Cheekiam/DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb

## DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb
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   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
    "\n",
    "<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
   ]
  },
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Pie Charts](#6) <br>\n",
    "5. [Box Plots](#8) <br>\n",
    "6. [Scatter Plots](#10) <br>\n",
    "7. [Bubble Plots](#12) <br> \n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
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   },
   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Import primary modules."
   ]
  },
  {
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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   },
   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset."
   ]
  },
  {
   "cell_type": "code",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
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       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
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       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
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       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>...</td>\n",
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       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
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     },
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   ],
   "source": [
    "df_can.head()"
   ]
  },
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   },
   "source": [
    "Let's find out how many entries there are in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   },
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.0.3\n"
     ]
    }
   ],
   "source": [
    "%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": "markdown",
   "metadata": {
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   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Gather data. \n",
    "\n",
    "We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1. **Split:** Splitting the data into groups based on some criteria.\n",
    "2. **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3. **Combine:** Combining the results into a data structure."
   ]
  },
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   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">"
   ]
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    "scrolled": false
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   "outputs": [
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      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
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       "      <th>1980</th>\n",
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       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <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",
       "    <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "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": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "colors_list = ['gold', 'yellowgreen', 'yellow', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0.05, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            colors=colors_list,\n",
    "                            explode=explode_list         # 'explode' lowest 3 continent\n",
    "                            )\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\n",
    "<!--\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\" width=440, align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI = df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.plot(kind='box', figsize=(10,8))\n",
    "\n",
    "plt.title('Box plot of Chinese and Indian immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Countries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\n",
    "- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,  \n",
    "- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jxq8LfqvDt7ytFFDDn29+n87fp48+2frEH+sSjJ9B5YvBj+LSPEjQejz4vga4Pi7NmGC/alvFvA6eQ/Hnn28Hw64PhlU4rpHaufRh4lon4esKuwju6qewjJoFx4XvxA1/Dngz1WUZpOlB4kBUTetTY4Pvk2LSXB4MuzTutzqgRdx05eekO4P1FduK+Zy4dVFexp+nsKwqnL/wddNSYs4J+AvqKNAk+P51wMXlU17vHFuDbbnCso3ZZhxxASx8dwWRuGHfx1/oN4rZ1uMDBp8AH8cNWwTcXUW5phK3zyRJV75uLokZVn6Tc0LMsPi6pgP+X1xey4DfVDGv8pbQp6W6fIPpXgD+GvN9Rvxvo/LxYB1xrcbwfVzVJhBV53mRpH4UN02F+maSNDup+kbwbSQORJ0ZLPvr8C0t2+OPJQ54KG7dz+TLetguYq5/qUUdqC6foDxrY7d1fEMKR1xAEx+4/yRJPhW237hxrfHXHSX4QPEvkqTbDnw33b+xvj/qI6rhmQsMDT7D8I/8/N3MJgTj+wd/Z8ZNNxPf0gIA59w+/N2NS4BO+AhuKn5uZnvxd/SfxbdGuS1J2gHAHBfTF5NzbhH+QDEgyTTg71a9Biw2/3aV75tZ92SJzawVvhVN/G9+G+hhZs2q/kkpWVT+j3NuC37n/yhm2A78QaFTsun4su+VjxIMi5+uXH9gqYt5jt85twx/cI83L36AmV1iZjPNd6i8F3/ns7x10sEs48q5Pvh7VJIylbsb3zfFDDO7zWreqXdVZR9A4vVpfLmNg182sX19FQDLnXPFccOSLV+CPGtiYfk/zrlS/F226pbVwrjv62OnMbOeQYeHK81sN74lYWv8nejEhTYLmdktZrbQfAfSe/EtAeOnWRq3PCrMG7884zvun1XN70kmI/Mys2vMdzC+Kfidv6GKZRNoij9O1Uiwf12BvztaiA8INsHfAY/tz+Q2vmwJeCLwI+BPZnZ+qrNKNNDM/st8J8Tln9HVZWT+bTQv4i9mUu24OOH8UzAaf+65En/3+BcJ0uzHL3+RI1XSY76ZdcQfv+6N3dfxrazB31CsysMx9bD78C1zH0qSNtVzabzp+Na/q83sSTO71sw6VJG+N75ukWheVdX3aqK29al01MPmu4r9Wb2XJG2ielgq568NQd2y3Hr8OkpadwnqnQ8DrwWdHN9iZsclS5+C+XHfk207TfCtf8otiktTQMXlWz6sunpYTc5JsfWwAvy5ua71sKHBNcdqM9uDbzkIVdfDmpnZXWb2ifkOtffiHwWNn6aq40ErfKu7dNXD0j6vGtQ349W2HvZvfOuy3+ADn+vxjwNuIqiHBX1F3Y+v94zFB6f/CDwZ3xF4stkkG2G+g/zYetjR1WVmZp3wT5R8hG9FmIra1MP24GMAJ+EDav9hZt9OkO6wqIcpENXw7HPOrQw+C51zv8OfKG6tZrpEB/nyHvXbUPUJItb/4HeAY4GmzrlrXdUdAibbyZLufMHJ/lx8E+H5+OdtPzWzidWULT7PdL5CPFHn4fHDHJX3mQNx45MNq2pfS/VAVaGDbDM7Bf+Y5kz8Hb9h+BMH+ApjuWhcBSuVMuGcux3frDeCfxRrjpnVqEPJZGWPK0dVwxOtg1TWS6zlQCtL/VXN8Z3cV5d/KtO8jH+DzXfxLdKGErT2qiLPm/Enuz/h+0sYiq+Uxk+TaN7x+0ZtgxLx0j6voBPe/8E/0nEe/q73r/B3yaqyBX9Hs8acc08557rhO3bt4Jz7JtAd/2gIwcshfohvCfeqc+4j59z/wzcRL6+AbMVXkPLjsj8K33JgR5LZP8iXNxuG4vv4S8rMWuMD982Bs1zFzmc3Bn8TlSFZp+RVcs6tds594px7DN8v38+s8muZ2+GXv8iRqqpjfvnf71NxXx+Cf9To42ryvjVI3x3fYumnLrgFnkRt6mF78Rc6F+P7prweWJnCBV6ieli6zi+1rU9loh6W7DfF18NSPX8l2l6qKxPOuWvwN0Km4/utWmxm11U1TRJlSeryyerVmaiH9a5BB++JXjZU63pYcMP69WDYt/CPPJW3uquqTL/Ht1b7Ff4RrKH41l2p1MPKy5tomdZFJuaVan0zXl3qYffjW0J1D/7egX8i5bMgyTiCt6E7594Orol/gb9u/I8gTW3rQL+g4rG5yjdoBtcPb+O7o7jEVXzhVFrrYc65aBAD+Mg59yD+EdhE116HRT1MgahDQym+WTT4JrHgm3jHGh0zDjMbgH+u/Dr8XbgnzaxxCvPaHuwA61z1bxT5BDg19sRiZkPwLT3Ky1KCf869AufNc8792jk3Br+DfzPRTJx/W806/Ek41hhgtfNvEDhULQH6BRebAAR3vNokn+SgUcBW59zPnHNznXOf4luO1VT5SS3RelrlnHvAOXcZ/sBd1dseauITKq/P0/EnzyVpmke5p/CBgUqvrAcws7Zpnl98/u3xd1zvcs695pxbgr+TUV1weAzwqnPub865Bc65lfiLmJpagu+sPlb893SpzbzG4DvEvtc594FzbgX+8YLqfEgd78Q75zY753aZ2Vn49fFsMKr8eBt/DCwjqOgFLfXm4/vqi3UOvqVowrdFOf8GrpUxn33Jyhe0UHgr+Hq2cy6+peQafAUqURlqe7c1Vij4HLyoCt4s2ItqAmgiRyrn30a1Fv/4+8oEn+paEGwK0m2sJgAFqZ1Lk9XDypxzM4OLuxPxF1RfSzKflfjzaKJ62CeVkx9SlgAnx76NFDg1xWlre/6KVwIQVwYAnHOLg/zPxXccfm0t8k8k0bYzhi87XU+nx/Dn1R8mGpnpehi+r7COwK3Oubecc0vxjz5Wd0N7DL67gX8FT3yswt+gTVlw82g99VAPq8O8alvfrFM9LLgW3Oj8m6i/Egx+IfhbfgMsvi51sB5GLetAQd0v9phcmixtcGPyHfxx4pK4pwLgyxdFTIiZJoR/AUK66mEVrt/NrE8w7JCvh+VmuwBSSaOY1z02x2/YE4BfAjjnPjOzp4AHgrsin+ODAwMJKhBm1gR/5/5F59zfzOxZfFPOu/FNIdPlfvwdv0fM7Nf44MkDwCzn3DtBmtVAvpmdiu8UsAgffT4Tf3diI/5gNxh/gk3mN8A9ZrYC/4z0Gfjf/d00/p5seBx/p+UfZvZzfDPLe/AVgeoqoMuBjmZ2Nf5idRT+rSE19Tn+gvs8M/sX/oBahu+c8hn8OmyDP7CnK0j0e+BDM7sX/9a7Hvg7MY87576oasKacs6tN7MbgYfMrA1fduTfDt+XxjgqB3bTaQf+rsU1ZvYZ/s7P7/DruCrLgSvNbBy+YnEVvkPUZC1tkrkHeMrM5uHv5I3CP3aVCbWZ13LgajO7CFiMfxzskhTmNRX4lpk1jQ3mmFlvfH9KRwffhwajVgatADCz7+Ifg94ZlPE+fMfn5UGfpfhWAveb2Q/xFZ1x+HUQ2zr1d8DTwe99Fd8/1CX41/vWiZl1Bv6N306+ATSLeQx5u/OvIHdm9nvg12a2FF8pmYJveXFNTF4t+PJxoEb4Y/JQYG9Q4cTMbsb3qfEp/thzUvD7XowLgI3CHyPerutvFGlAWsQcK8rtd/5R3tq4Ffibme0Ense3IOmH71y2Ni1akknlXLoauDy4QbkJ/+jHOfiW7zPx56cT8S0TEp7jnXNFZvb/gNvNbAu+Tnk5/hx6dhp/TzY8gG9h8Wczuw/fsuHOYFwq9bDanL/irQ7+Xmhms/DH/Xz8cfwlfGCzC/6m84e1yD+R3+Bfc38L/ibMUPwj6fe4il0i1Jlz7n0z+xX+XNUd34Lsc/xvCuMfJwunc55xPseft75nZvfg95O7SG39XmRmz+D77fkhvsybajj/e/D7zjL8W7IvxAcqMqE286ptfXMqvuV0BWbWny+7CWkUc2xdUr5tmdmP8NeB5UGcu/Bvv1sZpJ2NPzY9Elwf7cbvW2cRbCup1oFqK/gdb+Afx7sJaO+fGARgSxDM321mDwZl2Ijfl3+Ev557KCavdgT10sDRwXLZXn6sNrP/xge9VuFvAI7BdyL/f3FFGwt87pw71G8CkPVOqvT58oPv5NHFfIrwdyz+k6Dz6SBdK/zGvQW/A79PTIe5+I53V1GxA+xR+IrQhVXMfw1Vd2I7lpgOHoNhI/AVmX34i7ongE4x4/OCYduDaW/DR8+n4pssFuNPEL+n6jdXGH7HXh38jlXEdGwZpLmN2ndWHv+WmlLiOurGt2L5dvB/Dyp3CNmNuI4l+bKTxbOqmO4E/MmiGB+suwz/2NbNVZUxGH47/oRYGCzTrwZpewTjp1D5Vc+Jyvlj/MmnDB/oaxKst9XB796Mrzh0r+GyrbTNxIyLfeX0Fvx2G/sK2keI6/SUxJ2uPogPfla33kfhLwg2B/NchX8by9Bk6yYYvpKYV7CSuLPy+LdNVign/q7jomBZLsc3N66Qb4LytsY/Frkb/6ai/wnW95pqllGiTk+/H6zfffiT6jeSrZdkeadrXvHbBP4Y8RD+GFH+Fs4b4/NNUL4QPqD4tbjhM6h4HC3/xG7v/xtscyX4wMstxHXKjb9IexIfLN+HD9L8iJhjccw+9mmQ13JSfCVvCtvrlCS/o8Jvidl/v8Bv1wup/KrrsUnyid1Gb8FfhBbiL1QX49+K1Swur78T05moPvoc6h8q173KP8uC8TNI8Ar1uDwSvZhlEr6voaLg2LaQJB3PxkyT8FwfM34Klc/p1Z1L2+HrB7uC/KfgL3DeDNLvx9c9bqGK17Xjj9V34Y/vJcHxIv74+wi176y8pvWp8uNabJ200stX+PIFLb2rmO6s4JhXjL/oLO+s/NJkZYxZJlWev0hQN01Szj/g63PRYDl2xgeIyl8gsQF/I611qss22TYTM+4b+BsvJcF6vZPgbWBVbOtvAI/EDXuVKt5iG5PuAnzwYQdf1oceqGrdBMMr1MlJrR5foZz4uvWKYL4L8PWySnX9uDy64x+NLyR4GyL+pvmMmhwP8PWVX+Mf6S/Edx7+H8nWS7K80zWv+G2CFOqbScrXMphmZNzwNSQ+psZu7+XbQfk+d02C/IfiX1K0BR8IXESCN/lRTR2oth/8vpusHhb7W/LwN+4Kgu3rXeCkuLymJMkndhu9D39tsC9YNh/gG1zE10/fBn6ajt+Y7Y8FP0hEGggzOwZ/EL/QOfdSlosj0mCZ2ZX4O5TDnE5mGRfcyf4IOME5tybLxRERyQgzK+8yYrBzrro+vUSOWEFrpROdc5OyXZYjgZkNx7+8pq/zXdcc0vRonkiWmdnX8XeiVuPfUPE7fCux17NZLpFDwGP4u+Rd+PJtkJI5PfB3LddkuRwiImljZjfgW1tswPfpeB8wV0EokWr9HviRmTV3vq8nyayj8K3/DvkgFKAWUSLZZmbfxz973BXfvPtd/GN5ae0rSUREREQqMrO78P2slr/pajrwE+fctqwWTETkMKZAlIiIiIiIiIiI1ItQtgsgIiIiIiIiIiJHBgWiRERERERERESkXhyJnZXrWUQREZHDn2W7AFKJ6mAiIiKHv2rrYEdiIIoNGzZUGtahQwe2bt2ahdJIOmj9Hdq0/g59WoeHtsNt/XXp0iXbRZAkEtXBJLHDbb+sD1pmNadlVnNaZjWnZVZzh+oyS7UOpkfzRERERERERESkXigQJSIiIiIiIiIi9UKBKBERERERERERqRdHZB9RIiIiIiIiIiK14Zxj//79RKNRzNL/fpRNmzZRXFyc9nzTwTlHKBSiSZMmtf7tCkSJiIiIiIiIiKRo//795OXlkZubmZBKbm4uOTk5Gck7HUpLS9m/fz9Nmzat1fR6NE9EREREREREJEXRaDRjQahDQW5uLtFotNbTKxAlIiIiIiIiIpKiTDyOd6ipyzJQIEpEREREREREJMM2b97MDTfcwMiRIxk7dixXXnkln332Wdrynz17NvPnz09bfply5LYlExERERERERGpB845rr76ai6//HL+/Oc/A7B48WK2bt1Kr1690jKP9957j+bNm3PyySdXGldaWtpgHidsGKUQERERERERETlMvfvuu+Tl5XHVVVcdHDZw4ECcc9x+++289dZbmBk33XQTl156KbNnz+bBBx/kH//4BwC33norgwcPZvLkyZxyyileLqN/AAAgAElEQVRcfvnlTJ8+ndLSUh566CEaN27Mo48+Sk5ODs888wx33HEH//znP2nTpg2LFy9mwIABvPHGG7z44ou0b9+eaDTK6NGjeemll2jXrl29LgsFokREREREREREMmj58uUMGjSo0vCpU6fyySefMH36dLZv3855553HqFGjqs2vXbt2vPbaazzyyCM8+OCD3H333Vx55ZU0b96c66+/HoB//vOfrFq1in/961/k5OTQunVrnn32Wa655hreeecd+vfvX+9BKFAfUSIiIiIiIiKSAW7bZqKFe7NdjAZt3rx5TJo0iZycHDp27MiIESNYsGBBtdOde+65AAwePJi1a9cmTTdx4kRycnIAmDx5Mk8//TQATz75JOFwOA2/oOYUiBIRERERERGRtHIHDhC944fsfeRP2S5Kg9C3b18+/vjjSsOdcwnT5+bmVhhXXFxcYXzjxo0ByMnJoaysLOl8mzVrdvD/rl270rFjR2bNmsWCBQs444wzavQb0kWBKBERERERERFJr4/fh727KVn8YbZL0iCMGjWKkpISHn/88YPDFi5cSJs2bXjxxRcpKytj27ZtzJ07l2HDhtG1a1c+/fRTiouL2b17N7Nmzap2Hs2bN2fv3qpboH31q1/lpptu4oILLjjYUqq+KRAlIiIiIiIiImkVnTsDgLKC9bid27NbmAbAzHj44YeZOXMmI0eOZNy4cdxzzz1MmjSJfv36cfbZZxMOh7n11lvp1KkTXbt25YILLuCss87ixhtvZODAgdXO4+yzz+bVV1/l7LPPZu7cuQnTjB8/nsLCQiZPnpzun5gyS9YM7DDmNmzYUGlghw4d2Lp1axaKI+mg9Xdo0/o79GkdHtoOt/XXpUsXAMt2OaSShHUwSexw2y/rg5ZZzWmZ1ZyWWWpc0V6iN18Fx/SGz5YRuv4n2ImnZbtYaVNUVFThkbd0y83NpbS0NCN5L1q0iNtuu43nnnuuTvkkWgap1sHUIkpERERERERE0sZ9MBtKSwld/i1o1Bi3Ykm2iyTA/fffzzXXXMNPf/rTrJZDgSgRERERERERSRs39204qiscexx5fQcoENVA3HjjjcybN4/hw4dntRwKRImIiIiIiIhIWrjtW+HTxdiI0zEzGvUbAmtX4/YXZbto0kAoECUiIiIiIiIiaeHmzwTnsOGnA5DXfzC4KKxanuWSSUOhQJSIiIiIiIiIpIWbMwOOPQ7r1BmAvL4DwUK4FUuzWzBpMBSIEhEREREREZE6c+vWwLo12IixB4eFmjWH7j1wK9VPlHgKRImIiIiIiIhInbm5b0MohJ00qsJw690fVi3HlZZmqWSHp82bN3PDDTcwcuRIxo4dy5VXXsljjz3GVVddlTD9f/7nf/Lpp5/WcykrUyBKREREREREROrERaO4eW/DgGFYy9YVxlmf/lBSDGtXZ6l0hx/nHFdffTWnnnoqs2fPZsaMGfzkJz9hy5YtSae5++676du3bz2WMjEFokRERERERESkblYuge1bsVNOrzyudz8APZ6XRu+++y55eXkVWj8NHDiQU045haKiIq655hrGjBnDjTfeiHMOgMsuu4xFixYB0KdPH+666y7OOussJk6ceDCA9frrrzNx4kTGjx/P5MmTqwxs1VZu2nMUERERERERkSOKmzMDGjfBhp5SaZy1aQ8d830g6uyL6r9wGRR98q+4dLf0OqYXhK+uMsny5csZNGhQwnGLFy/mzTffJD8/n4suuoj58+czfPjwCmmKiooYNmwYt9xyC3fccQePP/44P/jBDxg+fDgvvfQSZsYTTzzBAw88wC9/+cu0/TRQIEpERERERERE6sAdOID74F3shBFY4yYJ01jvfrjFH+Kcw8zquYRHlqFDh9KlSxcABgwYwNq1aysFoho1asTZZ58NwKBBg3jnnXcA2LhxIzfccAObN2+mpKSEo48+Ou3lUyBKRERERERERGpv8QdQVIidMjZ5mt794b23YNMGyO9ab0XLtNBXrkl7nrm5uZRW07F73759eeWVVxKOa9So0cH/c3JyEuaVm5t7MCAYm+bnP/851157LePHj2f27Nnce++9tf0ZSSkQJSIiInIYCofDOcD7wPpIJDIxHA73BJ4E2gEfAldGIpGScDjcGPgHcCKwDZgciUTWBHn8FLgaKANuikQirwXDzwH+COQAD0cikbvq9ceJiEiDEp0zA1q2hn5DkqaxPv1x+H6i7DAKRGXLqFGj+O1vf8vjjz/OFVdcAcDChQuZM2dOnfLdvXs3+fn5ADz11FN1Lmci6qxcRERE5PD0fWBpzPffAvdFIpE+wA58gIng745IJNIbuC9IRzgc7g98BRgAnAM8EA6Hc4IA1/8A5wL9ga8GaUVE5Ajkigrho/nY8DFYTk7yhPndoEVL36m51JmZ8fDDDzNz5kxGjhzJuHHjuOeeezjqqKPqlO/NN9/Mddddx8UXX0y7du3SVNqK1CJKRERE5DATDoe7AecDdwI/DIfDBpwBfC1I8nfgNuDPwEXB/wBPA/cH6S8CnoxEIsXA6nA4vBIo72BiZSQSWRXM68kgra4sRESOQO7D2VB6oOrH8vCBE3r1w61YWmU6SV1+fj4PPfRQpeHlLaQA7rzzzoP/P/300wf/X7FixcH/J06cyMSJEwGYMGECEyZMyERxD1IgSkREROTw8wfgx0DL4Ht7YGckEinvJGIdUP5cRFdgLUAkEikNh8O7gvRdgdj2/bHTrI0bXvkVSUA4HL4WuDbImw4dOtThJx1ZcnNztbxqSMus5rTMak7LrLLtH7xLtHN32p80ImEn5LHLrHDocPYumkfb3BA5bTLT2qY+bNq0idzczIZTMp1/XTVu3LjW+0LD/mUiIiIiUiPhcHgisDkSiXwQDofHBoMTvZ7IVTMu2fBEXTu4BMOIRCJ/Af5Snmbr1q3Jii1xOnTogJZXzWiZ1ZyWWc1pmVXkdmwj+skCbOJX2LZtW8I0scvMdfZvYNs+bxY2bGS9lTPdiouLyanqMcQ6SqWz8mwrLi6utC+Uv6mvOuojSkREROTwchpwYTgcXoPvnPwMfAupNuFwuPwmZDdgQ/D/OqA7QDC+NbA9dnjcNMmGi4jIEcbNmwnOYSNOT22CY3pBXiM9nneEUyBKRERE5DASiUR+GolEukUikR74zsbfjEQiVwBvAZcFyb4BvBD8/2LwnWD8m5FIxAXDvxIOhxsHb9zrA8wD5gN9wuFwz3A43CiYx4v18NNERKSBcXNmQM++WKfUWsJYbh707ItTh+VHNAWiRERERI4MP8F3XL4S3wfU34LhfwPaB8N/CNwCEIlEPgEi+E7IXwW+G4lEyoJ+pm4EXsO/lS8SpBURkSOIW/85rFtdbSfl8ax3f/jiM1zx/swUTBo89RElIiIicpiKRCIzgBnB/6v48q13sWn2A5cnmf5O/Jv34odPBaamsagiInKIcXNnQCiEnTyqRtNZn364qVFYtRz6DclM4aRBU4soEREREREREUmZi0Zxc2dC/xOwVm1qNvGxx4MZboUez6uLPn361Cj97NmzueqqqwB4/fXXuf/++zNRrJSoRZSIiIiIiIiIpG7lUti+Bbv4yhpPas2aQ9ce6icqi8aPH8/48eOzNn+1iBIRERERERGRlLm5M6BxE+yEEbWa3vr0g1XLcWVl6S3YEWj27NlcdtllXHPNNYwZM4Ybb7wR5xwAb731FmPGjGHSpElMmzbt4DT/+te/uPXWWwHfOmrixImMHz+eyZMns2XLloyXWS2iRERERERERCQlrvQA7v13saGnYI2b1C6TPgPgramwbjUc0zu9BaxnD7+/idU70tvxeq/2zfjWsI4pp1+8eDFvvvkm+fn5XHTRRcyfP5/Bgwfzox/9iEgkQs+ePbn++usTTjt8+HBeeuklzIwnnniCBx54gF/+8pfp+ikJKRAlIiIiIiIiIqlZ/AEU7a3x2/JiWa9+OMCtWIId4oGohmDo0KF06dIFgAEDBrB27VqaNWvG0UcfzbHHHgvApZdeymOPPVZp2o0bN3LDDTewefNmSkpKOProozNeXgWiRERERERERCQl0TkzoGVr6D+01nlYuw7QvpPvJ+qsC9NXuCz49klHpT3P3NxcSktLU07fqFGjg//n5OQcnNbMqp325z//Oddeey3jx49n9uzZ3HvvvTUvcA2pjygRERERERERqZYrKoRF87GTR2M5OXXKy/r0h5VLD/ZnJOnVu3dvvvjiC9asWQPA888/nzDd7t27yc/PB+Cpp56ql7IpECUiIiIiIiIi1XIL3oPSA9gpp9c9s979YdcO2FJQ97ykkiZNmvC73/2Oq666ikmTJtGtW7eE6W6++Wauu+46Lr74Ytq1a1cvZdOjeSIiIiIiIiJSLTdnBnTqDD371jkv693/y36iOnWuc35HmhUrVgAwcuRIRo4ceXD4nXfeefD/cePGMW7cuErTTp48mcmTJwMwYcIEJkyYkOHSVqQWUSIiIiIiIiJSJbdjGyz/GDvl9JT6HqpW527QrAWsXFL3vOSQokCUiIiIiIiIiFTJzZsJztXpbXmxLBSC3v18h+VyRFEgSkRERERERESq5ObOgJ59saO6pC1P690fCtbj9uxKW57S8CkQJSIiIiIiIiJJufVfwNrV6emkPIb16e//Wbk0rflmmt70V7dloECUiIiIiIiIiCTl5s6AUAg7eVR6Mz6mN+TmHXKP54VCIUpLS7NdjKwpLS0lFKp9OElvzRMRERERERGRhFw06vuH6j8Ua9U2rXlbXh707INbcWgFopo0acL+/fspLi5OT8ftcRo3bkxxcXHa800H5xyhUIgmTZrUOg8FokREREREREQksc+WwbbN2KQrMpK99e6Pe/05XHEx1rhxRuaRbmZG06ZNM5Z/hw4d2Lp1a8byzzY9miciIiIiIiIiCbk5M6BRY2zoiIzkb336Q1kZrPk0I/lLw6NAlIiIiIiIiIgc5JzD7d+H27wR98G72NARWJMMtQA69ngwO+Qez5Pa06N5IiIiIiIiIoc5d6AE9uw6+HG7d1X8vmcX7N4Je3fDnp1QUnJwWjt1bMbKZc1bQJejFYg6gigQJSIiIiIiIpIhrmAd7q2puHWrCX3nVh94qUfR6S/gXvon7CtKnCA3F1q2gZatoWUrrHN3aNUaWrSGVq2xdh3h+MEZLaP16Y97bwaurAzLycnovCT7FIgSERERERERSSMXjcLHHxB982VYsgBycqGsFPfWK9jEyfVXjj27cM8/Bt17YoNOgpatsVatg8BTK/+3SdOMvPkNYOe+UlqWRqtP2Ls/zJgG69fA0b0yUhZpOBSIEhEREREREUkDV7QXN+sN3IypsKUA2rTDLroCGzOe6CN/wv37RdzZF2GNm9RPef79EhwoIfSNm7DO3eplnuWizvHDV9fQrvlGfjWuC83ykrd0sj79cYBbsRRTIOqwp87KRUREREREROrArf+c6KMPEP3RN3FP/S+0bodd+2NCv3mY0MTJWKu2hM67HPbuwb3zWv2UaV8R7s1X4IRT6z0IBbB6RzHbikpZsaWQ37y9ngNlyVtGWbuO0K4jrFQ/UUcCtYgSERERERERqSFXVgaL5hJ98xVY/jHkNcKGj8HOmIgdfWyl9Na7H/QdiHvtedzp52F5eZkt34xpsK+Q0HmXZXQ+ySzcWAjAd0b14IFZa/jDexu5+bQuhJI8Bmi9++M+/RjnXMYeFZSGQYEoERERERERkRS5Pbtxs173j99t3wrtOmKXfAMbfTbWolWV04bOu5zoH36Jm/MWNnp85spYUox74wXofwJ2TO+MzacqiwoKOaZ1Y644sRuFhYX8fcEW2jbZzNUndkocaOrTD+a9DVs3Qcf8+i+w1BsFokRERERERESq4TZvYNc/HyQ6czqUHoDjBxP6yrUw5GQslOKb3voPhWN64159BnfamalPV9Oyvvtv2L3TPw6YBcWlUZZs3sc5fdsAcHG/duzYV8qLy3bQtmkulw5oX2ka6x30E7VyKaZA1GFNgSgRERERERGRKjjniP7pDvbv2IqddiY2biLW9ega52NmhM67jOif78J9MBs7eXT6y1painvtWeh1PPQdkPb8U7F0yz4ORB1D85sD/nd/c1gndu4r4x8Lt9C2aS5nHNu64kRdjoamzX0/UaeOy0Kppb4oECUiIiIiIiJSlRVLoGAdrb73MwoHD69bXkNHQH433NSncCeNSnt/SG7+O7BtM6GvXZe1vpYWFRSSYzCgU7ODw0Jm3HRqZ3YVl/KnORtp3TiHE7u2ODjeQiHo3Q+3Qh2WH+701jwRERERERGRKrhZr0PTZjQZWfeWOhYKYedeCuvWwMfv171wMVw0ipv2NHTrAYNOSmveNbGooIjjOjSlaV7FkENejnHLmK70bNuY376znuVb91UYb737wca1uD2767O4Us8UiBIRERERERFJwhUV4j54Fzt5DNakaVrytOGnQ7uORKc+hXMuLXkCsHAubFyLnXtZ1lpD7S4uY9X2/Qzt3Dzh+GZ5OfxibHfaNs3l9hnrWL+75OA46xM8SvjZ0vooqmSJHs2Tw96AAfns3KmYa8PXJdsFOOK1aRPlk08Ksl0MERERkQbFzZsJJSXY6LPTlqfl5mLnXIJ74iH49BM4bmCd83TOEZ32NHTqjJ10WhpKWTsfFxTigCH5iQNRAG2a5nLbGd35yeufc9uba/nthGNo1zQXevSG3FzcyiXY0FPqr9BSrxSIksPezp0h1q/fkO1iSBU6dOjA1q1bAejatYvWV5Z07apgoIiIiEg8N2s6dOsJx/ROa7522lm4l54kOu0pctIQiGLpIlizArvyuxl7G18qFhUU0SwvRJ/2TapM17llI34xtju3vvEFv3prLXeedTTNGzWCHn1wK9Ui6nCmZiIiIiIiIiIiCbgvPoPPV2Kjz077o27WqDF29iT4ZAHu85V1zi869Slo0w479Yw0lK72FhYUMuioZuSEql9evds34adjuvLFzmJ+PXM9JWVRrHd/WLMSV1JcD6WVbFAgSkRERERERCQBN2s65OZhp4zNSP429lxo2pzo1KfrlI/7bBks/xgbfzGWl5em0tVcwZ4SNu09UOVjefGGdm7OTad2ZvGmIu6bvZFor/5QVgprVmSwpJJNCkSJiIiIiIiIxHElxbg5b2MnjsSat8jIPKxpM+yM82HBe7iNa2udT3Ta09C8JTZ6fBpLV3OLCooAGJLfrEbTje3Zmm8N68TsL/bwt6J8HOBWLMlACaUhUCBKREREREREJI778D3YV4iNSl8n5YnYmRdAXh5u2jO1mt6tWwOL5mFnXpC2t/rV1sKCQto3y6Vrq0Y1nvaifu2Y1K8dU1cX8szAi4/IfqKKDpTxnZdW8canW7JdlBpzG75IOa0CUYcBdTAsItLw6NgsIiJyaHOzpkPHfOibho7Eq2AtW2OjJ+DmvY3btrnG07tpz0Djpr5lVRaVRR0fFxQyJL95rfvT+sYJHTm9Ryue6HAq/97VGBctS3MpG7bpK3exfncJLy3elO2ipMxt3kj0b/cSve17KU+jQJSIiIiIiIhIDLdpg+9zadTZWCjzl802fhJguNeeq9F0bksBbv472OnnYM1bZqZwKVq9o5g9JdEaP5YXK2TG90Z0ZmjTYv587AXM/2h1GkvYsJVFHS8v3w7AgvW72FvcsINwbvtWoo/+D9FffAf34exgG06NAlEiIiIiIiIiMdy70yEUwkbWzxvorF1H7NRxuFnTcbt3pDyde/VZyAlhZ1+UwdKlZmFBIQBDa9BReSJ5OcZPTsun554N3LukhMKShh2QSZc5a/ewubCUywa0pyzqmL9+b7aLlJDbvZPovx4meut1uHf/jY2ZQOjOvxC67Jsp55GVQFQ4HK7REg2Hw2PD4fDLwf8XhsPhWzJTMhERERERETmSudJS3Ow3YdBJWJv29TZfO+dSKC3FvfFiSundzm242W9gp52FtWmX4dJVb1FBIce0aUybprl1zqtpp05cX/Am+1yIN1ftSkPpGr4Xlm2nc8s8vja4Ax2aN2LOuj3ZLlIFrnAv0eceJfpf1+L+/TJ2yhhCd/yZ0Neur/H2V/ctpJ5FIpEXgdT2TBEREREREZGaWPw+7NpBKMOdlMezo7pgJ52Ge2sq7pxLsWZVv6nPTX8RolFswiX1VMLkikujLN28j3P7tklLfmZG767t6FO4gWkrGjHxuLa17nfqULBsyz6Wb93PtScdRU7IGNOrPS9/UkBxaZTGudl9kM3tL8K98RLu9ed95/0nj8Yu/CqW363WeWY1EBUOh8cCtwFbgYHAB8DXI5GIC4fD5wB/CMZ9GDPNFOCkSCRyYzgcvgD4GdAI2AZcEYlEDp1evURERERERKRBic56A1q3g0En1fu87dzLcPPfwb01FTs/nDSdK9yDe3uaDwp0zK/HEia2dMs+DkRdnR/Lq2DAMM6ZNpM/Ne/CR5uKGJLOvBuYF5Ztp0WjEGf2ag3AmF7tefajjSzYWMiI7tnp+8uVFONmTMNNexr27oYhwwlNugLr1rPOeTeEFlEnAAOADcC7wGnhcPh94K/AGcBK4F9Jpp0FjAgCV98GfgzcHJ8oHA5fC1wLEIlE6NChQ6WMcnNzEw4/VOjtTADJl8GhvG6PBPH7n9ZX9tTtWKLjULxDZVs+1M+BIiIi6eJ2bIOP3sfOuQTLyan3+Vv3njDoJNwbL+LOughr3DhhOvfvl6F4P3buZfVcwsQWFRSSG4L+nWrfUXk8G3Yqpz3xV/5OCVM/3XHYBqI27S1hzto9XNyvHU2C1k8ndG1Fi0Yh5qzdU++BKFd6ADfrDdwrEdi5DfoNITTp69ixx1U9nXMpz6MhBKLmRSKRdQDhcHgh0APYC6yORCIrguGPEQSS4nQD/hUOhzvjW0Ul7FI/Eon8BfhL8NVt3bq1UpoOHTqQaPihoQvr12/IdiGyqqr117Vrl0N43R4ZKq4/ra/sqf2x5NA+hmbGoXTsOdzWX5cuCoqKiEjtuNn/BhfFRp2VtTKEzruM6G9vwc16HTvzgkrj3f59uDdfhqGnYF2PyUIJK1tUUMhxHZrSNC99j5FZ02Y0HnIiZ26czwt2GluLDtChWV7a8m8oXlq2AwPOP67twWG5OSFO6tqC+ev3Uhp15IYy/1iicw43ZwbuxSdg6ybodTyhb/8QO25QtdOWRh1/mL2Be8NdU5pXQ3hrXnHM/2V8GRxLJZz2J+D+SCQyCLgOaJLmsomIiIiIiMgRwEWjuHffgOMGYZ2yd1PDeveHvgNwrz2HKz1Qabyb+RoU7iHUQFpD7d5fyqrtxel9LC9gp45j/Oczcc7x2oqdac8/2/aWlDH9s12MPqYV7eOCbCO6t2RvSZRPNhfVS1nce2/h/vc+aNaC0E2/JPST36YUhCqLOu59dwPvfJ565+oNIRCVyDKgZzgc7hV8/2qSdK2B9cH/38h4qUREREREROTwtPxj2FKA1XMn5YmEzr0cdmzFzZlRYbg7cAA3/Xk4fnC1j0rVl482FeGAIZ0z8Ohcv6EclRflxAObeH3lTg6Upf7416Fg+sqd7C+NcmG/ym+dG9a5OY1yjDlrM//2PBeN+r6guvUkdOs92KATU+ocvizquG/2Bt79Yg/fHNYx5fk1yEBUJBLZj38U75VwODwL+DxJ0tuAp8Lh8Dv4Ts1FREREREREaszNmg7NmmPDTs12UWDACXB0L9y0Z3DRsoOD3Xtvws7thM67PIuFq2hRQSHN80L0bpf+B5QsNxcbPoZzlk9j5/4y3quHoEx9KY06Xl6+g4FHNaNXgmXXODfECZ2bM3ftXqI16H+pVhbOhYJ12LmXYqHUwkRlUccf3tvIO5/v4RtDOzKpX/uUZ5eVPqIikUiL4O8MYEbM8Btj/n8VOD7BtI8AjwT/vwC8kMmyioiIiIiIyOHNFe7BffgeNno81ihxB+H1ycwInXc50Qfvwn3wHnbyKFxZGe7VZ6BHHzh+cLaLeNCigiIGHtWMnAz1Y2QjxjL03y+Tn3uAaZ/uYEyPVhmZT32b/cUethaVct3JRyVNM6J7S+au28vKbfvp26FpRsrhnCM67WnomI+deFpK05RFHf9vzkZmrtnNlUM6csmA1INQ0EBbRImIiIiIiIjUFzfnbSg9gI0en+2ifOmEEZDfDTf1Kd+R9PuzYEsBofMuT+mxqfqwcU8Jm/YeyOwb7Y7pTSi/KxO2LWTJln2s2bE/c/OqJ845Xli6nS4t8zipa4uk6U7u2oKQkdnH85Z9BGtWYBNSe1Nk1Dnun1vAjNW7uWJwBy4bWLMgFCgQdVg40t+YJyLSEOnYLCIicmhwzuHeeQ2O6Y1175nt4hxkoRB2zqWwbjUs/sD34dO5OwwZnu2iHbSooBCAIZ2bZWweZoaNGMsZH79MoxBM/fTQ77R86ZZ9rNy+nwuPb0eoiqBiy8Y5DDyqGXPW7c1YWaLTnobWbbGRZ1Sf1jn+Z24Bb67axVcHdSA8qEOt5qlAlIiIiIiIiBy51qyE9Z83iE7K49kpp0O7jkT/74++jOdelnIfPvVhUUER7Zvl0rVlo4zOx045nZal+xidu50Zq3dRWFJW/UQN2AvLttOyUYhxx7auNu2Ibi1Zv7uEtbuK014Ot2YFLF2EnXUhllf1Oow6x5/nFfDGZ7sID2zPVwbXLggFCkSJiIiIiIjIEczNeh0aNcKGj8l2USqx3FxswsW4PbugfSfs5NHZLtJBZVHHRwWFDM1vnvFHBa3DUdB3AOcsf43iMsebq3ZldH6ZtHFPCXPX7mVCn7Y0ya0+JDOiu390LxOP50WnPQNNm2Onn1tlOuccD83fxOsrd3HZgPZ8rQ5BKFAgSkRERERERI5Qrng/bt5M7MRRWLMM9nNUCzv3lzJ95U7uZBBfHfNrpo27FsvNyvvGElq1Yz97S6IMyc/cY3mx7JSx9DQT1iQAACAASURBVPp8AX1bwLQVO3GZfpNchry0fAc5ITivb5uU0rdvlkef9k2Ysza9j+e5gnWw4D1s3HlY0+TrsDwI9eqKnVzSvx1fH9KhzoFHBaJERERERETkiOTefxf272swnZRv3FPCC0u389PXP2fKMyu5f24BX+w+QLtWTXmmsB0HyhpO8GVRQRFAZjsqj2EnnQa5eZxbuIz1u0sOzv9Qsre4jH9/tpPRx7SifbO8lKcb0b0lK7fvZ0vhgbSVxb36LOTmYWdekDyNc/z1g81MW7GTSf3acdXQjmlp/dZwwqkiGdS1a5dsF0Gq9eU60vrKjjZtotkugoiIiEi9crNeh/yu0LtfdubvHKt2FDNn7R7mrtvL5zt9P0A92zZm8qD2jOjekh5tGrNgYyH//dY6Zn2+O6V+herDoo2F9GjTmDZN6yesYM1awJCTOfWD5/m/U/+LaSt2MLRzw2rFVp3XV+5kf6njwuPb1Wi6Ed1b8OjCLcxdt4eJx9Vs2kTc9q24OTOwMeOxVolbZjnn+NuHm3ll+Q4uPL4tU05ITxAKFIiSI4DeXNXwdejQga1bt2a7GCIiIiJyBHEb18LKpdhlUzLex1Gs0qhjyeYi5qzby9y1e9haVErIoH/Hplx9YidO6daCo1pU7Dj6hM7NObp1I15Ytp2xPVvVa3kTKS6NsmTLPs5P8fGydAmNGEejD2ZzVqt9PL8uypbCA3RsnnrLomwqjTpeXr6DwUc149h2TWo0bbdWjenWqhFz1u5NTyBq+gvgotj4ixOPd45HFmzhpWU7uOC4tnxrWKe0bnMKRImIiIiIiMgRx82aDjk52KnjMj6vsqhj3vq9zFm7h/fX72VvSZRGOcYJnZvztcEtOLlrC1o1SX55bmZc1K8df5pTwKKCoqy3BFq6ZR+lUVdvj+UdNHAYtGjJhM/f4blmZ/Haip18fWjH+i1DLb37+W627SvlO6fk12r6Ed1b8uySbewuLqNV45xal8Pt3Y175zVs+BjfCXz8eOf4x8ItPL90O+f3bcPVJ6Y3CAXqI0pERESkQQuHw03D4XBm34stInKEcaUHcLPfhCHDsVZtMz6/v7y/ibtmruf99Xs5uWsLbhnTlUcv68N/nd6NM3u1qTIIVe70Hq1o0ySHF5Zuz3h5q7NwYyG5IRhwVP10VF7OcvOwk0bTccEMTspvwuuf7WxQ/WYl45zjhWXb6dqqEcO61C54N6J7C6IO5q+r29vz3JuvQPF+7JxLE5bzsUVbeXbJds7t04ZrTjoqI63vFIgSERERaUDC4fDd4XB4ePD/+cB2YGc4HE7em6iIiNTMovmwdzehUZnvpPy1FTt5Nejs+R+X9uEHI7twaveWNMmt2eV4Xk6I8/u25cONhXwR9CWVLYsKCjm+Q9Ma/4Z0sBFj4UAJ50TXsmt/Ge+trVtgpj4s2byPz7YXc+HxbQnVMrDTu10TOjTLZc662r89zxXvx735sg/Adj2m4jjneOKjrTz9yTYm9G7DtSdnJggFCkSJiIiINDRXAIuD/38BfB24EPh11kokInKYic56Hdp2gAFDMzqfpVuK+Mv7BZzQuTlXDe1ITqhuF/bn9G1LoxzjhWXZaxW1e38pq3YU1/9jeeWOPQ46dWbIolfp3DKPqZ/uyE45auCFZdtp2TiHcT1r39G8mXFK95Ys3FjI/tLavWTIvfMaFO4hdO5llcb9f/buMzzO6lz3+P8d9WpVW7LcuzEGU1wAUw0Gm5qELEggIZXDTiWFkGSfNE4CO8lObzskZAdCgKyEDgYM2IAxsakuuMiSe5Fk9d5nnQ8zNrKRrTIjzci6f9elS9Jb1twzyEJ6tNazXtpZh323kksmj+CWeaP6XTDrDRWiRERERKJLsrW2yRiTDUyy1j5srX0BGN/TjSIi0jNXWQ6b3sE7ZxGer/+9dnpS2dTOj1/ZT05yHF8/Z3TIRSiA9IQYFk0awUs766hu7ghDyr5bX9oEwKkR6lPleR7e/AvwbXuXywri2FLezM7qlohk6Y0DdW28vq+BJVMzSAhxBtmCMam0dTrePtD3WVGuox23/HGYNgtv8owjztW1dvKXtw8yPSeR/5iXN6BFKFAhSkRERCTabDPG3AB8AXgewBiTAzRHNJWIyAnCrX4BAO+ciwfsMdo7/fx41X6aO/x8+/wxpIbQXPpoV83IotPvIjYTaH1pIylxPqb0cee3cPIWXADOcVHpm8THeDyzrSZiWXryZGEVMT6PpdNC70U2a2QyaQkxrNnbj0LU2pehuqLb2VD3vnOQhrZOPjcvLywF056oECUiIiISXT4HfB64EPhO8NilwPKIJRIROUE4f2egEDXz1G53DAvLYzjH/7xRRmFFC18+K5/xGQlhHX90ejzzxqTyTFENrf1cotVfzjnWlzZy8qjkQSlYHIs3Mh8mzyBl7YucOz6dl3bW0tDWGbE8x1Lf2smL22s5b0I6mUk9N6R3zuEqD+I6u5/tFuPzmFuQypv7G/rUpN35/bhnH4YxE2HW6Uec23SwiRe213L1jCwmZA5OcbHnV0JEREREBtNea+3ZXQ9Ya/9ujHmxNzcbYxKBV4AEAj/r/cta+z1jzETgISALeBv4mLW2zRiTANwHnAFUAtdZa3cFx/oW8GmgE/iStfa54PHLgF8BMcCfrbX/FeJzFhEZHJvXQ1U53rWfHLCHeLaohhe21/LhWdmcPS59QB7j6plZrN23hxU7alkShpk2vVXa0M7Bxg4+cFKE+kN14S24EPf3P7B0RCMvdjpW7qjlyhlZkY51hOeKa2jtdFw94/j/jVxFGW7ty7g1K6F0P9UnzcHd/A28lNT3XbtgbCordtTy7sEmTuvt8sh1a6B0P95nv35EA/L2Tsfv15YyMiWW60/J6dNzC4VmRImIiIhEl23HOL65l/e3AhdZa08F5gCXGWMWAD8GfmGtnQpUEygwEXxfba2dAvwieB3GmJOA64FZwGXA740xMcaYGOB3wBLgJOAjwWtFRKKee/V5SE3DmzN/QMbfdLCJP71ZxhmjU/jIAP5if1JuElOzE3liaxWd/t7PjAnVupJGgMg1Ku/CO/MciIll0saXmZ6TyLJtNTg3eK9FT9o7HU8XVjMnL7nbmUausQH/K8/S+ZNv4v/WZ3GP3Q/pGXhLPkT7tnfx/+SbuKry9903Jy+FxFiPNb3cLdA5h/+ZhyE3D++Mc44499iWSvbVtXHzmXmDugOiClEiIiIi0eV9ax2MMelAr9ZfWGudtfZQ84i44JsDLgL+FTx+L3BN8OOrg58TPL/IGOMFjz9krW211u4EioF5wbdia+0Oa20bgVlWV/ftKYqIDD7XWI9btxZvwYV4cXFhH7+iqZ0fr9rPqNQ4vhqm5uTH4nke18zM4kB9O2/s73u/oP5aX9pETnIso9PC//r1lZeaDrPPxL3+MkumjOBAfdvhRuqh8q9ajv+B/8G/ajluz3ZcR3ufx1i9p46q5g6unvneLC3X3o57Zw2df7gL/9c/jvvb76G+Du+aG/Hd9SdibrsL3wdvIvM7P4fqCvx33Ybbt/OIcRNifZw+OpW1+xrw96bwtnUD7CrCu/SDeDHv9SorqW/DvlvJWWPTmDvm/TOvBpKW5omIiIhEAWPMXgIFoyRjzJ6jTmcDD/ZhrBjgLWAKgdlL24Eaa+2hphP7gILgxwXAXgBrbYcxpjb4eAXAmi7Ddr1n71HHu51aYIy5Gbg5ODY5OYM37X+oi42N1evVR3rN+m64vWYt2zdT29lBxoVLiO/n8z7Wa9ba4eebL2yg3Q8/vWY2E7KSQ43boyuysvnb+kqeLq7nitMmDvjjdfod7x4s5vzJ2eTm5vb6voH8OmtZfBW1P1nDZbEV/DUplhd3N3Lx7NA2mW165mHq7/stxMZBRzsOIDaW2HGTiZs0jdjJ04mdOI24CVPwErrvqeSc4+nle5mQlczFJ4+jo/BdWl5+jpbVL+Aa6vGNyCTxsg+SeMFlxE6afsRyOYDYvDy8O/+Hmh9+DffTbzPi9ruIP+XMw+cvmenntT3bONiRwMn5x1/+Wf3iE3RkZpNz5Yfx4hMO57vz1U3Exvi4ffEMclLD28esJypEiYiIiESHGwnMhloGfKzLcQeUWWsLezuQtbYTmGOMyQAeBWZ2c9mhP6N29yd7d5zj3c2o7/ZPstbau4G7D11TUVFxvNjSRU5ODnq9+kavWd8Nt9fM//YaiI2jNnMkXj+fd3evmXOOX68pYUtZA98+r4BUfxMVFeGZmdOTy6eN4J63DvLa1r1My0ka0McqqmymvrWD6Zkxffq6GcivMzdhOiSn0Pz8Eyw642M8uqWKLbtLyE3p34wt9/a/8f/p53DqPHy3fBOqDuJ274A92+nYs52ONS/BC08GLvZ8kD8Gb9xkGD8p8H7sJLykZDaWNbKtvJHPJeyi/JYfQnkpxMfjzTkL34IL4KQ5tMbE0ApQWfm+HDk5OdSmZsA3/gv3qx9QfcdX8T75ZXzzzwdgeroj1gfPbtxHXtzIYz+fXUX417+B96GbqKyrBwLL+VbtqmPt7ho+c8ZIvJZ6Klp6t8yvJ6NHj+7VdSpEiYiIiEQBa+3LAMaYHGttWH6DsdbWGGNeAhYAGcaY2OCsqDHAgeBl+4CxwD5jTCwwAqjqcvyQrvcc67iISNRyRZthwtSwL8t7els1K3bUcf3sbOaPTQvr2D25ePIIHtpQwWNbqvjGuQU93xCC9SWB/zWdkjfws716y4uLwztzIW7NS1z6gZt5ZDM8V1TDjXN6P2PrEFe8Bf+ffwYTpuL77G14sbEwcjTeyNEwd2HgGuegqgL2bA8s2du9Hbd1PaxZ+d5fZEaO5rHx15Ael8O5q/4E007Cu+J6vNMX4CX27bXzsnLxfeO/8P/+R7g//wx/TRXe4mtIiY9h9qgU1uyr56bTct83o+oQ/zP/gqQUvPOXHD7W0NbJPW+VMTkrkaWD2Oi+KxWiRERERKJLR3BJ2xzgiKYN1tqP93SzMSYXaA8WoZKAiwk0IF8JXEugp9NNwOPBW54Ifv7v4PkV1lpnjHkCeMAY83NgNDAVeJ3ATKmpwV349hNoaP7R0J6yiMjAcq0tsGc73uJrer64DzaWNXLPWweZNyaV62YP/jLH5LgYFk/J4PGtVZQ1tDEqNX7AHmtdaSMTMxPISIyuMoK34ELcK8+RW/gGZxZMY/n2Gq6bnU1cTO9bYruSffh/+0PIzMH3xe/iJXS/VM3zPMjOhexcvNMWvHd/bTXs2YHbs53ifRW8mTKB65LKSbrrbrzM7NCeX0oqvlt/gPvLL3H/+l+orgDzKRaMTeUPr5exp7aN8Rnvz+tK9sE7a/CWXIuX9F4B7P515dS2dvJ/Lxg7oH3MjkfNykVERESiy33ArQTmz28/6q038oGVxpgNwBvA89bap4Dbga8aY4oJ9IC6J3j9PUB28PhXgW8CWGs3AZbAbn3PAp+31nYGZ1R9AXgO2BK41G4K7SmLiAywndugsxNv6qywDVne2M5PVh1gdFo8Xzk7H98xZqUMtCtmZOIBTxZWD9hjtHb42VLeHBW75b3PlJmQPRK39iWWTsugtqWT1/b0fqmZq6nC/6vvg8+H79bv46Udv+dSd7wRmTRMm8Nf8s7nW+kXkRrvY+mSs0IuQh0ePy4e77Nfx7v4atyLT+L/40+ZNyoBD465e5577mGIjcNbdOXhY4UVzTxbVMPSaZlMye6+v9VgiK5SpoiIiIhcCky01tb052Zr7QbgtG6O7yCw493Rx1uADx9jrB8BP+rm+DICvaxERIYEV7wZPA8mzwjLeK0dfu56ZR8dfse3zi8gOS6m55sGSE5yHAvHp/N8cS3Xz84hNT78WTaXN9Phd5waRcvyDvE8D2/BBbhl/+LUpBby0+J4pqiG8yeO6PFe19yE/9c/gIY6fLfdiZeb1+fHb+90PFtUzUMbK2hq93Px5BF89JRcMpLCW27xfD686z6NPysHZ+9hRF0N00+5mTV76983G89VlePWvIx33mK89Awg0Gz+D6+XkpkUyw2nRnaTAs2IEhEREYkue4DB3b5GROQE54o2Q8F4vOTQt6l3zvH7taXsqGrlq2ePZkx65L9lXzMzi5YOP8uL+/U3jB6tK2kk1udx0sjoK0RBYHkezo/3+iqWTM1kS3kzO6tbjnuP62jH/4e7YP9ufLfcjjd+Sp8e0znHmr31fPHpHfz5rYNMyUrkF0sm8Pn5+WSGuQjVle+Sq/Fuvg12bWPe1hXsqG6lrKHtyGzPPx54PRZ/4PCxJwur2Fndys1njopo4RQ0I0pEREQk2twHPG6M+RVQ1vWEtXZFZCKJiAxdrrMTthfinXVhWMZ7srCal3bV8dFTcpg7JvTCVjhMykpk9qhkniqs5qoZWcSGuffP+tJGZuQmkRgbnXNZvLwCmDgNt2Yliy64ivvXl7NsWzWfn5/f7fXOOdy9v4Ut6/E+8WW8k8/o0+Ntr2rhL2+V8e7BZsakx/PdC8Zw+uiUYzYNDzff3HNx6Rks+PMfuG/kQtZs3M3VZ00FwDXU4V55Dm/eeXg5o4DAMtIH1lcwtyCFBWMj/zUbnV9FIiIiIsPXF4BRwJ0E+jcdevtzJEOJiAxZ+3ZCazNMPSnkod7aW8P/vn2QBWNT+fDJ4en/Ey7XzMyisqmDV3fXhXXc2pYOdla3RuWyvK68BRfAvl2klO/hvAnpvLyzjn21rYGd7o7iHv0bbs1KvKtvwHfOol4/RkVTO7987QBfe2YXe2vbuGXuKH59+UTOKEgdtCLUId702Yy+9XbGNx9kzbrtuM3rAHArnoK2VrzLPnT42rvfDPxd6+Yz8wY9Z3c0I0pEREQkilhrJ0Y6g4jIicQVbQbAmxJaIaqsoY3vPFdMQXo8Xz4rcs3Jj+X00SmMSY/nsS1VnD8hPWwFh/WlTQDMicZG5V14c8/F2Xtwa17iikUfYeWOWj7/1E5GJMYwIyeJGblJzMxJYuK7LxP3zL/wzrsM73LTq7Gb2/08uqWSRzdX4XfwgZOyuHZWNikD0I+rL7yC8SyYDbaoiao//IjMa2/ErXgaTp2HVzAeCDQzf31fAzedlsvI1LiI5j1EhSgRERERERE5YbnizZA9Ei8rtAbNf3yjjE6/49vnjYl4j53u+DyPq2dm8bu1pWwsa+KUMBWO1pc2khLnY3JW5HZZ6w0vbQTMOh239mUmfPDj/OaKSWwsa2RreTNbK5pZu68BgFj/WCYtvJ0ZMyczY289M3KSyE7uvkDT6Xes3FnL/esrqG7uYOH4ND4+J5dRqfGD+dSOa8HUUfyjeBdvzFjEJff/HgDfkmsBaGrv5O43yxifkcBVM7IiGfMIKkSJiIiIRBFjTDrwfeB8IAc4/Cdta+24CMUSERmSnHNQtBnvpDkhjdPpd7xb1sSVJ+cxOj16ihBHu2BiOvevL+exLVVhKUQ551hf0sjsvGRiwtx3aiB4Cy7EbXgDtm6k4KQ5FKTHc9nUTACqt2xh69//ztbRsymcNI9ni2t5ojDQ3H1kSiwzcpKZnpvIjJxkJmQmsOlgE//79kF2VrcyPSeRb55bwIzcpEg+vW5NzExgZEocr+cvYnFaI3R04AV3h3xgQwVVTR18Y2FB2PuGhUKFKBEREZHo8ntgDHAHcD9wI3Ab8HAkQ4mIDEnlJVBXAyEuy9td00prp+Pk/PQwBRsY8TE+lk7L5MENFeytbWXsiNB29HtzfyPlTR18aFZ0L8s7xDt1Li4pGbfmpSOKj65kH+l//CHzUtJY8LEleGnptHc6dla3sLWima3lzbx7sIlXgv214mM82jodI1Pi+Po5o1k4Pi0qeit1x/M8FoxNZdm2Glo+8h+HZ+ttr2rh6cJqLp2aEXUFNBWiRERERKLLYmCmtbbSGNNprX3cGPMm8CTwiwhnExEZUg73hwqxUXlhRTMAs/LSoL0h5FwDacnUDB7eVMnjW6r4woLud43ryaayJh7YUM67B5vJSY5l/ti0MKccGF58At7pZ+PeXI274T/wEhJwNVX4f/V98Pnw3fp9vLRAMTEuxmNaThLTcpK4akZg9ldFUwdby5sprGgmNyWOJdMyiI+J/j3eFoxN44mt1by1v5FzJ6TT6Xf8fm0p6QkxfGxObqTjvY8KUSIiIiLRxQfUBj9uMMZkACXAlMhFEhEZooo2Q0oa5I0JaZitFc1kJMaQn55AZWV0F6JGJMZy4cQRrNhRy42n5pKR1Ptf+7eUN/HAhgo2lDaRmRjDZ88cyeIpQ6MYc4h31oW41S/g1q2BU+bi//UPoKEO32134uXmHfs+zyM3JY7clDjOnRDdM9+ONiMniREJMazZV8+5E9J5tqiG4qoWvnbOaFIj3FC9OypEiYiIiESX9QT6Q70IrAJ+BzQA2yIZSkRkKHLFW2DKTDxfaIWUwopmpuckRe3yrKNdNTOT54prWFZUzUdP6XlGTGFFMw9uqOCdkkZGJMbwqdNHctnUDBJih04B6rCpsyArJ1CMeu1F2L8b3xe/gzf+xP17TozPY96YVF7dXU9ZQxt/W1fOnLxkzh0fnTPZVIgSERERiS6f5b0G5V8C7gIygI9HLJGIyBDk6qqhbD/euZeENE5tSwcl9e0snpwRpmQDb0x6AnMLUnlmWw0fOin7mAWl4soWHtxQzpsHGklLiOGm03JZOi2TxKFYgAryfD68+efjngm0VvQ+8WW8k8+IcKqBt2BsGs9vr+W7L+6lw++4ZV5e1BZOVYgSERERiRLGmBjgE8CPAKy15cBnIplJRGTIKt4CgBdio/JD/aGmR1nD555cMzOL/3xhDyt31h7eOe6QHVUtPLixgtf3NZAa7+Njp+aydHrG4UbXQ5139iLcimV4Sz6E75xFkY4zKE7NSyYp1kdpQzs3nJJDflr07u6oQpSIiIhIlLDWdhpjPg98P9JZRESGOle0GeLiYfzkkMYprGghxoMpWYlhSjY4Zo1MYnJWIo9vqWbxlAx8nseu6hYe2ljBv/c2kBLn46On5HDljMwTpgB1iJc3Bt8v7seLi4t0lEETF+Nj4fg0iqta+MBJWZGOc1wqRImIiIhEl3uBW4DfRzqIiMhQ5oo2w8RpeLGhFSMKK5qZkJk45PoleZ7HNTOz+NnqAzy5tZrCimZW76knKdbHdbOzuWpGVlQ2sg6X4VSEOuTz8/Pwu0DPqGimQpSIiIhIdJkHfNEY8w1gL+AOnbDWnhexVCIiQ4hraYa9O/AuuzakcTr9jqLKZhZNGhGmZIPr7HFp3PdOLH95+yCJsT4+PCubq2dmkZZw4haghjPP84iJ7hoUoEKUiIiISLT5U/BNRET6a0ch+P14U0PrD7W7ppWWDsf0nKHVH+qQWJ/HFxbks7WimaVTM0hPVAlAIk9fhSIiIiJRxFp7b6QziIgMda54M3g+mDwjpHEONyofooUogDn5KczJT4l0DJHDVIgSERERiSLGmE8d41QrsA9YY61tHcRIIiJDjivaDGMn4CUlhzROYUUzIxJjGJU6/PoNiQwUFaJEREREosvHgbOAMgKFpzHAKOBNYAKAMeZqa+2bkQooIhLNXEcH7CjEW3hJyGMVVjQzIycJzxsCjXdEhggVokRERESiyybgEWvtrw8dMMZ8AZgBLAT+E/gNgWKViIgcbe9OaGuFKaH1h6pr6eBAfTsXT84IUzARARha+0+KiIiInPg+Cvz2qGN/AG6w1jrgp0Bov12JiJzAXNEmALypM0Map7CiBRja/aFEopEKUSIiIiLRpQy48qhjlwMHgx8nAu2DmkhEZAhxxZshNw8vIzukcQormvF5MCU7MUzJRAS0NE9EREQk2nwJ+Kcx5l1gLzAWOBn4cPD8fAJL80RE5CjOOSjegnfyGSGPVVjRzMTMBBJjNX9DJJxUiBIRERGJItba5caYycASYDSwDHjaWlt56DywPIIRRUSiV9l+qK+FqaGtYO70O7ZVtnDRpPQwBRORQ1SIEhEREYky1toK4G+RziEiMtS4os0AeCEWovbUttLS4Vd/KJEB0O9ClDEmCei01raFMY+IiIjIsGOMedZae1nw41WA6+46a+15gxpMRGSoKdoMqekwqiCkYQormgE1KhcZCL0uRBlj/huw1trXjTGXA/8CnDHmOmvtkwOWUEREROTEd1+Xj/8csRQiIkOcK94MU07C87yQximsaGZEQgx5qXFhSiYih/RlRtQNwHeDH38XuBGoBX4BqBAlIiIi0k/W2ge6fHxvJLOIiAxVrqYKykvxLlga8lhby1uYnpsUckFLRN6vL4WoZGttkzEmG5hkrX0YwBgzfmCiiYiIiAxPxphzgdOA1K7HrbV3RiaRiMgQUBye/lB1rZ0cqG9j0eQR4UglIkfpSyFqmzHmBmAK8DyAMSYHaB6IYCIiIiLDkTHmN4ABVnHkz1nd9o0SEZEAV7wF4hNg7KSQxtl2uD9UYjhiichR+lKI+hzwK6Ad+FTw2KVo+2ARERGRcLoBONlaeyDSQUREhhJXtAkmTceLDW1z+MKKZnweTM1Wo3KRgdDrf6HW2jeAs4869nfg7+EOJSIiIjKM7QVaIx1CRGQocc1NsHcX3uUm5LG2VjQzISOBxFhfGJKJyNH6VCo2xlwCXA+MtNZeaYw5E0i31q4YkHQiIiIiw8+ngT8ZYx4EyrqesNa+EplIIiJRbkchOH/I/aE6/Y5tFS1cODE9TMFE5Gi9LkQZY74IfJnAlsLXBg83A7/mqJlSIiIiItJvZwBLgPN4f4+ocRFJJCIS5VzRJvD5YNL0kMbZW9tKS4ef6TlalicyUPoyI+pWYJG1dpcx5vbgsa1AaP/SRURERKSrO4ErrbUvRDqIiMhQ4Yq3wNhJeImhFZAKK1oAmJGrQpTIQOnLotc0Aj0L4L1dW+KAtrAmEhERERneGgEtwRMR6SXX0Q47CkNelgeB/lDpCTHkpcaFIZmIdKcvM6JeN+HndgAAIABJREFUAb4J/KjLsS8BK8OaSERERGR4+y7wS2PMHcDBriestf7IRBIRiWK7t0N7G96U0AtRhRXNTM9JwvO8MAQTke70pRD1ReBJY8xngTRjTCFQB1w5IMlEREREhqe/BN//ny7HPAIz0mMGP46IyPs556KmWOOKtwQ+mDozpHHqWzvZX9emRuUiA6zXS/OstSXAXOA64KPATcB8a23pAGUTERERGY4mBt8mdXk79LmISMQVVTbz0X8W8eCGcjr9rucbBpgr2gQjR+OlZ4Y0zraKwP4QalQuMrD6MiMKa60D1gbfRERERCTMrLW7I51BROR4ni6spqXDz0MbK9l0sJmvnTOazKQ+/WoZNs7vh+1b8E6dH/JYWyua8XkwNVuFKJGBdNzvFsaYLdbamcGP9/Jek/IjWGu1lbCIiIhIGBhjRhDow3kakNr1nLV2cURCiYgENbR1snpPPZdMzmB6TiL/80YZX162k6+ePZo5+SmDH6h0HzTUQxgalRdWNDM+I4GkuL7s6SUifdVT2fqzXT6+cSCDiIiIiAgA/yTQC+pRoDnCWUREjvDyzjraOh2Lp2QwJTuRqdlJ/OTV/Xx/xV4+fHI218/OIcY3eL2jXPFmgJAblXf6HdsqWjhf/aFEBtxxC1HW2lcBjDExwKeAm621rYMRTERERGSYWgBkW2vbIx1ERKQr5xzLi2uYnJXAlOxEAMZlJPDfl03g7jfKsO9WsvlgE189ZzTZyXGDE6poC6RnwMj8kIbZW9tKc4df/aFEBkGv5hxaazuBxYC2DBYREREZWK8CoW39JCIyAIoqW9hV08olkzOOOJ4Y6+NLZ+Xz5bPyKaps4SvLdvFOSeOgZHJFm2DKSSHv4LetsgWAGSpEiQy4vnSU+wXwA2PM9/QXOhEREZEB8wlgmTFmLVDW9YS19o6IJBIRAZ4rriEhxjvm8rWLJo1gSnYiP121nx+s2Mu1s7L5yCkDt1TPVVVA5UG8i68Keayt5c2kJ8SQnzZIM7lEhrG+FKK+COQBXzXGlNOlcbmalYuIiIiEzY+AscAuoOtve5HfI11Ehq2m9k5e3V3HuRPSSY6LOeZ140YEl+q9WcY/N1Wy6WATX184MEv1DveHClOj8uk5iSHPrBKRnvWlEKVm5SIiIiID73pgmrW2JNJBREQOeWVXHS0dgSblPUmI9fHFBfnMHpXMH14v5dZlu/jK2fmcPjq1x3v7pHgzJCTBmIkhDVPf2sm+ujY1KhcZJH0pRI201v7z6IPGmGvDmEdERERkuNsBqA2CiESV5cW1jM9IYFqwSXlvXDBxBFOyEvnJqwf4wcp9fOikLG44NTdsS/Vc0RaYPB0v5tgztHqjqDKwQan6Q4kMjr4Uou4hsJ3w0e4G/hWeOCIiIiLD3t+AJ4wxv+H9PaJWRCaSiAxn26ta2F7Vws1njurz0rUxIxL46aXj+fNbZTy8uYot5c18beFockJcqueaGmD/LrzTPxLSOABbK5rxeTA1W4UokcHQYyHKGDMp+KHPGDMR6PqdZxLQMhDBRERERIapzwff33nUcUfgZy8RkUG1vLiG+BiP8yf0b+laQqyPz8/PZ/aoFH63NrBU71vnFjBrVHL/Q20vBOfC0x+qvJnxGQkkxfVqU3kRCVFvZkQVE/jBxwO2H3WuFPh+mDOJiIiIDFvW2tCanYiIhFFzu5+Xd9Zxzrg0UhNCWwJ33oR0JmclcsfKvfzxzTJ+tXRCv5uDu6JNEBMDE6eHlMnvHNsqWzh3vPpDiQyWHgtR1lofgDHmZWvt+QMfSURERERERKLB6j11NHf4e9WkvDcK0uO5dlY2v11byqaDzZzcz1lRrngzjJuMl5AQUp69tW00tfuZkatleSKDpdc9olSEEhERERk4xphVBGahH5O19rxBiiMiAsBzRTWMSY9nZhgLNedNSOev7xzk6W3V/SpEufZ22FmEd9HlIWcprAg0Kp+uRuUig6bXhahgf6gfAXOAI/bdtNaOC3MuERERkeHmz5EOICLS1a7qFrZVtvCp00f2ewlddxJifVwyOYPHt1ZR0dTe98blu4ugox1vShj6Q1U0kxbvY3RaaM3TRaT3+rJr3gMEekR9DWgamDgiIiIiw5O19t5IZxAR6Wp5cQ2xPo8LJ40I+9hLpmXw2JYqnt1Ww41zcvt0ryvaEvhgysyQc2wtb2ZaTlJYC20icnx9KUTNAs6x1voHKoyIiIiIiMhwsbGskV++VsL3LhzLuIzQeh2FW2uHn5d21XH22DTSQ2xS3p1RqfHMHZPK8uIarpudTVxM73esc0WbIG8MXlpoBbKG1k721bX1ezdAEemfvuxP+Qpw2kAFERERERERGU5W7aqnoqmDn766n9aO6Pp7/+o99TS2+blkSvhnQx1y+bRMals7eXV3fa/vcX4/bN+CNzX0ZXnbKoP9odSoXGRQ9WVG1C7gOWPMI0Bp1xPW2u+GM5SIiIiIiMiJbl1pI/lpceytbePPb5Xx+fn5kY502PPFNeSnxTG7n7va9cYpeckUpMfz9Lbq3i//K9kLTY0Qpv5QHjA1OzHksUSk9/oyIyoFeBKIA8Ye9SYiIiIi/WSMWdPl4+9FMouIDI6S+jbKGtq5akYWH5qVzfLiWl7ZVRfpWADsrW1lc3kziydnDGjvJJ/ncfm0TIoqW9gW3L3uWFxTA+7t1/A/+jeAsMyI2lrRwriMBJLjwr/0UESOrdczoqy1nxzIICIiIiLD2DRjTKK1toXAxjA/iHQgERlY60oaAZiTl8Ko1Dg2HWzi92tLmZqdSH5afESzBZqUw0WTB25Z3iEXTkrnvnXlPL2tmmk57y2Rcx3tsKMQt3kdbvM62FUMzg8JSXgXLoWcUSE9rt85iiqaWThe/aFEBluvC1HGmEnHOmet3RGeOCIiIiLD0uPANmPMLiDJGPNKdxdZa88b1FQiMmDeKWlkZEoc+WlxeJ7H184Zza3LdvLTV/fz48Xj+9S8O5zaOv2s3FnHvDFpZCT2pZNL/yTHxXDRpHSWF9fyidG7GVG8PlB42rYJWpvB88HEqXiXfxhv5hyYNB0vNvRc++raaGz3Mz1Hy/JEBltf/gUXAw7oOjfTBd9rLqOIiIhIP1lrP2mMWQhMAOYC90Q2kYgMpE6/Y2NZEwvHpx1e+pabEseXzsrnzpf3c+875XzmzNBm/PTXmr0N1Ld2cumUjAF/LFdXjdu8niVbilgWfy7L//Yo1+5ZASPz8c66IFB4mjEbLzk17I9dWK5G5SKR0peleUeU5I0xecD3gFXhDiUiIiIy3FhrXwVeNcbEW2vvjXQeERk42yqbaWr3Myc/5Yjj88ekceX0TJ4srGb2qGTmj00b9GzLi2sYlRrHKXkD06Tc1dfhVjyFW7cG9u0CoCAljVNPm85zUy/mQ//nemJH5g3IY3e1taKZ1HgfBRFeBikyHPV7TqO1ttQYcyuwDXggfJFEREREhi9r7V+MMRcCHwMKgP3A/dbaFb253xgzFrgPyAP8wN3W2l8ZY7KAfxCYdbULMNbaamOMB/wKWAo0AZ+w1r4dHOsm4P8Gh/7hoQKZMeYM4K9AErAM+LK19tBMeRHpwbqSRnwenDIq5X3nbjotl83lzfx6TQm/zEokNyVu0HIdqGtjY1kTN56agy/MTcpdfR3u+UdxK56GtlaYdjLeBz6Gd9IcGDeJyw80cefL+3m9JZmzw/rI3SusaGZ6TtKANmMXke6FuvB4OjBw+3mKiIiIDDPGmM8QKBiVAo8AJcADxpjP9nKIDuBr1tqZwALg88aYk4BvAi9aa6cCLwY/B1gCTA2+3Qz8IZgji8Ds9/nAPOB7xpjM4D1/CF576L7L+v2ERYahdSVNTM5KJC3h/R1O4mJ83LZwNJ1++O9XD9DhH7wa7/Pba/B5sGhy+Jblufpa/A/fi/9bn8E9+wjeKXPxfe83xHz9R/iWfhhvwlQ8Xwxnjk5lZEosTxdWh+2xj6WhrZO9tW1Mz9GyPJFI6Euz8lW81xMKAgWoWcAd4Q4lIiIiMox9A7jEWrv+0AFjzD+Ah4E/9XSztbaEQPEKa229MWYLgZlVVwMXBC+7F3gJuD14/L7gjKY1xpgMY0x+8NrnrbVVwQzPA5cZY14C0q21/w4evw+4BngmpGctMkw0tHWyrbKZa2dlH/Oa/LR4Pjc/j5+tPsCDGyr42JzcAc/V3ul4cXstcwtSyUoKvRm4q6/FPfco7qVl0NaKd+ZCvCuuwxs9rtvrY3weS6Zmcu+6cnZVtzAhc+CaiBdVtgCoECUSIX35DvPnoz5vBNZba4vCmEdERERkuMsGNh91rBDI6utAxpgJwGnAWmBUsEiFtbbEGDMyeFkBsLfLbfuCx453fF83x0WkFzaWNeF3vK8/1NHOm5DOxrJG/rWpkpNHJXNaD9eH6vX99dS2drI4xCblrq4Gt/xR3Mpl0N6GN/fcQAEqf2yP9148JYMHN1awbFsNn5s/cH2iCsub8YBp2jFPJCL60qxcTTNFREREBt6rwM+NMbdba5uMMSnAXcBrfRnEGJNKYBbVrdbaOmPMsS7trkHK0Tsl9+Z4dxluJrCED2stOTk5PcWWoNjYWL1efTRUXrPCDTUkxcVwzvQxxMYcv1PK7YszKa5ez6/+XcpfbziNnJTwNtbu+pqtXFXKqNQELpk9nhhf3/sm+WuqaHzsAZqefQTa20hceDEp136C2LETej1GDrB4eh0vbCvn1kUzSE8MfWZWd3bUlTIxO5lx+X3fmXCofJ1FE71mfXeiv2Z9WZoXR6BZ5ceA0cAB4G/Aj6y1bQMTT0RERGTYuQV4CKg1xlQRmAn1GvCR3g4Q/LntYeDv1tpHgofLjDH5wdlQ+cDB4PF9QNepCmMI/Jy3j/eW8h06/lLw+Jhurn8fa+3dwN3BT11FRUVvn8Kwl5OTg16vvhkqr9m/d1Zy8sgkaqqrenX9V84axdee2cV3nnyX7180tl9FomM59JqVNbTxxp4aPjI7h+qqyj6N4eqqg0vwnoH2drz55+FdbmjPG0MNQB//mywan8RTm/38840dXD2zzxNBe+R3jncP1HHO+LR+fb0Mla+zaKLXrO+G6ms2evToXl3XlxLzTwg0qrwF2A2MB74DpANf6WM+EREREelGcPnc+caYMQT/+Get3dfDbYcFd8G7B9hirf15l1NPADcB/xV8/3iX418wxjxEoDF5bbBY9RxwZ5cG5YuBb1lrq4wx9caYBQSW/H0c+E1/n6/IcFJa30ZpQztXzeh9gWXciAT+z9xR/GZNKQ9vqsTMDv8sieXFtcEm5SN6fY+rrcY99wju5WegvQNv/vl4lxu8vNBW6k7KSmRmbhLLtlVz5YzMsO/et7+ujcZ2v/pDiURQXwpRHwZOtdYeKpEXGmPeBtajQpSIiIhIWAWLT70uQHVxDoEZ7BuNMeuCx75NoABljTGfBvYQ+NkOYBmwFCgGmoBPBh+/yhjz/4A3gtfdcahxOfAfwF+BJAJNytWoXKQX1pU2AnBqft82Hl80aQQbSpt4cGMFs0YmM2tU+DYu7/A7XtxRy+n5KeSmxPXqHrd/D/67vg5tbXgLzsdbGnoBqqul0zL52eoDvH2gkTMLUsM2LkBhRTMAM1SIEomYvhSijlWKDm+JWkRERET6zVr7Ksf++WxRN9c74PPHGOsvwF+6Of4mcHIIMUWGpXUljeQmx1KQ1rdeT57nccu8URRVNvOz1Qf45dIJYeuf9Ob+BqqbO1g8r/f9ktzzj4Jz+O74LV7emJ5v6KOzxqaRmRTLsm3VYS9EbS1vJiXex+j08PbbEpHeO353vCP9E3jSGHOpMWamMeYy4LHgcRERERERETmGTr9jQ2kTc/JT8Pqx3Cw5LobbFhZQ29rJr/5dgnPd7hHQZ8uLa8hKiuXM0b0r+LiGOtzaV/DOunBAilAAcTEel03J4K0DjRyoC2874sKKZqZnJ4V9yZ+I9F5fClHfAF4Afge8RaAXwArgtgHIJSIiIjLsGGN8xpiLjDH6U73ICaa4qoXGdj9z8lP6PcakrEQ+dfpI3jzQyBNbq0POVFrXwtsHGrl48oheN0F3q56Hjna8Cy8P+fGP59KpGcT6YFlR6M/zkH21reytbWN6rpbliURSj/M5jTHnAFdZa28Hvht8O3Tux8DpwJoBSygiIiIyTFhr/caYx621aZHOIiLh9U5JIx5wSl7/C1EAS6dlsKGskXvfOcjM3CSmhdDr6OnNZQBc3Msm5a6zE/fSMpg+G69gfL8ftzcyk2I5e2w6L26v5YZTckmK68scivd7a38DP1t9gLSEGM4dnx6mlCLSH7351/xt4JVjnFsJ/Gf44oiIiIgMe68Ed6QTkRPI+pJGpmQnkp4QE9I4nufxxfn5ZCfH8t+rD7CjqoXyxnaa2jv7tFyv0+94alMZc/JTGJXay0mYG96AqnJ8Fw3sbKhDlk7PoKndz0s7a/s9hnOORzZX8v9e2sfI1Dh+dtkECtQfSiSietPhbg7w7DHOvUA3DSxFREREpN92A88YYx4H9gKHf7O01n73mHeJSNRqau9ka0UzHzwpOyzjpSbE8PWFBXxr+W6+8syuw8d9HiTH+UiJjzn8PuWI9+99XNncwcGGNj55Wk6vH9e/4inIyoFT54flefRkRk4Sk7MSWLatmsumZvS5t1Zrh5/fri3llV11nDMujS+dlU9ibGgzq0QkdL0pRKUD8UBzN+fiAE0dFxEREQmfJAIbwgAMTCdgERlUG0ub8Ds4LYT+UEebnpPEry+fyO7aVhrb/DS2ddLUHnjf2OansT3wvrShnca2Fpra/TS1+48YIzslnrkFvft1zu3fA1s34H3w43gxoc3q6i3P81g6LZPfrCllY1lTn5Y1VjS1c+fL+9lR1cKNp+Zw7azsfjWJF5Hw600haiuwGHi8m3OLg+dFREREJAystZ+MdAYRCa91pY0kxnpMD6GfU3fGjEhgzIiEXl/f6Xc0t79XpJo4Ohevpb5X97qXnobYOLyFi/sbt1/OHZ/OX98pZ9m26l4XorYcbOK/Vu2ntcPx7fMLmDdGcydEoklvClG/AP5ojIkBHgs20fQB1xDYQe+rAxlQREREZLgxxswErgVGWWu/YIyZDiRYazdEOJqI9MO6kkZOHplMXExkZ+TE+DxSE2JIDfapyklNoKIXhSjX1Ij790q8eefhpQ1uo++EWB+XTB7BY1uqKG9sJzcl7rjXLy+u4Y9vlJKbEsf/u3gM4/pQqBORwdHjAllr7QPAT4B7gRZjzAGgBfgr8BNr7YMDmlBERERkGDHGfJjARjEFwMeDh9OAn0cslIj0W1lDGwfq25kTxmV5g8299iK0tuBddEVEHn/J1EwAni2qOeY1HX7H3W+U8ru1pZw8KoX/vnSCilAiUapXndqstT8n8MPQlcDXg+/HWGt/MYDZRERERIajO4BLrLW3AJ3BY+uBUyMXSUT6a31pE8CQLUQ5vx+38mmYPANv/OSIZBiZGsfcglSWF9fQ1ul/3/m6lg6+v2IvT2+r4ZqZWXz3gjGHZ32JSPTpzdI8AKy1dcBzA5hFRERERGAkgcITvLdjnuvysYgMIe+UNJKdHMuY9PhIR+mfTe/AwRK8qz4a0RiXT89k7b4GXt1dz0WTRhw+vqu6hR+9vJ/q5g6+fFb+EedEJDpp70oRERGR6PIW8LGjjl0PvB6BLCISgk6/Y0NpI6flpwzZHdv8K5+GEZl4Z5wd0RynjEpmTHo8TxVW41ygLv/anjpuX76bDr/jzkvGqQglMkT0ekaUiIiIiAyKLwHLjTGfBlKMMc8B0wjsViwiQ8j2qhYa2vyc2svd3qKNO3gA3n0L74rr8GKP3yR8oHmex+XTM/njG2UUVrTwdkkD/9hYyfScRL553hiykvSrrchQoRlRIiIiIlHEWrsVmEFgd+L/C/wvMNtaWxTRYCLSZ+tKGvGAOXnJkY7SL27lM+Dz4Z13WaSjAHDBxHSSYn3csXIv/9hYyUWTRvDDi8epCCUyxKgQJSIiIhJlrLVNwGrgJWCVtbYhsolEpD/eKWlkUlYi6YlDr1DiWppxq1/AO/1svIysSMcBIDkuhsVTRtDc4eczZ4zkSwvyiI/Rr7QiQ83Q+44oIiIicgIzxowD/g4sAKqBTGPMWuAGa+3uiIYTkV5rau+ksKKZa2ZGRxGnr9yal6C5Ee+iKyId5Qg3nTaSK2dkkZsS2aWCItJ/Kh+LiIiIRJd7CTQsz7DWjgQygTeCx0VkiHi3rIlOB3Pyh15/KOccbuXTMG4STJ4R6ThHiPF5KkKJDHEqRImIiIhElzOA26y1jQDBZXm3B4+LyBCxrqSRhBiPmblJkY7Sd4Ub4cAevIuuGLK7/YlI9FIhSkRERCS6rAHmHXXsTODfEcgiIv20rrSJk0clEzcEexj5VzwFqWl4c8+NdBQROQGpR5SIiIhIhBlj7ujy6XZgmTHmaWAvMBZYCjwQiWwi0ncHG9rZX9fGZVMzIh2lz1xlOax7He/SD+DFJ0Q6joicgFSIEhEREYm8sUd9/kjw/UigFXgUSBzURCLSb+tKG4Eh2h/q5WUAeBcsiXASETlRqRAlIiIiEmHW2k9GOoOIhM+6kkaykmIZmx4f6Sh94trbcKuWw5x5eNkjIx1HRE5QKkSJiIiIRBljTDIwBUjtetxa+1pkEolIb3X6HRtKG5k7Jm3INfp2r6+Chnp8F14e6SgicgJTIUpEREQkihhjPg78FmgDmruccsC4iIQSkV7bUd1CfZuf04bYsjznHG7FU5A/FmacEuk4InICUyFKREREJLr8BPiQtfb5SAcRkb5bVxLoD3VKXnKEk/TRjkLYsx3vhluG3EwuERlaht5eoiIiIiIntjbgpUiHEJH+WVfSyKTMBDISh9bf/N2KpyApBW/BhZGOIiInOBWiRERERKLLd4CfG2NyIh1ERPqmud3P1ormIbdbnqupwr21Gu+cRXiJSZGOIyInuKFVphcRERE58W0D7gA+Z4w5dMwDnLU2JmKpRKRHmw420eFn6BWiXnkOOjvxLlga6SgiMgyoECUiIiISXf4G3Af8gyOblYtIlHunpJH4GI+ZuUNnVpHraMe98iycfAbeqNGRjiMiw4AKUSIiIiLRJRv4rrXWRTqIiPTNupJGZo1MJj5m6HRAcW//G2qr8d10eaSjiMgwMXS+Q4qIiIgMD/8LfCzSIUSkb8ob29lX18ZpQ21Z3oqnYGQ+zDo90lFEZJjQjCgRERGR6DIP+IIx5j+Bsq4nrLXnRSaSiPRkfWkjMLT6Q7VvL4TtW/Gu+zSeT3MURGRwqBAlIiIiEl3+FHwTkSHknZJGMpNiGTciPtJReq1p2b8gPgHv7EWRjiIiw4gKUSIiIiJRxFp7b6QziEjf+J1jfWkTZ45OwfO8SMfpFVdfR8uq5/HOvggvOTXScURkGFEhSkRERCSKGGM+daxz1tq/DGYWEemdHVWt1Ld2Dpllea69HffY/dDehnfhFZGOIyLDjApRIiIiItHl6EblecBkYDWgQpRImLmOdpy9B++CpXijx/VrjHUlwf5QedFfiHIb38L/0N1wsISkxdfQVtC/5ywi0l8qRImIiIhEEWvthUcfC86SmhmBOCInvnffwq1chtuxDd+3f4rni+nzEOtKG5mYmUBGUvT+euXKS/Hbe2DdWsgrwPeVH5B+3iVUVFREOpqIDDPR+51SRERERA75K1AB3BbhHCInHPf6KoiJgd3FuJXL8BZd2af7Wzr8bClv4srpWQOUMDSurRX37CO4Zx8Gnw/vQzfhXXwVXmxcpKOJyDClQpSIiIhIFDHGHL2HejJwI1ATgTgiJzTX2oJb/zreOZfgqg7iHr0fd9pZeFk5vR7j3bImOvxEZX8ot/51/A/9CSrK8Oaei3ftJ/v03EREBoIKUSIiIiLRpQNwRx3bD3w2AllETmhuwxtsTB7DH5MupDHfR1xaNXFP7SIuq464GB9xPo/4GI+4GI/YLh/H+XzB9x7bKpuJj/GYmZsU6adzmDt4AP9Df4aNb0L+WHxf+yHejFMiHUtEBFAhSkRERCTaTDzq80ZrrZq4iISZc44n1pdw76mfJT8+nrPyUmjbXUX73iLaM06mPSWDdr+jo9PR1O6n3e9o73S0d3b5OPj+vAnpJMQePZkxAs+ptRX3zD9xzz0CsXF4H/4U3kVX4MXq1z4RiR76jiQiIiISRay1uyOdQeRE19rh57er9/FK0inM9yq4dclZJMfF4E7Pwf/DB2D18/h+8Du8xJ5nOTl39ATGweecg3fWBJqRVx7EW3AB3oc+gZcRnX2rRGR4UyFKREREJAoYY1by/iV5XTlr7aLByiNyoipraOOuV/azq7qFj+54lms/uoSYuMBOeV5sLL4bP4f/x7fjnngAz3y6x/E8zxvoyMflSvfjf/Bu2PwOFIzHd9tdeNNmRTSTiMjxqBAlIiIiEh3uP8bxAuBLBJqWi0gI3ilp5Gev7scP/Gfty5zetBnfpFuPuMabMhPvvMtwLzyJW3AB3rjJkQnbA+cc7smHcMv+CfHxeNd/Fu+CpXgxMZGOJiJyXCpEiYiIiEQBa+09XT83xmQD3yLQpPwfwB2RyCVyInDO8cjmKu5fX87YEQl884x0Rn3vWbxLP9jtjCbvgx/HrVuD/77f4fv2T/F8UVjceWs17skHA7vhXf8ZvPTMSCcSEekVFaJEREREoogxJh24DfgC8BRwurV2e2RTiQxdze1+frOmhNV76lk4Po0vLsgn/tXncH4/3rxzu73HS0nFu+4zuD/9N27lM3iLrhjk1MfnGuvxP/BHGD8F79Nf1SwoERlSVIgSERERiQLGmCTgVuBrwEvAQmvtpoiGEhniSurbuPPlfeyra+MTp+VyzcwsPM+j841VkD8WCiYc815v7rm41S/iHvsb7vSz8DKzBy94D9w//wKN9fi+coeKUCIy5KhZ6si4AAAgAElEQVQQJSIiIhIddgIxwE+AN4FRxphRXS+w1q6IRDCRoejN/Q38fPUBfD6P7104ljn5KQC46koo2oR35UeO22jc8zx8N9yC//tfxP/Qn4j5j28OVvTjcpvX4Va/iLfkWryxEyMdR0Skz1SIEhEREYkOLQR2zfuPY5x3wKTBiyMyNPmd45/vVvLghgomZCbwrfMKGJUaf/i8e/NVcA5v7sIex/JG5uNdcR3u0b/h1r+Od+q8gYzeI9faiv/+38OoArwrr49oFhGR/lIhapibNSuPmhpfpGOEyehIB5CQ6L/f0Bfaf8OMDD+bNpWGKYvI0GOtnRDpDCJDXVN7J798rYS1+xq4YEI6n5ufR0LskT/rujdWwbhJeHljejWmt/ga3NqX8T/wR3zTZ+MlJg1E9F5xT/wdykvx3XYnXlx8zzeIiEQhFaKGuZoaH/v3H4h0jJDl5ORQUVER6RjSTwUFo0+Ir8PhLBz/BgsKVIwUEZH+213VxG3P7qakvo3PnDGSK6Znvm/pnSsv5f+3d+fhUVX3H8ffd5KQDQhLEkjYd0FRUBBwoSqiuGvV4y5uVVvbqrW2VttqXeuvdaFWbd3ABZfjvuGKVRERWcQFXEFkJyFhTwhJ5vz+uBeNGCBDJnOTyef1PPNkcuYu33vOZObme885l2+/wjvhrDpv10tNI3LGr4jefIV/l7oTz4lz5HXjFn6Ne+MFvJFj8PruFkoMIiLxkCxdYUREREREpJmavng95z3xMRsqqrluVFeO2qVdrfM/uRlTAPCG7HhYXk1e7wF4Iw/FvfkCbtGCuMQcC1dVRfTBOyCnDd7xYxO+fxGReFIiSkREREREmqSoc0z8uJgb311Kt7aZ3HJYd3brkLXN5d2MKdBrF7z2+THvy/v5WMhuRfSRu3DR6vqEHTP3+rOwZCGR0y7Ey8pO6L5FROJNiSgREREREWlyNmyu5oa3l2A/K2FUzxzuPGF38rLTtrm8W7YIlizEGzpyp/bnZbfEO+k8+PYr3Duv7mzYMXMrluBefBz22gdv0PCE7VdEpKEoESUiIiIiIk3KorUVXP7qQj5avpELhnbgN8N/Oin51tyMKeBF8Ibsu9P79fYeCQMG4Z55CLemZKe3U1cuGiX68J3QIp3IKRc0+P5ERBJBiag40kS7IiIidaPvTJHkUh11PDuvhF++sICHPipi4+aGG7o2bdF6Ln/1O8oqo1x/cFcO7/vTScm35pzDfTgFdhmIl9N2p/fteR6R034J1dVEH793p7dTV27K6/DVXDxzTr3iFhFpTJSIEhERERGRnfbVqnIue3UhEz4qJj3V4+l5pVz4wgJe/nI1VVEXt/1URx0Pzynm71OW0jWnBbce1p0B+dueD+pHFi2AomV4Q/evdxxefgHeEQZmvY/7ZEa9t7ctbnUJ7ukJ0H8PvH1GNdh+REQSLTXsAEREREREpOkpq6zmkTnFTPpqDe0yU7liZCeGd27JgtUVjJ9dxD0zV/LSl6sZOziPYZ1b7rDX0vZsqKjm1veXMWvZRkb3yuGCoR1IS6n7NXU3411IScHbc8ROx1CTd+hxuOnvEH30v0T6DcRLz4jLdrdwzhGdeDdUVxE5/Vf1qjsRkcYmYYkoY0xH4HZgKFABLASeA4621h5Zy/L3Abdaa+clKkYREREREdk+5xzTFq/n3plFrC6v4oh+bTltj1yy0lIA6NUug+tGdWHWso2Mn13ETe8uZUBeJmfvmU/f3MyY9/fdmgpufGcJq8oq+eXeHRjTJ7Yhai4axc14DwYMxstuFfP+a+OlphE54yKi/3cF7sXH8E44Oy7b/d6sqfDxh3gnnI2XXxDfbYuIhCwhiShjjAc8CzxorT05KBsEHLWtday15yUiNhERERERqZvijZX8d8ZKZizdQI+26Vz5s070af/T5JLneQzp1JLBBdm8MX8Nj36yistf+479u7XijEF5dGjZok77m/rdOv71wXIyUyNcf3BX+ufVcSheTQu+gNJivONOj33d7fD6DMDb/xDcG8/jhh2A16VHXLbrNq4n+uh/oVtvvIOPjss2RUQak0T1iDoQqLTW/mdLgbV2jjGmDTDKGPMUsBswCzjdWuuMMW8Dv7fWzjTGbADGAUcC5cAx1tqVxpijgD8DLYAS4DRr7coEHZOIiIiISLNQHXW89OVqHv2kGOfg7D3zOKpfO1Ii2x8ylhLxGNOnLSO7t+bZeaU893kp0xZv4Mh+bTlx1/a0TE/Z5v4mflzM0/NK6ZebyR/3L6R9VtpOxe4+nAJpLfAGDdup9bfHO34sbs50ouOuwTv053gjD633MD335AOwcT2RS/6Gl1J7/YiINGWJSkRtSTLVZjCwK7AMmArsC7y31TLZwAfW2quMMf8H/AK4PlhueJC4Og/4A3DZ1jswxpwPnA9grSU3N/cnQaSmptZaHqumeBegeBx32OLVfhIetV/T1pw/Q5NH4utef/ciTcPXJeXcNX0FC1ZXMKQwmwuGdiS/ZWxJoay0FE7bI48xfdow8eNVPP95KZPnr+GkgbmM6dOWtJQfElrrK6r559RlzFm+kUN7t+EXQ/Jjmg+qJlddjZv5Huw+BC9jJ3pT7YCX3YrIxVcTfWoCzt6Pm/Qk3sFH4x14BF5WduzxzpuDmzoZ77AT8Lr2jHu8IiKNQWOYrPxDa+0SAGPMHKA7P01EbQZeCp7PAkYHzzsDTxhjCvB7RX1b2w6stfcA9wS/ulWrVv1kmdzcXGorj00hS5cuq+c2EqtTp8I4HHf44tN+Ep7keB82Z831MzRZhPEZ2pDfP4WFSmiKxENZZTUTP17FpK9Wk5ORyh/3L2REl1b1mji7fVYavx1RwFG7tGX87CLum1XES1+u5szBeezTpZU/H9S7Sykpq+KiYR05pHeb+h3EV5/B+rVEho6s33a2w+vWm5TLrsd98znRSU/innsE99qzeKOOxBt1FF7L1nXajquoIPrIXZBfiHfkSQ0Wr4hI2BKViJoLnLCN1ypqPK+m9pgqrbWulmXuwJ/Q/AVjzAHANfUPVURERKTpMsY8gD+dQZG1dregrB3wBP4Fv4WAsdauDubxHAccDpQBZ1lrZwfrjMWfAgHgemvtg0H5XsAEIBOYBFxc4zxNksQHi9dzz4yVlJZXMaZPG84YlEd2i/gNE+vRNoO/HdSFj5ZvZMLsYv5vyjJ6t8tg8doKslukcOPorvTbiYnNt+ZmTIH0TBi4Vxyi3j6vd39SfvtX3HfziU6yuJeewL3xPN7PDsM75Fi8nO1Psu5emAjFK4j8/ka8FukNHq+ISFh2ro9r7N4C0o0xv9hSYIwZCvysntvNAZYGz8fWc1siIiIiyWACMGarsiuAydbaPsDk4HeAw4A+weN84G74PnF1NTAM2Bu42hiz5b/ou4Nlt6y39b6kCSstr+LGd5Zw07tLaZWews2HduPCvTvGNQm1hed57FnYktsO786vh3WkpLyKXu0yuPWw7vFJQlVV4ma9jzd4WEITO163XqT88k9Ervk33qBhuDeeJ3rFeUQf/Q+upLj2WBd+jXvjBX+OqX67JSxWEZEwJKRHVDCH03HA7caYK4BN+Ffjnqvnpq8BnjTGLAU+AOJzqwoRERGRJspa+64xpvtWxccABwTPHwTeBv4YlD8U9Gj6wBjTJpjy4ADgDWttKYAx5g1gTHAzmdbW2mlB+UPAscArDXhIkiCV1Y7r317M4rWbGTsoj6P7tyN1B5ORx0NKxGN07zYc1DOHiEe9hv79yNw5ULYBb+j+8dlejLxOXfHOuwx39Cm4V57Gvfs67t3X8EYchHfY8Xj5/jBiV1VF9ME7oHUbvOPPCiVWEZFEStgcUdbaZYCp5aV7ayzz6xrPD6jxvGWN508BTwXPnweeb4BwRURERJJJB2vtcgBr7XJjTH5Q3glYXGO5JUHZ9sqX1FIuSeDxT1cxv7SCK0Z2YkSXVgnf/47uwBcrN+NdyG4FAwbFdbux8vIL8cb+BnfkybjXnsFNed2fkHzv/fEOOxH38XRYspDIr67cqQnORUSamsYwWbmIiIiIhKO2//zdTpTXqi53LpbaJfqOwB8vXcsz80o4ckAHjhrcNAcZ1KwzV7GJ4o9nkLn/wbTuWBByZIHcXOh3FdWnX0DZC49T/tqzRKe/A5EU0kccSJvRRyY8JN15Onaqs9ipzmKX7HWmRFQc6W5PIiIidaPvzIRbaYwpCHpDFQBFQfkSoEuN5ToDy4LyA7Yqfzso71zL8rWqy52LpXaJvJtlWWU117yykPzsNE7brXWTvZNtzTpzs6biNpVRMXBo4zyeI0/GO+BwmPwi7qu5VP58bChx6s7TsVOdxU51FrumWmd1vXOxElEiIiIiye8F/Bu7/D34+XyN8l8bYx7Hn5h8bZCseg24scYE5YcAf7LWlhpj1htjhgPTgTPx72IsTdi9M4tYVVbJjaO7kpUW/0nJwxD9cAq0bgONeOJvr2VrvGNOCzsMEZGEUyJKREREJIkYYx7D782Ua4xZgn/3u78D1hhzLrAIODFYfBJwOPANUAacDRAknK4DZgTLXbtl4nLgl/h35svEn6RcE5U3Ye8vWsdbC9ZidmtP/7yssMOJC1deBp/M8O9AF0mOxJqISDJRIkpEREQkiVhrT9nGS6NqWdYBF21jOw8AD9RSPhNovN1MpM5Kyiq5a/oKerfL4KSByTMXiZszHaoq8fYeGXYoIiJSi0jYAYiIiIiISGI557jjgxVUVDsu3beA1DjfsS5MbsYUaJ8PPfuFHYqIiNRCPaKETp3qNqFY45csx9E8Jc/7sDmrXxu2aRONUxwiIrIjk75aw0fLN3Lh0A50bp0edjhx4zasg3kf4R18DJ6XPMk1EZFkokRUM5csdy1qqncVEJ/ar+lTG4pIXdzwzhLaZqTSLjOVdlmptM1IpW1mKm0zU2iTkUpKEvXKacwWra1gwkdF7FWYzZg+bcIOJ67c7GlQXY239/5hhyIiItugRJSIiIiIJETRhkq+LC5nbUX1T16LeJCTnhIkpvxHu+DRJSedvrkZtEjRrBL1VVntuG3qMjJSI/xmeEHS9RpyM6ZAh07QpWfYoYiIyDYoESUiIiIiCTHuiB6AnwxZs6mK1eX+ozR41Px9Qekm1lZUE3X+umkRj765Geyan8VuHbLYJTeT9FQlpmL1+KerWLC6gitHdqJtZnL9K1Bdugq+/BTviJOSLsEmIpJMkuvbR0REREQavbQUj7zsNPKy07a7XHXUT1jNL93E3KJyPltZxlNzS7CflZAagd7tMtk1P9NPTOVlkpWWkqAjaJrmFpXx9NwSDu6Vw7AurcIOJ+4q3n8LnNOwPBGRRk6JKBERERFplFIiHu2z0miflcbenf3ESVllNZ8XlfNZURlzi8p47vNSnp5XSsSDXu2CHlP5WfTPz6RlCyWmtiirrOb295fRoWUa5+6VH3Y4DWLTe29C5x54BV3CDkVERLZDiSgRERERaTKy0lLYq1NL9urUEoBNVVG+KC5nblEZn60s46UvV/Pc56V4QI+26ezeMZsDerSmR9uMcAMP2b0zV7KqrIqbRndLyp5jbtVKKr/8DO/nZ4YdioiI7IASUSIiIiLSZGWkRhhUkM2ggmwAKqqifFVSztyVfq+pl74s5bnPS+nVLp1RPdswsntrWqUnXyJme6YuWsdbC9ZhdmvPLnmZYYfTINzUNwHwhuwXciQiIrIjSkSJiIiISNJIT40wsEM2Azv4ial1FdW8u3Atk+ev5Z6ZK3lgdhHDOrfk4F457NExm5RIck9qXVJWyd3TV9CnfQYnDcwNO5wG4VaX4F5/jvR9DqIqr2PY4YiIyA4oESUiIiIiSat1egpH9mvHkf3asaB0E5MXrOWdheuYumg97TNTObBnDqN65lDYukXYocZd1Dn+NW05m6sdl+5TSGqSJt3csw9BtJpWZ/6K1WEHIyIiO6RElIiIiIg0Cz3bZdCzXQZnDc5jxtINvDl/Lc/MK+GpuSUMyMtkVK8c9u3amsy0SNihxsXLX65mzooyLhzagU5JmGgDcAu/xk37H96Y40npUAirVoUdkoiI7IASUSIiIiLSrKSlRNina2v26dqakrJK/vftOibPX8sdH6zg3pkr2adraw7ulcOAvEw8r2n2Ilq0toKH5hQzpDCbMX3ahB1Og3DOEX3ifmiVg3f4iWGHIyIidaRElIiIiIg0W+2z0jhh1/YcP6AdX6wq5835a3nvu/W8tWAtBa3SOHvPfIZ1bhV2mDGprHbcOnUZmakRfjO8oMkm03Zo9vvwzTy8My7Cy8wKOxoREamj5Oh3LCIiIiJSD57n0T8vi98ML+DB43tz8YgC0lMi3PjOUh78qIjqqAs7xDp79JNivl1dwUXDO9ImMzmvO7vKzUSfmgCdu+Ptd3DY4YiISAyUiBIRERERqSEjNcJBPXP4x5huHNq7Dc/MK+Wvkxexurwq7NB26JMVG3l2Ximje+U0uZ5csXBvvgirVhIx5+JFUsIOR0REYqBElIiIiIhILVqkRPjVsI5cMqKAr0o2cemkb/lsZVnYYW3Tuk1V3Pb+cgpbt+C8IR3CDqfBuHWrcZMs7LE3Xv89wg5HRERipESUiIiIiMh2HNgzh38c2o3MtBT+MnkRT88twbnGNVTPOccd01ewrqKa3+9bSEZq8p7mu+cfhcrNRE44O+xQRERkJyTvN5SIiIiINGluybdUX3sx0Yf+HXYodG+bwS2HdWN4l1Y8NKeYG99dyobN1WGH9b1JX63hwyUbGDs4j57tMsIOp8G4JQtxU97AO+BwvI6dwg5HRER2ghJRIiIiItKoOOeI/m8S0Rt+D8sX46a8jvt0ZthhkZWWwh/2K+S8vfKZtXQDv3tlIQtKN4UdFgtXb2L87CL2KszmqH5tww6nwTjniNr7ISsb76iTww5HRER2khJRIiIiItJouI3rid59E+7R/8AuuxO54R4o6EJ04n9wm8rDDg/P8zhql3bcOLobVdWOP7z2Ha9/sya0oXoVVVFumbqM7BYRfjuiAM/zQokjIT6ZAZ9/jHfUKXjZyTsRu4hIslMiSkREREQaBffNPKLXXgyfzMQ78Wwiv/kLXrtcImdcBCVFuBceDTvE7+2Sl8lth3dn1/xM7py+gn99sJyKqmjC43hgdhGL1m7mkn0KaZORmvD9J4qrqiT65Hjo2BnvZ2PCDkdEROpBiSgRERERCZWLVhN96Qmi/7gSUlKJ/PFmIocchxfxT1W9PgPwRh6Ke/NF3HfzQ472BzkZqfz1wC6cPLA9/1uwjstf+46l6zYnbP/TFq/n1a/XcGz/dgwuyE7YfsPg3n4FVi4lcuLZeKnJm3ATEWkOlIgSERERkdC4NSVEb/0r7vmJeEP2J/KX2/F69PnJct7xY6F1DtGH/o2rbjyThKdEPE7ZPY+/HtiZ0rJKLntlIe8vWtfg+y3eWMm/P1hOr3YZnL5HXoPvL0xuwzrci4/DgEEwcEjY4YiISD0pESUiIiIioXCfzCD6t4vh26/wzroY77zf4WVm1bqsl9US76RfwKL5uLdeSnCkO7ZnYUtuO7wHnXNacPOUZdw/ayVV0YaZN6o66rj9/WVURR2/37eQtJQknhcK/CRUeRkRc25yz4ElItJMKBElIiIiIgnlKiuJPnE/0TuugzbtiPz5NiL7jtphksEbsi8MHIJ77hFcSVGCoq27vOw0bhrdjSP6tuGFL1Zz5RvfsXx9/IfqPT23hM+KyrlgaEcKW7eI+/YbE7d8Ce7tSXgjD8Hr1C3scEREJA6UiBIRERGRhHFFy4je/Efcm8/jHXgEkSv/iVfQuU7rep5H5LQLAfy76IV0p7rtSUvxOH9oRy7fr5AlazdzyaSFvDk/fnfV+7y4jMc+XcXI7q05sEfruGyzMYs++QCkZ+Adc1rYoYiISJwoESUiIiIiCRH94H9Er70UilcQ+dWVRE69AC8tth49Xvt8vGNPh09n4mZObaBI62+/bq0Zd0QPerfP4I4PVnDTu0tZt6mqXtvcsLmaW6cuIy87jV/u3SHph6m5eR/BpzPxjjB4rXLCDkdEROJEiSgRERERSQh3/23QpQeRq8fhDR6+09vxDjoSuvbCPX4PbuOGOEYYX3nZaVw3qgtn75nHrGUb+e3L3zJr6c7F65zj7g9XUFJWxWX7FpKVlhLnaBsXV11N1D4AeR3xDjoq7HBERCSOlIgSERERkYTwjjyZyO9vwGtXv7u8eSkpRM78Naxfh3vmwThF1zAinsex/dvzzzHdaJWewrVvL+G/M1ZQURWNaTuTF6zlve/Wc+ruefTLzWygaBsPN+V1WPodkePPwktLCzscERGJIyWiRERERCQhIsecipcSn548XrdeeAcfhXv3NdzX8+KyzYbUo20GtxzWnaN3acukr9bwu1cWMr90U53WXbKugntmrGRghyyOG9CugSMNnyvbiHvhUei7K+w5IuxwREQkzpSIEhEREZEmyTv6VGifT/ThO3GVlWGHs0MtUiKcu1cH/nZQF8oro1z+6kKe+qyE6ui2JzKvrI5yy3vLaJEa4dJ9CkiJJPe8UABukoUN64iY85J+HiwRkeZIiSgRERERaZK8jEz/LnrLF+NeezrscOpsUEE2447owfAurXj442L+/OYiVm7YXOuyD80pZsHqCn47vCPts5J/iJorXoGb/CLeiIPwuvUKOxwREWkASkSJiIiISJPlDRyCN3R/3MsWt2JJ2OHUWav0FC7fr5BL9ylg4ZoKLn55IW8tWItzP/SOmr1sAy98sZoj+rZh786tQow2caJPTYCUVLzjTg87FBERaSBKRImIiIhIk+addB60SCf68F0/SuQ0dp7ncUCPHMYd3oOe7dIZN205/3hvGesrqinZuJnbpy2nW5t0ztozP+xQE8J9+RnMfh9vzM/x2rQPOxwREWkgqWEHICIiIiJSH15OW7zjz8I9fCdu6pt4+40OO6SY5LdM47pRXXnu81Ie/aSYz4vLKchZQXlllOsPLqRFSvJfO3bLFhF96A5om4s3+riwwxERkQaU/N9qIiIiIpL0vP1GQ+8BuCfH49atCTucmKVEPI7ftT3/OLQ7WWkR5q5Yz7l75dM1Jz3s0BqUc47olNeJ3vA7KC8jct5leOnJfcwiIs2dekSJiIiISJPnRSJEzryI6N8uxj1xP94vLgs7pJ3Ss10Gtx7WnVXV6RS2qH0C82ThyjbiHrkLN2MK9N+DyDmX4rVpF3ZYIiLSwNQjSkRERESSglfQBe+wE3AfvoP7bHbY4ey09NQIe3TKwfO8sENpMO7br4lefylu1lS8Y08ncsk1SkKJiDQTSkSJiIiISNLwDj8BOnQiOvFuXEVF2OHIVlw0SvT1Z4ne/AeoriZy+Y1EjjB4kZSwQxMRkQRRIkpEREREkoaX1oLIGRfBqpW4Fx8LOxypwa1fS/Tf1+OeHA+7DyXy19vxeg8IOywREUkwzRElIiIiIknF67cb3r4H4954Drf3SLyuPcMOqdlzX3xC9L5bYeN6vFMvxDvgsKQeeigiItumHlEiIiIiknS8E8+G7FZEH7wDt35d2OE0W666mujzE4ne+hfIzCTyp38QOfBwJaFERJoxJaJEREREJOl42a2InP4rWPod0Wt+jZszPeyQmh1XWkz0lqtwLz2BN+IgIlfdqt5pIiKiRJSIiIiIJCdvzxFErroFWrcheucNRMePw5VtDDusZsHNmU702ktg0bd4515K5OyL8TIyww5LREQaAc0RJSIiIiJJy+vSg8hVt+BefAL3ylO4zz8mctZv8AYMDju0pOQqK3FPT8BNfhG69iJy/uV4HQrDDktERBoRJaJEREREJKl5qWl4x52O22Mo0fG3E73tan+y7OPPUi+dHXBVlbCpHMrLfvhZUY4rL4NNZVBe7v8MXnPffgXLFuGNOsqv37S0sA9BREQaGSWiRERERKRZ8Hr2I/KX23HPPoKb/AJu7kdEzroYr++uYYcWOldehvt0Jsyehls0/4ekU1Xljlf2PMjIhIwsaNmKyEVX4Q0a1vBBi4hIk6RElIiIiIg0G16LdLyTzsUNHkZ0/Dii/7wSb/QxeMechtciPezwEsqtX4f7eDpu9jT4fA5UVUHrNnh9d4OslpAZJJcysyAjC2/L7xmZ35eRmQktMvAimnpWRETqRokoEREREWl2vL67Ebn6X7inxuNefw73yUwi51yK16NP2KE1KLe6BPfRND/59NVccFFon493wBF4e46AXv3wIilhhykiIklMiSgRERERaZa8jEy803+FGzyC6IN3EP375XiHnYB35El4qckzt5ErWv5D8mnBl35hQRf/WPccAV174nleuEGKiEizoUSUiIiIiDRr3q6DiVzzL9zj9+FetriPZxA59xK8zj3CDm2nOOdg2SLcrPdxH02DJQv9F7r2wjv2dLw9R+AVdAk1RhERab6UiBIRERGRZs/Laol3ziW4PYcTfehOotdfhnfUyXgjD4WWrUPrMeSqq6FsI5Rt8B8b1+M2bvDLNq7/vtxt3Ahl62HjBtiwDtau9icR79Ufz5yLN3g4Xm6HUI5BRESkJiWiREREREQC3qDhRHoNwE28G/fcI7jnHoHsVtCxk9+LqGNnvILO0LEz5ObXez4lt3E9FK3AFS+H4hVQvAJXvILiNSVE16/171y3PS3S/YnFs4NHXke8br2hR18/+ZTTtl7xiYiIxJsSUSIiIiIiNXitWuNd+EfcV3Nxi76B5UtxKxbjPv4Q3nsDt2XB1DToUIjXsTMUdAmSVZ2hQ2e8dP8OfC5aDatLoGg5rngFrFoRJJ6C52Ubf7zznLaQ15EWuwykIq1GkimrJV7w8/ukU2ZLvLTkmctKRESaByWiRERERERq4fXdFa/vrj8qcxvXw/IluOWLYcVS3IoluEXzYfY0cNEfklTt8yElFUqKoLrqhw2kpPiv5XXE68DqjukAABI9SURBVNnP/5nfEXI7+s/TMwDIyc1l1apViTlQERGRBFIiSkRERESkjrzsVtC7P17v/j8qd5WboWg5LF+MW7HET1ZVVeENHg75HfHyCiCvI7TLrfdwPhERkaZMiSgRERERkXry0lpAp27QqRvhTGsuIiLSNETCDkBERERERERERJoHJaJERERERERERCQhlIgSEREREREREZGEUCJKREREREREREQSQokoERERERERERFJCCWiREREREREREQkIVLDDkBEREREmh5jzBhgHJAC3Get/XvIIYmIiEgToB5RIiIiIhITY0wKcCdwGDAAOMUYMyDcqERERKQpUCJKRERERGK1N/CNtXaBtXYz8DhwTMgxiYiISBOgoXkiIiIiEqtOwOIavy8Bhm29kDHmfOB8AGstubm5iYkuCaSmpqq+YqQ6i53qLHaqs9ipzmKX7HWmRJSIiIiIxMqrpcxtXWCtvQe4Z8vrq1atatCgkklubi6qr9iozmKnOoud6ix2qrPYNdU6KywsrNNyGponIiIiIrFaAnSp8XtnYFlIsYiIiEgToh5RIiIiIhKrGUAfY0wPYClwMnBquCGJiIhIU+A595Ne1Mmu2R2wiIhIM1Tb0DGJI2PM4cDtQArwgLX2hh2sonMwERGR5LfDc7DmODTPq+1hjJm1rdf0aPwPtV/Tfqj9mv5Dbdi0H0naftLArLWTrLV9rbW96pCEIknfZ/q7bEQP1ZnqTHXWOB+qs2ZXZzvUHBNRIiIiIiIiIiISAiWiREREREREREQkIZSI+sE9O15EGjG1X9Om9mv61IZNm9pPEkHvs9iovmKnOoud6ix2qrPYqc5il9R11hwnKxcRERERERERkRCoR5SIiIiIiIiIiCREatgBhM0YMwYYh3/r4fustX8POaRmyxjzAHAkUGSt3S0oawc8AXQHFgLGWrvaGOPht9vhQBlwlrV2drDOWODPwWavt9Y+GJTvBUwAMoFJwMXWWnUJjBNjTBfgIaAjEAXusdaOUxs2HcaYDOBdIB3/++Epa+3VxpgewONAO2A2cIa1drMxJh2/zfcCSoCTrLULg239CTgXqAZ+a619LSjXZ24DM8akADOBpdbaI9V+0lC28b29B/AfoCX+Z/5p1tp1xpg04D5gT/zPl4estTcF6zSb91Uc62whsB7/b7TKWjskwYeSMDHWWQvgv8AQ/HORi621bwfrNJtziDjW2dtAAVAebPoQa21R4o4kMRJxDpts4lxn1cCnwaYXWWuPTuSxJMpO1NkuwHj874CrrLX/rLGtJv+92ax7RAUn63cChwEDgFOMMQPCjapZmwCM2arsCmCytbYPMDn4Hfw26xM8zgfuhu8TV1cDw4C9gauNMW2Dde4Olt2y3tb7kvqpAi6z1vYHhgMXBX9PasOmowI4yFq7BzAIGGOMGQ7cDNwWtOFq/AQFwc/V1trewG3BcgTtfjKwK34b3WWMSdFnbsJcDHxe43e1nzSUCfz0c/g+4Apr7UDgWeDyoPxEID0o3wu4wBjTvRm+ryZQzzqrsd6B1tpByZyECkyg7nX2C4CgfDRwizFmy/87zekcYgLxqTPwE1aDgkfSJaECiTiHTTZxqbNAeY33WFImoQKx1lkp8FvgnzU3kizfm806EYX/AfGNtXaBtXYz/hXjY0KOqdmy1r6L/wdX0zHAlisJDwLH1ih/yFrrrLUfAG2MMQXAocAb1tpSa+1q4A38f6YLgNbW2mnB1a+HamxL4sBau3zLlQ1r7Xr8f4Q7oTZsMoK22BD8mhY8HHAQ8FRQvnUbbmnbp4BRwRWvY4DHrbUV1tpvgW/wP2/1mdvAjDGdgSPw/+EgaA+1nzSIbXxv98PvWQn+5/fxwXMHZBtjUvF7pGwG1tHM3ldxqrNmJcY6G4D/jxxB0mQNMKS5nUPEo84SEGaj0dDnsAk8lISJY501G7HWmbW2yFo7A6jcalNJ8b3Z3BNRnYDFNX5fEpRJ49HBWrsc/D9eID8o31bbba98SS3l0gCCK7aDgemoDZuUoOfLHKAI/wRqPrDGWlsVLFKz3r9vq+D1tUB7Ym9biZ/bgT/gd/kGvz3UfpJInwFbrmifCHQJnj8FbASWA4uAf1prS9H7CmKvM/CTVK8bY2YZY85PZLCNxLbq7GPgGGNMajAsea/gNZ1DxF5nW4w3xswxxvwluFiR1BroHDap1bPOADKMMTONMR8YY5I2QVxTHetsW5LifdbcE1G1fZgm5VjxJLSttou1XOLMGNMSeBq4xFq7vSu3asNGyFpbba0dBHTGv+LSv5bFttS72rARMcZsmQ9kVo3i7dW52k8awjn4ww1mAa3we/GA/3lSDRQCPYDLjDE90fsKYq8zgH2ttXviD824yBgzMsExh21bdfYA/j9lM/ET8+/jD4fR+yz2OgN/WN5AYP/gcUZCI06wBjyHTVpxqDOArsEQ41OB240xveIcZqMSQ51tS1K8z5p7ImoJP874dwaWhRSL1G7llm6bwc8tY9O31XbbK+9cS7nEUTCx6tPARGvtM0Gx2rAJstauAd7GH8PeJhgaAj+u9+/bKng9B38oQKxtK/GxL3B0MInx4/hD8m5H7ScJZK39wlp7iLV2L+Ax/F6V4P+D8aq1tjIY/jMVf/hPs39f7USdYa1dFvwswp/vZ+/ERx6ebdWZtbbKWntpMNfMMUAb4Gt0DrEzdYa1dmnwcz3wKEn8Pmvgc9ikFKc6q/l5tgD/3HNwgwcfkhjrbFuS4n3W3BNRM4A+xpgewR0jTgZeCDkm+bEXgLHB87HA8zXKzzTGeMFkymuDroyvAYcYY9oGkwMeArwWvLbeGDM86FZ8Zo1tSRwE9Xo/8Lm19tYaL6kNmwhjTJ4xpk3wPBM4GH/8+v+AE4LFtm7DLW17AvBWMPfGC8DJxpj0oJt/H+BD9JnboKy1f7LWdrbWdsev27estaeh9pMEMsbkBz8j+HeO+k/w0iLgoOAzPxs/yf0Fel/FXGfGmGxjTKtgnWz878nPEh95eLZVZ8aYrKBOMMaMxr+j4DydQ8ReZ8FQvdygPA3/LnxJ+T5r6HPYhBxEgsWrzoK6Sg+2mYt/UW1eQg4iwXaizrYlKb43U3e8SPKy1lYZY36N/wGRAjxgrZ0bcljNljHmMeAAINcYswT/rhN/B6wx5lz8E7ITg8Un4d/+8xv8W4CeDWCtLTXGXIf/BwpwbY35FH7JD7ftfSV4SPzsi99l+9NgjiGAK1EbNiUFwIPGvxtHBLDW2peMMfOAx40x1wMf4X+JEvx82BjzDX5PmpPxV5prjLH4JxJVwEXW2moAfeaG4o+o/aQBbON7u6Ux5qJgkWfwbz0N/h1+xuP/I+sB4621nwTbaTbvq3jUWTA871ljDPjn8o9aa19N3FEkVox1lg+8ZoyJAkv58VCyZnMOEac6Sw/K0/D/Nt8E7k3MESRcIs5hk01c6gx/Coj/Bu+/CPB3a21SJqKIsc6MMR3xh8y2BqLGmEuAAdbadcnwvek51+SGE4qIiIiIiIiISBPU3IfmiYiIiIiIiIhIgigRJSIiIiIiIiIiCaFElIiIiIiIiIiIJIQSUSIiIiIiIiIikhBKRImIiIiIiIiISEKkhh2AiDR9xpgJwBJr7Z9D2LcHPAAcC3xtrd070THUiOU0YKy19pCwYhAREREREWnMlIgSSULGmIVAJtDTWrsxKDsPON1ae0CIoTWE/YDRQOctx1qTMeYs4Dxr7X4NHYi1diIwsaH3UxtjzAHAI9bazmHsX0RERJKHMWYiUGGtPadG2c+AZ4DdrLXLQwtORJo8Dc0TSV6pwMVhBxErY0xKjKt0AxbWloRqSowxujAgIiIijcVvgcONMaMBjDEZwL3AZfFMQu3EeZ+IJAH94yOSvP4B/MEYc5e1dk3NF4wx3YFvgTRrbVVQ9jZ+j5r7gl5EvwA+BM4GSoHTgb7AdUA6cLm19sEam801xrwBDAdmA2daa78Ltr0LcAewF1AM/MVaa4PXJgDl+AmlnwHHAG9uFW8h8B/83k+lwM3W2nuNMecCdwJpxpgNwC3W2qu3VylBb7E7gTOAXsDjwJXAhGD704ETrbWra9TTOcC1QEvgT8As4H6ga1Bnvw62fRY1el8ZYw4Jjrsjfk+pXYGHa6njscBdxpjx+Cd5ewAOeA24aEv7BbH/GzgzqK9Xg3VTgFeA9KAewG+rzsBdwfNyYKK19nfbqx8RERERa22JMeY3wD3GmN2APwPzrbUTjDER4ArgXCAH/7ztl8G5UwSw+OdUGcCc4LXPAYwxjwBr8c/B9geOMMa0xD9v7Ry8dou19rYEHq6IJJh6RIkkr5nA28Dvd3L9YcAnQHvgUfyEzVCgN35S6t/BicMWp+EnqXLxTzomAhhjsoE3gm3kA6fgJ112rbHuqcANQCvgvVpieQxYAhQCJwA3GmNGWWvvBy4EpllrW+4oCVXD8fjD+foCR+Enca4MYo/gXwXcui76ACcBtwNXAQfjJ5ZM0FX9R4wxucBT+Imr9sCXwD61bHcBfr3cAHjATcFx9ge6ANdsvWlgDNAD2B04K+gNdhiwLKiHltbaZcA4YJy1tjX+CZ+tU+2IiIhIs2etfRL/4ttjwPnABcFLvwOOAEbiJ482Av+qsepL+OdNHYHPgIe32vSpwN/wz/umAeOBc621rfDPbd5pgMMRkUZEPaJEkttfganGmHE7se631trxAMaYJ/CTL9daayuA140xm/GTUnOC5V+21r4bLH8VsNYY0wU/+bJwy7aA2caYp/ETSnODsuettVOD55tqBhFsYz/gSGvtJmCOMeY+/B5Nk3fiuADusNauDLY/BSiy1n4U/P4sMGqr5a8L9v26MWYj8Ji1tqjG+oP56UnT4cBca+0zwXL/4qdJwWXW2juC51XAN8EDoNgYcyuwdXLtX0GSCWPMi8Cg7RxnJdDbGJNrrV0FfLCdZUVERES2dhEwH7jKWrsoKLsAvwf4UgBjzDXAN8aYsdbaKH4vc2q8VmyMya4xjcKz1tppwfMKY0wlMMAY86m1thS/97uIJDElokSSmLX2M2PMS/jdpz+PcfWVNZ6XB9vbuqxmj6jFNfa7wRhTit+zpxswzBhTc3hgKj++OraYbSsESq2162uUfQcMqctBbMPWx7G949qZ5cGPu2adOGPMkq2W+dFxG2Py8a8o7o9/lTACrN5qnRU1npcF+9mWc/GHFH5hjPkW+Ju19qXtLC8iIiLyPWvtSmPMKn64eAj+1AQvGmOiNcockG+MKcbv3X0Cfk/zLcvk4vecgp+e9x2Hf8HzH8aYj4E/Wmunx/dIRKQxUSJKJPldjT9n0y01yracCGQB64LnHeu5ny5bngRD9toBy/BPNt6x1o7ezrpuO68tA9oZY1rVSEZ1BZbWM96Gthy/uzoAxhiv5u+BrY/7pqBs92BuhmPx54Sqi5/UobX2a+CUYL6GnwNPGWPaN/WJ3UVERCRUS4BTa0sWGWPOxu8VfhD+hcP2+PODejUW+9E5S7Cdo40xafg32nkcfwoCEUlSmiNKJMlZa78BnqDGvEfW2mL8RM7pxpgUY8w5+HMI1cfhxpj9jDEt8OeKmm6tXYw/T0BfY8wZxpi04DHUGNO/jvEvBt4HbjLGZBhjdsfv6TOxnvE2tJeBgcaYY4M74l3EjpN9rYANwBpjTCfg8hj2txJob4zJ2VJgjDndGJMXdJPf0iOtOoZtioiIiGztP/jzdXYFv0e3Mebo4LVWQAVQgn/B84btbcgYk2mMOdUY09paWwmsR+cqIklPiSiR5uFaIHursl/gJzpK8Cfdfr+e+3gUv/dVKf7d8U4DCHoxHQKcjN+7aQVwM/6d9+rqFKB7sP6zwNXW2jfqGW+DCuZkOhH4P/w6HoA/gXzFdlb7G7An/h1jXgaeiWF/X+BPJrrAGLMmuNPgGGBucCe9ccDJwVxXIiIiIjvrVvw79042xqzHP4ccGrw2Hv98bRn+cL66nF+OBb4zxqzDv9h4RtwjFpFGxXNueyNiREQkHoLhcUuA06y1/ws7HhERERERkTBojigRkQZijDkUmI4/ofnl+PMj6M51IiIiIiLSbGlonohIwxmBf8vjVcBRwLHW2vJwQxIREREREQmPhuaJiIiIiIiIiEhCqEeUiIiIiIiIiIgkhBJRIiIiIiIiIiKSEEpEiYiIiIiIiIhIQigRJSIiIiIiIiIiCaFElIiIiIiIiIiIJIQSUSIiIiIiIiIikhD/DzJtbGN7/Qk/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <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",
       "    <tr>\n",
       "      <th>Country</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>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\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>...</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>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "   1. Create a list of all years in decades 80's, 90's, and 00's.\n",
    "   2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "   3. Merge the three series into a new data frame. Call your dataframe **new_df**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "### type your answer here\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "# slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "# display dataframe\n",
    "new_df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # display dataframe\n",
    "new_df.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.plot(kind = \"box\", figsize = (10,7))\n",
    "plt.title(\"Immigration from top 15 countries for decades 80's, 90's and 2000s\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "* larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "* smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "* Q1 (25%) = 36,101.5 <br>\n",
    "* Q3 (75%) = 105,505.5 <br>\n",
    "* IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1980s   1990s   2000s\n",
       "Country                       \n",
       "India    82154  180395  303591\n",
       "China    32003  161528  340385"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) on box plots for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 24,
     "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": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
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    "run_control": {
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    }
   },
   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data:\n",
    "   1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "   2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "   3. Reset the index in place.\n",
    "   4. Rename the columns to **year** and **total**.\n",
    "   5. Display the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show resulting dataframe\n",
    "df_total.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10,6), color='red')\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",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\n",
    "<!--\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",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
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   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
   ]
  },
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   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
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       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
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       "      <th>Western Sahara</th>\n",
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       "      <th>Zambia</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
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       "      <td>80</td>\n",
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       "      <td>9378</td>\n",
       "      <td>128</td>\n",
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       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
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       "      <td>17</td>\n",
       "      <td>114</td>\n",
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       "      <th>2</th>\n",
       "      <td>1982</td>\n",
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       "      <td>71</td>\n",
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       "      <td>6</td>\n",
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       "      <td>9074</td>\n",
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       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
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       "      <td>63</td>\n",
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       "      <td>16</td>\n",
       "      <td>32</td>\n",
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       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
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   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
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   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
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   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
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   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
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       "<matplotlib.legend.Legend at 0x7fe9a6ab82b0>"
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "-->\n",
    "\n",
    "<!--\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe9a68219b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='yellow',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "\n",
    "# India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"brown\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week2_LAB2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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