{
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    "# <center> Where To Look For A Movie Rating? (A Data Science Approach)</center>\n",
    "---\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;This is the Jupyter Notebook version of [this Medium article](https://medium.freecodecamp.org/whose-reviews-should-you-trust-imdb-rotten-tomatoes-metacritic-or-fandango-7d1010c6cf19). The notebook aims to show that all the quantitative and visual elements presented in the article are reproducible. The notebook is self-contained as the article's written content was also added.\n",
    "\n",
    "\n",
    "### Rating, Review Or Trailer?\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Whenever we’re trying to decide whether to watch a certain movie or not, we could consider a lot of factors (the director, the actors, the movie’s budget etc.), but most of us only resume to reading a couple of reviews, watching a short trailer, or just quickly checking the movie’s rating. \n",
    "There are a few good reasons you would want to avoid reading reviews, or watching a trailer, although they bring much more information than a rating. \n",
    "First, you may want to completely avoid spoilers, no matter how small. I understand that! \n",
    "Second, it could be that you want an uninfluenced experience of watching that movie. This usually applies only to reviews, which are sprinkled with frames, like “this is a movie about the complexity of the universe” or “this movie is really not about love”. Once these frames get encoded in your short-term memory, it’s really hard to stop them from interfering with your own movie experience.\n",
    "Another good reason is that if you are tired or hurried, you might find it pretty uncomfortable to read a review, or even watch a 2-minute trailer. \n",
    "So, a movie rating seems to be the solution in quite a few situations, for quite a few people. This article aims to recommend one single place to look quickly for a movie rating, offering a robust argumentation for that.\n",
    "\n",
    "\n",
    "### Criteria For *“The Best”*\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Making such a recommendation it’s a lot like saying “this is the best place to look for a movie rating”, which is an evaluative statement, resting on some criteria used to determine what is better, what is worse or worst, and what is best, in this case. For my recommendation I will use one single criterion: a normal distribution.\n",
    "The best place to look for a movie rating is that whose ratings are distributed in a pattern which resembles the most, or is identical to, the pattern of a normal distribution, which is this: given a set of values lying in a certain interval, most of them are in the middle of it, and the few others at that interval’s extremes. Generally, this is how a normal (also called Gaussian) distribution looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   },
   "outputs": [],
   "source": [
    "# Importing .pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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RESumYEBERERERETEiikYEBEREREREbFiCgZERERERERErJiCAREREREREREr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1d7055c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the default style\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "# Generate a figure with a single ax\n",
    "fig, ax = plt.subplots(figsize = (18,9))\n",
    "\n",
    "# Generate a hist on the ax\n",
    "from numpy.random import normal\n",
    "ax.hist(normal(size = 1000000, scale = 6), bins = 29, range = (-20, 20))\n",
    "\n",
    "# Hide spines and remove grid\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "# Axes labels\n",
    "ax.set_ylabel('Number Of Values (Frequency)', fontsize = 18, weight = 'bold')\n",
    "ax.set_xlabel('Values', fontsize = 18, weight = 'bold')\n",
    "\n",
    "# Tweak tick parameters\n",
    "ax.set_yticks([0, 170000])\n",
    "ax.tick_params(axis = 'both', which = 'both', labelleft = False)\n",
    "\n",
    "ax.set_xticks([-20,20])\n",
    "ax.set_xticklabels(['Min', 'Max'], fontsize = 14, weight = 'bold')\n",
    "\n",
    "# Title\n",
    "fig.suptitle('  Meet The Normal Distribution', fontsize = 26, weight = 'bold')\n",
    "\n",
    "# Delimiting areas by generating vertical lines\n",
    "ax.axvline(-7.5, color = 'black', alpha = 0.4)\n",
    "ax.axvline(7.5, color = 'black', alpha = 0.4)\n",
    "\n",
    "# Explanatory text\n",
    "ax.text(0,125000, 'The tallest bars are here.\\n It means most of the values\\n are average.', fontsize = 20,\n",
    "        weight = 'bold', ha ='center')\n",
    "ax.text(-15,75000, 'Few values are low,\\n and fewer extremely low.', fontsize = 20, weight = 'bold', ha ='center')\n",
    "ax.text(15,75000, 'Few values are high,\\n and fewer extremely high.', fontsize = 20, weight = 'bold', ha ='center')\n",
    "\n",
    "# Increase pad btw the title and graph\n",
    "plt.tight_layout(pad = 10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; What’s the rationale behind this criterion? Well, from my own experience consisting of several hundred movies, I can tell that I’ve seen a few outstanding ones that I’ve watched several times, a couple that were really appalling, and made me regret the time spent watching them, and a whole bunch of average ones for most of which I can’t even remember the plot anymore. \n",
    "I believe that most of the people, whether critics, cinephiles, or just regular moviegoers, have had the same experience.\n",
    "If movie ratings express indeed movie quality, then we should see the same pattern for both. Given that most of us asses the bulk of movies as being of an average quality, we should see the same pattern when we analyze movie ratings. A similar logic applies for bad and good movies.\n",
    "If you are not yet persuaded that there should be such a correspondence between the patterns, think about the distribution of ratings for a single movie. As many people rate the movie, it is not a leap of faith to assume that most often there will be many of them having quite the same preferences, and, consequently, agreeing that the movie is either bad, average, or good (I will quantify later these qualitative values). Also, there will be a few others who assess the movie with one of the other two qualitative values. \n",
    "If we took all the ratings for an individual movie, and visualized their distribution, we would most likely see that one single cluster forms in one of the areas corresponding to a low, an average, or a high rating. Provided most movies are considered average, the cluster around the average area has the greatest likelihood of occurring, and the other two clusters have a smaller (but still significant) likelihood (all these likelihoods can be quantified in principle, but this would require a lot of data, and would have the potential to turn this article into a book). \n",
    "The least likely would be a uniform distribution in which there are no clusters, and people’s preferences are split almost equally across the three qualitative values. \n",
    "Given these likelihoods, the distribution of ratings for a large enough sample of movies should be one with a blunt cluster in the average area, bordered by bars of decreasing height (frequency), resembling, thus, a normal distribution. If you have found this hard to understand, consider this illustration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uXZEcN7/DjMqi3H43muLcuXNITk4GIA+i1KxZE8+fPwcgz+ApaFDnxIkTQkAH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85D9alw4SLq23eQPvzwHUnSrl3b1axZS+3bt0eDB/eVJH3yyWc6f/6cfvllqfLmza9Z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\np09Rv349FRUV9cDn9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coOaq3jVN3uwMu253fOLioloSjH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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1d748f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the default style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Generate a figure with 4 axes (2 rows by 2 columns)\n",
    "fig = plt.figure(figsize = (18,10))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "# Removie tick marks, labels and grids for all axes\n",
    "for ax in fig.axes:\n",
    "    ax.tick_params(axis = 'both', which = 'both', labelleft = False, labelbottom = False)\n",
    "    ax.grid(False)\n",
    "\n",
    "# Values for hists\n",
    "bad = [0,0,0,1,1,1,1,1,1,1,1,1,2,2,2,2,2,3,3,3,4,4,5,5,6]\n",
    "average = [0,1,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4,4,4,5,5,6]\n",
    "good = [0,1,1,2,2,3,3,3,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,6]\n",
    "uniform = [0,0,0,0,1,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,4,5,5,5,5,6,6,6,6]\n",
    "\n",
    "# Good cluster ax \n",
    "ax1.hist(good, bins = 7)\n",
    "\n",
    "# Average cluster ax\n",
    "ax2.hist(average, bins = 7)\n",
    "\n",
    "# Bad cluster ax\n",
    "ax3.hist(bad, bins = 7)\n",
    "\n",
    "# Uniform cluster ax\n",
    "ax4.hist(uniform, bins = 7)\n",
    "ax4.set_ylim(0,10) # makes the bin's height appear shorter\n",
    "\n",
    "# Text and arrows\n",
    "fig.suptitle('Four Possible Distributions Of\\n The Ratings For A Single Movie', fontsize = 26, \n",
    "             weight = 'bold')\n",
    "\n",
    "ax1.text(1.5,7,'Cluster In The High\\n Ratings Area (Likely)', fontsize = 20, weight = 'bold', ha = 'center')\n",
    "ax1.arrow(3,8,0.9,0, width = 0.075, color = 'b', alpha = 0.5)\n",
    "\n",
    "ax2.text(4.9,6, 'Cluster In The\\n Average Ratings\\n Area (Very Likely)', fontsize = 20, weight = 'bold',\n",
    "         ha = 'center')\n",
    "ax2.arrow(3.90,7.85,-0.15,0, width = 0.060, color = 'b', alpha = 0.5)\n",
    "\n",
    "ax3.text(4.5,6.5, 'Cluster In The Low\\n Ratings Area (Likely)', fontsize = 20, weight ='bold', ha = 'center')\n",
    "ax3.arrow(3.05,7.55,-0.9,0, width = 0.075, color = 'b', alpha = 0.5)\n",
    "\n",
    "ax4.text(3,6.5, 'No Prominent Clusters\\n (Unlikely)', fontsize = 20, weight = 'bold', ha = 'center')\n",
    "\n",
    "ax3.text(7,-4,'''A Large Enough Sample Of Averaged Ratings For Different Movies Should \n",
    "Render A Normal Distribution, Given These Likelihoods''', fontsize = 20, weight = 'bold', ha ='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IMDB, Rotten Tomatoes, Fandango Or Metacritic?\n",
    "\n",
    "\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Now that there’s a criterion to work with, let’s proceed with analyzing some data. \n",
    "There are a lot of websites out there that come up with their own movie ratings, but I have chosen only four, mainly based on their popularity, so I could get ratings for movies with an acceptable number of votes. The happy winners are [IMDB][1], [Fandango][2], [Rotten Tomatoes][3], and [Metacritic][4]. \n",
    "For the last two, I have focused only on their iconic rating types, namely the tomatometer, and the metascore, mainly because these are more visible to the user on each of the websites (meaning it’s quicker to find them), and are also shared on the other two websites (the metascore is shared on IMDB and the tomatometer on Fandango). Besides these iconic ratings, both websites also have a less featured rating type where only users get to contribute.\n",
    "I have collected ratings for some of the most voted and reviewed movies in 2016 and 2017. The cleaned dataset has ratings for 214 movies, and can be downloaded from [this Github repo][5]. I haven’t collected ratings for movies released before 2016, simply because a slight change has occurred in Fandango’s rating system, soon after [Walt Hickey’s analysis][6], which I will refer to later in this article. I am aware that working with a small sample is risky, but at least is compensated in this case by getting the most recent snapshot of the ratings’ distributions.\n",
    "Before plotting and interpreting the distributions, I need to quantify the qualitative values I’ve used earlier: on a 0 to 10 scale, a bad movie is somewhere between 0 and 3, an average one between 3 and 7, and a good one between 7 and 10. \n",
    "Please take note of the distinction between quality and quantity. To keep it discernible in what follows, I will refer to ratings (quantity) as being low, average, or high. As before, the movie quality is expressed as bad, average, or good. If you worry about the “average” term being the same, don’t, because I will take care to avoid any ambiguity. \n",
    "Now let’s take a look at the distributions:\n",
    "\n",
    "[1]: http://www.imdb.com\n",
    "[2]: http://www.fandango.com\n",
    "[3]: https://rottentomatoes.com\n",
    "[4]: http://www.metacritic.com/\n",
    "[5]: https://github.com/mircealex/Movie_ratings_2016_17\n",
    "[6]: https://fivethirtyeight.com/features/fandango-movies-ratings/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(214, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>metascore</th>\n",
       "      <th>imdb</th>\n",
       "      <th>tmeter</th>\n",
       "      <th>audience</th>\n",
       "      <th>fandango</th>\n",
       "      <th>n_metascore</th>\n",
       "      <th>n_imdb</th>\n",
       "      <th>n_tmeter</th>\n",
       "      <th>n_audience</th>\n",
       "      <th>nr_metascore</th>\n",
       "      <th>nr_imdb</th>\n",
       "      <th>nr_tmeter</th>\n",
       "      <th>nr_audience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>76</td>\n",
       "      <td>7.2</td>\n",
       "      <td>90</td>\n",
       "      <td>79</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>48</td>\n",
       "      <td>7.3</td>\n",
       "      <td>50</td>\n",
       "      <td>83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>47</td>\n",
       "      <td>6.6</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>43</td>\n",
       "      <td>5.2</td>\n",
       "      <td>33</td>\n",
       "      <td>76</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>58</td>\n",
       "      <td>6.1</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  metascore  imdb  tmeter  audience  fandango  \\\n",
       "0      10 Cloverfield Lane  2016         76   7.2      90        79       3.5   \n",
       "1                 13 Hours  2016         48   7.3      50        83       4.5   \n",
       "2      A Cure for Wellness  2016         47   6.6      40        47       3.0   \n",
       "3          A Dog's Purpose  2017         43   5.2      33        76       4.5   \n",
       "4  A Hologram for the King  2016         58   6.1      70        57       3.0   \n",
       "\n",
       "   n_metascore  n_imdb  n_tmeter  n_audience  nr_metascore  nr_imdb  \\\n",
       "0         3.80    3.60      4.50        3.95           4.0      3.5   \n",
       "1         2.40    3.65      2.50        4.15           2.5      3.5   \n",
       "2         2.35    3.30      2.00        2.35           2.5      3.5   \n",
       "3         2.15    2.60      1.65        3.80           2.0      2.5   \n",
       "4         2.90    3.05      3.50        2.85           3.0      3.0   \n",
       "\n",
       "   nr_tmeter  nr_audience  \n",
       "0        4.5          4.0  \n",
       "1        2.5          4.0  \n",
       "2        2.0          2.5  \n",
       "3        1.5          4.0  \n",
       "4        3.5          3.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the dataset\n",
    "new_ds = pd.read_csv('movie_ratings_16_17.csv')\n",
    "\n",
    "# Print some info to help the reader understand the structure of the dataset\n",
    "print(new_ds.shape)\n",
    "new_ds.head(5) # Check the github link given to understand what the values of each column describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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T3Llz57HuaW+pSZMmRkCX7t0m0DKUW55rj6pYsWJW56uzs7PVuG3LfVpeJCtQoICqVatm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\nQxDSAQAAAAAwBCEdAAAAAABDENIBAAAAADAEIR0AAAAAAEM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PnqzMzEx5enoqLCxM5cqV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VAAAT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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1d519588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the FTE style\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Generate a figure with 4 axes (2 rows by 2 columns)\n",
    "fig = plt.figure(figsize = (15,10))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "# Remove grids for all axes\n",
    "for ax in fig.axes:\n",
    "    ax.grid(False)\n",
    "\n",
    "    \n",
    "# IMDB\n",
    "ax1.hist(new_ds.imdb, bins = 20, range = (0,10), align = 'left') # bin range = 0.5\n",
    "ax1.axvline(3, color = 'black', alpha = 0.4)\n",
    "ax1.axvline(7, color = 'black', alpha = 0.4)\n",
    "ax1.set_ylim(0, 60)\n",
    "ax1.text(5,50, 'The Average\\n Area', fontsize = 17, weight = 'bold', ha = 'center')\n",
    "ax1.set_yticks([0,15,30,45,60])\n",
    "ax1.set_xticks([0,3,7,10])\n",
    "ax1.text(5,-7.5, 'IMDB\\n (0-10)', fontsize = 14, weight = 'bold', ha = 'center')\n",
    "ax1.text(-0.86,59.2, 'movies', fontsize = 11)\n",
    "\n",
    "\n",
    "# Fandango\n",
    "ax2.hist(new_ds.fandango, bins = 10, range = (0,5), align = 'left') # bin range = 0.5\n",
    "ax2.axvline(1.5, color = 'black', alpha = 0.4)\n",
    "ax2.axvline(3.5, color = 'black', alpha = 0.4)\n",
    "ax2.set_ylim(0,90)\n",
    "ax2.set_yticks([0,23,45,68,90])\n",
    "ax2.set_xticks([0,1.5,3.5,5])\n",
    "ax2.text(2.5,-11, 'Fandango\\n (0-5 stars)', fontsize = 14, weight = 'bold', ha = 'center')\n",
    "ax2.text(-0.56,88.7, 'movies', fontsize = 11)\n",
    "\n",
    "\n",
    "# Metacritic\n",
    "ax3.hist(new_ds.metascore, bins = 20, range = (0,100), align = 'left') \n",
    "# bin range = 5 (equivalent to 0.5 if normalized to 0-10)\n",
    "ax3.axvline(30, color = 'black', alpha = 0.4)\n",
    "ax3.axvline(70, color = 'black', alpha = 0.4)\n",
    "ax3.set_ylim(0,30)\n",
    "ax3.set_yticks([0,8,15,23,30])\n",
    "ax3.set_xticks([0,30,70,100])\n",
    "ax3.text(50,-3.65, 'Metascore\\n (0-100)', fontsize = 14, weight = 'bold', ha = 'center')\n",
    "\n",
    "# RT\n",
    "ax4.hist(new_ds.tmeter, bins = 20, range = (0,100), align = 'left') # bin range = 5 \n",
    "ax4.axvline(30, color = 'black', alpha = 0.4)\n",
    "ax4.axvline(70, color = 'black', alpha = 0.4)\n",
    "ax4.set_ylim(0,30)\n",
    "ax4.set_yticks([0,8,15,23,30])\n",
    "ax4.set_xticks([0,30,70,100])\n",
    "ax4.text(50,-3.65, 'Tomatometer\\n (0-100%)', fontsize = 14, weight = 'bold', ha = 'center')\n",
    "\n",
    "# Text\n",
    "fig.suptitle('Looking For Something Normal', fontsize = 24, weight = 'bold')\n",
    "ax3.text(-12,-10, 'Author: Alex Olteanu', fontsize = 10, weight = 'bold')\n",
    "ax4.text(30,-10, 'Source: IMDB, Fandango, Metacritic, Rotten Tomatoes (Websites)', fontsize = 10, weight = 'bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; At a simple glance, it can be noticed that the metascore’s [histogram][1] (that’s how this kind of graph is called) resembles the most a normal distribution. It has a thick cluster in the average area composed of bars of irregular heights, which makes the top neither blunt, neither sharp. However, they are more numerous and taller than  the bars in each of the other two areas, which decrease in height towards extremes, more or less gradually. All these clearly indicate that most of the metascores have an average value, which is pretty much what we’re looking for.\n",
    "In the case of IMDB, the bulk of the distribution is in the average area as well, but there is an obvious skew towards the highest average values. The high ratings area looks similar to what would be expected to be seen for a normal distribution in that part of the histogram. However, the striking feature is that the area representing low movie ratings is completely empty, which raises a big question mark. Initially, I put the blame on the small sample, thinking that a larger one would do more justice to IMDB. Luckily, I was able to find [a ready-made dataset on Kaggle][2] containing IMDB ratings for 4917 different movies. To my great surprise, the distribution looked like this:\n",
    "\n",
    "\n",
    "[1]: http://www.datavizcatalogue.com/methods/histogram.html\n",
    "[2]: https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4917, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the dataset\n",
    "ds = pd.read_csv('movie_metadata.csv') # I removed a few duplicates before importing\n",
    "\n",
    "# Make the dataset easier to be processed\n",
    "titles = ds[['movie_title', 'imdb_score']]\n",
    "\n",
    "# Drop rows with missing scores\n",
    "titles = titles.dropna(subset = ['imdb_score'])\n",
    "titles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UqUxD07Vr19SxY0fj7zJlyhg3i3Z1dTXu0efq6mpyiQtHbn7U69evn+no/XdT\ncnKyVq9ereDgYJ04cUI3btyQu7u7ypUrp8aNG+vFF1/M8Q2h76YdO3bonXfeMf4ePHhwru4iAeDB\nRODCAycsLExHjx5VjRo17J7funVrlu9p27at3a1lHkS33/Q5ISHBuPdm0aJF7QZcNHNE95w4d+6c\nhg8frnPnzhnPFS1aVHFxcTp+/LiOHz+uJUuWaPTo0Xr22WcLsaQZrVu3zu7v9evXE7gAELjwYClS\npIgSExMVHBycZeCyTvN3s3HjRru/AwMDFRQUJCn9XnodOnQojGJlcOPGDQ0dOlRXrlyRJL322mt6\n9dVX5efnp+TkZAUHB2vatGmKiorSuHHj5OXlpaZNmxZyqdPFxcUZ+9nTTz+tzZs36/Tp0zpx4gSX\n9gN/cwQuPFDq16+v3377TSEhIRo8eLDx/K1bt7Rv3z5JUr169bRr1y67961Zs0YTJkyQlH47DWv4\n6NSpk65cuSJ/f3/1799fkyZN0oULF/TNN9/I3d3daMocOHCgfHx8FBQUpNTUVON2HJK0ZcsWrVix\nQseOHVN8fLx8fHzUoEEDde/eXY899pgx3b59+zRo0CBJ0oQJE3T+/HktX75c5cqV04IFCyRJoaGh\n+uabb3Tq1CmFh4fLw8NDVapUUdeuXdWqVasC3poZ/fzzz1q0aJEuXbokPz8/vfLKKxnu3ZmSkqIf\nfvhB69at07lz5+Tk5KQqVaqoe/fuOSrj4sWLjbDVo0cPvfnmm8ZrLi4uCggIUMWKFdWzZ0+lpqZq\n6tSpaty4sZycnOyalwcOHKinn35aM2bM0IEDB2SxWNSgQQO9++67GW6CffLkSS1cuFD79+9XTEyM\nSpQooRYtWqh///45vom1JAUHBxsDVg4ePFj79+9XdHS01q9ff8fAlZycrHnz5mn9+vWKiopShQoV\n1L9//wzbLC0tTWvWrNHq1av1119/KSkpSb6+vmrUqJF69uxp3PTbNlD/3//9n11zuiQNGTJEe/bs\nkYODg1atWmXcu3D16tVauXKl/vrrL6WmpqpSpUrq2rWrOnXqlOPtACAjOs3jgeLv7y9HR0edOXPG\nrjlq586dSklJUeXKleXr65vr+d66dUujR4/W2bNn5ejoaHfzbUk6evSopkyZopiYGFks/xvaburU\nqRo1apR27dqlGzduyMHBQWFhYVq3bp169eqVZTPn5s2bFRQUpPj4eGNZBw4cUP/+/RUcHKyLFy/K\n2dlZMTHaK5wRAAAgAElEQVQx2r17t0aOHKklS5bker1yY/369Ro3bpwuXryopKQkXbp0SdOmTdOm\nTZuMaVJTUzVixAjNmDFDJ06cUFpamhITE3Xo0CGNHDlSK1asuONy1q5dKym9JjKrpriqVauqdevW\nkqRLly4ZYdrWlStX1L9/f+3atUuxsbGKi4vTtm3b9N5779l9Rnv37lXv3r21ceNGRUZGysnJSWFh\nYVq+fLn69+9vd/PsO7E2J1arVk0VKlQwwtKGDRuUlpaW7XunTJmihQsXKjIyUklJSTp16pTef/99\n7d6925gmLS1No0aN0scff6yDBw/q1q1bslgsunz5sn788Ud1795dR44ckSS7JvLt27fbLevmzZva\nv3+/pPSTFGvYmjx5siZOnKjDhw8rMTFRqamp+vPPP/Wvf/1Ls2bNyvF2AJARgQsPlOLFi+vxxx+X\nlF7bYGUNNnm9GuzYsWOqWLGiNmzYoK1bt6pKlSp2r2/btk09evTQli1b9PPPPxvPLV26VJJUq1Yt\n/fTTT9q2bZtmzpwpDw8PJScna8KECYqNjc2wvJCQEI0cOVLBwcFG7dayZcuUkpIiX19frV69Wlu2\nbNGWLVsUEBAgKb1Gw8ym0uXLl2vBggXaunWrhg0bZjz/ww8/GI9Xr16tHTt2SJK6d++ukJAQbd68\n2SjjzJkzjXvFZeby5cvG1ZA1atTIti9Zw4YNjceHDh3K8PqaNWvUunVrbd68WatXr1bFihUlSadP\nn9aePXskpdfGTZw40agl+uGHH7Rt2zYFBgaqSJEiOnv2rBYtWnTHbSNJERER2rt3ryQZYbBNmzaS\n0vsVZhYKbR09elSrV69WSEiI3nrrLUnpAWvevHnGNMuWLVNISIgkqVmzZvrll18UEhKiCRMmyMnJ\nSbdu3dJHH32ktLQ0VapUyahB3b17t5KTk435bN++3QjyzzzzjDHNjz/+KElq166dtmzZoq1bt+rV\nV1+VlF7zePr06RxtCwAZEbjwwGnWrJkkGT9MKSkp2rlzpySpRYsWeZqnxWLRu+++Ky8vLzk4OMjR\n0f6rU6JECfXv318uLi5ycnKSJLvanA8//FBlypSRg4ODcYWdlN5fKbNarscff1xdu3aVk5OTMT9r\nMEtLSzNqS9zc3PTBBx9o7dq12rRpk4oUKZKn9cuJbt26qWbNmnJyclK3bt3k4+MjSTpz5owxjbWG\nx8XFRYMGDZKzs7Pc3d3Vv39/SfZ9nDITFRVlPL7TFYjWWhlJmQ5ZUbx4cb3zzjtyc3NT6dKl1bVr\nV+O1s2fPSkqvNbQ2X3bu3FmVK1eWlN7s3LJlS0myax7OzsaNG40QYw1aDRo0MLbTneYzaNAglS5d\nWs7OzurevbuxfocPHzaaKa37lIuLi8aNGycfHx85OzurXbt2Rsi7cOGCEUCttVyxsbFGjZb0v++G\ni4uLnn76aUkyThSk9OZYNzc3OTs7a+DAgXJ0dFRaWlqGfoAAco7AhQeO9YfyyJEjCg8P1759+3Tr\n1i15eXnpH//4R57m6ebmpkcffTTL16tVqyZnZ/sukdamHU9Pzwzvte3Qf+LEiQzzq1WrVobnrOsV\nFRWlLl26qEePHvr000+1Z88eeXl5ZQiBBa1u3brGY0dHR6MfVExMjPH8X3/9JSm9abF9+/YKCAhQ\nQECA+vTpY0zz559/ZrmM3KyDbW2eNZTaqlmzpt0QHxUqVDAeW8tsLa8kfffdd0Z5AwICjFBy7dq1\nbGvlrKxhs3r16sa2cXJy0lNPPSUpvZk4uxpI2+3r4OBg1KKmpaUpLCxM8fHxRritVKmSvLy8Mqyv\nlXWfstYsSv9rVkxKStLvv/8uSWrSpIkxH9tt0bNnT2M7dOzY0WiCze6zA5A9Os3jgVO1alWVLVtW\nly9f1u+//67jx49LSq/5uj0U5dTtQyrc7vYfPym9n4wkeXh4ZHitaNGixuPM+ghltrwuXbooISFB\nCxcu1PXr143hEZYtW6YSJUroww8/VPPmze+4Lnl1+zpmNl6ZbS2cbRCzdf369SyXYdu/7vLly9mW\n59q1a8bjzGrDsiuvNUDYNufGxcVluazr169n23n+7Nmzxn527NixTJuuY2NjtX37dqMm6na3N5/a\nlj8mJkZubm7G3zndp8qWLatatWopNDRUO3bs0Hvvvafdu3cb69quXTvjPbbrn5fPDkD2CFx4ILVs\n2VJLlizRrl27dOzYMeO5vLpTzUtmrxcrVkzR0dGZBirb54oVK5bhdQcHh0yX061bN7344osKDQ3V\ngQMHdODAAe3atUtRUVEaPXq0li5dqjJlytxpdUzj4eGhGzduyNvbWxs2bMj1+0uVKqUyZcroypUr\nOn78uKKjo7MMOrb9turXr5/n8lqNGDHCaOrNrdvH3spuuqwCV2xsrN2+YBsGfXx87F6zhnlbtvuU\nbVgLCAhQaGioLl68qDNnzhhNuh4eHnYB3XZbhISEyN3dPUfrBCBnaFLEA8karnbs2KELFy6oSJEi\natKkyV0tg7WJJzY21mhetLK98iyz5sPMJCYm6uTJk7p586bq1aunXr166fPPP9cnn3xivB4aGlpA\npc8bayftmJgYhYeHG8+npKQoPDzcruN2VqzDD6SkpOirr77KdJoTJ05o8+bNkqQqVaqodu3a+Sqv\nJJ06dcrutevXr2cabDLzyy+/SEq/e8H8+fMz/LPWeP3222+6ceNGpvM4fPiw8dhisRjNgo6OjvL1\n9ZW7u7seeeQRSek1arbbV7Lfp2ybF9u0aWOcEGzdulXbtm2TJLVq1cquz5/thSAnT560K8u1a9f+\nlmPXAQWJwIUHUt26deXj42PUEjRq1MiuSeZueP75543H06dPV0REhNLS0hQSEmIMfVCqVCmjk392\n4uPjFRAQoNdee02TJ082ajNSU1N1/vx5Y7oSJUoU8FrkjvWKN4vFoqlTp+rWrVtGcHruuefUtGlT\n4wKGrHTr1k2VKlWSJK1cuVKTJk0ymg+Tk5O1ceNGDR06VKmpqXJ2dtb777+fZY3gndStW9eoEVy3\nbp1+//13WSwWnTt3Tt26dVPr1q31xhtvZDuPgwcPGs2frVq1Uu3atTP8szbdJScn69dff810Pl9+\n+aWuXbumlJQUBQUF6erVq5LSa++s+651n0pNTdUnn3yimzdvKiUlRStXrjT6ZVWvXl3Vq1c35uvn\n56d69epJkpYsWaLIyEhJ//usrGybF7/44gtFRETIYrFo2bJl6tixo5o3b67//ve/d9qkALJAkyIe\nSE5OTmrWrJlWr14tSXdlUNDbNW/eXC+99JKWLl2qw4cPq3379nJ1dVVSUpKk9CacCRMm5OjejdYr\n/WbOnKng4GBt3bpVHh4eSkxMNObXsGHDPF8UUFA6duyoX3/9Vbt379aWLVsUEhIiFxcXo3bkueee\n05NPPpntPNzd3fXFF19o+PDh+vPPP7Vy5UqtXLlSRYsWNcaGktL7PE2YMEF16tTJc3mdnJw0evRo\nvfvuu0pISNDQoUPt7kTg4eGh999/P9t52DYnWq/4u12LFi3k7OyslJQUrV+/3i6MW8tRtmxZdezY\nUS4uLkZNoPVqT6uuXbtqz549CgkJUUhIiNq0aSNnZ2dj+pIlS2rcuHEZlh8QEKD9+/cbV4GWKFFC\nTzzxhN00/v7+6tixo1avXq2DBw+qffv2dtvC39+fwU+BfKCGCw8sa8iyhq/CMHz4cP373/9Ww4YN\nVaxYMVksFpUpU0adOnXS4sWLc9X3qHv37poyZYoaNWokb29vxcfHy8nJSdWqVdObb76pzz77zPQr\nFe/EyclJn332mYYMGaJHH33UuD/j448/rpEjR2rMmDE5mk/p0qW1cOFCffTRR2rSpIlKliyppKQk\nFS1aVDVq1FDfvn21YsWKAvlcGzVqpPnz56tVq1by8fFRSkqKSpQooXbt2mnhwoXZjhCfkpJiDPxa\nsmTJLJs2vby81KBBA0npNWLWoShsw+PEiRP1wgsvyMPDQ0WKFFGtWrU0c+ZMu3k6OjpqypQpGj16\ntGrXrq2iRYvKwcFB5cuXV7du3bR48WJjaAtbrVu3truSMyAgINMrO8eMGaP3339f1atXl5ubm9LS\n0lS5cmUNGjRI06ZNy/NFJwAkh+joaMudJwMAAEBeUcMFAABgMgIXAACAyQhcAAAAJiNwAQAAmIzA\nBQAAYDICFwAAgMkIXAAAACYjcAEAAJiMwAUAAGAyAhcAAIDJCFwAAAAmI3ABAACYjMAFAABgMgIX\nAACAyQhcAAAAJiNwAQAAmIzABQAAYDICFwAAgMkIXAAAACYjcAEAAJiMwAUAAGAyAhcAAIDJCFwA\nAAAmI3ABAACYjMAFAABgMgIXAACAyQhcAAAAJiNwAQAAmIzABQAAYDICFwAAgMkIXAAAACYjcAEA\nAJiMwAUAAGAyAhcAAIDJCFwAAAAmI3ABAACYjMAFAABgshwHrr/++ktdunTR0qVLJUnXrl3TkCFD\nNGDAAA0ZMkQRERGSpPXr16tnz57q3bu3fvrpJ0lSSkqK/u///k/9+vXTgAEDdOnSJRNWxd5vv/2m\nr7/+2vTlAAAA3EmOAld8fLymTp2qJ554wnhu9uzZ6ty5s+bOnatWrVrpu+++U3x8vIKCgjRr1izN\nnj1bS5YsUUxMjNavX69ixYpp3rx56t27t2bNmmXaClk1adJEb7zxhunLAQAAuBPnnEzk4uKizz77\nTN98843x3KhRo+Tq6ipJ8vb21vHjxxUaGqoaNWrI09NTklS3bl0dOnRIe/bs0XPPPSdJ8vf318cf\nf5xhGfv27dOCBQtUqlQpHT16VLVq1dJjjz2m4OBgRUdH6/PPP9fJkyc1f/58ubm5yc3NTR988IFC\nQkJ08uRJjR49WpK0bt06bdu2TU8++aR2796tCRMm6OTJk5oxY4ZSUlKUkpKiESNG6PHHH9f333+v\ndevWGfMbP368vL2987dFAQAAbpOjwOXs7CxnZ/tJ3d3dJUmpqalavny5+vTpo8jISLvA4uPjo4iI\nCEVFRRnPOzqmV6olJyfLxcXFbp5HjhzRv/71L7m5ualNmzb6xz/+odmzZ2v8+PH6+eeftXTpUi1c\nuFClS5fW0qVLNWfOHL311ltauHChUlNT5eTkpI0bN6pLly6KiYkx5vvRRx/p008/1cMPP6wTJ07o\n448/1jfffKO5c+dq+fLlKlmypH777TdFREQQuAAAQIHLUeDKSmpqqsaOHauGDRvK399f69evt3vd\nYrHkan6VK1dW8eLFJUnFixdXnTp1JEmlSpVSSkqKSpQoodKlS0uSGjRooBUrVsjHx0dVq1bV/v37\nVa1aNZ04cUJNmjQxyhIVFaVz587Z1arFxsYqLS1N//znPzVs2DA9/fTTat26tSpWrJjnbQEAAJCV\nfAWuCRMmqEKFCurXr58kyc/PT1FRUcbr4eHhqlWrlnx9fRUZGSkpvQO9xWLJULslSU5OTln+feLE\nCbvXLBaLHBwcJEnPPPOMNm/erKtXr6ply5Z2tXGurq5ydXXVnDlzMizvnXfe0ZUrV7Rjxw6NHDlS\nw4YN05NPPpnbzQBksHDhQru/e/XqVSjlAO4m9nsga3keFmL9+vVycXFR//79jedq1qypo0eP6ubN\nm4qLi9PBgwf1j3/8Q40aNdKmTZskSdu2bVODBg1yvbwKFSro+vXrunr1qiRp9+7dqlWrliSpZcuW\n2rt3r4KDg9W+fXu793l6eqpMmTLasWOHJOncuXMKCgrSjRs3FBgYqNKlS6tr167q2rWrjhw5kqdt\nAQAAkJ0c1XAdO3ZMM2bM0JUrV+Ts7KzNmzfr+vXrcnV11cCBAyWlNweOGjVKQ4YM0dChQ+Xg4KB+\n/frJ09NTAQEB2r17t/r16ycXFxeNHTs21wUtUqSIxowZo9GjR8vV1VXu7u4aM2aMpPT+ZNbmxJo1\na2Z477hx4zR16lQtWrRIKSkpevvtt+Xl5aW4uDj16tVLxYoVk7OzszE/AACAguQQHR2du45WAO6I\nphX8HbHfA1ljpHkAAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAA\nwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAA\nkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABM\nRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZ\ngQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQE\nLgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4\nAAAATOZc2AUAAOSf94JLps07unc50+YN/F1QwwUAAGAyAhcAAIDJCFwAAAAmI3ABAACYjMAFAABg\nMgIXAACAyQhcAAAAJiNwAQAAmIzABQAAYDICFwAAgMkIXAAAACYjcAEAAJiMwAUAAGAyAhcAAIDJ\nCFwAAAAmI3ABAACYjMAFAABgMgIXAACAyZwLuwAAgHub94JLOZtw53W7P9+25Ox90b3L5bZIwH2H\nGi4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBnjcAHAXZDjsawAPJCo\n4QIAADAZgQsAAMBk+WpSjIuL07hx43Tz5k0lJSWpb9++KlmypKZMmSIHBwdVqVJF77//viRp8eLF\n2rRpkxwcHNS3b181bdq0QFYAAADgXpevwLVmzRpVrFhRQ4YMUXh4uAYPHixfX1+99957qlGjhsaM\nGaOdO3eqYsWK2rhxo+bPn69bt26pf//+aty4sZycnApqPQAAAO5Z+WpS9Pb2VkxMjCTpxo0b8vLy\n0uXLl1WjRg1JUvPmzbV7927t27dPTZo0kYuLi3x8fPTQQw/pzJkz+S89AADAfSBfgatt27a6evWq\nnn/+eQ0YMEDDhg1TsWLFjNd9fHwUERGhyMhI+fj4GM+XKFFCERER+Vk0AADAfSNfTYrr1q3TQw89\npJkzZ+rEiRMaOXKkPD097/g+i8WSn8UCAADcV/JVw3Xw4EE1btxYklS1alUlJiYqOjraeD0sLEx+\nfn7y8/NTZGSk8Xx4eLj8/Pzys2gAAID7Rr4CV/ny5RUaGipJunLliooWLarKlSvrwIEDkqTg4GA1\nadJEDRs21I4dO5ScnKzw8HCFhYWpcuXK+S89AADAfSBfTYpdunTRxIkTNWDAAKWmpur9999XyZIl\nNXnyZFksFtWsWVP+/v6SpE6dOmnAgAGSpFGjRsnRkSHAAADA34NDdHQ0HaqAArZw4UK7v3v16lUo\n5cC9429xa5+dS+3/fvKlHL0tunc5EwoD3FuoZgIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAw\nGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBk\nBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMR\nuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbg\nAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYEL\nAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGTOhV0AALgXeC+4VNhFAPAAo4YLAADAZAQuAAAA\nkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZA58CAAqV2YPORvcuZ+r8gZyg\nhgsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQE\nLgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4\nAAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuAC\nAAAwmXN+Z5CQkKBu3bqpT58+euKJJzR27FilpqbK19dX48ePl6urq9avX68lS5bI0dFRnTt3VqdO\nnQqi7AAAAPeFfNdwff311/Ly8pIkzZ07V127dtW8efNUvnx5rVq1SvHx8QoKCtKsWbM0e/ZsLVmy\nRDExMfkuOAAAwP0iX4Hr7NmzOnPmjJo2bSpJ2rdvn1q0aCFJatasmfbs2aPQ0FDVqFFDnp6ecnNz\nU926dXXo0KH8lxwAAOA+ka/ANWPGDL399tvG3wkJCXJ1dZUklShRQhEREYqMjJS3t7cxjY+PjyIi\nIvKzWAAAgPtKngPX2rVrVatWLZUrVy7T1y0WS66eBwAAeFDludP8jh07dOnSJe3YsUNhYWFycXGR\nu7u7EhIS5ObmpvDwcPn5+cnPz09RUVHG+8LDw1WrVq0CKTwAAMD9IM+Ba9KkScbjwMBAlS1bVocO\nHdKWLVv07LPPavPmzWrcuLFq1qypf/3rX7p586acnJx08OBBvfvuuwVSeAAAgPtBvoeFsNW/f3+N\nGzdOK1asUJkyZdShQwc5OztryJAhGjp0qBwcHNSvXz95enoW5GIBAADuaQUSuPr37288/vLLLzO8\n3rp1a7Vu3bogFgUAAHDfYaR5AAAAkxVokyIAmMV7waXCLgIA5Bk1XAAAACYjcAEAAJiMwAUAAGAy\nAhcAAIDJCFwAAAAmI3ABAACYjMAFAABgMgIXAACAyQhcAAAAJiNwAQAAmIzABQAAYDICFwAAgMkI\nXAAAACYjcAEAAJiMwAUAAGAyAhcAAIDJCFwAAAAmI3ABAACYjMAFAABgMgIXAACAyQhcAAAAJiNw\nAQAAmIzABQAAYDICFwAAgMkIXAAAACYjcAEAAJiMwAUAAGAy58IuAAAAZvJecMnU+Uf3Lmfq/PFg\noIYLAADAZAQuAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG4AIAADAZgQsAAMBk\nBC4AAACTcWsfAAXG7FuoAMD9ihouAAAAkxG4AAAATEbgAgAAMBmBCwAAwGQELgAAAJMRuAAAAExG\n4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAkxG4AAAATEbgAgAAMBmB\nCwAAwGQELgAAAJMRuAAAAExG4AIAADCZc2EXAMDd473gUmEXAQD+lqjhAgAAMBmBCwAAwGQELgAA\nAJMRuAAAAExG4AIAADAZgQsAAMBkBC4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAk3EvReAe\nwr0OAeDBROACACAfzDxRiu5dzrR54+6iSREAAMBkBC4AAACT0aQI5EKOmw52Xrf7820LfbMA4O+M\nGi4AAACTEbgAAABMRuACAAAwGYELAADAZAQuAAAAk3GVIh44jNYOALjXUMMFAABgsrtawzV9+nSF\nhobKwcFB7733nmrUqHE3F497BDVQAIC/m7tWw7V//35duHBBX3/9tcaMGaOpU6ferUUDAAAUqrtW\nw7Vnzx61bNlSklS5cmXdvHlTt27dkqen590qAu4Rf4ubsfZ+p7BLANx97PdAlu5aDVdkZKR8fHyM\nv729vRUZGXm3Fg8AAFBo6DQPAABgsrsWuHx9fe1qtMLDw+Xr63u3Fg8AAFBo7lrgaty4sTZv3ixJ\nOn78uPz8/OTh4XG3Fg8AAFBo7lqn+Tp16qhatWrq06ePHB0dNWLEiLu1aAAAgELlEB0dbSnsQgCA\nGfbt26dBgwbpueee09ixYwu7OAD+xghcAO4Jly9fVufOne2ec3JyUtmyZfX000+rT58+cnNzy3Ye\nR44cUe/evbVy5UqVLVtWERER2r59uypUqKD69eubWXwAyBb3UgRwTyldurT69OkjSUpKStLGjRu1\naNEixcXF3bErwvbt2+3+9vX1zRDiAKAwUMN1B9yOCHmRkJCg8ePHKyoqSklJSXrjjTfUvHnzwi7W\nPc1aw/XYY4/p22+/NZ6/efOm2rRpoxIlSmjdunVaunSpli5dqmvXrumhhx7SwIED1bp1a40fP15r\n16413te3b181aNDArkkxMDBQQUFBGjt2rLZv364dO3aoUqVKmjBhgipVqiRJWrVqlebMmaOEhAS9\n9NJLOn36tIKDg41aM2Ttp59+0rp164y/jx07ppCQkEIsEe51f/31l4YPH65u3brppZde0rVr1zR2\n7FilpqbK19dX48ePl6ura2EXs0AwDlc2uB0R8mrbtm2qXr265s6dq0mTJunzzz8v7CLdt9zd3eXo\n6Kjk5GTt2LFDU6dOlaenp958800lJSXpo48+0tWrV9WxY0dVqFBBkjRkyBC1aNEiy3kGBgaqTJky\nevLJJ3X8+HFNmzZNknTp0iVNmjRJt27d0uuvv64///xTe/fuvSvr+SDo1KmT5syZozlz5qh///56\n7rnnCrtIuIfFx8dr6tSpeuKJJ4zn5s6dq65du2revHkqX768Vq1aVYglLFgErmxkdTsi4E4CAgLU\no0cPSdK1a9dUqlSpQi7R/SM1NVURERGKiIjQlStX9NVXXyk1NVVNmjRRlSpVNH/+fE2aNEmtW7dW\n8+bNlZycrCNHjqh+/frG2H4BAQGqVq1alsuoWbOmhg4dqnHjxqlIkSIKDQ2VJG3atElpaWl6+eWX\n1bt3b02aNEkWC40AeREUFKQ33nijsIuBe5iLi4s+++wzuzE59+3bZ5wsNWvWTHv27Cms4hU4+nBl\nIxkSLMkAABjsSURBVDIy0u6gbb0dEfd/RE716dNHYWFhmj59emEX5b5x+vRptW/f3u65Vq1aaeTI\nkYqNjdUXX3yhgwcP2gWh+Pj4XC3j8ccflyS5ubnJ29tb165dkyTj/0cffVRSeu1a5cqVjUCGnDl6\n9KhKly7N4NbIlrOzs5yd7WNIQkKC0YRYokQJRUREFEbRTEENF2Ci+fPna9q0aRo7diw1JTlUrlw5\nzZgxQzNmzFC9evUkSe3bt1exYsU0Y8YMHThwQF26dNH06dPVtm3bPC3Dtk+Io2PGw6CDg0PeCg9J\n6X25OnToUNjFwH3uQTtmEriywe2IkFfHjh0zakuqVq2q1NRUXb9+vZBLdX8oWrSomjRpoiZNmmjE\niBFycnLS9OnTFR8fr9OnT0tK7xDfrFkzJSQkSMp4YE5LS8vTsq1Nv9blJCQk6MyZM3ldlb+tffv2\nqU6dOoVdDNyH3N3dje91eHi4/Pz8CrlEBYfAlQ1uR4S8+uOPP4wr7SIjIxUXFydvb+9CLtX957HH\nHlOXLl109epVzZs3Tw8//LAkad68efrkk090+fJlSdLmzZt16dIlFStWTFJ6/6G89P1o1aqVHBwc\n9MMPP2jhwoUaPXo0tV25FB4erqJFi8rFxaWwi4L7kL+/v7Zs2SIp/XvduHHjQi5RwSFwZcP2dkTT\npk3jdkTIsf9v787Da7zzPo6/IwlpY0kItVz2xAhFKhxbsyAqVCchlgm1C0kNmahBbWUsqXE1yqiS\niHRIJ8EYpDF2EmMpKqmtSIuYiBSJCumREMnzh8l5Elnw1Hk68zyf13W5Lue4z+/+5Xfu8/197t99\n5xgwYAB37twhICCAkJAQpk2bVualK3m2CRMmUKNGDWJiYpgwYQJOTk7s3LmTrKwsPv30U1q2bElS\nUhI3b95kyJAh1KpViwMHDnD+/PkX3lfjxo2ZOXMmVlZWREdH07p1a5ycnIAnX8Iqz5aZmYm9vf0v\n3Q35D3DhwgUCAwPZsWMHGzduJDAwkHHjxrFjxw4CAgK4d+/e/6lL0/oeLhGRf3n06BHp6ekApu/l\n8vHxISsri8TERIUuEfkf028pioj8y+3bt/H398fW1pZhw4Zx/fp1MjIy8Pb2VtgSkZ9FK1wiIsUc\nOnSI1atXk5aWRvXq1XnzzTeZNGmSvg5GRH4WBS4RERERM9NdvCIiIiJmpsAlIiIiYmYKXCIiIiJm\npsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIi\nIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYK\nXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIi\nYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAl\nIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJm\npsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIi\nIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmYKXCIiIiJmpsAlIiIiYmbPFbi2bduGwWBg4cKFz9Xo\nmTNnOHXqFADz589n2LBh//MePsNnn32GwWAgMjLS9NyNGzcwGAwkJCT8rLZTUlIICgrCzc0NLy8v\nPv74Y/Ly8gAIDw+nR48eAGRmZhIfH/+z9iUi/3k2bNiAj48Pbm5uDB48mIMHD/7SXTI5deoUBoMB\ng8GAp6cnY8aMeWb/cnNziYmJeWl9KK82FhYWEhUVxTvvvIObmxtDhw7lwIEDL22/Tyter190TvLx\n8SEsLKzEc/Hx8aaxNRgMeHt7s2LFCgoLC5+rzcDAQKZOnWp6PHfuXLy9vblx48Zz9+tF91GRp8fk\n/v37GAyG/5V5zWAwvNRjzhyKv9cGg4HAwEAAkpKS8PX1xcPDg+XLlz+znecKXImJiVhbW3P48GEK\nCgqeuf1f/vIXU+Ayt4SEBKytrUlMTHyp7RqNRiZPnkyNGjWIjY0lLCyMxMREPvroo1LbHjx4UIFL\n5P+Z8+fP86c//Ylp06YRFxeHs7MzoaGhPHr06JfuWglr165l06ZNuLq6MmPGjApr89dff01sbOxL\n23d5tTE+Pp61a9fy3nvvsXXrVry8vJg5cyaXLl16afsubvTo0ezYseOltxsfH8/Bgwf57W9/S3R0\nNGfPnn3hNsLDw0lISCAsLIz69eu/9D7Kz5eQkEBCQgIHDx7E0dERV1dX8vPzmT17Nr1792bx4sXs\n3buX9PT0Ctt5ZuD66aefOHnyJIMGDeLOnTucOXMGKL2KVJSmw8PDOXDgAGvXriU8PNzUzsKFC/H0\n9DSdKeTk5DB79mx69uyJr68vcXFxwJODz8/Pj5CQEEJDQ01naSkpKaX69s9//pOrV68yaNAgLl68\nyM2bN0ttU1hYSFhYGD179sTPz48jR46Qm5tL//79+eSTTwAICAjg97//fYnX7dy5k3v37jFjxgwa\nNGhA27ZtCQgIYOfOndy5c8e03alTp1i6dClJSUkEBgaSl5fH3Llz6d69O0OHDuXbb78FYNeuXfj4\n+ODh4cGcOXMoKCggPj6eLl26EB0djaenJyEhIeTn55vOnu7fvw/8Z5wBiPx/k5GRAUClSpWwt7dn\n1qxZ7Nq1C2tr6wrrW9FKS/EaWlQLlixZQnBwMHl5eSxYsIAePXrg5+fHiRMnAPjqq6/w8/PDy8uL\npUuXmk6AK6oRNjY21KlTh4kTJ9KsWTM2btwIwPLly/Hy8sLLy4uYmBhu3LjBlClTyMjIwMfHh/v3\n7xMSEoKHhwe+vr58/fXXwJOQ0a9fP9zd3Zk1axZ5eXll1tmna2NxmzdvxtPTkz59+uDg4MCYMWMI\nCwujSZMmz5wbFi9ejJubG5GRkYSGhuLh4cGnn34KwLx58xgxYgRTp07F09OTZcuWARAVFcXbb79d\namwuXLjAsGHD6N69O7NnzyY3N5fCwkLTfDVt2rQKFxleeeUVbG1tcXBwAMDW1pbw8HD69OljWu3y\n9/cvtUJWZNeuXURFRbFgwQJatWoFwKVLlxg+fDhubm6MGTOGW7dulTh2goKCCAoKMq1ebdu2jV69\nejFs2DCmT59e5updecfN88jOzmbSpEm4u7vj6+vLkSNHyh27ovl66dKlDBkypFQ7U6dOpXv37rz7\n7rsl5vSMjAwGDx6Mt7c3SUlJQMljuvgK3IvMpeX1vfiqZfHPZGBgILNnz2bChAn07NmTrVu3AvDq\nq6/y6quvkpiYyK1bt/D39+fChQtkZmYyYMAAunXrRnx8PA0aNKhwLJ8ZuI4ePUp+fj7Dhw+nQYMG\nz1xJGj16NHXr1mXUqFGMHj0aeBKM+vXrx4ABA9i4cSN37twhIiKC06dPExUVhb+/P6GhoVy/fh2A\n9PR0evfuTXBwMC4uLiQkJODo6FhqXwkJCVSrVo2AgABsbGzK7Nu+ffuIi4sjMjKS0aNHs2DBAqys\nrJgxYwabN28mMjKS77//vlTgunbtGnXr1sXOzs70XMuWLSkoKCA1NdX0nIuLC97e3rRr145PPvmE\n2NhYTp8+TUxMDD179iQ0NBR4cnAGBASwbt06du/ezfHjxwF4/PixqT9FRUpE/v25urpSs2ZNgoOD\n8fX1ZdmyZaYaVlF9K8/jx4+pV68eixYt4m9/+xsJCQlERUXh4eHBokWLyM3NZc6cOfj6+rJ+/XoO\nHjzI4cOHgSe1cNCgQc/ss7OzM6mpqWRmZnL//n2WLl1KUFAQK1aswN7enlGjRlG3bl02btxISkoK\nderUYdOmTbRt25ZVq1YBEBYWxqhRo9iyZQuWlpZcuXKlzDrbpk2bErWxuLS0tFI1vWvXrlSpUqXC\nsbtx4wYeHh5069aNyMhIXF1d8fHxITo6mtzcXCwsLEhJSWHo0KEEBAQQExNTol4/7cMPP+SNN95g\n8+bNpKamsn37dk6ePElcXBwLFizAz8+vzBP5Iv3798fLy4vg4GDeeecdmjdvTt++fcnKyiIlJYXM\nzEwuX75Mr169Sr32ypUrLFy4kA4dOuDh4WF6/vz587i5ubFt2zays7PZvHkz6enpREZGMmbMGCZN\nmmRa+DAajSxbtgxvb28WL15McnJyqf1UdNw8j7///e/cuHGDuLg4xo0bZ5qjyhq74oovuABERkZy\n5coVYmJiaNKkCUuXLjX928WLF1m+fDm2trZs2LChwv68yFxaXt8rcvz4caZPn46Liwtr1641PV9Q\nUMC6desYOHAgVatWNZ1w/fnPf8bX15fQ0FAeP35cYdtWz9p5YmIirVq1olatWri5uXHo0CGCg4PL\n3d7a2hoLCwusrKywtrYG4LXXXsPFxYWHDx8SHR1NVlYWp06donPnzjRq1IjatWvz8ccf88033wBg\naWlJ7969sbCwAJ6ky/L61qVLF2xtbenUqROJiYkMHjy4xDZJSUnk5eUREBBAYWEh9+7dIz09nU6d\nOuHl5cWaNWt4//33qVOnTonXWVhYlHs9vvglA0tLS9MfGxsbkpOTuX37NsOHDyc/Px+j0cjDhw+p\nUaMGERERGI1GALKyskxt9OrVC0tLS+DJPQ8i8u/P3t6e2NhYduzYwdGjR9m2bRt79uxhy5YtFda3\nivTp04eqVauSnJxMq1ataNy4MZMnT2by5MmcP3+e7OxsoqKiWL9+PTk5OZw7dw53d/dya+TTCgoK\nsLCwoGrVquTm5jJ79myMRiOPHz/m7t27WFlZYWFhgY2NDQ4ODqSmpjJy5Eh++uknatasCYCTkxPr\n1q3j4sWL9O3bF2dnZ+Li4krV2YyMjBK1sbhKlSqVu8pS0dhZWVnRrVs3UlNTOXDgAD169MDKyorY\n2Fju3r0LQP369Wnfvj0NGzZk+fLlXL58ucz93Llzh9TUVG7fvs3u3bt58OABZ8+e5dGjR1SuXBk3\nNzeAEifdTwsPD8fW1pa0tDSmT59OkyZNGD58OO3atePIkSPUrVuXevXq0aZNm1Kv/eGHH+jTpw9f\nfvklhw8f5s033wSgdu3abN68mb/+9a/cv3+frKwsLl26RGFhId7e3jg4ONC8eXPgycLAgwcP6NWr\nF40bN6Z9+/akpaWV2M/ly5fLPW6KFM21ZXF0dCQ9PZ2ZM2fSqVMnRowYUe7YFYXo3r17U6NGjRLt\nJCcn07FjR+rWrVvqfvCuXbtSr149Xn/9da5cuVJuX4AXmkvL6vuztGrVimbNmtG+ffsSwfTIkSOk\npaUxcOBA4L/HrEWLFnTs2JHp06fTqVMn02pZWSoMXPn5+Rw9epScnBwMBoPp+cuXL5s+4EUfmocP\nH5bbTpUqVQBMA1FYWFjiDS5qo1KlSqbtKzoA4Mlgnjt3jrNnz7Jnzx5T+0WX4YpYW1tTp06dEmm7\nVq1aANy6dQtLS0t++OGHUu03a9aMTZs2kZmZaVouvnjxIpaWljg6OnL69Oky+2VtbU3r1q1LHFAZ\nGRmsXr2akJAQ3NzcGDBgQInXVK5c2fT34iHv8ePHFY6riPxycnJyyM7OZtiwYQwbNox//OMfvP/+\n+1y6dKnc+mZhYWF6XNa9XkXBpHgYyc/PJycnx3QCO2fOHNPlp+cNWkUuXLjAr371K3bt2sW+ffsI\nDw8nPT2dDz/8sNQJZmRkJFlZWaxZs4aNGzeaLscsX76cAwcOcPz4caZMmcL8+fMrrLNlady4canb\nRL744gveeuutCueGojEoemxlZWX6+9MB7unXPq2o7gYGBuLp6Qk8mXvi4+MpLCw0zVP5+fnl/hy1\na9emWrVqvPbaa7Rq1YqkpCSGDx/O22+/zY4dO6hfv36Zq1vwJGTMmTOHe/fusWjRImJiYrCzs2PJ\nkiW4uLgQFhbGtGnTKCwsNP0sT79Hz3oMPNdxY2dnR05Ojunxjz/+CECdOnXo2LEjGzZs4ODBg+zc\nuZMdO3awbt26MseuKNw+HbDhyftR1L+HDx9iNBpNYbYoIzwdxJ/+rKSlpb3QXFpW3zdt2gRgWo16\n+nNY/BgrPp4JCQk4Ozub8kDR8e3p6YmdnR1VqlR55ip2hZcUT548SU5ODkuWLCE6Opr169dja2tL\nQkIC9vb2WFpacubMGVJTU/n+++9Nr7O0tCQ9PZ3c3Nxy2+7QoQPHjh0jLS2Nbdu2YW1tzRtvvFFq\nu8ePH2M0Gkt9mA4dOkSlSpWIiIggOjqa1atXU1BQUGqptE2bNty8eZOMjAySk5NNy5jx8fF89913\nLFmyhNjY2FIf/t69e2NnZ2dazj5z5gwRERH4+PiUKiSWlpbcvn2bu3fv0qZNGy5fvkxOTg779+8n\nPDzcNA7VqlXj0KFDWFtb8+OPP5Z7hlf0hp45c4Z9+/Y9M3yKyP++2NhYRo0axalTp8jOziYtLQ1L\nS0saNmxYbn1zcHDAaDSSkpLC/v37y227Xbt2nDt3jqtXrxIREcHAgQNp0KAB1atXJykpiUePHvHR\nRx+Z6pbRaCw3GOTm5nLnzh3Cw8O5du0aI0aMwGg0mla6vvrqK+DJio+lpSXZ2dncunULo9FI5cqV\nqVSpEsnJyTx48ICcnBw++OADGjZsSEhICI6Ojly7dq3cOlu8Nhbn7+/PoUOH+PLLL8nMzGTdunWs\nXLmSjIyM554bypOens6ZM2fYv38/FhYWtGjRosztqlatStOmTUlKSqKwsJAVK1Zw8uRJHB0defTo\nEfv37+fAgQMlgsjTHjx4gNFo5MqVK1y8eJGmTZsC4OXlxXfffcexY8d46623KuzvjBkzyM/PZ8mS\nJcCT+6ZfeeUVbt26RVpaGvfv38fJyQmA3bt38+2335rm20aNGlG5cmX27dvHtWvXyryk2KhRo3KP\nmyIGg4GMjAx27dpFZmYmUVFR1KxZk1atWrF9+3bi4+P5zW9+w+jRo0lPT8fGxqbMsatIu3btOHHi\nBBkZGSxevJj33nuvwu0dHBw4d+4ct2/fNq1wFq1qPe9cWlbf8/PzcXBw4MKFC9y9e5djx45V2I8i\np0+fpnXr1qbHbdq0wd7ent27d3PixAny8vLKvPWpuAoDV0JCAg0bNqR79+60aNGCli1b4u7uTmJi\nIjY2NowcOZKtW7cSFhaGq6ur6XUeHh7s27evwmuxY8eOxcX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e29a4efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keep the FTE style\n",
    "\n",
    "# Add one ax\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,6.5)\n",
    "ax.grid(False) # removes the grid\n",
    "\n",
    "# Generate the hist\n",
    "ax.hist(titles.imdb_score, bins = 20, range = (0,10), align = 'left') # bin range = 0.5\n",
    "ax.set_yticks([0,400,800,1200])\n",
    "ax.set_xticks([0,3,7,10])\n",
    "ax.axvline(3, color = 'black', alpha = 0.4)\n",
    "ax.axvline(7, color = 'black', alpha = 0.4)\n",
    "fig.suptitle('The Distribution For 4917 IMDB Movie Ratings\\n Mirrors The One Above',fontsize = 20,\n",
    "             weight = 'bold')\n",
    "\n",
    "# Increase the pad btw title and graph; this function works well when dealing with one ax\n",
    "plt.tight_layout(pad = 6)\n",
    "\n",
    "# Text\n",
    "ax.text(-0.85,1180, 'movies', fontsize = 11)\n",
    "ax.text(5, -65, 'Rating', fontsize = 13.5, weight = 'bold', ha = 'center')\n",
    "ax.text(-1.4, -350, 'Author: Alex Olteanu', fontsize = 10, weight = 'bold')\n",
    "ax.text(5.5, -350, 'Source: Dataset Compiled By Kaggle User chuansun76', fontsize = 10, weight = 'bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The shape of the distribution looks almost the same as that for the sample with 214 movies, except for the low ratings area, which is in this case feebly populated with 46 movies (out of 4917). The bulk of the values is still in the average area, which makes the IMDB rating worth considering further for a recommendation, although is clearly hard to rival the metascore, with that skew. \n",
    "Anyway, what’s really great about this outcome, is that it can be used as a strong argument to support the thesis that the 214-movies sample is fairly representative for the whole population. In other words, there’s a greater confidence now that the results of this analysis would be the same, or at least similar, to the results reached if absolutely all the movie ratings from all the four websites were analyzed.\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; With this increased confidence, let’s move on to examining the distribution of Fandango’s ratings, which doesn’t seem to have changed much since Hickey’s analysis. The skew is still visibly towards the higher part of the movie rating spectrum, where most of the ratings reside. The area for the lower half of the average ratings is completely empty, just like the one for bad ratings. It can easily be concluded that the distribution is quite far from fitting my criterion. Consequently, I won’t consider it further for a possible recommendation. \n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Lastly, the tomatometer’s distribution is unexpectedly uniform, and would look even flatter under a different binning strategy. This distribution is not easy to interpret in context, because the tomatometer it’s not a classical rating, but rather represents the percentage of critics who gave a positive review to a movie. This makes it unfit for the bad-average-good framework, because it makes movies either good, either bad. Anyway, I guess it should still boil down to the same normal distribution, with most of the movies having a moderate difference between the number of positive reviews and the negative ones (rendering many ratings of 30% - 70% positive reviews), and a few movies having a significantly bigger difference, in one way or the other. Given this consideration, and the shape of the distribution, the tomatometer doesn’t meet my criterion. It *could* be that a larger sample would do it more justice, but even if I were to recommend it, I would do it with some reserves because of the vague positive or negative rating system.\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; At this point of the analysis, I could say that by looking at the distributions, my recommendation is the metascore. \n",
    "However, the IMDB’s distribution seems to be worth considering as well, especially if you tweak a little the rating intervals for the three qualitative categories (intervals which I defined myself, more or less arbitrarily). From this perspective, recommending the metascore by mostly doing a visual examination is clearly not enough.\n",
    "So, I will try to delimit between these two by using a quantitative method.\n",
    "The idea is to use the Fandango variable as a negative reference, and then determine which variable, from the IMDB rating and the metascore, is the least correlated with it (I call these variables because they can take different values — for example, the metascore is a variable because it takes different values, depending on the movie). \n",
    "I will simply compute some correlation coefficients, and the variable with the smallest value will be my recommendation (I will explain then how these correlation coefficients work). But before that, let me briefly justify choosing the Fandango variable as a negative reference.\n",
    "\n",
    "\n",
    "\n",
    "### Fandango’s users love movies simply too much\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; One reason for this choice is that the distribution of Fandango’s movie ratings is the furthest from that of a normal one, having that obvious skew towards the higher part of the movie ratings spectrum. \n",
    "The other reason is the cloud of suspicion around Fandango left by [Walt Hickey’s analysis][1]. On October 2015, he was also puzzled by a similar distribution, and discovered that on Fandango’s website the numerical ratings were always rounded to the next highest half-star, not to the nearest one (for example, a 4.1 average rating for a movie would have been rounded to 4.5 stars, instead of 4.0).\n",
    "The Fandango team fixed the biased rating system, and replied Hickey that the rating logic was rather a “software glitch” on their website, pointing towards an unbiased system on their mobile app (more about this on Hickey’s article). The adjustment did change some statistical parameters for the better, but not enough to convince me not to work with the Fandango variable as a negative reference. This is how the change looks like:\n",
    "\n",
    "[1]: https://fivethirtyeight.com/features/fandango-movies-ratings/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146, 22)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "0  Avengers: Age of Ultron (2015)              74                   86   \n",
       "1               Cinderella (2015)              85                   80   \n",
       "2                  Ant-Man (2015)              80                   90   \n",
       "\n",
       "   Metacritic  Metacritic_User  IMDB  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0          66              7.1   7.8             5.0                   4.5   \n",
       "1          67              7.5   7.1             5.0                   4.5   \n",
       "2          64              8.1   7.8             5.0                   4.5   \n",
       "\n",
       "   RT_norm  RT_user_norm         ...           IMDB_norm  RT_norm_round  \\\n",
       "0     3.70           4.3         ...                3.90            3.5   \n",
       "1     4.25           4.0         ...                3.55            4.5   \n",
       "2     4.00           4.5         ...                3.90            4.0   \n",
       "\n",
       "   RT_user_norm_round  Metacritic_norm_round  Metacritic_user_norm_round  \\\n",
       "0                 4.5                    3.5                         3.5   \n",
       "1                 4.0                    3.5                         4.0   \n",
       "2                 4.5                    3.0                         4.0   \n",
       "\n",
       "   IMDB_norm_round  Metacritic_user_vote_count  IMDB_user_vote_count  \\\n",
       "0              4.0                        1330                271107   \n",
       "1              3.5                         249                 65709   \n",
       "2              4.0                         627                103660   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Hickey's dataset to use for generating a comparative graph\n",
    "fte_ds = pd.read_csv('fandango_score_comparison.csv')\n",
    "print(fte_ds.shape)\n",
    "fte_ds.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "###### Generate a figure with two axes containing comparative line plots ######\n",
    "\n",
    "### Getting the values ###\n",
    "'''First, get the values and their frequencies. Then, normalize the frequencies to percent, so you can compare\n",
    "the two datasets which have different number of datapoints.'''\n",
    "\n",
    "\n",
    "# Fandango\n",
    "fdg_vals = new_ds.fandango.value_counts(normalize = True).sort_index() * 100 \n",
    "# 'normalize' gives the quotient of (frequency of a value/total nr of values); multiply by 100 to get percentages\n",
    "# Sort all indexes, otherwise the line plots will look chaotic;\n",
    "fte_fdg = fte_ds.Fandango_Stars.value_counts(normalize = True).sort_index() * 100\n",
    "\n",
    "# Metascore\n",
    "ms_vals = new_ds.nr_metascore.value_counts(normalize = True).sort_index() * 100\n",
    "fte_ms = fte_ds.Metacritic_norm_round.value_counts(normalize = True).sort_index() * 100\n",
    "\n",
    "# IMDB\n",
    "imdb_vals = new_ds.nr_imdb.value_counts(normalize = True).sort_index() * 100\n",
    "fte_imdb = fte_ds.IMDB_norm_round.value_counts(normalize = True).sort_index() * 100\n",
    "\n",
    "# Tomatometer\n",
    "tmeter_vals = new_ds.nr_tmeter.value_counts(normalize = True).sort_index() * 100\n",
    "fte_tmeter = fte_ds.RT_norm_round.value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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4MdasWSNKxqrvnO/du1co0z8NL0pycjKsrKwQHBwMb29vobx169Z4//33RYkKNRoN/vzz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\nQ6ny/7SGTl7H+pPXMDYaw+t4tPd73NNpkX0JGQf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fTcS1K2q/SDQ5UvgQQoi6qjrbQ+0F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VoD8wcFNBjj7qCmqFrP0lsiJKknF2PRrjQMJpGJ4R58Pj0CT0c1E5Ye43BWhWXMecHDuoWuwSRa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e32163320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### The graph ###\n",
    "\n",
    "# Keep the FTE style\n",
    "\n",
    "# Generate a figure with 2 axes (1 row by 2 columns)\n",
    "fig = plt.figure(figsize = (17.5, 7))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "\n",
    "# Plot the lines (consider colorblindness when choosing colors)\n",
    "# FTE dataset\n",
    "ax1.plot(fte_fdg.index, fte_fdg.values, c = (230/255, 159/255, 0))\n",
    "ax1.plot(fte_ms.index, fte_ms.values, c = (0, 114/255, 178/255))\n",
    "ax1.plot(fte_imdb.index, fte_imdb.values, c = (240/255, 228/255, 66/255))\n",
    "ax1.plot(fte_tmeter.index, fte_tmeter.values, c = (0, 158/255, 115/255))\n",
    "\n",
    "# New dataset\n",
    "ax2.plot(fdg_vals.index, fdg_vals.values, c = (230/255, 159/255, 0))\n",
    "ax2.plot(ms_vals.index, ms_vals.values, c = (0, 114/255, 178/255))\n",
    "ax2.plot(imdb_vals.index, imdb_vals.values, c = (240/255, 228/255, 66/255))\n",
    "ax2.plot(tmeter_vals.index, tmeter_vals.values, c = (0, 158/255, 115/255))\n",
    "\n",
    "# Tweak the axes\n",
    "# Ax1\n",
    "# Ticks\n",
    "ax1.set_yticks([0,10,20,30,40,50])\n",
    "ax1.set_xticks([0,1,2,3,4,5])\n",
    "ax1.set_yticklabels(['', '10%', '', '', '40%', '']) # Had to do it this way to keep the gridlines for 0,20, and 30\n",
    "ax1.set_xticklabels(['0 stars', '1', '2', '3', '4', '5 stars', ])\n",
    "ax1.tick_params(labelsize = 14) # font size for tick labels\n",
    "# Legend\n",
    "ax1.text(3.25,44, 'IMDB', color = (220/255, 208/255, 46/255), fontsize = 16, weight = 'bold')\n",
    "ax1.text(4.1,39.5, 'Fandango', color = (230/255, 159/255, 0), fontsize = 16, weight = 'bold')\n",
    "ax1.text(1.05,15.1, 'Metacritic', color = (0, 114/255, 178/255), fontsize = 16, weight = 'bold')\n",
    "ax1.text(2.7, 4, 'RottenTomatoes', color = (0, 158/255, 115/255), fontsize = 16, weight = 'bold')\n",
    "ax1.text(-0.3, 33, 'From 146 movies', fontsize = 12, weight = 'bold', rotation = 'vertical')\n",
    "\n",
    "# Ax2\n",
    "# Ticksb\n",
    "ax2.yaxis.tick_right() # moves the y-axis to the right\n",
    "ax2.set_yticks([0,10,20,30,40,50])\n",
    "ax2.set_xticks([0,1,2,3,4,5])\n",
    "ax2.set_yticklabels(['', '10%', '', '', '40%', ''])\n",
    "ax2.set_xticklabels(['0 stars', '1', '2', '3', '4', '5 stars'])\n",
    "ax2.tick_params(labelsize = 14)\n",
    "# Legend\n",
    "ax2.text(2.8,37, 'IMDB', color = (220/255, 208/255, 46/255), fontsize = 16, weight = 'bold')\n",
    "ax2.text(3.6,40, 'Fandango', color = (230/255, 159/255, 0), fontsize = 16, weight = 'bold')\n",
    "ax2.text(1.5,20.2, 'Metacritic', color = (0, 114/255, 178/255), fontsize = 16, weight = 'bold')\n",
    "ax2.text(-0.1,13.5, 'RottenTomatoes', color = (0, 158/255, 115/255), fontsize = 16, weight = 'bold')\n",
    "ax2.text(5.4, 33, 'From 214 movies', fontsize = 12, weight = 'bold', rotation = 270)\n",
    "\n",
    "# Titles & Subtitles\n",
    "fig.suptitle('Different Movie, Same Story', fontsize = 34, weight = 'bold')\n",
    "ax1.set_title('October 2015', loc = 'left', weight = 'bold')\n",
    "ax2.set_title('March 2017', loc = 'right', weight = 'bold')\n",
    "ax1.text(2.5, -17, 'Walt Hickey From FTE Was \\n Puzzled By Fandango\\'s Skewed Distribution', fontsize = 21, \n",
    "         weight = 'bold', ha = 'center')\n",
    "ax2.text(2.5, -17, 'We Could Name This Kind Of Distribution \\n \"The Fandango Distribution\"', fontsize = 21, weight = 'bold', ha = 'center')\n",
    "ax1.text(-0.5, -38, 'Author: Alex Olteanu', fontsize = 11)\n",
    "ax2.text(0, -38, 'Source: Walt Hickey\\'s Dataset; Fandango, Metacritic, IMDB, and Rotten Tomatoes (Websites)', \n",
    "         fontsize = 11)\n",
    "plt.tight_layout(pad = 7) # increases the padding btw the fig title and the axes objects\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('small_multiple_fdg.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A magnified picture of the changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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fR/d/rbByRw8h3AKAolZ9qLza6hdys5AV9rfo+ax9e8Nj/r9w7Pc6ZPKC+/UR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\no/L/EKp7/2zqkA0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1d46a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keep the FTE style\n",
    "\n",
    "# One ax\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(12,7.5)\n",
    "ax.grid(False) # removes all gridlines\n",
    "\n",
    "# Plot the bar graphs\n",
    "ax.bar(fte_fdg.index, fte_fdg.values, width = 0.2, color = (213/255, 94/255, 0)) # red; 2015\n",
    "ax.bar(fdg_vals.index, fdg_vals.values, width = 0.2, align = 'edge',color = (0/255, 114/255, 178/255)) # blue;2017\n",
    "\n",
    "# Tweak the graph\n",
    "from numpy import arange\n",
    "ax.set_xticks(arange(0,5.1,0.5))\n",
    "ax.set_xticklabels(('0', '', '', '', '', '2.5', '3.0', '3.5', '4.0', '4.5', '5.0 stars'))\n",
    "ax.set_yticks(range(0, 70, 10)) \n",
    "ax.set_yticklabels(('', '10%', '', '', '40%', '', '', ''), weight = 'bold', fontsize = 12)\n",
    "ax.yaxis.tick_right()\n",
    "\n",
    "# Text\n",
    "ax.text(0, 55, '2015', fontsize = 24, weight = 1000, color = (213/255, 94/255, 0)) # year\n",
    "ax.text(2, 55, '2017', fontsize = 24, weight = 1000, color = (0/255, 114/255, 178/255)) # year\n",
    "# Means and modes computed before\n",
    "ax.text(0.12,50, '4.1', fontsize = 18, weight = 1000, color = (213/255, 94/255, 0)) # mean 2015\n",
    "ax.text(2.135,50, '3.9', fontsize = 18, weight = 1000, color = (0/255, 114/255, 178/255)) # mean 2017\n",
    "ax.text(1,50, 'Mean', fontsize = 22, weight = 'bold', color = 'black') # mean \n",
    "ax.text(1,45, 'Mode', fontsize = 22, weight = 'bold', color = 'black') # mode\n",
    "ax.text(0.12,45, '4.5', fontsize = 18, weight = 1000, color = (213/255, 94/255, 0)) # mode 2015\n",
    "ax.text(2.135,45, '4.0', fontsize = 18, weight = 1000, color = (0/255, 114/255, 178/255)) # mode 2017\n",
    "ax.text(0, -9, 'Author: Alex Olteanu', fontsize = 10)\n",
    "ax.text(3.8, -9, 'Source: FiveThirtyEight\\'s dataset, fandango.com', fontsize = 10)\n",
    "ax.annotate(\"The barren area of \\n movies not worth seeing\", xy=(0.2, 0.2), xycoords='data', xytext=(1.2, 18), \n",
    "            textcoords='data', size=20, va=\"center\", ha=\"center\", bbox=dict(boxstyle=\"circle\", fc=\"w\", alpha = 0.5))\n",
    "                  \n",
    "# Title\n",
    "fig.suptitle('Magnifying On Fandango', fontsize = 28, weight = 'bold')\n",
    "plt.tight_layout(pad = 6.5) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Between the metascore and the IMDB rating, which is the least correlated with the Fandango rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "metascore    0.378537\n",
      "imdb         0.633383\n",
      "Name: fandango, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Correlation Values\n",
    "print(new_ds.corr().loc['fandango'][['metascore', 'imdb']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The least correlated with the Fandango rating is the metascore. It has a Pearson’s r value of 0.38 with respect to Fandango, while the IMDB rating has a value of 0.63.\n",
    "Now let me explain all this. \n",
    "As two variables change, taking different values, they are correlated if there’s a pattern corresponding to both changes. Measuring [correlation][1] simply means measuring the extent to which there is such a pattern. One of the ways to perform this measure is to compute the Pearson’s r. If the value is +1.0, it means there’s a perfect positive correlation, and if it’s -1.0, it means there’s a perfect negative correlation. The extent to which the variables are correlated decrease as the Pearson’s r approaches 0, from both the negative and the positive side. Let’s better visualize this:\n",
    "\n",
    "[1]: http://www.mathsisfun.com/data/correlation.html\n",
    "\n",
    "<figure>\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d4/Correlation_examples2.svg/506px-Correlation_examples2.svg.png\" alt=\"Pearson's r\">\n",
    "<center><figcaption><b>Visualizing Pearson's r Values (Author: Denis Boigelot; Source: Wikipedia)</b></figcaption></center>\n",
    "<figure>\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Now, to put the abstraction above into context, if we compare how the values for two rating types (say Fandango’s and IMDB’s) change, we can determine the degree to which there’s a pattern corresponding to both changes. Given the correlation coefficients just mentioned, there is a pattern between Fandango and IMDB to a greater extent than is for Fandango and the metascore. Both coefficients are positive, and, as such, the correlation is said to be positive, which means that as Fandango’s ratings go up, IMDB’s ratings tend to go up as well, more than the metascores do. \n",
    "Put differently, for any given movie rating on Fandango, it is more probable that the metascore is going to be more different from it than the IMDB rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The metascore: how it works & downsides\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; All in all, I recommend checking the metascore whenever you are looking for a movie rating.\n",
    "In a nutshell, the metascore is a weighted average of many reviews coming from reputed critics. The Metacritic team reads the reviews and assigns each a 0–100 score, which is then given a weight, mainly based on the review’s quality and source. More about their rating system can be found [here][1]. \n",
    "Now, I just want to point out a few downsides of the metascore: \n",
    "- the weighting coefficients are confidential, so you won’t get to see the extent to which each review counted in the metascore; \n",
    "- you will have a rough time finding metascores for less-known movies that appeared before 1999, the year Metacritic was created; \n",
    "- some recent movies whose main language is not English, not only that don’t have a metascore, they are not even listed on Metacritic — for example, the Romanian movies [Two Lottery Tickets (2016)][2] and [Eastern Business (2016)][3] are not listed on Metacritic, while on IMDB are, being also rated.\n",
    "\n",
    "### Few more words\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; To sum up, in this article I made a single recommendation of where to look for a movie rating. I recommended the metascore, based on two arguments: its distribution resembles the most a normal one, and it is the least correlated with the Fandango rating.\n",
    "\n",
    "[1]: http://www.metacritic.com/faq#item11\n",
    "[2]: http://www.imdb.com/title/tt5700224/?ref_=nv_sr_1\n",
    "[3]: http://www.imdb.com/title/tt5610362/?ref_=nv_sr_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HDx40LvZy6igpLy1bttSePXu0cOFCSTlXvZOyLmRLlSql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\nfAAAAACFJfgAAAAACkvwAQAAABSW4AMAAAAoLMEHAAAAUFi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1d2acc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Generating the top-image ###\n",
    "\n",
    "# Keep the fivethirtyeight style (remains from above)\n",
    "\n",
    "# Generate a figure with a single ax\n",
    "fig, ax = plt.subplots(figsize = (18,9))\n",
    "\n",
    "# Generate a hist on the ax; normal was imported earlier from numpy.random\n",
    "ax.hist(normal(size = 1000000, scale = 6), bins = 29, range = (-20, 20))\n",
    "\n",
    "# Hide grid and tweak tick parameters\n",
    "ax.grid(False)\n",
    "\n",
    "ax.set_yticks([0, 170000])\n",
    "ax.tick_params(axis = 'both', which = 'both', labelleft = False)\n",
    "\n",
    "ax.set_xticks([-20,20])\n",
    "ax.set_xticklabels(['Min', 'Max'])\n",
    "\n",
    "# Explanatory text\n",
    "ax.text(0, -7000, 'Rating Value', weight = 'bold', fontsize = 14, ha = 'center')\n",
    "ax.text(0,115000, 'Most Of The Movies\\n Are Average', fontsize = 18, weight = 'bold', ha ='center')\n",
    "ax.text(-15,115000, 'Few Movies Are\\n Terrible', fontsize = 18, weight = 'bold', ha ='center')\n",
    "ax.text(15,115000, 'Few Movies Are\\n Outstanding', fontsize = 18, weight = 'bold', ha ='center')\n",
    "fig.suptitle('Movie Ratings Should Reflect\\n Movie Quality', fontsize = 22, weight = 'bold')\n",
    "\n",
    "# Delimitating areas\n",
    "ax.axvline(-7.5, color = 'black', alpha = 0.4)\n",
    "ax.axvline(7.5, color = 'black', alpha = 0.4)\n",
    "\n",
    "# Increase the pad btw title and graph\n",
    "plt.tight_layout(pad = 8)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}