{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'datascience'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-9175ae7c828c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdatascience\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpath_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../../../data/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Agg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'datascience'"
     ]
    }
   ],
   "source": [
    "from datascience import *\n",
    "import matplotlib\n",
    "path_data = '../../../data/'\n",
    "matplotlib.use('Agg', warn=False)\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": [
    "### Scatter Plots and Line Graphs ###"
   ]
  },
  {
   "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. In total, his movies have brought in more money at domestic box office than the movies of any other actor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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\", such as 'number of movies.' The term *variable* emphasizes that the feature can have different values for different individuals – the numbers of movies that actors have been in varies across all the actors.\n",
    "\n",
    "Variables that have numerical values, such as 'number of movies' or 'average gross receipts per movie' are called *quantitative* or *numerical* variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots ###\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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_8_0.png"
      }
     },
     "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_11_0.png"
      }
     },
     "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_14_0.png"
      }
     },
     "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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",
    "![C-3PO](../../images/C-3PO_droid.png)\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": [
    "### Line Graphs ###\n",
    "Line graphs are among the most common visualizations and 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": {
    "collapsed": false
   },
   "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 graph. 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 graph of the number of movies released each year over the years 1980 through 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_23_0.png"
      }
     },
     "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "century_21 = movies_by_year.where('Year', are.above(1999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_26_0.png"
      }
     },
     "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/07/Visualization_28_0.png"
      }
     },
     "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 the second highest grossing movie of all time, as we will see later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}