{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import matplotlib\n",
    "path_data = '../../../assets/data/'\n",
    "matplotlib.use('Agg')\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "import numpy as np\n",
    "np.set_printoptions(threshold=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Categorical Distributions\n",
    "\n",
    "Data come in many forms that are not numerical. Data can be pieces of music, or places on a map. They can also be categories into which you can place individuals. Here are some examples of *categorical* variables.\n",
    "\n",
    "- The individuals are cartons of ice-cream, and the variable is the flavor in the carton.\n",
    "- The individuals are professional basketball players, and the variable is the player's team.\n",
    "- The individuals are years, and the variable is the genre of the highest grossing movie of the year.\n",
    "- The individuals are survey respondents, and the variable is the response they choose from among \"Not at all satisfied,\" \"Somewhat satisfied,\" and \"Very satisfied.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table `icecream` contains data on 30 cartons of ice-cream. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Flavor</th> <th>Number of Cartons</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Chocolate </td> <td>16               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Strawberry</td> <td>5                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vanilla   </td> <td>9                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Flavor     | Number of Cartons\n",
       "Chocolate  | 16\n",
       "Strawberry | 5\n",
       "Vanilla    | 9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icecream = Table().with_columns(\n",
    "    'Flavor', make_array('Chocolate', 'Strawberry', 'Vanilla'),\n",
    "    'Number of Cartons', make_array(16, 5, 9)\n",
    ")\n",
    "icecream"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *values* of the categorical variable \"flavor\" are chocolate, strawberry, and vanilla. \n",
    "\n",
    "Each of the cartons had exactly one of the three flavors. In a *distribution*, each individual belongs to exactly one category and thus has exactly one value. So we have a distribution of flavors.\n",
    "\n",
    "The table shows the number of cartons of each flavor. We call this a *distribution table*. A distribution table shows all the values of the variable along with the frequency of each one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Chart\n",
    "The bar chart is a familiar way of visualizing categorical distributions. It displays a bar for each category. The bars are equally spaced and equally wide. The length of each bar is proportional to the frequency of the corresponding category.\n",
    "\n",
    "We will draw bar charts with horizontal bars because it's easier to label the bars that way. The Table method is therefore called `barh`. It takes two arguments: the first is the column label of the categories, and the second is the column label of the frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icecream.barh('Flavor', 'Number of Cartons')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the table consists just of a column of categories and a column of frequencies, as in `icecream`, the method call is even simpler. You can just specify the column containing the categories, and `barh` will use the values in the other column as frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icecream.barh('Flavor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Aspects of Bar Charts\n",
    "Apart from purely visual differences, there is an important fundamental distinction between bar charts and the two graphs that we saw in the previous sections. Those were the scatter plot and the line plot, both of which display two quantitative variables – the variables on both axes are quantitative. In contrast, the bar chart has categories on one axis and numerical quantities on the other.\n",
    "\n",
    "This has consequences for the chart. First, the width of each bar and the space between consecutive bars is entirely up to the person who is producing the graph, or to the program being used to produce it. Python made those choices for us. If you were to draw the bar graph by hand, you could make completely different choices and still have a perfectly correct bar graph, provided you drew all the bars with the same width and kept all the spaces the same.\n",
    "\n",
    "Most importantly, the bars can be drawn in any order. The categories \"chocolate,\" \"vanilla,\" and \"strawberry\" have no universal rank order, unlike for example the numbers 5, 7, and 10.\n",
    "\n",
    "This means that we can draw a bar chart that is easier to interpret, by rearranging the bars in decreasing order. To do this, we first rearrange the rows of `icecream` in decreasing order of `Number of Cartons`, and then draw the bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icecream.sort('Number of Cartons', descending=True).barh('Flavor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar chart contains exactly the same information as the previous ones, but it is a little easier to read. While this is not a huge gain in reading a chart with just three bars, it can be quite significant when the number of categories is large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping Categorical Data\n",
    "To construct the table `icecream`, someone had to look at all 30 cartons of ice-cream and count the number of each flavor. But if our table does not already include frequencies, we have to compute the frequencies before we can draw a bar chart. Here is an example where this is necessary.\n",
    "\n",
    "The table `top` consists of U.S.A.'s top grossing movies of all time, as of 2017. 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 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 year in which the movie was released. \n",
    "\n",
    "There are 200 movies on the list. Here are the top ten according to unadjusted gross receipts."
   ]
  },
  {
   "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>Studio</th> <th>Gross</th> <th>Gross (Adjusted)</th> <th>Year</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Gone with the Wind              </td> <td>MGM            </td> <td>198676459</td> <td>1796176700      </td> <td>1939</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Star Wars                       </td> <td>Fox            </td> <td>460998007</td> <td>1583483200      </td> <td>1977</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>The Sound of Music              </td> <td>Fox            </td> <td>158671368</td> <td>1266072700      </td> <td>1965</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>E.T.: The Extra-Terrestrial     </td> <td>Universal      </td> <td>435110554</td> <td>1261085000      </td> <td>1982</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Titanic                         </td> <td>Paramount      </td> <td>658672302</td> <td>1204368000      </td> <td>1997</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>The Ten Commandments            </td> <td>Paramount      </td> <td>65500000 </td> <td>1164590000      </td> <td>1956</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Jaws                            </td> <td>Universal      </td> <td>260000000</td> <td>1138620700      </td> <td>1975</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Doctor Zhivago                  </td> <td>MGM            </td> <td>111721910</td> <td>1103564200      </td> <td>1965</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>The Exorcist                    </td> <td>Warner Brothers</td> <td>232906145</td> <td>983226600       </td> <td>1973</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Snow White and the Seven Dwarves</td> <td>Disney         </td> <td>184925486</td> <td>969010000       </td> <td>1937</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (190 rows omitted)</p>"
      ],
      "text/plain": [
       "Title                            | Studio          | Gross     | Gross (Adjusted) | Year\n",
       "Gone with the Wind               | MGM             | 198676459 | 1796176700       | 1939\n",
       "Star Wars                        | Fox             | 460998007 | 1583483200       | 1977\n",
       "The Sound of Music               | Fox             | 158671368 | 1266072700       | 1965\n",
       "E.T.: The Extra-Terrestrial      | Universal       | 435110554 | 1261085000       | 1982\n",
       "Titanic                          | Paramount       | 658672302 | 1204368000       | 1997\n",
       "The Ten Commandments             | Paramount       | 65500000  | 1164590000       | 1956\n",
       "Jaws                             | Universal       | 260000000 | 1138620700       | 1975\n",
       "Doctor Zhivago                   | MGM             | 111721910 | 1103564200       | 1965\n",
       "The Exorcist                     | Warner Brothers | 232906145 | 983226600        | 1973\n",
       "Snow White and the Seven Dwarves | Disney          | 184925486 | 969010000        | 1937\n",
       "... (190 rows omitted)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = Table.read_table(path_data + 'top_movies_2017.csv')\n",
    "top"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The studios MGM, Fox, Universal, and Paramount show up more than once in the top ten. Which studios will appear most frequently if we look among all 200 rows?\n",
    "\n",
    "To figure this out, first notice that all we need is a table with the movies and the studios; the other information is unnecessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_and_studios = top.select('Title', 'Studio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Table method `group` allows us to count how frequently each studio appears in the table, by calling each studio a category and collecting all the rows in each of these new categories. \n",
    "\n",
    "The `group` method takes as its argument the label of the column that contains the categories. It returns a table of counts of rows in each category. \n",
    "\n",
    "Thus `group` creates a distribution table that shows how the individuals (movies) are distributed among the categories (studios). \n",
    "\n",
    "The `group` method lists the categories in ascending order. Since our categories are studio names and therefore represented as strings, ascending order means alphabetical order.\n",
    "\n",
    "The column of counts is always called `count`, but you can change that if you like by using `relabeled`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Studio</th> <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AVCO       </td> <td>1    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Buena Vista</td> <td>35   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Columbia   </td> <td>9    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Disney     </td> <td>11   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Dreamworks </td> <td>3    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Fox        </td> <td>24   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IFC        </td> <td>1    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Lionsgate  </td> <td>3    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MGM        </td> <td>7    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Metro      </td> <td>1    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (13 rows omitted)</p>"
      ],
      "text/plain": [
       "Studio      | count\n",
       "AVCO        | 1\n",
       "Buena Vista | 35\n",
       "Columbia    | 9\n",
       "Disney      | 11\n",
       "Dreamworks  | 3\n",
       "Fox         | 24\n",
       "IFC         | 1\n",
       "Lionsgate   | 3\n",
       "MGM         | 7\n",
       "Metro       | 1\n",
       "... (13 rows omitted)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "studio_distribution = movies_and_studios.group('Studio')\n",
    "studio_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table shows that there are 23 different studios and provides the count of movies released by each one. The total of the count is 200, the total number of movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(studio_distribution.column('count'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use this table, along with the graphing skills acquired above, to draw a bar chart that shows which studios are most frequent among the 200 highest grossing movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x828 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "studio_distribution.sort('count', descending=True).barh('Studio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buena Vista and Warner Brothers are the most common studios among the top 200 movies. Warner Brothers produces the Harry Potter movies and Buena Vista produces Star Wars. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Towards Quantitative Variables\n",
    "\n",
    "Though years are numerical, we could treat the year of release as a categorical variable and plot the its distribution. That is, for every year we could find how many movies were released that year, and then draw the bar chart of that distribution.\n",
    "\n",
    "Let's do that and just look at the 10 earliest years in the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_and_years = top.select('Title', 'Year')\n",
    "movies_and_years.group('Year').take(np.arange(10)).barh('Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The years appear in increasing order because `group` sorts the categories from lowest to highest. That is important here because years have a chronological order that has to be maintained. But there is something disquieting about this bar chart. The bars at 1921 and 1937 are just as far apart from each other as the bars at 1937 and 1939. The bar chart doesn't show that none of the 200 movies were released in the years 1922 through 1936, nor in 1938. Such inconsistencies and omissions make the distribution in the early years hard to understand based on this visualization.\n",
    "\n",
    "The distribution of a categorical variable can be displayed using a bar chart. But if the variable is not categorical but quantitative, then the numerical relations between its values have to be taken into account when we create visualizations. That is the topic of the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}