{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# HIDDEN\n", "from datascience import *\n", "import numpy as np\n", "path_data = '../../../../data/'\n", "import matplotlib\n", "matplotlib.use('Agg', warn=False)\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import warnings\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", "\n", "from urllib.request import urlopen \n", "import re\n", "def read_url(url): \n", " return re.sub('\\\\s+', ' ', urlopen(url).read().decode())" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# HIDDEN\n", "\n", "# Read two books, fast (again)!\n", "\n", "huck_finn_url = 'https://www.inferentialthinking.com/data/huck_finn.txt'\n", "huck_finn_text = read_url(huck_finn_url)\n", "huck_finn_chapters = huck_finn_text.split('CHAPTER ')[44:]\n", "\n", "little_women_url = 'https://www.inferentialthinking.com/data/little_women.txt'\n", "little_women_text = read_url(little_women_url)\n", "little_women_chapters = little_women_text.split('CHAPTER ')[1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Another Kind of Character\n", "\n", "In some situations, the relationships between quantities allow us to make predictions. This text will explore how to make accurate predictions based on incomplete information and develop methods for combining multiple sources of uncertain information to make decisions.\n", "\n", "As an example of visualizing information derived from multiple sources, let us first use the computer to get some information that would be tedious to acquire by hand. In the context of novels, the word \"character\" has a second meaning: a printed symbol such as a letter or number or punctuation symbol. Here, we ask the computer to count the number of characters and the number of periods in each chapter of both *Huckleberry Finn* and *Little Women*." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# In each chapter, count the number of all characters;\n", "# call this the \"length\" of the chapter.\n", "# Also count the number of periods.\n", "\n", "chars_periods_huck_finn = Table().with_columns([\n", " 'Huck Finn Chapter Length', [len(s) for s in huck_finn_chapters],\n", " 'Number of Periods', np.char.count(huck_finn_chapters, '.')\n", " ])\n", "chars_periods_little_women = Table().with_columns([\n", " 'Little Women Chapter Length', [len(s) for s in little_women_chapters],\n", " 'Number of Periods', np.char.count(little_women_chapters, '.')\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the data for *Huckleberry Finn*. Each row of the table corresponds to one chapter of the novel and displays the number of characters as well as the number of periods in the chapter. Not surprisingly, chapters with fewer characters also tend to have fewer periods, in general – the shorter the chapter, the fewer sentences there tend to be, and vice versa. The relation is not entirely predictable, however, as sentences are of varying lengths and can involve other punctuation such as question marks. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Huck Finn Chapter Length | Number of Periods | \n", "
---|---|
7,026 | 66 | \n", "
11,982 | 117 | \n", "
8,529 | 72 | \n", "
6,799 | 84 | \n", "
8,166 | 91 | \n", "
14,550 | 125 | \n", "
13,218 | 127 | \n", "
22,208 | 249 | \n", "
8,081 | 71 | \n", "
7,036 | 70 | \n", "
... (33 rows omitted)
" ], "text/plain": [ "Huck Finn Chapter Length | Number of Periods\n", "7,026 | 66\n", "11,982 | 117\n", "8,529 | 72\n", "6,799 | 84\n", "8,166 | 91\n", "14,550 | 125\n", "13,218 | 127\n", "22,208 | 249\n", "8,081 | 71\n", "7,036 | 70\n", "... (33 rows omitted)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chars_periods_huck_finn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the corresponding data for *Little Women*." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Little Women Chapter Length | Number of Periods | \n", "
---|---|
21,759 | 189 | \n", "
22,148 | 188 | \n", "
20,558 | 231 | \n", "
25,526 | 195 | \n", "
23,395 | 255 | \n", "
14,622 | 140 | \n", "
14,431 | 131 | \n", "
22,476 | 214 | \n", "
33,767 | 337 | \n", "
18,508 | 185 | \n", "
... (37 rows omitted)
" ], "text/plain": [ "Little Women Chapter Length | Number of Periods\n", "21,759 | 189\n", "22,148 | 188\n", "20,558 | 231\n", "25,526 | 195\n", "23,395 | 255\n", "14,622 | 140\n", "14,431 | 131\n", "22,476 | 214\n", "33,767 | 337\n", "18,508 | 185\n", "... (37 rows omitted)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chars_periods_little_women" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the chapters of *Little Women* are in general longer than those of *Huckleberry Finn*. Let us see if these two simple variables – the length and number of periods in each chapter – can tell us anything more about the two books. One way for us to do this is to plot both sets of data on the same axes. \n", "\n", "In the plot below, there is a dot for each chapter in each book. Blue dots correspond to *Huckleberry Finn* and gold dots to *Little Women*. The horizontal axis represents the number of periods and the vertical axis represents the number of characters." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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umveJ5WbBqDT2R7lZMIsFUIsnOFlevHgR/fv3x549e9CpUycUFxcr111aW1vj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\nZcsWAgMDcXBwwNTUFE9PT0aNGsUPP/zA3LlzOX78OL169UKlUpGYmEh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