SWC Final Project

Final_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/afghanistan-TAVG-Trend.txt'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from lxml import html\n",
    "from urllib.request import unquote\n",
    "\n",
    "base_url = \"http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/\"\n",
    "\n",
    "page = requests.get(base_url)\n",
    "tree = html.fromstring(page.content)\n",
    "\n",
    "file_links = tree.xpath(\"//a[contains(@href,'.txt')]/@href\")\n",
    "region_names = [unquote(link.split('-')[0]) for link in file_links]\n",
    "\n",
    "regions = {k: base_url + v for k, v in zip(region_names, file_links)}\n",
    "\n",
    "regions['afghanistan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anomaly</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>10-year-anomaly</th>\n",
       "      <th>10-year-uncertainty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.095</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855-02-01</th>\n",
       "      <td>-1.233</td>\n",
       "      <td>1.771</td>\n",
       "      <td>-0.463</td>\n",
       "      <td>0.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862-04-01</th>\n",
       "      <td>-1.298</td>\n",
       "      <td>1.033</td>\n",
       "      <td>-0.428</td>\n",
       "      <td>0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872-07-01</th>\n",
       "      <td>-0.435</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-0.420</td>\n",
       "      <td>0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927-08-01</th>\n",
       "      <td>-1.366</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877-08-01</th>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.834</td>\n",
       "      <td>-0.397</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>3.074</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871-03-01</th>\n",
       "      <td>0.719</td>\n",
       "      <td>1.151</td>\n",
       "      <td>-0.424</td>\n",
       "      <td>0.245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>1.474</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-04-01</th>\n",
       "      <td>1.246</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>-1.100</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872-10-01</th>\n",
       "      <td>-0.839</td>\n",
       "      <td>0.608</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-04-01</th>\n",
       "      <td>-1.341</td>\n",
       "      <td>0.155</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1800-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890-02-01</th>\n",
       "      <td>0.090</td>\n",
       "      <td>1.010</td>\n",
       "      <td>-0.482</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-01</th>\n",
       "      <td>0.255</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01</th>\n",
       "      <td>-1.195</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            anomaly  uncertainty  10-year-anomaly  10-year-uncertainty\n",
       "date                                                                  \n",
       "1960-09-01    0.516        0.095           -0.027                0.043\n",
       "1855-02-01   -1.233        1.771           -0.463                0.555\n",
       "1862-04-01   -1.298        1.033           -0.428                0.373\n",
       "1778-08-01      NaN          NaN              NaN                  NaN\n",
       "1872-07-01   -0.435        0.994           -0.420                0.240\n",
       "1927-08-01   -1.366        0.174            0.038                0.081\n",
       "1877-08-01   -0.019        0.834           -0.397                0.221\n",
       "1793-02-01      NaN          NaN              NaN                  NaN\n",
       "1981-01-01    3.074        0.187            0.123                0.028\n",
       "1871-03-01    0.719        1.151           -0.424                0.245\n",
       "1983-01-01    1.474        0.198            0.232                0.023\n",
       "1969-04-01    1.246        0.211           -0.130                0.038\n",
       "1964-04-01   -0.211        0.173           -0.076                0.039\n",
       "1950-07-01   -1.100        0.146            0.092                0.045\n",
       "1872-10-01   -0.839        0.608           -0.431                0.246\n",
       "1928-04-01   -1.341        0.155           -0.006                0.083\n",
       "1800-07-01      NaN          NaN              NaN                  NaN\n",
       "1890-02-01    0.090        1.010           -0.482                0.181\n",
       "2000-09-01    0.255        0.113            0.811                0.047\n",
       "1962-03-01   -1.195        0.174            0.005                0.035"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"http://berkeleyearth.lbl.gov/auto/Regional/TAVG/Text/united-states-TAVG-Trend.txt\",\n",
    "    delim_whitespace=True,\n",
    "    comment='%',\n",
    "    header=None,\n",
    "    parse_dates=[[0,1]],\n",
    "    index_col=(0),\n",
    "    usecols=(0, 1, 2, 3, 8, 9),\n",
    "    names=(\"year\", \"month\", \"anomaly\", \"uncertainty\", \"10-year-anomaly\", \"10-year-uncertainty\")\n",
    ")\n",
    "df.index.name = \"date\"\n",
    "df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2YuueKVfHJmus9/DUaIrxVJa9Uxni4V5CpsGO0WRF5VCJsaTFfTsnsG3FU6NJ\n0tk8R6zqJm3lyeVtnhhJIsARB3RhGsJEymLnaIpkNs+GwTjR0PKNWXCL8ZevEyyjHO499h9RK4/n\n4E2fJi8Bkm/6DSHZb8SsVznMIoL9sq/A135G3ggx9te/xF51EgD5/mcx/k6n7P1S3bEKd7Hn7x6n\n7z/PIjS5Y8H+YroLgJkjXwdWipmXXoYKxWe3m3sfRMVXYI5vJ7TjT0Tv+y5man/b5FYcOxvD0Ejc\nri36QTWL1J9XSn1kqW118DTOOkeRtYVtbYMfwXdesX1kpnxWT6XYeN/HOWjnr3jkkHfwwJEfIGXl\neXosxZq+aNlzlSs7Opm2GEtmQYFl2zy8a5JcXtVVjrTEFVbB8FSGTG6cpJXHYH+Q4KYdY8TDASYL\nEc6j01ni4QAHDVS+vWcyOZ4eS3HIisSyXO8wpnYxdPtCC+7w635N38qTiARNnt5wOpGAQSLon7I1\ng2F2XvIMeVvVp3iCccYuvAPSkxDpBjtP5N7v0nPjx0sOi2/+X+f/5Vczev4PSPzhQ4SSuyue2ho4\nnNzQMbXLVgfdkSCGIU2pdleNgfzFZbadW2ZbrdwJHCoiBxeyxL4e+JWH529bxl2mdlgsrfNk2mLX\nRKrsvjNvv5CDdjrNfPhj35nd/vi+aTK5PEopUtncbAbXZybS3PPkOKMzWXaNOedUSrF193SJAXIm\nk/OlVvVkyiKXs0vcc62czfhMtuT7z11/mh9tnrbybN09xT1PjbFnMl1SMrVV1uTcUDTDbd41ych0\n9TEsoS1Xzb5+5t0P89C7nuShi57EXnvybEc9mAg3JMI+aBrezUoiBau3YZI+6UJG3vh78qEupo95\n04JD+3/55iWVQz6+ktG3/GnZmJXm4mYN4r3AxcAGEbl/zq4u4FavBFFK5UTkfcDvcNxcv6OUesir\n87czeZd5wxeLhdi+b2bWzHPYY9/luM1fYu/Ayexa+UJWjPyl5NihfXcwPHgKKCdeImQaTGVyJMIm\n6/tjPDY8hbLhoacnMExhZU+EfdMZUtnWCnobnc7yxL4ZptIWpiEcMpRgLGkRCxmMJ62S0dh0xqI3\nFmTL7klSmTwH9EZY2xdrovQOSikmUha9sYWxKWkrz1ObN9G760/EDJOxtWcycMziKS9mdm8jctNn\nMLpWIi/9HJn0DPF7/wuA4XOvwIgP0Klx9LmVx7PvfY8CkDn2b8hNjxGdeZLuP35s0c+Mvu4X5LvX\ngRlqilkmn38hAAAgAElEQVTJD/rjIUZnqhtIuJk3/gj4LfAvwFyH5yml1GhVV1sCpdQ1wDVennO5\nk8rmSpLLHf3IVwFYMXInK0bunN2+fe35HLTzlxy99XJuHHQKmYzNZGfdl6y8zb6Z8ZL1BDuveHTv\nFPuqvOkaxc7R/VG2ufwUkymLUJmgwsl0jmcmUowWRuHbR2aIhQL0RoNNWexOW3kiQZPpTI4Hdk5w\nxKpuuqMBptI5BhOOU+nIric484/n7//Qw19gy9SX6Dt94Sg5O7KDDT/av7A7Nfk4K5++CQArMoD9\nrHP8/UItRG7VswFIAZlnnUfsj58gedansbuciG/JTKICETBbotRNWUxDXA8a5xKo4V52s0g9AUwA\nbxCRPuBQIAKOzV0pdVPVV20A7WQq8JNnJtKzpp+B0bsx7YXmqqtffCNmPs2BO69mcPRuVu++nl0H\nnO3sLHzWWmSRbO/k0u63jWRwZBO2EWCi6zDygf2zgOL6xFxz08Do3Rz/0Oe4+dRvMj7TN7td2bB1\nzxQh02B9f4xoyMRWiq4aTC1F54DDVnaVKJtc3mYqnSMaMokETfK2YvdEmmQ2x9iMRTxiktizieOf\n+DUPZ/8WFe3GtrIkV/QTtcY5+lcvW3CtI277IPtiBjNH/TWjOx5iZcIk0LuGdf9ZOrPoenr/Izty\n7n8gwfJrTZ2OnTiA6VeWpuVWYa+cMksJmdVlO5hLMy1b1SxSXwhcgrN4fC9wGnAb8EJ/RNNA/bUH\n5trXz7r1LbOvH1v/Wg558ifce9RHyIQd48L2da/k4Kd+wQkPfpY9g6eXdLAtj1K89IZzSSR3ApA3\nglx35s+YSmwoc6zNKfd8lPW7nMnqOX+6gN+cfR1KTAK5JLlgAitnY+VsHtnj5KoSA049eIDgnLxf\nO0ZmOHAgXnJqx9zmzADChdnK8FSGoa4I/fEgVl4xmbbYvi9JKpvDNIWeaBBbwfhMlv6xeznh8R9w\n39Ef4Yyb30PImuTQ7fszhO5Y8wqywS7C1jgAfznx8+zrP4mXXe8sEQ5e/34Gr38/RQfw8RX7lcOj\nF1zN2t9fRCTpZBAdPvlDyMFn1tfuGlcYhoDPZUT8KOpVjWvCJcDJwO1KqbNE5Aigcvy7pm7qKe+Z\nyu4PFjxmy7/Nbv/DmT9lovsIHjjyA+QCidntdx/7KfrHH6Jnahur9t7EztUvrV3wCnRPbiOW3k00\ntZvt616FMha/DcXO0Tu5mfHuI0uOM3NJ1j/9G6xAnIHxBzj0iR+UfM60LY7ZchlWIMHA2H38+dQr\nSMZWY+QzvPq3zy45NpIZ4QW3vpWB8f1LbA8ddjGbD7t4duFb2U4diIF4aNZbbed4ilDAZEVXmJls\njh0jScbnmdsGJh/kzIe+wtPHvIdta57HZMoiZVkc9uj3OG7LlwH41Tl/pmdyG+fd+4/E0s6C6bpn\nfle2PQ58+urZ1w8c/SECJ7yO9abJU8f9Pevu/+qC43v33gHAvpMuoWvDKYy85UZy1/5fAoc8n8Dx\nr1m03TXNoTcWZCJlua4S2R0JMp3JkYgEMC2pGBxci5dlNQoirZRKiwgiElZKbRGRw6u+YoNoZZfT\naqgnGeFU2hmyGPksRzzqTKX3DpzKRPcRAOSCpaEtygiyfe35HL/5i6ze/UdfFEQoO8Y5N+1P9mYF\nE+xcfS5mPsWrfntyybFPrn4ZBwz/mZA1CcBMdA03nfYtNt73SYZGN5U9fzbYTTKykt6pbazZff3s\n9vP+WN7OvmvlC1i958YS5QBw9NZvcPTWb2BLAEM5D90dZ/2IfQecTDavsPK2swazZ4rHh6fLepCJ\nbXHWTa8HoO/mB/jt2b/jtLv+zwLHgFf+/nllZVuK6OkXsaLbmeWlz/wo4ztvond0YSacZN/h5J/v\nLB8GYr2o8y/zPQOyIeJL7fJ2pC8Wcp0PKxwwCRh51+VjoyGTaMi/LAPVKIidItIL/AL4g4iMAQsj\nUloEN91qp9++w9Npp5O65Y2z2245+WsVP7PrgBdy/OYvsn7XNWw+9N1MdXlXOH3l3lt43h3vLtl2\n2t0f4oHkLo7d8pUFx6/f9ZuS9/HU05x7w3mLnv+m065k7+BpoGxecuMr6JpZ/PYc7t/IrRsvo3/i\nQVbvuXF2+yMb3s7hj3939n1ROQAcc9sHuObsP4AIA6N3c+a9H+em064kGSvUUlN5jnj0SlYO30Yq\nsmLWhAUQzCddKYJ9/SexY80r6ZncQibcz+hRb2ZPvgsRSIQC9Dx+NQfuvJrtL/4WG7r328sjIZPM\nG3/BvX/+PlOHvIINa1Ywfut3CSiL+HPfTXBOnqBgA9LjB00hkyt9wmpdXG1VAoYQCwWYTFd2Qw8F\njJoTJkJzB7vVlBwtDvsuFZEbgB7gWl+k8oDpZV5rYDptMTqd5cQHP0vfpFMZ9umVL1xyXWEmvp7x\nrsPondrKczb9Pb876zcVjy9BKU548DNYwW62HPou8maU/rF7OfO2C3lqzbkc/NR+v/viNYCyymEu\nt268jI33foJQrjQSfLTnaG5/9pdIRleXJkoTgxue+19EU3uYThzIOTeeTzy1a3b3vr4TuP3ZX8IK\n9bBn8HR2rHk5XdPbueXky8lEBtl86Lu54HenLZAjlt5NLPU0ydhajnv4iySST3HeH1/CT192H0Mj\nd/H829/huqk2HXcp2w98DcHsJEdsu4KQNcl9R3+EXCBOIhLkibRFNGSy8aB+EmMpxBBWdoW5X17J\n5sPO57DBMokNglH6zriQVYURZf8ZF5JXqiEKwQ2xkMlUurOey2jIXFJBeE04YGDlFeF5ed78cLhr\nmWR9tVApWd9SUYeDiTC2UiV+wfUm65t/3YF4qO6pfCqbr+kG3LxrkrGJ8RKzzc/OuwdllPfECQWM\nWQ+f7smtnHPTq2f3FdcsKhGfeYrn3nkx3dNPzG7bcsg7OeKxby84dtvBb+a+oz7M6ZsuYc2eP85u\nv+nUbzHSdzwAeTNK0JrECnY7bhwqz6t+ewqmneHWjZexe+h52G5dEZWid+JhJroPA6RkLUMMFk8F\nomz6xx9krOcozrjjvazcd5u7683hydXn8eQJH+DYP7+Xnqlt/OWEz/HU2pfTEwuxvj9KyrJ5fHga\nwxDW98WIh03i4QDJbA6lWBADoZRCKVoq11QsZC7IGFsuPURXJNBRCiJgCAOJ8JJ9zcruiOvneGV3\nhNGZbImJqdgvFa8TDwdIlCl9q5RiMpUjHDTK1k1f2R2ZPYeXyfrmJukr3pUK75P1NRU/7XiNZvdE\nmpFkhlXDThxjOtTPb150/axymN8pigHHrunhnqfGsW3FZPdhbDv4TRz6hBNI9eKbXkMyspJrz/oN\nR239d6bj69i+fv8Cp9gW596wMKi+nHK48fTvsm/AUVq3bbyM0+7+ICibv5z0hQXKywr1zBHS5Krz\n7qqtQUQY7z16weZEJMjhKxM8+PTk/qjvua4gYjDadxxiwJ7B010piFs2fm1WyWXD/YQDJqds6OfW\n6NXMZHKs6o1wZDQ0W3OjF+iLBcnmbLqj+7//YoWbnDVA199c0yLUOtOoppywiNATc+6hcgqiFtzE\nQbREkr7lSi2dwfB0BmXDqj03APDowW8q6XyPWNnN1r1T5PNOT9gXCxELB1jbF+XJESe47P4jPzir\nIABi6T2c86cLZt1Ic4HE7CL2in2li67z2Xzou+md2Mx9R32E6cScDLwi3P7sL1f/BZcgEQkSDshs\n+gnDFOz8/pmyYQqruiOs7o0SCZoMJEIMT2dY3RtlRVeYTC7PjpEU4YCQsxUHdEexh/6OsWeupW9i\nf3mS20/6AunwILHUM+wdPI10pDTiVgxms9r2xYOs6A6XjdCOBE3v0kw0gfa1QbQ+tQ4GEuFAiZm9\n1vurmjgIAd4IHKyU+rSIrANWKaXuqOnKTaaTBmGP7Z2mPx6iLx4inc0zkcoitsWaZxwvnqeLQW84\n09PBrjDBgLB19zQDiRCrCqm9B+KhWQWhjCA3POcHxJNPctID/0wgn5pVDuDkbdq5+qWgFMdsuQyA\nhw99Dw8f/j4Awul9HL31ckZ7j2X7+r9qSDtEgyYreyL0xYLYOJ40AVMIB0x2T6RnTTMDiSAHD+53\n713dG6E7GmSoMKqPBE2OWhXAEMGy7UJK8RB/PuunJYF28xUPOKa6fCGobigRZkXhnHOvp6mNoGm4\n9u5Z7sTnKYieaG35tKrxYvoGYOMExn0amMapAHdypQ9p/GcsmWXXeIpj1vTw6F6n9kL/xGZCuSkm\nEwfPeiKJAUeuciaEPdEQx67tKRlZJCJBDhqMs32fU2lupP9E56/vxAXeQ30TD/OaXx/DbSd9aXYR\nfNvBb57dn4kMcvdxl/r5tUtY1RvhkKFEicdH9yrnoVBKMZgIEQqYzGRzROeNpqKhwILU4MW1o7Cx\n/9iuSKAkKd7hK7ucQLqCkoiHA2wYipMIBzAN6RhX61ahPx5iZDpDroM8oVqdahTEqUqpk0TkHgCl\n1Fgh66qmyRQXAx/ZMzWbEmNwxIkTGOk7cfa41T3Rko6w3LRzXX+MPZPpksJCM/H13HHCZ+ieepyt\nh7ydA5/6Bcdv/iIAp9/9QQB2Dz2ndM2ggfTEgguUw1xEZPZ7d9eRmXR1b5RQwGA6k2cqbdEfC3Hs\n6h6emUixujdKIhzQSqFAufgdL/xhQgGDXJmiVyLenL8VkJLX5dqxcV+0GgVhFepGKwARGcKZUXQE\n7Xpz7ZlMzwZpzeZLUmo24nZ4wHFUCAUMDh6Mlz3HfFZ0R9ixr7Re9ZNr9yeG27bhrRz6xA+IpffM\nbtt0/Gdq/g61EA6YGIYQD5kcuboxfhK9sRC9sRBKKTI5G8MQuqPBksVljb+Ue0z74yGCptGUegmd\nTjU+mF8FrgJWiMhngJvRqTaazq7xhQ/Fqj030DO1jXSon52rXkIoYLCyJ+J6dNsfW6LDE+GaF13P\nk6sds9Odx3+adGSoatndIIZj+jJNIVDw+x7qCnPoygTHre1pmHIokUmkrReVm4VfbabnbP7hagZR\nWKC+CbgLOBvnN7lAKbXZR9k0S2DbiukyrnMnPuiM5p9e9WIkFOHQlV10RdxPFhORIGv6ojw9Vr7I\nUJG7j/sUjx78RkZ7j6tOcJdEgyareqOs6YuSyeXZPZFmeDLD4Qd0aVNOm9EbC2IaQnckSNrK15zZ\ntJH0RIOeuYtWg9e3dj3VEl31GkopJSLXKKWOBbbUfLUWo13NSpVYufeWWdPPowe/kZ5okP549UtF\nBw7EGU9aFZN/5QJxRgs+/15imk5KhhU9kdmyp+GAycquCCu6wlo5eEg8HKj4G9eMzH/rbIiGTExD\nyCZbs4bIXJoVge71/V3P2appgbtFpOU8lmIdFOBWD5H0XlD52VxHY91HMpXYQH88XNP5TEM4alV3\nU+bvR6/u5ujVPbPut0UiIXOBt5GmPuL6+VmURt76g4nwbMR0K41/qvJiAt4oIjuAGfZHUvtjX3BJ\nq9mC/cjJXvF6do6/uuaEBdvvPOEzGIYwmKjd0SwSMtkwmODx4el6RFxAKGBgFp6CuUnMemJBooWg\nMSf2QOMnnZQ9oBl4+ZzPNQPFguasF2Es3NzfqBoF8RLfpNDUzBGPfmvBtpnoGia7DqU/Fqx7mrym\nL4oh8MTIzGzk9VLMzetUjt5YkENXdPHEvhlS4ykQJ4DtsJVdBAzxPRW1xmF+sjcvqXYUvJyD4Obn\nVQqYBkOJMCLNL1tQTTbXlkzt3UKzsaawcviWkvfZYDe/PdspNlMuoddcoiGzJN5hMVb1RplK59gz\nmXbWB8ooCjGcyOXuSJADB2LsGEkiAqmszbr+KFt3TyEGHDLURXck4MxuukIoFL2xELE2TzehcU/Q\nlAUz7aAp1Fk8seUJGFI2yC9e5jltlWSM2qDrI43Q/oNjToGYa1/wawCm4+tm9/Uu5a5aBd3RIHsm\n02wYTLB3Ks1EstS7Ix4K0hcPkggH6IoEOXp1N7vG0xwy5Piob5UpVnZFStYVeqIhwqZJRJs62op6\niwGJCCvmZBYFZwbRGzNKSuQ2C0MEhfK8k56b+bW+GtW1y9UTDVY1U9MKoo3pvu5Ds6+n4+tA9ne0\n0ZC5aEbQWigmm+uOBLDt8AIFkQibHDSnPrOIsLp3f+zFsWt6FqSuBlpOOSw2ytPsR1wstM3vwtz0\nac66kzX7eT9/hcVcWAXqWrdrBGYdCqLaxJCujZAi8nci0leTVBrPkcwk8QedOsy2BEqUAzjR0F4S\nDpgcfkA3sXCA7ui8cYU4brELZJxzI5dTDq1Is2y+9fiqVyLgw3l7fYwcjxScE8I+mxsjQbOsebqv\nUHO82bb/cnRHgkQCJpGgu267mCq8HtNtNatUK4E7ReR/ReSl0ootuIwQK0nyyNeRDvVz1bl3Lthf\nzCLqB/FwoMTLqD8eIuTjgudyoFk+99VaikyXTgS1KrzuaICeaJDuKgI7i7RK5by5eKmgoyGTnljQ\ntfLqjQXpjQXLrnG4xXWLKqU+ARwKfBt4G7BNRD4rIt4VLda4xk4cwMRLvsqvX/ynBYV2BhNhV6OG\nWm9dEWFtIYANocS0VC/VRHxr6qfWYV6lzw0mwhg1nriYxmR+J1jr+ZpNLUGqXiEidbuLV6VylZNG\ncHfhLwf0AT8VkX+tS4om0Kb320LmfZHeeGi2roGfDHSFMExhZVekrhFKLfhhNlmOCI4pZaCGTixo\nLN51eG0uE1hURvHhel6y2GjfNB1FuJSnYbOppmDQJcBbgH3AlcCHlFKWiBjANuDD/oioccNQV5hQ\nwOCA7ojv9ltw1iSevb6PyVTn1BherrR63EkoYCzqUTTUxqlXai3i00iqUV/9wKvnx0MopWwRebm3\nYrmnnptDzfWT8MFlwovb1u3XO3Ag1vA0FO1eKtNvGh1VPx/HXbOUxeomlPPeqtedtRFU8/w3+/do\nR6pZg/jUYsFyOqvrHDwezLh5Pg8ajOscRT7STiatuUXuy7lrLmbLb4fRLFS/qD6YmGNudfEzttNv\n3QiW7FVEZIpSxVtUxMVcTI1PyO8B7TotLUefhwFxmoUEDIOc7S7Mt5oU0dWMaMMBY7ZyYCWiIZNs\nyiYcMKq6x702M4UDxpLpM+bOUPzqmAXH6y6fV+SVqihPf8HFtZ5rddoMZUkFoZTqaoQgnYgXOsjN\nOUI6sV1LEA05Jjc3CqInGkQEzyOHI0GToGksunDbqGGRiNAfD1Ws8tYXCzKZdmqE+7kOUlwIHp2p\nnGK8c4aM3lGVXaIQKHcoMBuFpZS6yWuh3GJIa3sw+I1hCEeu6tYxCC1KpdFkJGiSydWefKjSjKJd\nnomAaVR0Ay2OyBsR3xCpQUm1+PKMJ1QTSX0hTlW53wH/VPh/qT9iuaNdHgQ/GazBpTUeDmAaQnQZ\nLTB3R1rHDFfvwr5Q+/dpp8HEQCJMVyRQV80Xt7P4dlmDaTTV3C2XACcDO5RSZwEnAuO+SKXxlUQ4\nwGCiNdwDG6Xkm/FVW3FBON4kZ4Zamt80hFgoUNN96kRjl0YdB0zn9eym5t/+LU81CiKtlEoDiEhY\nKbUFONwfsTTLhfmJxzppVhg0peNGpq0e2FUkEjQXFETqCgdIhAMMFKosejWrLM4I51+v3G/fFXFm\n781S1NVSjZQ7RaQX+AXwBxEZA1qyRkSr0Aoj9Pm0cgfcEw2WZPTsBNwuWldFk37CnmiwrHksHDBc\n1RXxgpBpkF6kcMRSzSIiJVH/Xq1tdEcCRIMmoYBBstAOfbHy+clioQCxNlEOUF3BoFcVXl4qIjcA\nPcC1vkil8YWQabT0iFYH3TnUUytgPo1YSA0HTEJm3jOZKxENmUxnci0VwCcihAKtO/Cqh5pUmVLq\nT14KISJfAF4BZIHHgLcrpfT6Rp3Mj46NhsyWqVTVibRrQrlyREMmGct9h2+aAhUmEV7OXCPB/SP1\nejANIRIwqZBWatlTjRfTRhG5SkTuFpH7i38eyfEH4Bil1HHAVuBjHp13WVPPGCtgSElUrmZpYoUZ\nUE80iGkIXT55TkkDbExe2edXdIUL9ZVbU3n2xIK+/U6dQDUziB8CHwIeADydSyqlfj/n7e3Aa7w8\n/+LXbcRVmke+yspokYBJeo5vfl88xGTa8tW+3G4/gZuKc25zVNV6/9XT17r5rJdduVN8x8MTNglD\nBENYdtUGq1EQw0qpX/kmyX7eAfy4Addp6QVbL5gf+r/Ug9odDZCeqk0ZLNVxRkNmwxYym4XfHWFv\nLMhUOtewdaSAIWTzy6tDXIxoyEnNXSkyvBOpRkF8SkSuBK4HMsWNSqmfu/mwiFwHHFBm18eVUr8s\nHPNxnDoTP6xwnouAiwDWr1/vWvhytGIFKi8xDKlqFlGLGaA/HsLK20QCJsPTmUWP644EO0JBzG/O\n+bOuaqjWhBcOmIQTzszEbsBItjsaZDqdK/v9YmGTbNJe4Nqp6SyqURBvB44Aguw3MSnAlYJQSr2o\n0n4ReRvwcuDsQmGixc5zBXAFwMaNG5fl8KYrEmAqvXQdhqBhkJ+TZG6+7dqLGVTQNHxXtPV0wovh\nNvndfCrcmlUz12Fgqd+iGeZQ0xB6YkHSkwvbPhwwGUosXqeh01ge33Ih1SiIk5VSvgTGichLcQoO\nPV8plfTjGkuh2sgaHgu5UxDzKTdBaIcMlD2xIPaMaogbpSmyMApjTrv51VamOEF1hggzmfYowtQs\n5dDI+7U7EiRt5WfTfSxWT6NTqWbod6uIHOWTHJcDXTgBePeKyH/4dJ1lTaeb1GplruLsigSIhswF\nSeQihYy5fsZqRArBVrVSrZttJywe+000ZNI3Jw34UML/cr6tRDUziNOAe0XkCZw1iGI9iOPqFUIp\n9ax6z1ENtdTgbXeaoRzczE5arY8yDCnr4tkTC9JlB1CwaCRvsyimvyinvGqaGbfaj+KSgClk8/6K\n36ruun5RjYJ4qW9SNJCgabR8DV6vaLbZrLtQPKfodWMWFs3NOYvnIk522XqtFYlwgOl5ppmBeIiR\nJWoAVEOjTCrV9kGGiF4sxrkHDJGGReQ3+/lqBNWUHN0B9OJEPL8C6F2sBGk7stxGBkUWu8W9iAqO\nBE1WdkdmH9jeQi6f3nlumolw/flpyi3ytutAoCsSJGBUTvTnx+1abMNgm4YWF3MttYL7eqd0J9VE\nUl+C4366ovD3XyLyd34J5iVz7cnzf7eeaJCgadDVJlkq25lAIRfU3I67JR7mFrOpmIYwkAg3PDdV\nXyxEPBxo6XxdoOtGN5JqesV3AqcqpWYAROTzwG3A1/wQzCtEKtvf3Ua9NotO9ZgYiIdI52zidZpG\nBuIhDJElPZyc2gJmTTl8KsYcdFBfZRpSMZ13q+SaigZNbOW4Kmv8pZoWFkrTceXpqMdD00gCpkEi\nXFsxmPnnMQxxtQhfa86d+cpnuXmy9MVCdEeCLVONTsRRZNorz3+qmUF8F/iLiFyFoxjOB77ji1Re\n0qEjcE0ppiEMJsKMTGfK/uT1qKH5I9V2Cg6LhQJ1x1W0imLQNJ5q6kF8WURuBM7A6XbfrpS6xy/B\nNJpqMQ1BRDyNdobWX+yu1IEnwvUrCM3ypZpF6jBOqo0E0Ae8QkQ+6ZdgmsbQGwsitFfR9uXqcbYY\nrbDQr+lMqhka/RLHrJQDZub8tSRF+6S2U1YmHDBZMccVtR76Yo0JQOwu1PVtJ6XWTHQ7aWqlmjWI\ntUqptgmW640GSVl5oi3sodRphAIGhojv5SADpsHgMlsorgc9SNLUSrW5mI71TRKPMQwnaGb+gqLf\n1olG+dS7MSs0w323mMK6VcweRTnmyzP3d/K7AE89FF1Lgx4tFM81z7WK26qmdalmBnEG8DY/cjE1\ngp5okGQ23zHlBaNBc0Fqibn0xUKEAgYTqQW5SX2lKxIgYNWX7iBoGlgeZW4dTIRRSrXtusVAPEQ2\nb9fl8z9/0b4/HsJWqmWUuKZ1qUZBnOubFA2g1QPiqmUpI44fD/+KrjApK18x1Xhx5lYPfbEgIzPZ\nqkumLka7Kgdw2jNi1HffFu+FYjNok5P/tO8dV0o1bq47RKQPOBSIzNnVMfmYOplYlRHLxa45FnRK\nhUZDplNfuAG3vogQqLIanmZxRIShRLhj8gO1Mt2RIDnbbnnXaLe4VhAiciFwCbAWuBcn/fdtwAv9\nEU0D3tmJa81fEzANVnSF23oUrmmv4L62ocz4xcmq2zmWimrU3CXAycAOpdRZwInAuC9StTFBs/yi\naK3UEsUaCZqejvM7Ujl04FdqJro5O5NqjMVppVRaRBCRsFJqi4j4UoK0nemOBEk20b22kUFvnag3\nNLWhjYGdSTUKYqeI9AK/wCkNOoZef1iAsURGzE6iU5LWtaOe69Qsv5rWoppF6lcVXl4qIjcAPcC1\nvkilaQv8ND0FTYNMztazlDZB/0ydSU1DXaXUn7wWROOOUId4RyxFLGRiiPiWSdRLxVMsSLWYwvQj\nulwrTk0jWB69TYdgiNAXb0y+o2YjhTrLXsdzFGNhvFwjCppGxdiCgXjI8w5dm5g0jUAriDai6CG1\nHCmmmqi3BXqiQVZ0hRsaLGYYQjjQOa6PmuXD8lhN7UDMMkNSp0JbE4RpAPGQiSEs2dF2RwOMJ62K\nnlzzTUEBwyBnL16KtBFN2qm/m6a9qSZQLgJczP6CQTcD/66USvskm6YC0ZBJysqX5CyKFaKdOxER\nIRZa+nYNB0xWdlc3WjcMZx1BJ6/TaEqpZp79feBo4GvA5cBRwA/8EErjjnjYfUfotvMr2uirTc3R\n7gRNQyev02jmUY2J6Ril1FFz3t8gIg97LZDGPW4WKouZO92mWuiJBkmEA7qzbHEMQwiZhp71aHyl\nmhnE3SJyWvGNiJwKbPJeJM1i1OK5EjSNqhdItXJoD/riIXpinZG+XtOaVDODeDZO0aAnC+/XA4+I\nyAO0UV2IdkZ7Nmo0mkZSjYIoV25UoYMofSdkGmTzNpGg9kr2g0Ys7EeDJmkrX1fhH42m0VSjIIaA\nj51z/fAAAAh8SURBVAMHzv2cnjn4T28sWKgqVtlU1KkeTH5Tayp0KO9uXI5QwGAoEdZptzuI5TCj\nr0ZB/BD4EPAA4E09SI0rRCoHWgVNg94GZXDtJAYTYay8XVOlwb5YiHQuX5W3l1YOnUFPNMhEqnKs\nTadQjYIYVkr9yjdJNDVjiO58asE0BLPGcp6hgOFbnihNa9Np5YsrUY2C+JSIXAlcD2SKG5VSP/dc\nKo1Go9E0nWoUxNuBI4Ag+01MCtAKQqPRaDqQahTEyUopXUFOo9FolgnVGFFvFZGjlj5Mo9FoNJ1A\nNTOI04B7ReQJnDUIQQfIaTQaTcdSb6Ccp4jIB4EvAkNKqX1+X0+j0Wg0i1NNTeodfgoiIuuAc4An\nlzpWo9FoNP7jeg1CHN4kIp8svF8vIqd4KMtXgA+zPAIUNRqNpuWpZpH6G8DpwBsK76eAr3shhIic\nDzytlLrPi/NpNBqNpn6qWYM4VSl1kojcA6CUGhORkNsPi8h1wAFldn0c+Ecc85Kb81wEXASwfv16\nt5fXaDQaTZVUoyAsETEpmIBEZIgqcjIppV5UbruIHAscDNxXSDa3Fqf2xClKqd1lznMFcAXAxo0b\ntTmqwQRNJ6WHLlSj0XQ+1SiIrwJXAStE5DPAa4BP1CuAUuoBYEXxvYhsBzZqLyb3SAMzrgdMg8FE\nGJ36SaPpfKpZgzgOZxH5X4BngAuAjX4IpWltTEN0anGNZhlQzQzixUqpjwBbihtE5FzgI14KpJQ6\nyMvzaTT1EjR11lbN8mRJBSEi7wUuBjaIyP1zdnUBt/glmEbTbAYTYXK2rdN6u0DPKDsTNzOIHwG/\nxTEtfXTO9iml1KgvUmk0LUA99SLcEgmYZHLtX4o0HjKxcjbRKgooaVqfJRWEUmoCmGB//IOmhRBB\nVwVvY3piQZwM+u2NiNAXd+31rmkTqlmD0LQYy6mylUajaTztPa/VaDQajW9oBaHRaDSasmgFodFo\nNJqyaAWh0Wg0mrJoBaHRaDSasmgFodFoNJqyaAWh0Wg0mrJoBaHRaDSasohS7VtSQURSwEM1fLQH\nJzq8EZ9r5LUA1lN9Xe9Gy9jIdqylPWq9Xju0YyPbo9bP6WfGm89V+syBSqmhJc+glGrbP2C4xs9d\n0ajPNfJatbZJE2RsZDs27B5pk3bUz4wHbdImv3VN15r71+4mpvEaP3d1Az/XyGtBbW3SaBkb2Y6N\nvEfaoR31M7MQ/cwsQrubmDYppXTRojnoNilFt0cpuj0Wottkcdp9BnFFswVoQXSblKLboxTdHgvR\nbbIIbT2D0Gg0Go1/tPsMQqPRaDQ+0XIKQkS+IyJ7ReTBOdt+LCL3Fv62i8i9c/YdJyK3ichDIvKA\niEQK2/9aRO4vbP98M76LFyzSHieIyO2F9tgkIqcUtg+IyA0iMi0il887z7Jrj8K+jr4/YNE2Ob7w\nvR8QkatFpLuwfbneI2Xbo7Cv4++RmqnXDcrrP+BM4CTgwUX2fwn4ZOF1ALgfOL7wfgAwC/+fBIYK\n2/8TOLvZ382r9gB+D5xbeH0ecGPhdRw4A3gPcPmc45dre3T8/VGhTe4Enl94/Q7g08v8HlmsPZbF\nPVLrX8vNIJRSNwFla12LUxn9dcB/FzadA9yvlLqv8NkRpVQe2ABsU0oNF467DvgrXwX3iUXaQwHF\nEVAPsKtw7IxS6mYgPe/4ZdkeLIP7AxZtk8OAmwqv/0Dh+y3je6Rse7BM7pFaaTkFsQTPA/YopbYV\n3h8GKBH5nYjcLSIfLmx/FDhcRA4SkQBwAbCuCfL6xT8AXxCRp4AvAh9b4vjl2h7L9f4AJ8PA+YXX\nr2Xp79fpbbJYeyzne2RJ2k1BvIH9swdwpodnAG8s/H+ViJytlBoD3gv8GPgzsB3IN1ZUX3kv8H6l\n1Drg/cC3Kx28jNtjud4f4JhRLhaRu4AuIFvp4GXQJou1x3K+R5akbRREQYu/GucHK7ITuEkptU8p\nlQSuwbE9opS6Wil1qlLqdOARYGujZfaRtwI/L7z+CXBKhWOBZdsey/X+QCm1RSl1jlLq2TiDqsdc\nfKZj26RCeyzbe8QNbaMggBcBW5RSO+ds+x1wrIjECgrk+cDDACKyovC/D7gYuLLB8vrJLpzvCvBC\nYFuFY4Fl2x7L9f6Y+/0M4BPAf1TxmY5rkwrtsWzvEVc0e5V8/h+Odn8GsHC0+zsL278HvKfM8W/C\nsS8+CPzrvPM8XPh7fbO/l5ftgTMVvgu4D/gL8Ow5x2/HWaCbLhx/1DJvj46+Pyq0ySU4I96twOco\nBMUu43ukUnt0/D1S65+OpNZoNBpNWdrJxKTRaDSaBqIVhEaj0WjKohWERqPRaMqiFYRGo9FoyqIV\nhEaj0WjKohWERlMHInKpiPyfCvsvEJGjGimTRuMVWkFoNP5yAaAVhKYt0XEQGk2ViMjHcdJ77AWe\nwgnSmwAuAkI4id7eDJwA/Lqwb4L92UC/DgwBSeBdSqktjZRfo3GLVhAaTRWIyLNxovpPxUn0djdO\n2obvKqVGCsf8M07W4a+JyPeAXyulflrYdz1ORoBtInIq8C9KqRc2/ptoNEsTaLYAGk2b8TzgKuUk\ndkNEflXYfkxBMfQCCZwcPyWISAJ4DvATp7QJAGHfJdZoakQrCI3GG74HXKCUuk9E3ga8oMwxBjCu\nlDqhgXJpNDWjF6k1muq4CbhARKIi0gW8orC9C3hGRII4tQWKTBX2oZSaBJ4QkdeCUyFRRI5vnOga\nTXVoBaHRVIFS6m6cmiT3Ab/FqXUM8H9xMsneAsxddP4f4EMico+IHIKjPN4pIvdRWuVMo2k59CK1\nRqPRaMqiZxAajUajKYtWEBqNRqMpi1YQGo1GoymLVhAajUajKYtWEBqNRqMpi1YQGo1GoymLVhAa\njUajKYtWEBqNRqMpy/8HliGDfrPpnywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134af0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"anomaly\"].plot(alpha=0.1, linewidth=2)\n",
    "df[\"10-year-anomaly\"].plot(linewidth=2)\n",
    "\n",
    "upper = df[\"10-year-anomaly\"] + df[\"10-year-uncertainty\"]\n",
    "mask = ~np.isnan(upper)\n",
    "lower = df[\"10-year-anomaly\"] - df[\"10-year-uncertainty\"]\n",
    "mask = mask & (~np.isnan(lower))\n",
    "\n",
    "lower.head()\n",
    "\n",
    "plt.fill_between(lower.index[mask], lower[mask], upper[mask], alpha=0.3)\n",
    "\n",
    "plt.ylabel(\"temp anomaly relative 1951-1980 [$^\\circ C$]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kWZbkq0nuTbIryRXDrmk+SXJkkruS3DTsWhZl0CdZBrwB+N6wa5lntgMvraqXAQ8AVw65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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173c4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m9o3kfdzdzazH3yTuvhxYDlBdXe01NTWZlpKS+vp6ct1GJlRXepLremb9trSOzeUNU1vb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173c4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"anomaly\"].hist(bins=20);\n",
    "plt.xlabel(\"anomaly\")\n",
    "plt.figure()\n",
    "df[\"anomaly\"].loc[\"1850\":\"1900\"].hist(bins=20, alpha=0.3)\n",
    "df[\"anomaly\"].loc[\"1950\":\"2000\"].hist(bins=20, alpha=0.3)\n",
    "plt.legend([\"1850-1900\", \"1950-2000\"])\n",
    "plt.xlabel(\"anomaly\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}