lessons/python/pandas2/UofT-pandas.ipynb

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  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "- Read tabular data into an IPython notebook\n",
    "- Access columns of the data\n",
    "- Isolate subsets of the data\n",
    "- Generate plots based on subsetted data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "Pandas has lots of great documentation, tutorials and walkthroughs.\n",
    "\n",
    "This tutorial was based largely off of a SWC inspired lesson by Nancy Soontiens found at:\n",
    "https://nsoontie.github.io/2015-03-05-ubc/novice/python/Pandas-Lesson.html\n",
    "\n",
    "I adapted other parts from a great tutorial by Greg Reda:\n",
    "http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/\n",
    "\n",
    "More can be found in the pandas documentation:\n",
    "http://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "A great youtube walkthrough from PyCon 2015:\n",
    "https://www.youtube.com/watch?v=5JnMutdy6Fw\n",
    "\n",
    "Lastly, a set of recent helpful blogposts for intermediate and advanced users can be found at:\n",
    "https://tomaugspurger.github.io/modern-1.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas introduces two new data structures to Python - **Series** and **DataFrame**, both of which are built on top of NumPy.\n",
    "\n",
    "We can load in a tabular data set as a dataframe in a number of different ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_table('./gapminderDataFiveYear.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>31889923</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.828</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1952</td>\n",
       "      <td>1282697</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1601.056136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1957</td>\n",
       "      <td>1476505</td>\n",
       "      <td>Europe</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1942.284244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1962</td>\n",
       "      <td>1728137</td>\n",
       "      <td>Europe</td>\n",
       "      <td>64.820</td>\n",
       "      <td>2312.888958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1967</td>\n",
       "      <td>1984060</td>\n",
       "      <td>Europe</td>\n",
       "      <td>66.220</td>\n",
       "      <td>2760.196931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1972</td>\n",
       "      <td>2263554</td>\n",
       "      <td>Europe</td>\n",
       "      <td>67.690</td>\n",
       "      <td>3313.422188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1977</td>\n",
       "      <td>2509048</td>\n",
       "      <td>Europe</td>\n",
       "      <td>68.930</td>\n",
       "      <td>3533.003910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1982</td>\n",
       "      <td>2780097</td>\n",
       "      <td>Europe</td>\n",
       "      <td>70.420</td>\n",
       "      <td>3630.880722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>3075321</td>\n",
       "      <td>Europe</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3738.932735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1992</td>\n",
       "      <td>3326498</td>\n",
       "      <td>Europe</td>\n",
       "      <td>71.581</td>\n",
       "      <td>2497.437901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1997</td>\n",
       "      <td>3428038</td>\n",
       "      <td>Europe</td>\n",
       "      <td>72.950</td>\n",
       "      <td>3193.054604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2002</td>\n",
       "      <td>3508512</td>\n",
       "      <td>Europe</td>\n",
       "      <td>75.651</td>\n",
       "      <td>4604.211737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2007</td>\n",
       "      <td>3600523</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.423</td>\n",
       "      <td>5937.029526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1952</td>\n",
       "      <td>9279525</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.077</td>\n",
       "      <td>2449.008185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1957</td>\n",
       "      <td>10270856</td>\n",
       "      <td>Africa</td>\n",
       "      <td>45.685</td>\n",
       "      <td>3013.976023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1962</td>\n",
       "      <td>11000948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>48.303</td>\n",
       "      <td>2550.816880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1967</td>\n",
       "      <td>12760499</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.407</td>\n",
       "      <td>3246.991771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1972</td>\n",
       "      <td>14760787</td>\n",
       "      <td>Africa</td>\n",
       "      <td>54.518</td>\n",
       "      <td>4182.663766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1977</td>\n",
       "      <td>17152804</td>\n",
       "      <td>Africa</td>\n",
       "      <td>58.014</td>\n",
       "      <td>4910.416756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>1982</td>\n",
       "      <td>9657618</td>\n",
       "      <td>Asia</td>\n",
       "      <td>49.113</td>\n",
       "      <td>1977.557010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>1987</td>\n",
       "      <td>11219340</td>\n",
       "      <td>Asia</td>\n",
       "      <td>52.922</td>\n",
       "      <td>1971.741538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>1992</td>\n",
       "      <td>13367997</td>\n",
       "      <td>Asia</td>\n",
       "      <td>55.599</td>\n",
       "      <td>1879.496673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>1997</td>\n",
       "      <td>15826497</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58.020</td>\n",
       "      <td>2117.484526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>2002</td>\n",
       "      <td>18701257</td>\n",
       "      <td>Asia</td>\n",
       "      <td>60.308</td>\n",
       "      <td>2234.820827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>2007</td>\n",
       "      <td>22211743</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.698</td>\n",
       "      <td>2280.769906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>2672000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.038</td>\n",
       "      <td>1147.388831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1957</td>\n",
       "      <td>3016000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>44.077</td>\n",
       "      <td>1311.956766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1962</td>\n",
       "      <td>3421000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.023</td>\n",
       "      <td>1452.725766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1967</td>\n",
       "      <td>3900000</td>\n",
       "      <td>Africa</td>\n",
       "      <td>47.768</td>\n",
       "      <td>1777.077318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1972</td>\n",
       "      <td>4506497</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.107</td>\n",
       "      <td>1773.498265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1977</td>\n",
       "      <td>5216550</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.386</td>\n",
       "      <td>1588.688299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1982</td>\n",
       "      <td>6100407</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.821</td>\n",
       "      <td>1408.678565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1987</td>\n",
       "      <td>7272406</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.821</td>\n",
       "      <td>1213.315116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1992</td>\n",
       "      <td>8381163</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.100</td>\n",
       "      <td>1210.884633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1997</td>\n",
       "      <td>9417789</td>\n",
       "      <td>Africa</td>\n",
       "      <td>40.238</td>\n",
       "      <td>1071.353818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2002</td>\n",
       "      <td>10595811</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.193</td>\n",
       "      <td>1071.613938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2007</td>\n",
       "      <td>11746035</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.384</td>\n",
       "      <td>1271.211593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1952</td>\n",
       "      <td>3080907</td>\n",
       "      <td>Africa</td>\n",
       "      <td>48.451</td>\n",
       "      <td>406.884115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1957</td>\n",
       "      <td>3646340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>50.469</td>\n",
       "      <td>518.764268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1962</td>\n",
       "      <td>4277736</td>\n",
       "      <td>Africa</td>\n",
       "      <td>52.358</td>\n",
       "      <td>527.272182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1967</td>\n",
       "      <td>4995432</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.995</td>\n",
       "      <td>569.795071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1972</td>\n",
       "      <td>5861135</td>\n",
       "      <td>Africa</td>\n",
       "      <td>55.635</td>\n",
       "      <td>799.362176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1977</td>\n",
       "      <td>6642107</td>\n",
       "      <td>Africa</td>\n",
       "      <td>57.674</td>\n",
       "      <td>685.587682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1982</td>\n",
       "      <td>7636524</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.363</td>\n",
       "      <td>788.855041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year       pop continent  lifeExp    gdpPercap\n",
       "0     Afghanistan  1952   8425333      Asia   28.801   779.445314\n",
       "1     Afghanistan  1957   9240934      Asia   30.332   820.853030\n",
       "2     Afghanistan  1962  10267083      Asia   31.997   853.100710\n",
       "3     Afghanistan  1967  11537966      Asia   34.020   836.197138\n",
       "4     Afghanistan  1972  13079460      Asia   36.088   739.981106\n",
       "5     Afghanistan  1977  14880372      Asia   38.438   786.113360\n",
       "6     Afghanistan  1982  12881816      Asia   39.854   978.011439\n",
       "7     Afghanistan  1987  13867957      Asia   40.822   852.395945\n",
       "8     Afghanistan  1992  16317921      Asia   41.674   649.341395\n",
       "9     Afghanistan  1997  22227415      Asia   41.763   635.341351\n",
       "10    Afghanistan  2002  25268405      Asia   42.129   726.734055\n",
       "11    Afghanistan  2007  31889923      Asia   43.828   974.580338\n",
       "12        Albania  1952   1282697    Europe   55.230  1601.056136\n",
       "13        Albania  1957   1476505    Europe   59.280  1942.284244\n",
       "14        Albania  1962   1728137    Europe   64.820  2312.888958\n",
       "15        Albania  1967   1984060    Europe   66.220  2760.196931\n",
       "16        Albania  1972   2263554    Europe   67.690  3313.422188\n",
       "17        Albania  1977   2509048    Europe   68.930  3533.003910\n",
       "18        Albania  1982   2780097    Europe   70.420  3630.880722\n",
       "19        Albania  1987   3075321    Europe   72.000  3738.932735\n",
       "20        Albania  1992   3326498    Europe   71.581  2497.437901\n",
       "21        Albania  1997   3428038    Europe   72.950  3193.054604\n",
       "22        Albania  2002   3508512    Europe   75.651  4604.211737\n",
       "23        Albania  2007   3600523    Europe   76.423  5937.029526\n",
       "24        Algeria  1952   9279525    Africa   43.077  2449.008185\n",
       "25        Algeria  1957  10270856    Africa   45.685  3013.976023\n",
       "26        Algeria  1962  11000948    Africa   48.303  2550.816880\n",
       "27        Algeria  1967  12760499    Africa   51.407  3246.991771\n",
       "28        Algeria  1972  14760787    Africa   54.518  4182.663766\n",
       "29        Algeria  1977  17152804    Africa   58.014  4910.416756\n",
       "...           ...   ...       ...       ...      ...          ...\n",
       "1674  Yemen, Rep.  1982   9657618      Asia   49.113  1977.557010\n",
       "1675  Yemen, Rep.  1987  11219340      Asia   52.922  1971.741538\n",
       "1676  Yemen, Rep.  1992  13367997      Asia   55.599  1879.496673\n",
       "1677  Yemen, Rep.  1997  15826497      Asia   58.020  2117.484526\n",
       "1678  Yemen, Rep.  2002  18701257      Asia   60.308  2234.820827\n",
       "1679  Yemen, Rep.  2007  22211743      Asia   62.698  2280.769906\n",
       "1680       Zambia  1952   2672000    Africa   42.038  1147.388831\n",
       "1681       Zambia  1957   3016000    Africa   44.077  1311.956766\n",
       "1682       Zambia  1962   3421000    Africa   46.023  1452.725766\n",
       "1683       Zambia  1967   3900000    Africa   47.768  1777.077318\n",
       "1684       Zambia  1972   4506497    Africa   50.107  1773.498265\n",
       "1685       Zambia  1977   5216550    Africa   51.386  1588.688299\n",
       "1686       Zambia  1982   6100407    Africa   51.821  1408.678565\n",
       "1687       Zambia  1987   7272406    Africa   50.821  1213.315116\n",
       "1688       Zambia  1992   8381163    Africa   46.100  1210.884633\n",
       "1689       Zambia  1997   9417789    Africa   40.238  1071.353818\n",
       "1690       Zambia  2002  10595811    Africa   39.193  1071.613938\n",
       "1691       Zambia  2007  11746035    Africa   42.384  1271.211593\n",
       "1692     Zimbabwe  1952   3080907    Africa   48.451   406.884115\n",
       "1693     Zimbabwe  1957   3646340    Africa   50.469   518.764268\n",
       "1694     Zimbabwe  1962   4277736    Africa   52.358   527.272182\n",
       "1695     Zimbabwe  1967   4995432    Africa   53.995   569.795071\n",
       "1696     Zimbabwe  1972   5861135    Africa   55.635   799.362176\n",
       "1697     Zimbabwe  1977   6642107    Africa   57.674   685.587682\n",
       "1698     Zimbabwe  1982   7636524    Africa   60.363   788.855041\n",
       "1699     Zimbabwe  1987   9216418    Africa   62.351   706.157306\n",
       "1700     Zimbabwe  1992  10704340    Africa   60.377   693.420786\n",
       "1701     Zimbabwe  1997  11404948    Africa   46.809   792.449960\n",
       "1702     Zimbabwe  2002  11926563    Africa   39.989   672.038623\n",
       "1703     Zimbabwe  2007  12311143    Africa   43.487   469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'pop', 'continent', 'lifeExp', 'gdpPercap'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106\n",
       "5  Afghanistan  1977  14880372      Asia   38.438  786.113360"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "1699  Zimbabwe  1987   9216418    Africa   62.351  706.157306\n",
       "1700  Zimbabwe  1992  10704340    Africa   60.377  693.420786\n",
       "1701  Zimbabwe  1997  11404948    Africa   46.809  792.449960\n",
       "1702  Zimbabwe  2002  11926563    Africa   39.989  672.038623\n",
       "1703  Zimbabwe  2007  12311143    Africa   43.487  469.709298"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      "country      1704 non-null object\n",
      "year         1704 non-null int64\n",
      "pop          1704 non-null float64\n",
      "continent    1704 non-null object\n",
      "lifeExp      1704 non-null float64\n",
      "gdpPercap    1704 non-null float64\n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 93.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country       object\n",
       "year           int64\n",
       "pop          float64\n",
       "continent     object\n",
       "lifeExp      float64\n",
       "gdpPercap    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.00000</td>\n",
       "      <td>1.704000e+03</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>1704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>2.960121e+07</td>\n",
       "      <td>59.474439</td>\n",
       "      <td>7215.327081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.26533</td>\n",
       "      <td>1.061579e+08</td>\n",
       "      <td>12.917107</td>\n",
       "      <td>9857.454543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>23.599000</td>\n",
       "      <td>241.165877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "      <td>2.793664e+06</td>\n",
       "      <td>48.198000</td>\n",
       "      <td>1202.060309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>7.023596e+06</td>\n",
       "      <td>60.712500</td>\n",
       "      <td>3531.846989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "      <td>1.958522e+07</td>\n",
       "      <td>70.845500</td>\n",
       "      <td>9325.462346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>82.603000</td>\n",
       "      <td>113523.132900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year           pop      lifeExp      gdpPercap\n",
       "count  1704.00000  1.704000e+03  1704.000000    1704.000000\n",
       "mean   1979.50000  2.960121e+07    59.474439    7215.327081\n",
       "std      17.26533  1.061579e+08    12.917107    9857.454543\n",
       "min    1952.00000  6.001100e+04    23.599000     241.165877\n",
       "25%    1965.75000  2.793664e+06    48.198000    1202.060309\n",
       "50%    1979.50000  7.023596e+06    60.712500    3531.846989\n",
       "75%    1993.25000  1.958522e+07    70.845500    9325.462346\n",
       "max    2007.00000  1.318683e+09    82.603000  113523.132900"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data selection\n",
    "Sometimes we need to look at only parts of the data. For example, we might want to look at the data for a particular country or in a particular year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1977</td>\n",
       "      <td>38.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1982</td>\n",
       "      <td>39.854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>40.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1992</td>\n",
       "      <td>41.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1997</td>\n",
       "      <td>41.763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2002</td>\n",
       "      <td>42.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1952</td>\n",
       "      <td>55.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1957</td>\n",
       "      <td>59.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1962</td>\n",
       "      <td>64.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1967</td>\n",
       "      <td>66.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1972</td>\n",
       "      <td>67.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>68.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1982</td>\n",
       "      <td>70.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1987</td>\n",
       "      <td>72.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1992</td>\n",
       "      <td>71.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1997</td>\n",
       "      <td>72.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>75.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2007</td>\n",
       "      <td>76.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1952</td>\n",
       "      <td>43.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1957</td>\n",
       "      <td>45.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1962</td>\n",
       "      <td>48.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1967</td>\n",
       "      <td>51.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1972</td>\n",
       "      <td>54.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1977</td>\n",
       "      <td>58.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>1982</td>\n",
       "      <td>49.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>1987</td>\n",
       "      <td>52.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1992</td>\n",
       "      <td>55.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>1997</td>\n",
       "      <td>58.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>2002</td>\n",
       "      <td>60.308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>2007</td>\n",
       "      <td>62.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>1952</td>\n",
       "      <td>42.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>1957</td>\n",
       "      <td>44.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>1962</td>\n",
       "      <td>46.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>1967</td>\n",
       "      <td>47.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>1972</td>\n",
       "      <td>50.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>1977</td>\n",
       "      <td>51.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>1982</td>\n",
       "      <td>51.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>1987</td>\n",
       "      <td>50.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>1992</td>\n",
       "      <td>46.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>1997</td>\n",
       "      <td>40.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>2002</td>\n",
       "      <td>39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>2007</td>\n",
       "      <td>42.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>1952</td>\n",
       "      <td>48.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>1957</td>\n",
       "      <td>50.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>1962</td>\n",
       "      <td>52.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>1967</td>\n",
       "      <td>53.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>1972</td>\n",
       "      <td>55.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>1977</td>\n",
       "      <td>57.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>1982</td>\n",
       "      <td>60.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  lifeExp\n",
       "0     1952   28.801\n",
       "1     1957   30.332\n",
       "2     1962   31.997\n",
       "3     1967   34.020\n",
       "4     1972   36.088\n",
       "5     1977   38.438\n",
       "6     1982   39.854\n",
       "7     1987   40.822\n",
       "8     1992   41.674\n",
       "9     1997   41.763\n",
       "10    2002   42.129\n",
       "11    2007   43.828\n",
       "12    1952   55.230\n",
       "13    1957   59.280\n",
       "14    1962   64.820\n",
       "15    1967   66.220\n",
       "16    1972   67.690\n",
       "17    1977   68.930\n",
       "18    1982   70.420\n",
       "19    1987   72.000\n",
       "20    1992   71.581\n",
       "21    1997   72.950\n",
       "22    2002   75.651\n",
       "23    2007   76.423\n",
       "24    1952   43.077\n",
       "25    1957   45.685\n",
       "26    1962   48.303\n",
       "27    1967   51.407\n",
       "28    1972   54.518\n",
       "29    1977   58.014\n",
       "...    ...      ...\n",
       "1674  1982   49.113\n",
       "1675  1987   52.922\n",
       "1676  1992   55.599\n",
       "1677  1997   58.020\n",
       "1678  2002   60.308\n",
       "1679  2007   62.698\n",
       "1680  1952   42.038\n",
       "1681  1957   44.077\n",
       "1682  1962   46.023\n",
       "1683  1967   47.768\n",
       "1684  1972   50.107\n",
       "1685  1977   51.386\n",
       "1686  1982   51.821\n",
       "1687  1987   50.821\n",
       "1688  1992   46.100\n",
       "1689  1997   40.238\n",
       "1690  2002   39.193\n",
       "1691  2007   42.384\n",
       "1692  1952   48.451\n",
       "1693  1957   50.469\n",
       "1694  1962   52.358\n",
       "1695  1967   53.995\n",
       "1696  1972   55.635\n",
       "1697  1977   57.674\n",
       "1698  1982   60.363\n",
       "1699  1987   62.351\n",
       "1700  1992   60.377\n",
       "1701  1997   46.809\n",
       "1702  2002   39.989\n",
       "1703  2007   43.487\n",
       "\n",
       "[1704 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select multiple columns with a list of column names\n",
    "df[['year','lifeExp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       28.801\n",
       "1       30.332\n",
       "2       31.997\n",
       "3       34.020\n",
       "4       36.088\n",
       "5       38.438\n",
       "6       39.854\n",
       "7       40.822\n",
       "8       41.674\n",
       "9       41.763\n",
       "10      42.129\n",
       "11      43.828\n",
       "12      55.230\n",
       "13      59.280\n",
       "14      64.820\n",
       "15      66.220\n",
       "16      67.690\n",
       "17      68.930\n",
       "18      70.420\n",
       "19      72.000\n",
       "20      71.581\n",
       "21      72.950\n",
       "22      75.651\n",
       "23      76.423\n",
       "24      43.077\n",
       "25      45.685\n",
       "26      48.303\n",
       "27      51.407\n",
       "28      54.518\n",
       "29      58.014\n",
       "         ...  \n",
       "1674    49.113\n",
       "1675    52.922\n",
       "1676    55.599\n",
       "1677    58.020\n",
       "1678    60.308\n",
       "1679    62.698\n",
       "1680    42.038\n",
       "1681    44.077\n",
       "1682    46.023\n",
       "1683    47.768\n",
       "1684    50.107\n",
       "1685    51.386\n",
       "1686    51.821\n",
       "1687    50.821\n",
       "1688    46.100\n",
       "1689    40.238\n",
       "1690    39.193\n",
       "1691    42.384\n",
       "1692    48.451\n",
       "1693    50.469\n",
       "1694    52.358\n",
       "1695    53.995\n",
       "1696    55.635\n",
       "1697    57.674\n",
       "1698    60.363\n",
       "1699    62.351\n",
       "1700    60.377\n",
       "1701    46.809\n",
       "1702    39.989\n",
       "1703    43.487\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#alternative selection with dot notation won't work if column names have spaces, uncommon characters or leading numbers \n",
    "df.lifeExp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year      pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952  8425333      Asia   28.801  779.445314"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#by index location\n",
    "df.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year       pop continent  lifeExp   gdpPercap\n",
       "0   Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "5   Afghanistan  1977  14880372      Asia   38.438  786.113360\n",
       "10  Afghanistan  2002  25268405      Asia   42.129  726.734055"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#you can provide a list of index values to select\n",
    "df.iloc[[0,5,10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or select with the slice notation\n",
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#select by index label\n",
    "#would require named index\n",
    "country_index = df.set_index('country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Canada</th>\n",
       "      <td>1952</td>\n",
       "      <td>14785584</td>\n",
       "      <td>Americas</td>\n",
       "      <td>68.750</td>\n",
       "      <td>11367.16112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1957</td>\n",
       "      <td>17010154</td>\n",
       "      <td>Americas</td>\n",
       "      <td>69.960</td>\n",
       "      <td>12489.95006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1962</td>\n",
       "      <td>18985849</td>\n",
       "      <td>Americas</td>\n",
       "      <td>71.300</td>\n",
       "      <td>13462.48555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1967</td>\n",
       "      <td>20819767</td>\n",
       "      <td>Americas</td>\n",
       "      <td>72.130</td>\n",
       "      <td>16076.58803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1972</td>\n",
       "      <td>22284500</td>\n",
       "      <td>Americas</td>\n",
       "      <td>72.880</td>\n",
       "      <td>18970.57086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1977</td>\n",
       "      <td>23796400</td>\n",
       "      <td>Americas</td>\n",
       "      <td>74.210</td>\n",
       "      <td>22090.88306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1982</td>\n",
       "      <td>25201900</td>\n",
       "      <td>Americas</td>\n",
       "      <td>75.760</td>\n",
       "      <td>22898.79214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1987</td>\n",
       "      <td>26549700</td>\n",
       "      <td>Americas</td>\n",
       "      <td>76.860</td>\n",
       "      <td>26626.51503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1992</td>\n",
       "      <td>28523502</td>\n",
       "      <td>Americas</td>\n",
       "      <td>77.950</td>\n",
       "      <td>26342.88426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1997</td>\n",
       "      <td>30305843</td>\n",
       "      <td>Americas</td>\n",
       "      <td>78.610</td>\n",
       "      <td>28954.92589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>2002</td>\n",
       "      <td>31902268</td>\n",
       "      <td>Americas</td>\n",
       "      <td>79.770</td>\n",
       "      <td>33328.96507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>2007</td>\n",
       "      <td>33390141</td>\n",
       "      <td>Americas</td>\n",
       "      <td>80.653</td>\n",
       "      <td>36319.23501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year       pop continent  lifeExp    gdpPercap\n",
       "country                                                \n",
       "Canada   1952  14785584  Americas   68.750  11367.16112\n",
       "Canada   1957  17010154  Americas   69.960  12489.95006\n",
       "Canada   1962  18985849  Americas   71.300  13462.48555\n",
       "Canada   1967  20819767  Americas   72.130  16076.58803\n",
       "Canada   1972  22284500  Americas   72.880  18970.57086\n",
       "Canada   1977  23796400  Americas   74.210  22090.88306\n",
       "Canada   1982  25201900  Americas   75.760  22898.79214\n",
       "Canada   1987  26549700  Americas   76.860  26626.51503\n",
       "Canada   1992  28523502  Americas   77.950  26342.88426\n",
       "Canada   1997  30305843  Americas   78.610  28954.92589\n",
       "Canada   2002  31902268  Americas   79.770  33328.96507\n",
       "Canada   2007  33390141  Americas   80.653  36319.23501"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_index.loc['Canada']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#boolean indexing\n",
    "large_pop = df[df['pop'] > 300000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>China</td>\n",
       "      <td>1952</td>\n",
       "      <td>5.562635e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>400.448611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>China</td>\n",
       "      <td>1957</td>\n",
       "      <td>6.374080e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>50.54896</td>\n",
       "      <td>575.987001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>China</td>\n",
       "      <td>1962</td>\n",
       "      <td>6.657700e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>44.50136</td>\n",
       "      <td>487.674018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>China</td>\n",
       "      <td>1967</td>\n",
       "      <td>7.545500e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58.38112</td>\n",
       "      <td>612.705693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>China</td>\n",
       "      <td>1972</td>\n",
       "      <td>8.620300e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>63.11888</td>\n",
       "      <td>676.900092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>China</td>\n",
       "      <td>1977</td>\n",
       "      <td>9.434550e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>63.96736</td>\n",
       "      <td>741.237470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>China</td>\n",
       "      <td>1982</td>\n",
       "      <td>1.000281e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>65.52500</td>\n",
       "      <td>962.421380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>China</td>\n",
       "      <td>1987</td>\n",
       "      <td>1.084035e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>67.27400</td>\n",
       "      <td>1378.904018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "      <td>1.164970e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>68.69000</td>\n",
       "      <td>1655.784158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>China</td>\n",
       "      <td>1997</td>\n",
       "      <td>1.230075e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>70.42600</td>\n",
       "      <td>2289.234136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>China</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.280400e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>72.02800</td>\n",
       "      <td>3119.280896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>China</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>72.96100</td>\n",
       "      <td>4959.114854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>India</td>\n",
       "      <td>1952</td>\n",
       "      <td>3.720000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>37.37300</td>\n",
       "      <td>546.565749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>India</td>\n",
       "      <td>1957</td>\n",
       "      <td>4.090000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.24900</td>\n",
       "      <td>590.061996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>India</td>\n",
       "      <td>1962</td>\n",
       "      <td>4.540000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.60500</td>\n",
       "      <td>658.347151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>India</td>\n",
       "      <td>1967</td>\n",
       "      <td>5.060000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>47.19300</td>\n",
       "      <td>700.770611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>India</td>\n",
       "      <td>1972</td>\n",
       "      <td>5.670000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>50.65100</td>\n",
       "      <td>724.032527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>India</td>\n",
       "      <td>1977</td>\n",
       "      <td>6.340000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>54.20800</td>\n",
       "      <td>813.337323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>India</td>\n",
       "      <td>1982</td>\n",
       "      <td>7.080000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>56.59600</td>\n",
       "      <td>855.723538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>India</td>\n",
       "      <td>1987</td>\n",
       "      <td>7.880000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58.55300</td>\n",
       "      <td>976.512676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>India</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.720000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>60.22300</td>\n",
       "      <td>1164.406809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>India</td>\n",
       "      <td>1997</td>\n",
       "      <td>9.590000e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>61.76500</td>\n",
       "      <td>1458.817442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>India</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.034173e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.87900</td>\n",
       "      <td>1746.769454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>India</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.110396e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>64.69800</td>\n",
       "      <td>2452.210407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>United States</td>\n",
       "      <td>2007</td>\n",
       "      <td>3.011399e+08</td>\n",
       "      <td>Americas</td>\n",
       "      <td>78.24200</td>\n",
       "      <td>42951.653090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  year           pop continent   lifeExp     gdpPercap\n",
       "288           China  1952  5.562635e+08      Asia  44.00000    400.448611\n",
       "289           China  1957  6.374080e+08      Asia  50.54896    575.987001\n",
       "290           China  1962  6.657700e+08      Asia  44.50136    487.674018\n",
       "291           China  1967  7.545500e+08      Asia  58.38112    612.705693\n",
       "292           China  1972  8.620300e+08      Asia  63.11888    676.900092\n",
       "293           China  1977  9.434550e+08      Asia  63.96736    741.237470\n",
       "294           China  1982  1.000281e+09      Asia  65.52500    962.421380\n",
       "295           China  1987  1.084035e+09      Asia  67.27400   1378.904018\n",
       "296           China  1992  1.164970e+09      Asia  68.69000   1655.784158\n",
       "297           China  1997  1.230075e+09      Asia  70.42600   2289.234136\n",
       "298           China  2002  1.280400e+09      Asia  72.02800   3119.280896\n",
       "299           China  2007  1.318683e+09      Asia  72.96100   4959.114854\n",
       "696           India  1952  3.720000e+08      Asia  37.37300    546.565749\n",
       "697           India  1957  4.090000e+08      Asia  40.24900    590.061996\n",
       "698           India  1962  4.540000e+08      Asia  43.60500    658.347151\n",
       "699           India  1967  5.060000e+08      Asia  47.19300    700.770611\n",
       "700           India  1972  5.670000e+08      Asia  50.65100    724.032527\n",
       "701           India  1977  6.340000e+08      Asia  54.20800    813.337323\n",
       "702           India  1982  7.080000e+08      Asia  56.59600    855.723538\n",
       "703           India  1987  7.880000e+08      Asia  58.55300    976.512676\n",
       "704           India  1992  8.720000e+08      Asia  60.22300   1164.406809\n",
       "705           India  1997  9.590000e+08      Asia  61.76500   1458.817442\n",
       "706           India  2002  1.034173e+09      Asia  62.87900   1746.769454\n",
       "707           India  2007  1.110396e+09      Asia  64.69800   2452.210407\n",
       "1619  United States  2007  3.011399e+08  Americas  78.24200  42951.653090"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['China', 'India', 'United States'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_pop['country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also chain together multiple criteria for boolean indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "multi_criteria = df[(df['country']=='Canada') & (df['year'] > 1990)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1992</td>\n",
       "      <td>28523502</td>\n",
       "      <td>Americas</td>\n",
       "      <td>77.950</td>\n",
       "      <td>26342.88426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1997</td>\n",
       "      <td>30305843</td>\n",
       "      <td>Americas</td>\n",
       "      <td>78.610</td>\n",
       "      <td>28954.92589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2002</td>\n",
       "      <td>31902268</td>\n",
       "      <td>Americas</td>\n",
       "      <td>79.770</td>\n",
       "      <td>33328.96507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2007</td>\n",
       "      <td>33390141</td>\n",
       "      <td>Americas</td>\n",
       "      <td>80.653</td>\n",
       "      <td>36319.23501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year       pop continent  lifeExp    gdpPercap\n",
       "248  Canada  1992  28523502  Americas   77.950  26342.88426\n",
       "249  Canada  1997  30305843  Americas   78.610  28954.92589\n",
       "250  Canada  2002  31902268  Americas   79.770  33328.96507\n",
       "251  Canada  2007  33390141  Americas   80.653  36319.23501"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q:\n",
    "How many unique countries are there in our dataframe? Years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Write a function 'print_stats()' that will print a given country's life expectancy, population and gdp per capita in a given year. (note data is available only for every 5 years between 1952 and 2007)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_stats(df,country,year):\n",
    "    \"\"\" Prints the life expectancy, gdp per capita and population\n",
    "    of country in year. \"\"\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_stats(df, 'Canada', 2007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby\n",
    "We can use the groupby method to split up the data according to repeated values in each column. For example, group the data by continent. This is helpful if we want to repeat an analysis on each group of data from a continent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f7688f1db38>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents = df.groupby('continent')\n",
    "continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</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>Africa</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1952</td>\n",
       "      <td>9279525</td>\n",
       "      <td>43.077</td>\n",
       "      <td>2449.008185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1952</td>\n",
       "      <td>17876956</td>\n",
       "      <td>62.485</td>\n",
       "      <td>5911.315053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1952</td>\n",
       "      <td>1282697</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1601.056136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1952</td>\n",
       "      <td>8691212</td>\n",
       "      <td>69.120</td>\n",
       "      <td>10039.595640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year       pop  lifeExp     gdpPercap\n",
       "continent                                                    \n",
       "Africa         Algeria  1952   9279525   43.077   2449.008185\n",
       "Americas     Argentina  1952  17876956   62.485   5911.315053\n",
       "Asia       Afghanistan  1952   8425333   28.801    779.445314\n",
       "Europe         Albania  1952   1282697   55.230   1601.056136\n",
       "Oceania      Australia  1952   8691212   69.120  10039.595640"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#helpful way to visualize the groupby object: gives first row of each group\n",
    "continents.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q:\n",
    "List the names of the continents and the number of data points in each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q:\n",
    "How many unique countries are there grouped together in the Americas continent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use an aggregate function to get the mean life expectancy in the different continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      48.865330\n",
       "Americas    64.658737\n",
       "Asia        60.064903\n",
       "Europe      71.903686\n",
       "Oceania     74.326208\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents.lifeExp.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell showed mean life expectancy values aggregated over all the years.\n",
    "\n",
    "Alternatively, we can groupby multiple columns and use an aggregate function to get the mean life expectancy/population/gdpPercap in a specific continent in a specific year of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Africa</th>\n",
       "      <th>1952</th>\n",
       "      <td>4.570010e+06</td>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>5.093033e+06</td>\n",
       "      <td>41.266346</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>5.702247e+06</td>\n",
       "      <td>43.319442</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>6.447875e+06</td>\n",
       "      <td>45.334538</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>7.305376e+06</td>\n",
       "      <td>47.450942</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>8.328097e+06</td>\n",
       "      <td>49.580423</td>\n",
       "      <td>2585.938508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>9.602857e+06</td>\n",
       "      <td>51.592865</td>\n",
       "      <td>2481.592960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.105450e+07</td>\n",
       "      <td>53.344788</td>\n",
       "      <td>2282.668991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.267464e+07</td>\n",
       "      <td>53.629577</td>\n",
       "      <td>2281.810333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.430448e+07</td>\n",
       "      <td>53.598269</td>\n",
       "      <td>2378.759555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.603315e+07</td>\n",
       "      <td>53.325231</td>\n",
       "      <td>2599.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.787576e+07</td>\n",
       "      <td>54.806038</td>\n",
       "      <td>3089.032605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Americas</th>\n",
       "      <th>1952</th>\n",
       "      <td>1.380610e+07</td>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>1.547816e+07</td>\n",
       "      <td>55.960280</td>\n",
       "      <td>4616.043733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>1.733081e+07</td>\n",
       "      <td>58.398760</td>\n",
       "      <td>4901.541870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>1.922986e+07</td>\n",
       "      <td>60.410920</td>\n",
       "      <td>5668.253496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>2.117537e+07</td>\n",
       "      <td>62.394920</td>\n",
       "      <td>6491.334139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>2.312271e+07</td>\n",
       "      <td>64.391560</td>\n",
       "      <td>7352.007126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>2.521164e+07</td>\n",
       "      <td>66.228840</td>\n",
       "      <td>7506.737088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>2.731016e+07</td>\n",
       "      <td>68.090720</td>\n",
       "      <td>7793.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>2.957096e+07</td>\n",
       "      <td>69.568360</td>\n",
       "      <td>8044.934406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>3.187602e+07</td>\n",
       "      <td>71.150480</td>\n",
       "      <td>8889.300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.399091e+07</td>\n",
       "      <td>72.422040</td>\n",
       "      <td>9287.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3.595485e+07</td>\n",
       "      <td>73.608120</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Asia</th>\n",
       "      <th>1952</th>\n",
       "      <td>4.228356e+07</td>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>4.735699e+07</td>\n",
       "      <td>49.318544</td>\n",
       "      <td>5787.732940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>5.140476e+07</td>\n",
       "      <td>51.563223</td>\n",
       "      <td>5729.369625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>5.774736e+07</td>\n",
       "      <td>54.663640</td>\n",
       "      <td>5971.173374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>6.518098e+07</td>\n",
       "      <td>57.319269</td>\n",
       "      <td>8187.468699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>7.225799e+07</td>\n",
       "      <td>59.610556</td>\n",
       "      <td>7791.314020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>7.909502e+07</td>\n",
       "      <td>62.617939</td>\n",
       "      <td>7434.135157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>8.700669e+07</td>\n",
       "      <td>64.851182</td>\n",
       "      <td>7608.226508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>9.494825e+07</td>\n",
       "      <td>66.537212</td>\n",
       "      <td>8639.690248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.025238e+08</td>\n",
       "      <td>68.020515</td>\n",
       "      <td>9834.093295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.091455e+08</td>\n",
       "      <td>69.233879</td>\n",
       "      <td>10174.090397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.155138e+08</td>\n",
       "      <td>70.728485</td>\n",
       "      <td>12473.026870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Europe</th>\n",
       "      <th>1952</th>\n",
       "      <td>1.393736e+07</td>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>1.459635e+07</td>\n",
       "      <td>66.703067</td>\n",
       "      <td>6963.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>1.534517e+07</td>\n",
       "      <td>68.539233</td>\n",
       "      <td>8365.486814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>1.603930e+07</td>\n",
       "      <td>69.737600</td>\n",
       "      <td>10143.823757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>1.668784e+07</td>\n",
       "      <td>70.775033</td>\n",
       "      <td>12479.575246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>1.723882e+07</td>\n",
       "      <td>71.937767</td>\n",
       "      <td>14283.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1.770890e+07</td>\n",
       "      <td>72.806400</td>\n",
       "      <td>15617.896551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.810314e+07</td>\n",
       "      <td>73.642167</td>\n",
       "      <td>17214.310727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.860476e+07</td>\n",
       "      <td>74.440100</td>\n",
       "      <td>17061.568084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.896480e+07</td>\n",
       "      <td>75.505167</td>\n",
       "      <td>19076.781802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.927413e+07</td>\n",
       "      <td>76.700600</td>\n",
       "      <td>21711.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.953662e+07</td>\n",
       "      <td>77.648600</td>\n",
       "      <td>25054.481636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Oceania</th>\n",
       "      <th>1952</th>\n",
       "      <td>5.343003e+06</td>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>5.970988e+06</td>\n",
       "      <td>70.295000</td>\n",
       "      <td>11598.522455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>6.641759e+06</td>\n",
       "      <td>71.085000</td>\n",
       "      <td>12696.452430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>7.300207e+06</td>\n",
       "      <td>71.310000</td>\n",
       "      <td>14495.021790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>8.053050e+06</td>\n",
       "      <td>71.910000</td>\n",
       "      <td>16417.333380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>8.619500e+06</td>\n",
       "      <td>72.855000</td>\n",
       "      <td>17283.957605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>9.197425e+06</td>\n",
       "      <td>74.290000</td>\n",
       "      <td>18554.709840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>9.787208e+06</td>\n",
       "      <td>75.320000</td>\n",
       "      <td>20448.040160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.045983e+07</td>\n",
       "      <td>76.945000</td>\n",
       "      <td>20894.045885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.112072e+07</td>\n",
       "      <td>78.190000</td>\n",
       "      <td>24024.175170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.172741e+07</td>\n",
       "      <td>79.740000</td>\n",
       "      <td>26938.778040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.227497e+07</td>\n",
       "      <td>80.719500</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         pop    lifeExp     gdpPercap\n",
       "continent year                                       \n",
       "Africa    1952  4.570010e+06  39.135500   1252.572466\n",
       "          1957  5.093033e+06  41.266346   1385.236062\n",
       "          1962  5.702247e+06  43.319442   1598.078825\n",
       "          1967  6.447875e+06  45.334538   2050.363801\n",
       "          1972  7.305376e+06  47.450942   2339.615674\n",
       "          1977  8.328097e+06  49.580423   2585.938508\n",
       "          1982  9.602857e+06  51.592865   2481.592960\n",
       "          1987  1.105450e+07  53.344788   2282.668991\n",
       "          1992  1.267464e+07  53.629577   2281.810333\n",
       "          1997  1.430448e+07  53.598269   2378.759555\n",
       "          2002  1.603315e+07  53.325231   2599.385159\n",
       "          2007  1.787576e+07  54.806038   3089.032605\n",
       "Americas  1952  1.380610e+07  53.279840   4079.062552\n",
       "          1957  1.547816e+07  55.960280   4616.043733\n",
       "          1962  1.733081e+07  58.398760   4901.541870\n",
       "          1967  1.922986e+07  60.410920   5668.253496\n",
       "          1972  2.117537e+07  62.394920   6491.334139\n",
       "          1977  2.312271e+07  64.391560   7352.007126\n",
       "          1982  2.521164e+07  66.228840   7506.737088\n",
       "          1987  2.731016e+07  68.090720   7793.400261\n",
       "          1992  2.957096e+07  69.568360   8044.934406\n",
       "          1997  3.187602e+07  71.150480   8889.300863\n",
       "          2002  3.399091e+07  72.422040   9287.677107\n",
       "          2007  3.595485e+07  73.608120  11003.031625\n",
       "Asia      1952  4.228356e+07  46.314394   5195.484004\n",
       "          1957  4.735699e+07  49.318544   5787.732940\n",
       "          1962  5.140476e+07  51.563223   5729.369625\n",
       "          1967  5.774736e+07  54.663640   5971.173374\n",
       "          1972  6.518098e+07  57.319269   8187.468699\n",
       "          1977  7.225799e+07  59.610556   7791.314020\n",
       "          1982  7.909502e+07  62.617939   7434.135157\n",
       "          1987  8.700669e+07  64.851182   7608.226508\n",
       "          1992  9.494825e+07  66.537212   8639.690248\n",
       "          1997  1.025238e+08  68.020515   9834.093295\n",
       "          2002  1.091455e+08  69.233879  10174.090397\n",
       "          2007  1.155138e+08  70.728485  12473.026870\n",
       "Europe    1952  1.393736e+07  64.408500   5661.057435\n",
       "          1957  1.459635e+07  66.703067   6963.012816\n",
       "          1962  1.534517e+07  68.539233   8365.486814\n",
       "          1967  1.603930e+07  69.737600  10143.823757\n",
       "          1972  1.668784e+07  70.775033  12479.575246\n",
       "          1977  1.723882e+07  71.937767  14283.979110\n",
       "          1982  1.770890e+07  72.806400  15617.896551\n",
       "          1987  1.810314e+07  73.642167  17214.310727\n",
       "          1992  1.860476e+07  74.440100  17061.568084\n",
       "          1997  1.896480e+07  75.505167  19076.781802\n",
       "          2002  1.927413e+07  76.700600  21711.732422\n",
       "          2007  1.953662e+07  77.648600  25054.481636\n",
       "Oceania   1952  5.343003e+06  69.255000  10298.085650\n",
       "          1957  5.970988e+06  70.295000  11598.522455\n",
       "          1962  6.641759e+06  71.085000  12696.452430\n",
       "          1967  7.300207e+06  71.310000  14495.021790\n",
       "          1972  8.053050e+06  71.910000  16417.333380\n",
       "          1977  8.619500e+06  72.855000  17283.957605\n",
       "          1982  9.197425e+06  74.290000  18554.709840\n",
       "          1987  9.787208e+06  75.320000  20448.040160\n",
       "          1992  1.045983e+07  76.945000  20894.045885\n",
       "          1997  1.112072e+07  78.190000  24024.175170\n",
       "          2002  1.172741e+07  79.740000  26938.778040\n",
       "          2007  1.227497e+07  80.719500  29810.188275"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['continent', 'year']).agg(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also retrieve a particular group with the get_group() command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>624.00000</td>\n",
       "      <td>6.240000e+02</td>\n",
       "      <td>624.00000</td>\n",
       "      <td>624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>9.916003e+06</td>\n",
       "      <td>48.86533</td>\n",
       "      <td>2193.754578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.27411</td>\n",
       "      <td>1.549092e+07</td>\n",
       "      <td>9.15021</td>\n",
       "      <td>2827.929863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>23.59900</td>\n",
       "      <td>241.165877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "      <td>1.342075e+06</td>\n",
       "      <td>42.37250</td>\n",
       "      <td>761.247010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>4.579311e+06</td>\n",
       "      <td>47.79200</td>\n",
       "      <td>1192.138217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "      <td>1.080149e+07</td>\n",
       "      <td>54.41150</td>\n",
       "      <td>2377.417422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "      <td>1.350312e+08</td>\n",
       "      <td>76.44200</td>\n",
       "      <td>21951.211760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year           pop    lifeExp     gdpPercap\n",
       "count   624.00000  6.240000e+02  624.00000    624.000000\n",
       "mean   1979.50000  9.916003e+06   48.86533   2193.754578\n",
       "std      17.27411  1.549092e+07    9.15021   2827.929863\n",
       "min    1952.00000  6.001100e+04   23.59900    241.165877\n",
       "25%    1965.75000  1.342075e+06   42.37250    761.247010\n",
       "50%    1979.50000  4.579311e+06   47.79200   1192.138217\n",
       "75%    1993.25000  1.080149e+07   54.41150   2377.417422\n",
       "max    2007.00000  1.350312e+08   76.44200  21951.211760"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents.get_group('Africa').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q:\n",
    "What is the maximum life expectancy for a country in Asia?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What country is this? When was the measurement taken? We can figure this out in a few different ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "803"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents.get_group('Asia').lifeExp.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "803"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#idxmax convenience function will return the index with max value\n",
    "df[df['continent']=='Asia']['lifeExp'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country            Japan\n",
       "year                2007\n",
       "pop          1.27468e+08\n",
       "continent           Asia\n",
       "lifeExp           82.603\n",
       "gdpPercap        31656.1\n",
       "Name: 803, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[803]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How can we rank each country based on their lifeExp?\n",
    "\n",
    "Let's create a new column 'lifeExp_rank' that creates an ordered ranking based on the longest life expectancy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_by_lifeExp = df.sort_values('lifeExp', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_by_lifeExp['lifeExp_rank'] = np.arange(len(sorted_by_lifeExp)) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>lifeExp_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2007</td>\n",
       "      <td>127467972</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.603</td>\n",
       "      <td>31656.06806</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>2007</td>\n",
       "      <td>6980412</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.208</td>\n",
       "      <td>39724.97867</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2002</td>\n",
       "      <td>127065841</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.000</td>\n",
       "      <td>28604.59190</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2007</td>\n",
       "      <td>301931</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.757</td>\n",
       "      <td>36180.78919</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2007</td>\n",
       "      <td>7554661</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.701</td>\n",
       "      <td>37506.41907</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year        pop continent  lifeExp    gdpPercap  \\\n",
       "803              Japan  2007  127467972      Asia   82.603  31656.06806   \n",
       "671   Hong Kong, China  2007    6980412      Asia   82.208  39724.97867   \n",
       "802              Japan  2002  127065841      Asia   82.000  28604.59190   \n",
       "695            Iceland  2007     301931    Europe   81.757  36180.78919   \n",
       "1487       Switzerland  2007    7554661    Europe   81.701  37506.41907   \n",
       "\n",
       "      lifeExp_rank  \n",
       "803              1  \n",
       "671              2  \n",
       "802              3  \n",
       "695              4  \n",
       "1487             5  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lists all rows in order of lifeExp\n",
    "sorted_by_lifeExp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split,apply and combine: the power of groupby\n",
    "What if we want to rank each country by max life expectancy for each year that data was collected?\n",
    "\n",
    "Applying a function on grouped selections can simplify this process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ranker(df):\n",
    "    \"\"\"Assigns a rank to each country based on lifeExp, with 1 having the highest lifeExp.\n",
    "    Assumes the data is DESC sorted by lifeExp.\"\"\"\n",
    "    df['lifeExp_rank'] = np.arange(len(df)) + 1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#apply the ranking function on a per year basis:\n",
    "sorted_by_lifeExp = sorted_by_lifeExp.groupby('year').apply(ranker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now subset my new dataframe by year to view the lifeExp ranks for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>lifeExp_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2002</td>\n",
       "      <td>127065841</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.000</td>\n",
       "      <td>28604.59190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>2002</td>\n",
       "      <td>6762476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>81.495</td>\n",
       "      <td>30209.01516</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2002</td>\n",
       "      <td>7361757</td>\n",
       "      <td>Europe</td>\n",
       "      <td>80.620</td>\n",
       "      <td>34480.95771</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2002</td>\n",
       "      <td>288030</td>\n",
       "      <td>Europe</td>\n",
       "      <td>80.500</td>\n",
       "      <td>31163.20196</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2002</td>\n",
       "      <td>19546792</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>80.370</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year        pop continent  lifeExp    gdpPercap  \\\n",
       "802              Japan  2002  127065841      Asia   82.000  28604.59190   \n",
       "670   Hong Kong, China  2002    6762476      Asia   81.495  30209.01516   \n",
       "1486       Switzerland  2002    7361757    Europe   80.620  34480.95771   \n",
       "694            Iceland  2002     288030    Europe   80.500  31163.20196   \n",
       "70           Australia  2002   19546792   Oceania   80.370  30687.75473   \n",
       "\n",
       "      lifeExp_rank  \n",
       "802              1  \n",
       "670              2  \n",
       "1486             3  \n",
       "694              4  \n",
       "70               5  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_lifeExp[sorted_by_lifeExp.year == 2002].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also subset by country=='Canada' to see how Canada's ranking has changed over the years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>lifeExp_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2007</td>\n",
       "      <td>33390141</td>\n",
       "      <td>Americas</td>\n",
       "      <td>80.653</td>\n",
       "      <td>36319.23501</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2002</td>\n",
       "      <td>31902268</td>\n",
       "      <td>Americas</td>\n",
       "      <td>79.770</td>\n",
       "      <td>33328.96507</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1997</td>\n",
       "      <td>30305843</td>\n",
       "      <td>Americas</td>\n",
       "      <td>78.610</td>\n",
       "      <td>28954.92589</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1992</td>\n",
       "      <td>28523502</td>\n",
       "      <td>Americas</td>\n",
       "      <td>77.950</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1987</td>\n",
       "      <td>26549700</td>\n",
       "      <td>Americas</td>\n",
       "      <td>76.860</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1982</td>\n",
       "      <td>25201900</td>\n",
       "      <td>Americas</td>\n",
       "      <td>75.760</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1977</td>\n",
       "      <td>23796400</td>\n",
       "      <td>Americas</td>\n",
       "      <td>74.210</td>\n",
       "      <td>22090.88306</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1972</td>\n",
       "      <td>22284500</td>\n",
       "      <td>Americas</td>\n",
       "      <td>72.880</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1967</td>\n",
       "      <td>20819767</td>\n",
       "      <td>Americas</td>\n",
       "      <td>72.130</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1962</td>\n",
       "      <td>18985849</td>\n",
       "      <td>Americas</td>\n",
       "      <td>71.300</td>\n",
       "      <td>13462.48555</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1957</td>\n",
       "      <td>17010154</td>\n",
       "      <td>Americas</td>\n",
       "      <td>69.960</td>\n",
       "      <td>12489.95006</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1952</td>\n",
       "      <td>14785584</td>\n",
       "      <td>Americas</td>\n",
       "      <td>68.750</td>\n",
       "      <td>11367.16112</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year       pop continent  lifeExp    gdpPercap  lifeExp_rank\n",
       "251  Canada  2007  33390141  Americas   80.653  36319.23501            10\n",
       "250  Canada  2002  31902268  Americas   79.770  33328.96507             9\n",
       "249  Canada  1997  30305843  Americas   78.610  28954.92589            10\n",
       "248  Canada  1992  28523502  Americas   77.950  26342.88426             5\n",
       "247  Canada  1987  26549700  Americas   76.860  26626.51503             6\n",
       "246  Canada  1982  25201900  Americas   75.760  22898.79214             8\n",
       "245  Canada  1977  23796400  Americas   74.210  22090.88306             9\n",
       "244  Canada  1972  22284500  Americas   72.880  18970.57086             9\n",
       "243  Canada  1967  20819767  Americas   72.130  16076.58803             7\n",
       "242  Canada  1962  18985849  Americas   71.300  13462.48555             7\n",
       "241  Canada  1957  17010154  Americas   69.960  12489.95006            10\n",
       "240  Canada  1952  14785584  Americas   68.750  11367.16112            10"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_lifeExp[(sorted_by_lifeExp['country']=='Canada')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure you use the following %magic command to allow for inline plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify the type of plot with the kind argument. Also, choose the independent and dependent variables with x and y arguments. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Plot year vs life expectancy in a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7688e79320>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vDBKXLaolxoMSzG7C3EHLly9X6TytLJEMqKq1LnGpYvJZToM+D63qJillnkR1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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7688ed3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='year',y='lifeExp',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot gdp per capita vs life expectancy in a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76867dd128>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P00yzd68kldrH0ND0rWtRr68yMxPgOGscOZJgfHzsR9ZX6HoMjebHR9eBPAFs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\n/wG05oZk44C4xQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7688efd4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='gdpPercap',y='lifeExp',kind='scatter', alpha = 0.2, s=50, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's going on with those points on the right? \n",
    "\n",
    "High gdp per capita, yet not particularly high lifeExp. We can use boolean selection to rapidly subset and check them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1952</td>\n",
       "      <td>160000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>55.565</td>\n",
       "      <td>108382.35290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1957</td>\n",
       "      <td>212846</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58.033</td>\n",
       "      <td>113523.13290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1962</td>\n",
       "      <td>358266</td>\n",
       "      <td>Asia</td>\n",
       "      <td>60.470</td>\n",
       "      <td>95458.11176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1967</td>\n",
       "      <td>575003</td>\n",
       "      <td>Asia</td>\n",
       "      <td>64.624</td>\n",
       "      <td>80894.88326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1972</td>\n",
       "      <td>841934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>67.712</td>\n",
       "      <td>109347.86700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>1977</td>\n",
       "      <td>1140357</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.343</td>\n",
       "      <td>59265.47714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year      pop continent  lifeExp     gdpPercap\n",
       "852  Kuwait  1952   160000      Asia   55.565  108382.35290\n",
       "853  Kuwait  1957   212846      Asia   58.033  113523.13290\n",
       "854  Kuwait  1962   358266      Asia   60.470   95458.11176\n",
       "855  Kuwait  1967   575003      Asia   64.624   80894.88326\n",
       "856  Kuwait  1972   841934      Asia   67.712  109347.86700\n",
       "857  Kuwait  1977  1140357      Asia   69.343   59265.47714"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['gdpPercap'] > 55000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f76866f2940>]], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76866fc550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='lifeExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7686750978>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kmQwkSZgMdrRxh4YevO585TbGt5ltqrW79SGaJ9FGtTY3flbVh/+uPr+O9zNJw/4tzK95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\nwmQgScJkIEnCZCBJwmQgSQL+Pz28Lchm1IZCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f768666b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.lifeExp.plot.hist(bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7686611e80>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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slRO6iocbm7Vg6dIkuZxwQt6RWGd0757UWpYsyTsSK1XmiUXSREnLJa2UdGUL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f768664fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['lifeExp'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Write a function that will take two countries as an argument and plot the life expectancy vs year for each country on the same axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compare_lifeExp(country1, country2):\n",
    "    \"\"\"Plot life expectancy vs year for country1 and country2\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "compare_lifeExp('Canada', 'Mexico')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Suzy wrote some code to determine which country had the lowest life expectancy in 1982.\n",
    "\n",
    "What is wrong with her solution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    Afghanistan\n",
       "lifeExp         38.445\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec=['country','lifeExp']\n",
    "df[df['year']==1982][spec].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do a quick check to look up Afghanistan's life expectancy in 1982."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "6  Afghanistan  1982  12881816      Asia   39.854  978.011439"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['year']==1982) & (df['country']=='Afghanistan')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesnt match with the answer above because the min() function was applied to each column (country and lifeExp).\n",
    "\n",
    "She should have done this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Putting it together:\n",
    "\n",
    "We can use all of these ideas to generate a plot that looks at a subset of the data. \n",
    "\n",
    "* Plot GDP per capita vs life expectancy in 2007 for each continent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bgJeBDwE/BtYCXUA/cFlE7Kizb+n7MIZ5cp6ZNUtL7+kt6b8Dtw1fvdMk8JcR0ZNh3y8A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\nCUa7v8Bv0lrE/oM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7680051400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76800512b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k+9D/rqTV+X4jgduAq/LmrOOA7WWnfwb4Ltk9D16KiAf68x6YFcXLm9ugI2l0\nRGzPawyPA/8+Ilb3sO+VwOkR8Y2aBrl3HOcBfxwRF9YzDrPeuA/DBqPbJX2c7Bv9nT0lCzPrG9cw\nzMwsifswzMwsiROGmZklccIwM7MkThhmZpbECcPMzJI4YZiZWZL/Dw2yJzbYnemBAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fcf9588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DjhUifqAJ+PUgx2sW/2hZfPCVwPqyx0+mx14JPFF2/In0WL9zImIHsFlJMX2o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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fcee4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2AtcBlwOzgP8aES/mGVgz8gORzKyRZW3DWAocC3wGOA64CfiHiPhKhmPHAF8Hfh/YC3wW\neBpYAbQC7cDciNhR5timacPwA5HMrFpq3YaxEZgVEb+MiIeADwHTMx57E/BgRJwAnAxsAhYBK9OB\nf6uAxf0Lu/H4gUhm1uj6LGFIGh0Rv+ll2+SIeL7Pk0ujgbaIOLbH+k3AmRHRkc5RVYyIqWWOdwnD\nzKyfalXCKJYE8P0e2+7LcP73Aa9Iul3SOkm3SjoEmBgRHQDpJIYT+hFzQ/IDkcys0R2ol1RphhrX\nx7a+zj8duDIiHpd0I0l1VM9iQ6/FiCVLluxbLhQKFAqFDJetT/Pnz2P27LPcS8rMKqpYLFIsFnO/\nzoGqpNZFxPSey+Xe93L8ROCxiDgmfX8GScI4FiiUVEmtTts4eh7fNFVSZmbVUqupQSZI+jOS0kTX\nMun7A/55nCaEFyQdFxFPA2cDT6avS4EbgAW8M9WImZnVqQOVMK7t6+CI+KsDXkA6maRb7UHAcyRd\nc4cDdwNHA1tIutW+VuZYlzDMzPrJz/Q2M7NMajoOQ9Jxkr4v6Yn0/UmSvlTpYMzMrH5lHbj3NZLB\ndbsBImID8Om8gjIzs/qTNWEcEhFreqz7XaWDMTOz+pU1Ybwi6VjS8RKSLgJeyi0qMzOrO1knHzwG\nuBX4CLAd+CVwcURsyTU4N3qbmfVbTcZhSLoqIm4CjoqI2ZIOBYZFxOuVDsTMzOrbgcZhrI+IU7KM\n6s6DSxhmZv1Xq5HeT0l6Bni3pA2l8QARESf1cpyZmTWZA7ZhpHM9PQSc13Ob2zDMzOqPR3qbmVkm\ntWr0vjsi5kraSPcpyF0lZWY2xByo0fuoiHhJUmu57a6SMjOrP66SMjOzTGpVJfU65Z+G11UlNbrS\nAZmZWX3qM2FExLuqFYiZmdW3rHNJmZnZEOeEYWZmmThhmJlZJk4YZmaWiROGmZll4oRhZmaZOGGY\nmVkmThhmZpaJE4aZmWXihGFmZpk4YZiZWSZOGGZmlokThpmZZdLnbLWVIKkd2AHsBXZHxExJY4EV\nQCvQDsyNiB15x2JmZgNXjRLGXqAQEdMiYma6bhGwMiKOB1YBi6sQh5mZDUI1EobKXOd8YFm6vAy4\noApxmJnZIFQjYQTwiKS1ki5L102MiA6AiNgKTKhCHGZmNgi5t2EAp0fES5LGAw9L2sz+j33t9cHd\nS5Ys2bdcKBQoFAp5xGhm1rCKxSLFYjH36yii13t15S8mXQu8AVxG0q7RIWkSsDoiTiizf1QzPjOz\nZiCJiFClz5trlZSkQyQdli4fCpwDbAQeAC5Nd1sA3J9nHGZmNni5ljAkvQ+4l6TKaQTwzYi4XtI4\n4G7gaGALSbfa18oc7xKGmVk/5VXCqGqVVH85YZiZ9V9DVkmZmVnzcMIwM7NMnDDMzCwTJwwzM8vE\nCcPMzDJxwjAzs0ycMMzMLBMnDDMzy8QJw8zMMnHCMDOzTJwwzMwsEycMMzPLxAnDzMwyccIwM7NM\nnDDMzCwTJwwzM8vECcPMzDJxwjAzs0ycMMzMLBMnDDMzy8QJw8zMMnHCMDOzTJwwzMwsEycMMzPL\nxAnDzMwyccIwM7NMnDDMzCwTJwwzM8ukKglD0jBJ6yQ9kL4fK+lhSZslPSRpTDXiMDOzgatWCeMq\n4Ocl7xcBKyPieGAVsLhKcZiZ2QDlnjAkvRc4F/h6yerzgWXp8jLggrzjMDOzwalGCeNG4C+AKFk3\nMSI6ACJiKzChCnGYmdkg5JowJH0c6IiI9YD62DX62GZmZnVgRM7nPx04T9K5QAvwLkl3AFslTYyI\nDkmTgJd7O8GSJUv2LRcKBQqFQr4Rm5k1mGKxSLFYzP06iqjOH/eSzgT+PCLOk/Q3wKsRcYOkq4Gx\nEbGozDFRrfjMzJqFJCKir1qdAanVOIzrgTmSNgNnp+/NzKyOVa2EMRAuYZiZ9V+zlTDMzKzBOGGY\nmVkmThhmZpaJE4aZmWXihGFmZpk4YZiZWSZOGGZmlokThpmZZeKEYWZmmThhmJlZJk4YZmaWiROG\nmZll4oRhZmaZOGGYmVkmThhmZpaJE4aZmWXihGFmZpk4YZiZWSZOGGZmlokThpmZZeKEYWZmmThh\nmJlZJk4YZmaWiROGmZll4oRhZmaZOGGYmVkmThhmZpaJE4aZmWWSa8KQNErSTyW1Sdoo6dp0/VhJ\nD0vaLOkhSWPyjMPMzAYv14QREW8BsyJiGnAK8EeSZgKLgJURcTywClicZxy1UiwWax3CoDRy/I0c\nOzj+Wmv0+POSe5VURPw2XRwFjAACOB9Ylq5fBlyQdxy10Oi/dI0cfyPHDo6/1ho9/rzknjAkDZPU\nBmwFHomItcDEiOgAiIitwIS84zAzs8GpRgljb1ol9V5gpqQPkpQyuu2WdxxmZjY4iqjevVrSNcBv\ngcuAQkR0SJoErI6IE8rs70RiZjYAEaFKnzPXhCHpSGB3ROyQ1AI8BFwPnAlsi4gbJF0NjI2IRbkF\nYmZmg5Z3wjiRpFF7WPpaERHXSRoH3A0cDWwB5kbEa7kFYmZmg1bVKikzM2tcVR3pLekiSU9I2iNp\neo9tiyU9I+kpSeeUrJ8uaYOkpyX975L1IyXdlR7zmKTJJdsWpPtvlnRJdT5dt8/yMUmb0hiurvb1\ne8SyVFKHpA0l63odOFnJn0MFYn+vpFWSnkwHfv5pg8Xf74Gr9RR/yTWGSVon6YFGi19Su6SfpT+D\nNQ0Y/xhJ96TxPCnpQzWNPyKq9gKOB36PZLDe9JL1JwBtJOM0pgC/4J3Sz0+BGenyg8BH0+XPATen\ny/OAu9LlscCzwBjg8K7lKn7GYWn8rcBBwHpgajW/5x7xnEEyaHJDybobgC+ky1cD16fLH6jUz6FC\nsU8CTkmXDwM2A1MbJf70nIek/w4HfgLMbKT40/N+Hvhn4IFG+v1Jz/kcSRtp6bpGiv8bwGfS5REk\n97WaxV+rm9hquieMRcDVJe+/B3yI5Ibx85L1nwa+mi7/C/ChdHk48HLPfdL3XwXmVfGzfRj4Xm+f\nrUbfdyvdE8YmkrEwpN/xpgr+HDpz/Bz3AbMbMX7gEOBxYEYjxU/SHf4RoMA7CaOR4v8lcESPdQ0R\nPzAaeLbM+prFXy+TD74HeKHk/a/Sde8BXixZ/2K6rtsxEbEH2KGkMb23c1VLz+uXxlwvJkT5gZOV\n+Dm8lv4cKkrSFJKS0k/ofeBn3cWv/g1crbv4gRuBv6D7WKlGij+ARyStlXRZg8X/PuAVSbenVYK3\nSjqklvGPGPxn6k7SI8DE0lUkP7QvRsR3K329Htexgalkz4fK9/2WDgO+BVwVEW9o//E5dRt/ROwF\npkkaDdyr/AeuVix+SR8HOiJivaRCH7vWZfyp0yPiJUnjgYclbaZBvn+S+/N04MqIeFzSjSSliJrF\nX/ESRkTMiYiTSl4npv/2lSx+RdLFtst703W9re92jKThwOiI2Jaun9zLMdVQ6+tn0SFpIoCSgZMv\np+sr+XOoCEkjSJLFHRFxf6PF3yUifgMUgY81UPynA+dJeg5YDpwl6Q5ga4PET0S8lP7bSVKlOZPG\n+f5fBF6IiMfT998mSSA1i7+WVVKlmewB4NNpi/37gPcDa9Li1g5JMyUJuAS4v+SYBenyp0ga0iEZ\nHDgn7V0wFpiTrquWtcD7JbVKGklSX/hAFa9fjtj/+740XV5A9++0Uj+HSrmNpP71pkaLX9KRXT1Y\nlAxcnQM81SjxR8RfRsTkiDiG5Pd4VUT8MfDdRohf0iFp6RRJhwLnABtpnO+/A3hB0nHpqrOBJ2sa\nfyUbmDI04lxAUl+2E3iJ7o3Di0la9Z8CzilZfyrJD/kZ4KaS9aNIBv89Q1KvPaVk26Xp+qeBS6r5\nGdPrf4ykR88zwKJqX79HLHcCvwbeAp4HPkPSk2xlGuPDwOF5/BwqEPvpwB6SnmZtwLr0ux3XIPGf\nmMa8HthAUi1Lo8Tf47OcyTuN3g0RP0kbQNfvzsau/4uNEn96/pNJ/ghdD3yHpJdUzeL3wD0zM8uk\nXnpJmZlZnXPCMDOzTJwwzMwsEycMMzPLxAnDzMwyccIwM7NMnDBsyEkHVW7MsN+edA6fjZJWSDq4\nGvGZ1SsnDBuqsgxAejMipkfEicBu4PKsJ5fk/1vWdPxLbU1H0jVKHmD1qKQ7Jf1Z+gCZ9enMsVeW\n7LtA0n2SVit5IM1/7+W0PySZagFJFyt5MNI6SV9Np1tA0uuS/md6jQ9LOk3Sj9Pr/kTSoWnp5lFJ\nj6evD6fHninpB5L+bxr7zfl+S2b954RhTUXSacAnSablOBc4Ld10O8msn9PKHDYjPeZk4FN652mQ\nXYlgBPBHwEZJU0keNPORiJgO7AUuTvc/FHgsvcZaYAXwJxFxCslzPHYCHcDsiDiNZH6mf+gRx5Uk\nDxR7v6QLB/NdmFWaE4Y1m9OB+yNid0S8QTK5mkieuvjjdJ87ehzzSES8FhG7SObrOSNd3yJpHbAG\naAeWkkwANx1Ym5YkziKZswiSea++ky4fD/w6ItYBRMQbkUx1PhL4upJH5t5Dkhy6rImILZHM17O8\nJA6zulDx52GY1Zkszyfo7fkCv01LEe+cLKl+WhYRXyxznp3RfXK2ctf+PLA1Ik5Kp5PemSEOs7rg\nEoY1mx8D/17SqHRq60+Q3Hhfk3R6us9/6nHMHEmHp1OQXwD8KF1f7ob/feAiJQ/kQdJYSUeX2X8z\nMEnSqel+h6UJYgzJTM2QTDM9vOSYmWkbxzCSaq8fYVZHnDCsqUTysJkHgJ8B/49kWvEdJNO6fyWt\nYur5l/sakqqk9cA9EdHWdboy538K+BLJ09t+RjK99FE994+I3SQ3/X+UtD7dbxRwM3BpWp11HPBm\nyekfB/6R5JkHz0bEvQP5Dszy4unNrelIOjQi3kxLDI8C/zki1vey7wLg1Ij406oGuX8cZwJ/HhHn\n1TIOs764DcOa0a2SPkDyF/03eksWZtY/LmGYmVkmbsMwM7NMnDDMzCwTJwwzM8vECcPMzDJxwjAz\ns0ycMMzMLJP/DzsfR6rOiec9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fbae630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "continents = df.groupby(['continent'])\n",
    "for continent in continents.groups:\n",
    "    group = continents.get_group(continent)\n",
    "    group[group['year']==2007].plot(kind='scatter', x='gdpPercap', y='lifeExp', title=continent)\n",
    "    plt.axis([-10000,60000,30,90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f767fa941d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EHzv/kczcTPx8/IhJiWHMc2MICwuTY0KEEFUmOYwGLDIyktE3jybnqxyuzrqa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\nQy2Z2nEqny36TKalRKPStVs3RsycSc7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37lx27NhBaWkpDQ0NXZ68yjfvuOecS1tSOu59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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fb5f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Example \n",
    "fig,ax = plt.subplots(1,1)\n",
    "colours = ['m','b','r','g','y']\n",
    "for continent, colour in zip(continents.groups, colours):\n",
    "    group = continents.get_group(continent)\n",
    "    group[group['year']==2007].plot(kind='scatter',x='gdpPercap',y='lifeExp',label=continent,ax=ax,color=colour,alpha=0.5)\n",
    "ax.set_title(2007)\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Write a function the takes a country as an argument and plots the life expectancy against GDP per capita for all years in a scatter plot. Also print the year of the minimum/maximum lifeExp and the year of the miniimim/maximum GDP per capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_gdp_lifeExp(df,country):\n",
    "    \"\"\" plot GDP per capita against life expectancy for a given country.\n",
    "    print year of min/max gdp per capita and life expectancy\n",
    "    \"\"\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "compare_gdp_lifeExp(df,'Afghanistan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "compare_gdp_lifeExp(df,'Canada')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rapid plotting with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/derek/anaconda2/envs/py3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       pop continent  lifeExp   gdpPercap\n",
       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f767e8a5b00>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MhMb+yAC8sSHIijz/i+fBae21g0GSiIhyEhs7hZGuX855TXI8ULrXfR6CSoNkrB/JcGZX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5OfEULAlHMBpXv3oPTAPliK635kV6ZWQ9unH4IHDIQ6oSsK/ru2zrF9gR06IGbOA+4q22XqDh2h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767e8a5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "sns.factorplot(data=df, x='year', y='lifeExp', hue='continent', size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f767e12b518>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DpMewbJstu49ScbyRQf0C3DD6ckzDcLpbIiLSw7nmdv+RI0eor6/nhhtuAOCyyy5jxIgR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\njNemTZsW23qqvREjRvDSSy+dW0dFJI7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725PqtvmNANC5wGsLqZ0LnB2qPb9rHnZVHzMD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fcdb320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=df, x='year', y='gdpPercap', fit_reg=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f767e36ef98>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xefNmli1bxo4dO7jlllvYvXs3R44cOeu5pz1dVVV9p34RTCaN5OREKfcsGUqx\nZVc5ZUcbGDzAyTWXnLoP9Vx5v1Ju2+XGW9Qt1AcffJD09HScTie//vWvWbRoEfn5+QwePJilS5fG\nvWJCdARd07hu7IVdXQ1xjmrXtKmpU6diGAYJCQksXboUj8fTauqREEL0VlFfy3/w4EH++Z//mQ8/\n/DD82Ouvv8706dM5ePBgh1ROCCF6kqgD9emnn+Y73/kOV155Zfix6dOnc+mll/Kb3/ymQyonhBA9\nSdSn/F9++SVbt27FarWGH+vXrx+PPfZYh17jL4QQPUXULVSHw0F5eXmrx0tKSiIu/RRCiN4q6hbq\nzJkzuf/++7n11lsZPHgwSilKSkp46623+NGPftSRdRRCiB6hXdOmkpKSWLt2LaWlpei6TlpaGg88\n8ED4+nshhOjNog7Uuro67r77bu6+++6OrI8QQvRYUfehTpgwAcMwzvxEIYTopaIO1JycHP785z9L\nqAohxGlEfcp/6NAhNm3axEsvvcSgQYNaLfrcW6/lF0KIJlEH6hVXXMEVV1zRkXURQogeLS73lBJC\nCNGOPlSATz/9lF/96lfheaeBQIC1a9d2SMWEEKKniTpQX3/9debNm4fdbmfnzp0AHD9+nBdffLHV\nrUSEEKI3ijpQX3vtNV555RWefvrp8GMDBw7k5Zdf5vXXX++QygkhRE8SdaAeO3YsPCilNVvhfMSI\nEVRWVsa/ZkII0cNEHaiDBw8On+o399FHHzFo0KC4VkoIIXqiqEf577rrLn7yk5/wwx/+kGAwyMsv\nv0xBQQGbN28mLy+vA6sohBA9Q9SBeuutt3L++eezZs0aUlNT+fDDDxkyZAivvfYa48eP78g6CiFE\nj9Cue0rdcMMN3HDDDR1VFyGE6NGiCtQtW7bwwQcfYDabycnJkSumhBBtChgGf9pYyMHKelJTErl7\n8ijMerumvfdIZ3yHGzdu5Kc//SmVlZUcPHiQOXPm8Le//a0z6iaE6KH+tLGQzwsrOVLl4vPCSv60\nsbCrq9QpzthC/Y//+A9++9vfMnnyZADee+89XnnlFTn1F0Kc1sHK+ja3z1VnbKHu37+fzMzM8PbN\nN9/Mvn37OrRSQoieLTUlsc3tc9UZW6iBQCBiqT6bzYbP5+vQSgkhera7J48CiOhD7Q3aNcovhBDR\nMOs69035XldXo9OdMVCDwSD/+Z//iVKqzcfuuuuujqmhEEL0EGcM1JSUlFarSbV8TNM0CVQhRK93\nxkDdvHlR6SsBAAAgAElEQVRzZ9RDCCF6vHb1oRqGwc6dO6msrETTNAYNGsQll1zSUXUTQogeJepA\n3bVrF3PnzuXYsWNYLBaUUgQCAQYOHMiKFSu4+OKLO7KeQgjR7UV9LdgjjzzCpEmT+N///V92797N\n119/zd///nduvPFGfvnLX3ZkHYUQokeIOlAPHz7MI488QkpKSvixgQMH8qtf/YrDhw93SOWEEKIn\niTpQR48eTWlpaavHDx8+HPfT/bq6Oq677joee+wxAKqrq5k7dy6ZmZlkZWWxbNmy8HOVUixdupRJ\nkyaRlZXFvHnzqKmpCe9fv349kydPJisri9zcXHbv3h3et2vXLnJzc5k0aRI5OTmsX78+ru9DCNG7\nRN2HOn36dB566CFuueUW0tPTMQyD0tJS8vPzmTVrFh999FH4uTfddFNMlfrXf/1X7HZ7ePupp54K\n99W63W7uvPNO3njjDWbPns3q1avZsWMH+fn52Gw2Fi1aRF5eHsuXL6ewsJDFixezdu1aUlNT2bhx\nI/Pnz2fTpk0YhsH8+fN59NFHyc7OprS0lJkzZ3LxxReTkZERU/2FEL1T1IG6cOFCAJYvX95q35Il\nS8I/a5pGQUHBWVfob3/7GwcPHmTq1KkcPnyYhoYGPvroI95//30AEhISuO2221i7di2zZ89mw4YN\n5ObmYrPZAJgzZw45OTl4PB7y8/OZOHEiqampAEyePJnf/va3bN++Hb/fj6ZpZGdnA5CWlsaECRN4\n9913WbBgwVnXX4juxFCKLbvKKTvawOABTq655AL0ZveEE/EVdaAWFnb88lsnTpzgmWee4d///d/J\nz88H4MCBA2iaFg5FgPT0dPbu3QtAcXExQ4cODe9LS0tDKcX+/fspLi5m9OjREWWkpaVRVFSEUooh\nQ4ZE7EtPT4/pj4EQ3c2WXeVs/vIQAHvKQl1h1429sCurdE5rM1Dr66NfcisxMfbVZJ555hnuvPPO\niKBzuVwRi7NAaIEWt9sNgNvtDrdOIdRCtlgsuFwu3G53RNcBgN1ux+12YxjGafe1h6537l/7pvKk\nXCk3GoeON6C12DaZWr92vMs1lOKTneUcPFpP6oBErh176pZxV3/O8dZmoI4bNy7iltFtibVlt3nz\nZg4ePBgx4ATgdDpbrW7ldrtxOBwAOBwOvF5veJ9hGPh8PhwOBw6HA4/Hc8pjDcM47b72SEpytuv5\n8SLlSrnRGJXen+LDtRHbycmnb/zEq9y/fnaAv+8Mzf4pPlyL02kj86ohp31+V33O8dZmoP75z38O\n/1xcXMzq1av553/+ZzIyMjAMg8LCQtatW8fPfvazmCvyl7/8hbKyMm666SaUUtTV1REMBiksLETX\ndQ4cOBBuue7du5eRI0cCMGLECEpKShg3bly4nmazmWHDhpGRkUFJSUlEOcXFxYwcOZJAINBqjYJ9\n+/aFXzdaNTUNGIY68xPjRNc1kpKcUq6UG5XLRiTT0OANtxQvG5FMVVXrM894l1u4/ziBgBGxfUVG\n/w4vN1pN5cZbm4F65ZVXhn9+7rnn+Ld/+zeGDRsWfuzaa6/l+uuvZ+HChUyZMiWmijz77LMR2y+9\n9BKHDh1iyZIl/Mu//AsrV65kyZIl1NbW8uabb3LfffcBMGPGDFatWkV2djZOp5NXX32VnJwcrFYr\nU6dOZfbs2RQVFZGRkcGaNWtwOp2MHz8ewzAwmUysW7eO6dOnU1hYyNatW9s9IGUYimCw874IUq6U\n217XjLkg/LMyIMjpXzde5V7U38m3pTUR2229bld9zvEW9aDUnj17GDx4cKvH09PTKSoqimulWnrq\nqadYuHAhmZmZmEwmpkyZwrRp0wCYNWsWZWVlzJw5EwjNl33yyScBGD58OHl5eSxYsAC/309KSgor\nVqxA13V0XWfFihXk5eWxcuVKbDYbzzzzTKuBKiFE+11zSSjEm88u6A001XxR0zbMnDmTSy+9lHnz\n5pGcnAxAVVUVK1euZPv27b12UnxVVX2n/mU1mTSSkxOlXClXyo1DufEWdQv16aef5sEHH+T111/H\nbrdjGAZer5ekpCRWrlwZ94oJIURPE3WgDhkyhLVr13LgwAGOHDmCz+dj0KBBDB06tNX0IyGE6I2i\nDtQzTaFyOp1MnDiRhQsXkpSUFJfKCSG6jlxl1X5RB+oLL7zA7373O6ZOncoll1yCruvs3LmTv/zl\nL8ydOxe/38+qVatYsmRJq7mkQoieR66yar+oA/XPf/4zv/vd7yJW6L/22mu57rrreOmll3j11Vf5\nwQ9+wIwZMzqkokL0Vl3VUjx4tJ56lx9fIIjVbOLg0eivnOytog7Ur7/+mlGjWt9be9SoUXz++ecA\nJCUltbr6SAgRm65qKbo9AepcoasUvb4gbk+gw8vs6aJeDzU1NZXFixdTWVkZfqyqqorf/e539O/f\nH8MwWLZsGd/97nc7pKJCxIOhFB/vPMwbm4r4eOdhjOhmDXapsqMNbW53lASbmT4OKzariT4OKwm2\ndt2CrleK+hNaunQpP/nJT1izZg0WiwWz2Yzb7SYxMZEXX3wRgC1btvBv//ZvHVZZIWIVS2uvq069\nBw9whuvatB2tWOqcmpJI0aETgCW8LdoWdaBefPHF/O1vf+Prr7/m6NGjGIZB//79GT16dHhBkaY1\nS4XormJp7XXVqXcsVx3FUufeerVTLNrVhrdarVx++eUdVRchOlwsrb2uOvXWNe2sgzuWOsdSbm8l\nnSKiV4ml1RVLGHeVnljnnkwCVfQqsbS6euIpcE+sc08mgSrEOayrTtt761VWEqhCREmuHIpeb/2s\nop6HKkRv11WDUj1Rb/2sJFCFiFLLAZ1zfYAnlosgettn1URO+YWIUm8b4InltP0HYwax52ANByvr\nSU1J5AdjBnVYPbsTCVQhotTb5mXGctq+bfcRyo41oOkaZcca2Lb7SK/47CRQhTiHxTLaftH5Dr7Y\nczS82tRF50d/i/WDlS1WqqrsHStVSaAK0Qm6ahrRJ7vKyd+yPxxsCrg+2pZiy/q1o75ub4uVqry9\nY6UqCVQhOkFXTSPaXlAREWzbCyqiDtRDRxtIdFhoWhzlUDtO+RPsoZWqmoI8wd47okZG+YXoBD1x\nGlEsI/WpAxJJdFhI7msn0WEhdUDvWKmqd/zZEKKLddUSfFeOSqGiyh1uKV45KiXqcmOZ1dDbZkQ0\nkUAVohN01RJ81469EE3TzqrcWGY19LYZEU0kUIXoBLIEX+8gfahCdHM98aqjnnirmXiQFqoQ3VxP\n7I/srYujSKCKHqe3LQ0XMAw+3nWYiio3A5MTuGr0QKwmU1dXq009cVZDPMgpv+hxmlo/e8pq2Pzl\nIbbsKu/qKnWo373+JfsO1VLv9rPvUC2/e/3Lrq7SGfXEbop4kBaq6HF6W+unosrd5nZ31BO7KeJB\nAlX0OL3tPkkDkxOoP+SP2O4MAcPgTxsLwytG3T15FGZdTmrbIoEqepze1vr55e2X8bvXvwz3of7y\n9ss6pdw/bSzk88JKAI5UuQC4b8r3ojpWBqWE6CF64tzKWAbSrCYTv/7RuA6uYWstV4hqz4pRstqU\nEKLD9MQWW2pKIoeONaAArXE7Wr11talu1SGybds2Zs2aRXZ2NllZWfy///f/AKiurmbu3LlkZmaS\nlZXFsmXLwscopVi6dCmTJk0iKyuLefPmUVNzsn9t/fr1TJ48maysLHJzc9m9e3d4365du8jNzWXS\npEnk5OSwfv36TnuvonfpiQNpIwafh91qwqRr2K0mRgw+L+pj7TYTFrOOAixmHbute0/zipdu00I9\nduwYc+fOZeXKlVx11VUcPHiQH/7wh1x66aX88Y9/ZODAgaxYsQK3282dd97JG2+8wezZs1m9ejU7\nduwgPz8fm83GokWLyMvLY/ny5RQWFrJ48WLWrl1LamoqGzduZP78+WzatAnDMJg/fz6PPvoo2dnZ\nlJaWMnPmTC6++GIyMjK6+uMQ55hYFmuORSxdDYePuTg/KSFiO1oebxB/wEAD/AEDjzfY3qr3SN2m\nharrOs8++yxXXXUVAKmpqYwYMYJdu3axefNm7r33XgASEhK47bbbeOeddwDYsGEDubm52Gw2AObM\nmcOmTZvweDzk5+czceJEUlNTAZg8eTIA27dvZ9u2bWiaRnZ2NgBpaWlMmDCBd999t1Pft+glYlis\nORaf7DzMO1v28+k/jvDOlv18svNw1MfGMpe0aT1Um9VEH4e116yH2m3eZXJyMjfffHN4u7S0lKKi\nIr73vdCoYlMoAqSnp7N3714AiouLGTp0aHhfWloaSin2799PcXExo0ePjignLS2NoqIilFIMGTIk\nYl96ejoFBQVxf29ClB2tb3O7o3xaUEFVnQelQNP8fFpQwfWXXhTVsbHMpkgdkEhR2QmaFqeW9VC7\n0JEjR/jZz37Gj3/8YwAsFkvEfpvNhtsdmtzsdrvDrVMATdOwWCy4XC7cbjd2uz3iWLvdjtvtxjCM\n0+5rD13v3Esem8qTcntWuV5fMGKQxusLYjK1fu14l3vkmIumdUmUCm1HW66mQtuaHvqvyaRF3V1w\n/WUXousaB4/WkzogkWvHnrqroav/feOt2wXqN998w7x58/jRj37EfffdR0FBAT6fL+I5brcbhyPU\nB+VwOPB6veF9hmHg8/lwOBw4HA48Hs8pjzUM47T72iMpqWsmlUu5Z8cwFB99Xsr+I7WkD+rLTePT\n2vzlal5uIGDw0n99RUn5CYZecB4/v/VSzOboes2SzrOT1MeG1x/EZjGRdJ6d5OTTt9ri9X4Dhmq1\nHW25H356gPyt+/H6g3xlOYbDYWPS94ec9tiWpt3YJ+rndtX3Kt66VaB+8803/PSnPyUvLy98+p+e\nno6u6xw4cCB8ir53715GjhwJwIgRIygpKWHcuNA8veLiYsxmM8OGDSMjI4OSkpKIMoqLixk5ciSB\nQIDXXnstYt++ffvCrxutmpoGDKPzlibTdY2kJKeUe5b+96vDfPR/ZQDs2nOUhgYv11/aevrSqcr9\n9/x/8Nk/KgAoq6jH5wtw/y3RTXQf0MdGMKhQhiIYVAzoY6OqqvVp/6nKNZTik53lZ2ztnUofh4V6\ntz9iO9pyP/r8AMdPeFBKUa9pfPT5AcZ9p39U5Uarq79X8dZtAtXn8/HQQw/x1FNPRfSlJiQkkJWV\nxcqVK1myZAm1tbW8+eab3HfffQDMmDGDVatWkZ2djdPp5NVXXyUnJwer1crUqVOZPXs2RUVFZGRk\nsGbNGpxOJ+PHj8cwDEwmE+vWrWP69OkUFhaydetWFixY0K56G42/IJ2tN5cby8h1aUU9qsV2W++n\nebkHjtRF7DtwpC7qzyJgKDy+AP6AQdBQBM7wOTYv93+/OkT+1gP4AkH+z2wiGDSi7gedND6Vt/9n\nH76AgdWsM2l8atTlVtd5wyGnlKK6ztth//Zd9b2Kt24TqH/96185fPgwzz//PL///e+BUH/o5MmT\neeqpp3j88cfJzMzEZDIxZcoUpk2bBsCsWbMoKytj5syZAIwePZonn3wSgOHDh5OXl8eCBQvw+/2k\npKSwYsUKdF1H13VWrFhBXl4eK1euxGaz8cwzz7QaqBLdTyyT5GNZByA1JTF8CWbTdrR2FFTi8QVR\nQNAXZEdBJROjDMXPCiqpqfeiABcBPiuojDpQrxt7IfpZ3gKlX6KNymp3eGJ/v0TbmQ7p9bpNoObk\n5JCTk3Pa/S+88MJp9z388MM8/PDDp9w3efLk8HSplkaNGsWbb77ZvoqKLhfLJPlYRq7vnjwKIGKx\nkGhV1zdr7TVuR6umxbE17Tg2lst0r/zeQCqqm93g73sDz+p1epNuE6iid4nltD2WVmYsAWPW9agX\nB2kpyWnlSJWrcfpSaDvqY/tEthST+nROS/HaSy5Ao/csQhMPEqiiS8Ry2h5LK7OrVvtP7mtH1zSU\nFgrF5L72Mx7T5KpRKVQ2uxX0Ve24FXQseuIiNF1NAlV0ia66k2dXLVKSYDOTlGgLh2KCLfpfvVhu\nBd3bbhfT1SRQRZfoqkWiu2qRktSURIoONbtyqB0DWj3xD0hvJYEqukRXLRIdS5DHsoJ9V3VT9MRV\nrnoyCVTR48QSMD8YM4g9B2vCofiDMYOiLjeWFey7qpXZ224XEy3DMAgEApjN8Y1ACVTRJWIJiViO\n3bb7CGXHGtB0jbJjDWzbfSTqY0sr6wkEjfBIfWknrULfVdPEerpAIIDP58Pj9REMGgQMhRFUBBv/\nAetdDaSnxffzkEAVXSKWkOiqY72+AE1XRyoV2u4MXTVNrCcIBAJ4vF68Xi+BIAQNhRE0CCqF0nRM\nuhmzxYLWuMiLyQwmwKRr6B2w5rUEqugSsYRELMfGstCz1ayjaYRbqNYoF0aJVW9uZSql8Pv9eL0+\nfH7fydA0FEHDQKFhMlsxm21oplBI6pauCzYJVNElYgmJWPpBY1noOW1gH8qr3CgUmqaRNjD61ZRi\nca63MoPBIG63G5fbiz8QJBhs7ONsDE50HbPZitlsBxOh0KR7hld3rJPoIWIZHIolJGLpBz10tIFE\nh4Wm6UuH2nHKPyI1iZ37juMPGFjMOiNSk86m+r1Cy+/GVd8bgM/nw+fzhxeIUYZCoehT66C+wY+m\nm9Ebm5c6EP21ZGfmCwRpcAdo8PhpcPtxe4NMuGxAHEsIkUAVZ62r5jjG0g960QBn5Cl/O7oLylvc\nY6m8HfdYikV3n5yvlAoNADWemgeCBp9+c4Qt3xxBKfjyWzh6vIbx37sw1J9pOtl/adI1EpwOfH4X\nwSiX71NK4QsYNLj9NHhOhmQ4MD3Nfw7Q4PbjCxitXkcCVcRdLL+sXXXv9Vj6UJVh4PEF8AUMDEOh\njNa/aB1Rbiyfc1dPzg8GgwQCAbw+Lz5fgKBxsh/TUKH/arqO3mwAqKJO4Q9a8QcNLCad4y6FxXrq\nNqdSoaUNa13+kyHpbhmMoZ/rGx8PdNOl/iRQe7lYflm76t7rsfS/flZYicsbQCkIBA0+K6xkwmWD\nO7zcT3YeDq9pajWbUEpFvQRfR07Ob2pder0BFAGOHavF5w/9sTGM0BQjpTR0kwmT2YLJFNmPebrX\nbHD5qXf7UIAHOFTZwP98eahVq7LeE8DVjQOyvSRQe7lYflmb7mwZvj69B9zZsrzFPZbac9oeS7/v\n9sLKiD8+2wujX9M0pha5UgSDwdBIuc/XOOijCBoq3MrUdB2z2cL51gSCmh3MCp2TAz+GUni8QU7U\n+Wjw+Kl3N2tBNrYmXZ6TrUeXJ9Dq9L20sr7D5+2aTRpOuwWn3YwzwRL6OcHc+N/Gxxsf6+uIZw9t\nszp0yKuKHiOmBZe76M6WsbSqTS3uH9Vyuztqq2XcNHnd5/fjDwQxGk/HVWPrMmgovtxzjMoTPi48\nvw/jvjcIdHD7m1qJQRo8XtzeOgJ7qjhe7WoMxlBQ1tT78PqDXfK+LSY9FIhN4dgYlIkRQdn4X7sF\nq0VHi7IbpaP+3SVQzwGx9M/FchrbE69P/+6Qfnz6j4rw2qLfHdIv6mNjceV3B1LRbAm+K7975sWa\ng8FgOCy/O9jOdy60YyhFecXxcP+l0nQUOv6AhstnRPY7uv2UlNdRfjx0v6Yv9lbzl+0H8QWMcCu9\nM1nN+smWYmNI9kmw0D/ZgUkpEhpbkImNIWm1dMDM+w4mgXoOiKXFFstpbFddn37B+Q62fl0enr40\n4RQ32TudWFbdj0XzxZovSLYzfmQy9Q0NBAIBgsGmliWgKapr6yg7UkudO4jbp3D7DNy+5tN+Ao2n\n3qGWpMcbINp8jGdfpc1iimghOhPMJDYG5Z6DNVTVedD10K2nh17Qlx9eO7TVa5h0jfOSHJyoiX6U\nvzuTQD0H9MQVhQ4ebTFD4Gj0/Wt7D9aE7s/UeEq792BN1PdnimXV/ZaCwWBjGBoEgkEMI0gwaGAY\nBoYBhmo2xccVwGoKMPA8nboGD+9s2Y/La+DyBFqEpR+3r2tOsR02Mw576H+JCRYOHKmjwXNyoHHA\neTZyb/pOuIVpNp3+SjG71cSnjXeIBbjo/N6xKIsE6jmgq1YUiuW03e0JRNx4zu2JfoZAaWV96HRX\ngYaKerDDMAyChsGWXYcpq6znwv4Ovj96ICgVCkJlEAgEMZRC08Dt81Bd3UAwYIRagCr0nr3+IPUe\nPy6vwu0LNgZjEJc3gMsTPDmK3dia7Ko+yASbGZOuhWdfmPRQSzEj9byIVqUzwULfBAvJ/RMjWoov\n/NdO3I0zIjQNTLrOhVEG4+UjQ3M8j1S5GJTsCG+f6yRQzwEd3ZepGucaNs1HDDROkv545yH+56vQ\nafs3xQqPx80PRg+CxpaZItRXF2qxKUCFFxc5dOQoPk99+Je17IhOxdGqxr69kzek03WNBo+bmpqG\n0G2GFdSeOIHPc/JGdSdOBCg7fDTitLfpZ9UYvAqFjs7/7TnKZ4WVaGh8XaJzwuXjkuHnh1qJngBu\nr9EYjAH8qoqqGnfo1LrZdB+fP/q5q/GiQas+xsiR6+YDNGYcdgsmXSN/awkFJdXh+aDJ59m4enTr\n78epBmkuPN/J8VpPxHa0dE1jXCfdquVMQt8BFfGz0jWUEf+1GCRQY9A0h6/l/cRb/uM1/zkUNCef\n0/SbH94mdDmeYahwGDRnMmkYykdVdT3BgAq3nEZeFBq0ADh6rLrxNVvVOPz/qvH/Pi+oYGvjqVno\nipYqrhiZEj5eodDQ0E06JxrcnKh1h+oGfFtaFQ4XTYN95fWMSg80bmvhEVddt5wcfW38j8nmxJ5w\nMpjMNifKFHmfJY1QoOqWBEwWBaZQ/R3OPtT6Tn51nc4EzHZn+HM8eRWNv1W/Y2FpDbVurXFCeoD3\nPj1E/raylh9Up2gKxMRmwZjosHB+sjM0SGMzh0PSYTOjn8XItNcbpMHjRymFF/C4/QSDp2gxK63x\nj2WAQNAApcj5wUX4fV6OHK8npZ+d7CtT8Hladydpzf+raY0LyCj+79tKyo+7uSA5gStGpaChhZ/c\n9FzdpKEFdTTDjdZGQ775d7n53/uIr1Wzn08+p7GM8HdRQ0PDZNIZOCD+lw5LoMagovI4hyrrMJoF\nqqZp4daRhhbebvoH1cL/6lrEMc2FwujUU0BMaHgNKwFlJ6ippoLaRWt2yNEGDYvtZMujyqVjS2g9\n/cmka9gdDrw+LXxKOHhgP4oOucKtn4sG9MVisURVh0uGn8/xGk/42EuGn3/K5ymlcHsCHDvhpq7x\nShpfIPI3r6bBx0trd4dDtCsmieuadsqpPE0/H6lysffQCXRNw6Rr/ODigYw/xUi/hiKxj42a6nr8\ngQAoCBoe3vywmCPHXVzQ38GMicOxmExo4XwKhdjJ7fCLYdF8qIAbf1BhNevYzUH62E9+Pk1nCLoO\n/ZwmNL+JoKGjaxq6rjNv5lhMJhMmU/tG3D/eeZiv9ofm+FbUuUhO9p9y0NFk0khOTsRutbdqmHQk\nk0nDbo/+RonRkkCNhaaRYHd26uhktPPsojUo2cH+I3UR29FSgNcfxB80wq3WMx6jFB5fkCEDE7lk\nxPlUVLtw2MzUuXy8u3X/Ka/DPtPn6/EFOXwsvgNxJl2jj9OKw2rCYbeQmGDh65LjEWFtt5iYO2M0\nTrsFu9XU6t8m1NVhYBhB/AEnrvrQFKbz+zsYPcRB0OtC00MBqzeGmtVq5vy+VmyaI3SFkq7z2sZC\nCso8KHSqyzz0/eJY1ANrZusRdIsDW+PfOd2SQN8+fVu/X5NG376JBAJaXIKtJw6UxoME6jnAUIov\nvj0aMQAQ7eDQ2Iz+7Cis4PgJL/3PszE2o3/U5e4uPo6/sbXoDwTZXlCBw2Zudmnhyek9rvD12QGM\nLpgEGb6KpsUVM02PJTbNjWzc77CZSernjBikOb62gQMVtSjDQCmDpL4JnGcz0PGhAk2txJPhaLKY\nMOkmzGYL2745QY0HnH36UueDsuNBrhvbuoVqMmkkJjrx+VQ42AoP1jT2QYf+iBWUVkf9vrvqarbe\neusVCdRzwBffHg1PUWlqbUY7ILDhf0soPx46NSs/7mL934vJ/kF660sLvQH8hqKqxhO+xPD4CU9o\n7mSjw8dcvL6pKL5v7jRMuobZpJFgs5DSL+HkQE2zkevmV9eEFofWIv74DOwX+uOjQWNfuB+MAFow\nQMCrE/BqGAE3WmhJTn42/Ts8u3ontS6D5L52nvzJ97FHeU+iiho/erMl4tvTYrOa9YjWf3sWth48\nIJEv9xyL2I5WV10w0pNJoJ4Dmm4Yd6rtoKFwNQvG8Ko9jXMev9lfjb/ZKd6Xe4/z5d7jnVb3JhqQ\n1MfWbJCmaaK4lQH9nejKCA3SND5uNUfXpxeaKxrA6w2CYfDFnko+K6gEDfaWAgE3V4+5gD6JFmxW\nB2azGU3Twn17VVX14ZbixzsP07dvH/o2njF//k1l1BcjXNg/ga1fl+MLGFjNOhPHRh8wwy48j8pq\nd7i/c9iF50V9bKuRyXacHXTVBSM9mQRqDxMIGtTUeSk/1kCty0eDJ0Cd28/xE26ChkIjtPjGnoNf\nNU4D6pwVoFqyW00tBmgiF6x4d2sJbu/J5m2iw8z/N/uyVq9zpitpmgLTCAbRMDDpOnpjv6RJD83F\ntFrsWCwWzGYz2wpP4HCeXGm/zmcmuV90o72xLFdYVHYiYpWrorITUa9y5bCb6dfHHi7X0Y7T9kPH\nXJELardjMZhY+kG7+xquHUUCtYsFgi0Xyj25ak9Eq7LxZ08UV9E0nzsYLwk2E32dNhKsoV9op91C\naUUdR2vc4SkrGYOT+OF1w3DazW1eRQPw0Y6DuL2+8LblFM8P3U8ogMftwu0KhVdo9Dl0OaPJFApu\nm9WO1WqNaiQ6lgWmXR5/6GIEpXBpoWXnolVQWhOxylVBaU3bBzQTy2l7LH2ZFzZe4htuVbfjEt+P\nvzrE238vDh9rGEbUf0B6MgnUOPMHmhaoOBmC9S0WrGhoNkDTFVfRaBCe4+j2BGjwBMKzuC4838k1\nY69Qf8UAACAASURBVC6ImCPpsIdOsVu2FPM/Kaa2wRee+tQv0cp5zuiWRbNazGC4CRpBMIJohsLv\naQiNeDe1ME06CU4rgwYmkeRMoGkt6Natn75Rt34Mw6DOFVpV3msKXTYarap6b8QVWlX13jMfFC5X\ntbndphhO22O5/9aeg9W4PKF1AgIBgz0Hq5kQ5SW+728vDV+26g8YvL+9VAJVtO2P7+2hutYXsdzZ\nqW610NkcNhNjhp9/crmzFlfUNF2SCJC/pYSvio4RCBqYTTqpKYmMHXHqOaEteXxBvP5g49VQwYjW\ns1KKgN+PYQRQhhEKSU0LTxMa1Fej4qgfq9mCrlsYNfQCUi9sfXmiyaRhtVrRNB9NFyZ8squc/C37\nTy7WDFwfZX/dh58fDP8b+QIGH35+kIlR/qKXH2tosZZqO9aOtZk40RC5Ha3So/WcaPASDCpMpgCl\n7Vj3YOvuIxSW1oTuqeQJsHX3kag/q137qk5ecda4Ha3qOl+b2+cqCdQYbPu6skNeV9fAcZqFcvs4\nLKFBGsMgwRZ6/NV3vuZozcnWUqLDcsqVfU7F2xSKQNAI4o1yYQ6lFFW1Dfh9HgwjiAYcr9Yh4MGk\ng9msY02wYrU4wwM9zc2dNZ4/bSw8q1WfPvvmCFV1nsZBGj+ffRN9SNS6fG1ut8Xf4o9ly+22nOe0\ncKTKHbEdrc//URGe/xoIKj7/RwV3Zo6M6tjP/lFBdfPP6h8VUX9WsVAtWtEtt89VEqidoPlVNE2n\n0G0tlGu3mU57CnuqQRqXO3LgqcEVfd9eTb0nfH29atyGk5fVGsEAGAYmEwQSQAXcoZXcTToNLi+6\nyYLZErrixI+dQSnJUZVrKEVFtYvqOi9Wi96uuamHjke2FA8dj76laDGZgGCL7ej0dVhxed0R29EK\nGm1vt8XtDba53ZZDR+tO/vuq0Ha0EqymiNWmEqzRf1Yt/zV7R5xKoMYk/YJELCZT5Cj2KSaOn+oq\nmng62/45pRTVtQ0EfG6UCv2GV1UH0IIeLCbo47RhtSZgNpuxWEytphEl9+vDsfra8C9LH0f0X6dn\nV3/BvsOhX+66Q36eXf0Fj981PqpjW85caM9MBkeLU29HO069h1yQyJFqd8R2tLz+YOM17qGpT+3p\nO0/qY+XYCW/EdrQaWqzi1XK7LfVuX5vbbWl5+e+5cs+oM5FAjcHjd11K1Ylgly+Ma7PquJutgGSz\nRo6YN01aV8FgeLDHpGtYzTpurx+T2YrWOOk8qJsZOCC6VmbLe9q35x73JeV1bW63xWjxy9lyuy3B\nFoNQLbfbUnCgps3ttjQtgwehUG3PH4FEu4ljJyK3o9Xyq9mer6rXr9rcbotGZKv03J8wFdLrA3XX\nrl0sXryY6upqLBYLP/7xj5k2bVpXV6td6l3e0JqdRgClDGoCYPjd4bmYzgQLNlsfLBZLq5ayJ2BG\na5a/da7of9EbPME2t9sSyy96LL+ttQ3+Nrfb4mrRumu53ZaqWm+b2205UOFqc7stMUwQiOm03WSC\n5mvYtHNtlR6rVweq7/9n797jo6jv/Y+/Zjd7yW6AECAJai5cYmgBkZtaUQE1xCTIEWhB6g2LnloR\nFfVXL6AEewDReql6MNJzyqkFobQHwSgKKp56QYq0FrwkkhshISEEkpDL7mYvM78/YlaWS5iQyc18\nno+Hjwezs7Of76zJOzPf+c53vF4WLFjAI488QlpaGgcPHmTmzJkMHz6cpKSkzm5eUPMV80DAh1kB\nh10j4HM3TatnAk+jF8VkDjnSPC9G/z353U1bwthz0lymJy+37Nwjpi1tbkuw9dQjxc7SowP1s88+\nQ1EU0tLSAIiPj2fixIm89dZbLFy4sEPboqoqfr8P1e9HUbSQO36sYSZ697Zis0Zgs1lO6csMs4Z3\naFs7W1uOutripFkDT1nuijrr4tDJPSmt6Fnp1np0oBYVFZGQkBDyWmJiIjk5Oe1ST1VV/D5v022S\nikaYyYTJrBBmUrBbzNicdmw2GyaT8TOJC9FaJlNoELbmx9JuM+M6oQvI3oqLf91Zjw5Ul8t1yiSz\ndrsdt9t9hi1OpZiaJn2GE2fw94GmBm+TNJubZkayWM3Y+zixWq3nHJrNs7afbfZ2s/ncT+5Ot63U\nPf3ps966ZlPoUCmzSX9di1kJmcDGYlZ017VbTCHdGnaLSXfd3uFWahq8Ict6644e2p+dX1cERzWM\nHtq/Tf9/jdZe9Xp0oDocDjye0Pve3W43Doe+SZZNJgWHTQteNQ8zm7DbI7BZLae9AGSkyMjv78m2\nmOHEUTgWM0RF6RvSE24Bty90uaVtT6w7KNZB0WFXyLLeuoPPi6CwrD5kWW/dK0fF8vHewyHLeute\ndfFAPvpXeciy3rp3ThvO6je/DlnWW/eO60ewestXTU9v+G5Zb91504azevNXqFrTTR/zWlH39uuH\n8/stX+EPaISZFW6/vuVtT6z78+uG8Yfsr4N3pP38umG66z50yyW8/Jd/UVR+nEED+3DPzy4mrIVp\nB0+s250pWk+5heE0Pv30Ux577DH+9re/BV+7//77GTJkCAsWLND1GTU1Da27L7uNTCaFyEhnSN16\nr5cHf7cTjzeA3Wrm2fsuJ8Kqb6yiy+fj1y9/RoPbhzPcwtP3/ATHaR5jcrq63kCAp9d+EZzY+tc3\nj8aq83Ku3m1PV9evqvzP27kUV9SRENOLuRnDCNN5xK9329PVVTWNT/aWU1JZT9yACK4YpX8GJb3b\n/lDq6nW6uh2hua7RenSg+v1+pkyZwoIFC5g+fTq5ubnceuut/OUvfzmlb/VMTrw41BFON0+n1JW6\nUvfc6hqtR5/yh4WFsWrVKjIzM8nKysJms7F8+XLdYSqEECfq0YEKMGzYMDZs2NDZzRBC/ADI+Bwh\nhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQC\nVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAG\nkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAoh\nhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGCSssxvQTNM0fve737F9+3ZUVSUqKopFixYxfPhwAD76\n6COeffZZPB4P4eHhPPjgg1x55ZUAlJSUsHjxYsrKyjCbzfz0pz/ljjvuAKCxsZElS5bwj3/8A5PJ\nxJgxY1i6dClWqxWA3//+9/z1r38FYODAgfzmN78hLi6uE74BIUR312WOUNetW8eHH37Ixo0beffd\nd7n66qt58MEHATh69CgLFy5k6dKlbNu2jczMTBYuXEhVVRUACxcu5Morr+S9995j/fr1rFu3jo8+\n+giAF154gePHj7Nt2zbeeecdampqePHFFwH48MMPWb9+PRs2bGDbtm1MmDCBBx54oHO+ACFEt9dl\nAvXiiy9m5cqVREREADB58mSKi4vx+Xxs376d5ORkLr744uB7k5KS+OCDDygoKODbb7/llltuAaBv\n375MmzaNN998E4AtW7Zw6623YjKZMJlM3HLLLSHrpk2bRt++fQG45ZZbyMnJobi4uKN3XwjxA9Bl\nAnXEiBEMGzYsuLx9+3ZGjhyJxWKhsLCQxMTEkPcnJiaSl5dHYWEhMTEx2Gy24LpBgwaRn5/P8ePH\nqaqqCtk2MTGRyspK6urqTvlcu91OTEwMeXl57bWbQogfsA7tQ926dStPPvkkiqIEX9M0jd69e7N9\n+/aQ97322mu89tprALjdbux2e8hn2e123G43LpfrlHU2mw23243b7Q4un7gdgMvlavFz9TKZlLO/\nyUDN9aSu1JW6ba9rtA4N1PT0dNLT01t8z6uvvsqGDRv4n//5H5KSkgBwOBw0NDSEvM/tdtOnTx+c\nTicej+eUdQ6HA4fDATRdmGrmcrkAcDqdOByOM26rV2SkU/d7jSR1pa7U7Xq6zCk/NF1A2rZtG3/5\ny19ITk4Ovp6UlERRUVHIewsKCkhOTmbo0KEcPnw4JDSb1/Xu3ZsBAwaEbFtQUMDAgQOJiIhg6NCh\nIevq6+upqKjgwgsvbMe9FEL8UHWZQP3kk0948803WbNmDf379w9Zl5KSQkFBAbt27Qq+9+DBg6Sk\npJCYmMjIkSP5/e9/D0BZWRlbtmxh5syZAMyYMYP//u//xufz4fV6+cMf/sCMGTOC6zZv3kxFRQXQ\nNIRqzJgxMmxKCHFOFE3TtM5uBMC8efP46quv6NevH9DUt6ooCs8//zzJycns2rWLlStX4nK56NWr\nF48++ihjx44FoLy8nEWLFlFaWorFYuGWW27hxhtvBMDr9fKb3/yGv//97yiKwhVXXMGjjz5KWFhT\nb8cf//hH1q9fj6ZpJCYm8uSTTxITE9M5X4IQolvrMoEqhBDdXZc55RdCiO5OAlUIIQwigSqEEAaR\nQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGE\nQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVXdZ1113HunXrOrsZQugmD+kT\nXUZubi6VlZVceeWVHV67rq6Ot99+O/i03LY6dOgQ11xzDVarFUVRgq83P8335Zdf7pT9FO0rrLMb\nIESzv/71r5hMpk4Jmp07d7JhwwbDAhVAURT+8pe/kJycbNhniq5NTvlFqxw6dIi77rqLMWPGcNVV\nV/H888/TfJJTX1/P4sWLmTRpEqNHj2bOnDl88cUXwW2vvvpqNmzYwL333svYsWO56qqr2Lx5MwCP\nP/4469atY926dfzkJz8Jvv+1114D4NFHH+WJJ57ghRde4PLLL+eSSy7hySefDGlbVlYWqampXHzx\nxUydOpVt27YF17W0/ZYtW3jggQfYv38/o0aNIicn55T9vu666xg1alTIfxdddBGjRo0643elaRpn\nOwE8cR+bv99hw4aRm5sbXJ+VlUVaWhr33nsvAJWVldx///1MmDCBMWPGMG/ePAoKCoKfMWzYMP76\n179y0003MWrUKNLS0ti3b19wfUVFBffeey8TJkxg7Nix/OpXv6K8vLzFdgqdNCFa4YYbbtCWLFmi\nud1u7eDBg9pVV12lrV27VtM0Tbv//vu1G2+8UTty5IjW2NioPfPMM9oll1yi1dXVaZqmaZMnT9au\nueYabc+ePZrf79deeOEFbfTo0VpjY6OmaZp28803a8uXLw/Wmjx5svbHP/5R0zRNe+SRR7TLLrtM\nW7t2rebz+bRPPvlES05O1j7//HNN0zTtT3/6kzZ58mQtPz9fCwQC2vbt27URI0ZoBw4c0LX9Sy+9\npN1www2GfU+lpaVacnKylpOT0+L7TtzH5u2GDRsW3G7y5Mnaddddp+Xn5wffM2vWLO2ee+7Ramtr\ntfr6eu2hhx7SpkyZElyfnJyspaena/v379c8Ho+2dOlSbcKECVogENA0TdNmzJihPfbYY1pDQ4NW\nV1en/b//9/+0n//854bte08mR6hCt5ycHHJzc1mwYAF2u524uDhefPFFRo4cSV1dHe+++y4PPPAA\nAwYMwGq1ct999+H1evnkk0+Cn3HllVcyduxYzGYzU6dOxe12U1ZWpqt+VFQUN910E2FhYUyYMIGo\nqCgKCwsB2LhxI7feeitDhgzBZDKRkpLCJZdcEjwCPtv27WXWrFmnHNWmpaW1uI120lHtZZddxpAh\nQ4CmfuZ9+/bx6KOP0qtXL5xOJw899BDFxcV8+eWXwW2uv/56kpKSsNls3HXXXRw9epS9e/fyzTff\nkJOTw69//WscDgcRERE8+OCD/OMf/6CkpMT4L6CHkT5UodvBgwexWq3069cv+FrzKW/zaXLzLz6A\nxWLhvPPO4+DBg8HXEhISgv+22+1omobH49FVPz4+PmTZbrcHtz1w4ADPPvsszz//PPD96XZ0dLSu\n7duLEX2oF1xwQfDfpaWl2O12zjvvvOBrMTEx2O12SkpKGDlyJACJiYnB9dHR0VgsFo4cOUIgEEBV\nVa666qrgek3TsFgslJaWEhcX16a29nQSqEI3k8l0xj5Br9d7xu1OvMptMp37SZHZbD7jOrvdzuOP\nP871119/TtufzXXXXXdKP6P23RX7vXv3nnG7M31fZ6Kq6imvWSyW4L9b+p5b+pzmttpsNsLCwlps\nszh3EqhCt7i4OHw+H4cOHeL8888HYPfu3Rw/fpyxY8eiaRr79+/nsssuA8DlcnHo0CEGDRrU7m1L\nSEjgm2++CQnU8vJyYmNjQwL9XL377rut3kZPXZvNFnKUXFxc3OJ2cXFxeDweSkpKgkeTJSUleDye\nkO/5xLOCiooK/H4/sbGxOJ1OAoEAeXl5JCUlAU3he+TIEWJjY1u9jyKU9KEK3YYNG8aIESN4/vnn\nqa+vp7S0lEWLFlFeXk5UVBSTJk3ipZde4tixY7hcLp599ll69+7NFVdcoevz7XY7paWl1NXVtfrI\n7qabbuKvf/0rf//731FVld27dzNt2jR2796ta3ubzcbRo0eprq7WfRR4Nnr2ISEhgb/97W+4XC4q\nKytZv359i+8fOXIkF154Ib/97W+pr6/n+PHjPP300/z4xz/mRz/6UfB92dnZFBQU4Ha7ycrKIiYm\nhosuuoghQ4Ywfvx4li9fztGjR/F4PDz33HPccsstrf7Oxak6LVD//Oc/M3r0aNasWRN8raSkhLvu\nuou0tDSmTJnC4sWLgz/cmqbx1FNPMWXKFFJTU5k/fz41NTXBbTdv3kx6ejqpqanMnj07pIN+3759\nzJ49mylTppCRkRFyoaK6upq7776blJQUUlNTWblyZQfsffeVlZXF8ePHufLKK7n55ptJT0/n1ltv\nBWDlypUMHDiQG264gWuuuYbS0lLWrl2L3W4HTn/EduJrM2fOZNeuXVx77bXU19ejKEqLR2snrrvh\nhhu44447ePjhhxkzZgxLly7l8ccf59JLL9W1fUpKChaLhauvvlp3CJ+NoiinvSg1atQonnrqKQAe\neOAB3G43EyZM4M4772Tu3LlnbGOzV155Ba/XS0pKChkZGVitVn7/+9+HvGfWrFk8/vjjXHbZZeza\ntYtVq1YF1/32t78lIiKCKVOmMHHiRL799lv+67/+y5Aj+R6vM4YWLF26VLv//vu1GTNmaH/4wx+C\nr0+fPl176aWXNE3TNLfbrc2cOVN79dVXNU1rGhYzc+ZMzePxaJqmaZmZmdp9992naZqm5eTkaOPG\njdMOHjyoaZqmvf3229rEiRM1n8+nNTY2aldddZW2detWTdM0rbi4WBs3bpy2f/9+TdM0bcGCBVpm\nZqamaZrmcrm0GTNmaK+//noHfAtCtI/k5GTt/fff7+xm9EidcoQ6depUnn/+eRwOx4nBzi9/+Utu\nv/12oOn077LLLuPbb78FmgZfz549G5vNBsDcuXN5//338Xg8ZGdnM2nSpGCfUnp6OtDUv/fZZ5+h\nKEpwqEp8fDwTJ07krbfeoqGhgQ8++IBf/OIXAISHh3PjjTfy5ptvdswXIYT4QemUQB0zZswprymK\nQmpqKk6nE2i6mvm3v/2N8ePHA1BYWBjS6R4fH4+maRw4cIDCwsKQYSLN6/Py8igqKgoZqgNNQ0ry\n8/ODFwBOHCrSvE6I7kpO3TtPl7zK7/V6eeihh4iJiWHWrFkAuN3u4NEpNP3QWCwWXC4Xbrc72E/X\nzG6343a7UVX1jOtcLlfIkBRoujjhdrvbac+EaH+nu3VWdIwud5W/qqqK22+/HZPJxKpVq4LjFh0O\nB42NjcH3qaqK1+vF4XDgcDhOGaDtdrvPus7pdJ5yRbd5nR6aXBUVQpygSx2h1tbWcvvttzNp0iQW\nLlwYsm7o0KEUFRUxbtw4oKkLICwsjMGDB5OUlERRUVHI+wsLC0lOTsbv94eMJAAoKCggOTmZxMRE\nTCYTxcXFwW6B5nV6KIpCTU0DqtpxwWoyKURGOqWu1JW6BtQ1WpcK1KVLlzJ+/PhTwhRgxowZrF27\nlrS0NJxOJ6tXrw4OGZk2bRpz5swJDlbeuHEjTqeT8ePHo6oqZrOZN954g+nTp5Obm8vOnTt54IEH\nCA8PJzU1laysLFasWEFtbS3r169n3rx5utusqhqBQMcfqUpdqSt1u54On2BaVVUyMjJQFIXy8nIc\nDgd9+vRh1KhRbN68mfPPPx+LxYKiKGiaRnx8PK+++ioAzz33XHBKthEjRrB06VIiIiIA2Lp1K6tW\nrcLn8xEdHc2SJUsYOnQo0DShRGZmJtXV1dhsNu69916uvfZaoOmoePHixeTk5AQn7Ljnnnt0709V\nVX2H/iCYzQpRURFSV+pKXQPqGk1m7G+jnvYDKHWl7g+prtG63EUpIYToriRQhRDCIBKoQghhEAlU\nIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhE\nAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQ\nBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAK\nIYRBJFCFEMIgnRaof/7znxk9ejRr1qwJvlZdXc3dd99NSkoKqamprFy5MrhO0zSeeuoppkyZQmpq\nKvPnz6empia4fvPmzaSnp5Oamsrs2bP58ssvg+v27dvH7NmzmTJlChkZGWzevFlXTSGEaI1OCdQn\nn3ySXbt2MXjw4JDXlyxZQkxMDO+99x6bN29m9+7drF+/HoB169axZ88esrOz2bZtG9HR0WRmZgKQ\nm5vLsmXLePXVV9m2bRu33XYbCxYswO/34/V6WbBgAXPnzmX79u288sorLFu2jLy8vLPWFEKI1uiU\nQJ06dSrPP/88Docj+FpDQwMffPABv/jFLwAIDw/nxhtv5M033wRgy5YtzJ49G5vNBsDcuXN5//33\n8Xg8ZGdnM2nSJOLi4gBIT08HYPfu3Xz22WcoikJaWhoA8fHxTJw4kbfeeuusNYUQojXCOqPomDFj\nTnmtuLgYRVGCoQiQmJhIfn4+AIWFhQwaNCi4Lj4+Hk3TOHDgAIWFhYwYMSLk8+Lj48nLy0PTNBIS\nEkLWJSYmkpOTc9aaQgjRGp0SqKfjcrmwWCwhr9lsNtxuNwButzt4dAqgKAoWiwWXy4Xb7cZut4ds\na7fbcbvdqKp6xnVnqymEEK3RZQLV6XTi9XpDXnO73cFuAYfDQWNjY3Cdqqp4vV4cDgcOhwOPx3Pa\nbVVVPeO6s9XUw2RSdL/XCM31pK7Ulbptr2u0LhOoiYmJmEwmiouLg6fo+fn5JCcnAzB06FCKiooY\nN24c0NQFEBYWxuDBg0lKSqKoqCjk8woLC0lOTsbv94eMJAAoKCggOTn5tDWb1+kVGek8531uC6kr\ndaVu19NlAjU8PJzU1FSysrJYsWIFtbW1bNiwgXnz5gEwY8YM1q5dS1paGk6nk9WrV5ORkYHVamXa\ntGnMmTOHvLw8kpKS2LhxI06nk/Hjx6OqKmazmTfeeIPp06eTm5vLzp07eeCBB05bc/369cGaetTU\nNKCqWnt9LacwmRQiI51SV+pKXQPqGk3RNK3j9oKmU/WMjAwURaG8vByHw0GfPn1ISUnhjjvuYNGi\nReTk5GA2m5k6dSr33HNPcNvnnnuObdu2ATBixAiWLl1KREQEAFu3bmXVqlX4fD6io6NZsmQJQ4cO\nBZqGVWVmZlJdXY3NZuPee+/l2muvBaC2tpbFixefsebZVFXVEwh03FdoNitERUVIXakrdQ2oa7QO\nD9Qfmp72Ayh1pe4Pqa7R5NZTIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIg\nEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGE\nMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJV\nCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwS1tkNONHn\nn3/OM888Q319PWazmZ/97GfceuutVFdXs2jRIvLy8jCZTFx99dU8/PDDAGiaxsqVK9mxYweKojB0\n6FCWLVtGZGQkAJs3b2b16tUEAgEiIyNZvHgxI0eOBGDfvn0sW7aM6upqLBYLd955JzfccEOn7b8Q\nonvrMkeoHo+H+fPnc/fdd7N161bWrFnDK6+8wscff8ySJUuIiYnhvffeY/PmzezevZv169cDsG7d\nOvbs2UN2djbbtm0jOjqazMxMAHJzc1m2bBmvvvoq27Zt47bbbmPBggX4/X68Xi8LFixg7ty5bN++\nnVdeeYVly5aRl5fXid+CEKI76zKBWlZWRl1dHRMmTACgf//+DBs2jC+//JIdO3Zw++23AxAeHs6N\nN97Im2++CcCWLVuYPXs2NpsNgLlz5/L+++/j8XjIzs5m0qRJxMXFAZCeng7A7t27+eyzz1AUhbS0\nNADi4+OZOHEib731VofutxDih6PLBGpCQgKJiYnBoCwpKSEvL49JkyYBTYHXLDExkfz8fAAKCwsZ\nNGhQcF18fDyapnHgwAEKCwtJTEwMqRMfH09eXh5FRUUkJCSErDvxc4UQorW6TB+q2WxmxYoV/PKX\nv+SZZ56hrq6Oe+65B5fLhcViCXmvzWbD7XYD4Ha7g0enAIqiYLFYcLlcuN1u7HZ7yLZ2ux23242q\nqmdc1xomk9Kq97dVcz2pK3WlbtvrGq3LBGplZSV33XUXzz33HBMmTKCmpoY777wTTdPwer0hEt4g\npQAAIABJREFU73W73TgcDgAcDgeNjY3Bdaqq4vV6cTgcOBwOPB7PabdVVfWM61ojMtLZqvcbRepK\nXanb9XSZQP3nP/9J7969g32okZGRTJ48mV27dmEymSguLg6eoufn55OcnAzA0KFDKSoqYty4cUBT\nF0BYWBiDBw8mKSmJoqKikDqFhYUkJyfj9/tZs2ZNyLqCgoLg5+pVU9OAqmrntM/nwmRSiIx0Sl2p\nK3UNqGu0LhOoQ4cOpaKigi+//JKRI0fidrvZuXMn48ePJzo6mqysLFasWEFtbS0bNmxg3rx5AMyY\nMYO1a9eSlpaG0+lk9erVZGRkYLVamTZtGnPmzCEvL4+kpCQ2btyI0+lk/PjxqKqK2WzmjTfeYPr0\n6eTm5rJz504WLlzYqnarqkYg0HE/CFJX6krdrqvLBOqQIUNYtmwZixYtwufzoWkal19+OXfddReN\njY0sXryYlJQUzGYzU6dODY4XnTVrFqWlpcycOROAESNG8MQTTwQ/MzMzk4ULF+Lz+YiOjmbVqlWY\nTCZMJhOrVq0iMzOTrKwsbDYby5cvP+VClRBC6KVomtb9/yx0oqqq+g79y2o2K0RFRUhdqSt1Dahr\ntC4zbEoIIbo7CVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJ\nVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIY\nRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqE\nEAaRQBVCCIOE6X2jpmm8/vrr7NixgyNHjgAQGxtLSkoKs2bNarcGCiFEd6E7UJ988km2bt1Kamoq\nl156KZqmUVpayrPPPsv+/ftZvHhxe7ZTCCG6PN2BumXLFl5//XWGDRsW8vrPf/5zfv7zn0ugCiF6\nPN19qOHh4QwePPiU14cMGYLD4TC0UUII0R3pDtQ77riDl156CZ/PF3zN5/PxyiuvcPvttxvSmOPH\nj3PfffcxadIkUlJS+M///E8Aqqurufvuu0lJSSE1NZWVK1cGt9E0jaeeeoopU6aQmprK/Pnzqamp\nCa7fvHkz6enppKamMnv2bL788svgun379jF79mymTJlCRkYGmzdvNmQ/hBA9k+5T/vfee4/c3FzW\nrl3L+eefTyAQoKKiAoCEhATefvvt4HvfeOONc2rMI488wvnnn8///d//UV1dzYIFC5g6dSrPPvss\nMTExrFq1Crfbzc0338z69euZM2cO69atY8+ePWRnZ2Oz2Vi6dCmZmZm88MIL5ObmsmzZMjZt2kRc\nXBxbt25lwYIFvP/++6iqyoIFC3jkkUdIS0vj4MGDzJw5k+HDh5OUlHRO7RdC9Gy6A/Xyyy/n8ssv\nb7eGHDlyhI8//piPP/4YgL59+7J27VoaGhr44IMPePfdd4Gmrocbb7yRTZs2MWfOHLZs2cLs2bOx\n2WwAzJ07l4yMDDweD9nZ2UyaNIm4uDgA0tPTefrpp9m9ezc+nw9FUUhLSwMgPj6eiRMn8tZbb7Fw\n4cJ2208hxA+X7kC95557zriu+WixLXJycujXrx//+7//y5YtWzCbzcyePZtRo0ahKEowFAESExPJ\nz88HoLCwkEGDBgXXxcfHo2kaBw4coLCwkBEjRoTUiY+PJy8vD03TSEhICFmXmJhITk5Om/ZDCNFz\n6Q5UgOLiYr7++mu8Xm/wtYqKCl555ZU2B2ptbS3Hjh3DbreTnZ3Nt99+y0033cQvfvELLBZLyHtt\nNhtutxsAt9sdPDoFUBQFi8WCy+XC7XZjt9tDtrXb7bjdblRVPeO61jCZlFa9v62a60ldqSt1217X\naLoDdfPmzTz22GNYrVYaGxsJDw/H5XIRHR3NHXfc0eaG9O7dG0VRuOmmmwBITk5m4sSJ7Nq1KyTA\noSlEm0cWOBwOGhsbg+tUVcXr9eJwOHA4HHg8ntNuq6rqGde1RmSks1XvN4rUlbpSt+vRHahZWVm8\n+OKLXHvttVx00UX885//JD8/nxdeeIGUlJQ2NyQ+Ph6/34/L5cLpbPpyFUVh5MiRfPHFFxQXFwdP\n0fPz80lOTgZg6NChFBUVMW7cOKCpCyAsLIzBgweTlJREUVFRSJ3CwkKSk5Px+/2sWbMmZF1BQUHw\nc/WqqWlAVbVz2udzYTIpREY6pa7UlboG1DX8c/W+saKigmuvvRZoCjpoCrP77ruPxx9/vM0NGTRo\nEGPGjOHVV18FoLS0lI8//pjJkyeTmppKVlYW0NQ1sGHDBmbOnAnAjBkzWLt2LfX19WiaxurVq8nI\nyMBqtTJt2jQ++ugj8vLyANi4cSNOp5Px48dz6aWXYjabgyMScnNz2blzJ9OmTWtVu1VVIxDouP+a\nf+ikrtSVum2vazTdR6hRUVHBo8TevXtTUFDAkCFDSEhICAZWW61cuZJFixZx9dVX43A4ePDBBxk3\nbhwXXnghixcvJiUlBbPZzNSpU7nhhhsAmDVrFqWlpcGAHTFiBE888QTQdNNBZmYmCxcuxOfzER0d\nzapVqzCZTJhMJlatWkVmZiZZWVnYbDaWL19+yoUqIYTQS9E0TVdUv/jii6xdu5YdO3awcuVK9uzZ\nw/XXX8+XX37J4cOHz3nsaXdXVVVPINBxpypms0JUVITUlbpS14C6RtN9yn/vvfeyePFinE4njz32\nGKNGjSI7Oxu/389TTz1leMOEEKK7adWwqWnTpqGqKuHh4Tz11FN4PJ5Thh4JIURPpfsItaSkhJ/+\n9Kds3749+Nrrr7/O9OnTKSkpaZfGCSFEd6I7UJ988kkuvPBCLrnkkuBr06dP5+KLL+Y3v/lNuzRO\nCCG6E92n/F988QU7d+7EarUGX+vbty+PPvpou97jL4QQ3YXuI1SHw0F5efkprxcVFYXc+imEED2V\n7iPUmTNncscdd/Czn/2MCy64AE3TKCoq4s9//jO33HJLe7ZRCCG6Bd2Beu+99xIZGcmmTZs4ePAg\nJpOJ+Ph47rrrruD990II0ZPpDtS6ujpuu+02brvttvZsjxBCdFu6+1AnTpyIqqrt2RYhhOjWdAdq\nRkYGr732moSqEEKcge5T/kOHDvH+++/z8ssvExsbe8qkzz31Xn4hhGimO1DHjh3L2LFj27MtQgjR\nrRnyTCkhhBCt6EMF2LVrF7/+9a+D4079fj+bNm1ql4YJIUR3oztQX3/9debPn4/dbmfv3r0AHDt2\njJdeeumUR4kIIURPpDtQ16xZw6uvvsqTTz4ZfC0mJoZXXnmF119/vV0aJ4QQ3YnuPtSjR48GL0o1\nP1MKmp4rdeTIEeNbJoTotlRN49N95ZRWNnDBACcTLhqISenYR0V3Bt1HqBdccEHwVP9EH3zwAbGx\nsYY2SgjRvX26r5wdXxxif2kNO744xKf7Tp1Y6YdI9xHqrbfeyr//+7/zb//2bwQCAV555RVycnLY\nsWMHmZmZ7dhEIUR3U1rZ0OLyD5XuQP3Zz35G//792bhxI3FxcWzfvp2EhATWrFnD+PHj27ONQohu\n5oIBTvaX1oQs9wSteqbU5MmTmTx5cnu1RQjxAzHhooEAIX2oPYGuQP3000/Ztm0bYWFhZGRkyB1T\nQogWmRSFK0ed19nN6HBnvSi1detWfvnLX3LkyBFKSkqYO3cuH374YUe0TQghupWzHqH+93//N08/\n/TTp6ekAvP3227z66qty6i+EECc56xHqgQMHSElJCS5fe+21FBQUtGujhBCiOzproPr9/pCp+mw2\nG16vt10bJYQQ3VGrJkcRQghxZmftQw0EAvzpT39C07QWX7v11lvbp4VCiE7RU28fbYuzBmp0dPQp\ns0md/JqiKBKoQvzANN8+CgQH6ffEoVCtcdZA3bFjR0e0QwjRxfTU20fbolV3Sqmqyt69ezly5AiK\nohAbG8tFF13UXm0TQnSinnr7aFvoDtR9+/Zx9913c/ToUSwWC5qm4ff7iYmJYdWqVQwfPrw92ymE\n6GA99fbRttAdqA8//DBTpkzhrrvuIjo6GoCKigqysrJ46KGHeOedd9qtkUKIjtdTbx9tC93DpsrK\nynj44YeDYQpNM/b/+te/pqysrF0aJ4QQ3YnuQB0xYgQHDx485fWysjLDT/fr6uq48sorefTRRwGo\nrq7m7rvvJiUlhdTUVFauXBl8r6ZpPPXUU0yZMoXU1FTmz59PTc33/T6bN28mPT2d1NRUZs+ezZdf\nfhlct2/fPmbPns2UKVPIyMhg8+bNhu6HEJ1N1TQ+3lvG+vfz+HhvGeoJQx2F8XSf8k+fPp3777+f\n66+/nsTERFRV5eDBg2RnZzNr1iw++OCD4HuvueaaNjXqP/7jP7Db7cHlJUuWBPtq3W43N998M+vX\nr2fOnDmsW7eOPXv2kJ2djc1mY+nSpWRmZvLCCy+Qm5vLsmXL2LRpE3FxcWzdupUFCxbw/vvvo6oq\nCxYs4JFHHiEtLY2DBw8yc+ZMhg8fTlJSUpvaL0RX0VlDn3rqGFbdgbp48WIAXnjhhVPWrVixIvhv\nRVHIyck55wZ9+OGHlJSUMG3aNMrKymhoaOCDDz7g3XffBSA8PJwbb7yRTZs2MWfOHLZs2cLs2bOx\n2WwAzJ07l4yMDDweD9nZ2UyaNIm4uDgA0tPTefrpp9m9ezc+nw9FUUhLSwMgPj6eiRMn8tZbb7Fw\n4cJzbr8QXUlnDX3qqWNYdQdqbm5ue7YDgOPHj7N8+XL+67/+i+zsbACKi4tRFCUYigCJiYnk5+cD\nUFhYyKBBg4Lr4uPj0TSNAwcOUFhYyIgRI0JqxMfHk5eXh6ZpJCQkhKxLTExs0x8DIbqazhr61FPH\nsLYYqPX19bo/KCIios2NWb58OTfffHNI0LlcrpDJWaBpgha32w2A2+0OHp1C0xGyxWLB5XLhdrtD\nug4A7HY7brcbVVXPuK41TKaOPY1prid1pa4eV40+D5NJoaSynrgBEVwx6vSn3kbXjY+JIO+EII+P\nicBsbv+6erVXvRYDddy4cSGPjG5JW4/sduzYQUlJScgFJwCn03nK7FZutxuHwwGAw+GgsbExuE5V\nVbxeLw6HA4fDgcfjOe22qqqecV1rREZ2zmBnqSt19brh6l4dXnfapCScThsHDteSGNuba8bHtxhi\nnfU9G63FQH3ttdeC/y4sLGTdunX89Kc/JSkpCVVVyc3N5Y033uBXv/pVmxvyzjvvUFpayjXXXIOm\nadTV1REIBMjNzcVkMlFcXBw8cs3Pzyc5ORmAoUOHUlRUxLhx44LtDAsLY/DgwSQlJVFUVBRSp7Cw\nkOTkZPx+/ylzFBQUFAQ/V6+amgZUteOunJpMCpGRTqkrdbt83bFJ/Rib1A9o+j3pqLp6NNc1WouB\neskllwT//eyzz/K73/2OwYMHB1+74ooruOqqq1i8eDFTp05tU0OeeeaZkOWXX36ZQ4cOsWLFCh58\n8EGysrJYsWIFtbW1bNiwgXnz5gEwY8YM1q5dS1paGk6nk9WrV5ORkYHVamXatGnMmTOHvLw8kpKS\n2LhxI06nk/Hjx6OqKmazmTfeeIPp06eTm5vLzp07W31BSlU1AoGOH4oidaWu1O16dF+U2r9/Pxdc\ncMEprycmJpKXl2doo062ZMkSFi9eTEpKCmazmalTp3LDDTcAMGvWLEpLS5k5cybQNF72iSeeAGDI\nkCFkZmaycOFCfD4f0dHRrFq1CpPJhMlkYtWqVWRmZpKVlYXNZmP58uWnXKgSQrReTx02pWiavpG+\nM2fO5OKLL2b+/PlERUUBUFVVRVZWFrt37+6xg+Krquo79C+r2awQFRUhdaVul6778d6y4LApgKtH\nn3/aYVOdvb9G032E+uSTT3Lvvffy+uuvY7fbUVWVxsZGIiMjycrKMrxhQojuq6SynnqXD68/gDXM\nTEml/hFD3ZnuQE1ISGDTpk0UFxdz+PBhvF4vsbGxDBo06JThR0KIns3t8VNT34gGuPDj9vg7u0kd\nQnegnm0IldPpZNKkSSxevJjIyEhDGieE6J6q6jyomoamgaI0LfcEugP1xRdf5Le//S3Tpk3joosu\nwmQysXfvXt555x3uvvtufD4fa9euZcWKFaeMJRVC9Cw19V6ar85oWtNyT6A7UF977TV++9vfhszQ\nf8UVV3DllVfy8ssvs3r1an7yk58wY8aMdmmoEKL7OPlat85r392e7kD96quvGDZs2CmvDxs2jM8/\n/xyAyMjIU+4+EkJ0T20Z+tS3l43KGg+apqEoCn172c6+0Q+A7kCNi4tj2bJlzJ8/PzjJdPOwqX79\n+qGqKitXruRHP/pRuzVWiJ6os8Z0frKvnOxPDwSv1GvAVTpnjLr0x7EcqfYEt730x7Ht29guQneg\nPvXUU/z7v/87GzduxGKxEBYWhtvtJiIigpdeegmATz/9lN/97nft1lgheqLOmgpv9zcVIVfqd39T\noTtQLxsRwyf7yqioctO3l5XLRsS0b2O7CN2BOnz4cD788EO++uorKisrUVWVfv36MWLEiOCEIs1z\nlgohjNNZU+FV1zcG76/XvlvW67WtuRSU1aJpUF/m47Wtudxx/Q//QZ6teoy01WplzJgx7dUWIcRp\ndNacppERVo5Uu4P9oJERVt3b7i04FnKVf2/BsXZqZdfSqkAVQnS8tjzOuS39r+N+FE1hWS0+P4SZ\nFcb9KPrsGwWdfFW/Z1zl1/2QPiFE99Pc/7q/tIYdXxzi033lurctKDmOP6ABGv6ARkHJcd3bxkY5\nW1z+oZIjVCE6QFuOFNtyUaot/a+llQ2YTQqgtHrbxIG9OFzlwutXsYaZSByof5Lr7kwCVYgO0Fmh\n2Jb+1wsGODl0tCHYh9qabeOje5F/qDZkuSeQQBWiA5QcOWn2pSP6Z19qSyi2pf91SFwf/rG/MtiH\nOiSuj+5tfzIylv0lNZQcqScuOoKfjOwZ41ClD1WIDuBu9FPn8tLoDVDn8uJu1D/70oSLBnL16PO5\n8IJIrh59fqtCsS3+kVPZ1IeqgD+g8Y+cSt3bfvblYUqPNqCYFEqPNvDZl4fbsaVdhxyhCtEBwu1h\n9HJYg0eo4Xb9v3omRTnngfxtudupqs5D4ITnPLVmxqiu/hjp5vmcT3xishHkCFWIDhA3IIIIh4Wo\n3nYiHBbiBhg/W/zp7M5putup4bv5SXfnVOjfuA0jn07uluiosbMt8Xg8VB6rouzwUQ6WHaW8osrw\nGnKEKkQHaEtfZltU1510t1Od/rud3N5Ai8stuXREDB9/d+tpTFQ4l3bCrad+v5/6hgbcjX58fhUU\nM1abHZMVLCYFk9n4mhKoQujUlqFPbTltb4s+TguHqwhO9NzHadG9rcnU8nJL/vTOtxysaLrwdrCi\nnj+98y3zpv5Y/wecA03TaHC5cLkb8fpU/CpNARpmwdpBSSeBKoROnTVJSVtE9bKj0DQgX/luWa++\nETaq67why3qdPIqhNaMaWsPr9VJXX0+jT8PnD2AyW7FY7YTZTg03VdM4Uu2mqLyW0iP13HF9kuHt\nkUAV3U5nTWfX1S+0nE71STPln7zcEqvFjKJ8f3Rrteg/R46Ljmgaw0pTkMdFG9NnHAgEqKuvx+3x\n4wuoqCjYbOGYLAq2kw6+VVWjvMrFgfJaisprOVBehytkdIUEqhBtOlJsSxi3ZTxoW+r6VZU/bs0N\njum8LX0YYTrPv2vqG096FIn+PlSTScFiNoUs6zX0/N78K/9o8E6poef31r3tiTRNw+V243J5mk7j\nAxphNjtmSzjWkwI0oKocqmwIhueBw3U0+vT3+xpBAlV0O205UmxLGLdlsHpb6v7P2zns+qYCTYPS\nyno0TdM9FV5bZoy65EcxVFS5g0OuLvmR/gtLZcfc9I8MD1nWy+/3U1tXj8cb+O403oLFasdsgxOP\nkX1+ldLK+mCAFlfUNV180qFf7/Z5UrMEquh22nKk2JYwPt1g9Y64fXRvwTHUc5wKb3zyAAoO1eIP\naJhNCuOTB+je9oqLBqJwbiMTWvP/SNM06urqqWtoxOcL4EfBarVjslhDTuO9vgDFFXUcKK+j6HBT\nP2jT5C1nF903nEEDe5MY24tBA3u32yNZJFBFt9OWIUidFcadNadpQVld0+B8RSGgahSU1TFJ55TG\nbRmZcLajeY/HQ32DC7+qUV1XT31DAJPZTpj5+1DyeP0cOFz3XR9oHYcqG1B1POxPAWL7OZoC9LsQ\njQjXP7qhLSRQRbfTll/0zgrjtnQXxPS1U1heH7Ks18GTrq6fvNxeTj6a/+RfpVw8pE9wTKimmLBa\n7YRZTNgdDhq9LupcXorK64IXkcqPuXTdS2BS4Lz+TgYN7M2ggb1JiO1FuK1zok0CVfQonRXGO/eV\nk3uwBq8/QIPHz8595Vx18fm6trVawlAgeMXcatH/a2sNM4XcPmoN0z+YtC0X0kqO1ONtdBMI+NE0\n+KbIxIXxUSFjQutcXooP13GoykVuURUV1fr6Wc0mhQuiI74L0F7Ex/TC1ooRCNB066mmr7u1VSRQ\nhdCpLWG8O/cIda6mIUuN3gC7c4/oDtTjDU3bKSct69Hg8bW43JLWXkjz+XzU1dfj8arYzT40zIRZ\nm/oq4wf2o9blo6islqLDdRSV13LsuL65ASxmE/GxESTGNgVoXHQvLK34w6BpGj5vI2rAj8VsIizM\nhCM8jPNiW/MEAn0kUIXoAKqq4j3hCrSq6j886hNh5XCV6/u7nVpxpb7Rp7a43JKz9RlrmkZ9fQMu\njxevL4CKgvW7MaGXXpSAV7NScOg4Pr/Kjn+WsumjQl11bRYzCbG9GDSw6QLSef2dhJn1Baimafh9\nXgJ+H2FmE2FmEzaLib59HdhsNpTvjrDNZgWLxfh+VQlU0aO05TS2LdtW1Ta2uNySfr3smBQFTWk6\nSu3XirudonpZQ+7fj+qlP4xP12fc2NhIXX0DXp+Kz69hslixWOyEmTSO1LgpKqygqKyOA4drqXPp\nOxp22MNIjO0VPAKN7ef87kkBZ+fz+Qj4vJhNEBZmwmJWiOwTjt0eGQzPjiSBKjpFZ93t1JbxoG3Z\n1tUYaHG5JXabGbvVHBwkb7fp7y9MjO3N4Sr3948iidU/wH7CRQNRVZWC0qMM6G0lvn8Yh6vqsVrt\nEAbHal0UlR8NDmNyefTN8eoMtzQdfcb2Zsj5vblwcH/qjrtD+npPJxAI4PN6MCkQZm4KzwinjfDw\nCMzmdpjp5BxIoIpO0Vn3xbf1GUvnum1sVDj5h3why3p5GgP4/CoKTYPZPa0I4/iYXuSX1YYsn7We\nx0Ndg4tGb4CjVcfxa2Y0cziHazWKD1dTVF5L8eE6PDpnnzKZFM7v72Rs8gAGDexN/z7270+9Tcpp\n/5CG9HuGNZ26O61mnJGR7XKqbhQJVNEpOuu++LYMfTp/gJN/7q8M3jl0fmuGTQ2Poai8aUyo2aTw\nk+H67zqy28KwhJnw+VUsYSbsrRgSpGdkQtP98Q24Gt1UVtYRwITJbOWDf1XwRV4VXl8Ar69S93So\nUb1thJlMTd+TxYzZpBAXHdHinVZerxe/9/tT99P1e3YHXSpQP/vsM55//nnq6upQVZU5c+Ywd+5c\nqqurWbRoEXl5eZhMJq6++moefvhhoOkv2cqVK9mxYweKojB06FCWLVtGZGQkAJs3b2b16tUEAgEi\nIyNZvHgxI0eOBGDfvn0sW7aM6upqLBYLd955JzfccEOn7X9P0lkD3dsy9MkfCFBT3/hdKPrxB/Qf\nKe75tunxIc1dg3u+rWTymDhd23qa5/Ok+QhV/+NTTjcy4cSLST5/0zR3JrOF6qNuvsqrorCslpJW\n3IU0IDI8eAEpMbYXfSJs7Mk9wq5vvp/MOjbKEfy33+/H7/NiUjRsVjNhmInqbcFm7YWpNXMEdkFd\nJlCPHj3K3XffTVZWFpdeeiklJSX827/9GxdffDF/+MMfiImJYdWqVbjdbm6++WbWr1/PnDlzWLdu\nHXv27CE7OxubzcbSpUvJzMzkhRdeIDc3l2XLlrFp0ybi4uLYunUrCxYs4P3330dVVRYsWMAjjzxC\nWloaBw8eZObMmQwfPpykJONnoRGhOmvC5bbI/rQ42M8XUDWyPy3map2hWFPv/b6PUNOoacWsT+G2\nkx6fcg6D1k++mOTVzJQdczcNpD9cS2llQ3Ai6pY034XUfAEpcWDv096FNOa7W1zLjzXQP8LM8Phw\nVK+LsDATfcKtOPr1xWw2YzYrREVFUFVVT0BngHdlXSZQTSYTzzzzDJdeeikAcXFxDB06lH379rFj\nxw7eeecdAMLDw7nxxhvZtGkTc+bMYcuWLcyePTv4bJi5c+eSkZGBx+MhOzubSZMmERfX9EOfnp7O\n008/ze7du/H5fCiKQlpaGgDx8fFMnDiRt956i4ULF3bCN9D9dMcJlz/aW8b//l9B8PQ5oGlM0jke\ntN7tbXG5vVwQHcEXeUdDls9GVVXq6utp8Pj4+1fllB7zYLXZMZtMFB+uo+xYAzru4gSaxoH27WXj\n2vEXMOS8Pi0Gus/rJeD3YjIpaD4XpkAjfXpFkXB+dIdcdOxsXSZQo6KiuPbaa4PLBw8eJC8vjx//\nuGmW7+ZQBEhMTCQ/Px+AwsJCBg0aFFwXHx+PpmkcOHCAwsJCRowYEVInPj6evLw8NE0jISEhZF1i\nYiI5OTmG79sPVXeccPm9z0uCV6N9fpX3Pi/RHaiWMBMBrxqyrFdkhK1p1ieajvIiWzFZ8ynJd4Yk\nbL6Y5PWp1NR5KanysefbSg5W1H13+l572u1OZDYpXDAggkEDe+H1q3xdeAy/quH1BXA3BkLC9MRT\nd8t3Q5Z69bbjCO/DJ/vK+WdRU794aU0lFou1y/9sGKHLBOqJDh8+zK9+9SvuvPNOgFOu6tlsNtzu\nptvU3G53yJMLFaVpwK7L5cLtdmO3h47Zs9vtuN1uVFU947rWaM0ckUZortcV6h461sA+EgdmAAAg\nAElEQVSJrTh0rAGz2dh2Gb2/Hq8/5OKKx+s/bZtPV7dvLzvlx1why3r39yfDYzhS7cbnD2AJM/OT\n4TG665ZVuejlsACW4LLZrDQdhdbV4270UVnjoajCRckRD4XltRzVeRdSmFkhIaYXg87rzcikAfTv\nZSHsu4ct/f6tb4ITMjd6ffwz9xCjBzmbxnuGKfSKsOJ0RJ12yJLen43O/nk2WpcL1K+//pr58+dz\nyy23MG/ePHJycvB6Q0+t3G43DkdTJ7fD4aCx8fuBy6qq4vV6cTgcOBwOPB7PabdVVfWM61ojMrJz\nnubYFeoOS+xH4QlDcoYl9iMqqn2e5mnU/p58z7fNYm6xzSfWjY5yUFnjDt6xFB3l0L2/109MovhI\nA0Xlxxk0sA/XT0wirIUj3NN9z013ATUS08dEYVkV3x48TtHhBvJLj+u+jTPMbOLC+EgujO9LUlwk\nCQN7n3IXUtOQJS+a343f2/wHRKF3RCSjLxqsq05rfzY66+fZaF0qUL/++mt++ctfkpmZGTz9T0xM\nxGQyUVxcHDxFz8/PJzk5GYChQ4dSVFTEuHHjgKYugLCwMAYPHkxSUhJFRUUhNQoLC0lOTsbv97Nm\nzZqQdQUFBcHP1aumRl9nvlFMJoXISGeXqDt6aBQNDY2UVNYTNyCC0UOjqKoydjaj09X1BgI8vfYL\nDle5iI1y8OubR2PVObC70es/Zfl0bT5d3bFJ/SmtqA8eZY5N6q97f//2xSH+tb8Snz/A8Tov2X/L\nY+LoU7saTq7r9Xq5oK9GmOql5JgLS5iVLTvLqG0o1lU33GbGGmZG1TTO7+/k51MuxBb2/XfVUOdp\nOnX3e4mKDMft9mA2KTjDw7lq9CBqXMXB/R0/bKDu/R01pC9f5FZQXFFHQkwvRg3pq/t77gjNdY3W\nZQLV6/Vy//33s2TJkpC+1PDwcFJTU8nKymLFihXU1tayYcMG5s2bB8CMGTNYu3YtaWlpOJ1OVq9e\nTUZGBlarlWnTpjFnzhzy8vJISkpi48aNOJ1Oxo8fj6qqmM1m3njjDaZPn05ubi47d+5s9QUpVdU6\n5epkV6k7YeT3V+c1FQKteXj7OdZd+ad/UnCo6egn33WclX/6J4/dMk7f52inLrf0PZ5Y95LhMXy0\nt+nRyH17WblkeIzu/we7vqkITo7i8QbY9U0FV1x0ap+iqqrUHK9lz9dl7C+p5UCFi4LyOrw+FVCg\nseXbOZ3hFgbFNl19HzSwF6VH6vl7zhEAal0+vvi2kosG9wY1gMVswhJmIsJmpVf/vgwY0CfkavuE\ni84DlOBFx8tHDtS9v5/sLefgkXoUReHgkXo++Vd5i32onfXzbLQuE6jvvfceZWVlPP/88zz33HNA\nU39oeno6S5YsYdGiRaSkpGA2m5k6dWpwvOisWbMoLS1l5syZAIwYMYInnngCgCFDhpCZmcnChQvx\n+XxER0ezatUqTCYTJpOJVatWkZmZSVZWFjabjeXLl59yoeqHrrNuAW2Lk+fJPLFf82wcNgvHG3wh\ny3r96Z1vKSqvQwOKyusMezRyfYOL/QePkXeongOH6zlY6catc6xpb6e1afhSbG8GndebASfchQTw\n96/L8DW6AA1FgSOV1Qwcfz5Wa+g9/aft32zDSIySynrqXb7gUK+Syo6Zh7WzdZlAzcjIICMj44zr\nX3zxxTOue+CBB3jggQdOuy49PZ309PTTrhs2bBgbNmxoXUN/YLrjlfqTrye05vpCn15WDlefMHNT\nKyYLyTlYHTwt1b5b1uvE5zOZURnQy8Tr27+hqLyekkpXyExULVFoGjZ1yY+iSRzYm6he399J5Pf7\naXS7QiYKGRzrJL+0Dp+qYQ0zk5QYc0qYnklb/ti6Pf6Q6QrdOu/z7+66TKCKztEdH40c2ctGvdv/\n/RCkVjwfqC0zN53ugpYe7kYf4ZYAA3qbOXTMR60nwN++1PdcqAGRdlRVo6q2sWl/Fejf28bopP54\nvR68nobgqbsz3IqjX+hVd1uJm0Z/09SBqqq1qkOmLX9sjbgZoTvqGXspzqizbgFt0+Oco3txqLIB\nvjvKvCD67BN+NAu3hxEZYfv+F92u/1fg2nEX8OcP8vEHVMLMJq4dd8Fp3+fy+Piq8Ai5xdUUltVT\nesyt+4JLbJSD5MQoLujnID4mgl4OKy/+ZS+K6kVT/ahA6WFwWBLp37v3WY82P889EjKxyue5R5io\nc9xtW/7YxkVHkHfoOM1DveJ03IzwQyCB2sN11i2gn+wrJ/vTA8Fg04CrdB79qH5/yFNAVb/+08m4\nARHklZ7wiz5A/y+6WVFCjrrM3/0BaPD4+Lqgkq8PHKOwvJ6yY25ddyEpCpzXz/ndw+Sa+kEjwsNw\nOMKorjqOgh/8KrF9TFRUmTBZHCiKQlJCNJF9+uhud+C7I9PW9oy35Y9td7y12AgSqD1cZ90Cujun\nIvSRIDkVugP1y6LqFpdb0paH5R066iLCYSEQMPP/2bvz+KjKu///rzOTyUxmQggBErYsQGKwgAuC\n9HZjkZAmQcqiLK64VYuiYv1VLShBbzatitg7BHq3VAuCfC2LWCqI2LoAInetoBLMRhZ2TEKWmWQm\nOef3R2RkAoQzzMlGPs/Ho4965szJ50xI3rnOda5zXa4aD1u+yOeDLwo4WqpvDOiZunS0Mn38QMyK\ndsYEyR4sSh3dOocRFhLM6Un9r+jXi+yjNd7HZeOjw3XX6dTgiayG240JJBRb6ueqpUmgihahqhqe\nOtV7c8ifMYgNZ0HSOysSXNxieaUVNWQVlpJz6AcKjlSCou+R0yCzQnRk/VIen/znMJ66OtRaD6pa\nS0mJC0dQLTZrMPYznjYymxVCQkJwuSrhxx7PIyeddAn/af7UIyf1j2oIsTbo4vCjLzOQUGyLo0eM\nIIF6CWiLP7yeWtV7WaxpeKen06NzR99HQDt31H9j6Yus45RV1t/gcVLLFw0Wy9M0jZOnqskuLiPn\n8Cn25Zzkh/IzntRrJEyDg0zERNVPY9e7RwciO1owaXUEmU3s+E8+1Z46TOYgLBYHHRzBdO0Soeuc\nA7n0DqQvM5Cfq0C6dNoyCdRLQFsc+lRSUd3odmP03hw6l7Lyap+lNkpPuTjyQxUHisrIKijh+8Iy\nyqr0rwzaLyac3t3DiI60ExlmwWL+aciSPcRGSEgIiqJw2+gBrP0oB0+thiVIYeyNcbprDB0Qxad7\n6x8oiIoIYegA/ZNTB3LZHsjPVSBdOm2ZBOoloC0OfWq46Kcfi4Ce9+aQHlVnjIfUNI2jpS5m/fEL\nXcealPonqzRNQ6310CMimDtvjsViVrBZLdjt9vOubXT9wO7s3HfUG4pnPmF2IX/9xwEKj9UPjC88\nVunXAwUBDc4/3mBw/vH2MTg/EBKol4CWGvpUq6q8uTnLe4PnntR+BOmccb1HZzvlVW7v3ecenfVP\nSnP65tDpy9hDjfQp1qkqhccqOVBYxvdFZZQ73Zy+332hpTU6hgYT160DvbrYieliJf9oGZt3FlCn\nKlitwSRddxk9ojrrOudAQrFhkDVXsLlqGgzO92OlgGv7RXofZAgOMnNtv8imOs1WRQL1EtBSQ1T+\n8vf97PruGJoGxScq0TSNB27pr+vYjh2s3kHm2o/bevXoYmfHN0e8K3kOv+qnFlhtncrBIxUcKCrl\nQFEZOcWnGiwmd/4QDf8xQKO7hhDf3cFlvSNwVlVjDbYSEhLCv775AVvIT2Ne/33gBCOv1tfdUHCs\nwucmXMGxCt2fNzoylKMlTp/t5hBiazA4348xuzdc2QNFUWTYlGh7WmqIylfZJ3zGg36VfUL3sd/l\nlzS63ZjvC0u9l+6eWpUv9x+jrNLN90Vl5Bw6pf8Gl6YRGhLEqEHd6dsjlC4dbYTYgnDYHQQHB521\nNIematSeEYqaHyMT3J46n5twbo/+9ajuSe0H4HMl0BwCGbMrw6aE8JO7Vmt0u9FjPWqj2+fjqqnl\n32csB6JpGt8VlPFdQVkjR9WL6hRClauGyioXaPWB1j2iM7fcmKBrZU13bZ3PHxB3rf5QDLaYUMDb\nxRFs0T/bf5DJdNGTsARypz6QMbvtlQRqOxfIL5zNYqbqjH41m85n26H+sj3vSIXP9rlUVXvI+e4o\ne749QlZBKQVHK3ym4DtfECoKdOsUQkyknT7dQ+nbowMRYVb+nVPG+k8LvN0F110Ro3uZ4hqPWt8y\n/bGFWqPzjwCALTjIp4vDFtw8v3qBDF/ase+o75jdfUfbxZ36QEigtnP/+s8hnyFItarKCJ39glck\ndGbXt8e8AXNFgr4bNADXDexGwbFK7zr11/3Y+il3uvn+xxtIp1tHetq9JkWhR5f6AO3rDVA7dnuI\nz9LEilJOtbuOOlVDVTVUP4YXBAeZfC7bg/1YU8oSZMZ0RhhbgvT/8QlEIMOX2uvQp0BIoLZz7312\n0Dt1nLtW5b3PDuoO1PgeYez+7jh1moZJUYjvEaa77r+/P4nZpHiHIv19ZwEf/d8hv+Y2RVMJNsPk\nkb1J7BVGp46h2Gy2Rluc7zVYCvq9zwsYoXMpaJe7rtHtxigKPkuN+PPcRVt8cCOQESBtmQTqJSCQ\nX7jqBqHQcLsxH+4p8gmnD/cUXTCcTpa5OFBUxqETVT5zgJZWuIHzL8tsCTLRq4ud3t0cFBwpI7u4\nDJPFSh1m8o7WMGJwV13nXN1gCZSG242p8dT5jBGo8ePGUiDDiAIZYD8ksSsFRyu8XRxDEvV9nwI9\n5zc3Z/FlVv1KAadHKBgxGXdrJ4F6Cfj068O8+89c7y+Nqmm6p2jrYA+i5lSdz7ZeJeXuRrc1TeNY\nqYvvi8o4UFg/jKmkvAY9rBYTMZEO+nQP5ep+XYkKsxDqCMVsNvPU/3yOyVL/bHudqrG/QP/kKGGO\nYE6UVfts69UvOtw7TExR6rf1uu6K7mQXn/K22K7zYxhRIA9uaD/+YVUabOsRyNCnwmMV9bNcaVr9\nMih+DBNryyRQLwFbz1hrvrZWZeuXRboDteFTRv48dWQxK5zZwAsy1Y9HPXBGH+ipqvO3Os8UZFKI\n79mB3t0cxPfsQN8e4YSG2rH8uCrpmcOX6hr0ezbcbkynDr6B2smPGfsTosP5OvcH7x+uBD8Cdee+\noxSfrEIxKRSfrGLnvqO6W5k9Oof4jru9Un+w7flxPlSoH2K2J+s4w3X+bAQy9MkabP7pEV9Nwxrc\nPH3GLU0C9RJQ4/ZdGq/Gj8v2ympPo9uNiYrwvVNf7VF5/k+7dR1rUuqX7NDUOlStjuhuHXhq6iCf\nG0jnozaYbLThdmOOlVQ3ut2Yww1mfTrsx6xPgayxlHOo/kaaRn2XTM6hcoZdrfvwFtFSN+FamgTq\nJaBTaDAlFTU+23rVqY1vn8lTq5JTfIr9BaUcKCyl4JhvKDQ2zr2jI7h+CFM3B317dmDl1iyOnzKB\nOaj+hzDIpitM/T3nhswNFqBquN2Y7j8+oXV6XtJhV+lvvbmqa31mufJnjaXCBo+aNtxuTMs9Aqr9\neHPwp/9vDyRQLwFVDZ6xbrjdmMYWvPPU1pF3uJwDP/aB5h4u1z0Av3OYlZhIO72jHMT3DKNHVzsO\nu8M7eYj6YT4o9X8ENPDrOfEONrO3i+P0tl6Xx3aq7welvl/x8thOuo/NKSqrbylqGnWqRk5Rme7L\nZ+9sWj822fyZXctqMfksDmj146GAQPpuA7nZGdHBBpzyDmeI8GPtrrZMAvUS4P5xzaAzt/UKc1hw\n1vzURWANNrHuX7lkFZaSf6TCZ6q7xml0CQtm+JXdiO/ZgchOIYQ6HAQFnftHzNmghdZwuzFqg+fx\nG2435q6URI6VOr2zPt2Vkqj72OITVT+2aBXvtl6Kovgcq/dhAoCYyFAKj1V6xwrH+PEsfyCD8wN5\nKMBmDcIWbPa25m3tZJG+S39gWDtwWc8wNPD+77Ke+saDOqs9hIfa0DQVTVXRNI3SCg/v7ywg51D5\necNUAbp3thNshlpPNZ6aKjw1LjRVJfX6BC6L60Z4x47nDVMIbNb9hq1Zf1q3X3xzDE+dRkRHG546\njS++Oab72IaTkvgzScm1/SLpYA/GGmymgz3Yr0vvGo9a/2+hKNSpml9PaJ0enF/jrqPC6Wb3fv2f\n94vvjlJWWUOVy0NZZQ1ffHdU97HVNbU+N8Oq/fg3asvax5+NS1zO4fJGt0+rcNZPIPJt/km+LzrF\nkR9cPy5NbGp0BTeTAj27hnJZTDhRYWbiokLp6LAw5y//hznop3Xha+r0t7psFpNPS9rmx2Vstwg7\nOYfKfbb1CmQIUiCTlAQyBKmkohpV07w3ePzpLlC1BkvN+HEDr7SixudOfWmFviFvIMtIizasflD8\n2dtllTV8l3+Sb/N/IPdwJcfL9P0imk0K0ZGhxEaF0quLlbgoBx1DLfTs0Rl3jYqm1Qdnh1AH1Wd8\nTX9+adQGNykabjfmuv5RPpfA1/XXP4N9IHPHBjJJSSBDkMoq3T6PvJZV6huKBuDxNFhqxo/Wraum\nrtHtxsgy0qLNMilQB/VDkNQ6NJPC/5fxme9aSDpomkbnDkH8emw/HDYLNquZDqH1g+nNZoWwDqGU\neH4aD6o0CMGG243Xany7MUdKqunexeGzrVdbXN644TSB/kwb2LBV6U8rs7bB8ImG241przNVSaC2\nUaqqUnC4lO8KSggyqbhrNRSTGbOp/o53Y2FqtZiJ7Va/GueWL/LxuE//kimUVQbTN0Zfi6+yuq7R\n7cbPv/HtxgTSymypeToDuWOuNBiK0XC7MdZgs09vjj8D7MMcFp8lY8IcFt3HBvIgQ1smgdpG1Ljd\n5B8q4fvicgpPuMguOkWF6/QPu9LoXeMQaxBx3epX44yJctDFYcJqMWGxmPhwVw5BwXbv8UFB+n9Z\nG87U5M/MTVaLyWdyEX+GArXFVmYgz+N3CrVyvNTlHerVKVT/6gZJg3ux5ozZxJL8WNBw9OBo3v1X\nnvcJrdGD9U0iA21znTMjSKC2QqqqUuV0kXuolJxDleQdraTgWJVffVgD+0QQ1z2MuG4d6ORQMKER\nHGT2uYwHCLHbqTqjZWnxY0q6hv1x/vTP/Swugi/2H/c+6/2zOH1LKkPLrRcfyLGBPCl17c+iOFZ6\nxuD8n+nvMzY1WNDQn1mqrr+yBzmHyr2X7df78T1vqXXOWpoEagvTNI2amhoqKl0cPFpJ7pEK8o9W\nUXTcSbUfsxmdyUQd46/rQXCQiWCLiVCHHZvt3AOrqxuEdMPtxjTsyvOja49paZejKEqzL+sRSEsx\nkHGZgTwpdcMV3VG4uBa5PwsaNhTIZXtbvIowggRqM6utraXK6aTS6ebg0Spyj1RQeNxFcYPp7BrT\nNTyE2G6hxHULo3f3DvzP+r2UVzjR1PowDHNYie3ZVdfg8YYZ6M8Dgh0cFsrPWMO+gx99bIHcMQ9k\nrs3C45WcLHN5B5z78xjn7v3HfELRnwmXA3lSKpAWeSAtxUAu22VNKWE4TdOorq6myumi0lVL/tEq\nDh6voui4i+ITlboHs3cND6F39/o+0L49wojp1YkTx0up9bixBGk4K6swmYMxWepboXWYdD+JE2RS\ncJ9xHkF+3PAYd30ca7bnevvnxl0fp/vYQC6f39yc5X189NDJ+l9yveFccLTc+1SWp1al4Oi5x+ye\nS2lFjc8joP7cMQ/kSalABHK3vb1etgdCAtVAHo+HKqeTGncdlU4PeUcrKTrhouC4k0MnqnQNqlao\nn8Wpd/f61mdc9zBCQyzU1dVRW1NNcHAdZq2Gzh2tWIM7oigKSlCIzyNvulf9pP4RQbfT47Ot101X\n98JsNl/UZV0gl8/7C0t9gm1/of75UGvcdZhMirfv1p+ZucJDg+tvDv14bLgfk9AEMklJIC1yuWxv\nXhKoAaiuruHEyRKq3SrlVW4KjjspOllNwdFKDv9QpWtspUmBHl0c9O4e5r2JFGINQlVV3DUuzIob\nausItQURGhFx7uWNG3xNfy7bu3UOofyMQO3WOaSRd/tSNc3b+nFWe/ivgd10tzJ3f9fg8vk7/ZfP\n1gaLATbcbkxMVAeOlbo43VKMieqg+9ihl0dxvLTaG4pDL9d/cyiQJ6UCmf1eLtublwRqAD78Ip99\nuWXkHS7/8Zf0wswmhV5dQ72tz9ioDt6xgR53DWpdNVqtGZvFTNfIcCyWC/dLmhU4s/fA7MfVZF1d\n49uNCeTSu7SyweVzpf7L59FDon1WKBg9RP9wnpZ6fDSQcCpq0M/bcLsxctnevNp9oO7du5d58+ZR\nWlqKxWLhwQcfZNy4cbqOXf7egQu+x2I2ER0V+mMLtAMxkR28Q5Pq6urw1FRTpykEW0x0CLNhDwn3\nu3/NEWLxaWU6QvTfHHLX1vlMBOzPWvOBXHoHcvl845U9MF1ksLXU46OBiI4M9bZMT2/rJZftzatd\nB6rb7WbGjBk888wzpKSkUFhYyMSJE+nfvz8JCQkX9TWDLSbiunX48Q58GD27OnxWu3TX1F8yWswK\nDpuFDhER3jGhF6tHFwcVhWXegd89uuhvhVgtZp9nvf25fA7k0nvoz7r5Xj7/TP/NkvZ2KRpIq7q9\nfa9aWrsO1J07d6IoCikpKQDExMQwbNgw3n//fWbOnKnra4RYzcRGhdG7R/1d+O6dHT6zwNfV1VHt\nrMISVN8Kjex0/jGhFyuig9XnRktEB/1P0sR268CRH5zey+fYbvr7FAO59A5kbGV7E0irWjSvdh2o\n+fn5xMbG+rwWFxfH/v37dR0//6HBKASddfPJXVMNmrGt0MbYQyyEh1q9rT27H5f8MZEdfKbCi4nU\nH6iBXHpLy0lcitp1oDqdzrNaizabDZdL3w2mruE2Sivq8LhrqfVUE2Q2YQ020bmLw/BW6GmmH1u/\npjNawbFRHcgp/mmqtNioDph13pm66eoemEwKRScqie4ayg1Xnns86LnqmlEYPkjfEiAX61x1m4PU\nbR91jdauA9Vut1Nd7fvEisvlwm7XN2GxJchER4eGo0soHTp0073InBHCw3/qJx07PAGHw8rBo+XE\ndQvj5iExfv3AjBupv1V6Zt3mJHWlblvQrgM1ISGBFStW+LyWm5tLYqK+dYY6R3SirKyKujqNsjL9\nz0gHwmRSCA93UFZW5b3DDnBNQmeuSegMQFmZ8TP7nK9uU5O6Urcp6xqtXQfq0KFDMZvNrF+/nvHj\nx5OVlcWOHTt035ACUFXNO8C+OUldqSt1W592HahBQUFkZGSQnp5OZmYmVquV+fPnn3WjSggh9GjX\ngQrQr18/1qxZ09KnIYS4BMgy0kIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGE\nQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhC\nCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgE\nqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwSFBLn8Bpmqbx+uuv\ns3XrVlRVJSIiglmzZtG/f38APvnkE1555RWqq6sJCQnhN7/5DTfeeCMARUVFzJ49m8OHD2M2m7n1\n1lt54IEHAKipqWHOnDn83//9HyaTiUGDBjF37lyCg4MB+OMf/8i7774LQPfu3XnxxReJjo5uge+A\nEKKtazUt1FWrVvHxxx+zdu1aPvjgA0aOHMlvfvMbAE6ePMnMmTOZO3cuW7ZsIT09nZkzZ1JSUgLA\nzJkzufHGG/nwww9ZvXo1q1at4pNPPgFg8eLFnDp1ii1btvCPf/yDsrIylixZAsDHH3/M6tWrWbNm\nDVu2bOH666/nySefbJlvgBCizWs1gXrVVVexaNEiQkNDARgxYgQFBQV4PB62bt1KYmIiV111lfe9\nCQkJfPTRR+Tm5nLgwAHuuusuADp16sTYsWN57733ANi4cSN33303JpMJk8nEXXfd5bNv7NixdOrU\nCYC77rqL/fv3U1BQ0NwfXwhxCWg1gTpgwAD69evn3d66dSsDBw7EYrGQl5dHXFycz/vj4uLIzs4m\nLy+PqKgorFard1/v3r3Jycnh1KlTlJSU+BwbFxfHiRMnqKioOOvr2mw2oqKiyM7ObqqPKYS4hDVr\nH+rmzZt54YUXUBTF+5qmaYSFhbF161af97311lu89dZbALhcLmw2m8/XstlsuFwunE7nWfusVisu\nlwuXy+XdPvM4AKfT2ejX1ctkUi78JgOdrid1pa7UDbyu0Zo1UFNTU0lNTW30PcuWLWPNmjX85S9/\nISEhAQC73U5VVZXP+1wuFx07dsThcFBdXX3WPrvdjt1uB+pvTJ3mdDoBcDgc2O328x6rV3i4Q/d7\njSR1pa7UbX1azSU/1N9A2rJlC//v//0/EhMTva8nJCSQn5/v897c3FwSExOJj4/n6NGjPqF5el9Y\nWBhdu3b1OTY3N5fu3bsTGhpKfHy8z77KykqOHTvGZZdd1oSfUghxqWo1gfrZZ5/x3nvvsWLFCrp0\n6eKzLykpidzcXHbt2uV9b2FhIUlJScTFxTFw4ED++Mc/AnD48GE2btzIxIkTAZgwYQJ/+tOf8Hg8\nuN1u/vznPzNhwgTvvg0bNnDs2DGgfgjVoEGDZNiUEOKiKJqmaS19EgD3338/33zzDZ07dwbq+1YV\nReG1114jMTGRXbt2sWjRIpxOJx06dODZZ5/lmmuuAeDIkSPMmjWL4uJiLBYLd911F1OmTAHA7Xbz\n4osv8sUXX6AoCjfccAPPPvssQUH1vR1vvvkmq1evRtM04uLieOGFF4iKimqZb4IQok1rNYEqhBBt\nXau55BdCiLZOAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQC\nVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRIYS1TcAACAASURB\nVAJVCCEMIoEqhBAGkUAVbcYvfvELVq1a1dKn0aROnjzJrbfeytVXX81XX311zve0h+9DWyWBKlqt\nrKwsPv30U+/2Bx98wB133NEstSsqKlizZk2TfO1Vq1bRr18/MjIyztq3efNmjhw5wueff87VV199\nzuOb8/sg/COBKlqtd9991ydQm9OOHTuaLFDXrl1LWloaf/vb387aV15eTmRkJHa7vUlqi6Yly0iL\ngBw6dIgXX3yR3bt3Exoayvjx43niiSdQFIXKykoWLlzIZ599xqlTp+jXrx+//e1vvS2vkSNH8qtf\n/YodO3bw+eef43A4ePLJJxk3bhzPPfcc7777LiaTibCwMHbu3MnIkSOZNm0ad999N88++ywWi4WI\niAjWrl1LbW0tY8aM4fnnn/eeW2ZmJuvXr+fYsWP06tWLGTNmkJycDNDo8Rs3buR3v/sdmqZhsVhY\ns2YNl19+uc/n/sUvfsGRI0d8XtM0DUVR+Prrr8/7/frPf/7Dfffdx/bt20lKSmLx4sVcf/31ALz0\n0ku8+eabqKpKcHAwf/rTn3j99dcZOHAge/bsQVVV3n33XZ/vg6ZpLFmyhL/97W84nU6uvvpqnn/+\neaKjowFYsmQJGzdupKSkhKioKB5++GHGjRsX+D+8ODdNiACMGzdOmzNnjuZyubTCwkLtpptu0lau\nXKlpmqY98cQT2pQpU7Tjx49rNTU12ssvv6xde+21WkVFhaZpmjZixAjt5ptv1vbs2aPV1tZqixcv\n1q6++mqtpqZG0zRNu/POO7X58+d7a40YMUJ78803NU3TtGeeeUb7+c9/rq1cuVLzeDzaZ599piUm\nJmpffvmlpmma9te//lUbMWKElpOTo9XV1Wlbt27VBgwYoB08eFDX8W+88YY2btw4w79fzzzzjPb0\n009rmqZpzz33nPbYY4/57G9Y984779Ruuukmbc+ePef8PvzlL3/RRo0apR08eFCrqanRnn32WW3i\nxImapmnaxo0btSFDhng/84cffqhdfvnl3m1hPLnkFxdt//79ZGVlMWPGDGw2G9HR0SxZsoSBAwdS\nUVHBBx98wJNPPknXrl0JDg7m8ccfx+1289lnn3m/xo033sg111yD2WxmzJgxuFwuDh8+rKt+REQE\nd9xxB0FBQVx//fVERESQl5cH1F9W33333fTt2xeTyURSUhLXXnstGzZs0HV8U6isrOSDDz5gwoQJ\nAEyYMIHt27dTUlLS6HGJiYlcc80159y3bt06brvtNmJjYwkODuapp57igQceQFVVbrnlFrZv305s\nbCwAo0aNIjg4mG+//dbYDya8glr6BETbVVhYSHBwMJ07d/a+duWVVwL1YQvQt29f7z6LxUKPHj0o\nLCz0vnb6lx3AZrOhaRrV1dW66sfExPhs22w277EHDx7klVde4bXXXgPqL8c1TSMyMlLX8U1h48aN\ndOrUiWuvvRaAq666il69erF+/Xruv//+8x7Xq1ev8+4rLCz02R8REcEvfvELAFwuF4sWLeKf//wn\n5eXlaJqGx+PB7XYb9IlEQxKo4qKZTCa083TBN/ZLqyiKz9e4WGaz+bz7bDYbzz33HLfccstFHX8h\nF9OHunbtWo4fP+5z997j8fDuu+82GqgWi+W8+xr7N5g7dy5ff/01f/nLX7x/2AYNGnTeryUCJ4Eq\nLlp0dDQej4dDhw7Rs2dPAHbv3s2pU6e45ppr0DSN77//np///OcAOJ1ODh06RO/evZv83GJjY/nu\nu+98AvXIkSN069bNJ9Av1gcffODX+//zn/+QnZ3NypUr6dq1q/f1kpISbr/9dvbs2cPgwYP9Po+Y\nmBjy8/O926Wlpaxfv57bb7+dr7/+mltuucUbpjk5OTidTr9rCP2kD1VctH79+jFgwABee+01Kisr\nKS4uZtasWRw5coSIiAiGDx/OG2+8wQ8//IDT6eSVV14hLCyMG264QdfXt9lsFBcXU1FRcd5W2Pnc\ncccdvPvuu3zxxReoqsru3bsZO3Ysu3fv1nW81Wrl5MmTlJaWGnKJ/M4773DttdcyaNAgoqOjvf+7\n8sorue6661i7du1Ffd1bb72Vd955hwMHDuB2u1myZAmbNm3CZrPRq1cv9u3bh9vtJjs7m8WLFxMZ\nGcmxY8cC/jzi3FosUN955x2uvvpqVqxY4X2tqKiIhx9+mJSUFEaPHs3s2bO9P8yaprFw4UJGjx5N\ncnIyjzzyCGVlZd5jN2zYQGpqKsnJyUyePJl9+/Z59+3du5fJkyczevRo0tLSfG5MlJaWMn36dJKS\nkkhOTmbRokXN8OkvHZmZmZw6dYobb7yRO++8k9TUVO6++24AFi1aRPfu3Rk3bhw333wzxcXFrFy5\nEpvNBnDOluKZr02cOJFdu3YxatQoKisrURSl0dblmfvGjRvHAw88wNNPP82gQYOYO3cuzz33HEOH\nDtV1fFJSEhaLhZEjR+oO4fM5fTNq0qRJ59w/adIktm7dSkVFRaPndK7X7rjjDu644w7uu+8+rrvu\nOg4dOsSSJUsAeOqppzh69ChDhw5l1qxZzJgxg0mTJrF06VJWr14d0GcS59ESQwvmzp2rPfHEE9qE\nCRO0P//5z97Xx48fr73xxhuapmmay+XSJk6cqC1btkzTtPphMBMnTtSqq6s1TdO09PR07fHHH9c0\nTdP279+vDR48WCssLNQ0TdP+/ve/a8OGDdM8Ho9WU1Oj3XTTTdrmzZs1TdO0goICbfDgwdr333+v\naZqmzZgxQ0tPT9c0TdOcTqc2YcIE7e23326G74IQ4lLTIi3UMWPG8Nprr/k8DaJpGg899BD33nsv\nUH+59/Of/5wDBw4A9XdIJ0+ejNVqBWDatGls27aN6upqNm3axPDhw72DmVNTU4H6/rydO3eiKAop\nKSlAfZ/TsGHDeP/996mqquKjjz7ivvvuAyAkJIQpU6bw3nvvNc83QghxSWmRQD3XnUZFUUhOTsbh\ncAD1d4n/9a9/MWTIEADy8vJ8bmbExMSgaRoHDx4kLy+PuLg4n68XExNDdnY2+fn5PkNzAOLi4sjJ\nyaGgoABFUbxBfOY+IYTwV6u8y+92u3nqqaeIiory9ju5XC5v6xTqA9hiseB0OnG5XN5+udNsNhsu\nlwtVVc+7z+l0njUkxWq14nK5muiTCSEuZa3uLn9JSQn33nsvJpOJjIwM7zhFu91OTU2N932qquJ2\nu7Hb7djt9rMGZLtcrgvuczgcZ93BPb1PD02mQRBCnKFVtVDLy8u59957GT58ODNnzvTZFx8fT35+\nvnesXl5eHkFBQfTp04eEhASfsXin9ycmJlJbW+szkgAgNzeXxMRE4uLiMJlMFBQUeLsFTu/TQ1EU\nysqqUNXmC1aTSSE83CF1pa7UNaCu0VpVoM6dO5chQ4acFaZQ/9zzypUrSUlJweFwsHz5ctLS0ggO\nDmbs2LFMnTqV7OxsEhISWLt2LQ6HgyFDhqCqKmazmfXr1zN+/HiysrLYsWMHTz75JCEhISQnJ5OZ\nmcmCBQsoLy9n9erVjT610pCqatTVNX9LVepKXanb+jT79H2qqpKWloaiKBw5cgS73U7Hjh258sor\n2bBhAz179sRisaAoCpqmERMTw7JlywB49dVX2bJlCwADBgxg7ty5hIaGAvUT82ZkZODxeIiMjGTO\nnDnEx8cD9RMVp6enU1paitVq5bHHHmPUqFFAfat49uzZ7N+/3ztBx6OPPqr785SUVDbrD4LZrBAR\nESp1pa7UNaCu0WQ+1AC1tx9AqSt1L6W6Rmt1N6WEEKKtkkAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQ\nCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBC\nGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEq\nhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCIO0\nWKC+8847XH311axYscL7WmlpKdOnTycpKYnk5GQWLVrk3adpGgsXLmT06NEkJyfzyCOPUFZW5t2/\nYcMGUlNTSU5OZvLkyezbt8+7b+/evUyePJnRo0eTlpbGhg0bdNUUQgh/tEigvvDCC+zatYs+ffr4\nvD5nzhyioqL48MMP2bBhA7t372b16tUArFq1ij179rBp0ya2bNlCZGQk6enpAGRlZTFv3jyWLVvG\nli1buOeee5gxYwa1tbW43W5mzJjBtGnT2Lp1K0uXLmXevHlkZ2dfsKYQQvijRQJ1zJgxvPbaa9jt\ndu9rVVVVfPTRR9x3330AhISEMGXKFN577z0ANm7cyOTJk7FarQBMmzaNbdu2UV1dzaZNmxg+fDjR\n0dEApKamArB792527tyJoiikpKQAEBMTw7Bhw3j//fcvWFMIIfwR1BJFBw0adNZrBQUFKIriDUWA\nuLg4cnJyAMjLy6N3797efTExMWiaxsGDB8nLy2PAgAE+Xy8mJobs7Gw0TSM2NtZnX1xcHPv3779g\nTSGE8EeLBOq5OJ1OLBaLz2tWqxWXywWAy+Xytk4BFEXBYrHgdDpxuVzYbDafY202Gy6XC1VVz7vv\nQjWFEMIfrSZQHQ4Hbrfb5zWXy+XtFrDb7dTU1Hj3qaqK2+3Gbrdjt9uprq4+57Gqqp5334Vq6mEy\nKbrfa4TT9aSu1JW6gdc1WqsJ1Li4OEwmEwUFBd5L9JycHBITEwGIj48nPz+fwYMHA/VdAEFBQfTp\n04eEhATy8/N9vl5eXh6JiYnU1tb6jCQAyM3NJTEx8Zw1T+/TKzzccdGfORBSV+pK3dan1QRqSEgI\nycnJZGZmsmDBAsrLy1mzZg33338/ABMmTGDlypWkpKTgcDhYvnw5aWlpBAcHM3bsWKZOnUp2djYJ\nCQmsXbsWh8PBkCFDUFUVs9nM+vXrGT9+PFlZWezYsYMnn3zynDVXr17tralHWVkVqqo11bflLCaT\nQni4Q+pKXalrQF2jKZqmNd+noP5SPS0tDUVROHLkCHa7nY4dO5KUlMQDDzzArFmz2L9/P2azmTFj\nxvDoo496j3311VfZsmULAAMGDGDu3LmEhoYCsHnzZjIyMvB4PERGRjJnzhzi4+OB+mFV6enplJaW\nYrVaeeyxxxg1ahQA5eXlzJ49+7w1L6SkpJK6uub7FprNChERoVJX6kpdA+oardkD9VLT3n4Apa7U\nvZTqGk0ePRVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUII\ng0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCF\nEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJ\nVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIEEtfQJn+vLLL3n55ZeprKzE\nbDZz2223cffdd1NaWsqsWbPIzs7GZDIxcuRInn76aQA0TWPRokVs374dRVGIj49n3rx5hIeHA7Bh\nwwaWL19OXV0d4eHhzJ49m4EDBwKwd+9e5s2bR2lpKRaLhQcffJBx48a12OcXQrRtraaFWl1dzSOP\nPML06dPZvHkzK1asYOnSpXz66afMmTOHqKgoPvzwQzZs2MDu3btZvXo1AKtWrWLPnj1s2rSJLVu2\nEBkZSXp6OgBZWVnMmzePZcuWsWXLFu655x5mzJhBbW0tbrebGTNmMG3aNLZu3crSpUuZN28e2dnZ\nLfhdEEK0Za0mUA8fPkxFRQXXX389AF26dKFfv37s27eP7du3c++99wIQEhLClClTeO+99wDYuHEj\nkydPxmq1AjBt2jS2bdtGdXU1mzZtYvjw4URHRwOQmpoKwO7du9m5cyeKopCSkgJATEwMw4YN4/33\n32/Wzy2EuHS0mkCNjY0lLi7OG5RFRUVkZ2czfPhwoD7wTouLiyMnJweAvLw8evfu7d0XExODpmkc\nPHiQvLw84uLifOrExMSQnZ1Nfn4+sbGxPvvO/LpCCOGvVtOHajabWbBgAQ899BAvv/wyFRUVPPro\nozidTiwWi897rVYrLpcLAJfL5W2dAiiKgsViwel04nK5sNlsPsfabDZcLheqqp53nz9MJsWv9wfq\ndD2pK3WlbuB1jdZqAvXEiRM8/PDDvPrqq1x//fWUlZXx4IMPomkabrfb570ulwu73Q6A3W6npqbG\nu09VVdxuN3a7HbvdTnV19TmPVVX1vPv8ER7u8Ov9RpG6Ulfqtj6tJlD//e9/ExYW5u1DDQ8PZ8SI\nEezatQuTyURBQYH3Ej0nJ4fExEQA4uPjyc/PZ/DgwUB9F0BQUBB9+vQhISGB/Px8nzp5eXkkJiZS\nW1vLihUrfPbl5uZ6v65eZWVVqKp2UZ/5YphMCuHhDqkrdaWuAXWN1moCNT4+nmPHjrFv3z4GDhyI\ny+Vix44dDBkyhMjISDIzM1mwYAHl5eWsWbOG+++/H4AJEyawcuVKUlJScDgcLF++nLS0NIKDgxk7\ndixTp04lOzubhIQE1q5di8PhYMiQIaiqitlsZv369YwfP56srCx27NjBzJkz/TpvVdWoq2u+HwSp\nK3WlbuvVagK1b9++zJs3j1mzZuHxeNA0jeuuu46HH36YmpoaZs+eTVJSEmazmTFjxnjHi06aNIni\n4mImTpwIwIABA3j++ee9XzM9PZ2ZM2fi8XiIjIwkIyMDk8mEyWQiIyOD9PR0MjMzsVqtzJ8//6wb\nVUIIoZeiaVrb/7PQgkpKKpv1L6vZrBARESp1pa7UNaCu0VrNsCkhhGjrJFCFEMIgEqhCCGEQCVQh\nhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQC\nVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAG\nkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwSpPeNmqbx9ttvs337do4f\nPw5At27dSEpKYtKkSU12gkII0VboDtQXXniBzZs3k5yczNChQ9E0jeLiYl555RW+//57Zs+e3ZTn\nKYQQrZ7uQN24cSNvv/02/fr183n99ttv5/bbb5dAFUK0e7r7UENCQujTp89Zr/ft2xe73W7oSQkh\nRFukO1AfeOAB3njjDTwej/c1j8fD0qVLuffeew05mVOnTvH4448zfPhwkpKS+J//+R8ASktLmT59\nOklJSSQnJ7No0SLvMZqmsXDhQkaPHk1ycjKPPPIIZWVl3v0bNmwgNTWV5ORkJk+ezL59+7z79u7d\ny+TJkxk9ejRpaWls2LDBkM8hhGifdF/yf/jhh2RlZbFy5Up69uxJXV0dx44dAyA2Npa///3v3veu\nX7/+ok7mmWeeoWfPnvzzn/+ktLSUGTNmMGbMGF555RWioqLIyMjA5XJx5513snr1aqZOncqqVavY\ns2cPmzZtwmq1MnfuXNLT01m8eDFZWVnMmzePdevWER0dzebNm5kxYwbbtm1DVVVmzJjBM888Q0pK\nCoWFhUycOJH+/fuTkJBwUecvhGjfdAfqddddx3XXXddkJ3L8+HE+/fRTPv30UwA6derEypUrqaqq\n4qOPPuKDDz4A6rsepkyZwrp165g6dSobN25k8uTJWK1WAKZNm0ZaWhrV1dVs2rSJ4cOHEx0dDUBq\naiovvfQSu3fvxuPxoCgKKSkpAMTExDBs2DDef/99Zs6c2WSfUwhx6dIdqI8++uh5951uLQZi//79\ndO7cmb/97W9s3LgRs9nM5MmTufLKK1EUxRuKAHFxceTk5ACQl5dH7969vftiYmLQNI2DBw+Sl5fH\ngAEDfOrExMSQnZ2NpmnExsb67IuLi2P//v0BfQ4hRPulO1ABCgoK+Pbbb3G73d7Xjh07xtKlSwMO\n1PLycn744QdsNhubNm3iwIED3HHHHdx3331YLBaf91qtVlwuFwAul8vbOgVQFAWLxYLT6cTlcmGz\n2XyOtdlsuFwuVFU97z5/mEyKX+8P1Ol6UlfqSt3A6xpNd6Bu2LCB3/3udwQHB1NTU0NISAhOp5PI\nyEgeeOCBgE8kLCwMRVG44447AEhMTGTYsGHs2rXLJ8ChPkRPjyyw2+3U1NR496mqitvtxm63Y7fb\nqa6uPuexqqqed58/wsMdfr3fKFJX6krd1kd3oGZmZrJkyRJGjRrFFVdcwb///W9ycnJYvHgxSUlJ\nAZ9ITEwMtbW1OJ1OHI76b66iKAwcOJCvvvqKgoIC7yV6Tk4OiYmJAMTHx5Ofn8/gwYOB+i6AoKAg\n+vTpQ0JCAvn5+T518vLySExMpLa2lhUrVvjsy83N9X5dvcrKqlBV7aI+88UwmRTCwx1SV+pKXQPq\nGv519b7x2LFjjBo1CqgPOqgPs8cff5znnnsu4BPp3bs3gwYNYtmyZQAUFxfz6aefMmLECJKTk8nM\nzATquwbWrFnDxIkTAZgwYQIrV66ksrISTdNYvnw5aWlpBAcHM3bsWD755BOys7MBWLt2LQ6HgyFD\nhjB06FDMZrN3REJWVhY7duxg7Nixfp23qmrU1TXf/07/0EldqSt1A69rNN0t1IiICG8rMSwsjNzc\nXPr27UtsbKw3sAK1aNEiZs2axciRI7Hb7fzmN79h8ODBXHbZZcyePZukpCTMZjNjxoxh3LhxAEya\nNIni4mJvwA4YMIDnn38eqH/oID09nZkzZ+LxeIiMjCQjIwOTyYTJZCIjI4P09HQyMzOxWq3Mnz//\nrBtVQgihl6Jpmq6oXrJkCStXrmT79u0sWrSIPXv2cMstt7Bv3z6OHj160WNP27qSkkrq6prvUsVs\nVoiICJW6UlfqGlDXaLov+R977DFmz56Nw+Hgd7/7HVdeeSWbNm2itraWhQsXGn5iQgjR1vg1bGrs\n2LGoqkpISAgLFy6kurr6rKFHQgjRXuluoRYVFXHrrbeydetW72tvv/0248ePp6ioqElOTggh2hLd\ngfrCCy9w2WWXce2113pfGz9+PFdddRUvvvhik5ycEEK0Jbov+b/66it27NhBcHCw97VOnTrx7LPP\nNukz/kII0VbobqHa7XaOHDly1uv5+fk+j34KIUR7pbuFOnHiRB544AFuu+02evXqhaZp5Ofn8847\n73DXXXc15TkKIUSboDtQH3vsMcLDw1m3bh2FhYWYTCZiYmJ4+OGHvc/fCyFEe6Y7UCsqKrjnnnu4\n5557mvJ8hBCizdLdhzps2DBUVW3KcxFCiDZNd6CmpaXx1ltvSagKIcR56L7kP3ToENu2beMPf/gD\n3bp1O2vS5/b6LL8QQpymO1CvueYarrnmmqY8FyGEaNMMWVNKCCGEH32oALt27eK3v/2td9xpbW0t\n69ata5ITE0KItkZ3oL799ts88sgj2Gw2vv76awB++OEH3njjjbOWEhFCiPZId6CuWLGCZcuW8cIL\nL3hfi4qKYunSpbz99ttNcnJCCNGW6A7UkydPem9KnV5TCurXlTp+/LjxZyaEEG2M7kDt1auX91L/\nTB999BHdunUz9KSEEKIt0n2X/+677+ZXv/oVv/zlL6mrq2Pp0qXs37+f7du3k56e3oSnKIQQbYPu\nQL3tttvo0qULa9euJTo6mq1btxIbG8uKFSsYMmRIU56jEEK0CX6tKTVixAhGjBjRVOcihBBtmq5A\n/fzzz9myZQtBQUGkpaXJE1NCCHEOF7wptXnzZh566CGOHz9OUVER06ZN4+OPP26OcxNCiDblgi3U\nP/3pT7z00kukpqYC8Pe//51ly5bJpb8QQjRwwRbqwYMHSUpK8m6PGjWK3NzcJj0pIYRoiy4YqLW1\ntT5T9VmtVtxud5OelBBCtEV+TY4ihBDi/C7Yh1pXV8df//pXNE1r9LW77767ac5QCCHaiAsGamRk\n5FmzSTV8TVEUCVQhRLt3wUDdvn17c5yHEEK0eX49KaWqKl9//TXHjx9HURS6devGFVdc0VTnJoQQ\nbYruQN27dy/Tp0/n5MmTWCwWNE2jtraWqKgoMjIy6N+/f1OepxBCtHq67/I//fTTjB49mk8++YR9\n+/bxzTff8K9//YuRI0fy1FNPNeU5CiFEm6A7UA8fPszTTz9NZGSk97WoqCh++9vfcvjw4SY5OSGE\naEt0B+qAAQMoLCw86/XDhw8bfrlfUVHBjTfeyLPPPgtAaWkp06dPJykpieTkZBYtWuR9r6ZpLFy4\nkNGjR5OcnMwjjzxCWVmZd/+GDRtITU0lOTmZyZMns2/fPu++vXv3MnnyZEaPHk1aWhobNmww9HMI\nIdoX3X2o48eP54knnuCWW24hLi4OVVUpLCxk06ZNTJo0iY8++sj73ptvvjmgk/rv//5vbDabd3vO\nnDnevlqXy8Wdd97J6tWrmTp1KqtWrWLPnj1s2rQJq9XK3LlzSU9PZ/HixWRlZTFv3jzWrVtHdHQ0\nmzdvZsaMGWzbtg1VVZkxYwbPPPMMKSkpFBYWMnHiRPr3709CQkJA5y+EaJ90B+rs2bMBWLx48Vn7\nFixY4P1vRVHYv3//RZ/Qxx9/TFFREWPHjuXw4cNUVVXx0Ucf8cEHHwAQEhLClClTWLduHVOnTmXj\nxo1MnjwZq9UKwLRp00hLS6O6uppNmzYxfPhwoqOjAUhNTeWll15i9+7deDweFEUhJSUFgJiYGIYN\nG8b777/PzJkzL/r8hRDtl+5AzcrKasrzAODUqVPMnz+f//3f/2XTpk0AFBQUoCiKNxQB4uLiyMnJ\nASAvL4/evXt798XExKBpGgcPHiQvL48BAwb41IiJiSE7OxtN04iNjfXZKeB9hgAAIABJREFUFxcX\nF9AfAyFE+9ZooFZWVur+QqGhoQGfzPz587nzzjt9gs7pdPpMzgL1E7S4XC4AXC6Xt3UK9S1ki8WC\n0+nE5XL5dB0A2Gw2XC4Xqqqed58/TCblwm8y0Ol6UlfqSt3A6xqt0UAdPHiwz5LRjQm0Zbd9+3aK\niop8bjgBOByOs2a3crlc2O12AOx2OzU1Nd59qqridrux2+3Y7Xaqq6vPeayqqufd54/wcIdf7zeK\n1JW6Urf1aTRQ33rrLe9/5+XlsWrVKm699VYSEhJQVZWsrCzWr1/Pr3/964BP5B//+AfFxcXcfPPN\naJpGRUUFdXV1ZGVlYTKZKCgo8LZcc3JySExMBCA+Pp78/HwGDx7sPc+goCD69OlDQkIC+fn5PnXy\n8vJITEyktrb2rDkKcnNzvV9Xr7KyKlRVu/AbDWIyKYSHO6Su1JW6BtQ1WqOBeu2113r/+5VXXuH1\n11+nT58+3tduuOEGbrrpJmbPns2YMWMCOpGXX37ZZ/sPf/gDhw4dYsGCBfzmN78hMzOTBQsWUF5e\nzpo1a7j//vsBmDBhAitXriQlJQWHw8Hy5ctJS0sjODiYsWPHMnXqVLKzs0lISGDt2rU4HA6GDBmC\nqqqYzWbWr1/P+PHjycrKYseOHX7fkFJVjbq65vtBkLpSV+q2XrpvSn3//ff06tXrrNfj4uLIzs42\n9KQamjNnDrNnzyYpKQmz2cyYMWMYN24cAJMmTaK4uJiJEycC9eNln3/+eQD69u1Leno6M2fOxOPx\nEBkZSUZGBiaTCZPJREZGBunp6WRmZmK1Wpk/f/5ZN6qEEEIvRTtzUtNGTJw4kauuuopHHnmEiIgI\nAEpKSsjMzGT37t3tdlB8SUlls/5lNZsVIiJCpa7UlboG1DWa7hbqCy+8wGOPPcbbb7+NzWZDVVVq\namoIDw8nMzPT8BMTQoi2RnegxsbGsm7dOgoKCjh69Chut5tu3brRu3fvs4YfCSFEe6Q7UC80hMrh\ncDB8+HBmz55NeHi4IScnhBBtie5AXbJkCb///e8ZO3YsV1xxBSaTia+//pp//OMfTJ8+HY/Hw8qV\nK1mwYMFZY0mFEKI90B2ob731Fr///e99Zui/4YYbuPHGG/nDH/7A8uXL+a//+i8mTJjQJCcqhBCt\nne7p+7755hv69et31uv9+vXjyy+/BCA8PPysp4+EEKK90B2o0dHRzJs3j+PHj3tfKykp4fe//z2d\nO3dGVVUWLVrE5Zdf3iQnKoQQrZ3uS/6FCxfyq1/9irVr12KxWAgKCsLlchEaGsobb7wBwOeff87r\nr7/eZCcrhBCtme5A7d+/Px9//DHffPMNJ06cQFVVOnfuzIABA7wTipyes1QIIdojv5aRDg4OZtCg\nQU11LkII0abp7kMVQgjROAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIg\nEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGE\nMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQYJa+gSEaE61qsqbm7MoOl5JdGQo96T2I8gk\n7QphjFYVqDt37uS1116joqICVVWZOnUq06ZNo7S0lFmzZpGdnY3JZGLkyJE8/fTTAGiaxqJFi9i+\nfTuKohAfH8+8efMIDw8HYMOGDSxfvpy6ujrCw8OZPXs2AwcOBGDv3r3MmzeP0tJSLBYLDz74IOPG\njWuxzy+a3pubs/gy6zgAR0ucANw/5mcteUriEtJqAvXkyZNMnz6dzMxMhg4dSlFREb/85S+56qqr\n+POf/0xUVBQZGRm4XC7uvPNOVq9ezdSpU1m1ahV79uxh06ZNWK1W5s6dS3p6OosXLyYrK4t58+ax\nbt06oqOj2bx5MzNmzGDbtm2oqsqMGTN45plnSElJobCwkIkTJ9K/f38SEhJa+tshGqFqGp/vPULx\niSp6dXVw/RXdMSmKrmOLjlc2ui1EIFrNtY7JZOLll19m6NChAERHRxMfH8/evXvZvn079913HwAh\nISFMmTKF9957D4CNGzcyefJkrFYrANOmTWPbtm1UV1ezadMmhg8fTnR0NACpqakA7N69m507d6Io\nCikpKQDExMQwbNgw3n///Wb93MJ/n+89wvavDvF9cRnbvzrE53uP6D42OjK00W0hAtFqWqgRERGM\nGjXKu11YWEh2djY/+1n95djpUASIi4sjJycHgLy8PHr37u3dFxMTg6ZpHDx4kLy8PAYMGOBTJyYm\nhuzsbDRNIzY21mdfXFwc+/fvN/yzCWMVn6hqdLsx96T2A/DpQxXCKK0mUM909OhRfv3rX/Pggw8C\nYLFYfPZbrVZcLhcALpfL2zoFUBQFi8WC0+nE5XJhs9l8jrXZbLhcLlRVPe8+f5hM+i41jXK6Xnuu\nGxMVSnZxmc+22azvvMxmM7/6Zf+LqtscpG7z1jVaqwvUb7/9lkceeYS77rqL+++/n/379+N2u33e\n43K5sNvtANjtdmpqarz7VFXF7XZjt9ux2+1UV1ef81hVVc+7zx/h4Q6/3m+U9lx37PAEHA4rB4+W\nE9ctjJuHxDTZL0hr+LxSt+1oVYH67bff8tBDD5Genu69/I+Li8NkMlFQUOC9RM/JySExMRGA+Ph4\n8vPzGTx4MFDfBRAUFESfPn1ISEggPz/fp0ZeXh6JiYnU1tayYsUKn325ubner6tXWVkVqqpd1Oe9\nGCaTQni4o83XVTWNz74+QtGJSqK7hnLDlee+sXS+utckdOaahM5A/b+B0S6V77PUbbyu0VpNoLrd\nbp544gnmzJnj05caEhJCcnIymZmZLFiwgPLyctasWcP9998PwIQJE1i5ciUpKSk4HA6WL19OWloa\nwcHBjB07lqlTp5KdnU1CQgJr167F4XAwZMgQVFXFbDazfv16xo8fT1ZWFjt27GDmzJl+nbeqatTV\nNd8PwqVS99OvD7P9q0MAHCgsQ1U1bryyR5PX9ZfUvbTrGq3VBOqHH37I4cOHee2113j11VeB+v7Q\n1NRU5syZw6xZs0hKSsJsNjNmzBjveNFJkyZRXFzMxIkTARgwYADPP/88AH379iU9PZ2ZM2fi8XiI\njIwkIyMDk8mEyWQiIyOD9PR0MjMzsVqtzJ8//6wbVaJpBHJjSYjWStE0re3/WWhBJSWVzfqX1WxW\niIgIbfN1z2yhAoy8uuc5W6it6fMGMv41kLrNob3WNVqraaGK9uX6K7oD+IRTa3d6/CvA9z+OMmis\nm0K0PxKookWYFKXNhZF0U4gLaTVPSgnR2vXq6mh0WwhpoQqhU1vsphDNS1qoQghhEGmhCqFTW7wp\n1RwjE8RPJFCF0Kkt3pRqi38E2jK55BdCp7Z4U6ot/hFoy6SFKoROLXVTKpDL9l5dHd6W6ent5tBe\nuxokUIXQqaXGzgZy2d5SfwTaa1eDBKoQrVwgl+2B/BEIaKmZE5VUOj24a+sIDjJTdKJ9LDUjgSpE\nMwhktdWWumwPpJXpdHkoqahG00BRPDhdniY7z9ZEAlW0OW2xfy6Q1VZb6rI9kJZx/tFyTk+7pGn1\n2+2BBKpoc9pi/1wgq622VN9tz64O/v39Ce9le08/WsbV7rpGty9VMmxKtDltcShQm1xtteHMnn7M\n9Nk5zNbo9qVKWqiizWmpPsVAtMXVVg+ddBJqtwAW77Zesd3COPKDE0+tiiXIRGy3sCY6y9ZFAlW0\nOW1xkpIgk0l3n2lDLdVn3LOL3feSv4v+BSxjIkPJORTis90e/P/s3Xl8VNX9//HXnSXLhGKIGrCS\nEJAYrKCCIK2KLBLSEKQsyqKAKLiBoFh/LgUl6BcBrYroFwL9tljLJrUQjFJZbRUBkS6AmmhCQkIM\ni5CELDPJLPf8/ggZM4Rlwtxs5PN8PHzI3eZz7iW8c+49996RQBXNTnN8l2ogArlmHFAYn7leHUL8\nV93a8f3hYm+P/Ffd2vm9bXMmgSpEExfINeMd+4+Q9sUhby9TAXf4Gcb5ZwycnTl9PrsOHCX/RDma\nSSP/RDm7DhxtEb8EJVBFo2iOtz41VpsDuWa8J/0YpXYnAJVOD3vSj/kdqI5Kt8+2jkq333Wb48Ch\nESRQRaNorNPYQLZtrDY31ulzcJAZALdHx2I2eaf90RwHDo0ggSoaRSA9mECCLZBtG6vNgZw+97z2\nCrLyT3lDsee1V/jd5rxjpbjcOgAut07esVK/t22p11DlPlTRKK6+wkaZ3UVhSQVldledRpADCbZA\ntg3k9X2B1D18vMznWNXloYCDBaV4dAWahkdXHCzwPxQrXTomk4Zm0jCZNCpdut/b7tx/hIy8YorK\nKsnIK2bn/iN+b9ucSQ9VXLRAnk8PZAQ5kNPJQLYN5HatQOoGci3z8PEyzCbNZ9pf0ZGtOFZo95n2\n156M477XbjOOc8dNV/u9fXMlgSouWiDPp//wY7nvTeN16LEFEmyBbBvI7VqBnAIHB5vRNHC5FVaL\nRnCw/9cyoyJb8cOJchSgUbcntMYnxnGsyM6xQgdtI0IZnxjn97YAHl1567YUEqgtXCCDJXnHqk4n\nlVJomlana2yBPCceiMZ6nd3OA0fJyCvG6fZQXuFm54Gjfo+25x0txXn6dNvpUuQd9f84d25/Gf/N\nOoHTrRNkMdG5/WV+b7v7wFGKSp2gQVGpk90HjvrdywwPC0IpVfW0qlY13RJIoLZwgQyWBAeZ0fWq\n57uVUnUaBQ7kOfEd+wpI25n7072VSjXI6WQgdb/89ijFZZUopbBrbr781v9ArXB6QOP0q/Dq9qKR\nghN2rggP9Zn215cZx6vaDNhx82UdTtuLyirRNA2o+mVbVFbpd93mTAalLgG6Uny+r4DVWzP5fF8B\neh3CqfpFwN4Bjzq8CLhDu9bYQixYLSZsIZY6Pa9d/Zx4ROsQWtmsdXpOvPr6XKXTQ6ndyZ7Tlx38\nEcix+vLbYxSVVh2notIKvvz2mN/bFpdV4tEVuqo6FS6uQ8CEBJlBnT51Vqen/fTzK2ycKHZQcKKc\nE8UOfl6Hwb/i0tNt1lVVm0vrEIqq6ljrp/+P/4e5WZMe6iUgkKdhHBVnDHhU+D/gERXZiv9mWjCZ\nqurW5fpcoPcpXuz1uUB6mQUny9FrvOOz4GQdblZX2vmnzyM6shV5x8q8tz7VZXAo83Axjko3SoHH\no5N5uJi+fu6vOuOXzZnT5+N0e3zeh+p0y+v7RDNR/TSMt8eW7n/PKSTYjNViQgFWi4mQOgx4BHLa\n3rtrW6xmjcJTFVjNGr27tvV728vCrOine066rrgszOr3tru/OUphSQWlp3vlu7856ve2TpfnvNPn\n49E9550+H4fTjcuto6uq+0EdTv9/6aXnFaGf7iDqqmraf2f+ffr/91t4Rm/2zOlLlfRQLwEK3x5b\nXc6uHBVuKpwelFJU6KpOPdRAXu/2l79/R86RUhSQc6SUv/z9O7/vEMguKPHuozo97a/cY2U+2+Ye\n8/8Sh67OP30+haXO806fz7eHinza/O0h/0OxotJz3unz0c7ob505fT66fv7pQFRdgtBxe6ouRXh0\nhcejn+PPp9et8WePR6FQDO7T2bhGnSaBegloUz2CenrUok0dRlRPllRU3fh9evuTJRV+b3tVRAj/\n/G+591S07w3+3wqUnlv0U93T0/4qLKk87/T5BPIm+UBOgQPZtvyMX3JnTp+PduavV6VTYnf+FC6n\ng0ahKCx3U1RcjtOl4/HonCr3Df3i8kp27D/is51H/ymk3Kf/rOsKl8f3uFa6PCxO/bpW8OlKoWkm\nKpzun5adIwSrr+cadTn25i5taHvl5QZ9WhUJ1EuA1YJPKFrr8Ld6Zg+tLj22HV8fxXn60USnW2fH\n10fp1yPKr23P/Ad35vT5BPBMgF8nsUpV/eOtdFXdRF8dMG6P79pujyLnSAluj14rXDy6wn06XDwe\nhVbr3EEj9fPsWj0pXSksFgt2h9P7ufoZXWGPrnj5z1/VqHPuHlr1o6PVHC7Fk4t2+H/Aam5b6eFP\nG9MvatsKp4e9dRg8bAiV9fC1LBKol4Dd3/5Ya/r+RP+2DeS6YPYZjzFmF5RS6fTg1s8ImLP0ZirO\neNrHUeFmT/oxn3Cpvsc1KDiI0rIK3O7TvZazBMwfP/r2guHi8Zy9bzP9rc9rhaE/dAUv/3mv38fr\nzDZ/+MWhi9pWqarLJCIwdTlL8Jem6uNTm5H9+/czd+5cioqKsFqtPPTQQwwbNszv7QsLy875D/V8\nlFLoqmbQ/PSP3ycUzggihcIWFkLxKTsuV9U6Sz/8ptbn39PvmrOEy5kBp7Prm9oDWHFR4d6a7hr1\nQcPl8lT1nHRVp1NPcenRNDCbNMwmDZN2+v+np4vKKlA6QNUputkEHdq29lnHZNKwmDVCQ6y43R5M\n3s8zVa2jaZjN+Gxz5n8ms+bTBotZw2zi9PomLGYTZrOGxVT1/6p5GkFWE506RBIaaux3XbXoQHU6\nncTHx/Pcc8+RmJhIXl4eI0eOZNWqVcTGxl5w+zn/txtHhavqdPAcgeUbZnXvBYlLS81/bq1CLWcN\niqAgC0rXvdPf55+q+QGgafzyF22rlp8OCfPpcDKbTViqA8ms8cE/DtaoXnVt5NHfXF+1jVmrChyT\nRpDVTJs2NuzllWhU1Z31f1/Wav+Sp/piNlcF3PmeEntw/vZa8/703IBa88xmjYiIVhfdMblY1XWN\n1qJP+Xft2oWmaSQmVp0fR0dH07dvXz766CNmzJhxwe331uH2JHFxTBqYzaYavReNEnvtEfKYdj/z\nhkR1L6dm4FSHzY7Tbz3SaoTB0NtiTgfR6W3NJqwWjdY/C6WiwukNmMWpX9eq+8L9PX8Kw9PhdOaf\nLWaNR37/z1p1336yb63PO1vAnC2cHh12g1/Hb91nh2rN++X1td9f4G+w1elpuBaoRQdqTk4OHTp0\n8JkXExNDevrFXXhvKpRSBFnNmE21T5fO7BEdOuO5cA3oce2VVQFjru75mLBYTLQKC8blclediplM\npO7IqVV7UtJ1ZwkX34CzmEzMeferWtsunH776VAzeXtBVovJr4CZ/UBvv47NFwdq33c6rE+nWvP8\nDZiOV7WMb/MU/mnRgWq32wkJ8b2GEhISgsPh8Gv7GztfjtPpqnG954zrOyatKiDO6CVZqqctVadn\nFoupxvWequWW04FmMZt9PjfIaiYiIoyyUgec7jlNf+tzn3ZpmsbSp/v5tQ9nC6dpd99Ua97ZAuZs\ngXpbt4v/BtLWtpbxAg1x6WrRgWqz2aio8L3v0uFwYLP597zz/zx2O8XF5bVua6lPJpNGeHgYxWbO\nW9dsvviXpp1tW9Ppd2pW/1/qSt2GrGu0+qrXogM1NjaW5cuX+8w7ePAgcXH+v/cxPLxxvivnQnUD\nueB+vm1r1g0yQ81b+YLM/tftee1l7P3+lM+0v3UnDOrIe5tzfKb9rfvW/7uDJ177zGfa37qjBnZi\n7dZsn2l/644ZeA1rth70mfa3btQVQRw+4fSZ9rdu1BVWDp9w+Uz7W/fmzq34V1aZz7S/dS//mYWT\npW6faX/rNmctepTf7XYzaNAgpk2bxvDhw8nIyGDChAn89a9/rXVt9VwarYdao+7x8nL+38Ld3nVe\ne/KXRIb59wNaUlnJjDe/wO1RWMwab864jdbBwX7VtbtcPPPOLsodLsJCrbz6+K+wWf17rt6t67z7\ncQa5x0rp0PZnTEw6+9v+z1ZXV4od+45w+Mcyoq5sxe03Gv/to0bX9Xfbs9V1ejy8uuI/HC200y7C\nxjPjuhNk9m9wyN9tz1bX37+js6lwu3lh2R6KSitp87NgXn74FkIstftvZ6vbEKrrGq1FBypARkYG\nycnJFBUVERwczPTp0xk4cKDf2zfW7R5SV+pK3cDrGq1Fn/IDdOnShTVr1jR2M4QQlwB5fZ8QQhhE\nAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQ\nBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAK\nIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiAS\nqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBrE0dgOqKaV466232Lx5M7quExERwcyZM7n++usB\n+Oyzz3j99depqKggNDSU3/72t/Tp0weAw4cPM2vWLAoKCjCbzdx9991MnjwZgMrKSmbPns2//vUv\nTCYTPXr0YM6cOQQFBQHwhz/8gQ8++ACAq666ipdffpmoqKhGOAJCiOauyfRQV65cyaeffsratWv5\n5JNPGDBgAL/97W8BOHHiBDNmzGDOnDls2rSJ5ORkZsyYQWFhIQAzZsygT58+bNmyhdWrV7Ny5Uo+\n++wzABYuXMipU6fYtGkTf//73ykuLmbRokUAfPrpp6xevZo1a9awadMmbrvtNp566qnGOQBCiGav\nyQTqTTfdxIIFC2jVqhUA/fv3Jzc3F5fLxebNm4mLi+Omm27yrhsbG8u2bds4ePAg3333HePHjweg\nTZs2DB06lA8//BCADRs2MGHCBEwmEyaTifHjx/ssGzp0KG3atAFg/PjxpKenk5ub29C7L4S4BDSZ\nQO3atStdunTxTm/evJlu3bphtVrJzs4mJibGZ/2YmBgyMzPJzs6mbdu2BAcHe5d17NiRrKwsTp06\nRWFhoc+2MTEx/Pjjj5SWltb63JCQENq2bUtmZmZ97aYQ4hLWoNdQN27cyEsvvYSmad55Silat27N\n5s2bfdZ77733eO+99wBwOByEhIT4fFZISAgOhwO73V5rWXBwMA6HA4fD4Z2uuR2A3W4/7+f6y2TS\nLrySgarrSV2pK3UDr2u0Bg3UwYMHM3jw4POus3TpUtasWcO7775LbGwsADabjfLycp/1HA4Hl112\nGWFhYVRUVNRaZrPZsNlsQNXAVDW73Q5AWFgYNpvtnNv6Kzw8zO91jSR1pa7UbXqazCk/VA0gbdq0\nib/+9a/ExcV558fGxpKTk+Oz7sGDB4mLi6Nz584cPXrUJzSrl7Vu3Zorr7zSZ9uDBw9y1VVX0apV\nKzp37uyzrKysjGPHjnHttdfW414KIS5VTSZQd+zYwYcffsjy5cu54oorfJbFx8dz8OBBdu/e7V03\nLy+P+Ph4YmJi6NatG3/4wx8AKCgoYMOGDYwcORKAESNG8Mc//hGXy4XT6eRPf/oTI0aM8C5LTU3l\n2LFjQNUtVD169JDbpoQQF0VTSqnGbgTApEmT+Prrr7n88suBqmurmqbx5ptvEhcXx+7du1mwYAF2\nu52f/exnPP/889x8880AHDlyhJkzZ5Kfn4/VamX8+PGMGTMGAKfTycsvv8yXX36JpmncfvvtPP/8\n81gsVVc7/vznP7N69WqUUsTExPDSSy/Rtm3bxjkIQohmrckEqhBCNHdN5pRfCCGaOwlUIYQwiASq\nEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwi\ngSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVTSqX//616xcubKxm9Hs\nyXFsGuRL+kSDysjI4Mcff6RPnz4NXru0tJSPP/7Y+424Rlq5ciUvv/wy06dPZ8qUKYZ/vmgepIcq\nGtQHH3zA559/3ii1d+7cyZo1a+rls9euXUtSUhJ/+9vf6uXzRfMgPVRRyw8//MDLL7/Mnj17aNWq\nFcOHD+fJJ59E0zTKysqYP38+O3bs4NSpU3Tp0oVnnnmG7t27AzBgwAAefvhhdu7cyRdffEFYWBhP\nPfUUw4YN44UXXuCDDz7AZDLRunVrdu3axYABA5g4cSITJkzg+eefx2q1EhERwdq1a3G73QwZMoQX\nX3zR27aUlBTWr1/PsWPHaN++PdOmTSMhIQHgvNtv2LCB3/3udyilsFqtrFmzhuuuu85nv3/9619z\n5MgRn3lKKTRNY9++fec8Xv/973958MEH2b59O/Hx8SxcuJDbbrvNu3z8+PH06tWL3Nxctm/fTmRk\nJK+++ip79+5l+fLluFwuJk2axMMPPwyA0+nkjTfeYMuWLZw8eZLOnTvzzDPPcMstt3g/r1u3buzd\nuxdd1/nggw98jqNSikWLFvG3v/0Nu91O9+7defHFF4mKigJg0aJFbNiwgcLCQtq2bcujjz7KsGHD\nADh58iTJycl89dVXuFwurrvuOmbNmkWXLl0u6mepxVFCnGHYsGFq9uzZyuFwqLy8PHXHHXeoFStW\nKKWUevLJJ9WYMWPU8ePHVWVlpXrttdfULbfcokpLS5VSSvXv31/deeedau/evcrtdquFCxeq7t27\nq8rKSqWUUuPGjVOvvPKKt1b//v3Vn//8Z6WUUs8995z65S9/qVasWKFcLpfasWOHiouLU1999ZVS\nSqm//OUvqn///iorK0t5PB61efNm1bVrV3Xo0CG/tn/77bfVsGHDDD9ezz33nHr22WeVUkq98MIL\navr06T7Lx40bp/r06aO++uorVVlZqcaNG6f69u2r3n77beVyudSqVavUL37xC3Xy5EmllFLz589X\nI0aMUAUFBcrlcqkVK1ao7t27e4/xuHHj1B133KH27t171uP47rvvqoEDB6pDhw6pyspK9fzzz6uR\nI0cqpZTasGGD6tWrl/eYbdmyRV133XXe6RdeeEFNnjxZ2e125XK51DvvvKPuuecew4/ZpUpO+YWP\n9PR0MjIymDZtGiEhIURFRbFo0SK6detGaWkpn3zyCU899RRXXnklQUFBPPHEEzidTnbs2OH9jD59\n+nDzzTdjNpsZMmQIDoeDgoICv+pHRERw3333YbFYuO2224iIiCBLsIG/AAAgAElEQVQ7OxuoOq2e\nMGEC11xzDSaTifj4eG655RZSU1P92r4+lJWV8cknnzBixAgARowYwfbt2yksLPRZ78Ybb6Rnz54E\nBQVx++23U1xczKOPPorFYuHOO+/E4/GQn5+PUooPPviAxx57jKuuugqLxcJ9993HlVdeySeffOL9\nvLi4OG6++eaztmndunXcc889dOjQgaCgIJ5++mkmT56MruvcddddbN++nQ4dOgAwcOBAgoKC+Oab\nbwAoKSnBYrEQFBSExWJh6tSprF27tj4O3SXJ0tgNEE1LXl4eQUFBXH755d55N954I1AVtgDXXHON\nd5nVauXnP/85eXl53nnV/1gBQkJCUEpRUVHhV/3o6Gif6ZCQEO+2hw4d4vXXX+fNN98Eqk7HlVJE\nRkb6tX192LBhA23atPGejt900020b9+e9evXM2nSJO967dq182lTREQEFkvVP7/Q0FAAKisrOXny\nJKWlpTz11FNomgb8tJ81L0e0b9/+nG3Ky8vzWR4REcGvf/1rABwOBwsWLOAf//gHJSUlKKVwuVw4\nnU4AJk+ezGOPPUbfvn3p06cPd955JwMHDgzoGLUkEqjCh8lkQp3jsnr1P7qzqf7HX/0ZF8tsNp9z\nWUhICC+88AJ33XXXRW1/IRdzDXXt2rUcP37cew0ZwOVy8cEHH/gE6pnHpObxqikkJASAd999lx49\nepyzrVar9ZzLzvd3OGfOHPbt28e7777r/cVYs07Xrl3Zvn07O3bs4NNPP+X5558nLS2Nt95665z1\nxE8kUIWPqKgoXC4XP/zwA1dffTUAe/bs4dSpU9x8880opfj+++/55S9/CYDdbueHH36gY8eO9d62\nDh068O233/oE6pEjR2jXrt05A6ouap5S++O///0vmZmZrFixgiuvvNI7v7CwkHvvvZe9e/fSs2fP\nOn1mq1atiIiIID093Sfoav59XEh0dDQ5OTne6aKiItavX8+9997Lvn37uOuuu7xhmpWVhd1u965b\nWlqKzWajf//+9O/fn7vuuovx48dz6tQpLrvssjrtS0sk11CFjy5dutC1a1fefPNNysrKyM/PZ+bM\nmRw5coSIiAj69evH22+/zcmTJ7Hb7bz++uu0bt2a22+/3a/PDwkJIT8/n9LS0nP2os7lvvvu44MP\nPuDLL79E13X27NnD0KFD2bNnj1/bBwcHc+LECYqKis7b2/bX+++/zy233EKPHj2Iiory/nfjjTdy\n6623XvS1x3vvvZc//OEPZGRkoOs6mzZtYsiQIRw6dMiv7e+++27ef/99vvvuO5xOJ4sWLSItLY2Q\nkBDat2/PgQMHcDqdZGZmsnDhQiIjIzl27BgAo0aNYtGiRVRUVOB2u9m3bx9t2rSRMPVTowXq+++/\nT/fu3Vm+fLl33uHDh3n00UdJTExk0KBBzJo1y/uDr5Ri/vz5DBo0iISEBKZOnUpxcbF329TUVAYP\nHkxCQgKjR4/mwIED3mX79+9n9OjRDBo0iKSkJJ9BjKKiIqZMmUJ8fDwJCQksWLCgAfa+aUtJSeHU\nqVP06dOHcePGMXjwYCZMmADAggULuOqqqxg2bBh33nkn+fn5rFixwnuqeraeYs15I0eOZPfu3Qwc\nOJCysjI0TTtv77LmsmHDhjF58mSeffZZevTowZw5c3jhhRfo3bu3X9vHx8djtVoZMGCA3yF8LtWD\nUaNGjTrr8lGjRrF582ZKS0v9+rya7XzkkUdITExk0qRJ3HzzzSxdupRFixYRExNTa92zbX/fffdx\n33338eCDD3Lrrbfyww8/sGjRIgCefvppjh49Su/evZk5cybTpk1j1KhRLFmyhNWrV/PWW2/xr3/9\ni9tuu41f/epX/OMf/yAlJcXfwyIa49aCOXPmqCeffFKNGDFC/elPf/LOHz58uHr77beVUko5HA41\ncuRItXTpUqVU1S0zI0eOVBUVFUoppZKTk9UTTzyhlFIqPT1d9ezZU+Xl5SmllPr4449V3759lcvl\nUpWVleqOO+5QGzduVEoplZubq3r27Km+//57pZRS06ZNU8nJyUoppex2uxoxYoRatWpVAxwFIcSl\nplF6qEOGDOHNN9/EZrPVDHYeeeQRHnjgAaDq1PCXv/wl3333HVA1mjp69GiCg4MBmDhxIlu3bqWi\nooK0tDT69evnvXF58ODBQNW1v127dqFpGomJiUDV9aW+ffvy0UcfUV5ezrZt23jwwQeBqtHWMWPG\n8OGHHzbMgRBCXFIaJVDPNnqpaRoJCQmEhYUBVSPK//znP+nVqxcA2dnZPgMf0dHRKKU4dOgQ2dnZ\n3tOhmsszMzPJycnxuY0HICYmhqysLHJzc9E0zRvENZcJIURdNclRfqfTydNPP03btm2916gcDoe3\ndwpVAWy1WrHb7TgcDu81vGohISE4HA50XT/nMrvdXuv2k+DgYBwORz3tmRDiUtbkRvkLCwt54IEH\nMJlMLF682Hv/ns1mo7Ky0ruerus4nU5sNhs2m63WzdsOh+OCy8LCwmqN9lYv84eS1yAIIWpoUj3U\nkpISHnjgAfr168eMGTN8lnXu3JmcnBzvfX3Z2dlYLBY6depEbGysz3131cvj4uJwu90+dxIAHDx4\nkLi4OGJiYjCZTOTm5novC1Qv84emaRQXl6PrDResJpNGeHiY1JW6UteAukZrUoE6Z84cevXqVStM\noeoZ6RUrVpCYmEhYWBjLli0jKSmJoKAghg4dytixY8nMzCQ2Npa1a9cSFhZGr1690HUds9nM+vXr\nGT58OBkZGezcuZOnnnqK0NBQEhISSElJYd68eZSUlLB69WqfJ1wuRNcVHk/D91SlrtSVuk1Pg7++\nT9d1kpKS0DSNI0eOYLPZuOyyy7jxxhtJTU3l6quvxmq1omkaSimio6NZunQpAG+88QabNm0Cqh6R\nmzNnDq1atQJg48aNLF68GJfLRWRkJLNnz6Zz585A1UuNk5OTKSoqIjg4mOnTp3ufTy4pKWHWrFmk\np6d7X+bx+OOP+70/hYVlDfqDYDZrRES0krpSV+oaUNdo8j7UALW0H0CpK3UvpbpGa3KDUkII0VxJ\noAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDC\nIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQh\nhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQC\nVQghDCKBKoQQBpFAFUIIg0igCiGEQRotUN9//326d+/O8uXLvfOKioqYMmUK8fHxJCQksGDBAu8y\npRTz589n0KBBJCQkMHXqVIqLi73LU1NTGTx4MAkJCYwePZoDBw54l+3fv5/Ro0czaNAgkpKSSE1N\n9aumEELURaME6ksvvcTu3bvp1KmTz/zZs2fTtm1btmzZQmpqKnv27GH16tUArFy5kr1795KWlsam\nTZuIjIwkOTkZgIyMDObOncvSpUvZtGkT999/P9OmTcPtduN0Opk2bRoTJ05k8+bNLFmyhLlz55KZ\nmXnBmkIIUReNEqhDhgzhzTffxGazeeeVl5ezbds2HnzwQQBCQ0MZM2YMH374IQAbNmxg9OjRBAcH\nAzBx4kS2bt1KRUUFaWlp9OvXj6ioKAAGDx4MwJ49e9i1axeappGYmAhAdHQ0ffv25aOPPrpgTSGE\nqAtLYxTt0aNHrXm5ublomuYNRYCYmBiysrIAyM7OpmPHjt5l0dHRKKU4dOgQ2dnZdO3a1efzoqOj\nyczMRClFhw4dfJbFxMSQnp5+wZpCCFEXjRKoZ2O327FarT7zgoODcTgcADgcDm/vFEDTNKxWK3a7\nHYfDQUhIiM+2ISEhOBwOdF0/57IL1fSHyaT5va4RqutJXakrdQOva7QmE6hhYWE4nU6feQ6Hw3tZ\nwGazUVlZ6V2m6zpOpxObzYbNZqOiouKs2+q6fs5lF6rpj/DwML/XNZLUlbpSt+lpMoEaExODyWQi\nNzfXe4qelZVFXFwcAJ07dyYnJ4eePXsCVZcALBYLnTp1IjY2lpycHJ/Py87OJi4uDrfb7XMnAcDB\ngweJi4s7a83qZf4qLi5H19VF73ddmUwa4eFhUlfqSl0D6hqtyQRqaGgoCQkJpKSkMG/ePEpKSliz\nZg2TJk0CYMSIEaxYsYLExETCwsJYtmwZSUlJBAUFMXToUMaOHUtmZiaxsbGsXbuWsLAwevXqha7r\nmM1m1q9fz/Dhw8nIyGDnzp089dRTZ625evVqb01/6LrC42m4HwSpK3WlbtOlKaUadC90XScpKQlN\n0zhy5Ag2m43LLruM+Ph4Jk+ezMyZM0lPT8dsNjNkyBAef/xx77ZvvPEGmzZtAqBr167MmTOHVq1a\nAbBx40YWL16My+UiMjKS2bNn07lzZ6Dqtqrk5GSKiooIDg5m+vTpDBw4EICSkhJmzZp1zpoXUlhY\n1qA/CGazRkREK6krdaWuAXWN1uCBeqlpaT+AUlfqXkp1jSaPngohhEEkUIUQwiASqEIIYRAJVCGE\nMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJV\nCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaR\nQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBCGEQCVQghDCKBKoQQBpFAFUIIg0igCiGE\nQSRQhRDCIBKoQghhEEtjN6Cmr776itdee42ysjLMZjP33HMPEyZMoKioiJkzZ5KZmYnJZGLAgAE8\n++yzACilWLBgAdu3b0fTNDp37szcuXMJDw8HIDU1lWXLluHxeAgPD2fWrFl069YNgP379zN37lyK\nioqwWq089NBDDBs2rNH2XwjRvDWZHmpFRQVTp05lypQpbNy4keXLl7NkyRI+//xzZs+eTdu2bdmy\nZQupqans2bOH1atXA7By5Ur27t1LWloamzZtIjIykuTkZAAyMjKYO3cuS5cuZdOmTdx///1MmzYN\nt9uN0+lk2rRpTJw4kc2bN7NkyRLmzp1LZmZmIx4FIURz1mQCtaCggNLSUm677TYArrjiCrp06cKB\nAwfYvn07DzzwAAChoaGMGTOGDz/8EIANGzYwevRogoODAZg4cSJbt26loqKCtLQ0+vXrR1RUFACD\nBw8GYM+ePezatQtN00hMTAQgOjqavn378tFHHzXofgshLh1NJlA7dOhATEyMNygPHz5MZmYm/fr1\nA6oCr1pMTAxZWVkAZGdn07FjR++y6OholFIcOnSI7OxsYmJifOpER0eTmZlJTk4OHTp08FlW83OF\nEKKumsw1VLPZzLx583jkkUd47bXXKC0t5fHHH8dut2O1Wn3WDQ4OxuFwAOBwOLy9UwBN07Bardjt\ndhwOByEhIT7bhoSE4HA40HX9nMvqwmTS6rR+oKrrSV2pK3UDr2u0JhOoP/74I48++ihvvPEGt912\nG8XFxTz00EMopXA6nT7rOhwObDYbADabjcrKSu8yXddxOp3YbDZsNhsVFRVn3VbX9XMuq4vw8LA6\nrW8UqSt1pW7T02QC9d///jetW7f2XkMNDw+nf//+7N69G5PJRG5urvcUPSsri7i4OAA6d+5MTk4O\nPXv2BKouAVgsFjp16kRsbCw5OTk+dbKzs4mLi8PtdrN8+XKfZQcPHvR+rr+Ki8vRdXVR+3wxTCaN\n8PAwqSt1pa4BdY3WZAK1c+fOHDt2jAMHDtCtWzccDgc7d+6kV69eREZGkpKSwrx58ygpKWHNmjVM\nmjQJgBEjRrBixQoSExMJCwtj2bJlJCUlERQUxNChQxk7diyZmZnExsaydu1awsLC6NWrF7quYzab\nWb9+PcOHDycjI4OdO3cyY8aMOrVb1xUeT8P9IEhdqSt1m64mE6jXXHMNc+fOZebMmbhcLpRS3Hrr\nrTz66KNUVlYya9Ys4uPjMZvNDBkyxHu/6KhRo8jPz2fkyJEAdO3alRdffNH7mcnJycyYMQOXy0Vk\nZCSLFy/GZDJhMplYvHgxycnJpKSkEBwczCuvvFJroEoIIfylKaWa/6+FRlRYWNagv1nNZo2IiFZS\nV+pKXQPqGq3J3DYlhBDNnQSqEEIYRAJVCCEMIoEqhBAGkUAVQgiDSKAKIYRBJFCFEMIgEqhCCGEQ\nCVQhhDCIBKoQQhhEAlUIIQwigSqEEAaRQBVCCINIoAohhEEkUIUQwiASqEIIYRAJVCGEMIgEqhBC\nGEQCVQghDCKBKoQQBpFAFUIIg0igCiGEQSRQhRDCIBKoQghhEAlUIYQwiASqEEIYRAJVCCEMIoEq\nhBAGkUAVQgiDSKAKIYRBLP6uqJRi1apVbN++nePHjwPQrl074uPjGTVqVL01UAghmgu/A/Wll15i\n48aNJCQk0Lt3b5RS5Ofn8/rrr/P9998za9as+mynEEI0eX4H6oYNG1i1ahVdunTxmX/vvfdy7733\nSqAKIVo8v6+hhoaG0qlTp1rzr7nmGmw2m6GNEkKI5sjvQJ08eTJvv/02LpfLO8/lcrFkyRIeeOAB\nQxpz6tQpnnjiCfr160d8fDz/+7//C0BRURFTpkwhPj6ehIQEFixY4N1GKcX8+fMZNGgQCQkJTJ06\nleLiYu/y1NRUBg8eTEJCAqNHj+bAgQPeZfv372f06NEMGjSIpKQkUlNTDdkPIUTL5Pcp/5YtW8jI\nyGDFihVcffXVeDwejh07BkCHDh34+OOPveuuX7/+ohrz3HPPcfXVV/OPf/yDoqIipk2bxpAhQ3j9\n9ddp27YtixcvxuFwMG7cOFavXs3YsWNZuXIle/fuJS0tjeDgYObMmUNycjILFy4kIyODuXPnsm7d\nOqKioti4cSPTpk1j69at6LrOtGnTeO6550hMTCQvL4+RI0dy/fXXExsbe1HtF0K0bH4H6q233sqt\nt95abw05fvw4n3/+OZ9//jkAbdq0YcWKFZSXl7Nt2zY++eQToOrSw5gxY1i3bh1jx45lw4YNjB49\nmuDgYAAmTpxIUlISFRUVpKWl0a9fP6KiogAYPHgwr776Knv27MHlcqFpGomJiQBER0fTt29fPvro\nI2bMmFFv+ymEuHT5HaiPP/74OZdV9xYDkZ6ezuWXX87f/vY3NmzYgNlsZvTo0dx4441omuYNRYCY\nmBiysrIAyM7OpmPHjt5l0dHRKKU4dOgQ2dnZdO3a1adOdHQ0mZmZKKXo0KGDz7KYmBjS09MD2g8h\nRMvld6AC5Obm8s033+B0Or3zjh07xpIlSwIO1JKSEk6ePElISAhpaWl899133HfffTz44INYrVaf\ndYODg3E4HAA4HA5v7xRA0zSsVit2ux2Hw0FISIjPtiEhITgcDnRdP+eyujCZtDqtH6jqelJX6krd\nwOsaze9ATU1N5Xe/+x1BQUFUVlYSGhqK3W4nMjKSyZMnB9yQ1q1bo2ka9913HwBxcXH07duX3bt3\n+wQ4VIVo9Z0FNpuNyspK7zJd13E6ndhsNmw2GxUVFWfdVtf1cy6ri/DwsDqtbxSpK3WlbtPjd6Cm\npKSwaNEiBg4cyA033MC///1vsrKyWLhwIfHx8QE3JDo6Grfbjd1uJyys6uBqmka3bt34z3/+Q25u\nrvcUPSsri7i4OAA6d+5MTk4OPXv2BKouAVgsFjp16kRsbCw5OTk+dbKzs4mLi8PtdrN8+XKfZQcP\nHvR+rr+Ki8vRdXVR+3wxTCaN8PAwqSt1pa4BdQ3/XH9XPHbsGAMHDgSqgg6qwuyJJ57ghRdeCLgh\nHTt2pEePHixduhSA/Px8Pv/8c/r3709CQgIpKSlA1aWBNWvWMHLkSABGjBjBihUrKCsrQynFsmXL\nSEpKIigoiKFDh/LZZ5+RmZkJwNq1awkLC6NXr1707t0bs9nsvSMhIyODnTt3MnTo0Dq1W9cVHk/D\n/Vf9Qyd1pa7UDbyu0fzuoUZERHh7ia1bt+bgwYNcc801dOjQwRtYgVqwYAEzZ85kwIAB2Gw2fvvb\n39KzZ0+uvfZaZs2aRXx8PGazmSFDhjBs2DAARo0aRX5+vjdgu3btyosvvghUPXSQnJzMjBkzcLlc\nREZGsnjxYkwmEyaTicWLF5OcnExKSgrBwcG88sortQaqhBDCX5pSyq+oXrRoEStWrGD79u0sWLCA\nvXv3ctddd3HgwAGOHj160feeNneFhWV4PA13qmI2a0REtJK6UlfqGlDXaH6f8k+fPp1Zs2YRFhbG\n7373O2688UbS0tJwu93Mnz/f8IYJIURzU6fbpoYOHYqu64SGhjJ//nwqKipq3XokhBAtld891MOH\nD3P33XezefNm77xVq1YxfPhwDh8+XC+NE0KI5sTvQH3ppZe49tprueWWW7zzhg8fzk033cTLL79c\nL40TQojmxO9T/v/85z/s3LmToKAg77w2bdrw/PPP1+sz/kII0Vz43UO12WwcOXKk1vycnByfRz+F\nEKKl8ruHOnLkSCZPnsw999xD+/btUUqRk5PD+++/z/jx4+uzjUII0Sz4HajTp08nPDycdevWkZeX\nh8lkIjo6mkcffdT7/L0QQrRkfgdqaWkp999/P/fff399tkcIIZotv6+h9u3bF13X67MtQgjRrPkd\nqElJSbz33nsSqkIIcQ5+n/L/8MMPbN26lXfeeYd27drVeulzS32WXwghqvkdqDfffDM333xzfbZF\nCCGaNUO+U0oIIUQdrqEC7N69m2eeecZ736nb7WbdunX10jAhhGhu/A7UVatWMXXqVEJCQti3bx8A\nJ0+e5O233671VSJCCNES+R2oy5cvZ+nSpbz00kveeW3btmXJkiWsWrWqXhonhBDNid+BeuLECe+g\nVPV3SkHV90odP37c+JYJIUQz43egtm/f3nuqX9O2bdto166doY0SQojmyO9R/gkTJvDwww/zm9/8\nBo/Hw5IlS0hPT2f79u0kJyfXYxOFEKJ58DtQ77nnHq644grWrl1LVFQUmzdvpkOHDixfvpxevXrV\nZxuFEKJZqNN3SvXv35/+/fvXV1uEEKJZ8ytQv/jiCzZt2oTFYiEpKUmemBJCiLO44KDUxo0beeSR\nRzh+/DiHDx9m4sSJfPrppw3RNiGEaFYu2EP94x//yKuvvsrgwYMB+Pjjj1m6dKmc+gshxBku2EM9\ndOgQ8fHx3umBAwdy8ODBem2UEEI0RxcMVLfb7fOqvuDgYJxOZ702SgghmqM6vRxFCCHEuV3wGqrH\n4+Evf/kLSqnzzpswYUL9tFAIIZqJCwZqZGRkrbdJnTlP0zQJVCFEi3fBQN2+fXtDtEMIIZq9Oj0p\npes6+/bt4/jx42iaRrt27bjhhhvqq21CiGZKV4ov9h8h/8dy2l8Zxm03XIWpxlvqLlV+B+r+/fuZ\nMmUKJ06cwGq1opTC7XbTtm1bFi9ezPXXX1+f7RRCNCNf7D/C9v/8AMD3+cUA9Lnx543ZpAbh9yj/\ns88+y6BBg/jss884cOAAX3/9Nf/85z8ZMGAATz/9dH22UQjRzOT/WH7e6UuV34FaUFDAs88+S2Rk\npHde27ZteeaZZygoKDC0UaWlpfTp04fnn38egKKiIqZMmUJ8fDwJCQksWLDAu65Sivnz5zNo0CAS\nEhKYOnUqxcXF3uWpqakMHjyYhIQERo8ezYEDB7zL9u/fz+jRoxk0aBBJSUmkpqYauh9CtFTtrww7\n7/Slyu9A7dq1K3l5ebXmFxQUGH66/z//8z+EhIR4p2fPnk3btm3ZsmULqamp7Nmzh9WrVwOwcuVK\n9u7dS1paGps2bSIyMtL7ftaMjAzmzp3L0qVL2bRpE/fffz/Tpk3D7XbjdDqZNm0aEydOZPPmzSxZ\nsoS5c+eSmZlp6L4I0RLddsNVDOh+Nde2D2dA96u57YarGrtJDcLva6jDhw/nySef5K677iImJgZd\n18nLyyMtLY1Ro0axbds277p33nnnRTfo008/5fDhwwwdOpSCggLKy8vZtm0bn3zyCQChoaGMGTOG\ndevWMXbsWDZs2MDo0aMJDg4GYOLEiSQlJVFRUUFaWhr9+vUjKioKgMGDB/Pqq6+yZ88eXC4XmqaR\nmJgIQHR0NH379uWjjz5ixowZF91+IQSYNK1FXDM9k9+BOmvWLAAWLlxYa9m8efO8f9Y0jfT09Itq\nzKlTp3jllVf4v//7P9LS0gDIzc1F0zRvKALExMSQlZUFQHZ2Nh07dvQui46ORinFoUOHyM7OpmvX\nrj41oqOjyczMRClFhw4dfJbFxMRcdNuFEMLvQM3IyKjPdgDwyiuvMG7cOJ+gs9vtPu8SgKr3CTgc\nDgAcDoe3dwpVgW61WrHb7TgcDp9LBwAhISE4HA50XT/nMiGEuBjnDdSysjK/P6hVq1YBNWT79u0c\nPnzYZ8AJICwsrNbLWBwOBzabDQCbzUZlZaV3ma7rOJ1ObDYbNpuNioqKs26r6/o5l9WFydSw99ZV\n15O6UlfqBl7XaOcN1J49e/p8ZfT5BHqq/Pe//538/HzuvPNOlFKUlpbi8XjIyMjAZDKRm5vr7blm\nZWURFxcHVH2NdU5ODj179gSqLgFYLBY6depEbGwsOTk5PnWys7OJi4vD7XbXeqT24MGD3s/1V3h4\n44xeSl2pW991dV2x7as8Dh0tIaZda+7sFV1vQdRY+2u08wbqe++95/1zdnY2K1eu5O677yY2NhZd\n18nIyGD9+vU89thjATfktdde85l+5513+OGHH5g3bx6//e1vSUlJYd68eZSUlLBmzRomTZoEwIgR\nI1ixYgWJiYmEhYWxbNkykpKSCAoKYujQoYwdO5bMzExiY2NZu3YtYWFh9OrVC13XMZvNrF+/nuHD\nh5ORkcHOnTvrPCBVXFyOrqsLr2gQk0kjPDxM6krdeq/72cQEG7cAACAASURBVH8L2PavfAD2f/8j\n5eWV3HGTsQNNjb2/RjtvoN5yyy3eP7/++uu89dZbdOrUyTvv9ttv54477mDWrFkMGTLE8MZVmz17\nNrNmzSI+Ph6z2cyQIUMYNmwYAKNGjSI/P5+RI0cCVbd3vfjiiwBcc801JCcnM2PGDFwuF5GRkSxe\nvBiTyYTJZGLx4sUkJyeTkpJCcHAwr7zySq2BqgvRdYXH03A/CFJX6jZU3bxjZdRsQd6xsnprU2Pt\nr9E0VfMdfOfRvXt3vvzyS4KCgnzmO51OevfuzX/+8596aWBTV1hYfz9kZ2M2a0REtJK6Urfe636+\nr8D7+CjAgO5XG34rVGPvr9H8vrG/U6dOLFiwgMLCQu+8wsJCfv/739e5VyeEaPpa6s35gfD7tqmX\nXnqJ6dOns2rVKkJCQtB1ncrKSsLDw0lJSanPNgohGkFLvTk/EH4HaocOHVi3bh25ubkcPXoUp9NJ\nu3bt6NixY637OYUQLZu8vu8CLnQLVVhYGP369WPWrFmEh4cb0jghRGAaK9ha6uv7/A7URYsW8fvf\n/56hQ4dyww03YDKZ2LdvH3//+9+ZMmUKLpeLFStWMG/evFo35wshGkdjBVtLfX2f34H63nvv8fvf\n/97nDf233347ffr04Z133mHZsmX86le/YsSIEfXSUCFE3TVWsLW/Mswb4NXTLYHfgfr111/TpUuX\nWvO7dOnCV199BUB4eHitxzmFEI2nsYKt+o6AmpcaWgK/AzUqKoq5c+cydepU70umCwsLSUlJ4fLL\nL0fXdRYsWMB1111Xb40VQtRNYwVbS71DwO9AnT9/Pg8//DBr167FarVisVhwOBy0atWKt99+G4Av\nvviCt956q94aK4Som0CCraWO1AfC70C9/vrr+fTTT/n666/58ccf0XWdyy+/nK5du3rf0FT9Emgh\nRPPXUkfqA1Gnr5EOCgqiR48e9dUWIUQT0lJH6gPh96OnQojGoSvF5/sKWL01k8/3FaD79/qNgLXU\nL9oLRJ16qEKIhtdYp94tdaQ+EBKoQjRxjXXq3VJH6gMhp/xCNHFy6t18SA9ViCZOTr2bDwlUIRpA\nIPd0yql38yGBKkQDkHs6WwYJVCEaQGMNLMnTTg1LAlWIBtBYLymRnnHDkkAVogE01sCSPO3UsCRQ\nhWgAjTWw1FLfS9pYJFCFuITJLVcNSwJViAbQWINDcstVw5JAFaIBBDI4JCP1zYcEqhANIJDBIRmp\nbz7kWX4hGkAgz+PLSH3zIT1UIfwUyKl3IINDMlLffEigCuGnQE69AxkckpH65kMCVbQogfQy5b2k\n4kIkUEWLEkgvU069xYVIoIoWJZBeppx6iwuRUX7Rosjb70V9kh6qaFF+1a0d3x8u5vDxMqIiW/Gr\nbu383lbuBxUXIj1U0ewE8rXKuw4cJf9EOZpJI/9EObsOHPV7W7kfVFxIk+qh7tq1izfffJPS0lJ0\nXWfs2LFMnDiRoqIiZs6cSWZmJiaTiQEDBvDss88CoJRiwYIFbN++HU3T6Ny5M3PnziU8PByA1NRU\nli1bhsfjITw8nFmzZtGtWzcA9u/fz9y5cykqKsJqtfLQQw8xbNiwRtt/4Z9AeoqBhKIMSokLaTKB\neuLECaZMmUJKSgq9e/fm8OHD/OY3v+Gmm27iT3/6E23btmXx4sU4HA7GjRvH6tWrGTt2LCtXrmTv\n3r2kpaURHBzMnDlzSE5OZuHChWRkZDB37lzWrVtHVFQUGzduZNq0aWzduhVd15k2bRrPPfcciYmJ\n5OXlMXLkSK6//npiY2Mb+3CI82isUJRBKXEhTeaU32Qy8dprr9G7d28AoqKi6Ny5M/v372f79u08\n+OCDAISGhjJmzBg+/PBDADZs2MDo0aMJDg4GYOLEiWzdupWKigrS0tLo168fUVFRAAwePBiAPXv2\nsGvXLjRNIzExEYDo6Gj69u3LRx991KD7LeoukIGl2264igHdr+ba9uEM6H51nUKx+n7QsQNj6XPj\nz+UFJaKWJtNDjYiIYODAgd7pvLw8MjMz+cUvfgHgDUWAmJgYsrKyAMjOzqZjx47eZdHR0SilOHTo\nENnZ2XTt2tWnTnR0NJmZmSil6NChg8+ymJgY0tPTDd83YaxAeopyk7yoT00mUGs6evQojz32GA89\n9BAAVqvVZ3lwcDAOhwMAh8Ph7Z0CaJqG1WrFbrfjcDgICQnx2TYkJASHw4Gu6+dcVhcmU8P2Uqrr\nNfe6ulLs2HeEwz+WEXVlK26/8exPLJ2trhmNfj2uNqQd53KpHGepe/66RmtygfrNN98wdepUxo8f\nz6RJk0hPT8fpdPqs43A4sNlsANhsNiorK73LdF3H6XRis9mw2WxUVFScdVtd18+5rC7CwxtnYKK5\n193yZS7/3FcAQHZBCWFhwcT37nDO9WvW1XXFtq/yOHS0hJh2rbmzV3S9/QNp7se5serW9e+osfbX\naE0qUL/55hseeeQRkpOTvaf/MTExmEwmcnNzvafoWVlZxMXFAdC5c2dycnLo2bMnUHUJwGKx0KlT\nJ2JjY8nJyfGpkZ2dTVxcHG63m+XLl/ssO3jwoPdz/VVcXI6u+3/bTqBMJo3w8LBmXzfj0Encbt1n\n+ubYy/2q+9l/C9j2r3wA9n//I+Xlldxxk7Gn8ZfKcQ6krr9nEWfj799RY++v0ZpMoDqdTp588klm\nz57tcy01NDSUhIQEUlJSmDdvHiUlJaxZs4ZJkyYBMGLECFasWEFiYiJhYWEsW7aMpKQkgoKCGDp0\nKGPHjiUzM5PY2FjWrl1LWFgYvXr1Qtd1zGYz69evZ/jw4WRkZLBz505mzJhRp3brusLjabgfhEul\n7tWXh/FdXrHP9Pk+t2bdvGNl1Fwz71hZvR2L5n6cA6n7+b4C7+1p3+UVo+vK7+vPuUdLKbW7cLo9\nBFnM5B4t9fvvtzlrMoG6ZcsWCgoKePPNN3njjTeAquuhgwcPZvbs2cycOZP4+HjMZjNDhgzx3i86\natQo8vPzGTlyJABdu3blxRdfBOCaa64hOTmZGTNm4HK5iIyMZPHixZhMJkwmE4sXLyY5OZmUlBSC\ng4N55ZVXag1Uifoh7wdt+gK5Pc1R6abUXnWprtLpwVHpNrRtTZWmVB0eMxG1FBbWX+/obMxmjYiI\nVi26bkN8x1JT2t/GqluzhwowoPvVfvdQV239nv98f8LbQ+1+7RXcO/Bav+o2hOq6RmsyPVQh/CW3\nPvmvsb5lIOrKVmTmnwKs3umWQAJViEuYfMtAw5JAFRctkN6PfDVyw5BvGWhYEqjiogXS+5FX4TUM\nGcBrWBKoLVxjfceSvAqvYbTUU+/GIoHawjXWdyxJz8l/gfzSa6mn3o1FAvUS0Fi9zEB6P9Jz8p9c\nHmk+JFAvAY3Vywyk9yM9J//J5ZHmQwL1EtBYvczmOFLfHNvcHC+PNMXjrJTC7Xbjcrnw6G7MFsVl\nrX9maA0J1EtAY/Uym+OpaHNsc3O8PLJj/xHSvjjkfVJKAXfU43FWSuFyuXC6XLhcLtxuHY9eFey6\nrrz/10wmTCYLQUFWLKUOCVRRW2P1MpvjqWhjtbmlDSztST/m8yz/nvRjAQWqx+PB6XSeDkw3Hh08\n+hlhaTZjMlmwWKxoJg1MVV9JcravJWkx70MVdddYvczmeCraWG1ujj3jQCil8OgKpRSapnGhV4a4\n3W4cjsqqwHR78Hiq3m2s6wqPUihMmMxmzGYLZnMImMFkrtt3OLk9OuUOF+UVbhyVbnrEhQe2k2ch\ngXoJaI6j/I2lsdrcHHvzgfxctWkVjK4USgFK52chJsrKy3E6XXg8Oh69KnA1oLC0lFMlFWhYMFss\nmExWsPzUuzxXSLk9OvYKN+UVLspOB2W5w0W5w0VZ9Z8rXJQ73JQ5XFS6PD7b94j7VQBH5+wkUC8B\nzXGUv7EE0uZAAqaxesaBtNmf66C6ruN2u3E6q65fVoUl5B87gbuyHAVoaBwtLKOozO1zOm4GzCaN\nUJsNp9OM063XDscagVj95+p1KpyeszW7UUmgXgJaWi+zsUaQAxloaazr3IH8sv3ym6MUnirH7XGD\n7uGfX7m59qoQn1NxlAYmEyaz77VLa1AYliDP6UAFt24hPe+UTyCWV7ixV7hwOD2UlDmxXwLvTJVA\nvQS0tF5mICERSDgZPdDir0D2N+9YKSeKHTjdOkEWE3nHSr3Lag706LqHSlcFJwvLcbl0dKUoOH4S\nt9uNZjKjWYModZkpd5spc+iUOzyne4w1epSnp8sr3BSWVHi/VUEBBSftrNmWZeRhOSuTBrYQK61C\nrdhCLLQKtRIWYiUs1HL6/1ZahVpobQuql/oSqJeA5ngvaSB1D/9YRlmNr9c4/GOZ33V37CsgbWfu\nT71MpbjjJv++QVVRNbJc3euqy+uQAwnFi9lfj8eD2+3m+9xjFJ8qARR24NssN4e7RlTth6ZR6YYK\np8LpVuhH3fx4sowSe9XptZtgzFaz9zNPnKpk7nv/qsNeB07TwBZsIaxGMLY6HYzVIdkq9HRQhlgI\nCbb49XNkllF+cS7N8V7SQOo6KtwUl1WiADtuHBX+nyruyTju28vMOO53oIbbrHhqfJFcuM16nrV9\nBfJL4Mz9Lbc7qays9PYuq28h8nh07JUeSu0uyit1HJUejpd4sASFop0OmeNlGks+OujtSeoN/IUd\nGhAabPkpDG1WIi4LxWrSsIVUB+RPAWoLtjT4V0wHQgK1hWus0edA6haWnv76b6VA036armeHapwu\nn236fPz9JVB9g3pZeTkORyVlDjc5PxzHWWkHNDRN49ucH/nzJwp7pQd7pcfnVNtzlm8O1Wr02Dy6\n4mih3e92+yM02PzT6fTpXuR/M0/grPGttmEhFqbffQO2EKtP79Bs0rgs3MapYvtZ2x6Iqmu9OkrX\nUUpH1z2g/j979x4fRX3vf/w1s9fsJiHcElAJAYlBBQQEtVUE1JByEbm0AvWGt9qDotL2UW3BA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4kD59+jBlyhR69+7N0qVLcTgcQKSz7K9//Sv/9V//xXXXXcfvfvc7Zs+ezZVXXonF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\nX1/1DSm3W2mQLjlS9xq3c+Gud+7JStRqFYr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z5s2jsLAQnU7HhAkTGDduHABOp5PFixez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\nCD+RQBVCCD/5f7btOZTKESCvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767e36ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=df, x='year', y='gdpPercap', row='continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7686655780>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tGy4uLsyePZvExEQWL15MRkYGSUlJbNy4ER8fH7Zv305cXBw7d+7E0dGuNl2I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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767fcb8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(data=df, x='continent', y='gdpPercap', kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f767fc5fd30>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col='continent', row='year')\n",
    "g.map(plt.hist, 'lifeExp')"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}