code/chap05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "Chapter 5\n",
    "\n",
    "Copyright 2017 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data\n",
    "\n",
    "Pandas is a library that provides tools for reading and processing data.  `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n",
    "\n",
    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'data/World_population_estimates.html'\n",
    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
    "len(tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
    "\n",
    "`head` selects the header and the first five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>United States Census Bureau (2017)[28]</th>\n",
       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
       "      <th>Maddison (2008)[17]</th>\n",
       "      <th>HYDE (2007)[24]</th>\n",
       "      <th>Tanton (1994)[18]</th>\n",
       "      <th>Biraben (1980)[19]</th>\n",
       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
       "      <th>Thomlinson (1975)[21]</th>\n",
       "      <th>Durand (1974)[22]</th>\n",
       "      <th>Clark (1967)[23]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2557628654</td>\n",
       "      <td>2.516000e+09</td>\n",
       "      <td>2.525149e+09</td>\n",
       "      <td>2.544000e+09</td>\n",
       "      <td>2.527960e+09</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>2.527000e+09</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.486000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>2594939877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.572851e+09</td>\n",
       "      <td>2.571663e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>2636772306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.619292e+09</td>\n",
       "      <td>2.617949e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2682053389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.665865e+09</td>\n",
       "      <td>2.665959e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2730228104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.713172e+09</td>\n",
       "      <td>2.716927e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      United States Census Bureau (2017)[28]  \\\n",
       "Year                                           \n",
       "1950                              2557628654   \n",
       "1951                              2594939877   \n",
       "1952                              2636772306   \n",
       "1953                              2682053389   \n",
       "1954                              2730228104   \n",
       "\n",
       "      Population Reference Bureau (1973–2016)[15]  \\\n",
       "Year                                                \n",
       "1950                                 2.516000e+09   \n",
       "1951                                          NaN   \n",
       "1952                                          NaN   \n",
       "1953                                          NaN   \n",
       "1954                                          NaN   \n",
       "\n",
       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
       "Year                                                                        \n",
       "1950                                       2.525149e+09                     \n",
       "1951                                       2.572851e+09                     \n",
       "1952                                       2.619292e+09                     \n",
       "1953                                       2.665865e+09                     \n",
       "1954                                       2.713172e+09                     \n",
       "\n",
       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
       "Year                                                            \n",
       "1950         2.544000e+09     2.527960e+09       2.400000e+09   \n",
       "1951         2.571663e+09              NaN                NaN   \n",
       "1952         2.617949e+09              NaN                NaN   \n",
       "1953         2.665959e+09              NaN                NaN   \n",
       "1954         2.716927e+09              NaN                NaN   \n",
       "\n",
       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
       "Year                                                                          \n",
       "1950        2.527000e+09                2.500000e+09           2.400000e+09   \n",
       "1951                 NaN                         NaN                    NaN   \n",
       "1952                 NaN                         NaN                    NaN   \n",
       "1953                 NaN                         NaN                    NaN   \n",
       "1954                 NaN                         NaN                    NaN   \n",
       "\n",
       "     Durand (1974)[22]  Clark (1967)[23]  \n",
       "Year                                      \n",
       "1950               NaN      2.486000e+09  \n",
       "1951               NaN               NaN  \n",
       "1952               NaN               NaN  \n",
       "1953               NaN               NaN  \n",
       "1954               NaN               NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2 = tables[2]\n",
    "table2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`tail` selects the last five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>United States Census Bureau (2017)[28]</th>\n",
       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
       "      <th>Maddison (2008)[17]</th>\n",
       "      <th>HYDE (2007)[24]</th>\n",
       "      <th>Tanton (1994)[18]</th>\n",
       "      <th>Biraben (1980)[19]</th>\n",
       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
       "      <th>Thomlinson (1975)[21]</th>\n",
       "      <th>Durand (1974)[22]</th>\n",
       "      <th>Clark (1967)[23]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>7013871313</td>\n",
       "      <td>7.057075e+09</td>\n",
       "      <td>7.080072e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7092128094</td>\n",
       "      <td>7.136796e+09</td>\n",
       "      <td>7.162119e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>7169968185</td>\n",
       "      <td>7.238184e+09</td>\n",
       "      <td>7.243784e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>7247892788</td>\n",
       "      <td>7.336435e+09</td>\n",
       "      <td>7.349472e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7325996709</td>\n",
       "      <td>7.418152e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      United States Census Bureau (2017)[28]  \\\n",
       "Year                                           \n",
       "2012                              7013871313   \n",
       "2013                              7092128094   \n",
       "2014                              7169968185   \n",
       "2015                              7247892788   \n",
       "2016                              7325996709   \n",
       "\n",
       "      Population Reference Bureau (1973–2016)[15]  \\\n",
       "Year                                                \n",
       "2012                                 7.057075e+09   \n",
       "2013                                 7.136796e+09   \n",
       "2014                                 7.238184e+09   \n",
       "2015                                 7.336435e+09   \n",
       "2016                                 7.418152e+09   \n",
       "\n",
       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
       "Year                                                                        \n",
       "2012                                       7.080072e+09                     \n",
       "2013                                       7.162119e+09                     \n",
       "2014                                       7.243784e+09                     \n",
       "2015                                       7.349472e+09                     \n",
       "2016                                                NaN                     \n",
       "\n",
       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
       "Year                                                            \n",
       "2012                  NaN              NaN                NaN   \n",
       "2013                  NaN              NaN                NaN   \n",
       "2014                  NaN              NaN                NaN   \n",
       "2015                  NaN              NaN                NaN   \n",
       "2016                  NaN              NaN                NaN   \n",
       "\n",
       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
       "Year                                                                          \n",
       "2012                 NaN                         NaN                    NaN   \n",
       "2013                 NaN                         NaN                    NaN   \n",
       "2014                 NaN                         NaN                    NaN   \n",
       "2015                 NaN                         NaN                    NaN   \n",
       "2016                 NaN                         NaN                    NaN   \n",
       "\n",
       "     Durand (1974)[22]  Clark (1967)[23]  \n",
       "Year                                      \n",
       "2012               NaN               NaN  \n",
       "2013               NaN               NaN  \n",
       "2014               NaN               NaN  \n",
       "2015               NaN               NaN  \n",
       "2016               NaN               NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Long column names are awkard to work with, but we can replace them with abbreviated names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
    "                  'thomlinson', 'durand', 'clark']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the DataFrame looks like now.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>census</th>\n",
       "      <th>prb</th>\n",
       "      <th>un</th>\n",
       "      <th>maddison</th>\n",
       "      <th>hyde</th>\n",
       "      <th>tanton</th>\n",
       "      <th>biraben</th>\n",
       "      <th>mj</th>\n",
       "      <th>thomlinson</th>\n",
       "      <th>durand</th>\n",
       "      <th>clark</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2557628654</td>\n",
       "      <td>2.516000e+09</td>\n",
       "      <td>2.525149e+09</td>\n",
       "      <td>2.544000e+09</td>\n",
       "      <td>2.527960e+09</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>2.527000e+09</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.486000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>2594939877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.572851e+09</td>\n",
       "      <td>2.571663e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>2636772306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.619292e+09</td>\n",
       "      <td>2.617949e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2682053389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.665865e+09</td>\n",
       "      <td>2.665959e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2730228104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.713172e+09</td>\n",
       "      <td>2.716927e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          census           prb            un      maddison          hyde  \\\n",
       "Year                                                                       \n",
       "1950  2557628654  2.516000e+09  2.525149e+09  2.544000e+09  2.527960e+09   \n",
       "1951  2594939877           NaN  2.572851e+09  2.571663e+09           NaN   \n",
       "1952  2636772306           NaN  2.619292e+09  2.617949e+09           NaN   \n",
       "1953  2682053389           NaN  2.665865e+09  2.665959e+09           NaN   \n",
       "1954  2730228104           NaN  2.713172e+09  2.716927e+09           NaN   \n",
       "\n",
       "            tanton       biraben            mj    thomlinson durand  \\\n",
       "Year                                                                  \n",
       "1950  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09    NaN   \n",
       "1951           NaN           NaN           NaN           NaN    NaN   \n",
       "1952           NaN           NaN           NaN           NaN    NaN   \n",
       "1953           NaN           NaN           NaN           NaN    NaN   \n",
       "1954           NaN           NaN           NaN           NaN    NaN   \n",
       "\n",
       "             clark  \n",
       "Year                \n",
       "1950  2.486000e+09  \n",
       "1951           NaN  \n",
       "1952           NaN  \n",
       "1953           NaN  \n",
       "1954           NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
    "\n",
    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
    "\n",
    "`NaN` is a special value that indicates missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series\n",
    "\n",
    "We can use dot notation to select a column from a `DataFrame`.  The result is a `Series`, which is like a `DataFrame` with a single column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1950    2557628654\n",
       "1951    2594939877\n",
       "1952    2636772306\n",
       "1953    2682053389\n",
       "1954    2730228104\n",
       "Name: census, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = table2.census\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2012    7013871313\n",
       "2013    7092128094\n",
       "2014    7169968185\n",
       "2015    7247892788\n",
       "2016    7325996709\n",
       "Name: census, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n",
    "\n",
    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here on, we will work in units of billions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1950    2.525149\n",
       "1951    2.572851\n",
       "1952    2.619292\n",
       "1953    2.665865\n",
       "1954    2.713172\n",
       "Name: un, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un = table2.un / 1e9\n",
    "un.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1950    2.557629\n",
       "1951    2.594940\n",
       "1952    2.636772\n",
       "1953    2.682053\n",
       "1954    2.730228\n",
       "Name: census, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = table2.census / 1e9\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what these estimates look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure to file figs/chap03-fig01.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(census, ':', label='US Census')\n",
    "plot(un, '--', label='UN DESA')\n",
    "    \n",
    "decorate(xlabel='Year',\n",
    "         ylabel='World population (billion)')\n",
    "savefig('figs/chap03-fig01.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n",
    "\n",
    "So the largest relative error between the estimates is about 1.3%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3821293828998855"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(abs(census - un) / un) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
    "\n",
    "1.  Compute the elementwise differences, `census - un`\n",
    "2.  Compute the absolute differences, `abs(census - un)`\n",
    "3.  Compute the relative differences, `abs(census - un) / un`\n",
    "4.  Compute the percent differences, `abs(census - un) / un * 100`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year\n",
      "1950    0.032480\n",
      "1951    0.022089\n",
      "1952    0.017480\n",
      "1953    0.016188\n",
      "1954    0.017056\n",
      "1955    0.020448\n",
      "1956    0.023728\n",
      "1957    0.028307\n",
      "1958    0.032107\n",
      "1959    0.030321\n",
      "1960    0.016999\n",
      "1961    0.001137\n",
      "1962   -0.000978\n",
      "1963    0.008650\n",
      "1964    0.017462\n",
      "1965    0.021303\n",
      "1966    0.023203\n",
      "1967    0.021812\n",
      "1968    0.020639\n",
      "1969    0.021050\n",
      "1970    0.021525\n",
      "1971    0.023573\n",
      "1972    0.023695\n",
      "1973    0.022914\n",
      "1974    0.021304\n",
      "1975    0.018063\n",
      "1976    0.014049\n",
      "1977    0.011268\n",
      "1978    0.008441\n",
      "1979    0.007486\n",
      "          ...   \n",
      "1987   -0.018115\n",
      "1988   -0.023658\n",
      "1989   -0.028560\n",
      "1990   -0.031861\n",
      "1991   -0.037323\n",
      "1992   -0.038763\n",
      "1993   -0.040597\n",
      "1994   -0.042404\n",
      "1995   -0.042619\n",
      "1996   -0.041576\n",
      "1997   -0.040716\n",
      "1998   -0.040090\n",
      "1999   -0.039403\n",
      "2000   -0.039129\n",
      "2001   -0.038928\n",
      "2002   -0.038837\n",
      "2003   -0.039401\n",
      "2004   -0.040006\n",
      "2005   -0.041050\n",
      "2006   -0.041964\n",
      "2007   -0.043192\n",
      "2008   -0.044599\n",
      "2009   -0.046508\n",
      "2010   -0.057599\n",
      "2011   -0.061999\n",
      "2012   -0.066201\n",
      "2013   -0.069991\n",
      "2014   -0.073816\n",
      "2015   -0.101579\n",
      "2016         NaN\n",
      "Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(census-un)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year\n",
      "1950    0.032480\n",
      "1951    0.022089\n",
      "1952    0.017480\n",
      "1953    0.016188\n",
      "1954    0.017056\n",
      "1955    0.020448\n",
      "1956    0.023728\n",
      "1957    0.028307\n",
      "1958    0.032107\n",
      "1959    0.030321\n",
      "1960    0.016999\n",
      "1961    0.001137\n",
      "1962    0.000978\n",
      "1963    0.008650\n",
      "1964    0.017462\n",
      "1965    0.021303\n",
      "1966    0.023203\n",
      "1967    0.021812\n",
      "1968    0.020639\n",
      "1969    0.021050\n",
      "1970    0.021525\n",
      "1971    0.023573\n",
      "1972    0.023695\n",
      "1973    0.022914\n",
      "1974    0.021304\n",
      "1975    0.018063\n",
      "1976    0.014049\n",
      "1977    0.011268\n",
      "1978    0.008441\n",
      "1979    0.007486\n",
      "          ...   \n",
      "1987    0.018115\n",
      "1988    0.023658\n",
      "1989    0.028560\n",
      "1990    0.031861\n",
      "1991    0.037323\n",
      "1992    0.038763\n",
      "1993    0.040597\n",
      "1994    0.042404\n",
      "1995    0.042619\n",
      "1996    0.041576\n",
      "1997    0.040716\n",
      "1998    0.040090\n",
      "1999    0.039403\n",
      "2000    0.039129\n",
      "2001    0.038928\n",
      "2002    0.038837\n",
      "2003    0.039401\n",
      "2004    0.040006\n",
      "2005    0.041050\n",
      "2006    0.041964\n",
      "2007    0.043192\n",
      "2008    0.044599\n",
      "2009    0.046508\n",
      "2010    0.057599\n",
      "2011    0.061999\n",
      "2012    0.066201\n",
      "2013    0.069991\n",
      "2014    0.073816\n",
      "2015    0.101579\n",
      "2016         NaN\n",
      "Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(abs(census-un))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1950    0.012862\n",
       "1951    0.008585\n",
       "1952    0.006674\n",
       "1953    0.006072\n",
       "1954    0.006286\n",
       "1955    0.007404\n",
       "1956    0.008439\n",
       "1957    0.009887\n",
       "1958    0.011011\n",
       "1959    0.010208\n",
       "1960    0.005617\n",
       "1961    0.000369\n",
       "1962    0.000311\n",
       "1963    0.002702\n",
       "1964    0.005350\n",
       "1965    0.006399\n",
       "1966    0.006829\n",
       "1967    0.006289\n",
       "1968    0.005827\n",
       "1969    0.005821\n",
       "1970    0.005832\n",
       "1971    0.006258\n",
       "1972    0.006166\n",
       "1973    0.005847\n",
       "1974    0.005332\n",
       "1975    0.004437\n",
       "1976    0.003388\n",
       "1977    0.002670\n",
       "1978    0.001965\n",
       "1979    0.001712\n",
       "          ...   \n",
       "1987    0.003591\n",
       "1988    0.004604\n",
       "1989    0.005461\n",
       "1990    0.005988\n",
       "1991    0.006900\n",
       "1992    0.007054\n",
       "1993    0.007277\n",
       "1994    0.007490\n",
       "1995    0.007423\n",
       "1996    0.007142\n",
       "1997    0.006903\n",
       "1998    0.006709\n",
       "1999    0.006511\n",
       "2000    0.006386\n",
       "2001    0.006274\n",
       "2002    0.006183\n",
       "2003    0.006197\n",
       "2004    0.006216\n",
       "2005    0.006302\n",
       "2006    0.006365\n",
       "2007    0.006473\n",
       "2008    0.006604\n",
       "2009    0.006805\n",
       "2010    0.008328\n",
       "2011    0.008860\n",
       "2012    0.009350\n",
       "2013    0.009772\n",
       "2014    0.010190\n",
       "2015    0.013821\n",
       "2016         NaN\n",
       "Length: 67, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(census - un) / un"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1950    1.286247\n",
       "1951    0.858540\n",
       "1952    0.667365\n",
       "1953    0.607232\n",
       "1954    0.628640\n",
       "1955    0.740425\n",
       "1956    0.843928\n",
       "1957    0.988701\n",
       "1958    1.101054\n",
       "1959    1.020766\n",
       "1960    0.561750\n",
       "1961    0.036871\n",
       "1962    0.031146\n",
       "1963    0.270201\n",
       "1964    0.535045\n",
       "1965    0.639908\n",
       "1966    0.682939\n",
       "1967    0.628856\n",
       "1968    0.582745\n",
       "1969    0.582126\n",
       "1970    0.583151\n",
       "1971    0.625807\n",
       "1972    0.616597\n",
       "1973    0.584666\n",
       "1974    0.533223\n",
       "1975    0.443692\n",
       "1976    0.338849\n",
       "1977    0.266959\n",
       "1978    0.196499\n",
       "1979    0.171246\n",
       "          ...   \n",
       "1987    0.359053\n",
       "1988    0.460423\n",
       "1989    0.546078\n",
       "1990    0.598794\n",
       "1991    0.690025\n",
       "1992    0.705441\n",
       "1993    0.727689\n",
       "1994    0.749047\n",
       "1995    0.742263\n",
       "1996    0.714242\n",
       "1997    0.690252\n",
       "1998    0.670935\n",
       "1999    0.651132\n",
       "2000    0.638560\n",
       "2001    0.627448\n",
       "2002    0.618347\n",
       "2003    0.619705\n",
       "2004    0.621627\n",
       "2005    0.630170\n",
       "2006    0.636478\n",
       "2007    0.647257\n",
       "2008    0.660376\n",
       "2009    0.680460\n",
       "2010    0.832811\n",
       "2011    0.885957\n",
       "2012    0.935034\n",
       "2013    0.977243\n",
       "2014    1.019023\n",
       "2015    1.382129\n",
       "2016         NaN\n",
       "Length: 67, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(census - un) / un * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general.  When you import `modsim`, you get the NumPy versions of these functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constant growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select a value from a `Series` using bracket notation.  Here's the first element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.557628654"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census[1950]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the last value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.325996709"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census[2016]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_0 = get_first_label(census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_end = get_last_label(census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elapsed_time = t_end - t_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can get the first and last values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.557628654"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_0 = get_first_value(census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.325996709"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_end = get_last_value(census)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can compute the average annual growth in billions of people per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.768368055"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_growth = p_end - p_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07224800083333333"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_growth = total_growth / elapsed_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TimeSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = TimeSeries()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2.557629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "1950    2.557629\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[t_0] = census[t_0]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, the population in the model grows by a constant amount each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in linrange(t_0, t_end):\n",
    "    results[t+1] = results[t] + annual_growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the results looks like, compared to the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure to file figs/chap03-fig02.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(census, ':', label='US Census')\n",
    "plot(un, '--', label='UN DESA')\n",
    "plot(results, color='gray', label='model')\n",
    "\n",
    "decorate(xlabel='Year', \n",
    "         ylabel='World population (billion)',\n",
    "         title='Constant growth')\n",
    "savefig('figs/chap03-fig02.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model fits the data pretty well after 1990, but not so well before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "**Optional Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
    "\n",
    "Hint: \n",
    "\n",
    "1. Copy the code from above and make a few changes.  Test your code after each small change.\n",
    "\n",
    "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n",
    "\n",
    "3. You might want to add a constant to the starting value to match the data better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2.141698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>2.220248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>2.298798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2.377348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2.455898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>2.534448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2.612998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>2.691548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>2.770098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>2.848648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>2.927198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>3.005748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>3.084298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>3.162848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>3.241398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>3.319948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>3.398498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>3.477048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>3.555598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>3.634148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>3.712698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>3.791248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>3.869798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>3.948348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>4.026898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>4.105448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>4.183998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>4.262548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>4.341098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>4.419648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>5.048047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5.126597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>5.205147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>5.283697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>5.362247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>5.440797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>5.519347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>5.597897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>5.676447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>5.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>5.833547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>5.912097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>5.990647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>6.069197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>6.147747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>6.226297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>6.304847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>6.383397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>6.461947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>6.540497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.619047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>6.697597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6.776147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>6.854697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6.933247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>7.011797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7.090347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>7.168897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>7.247447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7.325997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "1950    2.141698\n",
       "1951    2.220248\n",
       "1952    2.298798\n",
       "1953    2.377348\n",
       "1954    2.455898\n",
       "1955    2.534448\n",
       "1956    2.612998\n",
       "1957    2.691548\n",
       "1958    2.770098\n",
       "1959    2.848648\n",
       "1960    2.927198\n",
       "1961    3.005748\n",
       "1962    3.084298\n",
       "1963    3.162848\n",
       "1964    3.241398\n",
       "1965    3.319948\n",
       "1966    3.398498\n",
       "1967    3.477048\n",
       "1968    3.555598\n",
       "1969    3.634148\n",
       "1970    3.712698\n",
       "1971    3.791248\n",
       "1972    3.869798\n",
       "1973    3.948348\n",
       "1974    4.026898\n",
       "1975    4.105448\n",
       "1976    4.183998\n",
       "1977    4.262548\n",
       "1978    4.341098\n",
       "1979    4.419648\n",
       "          ...   \n",
       "1987    5.048047\n",
       "1988    5.126597\n",
       "1989    5.205147\n",
       "1990    5.283697\n",
       "1991    5.362247\n",
       "1992    5.440797\n",
       "1993    5.519347\n",
       "1994    5.597897\n",
       "1995    5.676447\n",
       "1996    5.754997\n",
       "1997    5.833547\n",
       "1998    5.912097\n",
       "1999    5.990647\n",
       "2000    6.069197\n",
       "2001    6.147747\n",
       "2002    6.226297\n",
       "2003    6.304847\n",
       "2004    6.383397\n",
       "2005    6.461947\n",
       "2006    6.540497\n",
       "2007    6.619047\n",
       "2008    6.697597\n",
       "2009    6.776147\n",
       "2010    6.854697\n",
       "2011    6.933247\n",
       "2012    7.011797\n",
       "2013    7.090347\n",
       "2014    7.168897\n",
       "2015    7.247447\n",
       "2016    7.325997\n",
       "Length: 67, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startingValue= census[1970]\n",
    "startingLabel = 1970\n",
    "endingLabel = get_last_label(census)\n",
    "endingValue= get_last_value(census)\n",
    "totalGrowth = endingValue - startingValue\n",
    "totalTime = endingLabel-startingLabel\n",
    "rate = totalGrowth/totalTime\n",
    "\n",
    "results = TimeSeries()\n",
    "count = 20\n",
    "for i in range(1950,1970):\n",
    "    results[i] = census[1970]-rate*count\n",
    "    count-=1\n",
    "results[startingLabel] = census[startingLabel]\n",
    "for i in range(startingLabel, endingLabel):\n",
    "    results[i+1] = results[i]+rate\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure to file figs/chap03-fig02.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(census, ':', label='US Census')\n",
    "plot(un, '--', label='UN DESA')\n",
    "plot(results, color='gray', label='model')\n",
    "\n",
    "decorate(xlabel='Year', \n",
    "         ylabel='World population (billion)',\n",
    "         title='Constant growth')\n",
    "savefig('figs/chap03-fig02.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}