{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import matplotlib\n",
    "path_data = '../../assets/data/'\n",
    "matplotlib.use('Agg')\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "import numpy as np\n",
    "np.set_printoptions(threshold=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tables are a powerful way of organizing and visualizing data. However, large tables of numbers can be difficult to interpret, no matter how organized they are. Sometimes it is much easier to interpret graphs than numbers.\n",
    "\n",
    "In this chapter we will develop some of the fundamental graphical methods of data analysis. Our source of data is the [Internet Movie Database](http://www.imdb.com), an online database that contains information about movies, television shows, video games, and so on. The site [Box Office Mojo](http://www.boxofficemojo.com) provides many summaries of IMDB data, some of which we have adapted. We have also used data summaries from [The Numbers](http://www.the-numbers.com), a site with a tagline that says it is \"where data and the movie business meet.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Scatter Plots and Line Graphs</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table `actors` contains data on Hollywood actors, both male and female. The columns are:\n",
    "\n",
    "| Column        | Contents |\n",
    "|---------------------|----------|\n",
    "|`Actor`              | Name of actor |\n",
    "|`Total Gross`        | Total gross domestic box office receipt, in millions of dollars, of all of the actor's movies |\n",
    "| `Number of Movies`  | The number of movies the actor has been in |\n",
    "| `Average per Movie` | Total gross divided by number of movies |\n",
    "| `#1 Movie`          | The highest grossing movie the actor has been in |\n",
    "| `Gross`             | Gross domestic box office receipt, in millions of dollars, of the actor's `#1 Movie` |\n",
    "\n",
    "In the calculation of the gross receipt, the data tabulators did not include movies where an actor had a cameo role or a speaking role that did not involve much screen time.\n",
    "\n",
    "The table has 50 rows, corresponding to the 50 top grossing actors. The table is already sorted by `Total Gross`, so it is easy to see that Harrison Ford is the highest grossing actor. At the time when the table was created, his movies had brought in more money at the domestic box office than the movies of any other actor in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Actor</th> <th>Total Gross</th> <th>Number of Movies</th> <th>Average per Movie</th> <th>#1 Movie</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Harrison Ford     </td> <td>4871.7     </td> <td>41              </td> <td>118.8            </td> <td>Star Wars: The Force Awakens</td> <td>936.7</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Samuel L. Jackson </td> <td>4772.8     </td> <td>69              </td> <td>69.2             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Morgan Freeman    </td> <td>4468.3     </td> <td>61              </td> <td>73.3             </td> <td>The Dark Knight             </td> <td>534.9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tom Hanks         </td> <td>4340.8     </td> <td>44              </td> <td>98.7             </td> <td>Toy Story 3                 </td> <td>415  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Robert Downey, Jr.</td> <td>3947.3     </td> <td>53              </td> <td>74.5             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Eddie Murphy      </td> <td>3810.4     </td> <td>38              </td> <td>100.3            </td> <td>Shrek 2                     </td> <td>441.2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tom Cruise        </td> <td>3587.2     </td> <td>36              </td> <td>99.6             </td> <td>War of the Worlds           </td> <td>234.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Johnny Depp       </td> <td>3368.6     </td> <td>45              </td> <td>74.9             </td> <td>Dead Man's Chest            </td> <td>423.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michael Caine     </td> <td>3351.5     </td> <td>58              </td> <td>57.8             </td> <td>The Dark Knight             </td> <td>534.9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Scarlett Johansson</td> <td>3341.2     </td> <td>37              </td> <td>90.3             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (40 rows omitted)</p>"
      ],
      "text/plain": [
       "Actor              | Total Gross | Number of Movies | Average per Movie | #1 Movie                     | Gross\n",
       "Harrison Ford      | 4871.7      | 41               | 118.8             | Star Wars: The Force Awakens | 936.7\n",
       "Samuel L. Jackson  | 4772.8      | 69               | 69.2              | The Avengers                 | 623.4\n",
       "Morgan Freeman     | 4468.3      | 61               | 73.3              | The Dark Knight              | 534.9\n",
       "Tom Hanks          | 4340.8      | 44               | 98.7              | Toy Story 3                  | 415\n",
       "Robert Downey, Jr. | 3947.3      | 53               | 74.5              | The Avengers                 | 623.4\n",
       "Eddie Murphy       | 3810.4      | 38               | 100.3             | Shrek 2                      | 441.2\n",
       "Tom Cruise         | 3587.2      | 36               | 99.6              | War of the Worlds            | 234.3\n",
       "Johnny Depp        | 3368.6      | 45               | 74.9              | Dead Man's Chest             | 423.3\n",
       "Michael Caine      | 3351.5      | 58               | 57.8              | The Dark Knight              | 534.9\n",
       "Scarlett Johansson | 3341.2      | 37               | 90.3              | The Avengers                 | 623.4\n",
       "... (40 rows omitted)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors = Table.read_table(path_data + 'actors.csv')\n",
    "actors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Terminology.**\n",
    "A *variable* is a formal name for what we have been calling a \"feature\" or \"attribute\", such as 'number of movies.' The term *variable* emphasizes the point that a feature can have different values for different individuals. For example, the numbers of movies that actors have been in vary across all the actors.\n",
    "\n",
    "Variables that have numerical values and can be measured numerically, such as 'number of movies' or 'average gross receipts per movie' are called *quantitative* or *numerical* variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Scatter Plots</h2>\n",
    "\n",
    "A *scatter plot* displays the relation between two numerical variables. You saw an example of a scatter plot in an early section where we looked at the number of periods and number of characters in two classic novels.\n",
    "\n",
    "The Table method `scatter` draws a scatter plot consisting of one point for each row of the table. Its first argument is the label of the column to be plotted on the horizontal axis, and its second argument is the label of the column on the vertical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actors.scatter('Number of Movies', 'Total Gross')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot contains 50 points, one point for each actor in the table. You can see that it slopes upwards, in general. The more movies an actor has been in, the more the total gross of all of those movies – in general.\n",
    "\n",
    "Formally, we say that the plot shows an *association* between the variables, and that the association is *positive*: high values of one variable tend to be associated with high values of the other, and low values of one with low values of the other, in general. \n",
    "\n",
    "Of course there is some variability. Some actors have high numbers of movies but middling total gross receipts. Others have middling numbers of movies but high receipts. That the association is positive is simply a statement about the broad general trend.\n",
    "\n",
    "Later in the course we will study how to quantify association. For the moment, we will just think about it qualitatively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have explored how the number of movies is related to the *total* gross receipt, let's turn our attention to how it is related to the *average* gross receipt per movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actors.scatter('Number of Movies', 'Average per Movie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a markedly different picture and shows a *negative* association. In general, the more movies an actor has been in, the *less* the average receipt per movie.\n",
    "\n",
    "Also, one of the points is quite high and off to the left of the plot. It corresponds to one actor who has a low number of movies and high average per movie. This point is an *outlier*. It lies outside the general range of the data. Indeed, it is quite far from all the other points in the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will examine the negative association further by looking at points at the right and left ends of the plot. \n",
    "\n",
    "For the right end, let's zoom in on the main body of the plot by just looking at the portion that doesn't have the outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WFmLr1pcvwCWXy1/4wNWLPgR1kslkivf6x4I4lJRVoGWr1igpq0DS/nTBPtT0bO6m5NncTWllTl34vJuS5pq71pusbdu2RUBAAL777jucO3cOQUFB+P777+Hq6orBgwdj48aNKCgoqPcbv0h5eTkmTJiAS5cuYdeuXTA1NVW0mZubo6KiAnl5eUo/k5ubC4lEopE89cWFxLSLK3MSKVNpFk3Hjh0xY8YMnDx5EqmpqejevTtmz56Nv/3tb2oP9OTJEwQFBeHSpUvYs2cPpFKpUruLiwsMDAyQmpqqOJaVlYVr167Bzc1N7XkawtTYCJV/3MPgQmKaxwsqkbI6bbp95swZJCUlYdeuXTAwMECPHj3q/IbFxcXIzMwEAFRWVuL27ds4f/48TExM0KFDB4wfPx6//PILtm7dCpFIhOzsbABAu3bt0KZNGxgbG2PcuHGIioqCRCKBiYkJIiMj4ezsjAEDBtQ5jyZFTg1E9LqtyCv4c8iANIcrcxIpe2mBv3XrFrZt24bk5GRkZGSgX79+iIqKwogRI2BsbFznN/zll1/wzjvvKL6PiYlBTEwMAgMDER4ejn379gFAtWIdGxuL999/HwAQHR0NfX19BAUFobS0FB4eHli/fj309fXrnEeTuJCYdvGCSqSs1gL/zTffICkpCenp6bC1tcWYMWMQEBAAW1vbBr3hm2+++cKxe1XG9Vu3bo3ly5dj+fLlDcpCuoUXVCJltRb40NBQGBkZISAgAP369QMApKWlIS0trcbzx40bp5mERERULy8coikqKsKWLVuwZcuWF76ISCRigSciEphaC/y5c+e0mYOIiNSs1gJvbW2tzRxERKRmWl9NkoiItIMFnohIR7HAExHpKBZ4IiIdpVKBLywsRFlZmaazEBGRGr20wJeXl8POzg6HDx/WRh4iIlKTlxb4Fi1awNzcXHDrvBAR0YupNEQzduxYfPPNN5rOQkREaqTScsHW1tbYvn07Bg4ciCFDhkAqlVbbPYlLFRARCYtKBX7mzJkAgDt37uDs2bPV2rkWDRGR8KhU4LkuDRFR06PyEA0RETUtddqy7+LFizhx4gTy8/MRFBQEqVSKzMxMSCQSGBlxezQiIiFRqcCXlZVh0qRJ2LNnD+RyOUQiEXx8fCCVShEVFQV7e3vMnz9fw1GJiKguVJom+dlnn+HIkSOIj4+HTCaDXC5XtA0ePBiHDh3SWEAiIqoflXrwO3bswNy5czFmzBhUVFQotdnY2ODWrVsaCdccZOc+QHRsEvIL/9wo2ry9uLFjEZEOUKkHn5+fjy5dutTYVllZicePH6s1VHMSHZuEOzn5ePy4HHdy8hG9bmtjRyIiHaFSgbexscHp06drbDtz5gzs7e3VGqo5yS8sgt4fD43piUTIKyhq5EREpCtUKvABAQFYs2YNtm3bpuiti0Qi/PTTT1i3bh0++OADjYbUZabGRqj8455GpVwOU2PORiIi9VCpwIeGhsLLywuTJ0/Gq6++CgDw9fXFiBEj8Pbbb2Py5MkaDanLIqcGoqPUFC1btoCluSkipwY2diQi0hEq3WTV19fHV199heDgYBw6dAi5ubkwNTWFp6cn+vfvr+mMOs28vRhroqY0dgwi0kF1etDJ3d0d7u7umspCRERqVKcC/9NPP+H06dO4e/cuOnTogL59++LNN9/UVDYiImoAlQr8gwcPMH78eKSlpUFPTw9isRgFBQWQy+Xo378/Nm3aBBMTE01nJSKiOlDpJuvs2bPxyy+/YMOGDbh37x6uX7+Oe/fuYf369Th79ixmz56t6ZzNWnbuA4QuiMO4T5YhdEEccnILGjsSETUBKhX4H374AVFRURgzZgwMDAwAAAYGBhg7diwiIyPxww8/aDRkc8eHoYioPlQaotHT00Pnzp1rbHNwcICenkrXCaonbT8MxeUTiHSDSpV5yJAhSElJqbFtx44d8PPzU/kNjx8/joCAAHTt2hVisRiJiYlK7XK5HDExMXBycoKFhQX8/Pxw5coVpXPKysoQFhYGOzs7WFpaIiAgAFlZWSpnaGq0/TAU/2Ig0g0qFXgfHx8cOXIEY8eORWJiIg4ePIjExESMGTMGR48eha+vL44ePar4epGSkhJ069YNS5YsQZs2baq1r127FrGxsVi6dCkOHz4MiUSCkSNHoqjoz15rREQE9uzZg40bN2Lfvn0oKiqCv79/tYXQdMWkAF/cuH0P569k4sbte5gcOESj78flE4h0g0pDNOPHjwcAZGVl4eDBg9XaP/zwQwBQrBWfn59f62t5eXnBy8sLABASEqLUJpfLERcXh+nTp2P48OEAgLi4ODg4OGD79u0ICgpCYWEhNm/ejNjYWAwcOBAAEB8fj+7du+PIkSPw9PRU5VdqUjYk7YetlQX0RCJUyuXYkLRPow9HmRob4U5OvuL9tL18AoeIiNRDpQK/Z88eTecAANy8eRPZ2dkYNGiQ4libNm3g7u6O9PR0BAUF4ezZs3jy5InSOVZWVnB0dER6erpOFnht96gjpwYiet1W5BX8WWDVRZXiXTVEpCcSKYaI+LQvUd2pVOC1tRxBdnY2AEAikSgdl0gkuHv3LgAgJycH+vr6MDMzq3ZOTk5Ora8tk8nUnLZ26n4vfVSiuLgYIpEIcrkcEtN2OHnqDOKTDqKw6CGMjdrio0AvmIkb1tN+NvfUwLcV/y58cB+FD+436LWrLI7bgfv5v//xl14BZkevx5wpo5TOuXX7Lp6U/zncdvO3shd+ptr831admFu7dDW3g4NDrW11epJVW0R/9FarVA39vMjLznnRh6BOMplM7e+1bM5H1XrUi2O3oqSsAi1btUZJWQWS9qc3qJeridw1qYAeXnnlFcX35dCr9r7WVh2UhogszU1rzaaO3I0xJKStz1vdmFu7GppbUPMbpVIpAFTriefm5ip69ebm5qioqEBeXl6t5+iaqgXJNq+ahbXzpsC8vbjJ3ghVZUaQtlfY5Kwh0lWCKvA2NjaQSqVITU1VHCstLcXJkyfh5uYGAHBxcYGBgYHSOVlZWbh27ZrinOagqa4jr0rxrumCpklN9WJJ9DJaH6IpLi5GZmYmgKfb/d2+fRvnz5+HiYkJOnXqhClTpmDlypVwcHCAvb09VqxYAUNDQ4wePRoAYGxsjHHjxiEqKgoSiQQmJiaIjIyEs7MzBgwYoO1fp9Fo8kaoJglxeeTGnjVEpClaL/C//PIL3nnnHcX3MTExiImJQWBgIOLi4hAaGopHjx4hLCwMBQUFcHV1RUpKCoyM/vyPLjo6Gvr6+ggKCkJpaSk8PDywfv166Ovra/vX0YraxoiFViibqqZ6sSR6GVFBQYFc1ZMrKytx9epV5Ofno1evXjA0NNRktiZJEzdzQhfEKfUwO0pN1V7cm+tNqMbC3NrVXHOrPAb/5ZdfokuXLujfvz+GDRummLrz3nvvYf369fUOQC/HMWIiqg+VCvymTZsQHh4OPz8//POf/4Rc/men//XXX8fu3bs1FpCa7g1VImpcKhX42NhYTJs2DWvXrsXQoUOV2rp06YKMjAyNhKOnuDE3EdWHSjdZb968qbQ0wLPatm2LwsJCtYYiZareUOUaLkT0LJV68GZmZrh161aNbRkZGejQoYNaQ1H98IEdInqWyssFL1u2DDdu3FAcE4lEyMvLw7p16+q0HjxpDm/GEtGzVCrwc+fORatWrfD6669j+PDhEIlECA8PR9++faGvr49Zs2ZpOiepgDdjiehZKhV4U1NTpKamYsaMGSgvL8err76K8vJyTJw4EQcOHICxsbGmc5IKeDOWiJ6l8pOsRkZGmDVrFnvrAsanW4noWYJabIyIiNRHpR78s2vHPE9PTw/t2rWDi4sLxo0bB3Nzc7WFIyKi+lOpwMvlcly/fh337t2DjY0NzM3NkZOTg5s3b8LCwgISiQQHDx7EunXrsHfvXjg5OWk6NxERvYRKQzRTp05Fq1atcOTIEZw9exYHDhzA2bNnkZqailatWmH27Nk4c+YM2rdvj88++0zTmUmHZOc+QOiCOIz7ZBlCF8QhJ7egsSMR6QyVCvyiRYsQHh6Onj17Kh13cXHB7NmzsXjxYnTs2BF/+9vfcOLECY0EJfUQWkHlw1lEmqNSgb9+/Xq1Ta6rtG/fXrGBx6uvvoqHDx+qLx2pnSYLan0uHnw4i0hzVCrw1tbW+Oabb2ps+/rrr2FtbQ0AyMvLg6mpqfrSkdppsqDW5+LBh7OINEelm6yzZs3CpEmT4O7ujmHDhkEikeD+/fvYvXs3rly5goSEBADA0aNH4erqqtHA1DCa3J6uPhcP7qZEpDkqFfjRo0fDzMwMMTExWLVqFZ48eQIDAwP06tULO3fuVOyFunjxYp3dNk9XaLKg1ufiwYeziDRH5SdZBw4ciIEDB6KyshJ5eXkwMzODnp7yCE/r1q3VHpDUS5MFlb1xImGp86bbenp6kEgkmshCTZy6Lh5c155IPVQu8I8fP8bBgweRkZGB0tJSpTaRSMQ1akhtqm7W6olEipu1HMYhqjuVCvzdu3fh4+ODW7duQSQSKfZkFf1xQw0ACzypDadOEqmHStMko6Ki0L59e1y8eBFyuRyHDh3C2bNn8emnn8LOzg5nz57VdE5qRjh1kkg9VCrwJ0+exLRp0xRb8+np6cHGxgaRkZEYPnw4Zs+erdGQ1DSo6ylZrmtPpB4qDdHk5+fDwsICenp6MDQ0REHBn//henh44Msvv9RYQGo61DV2zqmTROqhUg/e0tISeXl5AABbW1scPnxY0XbmzBm0atVKM+moSeHYOZGwqNSDf/PNN3H8+HEMHToUQUFBmDlzJi5cuAADAwMcOnQIQUFBms5JjUjVaYuafEqWiOpO5U23J0yYAACYMGEClixZgkePHuHevXsIDQ3FokWLNBqSGpeqa8xw7JxIWFTqwbdo0QKdOnVSfD958mRMnjxZY6FIWFQdeuHYOZGwvLQHX15eDjs7O6Vxd2peOG2RqGl6aYFv0aIFzM3NuYhYM8ahF6KmSaUx+LFjx9a6Hry6VVRUYNGiRejRowekUil69OiBRYsWoby8XHGOXC5HTEwMnJycYGFhAT8/P1y5ckUr+ZqjqqGXzatmYe28KVwXhqiJUGkM3traGtu3b8fAgQMxZMgQSKVSpWUKAGDcuHFqCbRmzRokJCQgLi4O3bp1w6VLlzBlyhS0bNlSsRzC2rVrERsbi9jYWDg4OGDZsmUYOXIkTp8+DSMjDh8QEQEqFviZM2cCAO7cuVPjsgQikUhtBf7UqVPw8fGBr68vAMDGxga+vr44c+YMgKe997i4OEyfPh3Dhw8HAMTFxcHBwQHbt2/nlE0ioj+oVODPnTun6RwK/fr1w8aNG/Hrr7+iS5cuuHr1KtLS0jBjxgwAwM2bN5GdnY1BgwYpfqZNmzZwd3dHeno6C7yO4hLCRHWn8hCNtkyfPh3FxcVwc3ODvr4+ysvLMXPmTAQHBwMAsrOzAaDamvQSiQR3796t9XVlMpnmQjfie6mTkHMvjtuB+/m/QyQSIT+/ALOj12POlFEAhJ37RZhbu3Q1t4ODQ61tddrw4+LFizhx4gTy8/MRFBQEqVSKzMxMSCQStY19p6SkICkpCQkJCXBycsKFCxcQHh4Oa2trfPjhh4rznr8HIJfLqx171os+BHWSyWRaey91EnruCujhlVdeUXxfDj04ODgIPndtmFu7mmtulQp8WVkZJk2ahD179igKqY+PD6RSKaKiomBvb4/58+fXO8SzoqKiMG3aNIwa9bR35uzsjN9++w2rV6/Ghx9+CKlUCgDIycmBlZWV4udyc3O501QTUN+hFi6DQFR3Kk2T/Oyzz3DkyBHEx8dDJpMpNvwAgMGDB+PQoUNqC/Tw4cNqc+719fVRWVkJ4OlNV6lUitTUVEV7aWkpTp48CTc3N7XloIaraflgVZc9eB7n4hPVnUo9+B07dmDu3LkYM2YMKioqlNpsbGxw69YttQXy8fHBmjVrYGNjAycnJ5w/fx6xsbEICAgA8HRoZsqUKVi5ciUcHBxgb2+PFStWwNDQEKNHj1ZbDmq4mpYPru+Kk1wGgajuVF4PvkuXLjW2VVZW4vHjx2oLtGzZMixevBiffvopcqjeSKIAABzJSURBVHNzIZVKMX78eKUtAUNDQ/Ho0SOEhYWhoKAArq6uSElJ4Rx4gampmHOohUh7VCrwNjY2OH36NN56661qbWfOnIG9vb3aAhkZGWHJkiVYsmRJreeIRCJEREQgIiJCbe9L6ldTMY+cGojodVsVxX5SoC9CF8Rx+iORBqg0Bh8QEIA1a9Zg27Ztit66SCTCTz/9hHXr1uGDDz7QaEhqmmoaN39+2YMNW/fXa0yeiF5OpR58aGgoLl68iMmTJ+Pjjz8GAPj6+qK0tBSjRo3i0sFUo5rGzZ+fRXMnJw96oqf9DO4CRaReKhV4fX19fPXVVwgODsbhw4dx//59mJqawtPTE/3799d0RtIhz994vXk7GzZWFoIak+dTs6Qr6vSgk7u7O9zd3TWVhZqB52+82lpZwFJqqhiTF8L0R3VtHq5OvOhQfahU4D08PBAQEIDRo0fD3Nxc05lIhz1/49XS3FSrxVOVQinEzcOFeNEh4VPpJqu5uTnmzZsHZ2dnjBkzBjt27EBpaamms5EOauwHllR50EqIO1gJ8aJT04NsJCwqFfjt27fj8uXLWLBgAe7fv4/g4GB06dIF06ZNw08//aTpjKRDGnvzEFUKZWNfhGoixItOfZ9KJu1ReQxeIpEgJCQEISEhuHbtGpKTk/Htt99iy5YtsLS0xMWLFzWZk0gtVHnQSohPzT7//IAQLjpC/KuClNXpJmsVR0dHzJo1C05OTliwYAHu3Lmj7lxEStR1k1GIhVIVQrzo8Klk4atzgT969CiSk5Px/fffo7i4GK6urorNOIg0RV03GYVYKJuqpnqxbE5UKvCXL1/Gtm3bsH37dty5cwedOnXC5MmTERAQgM6dO2s6I+mQ+vbEORwgPLxYCp9KBf6NN95Au3btMGLECPj7+3MuPKmkpmJe3544hwOI6k6lAv/Pf/4Tvr6+aNWqlabzkA5R53LBHA4gqjuVCvyIESNqbTt27Bi2bt2K2NhYtYUi3aDO5YI5HEBUdyrNg39eZmYmFi9ejB49euCdd97Bd999p+5cpANqmrstxDnmpBv44FV1Ks+iKSwsxM6dO5GUlIRTp04BAP7yl79gxowZiv1TiZ5V07AKe+KkKVzOoboXFvjKykr8+OOPSEpKwv79+1FaWooOHTogODgYCQkJiImJwRtvvKGtrNTEsJiTNnGmVXW1Fvi5c+fi22+/xf3799G6dWsMHToUgYGBGDBgAH7//Xd8+eWX2sxJRPRCnGlVXa0FPjY2FiKRCIMHD0ZcXBxMTU0VbaI/rpJEjaVqCuat23dhbdWBy+cSZ1rVoNYC/8EHH2DXrl04cOAA+vTpg1GjRiEgIACurq7azEcEoPqc+oePylBQVIIn5RWCHm/lOu7awyHB6mqdRfPFF1/g119/xYYNG+Di4oKvvvoKgwcPRt++fbF27Vr24kmrnl+5MP3c1SYx3soVF6kxvXCaZOvWrTFmzBikpKTg0qVLiIqKgr6+PlavXg25XI4FCxYgOTmZa8OTxj1/Aw2QC2753Jrwxh81JpXnwVtYWGD69Ok4efIkDh06hODgYFy/fh0fffQRHB0dNZmRqNqc+n4uXdFRagqDFvq1zqcXwrxoIa7jTs1HvR506t27N5YvX46rV69i06ZNnCpJGvf8A1KLPv0r1kRNwbJZ42rdOEQIwyN8sIsaU73Wg69iYGCAYcOGYdiwYerKQ1Sj+txAE8LwCG/8UWOqVw+eqCng8Ag1dyzwpLM4PELNXYOGaIiEjMMj1NyxwBNpEZ/AJW3iEA2RFlXN7Hn2CVwiTWEPnpqkptoTFsLMHmo+BNmDv3fvHj766CN07twZUqkUbm5uOHbsmKJdLpcjJiYGTk5OsLCwgJ+fH65cudKIiUnbmmpPmDN7SJsEV+ALCgrg7e0NuVyObdu2IT09HcuWLYNEIlGcs3btWsTGxmLp0qU4fPgwJBIJRo4ciaIi9oaai6baE66a2fOiJ3CJ1EVwQzSff/45LCwsEB8frzhma2ur+LdcLkdcXBymT5+O4cOHAwDi4uLg4OCA7du3IygoSNuRqRFUrf0NNK2ecNXMHplMBgcHh8aOQzpOcD34vXv3wtXVFUFBQbC3t0f//v2xYcMGyP/4s/bmzZvIzs7GoEGDFD/Tpk0buLu7Iz09vbFik5axJ0z0cqKCggJ5Y4d4llQqBQCEhIRgxIgRuHDhAmbPno158+Zh0qRJSE9Ph7e3Ny5cuIBOnTopfm7q1Km4e/cuUlJSanxdmUymlfxERNr0or8EBTdEU1lZiV69emHevHkAgJ49eyIzMxMJCQmYNGmS4rzn16OXy+UvXKNeW38ON9U/vZtLbqFswNFcPm+haK65BTdEI5VKqy0/3KVLF9y+fVvRDgA5OTlK5+Tm5irdiCWqSX1XmBTC0sNEdSW4At+vXz9kZGQoHcvIyFAMx9jY2EAqlSI1NVXRXlpaipMnT8LNzU2rWanpqe/sGyEsPawpvHjpLsEV+JCQEJw+fRorVqxAZmYmvvvuO2zYsAHBwcEAng7NTJkyBWvWrMHu3btx+fJlhISEwNDQEKNHj27k9CR09Z2H3lSnZapCly9ezZ3gxuB79+6NxMRELFy4EMuXL4eVlRXmzJmjKPAAEBoaikePHiEsLAwFBQVwdXVFSkoKjIyaxlQ5ajyRUwMRvW4r8gr+HINXRdW0TD2RqElNy1SFLl+8mjvBFXgA8Pb2hre3d63tIpEIERERiIiI0GIq0gX1XWGyvheG5wlxiQVdvng1d4Is8ERCo66lh2taYqGxlzRW18WLhIcFnkiLhDgcwnXzdZfgbrIS6TIuNkbaxAJPpEVcYoG0iUM0RFrExcZIm1jgieilhDj7h16OQzRE9FJNdYOV5o4FnoheSoizf+jlWOCJ6KU4+6dpYoEnopfi7J+miTdZieilOPunaWIPnohIR7HAExHpKBZ4IiIdxQJPRKSjWOCJiHQUCzwRkY5igSci0lEs8EREOooPOhGRxlStQplf+Od2gFyFUnvYgycijalahfLx43KuQtkIWOCJSGO4CmXjYoEnIo3hKpSNiwWeiDSmahXKli1bcBXKRsCbrESkMVWrUFLjYA+eiEhHscATEekoFngiIh3FAk9EpKNY4ImIdBQLPBGRjhJ8gV+5ciXEYjHCwsIUx+RyOWJiYuDk5AQLCwv4+fnhypUrjZiSiEh4BF3gT58+jU2bNsHZ2Vnp+Nq1axEbG4ulS5fi8OHDkEgkGDlyJIqK+Bg0EVEVwRb4wsJCTJw4EV988QXE4j9Xn5PL5YiLi8P06dMxfPhwdOvWDXFxcSguLsb27dsbMTERkbAItsBXFfC33npL6fjNmzeRnZ2NQYMGKY61adMG7u7uSE9P13ZMIiLBEuRSBZs2bUJmZibi4+OrtWVnZwMAJBKJ0nGJRIK7d+/W+poymUy9IV9Am++lTsytXcytXULNnfvgd8QnHURh0UMYG7XFR4FeMBP/uSjby3I7ODjU2ia4Ai+TybBw4ULs378fLVu2rPU80R9LkFaRy+XVjj3rRR+COslkMq29lzoxt3Yxt3YJOfc/FsShpKwCLVu1RklZBZL2pyvW72lobsEN0Zw6dQp5eXl4/fXXYWZmBjMzMxw/fhwJCQkwMzODqakpACAnJ0fp53Jzc6v16omIhE6Ta+YLrsD7+fnhxIkTSEtLU3z16tULo0aNQlpaGuzt7SGVSpGamqr4mdLSUpw8eRJubm6NmJyIqO40uWa+4IZoxGKx0qwZAGjbti1MTEzQrVs3AMCUKVOwcuVKODg4wN7eHitWrIChoSFGjx7dGJGJiOotcmogotdtRV7Bn/vWqovgCrwqQkND8ejRI4SFhaGgoACurq5ISUmBkRF3iyGipkWTa+Y3iQK/d+9epe9FIhEiIiIQERHRSImIiIRPcGPwRESkHizwREQ6igWeiEhHscATEekoFngiIh3FAk9EpKNY4ImIdJSooKBA3tghiIhI/diDJyLSUSzwREQ6igWeiEhHscATEekoFngiIh3FAl9Hq1atwsCBA9GpUyd07twZ/v7+uHz5stI5crkcMTExcHJygoWFBfz8/HDlypVGSvzUl19+CXd3d3Tq1AmdOnXC4MGD8e9//1vRLsTMNVm5ciXEYjHCwsIUx4SYPSYmRrG3QdVXly5dFO1CzFzl3r17+Oijj9C5c2dIpVK4ubnh2LFjinYhZu/evXu1z1ssFmPs2LGCzQwAFRUVWLRoEXr06AGpVIoePXpg0aJFKC8vV5zTkOws8HV07NgxTJgwAf/+97+xe/dutGjRAiNGjMCDBw8U56xduxaxsbFYunQpDh8+DIlEgpEjR6KoSH1bcdWVpaUlFixYgKNHjyI1NRUeHh54//33cfHiRcFmft7p06exadMmODs7Kx0XanYHBwdcu3ZN8XXixAlFm1AzFxQUwNvbG3K5HNu2bUN6ejqWLVumtB2mELOnpqYqfdZHjx6FSCTCiBEjBJsZANasWYOEhAQsXboUp06dwpIlS/Dll19i1apVinMakp3z4BuouLgY1tbWSExMhK+vL+RyOZycnDBx4kTMnDkTAPDo0SM4ODjgs88+Q1BQUCMn/pOtrS3mzZuHv/71r4LPXFhYiLfeegtr167FsmXL0K1bNyxfvlywn3dMTAx2796NkydPVmsTamYAWLhwIY4fP670192zhJz9WStWrMDnn3+Oq1evok2bNoLN7O/vDxMTE6xfv15x7KOPPsKDBw+QnJzc4M+bPfgGKi4uRmVlpWKbwZs3byI7OxuDBg1SnNOmTRu4u7sjPT29sWIqqaiowI4dO1BSUoK+ffs2iczTp0/H8OHD8dZbbykdF3L2GzduoGvXrujRowf+7//+Dzdu3AAg7Mx79+6Fq6srgoKCYG9vj/79+2PDhg2Q/7FnqJCzV5HL5di8eTP8/f3Rtm1bQWfu168fjh07hl9//RUAcPXqVaSlpWHw4MEAGv55N4kdnYQsPDwc3bt3R9++fQEA2dnZAKD0J23V93fv3tV6vmddunQJXl5eKC0thaGhIf71r3/B2dlZ8X8UIWYGgE2bNiEzMxPx8fHV2oT6effp0wfr1q2Dg4MDcnNzsXz5cnh5eeHnn38WbGbg6UVp48aNCAkJwfTp03HhwgXMnj0bADBp0iRBZ6+SmpqKmzdvYty4cQCE+/8R4GnHpbi4GG5ubtDX10d5eTlmzpyJ4OBgAA3PzgLfAHPmzMHPP/+MH374Afr6+kptIpFI6Xu5XF7tmLY5ODggLS0NhYWF2L17N6ZMmYLvv/9e0S7EzDKZDAsXLsT+/fvRsmXLWs8TWvaqHliVPn36wMXFBVu2bMFrr70GQHiZAaCyshK9evXCvHnzAAA9e/ZEZmYmEhISMGnSJMV5QsxeZdOmTejduzd69OihdFyImVNSUpCUlISEhAQ4OTnhwoULCA8Ph7W1NT788EPFefXNziGaeoqIiMCOHTuwe/du2NraKo5LpVIAQE5OjtL5ubm51a7C2tayZUvY2dkp/gPu3r071q1bJ+jMp06dQl5eHl5//XWYmZnBzMwMx48fR0JCAszMzGBqagpAmNmf9corr8DJyQmZmZmC/rylUikcHR2VjnXp0gW3b99WtAPCzA4A9+/fx759+zB+/HjFMSFnjoqKwrRp0zBq1Cg4OzsjICAAU6dOxerVqwE0PDsLfD3Mnj0b27dvx+7du5WmvgGAjY0NpFIpUlNTFcdKS0tx8uRJuLm5aTvqC1VWVuLx48eCzuzn54cTJ04gLS1N8dWrVy+MGjUKaWlpsLe3F2z2Z5WWlkImk0EqlQr68+7Xrx8yMjKUjmVkZKBTp04AhP//7y1btqBVq1Z49913FceEnPnhw4fV/vrX19dHZWUlgIZn1w8PD5+v1sQ6bubMmUhKSsLXX38NKysrlJSUoKSkBMDTHrJIJEJFRQVWr14Ne3t7VFRUIDIyEtnZ2VizZg1atWrVKLnnz5+Pli1borKyEllZWYiLi8O2bdswf/58dO7cWZCZAaB169aQSCRKX99++y2sra3x/vvvC/bznjt3ruLzzsjIQFhYGDIzM7F69WqIxWJBZgYAKysrLF26FHp6erCwsMDRo0exaNEizJgxA66uroL9vIGnwxZTp06Ft7e3YnokAEFnvnbtGpKTk2Fvbw8DAwOkpaXhs88+w7vvvgtPT88GZ+cYfB0lJCQAAIYPH650fPbs2YiIiAAAhIaG4tGjRwgLC0NBQQFcXV2RkpICIyMjreetkp2djUmTJiEnJwft2rWDs7Mztm/fDk9PT8FmVpUQs9+5cwfBwcHIy8tD+/bt0adPHxw8eBDW1taCzQwAvXv3RmJiIhYuXIjly5fDysoKc+bMUdz0A4SbPS0tDdevX8eGDRuqtQk187Jly7B48WJ8+umnyM3NhVQqxfjx4zFr1izFOQ3JznnwREQ6imPwREQ6igWeiEhHscATEekoFngiIh3FAk9EpKNY4ImIdBQLPNVZYmIixGIxrK2tUVBQoNRWXl4OsViMmJgYreeq2mTj2c0ShKiyshLh4eFwdHSEiYkJ3nvvvVrPrdrI4tl56M/y8/ODWCyGj4+PpuKie/fumDJlisZenzSHDzpRvf3+++9Ys2YN5s+f39hRmpRdu3Zh/fr1WLRoEfr27atYT6c2RkZG2Lt3L4qKipQebrl16xZOnDih8Yd1/vWvf6Fdu3YafQ/SDPbgqd4GDRqEDRs2KJY0bQ7Kysoa/BrXrl0DAISEhKBv376wt7d/4fkDBgyAgYEBdu/erXQ8OTkZ1tbW6N69e4MzvUjPnj3x6quvavQ9SDNY4KneqnaYWbFixQvPqxo6ed6UKVOUitPNmzchFovx1VdfYcGCBejSpQusrKwwadIkPHz4EJmZmXj33XfRsWNH9OrVC1u2bKnx/a5du4ahQ4eiQ4cOcHR0xOLFixWLN1XJy8vDJ598gq5du8Lc3ByvvfYavv76a6Vzqoaijh8/jvHjx8Pa2lqxtENtfvzxRwwePBgWFhawtrbGe++9B5lMpmjv3r07lixZAgAwNTWFWCxGYmLiC1+zTZs2eOedd5CcnKx0PDk5Gf7+/jUuG3vv3j1MnjwZdnZ2MDc3h7u7u9LPnzlzBmKxGPv376/2s5988gk6d+6MJ0+eKDI/P0Rz48YNTJw4EZ07d4a5uTn69++PPXv2KJ2TkZGB999/X7Eg3F/+8heMHz9e8ENouoQFnurNwsICEydOxKZNm3Dr1i21ve6qVatw9+5dxMXFISIiAikpKZgxYwY++OADeHl5KTYqmTp1ao2bD7///vsYMGAAEhMTMXr0aCxfvhxLly5VtP/+++/w9vbGgQMHEB4ejm3btsHHxweffPJJjZuKTJo0CTY2Nvjmm29eOBz1448/YuzYsTA0NMRXX32FlStX4sqVK/Dx8cGdO3cAPB3uqBpzP3jwIA4ePAhvb++XfiYBAQE4duwYsrKyADzdnzYjIwMBAQHVzi0pKYGfnx9+/PFHREVFITExEc7Ozpg8ebLiIubq6goHB4dqF43Hjx9j586dePfdd2FgYFBjltu3b+Ptt9/GxYsXER0dja1bt6Jnz5748MMPsW/fPsV5/v7+uHv3LlauXIkdO3Zg3rx5aNWqVbWLLWkOx+CpQaZPn45//vOfWLp0KWJjY9Xymq+++qpij0pPT0+cPHkSycnJiI+Ph7+/PwCgV69e2L9/P3bt2oWuXbsq/fz48eMxY8YMAE+HkYqKihAbG4spU6ZALBZj/fr1+O2333DixAl07twZwNNhkMLCQixduhQTJkxAixZ//qcxbNgwLFy48KW5Fy1aBFtbW2zfvl3x86+99hr69OmDf/zjH4iOjkbPnj1haWmpaFNV//790bFjR2zbtg0zZsxAUlIS3NzcYGdnV+3cxMREXL9+HXv27MGbb74J4OkGJDk5OVi0aBHGjRsHfX19+Pv7Y8WKFSgsLISxsTEA4MCBA3jw4EGNF44qS5YsgVwux969exX3Dzw9PZGVlYXo6GgMGTIEeXl5uH79OrZs2YIhQ4YofnbMmDEq/87UcOzBU4OYmJhg2rRpSEpKUhqKaIjnd0OqWnP/2eERsVgMiUSi6NE+a+TIkUrfjxo1CsXFxYre/qFDh+Dq6gobGxuUl5crvjw9PZGfn4+rV68q/fzQoUNfmrmkpATnzp3Du+++q3RxsLW1hZubG44fP/7S13gRkUgEf39/JCcn4/Hjx0hJSam1CJ84cQKWlpaK4l5l7NixyM3NVfx+Y8eORVlZGXbt2qU4Jzk5GQ4ODnB1da01y6FDhzB48GC0a9eu2ud38eJF/P777zA1NYWtrS0WLFiATZs24fr16w36/al+WOCpwUJCQmBiYoLo6Gi1vN7z4/VVQwU1Ha/ppmdN+1cCUOxhef/+fZw4cQLt27dX+qraBSg/P1/p5y0sLF6auaCgAHK5XLEDz7OkUikePHjw0td4mYCAAFy9ehVLly7Fw4cPq13Iqjx48KDWHFXtAGBtbQ13d3ckJSUpfocDBw4o/kqqzf3795GUlFTt8/v73/8O4OnnJxKJ8N1338HFxQULFiyAq6srevbsiY0bN9b796e64xANNdgrr7yCGTNmYO7cufj444+rtbdu3RrA0/HdZ/dVfb6Qqsv9+/dhaGio9D0AdOjQAcDTm5sSiURxs/N5z89qUWXvS7FYDJFIVOOMouzs7JdOhVSFvb09+vTpg9WrV+Odd96p8cY18PSvqud3ZarKAUApi7+/P0JDQ3Hr1i0cPnwYjx8/fukwiqmpKV5//XVMnz69xvaqz9nW1hbx8fGQy+W4cOECvvzyS3z66aewtrau9lcaaQZ78KQWwcHBsLS0xKJFi6q1VW33dvnyZcWxgoICnDp1SiNZdu7cqfT9jh078MorryjG6j09PfHrr7/CysoKvXr1qvZVn3nlhoaGcHFxwa5du1BRUaE4fuvWLZw6dQpvvPFGw36pP3z88cfw8fHBxIkTaz3njTfeQFZWFn7++Wel49u3b4dEIlHac3XEiBFo3bo1vv32WyQnJ8Pd3R02NjYvzODp6YlLly7Bycmpxs/v+V2GRCIRevTogcWLFwNAjTfGSTPYgye1aNWqFWbNmoXQ0NBqbW+//TbatWuH0NBQREREoKysDJ9//rlSL1udNm3ahMrKSvTu3RuHDh3CN998g/DwcEWPNyQkBDt37oSvry9CQkJgb2+Phw8fQiaT4cSJE9i6dWu93jcyMhJjx46Fv78/JkyYgJKSEsTExKBdu3aYNm2aWn63YcOGYdiwYS8857333sP69esxbtw4/P3vf4elpSW2bduG1NRUrFmzRmkP0Hbt2sHX1xcJCQm4d+8e1q5d+9IMc+bMgaenJ4YMGYKJEycqnmi+cuUKbty4gdjYWFy8eBHh4eF49913YWdnh4qKCmzZsgUtWrSAh4dHgz8HUg0LPKnN+++/j88//7zaDTWxWIzk5GTMmTMHQUFBsLS0xKxZs3DkyBEcO3ZM7Tm2bNmCWbNmYfny5WjXrh1mzpyptAWasbExDhw4gKVLl2LNmjW4e/cujI2N4eDggHfeeafe7/v2229j27ZtWLp0KYKCgtCyZUu88cYbWLhwoWLYQhsMDQ2xd+9eREVFYf78+SguLoa9vb3SLKRn+fv7IyUlBa1bt662FWVNOnXqhNTUVCxZsgSfffYZcnNzYWpqiq5duyIwMBDA0/F+KysrxMbG4s6dO2jVqhW6deuG5ORkuLi4qP13pppxyz4iIh3FMXgiIh3FAk9EpKNY4ImIdBQLPBGRjmKBJyLSUSzwREQ6igWeiEhHscATEekoFngiIh31/wtm1iGmv+ZQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_outlier = actors.where('Number of Movies', are.above(10))\n",
    "no_outlier.scatter('Number of Movies', 'Average per Movie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The negative association is still clearly visible. Let's identify the actors corresponding to the points that lie on the right hand side of the plot where the number of movies is large:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Actor</th> <th>Total Gross</th> <th>Number of Movies</th> <th>Average per Movie</th> <th>#1 Movie</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Samuel L. Jackson</td> <td>4772.8     </td> <td>69              </td> <td>69.2             </td> <td>The Avengers      </td> <td>623.4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Morgan Freeman   </td> <td>4468.3     </td> <td>61              </td> <td>73.3             </td> <td>The Dark Knight   </td> <td>534.9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Robert DeNiro    </td> <td>3081.3     </td> <td>79              </td> <td>39               </td> <td>Meet the Fockers  </td> <td>279.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Liam Neeson      </td> <td>2942.7     </td> <td>63              </td> <td>46.7             </td> <td>The Phantom Menace</td> <td>474.5</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Actor             | Total Gross | Number of Movies | Average per Movie | #1 Movie           | Gross\n",
       "Samuel L. Jackson | 4772.8      | 69               | 69.2              | The Avengers       | 623.4\n",
       "Morgan Freeman    | 4468.3      | 61               | 73.3              | The Dark Knight    | 534.9\n",
       "Robert DeNiro     | 3081.3      | 79               | 39                | Meet the Fockers   | 279.3\n",
       "Liam Neeson       | 2942.7      | 63               | 46.7              | The Phantom Menace | 474.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors.where('Number of Movies', are.above(60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The great actor Robert DeNiro has the highest number of movies and the lowest average receipt per movie. Other fine actors are at points that are not very far away, but DeNiro's is at the extreme end.\n",
    "\n",
    "To understand the negative association, note that the more movies an actor is in, the more variable those movies might be, in terms of style, genre, and box office draw. For example, an actor might be in some high-grossing action movies or comedies (such as Meet the Fockers), and also in a large number of smaller films that may be excellent but don't draw large crowds. Thus the actor's value of average receipts per movie might be relatively low.\n",
    "\n",
    "To approach this argument from a different direction, let us now take a look at the outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Actor</th> <th>Total Gross</th> <th>Number of Movies</th> <th>Average per Movie</th> <th>#1 Movie</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Anthony Daniels</td> <td>3162.9     </td> <td>7               </td> <td>451.8            </td> <td>Star Wars: The Force Awakens</td> <td>936.7</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Actor           | Total Gross | Number of Movies | Average per Movie | #1 Movie                     | Gross\n",
       "Anthony Daniels | 3162.9      | 7                | 451.8             | Star Wars: The Force Awakens | 936.7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors.where('Number of Movies', are.below(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an actor, Anthony Daniels might not have the stature of Robert DeNiro. But his 7 movies had an astonishingly high average receipt of nearly $452$ million dollars per movie.\n",
    "\n",
    "What were these movies? You might know about the droid C-3PO in Star Wars:\n",
    "\n",
    "![C-3PO](../../images/C-3PO_droid.png)\n",
    "\n",
    "That's [Anthony Daniels](https://en.wikipedia.org/wiki/Anthony_Daniels) inside the metallic suit. He plays C-3PO.\n",
    "\n",
    "Mr. Daniels' entire filmography (apart from cameos) consists of movies in the high-grossing Star Wars franchise. That explains both his high average receipt and his low number of movies.\n",
    "\n",
    "Variables such as genre and production budget have an effect on the association between the number of movies and the average receipt per movie. This example is a reminder that studying the association between two variables often involves understanding other related variables as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Line Plots</h2>\n",
    "\n",
    "Line plots, sometimes known as line graphs, are among the most common visualizations. They are often used to study chronological trends and patterns.\n",
    "\n",
    "The table `movies_by_year` contains data on movies produced by U.S. studios in each of the years 1980 through 2015. The columns are:\n",
    "\n",
    "| **Column** | Content |\n",
    "|------------|---------|\n",
    "| `Year` | Year |\n",
    "| `Total Gross` | Total domestic box office gross, in millions of dollars, of all movies released |\n",
    "| `Number of Movies` | Number of movies released |\n",
    "| `#1 Movie` | Highest grossing movie |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Total Gross</th> <th>Number of Movies</th> <th>#1 Movie</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2015</td> <td>11128.5    </td> <td>702             </td> <td>Star Wars: The Force Awakens       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2014</td> <td>10360.8    </td> <td>702             </td> <td>American Sniper                    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2013</td> <td>10923.6    </td> <td>688             </td> <td>Catching Fire                      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2012</td> <td>10837.4    </td> <td>667             </td> <td>The Avengers                       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2011</td> <td>10174.3    </td> <td>602             </td> <td>Harry Potter / Deathly Hallows (P2)</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2010</td> <td>10565.6    </td> <td>536             </td> <td>Toy Story 3                        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2009</td> <td>10595.5    </td> <td>521             </td> <td>Avatar                             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2008</td> <td>9630.7     </td> <td>608             </td> <td>The Dark Knight                    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2007</td> <td>9663.8     </td> <td>631             </td> <td>Spider-Man 3                       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2006</td> <td>9209.5     </td> <td>608             </td> <td>Dead Man's Chest                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (26 rows omitted)</p>"
      ],
      "text/plain": [
       "Year | Total Gross | Number of Movies | #1 Movie\n",
       "2015 | 11128.5     | 702              | Star Wars: The Force Awakens\n",
       "2014 | 10360.8     | 702              | American Sniper\n",
       "2013 | 10923.6     | 688              | Catching Fire\n",
       "2012 | 10837.4     | 667              | The Avengers\n",
       "2011 | 10174.3     | 602              | Harry Potter / Deathly Hallows (P2)\n",
       "2010 | 10565.6     | 536              | Toy Story 3\n",
       "2009 | 10595.5     | 521              | Avatar\n",
       "2008 | 9630.7      | 608              | The Dark Knight\n",
       "2007 | 9663.8      | 631              | Spider-Man 3\n",
       "2006 | 9209.5      | 608              | Dead Man's Chest\n",
       "... (26 rows omitted)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_by_year = Table.read_table(path_data + 'movies_by_year.csv')\n",
    "movies_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Table method `plot` produces a line plot. Its two arguments are the same as those for `scatter`: first the column on the horizontal axis, then the column on the vertical. Here is a line plot of the number of movies released each year over the years 1980 through 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_by_year.plot('Year', 'Number of Movies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph rises sharply and then has a gentle upwards trend though the numbers vary noticeably from year to year. The sharp rise in the early 1980's is due in part to studios returning to the forefront of movie production after some years of filmmaker driven movies in the 1970's. \n",
    "\n",
    "Our focus will be on more recent years. In keeping with the theme of movies, the table of rows corresponding to the years 2000 through 2015 have been assigned to the name `century_21`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "century_21 = movies_by_year.where('Year', are.above(1999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "century_21.plot('Year', 'Number of Movies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The global financial crisis of 2008 has a visible effect – in 2009 there is a sharp drop in the number of movies released.\n",
    "\n",
    "The dollar figures, however, didn't suffer much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "century_21.plot('Year', 'Total Gross')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total domestic gross receipt was higher in 2009 than in 2008, even though there was a financial crisis and a much smaller number of movies were released.\n",
    "\n",
    "One reason for this apparent contradiction is that people tend to go to the movies when there is a recession. [\"In Downturn, Americans Flock to the Movies,\"](http://www.nytimes.com/2009/03/01/movies/01films.html?_r=0) said the New York Times in February 2009. The article quotes Martin Kaplan of the University of Southern California saying, \"People want to forget their troubles, and they want to be with other people.\" When holidays and expensive treats are unaffordable, movies provide welcome entertainment and relief.\n",
    "\n",
    "In 2009, another reason for high box office receipts was the movie Avatar and its 3D release. Not only was Avatar the \\#1 movie of 2009, it is also by some calculations one of the highest grossing movies of all time, as we will see later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Total Gross</th> <th>Number of Movies</th> <th>#1 Movie</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2009</td> <td>10595.5    </td> <td>521             </td> <td>Avatar  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Year | Total Gross | Number of Movies | #1 Movie\n",
       "2009 | 10595.5     | 521              | Avatar"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "century_21.where('Year', are.equal_to(2009))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}