3ScatterPlot (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98d17542-8d57-43a5-a95c-aa7a5f541ccb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.5.3\n"
     ]
    }
   ],
   "source": [
    "#Import primary modules.\n",
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library\n",
    "\n",
    "#Importing Matplotlib\n",
    "#%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2468c985-f77e-4f86-b277-68b0a5406c6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read into a pandas dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.csv')\n",
    "\n",
    "print('Data read into a pandas dataframe!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f38d19fe-d929-4a4f-b2d6-464e7fe0b310",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
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       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
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       "      <td>3436</td>\n",
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       "      <td>2111</td>\n",
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       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
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       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "3     0     0     0     0  ...     0     1     0     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     1     1     0     0     0     0   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "3     0     0      6  \n",
       "4     1     1     15  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46b8b65b-760e-4209-9296-17843188c3b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 39)\n"
     ]
    }
   ],
   "source": [
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "359b1abc-5da5-4a9a-9cc5-a61acec30d6a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_can.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b2b0bdc-ac8b-4c31-946d-5f6bceed2813",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
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       "      <td>58639</td>\n",
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       "      <th>Albania</th>\n",
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       "      <td>561</td>\n",
       "      <td>539</td>\n",
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       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>15</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20ff9e47-d065-4826-ad50-09162b0cd780",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5cba820-bd55-403c-82ef-7bd4314cfb9f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = list(map(str, range(1980, 2014)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d8c3bf1-f3d6-4d58-93e9-6fe4ac9e0d13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
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       "      <th>1989</th>\n",
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       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
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       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f68bc971-f8f3-430e-985e-697bc35e9387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2c77d76-efb2-4f6d-90dc-2c9b57267d06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbd22b39-b026-4317-be77-bdf7d4886f28",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the equation of line of best fit. We will use Numpy's polyfit() method by passing x,y and degre =1 linear \n",
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc00b1e0-2769-4536-9b5d-660bf6bf4ee2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plot the regression line on the scatter plot\n",
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34749f00-6352-4179-9d1f-e61b97b6b506",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question: Create a scatter plot of the total immigration\n",
    "#from Denmark, Norway, and Sweden to Canada from 1980 to 2013?\n",
    "# create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "# create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "# reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "# change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "# show resulting dataframe\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bfcb530-7b09-4250-baa6-5776db4128d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zu+MLH8cPtBPPbk+aNMlIMnXq1DFDhgwx48ePd545bt26tVuO3nnnHRMUFGTCw8PN0KFDzQMPPGBSUlJMdHS06d69u8eC67XXXjPBwcGmVq1a5vrrrzf33Xefufrqq01QUJBJSEhwO2469mEDBgww8fHx5pZbbjFjxowxzZs397itlGfixImmffv2RpIZM2aMcx//zDPPONt8+eWXzuPN1VdfbR588EFz9dVXG4vFYqKiosrtqTqR4yTWX/7ylwq1P154eLjp2LGj8/McMWKEcxt3HJOPV5nPp2XLlua8884zN954o8nIyDC33Xabs4f9+uuvd2u/ceNGU6dOHSPJXHnllebBBx80AwYMMMHBwc7v9ok/0K+44grTuHFjc/3115vx48ebUaNGmQ4dOjiPiSUlJaf8LGrysflEpyq4+vTpYySZpUuXus07cOCAczs/vldqyZIlRpK57LLLyu3heu6551ymN2jQwEgyO3bscHufzz//3Ll/84evv/7aSDIdOnRwmf7KK68Yyb0Xy8GxDzvx98fxTqfg+u9//2sSEhJMWFiYS+9jRVBw/T/HD0hJZvLkyS7zHn/8cSPJREdHm9tuu83lyzx//nwjydxzzz0ur3EktHbt2i4H68OHD5vWrVuboKAgExMT4/LjpayszPllOfFsp6eCyxHXX/7yF5dLUfLz803dunVPWnAlJiaaLVu2uH02hw8fdvtBYsyxAum8884zsbGxbmesTnVJoacfbyNHjjSSzB133OEy/aeffjJRUVHGarW6xHd8b9rcuXNdXvPyyy8bSeb2228vN4YTOQ5c5cUrydx0001uB4rVq1c7z5T+97//dU4vKSkxffv29fj9cRxA+vXr53IG8tNPP3V+rzp16uRyVtlx1vTEnU1F1ulkBZcxxqSkpLgdNB3f1zFjxric9SwtLTXDhg0zklyK6OO3l8cee8xl+R9++KHHEweOAiA4ONisWrXKOf3o0aPmsssuc34W//znP11e5/iunFhQbNu2zeMZWsf3YerUqR7f32KxmA8++MDjZ3P8d/XQoUPmmmuuMZLMXXfd5XYQq4zjd/RHjx417dq1M0FBQS7be3kFl+MgPG3aNJfpn332mQkKCjKxsbEuReap1tdR8B1/cuOFF14wkkyfPn1MaGioOXDggHNehw4dTEREhMuPlt9++83jWfWlS5eaoKAgt4PijBkzjCTz/PPPu72mW7duxmKxeNW74Im3MVVm3+L4Tp643//6669NSEiIVwXXSy+9ZCSZqKgoM378ePPBBx+YXbt2ldv+4MGDJiwszFx00UXOafv27TPBwcHO78irr77qnPfss88aSeb11193Tlu+fLmRZLp27epWWDm+N2PGjHFO279/v4mLizMhISFulzfec889Hgu9DRs2GKvVapo3b+526U1OTo4JCgoy6enpLtMd+7COHTu69CTb7XZz7rnnenUZz8l63Y4ePWpatmzp8YfzG2+8YSSZFi1aVGibd1wCV5kfb5s2bXKbdvjwYdOzZ08TEhLidiyuzOfj6T2OHj3qPOHyxRdfuMxzfIeeffZZl+mLFi1y5vnEgmvz5s0eL4d19Lq++eabnj8AD2risdnTepys4Lr++uvd9s0OjhMonnI3YMAAIx07aTp69Ggzfvx406NHDxMaGmruvvtul+Ol3W43kozNZvMYw+7du40kU79+/Uqu5em5/fbbPZ5YGj9+vJFknn76aY+vGzVqlJFkXnrppXKX7U3B9fXXX5uJEyeaCRMmmKFDh5rY2FgTEhLi0vlQURRc/8/xA7JJkyZuP+J++eUXI8lERka6nTUvLS01VqvV9OzZ02W6I6GeumMdZxZvuukmt3nz5s3zeOD3VNQ4drCeDiiOa8rLK7iOP9NXUU8//bSR5PJjubzYjndiwXXkyBETERFhbDabyxkah4ceeshIMpMmTXJOc/wo8nS2pbi42ISEhJgLL7ywwutyqp16aGioy07bwXG/hacN9aeffjJBQUEmOTnZZbpjp75582a31yQnJxtJJicnx21er169THBwcLmXfZS3TqcquAYOHGgkmenTpxtjjh1869SpYxISEjy+V0FBgbFYLC49QSfbXowxJikpydSpU8dlmuOH3Mm+9926dXObt2rVKo+FXXnKyspM7dq13e6XcLz/iT/yjuf4rv7xxx+ma9euxmKxuBU4p+PEHb2jOO3du7dbnMcXXL/++qvzx4Sns8U33HCDkeRyKdKp1nfbtm1GOtYL79C/f3+TmJhoFi9ebCSZjz76yBhzrEfHYrGYPn36VHhd27Rp47Yt/PHHHyY8PNy0bdvWZfr69euNJJOamlrh5VeGp5i83bcUFxebyMhIExUV5bGnzrGPrWjBZYwxDz/8sLPn2fGvSZMm5tZbb/V4mVrPnj1NcHCw84egI18ffvihqVOnjkvPhaNX4rfffnNOc1wBcXwv9/E6dOhg6tWr5/zb0fvoqQelsLDQREdHuxU3jkLM0ftwIkdP1759+5zTHPswT2enHWfp33//fY/LO9HJCq7PPvvMWXB64rja5MRjnSfnnXeekVTuSZzKWLhwodv2bEzVfj7ffPON23HWsZ9JTk72uF93vH9F7+Has2ePkcq/jNiTmnhs9rQeJyu4HCfyzz33XJfCuqSkxHkSUHLvASsrKzOTJk0ywcHBLvuS3r17uxVn27dvP2kcxcXFzs/7TFu8eLGxWCwmMTHR7Tei42RXecWS4/fjk08+We7yvSm4/va3v7l8lrVr13Y7KVxRjFJ4gg4dOig4ONhlmmOknBYtWigqKsplXnBwsOrXr6/ffvvN4/IuvPBCt2mO5Z1sXnnLcygqKtLmzZvVqFEjj0Oyp6SknPT1nTt3LnfeunXrNGPGDH366afasWOH21C227dvP+myT2XDhg06dOiQUlJSFBsb6zb/sssu05NPPqnvvvvObV6nTp3cplmtVp1zzjkehwOtrCZNmqh+/fpu0x3DM/fq1cttXsuWLdWwYUNt3bpVhYWFiomJcc6LiYlR06ZN3V6TmJiorVu3lvtdOHr0qHbu3KkGDRqcxtqc3MaNG/XHH3+oefPm5Y66ExERoZ9++sltuqftRZIaNWqkL774wuOyqmqbKCkp0d///ne99dZbWr9+vfbt26eysjLn/PK+pyf77kvSf//7X3Xt2lVbtmxRZmamBg8efNL2p+OKK67Q5ZdfrmXLlmnp0qXq27evx3aO7123bt0UEuK+277sssv0xhtv6LvvvtOQIUNc5pW3vo0bN1bTpk21cuVK5+e2atUqpaWlqUePHgoODlZOTo4uv/xyrVixQsYYpaamuizDGKP58+dr7ty5+s9//qOCggKXIYtDQ0Nd2sfFxTkfUfDFF1/o0ksvlST9/e9/lyTddttt5X5WFeVtTA4V3bf89NNPOnjwoLp166bo6Gi31/Ts2VPz5s3zKuYnnnhC48aN00cffaQvv/xS3333nb766iv94x//0GuvvaaXX35Zw4cPd7ZPTU3VypUrtWrVKqWnp2v58uUKDQ1V9+7d1atXLy1fvlySdPToUX366adq2bKlyz7kiy++kNVq1dtvv+0xnuLiYu3evVt//PGH6tSp49wX9+jRw61tdHS0OnTooFWrVrlMd2z/K1euVF5entvrdu3apbKyMv38889u272nXDRq1EiSqmQ/f7L9uHRse/r888/13XffqXv37iddljFGkir1qJn8/HxNnz5dOTk5ys/P16FDh1zml7cP8+bz+eOPPzRjxgwtXbpUW7ZscXs0yPHv4fhcUlJSPO7Xe/bs6ZZn6dgocM8995yysrK0ceNG7d+/3/m5nGw9KuNsODb/5S9/0T//+U998MEHat26tf785z8rMjJSn3zyiTZv3qzmzZvr559/dsnRoUOHdOONN2rZsmV68cUXlZ6ersjISOXm5uruu+9W9+7d9c477yg9Pd2rWCryvf7++++1aNEil2kxMTHOkQG9kZubqxtuuEG1atXSv/71L4+/EU/mdLZHT26//XbdfvvtOnz4sLZu3aqXX35ZN954oz777DO9/PLLXi2LgusEng6gjh84nuY55peUlFTJ8hzzylueQ1FRkaRjQ1x6Ut50h/j4eI/Tv/zyS6Wmpqq0tFS9e/fWn//8Z9WuXVtBQUH6/vvvtXjxYh05cuSkyz6Vffv2nTSGhIQEl3bHO1kOyns2RWWUF1tFYs/Pz9e+fftcduoni7u8+RX9Lnjr999/lyTnUKt//PGHJOnnn3/WpEmTyn2d3W53m3ay9Tq++DnVayrzOQwaNEhZWVlq2rSp0tPTFR8fr7CwMEnSs88+W+73tLzcOezcuVNFRUVq2LChunXrdtK2VeHpp59Whw4ddP/995c7XP/pbDMnW9/evXvrlVde0ffffy9jjPbu3avevXsrOjpanTp1cj6OwvHf3r17u7x+7NixevbZZ5WQkKArrrhCDRo0UEREhKRjzxPz9OyXO+64Q/PmzdM//vEPXXrppTp8+LBef/111a9fX/379y831oqqTExSxfctjs+4vH3sqb5f5YmJidGgQYM0aNAgScd+xE6bNk2TJ0/WqFGj1K9fP+d79u7dW48++qhycnKUnp6unJwcdenSRREREerdu7cWLlyoH374QQcOHNC+ffvcThr88ccfKi0tPen2Lh3b5uvUqVOpdXbsV2bMmHHK9zjRyfYDVbGfP53t6USJiYn66aefTnmS9ERbtmzRxRdfrIKCAnXr1k2XX365oqOjFRwcrG3btmnevHnl7sMq+vkUFhbqoosu0tatW3XxxRdryJAhiouLU0hIiAoLC/Xcc8+5vEdl8uwYjjwvL09t2rTRoEGDVK9ePVmtVknSpEmTTvs3w6likGrGsdkhKChI7733np577jllZmYqMzNTVqtVXbp00bx583TXXXfp559/dik8p06dqnfffVfPPfecy4mrK6+8UgsXLlSHDh00ZswYZ8HlWK/yvuOO6eV9Psf7/vvv3fYljRs39rrgys3N1Z/+9CcFBQVp6dKlHp+/dqq4Hb+NKxK3N8LDw3XeeefpueeeU3FxsV5++WVddtlluu666yq8DAquAOV4dk95z3M41XMeyqv+J0+erEOHDmnFihXq2bOny7ypU6dq8eLF3gd7AseGsHPnTo/zd+zY4dLOH8r7fI6P/dxzz3WbXx1iP5n9+/fr22+/lSTnzswRa//+/fXuu+/6LTZvfPPNN8rKylLv3r31wQcfOA/uklRWVqannnqq3Nee6sxX+/btNWLECN18883q3r27VqxYoeTk5CqL/URt27bV0KFDNWfOHL322msu6+JwOtvMydY3NTVVr7zyinJycpxnBh29WL1799a0adO0d+9e5eTkKDo6Wh07dnS+dteuXfrrX/+qNm3a6PPPP3fr/X/zzTc9vmfnzp114YUXasGCBXrmmWf03nvvqaCgQA888IDHdfdGZWPyhuMzLm8fW16OvFWrVi098cQTWrlypVavXq3c3Fxdc801kuR8SGlOTo527dqlH374wdk77SiKP/nkEx08eFCS3Homo6OjVVZWpr1791Yolsqs8/E/jKrbs+aq8hiUkpKi5cuXKycnx6UX8lRmzZqlP/74Q3PmzNHNN9/sMu/NN9/0upfUk1dffVVbt27VxIkT9dhjj7nM++KLL/Tcc8+5TKtMnhcvXqy8vDwNHTpUc+fOdZm3Y8eOUxb13qqpx+YThYSEaNy4cRo3bpzL9EOHDun7779XRESEyzO1srOzJXnu4Wvfvr3i4uL0yy+/aM+ePapbt65q1aqlBg0aaPv27dqxY4fzJIPDzz//LOnYlV2ncvPNN7t9h721atUq9evXT8HBwfrwww/Lfdh1y5YtJR27MscTb+KurD/96U96+eWXtXLlSq8KriCfRQSfql27tpo2bart27dr27ZtbvNXr15dqeVu2rRJcXFxbsWWJI+XEkjHLqv05qxjy5YtFRkZqe+//97j5SErVqyQJJcfd9WF4+GjK1eudJu3adMm/fbbb0pOTnY5g1adzJgxQ4cOHdIFF1yg8847T5LUqlUrxcTE6Msvv/TZGbuqtmnTJklSenq624/0vLw8t0tzvHXjjTfqjTfe0Pbt29W9e3fnTtxXJk+erMjISD366KNul/xI//verV69WqWlpW7zK7vNpKamymKxOB/y2KJFC+elSb1791ZZWZkyMzP1888/q2fPni6XsGzZskVlZWW6/PLL3Qqb3377TVu2bCn3fe+44w4dOnRImZmZ+vvf/y6LxaKRI0d6FbsnpxNTRbVq1cq5//J0ltXTvuF0ONbj+Eu0QkJC1L17d61fv15vvPGGpP8VWs2bN1ejRo2Uk5Oj5cuXKygoyO1H2CWXXKKCggKtW7euQjE4vleejgH79u3T999/7zbd8YPps88+q9B7VDXHd9XTselk+/Hjp1dke7rllltktVr1r3/9S+vXrz9p2+N7ehz7sGuvvdatXXnHWm95+x7H72c8fW7lHfe8eQ9fCfRjc0VlZmbq8OHDGjhwoMuxr7i4WJK0e/dut9ccOXLE2fPjuApE+t+JmA8//NDtNR988IFLG1/KyclR3759ZbVa9fHHH5dbbEn/KyiXLVvmdhXN/v37lZubq4iIiJMu43Q5LpH1dHn/yVBwBbAhQ4aorKxMDz74oMvB+Ndff9Wzzz5bqWU2adJEe/fu1Zo1a1ymz549Wx999JHH19SpU0e7d+92u9erPKGhoRo8eLDsdrseffRRl3mbN2/WX//6V1mtVt10002VWgdfGjZsmKRjP5CP37EdPXpU48ePV1lZmVdnOc+Uw4cP68knn9SUKVMUGhqqv/71r855ISEhGj16tHbs2KG7777bY7GyY8eOU/6YOJMc9y2eeHDdtWuXRo0aVSXvMXDgQC1cuFC7du1Sjx499OOPP3qMw2KxeDzp4Y3ExESNGzdOO3fu9LjtNmzYUH369NG2bdvc5n/11Vd64403FBsb6/UlefXr19f555+vzz77TJ999pnLJYNdunRReHi4nnzySUnuB15HDk78cWa32zVy5EiPhaHD9ddfr5iYGE2dOlWff/65Lr/8co/3UTjep6Kf8enEVFFWq1WDBw/W/v373XoNvvnmG82fP9+r5c2YMaPcwmf16tVasWKFQkJCnPe7OTjyMXXqVEVFRemiiy5yzuvdu7dWrVql3Nxc59nt4917772SpJEjRzovMT7egQMH9OWXXzr/Tk9PV2xsrN544w198803Lm0fe+wxj4XnXXfdJavVqnvvvdfj2eji4mKfFmN16tSRdOx4eKKuXbuqZcuWWr16tRYuXOgyb+HChfr000/VokWLU94LLR37zj322GMqLi5WWlqa2+fj8OGHH+rKK690eZ30v5MlDh999JFeffXVU75vRZT3Hv/+9781depUt/aO/czWrVv1wgsvuMxbvHixxwKqvPfYsmWLMjIyTiN67wTqsbk8jgLpeF9//bUeeOAB2Ww2t99Ojsvfn3zySbdLOB977DGVlpbqoosucjkRdfvtt0uSpkyZ4nLye9u2bXrxxRcVFhamW265pcrWyZNly5apX79+ioiIUE5Ojst+zJNzzz1Xl19+uTPG402cOFEHDhzQkCFDVKtWrdOKa9WqVR5vi9i8ebOmTJkiSUpLS/NqmVxSGMDuv/9+LVq0SG+99ZY2bNigyy+/XPv27dPbb7+t7t27a9GiRQoK8q6mvueee/TRRx8pJSVFAwcOVHR0tL755hutXr1a1113ndvBSTp2cP/666915ZVXqlu3bgoNDVX79u111VVXlfs+06ZN02effaYXXnhBX3/9tXr16qU9e/bo7bff1v79+/XCCy/49DKuyurSpYvuv/9+PfXUU2rTpo2uu+461apVSx988IF++OEHpaSk6L777vNrjHPnznUWIna7XZs3b9ann36qvXv3KiEhQa+99prbD4lHHnlE//nPf/Tyyy/r/fffV2pqqho0aKBdu3bp559/Vm5urqZMmaLWrVv7YY3cXXTRRerataveffdddenSRSkpKfrvf/+rDz74QC1btnQOtHG60tPTtWjRIl1zzTXq0aOHPvnkE7Vr184537FD9vZMlyf333+//vGPfzjPGJ/o5ZdfVteuXXXfffdp2bJl6tSpk3799Ve98847CgoK0pw5c9x6dSqid+/ezkuLji+4wsPD1bVr13Lv34qPj9df/vIXvfXWW+rQoYNz//Pxxx8rPDxcHTp08NjzIUmRkZEaOnSo831PNliGN5/x6cTkjSeffFI5OTl69tln9c033yglJUU7duzQggUL1LdvX7333nsVXtb8+fN1//33q1WrVrrkkkuUkJCgAwcOaN26dVq+fLmMMZo5c6bbd9qRj127dqlfv34un0/v3r2dl3edmDfHtGnTpunBBx9U8+bN1bdvXyUnJ8tut+uXX37RqlWrlJKS4jzzbbPZ9I9//EODBg1St27dNGjQICUkJGj16tX64Ycf1L17d3366acu79GqVSu99tprGjZsmM4//3z96U9/UosWLVRSUqL8/Hx99tlnqlevnsfBeKpC7969NWPGDI0cOVLXXnutbDabYmJidNddd8lisWjevHnq06ePBg0apPT0dLVq1UobNmzQokWLFBUVpddff73Cx8+HHnrIeU/cRRddpC5duqhTp06y2Wz673//q08//VQ///yzy2AXd955p+bMmaOBAwfq2muvVYMGDfTDDz/oww8/1MCBA7VgwYLT/gyGDBmiGTNm6N5779XKlSudgy0sWbJE11xzjcf3ePHFF3XppZfqnnvu0bJly9S+fXtt2rRJWVlZuuqqq/T++++7tL/qqqvUrFkzPfPMM/rhhx90wQUXKD8/X0uWLFFaWpry8/NPez0qojofm3/66SdNmzbNZVpBQYHLZXhPP/206tat6/y7T58+ioyMVJs2bWSz2bRu3TotXbpUYWFhevfdd91OUE2YMEHvv/++cnJy1KpVK/3pT39SRESEcnNzlZeXp4iICLdLSLt06aKxY8dq1qxZateuna677joVFxdrwYIF2rt3r55//nmPg7JVlQ0bNig9PV2HDx9WWlqaFi9e7PG2lRNPbL300kvq0qWL7r77buXk5Oi8887TV199pRUrVqhFixbOguh406ZNc+5rHMeAOXPmOK8GS0lJ0YgRI5zt09PTFRMTo86dO6tRo0YqLS3V5s2b9eGHH6q0tFSjR49Wnz59vFvhSo1tGOB0kmHhTxxG/fjXnPgsKQdPw6I7hp30NER3eU9rN+Z/QxSfOKRweUOvFxQUmNGjR5uEhAQTGhpqWrZsaZ5++mnz1VdfGcn9OTEnGyrX4f333zedO3c2NpvNREdHmz59+phVq1aVG7fdbje33367adCggXM40uM/x/I+u4KCAnP//febZs2amdDQUBMdHW0uu+wy51DUFflcTvX5lOdUQ8+Wl2uHN99803Tt2tXYbDYTFhZmWrdubSZPnnzSp9l7G0dFcuVpWY5/wcHBJjo62rRs2dIMHDjQzJkzx9jt9nJfX1ZWZl5//XWTmppqYmNjjdVqNYmJiaZr165mypQpJj8/39n2VNuLp/WqzPf+ZO/1xx9/mDvuuMM0btzYhIWFmaZNm5oHH3zQHDhwwONnfrL3dygv95988omJjIw0cXFx5ttvvzXGHHs2XVBQULlDS3tyquFoHc99ktyfw2XMsWdM3X777SYpKclYrVZTp04dk56ebvLy8tzaVmR9jTHmvffeM9Kx53Xt2bPHZd6TTz5pJJlzzjnH42sPHDhgHnroIXPuueeasLAw07BhQ3PnnXeaPXv2nPS7bYwx//nPf4x07JmA5T0YtTKfsbcxVXbfsmPHDnPLLbeYunXrmvDwcNO+fXszZ86cUy7vRN9995154oknTK9evUyTJk1MeHi48/t8ww03mM8++8zj68rKypzPW5w1a5bLPMeQz/IwdPTxPvvsMzNgwACTkJBgrFarqVu3rmnfvr2599573Z63ZYwxy5YtM127djUREREmJibG/PnPfzY//vjjSfdVa9asMUOHDjVJSUkmNDTUxMbGmvPPP9/ceuutbkNun+w7U9Hv8/FmzpxpWrVqZUJDQ43+/7EKx/vpp5/MjTfeaOLj401ISIiJj483gwcPdnkYuDfWr19v7rrrLnP++ec7nycZHx9v/vSnP5lXX33V7flwubm5plevXiYmJsbYbDbTtWtXk5WVVe53qDKfz7p168xVV11l6tWrZyIjI03Hjh3NK6+8ctJ9+M8//2yuvfZaEx0dbSIjI80ll1xilixZUu575OfnmxtuuMEkJiaa8PBw07p1azN9+nRTUlJSoeNpRdcxUI/Nxz/rr7x/Jy7rqaeeMh07djTR0dEmNDTUNGnSxNx2220nfc9du3aZcePGmVatWpmwsDBjtVpNUlKSufnmm82PP/5Y7uvmzp1rOnXqZCIjI43NZjPdu3ev8OMFTkdFPpfycpCfn29uvvlmEx8f71zPu+++22UY/eOd+PvoxH8nbgfPPvus6du3r0lKSjIREREmNDTUNGrUyFx33XXmww8/rNT6Wow57lo01BivvPKKbr31Vr388stVMtQyAFfvvfee0tPTlZ2dXe5w7ijfa6+9puHDh+uRRx7R448/7rENnzEAoCag4Apwv//+u9ulJr/++qu6du2qnTt36pdffnEbfQbA6Rs3bpxycnKq5BK1s01paakuuOACbdiwQVu3bi33WTZ8xgCAmoCCK8BdeumlKikp0YUXXqiYmBht27ZNS5Ys0cGDB/XUU0/5/X4iAHD49NNPtWLFCq1cuVIrV67UmDFjKj3ADwAAgYJBMwLckCFDNH/+fGVlZamgoEA2m02XXHKJRo8erauvvtrf4QGA0/LlyzVp0iTVqVNHt99+u9uN5AAA1ET0cAEAAACAj/i9h2vUqFEeH9R2+eWXa8SIETLG6J133lFOTo7sdruaN2+u4cOHOx/OKUklJSXKzMxUbm6uiouL1aZNG40YMcL5HA4AAAAA8Ae/93AVFRW5PFwsPz9fkydP1sSJE3X++edr0aJFysrK0p133qmEhAS9++67+vHHH/Xss88qIiJC0rER+b799lvdeeedzudn2O12TZ8+3evnUAEAAABAVfF7NVK7dm3FxMQ4/3333Xc655xz1Lp1axljtHTpUvXv31+dO3dWUlKSRo0apSNHjjgfVnbw4EEtX75cQ4YMUbt27ZScnKzRo0crPz9fa9as8fPaAQAAADib+b3gOl5paak+++wz9erVSxaLRbt27VJhYaHat2/vbGO1WtW6dWtt2LBBkrRlyxYdPXpU7dq1c7aJi4tTUlKSNm7cWO57lZSU6ODBg85/hw8f9t2KAQAAADgr+f0eruPl5eXpwIED6tmzpySpsLBQkhQdHe3SLjo6Wnv27HG2CQkJkc1mc2vjeL0nWVlZWrhwofPvFi1aaPLkyae/EgAAAADw/6pVwbVixQp16NBBcXFxLtMtFovL3xW57exUbfr3769+/fq5vcfu3btVWlpa0ZB9wmKxKD4+Xjt37qzQusJ/yFXgIFeBgTwFDnIVOMhV4CBX1UdISIjq1atXNcuqkqVUgd27d2vNmjUaP368c1pMTIykY71YsbGxzulFRUXOXq+YmBiVlpbKbre79HIVFRWpZcuW5b6f1WqV1Wr1OK+6fMGNMdUmFpwcuQoc5CowkKfAQa4CB7kKHOSqZqk293CtWLFC0dHR6tixo3Na/fr1FRMT4zL4RWlpqdavX+8sppo2barg4GCXNgUFBcrPz1eLFi3O3AoAAAAAwAmqRQ9XWVmZVq5cqR49eig4ONg53WKxqG/fvsrKylJCQoLi4+OVlZWlsLAwpaSkSJIiIyOVmpqqzMxMRUVFyWazKTMzU0lJSS4DaQAAAADAmVYtCq61a9dqz5496tWrl9u89PR0FRcX69VXX9WBAwfUrFkzTZgwwfkMLkkaOnSogoOD9cwzzzgffJyRkcEzuAAAAAD4ld8ffFzd7N69WyUlJX6NwWKxKCEhQTt27OD63WqOXAUOchUYyFPgIFeBg1wFDnJVfVit1iobNIMuIAAAAADwEQouAAAAAPARCi4AAAAA8BEKLgAAAADwEQouAAAAAPARCi4AAAAA8BEKLgAAAADwEQouAAAAAPARCi4AAAAA8BEKLgAAAADwEQqus0B2dr5SU7N18cWLlJqarezsfH+HBAAAAJwVQvwdAHwrOztfGRl5Kigodk7LyMiTJKWlJfkrLAAAAOCsQA9XDTdz5lqXYkuSCgqKNWvWWj9FBAAAAJw9KLhqOLu9pJzppWc4EgAAAODsQ8FVw9ls1nKmczUpAAAA4GsUXDXcuHFtFRsb6jItNjZUY8e29VNEAAAAwNmDbo4azjEwxqxZa2W3l8pmC9HYsW0ZMAMAAAA4Ayi4zgJpaUkUWAAAAIAfcElhNXPsmVlL1KTJP9Sr1xKemQUAAAAEMHq4qhGemQUAAADULPRwVSM8MwsAAACoWSi4qhGemQUAAADULBRc1QjPzAIAAABqFgquaoRnZgEAAAA1C10n1cjxz8w6dMgoIsLCM7MAAACAAEbBVc2kpSWpX7/GSkhI0I4dO2SM8XdIAAAAACqJSwoBAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARyi4AAAAAMBHKLhQadnZ+UpNzdbFFy9Samq2srPz/R0SAAAAUK2E+DsABKbs7HxlZOSpoKDYOS0jI0+SlJaW5K+wAAAAgGqFHi5UysyZa12KLUkqKCjWrFlr/RQRAAAAUP1QcKFS7PaScqaXnuFIAAAAgOqLgguVYrNZy5nOVaoAAACAAwUXKmXcuLaKjQ11mRYbG6qxY9v6KSIAAACg+qE7ApXiGBhj1qy1sttLZbOFaOzYtgyYAQAAAByHgguVlpaWRIEFAAAAnASXFAIAAACAj1BwAQAAAICPUHABAAAAgI9QcAEAAACAj1BwAQAAAICPUHABAAAAgI9QcAEAAACAj1BwAQAAAICPUHABAAAAgI9QcAEAAACAj1BwAQAAAICPUHABAAAAgI9QcAEAAACAj1BwAQAAAICPUHABAAAAgI9QcAEAAACAj4T4OwBJ2rt3r/75z3/q+++/V3FxsRISEnTHHXeoadOmkiRjjN555x3l5OTIbrerefPmGj58uBo1auRcRklJiTIzM5Wbm6vi4mK1adNGI0aMUJ06dfy1WgAAAADOcn7v4bLb7XrkkUcUEhKihx56SLNmzdKQIUMUGRnpbLN48WJlZ2dr2LBhmjp1qmJiYjR58mQdOnTI2Wbu3LnKy8vTmDFj9Pjjj+vw4cOaNm2aysrK/LFaAAAAAOD/gmvx4sWqU6eO7rzzTjVr1kz169dX27ZtFR8fL+lY79bSpUvVv39/de7cWUlJSRo1apSOHDmi1atXS5IOHjyo5cuXa8iQIWrXrp2Sk5M1evRo5efna82aNf5cPQAAAABnMb9fUvjNN9+offv2mjVrltavX6+4uDhdfvnluuyyyyRJu3btUmFhodq3b+98jdVqVevWrbVhwwb16dNHW7Zs0dGjR9WuXTtnm7i4OCUlJWnjxo3q0KGD2/uWlJSopKTE+bfFYlFERITz//3J8f7+jgOnRq4CB7kKDOQpcJCrwEGuAge5qpn8XnDt2rVLH3/8sdLS0tS/f39t2rRJc+bMkdVqVY8ePVRYWChJio6OdnlddHS09uzZI0kqLCxUSEiIbDabWxvH60+UlZWlhQsXOv9OTk7W9OnTVa9evapbudPk6OVD9UeuAge5CgzkKXCQq8BBrgIHuapZ/F5wlZWV6dxzz9UNN9wg6Vjh8+uvv2rZsmXq0aOHs92Jlb4x5pTLPlmb/v37q1+/fm7L3717t0pLS71ah6pmsVgUHx+vnTt3Vmg94T/kKnCQq8BAngIHuQoc5CpwkKvqIyQkpMo6YvxecMXGxqphw4Yu0xo2bKivvvpKkhQTEyPpWC9WbGyss01RUZGz1ysmJkalpaWy2+0uvVxFRUVq2bKlx/e1Wq2yWq0e51WXL7gxptrEgpMjV4GDXAUG8hQ4yFXgIFeBg1zVLH4fNKNly5b6/fffXab9/vvvzoqyfv36iomJcRn8orS0VOvXr3cWU02bNlVwcLBLm4KCAuXn56tFixZnYC0AAAAAwJ3fe7jS0tL0yCOP6N1331WXLl20adMm5eTk6NZbb5V0rGu1b9++ysrKUkJCguLj45WVlaWwsDClpKRIkiIjI5WamqrMzExFRUXJZrMpMzNTSUlJLgNpAAAAAMCZ5PeCq1mzZho/frzeeOMN/etf/1L9+vU1dOhQdevWzdkmPT1dxcXFevXVV3XgwAE1a9ZMEyZMcI4qKElDhw5VcHCwnnnmGeeDjzMyMhQU5PdOPAAAAABnKYvhAlEXu3fvdhku3h8sFosSEhK0Y8cOrt+t5shV4CBXgYE8BQ5yFTjIVeAgV9WH1WqtskEz6P4BAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfqZKCa8+ePfr++++1f//+qlgcAAAAANQIId6+4K233tLhw4d18803S5LWrFmj6dOnq7S0VLVq1dKkSZPUqFGjqo4TAAAAAAKO1z1cX331lRo2bOj8e8GCBWrcuLHuu+8+1atXT//617+qNEAAAAAACFReF1x79+5VfHy8JGn//v3atGmTBg4cqE6dOik9PV0bNmyo8iABAAAAIBB5XXAZY2SMkSRt2LBBQUFBat26tSQpNjZWRUVFVRshAAAAAAQorwuuc845R99++60kKTc3V82aNVNoaKgkqaCgQDabrWojBAAAAIAA5fWgGX369NHs2bP16aef6sCBA7rjjjuc8zZs2OByfxcAAAAAnM28Lrguv/xy1apVSxs2bFCzZs3UvXt357zi4mL16NGjSgMEAAAAgEDldcElSV27dlXXrl3dpt92222nHRAAAAAA1BRe38M1aNAgbdq0yeO8LVu2aNCgQacdFAAAAADUBF4XXCdTVlZWlYsDAAAAgIBWpQXXli1bVKtWrapcJAAAAAAErArdw7V06VItXbrU+feMGTNktVpd2hQXF2vfvn265JJLqjZCAAAAAAhQFSq4ateu7Rzufffu3TrnnHMUGRnp0sZqtSopKUl9+/at+igBAAAAIABVqOBKSUlRSkqKJGnSpEkaMWKEGjRo4NPAAAAAACDQeT0s/MSJE30RBwAAAADUOJV6DpcxRps3b9bu3btVXFzsNp+HHwMAAABAJQqu33//XU899ZR27NhRbhsKLgAAAACoRME1e/ZslZSU6N5771VSUpLbaIUAAAAAgGO8Lrg2bdqk2267jeHfAQAAAOAUvH7wcXh4uNuQ8AAAAAAAd14XXL169dLq1at9EQsAAAAA1CheX1LYqFEj5ebmavr06brwwgsVFRXl1qZz585VEhwAAAAABDKvC66//vWvkqRdu3bpu+++89hmwYIFpxcVAAAAANQAPPgYAAAAAHzE64KrdevWvogDAAAAAGocrwfNAAAAAABUjNc9XJK0Y8cOffzxx9q+fbuKi4td5lksFj366KNVEhwAAAAABDKvC678/HxNmDBBcXFx2rlzpxo3bqz9+/dr7969qlOnjs455xxfxAkAAAAAAcfrSwrffPNNtW/fXrNmzZIk3X777frb3/6mjIwMlZSU6C9/+UuVBwkAAAAAgcjrgmvr1q3q2bOnLBaLJMkYI0nq2LGjrrrqKr3xxhtVGyEAAAAABCivC64DBw7IZrMpKChIwcHBOnDggHNe06ZNtXXr1ioNEAAAAAACldcFV1xcnIqKiiRJ8fHxWr9+vXNefn6+wsPDqy46AAAAAAhgXg+a0bJlS23cuFEXX3yxUlJS9M4776iwsFAhISFauXKlunXr5os4AQAAACDgeF1wXXPNNSooKJAkXX311SosLNTq1atlsVh06aWX6qabbqryIAEAAAAgEHldcNWtW9c59HtQUJCGDRumYcOGVXlgAAAAABDovLqHq7i4WIMHD9bXX3/tq3gAAAAAoMbwquAKDQ1VVFSUwsLCfBUPAAAAANQYXo9SeOGFFyovL88XsQAAAABAjeL1PVxdu3bV3/72N7300kvq3LmzYmNj3do0bdq0SoIDAAAAgEDmdcE1ZcoUSdKqVau0atUqj20WLFhwelEBAAAAQA3gdcF1xx13VGkAb7/9thYuXOgyLTo6Wq+88ookyRijd955Rzk5ObLb7WrevLmGDx+uRo0aOduXlJQoMzNTubm5Ki4uVps2bTRixAjVqVOnSmMFAAAAAG94XXD17NmzyoNo1KiRHnnkEeffQUH/u7Vs8eLFys7O1p133qmEhAS9++67mjx5sp599llFRERIkubOnatvv/1WY8aMUVRUlF5//XVNmzZN06dPd1kWAAAAAJxJ1aIaCQoKUkxMjPNf7dq1JR3r3Vq6dKn69++vzp07KykpSaNGjdKRI0e0evVqSdLBgwe1fPlyDRkyRO3atVNycrJGjx6t/Px8rVmzxp+rBQAAAOAs53UP10svvVTuvKCgIEVGRqpZs2a6+OKLFRJSscXv3LlTt912m0JCQtS8eXNdf/31Ouecc7Rr1y4VFhaqffv2zrZWq1WtW7fWhg0b1KdPH23ZskVHjx5Vu3btnG3i4uKUlJSkjRs3qkOHDh7fs6SkRCUlJc6/LRaLs8fMYrFUKG5fcby/v+PAqZGrwEGuAgN5ChzkKnCQq8BBrmomrwuudevW6eDBgzp48KCCgoIUFRWl/fv3q6ysTJGRkZKk7OxsJSYmauLEiYqJiTnp8po3b65Ro0YpMTFRhYWFevfdd/Xwww9r1qxZKiwslHTsnq7jRUdHa8+ePZKkwsJChYSEyGazubVxvN6TrKwsl3vHkpOTNX36dNWrV6+Cn4TvxcfH+zsEVBC5ChzkKjCQp8BBrgIHuQoc5Kpm8brgGjdunJ5++mmNHDlSl1xyiYKCglRWVqYvvvhC8+fP19ixY3X06FE9/fTTevPNN085yMYFF1zg/P+kpCS1aNFCo0eP1qpVq9S8eXNJ7lW+MeaUcZ6qTf/+/dWvXz/n34732L17t0pLS0+5fF+yWCyKj4/Xzp07K7Su8B9yFTjIVWAgT4GDXAUOchU4yFX1ERISUmUdMV4XXK+//rquuuoqdenSxTktKChIXbt21b59+zRv3jw98cQTSk9P1/vvv+91QOHh4UpKStKOHTt00UUXSTrWi3X8876KioqcvV4xMTEqLS2V3W536eUqKipSy5Yty30fq9Uqq9XqcV51+YIbY6pNLDg5chU4yFVgIE+Bg1wFDnIVOMhVzeL1oBmbN29Ww4YNPc5r1KiRtm3bJklq0qSJ9u/f73VAJSUl2r59u2JjY1W/fn3FxMS4DH5RWlqq9evXO4uppk2bKjg42KVNQUGB8vPz1aJFC6/fHwAAAACqitc9XBEREVq3bp3atm3rNu+HH35wDjxRXFzs/P+Tef3119WpUyfVrVtX+/bt07/+9S8dOnRIPXr0kMViUd++fZWVlaWEhATFx8crKytLYWFhSklJkSRFRkYqNTVVmZmZioqKks1mU2ZmppKSklwG0gAAAACAM83rgislJUWLFy+WMUaXXnqpoqOjtW/fPn3++ed6//331bdvX0nSli1b1KBBg1Mub+/evXruuedUVFSk2rVrq3nz5poyZYrzmsn09HQVFxfr1Vdf1YEDB9SsWTNNmDDBpZgbOnSogoOD9cwzzzgffJyRkcEzuAAAAAD4lcV4eYFoaWmpXnzxRX3++edu87p27ao777xTISEhWrNmjXOI+ECye/dul+Hi/cFisSghIUE7duzg+t1qjlwFDnIVGMhT4CBXgYNcBQ5yVX1YrVb/DZoREhKiMWPG6Nprr9X69eudg1W0bt3a5d4uLucDAAAAcLbzuuByaNiwYbmDZwAAAAAAKjFKIQAAAACgYirUwzVo0CBNmTJFzZo106BBg07a1mKx6K233qqS4AAAAAAgkFWo4LruuusUFxcnSbr22mtlsVh8GhQAAAAA1AQVKrgGDBjg/P+BAwf6LBgAAAAAqEm4hwsAAAAAfKRSoxSWlJRo7dq15T6zql+/fqcdGACgamRn52vmzLWy20tks1k1blxbpaUl+TssAADOCl4XXFu2bNH06dNVWFhYbhsKLgCoHrKz85WRkaeCgmLntIyMPEmi6AIA4AzwuuB69dVXFRERoZEjR6pBgwYKCan0o7wAAD42c+Zal2JLkgoKijVr1loKLgAAzgCvq6Vff/1VY8aMUadOnXwRDwCgCtnt7pd9H5teeoYjAQDg7OT1oBl169b1RRwAAB+w2azlTOfqBAAAzgSvC6709HS9//77HgfLAABUL+PGtVVsbKjLtNjYUI0d29ZPEQEAcHbx+hRnz549tWvXLo0ePVqtW7eWzWZzmW+xWHTLLbdUWYAAgMpz3Kc1a9Za2e2lstlCNHYsoxQCAHCmeF1wfffdd1q0aJGOHj2q3Nxcj20ouACg+khLS6LAAgDAT7wuuDIzM5WcnKxbb72VUQoBAAAA4CS8vodr9+7dGjBggBo3bkyxBQAAAAAn4XXB1aBBAx06dMgXsQAAAABAjeJ1wTVo0CC9++67Kiws9EE4AAAAAFBzeH1N4Mcffyy73a7Ro0erSZMmHkcpvP/++6ssQAAAAAAIVF4XXPn5+QoKClLt2rW1d+9e7d2712W+xWKpsuAAAAAAIJB5XXC9+OKLvogDAAAAAGocr+/hAgAAAABUTKXHdT98+LD27Nmj4uJit3lNmzY9raAAAAAAoCbwuuAqKirSyy+/rG+//bbcNgsWLDitoAAAAACgJvC64PrHP/6hdevWqW/fvmrQoAEPPwYAAACAcnhdLf3www+66aabdNlll/kiHgAAAACoMbweNCMsLEz16tXzRSwAAAAAUKN4XXB1795dX3zxhS9iAQAAAIAaxetLCv/yl7/ob3/7m2bMmKGOHTvKZrO5tencuXOVBAcAAAAAgczrgmvXrl3atGmTduzYoW+++cZjG0YpBAAAAIBKjlJ48OBBDR06VA0bNmSUQgAAAAAoh9fV0s8//6zbb79dKSkpvogHAAAAAGoMrwfNiI6OVq1atXwRCwAAAADUKF4XXJdffrk+/vhjX8QCAAAAADWK15cUWiwW5efnKyMjQxdccIHHUQr79etXJcEBAAAAQCDzuuCaP3++JGn37t3atm2bxzYUXAAAAABQiYLrhRde8EUcAAAAAFDjeF1w1atXzxdxAAAAAECN4/WgGQAAAACAiqlQD9f06dMrvECLxaL777+/0gEBAAAAQE1RoYIrPz+/wgu0WCyVDgYAAAAAapIKFVwvvviir+MAAAAAgBqHe7gAAAAAwEcouAAAAADARyi4AAAAAMBHKLgAAAAAwEcouAAAAADARypUcM2bN0979uyRJO3Zs0elpaU+DQoAAAAAaoIKFVxLly5VYWGhJGnUqFHatm2bD0MCAAAAgJqhQgVXrVq1nAUXAAAAAKBiKvTg4+bNm+vll19Ws2bNJEmZmZmKjIz02NZisej++++vuggBAAAAIEBVqOAaMWKE5s6dq99++02StHPnToWEeH6pxWKpuugAAAAAIIBVqOCqV6+e7rvvPknSoEGDdN999zl7uwAAAAAAnnk9LPzEiRPVsGFDX8QCAAAAADVKhXq4jte6dWtJxy4r/OGHH7R//35FRUWpTZs2io+Pr/IAAQAAACBQeV1wGWP02muv6eOPP5YxxjndYrHo8ssv17Bhw6o0QAAAAAAIVF4XXNnZ2Vq2bJn69Omjnj17Ki4uTnv37tWqVau0bNky1a9fX/369fNFrAAAAAAQULwuuHJycvSnP/1Jt9xyi3NaXFycmjVrpqCgIOXk5FBwAQAAAIAqMWjGrl27dOGFF3qcd+GFF2rXrl2nHRQAAAAA1AReF1yRkZHavXu3x3m7d+9WRETEaQcFAAAAADWB1wVX27Zt9dZbb2nLli0u07dt26a3335b7du3r7LgAAAAACCQeX0P1w033KCHH35YDz74oBo2bKjY2FgVFBTot99+U1xcnG644QZfxAkAAAAAAcfrgqtu3bp66qmntGTJEq1bt067du1SVFSUrr76aqWlpal27dq+iBMAAAAAAo7XBZck1a5dm54sAAAAADgFr+/hAgAAAABUDAUXAAAAAPgIBRcAAAAA+AgFFwAAAAD4iNcFV2lpqYwxvogFAAAAAGoUrwqu4uJiDR48WF9//bWv4gEAAACAGsOrgis0NFRRUVEKCwvzVTwAcNbKzs5Xamq2Lr54kVJTs5Wdne/vkAAAwGny+jlcF154ofLy8tS+fXtfxAMAZ6Xs7HxlZOSpoKDYOS0jI0+SlJaW5K+wAADAafL6Hq6uXbvqu+++00svvaRvv/1WW7ZscfsHAPDOzJlrXYotSSooKNasWWv9FBEAAKgKXvdwTZkyRZK0atUqrVq1ymObBQsWnF5UAHCWsdtLypleeoYjAQAAVcnrguuOO+7wRRwAcFaz2azlTPd6Nw0AAKoRr4/kPXv29EEYAHB2Gzeurds9XLGxoRo7tq0fowIAAKfrtE6d/v777yoqKlKTJk0UHh5eVTEBwFnHMTDGrFlrZbeXymYL0dixbRkwAwCAAFepgmvVqlV68803VVBQIEmaOnWqmjZtqlmzZqldu3a67LLLqjRIADgbpKUlUWABAFDDeD1K4RdffKGXXnpJycnJGj58uMu8pk2b6osvvqiy4AAAAAAgkHndw7Vo0SL17NlTd9xxh8rKyjR79mznvAYNGuiDDz6odDBZWVl688031bdvX918882SJGOM3nnnHeXk5Mhut6t58+YaPny4GjVq5HxdSUmJMjMzlZubq+LiYrVp00YjRoxQnTp1Kh0LAAAAAJwur3u4fvvtN3Xt2tXjPJvNJrvdXqlANm3apE8++USNGzd2mb548WJlZ2dr2LBhmjp1qmJiYjR58mQdOnTI2Wbu3LnKy8vTmDFj9Pjjj+vw4cOaNm2aysrKKhULAAAAAFQFrwuusLAwHTx40OO8vXv3qlatWl4HcfjwYT3//PO67bbbXF5vjNHSpUvVv39/de7cWUlJSRo1apSOHDmi1atXS5IOHjyo5cuXa8iQIWrXrp2Sk5M1evRo5efna82aNV7HAgAAAABVxetLClu2bKkPP/xQnTt3dpu3cuVKtW7d2usgXn31VV1wwQVq166d3n33Xef0Xbt2qbCwUO3bt3dOs1qtat26tTZs2KA+ffpoy5YtOnr0qNq1a+dsExcXp6SkJG3cuFEdOnTw+J4lJSUqKfnfg0YtFosiIiKc/+9Pjvf3dxw4NXIVOMhVYCBPgYNcBQ5yFTjIVc3kdcF13XXX6dFHH9VDDz3kvLQwLy9Pb7/9tn788Uc9+eSTXi0vNzdXW7du1dSpU93mFRYWSpKio6NdpkdHR2vPnj3ONiEhIbLZbG5tHK/3JCsrSwsXLnT+nZycrOnTp6tevXpexe9L8fHx/g4BFUSuAge5CgzkKXCQq8BBrgIHuapZvC64zj33XD344IOaPXu2MjMzJR0rXuLj4/Xggw8qKaniQxrv2bNHc+fO1YQJExQaGlpuuxOrfGPMKZd9qjb9+/dXv3793N5j9+7dKi0tPeXyfclisSg+Pl47d+6s0LrCf8hV4CBXgYE8BQ5yFTjIVeAgV9VHSEhIlXXEVOo5XG3atNEzzzyjnTt3at++fYqKilJiYqLXy9myZYv27dunBx54wDmtrKxMP/74oz788EM9++yzko71YsXGxjrbFBUVOXu9YmJiVFpaKrvd7tLLVVRUpJYtW5b73larVVar1eO86vIFN8ZUm1hwcuQqcJCrwECeAge5ChzkKnCQq5qlUgWXQ3x8/Gl1ebZt21ZPP/20y7S//e1vSkxMVHp6us455xzFxMRozZo1Sk5OliSVlpZq/fr1Gjx4sKRjz/4KDg7WmjVr1KVLF0lSQUGB8vPznW0AAAAAwB8qVXDt2rVLixYt0rp167R//35FRUXp/PPP19VXX6369etXeDkRERFulyCGhYUpKirKOb1v377KyspSQkKC4uPjlZWVpbCwMKWkpEiSIiMjlZqaqszMTEVFRclmsykzM1NJSUkuA2kAAAAAwJnmdcG1bds2TZo0SUeOHFHLli2VnJysffv2aeXKlfr888/12GOPqUmTJlUWYHp6uoqLi/Xqq6/qwIEDatasmSZMmOAcUVCShg4dquDgYD3zzDPOBx9nZGQoKMjrUe8BAAAAoMpYjJcXiD722GMqKCjQI488orp16zqn7969W5MnT1ZcXJwmTpxY5YGeKbt373YZLt4fLBaLEhIStGPHDq7frebIVeAgV4GBPAUOchU4yFXgIFfVh9VqrbJBM7zuAtq0aZMGDBjgUmxJUr169TRgwAD9/PPPVRIYAAAAAAQ6rwuuyMhIRUZGljvv+Ev9AAAAAOBs5nXBlZKSouXLl3ucl5OT43wYMoDqIzs7X6mp2br44kVKTc1Wdna+v0PyKFDiBAAAqKgKDZrx1VdfOf+/adOm+vLLL/Xggw+qa9euiomJUWFhoXJzc1VUVKRLL73UZ8EC8F52dr4yMvJUUFDsnJaRkSdJSkur+IPKfS1Q4gQAAPBGhQquWbNmuU37448/tGXLFrfpzz//vHPIdgD+N3PmWpciRpIKCoo1a9baalXIBEqcAAAA3qhQwRXIow4CZzu73fOom3Z76RmO5OQCJU4AAABvVKjgat26ta/jAOAjNpu1nOmVeu65zwRKnAAAAN7gycBADTduXFvFxoa6TIuNDdXYsW39FJFngRKnAwN8AACAiqjUqeO8vDytXr3a40OCLRaLZsyYUSXBATh9jvufZs1aK7u9VDZbiMaObVvt7osKlDglBvgAAAAV53XB9d5772n+/PmqXbu24uPjFR4e7ou4AFShtLSkgCgEAiVOBvgAAAAV5XXB9dFHH6lXr1669dZbFRTEFYkAzj4M8AEAACrK64rJbrcrJSWFYgvAWYsBPgAAQEV5XTW1bNlSv/32my9iAYCAEGgDfAAAAP/x+nTszTffrBkzZqhu3brq0KGDQkI4owvg7BJIA3wAAAD/8rpaio+PV9u2bTVjxgxZLBaFhYW5tZk3b16VBAcA1VWgDPABAAD8y+uC65///Kc++ugjNWnSRA0aNKCHCzhLZWfna+bMtbLbS2SzWTVuHD08AAAAJ/K6Wlq5cqXS09N1ww03+CIeAAGA51ABAABUjNeDZpSVlaldu3a+iAVAgDjZc6gAAADwP14XXO3atdPGjRt9EQuAAMFzqAAAACrG60sKr7vuOj3zzDMKDw9Xx44dZbPZ3Np4mgag5uA5VAAAABXj9a+j++67T9KxkQjLG41wwYIFpxcVgGpt3Li2bvdw8RwqAAAAd14XXNdee60sFosvYgEQIHgOFQAAQMV4XXANHDjQF3EACDA8hwoAAODUvB40AwAAAABQMV73cC1cuPCUba677rpKBQMAAICzU3Z2vmbOXCu7vUQ2m1XjxnGpOmoGrwuud95555RtKLgAAABQUdnZ+W6DMWVk5EkSRRcCntcFl6cRCO12u/Ly8rR06VI98MADVRIYAAAAzg4zZ651KbYkqaCgWLNmraXgQsCrknu4bDabUlNTlZKSojlz5lTFIgEAAHCWsNtLypleeoYjAapelQ6a0axZM/3www9VuUgAAADUcDabtZzpXl+MBVQ7VVpwbdu2TeHh4VW5SAAAANRw48a1VWxsqMu02NhQjR3b1k8RAVXH69MGq1atcptWUlKi/Px8rVixQt26dauSwAAAAHB2cNynNWvWWtntpbLZQjR2LKMUombwuuB66aWXPE63Wq3q1q2bbrrpptMOCgAAAGeXtLQkCizUSF4XXC+88ILbNKvVqpiYmKqIBwAAAABqDK8Lrnr16vkiDgAAAACocap00AwAACorOztfqalL1KTJP9Sr1xJlZ+f7OyQAAE5bhXq4xo8fX+EFWiwWzZgxo9IBAQDOPtnZ+crIyHN58GlGRp4kcU8HACCgVajgstlsslgsJ21z+PBhbdmypUqCAgCcXWbOXOtSbElSQUGxZs1aS8EFAAhoFSq4HnvssXLnHT16VJ988okWLlwoSUpJSamSwAAAZw+7vaSc6aVnOBIAAKrWaT2++4svvtBbb72lnTt3ql27dho8eLCaNGlSRaEBAM4WNpu1nOmndZgCAMDvKnUkW7dunebPn6/NmzcrOTlZDz/8sNq25UngAIDKGTeurds9XLGxoRo7lmMLACCweVVw5efna/78+fr+++9Vv3593X333eratauvYgMAVFPZ2fmaOXOt7PYS2WxWjRvX9rTutXK8dtastTp0yCgiwqKxY09vmQAAVAcVKrj27Nmjt956S7m5ubLZbLr55pvVp08fhYRwqQcAnG18NaJgWlqS+vVrrISEBO3YsUPGmNOOFQAAf6tQxTRmzBiVlpaqQ4cOSk9PV3h4uPLzy38+StOmTassQABA9cKIgvCFqu41BYDqokIFV2npsVGivv/+e33//fenbL9gwYLTCgoAUH0xoiCqGs9hA1CTVajguuOOO3wdBwAgQDCiIKoavaYAarIKHR179uzp4zAAAIGCEQVR1eg1BVCTcToSAOCV40cUtNtLZbOFnHUjCnK/UdWi1xS+wraK6oA9GQDAa2lpSWftjxbuN6p69JrCF9hWUV0E+TsAAP+TnZ2v1NRsXXzxIqWmZis7u/zRQAH4x8nuN0LlpKUlafr0i9WqVbQaNqylVq2iNX36xfwoxmlhW0V1QQ8XUE1wJg4IDNxv5Btnc68pfINtFdUFPVxANcGZOCAwcL8REBjYVlFdUHChWjmbL6njTBwQGMaNa6vY2FCXadxvBFQ/bKuoLijxUW2c7ZfUcSYOCAyM0ggEBrZVVBf8kkO1cbY/+JJRunwnOztfs2at1aFDRuHhFoYFxmnjfiMgMLCtojqg4EK1cbZfUseZON8423tOAQCAf1FwodrgkjrOxPnC2d5zCgAA/ItBM1BtcHMrfOFs7zkFAAD+dfZ0HaDa45I6+AI9pwCqWnZ2vmbOXCu7vUQ2m5X7QgGcFL84UK1wSR2qGoORAKhK3BcKwFsUXABqtON7Tg8dMoqIsNBzCqDSAu2+UEZpBfyPggtAjZeWlqR+/RorISFBO3bskDHG3yEBCFCBdF8ovXG+wSWl8BYFFwAAQAUF0n2hgdYbFwgoYlEZjFIIAABQQYE0om4g9cYFipMVsUB5qt/pGAAAgGoqkEbUDaTeuEBBEYvKYIsDAADwQqCMqMsorVWPIhaVwSWFAAAANVBaWpKmT79YrVpFq3Hj2mrVKlrTp18cEMVidRVIl5Si+qAcBwAAqKEYpbVqBdIlpag+KLgAAACACgqUS0pRfXBJIQAAAAD4CAUXAAAAAPgIBRcAAAAA+AgFFwAAAAD4CAUXAAAAAPgIBRcAAAAA+AgFFwAAAAD4CAUXAAAAAPgIBRcAAAAA+AgFFwAAAAD4CAUXAAAAAPgIBRcAAAAA+EiIvwNYtmyZli1bpt27d0uSGjZsqOuuu04XXHCBJMkYo3feeUc5OTmy2+1q3ry5hg8frkaNGjmXUVJSoszMTOXm5qq4uFht2rTRiBEjVKdOHb+sEwCg+sjOztfMmWtlt5fIZrNq3Li2SktL8ndYAICzhN97uOLi4nTDDTdo6tSpmjp1qtq0aaOnnnpKv/76qyRp8eLFys7O1rBhwzR16lTFxMRo8uTJOnTokHMZc+fOVV5ensaMGaPHH39chw8f1rRp01RWVuav1QIAVAPZ2fnKyMjThg37tH37QW3YsE8ZGXnKzs73d2gAgLOE3wuuTp06qWPHjkpMTFRiYqKuv/56hYeH6+eff5YxRkuXLlX//v3VuXNnJSUladSoUTpy5IhWr14tSTp48KCWL1+uIUOGqF27dkpOTtbo0aOVn5+vNWvW+HntAAD+NHPmWhUUFLtMKygo1qxZa/0UEQDgbOP3SwqPV1ZWpi+++EJHjhxRixYttGvXLhUWFqp9+/bONlarVa1bt9aGDRvUp08fbdmyRUePHlW7du2cbeLi4pSUlKSNGzeqQ4cOHt+rpKREJSUlzr8tFosiIiKc/+9Pjvf3dxw4NXIVOMhVYKjqPB04UOpxut1eynfhNLFNBQ5yFTjIVc1ULQqu/Px8TZgwQSUlJQoPD9f48ePVsGFDbdiwQZIUHR3t0j46Olp79uyRJBUWFiokJEQ2m82tTWFhYbnvmZWVpYULFzr/Tk5O1vTp01WvXr0qWqvTFx8f7+8QUEHkKnCQq8BQVXmKiYnQb78d8Dg9ISGhSt7jbMc2FTjIVfX2r39t0GOPfaH9+4tls1k1aVIXXXttS3+HhSpQLQquxMREzZgxQwcOHNBXX32lF198UZMmTXLOP7HKN8accpmnatO/f3/169fP7T12796t0lLPZ0TPFIvFovj4eO3cubNC6wr/IVeBg1wFhqrO0913n6eMjK9cLiuMjQ3V3Xefpx07dpz28s9mbFOBg1xVf0uW5Lvtq0aO/Eh79xaqXz8G+fGHkJCQKuuIqRYFV0hIiPOsy7nnnqvNmzdr6dKlSk9Pl3SsFys2NtbZvqioyNnrFRMTo9LSUtntdpderqKiIrVsWf5ZAavVKqvV6nFeddkZGWOqTSw4OXIVOMhVYKiqPKWlNZJkNGvWWtntpbLZQjR2bFulpTXie1BF2KYCB7mqvmbOXFPO/aZr/n8/hkBWLQquExljVFJSovr16ysmJkZr1qxRcnKyJKm0tFTr16/X4MGDJUlNmzZVcHCw1qxZoy5dukiSCgoKlJ+f72wDADh7paUlMQz8WYzHAlQ9PtOqZ7eXlDPdv1ddoWr4veB64403dMEFF6hOnTo6fPiwcnNztW7dOk2YMEEWi0V9+/ZVVlaWEhISFB8fr6ysLIWFhSklJUWSFBkZqdTUVGVmZioqKko2m02ZmZlKSkpyGUgDAACcXRyPBTi+5yAjI0+SKBAqic/UN2w2z1dd2Wx+/6mOKuD3LO7bt08vvPCCCgoKFBkZqcaNG2vChAnOYik9PV3FxcV69dVXdeDAATVr1kwTJkxwjigoSUOHDlVwcLCeeeYZ54OPMzIyFBTk91HvAQCAn5zssQDVsTgIhJ6jQPtMA8W4cW3dCtnY2FCNHdvWj1Ghqvi94LrjjjtOOt9isWjgwIEaOHBguW1CQ0M1bNgwDRs2rKrDAwAAASqQLtMKlJ6jQPpMA4kjx7NmrdWhQ0YREZb/v9+0+uQelef3ggsAAMAXAukyrUDpOQqkzzTQpKUlqV+/xkpISNCOHTsY4KQG4Zo7AABQI40b11axsaEu06rrZVqB0nMUSJ8pUF1wOgIAgBrMF/cFZWfnOy99Cg+3VMt7jSTXy7RcHwtQ/WINlJ6jQPpM4RuBcK9hdVO9tmIAAFBlfHFfUKDca+QQKI8FCKRBEwLlM0XVC7Ttv7rgkkKgkrKz85WaukRNmvxDvXotUXZ2vr9DAgAXJ7svqDotE8d+rE6ffrFatYpWw4a11KpVtKZPv5gfsahW2P4rhx4uoBI4wwMgEPjivqBAudcoENFzhOqO7b9y6OECKoEzPAACgS/uCwqUe40AVD22/8qh4AIqgTM8AAKBL0aUY5Q64OzF9l85lKNAJXCGB0Ag8MWIcjygFTh7+XKUypo8+iG/DoFKCKTRpACc3XxxXxAPaAXOXr7Yp9T0e+MpuIBK4AwvAABA1TjZvfE14bcVBRdQSZzhBQAAOH01/d54Bs0AAAAAaqBjzwzN1sUXL1Jqana1fWZoTb83noILAAAAqGEc90Vt2LBP27cf1IYN+5SRkVcti66aPvphzSgbAQAAADgF0n1Rvhz9sDqg4AIAAABqmEC7L8oXox9WF1xSCAAAANQwNf2+qEBCwQUAAADUMDX9vqhAQokLAAAA1DA1/b6oQELBBQAAANRANfm+qEDCJYUAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcOCtkZ+crNTVbF1+8SKmp2crOzvd3SAAAADgLhPg7AMDXsrPzlZGRp4KCYue0jIw8SVJaWpK/wgIAAMBZgB4u1HgzZ651KbYkqaCgWLNmrfVTRAAAADhbUHChxrPbS8qZXnqGIwEAAMDZhoILNZ7NZi1nOlfUAgAAwLcouFDjjRvXVrGxoS7TYmNDNXZsWz9FBAAAgLMFp/hR4zkGxpg1a63s9lLZbCEaO7YtA2YAAADA5yi4cFZIS0uiwAIAAMAZxyWFAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAAAAAOAjFFwAAAAA4CMUXAAAAADgIxRcAABUA9nZ+UpNzdbFFy9Samq2srPz/R0SAKAKhPg7AAAAznbZ2fnKyMhTQUGxc1pGRp4kKS0tyV9hAQCqAD1cAAD42cyZa12KLUkqKCjWrFlr/RQRAKCqUHABAOBndntJOdNLz3AkAICqRsEFAICf2WzWcqZz5T8ABDoKLgAA/GzcuLaKjQ11mRYbG6qxY9v6KSIAQFXh1BkAAH7mGBhj1qy1sttLZbOFaOzYtgyYAQA1AAUXAADVQFpaEgUWANRAXFIIAAAAAD5CwQUAAAAAPkLBBQAAAAA+QsEFAAAAAD5CwQUAAAAAPkLBBQAAAAA+4vdh4bOyspSXl6ft27crNDRULVq00I033qjExERnG2OM3nnnHeXk5Mhut6t58+YaPny4GjVq5GxTUlKizMxM5ebmqri4WG3atNGIESNUp04df6wWAAAAAPi/h2v9+vW64oorNGXKFD388MMqKyvT5MmTdfjwYWebxYsXKzs7W8OGDdPUqVMVExOjyZMn69ChQ842c+fOVV5ensaMGaPHH39chw8f1rRp01RWVuaP1QIAAAAA/xdcEyZMUM+ePdWoUSM1adJEd955p/bs2aMtW7ZIOta7tXTpUvXv31+dO3dWUlKSRo0apSNHjmj16tWSpIMHD2r58uUaMmSI2rVrp+TkZI0ePVr5+flas2aNP1cPAAAAwFnM75cUnujgwYOSJJvNJknatWuXCgsL1b59e2cbq9Wq1q1ba8OGDerTp4+2bNmio0ePql27ds42cXFxSkpK0saNG9WhQwe39ykpKVFJSYnzb4vFooiICOf/+5Pj/f0dB06NXAUOchUYyFPgIFeBg1wFDnJVM1WrgssYo3nz5qlVq1ZKSkqSJBUWFkqSoqOjXdpGR0drz549zjYhISHOIu34No7XnygrK0sLFy50/p2cnKzp06erXr16VbQ2py8+Pt7fIaCCyFXgIFeBgTwFDnIVOMhV4CBXNUu1Krhmz56t/Px8Pf74427zTqz0jTGnXN7J2vTv31/9+vVzW/7u3btVWlpa0ZB9wmKxKD4+Xjt37qzQesJ/yFXgIFeBgTwFDnIVOMhV4CBX1UdISEiVdcRUm4Lrtdde07fffqtJkya5jCwYExMj6VgvVmxsrHN6UVGRs9crJiZGpaWlstvtLr1cRUVFatmypcf3s1qtslqtHudVly+4MabaxIKTI1eBg1wFBvIUOMhV4CBXgYNc1Sx+HzTDGKPZs2frq6++0qOPPqr69eu7zK9fv75iYmJcBr8oLS3V+vXrncVU06ZNFRwc7NKmoKBA+fn5atGixZlZEQAAAAA4gd97uGbPnq3Vq1fr/vvvV0REhPOeq8jISIWGhspisahv377KyspSQkKC4uPjlZWVpbCwMKWkpDjbpqamKjMzU1FRUbLZbMrMzFRSUpLLQBoAAAAAcCb5veBatmyZJOmxxx5zmX7nnXeqZ8+ekqT09HQVFxfr1Vdf1YEDB9SsWTNNmDDBOaqgJA0dOlTBwcF65plnnA8+zsjIUFCQd514ISF+/0icqlMsODlyFTjIVWAgT4GDXAUOchU4yJX/VWUOLIYLRAEAAADAJ/x+DxfcHTp0SBkZGTp06JC/Q8EpkKvAQa4CA3kKHOQqcJCrwEGuaiYKrmrIGKOtW7cyOk0AIFeBg1wFBvIUOMhV4CBXgYNc1UwUXAAAAADgIxRcAAAAAOAjFFzVkNVq1XXXXVfug5lRfZCrwEGuAgN5ChzkKnCQq8BBrmomRikEAAAAAB+hhwsAAAAAfISCCwAAAAB8hIILAAAAAHyEggsAAAAAfCTE3wHUVOvXr9d7772nrVu3qqCgQOPHj9fFF1/snF9YWKj58+drzZo1OnDggM477zwNGzZMCQkJLm0yMzO1Zs0aHT58WImJierfv78uueQSZxu73a45c+bom2++kSR16tRJw4YNU61atc7cyga4M5WrUaNGaffu3S7vnZ6ersGDB/t+JWuAqsjTzp07lZmZqZ9++kmlpaVq3769hg0bppiYGGcbtqnTd6ZyxTZ1erKyspSXl6ft27crNDRULVq00I033qjExERnG2OM3nnnHeXk5Mhut6t58+YaPny4GjVq5GxTUlKizMxM5ebmqri4WG3atNGIESNUp04dZxu2q9NzJnPFdnV6qipXn3zyiVavXq2tW7fq0KFDmjNnjtv2wnYVOBil0Ef+/e9/a8OGDUpOTtbMmTNdfnAYY/Twww8rJCREN910kyIjI7VkyRJ9//33mjVrlsLDwyVJTzzxhA4ePKjhw4crKipKq1ev1ttvv61p06YpOTlZkvTkk0/qjz/+0G233SZJ+vvf/6569erpgQce8M+KB6AzlatRo0apV69euuyyy5zvHR4e7lwGTu5083T48GHdd999aty4sQYOHChJeuutt1RQUKApU6YoKOhYhz/b1Ok7U7limzo9U6ZMUdeuXXXuuefq6NGjeuutt5Sfn++yb1u0aJGysrJ05513KiEhQe+++65+/PFHPfvss4qIiJAkvfLKK/r222915513KioqSq+//rrsdrumT5/OdlVFzmSu2K5OT1XlKjs7WyUlJZKkN954w2PBxXYVQAx8bsCAAearr75y/r19+3YzYMAAk5+f75x29OhRc8stt5hPPvnEOe3GG280q1atclnWLbfcYnJycowxxvz6669mwIABZuPGjc75GzZsMAMGDDDbt2/31erUaL7KlTHG3HnnnWbJkiU+jP7sUZk8ff/992bgwIHmwIEDzjb79+83AwYMMP/5z3+MMWxTvuCrXBnDNlXV9u3bZwYMGGDWrVtnjDGmrKzMjBw50mRlZTnbFBcXm6FDh5ply5YZY4w5cOCA+ctf/mJyc3Odbf744w8zcOBA8+9//9sYw3blC77KlTFsV1WtMrk63g8//GAGDBhg7Ha7y3S2q8DCPVx+UFpaKkkuD7ULCgpSSEiIfvrpJ+e0Vq1a6fPPP5fdbldZWZlyc3NVUlKi1q1bS5I2btyoyMhINW/e3PmaFi1aKDIyUhs2bDhDa1OzVVWuHBYvXqxhw4bpvvvu07vvvutcPk5PRfJUUlIii8Xi0iY0NFQWi8XZhm3K96oqVw5sU1Xn4MGDkiSbzSZJ2rVrlwoLC9W+fXtnG6vVqtatWzu3hy1btujo0aNq166ds01cXJySkpK0ceNGSWxXvuCrXDmwXVWdyuSqItiuAgv3cPlBYmKi6tWrpzfeeEO33nqrwsPDtWTJEhUWFqqwsNDZ7t5779UzzzyjYcOGKTg4WKGhobrvvvsUHx8v6dh9ENHR0W7Lj46OdlkOKq+qciVJV155pZo2bapatWpp06ZNeuONN7Rr1y7dfvvtflizmqUieWrRooXCwsI0f/58XX/99TLGaP78+TLGONuwTfleVeVKYpuqSsYYzZs3T61atVJSUpIkOT/rE7eJ6Oho7dmzx9kmJCTE+WPy+DZsV77hy1xJbFdVqbK5qgi2q8BCweUHISEhGjdunP72t79p2LBhCgoKUtu2bXXBBRe4tHvrrbd04MABPfLII4qKitLXX3+tWbNm6fHHH3duuJ4YY2SxWHy9GmeFqsxVv379nO0bN26sWrVqadasWRo8eLCioqLO6HrVNBXJU+3atTV27Fi9+uqr+uCDD2SxWNS1a1clJyc7710oD9tU1anKXLFNVZ3Zs2crPz9fjz/+uNu8E7/7pgK3fle0DduV93ydK7arqlPVuaoItqvqiYLLT5o2baoZM2bo4MGDKi0tVe3atfXQQw+padOmko6N0PXhhx9q5syZzlFrmjRpop9++kkffvihbr31VsXExGjfvn1uyy4qKvJ41gOVUxW58qRFixbO13MQO32nypMktW/fXs8//7yKiooUHBysWrVqaeTIkapfv74ksU2dIVWRK0/Ypirntdde07fffqtJkya5jFbnGBGysLBQsbGxzunHbw8xMTEqLS2V3W536TkpKipSy5YtnW3YrqqGr3PlCdtV5ZxOriqC7SqwcA+Xn0VGRqp27drasWOHNm/erIsuukiSVFxcLMn9DEhQUJDzLEiLFi108OBBbdq0yTn/559/1sGDB0+680TlnE6uPNm6daskuexwcfrKy9PxateurVq1aumHH35QUVGROnXqJIlt6kw7nVx5wjblHWOMZs+era+++kqPPvqoWzFbv359xcTEaM2aNc5ppaWlWr9+vXN7aNq0qYKDg13aFBQUKD8/3/lDne3q9J2pXHnCduWdqshVRbBdBRZ6uHzk8OHD2rlzp/PvXbt2adu2bbLZbKpbt66++OIL1a5dW3Xr1lV+fr7mzp2riy66yHkTZWJiouLj4/XKK6/opptuks1m09dff601a9YoIyNDktSwYUN16NBBf//73zVy5EhJ0j/+8Q917NjR5XkPOLkzkauNGzdq48aNatOmjSIjI7Vp0ybNmzdPnTp1Ut26df2y3oHmdPMkSStWrFCDBg1Uu3Ztbdy4UXPnzlVaWppze2GbqhpnIldsU6dv9uzZWr16te6//35FREQ47/uIjIx0DlLSt29fZWVlKSEhQfHx8crKylJYWJhSUlKcbVNTU5WZmamoqCjZbDZlZmYqKSnJOTgD29XpO1O5Yrs6fVWRK0nO+1od+9L8/HxFRESobt26stlsbFcBhudw+ci6des0adIkt+k9evTQqFGjtHTpUr3//vvOLuXu3bvruuuuU0jI/2rgHTt2aP78+dqwYYMOHz6s+Ph4XXXVVerevbuzjd1ud3ZbS9KFF16o4cOH89A7L5yJXG3ZskWzZ8/W9u3bVVJSonr16qlLly5KT09XWFjYGVvXQFYVeZo/f75Wrlwpu92u+vXrq0+fPkpLS3PpnWSbOn1nIldsU6fP8YyzE915553q2bOnpP89oPWTTz7RgQMH1KxZMw0fPtzlPuLi4mL985//1OrVq10epnv8D3S2q9NzpnLFdnX6qipXb7/9thYuXHjS5bBdBQ4KLgAAAADwEe7hAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIAAAAAH6HgAgAAAAAfoeACAAAAAB+h4AIA1CjTpk3TzTffrD179rjNs9vtuvXWW/XII4+orKzMD9EBAM42FFwAgBrl9ttvV3BwsP7+97+7zZs9e7YOHTqkUaNGKSiIQyAAwPc42gAAapSYmBgNHz5c//nPf/Txxx87p+fl5Sk3N1c33XST4uPjfRpDWVmZSkpKfPoeAIDAEOLvAAAAqGpdunRRXl6eMjMz1b59e0VEROiVV15Ru3btdPnll2vz5s1auHChfvrpJxUXF6tBgwa6+uqr1aVLF+cyioqK9Pbbb2vdunXas2ePwsLC1KhRIw0cOFDnnXees92uXbt01113afDgwSotLdXy5cv1xx9/6IEHHlCHDh38sPYAgOrEYowx/g4CAICqZrfbNW7cOCUmJqp27dpas2aNnn76ae3YsUNPPvmkmjdvriuuuEKRkZH6/PPPtXLlSt15553q2bOnJOn333/X0qVL1bp1a8XExOjw4cPKy8vTihUr9Oijj+r888+X9L+CKy4uTgkJCc5lxsfHq379+n78BAAA1QE9XACAGslms+n222/X1KlTJUl33XWX6tSpo8mTJ6tRo0Z69NFHFRwcLEnq0KGDioqK9Oabb6p79+4KCgpSYmKiRowY4VxeWVmZ2rdvr927d+uDDz5wFlwOVqtVEyZMUEgIh1YAwP9wVAAA1FgXXHCBmjdvLrvdru7du2vnzp3avn27brrpJknS0aNHnW07duyo7777Tr///rsaNmwoSVq2bJlycnL022+/udyT1aBBA7f36tSpE8UWAMANRwYAQI1mtVqdhVBhYaEkKTMzU5mZmR7b79+/X5K0ZMkSvf766+rTp48GDRqkqKgoBQUFacGCBdq+fbvb62JjY32zAgCAgEbBBQA4a9SuXVuSdPXVV6tz584e2yQmJkqSPvvsM51//vkaOXKky/xDhw75NkgAQI1CwQUAOGskJiYqISFBv/zyi2644YZTtj/xEsFffvlFGzduVN26dX0VIgCghqHgAgCcVUaOHKmpU6dqypQp6tGjh+Li4mS327V9+3Zt3bpVY8eOlSRdeOGF+te//qW3335brVu31u+//66FCxeqfv36Kisr8/NaAAACBQUXAOCs0qZNGz355JN69913NW/ePNntdkVFRalhw4a69NJLne2uueYaHTlyRMuXL9fixYvVsGFDjRw5Unl5eVq/fr0f1wAAEEh4DhcAAAAA+EiQvwMAAAAAgJqKggsAAAAAfISCCwAAAAB8hIILAAAAAHyEggsAAAAAfISCCwAAAAB8hIILAAAAAHyEggsAAAAAfISCCwAAAAB8hIILAAAAAHyEggsAAAAAfOT/AOOOmleIyN0FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Question:Generate the scatter plot by plotting the total versus year in df_total.\n",
    "# generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "# add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43dd9f94-994c-4a26-89a8-d902ae205c3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}