investigation-of-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The TMDb movie data investigation\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   This dataset contains information about movies collected from The Movie Database (TMDb) containing columns like popularity ratings, budget, revenue, Title, cast, movie director, overview, runtime, genre, production companies, release date, release year. it also contains two columns, budget_adj and revenue_adj showing the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research questions\n",
    "1. What are the top 10 movies with the most expensive budgets?\n",
    "2. What year had the movies with the highest ratings?\n",
    "3. Has the general budget for movies been increasing in recent times?\n",
    "4. Is there a relationship between movie budgets and popularity ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue      original_title  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810      Jurassic World   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354  Mad Max: Fury Road   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201           Insurgent   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "\n",
       "                                         homepage          director  \\\n",
       "0                   http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                     http://www.madmaxmovie.com/     George Miller   \n",
       "2  http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "\n",
       "                      tagline  ...  \\\n",
       "0           The park is open.  ...   \n",
       "1          What a Lovely Day.  ...   \n",
       "2  One Choice Can Destroy You  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading of dataset, dataset should be loaded from personal directory\n",
    "df = pd.read_csv('tmdb-movies.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset was inspected and concluded that not all columns would be important for the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 10866 rows and 21 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking general info of columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that some columns had null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7930\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the number of null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicated values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one duplicated entry was found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping duplicated entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the duplicated entry\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicated entries\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping columns not needed for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'imdb_id', 'popularity', 'budget', 'revenue', 'original_title',\n",
       "       'cast', 'homepage', 'director', 'tagline', 'keywords', 'overview',\n",
       "       'runtime', 'genres', 'production_companies', 'release_date',\n",
       "       'vote_count', 'vote_average', 'release_year', 'budget_adj',\n",
       "       'revenue_adj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the columns\n",
    "df.drop(['id', 'imdb_id', 'budget', 'revenue','cast','homepage', \n",
    "         'director', 'tagline', 'keywords', 'overview','runtime',\n",
    "         'genres', 'production_companies', 'vote_count', 'vote_average',\n",
    "         'release_date'], axis=1, inplace=True)"
   ]
  },
  {
   "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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       "    .dataframe tbody tr th {\n",
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       "    }\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>popularity</th>\n",
       "      <th>original_title</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.173104</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.335014</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity                original_title  release_year    budget_adj  \\\n",
       "0   32.985763                Jurassic World          2015  1.379999e+08   \n",
       "1   28.419936            Mad Max: Fury Road          2015  1.379999e+08   \n",
       "2   13.112507                     Insurgent          2015  1.012000e+08   \n",
       "3   11.173104  Star Wars: The Force Awakens          2015  1.839999e+08   \n",
       "4    9.335014                     Furious 7          2015  1.747999e+08   \n",
       "\n",
       "    revenue_adj  \n",
       "0  1.392446e+09  \n",
       "1  3.481613e+08  \n",
       "2  2.716190e+08  \n",
       "3  1.902723e+09  \n",
       "4  1.385749e+09  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspecting dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping entries with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "popularity        0\n",
       "original_title    0\n",
       "release_year      0\n",
       "budget_adj        0\n",
       "revenue_adj       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No null values in the dataset being used for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General description of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.646446</td>\n",
       "      <td>2001.321859</td>\n",
       "      <td>1.754989e+07</td>\n",
       "      <td>5.136900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000231</td>\n",
       "      <td>12.813260</td>\n",
       "      <td>3.430753e+07</td>\n",
       "      <td>1.446383e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000065</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.207575</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.383831</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.713857</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.370173e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity  release_year    budget_adj   revenue_adj\n",
       "count  10865.000000  10865.000000  1.086500e+04  1.086500e+04\n",
       "mean       0.646446   2001.321859  1.754989e+07  5.136900e+07\n",
       "std        1.000231     12.813260  3.430753e+07  1.446383e+08\n",
       "min        0.000065   1960.000000  0.000000e+00  0.000000e+00\n",
       "25%        0.207575   1995.000000  0.000000e+00  0.000000e+00\n",
       "50%        0.383831   2006.000000  0.000000e+00  0.000000e+00\n",
       "75%        0.713857   2011.000000  2.085325e+07  3.370173e+07\n",
       "max       32.985763   2015.000000  4.250000e+08  2.827124e+09"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General description of variables\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the minimum, maximum and mean values for the various variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of years recorded\n",
    "df['release_year'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "56 years of movies information has been recorded "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of movies released each year\n",
    "df['release_year'].groupby(df['release_year']).count().plot(kind='bar', figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of movies released per year have increased over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEGCAYAAABB8K+FAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAARvElEQVR4nO3df5CdVX3H8ffdzSZLEtAYF6UmNrRxv4xSxVpcqesWLUJxUhg01dGqRARHS6y3behIIiBQ0M5k9DKmILXGpE4dNe2O06J1qKLGVYzWlmItnFhLWn45WWPCj9Ulu9nbP+4u7hKyezfZu/eePO/XDHP3Pj/u892Zy2dPzjnPeUrVahVJUutra3YBkqT6GNiSlAkDW5IyYWBLUiYMbEnKhIEtSZlYMB8XiYiTgG8Da1JKe6Y57jeBW4GFwP3AW1NKB+ajRklqdQ1vYUdEDzAAdNdx+E3A1SmllwAJ2NDI2iQpJ/PRwr4MuBz49MSGiHg7UKb2B+P7wOUppWGgHThp/LDFwM/moT5JykJpvu50jIg9wNnAEuDjwGtTSsMR8SFgKKX0FxHxCuB2YGj8v56U0r55KVCSWlwzBh1fDbwA+E5E3AVcCJwWEScAnwTOSSmdAtwM/G0T6pOkljQvg45P0Q58PqX0xwARsXS8jtOBX6SUvjt+3K3A9U2oT5JaUjNa2F8HLoqIkyOiBNxCrT/7v4GVERHjx10IfK8J9UlSS5r3wE4p/QdwLXAH8ENqLe4Pp5T2A+uAz0fE3cAlwDvmuz5JalXzNugoSTo23ukoSZlo5KDjIuBM4GHgUAOvI0nHk3bgFGpjeE9M3tHIwD4T+GYDP1+SjmevonaX+JMaGdgPA+zfP8TYmP3kai3Lly9l377Hm12GdJi2thLLli2B8QydrJGBfQhgbKxqYKsl+b1UizusK9lBR0nKhIEtSZkwsCUpEwa2JGXCwFah9PfvoK+vh/b2dvr6eujv39HskqS6NWO1Pqkp+vt3cOON11OpbGHNmnO57bbbKZfXA/D61/9Bk6uTZmYLW4VRqWymUtlCb28fHR0d9Pb2UalsoVLZ3OzSpLoY2CqM3bsTPT1nTdnW03MWu3enJlUkzY6BrcLo7g527bpzyrZdu+6kuzuOcIbUWgxsFUa5vIFyeT0DAzsZGRlhYGAn5fJ6yuUNzS5NqouDjiqMiYHFjRuvYO3aC+juDjZuvMoBR2WjkQ8wWAXct2/f467ZoJbT1XUig4OPNbsM6TBtbSWWL18KcCqwZ8q+ZhQkSZo9A1uSMmFgS1Im6g7siNgcEdsaWIskaRp1BXZE/C5wcYNrkSRNY8bAjohnATcANza+HEnSkdQzD/tWYBOw8mguMD49RWo5XV0nNrsEaVamDeyIuBS4P6X01YhYdzQXcB62WpHzsNWqJs3DPnzfDOe+CTg3Iu4CrgMuiIiPzm15kqR6TNvCTim9duLn8Rb22SmlP2l0UZKkwzkPW5IyUffiTymlbcC2hlUiSZqWLWxJyoSBLUmZMLAlKRMGtiRlwsCWpEwY2JKUCQNbhdLfv4O+vh7a29vp6+uhv39Hs0uS6uZDeFUY/f07uPHG66lUtrBmzbncdtvtlMvrAXwQr7JgC1uFUalsplLZQm9vHx0dHfT29lGpbKFS2dzs0qS6GNgqjN27Ez09Z03Z1tNzFrt3pyZVJM2Oga3C6O4Odu26c8q2XbvupLs7mlSRNDsGtgqjXN5AubyegYGdjIyMMDCwk3J5PeXyhmaXJtXFQUcVxsTA4saNV7B27QV0dwcbN17lgKOyUapWG/Y0mFXAfT5xRq3IJ86oVU164sypwJ4p+5pRkCRp9gxsScqEgS1JmTCwJSkTBrYkZcLAlqRMGNiSlAkDW5IyYWBLUiYMbEnKhIEtSZkwsCUpEwa2JGXCwJakTBjYkpQJA1uSMmFgS1ImDGwVSn//Dvr6emhvb6evr4f+/h3NLkmqm890VGH09+/gxhuvp1LZwpo153LbbbdTLq8H8LmOyoItbBVGpbKZSmULvb19dHR00NvbR6WyhUplc7NLk+piYKswdu9O9PScNWVbT89Z7N6dmlSRNDsGtgqjuzvYtevOKdt27bqT7u5oUkXS7BjYKoxyeQPl8noGBnYyMjLCwMBOyuX1lMsbml2aVJe6Bh0j4jpgLVAFPplS+khDq5IaYGJgcePGK1i79gK6u4ONG69ywFHZKFWr1WkPiIjfAW4AzgY6gP8Cfi+lNFPH3yrgvn37HmdsbPprSPOtq+tEBgcfa3YZ0mHa2kosX74U4FRgz5R9M52cUvoG8OqU0ihwMrVW+dDclylJmk5dfdgppZGIuJZa6/qrwIMNrUqSdJgZu0Qmi4jFwD8Bn0sp/fUMh68C7jv60iSp0A7rEplx0DEiTgM6U0p3pZR+HhH9wIvrvaJ92Gol/f07qFQ2s3t3ors7KJc3OOioljKpD/sw9cwS+TXg2ojopTZL5EJg69yVJ80Pb01X7uoZdPwS8EXg34HvA99OKX220YVJc81b05W7WfVhz9IqnNanFvLc5z6T++8fpKOj48lpfSMjI6xc2cVPfnKg2eVJwDFO65OOF96artwZ2CoMb01X7lwPW4XhrenKnX3YKiRvTVersg9bko4DBrYK5corN7BiRRelUokVK7q48kr7r5UPA1uFceWVG9i2bSubNl3D0NAQmzZdw7ZtWw1tZcM+bBXGihVdbNp0De95z/on+7BvuWULN9xwLQ88MNjs8iTAPmwJgIMHn+Diiy+Zsu3iiy/h4MEnmlSRNDsGtgpj4cJFbN8+dRmc7du3snDhoiZVJM2O87BVGG9728Vce+0H2LKlwuDgXrq6Tmbfvp/yjndc2uzSpLrYwlZhnHlmD4sXL2H//v0A7N+/n8WLl3DmmT1Nrkyqj4GtwqhUNnPZZe9m9erVtLW1sXr1ai677N2u1qds2CWiwkjpXvbu3cuSJUsAGBoaYvv2rezf/7MmVybVxxa2CqO9vZ1qdYybbrqZ4eFhbrrpZqrVMdrb25tdmlQXW9gqjNHRUYaHh3njGy9idHSEBQs66OhYwOjoaLNLk+piC1uFMjw8zLJlyyiVSixbtozh4eFmlyTVzcBWoSxcuIjOzk5KpRKdnZ3OwVZWDGwVysGDTzA8PMzY2BjDw8Pe5aisGNgqjFKpRMRpPPLIIwA88sgjRJxGqVRqcmVSfQxsFUa1WuXee+9h8eITKJVKLF58Avfeew8NXABNmlPOElFhtLcvYGzsEAcOHADgwIEDlEol2tqc1qc82MJWYRw6NEq1WuW8885ncHCQ8847n2q1yqFDTutTHmxhq1BWrFjJ1752B11dXSxcuIgVK1bywAP3N7ssqS4GtgplcjgfPPiEYa2s2CUiSZkwsFU4bW1tU16lXPiNVaGUSiXGxsYAGBsbcw62smJgq1Cq1eqTq/PVVu9zDrbyYWCrcOwSUa78xqpwRkZGprxKuTCwVTgTdzZ6h6NyY2CrcCbGGR1vVG4MbBXOxECjA47KjYGtQnFan3JmYKtQqtUq69a9kwMHDrBu3TttZSsrpQZ+YVcB9+3b9zhjY/5PoeY7+eST6OjooFrlyYfwlkq12SJ79z7a7PIkANraSixfvhTgVGDP5H0u/qRCmTyVb3TUaX3KS12BHRHXAG8cf/vFlNKfN64kSdLTmbEPOyLOAc4FXgqcAbwsIi5qcF2SpKeoZ9DxYeDPUkoHU0ojwD3A8xtbltQ4CxZ0THmVcjGrQceIeAHwLeCVKaUfzXD4KuC+oy9NmlvTTeFztoha0NEPOkbEi4AvAlfUEdZPcpaIWk1bWxvVanXKnOzBwceaXJVUM2mWyGHqHXR8JfAPQDml9Nk5rE2adxMhbatauZkxsCNiJfAF4E0ppTsaXpEk6WnV08LeAHQCH4mIiW0fTyl9vGFVSZIOM2Ngp5TeB7xvHmqRJE3DtUQkKRMGtgqns7OTUqlEZ2dns0uRZsW1RFQ4w8PDU16lXNjClqRMGNiSlAkDW5IyYWBLUiYMbEnKhIEtSZkwsCUpEwa2JGXCwJakTBjYkpQJA1uSMmFgS1ImDGxJyoSBLUmZMLAlKRMGtiRlwsCWpEwY2JKUCQNbkjJhYEtSJgxsScqEgS1JmTCwJSkTBrYkZcLAlqRMGNiSlAkDW5IyYWBLUiYMbEnKhIEtSZkwsCUpEwa2JGXCwJakTBjYkpSJBfUcFBEnAd8G1qSU9jS0IknS05qxhR0RPcAA0N34ciRJR1JPl8hlwOXAQw2uRZI0jRm7RFJKlwJEROOrkSQdUV192Mdi+fKljb6EdMy6uk5sdgnSjBoe2Pv2Pc7YWLXRl5GOyeDgY80uQQKgra10xIau0/okKRMGtiRlou4ukZTSqgbWIUmagS1sScqEgS1JmTCwJSkTBrYkZcLAlqRMGNiSlAkDW5IyYWBLUiYMbEnKhIEtSZkwsCUpEwa2JGXCwJakTBjYkpQJA1uSMmFgS1ImDGxJykTDH8IrzYer/mYXD/506KjPv+TDd8x4zPOevYTrL+056mtIx6pUrTbsieargPt8arpaxcknn3TEfXv3PjqPlUhHNump6acCe6bsa0ZBkqTZM7BVGEdqRdu6Vi4MbBXK3r2Psnfvo6z50y88+bOUCwNbkjJhYEtSJgxsScqEgS1JmTCwJSkTBrYkZcLAlqRMGNiSlAkDW5IyYWBLUiZcrU8t572VnQwNjza7jGO2pHMBHyv3NbsMZWa61fpcD1stZ2h4lK3vf01Dr9HVdSKDg4819Br1rLEtzYZdIpKUCQNbkjJhYEtSJgxsScpEXYOOEfEW4ANAB1BJKf1VQ6uSJB1mxhZ2RDwPuAHoBc4A3hURL2xwXZKkp6inS+Qc4I6U0s9SSkPA3wNrG1uWJOmp6ukS+RXg4UnvHwZeXu8FxieAS3VbdPoAl9/x5WaXccxOePGJdHVd2OwydBypJ7DbgMm3KpaAsXov4J2Omq1b1lzd8GvMx40zwLxcQ8eXSXc6Hr6vjvMfAE6Z9P65wENzUJckaRbqaWF/BfhgRHQBQ8AbgHc1tCpJ0mFmbGGnlB4ENgFfA+4CPpNS+m6D65IkPUVd87BTSp8BPtPgWiRJ0/BOR0nKhIEtSZkwsCUpE418gEE71OYUSq3I76Za0aTvZftT9zUysE8BWLZsSQMvIR0978JVizsF+PHkDY18puMi4Exqt7IfatRFJOk4004trL8HPDF5RyMDW5I0hxx0lKRMGNiSlAkDW5IyYWBLUiYMbEnKhIEtSZkwsCUpEwa2WkpEnB0RX2/CuS+PiL88mnOf5rM+GBEfHP/5rrn4TAkMbGnCC4HnzPWHppTOmOvPVHE1ci0R6Wg9OyK+DDwP2AVcDgynlEoAEbEOODultC4izgU+CgwD9058QEScDmyj9h3/JnB+Sml1RDwHuBVYSe1h0lcC/wpcByyNiE0ppRuerqiIWADcApxOLdzvBt6cUvpFRFxB7dF5PwX2A98dP6c6Ubd0rGxhqxWdCrwXeDFwIvDupzsoIhYB24G1KaWXAb+YtHs7cPV4C/d/+GXj5CZg6/jxF1AL70PA1cA/Himsx/02cDCldBawGngm8LqI+C3gEuClwDnAiln+vlJdDGy1op0ppR+llKrA3wFnH+G43wAeSindM/5+O0BEPAtYlVL60vj2rZPOOQe4brxv+Z+BDuDX6ykqpbQTuDkiLqcW/C8Alo7X96WU0uMppSFgRz2fJ82Wga1WNDrp5zZgBCAiJroWOsZfq0Dpac479JTtk7UDr0kpnTHe+u4BflBPURFxAbU/ID8HPgXsHL/OkeqQ5pSBrVbUGxHPj4g24O3AV6j1Db9oPLQvGD/ubuA5EfGS8fdvBkgpPQL8OCLOH9/+FmqhCnAH8EcAEfFC4D+BxdRCdqYxnXOAz6eUPgUcAF5N7Q/AV4Hfj4hnREQncNHR/uLSdAxstaIfUuvG+AHwIPBJ4P3AbcCdQAJIKY1QC+lPR8S/UQveCW8Hrh7f3sMv+7ffC7wiIu4GPge8NaX0GLVBwldExIenqesTwJsj4gfUuj2+BZyaUroLqFBbv/gbwP8eyy8vHYnrYeu4FBFXA59IKT0cEa8H/jCl9IZ5ruEZwIMpJR9toznhtD4dr/4P+JeIGKE2ze6d9ZwUEa8CPnaE3a9LKT1U5+f8KvAdav86kOaELWxJyoR92JKUCQNbkjJhYEtSJgxsScqEgS1JmTCwJSkT/w+5z9Prauk55gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of movie budgets\n",
    "df['budget_adj'].plot(kind='box');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows that majority of movies in the database had a budget of zero dollars, with outliers of movies with more expensive budgets stretching outwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of revenue from movies\n",
    "df['revenue_adj'].plot(kind='box');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows that majority of movies in the database had a revenue of zero dollars, but less than that of the budgets, showing that some movies with zero budgets gained revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the top 10 movies with the most expensive budgets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>original_title</th>\n",
       "      <th>budget_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Warrior's Way</td>\n",
       "      <td>4.250000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>3.683713e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>3.155006e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Superman Returns</td>\n",
       "      <td>2.920507e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>2.716921e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spider-Man 3</td>\n",
       "      <td>2.713305e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tangled</td>\n",
       "      <td>2.600000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>2.575999e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "      <td>2.541001e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Waterworld</td>\n",
       "      <td>2.504192e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                original_title    budget_adj\n",
       "0                            The Warrior's Way  4.250000e+08\n",
       "1  Pirates of the Caribbean: On Stranger Tides  3.683713e+08\n",
       "2     Pirates of the Caribbean: At World's End  3.155006e+08\n",
       "3                             Superman Returns  2.920507e+08\n",
       "4                                      Titanic  2.716921e+08\n",
       "5                                 Spider-Man 3  2.713305e+08\n",
       "6                                      Tangled  2.600000e+08\n",
       "7                      Avengers: Age of Ultron  2.575999e+08\n",
       "8       Harry Potter and the Half-Blood Prince  2.541001e+08\n",
       "9                                   Waterworld  2.504192e+08"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the top ten movies with the most expensive budgets\n",
    "Top_ten = df.sort_values('budget_adj', ascending=False, ignore_index=True).head(10).iloc[:,np.r_[1,3]]\n",
    "Top_ten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a function for plotting\n",
    "def ploty (df,x,y,kind,figsize,title):\n",
    "    df.plot(x=x, y=y, kind=kind, figsize=figsize,color='b' )\n",
    "    plt.xlabel(x, fontsize=15)\n",
    "    plt.ylabel(y, fontsize=15)\n",
    "    plt.title(title, fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a bar chart showing the top ten movies with the most expensive budgets\n",
    "ploty (Top_ten,'original_title','budget_adj','bar',(10,8),'Top Ten most expensive budgeted movies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the top 10 movies with the most expensive budgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What year had the movies with the highest average ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average ratings for movies for each year\n",
    "avg_ratings = df.groupby('release_year',as_index=False).mean().iloc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>release_year</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.030657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    release_year  popularity\n",
       "55          2015    1.030657"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Year with the highest Average ratings\n",
    "avg_ratings.sort_values('popularity',ascending=False).iloc[:,:2].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a bar chart for average ratings of movies per year\n",
    "ploty (avg_ratings,'release_year','popularity','bar',(15,12),'Average ratings for movies per year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the year 2015 had the highest average ratings for movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Has the general budget for movies increased over the recent years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2001</td>\n",
       "      <td>2.871069e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>2.686408e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2003</td>\n",
       "      <td>2.632182e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>2.696341e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>2.252539e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>1.936979e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>1.833353e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>1.588863e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>1.638843e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.913088e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2011</td>\n",
       "      <td>1.618922e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2012</td>\n",
       "      <td>1.336439e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2013</td>\n",
       "      <td>1.311873e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2014</td>\n",
       "      <td>1.042674e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.111100e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    release_year    budget_adj\n",
       "41          2001  2.871069e+07\n",
       "42          2002  2.686408e+07\n",
       "43          2003  2.632182e+07\n",
       "44          2004  2.696341e+07\n",
       "45          2005  2.252539e+07\n",
       "46          2006  1.936979e+07\n",
       "47          2007  1.833353e+07\n",
       "48          2008  1.588863e+07\n",
       "49          2009  1.638843e+07\n",
       "50          2010  1.913088e+07\n",
       "51          2011  1.618922e+07\n",
       "52          2012  1.336439e+07\n",
       "53          2013  1.311873e+07\n",
       "54          2014  1.042674e+07\n",
       "55          2015  1.111100e+07"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average budgets of movies per year in the recent years\n",
    "avg_budget = df.groupby('release_year',as_index=False).mean().iloc[:, np.r_[0,2]].tail(15)\n",
    "avg_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8yxjzb5y61qtxxmiGL0tCHgKuAe42zvjfs3D+ttyIc8PhFzcrHsZJxpiXcRKMEe5xnrXWTnfXv4eTxPzarXPf4m7Tpt5+nsD52/YfY0xvnJ/dlXy9d3EiToL9hDsixAqcNq09wah9b2/glOL82BjTFXgLp1f+Fpwe1b9/uUvKcU6Gvo3TobGkCe/7WNXWGv/WGPNunfY6LGttpTHmuzgJ9lzjjEpzEOdz0AW4orFh/owxRTifi13AfGPMlQ3svzkjeHyKk3T/0e1pXoBTT/5dnBKX2t7o2vf6bWNMe2tt/d+LWn/CqX2eboy5G6cz6jKcoR5Hu9v8DGeYzQ+MMyrRWpwbMMcBD1hrF39tr4dXG9uVxhl15TGcE8a7cD6LXXBKtW7AuRGyOZ7COZkG5+cjEpFaU093fafhXCb9xBgzH+cydd3L5VfijPl7xBvARILNHTXjeJzE5yKcL6bjcEYH+UZz6yfdETVOxOl5vBFnqLkPcCb9OJb4HscZLeCfOF/YP+GrIwk0x7dxxns+D6e84xvu/s9w159eb/sXcZKgudba+jcZHmv7NSkW64wDfDJOovk9nPZcgFMHDG69t1tvewbOyc8ZbjzjcCb6GNXEm74uxalZ/be7jz/g/J2qfc+VOEMnfowzbvFvcZK1ryQh7o2Ro91jX4wzfF0pTpJMvf2Nxjm5uQqnlGEXX/bW1763AE4b/wqnR/bfblwv4AzJt6XObu/AqV//P5zRUcLhPpwTp9v4+njOh2WtfQGndGM9zs/09zg3hp5rrT1c3fAp7v/pOCdUTzTwrzlxBHBque/H+dz8Byc5fQFnGMnaeyjewTlJGIuTyDZUu187M+ZxOFesbsKZ8MhHnaEvrbWrgOE445Rfj/Mz6wrcylfHuW/qe1iG87s0xN1XF2ttOc6VqlU4vzs/wZlU59fN3P3rOD+X9RzdGOAiYeELBI7qno4WyzizfZ2K82XazVr7PXd5ChBbW5NonDFF77TWvutNpCIS6dwh1HbUH+HDGPMjnB7ebtbahkYKiXjGmUBlT/1L9W6d9vPAGU3tNRYJJbekawtOD/xPvY5HpDGtuaf7PZzJQ3LdS1334Ux0gft8ME6PkYhIY/4BlLvlHMAXMzlehHM5fY1HcQXD94H9DYxFfSlOLfO8r79ExBOX4lxVmOhxHCKH1Wpquuuz1n5ujPktzs1YfpyJFmpnCcsBKo5i7FMRaV0m4ZRevGuMmYRT53whzmX564M8jFq4PYtT1/umcWas3I9TanEB8AerKbbFY+4VpRE4JSqvWWuXehySyGG1uvISEZFgckeE+RnOJDpxwELg79baFz0NLAjcoVbvwKnDbYNz0+W91toHPQ1MBDDG3Ibz+ZwFXFrv/gGRiBPtSXcCzsQZm2jC5BwiIiIiIkcpBmeCps9oYOK0aC8vGYruZBYRERGR8DkJZ1bkr4j2pHsTwI4d+6ipieoe/WOWnZ3Ctm3NHRpVmkJtGzpq29BR24aO2jZ01Laho7Y9Mr/fR2ZmG3Dzz/qiPemuBqipCSjpbgK1UeiobUNHbRs6atvQUduGjto2dNS2TdZgSXNrHjJQRERERCQslHSLiIiIiISYkm4RERERkRCL9ppuERERkahVXV3Fjh3lVFVVhPQ4ZWV+ampa8nxfwRMbG09mZg4xMc1Lo5V0i4iIiLRQO3aUk5iYTJs27fH5fCE7Tmysn6oqJd2BQIB9+3azY0c5bdt2aNZrVV4iIiIi0kJVVVXQpk1aSBNu+ZLP56NNm7SjurKgpFtERESkBVPCHV5H295KukVEREREQkxJt4iIiIgExdy5s7nllhvC/tolSxZx7713HdVr63v44Qd4+OEHALj22suDsk9Q0i0iIiIiLdyaNSXs2LE96PudOPGpoO1Lo5eIiIiIRIEPF25i5oJNIdn3KQM7clzv9k3adteundx663fZurWM3r37cOutP+X0009g5szZALzxxmvMmzeHX/7yN8ya9Ql33fVP4uPj6dKl4It9rF69kj/+8bdUV1fTv/8APvnkI5555mW2b9/G3/52J1u2bMHv93PjjTfTq1dvHnrofg4cOMBjjz3MNdd8q8G4qqqq+Mc//szq1avYvn073bt35ze/+SMJCYk89dTjvPrqS6SnZ5CamkpRUTEAJ5445Iu4j5V6ukVEREQkaDZt2sgPf/gTHnvsafbv38/LL7/Q4HYVFRX88Y938Ic//IVHHplEQkLCF+v+8IffMGHCjUyc+BQdO3aiuroagH//+++MHXsujzwyiT//+Z/87W93EhPjZ8KEmzjxxJMbTbgBFi1aQGxsHA888CjPPPMSe/bs4eOPP2TZsiVMnvwqjzzyJP/3f/dSXl4W3AZxqadbREREJAqM6NuBEX2bN3Z0UzVnnO7+/QeRl5cPwKhRY5g8+bUGt1u9eiXZ2TkUFBQCcNZZ43jwwfvYvXsXmzdv4vjjTwRg7NjzeO65pwGYPXsWa9eu5aGHnJrrqqoqNmxY36S4BgwYRFpaOi+88CylpWtYv34dBw4cYO7cORx33AiSk5MBOO20kV8k+cGkpFtEREREgiYmJuaLxzU1AWJjnXQzEAjg8/moqqpy1/qAwNde5/fHEAh8ubyu6uoa7rrrPtLS0gHYunUrmZmZrFix/IhxzZz5Pg899AAXXXQpZ599Ljt37vwipvpxhCLpVnmJiIiIiATNggXz2bx5MzU1NUydOpkhQ4aRkZFBSckqAoEAM2d+AED37j3Yvn37Fwnz229PAyAlJYVOnTrz8ccfAvDWW1O/GBt78OAhvPjicwCUlKzm6qsv4dChg01KlGfPnsXpp49k7NhzSUlJYd68OdTUVDNkyFA+/HAGe/fu5dChQ3zwwbshaRf1dIuIiIhI0BQWduVPf/od27ZtZfDgIYwbdx4xMX5uu+2HZGVl06/fAHbt2klsbCy/+c0f+cMffk1MTAw9e/b6Yh+/+tVv+dOffseDD95Lt249vqj3/uEPb+Ovf/0j11xzKYFAgNtv/x3JyW0oKirmkUf+y3333c23v/3dBuM655zx/Pa3v+Ttt6cRGxtH37792LhxI+PGnc9FF13GhAlXk5qaSrt2oSnR8TXWfR8lCoCSbdv2UlMT3vf5+kdryMtNoX/3tmE97tHKyUmlvHyP12FEJbVt6KhtQ0dtGzpq29BpjW27efNa2rfvEvLjNKemOxgeffRBzjlnPG3btuX996fz5ptT+OMf/xa24wPs3buX8ePP4q23ZnxtXUPt7vf7yM5OASgE1tR/jXq6Q2TVhl28NGM1V482nDKgk9fhiIiIiLQY7dq154c//A6xsbGkpqbxs5/d3qTXff75PP71r4aT87///d+0bZvTpP1s3ryJG2+8lrFjz2tyzEeinu4QOVRRzb0vL2Lh6m1ceEpXzj6uyxf1SJGoNfYOhIvaNnTUtqGjtg0dtW3otMa2jdae7kh3ND3dupEyRBLiY/juhX05rnc7Xnh/Nc9MX0lNdJ/giIiIiAeivAM14hxte6u8JIRiY/xMOKc3KUlxvPnZOvYeqOTas3oRG6NzHRERETl2fn8M1dVVxMbGeR1Kq1FdXYXfH3PkDetR0h1ifp+Py0b2IDU5jpdmlLDvQCU3nd+HhLjm/7BERERE6kpKSmHPnp1kZGTj86lTL9QCgRr27NlBUlJKs1+rpDsMfD4f54woJCU5nknTLP98Zj7f/0Y/khN1VioiIiJHLyUlnR07ytmyZT11J3gJNr/fT02NarrBR3x8Iikp6c1+pZLuMDptYCfaJMby4GtL+POTc7n1kgFkpCR4HZaIiIi0UD6fj6ys3JAfpzXepBpsug4RZsOK2vGDi/tTvvMgdz4xh7Id+70OSURERERCTEm3B4oLsrjt8oEcrKjmzklzKd2iM0cRERGRaKak2yOFHdL4+ZWDiI3x8Zen5mJLd3gdkoiIiIiEiJJuD3XIbsMvrhxMRkoC/3z2c+atKPc6JBEREREJASXdHstKS+RnVwyic04K97y4iJkLNnkdkoiIiIgEmZLuCJCaHM9PLhtAUZcMHnljKVM/LfU6JBEREREJIiXdESIxPpbvfaM/Q3vl8uy7K3nu3ZWa1lVEREQkSmic7ggSF+vnxnOLSUmKY8qnpew5UMk1Ywwxfp0biYiIiLRkSrojjN/v48pRPUlNjuPVD9c408afV0xcrKaNFxEREWmp1IUagXw+H+ef1JXLR/Zg3oqt/POZz9l/sMrrsERERETkKCnpjmAjh+Rxw7m9WblhF3/931x27avwOiQREREROQpKuiPccb3b871v9GPztv38adIcynce8DokEREREWkmJd0tQN+u2fz4soHsO1DJnZPmsL5sr9chiYiIiEgzKOluIbp3SudnVwzCB/z5ybmsWL/T65BEREREpImUdLcgnXJS+MWVg0lNjuMfT89nwaqtXockIiIiIk2gpLuFaZuRxM+vHEyH7Dbc/cJCPl682euQREREROQIwjpOtzHmDuBi9+lka+1tddYNACbW2TwH2GGt7WOMuQb4M7Clzmt/GfqII1Nam3huu3wgd7+wgAdfW8Le/ZWcOTTP67BEREREpBFhS7qNMSOBUcBAIABMNcaMt9a+BGCtnQ8McLdNBmYBN7kvHwLcaq39X7jijXRJCbH88OL+/PfVJfzvnRXsOVDB+JO64vP5vA5NREREROoJZ3nJJuBH1toKa20lsBTIb2TbnwPvW2tnus+HAtcYYxYaYyYZYzLDEG/Ei4uN4dvn9+Hk/h14/aO1PDHNUlMT8DosEREREanHFwiEP0kzxvQAPgRGWGtX1FuXDiwH+lpry9xlLwF/Bz4C7gTyrbVXNOFQBUBJEEOPSIFAgCemLOW5d1Ywol9HfnTFIE0bLyIiIuKNQmBN/YVhrekGMMYUA5OBn9RPuF1XAi/XJtwA1trxdV7/V2BVc465bdveqO8BPmtoHv5AgGemr2T7rgPcckFfkhKa/uPNyUmlvHxPCCNsvdS2oaO2DR21beiobUNHbRs6atsj8/t9ZGenNL4+jLFgjBkBvAP8zFr7WCObnQ88Xec16caYH9ZZ7wOqQhZkCzZ6WD7fGluELd3J3/43j937NW28iIiISCQIW9JtjMkDXgYut9Y+3cg2PmAw8HGdxXuB24wxw93ntwAvhTDUFm1E3w7cckFfNmzdx58nzWXbroNehyQiIiLS6oWzp/vHQCLwT2PMfPffTcaYN4wxQ9xtcoAKa+0XmaK1thpnmMH7jDFLcZLy2+rvXL40oEdbfnTJAHbtq+DOSXPYsHWf1yGJiIiItGqe3EgZRgVASWuo6W7IurK9/POZ+VRV1/CDi/vTrWN6o9uqVit01Laho7YNHbVt6KhtQ0dtGzpq2yOrU9Pd4I2UmpEyiuXlpvDzqwaTnBjL3/83n0Ul27wOSURERKRVUtId5XIzkvjFlYPJzUzi388tYNbSLUd+kYiIiIgElZLuViA9JYGfXj6Qbh3TeOCVxUyfu97rkERERERaFSXdrURyYhy3XjKA/t3bMunN5bwys4Qor+cXERERiRhKuluR+LgYbr6gDyP6tOeVmSU89dYKapR4i4iIiIRc2GekFG/F+P1cN7aIlOQ4ps1ax54DFUwY19vrsKLOjj2HmLeinDm2nJhYP9+/sC8xfp3jioiItFZKulshv8/HJaf3IC05nufeW8X+g1Xccf3xXofV4m3ZsZ+5y8uZa8tZtXE3AJmpCezYc4jZy8oZ3rudxxGKiIiIV5R0t2JnHdeFNklxPDZ1GT++6wOGmBx6F2TRpX0qfp/P6/AiXiAQYH35PubYMuYuL2d9uTMJUZd2qYw/uSuDeubQITuZOx75jKmfljKsKBef2lVERKRVUtLdyp3cvyOpyXG89tFaXnh/NS+8v5o2ibEUFWRRXJBJcUEWbTOSvA4zYtQEAqzeuJu5tpw5y8so33kQH9CjczqXntGDQT3afq29xp/ajf889zm2dCe9umR6E7iIiIh4Skm3MLBHDqNO6MrKkq0sWbuDJSXbWbRmO7OXlQHQLjOJ3oVZFBdk0Ss/k+TE1vWxqaquwa7byVxbztwV5ezaW0GM30dRQSZnH9eFAT1ySG8T3+jrTxucx+OTlzB1VqmSbhERkVaqdWVPcljpKQkcX9ye44vbEwgE2Lh1H4vX7GDJmu18tHAz787dgN/no7BjKsUFWfQuyKJrxzRiY6LvBsGKymoWl2xnzvJyPl+5lX0Hq4iP89O3azaDe+bQr1vbJp98xMfFcPrgzrw8o4QNW/fRqW2bEEcvIiIikUZJtzTI5/PRKSeFTjkpjBqaR1V1Das27GLxmu0sLtnBax+t4dUP15AYH0Ov/Ex6F2RSXJhF+6zkFlu3vP9gFQtWbWXO8nIWrt5GRWUNyQmxDOjRlsE9c+hdmEVCXMxR7fu0gZ144+O1vDmrlOvOLgpy5CIiIhLplHRLk8TG+DH5mZj8TC44GfYdrGSp2wu+eM125q/cCjijdRQXZNG7MJPeBVmkJTdedhEJdu+rcIb2W17O0jU7qK4JkN4mnhF9OjDI5GDyMoLSk5+aHM+Ifh2Y8flGLji5K+kpCUGIXkRERFoKJd1yVNokxjGkVy5DeuUCULbzAEtKnAR87vJyZi7cBEB+bsoX9eA9OqcTf5Q9xcG0ddcB5i7fylxbxor1uwgAORmJnDkkj0Emh64d00IyesuooXm8N3cD78xdzwUndwv6/kVERCRyKemWoMjNSCJ3YCdOHdiJmpoAazbvYfGa7Swp2c5bn61j6qelxMX66dE5neKCLIoLs+icmxK2oQk3bt3HHHcM7bVb9gDQOSeFc0YUMNjk0jmnTcjLYtplJjOoZw7vzt3A2OMKSIj3/gREREREwkNJtwSd3++ja8c0unZM45wTCjhYUcXydTtZXLKDxWu289x7q3juvVWkJsfRuyDLqQcvyCIrLTFoMQQCTuI/d7kzK+Tm7fsB6NYxjYtO68agnjm0y0wO2vGaavTwfOa4VwLOGNw57McXERERbyjplpBLjI+lX7e29OvWFnCmSK+tBV+yZgefLtkCQIfsZHoXOKUoJj+DpITmfTyra2pYsW6XMyvkinK27z6E3+ejV5cMRg7pzMAeOWSmeltL3b1TOt07pTNtVimnDeyE398ybzoVERGR5lHSLWGXmZrAiL4dGNG3wxezOi4u2c6SNduZ8flG3pmznhi3t9y5KTOLwg6pxPi/fkNjZVUNS9w68nkrtrL3QCVxsX76FGYx/qSu9O/elpSkOA/eZeNGD8vnnpcWMnd5+Rc18SIiIhLdlHSLp3w+H3m5KeTlpjBmeD6VVdWsXL+LxWucUpRXZpbw8swSkhJi6ZWfQXFhFiYvg43b9jPHlrFg1TYOVlSTlBBD/25tGdQzhz5ds0iMj9yP9sAebcnNTGLKp6UMNjktdohFERERabrIzUykVYqLjaGoIIuigiy+QTf27K9g6Vp3aMKSHcxbsfWLbVOT4xhW1I5BPXMo6pJJXGzLmKTH7/cxemgeT7y5nBXrd9EzL8PrkERERCTElHRLREtNjmdYUTuGFbUjEAhQtuMAy9ftJDcziR6dM1psTfQJfTvw0owSps0qVdItIiLSCijplhbD5/PRLiuZdlnhH3Uk2BLiYjh9UCde+3ANm7bto0O2poYXERGJZi3jerxIFDp9UGdiYvy89dk6r0MRERGREFPSLeKRtDbxjOjbng8XbWb3vgqvwxEREZEQUtIt4qFRQ/OorKph+tz1XociIiIiIaSkW8RDHbLbMKB7W6bP3cChymqvwxEREZEQUdIt4rExw/PZe6CSjxZt9joUERERCREl3SIe69E5ncIOabw5q5SamoDX4YiIiEgIKOkW8ZjP52PM8Hy27DjA/JVbj/wCERERaXGUdItEgEE929I2PZGps0q9DkVERERCQEm3SASI8fsZNTSPlet3sXLDLq/DERERkSBT0i0SIU7s14E2ibFMU2+3iIhI1FHSLRIhEuNjOXVgJ+bacsp27Pc6HBEREQkiJd0iEeSMwZ2JifHxpqaGFxERiSpKukUiSEZKAscVt2fmgk3sPVDpdTgiIiISJEq6RSLM6KF5VFTV8K6mhhcREYkaSrpFIkynnBT6dcvmnTnrqazS1PAiIiLRQEm3SAQaPSyf3fsr+XjxFq9DERERkSBQ0i0SgXrlZ9ClXSrTZpVSE9DU8CIiIi2dkm6RCOTz+Rg9PI9N2/azYNU2r8MRERGRY6SkWyRCDTG5ZKclMO1TTZYjIiLS0inpFolQsTF+zhySh123k5JNu70OR0RERI6Bkm6RCHZS/44kJWhqeBERkZZOSbdIBEtKiOXUAR35bFkZW3ce8DocEREROUpKukUi3Mghefh9Pt6cranhRUREWqrYcB7MGHMHcLH7dLK19rYG1n8T2OEuetBae48xJh+YBOQCFrjCWrs3TGGLeCozNYHhvdsx4/NNnHdiIW0S47wOSURERJopbD3dxpiRwChgIDAAGGyMGV9vsyHApdbaAe6/e9zl9wL3Wmt7AbOB28MUtkhEGD0sn0OV1bw3b4PXoYiIiMhRCGdP9ybgR9baCgBjzFIgv942Q4BfGGO6AB8APwaqgZOB891tJgLvAz8NfcgikSEvN4XiwizenrOeUUPziYtVZZiIiEhL4gt4MNudMaYH8CEwwlq7wl2WAjwL3AqsxEmu1wL/AT6z1nZ2t4sF9ltr45twqAKgJNjxi3hhni3j1//9mO9fMpCRw+qfr4qIiEiEKATW1F8Y1ppuAGNMMTAZ+Eltwg3g1mifXWe7fwCP4JSW1D8zqGnOMbdt20tNjabSPpycnFTKy/d4HUZUClbbdspMpHNOCs+/s5x+BRn4fL4gRNey6XMbOmrb0FHbho7aNnTUtkfm9/vIzk5pfH0YY8EYMwJ4B/iZtfaxeuvyjTHfrLPIB1QCZUC6MSbGXd4B2BiOeEUiic/nY8zwPDZs3ceiku1ehyMiIiLNEM4bKfOAl4HLrbVPN7DJAeCvxphCY4wPuBl4yVpbCcwALnG3uxqYEoaQRSLOsKJ2ZKYmMFVTw4uIiLQo4Swv+TGQCPzTGFO77H7gXODX1trZxpgbgdeAeGAm8A93u+8AjxljfgWUApeFMW6RiBEb42fkkM489+4q1m7eQ5f2qV6HJCIiIk0QtqTbWvt94PsNrLq/zjYvAC808Nq1wKkhC06kBTmlfyde+3AN0z4r5YZzir0OR0RERJpA446JtDDJibGc3L8js5aUsX33Qa/DERERkSZQ0i3SAp05JA+AtzQ1vIiISIugpFukBcpOT2RYUS7vz9/I/oNVXocjIiIiR6CkW6SFGj0sn4MV1XzwuUbQFBERiXRKukVaqC7tUynqkslbs9dRVd2s+aJEREQkzJR0i7Rgo4fls2PPIT5bWuZ1KCIiInIYSrpFWrC+XbPo2LYNU2eVEggEvA5HREREGqGkW6QF8/l8jB6Wx7qyvSxZu8PrcERERKQRSrpFWrjjercnvU080zQ1vIiISMRS0i3SwsXF+jljcGcWlWxnXdler8MRERGRBijpFokCpw7sRHycnzdnqbdbREQkEinpFokCKUlxnNSvI58s2cKOPYe8DkdERETqUdItEiXOHJpHTSDA23M0NbyIiEikUdItEiVyM5IYbHJ5b95GDhzS1PAiIiKRREm3SBQZPSyPA4eqmLFgk9ehiIiISB1KukWiSLeO6fTsnM5bn5VSXaOp4UVERCKFkm6RKDN6eD7bdh9i9rJyr0MRERERl5JukSjTv3tb2mUlM/VTTQ0vIiISKZR0i0QZvzs1/Note7ClO70OR0RERFDSLRKVTihuT2pyHFM1WY6IiEhEUNItEoXi42I4Y1BnFqzaxoat+7wOR0REpNVT0i0SpU4b1Im4WE0NLyIiEgmUdItEqdTkeE7s24GPF29m115NDS8iIuIlJd0iUWzU0DyqqwO8M3e916GIiIi0akq6RaJYu6xkBvbM4d25GzhUUe11OCIiIq2Wkm6RKDdmWD77DlYxc6GmhhcREfGKkm6RKNe9czrdOqUxbVYpNTWaLEdERMQLSrpFWoExw/LZuusgc5drangREREvKOkWaQUG9sghNyOJKZoaXkRExBNKukVaAb/fx6hheZRs2s2K9bu8DkdERKTVUdIt0kqM6NuBlKQ4pmmyHBERkbBT0i3SSiTExXDawE7MX7GVTds0NbyIiEg4KekWaUVOH9yZmBg/b322zutQREREWhUl3SKtSHqbeE7o054PF21m974Kr8MRERFpNZR0i7Qyo4flUVlVw3RNDS8iIhI2SrpFWpkO2W0Y0L0t0+du4FClpoYXEREJByXdIq3Q6GF57D1QyUeLNnsdioiISKugpFukFeqZl0Fhh1Te1NTwIiIiYaGkW6QV8vl8jB6Wz5YdB5i/cqvX4YiIiEQ9Jd0irdRgk0Pb9ESmarIcERGRkFPSLdJKxfj9nDk0j5Xrd7Fyg6aGFxERCSUl3SKt2En9OpCcEKup4UVEREJMSbdIK5YYH8tpgzox15ZTtmO/1+GIiIhELSXdIq3cGYM74/f7eFNTw4uIiISMkm6RVi4jJYHji9szc8Em9h6o9DocERGRqBQbzoMZY+4ALnafTrbW3lZv/XnAbwEfUAJcZ63dYYy5BvgzsKXOa38ZprBFot7oYXnMXLiJd+eu55wRhV6HIyIiEnXC1tNtjBkJjAIGAgOAwcaY8XXWpwH3AWOttf2BBcBv3NVDgFuttQPcf0q4RYKoU04Kfbtm886c9VRWaWp4ERGRYAtneckm4EfW2gprbSWwFMivsz4OuNlau8F9vqDO+qHANcaYhcaYScaYzLBFLdJKjBmWx+79lXzw+SavQxEREYk6vkAg/FNAG2N6AB8CI6y1KxpYnwTMAO621j5mjHkJ+DvwEXAnkG+tvaIJhyrAKVMRkSMIBAL86v6PWLl+J/fedjrZ6UlehyQiItISFQJr6i8Me9JtjCkGJgN3WGsfa2B9OvASUGKt/VYD6zOBVdbarCYcrgAo2bZtLzU14T+5aElyclIpL9/jdRhRqSW17ZYd+/n1w7Po2zWbWy7o63U4R9SS2ralUduGjto2dNS2oaO2PTK/30d2dgo0knSHdfQSY8wI4B3gZ40k3B1wergXABPcZenGmB/W2cwHVIUhXJFWp11mMuedWMjc5eXMseVehyMiIhI1wnkjZR7wMnC5tfbpBtbHAK8Bz1prf2Ctre2a3gvcZowZ7j6/BacnXERCYNTQPPJyU5j0lmX/QZ3fioiIBEM4hwz8MZAI/NMYU7vsfuBc4NdAHjAIiDXGfMNdP9taO8EYczFwn1vrvRy4Ooxxi7QqsTF+rj2rF394fDbPv7+Kq0ebI79IREREDitsSbe19vvA9xtYdb/7/2wa6Xm31s7ASchFJAwKO6Rx5pA83vxsHcf1bkfPvAyvQxIREWnRNCOliDTo/JMKyU5L5LGpy6isqvE6HBERkRZNSbeINCgxPparxxg2bdvP5I/XeB2OiIhIi6akW0Qa1bdrNscVt2Pyx2vZUL7X63BERERaLCXdInJYl57Rg6SEWB6baqnxYDItERGRaKCkW0QOKy05nktO787KDbt4b94Gr8MRERFpkZR0i8gRndCnPcUFmTz/3iq27z7odTgiIiItjpJuETkin8/HVWN6UVMT4Mm3lhNQmYmIiEizKOkWkSbJzUjivJMKmbdiq6aIFxERaSYl3SLSZKOG5pHfLoUn31rO/oOVXocjIiLSYijpFpEmi/E7U8Tv3l/Bs++u8jocERGRFkNJt4g0S0H7NEYPzeeDzzdiS3d4HY6IiEiLoKRbRJrtvJMKaZueyMSplsqqaq/DERERiXhKukWk2RLiYrhmTC+2bN/Pax+t9TocERGRiKekW0SOSnFhFscXt2fKJ2tZryniRUREDktJt4gctUvP6O5MET9lGTU1GrtbRESkMUq6ReSopSbHc9kZPVi1cTfvaop4ERGRRinpFpFjclxxO/oUZvH8+5oiXkREpDFKukXkmPh8Pq4abQgEAjwxzWqKeBERkQYo6RaRY5aTkcT4k7ry+aptfLaszOtwREREIo6SbhEJipFDOtOlfSpPvb2CfZoiXkRE5CuUdItIUMT4/Vx3Vi/27q/k2ekrvQ5HREQkoijpFpGgyW+XyuhhecxYsIllazVFvIiISC0l3SISVOeeWEhORiKPTV1GRaWmiBcREQEl3SISZAlxMVw9phdbdhzgtY/WeB2OiIhIRIg93EpjzOXA89baCvfx4VQApdbaWUGLTkRapOKCLEb0bc/UT0sZVtSOvNwUr0MSERHx1GGTbmAS8DZQ5j4+koAx5nFr7XXHHJmItGiXnN6DBau2MXHKUn551RD8fp/XIYmIiHjmsEm3tdbf0OOGGGNigPHAI4CSbpFWLiUpjstG9uC/ry7hnTnrOXNontchiYiIeCZoNd3W2mrgQ+A/wdqniLRsw4va0bdrNi9+sJqtuw54HY6IiIhnjlTTXQI0aU5na21Xa+0m4BfBCExEWj5nivie3P7QLCa9uZzvf6MfPp/KTEREpPU5Uk33Q3UetwVuAV4EPsG5cXIocDHwj5BEJyItXtv0JMaf3JWn31nBrKVlDO/dzuuQREREwu5INd1/rH1sjJkM/MRa+391tzHGfAxcEZLoRCQqjBzcmU+XbOapt5dTXJhFSlKc1yGJiIiEVXNquk8FXm9g+XRgSFCiEZGo5Pf7uGZML/YfrOKZ6Su8DkdERCTsmpN0r8UZnaS+awAbnHBEJFrlt0tlzPB8Ply4mSVrtnsdjoiISFgdqaa7rt8A/zPGjALmAD7gBGAYcE7wQxORaHPOCQV8tqyMx6dafvetYcTHxXgdkoiISFg0uafbWvsscBqwDRgHnA2sAU6w1r4ZkuhEJKrEx8VwzZhelO08wCsflngdjoiISNg0p6cba+0HwAchikVEWoGiLpmc2K8D0z5dx/CiduS3S/U6JBERkZBrctJtjEkEbgD6ArXXhH1AAjDEWtsz+OGJSDS6+LTuLFi5lYlTlvHLqwcT4w/aPF0iIiIRqTnfdPcAfwZ6AFcDBcDpwCXAy8EOTESiV0pSHJef2ZM1m/fw9uz1XocjIiIScs1Jus8BrrHWngqsBm4GugIvACnBD01EotnQXrn065bNSzNWU75TU8SLiEh0a07SnQ586j5eDAy21lYDf8K5qVJEpMl8Ph9XjTL4fD6emGYJBAJehyQiIhIyzUm6NwGd3MfLgX7u411ATjCDEpHWITs9kQtP7sqiku18smSL1+GIiIiETHOS7heBicaY44G3gWuMMecBtwOrQhGciES/0wd1pmvHNP739gr27K/wOhwREZGQaE7S/XPgTaDQWvsW8CpOPff5wI+CH5qItAZ+v49rx/TiwKEqnpm+0utwREREQqI5k+McstZ+11r7lPv8eiADyHGTcACMMb80xmQEO1ARiV6dc1M467h8Plq0mcUlmiJeRESizzENjmut3Wutraq3+KdA1rHsV0Ran3NOKKBdVjKPTV3Gocpqr8MREREJqmbNSNlEvsZWGGPuAC52n0621t5Wb/0A4CEgDWfmy5ustVXGmHxgEpALWOAKa+3eEMQuIh6Ji43h2jGGvzw1j1dmlHDx6d29DklERCRowjYNnDFmJDAKGAgMAAYbY8bX22wScIs7u6UPuN5dfi9wr7W2FzAb5+ZNEYkyJj+Tk/t3ZNpnpazdvMfrcERERIImnHMvbwJ+ZK2tsNZWAkuB/NqVxpguQJK19hN30UTgImNMHHAy8Hzd5eEKWkTC6+LTupGWHM+jU5ZSXVPjdTgiIiJBEYrykgZZaxfXPjbG9MApMxlRZ5OOOIl5rU1AZ6AtsLtO7Xjt8ibLztaEmU2Rk5PqdQhRS23bPN++sD9/fvwzPlpSzgWnHb7MRG0bOmrb0FHbho7aNnTUtscmbEl3LWNMMTAZ+Im1dkWdVX6g7pR0PqCmgeW4y5ts27a91NRotrvDyclJpbxcl/NDQW3bfD06pDCge1uenLoU0zmN3IykBrdT24aO2jZ01Laho7YNHbXtkfn9vsN29IaivKTR7NYYMwJ4B/iZtfaxeqvXAx3qPG8PbATKgHRjTIy7vIO7XESilM/n48pRPfH7fTwxdZmmiBcRkRYvFEl3g6OXGGPygJeBy621T9dfb61dCxx0E3OAq4Apbv33DOASd/nVwJRgBy0ikSUrLZELT+nG4jU7+HjxZq/DEREROSZNLi8xxkwHLrDW7qy3PAeYaq0d7C46E6fXur4fA4nAP40xtcvuB84Ffm2tnQ1cATxojEkD5gJ3udt9B3jMGPMroBS4rKlxi0jLddqgTnyyZDNPv7OSPl2zSUuO9zokERGRo+I73GVbY8wJQO1dTI8C3wd219usN/Ada21aSCI8NgVAiWq6j0y1WqGjtj02G8r38ptHP2NoUS43nFP8lXVq29BR24aO2jZ01Laho7Y9sjo13YXAmvrrj9TTXYMzWU1tyci/6q0PAHuA3x9TlCIijeiUk8LY47vw6odrOKG4PX26ZnsdkoiISLMdNul2x8yOBzDGlABDrbVbwxGYiEitsccX8NmyMh6fZvn9t4aTEB9z5BeJiIhEkCbfSGmtLaxNuI0xBcaYWHfiGhGRkIqL9XPNmF5s3XWQl2as9jocERGRZmvW6CXGmNuNMfuAlTizST5hjHlYybeIhFrPvAxOHdCRt2avo2RT/VtLREREIluTk25jzI+ACcD1wCF38TPAOOAPwQ9NROSrvnFqd9LaxPPYlGVUVWuKeBERaTma09P9LeBma+1TuDNCWmtfAr6JhvATkTBITozlyjN7Ulq2lzc/W+d1OCIiIk3WnKS7EFjcwHIL5AQnHBGRwxtschnUM4dXZpawceter8MRERFpkuYk3RY4uYHlF7rrRETC4oozexIb4+MfT85h49Z9XocjIiJyRM1Jun8D3GOM+QvOUINXGGMex6nn/nMIYhMRaVBmagLXjOnFui17uP3hT3no9SWU7djvdVgiIiKNas6QgS8DFwMnANXAD3FKTsZZa58OSXQiIo0YVtSOB39xJqOH5vPZsjJ++eCnPDZ1Gdt3H/Q6NBERka850oyUX2GtfQN4I0SxiIg0S3pKAhef3p1Rw/KY/NFa3pu/gQ8XbuLUAZ0Ye3wX0lMSvA5RREQEaEbSbYz5dSOrAkAFsB6YYq3dHozARESaKiMlgStG9WT08Dxe/2gN0+du4IPPN3L64M6cNTyf1OR4r0MUEZFWrjk93afi3Eh5CFjuLusBJAGlQBZQYYw5w1q7IJhBiog0Rdv0JK49q4izjuvCqzNLmPZpKe/O28CoIXmMHpZHcqLm8RJvrdqwi+Wb9tCzQ6rXoYhImDXnRsrZwHtAF2vtQGvtQJxZKacCk4Bs4GXgr0GOUUSkWdplJnP9OcX8bsJw+hZm8dpHa7jtvo95/aM1HKyo8jo8aaXKdx7gn89+zl8e/4zl63Z6HY6IhFlzku5vArdaa7fWLnBLSX4GfNtaWwX8Azg+uCGKiBydTm3b8J3xfbnj2qH06JzOix+s5qf3f8y0WaVUVFZ7HZ60IlXVNTzw6mIgQE5GEg9PXqITQJFWpjlJtw9Ia2B5Bl+WqVTjzlYpIhIpurRP5fsX9eeXVw0mLzeFZ6av5KcPfMz0ueuprNKfLAm9l2eUsHrjbq4Z04tbLx/M1p0HeXb6Sq/DEpEwak5N90vAg8aYm4BPcZLw4cA9wKvGmCTgpzhlKCIiEadbp3R+fOlAbOkOXvxgNZPeXM6UT0o5d0QBJ/RtT4y/Of0QIk2zqGQbb3yylpP7d2RYUTtyclIZPSyfqbNKGdgzh75ds70OUUTCoDnfMN8HlgDvAvuAvcDbOFPDfxcYBZwF/CTIMYqIBJXJz+RnVwzi1kv6k9YmjkenLOOXD37KJ4s3U1MT8Do8iSK79lXw0OtL6di2DZeN7PHF8vEnF9KpbRsefWMp+w5WehihiIRLcybH2WetvRDohjNJznigh7X2YmvtLmCqtbaTtXZ+aEIVEQken89Hn8JsfnX1EL57YV/iY2P472tLuOORWcyxZQQCSr7l2NQEAjz0+hIOHKripnOLSYiL+WJdXGwME8b1Zs/+Sp58c/lh9iIi0aJZk+MAWGtLgJIGlh8KSkQiImHk8/kY2COH/t3bMntZGS/PKOGelxaR3y6F8Sd1pV+3bHw+n9dhSgs07dNSFpds5+rRhs65KV9b36V9KuecUMDLM0sY1DOHIb1yPYhSRMLlsEm3MaYGZ/KbI7LWxhx5KxGRyOT3+RhW1I7BJodPFm/hlZkl/Pv5BXTrmMb4k7vSuyDL6xClBVm1YRcvfrCaISaHUwZ0bHS7s4/vwvyVW3l8mqVH53TNoioSxY5UXnJ1nX93AAeAvwPfAM4Ffg/sxLmBUkSkxYvx+xnRtwN33nAcV48xbN9ziL8/PZ+/PjWXFet3eh2etAD7D1bywKuLyUhJ4Nqzeh32SklsjJ8J43pzsKKax6ZalTWJRLHD9nRbayfVPjbGfIgzHvcTdTaZbIxZAvwcJxkXEYkKsTF+Th3QiRF92vP+/I28/vFa/jRpLn26ZjH+pK4UdmhoBFVp7QKBABOnWrbvPsTPrhzUpFlQO7ZtwzdO6crT01cyc+EmTurXeM+4iLRczRm9ZCDwSQPLPwd6BiccEZHIEhcbw8ghefzlpuO56LRulGzcze8fm83dLyxgfdler8OTCPPB5xuZvayM8ScX0r1TepNfN3JoHiYvg/+9vYKtuw6EMEIR8Upzku4lwLfrLjDG+IEfA3ODGZSISKRJiIvhrOFd+Ou3T+D8kwpZVrqDOx6Zxf2vLGLTtn1ehycRYEP5Xp56ewW9CzI567guzXqt3+fjm2OLCACPTF5KjcpMRKJOc0YvuQ2nnGQsMA9ncpyhQBZwRghiExGJOEkJsZw7opDTB3Vm2qxS3p69ns+WlXFCn/acO6KQnIwkr0MUD1RUVnP/K4tJio/h+nG98R/FiDc5GUlcdkYPJk5Zxjtz1nPmkLwQRCoiXmnOON3Tgb7Ay0AKkAw8CfSx1s4JSXQiIhEqJSmOC0/pxl9uOp4zh+Qxa2kZv/jvJzw+zbJ990Gvw5Mwe/qdFWzYuo8J43of0wgkJ/XrQL9u2Tz/3ipdQRGJMs0ap9tauxKNVCIi8oW0NvFcekYPRg/LZ/LHa3h//kZmLtjEqQM7Mvb4AtLbxHsdooTYZ8vKeG/+Rs4ank+fY5zS3efzce1Zvbj9oU956PWl/OKqQcT4m1MJKiKRqslJtzHmzcOtt9aOOvZwRERapszUBK4cZRgzPJ/XPlzD9Dkb+ODzjZwxuDNnDe9CStKRR7GQlmfrzgNMnLKMwg7OeO7BkJGSwFWjDfe/spg3PinlnBMKgrJfEfFWc06fN9T7twVIA0YAs4IfmohIy9M2PYnrzi7ij9cPZ1DPHKZ+Uspt933EyzNWc6iy2uvwJIiqqmt44NXFQIAbzysmNiZ4PdLDitoxrCiXV2eWULplT9D2KyLeaXJPt7X2uoaWG2PuADoFLSIRkSjQLiuZG84pZuxxXXh5ZgmvfriGdWV7ufmCvkd1k51EnpdnlLBq425uOq+Y3BDcQHvlKIMt3cmDry/h19cMJS5WZSYiLVkwfoMfAy4Own5ERKJOp5wUbh7fl8vO6MG8FVt5eUaJ1yFJECwu2c4bn6zl5P4dGFbULiTHSEmK47qze7GhfB8vz1wdkmOISPgEI+keDlQFYT8iIlFr5JDOnNSvA69/tIZZS7d4HY4cg137Knjw9SV0yE7mspGhnRuuX7e2nNy/A1M/LWXl+l0hPZaIhNax3kiZBgwC7g1aRCIiUcjn83HVaMPm7ft5ZPJScjOTKGivqeRbmppAgIdeX8KBQ1X8+JIBJMTFhPyYl5zegyVrdvDQ60v47TeHkRAf+mOKSPAdy42UG3CmgL8BZ1ZKERE5jNgYPzeP70tqchx3v7CQXXsPeR2SNNO0T0tZXLKdy87oQefclLAcMykhlm+NLaJ85wGefW9lWI4pIsF3zDdSiohI06W1iee7F/bjzklz+M+LC7nt8oHExarnsiVYtXEXL36wmsEmh1MGdAzrsU1+JmcOzePNz9YxqEcOxYVZYT2+iBy7ZtV0G2M6GmN+Z4x50RjzP2PML40x+s0XEWmG/HapTBjbm1Ubd/P4VEsgEPA6JDmC/QcreeCVxWSkJHDtWb3weTACzQUnd6VDdjKPvLGU/Qcrw358ETk2TU66jTHDgZXAZUAlTi/59cAKY8yg0IQnIhKdhvTK5bwTC/lw0WamzVrndThyGIFAgIlTLdt3H+LG84ppk+jNREfxcTFMGNebXXsrePKtFZ7EICJHrzk93fcBDwM9rbWXWGsvAroDrwF3hyI4EZFods6IAgabHJ57byULVm3zOhxpxAefb2T2sjLGn1xI907pnsZS2CGNcSd04ePFm5ljyz2NRUSapzlJd2/gbmvtF9dBrbVVwJ9xRjAREZFm8Pt8TBjbm7ycFB54dRGbtu3zOiSpZ0P5Xv739gp6F2Ry1nFdvA4HgHEnFNClXSqPT1vG7n0VXocjIk3UnKR7PnBcA8sHAMuCEYyISGuTEB/Ddy/sR2yMn7ueX8A+1epGjIrKau5/ZTGJ8TFcP653xMwkGhvjZ8K4Ig4cquaxqct0T4BIC3HY0UuMMZfXeToduNcYY4CPgWpgIHAb8PuQRSgiEuWy0xO55YK+/PWpedz/8iJ+cHF/Yvya8ttrT7+zgg1b93Hrxf1JT0nwOpyv6JSTwgUnd+XZd1fy0aLNjOjbweuQROQIjjRk4KQGlv28gWV/Bf5x7OGIiLROPTpncPVow6NTlvHs9FVcNrKH1yG1arOXlfHe/I2MGZ5Pn67ZXofToFFD85i/opyn3l5OUZdMstISvQ5JRA7jsEm3tVZdLSIiYXJS/46sK9/LW7PX0TmnDSf1D+9Y0OLYuvMAj05ZRmGHNC44uavX4TTK7/fxzXG9uePhWTzyxlJuvWRAxJTAiMjXKakWEYkgl5zeneKCTB6fZlmxfqfX4bQ6VdU1PPDqYiDAjecVExsT2V+TuRlJXHJ6d5as2cG7czd4HY6IHEaTZ6QMFmNMGvARMM5au6bO8gHAxDqb5gA7rLV9jDHX4IySssVdN9la+8uwBCwiEkYxfj83nd+HPzw2m3teXMjt1wwlO11lA+HyyswSVm3czU3nFZObkeR1OE1yyoCOzF1RznPvrqRPYRbtspK9DklEGhDWU3h3gp2ZQM/666y18621A6y1A4ATgB3ATe7qIcCtteuVcItINGuTGMf3vtGPyuoa7nphAYcqqr0OqVVYXLKdNz5ey8n9OzCsqJ3X4TSZz+fjurOKiI3x89DkJdTUaDQTkUgU7utm1wM3AxuPsN3PgfettTPd50OBa4wxC40xk4wxmaEMUkTEax2y23DjuX1YX77XSaQ0LFxI7dpXwYOvL6F9djKXjfxav1DEy0xN4MpRPVm1YTdTPl3rdTgi0oCwlpdYaycAOKMONswYkw7cAPSts3gT8HecspQ7gf8AVzT1uNnZKUcRbeuTk5PqdQhRS20bOtHctmfkpLL7YBWPvLaY6fM2ctnoXmE9fjS3bV01NQHufvFjDh6q4o/fHkHnDmkhP2Yo2nbcKSksXruTV2aWcMqQfAo7ejt7plday+fWC2rbYxP2mu4muBJ42VpbVrvAWju+9rEx5q/AqubscNu2vbrcdgQ5OamUl+/xOoyopLYNndbQtiN652JLtvHUm5aM5DiG9MoNy3FbQ9vWmvLpWuYtL+eq0YY2sb6Qv+9Qtu3Fp3Zl4aqt/O2J2dx+zZCIvxE02FrT5zbc1LZH5vf7DtvRG4m/jecDT9c+McakG2N+WGe9D6gKd1AiIl7w+XxcPcbQrWMaD01eQukWfekF06qNu3jx/dUMNjmcOqDlD9GYmhzPtWN6sa5sL6/MLPE6HBGpI6KSbmOMDxiMM+Nlrb3Abe5NmAC3AC+FOzYREa/ExcZwywV9aZMYx90vLGD3vgqvQ4oK+w9W8cAri8lISeDas3rhi5Ixrgf0aMuJfTvwxidrWbVhl9fhiIjL86TbGPOGMWaI+zQHqLDWHqxdb62tBi4G7jPGLMVJym8Lf6QiIt5JT0nguxf2Zc/+Su55aSFV1TVeh9SiBQIBHpu6jO27D3HjecW0SYzzOqSgumxkD7JSE3ho8lIOVWr0G5FI4ElNt7W2oM7js+s8LgPaN7D9DGBQWIITEYlQBe3T+ObYIu5/ZTFPTLNR1TsbbjMWbOKzZWVceEpXuneKvhsOkxJi+ebY3vztf/N4/r1VXHFmyxuRRSTaeN7TLSIiTTesqB3jTujCjAWbeHvOeq/DaZE2lO/lqbeW07sgk7OO6+J1OCFT1CWTkYM7886c9SxZs93rcERaPSXdIiItzPkndWVgj7Y8/c4KFpcomWqOispq7n9lMYnxMVw/rjf+KL9ScOGp3WiXlcwjbyxl/0GNQSDiJSXdIiItjN/nY8K43nRs24b7Xl7Elu37vQ6pxXh6+ko2bN3HhHG9SU9J8DqckEuIi2HCuCJ27DnE/95Z7nU4Iq2akm4RkRYoKSGW713YD7/fx10vLFAvZhPMXlbGe/M2MGZ4Pn26ZnsdTth065jO2OO78OHCzcxbUe51OCKtlpJuEZEWKicjiZvH96FsxwEeeHWxJgE7jK07D/DolGUUdkjjgpO7eh1O2J07opD83BQem7KM3fs15KSIF5R0i4i0YCY/kyvO7MnC1dt4/r1mTdbbalRV1/DAa4uBADeeV9zqZmkEiI3xM2Fcb/YfquKJaZZAQCdoIuHW+v7yiIhEmVMHduL0QZ2YOquUDxdu8jqciPPKzBJWbdjNNWN6kZuR5HU4numcm8L5J3Vlji3nkyVbvA5HpNVR0i0iEgUuPaMHvfIzeGzqMs1CWMfiNdt54+O1nNy/A8OK2nkdjufGDMune6d0nnxzOTv2HPI6HJFWRUm3iEgUiI3x853xfclMTeA/Ly5k++6DR35RlNu1r4KHXltC++xkLhupyWEA/H4f3xpXRFVNDY++sVRlJiJhpKRbRCRKpCTF8b0L+3Gospq7X1zYqqf/rgkEePj1Jew/VMW3z+tDQlyM1yFFjHaZyVx8WncWlWznvfkbvQ5HpNVQ0i0iEkU65aRwwznFlG7e06p7MqfNKmVRyXYuPaMHnXNTvA4n4pw2sBPFBZk8O30lZTs0zrtIOCjpFhGJMgN6tOWCU7oya2kZkz9e63U4Ybdq4y5efH81g00Opw7o6HU4Ecnn83Hd2UX4/T4emrxUw02KhIGSbhGRKHT2cV04rrgdL36wmnnLW8+EKPsPVvHAK4vJSInn2rN64Yvyad6PRVZaIlec2YOV63cx7bNSr8MRiXpKukVEopDP5+PaMb0o7JDKf19fwvqyvV6HFHKBQIDHpi5j++5D3HhuH9okxnkdUsQ7vrg9g3rm8NIHq1lfHv2fEREvKekWEYlS8XEx3HJBPxLjY7jrhQXsifKZCGcs2MRny8oYf3Ih3Tunex1Oi+Dz+bh6jCEpIZaHXl9CVXWN1yGJRC0l3SIiUSwzNYHvXtCPnXsruPelRVGbVG3Yuo+n3lpO74JMzjqui9fhtChpyfFcM6YXpVv28tqHa7wORyRqKekWEYlyXTumcd3ZvbDrdvLU2yu8DifoKiqruf+VRSTExzBhXG/8quNutkE9czihT3smf7yWkk27vQ5HJCop6RYRaQWOL27PWcfl8968Dbw7d73X4QTNocpqnnp7ORvK9zFhXG8yUhK8DqnFunxkD9JT4nno9SVUtOIx3kVCJdbrAEREJDwuPLkbG8r38eRbK2if3YaiLpleh9RshyqqWblxF7Z0B7Z0J6s37qa6JsCY4fn07ZrtdXgtWnJiHN8cW8Q/np7PC++v5rKRPbwOSSSqKOkWEWkl/H4fN55bzB8en829Ly3k9muHkpuR5HVYh3WwooqVG3ZhS3diS3dSsslJsv0+H13apzJqaB69umRSXJjldahRobggi9MHdeKt2esY2KMtvVrgiZlIpFLSLSLSiiQlxPK9b/TjD4/N5u7nF/CLqwaTlBA5XwUHK6pYuX4Xy0p3YtftYM2mPV8k2YUdUhk1LI9e+Zl075QeUXFHk4tOdaaIf3jyUn73rWFqZ5Eg0W+SiEgr0y4zme+c34d/PPM5D762hFsu6Ivf783NhwcOVbFi/S7sOqdcZM2mPdQEAsT4fRR0SGXM8HxMXgbdO6eTGK+vrHCovSH1T5Pm8PQ7K7ju7CKvQxKJCvoLJiLSChUVZHHZyB48+dZyXpqxmgtP6RaW4zpJ9k6nJ7t0J2s3f5lkF3ZM46zj8r/oyU6IjwlLTPJ13Tulc9bwLrzxyVoG9shhQI+2Xock0uIp6RYRaaVOH9SJ9eV7mfzxWjq1bcNxxe2Dfoz9B6tYvn4ny0t3sqx0B2u37CEQgBi/j64d0zj7+C6Y/Ay6d1SSHWnOO7GQhau38cgbS/n9t4aRrpFhRI6Jkm4RkVbK5/NxxZk92bRtP49OWUa7rGQKO6Qd0z73H6xk+bpdLCvdgV23k1I3yY6N8dG1Yzrjji+gV34GXTulkxCnJDuSxcX6ueHcYn438TMenryUH1zcX2OgixwDJd0iIq1YbIyf74zv49xY+cICbr9mKJmpTe/R3HugkhXrdmLXOT3Z67bsJeDut1vHNM45oQCTn0m3jmnEK8lucTq1bcMlp3dn0pvLeWfOes4ckud1SCItlpJuEZFWLi05nu9e2I87n5jDf15cyM+uGNjotnsPVLLcTbCXl+5kXZmTZMfFOkn2eScWYvIz6NoxjbhYJdnR4LSBnVi4ahvPvbuKovxMOuemeB2SSIukpFtERMjLTWHCuN7c89JCJk5Zxs+vGw7Anv0VLF/n3PS4rHQn68v3Ak6S3b1TOuedVEiv/EwKO6QRF6tJjqORz+fjurOL+PUjs3jgtcXcfvUQXbUQOQpKukVEBIDBJofxJxXy0owSfv/Ip2ws38uG8n0AxMf66d45nfFFXTF5GUqyW5m0NvF8a2wR/3r2c55/bxWXn9nT65BEWhwl3SIi8oVxJxSwaft+5q3YSveOaQwvakev/EwKOqQSG6MkuzXr2zWbkYM78/ac9fTpmk2/btlehyTSoijpFhGRL/h8Pq4f15u2bVPZtm2v1+FIhLnotG4sLd3BI28s5XffHEZam3ivQxJpMdRtISIiX+Hz+TyboVIiW1xsDDeeU8z+g1U8+sZSAoGA1yGJtBhKukVERKTJOuemcNFp3fh81TbenbfB63BEWgwl3SIiItIsIwd3pk/XLJ6ZvpINW/d5HY5Ii6CkW0RERJrF5/PxrbOLSIyP4b+vLqayqsbrkEQinpJuERERabb0lASuO7uIdWV7efGDVV6HIxLxlHSLiIjIURnQvS2nDerEtFnrWFyy3etwRCKakm4RERE5ahef1p0O2ck8NHkJe/ZXeB2OSMRS0i0iIiJHLSEuhhvPLWbfgUomTlmmYQRFGqGkW0RERI5JfrtULjylG/NWbOWDzzd6HY5IRFLSLSIiIsfszKF59C7I5H/vrGDTNg0jKFKfkm4RERE5Zn6fj2+N7U1cjJ//vrqEqmoNIyhSl5JuERERCYrMVGcYwbVb9vDSjNVehyMSUZR0i4iISNAM6pnDKQM6MvWTUpau3eF1OCIRQ0m3iIiIBNWlp/cgNyuZh15fwt4DlV6HIxIRYsN9QGNMGvARMM5au6beujuAbwK1p8YPWmvvMcbkA5OAXMACV1hr94YvahEREWmqhPgYbjy3N398fA6PT13Gt8/vg8/n8zosEU+FtafbGDMcmAn0bGSTIcCl1toB7r973OX3Avdaa3sBs4HbQx+tiIiIHK2C9mmMP7krs205Mxdu8jocEc+Fu6f7euBm4IlG1g8BfmGM6QJ8APwYqAZOBs53t5kIvA/8NJSBioiIyLEZMzyfRau38dRbK+iZl0G7zGSvQxLxTFh7uq21E6y1MxpaZ4xJAeYBPwEGARk4Pdptgd3W2ip3001A59BHKyIiIsfC7/MxYVxvYmN8GkZQWr2w13Q3xq3RPrv2uTHmH8AjOKUl9eeUbdZvbXZ2yjHH1xrk5KR6HULUUtuGjto2dNS2odOa2jYnJ5VbLh7AXx6fzdvzNnLVWUUhP56Ehtr22ERM0u3eLDnSWvuIu8gHVAJlQLoxJsZaWw10AJo1x+y2bXupqamft0tdOTmplJfv8TqMqKS2DR21beiobUOnNbat6ZjGiX078NzbyynMbYPJzwzJcVpj24aL2vbI/H7fYTt6I2nIwAPAX40xhcYYH07t90vW2kpgBnCJu93VwBSPYhQREZGjcPmZPcjJTOKh15ew/6CGEZTWx/Ok2xjzhjFmiLW2HLgReA1nWEAf8A93s+8ANxhjlgAnAb/yJFgRERE5KonxsdxwTjE79lTw+DRLIKAr0NK6eFJeYq0tqPP47DqPXwBeaGD7tcCp4YhNREREQqNrxzTOO6mQlz5YTf9ubTm+T3uvQxIJG897ukVERKT1GHtcF3p0TueJNy1lOw94HY5I2CjpFhERkbDx+31cf05vfD4fD762mOoaDSMorYOSbhEREQmrtulJXD3asGrDbl7/aK3X4YiEhZJuERERCbvhvdtxfHF7Xv2whJXrd3kdjkjIKekWERERT1w5qifZaYn897XFHDhUdeQXiLRgSrpFRETEE0kJzjCC23cfYtKby70ORySklHSLiIiIZ7p3TuecEQV8vHgznyzZ7HU4IiGjpFtEREQ8Ne6ELnTrlMYT05azdZeGEZTopKRbREREPBXj93P9OcUEAgEeem0JNTWarVKij5JuERER8VxuRhJXjurJ8vW7mPyJhhGU6KOkW0RERCLC8cXtGd67Ha/MKGH1xt1ehyMSVEq6RUREJCL4fD6uGtWTzNR4/vvqYg5WaBhBiR5KukVERCRiJCfGcf05xZTvOsBTb6/wOhyRoFHSLSIiIhGlZ14GY4/vwswFm5i9rMzrcESCQkm3iIiIRJxzRxRS2CGNiVOWsX33Qa/DETlmSrpFREQk4sTG+Lnh3N5U1wR46HUNIygtn5JuERERiUjtMpO5/MweLCvdybRZpV6HI3JMlHSLiIhIxDqxbweGmBxe/GA1azZrGEFpuZR0i4iISMTy+XxcPaYXaW3ieeDVJRyqqPY6JJGjoqRbREREIlpKUhwTxvWmbPt+np6uYQSlZVLSLSIiIhGvqEsmY47L5/35G5m7vNzrcESaTUm3iIiItAjjT+pKl3apTJyyjB17DnkdjkizKOkWERGRFqF2GMGKqmoenryEmoCGEZSWQ0m3iIiItBgdsttw2Rk9WLJmB299ts7rcESaTEm3iIiItCgn9+/IwB5teeH9VZRu2eN1OCJNoqRbREREWhSfz8e1Z/WiTVIcD7y6mEOVGkZQIp+SbhEREWlxUpPjmTC2N5u27ee5d1d6HY7IESnpFhERkRapuDCL0cPymD53A/NXbvU6HJHDUtItIiIiLdYFJ3cjLzeFR99Yyo7dB70OR6RRsV4HICIiInK04mL93HBuMb+b+Bm3/t/7dMhuQ3Z6Im3TE8lOS/zicVqbePw+n9fhSiumpFtERERatE5t2/Cd8/swy5azoWwPqzfuYt/Bqq9sExvjIyvNScTbpjvJeN3HmakJxPhVACCho6RbREREWrz+3dsy8vhCysudIQQPHKpi++6DbN11kG27D7Jt15ePF6zaxq59FV95vd/nIzM1nuz0pK/0kGenJ9I2LZGstETiYpWUy9FT0i0iIiJRJykhlk45KXTKSWlwfWVVNdt2H2Kbm4hv3XXAebzrIMvX7WD7kkPUn/AyPSWetm5CXpuMZ9cpY0mMV1oljdOnQ0RERFqduNgY2mcl0z4rucH1VdU17NxzyE3I3Z5yt8d8zaY9zLHlVNd8NStPSYr7ai95vR7z5IRYfKorb7WUdIuIiIjUExvjp21GEm0zkjANrK8JBNi1t8JNxt1e8t2H2LrrAJu27WNRyTYqKmu+8prE+JgvesjbZycz9vgCUpLiwvOGxHNKukVERESayakBTyAzNYHupH9tfSAQYM+Byi9KVur2mG/bfZBFJdvZuHU/37+on0ZVaSWUdIuIiIgEmc/nIy05nrTkeAo7pH1t/fS565n05nKmflrK2cd18SBCCTfdhisiIiISZqcN7MQQk8OL769mxfqdXocjYaCkW0RERCTMfD4f155VRHZ6Ave/spi9Byq9DklCTEm3iIiIiAeSE2O56bw+7N5XwcOvLyFQf4xCiSpKukVEREQ8UtghjUtO787nq7YxbdY6r8OREFLSLSIiIuKhMwZ3ZnDPHF54fxWrNuzyOhwJESXdIiIiIh7y+Xxcd3YvMlMTuP+VRarvjlJKukVEREQ8lpwYx03n9WHn3goembxU9d1RSEm3iIiISATo2jGNi07rzvyVW3lr9nqvw5EgC/vkOMaYNOAjYJy1dk29decBvwV8QAlwnbV2hzHmGuDPwBZ308nW2l+GL2oRERGR0DtzSGds6Q6ee3cl3Tul07Xj1yfWkZYprD3dxpjhwEygZwPr0oD7gLHW2v7AAuA37uohwK3W2gHuPyXcIiIiEnWc+u4iMlLiuf+VRew/qPruaBHu8pLrgZuBjQ2siwNuttZucJ8vAPLdx0OBa4wxC40xk4wxmaEPVURERCT8UpKc+u4dew7xyBvLVN8dJcKadFtrJ1hrZzSybpu19iUAY0wS8DPgZXf1JuD3QD9gHfCf0EcrIiIi4o1undK58JRuzF1ezjtzVN8dDXxenD0ZY9YAp9av6XbXpQMvASXW2m81sD4TWGWtzWrCoQpwasNFREREWpSamgB/ePRT5tly/vbdk+iel+F1SNI0hcCa+gvDfiPl4RhjOgDTgOnAD91l6cA3rbX/cjfzAVXN2e+2bXupqdGlmcPJyUmlvHyP12FEJbVt6KhtQ0dtGzpq29CJxra96syerFq/kzsnfsod1w4jOdGb1C0a2zbY/H4f2dkpja8PYyyHZYyJAV4DnrXW/sBaW5sl7wVuc2/CBLgFpydcREREJKqlJMVx07l92LbrEBOnaPzulszznm5jzBvAr4E8YBAQa4z5hrt6trV2gjHmYuA+t9Z7OXC1N9GKiIiIhFf3zulccEpXnn9vFe/O28Dpgzp7HZIcBU+SbmttQZ3HZ7sPZ9NIz7t78+Wg0EcmIiIiEnnGDM/Hlu7k6XdW0K1jOl3ap3odkjRTxJSXiIiIiEjD/D4fE8YVkZocz32vLOLAoWbd3iYRQEm3iIiISAuQmhzPjecWs3XnQR6bqvG7Wxol3SIiIiItRM+8DM4/qZBZS8t4f35Dcw1KpFLSLSIiItKCnH18F4oLs3jq7RWUbtEwfi2Fkm4RERGRFsTv83H9uN60SYrlvlcWq767hVDSLSIiItLCpLWJ56ZziynbsZ8nplnVd7cASrpFREREWiCTn8l5JxbyyZItzFiwyetw5AiUdIuIiIi0UOOOL6B3QSZPvrWc9WV7vQ5HDkNJt4iIiEgL5ff7uP6cYpITYrnvlUUcrFB9d6RS0i0iIiLSgqW3ieeGc3qzedt+Jr253OtwpBFKukVERERauKKCLM4ZUcBHizYzU/XdEUlJt4iIiEgUOHdEIb3yM5j0pmVDueq7I42SbhEREZEo4Pf7uPHcYhLjY7jvlcUcqqj2OiSpQ0m3iIiISJRIT0ng+nOL2bR1H0++pfruSKKkW0RERCSKFBdkMfaEAmYu3MSHC1XfHSmUdIuIiIhEmfNOLKBnXgZPvGnZuHWf1+EISrpFREREok6M38+N5xaTEBfDfa8s4lCl6ru9pqRbREREJAplpiZw/bjebCjfx//eVn2315R0i4iIiESpPl2zGXt8Fz74fBOfLN7sdTitmpJuERERkSh2/kmF9OiczmPTLJu2qb7bK0q6RURERKJYbX13XIyf+15eTIXquz2hpFtEREQkymWlJTJhXG/Wl+/l6XdWeB1Oq6SkW0RERKQV6Nctm7OG5/Pe/I3MWrrF63BaHSXdIiIiIq3E+JO70r1TOhOnLGPLjv1ehxN0gUCAA4eqvA6jQUq6RURERFqJ2BinvjvG7+O+lxdRWdXy67sDgQDryvbywvur+Pl/P+Hmf33Atl0HvQ7ra2K9DkBEREREwic7PZFvjevNXc8v4OnpK7lqlPE6pKOyads+Zi0tY9bSLWzath+/z0dRlwzOHVFAVlqC1+F9jZJuERERkVZmQPe2jB6Wx7RZ6yjKz2RIr1yvQ2qSsh373US7jPXle/EBPfMyGDkkj8Emh7TkeK9DbJSSbhEREZFW6MJTurFi/S4enbKU/HYp5GYmex1Sg7buOsBny5xEe+3mPQB075TOZSN7MMTkkpkaeb3aDVHSLSIiItIKxcb4uem8Yn7zyGfc98pifnHlYOJiI+N2vx17DjF7WRmzlm1h1YbdABR2SOXi07oztFcu2emJHkfYfEq6RURERFqptulJfGtsEXe/uJDn3l3J5Wf29CyW3fsqmG2dHu0V63YSAPJyU7jwlK4M7ZUbsT3xTaWkW0RERKQVG9gzhzOH5PHW7HWY/AwGm/DVd+89UMkcN9FeVrqDQAA6tm3DeScWMrQolw7ZbcIWS6gp6RYRERFp5S46rRsrN+zkkTeWkd8ulZyMpJAda//BKuatKGfW0jKWrNlOdU2AdplJjD2+gGFFuXTOSQnZsb2kpFtERESklXPqu/vwm0c/4/5XFvHzKwcTGxO8+u6DFVXMX7GVWUvLWFSyjarqANlpiYwamsewonbkt0vB5/MF7XiRSEm3iIiIiJCTkcQ3z+7FPS8t4vn3VnHpGT2OaX+HKqtZuGobs5Zu4fNV26isqiEzNYHTB3VmaFEuXTukRX2iXZeSbhEREREBYLDJ5YzBnXnzs3WYvAwG9sxp1usrq2pYtHobs5aVMX/FVg5VVpPWJp6T+3VkaFEu3Tun429FiXZdSrpFRERE5AsXn9adlRt28fDkpfymXQpt0w9f311VXcOSNduZtbSMeSvKOXCompSkOI4rbsewXrmY/Ez8/taZaNelpFtEREREvhAX6+fb5xXz24mf8cAri/npFYO+tk11TQ3LSnfy2dItzLHl7DtYRXJCLIN75jKsKJdeXTKDWhMeDZR0i4iIiMhX5GYmc+1ZRdz38iJefH81N18ykJqaACvW72TW0jJm2zL27K8kIT6GgT3aMqyoHcUFWREzuU4kUtItIiIiIl8ztFcuywZ1YuqsUg5W1zDPlrFrbwXxcX76d2vLsKJc+nbNJj4uxutQWwQl3SIiIiLSoEtP787qjbv58PON9O2azbCiXPp3a0tCvBLt5lLSLSIiIiINiouN4RdXDiIrK4U9uw94HU6LpsIbEREREWlUXGwMiQnqpz1WSrpFREREREJMSbeIiIiISIgp6RYRERERCTEl3SIiIiIiIRb2qnhjTBrwETDOWrum3roBwENAGvABcJO1tsoYkw9MAnIBC1xhrd0bzrhFRERERI5WWHu6jTHDgZlAz0Y2mQTcYq3tCfiA693l9wL3Wmt7AbOB20Mdq4iIiIhIsIS7vOR64GZgY/0VxpguQJK19hN30UTgImNMHHAy8Hzd5SGPVEREREQkSMJaXmKtnQBgjGlodUdgU53nm4DOQFtgt7W2qt7yJsvOTml2rK1RTk6q1yFELbVt6KhtQ0dtGzpq29BR24aO2vbYRNJI534gUOe5D6hpYDnu8ibbtm0vNTX1dyF15eSkUl6+x+swopLaNnTUtqGjtg0dtW3oqG1DR217ZH6/77AdvZE0esl6oEOd5+1xylDKgHRjTIy7vAMNlKeIiIiIiESqiEm6rbVrgYPGmBHuoquAKdbaSmAGcIm7/GpgigchioiIiIgcFc+TbmPMG8aYIe7TK4B/GWOWASnAXe7y7wA3GGOWACcBvwp/pCIiIiIiR8eTmm5rbUGdx2fXefw5MKyB7dcCp4YjNhERERGRYPO8p1tEREREJNop6RYRERERCTEl3SIiIiIiIRZJ43SHQgw44ybKkamdQkdtGzpq29BR24aO2jZ01Laho7Y9vDrtE9PQel8gENWTxpyIM9ygiIiIiEg4nATMrL8w2pPuBGAoztTx1R7HIiIiIiLRKwZnEsfPgEP1V0Z70i0iIiIi4jndSCkiIiIiEmJKukVEREREQkxJt4iIiIhIiCnpFhEREREJMSXdIiIiIiIhpqRbRERERCTElHSLiIiIiISYkm4RERERkRCL9ToACR1jzB3Axe7Tydba24wxI4F/AknAM9baX7nbDgAeAtKAD4CbrLVVxpgRwL+AeGAb8E1r7drwvpPIE4y2rbOvgcAn1tqEML6FiBWkz20Hd3lHYD9whbV2TVjfSAQKUtsWAI+7y3cC1+hvQvPats5rHgemW2snus/zgUlALmBxPrd7w/QWIlaQ2lbfZQ0IRtvWWa7vsiNQT3eUcn9pRgEDgQHAYGPMZcAjwHlAETDUGHOW+5JJwC3W2p6AD7jeXf4kMMFaO8B9fFe43kOkCmLbYoxJBu7G+SJo9YLYtk8Ar1lrB7qP/xK2NxGhgti2vwf+5/5NeAH4Y7jeQ6RqbtsaYzoaY14DvlFvV/cC91prewGzgdvD8w4iVxDbVt9l9QSxbfVd1kRKuqPXJuBH1toKa20lsBToCayw1pa4Pa2TgIuMMV2AJGvtJ+5rJ7rLE4BfWWsXuMsXAPlhfReR6Zjbts6+/gH8X9gij3zB+Ny2BfoDD7jLHwW+0lPTSgXrcxuD08sN0AY4EK43EMGa3Lbu9lcArwDP1u7AGBMHnAw87y6ayFf/VrRWwWhbfZc17Jjbtg59lzWBykuilLV2ce1jY0wPnMtHd+P8ktXaBHTGuQT/teXW2kM4v3AYY/zAb4CXQxl3SxCMtnVfey6QbK193hgT6rBbhCC1bTegFPiHMeYkYDNwS2gjj3zB+tzi9L5+ZIz5Hk6v1vEhDLtFaGbbYq39m7vtiXXWtwV21yk9q9vmrVYw2lbfZQ0L0udW32XNoJ7uKGeMKQbeAn4CrAYCdVb7gBqcz0FDy2v3EY9zOS4WuDPEIbcYx9K2xpj2OL2v3w1PtC3LMX5uY3Eul0631g7F6Zl5LAxhtwhB+JvwGHCDtbYTcBPwkjHGF+q4W4Imtm1j6rc5R9i+VTnGtq3dh77LGnAsbavvsuZR0h3F3BtH3gF+Zq19DFgPdKizSXtg42GWY4xJAabi/JE6z70E1eoFoW3HAdnAB8aY+e4+5xtjUkMffWQLQttuBvZYa193lz8FDAt13C3BsbatMSYH6GWtfQXAWvuCu65tGMKPaM1o28aUAenGmBj3eYcjbN9qBKFt9V3WiCC0rb7LmkFJd5QyxuThXD673Fr7tLv4U2eV6e7+Yb8cmOLewX3Q/eUDuAqY4j6eBKwELnEv0bV6wWhba+1D1tpu1toB7o09uI/3hPXNRJggte0qYH2dGwLPAeaE7U1EqCD9TdjqLj/J3ecInBOc8jC+lYjTnLZtbB9uEjgDuMRddPXhtm8tgtG2Ln2X1ROkz62+y5pBNd3R68dAIvDPOjVW9wPX4ow4kAi8wZc37VwBPGiMSQPmAne5w/+cBywB5rr72WitPTtM7yFSHXPbhjPYFiZYbXsB8IAx5m/AbuCacAQf4Y65ba21AWPMBcDdxpgkYA9wYdjeQeRqbts25jvAY8aYX+Hcl3BZKIJtYY65bfVd1qhgfW6liXyBQP0SMhERERERCSaVl4iIiIiIhJiSbhERERGREFPSLSIiIiISYkq6RURERERCTEm3iIiIiEiIKekWEYlwxpiJxpi3vY5DRESOnpJuEREREZEQU9ItIiIiIhJimpFSRCSMjDEB4PfAN91FQ4BDwD9wZs3zAZ8AP7TW2kb20cfd/iRgOzAZ+Km1dqe7vgD4G3AakA5sAO6x1v7NXW+Au4HjgADwLvADa+0ad30e8C9gFHDAXX+rtXZjE95fHLAR+Gvt8dzlvwfGWWsHGmMSgDtxpphuA8xz4//E3dYP/AJnJtEuwH7gHeAma225MeZUYCrwB+BW4HNr7WlHik1ExEvq6RYRCb/rgXE409VvwZlquSMwGjgRWAvMNMZk13+hMaYT8D6wABgIfAPoDbxYZ7PXcKZwPg0oAh4H/mqMGeCuf8o9xiCcxL0t8Ii7/zbAezjJ9gluTPHAdGNM/JHemLW20t3/lXVi9uFMK/+Yu+hx4GTgYpyTjunAu8aYnu76W4HvA98FeuBMh34i8Ms6h0pw398w4HtHiktExGvq6RYRCb+J1tr5AMaYkcBQIMtau9td/21jzBnADcCf6r3228Bqa+1PahcYYy4F1htjjgfmAxOBp621G9z1v8VJWPu667sDbwJrrLVVxpgrgfbu7i7D6X2+1lpb7b7+MmArcCHwv6a8P+B7xpi+1tqFwAggD3jSGNMdJ9nuY61d7G7/W2PMicCPgBsBC1xjrZ3qrl9rjJnqxl/XX621K5sQj4iI55R0i4iE3+o6jwcCMcBGp+rjC4k4vdT1DQQGGmP2NrCuyFr7sTHmP8AlxphhOD3FA3CubMa4292OU57yHWPMdOB1nN7p2v3nALvqxZPcSDxfY62dZ4z5HKe3+6fAVcAbdUpDAD6tt/8E9x/W2teMMccbY/4IGKCXe+wZ9Q61GhGRFkJJt4hI+B2o87gCpy57eAPbNZRYV+D0UjdUUlHulofMxPn7/jxOPfanOOUkAFhr7zLGPINT4nImTv32zW5PeQWwGKf0pb6dh31XX/UY8ENjzK9xerZra9gr3P+P56vtAE5tO8aYXwI/Bx4FpuDUf38fp767rvqvFxGJWEq6RUS8tRjIAqgtlTDGxABP4tRpP9vA9lcAa936aYwxhTg3Rv4MJzEdAGRba7e76w1OT7fPrRP/DfAXa+3DwMNuj/inQH93/xOAbdbaHe7r04BJOMn5u018X5OAv+CUjFTj3OxZGz9AO2vtF2OPG2PuAZYC/wF+APzaWvvPOut7AJVNPLaISMRR0i0i4q13cEYredYY832cGyt/BpwD/K6B7f8D3AJMNMb8Gack4x4gA1iOUwYCcIUx5lWc+u3a5DUB2AGcBXQ1xvwcZ2SQa3F6sS2wEKf++1l3/UHgzzg3LNYmzEfklpJMAX4FPGStrXCXr3R72f9rjLnZjfmbwE04o6UAlAOjjTFv4JTEfBunZ/zTph5fRCTSaPQSEREPWWsDwPk4Ce0rOMPn9QTGWGuXNLD9ZmAkzo2PnwLTgFLgTGtthbV2FvATnFrqZTgJ+SScHuqh1toaYKy7u9pRUIqB0dbaXdbaAzglJ/txRhX5EKeD5nRrbVkz397jQBJfjlpSawLOiC2PAotwTgIusNa+466/GuckYh7wFpCNcyLS2xiTjIhIC+QLBAJexyAiIlHIGHMLcIO1tp/XsYiIeE3lJSIiElTGmME4o438Evi1x+GIiEQEJd0iItJkxpidHP674yOcIQj/DDwHPByGsEREIp7KS0REpMmMMd1wpqpvzIHaSXlERORLSrpFREREREJMo5eIiIiIiISYkm4RERERkRBT0i0iIiIiEmJKukVEREREQuz/AVchAFFlEZteAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a line chart for average of movie budgets in the recent years\n",
    "ploty (avg_budget,'release_year','budget_adj','line',(12,8),'Trend for average budget in the 21st century')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the general average budget for movies has declined yearly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are more expensive budgeted movies rated better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a scatterplot of popularity against budget\n",
    "bud_rate = df.iloc[:,np.r_[0,3]]\n",
    "ploty (bud_rate,'budget_adj','popularity','scatter',(6,4),'Trend for average budget in the 21st century')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that there is no correlation between movies budgets and their ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From my exploratory data analysis of the TMDb movie dataset, the following conclusions where gotten;\n",
    "1. The warrior's way is the most expensive budgeted movie recorded as of 2015\n",
    "2. The latest year which is the year 2015, had the highest average ratings, this could mean that 2015 had the best movies leading to the highest average ratings, it could also imply that rating systems had increased overtime, leading more people to be involved in the rating sytems, further analysis would be needed to conclude.\n",
    "3. Contray to popular belief, the average money spent for budgets of movies yearly in recent times has actually been declining, this could be due to improvement in technology or other factors, further analysis would be needed to conclude.\n",
    "4. From my analysis, there seems to be no correlation between expensive budgets and popularity ratings. We would expect that expensive movies, tend to be better movies because of attention to details, thereby having higher ratings, futher analysis would be needed to conclude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIMITATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This was an exploratory data analysis of the TMDb movie dataset, it was used to get a general idea of the data we were working with, therefore conclusive results could not be made. \n",
    "2. Causal relationship between variables could not be confirmed from this analysis. Further statistical tests would be needed for better conclusions and actionable insights"
   ]
  }
 ],
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