osmya/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",
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       "      <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",
       "      <td>1</td>\n",
       "      <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",
       "      <td>3616</td>\n",
       "      <td>3626</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,
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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": 13,
   "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', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\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",
    "                            colors=colors_list\n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "\n",
    "\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\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",
    "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": 14,
   "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": 14,
     "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": 15,
   "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 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": 16,
   "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": 16,
     "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": 22,
   "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": 22,
     "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",
    "\n",
    "\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",
    "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": 23,
   "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": 23,
     "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": 24,
   "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 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Indian and Chinese immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\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",
    "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": 27,
   "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='yellowgreen', 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": 28,
   "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": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "\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",
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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": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 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",
    "\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",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "new_df.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 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": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()\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.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": 33,
   "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 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), color='red')\n",
    "\n",
    "plt.title('Box plots of Immigrants in 80s 90s 00s from')\n",
    "plt.xlabel('Decade')\n",
    "\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",
    "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": 34,
   "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": 34,
     "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": 35,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\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": 35,
     "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": 36,
   "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": 40,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.32504464e+02,  5.34657418e+05, -5.39077860e+08])"
      ]
     },
     "execution_count": 40,
     "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=2)\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": 46,
   "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 = -133 * Year + 534657'"
      ]
     },
     "execution_count": 46,
     "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[1] * x + fit[0] * (x**2) + fit[2], 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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "df_total.reset_index(inplace=True)\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "df_total.head()\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 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": 49,
   "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 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='darkblue')\n",
    "\n",
    "\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",
    "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",
    "\\\\ # 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>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>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",
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       "      <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>"
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      "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 0x7f984c115eb8>"
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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": 53,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\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": 54,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9847c61a58>"
      ]
     },
     "execution_count": 54,
     "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",
    "\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",
    "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",
    "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')"
   ]
  },
  {
   "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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  "widgets": {
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