FinalProject_group061.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of What Causes Movies to be Popular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Project COGS 108 Spring 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Team 61: The Best of The Best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calvin Tjoeng XXXXXXXXX \n",
    "- Dan Mu XXXXXXXXX \n",
    "- Anik Zingariello XXXXXXXXX \n",
    "- Xuege Gao XXXXXXXXX \n",
    "- Haonan Peng XXXXXXXXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Project Outline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sections:\n",
    "1. [Project Setup](#1.-Project-Setup)\n",
    "2. [Data Wrangling/Cleaning & Pre-processing](#2.-Data-Wrangling/Cleaning-&-Pre-processing)\n",
    "3. [Data Exploration](#3.-Data-Exploration)\n",
    "4. [Data Visualization](#4.-Data-Visualization)\n",
    "5. [Data Analysis and Results](#5.-Data-Analysis-and-Results)\n",
    "6. [Ethics and Privacy](#6.-Ethics-and-Privacy)\n",
    "7. [Conclusion and Discussion](#7.-Conclusion-and-Discussion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.1 Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some movies are popular amomg publics, others are not.\n",
    "\n",
    "When it comes to the factors that determine the popularity of movies, what come into our minds are cast & crew, rating, net profit...\n",
    "\n",
    "Among those possible factors, some indeed influence the popularity of the movies; however some factors in fact have no effect on popularity of the movie.\n",
    "\n",
    "In this project, we will first give a hypothesis about the factors that indeed determine the popularity of the movie. Then, we will dig into our large datasets. After cleaning the data and visualize the data, we will analyze the data intensively, and finally reach to the conclusion regarding whether or not our initial hypothesis is true or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.2 Research Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Which factors are most important for the movie to be popular and successful? \n",
    "- Are ratings and votes determining factors?\n",
    "- Does profit play its part in defining popularity of movies?\n",
    "- Do genres and production companies come into the mix too?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.3 Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that ratings and/or votes and profit are determining factors to movies' popularity; while genres and production companies are not. The reason can be explained with an answer to this one question/analogy:\n",
    "\n",
    "Suppose there is a popular movie called \"Foo\" and we know that the `x-factor` of \"Foo\" is some value. Now suppose that there is another movie \"Bar\", which we don't know if it is popular or not, but we know the fact that the `x-factor` of \"Bar\" has the same value as the `x-factor` of \"Foo\". Knowing all these, do you think the popularity of \"Bar\" would likely be somewhat similar to the popularity of \"Foo\"? Or would it be nowhere close?\n",
    "\n",
    "Now try to replace `x-factor` with either rating, vote, profit, genres, or production companies. If your answer is \"similar\", then the `x-factor` is a determining factor; otherwise, it is not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.4 Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some movies are popular among the public, others are not. The Movies Dataset on Kaggle that we are using contains several files, including `movie_metadata.csv` and `ratings_small.csv`, measuring about 45,000 movies. `movie_metadata.csv` contains data about budget, genres, original language, popularity, and almost all other elements that can label or describe a movie. `ratings_small.csv` contains information pertaining to the ratings for movies in the file. \n",
    "\n",
    "As the datasets show, a large number of factors specifically the genre, budgets & revenues, production companies and votes, can largely affect the popularity of movie. Our project’s goal is firstly to generate a meaningful and data science-based question, then find and read the appropriate datasets, later using codes and statistical methods to do data cleaning, data visualization, and data analysis, then finally draw a conclusion and present our findings! \n",
    "\n",
    "In Michal Bogacz’s project of “Are popular movies good”, he analyzed the 10,000 most popular movies to determine how “good” these films were by comparing their IMDb rating, Metascore, revenue and so on. From his project, we have the basic ideas of what factors should be collected to determine the popularity of films and how to visualize the data. https://www.kaggle.com/michau96/are-popular-movies-good/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.5 Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using some datasets that can be found from this webpage: https://www.kaggle.com/rounakbanik/the-movies-dataset \n",
    "\n",
    "Since we are wondering which factors are important in determining the popularity or success of movies, we found the useful datasets in the webpage above. Some of the datasets include the correlations between:\n",
    "- Genres, budget & revenue (net profit), production companies (all found in the dataset called `movies_metadata.csv`) influence the popularity of the movies (Link: https://www.kaggle.com/rounakbanik/the-movies-dataset#movies_metadata.csv). The dataset contains all the factors mentioned above, including some extra factors like overview, link to poster, runtime, etc. We are not sure how many entries are present in this dataset, but the filesize is 32.85 MB, and we are certain it has more than 10,000 rows, according to the file size. \n",
    "- Ratings and movies. This one can specifically be found in `ratings_small.csv` (Link: https://www.kaggle.com/rounakbanik/the-movies-dataset#ratings_small.csv). This dataset generally contains the ratings of each movie by different users, nothing more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Project Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Import libary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import json\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_1samp, normaltest, norm\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to ignore all warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Read data from csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store the data in variables called `movie` and `ratings`, correspondingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie = pd.read_csv(\"movies_metadata.csv\")\n",
    "ratings = pd.read_csv(\"ratings_small.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Check column labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['adult', 'belongs_to_collection', 'budget', 'genres', 'homepage',\n",
       "       'id', 'imdb_id', 'original_language', 'original_title', 'overview',\n",
       "       'popularity', 'poster_path', 'production_companies',\n",
       "       'production_countries', 'release_date', 'revenue', 'runtime',\n",
       "       'spoken_languages', 'status', 'tagline', 'title', 'video',\n",
       "       'vote_average', 'vote_count'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['userId', 'movieId', 'rating', 'timestamp'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Wrangling/Cleaning & Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Remove and/or rename unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
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       "      <td>Waiting to Exhale</td>\n",
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       "      <td>0</td>\n",
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       "      <td>11862</td>\n",
       "      <td>8.38752</td>\n",
       "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     budget                                             genres     id  \\\n",
       "0  30000000  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...    862   \n",
       "1  65000000  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   8844   \n",
       "2         0  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  15602   \n",
       "3  16000000  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  31357   \n",
       "4         0                     [{'id': 35, 'name': 'Comedy'}]  11862   \n",
       "\n",
       "  popularity                               production_companies release_date  \\\n",
       "0    21.9469     [{'name': 'Pixar Animation Studios', 'id': 3}]   1995-10-30   \n",
       "1    17.0155  [{'name': 'TriStar Pictures', 'id': 559}, {'na...   1995-12-15   \n",
       "2    11.7129  [{'name': 'Warner Bros.', 'id': 6194}, {'name'...   1995-12-22   \n",
       "3    3.85949  [{'name': 'Twentieth Century Fox Film Corporat...   1995-12-22   \n",
       "4    8.38752  [{'name': 'Sandollar Productions', 'id': 5842}...   1995-02-10   \n",
       "\n",
       "       revenue  runtime                        title  vote_average  \n",
       "0  373554033.0     81.0                    Toy Story           7.7  \n",
       "1  262797249.0    104.0                      Jumanji           6.9  \n",
       "2          0.0    101.0             Grumpier Old Men           6.5  \n",
       "3   81452156.0    127.0            Waiting to Exhale           6.1  \n",
       "4   76578911.0    106.0  Father of the Bride Part II           5.7  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.drop(columns = ['adult', 'belongs_to_collection', 'homepage', 'imdb_id',\n",
    "                      'original_language', 'original_title', 'overview', 'poster_path',\n",
    "                      'production_countries', 'spoken_languages', 'status', 'tagline',\n",
    "                      'video', 'vote_count'], inplace = True)\n",
    "movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "     id  rating\n",
       "0    31     2.5\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.drop(columns = ['userId', 'timestamp'], inplace = True)\n",
    "ratings.columns = ['id', 'rating']\n",
    "ratings['id'] = ratings['id'].astype(str)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Group rating by id and calculate the average rating of each id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = ratings.groupby('id', as_index=False)['rating'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Merge movie and ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store the DataFrame in a variable called `df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Heat</td>\n",
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       "      <th>1</th>\n",
       "      <td>58000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...</td>\n",
       "      <td>710</td>\n",
       "      <td>14.686</td>\n",
       "      <td>[{'name': 'United Artists', 'id': 60}, {'name'...</td>\n",
       "      <td>1995-11-16</td>\n",
       "      <td>352194034.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.500000</td>\n",
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       "      <th>2</th>\n",
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       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...</td>\n",
       "      <td>1408</td>\n",
       "      <td>7.28448</td>\n",
       "      <td>[{'name': 'Le Studio Canal+', 'id': 183}, {'na...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>10017322.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Cutthroat Island</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.616279</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52000000</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...</td>\n",
       "      <td>524</td>\n",
       "      <td>10.1374</td>\n",
       "      <td>[{'name': 'Universal Pictures', 'id': 33}, {'n...</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>116112375.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>Casino</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16500000</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
       "      <td>4584</td>\n",
       "      <td>10.6732</td>\n",
       "      <td>[{'name': 'Columbia Pictures Corporation', 'id...</td>\n",
       "      <td>1995-12-13</td>\n",
       "      <td>135000000.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>Sense and Sensibility</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     budget                                             genres    id  \\\n",
       "0  60000000  [{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...   949   \n",
       "1  58000000  [{'id': 12, 'name': 'Adventure'}, {'id': 28, '...   710   \n",
       "2  98000000  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  1408   \n",
       "3  52000000  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...   524   \n",
       "4  16500000  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  4584   \n",
       "\n",
       "  popularity                               production_companies release_date  \\\n",
       "0    17.9249  [{'name': 'Regency Enterprises', 'id': 508}, {...   1995-12-15   \n",
       "1     14.686  [{'name': 'United Artists', 'id': 60}, {'name'...   1995-11-16   \n",
       "2    7.28448  [{'name': 'Le Studio Canal+', 'id': 183}, {'na...   1995-12-22   \n",
       "3    10.1374  [{'name': 'Universal Pictures', 'id': 33}, {'n...   1995-11-22   \n",
       "4    10.6732  [{'name': 'Columbia Pictures Corporation', 'id...   1995-12-13   \n",
       "\n",
       "       revenue  runtime                  title  vote_average    rating  \n",
       "0  187436818.0    170.0                   Heat           7.7  3.593750  \n",
       "1  352194034.0    130.0              GoldenEye           6.6  1.500000  \n",
       "2   10017322.0    119.0       Cutthroat Island           5.7  3.616279  \n",
       "3  116112375.0    178.0                 Casino           7.8  3.555556  \n",
       "4  135000000.0    136.0  Sense and Sensibility           7.2  5.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(movie, ratings, on='id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Remove Na's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5 Remove unnecessary data in budget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believed that any movies with budget less than $1,000 are false records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['budget'] = df['budget'].astype(np.float64)\n",
    "df = df[df['budget'] > 1000.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6 Remove unnecessary data in revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['revenue'] = df['revenue'].astype(np.float64)\n",
    "df = df[df['revenue'] > 0.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7 Add a new column net_profit, which stores the value of `revenue - budget`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* if the value of is negative, it means that the corresponding movie suffers financial loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['net_profit'] = [df.loc[i, 'revenue'] - df.loc[i, 'budget'] for i in df.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.8 Drop columns budget and revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we now have the new column net_profit, we feel like having columns budget and revenue are not necessary any further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['budget', 'revenue'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.9 Remove unnecessary data in genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_genres(genres):\n",
    "    string = genres.replace(\"\\'\",\"\\\"\")\n",
    "    type_list = json.loads(string)\n",
    "    movie_type = []\n",
    "    for j in type_list:\n",
    "        movie_type.append(j['name'])\n",
    "    return movie_type if len(movie_type) > 0 else pd.NaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['genres'] = df['genres'].apply(clean_genres)\n",
    "df = df.dropna(subset=['genres'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.10 Remove unnecessary data in production_companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_production_companies(companies):\n",
    "    s = companies.replace(\"\\'\",\"\\\"\")\n",
    "    companies_list = []\n",
    "    try:\n",
    "        companies_list = json.loads(s)   # try to parse...\n",
    "    except Exception as e:\n",
    "        # \"Expecting , delimiter: line 34 column 54 (char 1158)\"\n",
    "        # position of unexpected character after '\"'\n",
    "        unexp = int(re.findall(r'\\(char (\\d+)\\)', str(e))[0])\n",
    "        # position of unescaped '\"' before that\n",
    "        unesc = s.rfind(r'\"', 0, unexp)\n",
    "        s = s[:unesc] + r'\\\"' + s[unesc+1:]\n",
    "        # position of correspondig closing '\"' (+2 for inserted '\\')\n",
    "        closg = s.find(r'\"', unesc + 2)\n",
    "        s = s[:closg] + r'\\\"' + s[closg+1:]\n",
    "        \n",
    "    result = []\n",
    "    for comp in companies_list:\n",
    "        result.append(comp['name'])\n",
    "    return result if len(result) > 0 else pd.NaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['production_companies'] = df['production_companies'].apply(clean_production_companies)\n",
    "df = df.dropna(subset=['production_companies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.11 Make sure `popularity` and `vote_average` are of type `np.float64`; while `id` and `runtime` are of type `np.int64`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['popularity'] = df['popularity'].astype(np.float64)\n",
    "df['vote_average'] = df['vote_average'].astype(np.float64)\n",
    "df['id'] = df['id'].astype(np.int64)\n",
    "df['runtime'] = df['runtime'].astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.12 Reorder the columns of `df`, sort it based on `id`, and reset the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['id', 'title', 'release_date', 'genres', 'runtime', 'popularity', 'rating',\n",
    "         'vote_average', 'net_profit', 'production_companies']]\n",
    "df = df.sort_values(by=['id'])\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.13 Drop any duplicate values, and any Na's again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset=['id'], inplace=True)\n",
    "df.dropna(how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.14 Make sure that the current dataset has at least 1000 observations, also make sure all rows are unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(df) >= 1000\n",
    "assert len(df) == len(df['id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.15 Display `df` post-cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>genres</th>\n",
       "      <th>runtime</th>\n",
       "      <th>popularity</th>\n",
       "      <th>rating</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>production_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Four Rooms</td>\n",
       "      <td>1995-12-09</td>\n",
       "      <td>[Crime, Comedy]</td>\n",
       "      <td>98</td>\n",
       "      <td>9.026586</td>\n",
       "      <td>3.267857</td>\n",
       "      <td>6.5</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>[Miramax Films, A Band Apart]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>1977-05-25</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>121</td>\n",
       "      <td>42.149697</td>\n",
       "      <td>3.689024</td>\n",
       "      <td>8.1</td>\n",
       "      <td>764398007.0</td>\n",
       "      <td>[Lucasfilm, Twentieth Century Fox Film Corpora...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>2003-05-30</td>\n",
       "      <td>[Animation, Family]</td>\n",
       "      <td>100</td>\n",
       "      <td>25.497794</td>\n",
       "      <td>2.861111</td>\n",
       "      <td>7.6</td>\n",
       "      <td>846335536.0</td>\n",
       "      <td>[Pixar Animation Studios]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>1994-07-06</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>142</td>\n",
       "      <td>48.307194</td>\n",
       "      <td>3.937500</td>\n",
       "      <td>8.2</td>\n",
       "      <td>622945399.0</td>\n",
       "      <td>[Paramount Pictures]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>American Beauty</td>\n",
       "      <td>1999-09-15</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>122</td>\n",
       "      <td>20.726578</td>\n",
       "      <td>3.451613</td>\n",
       "      <td>7.9</td>\n",
       "      <td>341296601.0</td>\n",
       "      <td>[DreamWorks SKG, Jinks/Cohen Company]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id            title release_date                                genres  \\\n",
       "0   5       Four Rooms   1995-12-09                       [Crime, Comedy]   \n",
       "1  11        Star Wars   1977-05-25  [Adventure, Action, Science Fiction]   \n",
       "2  12     Finding Nemo   2003-05-30                   [Animation, Family]   \n",
       "3  13     Forrest Gump   1994-07-06              [Comedy, Drama, Romance]   \n",
       "4  14  American Beauty   1999-09-15                               [Drama]   \n",
       "\n",
       "   runtime  popularity    rating  vote_average   net_profit  \\\n",
       "0       98    9.026586  3.267857           6.5     300000.0   \n",
       "1      121   42.149697  3.689024           8.1  764398007.0   \n",
       "2      100   25.497794  2.861111           7.6  846335536.0   \n",
       "3      142   48.307194  3.937500           8.2  622945399.0   \n",
       "4      122   20.726578  3.451613           7.9  341296601.0   \n",
       "\n",
       "                                production_companies  \n",
       "0                      [Miramax Films, A Band Apart]  \n",
       "1  [Lucasfilm, Twentieth Century Fox Film Corpora...  \n",
       "2                          [Pixar Animation Studios]  \n",
       "3                               [Paramount Pictures]  \n",
       "4              [DreamWorks SKG, Jinks/Cohen Company]  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 `popularity` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.1.1 Get the descriptive summary of `popularity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1001.000000\n",
       "mean       11.604160\n",
       "std         8.563963\n",
       "min         0.202478\n",
       "25%         7.738531\n",
       "50%        10.513367\n",
       "75%        13.328924\n",
       "max       140.950236\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.1.2 Get the quantile value of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in a variable called `top_pop`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided that the top 20% is the benchmark to define that movies can be considered popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.207001000000002"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pop = df['popularity'].quantile(.8)\n",
    "top_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store the top 20% movies DataFrame in `top_df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_df = df[df['popularity'] > top_pop]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 `net_profit` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.1 Get the descriptive summary of `net_profit`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.001000e+03\n",
       "mean     9.275597e+07\n",
       "std      1.542020e+08\n",
       "min     -9.830110e+07\n",
       "25%      4.419397e+06\n",
       "50%      3.564960e+07\n",
       "75%      1.201285e+08\n",
       "max      1.645034e+09\n",
       "Name: net_profit, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['net_profit'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.2 Get the average `net_profit` of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in `top_net`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215735435.955"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_net = top_df['net_profit'].mean()\n",
    "top_net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPECTED: Greater than overall average!\n"
     ]
    }
   ],
   "source": [
    "if top_net > df['net_profit'].mean():\n",
    "    print(\"EXPECTED: Greater than overall average!\")\n",
    "else:\n",
    "    print(\"UNEXPECTED: Lower than overall average!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can roughly see, `top_net` outnumbers all of the attributes of the descriptive summary of overall data, except for the max, as we expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.3 Make sure `top_net` is greater than the overall average of net profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert top_net > df['net_profit'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 `rating` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.3.1 Get the descriptive summary of `rating`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1001.000000\n",
       "mean        3.353050\n",
       "std         0.771349\n",
       "min         0.500000\n",
       "25%         3.000000\n",
       "50%         3.462963\n",
       "75%         3.875000\n",
       "max         5.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data above, we assume that the range value of column rating is [0.0, ..., 5.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.3.2 Get the average `rating` of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in `top_rating`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3990464741121955"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_rating = top_df['rating'].mean()\n",
    "top_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPECTED: Greater than overall average!\n"
     ]
    }
   ],
   "source": [
    "if top_rating > df['rating'].mean():\n",
    "    print(\"EXPECTED: Greater than overall average!\")\n",
    "else:\n",
    "    print(\"UNEXPECTED: Lower than overall average!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the value of `top_rating` above, we can see that the average rating of the top 20% movies, even though is slightly greater than the overall average, is lower than the 50th and 75th percentiles of the overall data. We sort of expected `top_rating` to be greater than the percentiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 `vote_average` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.4.1 Get the descriptive summary of `vote_average`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1001.000000\n",
       "mean        6.676623\n",
       "std         0.844567\n",
       "min         2.300000\n",
       "25%         6.100000\n",
       "50%         6.700000\n",
       "75%         7.300000\n",
       "max         8.500000\n",
       "Name: vote_average, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vote_average'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data above, we assume that the range value of column vote_average is [0.0, ..., 10.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.4.2 Get the average `vote_average` of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in `top_vote`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.122000000000001"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_vote = top_df['vote_average'].mean()\n",
    "top_vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPECTED: Greater than overall average!\n"
     ]
    }
   ],
   "source": [
    "if top_vote > df['vote_average'].mean():\n",
    "    print(\"EXPECTED: Greater than overall average!\")\n",
    "else:\n",
    "    print(\"UNEXPECTED: Lower than overall average!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can examine that the difference between `top_vote` and the average of the overall data is quite convincing; however, it does not surpass the 75th percentile. Again, this is pretty unexpected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 `genres` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.5.1 Collect different `genres` available in `df`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store them in a list called `genres` and its `value_counts()` in `genres_vc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres_raw = []\n",
    "for genre in df['genres']:\n",
    "    genres_raw += genre\n",
    "genres = list(set(genres_raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        500\n",
       "Thriller     342\n",
       "Comedy       289\n",
       "Action       276\n",
       "Adventure    211\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres_vc = pd.Series(genres_raw).value_counts()\n",
    "genres_vc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.5.2 Get the `genres` of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store them in `top_genres`, and in `top_genres_vc` for the `value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action       79\n",
       "Drama        77\n",
       "Adventure    68\n",
       "Thriller     67\n",
       "Fantasy      45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_genres_raw = top_df['genres']\n",
    "top_genres = []\n",
    "for top_gen in top_genres_raw:\n",
    "    top_genres += top_gen\n",
    "top_genres_vc = pd.Series(top_genres).value_counts()\n",
    "top_genres_vc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.5.3 Make sure that the top 3 genres in `genres_vc` are in `top_genres`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 'Drama' in top_genres\n",
    "assert 'Thriller' in top_genres\n",
    "assert 'Comedy' in top_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.5.4 Get the average `popularity` for each genre in the overall data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in a Series called `pop_genres`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy             10.160293\n",
       "Family             13.551611\n",
       "Science Fiction    12.225156\n",
       "Romance             9.792954\n",
       "Fantasy            14.917978\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_genres_dict = dict([(g, []) for g in genres])\n",
    "for idx, data in df[['genres', 'popularity']].iterrows():\n",
    "    for g in data['genres']:\n",
    "        pop_genres_dict[g].append(data['popularity'])\n",
    "        \n",
    "pop_genres = pd.Series({ key: np.mean(value) for key, value in pop_genres_dict.items() })\n",
    "pop_genres.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.5.5 Get the average `popularity` for each genre in the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in a Series called `top_pop_genres`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy             18.459326\n",
       "Family             20.319982\n",
       "Science Fiction    21.665384\n",
       "Romance            19.160698\n",
       "Fantasy            21.737466\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pop_genres_dict = dict([(g, []) for g in list(set(top_genres))])\n",
    "for idx, data in top_df[['genres', 'popularity']].iterrows():\n",
    "    for g in data['genres']:\n",
    "        top_pop_genres_dict[g].append(data['popularity'])\n",
    "        \n",
    "top_pop_genres = pd.Series({ key: np.mean(value) for key, value in top_pop_genres_dict.items() })\n",
    "top_pop_genres.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 `production_companies` exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.6.1 Collect different `production_companies` available in `df`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store them in a list called `companies` and its `value_counts()` in `companies_vc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies_raw = []\n",
    "for company in df['production_companies']:\n",
    "    companies_raw += company\n",
    "companies = list(set(companies_raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Bros.                              117\n",
       "Paramount Pictures                         99\n",
       "Universal Pictures                         92\n",
       "Twentieth Century Fox Film Corporation     70\n",
       "Metro-Goldwyn-Mayer (MGM)                  37\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies_vc = pd.Series(companies_raw).value_counts()\n",
    "companies_vc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.6.2 Get the `production_companies` of the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store them in `top_companies`, and in `top_companies_vc` for the `value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Bros.                              34\n",
       "Paramount Pictures                        26\n",
       "Twentieth Century Fox Film Corporation    20\n",
       "Universal Pictures                        20\n",
       "Miramax Films                             12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_companies_raw = top_df['production_companies']\n",
    "top_companies = []\n",
    "for top_comp in top_companies_raw:\n",
    "    top_companies += top_comp\n",
    "top_companies_vc = pd.Series(top_companies).value_counts()\n",
    "top_companies_vc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.6.3 Make sure that the top 3 production companies in `companies_vc` are in `top_companies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 'Warner Bros.' in top_companies\n",
    "assert 'Paramount Pictures' in top_companies\n",
    "assert 'Universal Pictures' in top_companies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.6.4 Get the average `popularity` for each production company in the overall data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in a Series called `pop_companies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stanley Kubrick Productions    16.568195\n",
       "Egg Films                      10.616859\n",
       "A&M Films                      12.627678\n",
       "Carolco Pictures               11.372142\n",
       "Saturn Films                   15.732864\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_companies_dict = dict([(c, []) for c in companies])\n",
    "for idx, data in df[['production_companies', 'popularity']].iterrows():\n",
    "    for c in data['production_companies']:\n",
    "        pop_companies_dict[c].append(data['popularity'])\n",
    "        \n",
    "pop_companies = pd.Series({ key: np.mean(value) for key, value in pop_companies_dict.items() })\n",
    "pop_companies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.6.5 Get the average `popularity` for each production company in the top 20% movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store it in a Series called `top_pop_companies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stanley Kubrick Productions    22.494622\n",
       "Cheyenne Enterprises           14.886039\n",
       "Mace Neufeld Productions       15.911260\n",
       "Carolco Pictures               22.661695\n",
       "Splendid Pictures              19.833077\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pop_companies_dict = dict([(c, []) for c in list(set(top_companies))])\n",
    "for idx, data in top_df[['production_companies', 'popularity']].iterrows():\n",
    "    for c in data['production_companies']:\n",
    "        top_pop_companies_dict[c].append(data['popularity'])\n",
    "        \n",
    "top_pop_companies = pd.Series({ key: np.mean(value) for key, value in top_pop_companies_dict.items() })\n",
    "top_pop_companies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 `net_profit`, `rating`, and `vote_average` charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.1.1 Plot a scatter matrix for the overall data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 125)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrv_plt = pd.plotting.scatter_matrix(df[['popularity', 'net_profit', 'rating', 'vote_average']],\n",
    "                                     grid=True, figsize=(14, 10), alpha=0.5)\n",
    "nrv_plt[0,1].set_ylim(0,125)\n",
    "nrv_plt[0,2].set_ylim(0,125)\n",
    "nrv_plt[0,3].set_ylim(0,125)\n",
    "nrv_plt[1,0].set_xlim(0,125)\n",
    "nrv_plt[2,0].set_xlim(0,125)\n",
    "nrv_plt[3,0].set_xlim(0,125)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.1.2 Plot a scatter matrix for the top 20% movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 125)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrv_plt = pd.plotting.scatter_matrix(top_df[['popularity', 'net_profit', 'rating', 'vote_average']],\n",
    "                                     grid=True, figsize=(14, 10), alpha=0.5)\n",
    "nrv_plt[1,0].set_xlim(0,125)\n",
    "nrv_plt[2,0].set_xlim(0,125)\n",
    "nrv_plt[3,0].set_xlim(0,125)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 `genres` charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.2.1 Plot bar charts for the number of movies in each genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to compare the number of movies of the overall data vs. the top 20% movies. In other words, plot `genres_vc` vs. `top_genres_vc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12df09d30>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "ax.invert_xaxis()\n",
    "ax.yaxis.tick_right()\n",
    "\n",
    "genres_vc.plot(kind='barh', title='Overall', figsize=(18,10), xlim=(550, 0), ax=ax)\n",
    "top_genres_vc.plot(kind='barh', title='Top 20%', figsize=(18,10), xlim=(0, 550), ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.2.2 Plot bar charts for the average popularity of movies in each genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to compare the average popularity of movies of the overall data vs. the top 20% movies, which means `pop_genres` vs. `top_pop_genres`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e2bc978>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WH5Xk6n3s65gkexX0q8pf1gGAFUrQB4Cl884k839/6eQkb0mSqrpdVX2mqg6dWv+Hqjq0qn62qj5ZVZdV1d9W1R2S/E6SJ1TVpVX1hKq6U1W9pqo+WlWfqKrHDP2cWlXnVNUHkry/ql5fVY+dL6aq3jTfFgAYL0EfAPbNuqraPPXasJM2Zyd5YlXdMcnRST6SJN19a5I3JjllaPfIJJd195eTPD/Jf+7u+yf5qe7+t2HbW7v7mO5+a5L/keQD3X18kpOSvKiq7jT0dWySx3f3w5P8aZJTk6Sq1iY5Icmy+IP0VbVh/me744btsy4HAJYVQR8A9s227j5u6rVpYYPuvjzJXCaj+e9csPs1SZ48LD81yWuH5QuTnFVVv5Jk1S7O/egkp1XVpUnOT3LHJPcY9r23u68Zzv83SY4YZg6cnOQd3X3LXl/pDHT3pvmf7apvWzvrcgBgWXH/HgAsrXOSvDjJiUm+Y35jd3+2qr5YVY9IcnyG0f3uflpV/VAmU/4vqaoH7KTPSvIz3f33/27j5LjrF7R9fZJfSPLEJE9ZlCsCAA5oRvQBYGm9JskZ3b1lJ/tenckU/rd1944kqap7dfdHuvv5Sb6c5LuTXJvkzlPHvTvJ06uqhmN+cDfnPyvJs5Kku6/8Fq8FAFgGBH0AWELd/bnufvkudp+TZE2+OW0/mdxvv6WqPpnkoiSXJflgkvvOP4wvyf9Kcvskl1fVFcP6rs7/xSR/t+AcAMCImboPAEugu9fsZNv5mdxTP+/+mTyE71NTbX56J91dk+SBC7b9l530f1YmI/jfUFXfluSIDE/8BwDGz4g+AMxAVZ2W5B1JfnMJz/HITEbzX9HdHl0PACuEEX0AmIHu3phk4xKf431J7rmU5wAADjyCPrDHtm5cP+sS4IBRZ866gpXjqMPXZrP3HwDYY6buAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMyOpZFwCM19xp5826BGAEtly93fsJwDKzdeP6WZewohnRBwAAgBER9AEAAGBEBH0AAAAYEUEfAAAARkTQB4BFVlXfUVWXDq9/raqrh+WvVtWVe9jH06rqycPyWVX1+GH5/Ko6binrBwCWN0/dB4BF1t1fSXJMklTV6Umu6+4XV9VcknNv6/iqWt3dr1qMWqpqVXfvWIy+AIDlwYg+AOxfq6rqT6rqiqp6T1UdnHxjpP5/V9XmJM+sqtOr6rm766iqHl1VF1fVx6vqbVW1Zti+tarOrKqPJ/nZpb8kAOBAIugDwL5ZV1Wbp14b9vC4I5L8YXffL8lXk/zM1L47dPdx3f2S2+qkqtYl+e0kj+zuY5NsTvKcqSZf6e5ju/vsPazrgFJVG+Z/tjtu2D7rcgBgWTF1HwD2zbbu3pd75a/q7kuH5UuSzE3te+te9POgJPdNcmFVJckdkly8j30dcLp7U5JNSXLQYUf0jMsBgGVF0AeA/eumqeUdSQ6eWr9+L/qpJO/t7pN3sX9v+gIARsTUfQBYnj6c5CFV9X1JUlV3qqp7z7gmAOAAIOgDwDLU3V9OcmqSt1TV5ZlM27/PTIsCAA4Ipu4DwBLq7tOnlrcmOXJq/cVTyyfu5rhTd9auuz+Q5IE7Oefct1Q0ALCsGdEHAACAERH0AQAAYERM3QeWzNaN62ddAiyZOnPWFawcRx2+Npu9nwDAHjOiDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIqtnXQDA3pg77bxZlwDsZ1uu3u7/PjAqWzeun3UJjJwRfQAAABgRQR8AAABGRNAHAACAERH0AQAAYEQEfQBYAlV13YL1U6vqlbOqBwBYOQR9ADgAVdXq3a3v6XEAwMrjwwAA7GdVNZfkNUnWJflykqd0979U1VlJvp7kB5NcWFVfS3KvJN+b5F+q6ilJ/jjJcUluSfKc7v5gVZ2a5KeTrEmyKsnD9+f1AAAHFkEfAPbNuqraPLW+qbs3Ta0fXFWXTq3fNck5w/Irkryuu19XVU9N8vIkjx32/ackJ3T3jqo6Pcl9kzy0u2+sql9P0t19VFXdJ8l7qurew3HHJjm6u69Z1KuckarakGRDkqw65NAZVwMAy4ugDwD7Zlt3H7eb/Td29zHzK8Oo+3z7B2cyAp8kb0jywqnj3tbdO6bWz+nuG4flh2byJUG6+1NV9c9J5oP+e8cS8pNk+NJkU5IcdNgRPeNyAGBZcY8+ABxYrr+N9T09DgBYoQR9ANj/LkryxGH5lCQX7OFxFwztM0zZv0eSv1/06gCAZU3QB4D97+lJnlJVlyd5UpJn7uFxf5TkdlW1Jclbk5za3TctUY0AwDLlHn0AWALdvWbB+llJzhqW/znJI3ZyzKkL1k9fsP71JE/ZyXHf6BsAwIg+AAAAjIigDwAAACNi6j6wrGzduH7WJUCSpM6cdQUrx1GHr81m//cBYI8Z0QcAAIAREfQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAREfQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAREfQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAREfQBAABgRAR9AAAAGJHVsy4AGL+5086bdQnAMrbl6u3eR4BlZevG9bMugRXOiD4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4ALIGq6qp649T66qr6clWdu4/9vbqq7rt4FQIAY+Wp+wCwNK5PcmRVHdzdNyZ5VJKr97Wz7v7lRasMABg1I/oAsHTemWT+byydnOQt8zuq6vSqeu7U+ieraq6q7lRV51XVZcO2Jwz7z6+q44blH62qjw9t3r8frwcAWAYEfQDYN+uqavPUa8NO2pyd5IlVdcckRyf5yB70+6NJPt/d9+/uI5O8a3pnVR2a5E+S/Ex33z/Jz35rl3FgqqoN8z/bHTdsn3U5ALCsmLoPAPtmW3cft7sG3X15Vc1lMpr/zj3sd0uSl1TVmUnO7e4LFux/UJK/7e6rhnNcs1dVLxPdvSnJpiQ56LAjesblAMCyYkQfAJbWOUlenKlp+4Nb8u9/D98xSbr700mOzSTw/25VPX9/FAkAjIegDwBL6zVJzujuLQu2b80k0Keqjk3yPcPy3ZPc0N1vTPKi+TZTPpzkh6tqvv1dl650AGA5MnUfAJZQd38uyct3susdSZ5cVVdkcu/+p4ftRyV5UVXdmuTmJL+6oL8vD88D+LOqul2SL2XyRH8AgCSCPgAsie5es5Nt5yc5f1i+Mcmjd3Lo1iTv3smxJ04t/3WSv16UQgGA0TF1HwAAAEZE0AcAAIARMXUfWHJbN66fdQmw6OrMWVewchx1+Nps9j4CAHvMiD4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIisnnUBwP4zd9p5sy4BYK9tuXq79y+AEdq6cf2sSxgtI/oAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCIeBgfACyBqtqRZEuS2ye5Jcnrk7y0u2+daWEAwOgJ+gCwNG7s7mOSpKq+M8mbkxyS5AXTjapqdXffMoP6AICRMnUfAJZYd38pyYYkv1YTp1bVOVX1gSTvr6o1VfX+qvp4VW2pqsckSVXNVdWnquqsqvp0Vb2pqh5ZVRdW1Weq6vih3fFVdXFVfaKqLqqq75/h5QIAM2ZEHwD2zbqq2jy1vqm7N+2qcXf/U1WtSvKdw6Zjkxzd3ddU1eokj+vur1XVuiQfrqpzhnbfl+Rnkzw1yceS/HyShyb5qSS/leSxST6V5GHdfUtVPTLJ7yX5mUW70hmoqg2ZfDmSVYccOuNqAGB5EfQBYN9s6+7jvoXj39vd1wzLleT3quqHk9ya5PAkdxv2XdXdW5Kkqq5I8v7u7qrakmRuaLM2yeuq6ogknclzAZa14UuTTUly0GFH9IzLAYBlxdR9ANgPqup7k+xI8qVh0/VTu09JcmiSBwz39X8xyR2HfTdNtbt1av3WfPML+/+V5IPdfWSSn5w6FgBYgQR9AFhiVXVoklcleWV372x0em2SL3X3zVV1UpJ77uUp1ia5elg+dZ8LBQBGQdAHgKVxcFVdOky3f1+S9yQ5Yxdt35TkuGE6/pMzued+b7wwye9X1SfitjwAWPF8GACAJdDdq3az76wkZ02tb0vy4F00P3Kq3alTy1vn93X3xUnuPXXMb+99xQDAWBjRBwAAgBER9AEAAGBETN2HFWTrxvWzLgFGo86cdQUrx1GHr81m718AsMeM6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjMjqWRcALA9zp5036xKAFWrL1du9BwHsga0b18+6BA4QRvQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAREfQBYIlU1WOrqqvqPrvYf1ZVPX6Rz3liVZ2wmH0CAMuLoA8AS+fkJB8a/t1fTkyyV0G/qvwVHgAYEUEfAJZAVa1J8tAkv5TkicO2qqpXVtXfV9X7knznsP1Hq+ptU8eeWFXnDsuPrqqLq+rjVfW2od9U1daqOmPYvqWq7lNVc0meluTZVXVpVT1s4ayBqrpu6hwXVNU5Sa4ctv1CVX10OPb/VNWqJf9BAQCLTtAHgH2zrqo2T702LNj/mCTv6u5PJ/lKVT0gyeOSfH+S+yZ5cr458v6+JD9UVXca1p+Q5OyqWpfkt5M8sruPTbI5yXOmzrFt2P7HSZ7b3VuTvCrJS7v7mO6+4Dau4dgkz+zue1fVDwznfUh3H5NkR5JT9u5HsniqasP8z3bHDdtnVQYALEum6gHAvtnW3cftZv/JSV42LJ89rK9O8pbu3pHk81X1gSTp7luq6l1JfrKq3p5kfZLfSPLwTL4UuLCqkuQOSS6eOsefDf9ekuSn9+EaPtrdVw3LP5LkAUk+Npzr4CRf2oc+F0V3b0qyKUkOOuyInlUdALAcCfoAsMiq6q5JHpHkqKrqJKuSdJI/381hZyf5tSTXJNnc3dfWJHG/t7t3dY//TcO/O7Lr3+m3ZJjBV1W3y+TLgnnXT5ed5HXd/Zu7qREAWAZM3QeAxff4JG/o7nt291x3f3eSq5J8JckTqmpVVR2W5KSpY/4mk6n0v5JJ6E+SDyd5SFV9X5JU1Z2q6t63ce5rk9x5an1rJiP1SfJTSW6/i+Pen+TxVTX/3IC7VtU9b/tSAYADjaAPAIvv5PzH0ft3JDksyWcyefjd6zM1DX+Yzn9ukh8b/k13fznJqUneUlWXD+13+qf6pvxVksfNP4wvyZ8keXhVXZbkwfn3o/jf0N1XZvI8gPcM53rvUC8AsMyYug8Ai6y7T9rJtpfvwXG/lsn0/eltH0jywJ20nZta3pzJn9XL8PC/oxc0f9DU8vOGducnOX9Bn29N8tbbqhMAOLAZ0QcAAIAREfQBAABgREzdB/bI1o3rZ10CHFDqzFlXsHIcdfjabPYeBAB7zIg+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+AAAAjIigDwAAACMi6AMAAMCIrJ51AcDemzvtvFmXALDfbLl6u/c9gL2wdeP6WZfAjBnRBwAAgBER9AEAAGBEBH0AAAAYEUEfAAAARkTQB4BFVlUvrapnTa2/u6pePbX+kqp6zmyqAwDGTtAHgMV3YZITkqSqbpdkXZL7Te0/IclFt9VJTfhdDQDsFR8eAGDxXZTkwcPy/ZJ8Msm1VfXtVXVQkh9IcmVVvaLVTFgAAB/XSURBVL+qPl5VW6rqMUlSVXNV9fdV9frhuO+exQUAAMvX6lkXAADL1Lqq2jy1vqm7NyVJd3++qm6pqntkMnp/cZLDMwn/25NsSXJDksd199eqal2SD1fVOUNfRyT5xe7+8P66mANNVW1IsiFJVh1y6IyrAYDlRdAHgH2zrbuP283+izIJ+Sck+YNMgv4JmQT9C5NUkt+rqh9Ocuuw/27Dsf+8kkN+kgxfmmxKkoMOO6JnXA4ALCuCPgAsjfn79I/KZAr+Z5P8epKvJXltklOSHJrkAd19c1VtTXLH4djr93u1AMBouEcfAJbGRUl+Isk13b2ju69JcpdMpu9flGRtki8NIf+kJPecXakAwJgI+gCwNLZk8rT9Dy/Ytr27tyV5U5LjqmpLkicn+dT+LxEAGCNT9wFgCXT3jiSHLNh26tTytnzzyfwLHbl0lQEAY2dEHwAAAEZE0AcAAIARMXUflqGtG9fPugRY8erMWVewchx1+Nps9r4HAHvMiD4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIisnnUBwGzMnXberEsA2CNbrt7uPQtgkW3duH7WJbCEjOgDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDwBKpqsdWVVfVfW6j3alVdfep9VdX1X2XvkIAYIwEfQBYOicn+dDw7+6cmuQbQb+7f7m7r1zCugCAERP0AWAJVNWaJA9N8ktJnji1/XlVtaWqLquqjVX1+CTHJXlTVV1aVQdX1flVddzQ/uSh/Ser6sypfq6rqv9v6OfDVXW3/XyJAMABStAHgH2zrqo2T702LNj/mCTv6u5PJ/lKVT2gqn5s2P5D3X3/JC/s7rcn2ZzklO4+prtvnO9gmM5/ZpJHJDkmyQOr6rHD7jsl+fDQz98m+ZWlvNj9rao2zP9sd9ywfdblAMCysnrWBQDAMrWtu4/bzf6Tk7xsWD57WK8kr+3uG5Kku6+5jXM8MMn53f3lJKmqNyX54SR/keTfkpw7tLskyaP25SIOVN29KcmmJDnosCN6xuUAwLIi6APAIququ2YyCn9UVXWSVUk6ydsW8TQ3d/d8AN4Rv9MBgIGp+wCw+B6f5A3dfc/unuvu705yVZLtSZ5SVd+WfOMLgSS5Nsmdd9LPR5M8vKrWVdWqTGYF/M3Slw8ALGeCPgAsvpOT/PmCbe9IcliSc5JsrqpLkzx32HdWklfNP4xv/oDu/kKS05J8MMllSS7p7r9c4toBgGXOND8AWGTdfdJOtr18anXjgn3vyOSLgHknTu17S5K37KS/NVPLb0/y9n2vGAAYEyP6AAAAMCKCPgAAAIyIqfuwQm3duH7WJcCyVmfOuoKV46jD12az9ywA2GNG9AEAAGBEBH0AAAAYEUEfAAAARkTQBwAAgBER9AEAAGBEBH0AAAAYEUEfAAAARkTQBwAAgBER9AEAAGBEBH0AAAAYEUEfAAAARkTQBwAAgBER9AEAAGBEBH0AAAAYEUEfAAAARmT1rAsAWEnmTjtv1iXAsrPl6u3+7wCwbGzduH7WJRjRBwAAgDER9AEAAGBEBH0AAAAYEUEfAAAARkTQB4AlUFXfVVVnV9U/VtUlVfXOqrr3TtpdNIv6AIDxEvQBYJFVVSX58yTnd/e9uvsBSX4zyd2m2qxOku4+YTZVAgBjJegDwOI7KcnN3f2q+Q3dfVmSVVV1QVWdk+TKJKmq64Z/T6yqv6mqv6yqf6qqjVV1SlV9tKq2VNW9hnaHVtU7qupjw+shM7g+AOAAtnrWBQDAMrWuqjZPrW/q7k3D8pFJLtnFcccmObK7r9rJvvsn+YEk1yT5pySv7u7jq+qZSZ6e5FlJXpbkpd39oaq6R5J3D8eMSlVtSLIhSVYdcuiMqwGA5UXQB4B9s627j9uH4z66i5CfJB/r7i8kSVX9Y5L3DNu3ZDJLIEkemeS+k7sDkiSHVNWa7r5uH2o5YA1fmmxKkoMOO6JnXA4ALCuCPgAsviuSPH4X+67fzXE3TS3fOrV+a775O/t2SR7U3V//lioEAEbLPfoAsPg+kOSgYfp5kqSqjk7ysEXo+z2ZTOOf7/eYRegTABgRQR8AFll3d5LHJXnk8Of1rkjy+0n+dRG6f0aS46rq8qq6MsnTFqFPAGBETN0HgCXQ3Z9P8nM72fUnC9qtGf49P8n5U9tPnFr+xr7u3pbkCYtbLQAwJkb0AQAAYEQEfQAAABgRU/cB9qOtG9fPugQWSZ056wpWjqMOX5vN/u8AwB4zog8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKrZ10AsLLNnXberEsADnBbrt7uvQJgBrZuXD/rEthHRvQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAREfQBYAlU1XUL1k+tqlcOy0+rqifv5tgTq+qEpa4RABgnT90HgP2su191G01OTHJdkov2tM+qWt3dt3wrdQEA42BEHwD2s6o6vaqeOyw/o6qurKrLq+rsqppL8rQkz66qS6vqYVU1V1UfGNq8v6ruMRx7VlW9qqo+kuSFVfWZqjp02He7qvqH+XUAYOUwog8A+2ZdVW2eWt/U3Zum1g+uqkun1u+a5Jyd9HNaku/p7puq6i7d/dWqelWS67r7xUlSVX+V5HXd/bqqemqSlyd57HD8f0pyQnfvqKrtSU5J8r+TPDLJZd395cW42P2tqjYk2ZAkqw7xXQUA7A1BHwD2zbbuPm43+2/s7mPmV6rq1CQ7a395kjdV1V8k+Ytd9PXgJD89LL8hyQun9r2tu3cMy69J8peZBP2nJnntbV3EgWr40mRTkhx02BE943IAYFkxdR8AZmt9kj9McmySj1XV3n4Jf/38Qnd/NskXq+oRSY5P8teLViUAsGwI+gAwI1V1uyTf3d0fTPK8JGuTrElybZI7TzW9KMkTh+VTklywm25fneSN+fcj/QDACiLoA8DsrEryxqrakuQTSV7e3V9N8ldJHjf/ML4kT0/ylKq6PMmTkjxzN32ek8mXBct22j4A8K1xjz4ALIHuXrNg/awkZw3Lp0/teuhOjv10kqMXbH7ETtqdupNT3z+Th/B9am/qBQDGQ9AHgJGoqtOS/Gom0/sBgBXK1H0AGInu3tjd9+zuD826FgBgdozoAzO1deP6WZcA+6TOnHUFK8dRh6/NZu8VALDHjOgDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIyIoA8AAAAjIugDAADAiAj6AAAAMCKCPgAAAIzI6lkXACvN3GnnzboEgGVly9XbvXcCzNDWjetnXQJ7yYg+AAAAjIigDwAAACMi6AMAAMCICPoAAAAwIoI+ACyBqtpRVZdOveb2sZ9nVdW3LW51AMCYeeo+ACyNG7v7mEXo51lJ3pjkhkXoCwBYAYzoA8B+UlVzVXVBVX18eJ0wbD+xqs6vqrdX1aeq6k018Ywkd0/ywar64ND2j6tqc1VdUVVnTPW9saqurKrLq+rFVXXnqrqqqm4/7D9keh0AGC8j+gCwb9ZV1eap9U3dvWlq/eCqunRYvqq7H5fkS0ke1d1fr6ojkrwlyXFDmx9Mcr8kn09yYZKHdPfLq+o5SU7q7m1Du//R3ddU1aok76+qo5NcneRxSe7T3V1Vd+nua6vq/CTrk/xFkicm+bPuvnmRfw5Loqo2JNmQJKsOOXTG1QDA8iLoA8C+2dbdx+1m/86m7t8+ySur6pgkO5Lce2rfR7v7c0kyfEEwl+RDO+n354YQvDrJYUnum+TKJF9P8qdVdW6Sc4e2r07yG5kE/ack+ZU9v7zZGr402ZQkBx12RM+4HABYVkzdB4D959lJvpjk/pmM5N9hat9NU8s7spMv46vqe5I8N8mPdPfRSc5LcsfuviXJ8UnenuQnkrwrSbr7wiRzVXViklXd/cnFviAA4MAj6APA/rM2yRe6+9YkT0qyag+OuTbJnYflQ5Jcn2R7Vd0tyY8lSVWtSbK2u9+ZyZcJ9586/vVJ3pzktYtyBQDAAU/QB4D954+S/GJVXZbkPpmE9tuyKcm7quqD3X1Zkk8k+VQm4f3Coc2dk5xbVZdnMt3/OVPHvynJt2fyPAAAYAVwjz4ALIHuXrOTbZ9JcvTUpucN289Pcv5Uu1+bWn5FkldMrZ+6i1Mev4vtD03y9u7+6p5VDgAsd4I+AIxUVb0ik+n9Pz7rWgCA/UfQB4CR6u6nz7oGAGD/E/RhP9u6cf2sSwAWQZ056wpWjqMOX5vN3jsBYI95GB8AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIgI+gAAADAigj4AAACMiKAPAAAAIyLoAwAAwIisnnUBAPtq7rTzZl0CsB9suXq7/+/AKG3duH7WJTBSRvQBAABgRAR9AAAAGBFBHwAAAEZE0AcAAIAR8TA+AFgCVbUjyZZMftdeleRJ3f3V2VYFAKwERvQBYGnc2N3HdPeRSa5J8t9mXRAAsDII+gCw9C5OcniS1MSLquqTVbWlqp4wbD+xqv6mqv6yqv6pqjZW1SlV9dGh3b2Gdj9ZVR+pqk9U1fuq6m7D9tOr6jVVdf5w/DPmT15VT66qy6vqsqp6w7Dt0Kp6R1V9bHg9ZL//VACAJWHqPgDsm3VVtXlqfVN3b1rYqKpWJfmRJH86bPrpJMckuX+Sdf+vvbsPlquu7zj+/hAEHQOIwmCKYpRJRAoSNFC1lAFHHS1Wa32I1gq004lUi9DxialOpe04E6T1CatOpFjaWqRWEVs6NYomgE4lCRLCQxHBUA0IBiqIQmyTb//YE9ne3nuTe3fvPXfPfb9mdjiPv/3uj7N3z2fP72yA9UmuatYdAzyL3giAO4ALq+r4JGcBZwJnA9cAz6uqSvL7wLuAtzf7HwGcDOwH3JrkE8BS4L3AC6pqW5InNtt+BPhQVV2T5DDgy81zzwlJVgIrARbsf3DL1UiSNFoM+pIkTc+2qlo+yfrHJbme3pX8W4CvNMtPAC6pqh3APUnWAccBDwLrq+pugCS3A2uafTbTC/AATwEuTbII2Ife/f+7XFFV24HtSe4FDgFeCHyuqrYBVNX9zbYvAo5Msmvf/ZMsrKqHptoRM6H50mQ1wL6LllTL5UiSNFIcui9J0sx4uKqWAU8Dwp7do7+9b3pn3/xOHv1y/gLgY1V1NPBm4LET7L+Dyb/Q34veyIBlzePQuRLyJUnSYAz6kiTNoKr6GfA24O1J9gauBlYkWZDkYOBE4NopNHkAsLWZPm0Ptv8a8NokTwLoG7q/ht7tADTLl02hBkmSNIcZ9CVJmmFV9W3gBuANwGXN9CZ6IfxdVfXDKTR3LvC5JBuBbXvw3DcB7wfWJdkEfLBZ9TZgefMjfTcDZ0yhBkmSNId5j74kSTOgqhaOmf+Nvtl3No/+9WuBtX3zJ423rqouBy4f5/nOHTN/VN/0xcDFY9ZvA1bswUuRJEkjxiv6kiRJkiR1iEFfkiRJkqQOcei+pJG1ZdUpbZegeSzntV3B/HH0oQewwfe7JEl7zCv6kiRJkiR1iEFfkiRJkqQOMehLkiRJktQhBn1JkiRJkjrEoC9JkiRJUocY9CVJkiRJ6hCDviRJkiRJHWLQlyRJkiSpQwz6kiRJkiR1iEFfkiRJkqQOMehLkiRJktQhBn1JkiRJkjrEoC9JkiRJUocY9CVJkiRJ6hCDviRJkiRJHbJ32wVImrsWn3NF2yVIEpu3PuDfI0lqyZZVp7RdgqbBK/qSJEmSJHWIQV+SJEmSpA4x6EuSJEmS1CEGfUmSJEmSOsSgL0nSDEjyniQ3JbkhyfVJfmWSbZcn+ehs1tc87+IkDzf17Xrsk+QVSc6ZZL8nJHlL3/wvJfmn2alakiTtjr+6L0nSkCV5PvBy4DlVtT3JQcA+E21fVRuADbNV3xi3V9WyMcu+1Dwm8gTgLcDHAarqLuA1M1OeJEmaKq/oS5I0fIuAbVW1HaCqtjVhmCTHJflmkk1Jrk2yX5KTkvxLs/7xSS5q1n07ySub5acn+UKSf0tyW5IP7HqyJC9Ncl3T5pWTtbMnmuf6WDN9SJLLmrY3JXkBsAo4vBkBcH4zMuDGZvvHJvl0ks3N8568u/olSdJweUVfkqTpOShJ/1X41VW1upleA/xJku8AXwUurap1SfYBLgVWVNX6JPsDD49p9z3A16rq95I8Abg2yVebdcuAY4HtwK1JLgAeAT4FnFhV30vyxMnaqaqfjnm+w5Nc30x/o6reOmb9R4F1VfWqJAuAhcA5wFG7RgIkWdy3/VuBqqqjkxwBrEmydKL6q+r74/QtSVYCKwEW7H/weJtIkqQJGPQlSZqebVW1fLwVVfVQkucCvwacDFza3PO+Ebi7qtY32z0IkKR/95cAr0jyjmb+scBhzfSVVfVAs8/NwNOAA4Grqup7TZv376adW8aUO97Q/X4vBE5t2t4BPJDkwEm2PwG4oNn+P5LcCewK+uPVP27Qb740WQ2w76IlNcnzSZKkMQz6kiTNgCYUrwXWJtkMnEYv6O9OgFdX1a3/Z2Hvx/y29y3aweSf4+O207Kp1C9JkqbJe/QlSRqyJM9MsqRv0TLgTuBWYFGS45rt9ksyNux+GTgzzWX+JMfu5un+HTgxydOb7XcN3Z9qOxO5EviDpo0FSQ4AfgLsN8H2VwNvbLZfSm8UwVz6skGSpM4z6EuSNHwLgYuT3JzkBuBI4Nyq+jmwArggySbgK/SG1Pf7c+AxwA1JbmrmJ1RVP6J3L/sXmjYvnU47kzgLOLkZlbAROLKq7gO+keTGJOeP2f7jwF7N9pcCp+/6UUJJkjQ7UjXxbW/Lly+vDRva+td+JLVt8TlXtF2CNGfded7LN050j76Ga99FS2rRaR9uuwxJmpe2rDql7RI0iSTjno94RV+SJEmSpA4x6EuSJEmS1CH+2q2kCTlUS5pYzmu7gvnj6EMPYIN/jyRJ2mNe0ZckSZIkqUMM+pIkSZIkdYhBX5IkSZKkDjHoS5IkSZLUIQZ9SZIkSZI6xKAvSZIkSVKHGPQlSZIkSeoQg74kSZIkSR1i0JckSZIkqUMM+pIkSZIkdYhBX5IkSZKkDjHoS5IkSZLUIQZ9SZIkSZI6xKAvSZIkSVKHGPQlSZIkSeqQvdsuQNJwLD7nirZLkKQZsXnrA/6Nk6Td2LLqlLZL0BziFX1JkiRJkjrEoC9JkiRJUocY9CVJkiRJ6hCDviRJkiRJHWLQlyRpBiTZkeT6vsfiIbR5RpJTm+m/SfKaQduUJEnd46/uS5I0Mx6uqmXDbLCqPjnM9iRJUjd5RV+SpFmSZHGSq5Nc1zxe0Cw/Kcm6JJcnuSPJqiRvTHJtks1JDm+2OzfJO8a0+cIkX+ybf3GSy2b3lUmSpLnEoC9J0vQclGRD32PlmPWP6xu2vyt43wu8uKqeA6wAPtq3/THAGcCzgDcBS6vqeOBC4MxJ6vg6cESSg5v53wUuGuyltS/Jyl19u+NnD7RdjiRJI8Wh+5IkTc+2qlo+yfrxhu4/BvhYkmXADmBp37r1VXU3QJLbgTXN8s3AyRM9SVVVkr8DfifJp4HnA6dO7aXMPVW1GlgNsO+iJdVyOZIkjRSDviRJs+ePgHvoXb3fC3ikb932vumdffM72f3n9aeBf27a+1xV/c9QqpUkSSPJoC9J0uw5APhBVe1MchqwYBiNVtVdSe4C3gu8aBhtSpKk0eU9+pIkzZ6PA6cl2QQcAfx0iG1/Bvh+Vd0yxDYlSdII8oq+JEkzoKoWjrPsNuDZfYve3SxfC6zt2+6kvulfrKuqc/uWnz6m+ROATw1UtCRJ6gSDviRJIy7JRnqjA97edi2SJKl9Bn1JkkZcVT237RokSdLcYdCXOmLLqlPaLkGaV3Je2xXMH0cfegAb/BsnSdIe88f4JEmSJEnqEIO+JEmSJEkdYtCXJEmSJKlDDPqSJEmSJHWIQV+SJEmSpA4x6EuSJEmS1CEGfUmSJEmSOsSgL0mSJElShxj0JUmSJEnqEIO+JEmSJEkdYtCXJEmSJKlDDPqSJEmSJHWIQV+SJEmSpA4x6EuSJEmS1CEGfUmSJEmSOmTvtguQpN1ZfM4VbZcgqUWbtz7g3wFJI2PLqlPaLkHyir4kSZIkSV1i0JckSZIkqUMM+pIkSZIkdYhBX5IkSZKkDjHoS5I0A5I8Oclnk9yeZGOSf02ydJaee0uSg2bjuSRJ0tzjr+5LkjRkSQJcBlxcVa9vlh0DHAJ8p83aJElS93lFX5Kk4TsZ+O+q+uSuBVW1CbgmyflJbkyyOckKgCQnJVmX5PIkdyRZleSNSa5ttju82e7gJJ9Psr55/Gqz/ElJ1iS5KcmFQJrlf5bk7F01JHl/krNmsR8kSVILDPqSJE3PQUk29D1W9q07Ctg4zj6/BSwDjgFeBJyfZFGz7hjgDOBZwJuApVV1PHAhcGazzUeAD1XVccCrm3UA7wOuqapfpjeS4LBm+UXAqQBJ9gJeD/z9YC97diRZuatvd/zsgbbLkSRppDh0X5Kk6dlWVcunuM8JwCVVtQO4J8k64DjgQWB9Vd0NkOR2YE2zz2Z6IwSg9+XAkb07AwDYP8lC4ER6XyJQVVck+a9mekuS+5IcS++2gW9X1X3TeK2zrqpWA6sB9l20pFouR5KkkWLQlyRp+G4CXjPFfbb3Te/sm9/Jo5/XewHPq6pH+nfsC/7juRA4HXgyvSv8kiSp4xy6L0nS8H0N2Ld/OH+SZwM/BlYkWZDkYHpX4q+dQrtreHQYP0mWNZNXAb/dLHsZcGDfPpcBL6U3cuDLU38pkiRp1HhFX5KkIauqSvIq4MNJ3g08AmwBzgYWApuAAt5VVT9McsQeNv024K+S3EDvM/wqevf1/ylwSZKbgG8C/9lXy8+TfB34cXPLgCRJ6jiDviRJM6Cq7gJeN86qdzaP/m3XAmv75k8ab11VbQNWjPNc9wEvGa+O5kf4nge8dir1S5Kk0eXQfUmSOirJkcB3gSur6ra265EkSbPDK/qSJHVUVd0MPKPtOiRJ0uwy6Eua87asOqXtEqT/J+e1XcH8cfShB7DBvwOSJO0xh+5LkiRJktQhBn1JkiRJkjrEoC9JkiRJUocY9CVJkiRJ6hCDviRJkiRJHWLQlyRJkiSpQwz6kiRJkiR1SKpq4pXJj4A7Z6+cGXEQsK3tIjrAfhycfTgc9uPg7MPheGZV7dd2EfNBkp8At7ZdRwf43h+cfTgc9uPg7MPh6EI/Pq2qDh67cO/J9hhvh1GTZENVLW+7jlFnPw7OPhwO+3Fw9uFwJNnQdg3zyK0es4PzvT84+3A47MfB2YfD0eV+dOi+JEmSJEkdYtCXJEmSJKlD5kPQX912AR1hPw7OPhwO+3Fw9uFw2I+zx74eDvtxcPbhcNiPg7MPh6Oz/Tjpj/FJkiRJkqTRMh+u6EuSJEmSNG8Y9CVJkiRJ6pDOBP0kT03y9SQ3J7kpyVnN8nOTbE1yffP49bZrncsm6ccnJvlKktua/x7Ydq1zWZKLktyb5Ma+ZR6LUzRBP3osDiDJliSbm2PQfx5uGpK8NMmtSb6b5Jy26+k6+3twvu+nx8+gwXk+NByenw9uPmbFztyjn2QRsKiqrkuyH7AR+E3gdcBDVfUXrRY4Iibpx9OB+6tqVXOidWBVvbvFUue0JCcCDwF/W1VHNcvOxWNxSiboxw/gsThtSbYAy6tqW9u1jKIkC4DvAC8GfgCsB95QVTe3WlhH2d/D4ft+evwMGpznQ8Ph+fng5mNW7MwV/aq6u6qua6Z/AtwCHNpuVaNnkn58JXBxs9nF9N4YmkBVXQXc33Ydo26CfvRYVJuOB75bVXdU1c+Bz9I7JjUz7G+1xs+gwXk+NByenw9uPmbFzgT9fkkWA8cC32oW/WGSG5rhQw5p2UNj+vGQqrq7WfVD4JCWyhp1HouD81gcTAFrkmxMsrLtYkbQocD3++Z/QMdPFFpmfw+H7/vh8TNoODwfmibPzwc3X7Ji54J+koXA54Gzq+pB4BPA4cAy4G7gL1ssb2SM04+/UL37Pbpxz8fs8lgcMo/FaTmhqp4DvAx4azOsUlK3+b6fAX4GTZvnQ9Pk+fng5lNW7FTQT/IYev/jPlNVXwCoqnuqakdV7QQ+RW8YoCYxXj8C9zT3tuy6x+XetuobVR6LQ+OxOICq2tr8917gMjwOp2or8NS++ac0yzQz7O8h8H0/VH4GDcjzoenx/Hxw8y0rdiboJwnw18AtVfXBvuWL+jZ7FXDj2H31qIn6EfgScFozfRpw+WzXNuo8FofGY3Gakjy++QEakjweeAkeh1O1HliS5OlJ9gFeT++Y1Mywvwfk+37o/AwakOdDU+f5+eDmY1bs0q/unwBcDWwGdjaL/xh4A72hGAVsAd7cdy+LxpikH78F/CNwGHAn8Lqq8sdVJpDkEuAk4CDgHuB9zbzH4hRM0I9fxGNxWpI8g97VPIC9gX+oqve3WNJIav7pnQ8DC4CL7MOZZX8Pxvf99PkZNDjPh4bD8/PBzces2JmgL0mSJEmSOjR0X5IkSZIkGfQlSZIkSeoUg74kSZIkSR1i0JckSZIkqUMM+pIkSZIkdYhBX5IkSZKkDjHoS5IkSZLUIf8L7V8S5aKmlFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "ax.invert_xaxis()\n",
    "ax.yaxis.tick_right()\n",
    "\n",
    "pop_genres.plot(kind='barh', title='Overall', figsize=(18,10), xlim=(28, 0), ax=ax)\n",
    "top_pop_genres.plot(kind='barh', title='Top 20%', figsize=(18,10), xlim=(0, 28), ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 `production_companies` charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* There are too many production companies to be shown within one bar chart; thus for presentation purposes, we are going to plot only the top 50 companies in the `value_counts()` list in each subsection below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.3.1 Plot bar charts for the number of movies produced by each company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to compare the number of movies produced of the overall data vs. the top 20% movies. In other words, plot `companies_vc` vs. `top_companies_vc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1311a34a8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "ax.invert_xaxis()\n",
    "ax.yaxis.tick_right()\n",
    "\n",
    "companies_vc.head(50).plot(kind='barh', title='Overall', figsize=(18,15), xlim=(120, 0), ax=ax)\n",
    "top_companies_vc.head(50).plot(kind='barh', title='Top 20%', figsize=(18,15), xlim=(0, 120), ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4.3.2 Plot bar charts for the average popularity of movies produced by each company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to compare the average popularity of movies of the overall data vs. the top 20% movies, which means `pop_companies` vs. `top_pop_companies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131bfc860>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "ax.invert_xaxis()\n",
    "ax.yaxis.tick_right()\n",
    "\n",
    "pop_companies.head(50).plot(kind='barh', title='Overall', figsize=(18,15), xlim=(80, 0), ax=ax)\n",
    "top_pop_companies.head(50).plot(kind='barh', title='Top 20%', figsize=(18,15), xlim=(0, 80), ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Data Analysis and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Analysis based *solely* on [Data Exploration](#3.-Data-Exploration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to Exploration [Section 3.2.2](#3.2.2-Get-the-average-net_profit-of-the-top-20%-movies), [Section 3.3.2](#3.3.2-Get-the-average-rating-of-the-top-20%-movies), and [Section 3.4.2](#3.4.2-Get-the-average-vote_average-of-the-top-20%-movies), we can say that `net_profit` is convincingly proportional to `popularity`, while `vote_average` may be a determining factor, but `rating` may somewhat be questionable. Therefore, if all three variables have correlations to `popularity`, then the order of the correlations from strongest to weakest would be:\n",
    "1. `net_profit`\n",
    "2. `vote_average`\n",
    "3. `rating`\n",
    "\n",
    "We think that the explorations done and shown in [Section 3.5](#3.5-genres-exploration) and [Section 3.6](#3.6-production_companies-exploration) have nothing deterministic towards our analysis, so we need to analyze further from the charts produced.\n",
    "\n",
    "But again, this part of the analysis is based solely on the Data Exploration, now let's dive deeper and analyze according to the charts!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Analysis based on [Data Visualization](#4.-Data-Visualization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see and compare the first two scatter matrices produced in [Section 4.1.1](#4.1.1-Plot-a-scatter-matrix-for-the-overall-data) and [Section 4.1.2](#4.1.2-Plot-a-scatter-matrix-for-the-top-20%-movies), specifically the scatter charts related to `popularity`, we can probably say that these data distribution charts do not give us any further information (as the top 20% movies charts are basically the same as the overall data charts except the fact that only movies with `popularity` greater than `top_pop` get displayed), except that now we know the three variables (`net_profit`, `rating`, `vote_average`) do not linearly correlate to `popularity`, meaning those three do not result in linear graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12cf25860>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2, ax3) = plt.subplots(ncols=3)\n",
    "\n",
    "df[['popularity', 'net_profit']].plot(x='net_profit', y='popularity', kind='scatter', alpha=0.3, title='Net Profit', figsize=(18,10), legend=False, ax=ax)\n",
    "df[['popularity', 'rating']].plot(x='rating', y='popularity', kind='scatter', alpha=0.3, title='Rating', figsize=(18,10), legend=False, ax=ax2)\n",
    "df[['popularity', 'vote_average']].plot(x='vote_average', y='popularity', kind='scatter', alpha=0.3, title='Vote Average', figsize=(18,10), legend=False, ax=ax3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12cd58588>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBFwFevDrZxf28j6cKvvoQDMDbUXAMZvWvrroNdHhyl37vxazpxdzXCrZbBQOX3qRG47vtL3sAAONbUXAMZvmvqrGRFwGevDrZw5u5qjS4PceuxIji4Ncubsau/pIcBhpvYCwPhNW38VRMBlrG1sZrjVsrK8mCRZWV7McKtlbWOz55EBHF5qLwCM37T1V0EEXMbK0mIGC5W19e0359r6ZgYLlZWlxZ5HBnB4qb0AMH7T1l8FEXAZy4OFnD51Ihc2hrn/oYu5sDHM6VMnsjzwtgGYFLUXAMZv2vqrxSrhCm47vpK77jiZtY3NrCwt2hEG6IDaCwDjN039VRABV7E8WLATDNAxtRcAxm9a+mv/IwAAAADmhiACAAAA6IwgAgAAAOiMIAIAAADojCACAAAA6MzEgoiq+tGqen9VvWXXtn9SVW+tqjdX1Wur6uZd131bVd1bVX9cVZ83qXEBzBO1GKBf6jDA401yRsSrknz+Jdt+NclzWmvPTXI2ybclSVV9cpKXJvlTo/v8UFUtTnBsAPPiVVGLAfr0qqjDAI8xsSCitfabST50ybZfaa0NRxd/N8nJ0c8vSvIzrbVHWmtvT3Jvks+c1NgA5oVaDNAvdRjg8fpcI+J/SvIfRj8/Jcm7d11332jb41TVK6rq7qq6e3V1dcJDBDj01GKAfqnDwNzpJYioqr+fZJjkJ6/1vq21V7bW7myt3XnixInxDw5gTqjFAP1Sh4F5Nej6Aavqa5J8cZIXtNbaaPN7kjx1181OjrYBMAFqMUC/1GFgnnU6I6KqPj/JNyf5ktbahV1XvT7JS6vqhqp6RpJnJfkvXY4NYF6oxQD9UoeBeTexGRFV9dNJPifJk6rqviTfme0VgW9I8qtVlSS/21r7+tbaH1XVa5L892xPT/uG1trmpMYGMC/UYoB+qcMAj1cfnQk2e+68885299139z0MgMepqje21u7sexxdUIuBaaQOA/TvcrW4z7NmAAAAAHNGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdEYQAQAAAHRGEAEAAAB0RhABAAAAdGZiQURV/WhVvb+q3rJr28dU1a9W1Z+M/vvE0faqqh+sqnur6s1V9bxJjQtgnqjFAP1ShwEeb5IzIl6V5PMv2fatSd7QWntWkjeMLifJFyR51ujfK5L88ATHBTBPXhW1GKBPr4o6DPAYEwsiWmu/meRDl2x+UZJXj35+dZIX79r+423b7ya5uao+blJjA5gXajFAv9RhgMfreo2IW1tr7xv9fC7JraOfn5Lk3btud99o2+NU1Suq6u6qunt1dXVyIwU4vNRigH6pw8Bc622xytZaS9IOcL9XttbubK3deeLEiQmMDGB+qMUA/VKHgXnUdRBx/870stF/3z/a/p4kT911u5OjbQCMn1oM0C91GJhrXQcRr0/y8tHPL0/yul3bXzZaKfj5Sc7vmq4GwHipxQD9UoeBuTaY1C+uqp9O8jlJnlRV9yX5ziTfm+Q1VfV1Sd6Z5CWjm/9yki9Mcm+SC0m+dlLjApgnajFAv9RhgMebWBDRWvuKy1z1gj1u25J8w6TGAjCv1GKAfqnDAI/X22KVAAAAwPwRRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEURcg/XhVs6vbWR9uNX3UACA6M0AHB7z1NMmdvrOw+bc+bWcObua4VbLYKFy+tSJ3HZ8pe9hAcDc0psBOCzmraeZEbEP68OtnDm7mqNLg9x67EiOLg1y5uzqXCRVADCN9GYADot57GmCiH1Y29jMcKtlZXkxSbKyvJjhVsvaxmbPIwOA+aQ3A3BYzGNPE0Tsw8rSYgYLlbX17T+EtfXNDBYqK0uLPY8MAOaT3gzAYTGPPU0QsQ/Lg4WcPnUiFzaGuf+hi7mwMczpUyeyPPDyAUAf9GYADot57GkWq9yn246v5K47TmZtYzMrS4uH+o8CAGaB3gzAYTFvPU0QcQ2WBwuH/g8CAGaJ3gzAYTFPPW0+niUAAAAwFQQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBn9hVEVNWnTHogAFyZWgzQL3UYYDz2OyPih6rqv1TV36iq4xMdEQCXoxYD9EsdBhiDfQURrbU/k+Srkjw1yRur6qeq6i9MdGQAPIZaDNAvdRhgPPa9RkRr7U+SfHuSb0lyOskPVtVbq+pLJzU4AB5LLQbolzoMcP32u0bEc6vq+5Pck+TPJ3lha+3Zo5+/f4LjA2BELQbolzoMMB6Dfd7unyf5N0n+XmttbWdja+29VfXtExkZAJdSiwH6pQ4DjMF+D814bWvtJ3YX3Kr6piRprf3EREYGwKXUYoB+qcMAY7DfIOJle2z7mjGOA4CrU4sB+qUOA4zBFQ/NqKqvSPKVSZ5RVa/fddWxJB+a5MAA2KYWA/RLHQYYr6utEfGfk7wvyZOS/NNd2x9K8uZJDQqAx1CLAfqlDgOM0RWDiNbaO5O8M8lndzMcAC6lFgP0Sx0GGK8rrhFRVb81+u9DVfXgrn8PVdWDB33QqvrbVfVHVfWWqvrpqjpSVc+oqt+rqnur6meravmgvx/gMFGLAfqlDgOM1xWDiNba/zj677HW2k27/h1rrd10kAesqqck+VtJ7mytPSfJYpKXJvlHSb6/tfYJST6c5OsO8vsBDhu1GKBf6jDAeF31rBlVtVhVbx3z4w6SrFTVIMnRbB9z9+eT/Pzo+lcnefGYHxNgZqnFAP1ShwHG56pBRGttM8kfV9XTxvGArbX3JPm+JO/KdrE9n+SNSR5orQ1HN7svyVP2un9VvaKq7q6qu1dXV8cxJICppxYD9EsdBhifq501Y8cTk/xRVf2XJA/vbGytfcm1PmBVPTHJi5I8I8kDSX4uyefv9/6ttVcmeWWS3Hnnne1aHx9ghqnFAP1ShwHGYL9BxD8Y42N+bpK3t9ZWk6Sq/l2SP53k5qoajBLgk0neM8bHBDgM1GKAfqnDAGOwryCitXZmjI/5riTPr6qjSdaSvCDJ3Ul+PcmXJfmZJC9P8roxPibAzFOLAfqlDgOMx1XXiEiSqnp+Vf3XqvpIVa1X1eZBT1XUWvu9bC/A8/tJ/nA0hlcm+ZYkf6eq7k1yS5IfOcjvBzis1GKAfqnDAOOx30Mz/q9sn07o55LcmeRlSU4d9EFba9+Z5Dsv2fy2JJ950N8JMAfUYoB+qcMAY7CvGRFJ0lq7N8lia22ztfZjuYbFdAAYD7UYoF/qMMD12++MiAtVtZzkv1XVP872KYb2HWIAMBZqMUC/1GGAMdhv4fzqJItJ/ma2T1X01CR/aVKDAmBPajFAv9RhgDHY71kz3jn6cS3JP5zccAC4HLUYoF/qMMB4XDGIqKo/TNIud31r7bljHxEAj6EWA/RLHQYYr6vNiPjiTkYBwJWoxQD9UocBxuiKQcSu6WcA9EQtBuiXOgwwXvtaI6KqHspHp6MtJ1lK8nBr7aZJDQyAx1KLAfqlDgOMx34Xqzy283NVVZIXJXn+pAYFwOOpxQD9UocBxuOaz3vctv1Cks+bwHgA2Ae1GKBf6jDAwe330Iwv3XVxIcmdSS5OZEQA7EktBuiXOgwwHvsKIpK8cNfPwyTvyPZUNAC6oxYD9EsdBhiD/a4R8bWTHggAV6YWA/RLHQYYj32tEVFVz6yqX6yq1ap6f1W9rqqeOenBAfBRajFAv9RhgPHY72KVP5XkNUk+LsmTk/xckp+e1KAA2JNaDNAvdRhgDPYbRBxtrf1Ea204+vdvkxyZ5MAAeBy1GKBf6jDAGOx3scr/UFXfmuRnkrQkfznJL1fVxyRJa+1DExofAB+lFgP0Sx0GGIP9BhEvGf33f75k+0uzXYQdGwcweWoxQL/UYYAx2O9ZM54x6YEAcGVqMUC/1GGA8dhXEFFVS0n+epI/O9r0G0n+VWttY0LjAuASajFAv9RhgPHY76EZP5xkKckPjS5/9WjbX53EoADYk1oM0C91GGAM9htEfEZr7VN3Xf61qvqDSQwIgMtSiwH6pQ4DjMF+T9+5WVUfv3Ohqp6ZZHMyQwLgMtRigH6pwwBjsN8ZEf9rkl+vqreNLt+e5GsnMiIALkctBuiXOgwwBvudEfHbSf5Vkq0kHxr9/DuTGhQAe1KLAfqlDgOMwX6DiB9P8owk/1uSf57tcyT/xKQGBcCe1GKAfqnDAGOw30MzntNa++Rdl3+9qv77JAYEwGWpxQD9UocBxmC/MyJ+v6qev3Ohqj4ryd2TGRIAl6EWA/RLHQYYg/3OiPj0JP+5qt41uvy0JH9cVX+YpLXWnjuR0QGwm1oM0C91GGAM9htEfP5ERwHAfqjFAP1ShwHGYF9BRGvtnZMeCABXphYD9EsdBhiP/a4RAQAAAHDdBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQRAAAAQGcEEQAAAEBnBBEAAABAZwQR12l9uJXzaxtZH271PRQAIHozAFzOtPTIQa+PPuPOnV/LmbOrGW61DBYqp0+dyG3HV/oeFgDMLb0ZAPY2TT3SjIgDWh9u5czZ1RxdGuTWY0dydGmQM2dXe0+WAGBe6c0AsLdp65GCiANa29jMcKtlZXkxSbKyvJjhVsvaxmbPIwOA+aQ3A8Depq1HCiIOaGVpMYOFytr69v+4tfXNDBYqK0uLPY8MAOaT3gwAe5u2HimIOKDlwUJOnzqRCxvD3P/QxVzYGOb0qRNZHnhJAaAPejMA7G3aeqTFKq/DbcdXctcdJ7O2sZmVpUU7OgDQM70ZAPY2TT1SEHGdlgcLdnIAYIrozQCwt2npkf2PAAAAAJgbgggAAACgM4IIAAAAoDOCCAAAAKAzgggAAACgM4IIAAAAoDOCCAAAAKAzvQQRVXVzVf18Vb21qu6pqs+uqo+pql+tqj8Z/feJfYwNYF6oxQD9UoeBedXXjIgfSMvSoPkAACAASURBVPIfW2uflORTk9yT5FuTvKG19qwkbxhdBmBy1GKAfqnDwFzqPIioquNJ/mySH0mS1tp6a+2BJC9K8urRzV6d5MVdjw1gXqjFAP1Sh4F51seMiGckWU3yY1X1pqr6N1V1Y5JbW2vvG93mXJJb97pzVb2iqu6uqrtXV1c7GjLAoaMWA/RLHQbmVh9BxCDJ85L8cGvtjiQP55IpZ621lqTtdefW2itba3e21u48ceLExAcLcEipxQD9UoeBudVHEHFfkvtaa783uvzz2S7C91fVxyXJ6L/v72FsAPNCLQbolzoMzK3Og4jW2rkk766qTxxtekGS/57k9UlePtr28iSv63psAPNCLQbolzoMzLNBT4/7jUl+sqqWk7wtyddmOxR5TVV9XZJ3JnlJT2MDmBdqMUC/1GFgLvUSRLTW/luSO/e46gVdjwVgXqnFAP1Sh4F51ccaEQAAAMCcEkQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFE7NP6cCvn1zayPtzqeygAcOjoswAwnSbRowdj+02H2LnzazlzdjXDrZbBQuX0qRO57fhK38MCgENBnwWA6TSpHm1GxFWsD7dy5uxqji4NcuuxIzm6NMiZs6u+sQGAMdBnAWA6TbJHCyKuYm1jM8OtlpXlxSTJyvJihlstaxubPY8MAGafPgsA02mSPVoQcRUrS4sZLFTW1rdf7LX1zQwWKitLiz2PDABmnz4LANNpkj1aEHEVy4OFnD51Ihc2hrn/oYu5sDHM6VMnsjzw0gHA9dJnAWA6TbJHW6xyH247vpK77jiZtY3NrCwt2jkCgDHSZwFgOk2qRwsi9ml5sGDHCAAmRJ8FgOk0iR6t4wMAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEMBPWh1s5v7aR9eFW30MBgLHS4wAYt2nvLU7fydQ7d34tZ86uZrjVMlionD51IrcdX+l7WABw3fQ4AMZtFnqLGRFMtfXhVs6cXc3RpUFuPXYkR5cGOXN2dWqTPQDYLz0OgHGbld4iiGCqrW1sZrjVsrK8mCRZWV7McKtlbWOz55EBwPXR4wAYt1npLYIIJu56jk9aWVrMYKGytr79xllb38xgobKytDjuYQJwiE3jsbJ6HMDsmMY+spdZ6S3WiGCirvf4pOXBQk6fOpEzZ1fz4CMbj/6O5YEMDYD9mdZjZfU4gNkwrX1kL7PSWwQRTMzu45NWlheztr6ZM2dXc9cdJ6/pjXDb8ZXcdcfJrG1sZmVpcereRABMr3H1oknR4wCm27T3kb3MQm+ZvhFxaIzz+KTlwUKOryxN5ZsIgOk1C8fK6nEA02sW+shepr23TOeoOBRm5fgkAA4vvQiA66GPTIYggonZOT7pwsYw9z90MRc2hlN5fBIAh5deBMD10EcmwxoRTNQsHJ8EwOGmFwFwPfSR8RNEMHHLgwVvVgB6pRcBcD30kfHySgIAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRAAAAACdEUQAAAAAnRFEAAAAAJ0RRDDX1odbOb+2kfXhVt9DAWAG6SMA9GlW+9Cg7wFAX86dX8uZs6sZbrUMFiqnT53IbcdX+h4WADNCHwGgT7Pch8yIOORmNSGbtPXhVs6cXc3RpUFuPXYkR5cGOXN21esE0JNZ61f6CAB9OmgfmpZ+a0bEITbLCdmkrW1sZrjVsrK8mCRZWV7Mg49sZG1jM8sD+RxAl2axX+kjAPTpIH1omvqtTjnDrpRm+abmylaWFjNYqKytbyZJ1tY3M1iorCwt9jwygPmy06+WFxfyhBsGWV5cmIl+pY8A0Kdr7UPX8/lwErMozIiYUZemWZ/9zFty/OhyVpYWszxY8E3NVSwPFnL61ImcObuaBx/ZeDQR9NoAdGttYzMf/Mh63vvAWtY3t7K8uJAn37wy9f1KHwGgT9fah3Y+Hy4NKg+vD3PDYCHDR9pV++2kZlEIImbQ7jRrZXkx7zu/lh98w5/kU556PEcGizl96kQ+5sYbHk3IVpYXfVOzh9uOr+SuO05mbWPz0QAHgG4tVuUt7zmfBy6sZ3lpIesbW/nQw+tZrOp7aFeljwDQp2vpQytLi3n44jBvue98Fhcrm5stz3jSjVf8fHjp58619c2cObuau+44ed09T8ecQbtnOwy3tnLv+z+SxYXKE1eWH51ikySnT53IhY1h7n/oYi5sDH1Ts4flwUKOryx5XQB6cnFjM5ttK4PFhaRVBosL2Wxbubix2ffQ9kUfAaBP19aH2lUuP9Zes+yHW9uzKK6XGREzaPfxQFtpH03AFhcyWFx49BAM39QAMPUqObo8yMmbb8hCJVst+dCFR5LpnxABADNjbWMzNx5ZyulTJx49FPKDF9aveGjG7s+d455l39sn06parKo3VdUvjS4/o6p+r6ruraqfrarlvsY27XaOB7qwMcwDFzaytdXyCSeekMHiwuP+OHxTA1yJWkzfbjqylGffdiwX1odZG27mwvowz77tWG46stT30KAT6jDQhZ1QYWOz5ejyIBub7aqhwu7PneOeZd/np9NvSnLPrsv/KMn3t9Y+IcmHk3xdL6OaETuzHV58x1PyTZ/7rCwvLTgEAzgItZheLQ8W8iWf9pQ85+RNuf2WG/OckzflSz7tKfoY80QdBibuoKHCzufOL37uk3PXHSfHdrrPXg7NqKqTSb4oyfck+TtVVUn+fJKvHN3k1Um+K8kP9zG+abQ+3HrcIRbLg4VHZzzcddOKQzCAa6IWM0579an9uu34Sr7805+mjzF31GGgS7cdX8kXfcqT8+G19TxxZTlPOLK/OGDnc+c49bVGxD9L8s1Jjo0u35LkgdbacHT5viRP2euOVfWKJK9Ikqc97WkTHuZ02M8pUybxxwEcemoxYzGOU3vpY8wpdRjozKROxXkQnXf8qvriJO9vrb3xIPdvrb2ytXZna+3OEydOjHl002f3KVNuPXbk0bNirA+3+h4aMMPUYsZFn4KDUYeBLk1bv+5jRsSfTvIlVfWFSY4kuSnJDyS5uaoGowT4ZJL39DC2qbPXKVN2zorhmyPgOqjFjIU+BQemDgOdmbZ+3fkjtta+rbV2srV2e5KXJvm11tpXJfn1JF82utnLk7yu67FNo92nTEky1lOmAPNLLWZc9Ck4GHUY6NK09etp+qriW7K9SM+92T4+7kd6Hs9UmOQpUziY9eFWzq9tmHbMYaUWc030qcnRb+aWOgxTaNZr8rT1674Wq0yStNZ+I8lvjH5+W5LP7HM802rnlClWE+/fNC3wAuOiFnO99Knx02/mizoM0+2w1ORp6tf2FGbEzmk67dz1Z9oWeAGYJvrU+Og3ANPjsNXkaenX9hZgn/Za4GW41bK2sdnzyAA4TPQbgOmhJk+GIAL2adoWeAHgcNJvAKaHmjwZgojrMOsLlnBtpm2BFwCuz7T2cf0GYHoctpo8Lb2v18UqZ9m582t5wz3vf3Shjxc8+2NncsESrs00LfACwMGsD7fyjg9+JP/17R9OKlO58Jh+AzA9DktNnqbPsIKIA1gfbuX1/+29efsHHs7iYmVzs+XhR4b5mj/9jJn9o2T/lgcL/j8DzKhz59fyhrfen7vf8eEcGSzmeU9/Yo4MFnPm7GruuuPkVNV3/QZgesx6TZ62z7Cz+0r26MG1jdxz7sEcO7KUm1eWc+zIUu4592AeXNvoe2gAwGXsrHw+qIXceMMgx44s5S3vOZ+lQVl4DIBDbdo+wwoiDqKucTsA0Ludlc+Pryxlsbab9mZrOb+2YeExAA63KfsMK4g4gJuOLOXZtx3LQxc38sDaeh66uJFn33YsNx1Zuux9pmVREAAON/3m8nZWPt/YbHnOk2/KQxc38vAjwww320wvPAYAV7PzGfb8hfWsPnQx5y+sX/Uz7CRZI+IAlgcL+ZJPe0re8Nb7s7a+lZXlhbzgk2697A7MufNrOXN2NcOtNpULYgFwOOg3V7az8vnOa/SckzflM2//mDz9licIIQA41JYHC3n+M2/JW9/3UNaGm1kZLOb5z7ylt/4niDig246v5Ms//WlXXTl153jUo0uDrCwvZm19cyoXxAJgtuk3+3NYVj4HgGuxPtzKPeceyp85dSILC5WtrZZ7zj2UT/q44xarnDXLg4UcX1m64v+4neNRV5a3jztdWV60IBYAY6ff7N9++jcAHCY7+wnHjizlxuXtBZv73E/QgSds53jUtfXt/8Fr65sWxAJg7PQbAOBypm0/QRAxYTvHo17YGOb+hy7mwsbQglgAjJ1+AwBczrTtJ1gjogOORwWgC/oNAHA507SfIIjoyPJgwQ4hABOn3wAAlzMt+wn9jwAAAACYG4IIrmh9uJXzaxtZH271PRQADjH9BoB5N0+90KEZXNa582s5c3Y1w62WwULl9KkTue34St/DAuCQ0W8AmHfz1gvNiGBP68OtnDm7mqNLg9x67EiOLg1y5uzqXKRzAHRHvwFg3s1jLxREsKe1jc0Mt1pWlrfPK7uyvJjhVsvaxmbPIwPgMNFvAJh389gLBRHsaWVpMYOFytr69h//2vpmBguVlaXFnkcGwGGi3wAw7+axFwoi2NPyYCGnT53IhY1h7n/oYi5sDHP61ImpONULAIeHfgPAvJvHXmixSi7rtuMrueuOk1nb2MzK0uKhfiMA0B/9BoB5N2+9UBDBFS0PFqb6TbA+3JqbNyvAYTbt/aZLehsw7+a1Ds5TLxREMLNv9Hk7xQ0A3emrN+ptMJ9mdX98EtTB+SCImHOz+kbffYqbleXFrK1v5szZ1dx1x8m5L94AXJ++eqPeBvNpVvfHJ0EdnB/+b86xWT5f7Tye4gaAyeuzN+ptMH9meX98EtTB+SGImGOz/Eafx1PcADB5ffZGvQ3mzyzvj0+COjg/BBFzbJbf6PN4ihsAJq/P3qi3wfyZ5f3xSVAH54c1IubYzhv9zNnVPPjIxqPHpM3KG33eTnEDwOT13Rv1NpgvfdecaaQOzgdBxJyb9Tf6PJ3iBoBu9N0b9TaYL33XnGmkDh5+ggi80a/R7tMrJZlo03AqJ2Be9V3/xtUbD/I8+n7uQPfsj+/PPNfHyz33WX1NBBFwDXafXunhi8MkLTceWZrIqZacygmYV4el/h3keRyW5w4wbvNcHy/33Gf5NZmdyAR6tvv0SrfcuJy3f+DhvP0DF3LL0eWxn2rJqZyAeXVY6t9Bnsdhee4A4zbP9fFyz/0jF4cz/ZoIImbQ+nAr59c2ZuaP7LDYfXqlR4ZbWVysLC5W1je3xn6qJadyAubVYal/B3keO/dZWqxcWB9mabFm8rkDjNs09Ia+PoNd7rl/eG2999fkejg0Y8bM8vSbSejymKjdp1e6YbCQzc2WJFleXBj7qZZ2P9bK8uLcn8oJmC6TrL2Hpf4d5HmsLC3m4Ysbect957O4WNncbHnGk47O3HMHGLe+e8NBP4ONo19e7rk/cWV5pvulGREzZJ6nJO3l3Pm1vPZN9+WX3vzevPZN9+Xc+bWJPt7u8xp/8OH1PONJN+YZTzqaD15YH/s5jp1DGZhWk669h6X+Hfx51FUuA8yfPnvDQT+DjatfXu65P+HIYKb7pRkRM2SvaTkPPrKRtY3NmfmDG5fdBWFpsXJ+bSNveOv9+fJPf9pEX4tLT6+UTO6sGU7lBEyb3bV359uXM2dXc9cdJ8dao/ZT/2ZhlfBrreNrG5u58cggpz/xRB4ZbuWGwUI++PD6XPZ5oH/TVmf72jc+yGewcffLyz33Wf68IIiYIX1PSZoGOwVxY7iV4VbLxY1h7n7ng9lsLQ8/Msxn3P4xOXXrTRMdw6WnV5rkG96pnIBp0mUgfqX6N0uHKV5LHd/p8xvDlhuXB3PZ5y81bR+EoGt9vQemtc72sW98kM9gk+iXl3vus/p5QRAxQ3am5Zw5u5oHH9l4tCjM4h/eQewuiEnLgxeGedeHLuTYkaUkyeZmy399+4dz+y1PmJvXBKBL0xCIdzUrow/z3ucvNa0fhKArfb0HDnOdPYiD1OZp6JfTThAxY2Z5+s312KsgfvChR/Lw+nD77BVVed7Tn5j1zS1TWAEmZBo+KB/2wxTntc9fygch5l2f74HDXmcP4lpr8zT0y2kniBizLqZPzer0m+uxV0E8fuNynntkKcduGOT4ylI2hi1brUkaGRtTgunCrP2d9f1B+Xq+ZZqV13oe+/ylfBBi3vX5HvBt/rXbq7/03S+nnSBijEwhnJy9CuKRwWL+3Klb8jtv+2A++PC6pJGx8n6mC7P6d9bnB+WDfss0q6/1vPJBiHnX53vAt/mPd6UecqXrBMuXJ4gYk3mcQtjlN0uXK4i3HV/JbcdXJI2M1Ty+n+mev7Oru1yfudZvmbzWs8cHIeZd3+8B3+Z/1E4PWV5cyJGlytZWe7SHJNFfDkgQMSbzNoWwj2+WLlcQJY2M27y9n+mHv7Mru1qfuZba77WeTT4IMe/6fg/Yx962trGZD35kPfc/eDGbrWWxKrfedCRrG5tJor8ckFdnTHZPn0oyc1MI14dbOb+2kfXh1r5uu5P83XrsSI4uDXLm7Oq+7nu9lgcLOb6y5I3NRM36+5nZ4O/s8vbqM2+45/35wEceOVCv8VrPLn2feec90L/Fqrxt9SOpVG5eWU5l+/JiVaf95Vo+r80CMyLGpO/pU9fjWmc3zPs3S7Oy2BkHN8vvZ2bHPP2dXWvdvLTPXBxu5u53fihrG5s5dmRwzbPw5um1BmC8NlvLM0/cmPsffCQPrK1nsSrPPHFjNlt7tL+84Z735/6HLmZlaTEvePbHjr2/HMZ1jgQRY3TQ6VN9frA9yHGz87yA1GEsAuyt7+mQzId5+Ds7SN1crMr6cCsPXdzIyvJifv+dH86RwWJO3rySjc12oONv5+G1BmD8VpYWc8sTbsjH3bSSxcXK5mbL+tbWYz/7VHvsf0fG8TnvsK5zNLsjn1LXOn3q3Pm1vPZN9+WX3vzevPZN9+Xc+bUJj/Cx9prdMNxqjx7ztJed5O/CxjD3P3QxFzaGc/HNUp+HpNAP0yHpwmH+OztI3Tx3fi3//g/fm7X1YX773g/knvc9mIvDzTzvaTdnsLiwrz51OYf5tQZgMnY++6xvbeWhR4ZZ39p69LPPTp+76Ybl3H7LjbnphuVH+9y4Pucd5PPaLDAjokfTkG4ddHbDLH+zdNBkct4PSQHYbT+19Frr5u6+eMut298+ffDCI/m0p96cI0vbuyzzNAsPgOlw2/GVfNGnPDkfXlvPE1eW84Qjo550mT734MWNsX3OO6yz0QURPZqGD7bXc9zsLK6kez2HVhzWIgBwrfZbS6+1bl7aF4+tLOXCcDOfcfsT86Z3PWB9BwB6cbm+d7k+lza+s2kc1nWOBBE9utwf7mJVzq9tdDbTYJZnN1yL652BcliLwP/P3psFyZWl932/u+a9uVZVVtYCoLAU0BigG71Oz/R0g8P2DKhpaUYeahQaB0WF6GHYVlARWhyhCFthvzCsF9kPdoj2g0KWLXpCjnCIkmlNaEg2SWjYQ05jpvduoBtooLGj9sqqyv3u1w83M5GVlZlVWcjagPN7QXdWZt5zT95zzne+833/TyAQCPqhn7m033mz27p4PJvkeDbZ1zolhIUFAoFA0IutrhObrXud1rm0qQ30APNx3K8JR8Qe0unBPTuR4seXZ3dcDLF94B3E6IZ+GUQEyuM4CQgEAkE/9DuXbnXebKxLr53M8vbNfEfHRT/6S0JYWCAQCHaXg+QA7med2Gzd67bODfoA83HbrwlHxADZzuBrfXAVSeLHl2d3XDPiSTXQBpVa8bhNAgKBQNDP+rWduXSzebN9XXp1Oksmrm/LmN0P+ksCgUDwONDP2nCQ9hf9rhNbWfc6rXPiALM3ojcGRDdVVMcLKNRcypZHoeZ2VApvqHj7YbjjiqhPcuWHJ7Xah0AgEPSiX1XvQc+lndalS7fy2zba9ou6eGP938r62s97BQKBYDfoZ23Y7/uL9jm233XiUda9fqs1PUnrgYiIGADdvGqN8NJ82eHWUpnpXIJsMjYwUa/t0GngrVQdFkoW4ynjsd+UC8+kQCAQPGS70QODnEsb65KmSlQcj5gq49nhtoWb94OwcD8ngwfpFFEgEDwZ9Ls27AcB/m50mmNHErG+14nd2EPMF2pcvLrYvMaFs2OP9XogdmEDoNPgs9yAi1cX0RWZhaJFMqaxULTRZbmrh3A3TuxbDTSAuUKNyw/W+I/XFh6pvu1BQtSRFwgEgohHiR4Y1FxqagoVy+Otz5d4984Kb32+RMXytu042Ovot35OBvf7KaJAIHgy6XdtaN9f7JfKct3mWGBb68RO7iEcL+BHH81yZabAnXyFKzMFfvTR7GO9HoiIiAHQ6fQlCENkGWRZwg9DhkyNtZqDokh4XveTnp32trUKZK5UHS4/WOP5I0NMZsxNvZ0HSYBGIBAIBJuzH6IHIsJN/r8/9jL6rZ+Twe2eIor1WCAQ7CT9rg37tbJcrzl2t9aJrc7XxZrL1fkiuWQUoe54AVfnixRrLqOp2I60ba8RjogB0Bh8F68uslCyolCaM2NcupUnCEIUSaJseSiShO+Hjyzq9ag0Bt5CyQIpZLIe8tPLABKhowKBQPD4sR+Mx5rrkzA0Xj+dw/EDdEUmX3UeOaR3N4WFWw3Nfgz47TiCxHosEAh2mu2sDfsx/XmzOXan14m+5msp+icIA2wvJAzDda8/jghHxCCRwua/rQN4PG00NSKcINgXHkJdlRlPGRiqsqkBJBTIBQKB4PFlr43HhqHo+iFxXd03Ib1bpZOhuVUDvl9jX6zHAoFgt9jO2rDfKsvtpbO93/k6bWgczpi8c2clcj6E8NXjI6QNbcfbulcIR8QAaDxo6ZjOeGr9g9ZamtMPwz0P/Wl/z1YGZ7ewpqLloinyvvF6CgQCwX5nv4bUD9p47Oc+B2ko7nb/9jI0t2rA92Ps72dBOIFA8PgxyLVhr9a/XnNstzYNoq3bma/TpsrxbKLpiEibj/dW/fG+u12i14PWb7mW7T70Wwn96faezQygTmFNFcvlzSvzICFCQwUCgWALPCkh9du5z0FEZexF/w5q/W839rvZA/tH00MgEAi2zl6vf50cKt3aNKi2NubrUs1FUTZPz2+kKX7zzNhA0xT3M4/nXe0y/SrFdqoP228d9/bv20x1u9d7NlOAbVcgL9oOIJE2NKHyLRAIBFtgO9URDmIt8UepAvEoauR7VX1iJ5Tie9kDe10RRCAQCPplP1YH6tamsuUNrK26KnN2IsVPv1jkD6/M89MvFjk7keo6X7enKbpb0BU86IiIiAHQT1hpt1q2j5LzuZXQn0cN52w9rXK9gDc/mxehoQKBQLBF+p2D9/r0aLvsVerAXl130PnHW8kp3mtND4FAIOiH/ZhS1q1NqzVnYG11vICf31pBkxU0Awjh57dWODOZGYhm0OOAcEQMiK0YBt0MjAtnxrG86HOeH2zpoe9XoXsQ4ZyNsCbHC0RoqEAgEPRBP3PwQRYk3KvUgb1MWRikY2ArBvtW0zj3qx6JQCDoj4M+lvdjSlm3tIlhUx9YW7dTjvNJczQLR0QXtjPoWzfqhZq74bPdDIzFksXl+wUUWcLQFE7lkuia3PWhbz8pe3U6y4tHh3j39mpXD1onL9ur01lqrt/8+1Z5Ej12AoFA8Cj0M2/ux9OjrbJX60O36wLN9Rjouq4/qqE/KEG3pnFsuciyRBCsD83daqTMQY2oEQgE69mtsTwoZ0en79nNdWGr99FIm/jhpbtNPYbfePUYSUMdXFu7ld18jMtx9otwRHRgK4O+24Pe67OdPIIQ8uG9NZ6fGuKLxTI11+fjB2v8w195qqOhVKy5XLy2QDqmY+oKc4Uav3PxBs9OZVBlia8eH+FYNtlxwLR62Yo1h7dv5rc9sXXy2B10j61AIBDsJN1OOtrnzv14etQPe3Wi037dlYrN73/4AC8IqVguIJEw1A1r3l5s2lt/c1jvIOlkHDfW2K1EyhzkiBqBQPCQ3RrLg5oDe33PbqwLva7fvs5G0Qklzp8abTp9r86XODOZ2VaVjU6kDY2zEyluL1ebURdnJ1I9y3EOej3a73sz4YhoYyuDvttDstlnO3kEv3p8hHfurDKZNsmlYthewFrVJW3q69rVuGbJ8vh0tsBr01k0VeKLxTJKPZRIQuKDe2scyya73l/jHv7oSv6RJ7bWEyBx+iIQCASb035y3m3uPOhRZ4MuB9rvdVvXY02RuPKgAMDrX8rhemFzzQN2fdPe+pu3O0henc5GxvHJ0abh2jCOtxopc5AjagQCwUN2YywPytmxle/ZyXWh1/VXKvaGddbUVbwgJJt46BRYKFnNvu2nykY3dFXmuy8c5uK1BWpOgKnLXDgz3jPtfpDr0UHYm+36iiRJ0pQkST+RJOkzSZI+lSTpH9ZfH5Ek6U8kSbpR/3d4t9sGnQe9F4TNFIZeyq+bfRYeegT/6nOH+N6LRziWTTZPvlRZRkbCaEnLcLyA5bLNxauLxDWVibQBwNs38xRqUUkXQ1PQFbnj9TqprndrZ9Fyt6XQXrY8/vDKPLoi7xs1XIFA0Jv9Phc/CfRaT9rXiv1mPOwkg6gW0rrOOX4AEvhhSLHmEhBiudGavZV1e5C0/ubZuM7t5Sq3lytkEzpxTeXitUXKjoeiSOiKTMrUovW55uL6AYRsWqFjs0oeB7Eay+OKmIcFvdiJE/XtYwAAIABJREFUqjzt439Qc+Buz6VbvX7Rcnnr+hK6IpOMqeiKzFvXl1Akqe+Kh9uppjGRMfn2M4d47VSWbz9zqOda/ih92P67NtqryzKpmIouy/tyb7YXEREe8I/CMPxAkqQU8L4kSX8C/AC4GIbhP5Uk6R8D/xj4b3e7cZuFxDYeEk2RqDoeuiLj2WEz7GUr4bTtXrZuJ1/tURDPTKb5fL7EnXyFpaLNWtXB1BWePZRBVeQN1+ukJZGJ683B1yrQUrFc3rwyDxJ9ec3mCzX+6Mo8H95bZTiuc+5whuG4Lk5fBIL9z76ei58ENjvt2quogkHTj7ji3XyZd+6sANJA6rfXHJ+K5XFzsYzt+dzLVxhNxjA0hW+eyTGeNvuq8/6otP7m1brDAcD2AhK6SjnvcmOhjKkrTc2omuvz5mdzgETFdqnY3roUk/Y+7ZWPfRBOyJ4wxDws6MqgtRW6Ve4bRCrgZvo2O023PRgh5MsOC0ULPwxRJInxtIEfhrx+OsfFq4sslCxMTeHC2bGufbvd6JRP7q9uSLV7bqqzX3G7aZnzhdqGqAtTV8mXbRaKdst9x/bd3mzXHRFhGM4Bc/X/LkmSdBU4DPwq8J/U3/Z/AX/GHky6jUF/8doCC6WNYTSKJJEvWXxybw1Nk/H9kBOj8aaB1c9D3aCb3kLD85YxNG7Ml3jz0wVimsyQoRNTZDKGxqmJFLIShRO1TlDt4T2tWhKGqpBL6vzB5XkcP0CR4XAmzmQm3lcoUOMaGUNjOB6lhlyZKfDC1NCGgbPfc5QEgieN/T4XPwnsteH2KGx1Tu9HXPHi1UXeu7uCoSq8dHQIo37itJ2w1Na1/L27q4wkVR6sePgBrFQc/tLT47x9M8/3XjzSVZNhJ2g1NHUlsiEAYqpMyXK5m6/y4rEh7ixXqbk+H9xb5fR4knTMjNbnmEbRdnjj6QnSpta1nZvZFQdJO+Jxth/EPCxopdOzPihthV7jfxDOjoa+ze/+7A41z8dUFX5w/viujdluThtDU7i1VCYZ0xgyNcqWx62lMopUV4yUwvX/1hmEdlPZ8vjhpbskYxpJQ23+/2/nUiQNdcM1tuN4cryAH300s06HomJ5fP/LR7m1VMHUVExNwXIDbi1VHt73PmFPNSIkSToOvAj8AhivT8gA88B4l8/8HeDvABw9enTnGhdK6//locfp8kyRfNnm6EgcU1cBqfkwuX7Q9aHuRfvJV7vn7ZnDaT6dK1BzwfNDhuIa8yWLo6Nx3nj6KFp9kDS+o/XzXhCs05Lwg5A/uDzPKydG0DWFiuXx7t0Vzh3O4PkBISGW52/qNWtcI5vQOHcozZXZIqtVh7Way185N7En+hGPs8EiEOwU+3oufozpJUy4n9nqnN6vuKKqSCRiKnFN5cpskVens82Iw+30yUTG5I2nJ7m5WObBgxpFy8P2AqazcbLJGCXbo2i5XQXLOl1zENU1GoamZ0cHGSCRrzg4XsB0LsHUcIJcvX2rFTda39edwkloXaJl2kUwWzmI2hFPUgSHmIefbHo964OIjus1/gfh7HC8gJ/fyqMpMpoqQwg/v5XvOpc+Ct3m4U73Uai5TOcSLBRt1moOiiQxnUtgedF6FFdVUrFoA9+qKfHmZ3OUaj4pU+GNpye3pd20WnNw/ICkEW23k4bKStVmteZQtt2Ov3e/v0VjDVtfIrTEquWQS8b4+MEaXhiiShLPHxnCD7e+N90N9swRIUlSEvh3wH8dhmFRavHQhGEYSlLnXXwYhv8C+BcAL7/88sB7s2EQpQ2N8bTRNJy+dXaCP7wyj2V7lCwXU1eYKVj86vOHKFouv/f+fWRJ4vKDNZ4/MsTxbGJbpw2NwdWau6QpUWjR4bTBfNkml4yhq0p0erJcxfGCDScjrZ471w8oWi6Jem6UTVCPhJCQgLQZPQb38hVmChaW6+MHId84PUbG1Da0rZN3cDgR44UjQxQsl7/x5anmoNvNE5gnyWARCAbFfp2LnwSaqt0dhAn368awnzm9X3HFbEJvntb4YUih5m7QNmisj34Y9qw80kCRJD66XyCuywyZGo4X8MVShYrtosqRsewFIRkj0pIwNYWi5bFQshhPGVsSFu3XOdFuaDb6QJEkfnx5lrm1Gl8slbFcv+6ciJMv2yR0hUq92lYnR8N6EUwPCEkY2sBDsHeLgxrBsR3EPLwzHJTDqc2e9UHcRzMCr0saWjdnR7drt7/e2BAPGRo+IQoSV+dLFC2X0WRsex3Tgc1s/fb7MDWFbDLGZNps3rcTBPWUDZvZgoXjBeiqzKGMQbHm8q8v3eGdO6tRmc0Qlgo2/+BXvtS3k2DY1NEVmbLlNSMidEUmqan88dX5nsUNtvw7dxn1ChJLZZuTY0kMTcFyfZbKtoiIAJAkSSOacP/vMAz/3/rLC5IkTYZhOCdJ0iSwuBdt62Q43V+t8v+8d49PZwosl22CEDIxjYrj8v69FaRQ4uunc8iyhCxLfLFUJpeK9X3a0D64zk6k+PmtPO/eXWG+YJOOqdQcjzVJIqYFZOMalh/wB1fmSNXr3rZ6T18/neNHH81yZXaN+ys1jg7HKdkeqizh+QG/uLXSTC+ZSBlcXSiiKQqmpnBqLMmlW3kmMmbPvNJ27+BfPjfRdEJ068+dOIF5kgwWgWBQ7Oe5+EmgNaqsQatq936knzl9q6Gsjfe5Xsi5wxk+uLuK5fl4YcCFL0WpkffylUjI0Y4c8NO5BNlkjNdP5wC6GqZl1yOb0PADSMTA8Rw0VWKt5vG9Fw+TNjUqlsuVBwUURaJs1cW+pBBDVTativXqdJZLt/ovhd1uaDb++7WTWf7Zn95ArvfT1IjJZ7MlPnmwxnLZZTwd46WjI6xU7HXXWVclRG2pEnI6h9ty0neQqrEcxAiO7SDm4Z3hIB1O9XrWO1V82M59bCcCr1sfdnpdlWVWyzZXZgrNDfzhjNF1o7wdtmPrr4tC8x6219AUrswUWas66JqM4waslB3eOOfws5t5RuI6hq5iOR4/u5nnb71qM1nfE211/kkaKr/x6jF+eOkuK1W72eeyIvWc2/oqEWpqnJ1Ic3u50lIiNE0ipjKdS7JQtJqO7ulcUkRESJGb9/8AroZh+D+3/OlHwH8O/NP6v/9+t9sGGw2nkuVyY7HIC1NDxHUVcFBlqDgeXgCWE/LUeIKUoeEFQT0Px8fxA9w+RK86Da7LswWqTkC+7JKKqUiyhKGrHB6O8+LUEFfnimiKzJFhc105ssZDO5KIkTAUzp/MEUwHfPKgyNs3l3lhaoiTuSTLZefhfesK5w4PMZqMEVNlVFluGsTQvbzZZt7B7Qqv9OqnTtd6UgwWgWBQ7Pe5+Elg0PPjbtBPm7ea77rOUAwiZ8RXTgxzPJtEV2Xur1T4nYs3kCSJmdUqR4bjLBRtJtMmF68tQCiRNrSOhumwqZMxdUxNxdBkKo5HyfL4tZePMpzU6wri0QmR64Xcy1c5MmQynjTWbeCLNZeS5TWjBE1dYaVmc/HaIqOJ2MAc4GlT59kjQwzFNRQJ3r2ziqkrJCWV0aSJIkvkUrEN16m5PpYbYGghVSdoimA6fkBcVwcagr1bHMTx0S9iHt4ZDtrhVLdnXZGkgd1HMwKvjzS0RtUFU3uYuvCdZw91bNM3To+Rr7gQQkyTsZ2AfMUd6An8dm39TvPecskmCANUJfqcqsgEYcBaxSUIaXsdLGd71T+emxrmv0mb3FmtcHw4wVjGwPGCrnPb9kqEHuLi1cXm/V04O0ba1MgmdSYzRvP3bkT97Sf2IiLiPPC3gcuSJH1Uf+2/I5ps/40kSf8FcBf4z/agbRsMp3zJpur4fD5XplKL1Ko1RWJ6LMnhIYOYqpCIqc1Qp+OjcT6dKbJac5qnKVuZLDoNrpmCz2dzBeK6QsbU8fyA1YrNneUKluuTL9t8+9lJVFlG1dkwGCMngkS2HhL1+ukYD9Zq/PLpHG/fzPPM4Qy2F6BI8GDNIgxBRkKV11fgeBRl90Eq/vYanE+CwSIQDJh9PRc/CQxaEX036LfNW934TmRMvvPsIVZrDsOmvi697+LVRRRZIm1qzBUklss2E2mDkDDSUFBkxuulrdvXp9YTqYIVnQL+V18/QcJQKdRcXC8gYag8dyTDe3dXkRWJxbLNYsni0FCcou02K3l8Olvg5mKZl44NY6gKYQCyJDW1mFrLgvYKYe6FqSkYWlTKOwhDLDcStZSk6HBhreYgyxKWFaxLHynWHC4/WEOWpXoosIupR+mY7evhIPLNd4ODOD62gZiHd4CDdjjV7Vn3w3Bg99FvBF6tvs9or7qwWnM6tmm5ajORieH6IW4QoiUkNEWi7HoMo/ds21bnyM3SS3qxYd6TwNRVjgzpSLJEGITkqw65dIzDQ+a6SInDQya5lLHpNTrxyf3VDQKez00NdyyMAN0PfnulIU5kTL7/8tSG1xsFFFodFPvt+d+Lqhl/QeP4YSMXdrMt3WgYTsWay48vz2KqCl8slVmpRE4JU4uqUNxYLJONaxzOGOSrHrIUGSW//soUZw8NrVOqbn84tqrGqqsylhvg+gFhCBXb5/mjKc6fHOW9O6vcWqowNRzvGH3R/p2uH5IyVHJJoxkG67g+H9xbw/J8To8lmStU1+WUNtr7KMrugxLB6TU4nxCDRSAYGAdhLn4SOEgn1A36bfNWNr6dyo9NZExqro8kg6EphGGIrshUXY+K7fHzWyvYno8sScR1hcmMSameWtF6Cvfc1DC/nUs1nRxl2+X3P3yAF4RASLHqcW+lynBcY6mk4vkB1xfKpGIahPDOnRXSMZ2vHhvh/Xur/PmNRV45McqFs2O8fTPPXKHGF4vlumC1z1eOD2Fq6Z5pjb36qmE8lm0Pxwt4birDneUqZctDkSRWKzZXZorN9JHXTmZ5+2ae548MPdSW8ENOjxjkq86BXg8P4vjoBzEP7wwH8XCqW6WbQd1Hv32iSBK3lipt1SYqJDW14/dMpsx10WeRU9Zj2OzthOg293diK+klW3VqpA2NsxMpbi5WmqkkZydSTKRNfuv1af7Vz+5geQHDpsxvnj++Lu18q5Qtj3/+1k1WKi6KLLEauPzzt27yP/2NF6I3tBVG2MyBth1BUy/0sVwfbU/LU3RnnzZr79HVuuorUWjUWjVSrjbC6G9rFY+UKVN2fH76xQoncyajSZMghH/7/gx/N2UwmjKYL9Q25I9C53zW9o30t54eJwgCPp0pslCysN2AREzhayeyjCRifOXECG/fXObBWq2pEdGec9ppc56sv/fitQXeu7NaN2RGMVSFouXyxjMTpI2H4pfbySvr1J+PYkBsxbv9uBssAoHg8eSgnFC3Msg2dys/9oPz01GEgKpwKpfki6UyiZjCatWh5nrEYyq/9FQOy/H5+P4aa1WHu/lIP+LHl2d5dTpLJq5jagpJQ22WS3vr8nqndr5kU3E8FEViPBVFEBYtl4Ll8tqpLO/eWcVyPa4tlNBUmZrr88LRDFMjCV4N4Xcu3kCpO+kDH/73P7/Ny8dG+PpTo1y6ld9eWLUUosgST40ncL2Q8XSMW0sVjmXjXJkp8vyRIXLJGIWay5ufzqMpSv3ULobjB6zWHL5z7tCGiloHkYM4PgR7y0E9nOqkHTOo++j3u/wwbGoMPKw2kURWpI7fM5zUN0Sf/carx3pu4HvN/d3SRXqllzTKQLdGAPRyanxtOsu1uVIzWuFr01l0Vea5qWH+yV9LbYjQa7Rhq/uMxbLF7XwFGQlJkgjDkILlMFuo8uG9tQ2FEb7z7KGuzqJ+043a+3ZmNcR2Zrr27V4hHBE9MDWFIAzxgYlMDFWWcfyAsuWyVHbwAxVJhortcn3B5/BQgkxc526+wj/70xs8f2yIqzNFnp8aYjJjUnN8Ll5dBCkkHdO3pLfw3RcOkzBUypZPKAWEvkQyFoVVGarCy8dGeOPcBIaq4IdhU/m1QSd17kLNZSQR442nJ6k5AUeGzUg9nCi9Q1PWT4T7Qdl9q55cYbAIBALB3rBdZfdu5ccaausNo3d6LMGJIMF/+UtDvHt39eHaFQcnDHDcgPMnR0mZGnOFGr9z8QbPTmXWiU52cmpnEjrPGRqpmErG1Kg5Pmu1qAKUrsq8e2eFD+4VSBkaMVWBED66V+CpsTSZuM6zUxlSusoH99YYScSouh6qInHx2iKyDNlErHmthgMd6KpE/9b1JdIxnfFUtNYVbYc3nj6CoSms1hz+47UFDFXm0q08fhhSqrmczCUZievN6EdDVTZU0xIIniT2+nBqUBU7Bnkf/XxXVG2is8ZAxtQ6fk979NlmUQSbzf3t9EovAfjRR7N8sVRu/q1ie/zg/ImeTo1GsYF2p0bDed1KvxFuKhJlyydtqBiaQs3xKFs+rhd0PFz1w7Crs6hQc/tK0+m3b/cK4Yjoga7KXDgzxuX7a5RtH0MNOTJs8rnlUnU80oaKJsvIkkTF9lFkmZrrkS/bHBqKY8gyfhDy+UKJXCqGpkqsVJ0onzW1Nb2FiYzJ9798tDnYoxCmReSqhKHJXDg7hucH/PjqwqalbNoH0Gsns6QMFdcLUXW6bu73g7L7QfVuCwQCwZPAI5W27CbiXX+9XT9CV2WuLZTWrV0KEnFdJWVGwtFfLJZRZIlhU0dCajr8Ozm1DVXhG6ej6hf5SpTK8FfqFaAcL+DpiTS/uJVHUaIIyZeODeP4D+/LUKPNfyPAXpEkErrKWjUSamt3oBdrDn90pXOljVbRSS8I6jaChFY3jHVVRpUlPri3RsqI1uRGnnTRcsX6KOiLg1Lecrvs1eHUoCt2dLuP7fx+W+2TVrvbc8MN80q37+m0ge/KJnN/O70OJYs1lw/urVC2/GaqRbHm8NdfOsJoqj+nRre+7iTe2SvCLW6oTOcSzK7WcHwHQpjOJXqWUu7m5GnqY2w1Tb7Pvt0rhCNiEyYyJt976RD/51/cZmbNIl+yOTFqMrtabdZjzSY01iyP1aqNqasMJTRcz+fjBwXmCjW8NRhPxZhds6i4HioSiXo+a6fNf/vE0upIuHQrjyxDGIS8djJK0fj9Dx9sKmxSrLlcvLbQjMQo1VwuXl3k60+N8t7d1Z7Gy37Jtdtr77ZAIBA8KWxF26j1vZ1CRhvaBZsZ443yY60nWWcn0qTrFSo6GfXtjumGXkPN8QkIH7ZTkVEVmZWa3RR37OTUnsiYzYiJwA8pux63lkpRWobno0gyU8Mmh4dMKrYPhM1+aKQ6VmwP3w85mUvwsy+WW7SXaiSMKK/65WPDXLy6SMbQyCa0DWt2q+hko5R2Q0cKIuP/q8dHeO/OKoq73jHyxjMTaMrDVIzHfZMpeDQOUnnLg8RuVezopa2wH6MxOrWrOfcvllt0Gh7O/e30OpR0g4D7K1U8P0RWJAI/ZLUq4QZBx+/qd2/TTbyz1XGx4f4MjfMns1xfqOCHAYokc3r8YenpdrHKXk6eRpp8u/Bl12t3KevZrW/3CuGI6EEj1+jPbyxyb6XGYslCQmKmUOXUaAIjphEG4IYBXz0xzOHhJFXH4/P5Ip4fMJyIMZ4ymFmr8adXF3lqLMnLR4fx/ZCP768RhGBo8rrNf6NWuixJzb+11zDPJmLUHJ+3b+a5cGZ8S8ImJcvj09kCr01nsVyPK7NFVqsOIXDhzFgzj7bTJLOVaIRBTXqbfY9IvRAIBIKdpdMGBTprG8FGDR9NkVgtuLz56QLjKWNTYzzK1R3h6lwRywsw6v/f2Ex3M+rbDeTXFZm3ri9huQFBEHIql0RVZObWalx+UIDw4braybjWVZlrcwV+eOkuluczu2bxzS/lODOZQT4qcenWMp/OFlFkibMTaVYqdtOB8f0vH+XFI8P8xRfLfDKzRkLXeG06i6Gp9dSKCaqOx8Vri3x4b5W0qfHUWJLxtIFnh83Q4obo5GdzRZZLNmtVm3/0rTPr+uxYNsnLx0ZQFYmMqeF6IUEYkjY0HC+qpuG4Pu/dXRWbTEFHDlp5y4PETlTsaLeNe2krrFTsXXEw9XJMd3q9m+Or29zfjW4VlsIAHM+nZAXNKhgpQybs7IfoO9K6Id5paiqmpmC5AbeWKk1h5G73990XDm9wGDWv0SZWuVl///xWHk2paxiG8PNbec5MZrr+5t994RBvfrpA0XJJGxpvPDO+78a3cER0oTFJSxLMrtV4sFoFSUIhKktze6XK145nUVSZ2TWLoYRBTJOxXPjW0+P85PoSjuexUPJ549wEf35jEbeuraArMseycb55dqxZegtoqZUOEhInRuNcvLrYLMnSaWJDYkvCJhlT4+ZimXfvrNZDO2WG4zpDpsalW/lNF59eXtFBedX3yjsvTo0Egt1HjLuH7Ke+6LRBuXhtAUKJtKF13LS0howWLZfPZouU7Uj88esnRzF1pacx3shdfe1kFi8MUSWpmavby6jP1E92Gpv41nXqm2eiMtUzhSqXHxTWaTU12p5pOxkqWx4/vHSXZExjOKGzXLZ5+2ae49kEuVQMVZV56egwE2kD11sflrtSsbk8WyAE/ACeO5JmuK4NUbQlkODSrTxDpkZMkbm1WOaLhRJHRxKcGkuuK5VtuR63l8s4fkgQhDxYrXA0m2i2U1ejtMy3ri81U0leP53b6EQ5k+PMROaRNpn76dkUDI6DVt7yIDHoKOJOtrGqyB3z//Nlm5/e2J6DqdNY72aX93q9U5RGM61BkTG0KK2gIc54db7EL3fRaejWpk7XkGTQVZW0GTSdM5oiI/W47W5OjU74YUguGePjB2vNder5I0NNfb5ujr32FPtWB3u7WGWv36mb5kO+YvPTLtcGUJX6M7lPC8YIR0QXGpO0JsvkKy5S/UmWZSAIcbyQe2tVDE1lasTk7GSaQs3lo3yVl44OUX83ASFLxRq3l6uUkj5xXWEiZVCxvWauKzyslW57Pvmyy2LJ4r27K5zKJfjqiWGOZZMdJ7a0oW1Z2OSlY8P82eeLlGwYS8U4dzhDytC2rPfQKRqh0W5Vkcgm9A3G2VbZK++8CE0UCHYfMe4est/6otMGZaEUHSmNp43ma+3aRo2Q0ZvLFWKqzDfPjHE3X+WDe2u8fjrXscR06zXzZYeFotUS8mo0DbduRn23vtNVOcqzTZsslCwIJXLJGFXHQ1fkZgRCu6G7WnNw/ICkoRKEAXFNpVBzKTseUr2C1kTaQJVlVJ11wpNNp39W485yhU8eFHn9dKx534TgBSEZM7p/VZGxPB+/XkIUImPRC3z+5OpiJJypyJQsl997b4bnjoysM5JbnS6KJFGsufzuz+6QNvWHTpQvIieKqavb2mTut2dTMDj2S8rt48ggNc262cZfPxVFqflBiO36BPW8f8vbnoOp01gfScQ6Xvs7zx7q+vqPPppZVw6zEaXRbY5frTnNedH2Ik2casWn5vodT/lHErGukSCaLDM1YlK2/eZ9JWMKmtzffXeb4xRJYqlsc3IsiaEpWK7fTNHfzLHXvn/aliOwi7aD5XT+rmLN3SB8vB+jnvZPS/YZjUnaCwKyCY0wDPCDoPkcSIBcf7h1RaZq+yR0FT8I+OhegZO5FLoq4wUhP72RR5clZtcsri+UePfuChlTi4weLzLwaq5PzfO5t1JjvlgjGdNQFYnlisvbt5YBeP10jqrrsVCyqLpeM1zW1FW+8+wh/upzh5ret9Z7KFkuFcdDlSW+emKELx8f5oWpIYbj+oYIiuWyzXLJbrZrM+7my7x3d4XLMwUu3cw3J8GGcbZVOg3K7XxPP7RO8OMpg7im8tb1pS3fu0Ag6J+DOu4cL6BQcwfazp3si/b2btb+xt8VSWpuUID6RiWKemh9rXXT0jiZefnYCGcmUjx7OMNcweL5qQyW5/NgrdZcszoZQFHIaxkJiaG6uOStpTKKJDWN+k5r38WriwBkE3rHvtNVmfGUge16vHV9iXfurPDW9SUqltt0ZPz+hw/4D5/M8vsfPsBxfXRFpmx5yJLMaDJGCFRcHy8MODuRxvXCDX3Qun6pssxLx4Y33Hfa1FBliULNJaYrnB5LcnI0yTe+lCNhaE0D9LlDQ9hegFtf/06NJ/HDkNWas6HfdFWm5nj8+PIs/+7DB9xcrgAhMqDJMlXXp2R7m24yG7992fKaz0irMFsqpqLL8oEYp4Kt0W1c7acNykGm4Shst8uh91zc/rdutrGuyRzOmFyeWePjmTUuz6xxOGOSSxob5u/NHEzd1qGitbFKgxeETeeBH/jcW6ngB1EbF8sW799bZa5QY6FkMVeo8f69VYqW25zjgyDS1gmCkFtLZZKaSsXyeOvzJd69s8Jbny9RsTwUSeo4/+TLNlfnS6QMjSFTJ2VozUoQaVPjpaMjTGYMcskYkxmDl46ONDUROq2J/ay/jXKmct3xINfLmfphuM6xt5V+7/f98FBLaa3qsFiyWKs6nJ1Ik0t1/s2R2PV91XYQERFdaApQXV3k0JBJ2faYWa3hBSFhABPDOpoCQ6bGF0sVJEmKqmGkTRaLFpKscHQkzkQ6xn/4ZI5AkhhLxZAkWK06vHMnz6Fhk2RM5bWTWW4vlfnRBw94sFbD8UPWdJfhuMbUkInrRSE5DYdD46Ffqdj83vv31oUntYaaNk6pfnjpLo7/sKbvWNrgretLLJSspgdwpWLzo49muDpfAiKxmO++cKjn6YfjBbxzZwVDVYhr0aP0wd1Vzh3O9O1V3wvvvAhNFAh2n4M47nbqZHin+qK9vWcnUlydL3Vtf3vt9WcPpyPjriEEeWYcoOsJX1N9PKljaAqKLGP7HpIkNUtMp43upSQjAy/BbMFisWShqzLTuQR+GG36O6UG3lgo8t7dFRIxFUWSOHc4gxeEFC13nWBjRHv+rdTxpPG9u6v8za9O8a9/fo+FkoWhyvz33z7DsdEkhFBzPd6+me/YB63rV2tp7db7btgUFdvDlWWeOZzeECny1HiaiXSMfMVFV2SuL5QYiesMm/qGfmu9hxM6H5hAAAAgAElEQVRZlZgqc/lBgaSpUnF8VsoO84Uah4a6n8g2npV82eHWUpnpXCSk9uLRoU2F2XYLkR6yMwgB8N2n11rSLSqhk23cKM97PJtoRh80yvX2G43RNfU77Jz6PWzq3Jgr8taNZQJCZCRef2qUr58cZbFoMxKPYegKluOzWLRx3ABNkckldT5+UGhJa8jg8zAi7CEhlhcJQ86uWc39y6EhA8uLNtF+GGDXtXHqH3moNTRfwPJCDElq6k20r3EXzo5h6mpf62+jnGkuqTfvIai/vlm/dypC0O/vtO7+3BBDi+4vaai8fjoXaUGsPNSCSBvagYh6Eo6IDjQemJFEjO+/PMWJ0Tj/5p37jCZ1Pp8voOkyri+xWHS4MV9CVVU8P2B6NMnkUIxcMkMypiLL8IubKxRrLpoq4/gBQRhSdTzOTqTIJWMUqi7/4x9+zu2lMmXHw1AVJHwc3ydtxjE0BdfzefPKfFMP4vXTOUxN6Rie9OuvHG86KgCuzpc4f3K0+Z5G7lXr4gPwe+/f4/ZylVwyCr29vVxp6lN0GxiRV03ipaNDXJkt4ofR5PGVE8N9L2iDDGXbKiI0USDYfQ7auNvJtLG+y3Fto72lmssPL93l/KnRjlUaItGz2XXK2hXb49dfOdZcSxr32W3T0rgP1w85dyjNB/fWoug4P+TC2bFNa5ZHn5ep2T5+GNbLUcrNSL3GNTMtJ1udnOBTw+bGtVJXSRgq50+NUnY8krpKwXKbp3rtRmja1HjhWIZSzSdlKmiKzMWW8tivTmc3iDu3rl8rVYcgDLlwZuN9T2TMpk3xb997wHt3V0GC7z47ieMFTQN1MmNwfaFMEIbIksSZ8VTHZ619A3HhzBj/+hd38cIYCV3hey8ewtBUvnF6jJiuNK/R/qzoisxC0SIZ01go2kymTS7dXOGLhTJpM9KSKlveOmG23aLTBkKkhwyO3RAAP2iOpEG0t59Uh0Yuf7e/dbKN/TAkYah88+wYthcQU2XyFYea6/ftYOq2DqVNrek8XShZzfHnegHv31tFkcFQVVzP5/17q/ztIGAsbVCzPXwnIPBDxtIGuibX0xocTuZSGJqM5QYslR0cNyBRTzFvOBzy1ej1KzMFVioOiiLj+wErFZu/+ZVjHBky+cXtlWb7XzkRRT00IvN++dTYuj3PdC7VdY3rxxbpdrjb6N9uehPdnE8TGZNvnZ1grlRjMmUynNzobG6l2/2dmcywWLT4+N5qs5rGi1MZJjJmz8oc+wXhiGij/YF5dTrL/dUaUyMmN5YrBKFM2QlwvSiX1Akgo8FC0SIMA2zP48xkmqvzBWpOQNrQOHc4zdW5Estlm4Suoiky8ZiKDHz8oMDdlQqrNRc/CEmaGkOSTr7iYHk+45koNFRVopKfFTsSDvvml8Y3iJa8f28NWb6HpkqEAXzlRBQeapr1Ema63NSDyJgPT2kKNZeaE4m7NF5TlCj0qNfpR2PyMjSVV6ezkSZFGHA8m9xW3++2d34vnB8CwZPOQRt3OxnBsZlhM4j2KoqE4wfIstSx/cWay9X5YpsAVhHL9dfVXu9lnLf+pl4Qcu5Imq8eH+FYfS1opHy0OzZaKdYcbucr+EGAIstMZmLNktXtBlzDCf7c4QyfzBSQgKrj4QXhBkHN7zx7iIrlceVBoWm8nRhNMGzqG4xQQnjnzgppXWfYlLBdf4MTp13cufXg4rWTWS5eXUSWI2FKSYK0ubEi1f3VGl8/nePBapW3ri/zv/7kJn9weZ4fnD/OoaE4BSsKk2+cNhasKN+39feAlg1EzUVRJJKGyvNTQ3ztZBYCuLFYZm6uyGezRc5Oppol49qrnRha9NsMmRprNQdFkXD9gCMjcQo1N3qtJQx5t+jmJPvB+RP7dr4QrGe3dEYG5ewYRHu7Oa8vnO1e5Q42htE3/tbJNna8IHL+eiEJXX0kh/6m65AUrvt3rlSrzxsKfgiGFoX8Vx2fLx8d2lCqMl1PPZvOJZldq1Gwomiv6VwSXZObTux4y31IMlQcn+WyTRCCLEFMU7A8n5ShcqIlEiRV3/A3I/MSDyPDF0oWS2WLq/NFRhI6siQRqGFzjesniqHhCDh/arSjsGYnEc1ezqeGuHBrnz83Ndzx2r3uL1+x+eGlu6RNnUOG2hRd/u1cKnqu/OizmrI/50zhiODhD97ISVqvFr7IStnmD67M47g+lhsgSVB2ApDADyEIQVMlZgs2SBLfPjfJeNrgL75Y4vR4kssPorrmnhfgqD6SJHFzqcwzhzLcyZcxVBkvCLDqG/+nxhIkDZO/9PQk50+N8Ltv3+XGYpmFgsVExiQk5NTY+s2+HwYsFi1qtsvNRRvXD/nZF0sEQchwwiARUzhVH/TtE5WpKZi6jO+Hzdwo348Mxvb3tg+OpvFpR5P2hS89mrdtN7zzrYjQRIFg9+lHqXon2YrxupMRHE3DpkPU2nbnovb2+n6IrsgEwUZtA2Bj1kKDlte3Ypx3+k0fhv3b3FqqcCybIGWoXDg7xtTIwyoQRcvl8myRQtXGC0CV4eOZAkPJBcaTG8t/mppCxXK5vVwFCWw34Hg2HkUqtBnyluvj+QGuHzRFpxuhwC8eHeLd26vNKIZXjo/wZ9cXuV4sNyMzCjWnqxOntV8IoVCzySYMErrCbMHif/nj6zxzJIMiSc17bhiTCV3m0s08GUPDD0NimsIPL93l737jJK4X9bOha/U12e34O7VvIBQZjo8kSKgqHz1YIwhCyrbPeMZsRjp0qnYSBFHqRbmem91c/3WZE6OJptHt+MGuRi41nGQjcT1qgxJtIDo5ZQT7j90SIR+Us2O77W1fR/pNdWiMqV5/a7eNezn0++2PbuvQdC7V1GlQYlJzj/RLJ0ep2JH4ftLQKFsuVcdnPGXwteksn84WqDgBCV3ma9PZlvQ1cP1oj+H6AapMV8H9IIDlko0iS2iyTBAELJdsSrZLwtD4+lMtEW6211PY2FAVqo7PQqHQdGqkTB2k7nuATn3YSOWIKbBScRg2tXWaCz/6aGadE6Ziefz1l6Y6PgvLJbtZoSnZ5jwo227H36/b/VmOH83NuoLtRn9bqdosli3+9NOFpiN3drWG7fr7zpH7xDki2ieM1ofN9QKqjk92PFrgTF0hLIVcnSviByFuGBKG4DZ0TOpOwqrtUbGj/16rOPzk+hJnJ5LMrdn8QWEeQ5PJJWMQgCJLjKUNlko2b11fxHajUMnjI3Hu5CuUbZ+5gsWvPneYN54Z5yefL6FIEoslG1NTWC7b5JIx3ruzyuGMyWLJRtdkao5PKqbys5t5VEXCD0Lur1TJmDoVxyeXivGx7fEPf+WpjqdZF86MU7G8dRoRF86OrXtvt8mt3438fgvT223nh0DwpLMf1Pi32oadjODodsLxKNEWndr7G68e4+p8aZ0uUOP704bG2YnUujS/sxMp0sbDNIitVEbqFE146VYeXZZZKNpIwKWbyxwainNlpsA/uPBUsyRl1fK4Pl+K1lRZwnJ9rs0V+fLRYY7XHRatToDIOI925oocRfIZmto00jQ1EoX0/JBbSyU+XygRj6mEITw3lWG14vJv37+PVo8IcX2fTELn3Xt5rswUGUsZDJkahapDvuLiuH7z1JEwMqLLlrdu03JzqcSbny1yOGOwXHZw/QAvCJtGaOOeJzImqiwxX7KwXD+KjgxgKK7zxWKRP7o8jxv4fDJT4MiwSUxV1v0erXQ6oVsq2eSrNqtVh4SuMpExyZh6M9LB8x5WDGl9VsbTRlMjwgkCLpwdA6Jwcc8NB/rcbxkpinTJl2xkRSLwQ8yY0t15JthX7IYe0CCdHdtpbz+6Do1Uh25rSb/rTLdIiX77o+kc1aTm5r7qRaKUd5bKG3QdXjs1yutPZfmLWytUixaKIvH6U1lkReLnt/LEVJWYBoTw81t5zkxm6leS8Pxwwxze6T7mCjUgjKoVyRAGUfiDoUZO6PURbvGeOg3JmEaxanNjqdoIouCpXPRdsHEP0K0Pv/PsIW7MF3nr+nIzbe7106OYX56iaLn87Gae2dVaM1JjqWzxn75wuGPai+X7zQpNAElDZaVqs1S2eOf2CrosY2rR/bX+fp1SLZIxDc8P+Pj+WvMZGIqrqEgdox33myP3iXJEtE8Yr53M8vbN/Lpc2iszBSYzBilDo2S52H7AyVyKa/MlClUHr0NUohOAoYAZU0jEFObWatxfqTKeirFquViez92VaFAZyOiKxJEhkyAMSRoy91dqmJrKkeEEo0mds4fTnD+Z5SefL/HJgzUIQ0qWRyauUrJcYqrEH39aZjQZVdZ4Kp3iUMZgrWzj+BLpmMaNpTK2FzKS0Dk2Gsfz4fRYknQHwSuIJoIfnJ+maLnrRG+a99hjYPYKt93sNxDlwASCJ4u9KtX7KG3YqcipnYq26NTe6VyqYwSKrsp894XDG0JKG/fYqIzULgrZapx36s+L16IUBdNUcPyAfMVBlmE4oVGouVy8tsj3vzyFH4YUHRfH86nYHkhRmK6uwJXZItlkjMmMuaFKRcJQef1LuXX50V85PsyfXVvi6nyRquMT+gE/uTZH2Q44PZYiaWh8eHcNN/B5bTo6/ft4uYIiS5wej0pwB2GAFwTRxl2WePnYMMsVh6obYLs+EPLmp/ORQ93xyI7H8IKAz+dLrJZtijUXVYoMTQ2Z5YrN88PD5Cs2F68t8rdeOcbZiRT/8i9us1iyKdQ8XjqaoWx55CsuY2mDX0lP8PaNZVarDq+eyvLd5w731IjIJjS8IIj6QlP41tPjKLJMMqZyZaawLtKh/flqLwO6VV2Q3cBQFRRJwgtAlyW8euRGYwMh2N/shh7Qdp0dnQ7ENmtvp1D9fnQddFXuuZYMYp1p9IemSB3LFXfC1BTmVqv8f7dWCIjKKb4yPUJMlvnFnVUSWhT5ULM9fnFnld88P81UNsELdoDtB8QUmalsArfuGF2/8S01BYTvr1T48P4qrh+1L67LXctbarLMkZE4pZoXbfpjEilTRZVlOokPt/Zhe2TeXKFG0fIYiWtN50XR8ijZbsdozG7P1ErV5v27bdoYd1epWB7VuoZO2lAxdZWaE/2/6wadiwYkjWaFpkZEhK7IGKrSFOl0/Ujk89CQsf73C6V1/+qqzMlckl/cXsFzopPyk7kkmtbl+dlnjtwnxhHR0VC6uogkSWQT0cOWMjWmcwnWai5zBYtbS2VGEhrv3V1jLKkxu2Zt+N6ELlN1orKeMUVmteZTdmpIwC+fHmWh6HA3X+HIkMlcsUYQwnzRZtjUmFmrMZKMSpXZfkAyDHjmUIo7y1VuL5UxVRVdkTA1ncWSzWg8RtWJynFlTI0To0kWCjZ3lqo8fSiDosi4NZeVWogE9XKiIamYTr5iI22yAOiq3FVUrNPAvL9abZ4qbTX8a683IAKBYG/ZD1UzttOGnYic2sloi9b2buYAnsiYfP/LRzcYwFutjNSpP+WqRBhE6Q2EUHWiUN6gvtEtWS6/9/59dFVmpWRH4a2SRBCCJEWnVmcmEnx8f40gBEOTN1SpaM+PPpSJkzAUvnJshPfu5HlQcVgqWYwmYnw+X+T0ZJqq45NLaVyZLVJzo/Jzk0Mmjh+QMTVMXeXpQ2kSMZXVis2VmSLjQwau7+MHIUeG49HBheXy6UyBybRJCNxbrZFLxSjWPPwwJF92GInr5EsOyyWbeExBliSKlsvV+RLf+NIYT40l+en1Za4vVpgaMnnlxAjDcZ3bSyXmijVKlscn94p87Xi249ra2DjNFWp8sVim5voEQcg3z+T4K+cmOkY6dHq+ej3bexkx6Ich5w4PMbtWa1HON3dVp0KwfXZDD2g7zo5u82G/6Q69qi70cioMarx1i8boFjHQDccL+GyuSL4aOV/9IOSzuSJrNYeEJrNUtvFLFookkUvqVBwPkDB0hYSiRnM8El2HZQi24/PW9WXiusJIIkrneOv6Mn//mz6YG6O90qbG6bEUl27lCYIQWZY4PZZC1+SOTuhO6XKNPrEcH1VRODQUQ5IkwjBksWRjOZ3LWHZ7pgoVt0UbI2xqY8yVahiaQtKI+tiqp2okjUjTolv65W+8eowfXrrLStVuOihShsaVmQJrVRddk3HcSKRTkR5WekobGuNp46H2yJlxJoZMfuPV4+sEmTVZ7hntuF94YhwRnQwlqQZBEK572LLJGN86O8G//3iGV06McGW2yKlcks8XSiiAIoMXyUMQAJoiIQMnRuNIcjR52G4k+jS3ZvOliRQ3FkvEdYUjwyYysFJx8cIQXVUYT5lkTJ+QkJLtcWOxyrNH0txfrRHXVFYqNiEhKUOl7HiR0eX6nMwl0ZVogpjIGEwMGWRXY8RUJXp4vZAgCBlPG6xUbIIgUvEeVN5xyXK5tVTm/MlRUuZGJfat/gb7vWyfQCAYLPuhasZ+aEODndap2aoDuJMBvNXKSJ3609DkZtThoSGDzxdK5GIxPD/k+GicT2eKnD81SsrQKNsusiyjEOL5US6xJsuMpgxyaZNvnhljPGU0r9ltwxBtUCVimsxswSJjqhRqCqauYHkBJ0eTSDJcmVkjY8QYTeo8WKkys1pDBty6oRaGsFZ1uTJT5PkjQ0wOmeTLNj+fzXMiF6WKpAyN6VySguViewG+H0VdzJcsZldrjNbDs50g4P5qhb98bgJDkyGkGcVwdjLDidEkt/MVfvW5Q1y6nWe1YvOTz5eIKQrxtMpwQmvmDref3umqzKvTWX7n4g2U+vN7Kpfk7ZuRoGavSIeDQKNc3mTG2DOdCsGjsdPzW7/Ojs3mw37SHb7z7KG+dB0GSa829YoY6MRSySJfcTmZS67bqFcdj4obMJrSiesaVcelUhe27+QM0DWZsxPpdeKyZyfSpE2NL5ZKxGORcL7VsPklWK7ajGWMju1KGSonR5PrRCkNVekq0tmtT751doLDQyZrVae5uT88ZJJLdb5ut2dKRuqojTEajxGPRQKaKxW36cwZSWik6joSndIvn5sa5rfbIhWXS5E4p1oXllQVmSCMnBt+GHbcQzWqRIUhjCZi61KBekU77heeGEdER0NJVZp5rOseNkVCU2X0utdrLG3geD6LRQsvCAiDgJWqjyKBrqhcODuCoSlcnimgqwqZuMY3v5Tj5nIFJwyYGjE5O5EmZWi8c2cFxy8zPZrkjlRBV2VCKWRqKM5CyeLpyTTHsolm9IWhKzw9nubUWJJvn5vkR5dnuT5XQlNkKo6PHwQkYypxXeGlY8O8fXOZpyaSHBuJR6FPhkoYsEEcrF/aB6bjBUznEqTqnsytOBX2k/EvEAj2ht04JTsIbWhvz05d+1EcwFutjNStPycyJt9LR1Uuvv3sJD+9sYxcN3Snc0lS9ZOZYTNGUleIx1TKtoeChKpIyKFETFPWOSEa9FKSt90AkAgCiWwiRhCC7QUghfzyUzlsN2ChaFG0PA4PmZRsl/mSTcpQ+e4LhxlJxFgoWSCFTNYjERqlQws1l2zd2Msmdb7z7CEs1ycIAu6t1EjFVBw/YDSpczQbJwggICQk6pO0ub62exjCWCrGxFBUau33P5yh7HiMJmIcyyZIxFQKtQqrNadjGHEmrvPsVIZhU4+qYymdq2MdRFqfqz3TqRA8MjsdVdOPs2Mr82F7e7t9xg/DPVtHurVpteb0jBjohKEryFJ0MGvoCpbjIUsQj6m8cmKEj++vsVZ1UGWJV06MEK/r8bQ7A9KGxndfOLSh3K6uykymTOK6SlxTiOsqVcej6vpMpjpHUUfpdxrfODO2rqxnrz4v1NyOfSIrEr/1+jT/6md3sLyAYVPmN88f7ymS3emZKtRcXn8qy9u3VlgqW6hSpI0R0xWShspvvX6S3/3ZnWYJzR+cP062i15IY9+TNNT17ZCidh8ZNptOoXzFiV7vsofqJvjZcKx1inbcTzwxjohehlKjHFi7MdOqJJ00ozKcNxYqDCU0JocDTueS/P0Lp/nFnRVkwNBUJEDTZA4PxxlOxHjj3ARV22uWIHthapgT2TiTGRNFlphdqxEQGUkvTg2TNjVcL+Tc4Qwf3F3F8nxCOeQvPz3BWMbgrz47yY/8SDnaD0NyqRhfmkihylFu0cvHRnjj3EQz9GaQD197LumPL8/25VTYb8a/QCDYG/ZDtZr90Ibd4FEcwOs2gptURurWnw2jPmNqzaoR7euHpkg8fSjNWs0lrquUai7HcwkUtfca0b5haBXzysQ1VisOR4ZNVFnmyLDJr30lKkmXTa6tO2WvOn5z3Wx833jKwFCVZhsb0RKeH64T/WwYkt976QgXry1QtnwUGZ45PMTR4Tg1x6dgufyNL081Dc5eRuOvvXyUz2aLDMU1ErGHucPDXfSdTE3BUBUkJFRFfuwc/E/KOBU8Glt1dmxnPuz1mYyp7cnz2a1NjbLE/ZT1zCZinD+Z5Z07q5QcD0I4fzLLoUyc46OJZtleRZIIwrCn6OZExuT7L09t6I/hpM7f+8ZJ/ref3Kx/RubvfeMkw8nu81qnsp69+rzX7/Tc1DD/5K911knqRvszZWoK56aGefpwBssNMDQZWX5YifC5qWH+hw7X6Gff00s8utcearupQPsBKTzAuXYvv/xy+N577/X1ma1WbHhYdsxp5leqsozlReU304bGG8+MM5ExN5Qom84lySb1dXm4rdddqdjN999YLHF0JMFYylivUl0vB/aVE8MczybXtdXxAoq1KByn1cmx2+KP2xWe3G9VMwSCnUCSpPfDMHx5r9uxG2xnLhbsHo8qErwTc3Z7m85OpPjwfoGi5ZLQFX7pqdENa18/7b2TL3Pp5gpuPZT/wtmx5j1vtT+65WB364tGPxWqzqbrcq8+/eT+atf68lvpSyEC/RAxDwva2c542Y9jrFubtnt/b342R6nmkzIV3nh6ctPv2s66sFp2mCvVmEyZXZ0Qm93foD/TD7ux75kv1DakU7Re46DuobrNxU+cI6IfGj92a34ldI4y6PTeXg9Ir/f3+5Dt5UN5UAeEQLDTCANYsJ/Yj3N1JxX6Qbax1/dt9VrbbdOj3kvZ8vo6vduPv+9+QMzDgk5sZ7zsxzHWrU2DvL+DtsfY6fbuRn/sx2ftUek2Fz8xqRnboVs4S7fX+nlYer1/kN+10+z3kB+BQCAQ7M+5ulNqxSDbOIh1drttetR72ZA7vMPXEwieJLYzXvbjGOu1TxnU/R20PcZOt3c3+mM/Pms7xZNxlwKBQCAQCAQCgUAgEAj2BcIRIRAIBAKBQCAQCAQCgWDXEI4IgUAgEAgEAoFAIBAIBLuGcEQIBAKBQCAQCAQCgUAg2DWEI0IgEAgEAoFAIBAIBALBriEcEQKBQCAQCAQCgUAgEAh2DeGIEAgEAoFAIBAIBAKBQLBrCEeEQCAQCAQCgUAgEAgEgl1DOCIEAoFAIBAIBAKBQCAQ7BrCESEQCAQCgUAgEPz/7d15sFxlncbx72MSdhSGIDskUiCChH1HJIAIjBVwYCC4EUVEZGAQwQGZQsUqoQpmBBxBEFmFsGRkChkUVMJAKXsSNllEArIatoRFRBJ+88f73nBouu/t5PZZ7r3Pp6rrnj7r0+85fc7b71mumZlVxg0RZmZmZmZmZlYZN0SYmZmZmZmZWWXcEGFmZmZmZmZmlXFDhJmZmZmZmZlVxg0RZmZmZmZmZlYZN0SYmZmZmZmZWWXcEGFmZmZmZmZmlXFDhJmZmZmZmZlVxg0RZmZmZmZmZlYZN0SYmZmZmZmZWWUUEXVnWGySngeeqHCRY4EXKlxet5qaC5xtcTQ1FzjbolgnIlauO0QVBrEvbsI6a0IGaEYOZ3hHE3I0IQM0I8fiZhiJ++EmrK86+HOPLCP1c8PQ/Oxt98VDuiGiapLuiogt687Rqqm5wNkWR1NzgbNZbzVhnTUhQ1NyOEOzcjQhQ1NyNCHDUDFSy8qfe2QZqZ8bhtdn960ZZmZmZmZmZlYZN0SYmZmZmZmZWWXcELFozq07QAdNzQXOtjiamguczXqrCeusCRmgGTmc4R1NyNGEDNCMHE3IMFSM1LLy5x5ZRurnhmH02f2MCDMzMzMzMzOrjK+IMDMzMzMzM7PKuCHCzMzMzMzMzCrjhghA0h6SHpb0qKTj2gxfUtIVefjtksYVhh2f+z8s6ZM1ZDta0h8k3Svpt5LWKQxbIGlWfl1TQ7Ypkp4vZPhyYdhBkv6YXwdVnOsHhUyPSJpbGFZ2mZ0vaY6k+zsMl6Qzc/Z7JW1eGFZmmQ2U67M5z32Sfi9pk8Kwx3P/WZLu6mWuLrPtLGleYb2dWBjW77Zg5RvMNl9hho7bUA8zrCVpet5fPyDpX9uMU0VZdJOj1PKQtJSkOyTdkzN8t804HY+7FefoeBzrcZZRkmZKurbNsNLLoosMVZVDv8eTKr4jQ1l/63A4K7se0lSSVpA0TdJDkh6UtF3dmcom6cOF/dAsSa9IOqruXFWQ9PV8rLpf0lRJS9WdadAiYkS/gFHAn4APAUsA9wAbtozzNeDHuXsycEXu3jCPvyQwPs9nVMXZJgLL5O7D+rLl96/VXG5TgP9qM+0/AI/lvyvm7hWrytUy/hHA+VWUWZ7/TsDmwP0dhu8F/BIQsC1we9ll1mWu7fuWB+zZlyu/fxwYW2OZ7QxcO9htwa/a1l/bbb4J21CPM6wGbJ67lwceabPPrKIsuslRannkz7dc7h4D3A5s2zJO2+NuDTmm0OY4VkKWo4HLOuzLSi+LLjJUVQ79Hk+q+I4M5Vd/63A4v8quhzT1BVwEfDl3LwGsUHemij//KOA5YJ26s1TwWdcAZgNL5/dXAlPqzjXYl6+IgK2BRyPisYj4O3A5sHfLOHuTvuwA04BdJSn3vzwi3oyI2cCjeX6VZYuI6RHx1/z2NmDNHi5/UNn68Ung1xHxUkS8DPwa2KOmXAcCU3u07AFFxM3AS/2MsjdwcSS3AStIWo1yy2zAXBHx+7xcqHY766bMOhnMNuyBf34AAAzNSURBVGo9MohtvsoMpYuIZyNiRu5+FXiQVLEoqqIsuslRqvz5Xstvx+RX65OzOx13q85ROklrAv8InNdhlNLLoosMTVH6d2SoGkLr0HpA0gdIjew/BYiIv0fE3P6nGnZ2Bf4UEU/UHaQio4GlJY0GlgGeqTnPoLkhIlXAniy8f4r3VsoWjhMR84F5wEpdTlt2tqKDSWcK+iwl6S5Jt0nap4e5FiXbvvnyyWmS1lrEacvMhdJtLOOBGwu9yyyzbnTKX/a2tihat7MAbpB0t6Sv1JRpu3x59S8lbZT7NanMrLOmrKd221Ap8qX1m5HOwBdVWhb95ICSyyNfQj4LmENqZO1YFi3H3apzQPvjWC+dDnwTeLvD8CrKYqAMUH45wMDHk6bsL5qom3U4XDWhHlK18cDzwAX5dpzzJC1bd6iKTabCk4l1ioingdOAPwPPAvMi4oZ6Uw2eGyKGCUmfA7YETi30XicitgQ+A5wuad2KY/0CGBcRE0hn8C8aYPyqTQamRcSCQr+6y6zRJE0kNUT8W6H3jhGxOemWjcMl7VRxrBmk9bYJ8EPgfypevg19lW1DkpYD/hs4KiJeKWs5g8xRenlExIKI2JR0ddXWkj7a62X0KEepxzFJnwLmRMTdvZxvCRmqOp7XfTwZkpqwHdVsJG43o0m3HJ4dEZsBrwMj5llYkpYAJgFX1Z2lCpJWJF0RNh5YHVg2//Yb0twQAU8DxZb9NXO/tuPky2E+ALzY5bRlZ0PSbsAJwKSIeLOvf249IyIeA24infmqLFtEvFjIcx6wRbfTlpmr4D0tqSWXWTc65S97WxuQpAmk9bh3RLzY179QZnOAq+nt7UkDiohX+i6vjojrgDGSxtKAMrOu1L6e+tmGekrSGNKP/0sj4udtRqmkLAbKUVV55PnPBabz3lvNOh13S9EpRz/HsV7ZAZgk6XHS7WO7SPpZyzhll8WAGSooh77lDHQ8qX1/0VDdbEfDVt31kJo8BTxVuIprGqlhYqTYE5gREX+pO0hFdgNmR8TzEfEW8HPS89uGNDdEwJ3AepLG59a1yUDrf0u4Buj7LwX7ATdGROT+k5WeaD0eWA+4o8pskjYDziE1Qswp9F9R0pK5eyzpIPWHirMV79ucRLoXGeB6YPeccUVg99yvklw52wakhz7eWuhXdpl14xrgC0q2JV169SzlltmAJK1N2ul9PiIeKfRfVtLyfd05V9v/TFBitlX77peWtDVpv/YiXW4LVrtO23xl+tmGerkMke7lfTAi/rPDaKWXRTc5yi4PSStLWiF3Lw18AnioZbROx92e6SZHP8exnoiI4yNizYgYR9pH3RgRrWe5Si2LbjKUXQ55Gd0cT2rfXzRRl9vRsNSEekgdIuI54ElJH869dqX6OmudKn3GWwP8GdhW0jL5+LwrJeyHqza67gB1i4j5kv6F9KNuFOk/KDwg6STgroi4hlRpu0TSo6QHnk3O0z4g6UrSF38+cHjLZf5VZDsVWA64Ktcb/xwRk4CPAOdIeptUiTwlInq2g+oy25GSJpHK5iXSU7eJiJckfY/0QxHgpIjoyYPkuswFaR1e3lKZK7XMACRNJT2Rfqykp4Bvkx6QRkT8GLiO9FTwR4G/Al/Mw0orsy5znUi6J/msvJ3Nz7ewrAJcnfuNBi6LiF/1KleX2fYDDpM0H3gDmJzXa9ttoZfZbGCLu81XnKHTNtRLOwCfB+5TeiYBwLeAtQs5Si+LLnOUXR6rARdJGkXa114ZEdd2c9ztsW5ytD2Ola2GshgoQxXl0PZ4IumrUOl3xIaW0ushDXYEcGk+2fIYI+T7kBucPgEcWneWqkTE7ZKmkW6dnA/MBM6tN9Xgqfd1LTMzMzMzMzOz9nxrhpmZmZmZmZlVxg0RZmZmZmZmZlYZN0SYmZmZmZmZWWXcEGFmZmZmZmZmlXFDhJlZgaTzJc2RNOC//5K0jqTfSrpX0k2S1qwio5mZmY1sksZJ+kzdOcwWlxsizMze7UJgjy7HPQ24OCImACcBJ5cVymw4knSUpGUK76+TtEKdmczMhohxQGMaIpT4t6V1zRuLDWuS9pG0YQXLmZrPin9d0kmSdsv931XJtuaLiJuBl4r9JK0r6VeS7pZ0i6QN8qANgRtz93Rg7wqjmg0JA1ROjwIW7iMjYq+ImFtNMjOzZpF0iqTDC++/I+lYSadKul/SfZIOyINPAT4maVauf47K492Z66SH9rOc5fIVnTPyPPfuZ/nH5O5jC/P+bu43TtLDki4G7gfWknS2pLskPdA3Xh53L0kP5brUmZKuzf2XzVej3iFpZl8WG/7cEGHD3T6kH4uDIml0P8NWBbaKiAkR8YOIODEifpMHv6uSbUPWucAREbEFcAxwVu5/D/BPufvTwPKSVqohn1mjtKmc/rS1YirpSGB1YLqk6bnf45LG5ukflPSTPM0NkpbO42yVK8Kz+irndX1OM7MeuwLYv/B+f2AOsCmwCbAbcKqk1YDjgFsiYtOI+AFwMDAvIrYCtgIOkTS+w3L+Bnw6IjYHJgL/IUkdln+FpN2B9YCtc5YtJO2Ux1kPOCsiNoqIJ4ATImJLYALwcUkTJC0FnAPsmetSKxeWcQJwY0RsnbOcKmnZRSo1G5LcEGFDSqfKabsz1pK2ByaRdmizJK3bYZ43STojj3O/pK1z/+9IukTS74BLJC0l6YLccjxT0sQ8ixuANfL0H5N0oaT92lWybeiRtBywPXCVpFmkA+lqefAxpIPsTODjwNPAglqCmjXPwsop8I3WimlEnAk8A0yMiIkdpv9Rnn4usG/ufwFwaERsir9vZjaMRMRM4IOSVpe0CfAy6Yf/1IhYEBF/Af6P1NDQanfgC7mucjuwEmk/2o6A70u6F/gNsAawSrvlR8STed67AzOBGcAGhXk/ERG3Fea9v6QZedyNSCcENwAei4jZeZypLbmPy7lvApYC1h6orGzo63iW16zB1gMOjIhDJF1Jqpx+EfhqRPxR0jakyu8ukq4Bro2IaQPMc5mI2DS37p4PfDT33xDYMSLekPQNICJiY6VL82+QtD6psePaXClG0sGkEc+UdDSpkv1Cb4vAKvQ+YG7f+i2KiGfIV0TkBot9fVm52ULFyun+kr5CqnesRtq33jvA9LMjYlbuvhsYp/T8iOUj4tbc/zLgUz3ObWZWp6uA/YBVSVcodLqqoZVIV29e38W4nyVdlbBFRLwl6XFSA0C75ffN++SIOOddC5TGAa8X3o8nnaTZKiJelnRhYb795d43Ih7uIrcNI74iwoai91RO6XzGultTYeHzAd6vdx6Wdk1EvJG7dwR+lsd7CHgCWH9xP4QNDRHxCjBb0j/DwvvdN8ndY/XOve/HkxqxzCx5Hd5VMd01P9j1fxm4YgrwZqF7AT55YmYjwxXAZFJjwFXALcABSs+AWBnYCbgDeBVYvjDd9cBhksYASFq/n1scPgDMyY0QE4F1+ll+37y/lE+6IGkNSR9sM9/3k/b98yStAuyZ+z8MfCg3XAAcUJjmeuCIfGsIkjbrkNmGGR/UbShqrZyuQocz1osgOrx/vXVEG94kTQV2BsZKegr4NunMwdmS/h0YA1xOej7EzsDJkgK4GTi83TzNRrh2FdOb8rC+inRXV41FxFxJr0raJiJuJ1WWzcyGjYh4QNLywNMR8aykq4HtSPWOAL4ZEc9JehFYIOke0n/8OoN0cm5G/lH/POlZae1cCvxC0n3AXcBDnZaf+90g6SPArbm94DXgc7TcHhcR9+TbVR8CngR+l/u/IelrwK8kvQ7cWZjse8DpwL355M5sfKXbiOCGCBsOFp6xjoir8s53QkTcw3tbizs5gPQshx1JD/qZl3e0RbeQfpDemG/JWJvUwtvf1ReLVMm2+kXEgR0GvedfeuZbfga67cdsROtUMc3OJVVMn+nwnIh2DgZ+Iult0r3S83oa2MysZhGxcaE7gGPzqzjOW8AuLZN+K78Gmv8LpMaNAZdf6HcGqbGj1UdbxpvSYbbTI2KDXE//EakBhHzlccf/8GHDlxsibLjodMb6clKF9Uhgv4j4U4fp/5YrymOAL3UY56y8jPuA+cCUiHizTYNF0eJUss3MhrSIeJxC5bRTxTQifgj8sPB+XO58oWX60wqTPZBv8UDSceTKrJmZNdohkg4CliA9yPKcAca3YU6pkc1s5JJ0E3BMRLgya2bWcJIOID2TZTTpWT1TIuL5elOZmTWPpI2BS1p6vxkR29SRx6zIDRE24rkhwszMzMzMrDq+NcNGDEk/AnZo6X1GROxcQxwzMzMzM7MRyVdEmJmZmZmZmVll3ld3ADMzMzMzMzMbOdwQYWZmZmZmZmaVcUOEmZmZmZmZmVXGDRFmZmZmZmZmVpn/B3yrYP/NOTrNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2, ax3) = plt.subplots(ncols=3)\n",
    "\n",
    "top_df[['popularity', 'net_profit']].plot(x='net_profit', y='popularity', kind='scatter', alpha=0.3, title='Net Profit', figsize=(18,10), legend=False, ax=ax)\n",
    "top_df[['popularity', 'rating']].plot(x='rating', y='popularity', kind='scatter', alpha=0.3, title='Rating', figsize=(18,10), legend=False, ax=ax2)\n",
    "top_df[['popularity', 'vote_average']].plot(x='vote_average', y='popularity', kind='scatter', alpha=0.3, title='Vote Average', figsize=(18,10), legend=False, ax=ax3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the datapoints are scattered all over the place with no particular pattern. Obviously. there are outliers in our data as shown above, which are any records with popularity greater than 30.0 for `df` and any record with popularity greater than 60.0 for `top_df`.\n",
    "\n",
    "Instead, we analyze these using two different methods:\n",
    "- Pairwise Correlation: between `popularity`, `net_profit`, `rating`, and `vote_average`\n",
    "- Linear Model: using OLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we are going to perform the two methods mentioned above, we are first going to remove the outliers from both `df` and `top_df` to avoid misconceptions and miscalculations. Then, we perform 1-sample T-tests to check for the distribution, expecting the null hypothesis of each test to be approximately the area with the darkest shade in the charts above, which are about 11.0 and 19.0 for `df` and `top_df`, correspondingly.\n",
    "\n",
    "Steps:\n",
    "1. [Outliers Removal](#5.2.1-Outliers-Removal)\n",
    "2. [1-sample T-tests](#5.2.2-1-sample-T-tests)\n",
    "3. [Pairwise Correlation](#5.2.3-Pairwise-Correlation)\n",
    "4. [Linear Model](#5.2.4-Linear-Model-OLS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 5.2.1 Outliers Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df['popularity'] <= 30.0]\n",
    "new_top_df = top_df[top_df['popularity'] <= 60.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 5.2.2 1-sample T-tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* Use alpha significance value of 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pop = new_df['popularity']\n",
    "df_top_pop = new_top_df['popularity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_df, p_df = ttest_1samp(df_pop.values, 11.0)\n",
    "st_top_df, p_top_df = ttest_1samp(df_top_pop.values, 19.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert p_df >= 0.01\n",
    "assert p_top_df >= 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 5.2.3 Pairwise Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>popularity</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>rating</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502541</td>\n",
       "      <td>-0.009622</td>\n",
       "      <td>0.304169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net_profit</th>\n",
       "      <td>0.502541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035096</td>\n",
       "      <td>0.097401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>-0.009622</td>\n",
       "      <td>-0.035096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>0.304169</td>\n",
       "      <td>0.097401</td>\n",
       "      <td>0.056829</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              popularity  net_profit    rating  vote_average\n",
       "popularity      1.000000    0.502541 -0.009622      0.304169\n",
       "net_profit      0.502541    1.000000 -0.035096      0.097401\n",
       "rating         -0.009622   -0.035096  1.000000      0.056829\n",
       "vote_average    0.304169    0.097401  0.056829      1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[['popularity', 'net_profit', 'rating', 'vote_average']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>popularity</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>rating</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394747</td>\n",
       "      <td>-0.041987</td>\n",
       "      <td>0.371825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net_profit</th>\n",
       "      <td>0.394747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010517</td>\n",
       "      <td>0.043137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>-0.041987</td>\n",
       "      <td>0.010517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.058792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>0.371825</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>-0.058792</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              popularity  net_profit    rating  vote_average\n",
       "popularity      1.000000    0.394747 -0.041987      0.371825\n",
       "net_profit      0.394747    1.000000  0.010517      0.043137\n",
       "rating         -0.041987    0.010517  1.000000     -0.058792\n",
       "vote_average    0.371825    0.043137 -0.058792      1.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_top_df[['popularity', 'net_profit', 'rating', 'vote_average']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have collected the correlations from the four variables mentioned from both `df` and `top_df`, but we want to focus to only the first row of each. After all, apparently those three variables are correlated to `popularity` in a way, since none of them have correlation coefficient of zero. Now we want to look deeper about how correlated they are to `popularity`.\n",
    "\n",
    "One thing that pops out the most is that `rating` and `popularity` have a negative correlation in both DataFrames. This suggests that as `rating` increases, `popularity` decreases. This is against our initial hypothesis, as we thought `rating` would contribute positively to movies' popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we pay attention to only the first row of the variables correlations and rank them from strongest to weakest, the order would be:\n",
    "1. `net_profit`\n",
    "2. `vote_average`\n",
    "3. `rating`\n",
    "\n",
    "This observation appears to be consistent with the analysis done in [Section 5.1](#5.1-Analysis-based-solely-on-Data-Exploration) above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 5.2.4 Linear Model OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>popularity</td>    <th>  R-squared:         </th> <td>   0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   152.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 02 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>5.71e-81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:12:13</td>     <th>  Log-Likelihood:    </th> <td> -2714.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   982</td>      <th>  AIC:               </th> <td>   5437.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   978</td>      <th>  BIC:               </th> <td>   5457.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>   -0.0383</td> <td>    1.102</td> <td>   -0.035</td> <td> 0.972</td> <td>   -2.201</td> <td>    2.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>net_profit</th>   <td> 1.514e-08</td> <td> 8.42e-10</td> <td>   17.972</td> <td> 0.000</td> <td> 1.35e-08</td> <td> 1.68e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rating</th>       <td>   -0.0452</td> <td>    0.159</td> <td>   -0.285</td> <td> 0.776</td> <td>   -0.357</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vote_average</th> <td>    1.4505</td> <td>    0.149</td> <td>    9.713</td> <td> 0.000</td> <td>    1.157</td> <td>    1.743</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>30.926</td> <th>  Durbin-Watson:     </th> <td>   1.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  48.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.273</td> <th>  Prob(JB):          </th> <td>3.17e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.940</td> <th>  Cond. No.          </th> <td>1.55e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.55e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             popularity   R-squared:                       0.318\n",
       "Model:                            OLS   Adj. R-squared:                  0.316\n",
       "Method:                 Least Squares   F-statistic:                     152.3\n",
       "Date:                Sun, 02 Jun 2019   Prob (F-statistic):           5.71e-81\n",
       "Time:                        01:12:13   Log-Likelihood:                -2714.7\n",
       "No. Observations:                 982   AIC:                             5437.\n",
       "Df Residuals:                     978   BIC:                             5457.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept       -0.0383      1.102     -0.035      0.972      -2.201       2.125\n",
       "net_profit    1.514e-08   8.42e-10     17.972      0.000    1.35e-08    1.68e-08\n",
       "rating          -0.0452      0.159     -0.285      0.776      -0.357       0.266\n",
       "vote_average     1.4505      0.149      9.713      0.000       1.157       1.743\n",
       "==============================================================================\n",
       "Omnibus:                       30.926   Durbin-Watson:                   1.836\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.350\n",
       "Skew:                           0.273   Prob(JB):                     3.17e-11\n",
       "Kurtosis:                       3.940   Cond. No.                     1.55e+09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.55e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('popularity ~ net_profit + rating + vote_average', new_df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>popularity</td>    <th>  R-squared:         </th> <td>   0.283</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   25.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 02 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>8.53e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:12:13</td>     <th>  Log-Likelihood:    </th> <td> -624.91</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   196</td>      <th>  AIC:               </th> <td>   1258.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   192</td>      <th>  BIC:               </th> <td>   1271.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>   -3.0153</td> <td>    4.346</td> <td>   -0.694</td> <td> 0.489</td> <td>  -11.587</td> <td>    5.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>net_profit</th>   <td> 1.113e-08</td> <td> 1.79e-09</td> <td>    6.206</td> <td> 0.000</td> <td> 7.59e-09</td> <td> 1.47e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rating</th>       <td>   -0.2526</td> <td>    0.615</td> <td>   -0.411</td> <td> 0.682</td> <td>   -1.465</td> <td>    0.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vote_average</th> <td>    2.9805</td> <td>    0.516</td> <td>    5.775</td> <td> 0.000</td> <td>    1.963</td> <td>    3.998</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>95.607</td> <th>  Durbin-Watson:     </th> <td>   1.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 368.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.988</td> <th>  Prob(JB):          </th> <td>8.84e-81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.416</td> <th>  Cond. No.          </th> <td>3.30e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.3e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             popularity   R-squared:                       0.283\n",
       "Model:                            OLS   Adj. R-squared:                  0.271\n",
       "Method:                 Least Squares   F-statistic:                     25.21\n",
       "Date:                Sun, 02 Jun 2019   Prob (F-statistic):           8.53e-14\n",
       "Time:                        01:12:13   Log-Likelihood:                -624.91\n",
       "No. Observations:                 196   AIC:                             1258.\n",
       "Df Residuals:                     192   BIC:                             1271.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept       -3.0153      4.346     -0.694      0.489     -11.587       5.556\n",
       "net_profit    1.113e-08   1.79e-09      6.206      0.000    7.59e-09    1.47e-08\n",
       "rating          -0.2526      0.615     -0.411      0.682      -1.465       0.960\n",
       "vote_average     2.9805      0.516      5.775      0.000       1.963       3.998\n",
       "==============================================================================\n",
       "Omnibus:                       95.607   Durbin-Watson:                   1.884\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              368.660\n",
       "Skew:                           1.988   Prob(JB):                     8.84e-81\n",
       "Kurtosis:                       8.416   Cond. No.                     3.30e+09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.3e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_top, predictors_top = patsy.dmatrices('popularity ~ net_profit + rating + vote_average', new_top_df)\n",
    "mod_top = sm.OLS(outcome_top, predictors_top)\n",
    "res_top = mod_top.fit()\n",
    "res_top.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the p-value shown above, the results suggest that both `net_profit` and `vote_average` significantly predict `popularity`; while `rating` does *not* predict `popularity` really well. Furthermore, looking at the standard errors of both results, it looks like `net_profit` predicts `popularity` better than `vote_average`. Thus, the order of correlations from this method is:\n",
    "1. `net_profit`\n",
    "2. `vote_average`\n",
    "3. `rating`\n",
    "\n",
    "Again, the order we obtained from [Section 5.1](#5.1-Analysis-based-solely-on-Data-Exploration) prevails."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, moving onto the analysis of impact of `genres`, we analyze only from the charts produced in:\n",
    "- [Section 4.2.1](#4.2.1-Plot-bar-charts-for-the-number-of-movies-in-each-genre)\n",
    "- [Section 4.2.2](#4.2.2-Plot-bar-charts-for-the-average-popularity-of-movies-in-each-genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From [Section 4.2.1](#4.2.1-Plot-bar-charts-for-the-number-of-movies-in-each-genre), we can argue that Action is the most popular `genre` in the movies industry according to the bar charts. However, according to [Section 4.2.2](#4.2.2-Plot-bar-charts-for-the-average-popularity-of-movies-in-each-genre), Action movies do not hold the highest `popularity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fantasy      14.917978\n",
       "Animation    13.820256\n",
       "Family       13.551611\n",
       "Adventure    12.982606\n",
       "Action       12.808466\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_genres.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crime              26.359366\n",
       "Drama              23.082636\n",
       "Thriller           22.524002\n",
       "Fantasy            21.737466\n",
       "Science Fiction    21.665384\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pop_genres.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the results above, Action movies are in the top 5 on the list of the overall data in terms of their `popularity` on average, but they are not even in the top 5 of the top 20% movies!\n",
    "\n",
    "These charts and results show that the `popularity` of movies does not depend on how popular the `genres` of the movies are. In other words, movies' `genres` do not determine their `popularity`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving onto the last part of the analysis, which is examining the impact of `production_companies`, we analyze only from the charts produced in:\n",
    "- [Section 4.3.1](#4.3.1-Plot-bar-charts-for-the-number-of-movies-produced-by-each-company)\n",
    "- [Section 4.3.2](#4.3.2-Plot-bar-charts-for-the-average-popularity-of-movies-produced-by-each-company)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From [Section 4.3.1](#4.3.1-Plot-bar-charts-for-the-number-of-movies-produced-by-each-company), we can argue that Warner Bros. is arguably the most popular `production_company` in the industry according to the bar charts. However, let's see what's the average `popularity` of the movies produced by Warner Bros. below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.669022538461538"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb_pop = pop_companies['Warner Bros.']\n",
    "wb_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.38225905882353"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb_top_pop = top_pop_companies['Warner Bros.']\n",
    "wb_top_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two values printed above are pretty low compared to the ones produced by some other particular companies, and if we try to fit in the two values above in the corresponding charts at [Section 4.3.2](#4.3.2-Plot-bar-charts-for-the-average-popularity-of-movies-produced-by-each-company), they obviously cannot afford to be the number one `production_company` to produce movies with the highest `popularity` on average.\n",
    "\n",
    "In fact, let's check what's their rank in terms of the movies' average `popularity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_companies.sort_values(ascending=False).tolist().index(wb_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pop_companies.sort_values(ascending=False).tolist().index(wb_top_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, Warner Bros. is not in the top 20 `production_companies` on the list of both `df` and `top_df`, and as a matter of fact, they are far from top 20 `production_companies` of `df`.\n",
    "\n",
    "Therefore, we can come and agree to the same argument as the one stated above in `genres` analysis, which is that `production_companies` do not define their products' (in this case, movies) `popularity`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Ethics and Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data we analyzed is public information that can be collected from film-review websites. That being said, the data we found online is always transparent and free for public use. As we go through our datasets, any data contains personal information will be discarded unhesitatingly to secure the privacy of reviewers. Since some films contain sensitive content, such as discrimination against different minor races and LGBTQ people, we should be aware of bias, and minimize the effect of it on rating as well. Based on that, the data sources, i.e the film-review websites, are credible and legal to ensure that no data will be used inappropriately. \n",
    "\n",
    "Regarding the datasets, as mentioned above, the data is accessible and free for the public. Since the dataset may contain certain biases, to minimize the effects of potential bias, we select two datasets that the ratings contained are collected from different reliable film-viewing websites. Additionally, as for the contents of the two particular datasets, the 'movie' dataset contains information only about films, including budgets, genres, popularity and so on. However, the 'ratings' dataset collects the IDs of reviewers, which brings the threat of privacy invasion. Therefore, no matter their IDs are allowed to be shown or not, we discard their IDs for privacy consideration. Besides that, there are no human subjects and no any other personal information exposed in this project. \n",
    "\n",
    "In our project, since our goal is to find the factors of popular and successful films, the results of this project are probably used by the movie production companies. What’s more, the results can be used by the entertainment supplier, especially the video websites like ‘YouTube’, ‘Hulu’, and ‘NetFlix’, to provide movies in a specific order. Since this type of results can be used in profit-making, we should be aware of the negative effect due to the expected changes in the film-related industries. The production companies have more interests in the genres of expected popular movies while spending less on indie films and other non-commercial films that usually have a low payback. What’s worse, the video websites will display films in a rank of popularity so the audiences are more likely to watch the popular ones first, which harms the profit of the non-commercial films. Therefore, instead of focusing on the short-term profit-earning, every viewer in film-related industry should consider more about the future of benign development of the industry.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conclusion and Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The factors that mainly determine the `popularity` of the movies are `net_profit` (revenue) and `vote_average`. Both `net_profit` and `vote_average` significantly predict `popularity`, they are positively proportional to movies' popularity; in particular, `net_profit` predicts popularity a little better than `vote_average`. As discussed above, `net_profit` is highly proportional to `popularity`; the top 20% of `net_profit` is greater than average `net_profit `and surpasses the 75% percentile. The `vote_average` of top 20% movies is greater than average `vote_average`, however it does not surpass the 75th percentile, which means the `vote_average` sufficiently determines the `popularity` of the movies, but is not as determining of a factor as `net_profit`.\n",
    "\n",
    "`rating` does not predict popularity well, and actually has negative correlation to `popularity`. We expected `rating` to have positive correlation to the `popularity` of movies, since the average `rating` of the top 20% movies is greater, though just slightly, than the overall average. However, after digging into and analyzing the data, we find out that `rating` is inversely proportional to the `popularity`. That being said, as `rating` increases, `popularity` decreases. They have negative correlation. \n",
    "\n",
    "Movies' `genres` do not determine their `popularity`. Our findings in data analysis show that action movies are not popular in top 20% movies in general, even though action is the most popular `genre` in the overall data. The charts and results show that the `popularity` of movies themselves does not depend on how popular the `genres` are. Even though some particular genres are popular among top movies, it does not mean that every movie in those genres have high `popularity` values.\n",
    "\n",
    "`production_companies` do not define the `popularity` of movies they produce. In Section 3, after we got the statistic result, we thought initially that `production_companies` determine the `popuarity`, since famous `production_companies` like Warner Bros, Paramount Pictures, and Twentieth Century Fox Film Corporation dominate the top movies dataset. However, after data analysis, we found out that the top companites like Warner Bros are not even in the list of top 20% popular movies. Hence, big `production_companies` may not have produce movies with high `popularity` values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All in all, we missed a bit from our hypothesis. We hypothesized that `net_profit`, `rating`, and `vote_average` are determining factors, but `genres` and `production_companies` are not. Now that we have reached to the conclusion part, we now realize that `rating` is not a determining factor of the movies, which is against the hypothesis, but the rest align just fine with the hypothesis. Thus, to put everything in an explicit manner, `net_profit` and `vote_average` are determining factors to define the `popularity` of movies; while `rating`, `genres`, `production_companies` are not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. What's Next"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believe that we're still able to improve this research when we have the skills that we are lacking right now, which include:\n",
    "\n",
    "**Math application (Probability and Statistics application)**\n",
    "\n",
    "In this project, we are limited by our math ability. The next step we should take is to hone our probability and statistics skills. With more professional statistics knowledge, we can deal with our datasets better and process them further. In fact, Python or other programming languages have all the useful tools. However, in order to make an excellent project, one has to be equipped with lots of statistic and probability knowledge, aside from only being equipped with strong programming ability. I think we should apply the MATH 180 and MATH 181 skills into our work later and come to a more accurate or precise conclusion.\n",
    "\n",
    "**Machine Learning Application (Predict the most popular movie better)**\n",
    "\n",
    "We would like to deliver our greatest appreciation to our professor and everyone in the instructors team, who gave us the idea or the big picture of machine learning. After all we did in the Data Analysis part above, we notice that we could have brought all these to the next step, which is predicting the `popularity` of movies. However, since predicting is not the goal of our research, we did not includef the prediction part. We do actually have a linear model, using OLS, to predict the `popularity` of movies based on their `net_profit`, `vote_average`, and `rating`, but the model can't account for `genres` and `production_companies`. If we were to have more skills and knowledge to make the prediction model , we would have pushed to the prediction part so that we could figure out the most correct combination of the factors. We might take COGS118 later on and apply the skills to do so."
   ]
  }
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