2plCDwsp6KOykuKmXl9DlkJkGBlwJNwUMN3RjDDdEJGo/nlQDaGq14RRw2zWgR9Og4lMFlTAajQJXQ9aO4YaIRMtoNGL7yUIAwKRB/gJXQx0R7usKuUyKqvpG5JXXCV0OWTlOBSciUfnjdGW1ph5ZpTWQSSUoq9aZ3uusKcud4UbTr7sLuZ0Uff1ccTK/EqcKKhHs5SR0SWTFGG6ISLRO5TfNrAn3dbHaLimGl7Yb2MOtKdzkV3a7lri2/H9iS6G9s7FbiohE62q4uTrThmzbgCuDik9xUDHdAMMNEYlSsaYexVVayCQS9Luy8SLZtgEBTf8dT+dzUDFdH8MNEYlSam7Tdgthvi5wlFtnlxS1Tz9/t6bxUzU6qDX1QpdDVozhhohEp1FvwNEre0kNDfEQuBqyFAd7GcJ8XABwMT+6PoYbIhKdUwWVqNXpoXS0R192SYnK1fVuTuZzGwZqHcMNEYnOoaxyAEBsiAdkUonA1ZAlRQW6AwCOX6oQtA6ybgw3RCQq6sp65JTXQioBhvb0FLocsrDBwe4AgGO5FRxUTK1iuCEiUdl/oRQA0N/fDW6O9gJXQ5YW4ecGhZ0UlXUNuFhaI3Q5ZKUYbohINM4WapB6ZSDxiDBvgauhziC3k2LQlXWLjuVWCFsMWS2GGyISBaPRiDe2nYURTYv29fRyFrok6iSmrqm8y8IWQlaL4YaIROGX9GLsyyyFTCrBhAF+QpdDnWhwcNP0frbcUGu4txQRCcZgNOJsoQaFlfWob9Ajs7gaw0I9EN/LG0qnto+XKa3WYtHW0wCAEb294Oks76ySyQpcbbk5p65Cra4RTnL+KqPm+H8EEXU5o9GIY7kV2JNRjNJqnen4gQtlWLP/IqQSYPwAP8y5uReGBF9/Eb5aXSMeXnsEeeV1CPJ0xOi+vp1dPrWiqzYB9Vc6QuWmQJFGi5OXKhHXy6tLnku2g91SRNSlDEYjtqQVYFPqJZRW6+BoL8PQEA/c0scHD93UE719nGEwAj+eUuOeFQdw78oD2HFKDb3h2mm/xVX1ePzLVBy/VAkPJ3usnT3Manf/JssaHHSlayqvQthCyCqx5YaIuozeYMS3KXk4cakSEgAJ/VUY3ssLiiuBZHpcMAAgo6gKn/yWhS3HCpCScxkpOSkIUDpgTIQvhoZ4otFgxPniKqw7kIO6Bj0UdlJ8MnMoevu4mBbwI3EbHOyOHafVOJbLQcV0LYYbIuoyW48X4MSlSsgkEjwwNMg0pffP+qhc8c59UXhuXF98npyNLw/moqCyHl8dysVXf+r6iApyx6LJ/W/YfUXiEtOz6b/30ezLMBiMkHIlavoDhhsi6hInLlXgSHY5JGhqoennf+M9n3zdHPCP8RF46tZwJF8owy/pxThXWAUHuQxKR3vcPsgP4wf4QSLhL7buJjLQHY72MpTV6JBRXIUI7iFGf8BwQ0SdLq+8Ft8dywcA3NLHp03B5o8c7GUYE+GLMRHWM1i4qwbPUsvkdlIMDfXE3owSHMgsY7ihZjigmIg6ldFoxN+/OQ5towHBnk4Y208ldEkkEsN7N82SOnChTOBKyNow3BBRp9p6vACHs8thL5NgamwQd+kmi7kabg5llaFRbxC4GrImDDdE1GlqtI14c/tZAMDovr7w4OJ6ZEEDApRwdbBDlbYRpws0QpdDVoThhog6zYo9mSjSaBHk6YiR3MiSLEwmleCmXuyaomsx3BBRp8ivqMPqvRcBAAtu7w97GX/ckOX9Pu6mVOBKyJrwpw0RdYr3fzoPnd6Am3p54rb+HERMnWN476YWwSPZ5dA26gWuhqwFp4IT/cmNpvheXUWXWpdVUo1NqZcAAP8YH8F1aKjT9FG5wNdVgeIqLQ5kllnVcgEkHLbcEJHF/een89AbjBgb4WtaSZaoM0gkEowf4AcA+PFUocDVkLWwinCzfPlyhISEwMHBAXFxcTh8+HCr565evRqjRo2Ch4cHPDw8kJCQcN3ziahrnS3U4H/HCwAAieP6CFwNdQcTBzaFm91nijglnABYQbjZuHEjEhMTsWjRIqSmpiIqKgrjx49HcXFxi+fv2bMH06ZNwy+//ILk5GQEBQVh3LhxyM/P7+LKiaglS3dnAABuj/THgICW944isqRhoZ7wcLLH5doGHM7mxqlkBeFm6dKlmDNnDmbPno3+/ftj1apVcHJywpo1a1o8/6uvvsKTTz6J6OhoRERE4JNPPoHBYEBSUlIXV05Ef3Y8rwK7zxRBKgGeTWCrDXUNO5nUNGh9xym1wNWQNRA03Oh0OqSkpCAhIcF0TCqVIiEhAcnJyW26R21tLRoaGuDp6dni+1qtFhqNptmLiDrHv3elAwDuHhyIMF8Xgauh7mTCla6pnafVMBiMAldDQhM03JSWlkKv10Olaj5NVKVSQa1uW/p+/vnnERAQ0Cwg/dHixYuhVCpNr6CgoA7XTUTXOpRVht/Ol8JOKsHTY8OFLoe6mRFh3nBR2KFIo8WxvMtCl0MCE7xbqiPeeustbNiwAd999x0cHBxaPGf+/PmorKw0vfLy8rq4SiLxMxqNWLKraazNA0ODEOzlJHBF1N0o7GQYN6Dpg/JX3LG92xM03Hh7e0Mmk6GoqKjZ8aKiIvj5+V332n//+9946623sGvXLkRGRrZ6nkKhgJubW7MXEVnWvsxSHM4uh9xOiqduDRO6HOqmHrqpJwDgh+OFKK3WClwNCUnQcCOXyxETE9NsMPDVwcHx8fGtXvfOO+/gtddew44dOxAbG9sVpRJRK4xGI/59pdXmwbhg+CsdBa6IuqvBwR6IClRCpzdgw2G23nRngndLJSYmYvXq1fj8889x9uxZPPHEE6ipqcHs2bMBADNmzMD8+fNN57/99ttYsGAB1qxZg5CQEKjVaqjValRXVwv1JRB1a0lni3E8rwKO9jI8OZqtNiSsmcNDAABfHMxBA9e86bYE335h6tSpKCkpwcKFC6FWqxEdHY0dO3aYBhnn5uZCKv09g61cuRI6nQ733Xdfs/ssWrQIr7zySleWTtTtNeoNphlSs0aEwMdVIXBF1B1cb4uURr0BzlcGFu84pcbkqIAurIysheDhBgDmzZuHefPmtfjenj17mv09Ozu78wsisnG55bVQV9ZD26iH3mDAuAF+ULm1POi+LVr7ZXLgQinOqavgaC/Do6N6mX3/rnaj/cPIdtnJpBgW4olf0ouxdHcGxg1QQWEnE7os6mJWEW6IyDIKK+uw87QaGUW/d9P+eEqNRVtPY1S4DxJv64OoIHeLPKuqvgG7zzRNBhg3QAUPZ7lF7kvUUaPCvXGqoBIXS2uwem8W5t3KpQm6G4YbIpFIzb2M/6ZcghGAVAKE+brASW4HmVSClJzL+DWjBPsyS/HELb3x1NiwDn+a3XFKDW2jAYEejhga0vIimkTm6GjLmoO9DC/f3g9Pb0jDBz9n4q7oHgjy5PIE3YngA4qJqONOXKowBZv+/m54NqEPZg0PxQOxQfjvE8Ox57nRmBwVAL3BiA9/ycR9K5NRWFln9vPOFGhwLK8CEgB3RgVAKpFY7GshsoQ7owIQ38sL2kYD5m8+ycHF3QzDDZGNyyiqwjdH82AEENvTA9PjguHl0nxgb4i3Mz6YNhgrHxwCDyd7nMyvxOQP9iMlp/2bDJbX6LAptWkxzBFh3gj04Cdisj4SiQSvTRkIhZ0U+zJL8ezGNOi5LUO3wXBDZMOqtY349mgeDEYgOsgdUwb3uG4rysRB/tg6byQi/FxRWq3FXz4+iI1H2t4F0Kg34OvDuahvMCDIw9G0IiyRNQrzdcGqh2JgL5PghxOF+Mem46hv0AtdFnUBhhsiG2U0GvF9Wj5qdHqo3BS45wbB5qogTyf894nhmDjQDw16I57/70m8svU0tI3X/6GvbdTji4M5yK+og6O9DNOGBcNOyh8hZN3G9PXF+38ZDKkE2Jyaj4Slv2L3mSJurily/MlEZKO2Hi/A6QINpBLg/pgg2Mna/s/ZWWGH5dOHIPG2PgCAtQeycfv7+1rtpqqqb8Cn+y7ifHE17GUSTBsWDHcnzo4i2zBxkD8+mRkLf6UDLl2uw5x1RzH8rZ+x8PtT+PFkIfIr6mA0MuyICWdLEdkgTX0D/vW/MwCAMRG+CHBv/5YHUqkEfxsbjv7+bnhh80lkFlfjvlXJGBXug7uiAtDLxxl1DXrsOl2Erw7loEFvhKO9DLOGh3DmCdmcWyNU+CnRCx/8nIkvkrOh1tRjXXIO1iXnAAD8lQ6I7+2F0X19MWGAH+R2/OxvyxhuqEvcaGrn9LjgLqpEHFbuuYCyGh28XRQY3ce3Q/dK6K/CTyEeeGPbWXybcgl7M0qwN6PkmvN6uDvi/phA+HZgMUBL4AJ8ZC5nhR1emBiBZxLCceBCKX66snXIOXUVCivrsTk1H5tT86FyU2D2iFA42stg344WUbIeDDdENia/og6f7rsIAJg40A8yacenYbs7yfHu/VF4ckwYtqYV4MdThajWNsLBXoZgTyeEeDmjt48zJJzyTSLgYC/DrREq3BrRNCC+TqdHSs5l7MssxebUSyjSaPHWj+fg5+aAvwwNEjzQU/sx3BDZmH/vTIeu0YCbenkiws/VovcO9XbG0wnheDqh+YqubC0hMXOUyzAy3Bsjw73x7G3h2JpWgLd3nINaU4/lezJx75BARAa6C10mtQPb24hsyJkCDb47lg8AeGlSf7akEFmYwk6G+2ODsP3pUQjzcUGD3ohvjubhTIFG6NKoHRhuiGzIe0kZAIA7Iv0xKFApcDVE4uXr6oBZI0IwJNgdBiPw9ZFcZBZX3/hCsgoMN0Q24nRBJXaeLoJEAjw9lhsBEnU2qUSCuwcHYkCAG/QGI746lIOyaq3QZVEbMNwQ2Yj3k84DAO6IDEC4yrJjbYioZTKpBFNjg9DTywnaRgM2Hs1Do4H7VFk7hhsiG9C81SZM6HKIuhU7mRRTY4PgaC/Dpct12H2mSOiS6AYYbohswHs/NbXaTI4MQJgvW22Iupq7kxz3DOkBAPjtfCmySjn+xpox3BBZuVP5ldh1pqnV5m8ca0MkmAEBSgwN8QAAfH+sAI16dk9ZK4YbIiv33pWxNndGBSDM10Xgaoi6twkD/OGisENJtRZ7z1+7kjdZB4YbIit2Kr8Su88UQSoBnrqVrTZEQnOUy3D7IH8AwJ70ElwsrRG4ImoJww2RFfvP7qZ1bdhqQ2Q9IgOVCPd1QaPBiH/977TQ5VALGG6IrNTR7HIknSuG7Mru3URkHSQSCSZHBkAmkeCX9BL8cq5Y6JLoTxhuiKyQ0WjE2zvOAQAeiA1CLx+22hBZE29XBYb39gIAvPbDGegaObjYmnDjTCIrtCe9BEeyL0NhJ+1WqxFzg06yJWMifHFWrUFWaQ3WJWfjkVG9hC6JrmDLDZGVadQbTK02s4aHwE/pIHBFRNQSB3sZ/jk+AkDTWlQlVdyawVqw5UbE2vIpeHpccBdUQu2xLjkH59RVUDra4/FbegtdDhFdx30xgfjiYA5O5ldiya50vHVvZJc+v6JWh4KKerg42KG0WgtvF0WXPt9aMdwQWZEiTT2WXpkh9fyECHg4ywWuiIiuRyqVYNHk/rhvVTI2Hs3Dg3E9MShQ2anPbDQYsD+zDCk55Sit1pmOr/r1AgYHu+OFCRGI6+XVqTVYO3ZLEVmR17edRbW2EVFB7vjL0CChyyGiNogN8cRd0QEwGoFX/3caRqOx056VVVqND5IysfO0GqXVOkglgL/SAUpHe0gkwLHcCkz9+CAe/yIF1drGTqvD2rHlhshK/HiyEP87XgCpBHhjykBIpRKhS2oXDgam7uyFiRHYdboIR3Mu49ujl/BAJ3w4OXChFNtOFMIIwFlhhwkDVBgQoISDvQwAkNDfF+8nncfXh/Ow47Qa+R/X4bPZQ7tlVxVbboiswMXSGvxj0wkAwKM398bAHp3brE1EluWvdMSztzXNbHxj+1mUVltucLHeYMQPJwrww5VgMyTYHYkJfRDT09MUbADA19UBr08ZhP8+MRyeznKczK/EfSsPQF1Zb7FabAXDDdENVNTqcDS7HN8czcOafRcx7eODeOTzI1j2Uwb2pBdD26jv0P3rG/R44sumJuRhIZ54blwfC1VORF3pryNC0d/fDZV1DXjthzMWuafeYMQ/vj2OAxfKAAATBvjh3iGBcJTLWr0mOsgdmx6PR6CHI7LLavHIuiOo1XWvLip2SxG1Qq2px89ni3CqQNPseGZJNQDgp7NNq5K6O9njzqgATI8LRoSfW7ueUaNtxGNfpOCcugreLnJ8MH0w7GT8zEFki+xkUrx17yBMWb4f36cVYNIgf4wf4Gf2/Rr1Bjz7zXFTd/X9sUGICnRv07W9fFzw9ZybcNfy/TiVr0HixuNY8eAQm+vuNhfDDdGf6BoN2HG6EIeyymEEIAEQ5OmE3j4u8HaRY1QfH5RXa3HiUiX2XyhFkUaLdck5WJecgxFhXnh4ZChG9/G94Q+RytoGzF57GKm5FXCSy7DiwRio3LimDZElCLUURmSgO+aM6oWP9mbhH98eR39/NwR5OrX7Pg16A57ecAzbT6phL5PggdggDAhoX3d1kKcTPn4oBtNXH8KO02q8l3Qez97WPVqGGW6I/uBMgQbL92SaFuMaGOCGW/up4PeH0HFnVIDpz3qDEfszS7HxSNMAvv2ZZdifWYZePs6YNTwEEwb6wde1eWDRG4z49mge/r0rA6XVWigd7bF29lAMDvbomi/yTzgQmMiy/j6uLw5dLEdaXgXmrU/Ft48Ph9yu7S2yukYDnvo6FTtPF0Euk2LFg0NQbOYCgbEhnlh8zyD8/dvj+ODn8xgZ7o2hIZ5m3cuWMNwQXbH7TBGe+joV9Q0GuDrY4b6YQIT7ul73GplUgpv7+ODmPj64dLkW65Jz8PXhXGSV1GDh96exaOtpRAa6o5e3Mzyc5Mi7XIu0vApTeOrp5YSPHoppd3cWEVkvuZ0UH04fjNvf34fjlyrxz03HseSBaMja0CVUVd+Ap74+hj3pJZDbSfHR/8VgTIRvhz6E3BsTiAMXyvDf1Et4ZkMatj89CkpHe7PvZwsYbogAfJGcjUVbT8NgBMJ9XfBAbBCcFe375xHo4YQXJ/XD38aGY9PRPHyXVoDjeRWm1x8pHe3x1K1hmBEf0q5PdERkGwI9nLBsajQeWXcUW9IKYDACSx+Iuu6Yutwrg38ziqqhsJNi9YxY3NzHxyL1vHrXABzNKUdOWS1e/O4kPpw2GBKJeMffMNxQt2YwGPH2znP46NcsAMDU2CAM7KFs0yes1rgo7DBrRChmjQhFkaYeB7PKUKSpR1m1Dj6uCkQFuWNggPK6sx2IyPaNifDF8umDMW/9MWw9XoDLtTq8dtdAhHg7NzuvUW/A+sO5WLIrA5V1DfB1VWD1jFhEBblbrBYXhR3e+8tg3LfyALadKMToPj64P1a8C4Uy3FC3Vd+gx3PfHscPJwoBAH+/rQ/m3RqGrw/nWewZKjcH3BXdw2L3IyLbMmGgP1b+nxRzv0rFb+dLMe4/e3F/bCCiAt3h7mSP45cqsPN0ETKLm2ZhRgYq8fFDsZ2yYW50kDsSx/XBOzvSsWjracSGeCL0T0FLLBhuSDC6RgOKNPXQNhqwP7MULgo79FG5dkmLRkWtDo9+kYLDF8thJ5Xg7XsjcW9MYKc/l4i6n9v6q7D96VF49X+n8dv5Unx1KBdf/WkMjYeTPRJv64Npw4I7dTmIx27ujd8ySpGcVYanNxzDpnYOdrYVDDfUpcprdDiWexmnCzQo0tTj6g4sa/ZfBABIJUCYrwsS+qlwR2QA+vm7WrxfOK+8FrPXHkFmcTVcFXZY9VAMRoR5W/QZRER/FObrgnV/HYY9GSX4Nb0E54urUFatw8AeSsT29MDEgf5QOnX+IF+ZVIL/TI3GhPf24sSlSizZnY75E/t1+nO7GsMNdYnCyjr8dKYIZ9VVzY47K+zgqrCDu5M9Sqq0KKvRIaOoGhlF1Vix5wIiA5V4ZFQvTBroZ5FPM3szSvC3DcdQUdsAPzcHrP3rUKudqWSpKdqc6k1kHSQSCcb09cWYvr6C1uGndMDb90bisS9S8NGvWRgZ5o1R4ZYZuGwtGG6oUxVp6vHGtrPYerwAQNOCeL19XDA42B29fV3g5tD0SWV6XDCMRiNKqrRIzirD9pOF+CW9BCcuVeJvXx/D2+6OmD0iBH8ZFgyXds5iApq6wFbsycR7SedhNAKDeijx8YwY+CsdLfnlEhHZhPED/PBgXDC+OpSLxG+O48enR4lqg02GG+oUeoMRXx7Mwb93pqNK27SnSWSgEmMjVPBxbfkfkEQige+VAbh3RfdAWbUWXxzMwRfJOcivqMPr287ivaTzmD4sGLNGhLQ5mBzJLsdL351ERlHTgL1pw4KwaPKAZhvOCYEtKkQkpJdv74/DF8txvrga89an4suH40Sz/QvDDVnciUsVeOm7UziZXwkAiApyx8gwb/Rwb18riZeLAs8k9MHjt/TG5tR8fLIvC1klNfhobxY+3XcRo8K9MTkqACPDvOH7p20LKmp12Hu+FOsOZONozuWm+znLseCO/pgymLOXiIgc5TKs/L8huOvD/TiYVY7FP57Dgjv6C12WRTDciEyNthFZJTWoqNPhbKEGUokErg52cHO0N6s7pz009Q1YsjMd6w7mwGgEXB3s8PyECEwbFoyNR8yfXu1gL8P0uGD8ZWgQfkkvxsd7s3DoYjl+SS/BL+klAAAfVwUClA6ARIKKWh1yympN19tJJbg/NhDPT4iAu5O8w18nEZFYhPm6YskDUXj8y1R8uu8iBgS44Z4htj9zlOHGxtXqGrE3owR70ktw4EIZcstrWz3XzcEOgR5O6O3jjAh/N3hY6Be93mDElmP5eGvHOdO2AlOiA/DS7f1b7YIyh1Qqwdh+Koztp0JmcTV+OFGAHafUyCiqQkmV1vTsq0K8nHBndA88GBfMDSmJSDA36oLujA0822PCQH88Obo3Vuy5gH9uOgEPZ7ngg547iuHGRqXmXsaGw7nYdqIQNTp9s/e8XeTwdlGgWtsIvcGI6vpGVGsboalvxJlCDc4UavC/E4Xwc3NAfkUtxvZTITrQ/Ya7WP9Zo96A3WeKsOyn80gvapoF1cvbGa9NGdjpU6vDfF3wTEIfPJPQB3U6Pc6qNbhco4PR2NTUOiDAja00RERt9Ny4vrh0uQ5bjxfgiS9T8NUjcYjpabsbbDLc2JBGvQG7zhRh9W9ZOJZbYToe5OmIsREq3NLXB1GB7vB0bvql/sdPC9pGPQoq6pFbXot0tQY5ZbVQa+qx/JcLWP7LBXi7KDA2whfDw7xwUy+vVls69AYjThdU4udzxdh4JA+FlfUAmlqFnhgdhr+ODIHCrmsH6jrKZRgi0I7aRERiIJVK8O/7o6Cpb8Ce9BLMXHMEK/9viM1OEWe4sQHV2kZ8cyQPnx24iLzyOgCAXCbF5KgATB0ahKEhHjdc6E5hJ0OotzNCvZ1xSx8f1GobkV5UhdoGPfaml6C0WouNR/Ow8WjT2BhvFwV6+TjDx1UBhUwKrd6Agoo6ZJXUoLKuwXRfT2c5pg8LxpxRvbpkASpbwZlQRGRr5HZSrHwwBg9/fgQHLpRh9mdHbHb1doYbK5ZVUo11yTnYlHIJ1VemU3s42eOhm3riofiQDo1ncVLYYXCwB6bHBUPXaMDhi+X4Jb0Yhy6W4XSBBqXVWpRWa1u81lVhh5t6e+H2Qf6YOMivy1tqrsfa+7aJiKyZo1yGz2YPxT++PYGtxwvw92+P49DFMiy4oz9cHWznAyzDjZXRNRrwa0YJvjiYg70ZJabjvX2c8deRobh3SKDF12eR20kxMtwbI8ObxslUaxuRVVLdNOuqVocGvRESCdDD3RFBnk7o6+cKe5GshUBERM0p7GRYNjUaPb2c8OEvmfjm6CUcuFCG+RP7YeJAv3aPzxSCVYSb5cuX491334VarUZUVBQ++OADDBs2rNXzv/32WyxYsADZ2dkIDw/H22+/jUmTJnVhxZZVq2tE8oUy/HS2GD+eKkRFbVO3j0QCjI3wxczhIRgZ5m3xPZZa46KwQ2SgOyID3bvkeV2J3UVEdJUlfh6I9WeKVCrB38f1xahwHyR+k4ZLl+swd30q+vm74ZGRoZgw0A/Onby8SEcIXtnGjRuRmJiIVatWIS4uDsuWLcP48eORnp4OX99rp6IdOHAA06ZNw+LFi3HHHXdg/fr1mDJlClJTUzFw4EABvoL2qW/Q42JpDTKKqnC6QIPUnMs4cakSOr3BdI63iwJ3Dw7AQzeFINjLScBq2/YPt6u6eqzlh4i11EFE1NmGhXpixzM345PfsvDpbxdxtlCDv397HC9vOYXRfX1wUy8vxPT0QJivi+Crvv+RxGg0Gm98WueJi4vD0KFD8eGHHwIADAYDgoKC8NRTT+GFF1645vypU6eipqYGP/zwg+nYTTfdhOjoaKxateqGz9NoNFAqlaisrISbm+U2TCzW1GPXmSLU6fSo1elR29CIep0eVfWNKK7SoriqHkUabbPBuH8U6OGI0X19MGGAP+J7e0FmgWY/SwSTrvpF3paAxFBBRGJkqZ9/nf1Bs6JWhy+Sc7D5WD4ultY0e08qAQI9nOCndIDKzQERfq6YOybMos9vz+9vQVtudDodUlJSMH/+fNMxqVSKhIQEJCcnt3hNcnIyEhMTmx0bP348tmzZ0uL5Wq0WWu3vA2MrK5u2BNBoNB2svrmzueV4ceORNp3r4iBDmI8rwlUuiAp0R3SwO3p6Opm6nWqqq25wh7aprbnxfW70fWjLPSyhLf89uqoWIqKu9MnPpy1yH0v/XvszKYCZQ1WYEeuLk/mVSL5QhqM5l3E6vxKa+kZkF9Yiu7Dp3OxAJR6KsexCgFe/vra0yQgabkpLS6HX66FSqZodV6lUOHfuXIvXqNXqFs9Xq9Utnr948WK8+uqr1xwPCgoys2rLOCvo0383R+gCrrCWOoiIbJU1/RzNA6B8rnPuXVVVBaVSed1zBB9z09nmz5/frKXHYDCgvLwcXl5eZg3Q1Wg0CAoKQl5enkW7tborfj8tj99Ty+L30/L4PbW87vA9NRqNqKqqQkBAwA3PFTTceHt7QyaToaioqNnxoqIi+Pn5tXiNn59fu85XKBRQKJqvB+Pu7m5+0Ve4ubmJ9n8gIfD7aXn8nloWv5+Wx++p5Yn9e3qjFpurBF2sRC6XIyYmBklJSaZjBoMBSUlJiI+Pb/Ga+Pj4ZucDwO7du1s9n4iIiLoXwbulEhMTMXPmTMTGxmLYsGFYtmwZampqMHv2bADAjBkz0KNHDyxevBgA8PTTT+OWW27BkiVLcPvtt2PDhg04evQoPv74YyG/DCIiIrISgoebqVOnoqSkBAsXLoRarUZ0dDR27NhhGjScm5sLqfT3Bqbhw4dj/fr1ePnll/Hiiy8iPDwcW7Zs6bI1bhQKBRYtWnRNVxeZh99Py+P31LL4/bQ8fk8tj9/T5gRf54aIiIjIkrhBEBEREYkKww0RERGJCsMNERERiQrDDREREYkKw007LF++HCEhIXBwcEBcXBwOHz4sdEk2a+/evZg8eTICAgIgkUha3RuM2mbx4sUYOnQoXF1d4evriylTpiA9PV3osmzaypUrERkZaVoULT4+Hj/++KPQZYnGW2+9BYlEgmeeeUboUmzWK6+8AolE0uwVEREhdFlWgeGmjTZu3IjExEQsWrQIqampiIqKwvjx41FcXCx0aTappqYGUVFRWL58udCliMKvv/6KuXPn4uDBg9i9ezcaGhowbtw41NTU3PhialFgYCDeeustpKSk4OjRo7j11ltx11134fRpy2xy2J0dOXIEH330ESIjI4UuxeYNGDAAhYWFpte+ffuELskqcCp4G8XFxWHo0KH48MMPATStpBwUFISnnnoKL7zwgsDV2TaJRILvvvsOU6ZMEboU0SgpKYGvry9+/fVX3HzzzUKXIxqenp5499138fDDDwtdis2qrq7GkCFDsGLFCrz++uuIjo7GsmXLhC7LJr3yyivYsmUL0tLShC7F6rDlpg10Oh1SUlKQkJBgOiaVSpGQkIDk5GQBKyNqWWVlJYCmX8bUcXq9Hhs2bEBNTQ23eumguXPn4vbbb2/285TMd/78eQQEBKBXr1548MEHkZubK3RJVkHwFYptQWlpKfR6vWnV5KtUKhXOnTsnUFVELTMYDHjmmWcwYsSILlu5W6xOnjyJ+Ph41NfXw8XFBd999x369+8vdFk2a8OGDUhNTcWRI0eELkUU4uLisHbtWvTt2xeFhYV49dVXMWrUKJw6dQqurq5ClycohhsikZk7dy5OnTrFvncL6Nu3L9LS0lBZWYlNmzZh5syZ+PXXXxlwzJCXl4enn34au3fvhoODg9DliMLEiRNNf46MjERcXBx69uyJb775ptt3nTLctIG3tzdkMhmKioqaHS8qKoKfn59AVRFda968efjhhx+wd+9eBAYGCl2OzZPL5QgLCwMAxMTE4MiRI3jvvffw0UcfCVyZ7UlJSUFxcTGGDBliOqbX67F37158+OGH0Gq1kMlkAlZo+9zd3dGnTx9kZmYKXYrgOOamDeRyOWJiYpCUlGQ6ZjAYkJSUxP53sgpGoxHz5s3Dd999h59//hmhoaFClyRKBoMBWq1W6DJs0tixY3Hy5EmkpaWZXrGxsXjwwQeRlpbGYGMB1dXVuHDhAvz9/YUuRXBsuWmjxMREzJw5E7GxsRg2bBiWLVuGmpoazJ49W+jSbFJ1dXWzTxcXL15EWloaPD09ERwcLGBltmnu3LlYv349vv/+e7i6ukKtVgMAlEolHB0dBa7ONs2fPx8TJ05EcHAwqqqqsH79euzZswc7d+4UujSb5Orqes0YMGdnZ3h5eXFsmJmee+45TJ48GT179kRBQQEWLVoEmUyGadOmCV2a4Bhu2mjq1KkoKSnBwoULoVarER0djR07dlwzyJja5ujRoxgzZozp74mJiQCAmTNnYu3atQJVZbtWrlwJABg9enSz45999hlmzZrV9QWJQHFxMWbMmIHCwkIolUpERkZi586duO2224QujQgAcOnSJUybNg1lZWXw8fHByJEjcfDgQfj4+AhdmuC4zg0RERGJCsfcEBERkagw3BAREZGoMNwQERGRqDDcEBERkagw3BAREZGoMNwQERGRqDDcEBERkagw3BAREZGoMNwQkaiEhIRg2bJlQpdBRAJiuCEim7R27Vq4u7tfc/zIkSN49NFHu74gIrIa3FuKiKyOTqeDXC4361ruq0NEbLkhIsGNHj0a8+bNwzPPPANvb2+MHz8eS5cuxaBBg+Ds7IygoCA8+eSTqK6uBgDs2bMHs2fPRmVlJSQSCSQSCV555RUA13ZLSSQSfPLJJ7j77rvh5OSE8PBwbN26tdnzt27divDwcDg4OGDMmDH4/PPPIZFIUFFR0UXfASKyJIYbIrIKn3/+OeRyOfbv349Vq1ZBKpXi/fffx+nTp/H555/j559/xj//+U8AwPDhw7Fs2TK4ubmhsLAQhYWFeO6551q996uvvooHHngAJ06cwKRJk/Dggw+ivLwcAHDx4kXcd999mDJlCo4fP47HHnsML730Upd8zUTUOdgtRURWITw8HO+8847p73379jX9OSQkBK+//joef/xxrFixAnK5HEqlEhKJBH5+fje896xZszBt2jQAwJtvvon3338fhw8fxoQJE/DRRx+hb9++ePfdd03PPXXqFN544w0Lf4VE1FUYbojIKsTExDT7+08//YTFixfj3Llz0Gg0aGxsRH19PWpra+Hk5NSue0dGRpr+7OzsDDc3NxQXFwMA0tPTMXTo0GbnDxs2zMyvgoisAbuliMgqODs7m/6cnZ2NO+64A5GRkfjvf/+LlJQULF++HEDTYOP2sre3b/Z3iUQCg8HQsYKJyGqx5YaIrE5KSgoMBgOWLFkCqbTpM9g333zT7By5XA69Xt/hZ/Xt2xfbt29vduzIkSMdvi8RCYctN0RkdcLCwtDQ0IAPPvgAWVlZ+OKLL7Bq1apm54SEhKC6uhpJSUkoLS1FbW2tWc967LHHcO7cOTz//PPIyMjAN998g7Vr1wJoauEhItvDcENEVicqKgpLly7F22+/jYEDB+Krr77C4sWLm50zfPhwPP7445g6dSp8fHyaDUZuj9DQUGzatAmbN29GZGQkVq5caZotpVAoOvy1EFHXkxiNRqPQRRARWZM33ngDq1atQl5entClEJEZOOaGiLq9FStWYOjQofDy8sL+/fvx7rvvYt68eUKXRURmYrghom7v/PnzeP3111FeXo7g4GD8/e9/x/z584Uui4jMxG4pIiIiEhUOKCYiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUfl/VMuz5K2FKEIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(rating_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Movies Rating Means Histogram')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(rating_means)\n",
    "plt.title('Movies Mean Rating Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='rating'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,3))\n",
    "sns.boxplot(x=rating_means)"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}