{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_run_growth_list = [\n",
    "    [-68000, 1, 1200, 0.1],\n",
    "    [-8000, 5, 1200, 2.5],\n",
    "    [-6000, 6.3, 900, 7],\n",
    "    [-3000, 9.2, 900, 15],\n",
    "    [-1000, 16.8, 900, 50],\n",
    "    [1, 30.9, 900, 170],\n",
    "    [800, 41.1, 900, 300],\n",
    "    [1500, 53, 900, 500],\n",
    "    [1770, 79.4, 1100, 750],\n",
    "    [1870, 123.5, 1300, 1300],\n",
    "    [2020, 2720.5, 11842, 7600]\n",
    "    ]\n",
    "\n",
    "long_run_growth_df = pd.DataFrame(\n",
    "  data=np.array(long_run_growth_list), columns = ['year', 'human_ideas', 'income_level', 'population']                             \n",
    "  )\n",
    "\n",
    "long_run_growth_df['year'] = long_run_growth_df['year'].apply(np.int64)\n",
    "initial_year = long_run_growth_df['year'][0:10]\n",
    "\n",
    "span = []\n",
    "g = []\n",
    "h = []\n",
    "n = []\n",
    "\n",
    "for t in range(10):\n",
    "    span = span +[long_run_growth_df['year'][t+1]-long_run_growth_df['year'][t]]\n",
    "    h = h + [np.log(long_run_growth_df['human_ideas'][t+1]/long_run_growth_df['human_ideas'][t])/span[t]]\n",
    "    g = g + [np.log(long_run_growth_df['income_level'][t+1]/long_run_growth_df['income_level'][t])/span[t]]\n",
    "    n = n + [np.log(long_run_growth_df['population'][t+1]/long_run_growth_df['population'][t])/span[t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "-68000     1200.0\n",
       "-8000      1200.0\n",
       "-6000       900.0\n",
       "-3000       900.0\n",
       "-1000       900.0\n",
       " 1          900.0\n",
       " 800        900.0\n",
       " 1500       900.0\n",
       " 1770      1100.0\n",
       " 1870      1300.0\n",
       " 2020     11842.0\n",
       "Name: income_level, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_run_growth_df.set_index('year', inplace=True)\n",
    "\n",
    "long_run_growth_df['income_level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Annual Income per Capita, 2020 Dollars')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_run_growth_df['income_level'].plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Annual Income per Capita, 2020 Dollars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Millions')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_run_growth_df['population'].plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Millions')\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Index of Useful Ideas Stock')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_run_growth_df['human_ideas'].plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Index of Useful Ideas Stock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Annual Income per Capita, 2020 Dollars')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(long_run_growth_df['income_level']).plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Log Annual Income per Capita, 2020 Dollars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Millions')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yYCywIeZ4REQKSltHZ07OAYAYE4C7rwf+DVgHbAQa3P336dOZ2RVmttzMlm/dujXfYYqIxGpYJgAzqwDeCRwAzALGmdnF6dO5+w3uvtTdl06dOjXfYYqIxKq9w3PSEyjE2wR0KvCqu2919zbgl8AbYoxHRKTgtHX48NsDIGr6Oc7MxlpU3j4FeC7GeERECk7UBDTM9gDc/VHgbuBJYHWI5Ya44hERKUTtnZ2U5WgPoCwnS+0jd/8S8KU4YxARKWSt7cOzCUhERHrR3jkMm4BERKR3w/UoIBER6UXrcDwPQEREeteuBCAiUpzaO101ABGRYtTanrvDQJUAREQKmPYARESK1LDsDE5ERHrX2q4EICJSlHa1djBuZGlOlq0EICJSwHa3djBmZG567VECEBEpUG0dnbR2dGoPQESk2Oxq7QBgjBKAiEhx2R0SwLhRagISESkqO1vbARirPQARkeKS3AMYM0IJQESkqOzcE+0BqAlIRKTI7GpTEVhEpChtqN8NwLQJo3KyfCUAEZEC9dLmZsaNLGV2YkxOlq8EICJSoNZsaWb+9AmYqTdQEZGi8tKWJg6aNj5ny1cCEBEpQA2729jcuEcJQESk2KzZ0gTAQdOVAEREispLm5sBOGjahJytQwlARKQAPbxmG5PHjczZEUAQcwIws4SZ3W1mz5vZc2Z2fJzxiIgUgt2tHfzhuS2cfsQMSkpycwQQQG7OL+67/wTuc/dzzWwkMDbmeEREYvfH57ewu62DMxfNyul6YksAZjYReBNwKYC7twKtccUjIlIo7l21gakTRnHMAZNyup44m4DmAVuBW8xshZndZGbj0icysyvMbLmZLd+6dWv+oxQRyaPmPe388fktnHHEDEpz2PwD8SaAMuBI4L/dvQbYCVyVPpG73+DuS9196dSpU/Mdo4hIXv3huc3sae/kzMW5bf6BeBNAHVDn7o+G53cTJQQRkaJ176qNzJg4mqOqK3K+rtgSgLtvAmrN7JAw6BTg2bjiERGJW2NLG396YStnLJyZ06N/kuI+CugTwO3hCKBXgMtijkdEJDYPPruZ1o5Ozlw8My/rizUBuPtKYGmcMYiIFIp7ntrA7MQYaqoSeVmfzgQWESkA23e28vBL2zhr8aycdf+cTglARKQA/Hb1Rto7nbPzcPRPkhKAiEgBuOepDcyfNp7DZuau87d0SgAiIjHb2LCbx9du5+w8Nv+AEoCISOzufWoj7uS1+QeUAEREYnfPUxtYVFnO3Cn79IaTU0oAIiIxenXbTlavb8j7v39QAhARidU9KzdgBu9YlJ+Tv1IpAYiIxOiep9Zz9NxJzCzP3ZW/eqIEICISk82NLby8dSenLZgey/qVAEREYrK6rgGAxXnq+iGdEoCISExWr2+gxGDBzImxrF8JQEQkJqvXNzB/2njGjYqnX85+JQAzqzCzRbkKRkSkWLg7q+oaWDg7nuYf6EMCMLNlZjbRzCYBTxFdw/c7uQ9NRGT42tTYwrbmPSycHU/zD/RtD6Dc3RuB9wC3uPtRwKm5DUtEZHhLFoAXVhbwHgBQZmYzgfOBe3Mcj4hIUVi9voHSEoutAAx9SwD/AtwPrHH3x81sHvBSbsMSERneVtU1cNC08YwZWRpbDL2Wnt3958DPU56/ApyTy6BERIYzd2f1+gZOOXRarHH0mgDMbCrwIWBu6vTufnnuwhIRGb42NLSwfWcriyrLY42jLwef/gr4M/Ag0JHbcEREhr/VdfVAvAVg6FsCGOvun8t5JCIiRWJVXQNlJcahM/J3+cdM+lIEvtfMzsh5JCIiRWL1+gYOnj6B0SPiKwBD3xLAlURJoMXMmsKtMdeBiYgMR8kCcNzt/9C3o4Di3UcRERlG6nbspn5XGwuHQgIAMLOzgTeFp8vcXSeEiYjsh1XJM4Bnx58A+tIX0NeJmoGeDbcrw7BBYWalZrbCzJRURGTYW72+gRGlxiExF4Chb3sAZwBL3L0TwMx+BKwArhqkGK4EngPiOx9aRCRPVq+v59AZExlVFm8BGPreHXTqwaqDtt9iZpXAO4CbBmuZIiKFqqsL6AJo/4e+7QF8DVhhZg8BRlQLuHqQ1v8fwD8BPe4LmdkVwBUA1dXVg7RaEZH8e+31XTS1tBdE+z/0YQ/A3e8AjgN+GW7Hu/udA12xmZ0JbHH3J3pZ/w3uvtTdl06dOnWgqxURic1ja7cDhVEAhiwJwMwODfdHAjOBOqAWmBWGDdQJwNlmtha4E3iLmd02CMsVESlIdzy2jnlTx3H4rMIoeWZrAvoHok7gvp1hnANvGciK3f1qQlOSmZ0M/KO7XzyQZYqIFKpnNjSwYl09XzhzAWYWdzhAlgTg7h8K92/OXzgiIsPTbY+sY/SIEs49sjLuULr0mADM7D3ZZnT3Xw5WEO6+DFg2WMsTESkkTS1t/Grles5aNIvysSPiDqdLtiags7KMc6KCsIiI9OJ/VqxnV2sHFx83J+5Q9pKtCeiyfAYiIjIcuTu3PfIaC2eXs7gq3v7/02VrAvpMthnd/TuDH46IyPDy+NodvLi5mW+cszDuUPaRrQko/o4qRESGuNseeY0Jo8s4a/GsuEPZR7YmoC/nMxARkeFmW/Mefvf0Rt537BzGjuxT58t5la0J6J/c/Ztm9j2iou9e3P2TOY1MRGSIu+vxWto6nIuPK8xubLKlpOfC/fJ8BCIiMpx0dDo/fXQdx82bxPxphdminq0J6Nfh/kf5C0dEZHj404tbWF+/m6vPODTuUHqUrQnonmwzuvvZgx+OiMjwcNsj65g6YRSnLZgRdyg9ytYEdDxR5293AI8SdQUtIiK9qN2+i4de2MLH3zyfkWV9vexK/mVLADOAtwIXAhcBvwHucPdn8hGYiMhQdcdj6zDgwmMKs/ib1GNqcvcOd7/P3S8huh7AGmCZmX0ib9GJiAwxre2d/Gx5LW85dDqzEmPiDierrAemmtkooks2XgjMBb6L+gASEenRfc9sYltza8Ee+pkqWxH4R8ARwO+AL7v703mLSkRkiLrtb69RPWksbzqo8K9gmG0P4P3ATuBg4JMpFzAwwN29MC5pIyJSIF7Y1MRja7dz1dsPpaSk8I+byXYeQOGWrkVECtDtj77GyNISzjuqcC76ko1+5EVEBsGaLc388sn1nLFwBpPHj4o7nD5RAhARGaC123Zy0Y2PMHpECZ869eC4w+kzJQARkQGo3b6Li258hLaOTm7/4HHMnTIu7pD6rPD6JxURGSI21O/mwhsfYWdrBz/90LEcMqMwO33rSa97AGbWZGaNabdaM/sfM5uXjyBFRArN5sYWLrrxERp2tfGTDxzD4bPK4w6p3/qyB/AdYAPwU6JDQC8g6ibiBeBm4ORcBSciUoi2Nu3hohsfYWvTHn78gWNZVFlY1/rtq77UAE539+vdvcndG939BuAMd78LqMhxfCIiBWX7zlYuvulRNtS3cPOlR3PUnKH7M9iXBNBpZuebWUm4nZ8ybp8rhYmIDFcNu9q4+KZHWfv6Tm66ZCnHzpscd0gD0pcE8D6is4K3hNv7gYvNbAzw8RzGJiJSMBpb2vi7mx9lzZZmrn//UZwwf0rcIQ1YrzUAd38FOKuH0Q8PbjgiIoWneU87l93yOM9saOS6i4/i5EOmxR3SoOjLUUCV4YifLWa22cx+YWYDPs/ZzKrM7CEze87MnjGzKwe6TBGRwba7tYMP3Po4K2vr+d6FNZy6YHrcIQ2avjQB3QLcA8wCZgO/DsMGqh34B3c/jOh6Ax8zswWDsFwRkUHR0tbBh368nMfXbuff37uEty+cGXdIg6ovCWCqu9/i7u3hdisw4H5O3X2juz8ZHjcBzxElGBGR2O1p7+DDtz3BX17exrfOXczZi2fFHdKg60sC2GZmF5tZabhdDLw+mEGY2Vyghujaw+njrjCz5Wa2fOvWrYO5WhGRjDo7nU/8dAXLXtjK1969kHOGSO+e/dWXBHA5cD6wCdgInAtcNlgBmNl44BfAp9y9MX28u9/g7kvdfenUqYV/gQURGfoeeG4zv392M9eccRgXFPh1fQei1wTg7uvc/Wx3n+ru09z9XcB7BmPlZjaC6Mf/dnfXpSZFJHbuzg8eWsOcyWO57IS5cYeTU/vbG+hnBrpiiy4x9kPgOXf/zkCXJyIyGP780jZW1TXw4ZMOpKx0eHeYvL+vbjCudXYC0UllbzGzleF2xiAsV0Rkv/3goTXMmDia9xw5/I9J2d/uoAfcBYS7P8zgJBIRkUGxfO12Hn11O188cwGjykrjDifnekwAZtZE5h96A8bkLCIRkZh8/6E1TB43kguHceE3VbaLwg+tKxuIiAzA0+sbWPbCVj77tkMYM3L4//sHXRJSRASI2v4njC7j/cfPiTuUvFECEJGit2ZLE/c9s4lL3zCXiaNHxB1O3igBiEjR+6+HXmZ0WSmXnXBA3KHklRKAiBS1da/v4ldPbeB9x1YzadzIuMPJKyUAESlq1/3fy5Sa8aE3zYs7lLxTAhCRorWpoYW7l9dx7tJKpk8cHXc4eacEICJF68Y/v0KHOx856cC4Q4mFEoCIFKXtO1v56aPreOfiWVRNGht3OLFQAhCRonTLX16lpb2Dj765OP/9gxKAiBShxpY2bv3rWk4/fAbzpxVvpwf72xmciMiQ4+68um0nt/xlLU0t7XzszfPjDilWSgAiMmzV72plZW09K9bVs7I2ujXsbgPgzEUzOWJ2ecwRxksJQESGhbaOTl7Y1MSKdTtYUVvPynX1vLJtJwAlBgdPn8AZC2ewpCpBTXUF86eOjzni+CkBiMiQtLFhd9c/+xXrdrB6fQMtbZ0ATBk/iprqBOcurWRJVYJFlQnGj9LPXTq9IyJS8Ha1trO6rqHrn/2K2h1sbtwDwMiyEo6YNZH3HTsn/LtPMDsxhuiqs5KNEoCIFJTOTueVbTtZsW5HV/v9C5ub6OiMrk81Z/JYjp83uasp57CZExlZpgMa94cSgIjEasfOUKgNTTkra+tpamkHYMKoMpZUJ/jYYQeypDrB4soEk8ePijni4UMJQETyprW9k+c3Ne7Vdr/29V1AVKg9ZMZEzlo8iyVVCY6sTjBvynhKStSUkytKACKSE+7OhoaW6F/9uugf/tPrG9jTHhVqp02ICrXvPbqamuoEC2eXM06F2rzSuy0ig2LnnnZW1TV0/bNfUVvP1qaoUDuqrISFs8t5/3FzqKmuoKY6wczy0SrUxkwJQET6rbPTeXlrMyvWdbfdv7i5iVCn5YAp4zhx/hSWVCeoqarg0JkTGFGqQm2hUQIQkV693ryn60zaFevqeaq2nqY9UaF24ugyllRXcNrhM6ipTrCkMkFFkV1Za6hSAhCRvbS2d/LsxkZWhmacFevqWbc9KtSWlhiHzpjA2UtmdTXlHDB5nAq1Q5QSgEgRc3fqduze6wSrZzY00hoKtTMmjqamOsH7jq2mprqCI2ZPZOxI/WwMF7F+kmZ2OvCfQClwk7t/Pc54RIa75j3trOo65j5q0tnWHBVqR48oYdHsBJe+YS41VQmWVCeYWT4m5ogll2JLAGZWCvwAeCtQBzxuZve4+7NxxSQynHR0Omu2NLOydkdUrF1Xz4tbmvBQqJ03dRxvOnhK1JRTleCQGSrUFps49wCOAda4+ysAZnYn8E5ACUBkP2xtShZqox/8VXUNNIdCbfmYESypSvD2hTOoqa5gSWWC8rEjYo5Y4hZnApgN1KY8rwOOTZ/IzK4ArgCorq7OT2QiBW5PewfPbGjsOsFqZe0OarfvBqCsxDhs5kTeXTM7OiqnKsEBU8bpmHvZR5wJINPW6PsMcL8BuAFg6dKl+4wXGe7cndrtu1kR/tmvrK3n2Q2NtHZEhdpZ5aNZUp3g746bS011giNmlzN6RGnMUctQEGcCqAOqUp5XAhtiikWkYDS2tLGqtqGrKWdlbT2v72wFYMyIUhZWlnPZG+dSUxUdhjl94uiYI5ahKs4E8DhwkJkdAKwHLgAuijEekbzr6HRe3NzU1X3Cytp6XtrS3FWonT9tPG8+dFpXU84h0ydQpkKtDJLYEoC7t5vZx4H7iQ4Dvdndn4krHpF82NLU0t1uv66eVXX17GztAKBibFSoPXPRLGqqo6tYlY9RoetGGjsAAA3cSURBVFZyJ9bzANz9t8Bv44xBJFda2qJCbeo1atfXdxdqF8yayLlHVUZH5VQlmDN5rAq1klc6pU9kELg7r72+a6+mnGc3NtLWEbXlzE6MYUl1gstOiAq1h89SoVbipwQgsh8adrexqq5+rwub7NjVBsDYkaUsqizngyfOiy5bWJVgmgq1UoCUAER60d7RyYubm/c6DHPNlmYAzOCgaeN564LpXU05B0+fQKk6R5MhQAlAJM3mxpbQz/2OUKhtYHdbVKidPG4kS6oSvGvJLJZUVbCoqpyJo1WolaFJCUCKWktbB0+vb9irKWdDQwsAI0qNBbPKee/RVdSEC5tUTRqjQq0MG0oAUjTcnbWv74qOygk/+M9tbKQ9XMaqsmIMR82dxAdDT5gLZk5UoVaGNSUAGbYadrWxsq77qJyVtfXUh0Lt+FFlLKos5+9PmseSqqjtfuqEUTFHLJJfSgAyLLR3dPL8pqa9LmzyytadQFSoPXjaBE4/fEZ0VE51BfOnjVehVoqeEoAMSZsaWrr+2a9YV8+q9fW0tEWdo00ZP5IlVRWcc2QlNVUJFlaWM0GFWpF9KAFIwdvd2sHq9Q17/eBvaowKtSNLSzh89kQuOmYOS6qjY+4rK1SoFekLJQApKJ2dzquv7wxF2qhY+/ymJjpCobZ60liOnTepqynnsJkTGFWmQq3I/lACkFjV72pNabevZ+W6HTS2RFexmjCqjMVVCT5y0oFdvWFOHq9CrchgUQKQvGnr6OT5jU1dJ1itqK3n1W1RobbE4ODpE3jHolnUVCWoqU5w4NTxlKhQK5IzSgCSE+7OxoaWvZpyVq9vYE97VKidOmEUNVUJzltaSU1VBYsqyxk3SpujSD7pGyeDYldrO6vqGrrOpl2xrp4tTXsAGFlWwsLZ5bz/uFCora5gVvloFWpFYqYEIP3W2em8sq059JcTHZXz4ubuQu3cyWM5Yf6UUKhNcOiMiYws01WsRAqNEoD0avvOVlamtNuvrK2nKVmoHV3GkqoEbz3sQGqqK1hclWDSuJExRywifaEEIHtpbe/kuY2N3U05tfW89vouICrUHjpjImct7i7UzpuiQq3IUKUEUMTcnfX1u7tOrlqxbgdPb2ikNRRqp00YxZHVFVx4THXXGbVjR2qTERku9G0uIs172llVV9/1g7+ytp6toVA7qqyERZXlXHL8nK4Lm8xUoVZkWFMCGKY6O501W5u7OkZLFmpDnZZ5U8Zx4vwp4QSrCg6dOYERpSrUihQTJYBh4vXmPd1NObU7WFXbQNOeqFBbPmYES6oSvO3wGV1n1CbGqlArUuyUAIagPe0dPLuhca+mnHXbo0JtaYlx2MwJvLNmFjVVFSypTnDA5HEq1IrIPpQACpy7U7djdzjePuoN85n1jbR2RIXameWjWVKV4OLjqllSVcHC2eWMGanO0USkd0oABaZ5Tzurauv3+sHf1twKwOgRJSyaneCyE+Z2td3PKB8dc8QiMlQpAcSoo9N5aUtTVKgNTTkvbmnCQ6H2wKnjOOngaV3t9ofMUKFWRAZPLAnAzL4FnAW0Ai8Dl7l7fRyx5NPWpj1dJ1itrK3nqdp6drZ2AJAYGxVqz1g4k5rqBIsrE5SP1VWsRCR34toDeAC42t3bzewbwNXA52KKJSda2jp4dmNj1wlWK2vrqduxG4CyEuOwmRM556jKrqacuZPH6ph7EcmrWBKAu/8+5ekjwLlxxDFY3J1123elHIZZz7MbGmjriNpyZpWPpqa6gkvfMJclVQmOmF3O6BEq1IpIvAqhBnA5cFfcQfRHY0sbq2pTrlFbW8/2nVGhduzIUhbOLucDb5zX1Rvm9Ikq1IpI4clZAjCzB4EZGUZd4+6/CtNcA7QDt2dZzhXAFQDV1dU5iDS7jk7nxc1Ne13YZM3W5q5C7fxp4znl0Gld3SccPH08ZSrUisgQYJ78Jcv3is0uAT4MnOLuu/oyz9KlS3358uU5jWtLY0tXH/cra3ewqq6BXaFQWzF2BDXVFdRUJVhSnWBRZYLyMSrUikhhM7Mn3H1p+vC4jgI6najoe1Jff/xzoaWtg2c2NHS1269cV8/6+qhQO6LUWDBzIucvrepqyqmepEKtiAwfcdUAvg+MAh4IP6iPuPuHc7lCd+e113ftdUHyZzc00h56R5udGENNdfIkqwoOnzVRhVoRGdbiOgpofj7X990/vMQtf3mVHbvagKhQu7gywRVvigq1S6oTTJugQq2IFJdCOAoo52ZMHM1pC2aEC5InOGjaBErVOZqIFLmiSADnH13F+UdXxR2GiEhB0fGKIiJFSglARKRIKQGIiBQpJQARkSKlBCAiUqSUAEREipQSgIhIkVICEBEpUrH1Bro/zGwr8FqGUVOAbXkOZyCGUrxDKVYYWvEOpVhhaMU7lGKF3Mc7x92npg8cUgmgJ2a2PFNXp4VqKMU7lGKFoRXvUIoVhla8QylWiC9eNQGJiBQpJQARkSI1XBLADXEH0E9DKd6hFCsMrXiHUqwwtOIdSrFCTPEOixqAiIj033DZAxARkX5SAhARKVIFmwDM7C4zWxlua81sZcq4q81sjZm9YGZvSxl+ehi2xsyuShl+gJk9amYvheWODMNHhedrwvi5A4j3E2Hdz5jZNws81mvNbH3K+3tGIccblvePZuZmNiU8NzP7blj+KjM7MmXaS0I8L5nZJSnDjzKz1WGe71q4ILWZTTKzB8L0D5hZxQDi/EqIZ6WZ/d7MZhVqvGb2LTN7PsTzP2aWSBlXcNuBmZ0Xvl+dZrY0bVzBxdvH15Qxvrxx94K/Ad8GvhgeLwCeIrqo/AHAy0BpuL0MzANGhmkWhHl+BlwQHl8HfCQ8/ihwXXh8AXDXfsb3ZuBBYFR4Pq1QYw3zXwv8Y4bhhRpvFXA/0UmAU8KwM4DfAQYcBzwahk8CXgn3FeFxRRj3GHB8mOd3wNvD8G8CV4XHVwHfGECsE1MefzLlPSi4eIHTgLLw+BvJ5RTwdnAYcAiwDFha6NttH15Pj/Hl65a3FQ3gTTKgFjgoPL8auDpl/P3hS3I8cH/K8KvDzYjOsEtu6F3TJecNj8vCdLYfMf4MODXD8IKLNcx/LZkTQKHGezewGFhLdwK4HrgwZZoXgJnAhcD1KcOvD8NmAs+nDO+aLjlveDwTeGGQtt2rgf8eCvEC7wZuL+TtIGW9y9g7ARR0vFleR8b4crGunm4F2wSU4kRgs7u/FJ7PJkoISXVhWE/DJwP17t6eNnyvZYXxDWH6/joYODHsMv7JzI4u4FiTPh52/W9OaUIouHjN7Gxgvbs/lTaqv7HODo/ThwNMd/eNIdaNwLT+xpkW87+aWS3wPuCLhR5vcDnRXsb+xJrP7TaToRZvb3HnTawXhTezB4EZGUZd4+6/Co8vBO5InS3D9E7meoZnmT7bsvoVK9H7WEG0a3808DMzmxdXrH2I97+Br4T5v0LUxHZ5XPH2EuvniZoq9pmtnzH16/3Lprft1t2vAa4xs6uBjwNfiivevnzHzOwaoB24PTlbD+uOdbtN+U3YZ7Ye1pHzeAcon+vKKNYE4O6nZhtvZmXAe4CjUgbXEbUJJ1UCG8LjTMO3AQkzKwsZPXX65LLqwrrKge39jdXMPgL80qP9uMfMrJOoc6dYYu0t3rTYbwTuTVtHXuPtKVYzW0jUpvtUqH9WAk+a2TFZYq0DTk4bviwMr+zhtW02s5nuvtHMZgJbMsXTW7wZ/BT4DVECiCXePnzHLgHOBE4J2y9ZYqWH4XnfbtPEFu8AZYs7P/LZ3rQfbWSnA39KG3Y4exd8XiEqppSFxwfQXVA5PMzzc/Yu+Hw0PP4Yexd8frafcX4Y+Jfw+GCi3TorxFjD/DNTHn8auLNQ39u0uNfSXQN4B3sXVR8LwycBrxLtkVWEx5PCuMfDtMmi6hlh+LfYu6j6zQHEeFDK408AdxdqvETfr2eBqYX+HUuLbxl71wAKOt4sr6PH+PJ1y9uK9vMNuhX4cIbh1xBVz18gHBkRhp8BvBjGXZMyfB7RERVrwgefPFpndHi+Joyft59xjgRuA54GngTeUqixhmX9BFgNrALuYe+EUHDxpqxrLd0JwIAfhHhWp/0gXB7Wuwa4LGX40vAZvQx8n+4z4ScDfwBeCveTBhDjL8I6VgG/BmYXarxhfbXAynC7rpC3A6JCdR2wB9jM3gXUgou3j68pY3z5uqkrCBGRIjUUjgISEZEcUAIQESlSSgAiIkVKCUBEpEgpAYiIFCklAJEehB48Hzazt6cMO9/M7oszLpHBosNARbIwsyOIjguvITq5aCVwuru/PIBlJs9AFYmVEoBILyy6vsNOYBzQ5O5fCV0ofIzoJMC/Ah93904zuwE4EhhD1JXwv4Rl1BH19Hk68B/u/vMYXorIXmLtC0hkiPgy0RnercDSsFfwbuAN7t4efvQvIOr75yp33x76kXnIzO5292fDcna6+wlxvACRTJQARHrh7jvN7C6g2d33mNmpRL2+Lg+d1I2hu1vfC83sA0TfrVlEFytJJoC78hu5SHZKACJ90xluEPXrc7O7fyF1AjM7CLgSOMbd683sNqK+ZZJ25iVSkT7SUUAi/fcgcL51X594splVAxOBJqAxdNP8tizLEImd9gBE+sndV5vZl4EHzawEaCPqEnw5UXPP00Td/P4lvihFeqejgEREipSagEREipQSgIhIkVICEBEpUkoAIiJFSglARKRIKQGIiBQpJQARkSL1/wHxfDvpj6hvigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(long_run_growth_df['population']).plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Log Millions')          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Index of Useful Ideas Stock')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(long_run_growth_df['human_ideas']).plot()\n",
    "plt.title('Human Economic History', size=24)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Log Index of Useful Ideas Stock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >human_ideas</th>        <th class=\"col_heading level0 col1\" >income_level</th>        <th class=\"col_heading level0 col2\" >population</th>    </tr>    <tr>        <th class=\"index_name level0\" >year</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row0\" class=\"row_heading level0 row0\" >-68000</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row0_col1\" class=\"data row0 col1\" >$1,200</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row0_col2\" class=\"data row0 col2\" >0.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row1\" class=\"row_heading level0 row1\" >-8000</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row1_col1\" class=\"data row1 col1\" >$1,200</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row1_col2\" class=\"data row1 col2\" >2.5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row2\" class=\"row_heading level0 row2\" >-6000</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row2_col0\" class=\"data row2 col0\" >6</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row2_col1\" class=\"data row2 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row2_col2\" class=\"data row2 col2\" >7.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row3\" class=\"row_heading level0 row3\" >-3000</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row3_col0\" class=\"data row3 col0\" >9</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row3_col1\" class=\"data row3 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row3_col2\" class=\"data row3 col2\" >15.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row4\" class=\"row_heading level0 row4\" >-1000</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row4_col0\" class=\"data row4 col0\" >17</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row4_col1\" class=\"data row4 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row4_col2\" class=\"data row4 col2\" >50.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row5_col0\" class=\"data row5 col0\" >31</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row5_col1\" class=\"data row5 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row5_col2\" class=\"data row5 col2\" >170.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row6\" class=\"row_heading level0 row6\" >800</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row6_col0\" class=\"data row6 col0\" >41</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row6_col1\" class=\"data row6 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row6_col2\" class=\"data row6 col2\" >300.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row7\" class=\"row_heading level0 row7\" >1500</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row7_col0\" class=\"data row7 col0\" >53</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row7_col1\" class=\"data row7 col1\" >$900</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row7_col2\" class=\"data row7 col2\" >500.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row8\" class=\"row_heading level0 row8\" >1770</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row8_col0\" class=\"data row8 col0\" >79</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row8_col1\" class=\"data row8 col1\" >$1,100</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row8_col2\" class=\"data row8 col2\" >750.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row9\" class=\"row_heading level0 row9\" >1870</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row9_col0\" class=\"data row9 col0\" >124</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row9_col1\" class=\"data row9 col1\" >$1,300</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row9_col2\" class=\"data row9 col2\" >1,300.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23level0_row10\" class=\"row_heading level0 row10\" >2020</th>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row10_col0\" class=\"data row10 col0\" >2,720</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row10_col1\" class=\"data row10 col1\" >$11,842</td>\n",
       "                        <td id=\"T_fdbeeede_9be3_11ea_845a_3e22fb6c5c23row10_col2\" class=\"data row10 col2\" >7,600.0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c448d90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.0f}'.format\n",
    "\n",
    "format_dict = {'year': '{d}', 'human_ideas': '{0:,.0f}', \n",
    "    'income_level': '${0:,.0f}', 'population': '{0:,.1f}'}\n",
    "\n",
    "long_run_growth_df.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >span</th>        <th class=\"col_heading level0 col1\" >h</th>        <th class=\"col_heading level0 col2\" >g</th>        <th class=\"col_heading level0 col3\" >n</th>    </tr>    <tr>        <th class=\"index_name level0\" >initial_year</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row0\" class=\"row_heading level0 row0\" >-68000</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row0_col0\" class=\"data row0 col0\" >60000</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row0_col1\" class=\"data row0 col1\" >0.003%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row0_col3\" class=\"data row0 col3\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row1\" class=\"row_heading level0 row1\" >-8000</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row1_col0\" class=\"data row1 col0\" >2000</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row1_col1\" class=\"data row1 col1\" >0.012%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row1_col2\" class=\"data row1 col2\" >-0.01%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row1_col3\" class=\"data row1 col3\" >0.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row2\" class=\"row_heading level0 row2\" >-6000</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row2_col0\" class=\"data row2 col0\" >3000</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row2_col1\" class=\"data row2 col1\" >0.013%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row2_col2\" class=\"data row2 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row2_col3\" class=\"data row2 col3\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row3\" class=\"row_heading level0 row3\" >-3000</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row3_col0\" class=\"data row3 col0\" >2000</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row3_col1\" class=\"data row3 col1\" >0.030%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row3_col3\" class=\"data row3 col3\" >0.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row4\" class=\"row_heading level0 row4\" >-1000</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row4_col0\" class=\"data row4 col0\" >1001</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row4_col1\" class=\"data row4 col1\" >0.061%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row4_col3\" class=\"data row4 col3\" >0.12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row5_col0\" class=\"data row5 col0\" >799</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row5_col1\" class=\"data row5 col1\" >0.036%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row5_col2\" class=\"data row5 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row5_col3\" class=\"data row5 col3\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row6\" class=\"row_heading level0 row6\" >800</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row6_col0\" class=\"data row6 col0\" >700</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row6_col1\" class=\"data row6 col1\" >0.036%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row6_col3\" class=\"data row6 col3\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row7\" class=\"row_heading level0 row7\" >1500</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row7_col0\" class=\"data row7 col0\" >270</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row7_col1\" class=\"data row7 col1\" >0.150%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row7_col2\" class=\"data row7 col2\" >0.07%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row7_col3\" class=\"data row7 col3\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row8\" class=\"row_heading level0 row8\" >1770</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row8_col0\" class=\"data row8 col0\" >100</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row8_col1\" class=\"data row8 col1\" >0.442%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row8_col2\" class=\"data row8 col2\" >0.17%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row8_col3\" class=\"data row8 col3\" >0.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23level0_row9\" class=\"row_heading level0 row9\" >1870</th>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row9_col0\" class=\"data row9 col0\" >150</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row9_col1\" class=\"data row9 col1\" >2.062%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row9_col2\" class=\"data row9 col2\" >1.47%</td>\n",
       "                        <td id=\"T_fdc165a6_9be3_11ea_845a_3e22fb6c5c23row9_col3\" class=\"data row9 col3\" >1.18%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c926310>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_run_growth_rates_df = pd.DataFrame(\n",
    "    data=np.array([initial_year, span, h, g, n]).transpose(),\n",
    "    columns = ['initial_year', 'span', 'h', 'g', 'n'])\n",
    "\n",
    "long_run_growth_rates_df['initial_year'] = long_run_growth_rates_df['initial_year'].apply(np.int64)\n",
    "long_run_growth_rates_df.set_index('initial_year', inplace=True)\n",
    "\n",
    "format_dict = { 'span': '{0:.0f}', 'h': '{0:,.3%}', \n",
    "    'g': '{0:,.2%}', 'n': '{0:,.2%}'}\n",
    "\n",
    "long_run_growth_rates_df.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>span</th>\n",
       "      <th>h</th>\n",
       "      <th>g</th>\n",
       "      <th>n</th>\n",
       "      <th>eras</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_year</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>-68000</th>\n",
       "      <td>60000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>speciation? language?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8000</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0</td>\n",
       "      <td>0</td>\n",
       "      <td>agriculture &amp; herding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-6000</th>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>proto-agrarian age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3000</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>writing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1000</th>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>axial age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>799</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>late-antiquity pause</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>middle age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>commercial revolution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>industrial revolution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>modern economic growth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              span  h  g  n                    eras\n",
       "initial_year                                       \n",
       "-68000       60000  0  0  0   speciation? language?\n",
       "-8000         2000  0 -0  0   agriculture & herding\n",
       "-6000         3000  0  0  0      proto-agrarian age\n",
       "-3000         2000  0  0  0                 writing\n",
       "-1000         1001  0  0  0               axial age\n",
       " 1             799  0  0  0    late-antiquity pause\n",
       " 800           700  0  0  0              middle age\n",
       " 1500          270  0  0  0   commercial revolution\n",
       " 1770          100  0  0  0   industrial revolution\n",
       " 1870          150  0  0  0  modern economic growth"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eras = eras[0:10]\n",
    "\n",
    "long_run_growth_rates_df['eras'] = eras\n",
    "\n",
    "long_run_growth_rates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >human_ideas</th>        <th class=\"col_heading level0 col1\" >income_level</th>        <th class=\"col_heading level0 col2\" >population</th>        <th class=\"col_heading level0 col3\" >eras</th>    </tr>    <tr>        <th class=\"index_name level0\" >year</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row0\" class=\"row_heading level0 row0\" >-68000</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row0_col1\" class=\"data row0 col1\" >$1,200</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row0_col2\" class=\"data row0 col2\" >0.1</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row0_col3\" class=\"data row0 col3\" >speciation? language?</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row1\" class=\"row_heading level0 row1\" >-8000</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row1_col1\" class=\"data row1 col1\" >$1,200</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row1_col2\" class=\"data row1 col2\" >2.5</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row1_col3\" class=\"data row1 col3\" >agriculture & herding</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row2\" class=\"row_heading level0 row2\" >-6000</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row2_col0\" class=\"data row2 col0\" >6</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row2_col1\" class=\"data row2 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row2_col2\" class=\"data row2 col2\" >7.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row2_col3\" class=\"data row2 col3\" >proto-agrarian age</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row3\" class=\"row_heading level0 row3\" >-3000</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row3_col0\" class=\"data row3 col0\" >9</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row3_col1\" class=\"data row3 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row3_col2\" class=\"data row3 col2\" >15.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row3_col3\" class=\"data row3 col3\" >writing</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row4\" class=\"row_heading level0 row4\" >-1000</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row4_col0\" class=\"data row4 col0\" >17</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row4_col1\" class=\"data row4 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row4_col2\" class=\"data row4 col2\" >50.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row4_col3\" class=\"data row4 col3\" >axial age</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row5_col0\" class=\"data row5 col0\" >31</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row5_col1\" class=\"data row5 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row5_col2\" class=\"data row5 col2\" >170.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row5_col3\" class=\"data row5 col3\" >late-antiquity pause</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row6\" class=\"row_heading level0 row6\" >800</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row6_col0\" class=\"data row6 col0\" >41</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row6_col1\" class=\"data row6 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row6_col2\" class=\"data row6 col2\" >300.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row6_col3\" class=\"data row6 col3\" >middle age</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row7\" class=\"row_heading level0 row7\" >1500</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row7_col0\" class=\"data row7 col0\" >53</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row7_col1\" class=\"data row7 col1\" >$900</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row7_col2\" class=\"data row7 col2\" >500.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row7_col3\" class=\"data row7 col3\" >commercial revolution</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row8\" class=\"row_heading level0 row8\" >1770</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row8_col0\" class=\"data row8 col0\" >79</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row8_col1\" class=\"data row8 col1\" >$1,100</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row8_col2\" class=\"data row8 col2\" >750.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row8_col3\" class=\"data row8 col3\" >industrial revolution</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row9\" class=\"row_heading level0 row9\" >1870</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row9_col0\" class=\"data row9 col0\" >124</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row9_col1\" class=\"data row9 col1\" >$1,300</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row9_col2\" class=\"data row9 col2\" >1,300.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row9_col3\" class=\"data row9 col3\" >modern economic growth</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23level0_row10\" class=\"row_heading level0 row10\" >2020</th>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row10_col0\" class=\"data row10 col0\" >2,720</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row10_col1\" class=\"data row10 col1\" >$11,842</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row10_col2\" class=\"data row10 col2\" >7,600.0</td>\n",
       "                        <td id=\"T_fdc49802_9be3_11ea_845a_3e22fb6c5c23row10_col3\" class=\"data row10 col3\" >whatever the 21st century brings</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c6a1210>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eras = ['speciation? language?', 'agriculture & herding', 'proto-agrarian age',\n",
    "        'writing', 'axial age', 'late-antiquity pause', 'middle age', 'commercial revolution',\n",
    "        'industrial revolution', 'modern economic growth', 'whatever the 21st century brings']\n",
    "\n",
    "long_run_growth_df['eras'] = eras\n",
    "\n",
    "format_dict = {'year': '{d}', 'human_ideas': '{0:,.0f}', \n",
    "    'income_level': '${0:,.0f}', 'population': '{0:,.1f}'}\n",
    "\n",
    "long_run_growth_df.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Employment-to-Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the environment for inline plotting, and download\n",
    "# the libraries\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the employment-population ratio data in from the internet\n",
    "# into a dataframe:\n",
    "\n",
    "employment_population_df = pd.read_csv(\n",
    "    'https://delong.typepad.com/files/employment-to-population2.csv'\n",
    "    )\n",
    "\n",
    "# set the date column to be the index of the dataframe:\n",
    "\n",
    "employment_population_df.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as downloaded, the employment_population_df dataframe includes\n",
    "# source and other such information in the first seven lines of \n",
    "# the dataframe. we would like to move that information to a more\n",
    "# convenient place so that it does not mess up presentation of \n",
    "# the data\n",
    "\n",
    "# therefore we create a related \"dict\" object, which starts out \n",
    "# empty:\n",
    "\n",
    "employment_population_dict = {}\n",
    "\n",
    "# and we load the source and related information into the dict\n",
    "# object:\n",
    "\n",
    "for i in range(7):\n",
    "    employment_population_dict[employment_population_df['Key'][i]] = employment_population_df['Value'][i]\n",
    "\n",
    "# then we drop the source information from the dataframe:\n",
    "\n",
    "employment_population_df.drop(['Key'], axis = 1, inplace=True)\n",
    "employment_population_df.drop(['Value'], axis = 1, inplace=True)\n",
    "\n",
    "# last, we add the dataframe as another element of the \"dict\"\n",
    "# object so that it is kept close and related to its source\n",
    "# information. If we ever want to see information about the\n",
    "# source, simply replace the \"df\" at the end of the dataframe's\n",
    "# name with a \"dict\", and then look in that object to see what\n",
    "# is there:\n",
    "\n",
    "employment_population_dict['Data'] = employment_population_df\n",
    "\n",
    "# why have we jumped through all these hoops? so that somebody\n",
    "# coming across this notebook file one or five or ten years from\n",
    "# now will not be hopelessly lost in trying to figure out what\n",
    "# is supposed to be going on here. if they want more information\n",
    "# about just what the data in employment_population_df is, the\n",
    "# fact that there is also an object called employment_population_dict\n",
    "# will make it a very obvious place to look..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employment to Population Ratio')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# having jumped through these hoops, we can now take a look at\n",
    "# what the share of american adults who every month tell the \n",
    "# cps interviewers that they have jobs looks like:\n",
    "\n",
    "employment_population_df.plot(legend=None)\n",
    "plt.ylabel('Percent of Adults')\n",
    "plt.title('Employment to Population Ratio', size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employment to Population Ratio')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if we just want to look at the data since 1990:\n",
    "\n",
    "employment_population_df['1989-12-01':].plot(legend=None)\n",
    "plt.ylabel('Percent of Adults')\n",
    "plt.title('Employment to Population Ratio', size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employment to Population Ratio')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and if we just want to look at the years 2005-2015—\n",
    "# the years from the peak of the housing bubble until\n",
    "# the federal reserve begins to try to \"normalize\" interest \n",
    "# rates because keeping interest rates as low as it \n",
    "# can in order to try to boost employment to offset \n",
    "# the damage done to the economy by the so-called \"great \n",
    "# recession\" is no longer its highest priority:\n",
    "\n",
    "employment_population_df['2004-12-01':'2015-12-01'].plot(legend=None)\n",
    "\n",
    "plt.ylabel('Percent of Adults')\n",
    "plt.title('Employment to Population Ratio', size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Data List': '20th Century Economic History',\n",
       " 'Data Updated': '2020-05-08',\n",
       " 'Link': 'https://research.stlouisfed.org/useraccount/datalists/244845',\n",
       " 'Series ID': 'LNU02300000',\n",
       " 'Title': 'Employment-Population Ratio, NSA',\n",
       " 'Frequency': 'Monthly',\n",
       " 'Source:': 'Current Population Survey',\n",
       " 'Data':             Ratio\n",
       " Date             \n",
       " 1948-01-01     55\n",
       " 1948-02-01     55\n",
       " 1948-03-01     55\n",
       " 1948-04-01     56\n",
       " 1948-05-01     56\n",
       " ...           ...\n",
       " 2019-12-01     61\n",
       " 2020-01-01     60\n",
       " 2020-02-01     61\n",
       " 2020-03-01     60\n",
       " 2020-04-01     51\n",
       " \n",
       " [868 rows x 1 columns]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remember that we stored information about the source\n",
    "# of the data in a \"dict\" object? now let's look at that \n",
    "# \"dict\" object for information about the source of the \n",
    "# data series:\n",
    "\n",
    "employment_population_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"dict\" object says that the source of the data is the Current Population Survey, that the data was last updated on 2020-05-08, that the series is called \"mployment-Population Ratio, NSA\", that it is a monthly data series, that it is a ratio, that its series ID is LNU02300000, and that it can be found in the \"20th Century Economic History\" data list.\n",
    "\n",
    "If we wanted to access any one of these, we could pull it out of the \"dict\" object directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LNU02300000'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employment_population_dict['Series ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spend some time playing with and analyzing this employment-population ratio series—this share of American adults who, each month, tell the CPS interviewers that they have jobs. See if you can identify all of these salient features of the series:\n",
    "\n",
    "1. the seasonal cycle—the substantial fluctuations in the share of American adults at work that happen each and every year\n",
    "    * the shrinking of the magnitude of the seasonal cycle: it is a much smaller deal now than it was in the first decade after World War II.\n",
    "2. the business cycle—the large but definitely not regular fluctuations with a roughly seven-year period in the share of American adults with jobs\n",
    "3. the large and rapid downward business-cycle fall in the employment share that is the \"Great Recession\" of 2008-2010\n",
    "    * the remarkably slow bounce-back of the employment share from its Great Recession low\n",
    "4. the thirty-year rise in the employment share with the coming of feminism: women were now not discouraged from taking jobs outside of housecleaners, waitresses, nurses, schoolteachers, and secretaries\n",
    "5. the tendency of the employment share to fall as more people are retired as the population ages and people enter the labor force later as years spent in school increase\n",
    "6. the collapse of the employment share in Coronavirus Spring"
   ]
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