{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "from datascience import *\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantitative Data and Histograms\n",
    "------------------------------\n",
    "\n",
    "Many of the variables that data scientists study are *quantitative*. For instance, we can study the amount of revenue earned by movies in recent decades. Our source is the [Internet Movie Database](http://www.imdb.com), an online database that contains information about movies, television shows, video games, and so on.\n",
    "\n",
    "The table `top` consists of [U.S.A.'s top grossing movies (http://www.boxofficemojo.com/alltime/adjusted.htm) of all time. The first column contains the title of the movie; *Star Wars: The Force Awakens* has the top rank, with a box office gross amount of more than 900 million dollars in the United States. The second column contains the name of the studio; the third contains the U.S. box office gross in dollars; the fourth contains the  gross amount that would have been earned from ticket sales at 2016 prices; and the fifth contains the release year of the movie. \n",
    "\n",
    "There are 200 movies on the list. Here are the top ten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Title</th> <th>Studio</th> <th>Gross</th> <th>Gross (Adjusted)</th> <th>Year</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars: The Force Awakens             </td> <td>Buena Vista (Disney)</td> <td>906,723,418</td> <td>906,723,400     </td> <td>2015</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Avatar                                   </td> <td>Fox                 </td> <td>760,507,625</td> <td>846,120,800     </td> <td>2009</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Titanic                                  </td> <td>Paramount           </td> <td>658,672,302</td> <td>1,178,627,900   </td> <td>1997</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Jurassic World                           </td> <td>Universal           </td> <td>652,270,625</td> <td>687,728,000     </td> <td>2015</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Marvel's The Avengers                    </td> <td>Buena Vista (Disney)</td> <td>623,357,910</td> <td>668,866,600     </td> <td>2012</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>The Dark Knight                          </td> <td>Warner Bros.        </td> <td>534,858,444</td> <td>647,761,600     </td> <td>2008</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars: Episode I - The Phantom Menace</td> <td>Fox                 </td> <td>474,544,677</td> <td>785,715,000     </td> <td>1999</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars                                </td> <td>Fox                 </td> <td>460,998,007</td> <td>1,549,640,500   </td> <td>1977</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Avengers: Age of Ultron                  </td> <td>Buena Vista (Disney)</td> <td>459,005,868</td> <td>465,684,200     </td> <td>2015</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>The Dark Knight Rises                    </td> <td>Warner Bros.        </td> <td>448,139,099</td> <td>500,961,700     </td> <td>2012</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (190 rows omitted)</p"
      ],
      "text/plain": [
       "Title                                     | Studio               | Gross       | Gross (Adjusted) | Year\n",
       "Star Wars: The Force Awakens              | Buena Vista (Disney) | 906,723,418 | 906,723,400      | 2015\n",
       "Avatar                                    | Fox                  | 760,507,625 | 846,120,800      | 2009\n",
       "Titanic                                   | Paramount            | 658,672,302 | 1,178,627,900    | 1997\n",
       "Jurassic World                            | Universal            | 652,270,625 | 687,728,000      | 2015\n",
       "Marvel's The Avengers                     | Buena Vista (Disney) | 623,357,910 | 668,866,600      | 2012\n",
       "The Dark Knight                           | Warner Bros.         | 534,858,444 | 647,761,600      | 2008\n",
       "Star Wars: Episode I - The Phantom Menace | Fox                  | 474,544,677 | 785,715,000      | 1999\n",
       "Star Wars                                 | Fox                  | 460,998,007 | 1,549,640,500    | 1977\n",
       "Avengers: Age of Ultron                   | Buena Vista (Disney) | 459,005,868 | 465,684,200      | 2015\n",
       "The Dark Knight Rises                     | Warner Bros.         | 448,139,099 | 500,961,700      | 2012\n",
       "... (190 rows omitted)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = Table.read_table('top_movies.csv')\n",
    "top.set_format([2, 3], NumberFormatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three-digit numbers are easier to work with than nine-digit numbers. So, we will add an additional *millions* column containing the adjusted gross box office revenue in millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Title</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars: The Force Awakens             </td> <td>906.72 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Avatar                                   </td> <td>846.12 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Titanic                                  </td> <td>1178.63</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Jurassic World                           </td> <td>687.73 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Marvel's The Avengers                    </td> <td>668.87 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>The Dark Knight                          </td> <td>647.76 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars: Episode I - The Phantom Menace</td> <td>785.72 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Star Wars                                </td> <td>1549.64</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Avengers: Age of Ultron                  </td> <td>465.68 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>The Dark Knight Rises                    </td> <td>500.96 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (190 rows omitted)</p"
      ],
      "text/plain": [
       "Title                                     | Gross\n",
       "Star Wars: The Force Awakens              | 906.72\n",
       "Avatar                                    | 846.12\n",
       "Titanic                                   | 1178.63\n",
       "Jurassic World                            | 687.73\n",
       "Marvel's The Avengers                     | 668.87\n",
       "The Dark Knight                           | 647.76\n",
       "Star Wars: Episode I - The Phantom Menace | 785.72\n",
       "Star Wars                                 | 1549.64\n",
       "Avengers: Age of Ultron                   | 465.68\n",
       "The Dark Knight Rises                     | 500.96\n",
       "... (190 rows omitted)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millions = top.select(0).with_column('Gross', np.round(top.column(3)/1000000, 2))\n",
    "millions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How to draw a histogram.**\n",
    "The `hist` method generates a *histogram* of the values in a column. The optional `unit` argument is used to label the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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hpKRELaEe5e3tjdLSUqxatQpFRUVwdHREYmKi4h7OoqIiFBYWKvq3bdsWMTExKCgogFwu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\niFTAwklERKSC/wMsQWnxb1XNWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c3c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above shows the distribution of the amounts grossed, in millions of dollars. The amounts have been grouped into contiguous intervals called *bins*. Although in this dataset no movie grossed an amount that is exactly on the edge between two bins, it is worth noting that *hist* has an *endpoint convention*: bins include the data at their left endpoint, but not the data at their right endpoint. Sometimes, adjustments have to be made in the first or last bin, to ensure that the smallest and largest values of the variable are included. You saw an example of such an adjustment in the Census data used in the Tables section, where an age of \"100\" years actually meant \"100 years old or older.\"\n",
    "\n",
    "We can see that there are 10 bins (some bars are so low that they are hard to see), and that they all have the same width. We can also see that there the list contains no movie that grossed fewer than 300 million dollars; that is because we are considering only the top grossing movies of all time. It is a little harder to see exactly where the edges of the bins are placed. For example, it is not clear exactly where the value 500 lies on the horizontal axis, and so it is hard to judge exactly where the first bar ends and the second begins.\n",
    "\n",
    "The optional argument *bins* can be used with *hist* to specify the edges of the bars. It must consist of a sequence of numbers that includes the left end of the first bar and the right end of the last bar. As the highest gross amount is somewhat over 760 on the horizontal scale, we will start by setting *bins* to be the array consisting of the numbers 300, 400, 500, and so on, ending with 2000.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Fhbh48SKSk5Pxxhtv4NixY5g2bZrKmLFEREStUb1bnPHx8Th9+jS++eYbeHt7q8zr1q0b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\nBicREZEABicREZEABicREZGAK3NaFdd4XVVWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104339978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', unit=\"Million Dollars\", bins=np.arange(300,2001,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure is easier to read. On the horizontal axis, the labels 200, 400, 600, and so on are centered at the corresponding values. The tallest bar is for movies that grossed between 300 and  million and 400 million (adjusted) dollars; the bar for 400 to 500 million is about 5/8th as high. The height of each bar is proportional to the number of movies that fall within the x-axis range of the bar.\n",
    "\n",
    "A very small number grossed more than 700 million dollars. This results in the figure being \"skewed to the right,\", or, less formally, having \"a long right hand tail.\" Distributions of variables like income or rent often have this kind of shape.\n",
    "\n",
    "The counts of values in different ranges can be computed from a table using the `bin` method, which takes a column label or index and an optional sequence or number of bins. The result is a tabular form of a histogram; it lists the counts of all values in the `'Millions'` column that are greater than or equal to the indicated `bin`, but less than the next `bin`'s starting value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Gross count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>300 </td> <td>81         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>400 </td> <td>52         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>500 </td> <td>28         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>600 </td> <td>16         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>700 </td> <td>7          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>800 </td> <td>5          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>900 </td> <td>3          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1000</td> <td>1          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1100</td> <td>3          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1200</td> <td>2          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (8 rows omitted)</p"
      ],
      "text/plain": [
       "bin  | Gross count\n",
       "300  | 81\n",
       "400  | 52\n",
       "500  | 28\n",
       "600  | 16\n",
       "700  | 7\n",
       "800  | 5\n",
       "900  | 3\n",
       "1000 | 1\n",
       "1100 | 3\n",
       "1200 | 2\n",
       "... (8 rows omitted)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = millions.bin('Gross', bins=np.arange(300,2001,100))\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**The vertical axis: Calculation.**\n",
    "We will start by explaining how to calculate the numbers on the vertical axis of the histogram. If the calculation seems a little strange, have patience – the rest of the section will explain the reasoning behind the calculation.\n",
    "\n",
    "The vertical axis of the histogram above is drawn to the *density scale*. The height of each bar is the percent of elements that fall into the corresponding bin, divided by the width of the bin. For example, the height of the bin from 300 to 400, which contains 81 of the 200 movies, is $\\frac{81}{200} \\cdot \\frac{1}{400-300}$ = 0.405% per million dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0040500000000000006"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starts = bins.column(0)\n",
    "counts = bins.column(1)\n",
    "counts.item(0) / sum(counts) / (starts.item(1) - starts.item(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Areas are percents, heights are densities.**\n",
    "The width of the bar is 100 million dollars, and its height is 0.405% per million dollars. So the total area of the bar is 40.5%. This area is the percent of all values in the `Millions` column that fall into the 300-400 bin. In other words, 40.5% of the movies grossed at least 300 million dollars but less than 400 million dollars.\n",
    "\n",
    "When a histogram is drawn to the density scale, the area of each bar is equal to the percent of data values that are in the corresponding bin. Thus, the total area of all the bars in a histogram is 100%. In terms of proportions, we can say that the area of all the bars in a histogram \"sums to 1\".\n",
    "\n",
    "An advantage of the histogram over a bar graph is that a histogram can contain bins of unequal width. The area of all the bars will still sum to 1. Below, the values in the `Millions` column are binned into three uneven categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fXLx4ESUlJbh48SJSU1Px5ptv4tixY5g+fbrCnLFERES6qNk9zsTERJw5cwbffPMNvL29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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c30a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', unit=\"Million Dollars\", bins=[300, 400, 600, 1500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the ranges 300-400 and 400-600 have nearly identical counts, the bar over the former is twice as tall as the latter because it is only half as wide. The density of values in that range is twice as much. Histograms help us visualize where on the number line the data are most concentrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Gross count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>300 </td> <td>81         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>400 </td> <td>80         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>600 </td> <td>37         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1500</td> <td>0          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "bin  | Gross count\n",
       "300  | 81\n",
       "400  | 80\n",
       "600  | 37\n",
       "1500 | 0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millions.bin('Gross', bins=[300, 400, 600, 1500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A graph of counts.** It is possible to display counts directly in a chart, using the `normed=False` option of the `hist` method. The resulting chart has the same shape when bins all have equal widths, but the bar areas do not sum to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HqFGjmixMVVUVCgsLMXToUHTp0gUajQYGgwF9+vSxrc/JyUFSUtJ95xBzpxVeLIeXl5do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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ef5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=np.arange(300,2001,100), normed=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the count scale is perhaps more natural to interpret than the density scale, the chart becomes highly misleading when bins have different widths. Below, it appears (due to the count scale) that high-grossing movies are quite common, when in fact we have seen that they are relatively rare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MoWvXroSFhWGz2ejXr59jfWFhIYsXL77oNlz9hAEcKCkjMDDQ5fMEtA4AcMtcAEFBQVgs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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1091bc710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=[300, 400, 500, 600, 1500], normed=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though the method used is called *hist*, **the figure above is NOT A HISTOGRAM.** It misleadingly exaggerates the proportion of movies grossing at least 600 million dollars. The height of each bar is simply plotted the number of movies in the bin, *without accounting for the difference in the widths of the bins*. The picture becomes even more absurd if the last two bins are combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bNPL9udIfKi8v79Aaf8jPz4+6ujqsVqtDtwNQX18P4JRtuYI39uWNPYH68iTO6qm2ttbh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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092c18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=[300, 400, 1500], normed=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this count-based figure, the shape of the distribution of movies is lost entirely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So exactly what is a histogram?**\n",
    "\n",
    "The figure above shows that what the eye perceives as \"big\" is area, not just height. This is particularly important when the bins have different widths.\n",
    "\n",
    "That is why a histogram has two defining properties:\n",
    "\n",
    "1. The bins are contiguous (though some might be empty) and are drawn to scale.\n",
    "2. The **area** of each bar is proportional to the number of entries in the bin. \n",
    "\n",
    "Property 2 is the key to drawing a histogram, and is usually achieved as follows:\n",
    "\n",
    "$$\n",
    "\\mbox{area of bar} ~=~ \\mbox{percent of entries in bin}\n",
    "$$\n",
    "\n",
    "When drawn using this method, the histogram is said to be drawn *on the density scale*, and the total area of the bars is equal to 100%.\n",
    "\n",
    "To calculate the height of each bar, use the fact that the bar is a rectangle:\n",
    "\n",
    "$$\n",
    "\\mbox{area of bar} = \\mbox{height of bar} \\times \\mbox{width of bin}\n",
    "$$\n",
    "\n",
    "and so\n",
    "\n",
    "$$\n",
    "\\mbox{height of bar} ~=~ \n",
    "\\frac{\\mbox{area of bar}}{\\mbox{width of bin}} ~=~\n",
    "\\frac{\\mbox{percent of entries in bin}}{\\mbox{width of bin}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The level of detail, and the flat tops of the bars**\n",
    "\n",
    "Even though the density scale correctly represents percents using area, some detail is lost by grouping values into bins.\n",
    "\n",
    "Take another look at the [300, 400) bin in the figure below. The flat top of the bar, at a level near 0.4% per million dollars, hides the fact that the movies are somewhat unevenly distributed across that bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CcOFMTk5GUFAQBg0ahPDwcIWHWkskEgwcOBA7duzQSJBERETaQnDhjIyMxJgx\nY5CUlISZM2cqLX/hhRfw+++/qzU4IiIibSO4cJ4/fx6vvPJKk8tNTU1RUlKilqCIiIi0leDC2blz\n52YnDi8qKoKJiYlagiIiItJWggvn6NGjsW3bNty7d09p2fXr17Fp0yZ4enqqNTgiIiJtI7hwLlu2\nDNevX4eHhwfi4uIAAD///DOWL18ONzc3iEQiLF26VGOBEhERaQPBhdPGxgY//fQTzM3NsWbNGgBA\nTEwMoqOj4ejoiP3798Pa2lpjgRIREWkDlR5kPWjQIOzevRu3bt1CYWEh6uvr0bdvX5iammoqPiIi\nIq2iUuF8SCwWa+QpKERERNpOpcJZXl6OL7/8Evv378fly5chEolgbW2NcePGYcGCBejevbum4iQi\nItIKgs9xFhYWYsSIEYiMjERdXR1GjRqFkSNH4v79+4iMjMSLL76IgoICTcZKRETU6gQXziVLluDO\nnTtITk5GZmYmtm7diq1btyIrKws//PAD7ty5gw8++EDlAOLi4uDo6AgLCwu4u7sjKyuryb737t3D\n22+/jREjRsDU1BSTJ09W6pORkQGxWKz0c+HCBZVjIyIiepTgwpmVlYWgoCCMHj1aadmYMWMwb948\nZGZmqjR4UlISQkJCsHjxYmRkZMDFxQXe3t64evVqo/3r6upgZGSEoKAgjBs3DiKRqMltZ2dn4/z5\n8/Kf/v37qxQbERFRYwQXzq5du0IsFje5vHv37ujWrZtKg8fExMDX1xd+fn6wtbVFREQEzM3NER8f\n32j/zp07IyoqCn5+fujZs6fCRPOPkkgkMDU1lf906PBET1AjIiJSILia+Pn5YevWrbh9+7bSsoqK\nCmzduhV+fn6CB66pqUFOTg48PDwU2j09PZGdnS14O01xd3eHnZ0dpFIpMjIynnp7REREgApX1dra\n2kIkEmHYsGGYMWMGBgwYAAC4cOECtm/fDjMzMwwcOBC7d+9WWM/Ly6vR7ZWWlqKurg5mZmYK7RKJ\nBMXFxarmIWdpaYl169ZhyJAhqKmpwY4dOyCVSpGSkgI3N7cn3i4RERGgQuGcO3eu/N/R0dFKy0tK\nSjBnzhyFNpFI1GTh1BQbGxvY2NjIXw8bNgyXL19GdHR0k4UzPz9fozEZGBigurq62Uny1aWqqgoA\nWmSs1qDpz6o16GJOAPNqS3QtJ1tbW41uX3Dh3LNnj1oHNjExgZ6entLeZUlJCczNzdU6lrOzs9Ke\n8N9p+k0b43rKAAAgAElEQVQGAENDQxgbG2t8HCMjIwBokbFaQ0t8Vi0pPz9f53ICmFdboos5aZrg\nwjlq1Ci1Dqyvrw8nJyekp6dDKpXK29PT0zFlyhS1jpWbmwsLCwu1bpOIiNqnJ5pyT12Cg4MRFBQE\nZ2dnuLq6Ij4+HsXFxfD39wcAhIWF4eTJk0hOTpavc+7cOdTU1KC0tBSVlZXIzc1FQ0MDHB0dAQCx\nsbHo06cP7OzsUFNTg4SEBKSmpmLLli2tkiMREemWVi2cXl5eKCsrw9q1ayGTyeDg4ICEhARYWVkB\nAGQyGYqKihTWmT59Oq5cuQLgwTnU0aNHQyQSoaysDABQW1uL0NBQ/PnnnzA0NIS9vT0SExMxduzY\nFs2NiIh0U6sWTgAIDAxEYGBgo8tiY2OV2k6fPt3s9hYuXIiFCxeqJTYiIqJHcVYAIiIiFbBwEhER\nqUBw4Vy9ejX++OOPJpefPXsWa9asUUtQRERE2kpw4VyzZg3OnDnT5PI//viDhZOIiHSe2g7V3r17\nFx07tvq1RkRERBrVbKXLzc3F77//Ln8KSVZWFmpra5X63bp1C/Hx8Zx9goiIdF6zhfPHH39ERESE\n/PXGjRuxcePGRvt2794d//nPf9QbHRERkZZptnC+9dZbmDBhAoAHj/v66KOPlCYSEIlE6Ny5M/r1\n64dOnTppLlIiIiIt0GzhtLS0hKWlJYAHk7zb2dnB1NS0RQIjIiLSRq02yTsREVFbpNJlsL/88gu2\nbNmCoqIilJeXyy8aEolEaGhogEgkQk5OjkYCJSIi0gaCC2d0dDSWL18Oc3NzODs7w8HBQamPSCRS\na3BERETaRnDh/PrrrzF69Gjs3LmTFwEREVG7JXgChPLyckyZMoVFk4iI2jXBhXPo0KHIz8/XZCxE\nRERaT3Dh/Oyzz7B3717s2LFDk/E0Ki4uDo6OjrCwsIC7uzuysrKa7Hvv3j28/fbbGDFiBExNTTF5\n8uQWjJSIiHSd4HOcfn5+uH//PubNm4dFixbB0tISenp68uUPr6rNzs5Wa4BJSUkICQlBZGQk3Nzc\nsH79enh7e+Po0aOwsrJS6l9XVwcjIyMEBQVh//79uH37tlrjISKi9k1w4TQ1NYWZmRkGDBjQZB9N\nXFUbExMDX19f+Pn5AQAiIiKQlpaG+Ph4hIaGKvXv3LkzoqKiADyYa7eiokLtMRERUfsluHCmpKRo\nMo5G1dTUICcnBwsXLlRo9/T0VPueLRERkRBqe6yYJpSWlqKurg5mZmYK7RKJBMXFxa0UFRERtWcq\nFc7S0lKEh4dj3LhxcHZ2xrFjxwAAZWVlWL16NfLy8jQSJBERkbYQfKj20qVLmDBhAm7dugV7e3tc\nvHgRVVVVAIAePXpg9+7duHnzJtauXau24ExMTKCnp6e0d1lSUgJzc3O1jaPp22wMDAxQXV2NyspK\njY4DQP6ZtMRYrUEXb4nSxZwA5tWW6FpOmn42tODCuXz5cjQ0NODo0aN45plnYGNjo7B84sSJSE1N\nVWtw+vr6cHJyQnp6OqRSqbw9PT0dU6ZMUds4LfEAbkNDQxgbG2t8HCMjIwBokbFag649LD0/P1/n\ncgKYV1uiizlpmuBDtQcPHsScOXPQt2/fRpf36dMH165dU1dccsHBwdi2bRs2b96MvLw8LF26FMXF\nxfD39wcAhIWFKRRVADh37hxOnz6N0tJSVFZWIjc3F6dPn1Z7bERE1P4I3uO8d+8exGJxk8srKirQ\noYP6rzXy8vJCWVkZ1q5dC5lMBgcHByQkJMjv4ZTJZCgqKlJYZ/r06bhy5QqAB7fIjB49GiKRCGVl\nZWqPj4iI2hfBhdPOzg6HDx9GQEBAo8tTU1Ph6OiotsD+LjAwEIGBgY0ui42NVWrj3iUREWmK4F3E\n+fPn44cffsBnn32GW7duAXgwS09eXh4CAwNx/PhxBAcHayxQIiIibSB4j9Pb2xtXr17Fv/71L3z6\n6acAgKlTpwIA9PT0sHLlSrz88suaiZKIiEhLCC6cAPD+++9j2rRp2Lt3LwoKClBfX4/+/fvjlVde\nafKiISIiIl2iUuEEgN69e2P+/PmaiIWIiEjrCT7HmZWVJZ88vTFRUVHymYSIiIh0leA9zoiICHTr\n1q3J5b///juOHDmCXbt2qSUwIiIibSR4j/P06dNwcXFpcvmwYcNw6tQptQRFRESkrQQXzr/++uux\nExzcvXv3qQMiIiLSZoIL54ABA5CWltbk8rS0NPTv318tQREREWkrwYXzzTffxC+//IIlS5bIJ0AA\nHjxqbMmSJUhLS8Mbb7yhkSCJiIi0heCLg2bPno3c3FzExcUhLi4O5ubmaGhokD/ya9asWXj77bc1\nFigREZE2EFw4RSIRoqOj4e3tjT179uDixYsAgH79+kEqlWLkyJEaC5KIiEhbCCqcVVVV+L//+z+M\nHz8eUqkUo0aN0nRcREREWknQOU4jIyMkJyejoqJC0/EQERFpNcEXBw0ZMgS5ubmajIWIiEjrCS6c\nn376KZKTk/HNN9+gpqZGkzERERFpLcGFc/bs2RCJRFi6dCmsrKzg6OgIV1dX+Y+LiwtcXV1VDiAu\nLg6Ojo6wsLCAu7s7srKymu1/5swZvPzyy7C0tISDgwMiIiIUlmdkZEAsFiv9XLhwQeXYiIiIHiX4\nqlpTU1OYmZnBxsamyT4ikUilwZOSkhASEoLIyEi4ublh/fr18Pb2xtGjR2FlZaXU//bt2/Dy8sLI\nkSORnp6OvLw8LFiwAJ07d8aCBQsU+mZnZ0MsFstfm5iYqBQbERFRYwQXzpSUFLUPHhMTA19fX/j5\n+QF4MJF8Wloa4uPjERoaqtQ/MTER1dXV+Oqrr2BgYAA7Ozvk5+cjNjZWqXBKJBL06NFD7TETEVH7\nJvhQrbrV1NQgJycHHh4eCu2enp7Izs5udJ1jx47Bzc0NBgYGCv2vX7+Oy5cvK/R1d3eHnZ0dpFIp\nMjIy1J8AERG1SyoVztLSUoSHh2PcuHFwdnaWP3+zrKwMq1evRl5enkrbqqurg5mZmUK7RCKRz0b0\nqOLiYqX+pqam8mUAYGlpiXXr1mHLli3YsmULbG1tIZVKH3vulIiISAjBh2ovXbqECRMm4NatW7C3\nt8fFixdRVVUFAOjRowd2796NmzdvYu3atRoLVsg5VBsbG4XzsMOGDcPly5cRHR0NNze3RtfJz89X\nW4yNMTAwQHV1NSorKzU6DgD5Z9ISY7UGTX9WrUEXcwKYV1uiaznZ2tpqdPuCC+fy5cvR0NCAo0eP\n4plnnlG6SGjixIlITU0VPLCJiQn09PSU9i5LSkpgbm7e6DpmZmaN9n+4rCnOzs7YvXt3k8s1/SYD\ngKGhIYyNjTU+jpGREQC0yFitoSU+q5aUn5+vczkBzKst0cWcNE3wodqDBw9izpw56Nu3b6PL+/Tp\ng2vXrgkeWF9fH05OTkhPT1doT09Pb/K2FhcXF2RlZeHevXsK/Xv27Alra+smx8rNzYWFhYXg2IiI\niJoiuHDeu3dP4faOR1VUVDz2QdePCg4OxrZt27B582bk5eVh6dKlKC4uhr+/PwAgLCwMUqlU3n/a\ntGkwMjLC/PnzcfbsWezZsweff/455s+fL+8TGxuLlJQUFBQU4OzZswgLC0NqairmzJmjUmxERESN\nEXyo1s7ODocPH0ZAQECjy1NTU+Ho6KjS4F5eXigrK8PatWshk8ng4OCAhIQE+T2cMpkMRUVF8v5d\nu3bF7t27sXjxYnh4eEAsFmPBggUIDg6W96mtrUVoaCj+/PNPGBoawt7eHomJiRg7dqxKsRERETVG\ncOGcP38+goKCYG9vDy8vLwBAXV0d8vLyEBERgePHj+O7775TOYDAwEAEBgY2uiw2NlapzcHBodlz\nqQsXLsTChQtVjoOIiEgIwYXT29sbV69exb/+9S98+umnAICpU6cCAPT09LBy5Uq8/PLLmomSiIhI\nSwgunADw/vvvY9q0adi7dy8KCgpQX1+P/v3745VXXmnyoiEiIiJd8tjCWVVVhdTUVFy+fBk9evTA\n+PHjFS7GISIiak+aLZzXr1/HxIkTcenSJXlb586d8f3332P06NEaD46IiEjbNHv/yCeffIIrV64g\nODgY27dvx6pVq2BgYIAPP/ywpeIjIiLSKs3ucR48eBAzZszAJ598Im8zMzNDYGAgrl27hl69emk8\nQCIiIm3S7B6nTCbD8OHDFdoezupz9epVzUVFRESkpZotnHV1dTA0NFRoe/i6urpac1ERERFpqcde\nVXvx4kX8+uuv8tcVFRUAgPPnz6NLly5K/YcOHarG8IiIiLTLYwvnqlWrsGrVKqX2Dz74QKlNJBKh\nrKxMPZERERFpoWYL55dfftlScRA9lnHnzjhz/tLjO7Yhd+/W6FxOAPNqS1oyJ1OTbjAz6d4iY2lS\ns4XT19e3peIgeqxbt+/iy29/bO0w1KqyslInn53KvNqOlswp9L1ZOlE4VXsOGBERUTvHwklERKQC\nFk4iIiIV6GzhjIuLg6OjIywsLODu7o6srKzWDomIiHSAThbOpKQkhISEYPHixcjIyICLi4v8eaJE\nRERPQycLZ0xMDHx9feHn5wdbW1tERETA3Nwc8fHxrR0aERG1cTpXOGtqapCTkwMPDw+Fdk9PT2Rn\nZ7dSVEREpCt0rnCWlpairq4OZmZmCu0SiQTFxcWtFBUREemKx065R+qxaO5rLTrejlj7Fh2vpYxy\nGdzaIRBRO6dze5wmJibQ09NT2rssKSmBubl5K0VFRES6QucKp76+PpycnJCenq7Qnp6eLn+WKBER\n0ZPSyUO1wcHBCAoKgrOzM1xdXREfH4/i4mL4+/u3dmhERNTG6WTh9PLyQllZGdauXQuZTAYHBwck\nJCTAysqqtUMjIqI2TlReXt7Q2kEQERG1FTp1jjMqKgoeHh6wtraGjY0NZsyYgbNnzyr1W7VqFezt\n7WFpaYnJkyfj3LlzCsvv3buHJUuWYMCAAejVqxdmzpyJP//8s6XSaFZUVBTEYjGWLFmi0N4Wc7px\n4wbmzZsHGxsbWFhYYPjw4Thy5IhCn7aWV21tLVauXInnn38eFhYWeP755/HJJ5+grq5OoZ8253Xk\nyBHMmDEDDg4OEIvF2LZtm1IfdcRfXl6OuXPnwtraGtbW1ggKCkJFRUWr5FVbW4vly5djxIgR6NWr\nF+zs7DBnzhyl2cbaWl6Peu+99yAWi/HFF18otLfVvC5cuIDXX38dffr0Qc+ePTFmzBicP39e43np\nVOE8cuQI5syZg59++gl79uxBx44dMWXKFJSXl8v7/Pvf/0ZsbCwiIiJw4MABmJqawsvLC3fv3pX3\nCQkJwY8//oj4+Hikpqbizp078PHxQX19fWukJXf8+HFs2rQJzz77LEQikby9LeZUXl6O8ePHQyQS\nITExEceOHUNERARMTU3lfdpiXpGRkdi4cSMiIiJw/PhxrF69Ghs2bEBUVJS8j7bn9ddff+G5557D\nqlWrYGRkpPC7ps74Z8+ejd9//x1JSUnYtWsXTp8+jaCgoFbJq7KyEqdPn8aSJUtw6NAhbNu2DVev\nXsW0adMU/uhpa3n9XXJyMk6ePAlLS0ulPm0xr6KiIowfPx79+vXD3r17kZWVhY8//ljh2aKaykun\nD9VWVlbC2toa27Ztw/jx49HQ0AA7OzsEBQVh0aJFAIDq6mrY2toiPDwcb731FioqKmBra4vY2FhM\nmzYNAHDt2jUMHjwYO3fuhKenZ6vkUlFRAXd3d3zxxRdYvXo1HBwcEBER0WZzWrlyJbKysvDf//63\n0eVtNS8fHx+YmJggNjZW3jZv3jzcunULO3bsaHN5WVlZ4bPPPsPMmTMBqO9zycvLw/Dhw7F//364\nuLgAAI4ePYqJEyfi+PHjsLGxadG8GvMwxszMTNjb27fpvC5fvowJEyYgOTkZU6dOxdy5c7FgwQIA\naLN5zZ49Gx06dMA333zT6DqazEun9jgfdefOHdTX16N79wdPHL906RKKi4sVvngMDQ3x4osvyqfj\nO3XqFO7fv6/Qp1evXhg0aFCrTtn33nvvYcqUKRg5ciQaGv7/3zptNaeUlBQ4OzvD398ftra2GDVq\nFNavXy9f3lbzeumll3Do0CHk5+cDAM6dO4fDhw9j/PjxANpuXg89bfzHjh0DABw7dgxdunSRf1kB\ngKurK4yNjeV9Wtvt27cBQP790Vbzqq2txezZs7FkyRLY2toqLW+LedXX12P//v0YNGgQpk6dChsb\nG3h6emL37t3yPprMS6cL54cffghHR0f5myKTyQBA4XAgoDgdX3FxMfT09NCjRw+FPqampigpKWmB\nqJVt2rQJRUVFWLZsGQAoHLJoqzkVFRVhw4YN6N+/P5KSkjBv3jyEhYXJi2dbzWv27NmYPn06XFxc\nYGpqCjc3N8ycORMBAQEA2m5eDz1t/H/vY2JiorBcJBJpzdSYNTU1WLZsGSZOnAhLS0sAbTevVatW\nQSKRNHk7XlvMq6SkBHfv3kVUVBT+8Y9/4IcffsDUqVPlp+oexqypvHTydhQA+Oijj3Ds2DH897//\nbfKY/98J6dMa8vPzER4ejn379kFPTw/Ag8Nlf9/rbIq25gQ8+Itx6NCh+PjjjwEAgwcPRmFhIeLi\n4jBnzpxm19XmvL7++mt89913iI+Ph52dHU6fPo0PP/wQ1tbWeOONN5pdV5vzEuJx8Qv5ndUGtbW1\nmDt3Lu7cuYMdO3Y8tr8255WRkYHvv/8eGRkZCu1CYtbmvB6eo5w0aRLmz58PAHjuuedw6tQprF+/\nHuPGjWtyXXXkpZN7nCEhIdi9ezf27NmDPn36yNsfTrn36F/tJSUl8knhzczMUFdXh7KyMoU+xcXF\nShPHt4Rjx46htLQUw4cPh0QigUQiQWZmJjZs2ABTU1P5X0ttKScAsLCwwKBBgxTabG1t5VcxtsXP\nCnhwcdCiRYvg5eUFe3t7+Pj4IDg4GOvWrQPQdvN66Gnif7RPaWmpwvKGhgbcvHmzVXOsra1FYGAg\nzp49i+TkZPlhWqBt5nXkyBHcuHEDgwYNkn9/XLlyBStWrMBzzz0nj7mt5WViYoKOHTs2+x2iybx0\nrnAuXbpUXjQfPbHbp08fmJub48CBA/K26upqHD16VD4dn5OTEzp16qTQ59q1azh//nyrTNk3efJk\nZGVl4fDhwzh8+DAyMjIwZMgQTJs2DRkZGRgwYECbywkAhg8frnDZOPDg0nJra2sAbfOzAh78T9eh\ng+L/Vh06dJD/ldtW83pIXfG7uLjg7t27CueRjh07hsrKylbL8f79+/D398fZs2exd+9epcPRbTGv\n2bNnIzMzU+H7w9LSEsHBwUhOTgbQNvPS19eHs7Nzs98hmsxL78MPP1yhxnxa1eLFi7Fjxw5s3LgR\nvXr1QmVlJSorKyESiaCvrw+RSIS6ujqsW7cONjY2qKurwz//+U8UFxfj3//+N/T19WFoaIgbN24g\nLi4Ozz33HCoqKvD++++jW7duCAsLa/HDaYaGhvK/FCUSCUxNTZGQkIDevXtj1qxZbTInAOjduzfW\nrFkDPT09WFhY4H//+x8++eQTLFq0CM7Ozm02r4KCAmzbtg22trbo2LEjMjIy8Mknn2Dq1Knw9PRs\nE3lVVlbi3LlzkMlk2LJlCxwcHPDMM8/g/v376Natm1ril0gk+PXXX5GYmAhHR0dcu3YN77//Pl54\n4YXHHqrXRF7Gxsbw8/PDb7/9hk2bNqFLly7y74+OHTuiY8eObTIvCwsLpe+P//znPxg9ejQmTJgA\nAG0yr65du6JHjx5YvXo1zMzM0LVrV+zZswfR0dH417/+hQEDBmg0L526HUUsFkMkEikdw/7www+x\ndOlS+evVq1fj22+/RXl5OV544QWsXbsWdnZ28uUPLwzYuXMnqqurMWbMGERGRqJnz54tlktzJk+e\nLL8d5aG2mNNPP/2ElStX4sKFC+jduzfmzJmDuXPnKvRpa3lVVlZi1apV2LNnj/yJPNOmTcMHH3wA\nfX19eT9tzisjIwOvvvoqACj8/zRr1izExMSoLf7y8nJ88MEH2LdvHwBg4sSJ+Oyzz9C1a9cWz2vp\n0qV4/vnnG/3+iI2Nld8G0dbyevh5/Z2jo6PC7ShtOa9t27YhKioK165dw4ABA7Bo0SK89tr/f4Sj\npvLSqcJJRESkaTp3jpOIiEiTWDiJiIhUwMJJRESkAhZOIiIiFbBwEhERqYCFk4iISAUsnERERCpg\n4SStdezYMQQEBODZZ5+FmZkZrK2t4enpiVWrVsmf0tEWREdHY9SoUQptYrEYYrEY4eHhSv0bGhrw\n/PPPQywWK0wIkZGRAbFYjCNHjsjbJk2ahMmTJ6vUp6U8zFEsFsPU1BQ2Njbym8tv3rz5VNtdvXq1\n/PWqVasgFovVEbJgMpkMvXr1wvHjx1t0XNIOLJyklb744gtMmDABZWVlWLZsGZKTkxEfHw9PT09s\n3LhRYdYTbVZaWorIyEj5I+H+7plnnkFCQoJSe2ZmJq5cuQJjY2OF6fWcnJzwyy+/wNHRUd4mEoke\nOwXfunXrEBUV9RRZPDlfX1/88ssvSE1NRUxMDEaMGIFvvvkGw4cPf6rnOD6ac0tPr2hubo7AwEB8\n9NFHLTouaQcWTtI6hw4dQmhoKN5++2388MMPmDlzJtzc3DB27FgsW7YMp06dUphWqzH3799voWib\nFx8fj27duskfZP13L7/8Mq5du4bDhw8rtG/fvh0jRoxQeo7gM888g6FDh+KZZ56Rtwl5RNLAgQMx\ncODAJ8zg6VhaWmLo0KEYNmwYxo8fj2XLliEzMxPdu3fHG2+8gaqqKrWMo65HYKnyexMQEIATJ04o\n7N1T+8DCSVrn888/h6mpKcLCwhpd3rlzZ/ncoQBw6dIliMVibNiwAaGhobCzs4O5uTkqKirQ0NCA\nmJgYvPDCCzAzM4OdnR2WLFmCO3fuKGzzq6++gouLCywtLdG3b194eHjgxx9/lC9PS0vDuHHjYG1t\nDSsrKwwbNkxhruCmbNq0CdOmTWt0mZWVFUaOHKnwzMfq6mrs2bNHIb+HGjsMK0Rjh2rz8/Ph6+uL\nPn36wNLSEi+99BLS0tIU+jw8BFpYWIjp06fDysoKgwcPRkRExFMVKlNTU6xcuRLFxcXYuXOnwjIh\nn5UQ33zzDV566SX069cPffr0wUsvvSR/wPFDzf3eyGQyzJs3D/b29jA3N4ednR18fHwUDjH37dsX\nL7zwAjZt2vRkbwS1WSycpFVqa2tx5MgRuLu7o2NH1Z6zHhkZicLCQkRHR+O7776DgYEBwsPDsWzZ\nMnh6emLHjh1YuHAhvv/+e0yfPl3+5Z+QkICPP/4Y3t7eSExMRFxcHKRSKcrLywEARUVFmDlzJvr2\n7Ytvv/0W33//PYKDgx+7t5SXl4dr165h+PDhjS4XiUSYMWMGkpOTUVNTAwBISUlBXV0dXn31VbXt\nRT16OPf69euYMGEC/vjjD6xduxYbN25Et27dMH36dPzyyy9K67/++usYM2YMvvvuO0yaNAmrVq3C\ntm3bniomDw8PdOzYUeFw7cqVKx/7WQl1+fJlvP766/j222/x7bffwsnJCT4+Pkp/HACN/94EBQXh\n119/RXh4OH744QesWbMGVlZW+OuvvxTWHT58ONLT05/sTaA2S7VvJiINKysrw71799C7d2+lZbW1\ntQqvHy2sZmZm2Lp1q/z1rVu38OWXX2LWrFnyvUMPDw9IJBIEBQVh3759mDhxIo4fP45nn30WS5Ys\nka87duxY+b9zcnJw//59REVFoUuXLgCgdLFPY06ePAkAsLe3b7KPVCrFkiVL8OOPP+K1117D9u3b\n8fLLL8vHUYeGhgaFwhkTE4OKigqkpaWhb9++AIBx48bB1dUV4eHhCrkDwIIFCzBr1iwAwJgxY3Do\n0CHs2rULvr6+TxyTkZERTExM5Bd5Cf2shPrkk0/k/66vr8eoUaNQUFCADRs24B//+IdC30d/bwDg\nxIkTCA0NVThaIJVKlcZ59tlncfPmTVy+fFn+HEjSfdzjpDZBJpPB1NRU4ae+vl6hz6RJkxReHz9+\nHPfv38f06dMV2l977TV07NgRmZmZAABnZ2fk5ubigw8+wMGDB5X2KhwdHdGpUycEBAQgOTkZJSUl\ngmJ+2O/Rc5V/Z2xsjEmTJmHHjh2QyWRIT09v9DCtOmVmZmLYsGHyogk8eNj2a6+9htzcXNy9e1eh\n/6PnZ+3t7XH16tWnjqO+vl5e0IV+VkKdOnUKPj4+GDhwoPw5lOnp6SgoKFDq++jvDQAMGTIE0dHR\n+Prrr3HmzJkm93hNTEwAAMXFxSrFR20bCydplR49esDQ0BBXrlxRaJdIJEhPT0d6ejrefPPNRq+i\nNDc3V3h969YtAICFhYVCe8eOHdGjRw/58pkzZyIqKgq//vorpk6div79++ONN97A5cuXAQD9+vXD\nrl27UF9fj3nz5mHQoEF46aWX1HZRyMyZM3HgwAHExsbCzMwM7u7uADR3peitW7eU3hPgwfvX0NAg\nP0T90KO3eujr66O6uvqpYqiqqkJpaan8MxP6WQlx9epVvPrqq6ioqMBnn32Gn3/+Genp6Rg7dmyj\ncT/6ewMAGzduxMSJExEdHY2RI0fKn3+rrsPn1LaxcJJW6dixI1588UWkp6crXOGop6cHJycnODk5\nyb/gH/VooXn4hX/jxg2F9traWpSVlSkUhLfeegtpaWkoLCzEV199hZMnTyIgIEC+fNSoUdi5cycu\nX76MH374AR07doSPjw/KysqazMXU1BQAmu0DAO7u7jA1NcWXX34Jb29vjd9a0aNHD6X3BHiwVy8S\nidC9e3eNjg88uNiqvr5efv5Xlc9KyLbv3LmDjRs3QiqVYujQoXByckJlZWWj/Rt7vyUSCT777DP8\n8ccfOHHiBGbNmoVVq1Zh48aNCv1KS0sBPDjcS+0HCydpnYULF6K0tBTLly9/qu24uLhAX18fSUlJ\nCjZ8XY8AAANySURBVO1JSUmora3FyJEjldbp1q0bvLy8IJVKcfbsWaXlnTp1wujRo/HOO++gsrJS\nvlfamCFDhgAAzpw502ycIpEIS5YswcSJE/H6668LSe2pjBgxAidOnFCIva6uDrt378bzzz8v6Pzq\n0xT3kpISLF++HJaWlpg6dSqAJ/usmvLwUPvfz4FfuHAB2dnZTxTvgAED8PHHH6N79+5KvxNnzpyB\nRCLh+c12hhcHkdYZM2YMVqxYgRUrVuDMmTOYMWMGrK2tce/ePVy4cAFJSUno0qXLY7+8u3fvjgUL\nFiAqKgqdO3fGSy+9hLy8PHz66adwc3OTn7t799138cwzz2DYsGGQSCQoKChAQkICPD09ATy4FzMr\nKwsvvfQSevbsidLSUqxbtw49e/Zs9sIfOzs79OrVC5mZmZgwYUKzsfr7+8Pf31+hTehhQSH9/t5n\n/vz52LZtG7y8vBASEoIuXbpgw4YNKCwsbHRChicdEwD+/PNPHD9+HPX19bh16xZOnDiBTZs2QSQS\nYfv27TAwMAAg/LMS4uEVu/PmzUNwcDBu3LiB1atXo3fv3krnxRtTUVGBKVOmYPr06bC1tUWnTp2Q\nkpKC8vJy+e/EQ0ePHpUfWqf2g4WTtNLChQvh6uqKr7/+GuHh4bh58yYMDQ1ha2uLqVOnIiAgQNBe\nz8cffwwTExNs3LgRGzZsgImJCWbMmKGwNzt8+HB899132LFjB27fvg0LCwv4+PggJCQEADB48GD8\n8ssvWLlyJUpKSiAWi+Hm5oYNGzbIv/ib8sYbb2DLli1YuXKlyu9BY/k1NmPO42bRebSPhYUF9u3b\nh+XLl2PRokWoqamBo6Ojwh8LTW27ufbGfP/999i2bRs6duyIrl27YuDAgZg3bx78/f2VLpoS8lk1\n5tF47OzssH79enz66aeYNWsW+vfvj7CwMPz888+CzksbGRnByckJmzdvxpUrV9ChQwfY2toiLi5O\n4creoqIi/PrrrwgNDRX0XpDuEJWXl/NsN5GG3Lx5E87OzvjPf/6j0u0UpP2WL1+OI0eONHrvK+k2\nFk4iDYuOjsauXbvwv//9r7VDITUpLi6Gs7Mzdu/ejWHDhrV2ONTCWDiJiIhUwKtqiYiIVMDCSURE\npAIWTiIiIhWwcBIREamAhZOIiEgFLJxEREQqYOEkIiJSwf8DOeUzj2flskAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093a1a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=[300, 400, 600, 1500], unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see this, let us split the [300, 400) bin into ten narrower bins of width 10 million dollars each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iPDwcw4YNw1dffVXnbRMREdXG4JBs0qQJDhw4gJkzZ6KwsBANGjTAiRMnoFKp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\n/wIEaPUJqn630AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092d2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=[300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 600, 1500 ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the skinny bars are taller than 0.4 and others are shorter. By putting a flat top at 0.4 over the whole bin, we are deciding to ignore the finer detail and use the flat level as a rough approximation. Often, though not always, this is sufficient for understanding the general shape of the distribution.\n",
    "\n",
    "Notice that because we have the entire dataset, we can draw the histogram with as fine a level of detail as the data and our patience will allow. However, if you are looking at a histogram in a book or on a website, and you don't have access to the underlying dataset, then it becomes important to have a clear understanding of the \"rough approximation\" created by the flat tops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The units of the density scale.**\n",
    "The height of each bar is a percent divided by a bin width. Thus, for this datset, the values on the vertical axis are in units of \"percent per million dollars.\" To understand this better, look again at the [300, 400) bin. The bin is 100 million dollars wide. So we can think of it as consisting of 100 narrow bins that are each 1 million dollars wide. The bar's height of roughly \"0.4% per million dollars\" means that roughly 0.4% of the movies are in each of those 100 skinny bins of width 1 million dollars.\n",
    "\n",
    "Because the height of a histogram bar is a percent per unit on the horizontal axis, it can be thought of as the *density* of entries per unit width. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heights versus areas.** To confirm our understanding of the density scale, let us draw the histogram again, this time with four bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BnV/rtq7HV1tvYl5vYk4A89IUYrFYqe8vuDC+mCn6xRdf4MsvvwQAeHh4AAC0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\nRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094a0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Gross', bins=[300, 350, 400, 450, 1500], unit='Million Dollars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the counts of movies in each of the bins. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Gross count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>300 </td> <td>32         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>350 </td> <td>49         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>400 </td> <td>25         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>450 </td> <td>92         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1500</td> <td>0          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "bin  | Gross count\n",
       "300  | 32\n",
       "350  | 49\n",
       "400  | 25\n",
       "450  | 92\n",
       "1500 | 0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millions.bin('Gross', bins=[300, 350, 400, 450, 1500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Heights and Densities: Q&A**\n",
    "\n",
    "Look again at the histogram, and compare the [400, 450) bin with the [450, 1500) bin.\n",
    "\n",
    "**Q**: Which has more movies in it? \n",
    "\n",
    "**A**: The [450, 1500) bin. It has 92 movies, compared with 25 movies in the [400, 450) bin.\n",
    "\n",
    "**Q**: Then why is the [450, 1500) bar so much shorter than the [400, 450) bar?\n",
    "\n",
    "**A**: Because height represents density per unit width, not the number of movies in the bin. The [450, 1500) bin does have more movies than the [400, 450) bin, but it is also a whole lot wider. So the density of movies in it is much lower."
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    "**Which to use: bar graph or histogram?** \n",
    "\n",
    "Bar charts display the distributions of categorical variables. All the bars in a bar chart have the same width. The lengths (or heights, if the bars are drawn vertically) of the bars are proportional to the number of entries.\n",
    "\n",
    "Histograms display the distributions of quantitative variables. The bars can have different widths. The areas of the bars are proportional to the number of entries. \n",
    "\n",
    "**Multiple histograms**\n",
    "\n",
    "In all the examples in this section, we have drawn a single histogram. However, if a data table contains several columns, then *barh* and *hist* can be used to draw several charts at once. We will cover this feature in a later section."
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