textbook/Visualizing_Numerical_Distributions.ipynb

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 "cells": [
  {
   "cell_type": "code",
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   "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": [
    "### Visualizing Numerical Distributions ###\n",
    "\n",
    "Many of the variables that data scientists study are *quantitative* or *numerical*. Their values are numbers on which you can perform arithmetic. Examples that we have seen include the number of periods in chapters of a book, the amount of money made by movies, and the age of people in the United States.\n",
    "\n",
    "The values of a categorical variable can be given numerical codes, but that doesn't make the variable quantitative. In the example in which we studied Census data broken down by age group, the categorial variable `SEX` had the numerical codes `1` for 'Male,' `2` for 'Female,' and `0` for the aggregate of both groups `1` and `2`. While 0, 1, and 2 are numbers, in this context it doesn't make sense to subtract 1 from 2, or take the average of 0, 1, and 2, or perform other arithmetic on the three values. `SEX` is a categorical variable even though the values have been given a numerical code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our main example, we will return to a dataset that we studied when we were visualizing categorical data. It is the table `top`, which consists of data from U.S.A.'s top grossing movies of all time. For convenience, here is the description of the table again.\n",
    "\n",
    "The first column contains the title of the movie. The second column contains the name of the studio that produced the movie. The third contains the domestic box office gross in dollars, and the fourth contains the  gross amount that would have been earned from ticket sales at 2016 prices. The fifth contains the release year of the movie. \n",
    "\n",
    "There are 200 movies on the list. Here are the top ten according to the unadjusted gross receipts in the column `Gross`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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",
    "# Make the numbers in the Gross and Gross (Adjusted) columns look nicer:\n",
    "top.set_format([2, 3], NumberFormatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Distribution of the Adjusted Receipts ###\n",
    "\n",
    "In this section we will draw graphs of the distribution of the numerical variable in the column `Gross (Adjusted)`. For simplicity, let's create a smaller table that has the information that we need. And since three-digit numbers are easier to work with than nine-digit numbers, let's measure the `Adjusted Gross` receipts in millions of dollars. Note how `round` is used to retain only two decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Title</th> <th>Adjusted 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                                     | Adjusted 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('Adjusted Gross', \n",
    "                                     np.round(top.column(3)/1e6, 2))\n",
    "millions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Histogram ###\n",
    "A *histogram* of a numerical dataset looks very much like a bar chart, though it has some important differences that we will examine in this section. First, let's just draw a histogram of the adjusted receipts.\n",
    "\n",
    "The `hist` method generates a histogram of the values in a column. The optional `unit` argument is used in the labels on the two axes. The histogram shows the distribution of the adjusted gross amounts, in millions of 2016 dollars. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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7O2bPnq2woERERPIkuACKRCKsW7cOnp6eOHr0qNQ4wKFDh8LMzExhIYmIiOStWgPhAaBr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\nSv8HoP6xSOreXd8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18099cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Adjusted Gross', unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Horizontal Axis ###\n",
    "\n",
    "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, `hist` does have to account for situations where there might have been values at the edges. So `hist` has an *endpoint convention*: bins include the data at their left endpoint, but not the data at their right endpoint. \n",
    "\n",
    "We will use the notation [*a*, *b*) for the bin that starts at *a* and ends at *b* but doesn't include *b*.\n",
    "\n",
    "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 studied earlier, 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 none of the movies grossed fewer than 300 million dollars; that is because we are considering only the top grossing movies of all time. \n",
    "\n",
    "It is a little harder to see exactly where the ends of the bins are situated. For example, it is not easy to pinpoint exactly where the value 500 lies on the horizontal axis. So it is hard to judge exactly where one bar ends and the next begins.\n",
    "\n",
    "The optional argument `bins` can be used with `hist` to specify the endpoints of the bins. It must consist of a sequence of numbers that starts with the left end of the first bin and ends with the right end of the last bin. We will start by setting the numbers in `bins` to be 300, 400, 500, and so on, ending with 2000.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RAEAsFkMsFsPb2xvTpk1TWqBERERCCE5sIpEIoaGh8PX1xcGDB6XuY+vTpw9sbGyUFqQq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\naXg1VUbGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d0f3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Adjusted Gross', bins=np.arange(300,2001,100), unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal axis of this figure is easier to read. The labels 200, 400, 600, and so on are centered at the corresponding values. The tallest bar is for movies that grossed between 300 million and 400 million dollars. \n",
    "\n",
    "A very small number of movies grossed 800 million dollars or more. 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 in large populations also often have this kind of shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Counts in the Bins ###\n",
    "\n",
    "The counts of values in the bins 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. The first column lists the left endpoints of the bins (but see the note about the final value, below). The second column contains the counts of all values in the `Adjusted Gross` column that are in the corresponding bin. That is, it counts all the `Adjusted Gross` values that are greater than or equal to the value in `bin`, but less than the next value in `bin`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Adjusted 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",
       "        <tr>\n",
       "            <td>1300</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1400</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1500</td> <td>1                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1600</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1700</td> <td>1                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1800</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1900</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>0                   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts = millions.bin('Adjusted Gross', bins=np.arange(300,2001,100))\n",
    "bin_counts.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the `bin` value 2000 in the last row. That's not the left end-point of any bar – it's the right end point of the last bar. By the endpoint convention, the data there are not included. So the corresponding `count` is recorded as 0, and would have been recorded as 0 even if there had been movies that made more than \\$2,000$ million dollars. When either `bin` or `hist` is called with a `bins` argument, the graph only considers values that are in the specified bins.\n",
    "\n",
    "Once values have been binned, the resulting counts can be used to generate a histogram using the `bin_column` named argument to specify which column contains the bin lower bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RAEAsFkMsFsPb2xvTpk1TWqBERERCCE5sIpEIoaGh8PX1xcGDB6XuY+vTpw9sbGyUFqQq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\njoYNGwJ4cxvGF198AV9fXzg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JdHG+evUqAgMD4erqCgsLCxQVFWHbtm2oU6cOevToAQCS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\naXg1VUbGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a203cd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts.hist('Adjusted Gross count', bin_column='bin', unit='Million Dollars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Vertical Axis: Density Scale ###\n",
    "\n",
    "The horizontal axis of a histogram is straightforward to read, once we have taken care of details like the ends of the bins. The features of the vertical axis require a little more attention. We will go over them one by one.\n",
    "\n",
    "Let's start by examining how to calculate the numbers on the vertical axis. If the calculation seems a little strange, have patience – the rest of the section will explain the reasoning.\n",
    "\n",
    "**Calculation.** The height of each bar is the percent of elements that fall into the corresponding bin, relative to the width of the bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Count</th> <th>Percent</th> <th>Height</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>300 </td> <td>81   </td> <td>40.5   </td> <td>0.405 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>400 </td> <td>52   </td> <td>26     </td> <td>0.26  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>500 </td> <td>28   </td> <td>14     </td> <td>0.14  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>600 </td> <td>16   </td> <td>8      </td> <td>0.08  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>700 </td> <td>7    </td> <td>3.5    </td> <td>0.035 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>800 </td> <td>5    </td> <td>2.5    </td> <td>0.025 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>900 </td> <td>3    </td> <td>1.5    </td> <td>0.015 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1000</td> <td>1    </td> <td>0.5    </td> <td>0.005 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1100</td> <td>3    </td> <td>1.5    </td> <td>0.015 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1200</td> <td>2    </td> <td>1      </td> <td>0.01  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (8 rows omitted)</p"
      ],
      "text/plain": [
       "bin  | Count | Percent | Height\n",
       "300  | 81    | 40.5    | 0.405\n",
       "400  | 52    | 26      | 0.26\n",
       "500  | 28    | 14      | 0.14\n",
       "600  | 16    | 8       | 0.08\n",
       "700  | 7     | 3.5     | 0.035\n",
       "800  | 5     | 2.5     | 0.025\n",
       "900  | 3     | 1.5     | 0.015\n",
       "1000 | 1     | 0.5     | 0.005\n",
       "1100 | 3     | 1.5     | 0.015\n",
       "1200 | 2     | 1       | 0.01\n",
       "... (8 rows omitted)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = bin_counts.relabeled('Adjusted Gross count', 'Count')\n",
    "percents = counts.with_column(\n",
    "    'Percent', (counts.column('Count')/200)*100\n",
    "    )\n",
    "heights = percents.with_column(\n",
    "    'Height', percents.column('Percent')/100\n",
    "    )\n",
    "heights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Go over the numbers on the vertical axis of the histogram above to check that the column `Heights` looks correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculations will become clear if we just examine the first row of the table. \n",
    "\n",
    "Remember that there are 200 movies in the dataset. The [300, 400) bin contains 81 movies. That's 40.5% of all the movies:\n",
    "$$\n",
    "\\mbox{Percent} = \\frac{81}{200} \\cdot 100 = 40.5\n",
    "$$\n",
    "\n",
    "The width of the [300, 400) bin is $ 400 - 300 = 100$. So\n",
    "$$\n",
    "\\mbox{Height} = \\frac{40.5}{100} = 0.405\n",
    "$$\n",
    "\n",
    "The code for calculating the heights used the facts that there are 200 movies in all and that the width of each bin is 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Units.** The height of the bar is 40.5% divided by 100 million dollars, and so the height is 0.405% per million dollars. \n",
    "\n",
    "This method of drawing histograms creates a vertical axis that is said to be *on the density scale*. The height of bar is **not** the percent of entries in the bin; it is the percent of entries in the bin relative to the amount of space in the bin. That is why the height measures crowdedness or *density*.\n",
    "\n",
    "Let's see why this matters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unequal Bins ###\n",
    "An advantage of the histogram over a bar chart is that a histogram can contain bins of unequal width. Below, the values in the `Millions` column are binned into three uneven categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmTO5ujQRETVqgieKmJubcxFQIiJq1AQntblz52Ljxo24fv26IuMhIiKqNcET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\nIqXx/wDHn+xkCmS3TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2055eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uneven = make_array(300, 400, 600, 1500)\n",
    "millions.hist('Adjusted Gross', bins=uneven, unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the counts in the three bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Adjusted 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  | Adjusted Gross count\n",
       "300  | 81\n",
       "400  | 80\n",
       "600  | 37\n",
       "1500 | 0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millions.bin('Adjusted Gross', bins=uneven)"
   ]
  },
  {
   "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 the [300, 400) is twice as much as the density in [400, 600). \n",
    "\n",
    "Histograms help us visualize where on the number line the data are most concentrated, epecially when the bins are uneven."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Problem with Simply Plotting Counts ###\n",
    "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 as a histogram when the bins all have equal widths, though the numbers on the vertical axis are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vERwcXO9u7a1bt8aIESPw7bffGqw4IiKiutQbYr///ju6dOmi08w6d+6MCxcuPHVRRERE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20684780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Adjusted 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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3U2KI/b//9/9o2bLlPe2sRYsWnDx58oGLEhERuRclhpjJZKKgoOCedlZQUHDXm/OKiIiU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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a207867b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Adjusted Gross', bins=uneven, 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 at the number of movies in the bin, *without accounting for the difference in the widths of the bins*. \n",
    "\n",
    "The picture becomes even more absurd if the last two bins are combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bti2HDh0iPj6eiIgIuxquZLVab3gbRHEfyo14eHjc8rq3Kjc31yHvW5HUozGoR9fniP5K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2085aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "very_uneven = make_array(300, 400, 1500)\n",
    "millions.hist('Adjusted Gross', bins=very_uneven, 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": [
    "### The Histogram: General Principles and Calculation ##\n",
    "\n",
    "The figure above shows that what the eye perceives as \"big\" is area, not just height. This observation becomes 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 drawn to scale and are contiguous (though some might be empty), because the values on the horizontal axis are numerical.\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",
    "The calculation of the heights just uses 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",
    "$$\n",
    "\\mbox{height of bar} ~=~ \n",
    "\\frac{\\mbox{area of bar}}{\\mbox{width of bin}} ~=~\n",
    "\\frac{\\mbox{percent of entries in bin}}{\\mbox{width of bin}}\n",
    "$$\n",
    "\n",
    "The units of height are \"percent per unit on the horizontal axis.\"\n",
    "\n",
    "When drawn using this method, the histogram is said to be drawn on the density scale. On this scale:\n",
    "- The area of each bar is equal to the percent of data values that are in the corresponding bin.\n",
    "- The total area of all the bars in the histogram is 100%. Speaking in terms of proportions, we say that the areas of all the bars in a histogram \"sum to 1\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flat Tops and the Level of Detail ###\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 the level 0.405% per million dollars, hides the fact that the movies are somewhat unevenly distributed across that bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmTO5ujQRETVqgieKmJubcxFQIiJq1AQntblz52Ljxo24fv26IuMhIiKqNcET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\nIqXx/wDHn+xkCmS3TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2088e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millions.hist('Adjusted Gross', bins=uneven, unit=\"Million Dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see this, let us split the [300, 400) bin into 10 narrower bins, each of width 10 million dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GMzMzwe2JiEhzCU5qTZo0gYmJCWxsbFTWuZ+fH8RiMZycnODs7IyIiAhkZmbC29sbACAW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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a208a05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_tiny_bins = make_array(300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 600, 1500)\n",
    "millions.hist('Adjusted Gross', bins=some_tiny_bins, unit='Million Dollars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the skinny bars are taller than 0.405 and others are shorter; the first two have heights of 0 because there are no data between 300 and 320. By putting a flat top at the level 0.405 across the whole bin, we are deciding to ignore the finer detail and are using the flat level as a rough approximation. Often, though not always, this is sufficient for understanding the general shape of the distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The height as a rough approximation.**\n",
    "This observation gives us a different way of thinking about the height.\n",
    "Look again at the [300, 400) bin in the earlier histograms. As we have seen, the bin is 100 million dollars wide and contains 40.5% of the data. Therefore the height of the corresponding bar is 0.405% per million dollars.\n",
    "\n",
    "Now think of the bin as consisting of 100 narrow bins that are each 1 million dollars wide. The bar's height of \"0.405% per million dollars\" means that as a rough approximation, 0.405% of the movies are in each of those 100 skinny bins of width 1 million dollars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that because we have the entire dataset that is being used to draw the histograms, we can draw the histograms to 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": [
    "### Histograms Q&A ###\n",
    "Let's draw the histogram again, this time with four bins, and check our understanding of the concepts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KBRAZGYl169YhKysLHTt2RGhoKPr06VPhtqdOncLixYuRnp6OFy9ewMjICOPHj8e0adMU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\nUVpaiu3bt+O9995Dv379AEDypRYZGQl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20b10f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uneven_again = make_array(300, 350, 400, 450, 1500)\n",
    "millions.hist('Adjusted Gross', bins=uneven_again, unit='Million Dollars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>bin</th> <th>Adjusted 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  | Adjusted Gross count\n",
       "300  | 32\n",
       "350  | 49\n",
       "400  | 25\n",
       "450  | 92\n",
       "1500 | 0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millions.bin('Adjusted Gross', bins=uneven_again)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 of space in the bin, 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 it is less crowded. The density of movies in it is much lower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differences Between Bar Charts and Histograms ### \n",
    "\n",
    "- Bar charts display one quantity per category. They are often used to display the distributions of categorical variables. Histograms display the distributions of quantitative variables. \n",
    "- All the bars in a bar chart have the same width, and there is an equal amount of space between consecutive bars. The bars of a histogram can have different widths, and they are contiguous.\n",
    "- The lengths (or heights, if the bars are drawn vertically) of the bars in a bar chart are proportional to the value for each category. The heights of bars in a histogram measure densities; the *areas* of bars in a histogram are proportional to the numbers of entries in the bins."
   ]
  }
 ],
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