abdatasci/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": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Explore Matplotlib library further\n",
    "-   Create pie charts, box plots, scatter plots and bubble charts\n"
   ]
  },
  {
   "cell_type": "markdown",
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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"
   ]
  },
  {
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   "metadata": {
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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/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740) and [**Numpy**](http://www.numpy.org/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\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?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740) 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.\n"
   ]
  },
  {
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Import primary modules.\n"
   ]
  },
  {
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Download the dataset and read it into a _pandas_ dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/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!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
  {
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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>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <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",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <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",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</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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    "new_sheet": false,
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    }
   },
   "source": [
    "Let's find out how many entries there are in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "collapsed": false,
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.3.4\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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {
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    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas.core.groupby.generic.DataFrameGroupBy\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <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,
    "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",
    "\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).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "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,
    "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,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yx4aFhdVITDcqLCwMP//8M+x2OzIyMhAdHY3PP/8cANCsWTMAwBtvvIEOHTrgpZdech33ww8/oHHjxli3bh2GDBmCBg0aYPXq1eXOn56eftWGItHR0UhMTHQ9rkoToXfffRcvv/wyFi9eXK4pR4MGDZCWlua27dLjq8VwLVFRUa4vveLi4hAdHY1Vq1Zh+PDh130uIvIOrJARkcdq0aIFDAYDEhIS3LZv27at0mOzs7Nx+PBhPP/88xg2bBjatm0Lk8lUrlplMBgq/fa5Xbt2SEhIcEsQ9u3bh/z8fLRr1+467qi8iq7frl07HDp0CFlZWa5t6enpOH78+FWvFxYWhhEjRuDjjz9Gfn5+uecdDgeKi4ur/LpU5siRI8jMzMTMmTMxcOBAtGnTBrm5uW6d7dq1a4fDhw8jLy/Pte3QoUNu8e3cuRMWiwUffPAB+vTpg9jY2CpXa44cOYKCggLX461btwIorZZe8sgjj2DJkiX4/PPPUVxcjD/96U/XPOe9996LtWvXlvuZu+RS2/t27dphw4YNbs9t3LgRZrMZzZs3r1L8QNV+/qqiXbt2MJlMOH36NFq0aFHuz6WqU23GVNVrNWzYEBqNBj/++CP69+/vqjIXFxeX63R66T4u/Zz16dMHZ86cwYkTJ1z7HDlyBMnJya5K65V0Op3baxMZGXnNGP/1r3/h3//+N5YtW1Zhh8Q+ffpg1apVbkntihUr4Ofnh86dO1fhVbi6S+e02Ww3dR4i8mxMyIjIY/n7++ORRx7BP//5TyxduhTHjx/HjBkzcOTIkUorL6GhoYiIiMCsWbNw/PhxJCQk4J577oHZbHbbr1mzZli3bh1SU1PdEqCyHnvsMRQUFGDq1Kk4ePAgNm/ejPvuuw99+/ZFv379buoemzVrht27d+PUqVPIysqCw+HA5MmTERERgbvuugt79uzB7t27cffddyMmJuaaTR8+/fRT6PV6dO3aFT/88AMOHz6MkydP4rvvvkO3bt1w4sSJKr8ulWnSpAmMRiM++ugjnDp1CmvWrMGTTz7p9u8yefJkBAYG4t5778W+ffuwbds2PPjgg27XatmyJSRJwnvvvYczZ85g0aJFePXVV6sUgyRJuP/++3Hw4EFs3LgRf/vb3zBq1Ci0bNnStU/fvn0RGxuLZ555BpMmTaq0OcKTTz6JwYMHY9iwYXj33Xexa9cunDt3DitWrMC4ceNczS9eeOEF/Prrr3jrrbdw/Phx/Pzzz3jllVfw9NNPl6tAXUuzZs1w9OhRVwJ+ox+8AwIC8OKLL+LFF1/Exx9/jGPHjuHQoUOYN28epk+ffl3nquhn8noUFRUhMTERiYmJsNvtSEtLQ2Jiotv6YTt37sT8+fNx6tQpJCQk4M4770RiYiL++9//uvYZN24cVqxYgffffx+nTp3Crl27MHXqVERHR6Nnz54AStdM69KlC+69917s2LED27dvx3333YdevXohPj7+uuKuyN///ne88847+PbbbxEbG4u0tDSkpaW5fanw6KOPIj8/H9OmTcOhQ4ewePFivPTSS3j88cfdKveXXpOcnBy31+iSX3/9FXPnzsXBgwdx7tw5rF69GpMmTULDhg0xaNCgm74XIvJcTMiIyKO9/fbbGDNmDCZPnowePXogNzcXU6dOLdeh7Uoajcb1ga9jx46YOnUq/v73v6NBgwZu+7333nvYvXs3mjVr5jb/q6z69etj5cqVSElJQffu3TF69Gi0b98ev/76603f39NPP43w8HDExcUhIiICW7ZsgdlsxsqVK2E0GtG/f3/Ex8fD398fK1asuOaH/caNG2PPnj0YO3YsXnnlFXTp0gW33norZs2ahWeffRbt27ev8utSmfDwcHz33XdYtWoV2rVrh2eeeQbvvvuuW0XDz8/PtXZUjx498Kc//QlPPfWUW0WiY8eO+Oijj/DFF1+gbdu2ePfdd6u86G2PHj3Qt29f3HbbbRg2bBjatWuHb775ptx+06ZNg91ux8MPP1zpOfV6PZYvX47XXnsN8+bNQ3x8PDp06IAXXngBPXr0wJQpUwAAI0eOxNdff405c+agffv2eOqpp/DXv/7VbYHkqnjooYfQvXt33HrrrYiIiMCPP/54XceX9dJLL+H999/Hl19+ibi4OPTt2xfvv/8+mjZtel3nqehn8nrs2rULnTt3RufOnXHhwgV88skn6Ny5M/785z+79rHZbPj3v/+N9u3bY/jw4bDZbNi6davbvNP77rsPn376Kb755ht07NgRI0eOhMlkwh9//OEaZqjRaLB06VI0btwYgwcPxm233YZbbrkFv/322w3N37rShx9+CKvVivHjx6NBgwauP08++aRrn0aNGmHlypU4cuQIunbtiocffhgPP/wwZs6c6XauS6/JkiVLsH37dtfjS4xGIz799FPXlwiPPPIIOnbsiK1bt161KygR+QZJiGpYOZGIqBYNGjQIoaGh1ZIQke977rnnsHz5chw4cEDtUHzKK6+8gu+++86t8kW1Y/bs2fjzn/8Mp9OpdihEVA3Y1IOIPNqBAwewZ88e9O7dG3a7Hd9++y3WrVuHZcuWqR0aebj8/HwcOHAAs2bNwvvvv692OD7p9OnTCAgIwJNPPlmuIkTVr7i4GPXr12ciRuRjWCEjIo928OBB/PnPf8aRI0egKApat26NGTNmYNy4cWqHRh5uwIAB2L59O+666y58/fXX5RpE0M3JyclBTk4OgNI5m/Xq1VM5It8nhHAtqyBJEm655RaVIyKi6sCEjIiIiIiISCX8upCIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIiIiISCVMyIiIiIiIiFTChIyIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIiIiISCVMyIiIiIiIiFTChIyIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIiIiISCVMyIiIiIiIiFTChIyIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIiIiISCVMyIiIiIiIiFTChIyIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIiIiISCVMyIiIiIiIiFTChIyIiIiIiEglTMiIiIiIiIhUwoSMiIiIiIhIJUzIiIiIiIiIVMKEjIiIvN6KFSsQGxuLFi1a4K233ir3fG5uLsaPH4+OHTuiR48eOHjwIAAgMzMTffv2Rfv27bFo0SLX/mPHjkVqampthU9ERHUYEzIiIvJqsizjb3/7G5YvX47Dhw/jxx9/xOHDh932eeONN9CpUyfs378fc+fOxZNPPgkA+PHHHzFlyhQkJCTgnXfeAQAsWbIEXbp0QXR0dK3fCxER1T1MyIiIyKvt2LEDLVq0QPPmzWEwGHD33Xfjt99+c9vn8OHDGDx4MACgdevWOHv2LNLT06HX62GxWGCz2aDRaOB0OvHBBx/g2WefVeNWiIioDmJCRkREXu38+fNo1KiR63HDhg1x/vx5t33i4uKwYMECAKUJ3Llz55CSkoLJkyfjjz/+wPDhw/HKK6/g008/xf333w8/Pz+3461WK3r06IG4uDi0a9cOL7/8crk48vPzMWbMGNc+33zzDQAOiyQiomtjQkZERF5NCFFumyRJbo+ff/555ObmolOnTvjoo4/QuXNn6HQ6BAcH4/fff8euXbvQpUsXLF26FBMmTMC0adNw5513IiEhAQBgNBqxdu1a7Nu3D4mJiVixYgW2bdvmdo1PPvkEbdu2xb59+7B+/Xo8/fTTsNvtHBZJRETXpFM7ACIiopvRsGFDJCcnux6npKSUS3SCgoJcFSshBJo1a4ZmzZq57fPqq69ixowZ+PHHH9G1a1dMnjwZY8eOxbp16yBJEgICAgAADocDDoejXNInSRIKCwshhEBRURHCwsKg0+muOixyyZIlNfFyEBGRl2GFjIiIvFr37t1x4sQJnDlzBna7HfPmzcPtt9/utk9eXh7sdjsA4Msvv0T//v0RFBTkev7EiRNITU1FfHw8SkpKoNFoIEkSrFarax9ZltGpUydERkbitttuQ8+ePd2u8dhjj+HIkSOIjo5Ghw4d8OGHH0Kj0VR5WCQREdVNTMiIiMir6XQ6fPzxxxg2bBjatGmDSZMmoV27dvj888/x+eefAwCOHDmCdu3aoXXr1li+fDk+/PBDt3PMmDEDr7/+OgDgnnvuwezZs9GrVy8888wzrn20Wi0SExORkpKCHTt2uFrnX/LHH3+gU6dOSE1NRWJiIh577DEUFBRUeVgkERHVTZKoaPA9ERH5lA8//BCzZs2CEALTpk3D3//+d7fn33nnHXz//fcAAKfTiSNHjiAzMxOyLGP8+PHIy8vD66+/jnHjxgEobUjx2Wef1dk5UP/+97/h7+/vlrCNGjUKzz//PPr16wcAGDRoEN566y306NHDtc9TTz2FcePG4fjx45Bl2W1YJBER1U2skBER+biDBw9i1qxZ2LFjB/bt24elS5fixIkTbvs8++yzSExMRGJiIt58803Ex8cjLCyMDSkuyszMRF5eHgDAYrFg9erVaN26tds+jRs3xpo1awAA6enpOHbsGJo3b+56virDIomIqO5hQkZE5OOOHDmCXr16wc/PDzqdDvHx8Vi4cOFV9//xxx9xzz33AADX6browoULGDhwIDp27Iju3bvjtttuw+jRo92GRb700kvYunUrOnTogMGDB+Ptt99GeHi46xxVGRZJRER1D4csEhH5uCNHjmDs2LFISEiA2WzG4MGD0a1bN3z00Ufl9i0pKUHDhg1x8uRJhIWFIT8/H5MnT0Z6ejrefvttHDp0CMHBwZgyZYoKd0JEROR7VGl7L4RAZmYmHA6HGpcn8mh6vR4RERHlWmoT3ag2bdpg+vTpuO222xAQEIC4uDjodBX/+l+yZAn69OmDsLAwAHA1pACA3NxcvP3221iwYAGmTZuG3NxcPP300+jdu3et3QsREZGvUaVClpGRAafTCb1eX9uXJvJ4DocDOp0OkZGRaodCPurFF19Ew4YN8de//rXcc+PHj8fEiRMxefLkcs+xIQUREVH1U2UOmcPhYDJGdBV6vZ7VY6p2GRkZAICkpCQsWLDANUesrPz8fGzYsAFjx44t9xwbUhAREdUMVYYsEhFR7ZowYQKys7Oh1+vxySefIDQ01NWM4i9/+QsAYOHChRg6dCj8/f3LHT9jxgzMnDkTQGlDinHjxuHDDz/Eq6++Wns3QURE5INUGbJ4/vx5GAyG2r5sOb///jseeOABbNmyBS1btkRWVhbuvfde2O12vPHGG+jVq5fb/k899RT+8pe/IDY2VqWIqa6w2+2IiYlROwwiIiIiqmEekZBFZFfvXJnMehlV2u/Pf/4z0tPT0a9fPzz33HNYuHAh1qxZg48//rjcvrIsQ6vVVmucRFfDhIyIiIiobqiz65AVFRVhx44d+OCDD7Bo0SIcOHAAr776KtasWYOBAwfCYrGgadOmeOuttzB8+HDs3LkT48aNQ2JiIgBg7dq1GDx4MAYMGIAJEyYAAPbs2YORI0di0KBBGDlyJE6ePKniHRIRERERkaers3PIli9fjkGDBuGWW25BSEgIhBCYPn06EhMT8dZbbwEoXY+nTZs2eP75592OzcrKwj/+8Q/89ttvaNKkCXJzcwEALVu2xOLFi6HT6bBhwwbMnDkT33zzTa3fGxEREREReYc6m5AtXLgQDz/8MIDSNs8LFixA69at3fbRarUYPXp0uWN3796NXr16oUmTJgCA0NBQAEBBQQEee+wxnDlzBpIksVMeERERERFdU51MyHJycrB582YcPXoUkiRBlmVIklSuWYfRaKxw3pgQosJFe9966y307dsXc+bMQVJSEsaPH19j90BERFV0OqX0v5IEaKTS/2o1gEYLBJiBQH84hAOykGGUjFyUnYiIalWdTMiWLFmCiRMn4r333nNtGzt2LFJTU6t0fLdu3fD888/j3LlzriGLoaGhKCgoQFRUFABg3rx5NRI7EZFPE3bAmQXI2YBcyX+VIgBOQMiAcAKQgfDXgJCp7udMTrv69WIigUB/HLUfxdqStQAAAwwwaozwl/wRqAlEgCbA7b+BmkD4SX5M3IiIqFrUyYRs4cKFeOKJJ9y2jR49Gq+99homT55c6fHh4eF477338MADD0BRFISHh+OXX37BY489hscffxyff/45+vXrV1PhExF5NyEAZwpgP+b+x3YMcCYBuInmv6Lo+vbXlY6CsAu7a5MddtgVOwpRiDS54mROAw2CNcEI04aV/tGEoZ62HsK0YdBK7MhLRERV5xFt74nIHdvek89wZgCWLYB1X5nk6zggimvmevU/gsP/YXw36ytodVpIkHBf685X3f2QXIA0k0Bug1ycDzl/05fXQIMwbRjCteGor62PBroGiNBGQCPV2abGRERUiTpZISMiohpiOwpYNpcmYSVbAMeJWg/h0veMOl3lb3HpuZk4VJgORa9ACrn5IYgKFGTJWciSs3AURwGUDoGM0kUhWheNaF00onRR0Ev6m74WERH5BiZkRER0YxQbYN1VmnxZNgOWraVzu7yIA0pp8yYDIG5mqOQ12GFHkjMJSc4kAKVVtAhtBBrqGqKpvimiddGsoBER1WFMyIiIqOocqUDxUqBwCVCyBhAWtSO6KQ5FLv1LLb4bKlCQLqcjXU7HbttuGGBAY31j9DP3Q5A2qPYCISIij8CEjIiIrs26HyhcABQtAWx71I6mWtlF7Sdk5WKAHWccZ3Cb/23qBUFERKphQkZEROVZdwOFv5b+sR9XO5oaY79YIRP6Wu9v5SZaFw2DxGZXRER1ERMyIiIq5UgF8r8G8r8BHKfVjqZW2FUYsliR5vrm6gZARESqqdOziH///XdERkbixIlrdwG75557kJ+fX0tRERHVIqEARb8DKeOAU02ArJfqTDIGeMaQRQBopm+mbgBERKQaj6iQLUoeXq3nG9doRZX2W7hwIXr27ImFCxfiueeeu+p+P/74Y3WFRkTkGRzngfyvgLyvLi7GXDd5QoUsTBOGYG2wegEQEZGq6myFrKioCDt27MAHH3yARYsWAQDS09Nx++23Y+DAgejfvz+2bdsGAOjatSuys0tbOd9///0YMmQI+vXrh7lz56oVPhHR9RNyaXfElNsvVsNertPJmEORIXCx3b2KCRmrY0REdZtHVMjUsHz5cgwaNAi33HILQkJCsH//fmzevBkDBw7EU089BVmWYbGUb+f84YcfIjQ0FBaLBcOGDcPo0aMRFhamwh0QEVWRYgPyvwSy/1OnE7ArOS4NV9QCuPk1oW9YMwMTMiKiuqzOJmQLFy7Eww8/DAAYP348FixYgGHDhuHJJ5+Ew+HAiBEj0KFDh3LHzZo1C8uWLQMAnD9/HqdPn2ZCRkSeSbEAeV8AOe8AzlS1o/E4Nln94YomyYQG2gbqBUBERKqrkwlZTk4ONm/ejKNHj0KSJMiyDEmS8PLLL2Px4sVYtWoVHnvsMfz1r3/FXXfd5Tpuy5Yt2LhxI5YtWwY/Pz+MGzcONptNxTshIqqAUgTkfgqR8x4kOUPtaDyWTXGW/kXFd8Km+qbQSHV29gAREaGOziFbsmQJJk6ciD179mD37t1ITExE48aNkZCQgPDwcNx3332YPHkyDhw44HZcQUEBQkJC4OfnhxMnTmD37t0q3QERUQXkAiBrJsSppkDmdCZjlbBfSsj06sXA+WNERFQnK2QLFy7EE0884bZt9OjReOKJJ+Dn5wedTgd/f398/PHHbvsMGjQIc+bMQXx8PFq0aIGuXbvWZthERBWT84Gc9yFyP4Sk5Kk5Hcqr2OTShEzo1FkUWgMNmuibqHJtIiLyHJIQotbfic6fPw+DwVDblyXyGna7HTExMWqHQZ5OKED+1xCZL0KSM9WOxjPU/wh2v2n4/suvYTQZAQCTW3SscNeD+RewIT8JSpQCpZtSm1ECABrqGmJC4IRavy4REXmWOlkhIyLyepZtEGmPQ7LtYkXsBqm9BhmHKxIREVBH55AREXktZxpE6hSIs7dCsu1SOxqv5hAXq2IqJWTN9c1RaBdIzFJQ7FBn2CQREamPFTIiIm8gHEDOB1CyXoVGFKm6bpavUDMhC9WEIkQbgt05MlalKPgjGYjxl9AyWEJsiAYhRv4DExHVFUzIiIg8XdEfUNKegMZ5nMMaqpHzYkKmRlOPpvqmAICT+aXXFgBSigVSigXWpSqIMAFtQzXoWE8Dfz2TMyIiX8aEjIjIUzkzINIehVS0gIlYDXBAvQrZrhXHkW524lxUX1RU7sy0AhsuKNiUpqBVsIRO4Ro0CZAgSUzOiIh8DRMyIiJPVPAL5NSHoUWu2pH4LNeQxVpeh0zj1AJ5BqQICUolY08VARzNEziaJyPMCMTVK62amXVMzIiIfEWdTciioqLQpk0b1+Px48eXW5uMiKjWydlwpjwMnWUBtGrH4uNU67KYpodW0sIRfH1LW+TYgHWpCjZeUNA6pLRq1iiAtVMiIm/nEQmZ4dNPq/V89r/+tdJ9TCYT1q1bd0Pndzqd0Ok84qUjIh8ihEDW0b8jQrtA7VDqBLsoTchqew6Z7ZyAEYAc1OCGjpcFcChX4FCujHCTjE7hGrQP08CkZdWMiMgb8au1K3Tt2hXZ2dkAgMTERIwbNw4A8J///AdPP/00Jk6ciMceewzJycmYMGEC4uPjMWHCBKSkpAAAHn/8cTzzzDMYM2YMevXqhZUrVwIAZFnGK6+8gqFDhyI+Ph5z5sxR5f6IyDNZLEX4fdEnWL41HPmWcLXDqRNUqZAJCdpMIxS/MAi96aZPl2UFVqco+OSgEyuSZOTbay65fPDBBxEZGYn27du7ts2fPx/t2rWDRqPBrl1XX4bhww8/RPv27dGuXTt88MEHru3Tp09Hx44dcf/997u2ffvtt/jwww9r5B6IiDxRnU3IrFYrBg4c6PqzaNGiSo/Zt28f5s6di88//xwvvPACJk6ciA0bNmDChAl48cUXXfslJyfjt99+ww8//IBnn30WVqsV33//PYKCgrBy5UqsXLkS3333Hc6dO1eDd0hE3uLE0Z2Y//2byM1Jh04fjK0nx0NW6uyv51rjUCEh0+YaoIcBzqDoaj2vQwESsxX877ATq1JkFNXAumZTp07FihUr3La1b98eCxYsQP/+/a963MGDBzFr1izs2LED+/btw9KlS3HixAnk5+dj69at2L9/P2RZxoEDB2CxWDB79mz8tQojXYiIfEWdHXd3I0MWhw0bBrPZDADYtWsXvvnmGwDAxIkT8eqrr7r2Gzt2LDQaDZo3b44mTZrgxIkTWL9+PQ4fPowlS5YAAAoLC3H69Gk0adKkmu6IiLxV6vmTEEKBTlfaXSK3OBqHzvdHx0br1Q3Mx10aslib74T2JMAAQA6u3oTsElkAuzMV7M9W0CVcg171q68BSP/+/XH27Fm3bWXnYl/NkSNH0KtXL/j5+QEA4uPjsXDhQjz66KOw2+0QQsBisUCv1+Odd97BE088Ab2+ljutEBGpiF/BXkGr1UJRSjtvWa1Wt+cuvZlUpGwr4ivbEkuSBCEE3njjDaxbtw7r1q3Drl27MHDgwGqMnIi8VZ/4O2E0miHE5arGofP9kFXYUMWofJsihGsdstpMyOTzWih6PyjmkBq9jkMBtmco+PyQE5suyLDJtb/W2iXt27fHxo0bkZ2djZKSEixbtgzJyckIDAzEhAkT0LlzZzRr1gzBwcHYuXMnxo4dq1qsRERqYEJ2hUaNGmH//v0AgN9///2q+3Xv3h0LFy4EAPz666/o0aOH67nFixdDURScOXMG586dQ4sWLTBw4EDMnj0bDocDAHDq1CkUFxfX4J0QkbfQ6fQYPPwB2O0W1zYBDRJOjYNDZqWgJlyaPyYkgdpqZ6kt0cNkN99wM48bYVOALWkKPjvkxLZ0GQ6l9hOzNm3aYPr06bjtttswfPhwxMXFuRpjPffcc0hMTMR7772Hl156Ca+++iq+/PJLTJo0Ca+//nqtx0pEpIY6m5BdOYfstddeAwA8++yzmDFjBsaMGQON5uovz8yZMzFv3jzEx8dj/vz5mDlzpuu5Fi1aYOzYsbjnnnvwzjvvwGQy4d5770VsbCyGDBmC/v3745lnnoEsyzV+n0TkHSIiG6Fth76wOy5X5gut9bD33FAVo/JddsVZ+pdarY5pIEkSnDU0XPFarDKwPrW0YrYrQ4azlhOzhx56CHv27MHGjRsRFhaGli1buj2/d+9eAECrVq0wd+5c/Pzzzzh48CBOnDhRq3ESEanBI+aQVaVNfXVLS0urcHuvXr2wbdu2ctufe+45t8eNGzfGggUVt6bu0aOHK8G7RKPRYMaMGZgxY8YNRkxEvq57r9FISToKS0mR6wuhkxndEBN6HDGh/GBanWyXErJaLEDaz0kwabSQAyJr76JXKHYCq88r2JGhYECMFm1Da+d72YyMDERGRiIpKQkLFixAQkKC2/MvvfQS/ve//8HhcLi+rNRoNCgpKamV+IiI1FRnK2RERJ5Go9FgyPAH4XTa3LZvP307rA6zSlH5ptpuea9xamAoNEMOqA9o1F/yu8ABLD4rY/4pJwqq2Cr/nnvuQe/evXHs2DE0bNgQX331FRYuXIiGDRsiISEBo0aNwrBhwwAAqampGDlypOvYCRMmoG3bthgzZgw++eQThIaGup5btGgRunfvjujoaISEhKB3797o0KEDJElCXFxc9d44EZEHkkTZWeS15Pz58zAYDLV9WSKvYbfbERMTo3YYpJL9e9dh947lMBovNxJqGHoE/WN/VjEqL1H/I9j9puH7L7+G0WQEAExu0bHcbmeKs7Es+yREmIB8a80PH9ekmiDtCIC1UTc46zWv8etdD4MGGBCtQedwTbmmVEREVPNYISMi8jAdOg1AeGQjt3mmKbltcDqT1YLqYpNLhywKXe18J2k7JyCAWm3oUVV2BViZouD7EzKyrep1YyQiqquYkBEReRhJkjBk+AMQl9qyX7Tr7AgU2YJVisq3XErIamXIogC0GQYo5lAIvecOPU0pFvj6qBNb0mTItT94hoiozmJCRkTkgczmAPTuNx426+XlMZyyEdtOjoMiOKzsZjlqcQ0ybZ4RemFUpbvi9ZIFsOmCgtlHnbhQrFR+ABER3TQmZEREHqplbDdENW8Ap8Pu2pZR2BTHLvRSMSrfUJsJmSOpNIGWgzw/Ibsk0wrMPS5jTYo6a5cREdUldTYha9q0aZX33bJlC3bs2OF6PHv2bPz000/Xfc3PP/8cjRo1QkFBwXUfW1UrVqzAf//73xo7f1WNGzcOiYmJ5bZ/8cUXbm2Mr+ffoSJ79uzB7bffjt69e+PWW2/FU089dV1tktPS0vDggw8CAObNm4fnn3++3D5JSUno37//TcVJdL2szlz8cfLvyKy/BJJZRtn+S/uSByG3WL3W6b7AjosLQ9fCHDJnigaK3gzFL7TynT2IALAzU8GXR5xIKmK1jIiopnjEOmRfpYRU6/keaphXrefbsmUL/P390aNHDwDA1KlTb+g8CxcuRKdOnbBs2TLcfffd1RhhKafTieHDh2P48OHVfu7q8r///Q933nkn/Pz8Kt+5EhkZGfjzn/+ML774At27d4cQAkuXLkVRUVGVzu90OhEVFYWvv/76pmMhqk4pBduw4dzLKHFkAQDC4zKQs6UezKYAAIAidEg4NR7D2n8JrYYLzN8I56UEt4bXIdNa9DDZzXCGe14zj6rKtwM/npDRv4FAr/rsxEhEVN08IiHzFH/88Qfef/992O12hIaG4rPPPoPVasWcOXOg1Wrxyy+/4M0338TGjRvh7++Pv/3tbxg3bhy6dOmCLVu2ID8/Hx988AF69So/nOjMmTMoLi7Gyy+/jA8++MCVkM2bNw/Lli2Doig4evQoHn30UdjtdsyfPx9GoxE//PADQkNDcebMGTz//PPIzs6G2WzG//3f/6Fly5Z4/PHHERISgoMHD6JDhw5o27YtEhMT8dZbbyEjIwPPPvsszp07BwD4z3/+gx49euD+++9HamoqbDYbpk2bhvvvv79cvO+++y5WrlwJq9WK7t27491334UkSVe9X4vFgieffBLHjh1Dq1atYLVay51z1qxZSEtLwx133IGwsDAsXLgQAPDGG29g5cqVMJvNmDNnDiIjI5GVlYVnn30W58+fBwC89tpr6Nmzp9v5vv76a0yaNAndu3cHUNoIYcyYMQBKK2f//Oc/YbVaYTKZ8N///hctWrTAvHnzsGrVKthsNpSUlOCDDz7Avffei40bNwIoXTvnrrvuQlJSEu644w48++yzAEqTt8ceewwHDhzALbfcgo8//hh+fn7Yt28f/vWvf6G4uBhhYWH46KOPUL9+fXz77bf49ttvYbfb0axZM3zyySfw8/PD448/jsDAQCQmJiIjIwMvv/yyK2YiWXFg89n3cDx3PlDmM2+WYz/qdxiC3ENW6A0mAEBeSRT2pwxA58ZrVIrWuzlRO0MW5fNaaCUJTi8arlgRAWDDBQUpxQKjm2hh1jEpIyKqLnV2yGJFevbsieXLl2Pt2rUYP348Pv74YzRu3BhTpkzBI488gnXr1lWYbDmdTvzxxx94/fXX8c4771R47oULF2L8+PHo1asXTp06hczMTNdzR48exeeff44VK1bgjTfegNlsxtq1a9GtWzf8/HPpukPPPPMM3nzzTaxevRqvvPIKpk+f7jr+9OnT+OWXX/Dqq6+6XXPGjBm49dZbsX79eqxZswatW7cGAHz44YdYvXo1Vq5ciS+//BI5OTnl4n3ooYewcuVKbNy4ERaLBStXrrzm/c6ePRtmsxkbNmzA3//+d+zbt6/cOadNm4aoqCgsWLDAlYyVlJSga9euWL9+PXr16oXvvvsOAPDPf/4TjzzyCFauXImvv/4a//jHP8qd7+jRo1ddNLRly5ZYvHgx1q5di+nTp2PmzJmu53bt2oWPPvoICxYsKHfcnj178Nlnn2Ht2rVYsmSJa9jlyZMncd9992HDhg0IDAzEN998A4fDgRdeeAFfffUVVq9ejcmTJ+ONN94AAIwaNQorV67E+vXr0apVK/zwww+ua6Snp2Pp0qX4/vvv8dprr1UYP9U9+dYkzNs3Ccfz3JOxS7L8NkEfDLehi0dS+yCjoHEtRuk7amthaPs5QEhayIG+McT0VIHAN8ecSGXDDyKiasMKWRmpqamYNm0aMjIyYLfb0bhx1T7ojBo1CgDQsWNHJCcnV7jPokWLMHv2bGg0GowcORKLFy/GQw89BADo27cvAgICEBAQgKCgIAwbNgwA0KZNGxw+fBhFRUXYuXOna3+gdOHgS8aMGQOtVlvumps3b8bHH38MANBqtQgKCgJQWqlatmwZgNJFuk+fPo2wsLByx37yySewWCzIzc1F69atXXFVdL8JCQmYNm0aAKBdu3Zo27ZtlV47g8GAoUOHAgDi4uKwYcMGAMDGjRtx7Ngx136FhYUoKipCQEBAlc5bUFCAxx57DGfOnIEkSXA4HK7n4uPjERpa8VyO+Ph412sxcuRIbN++HSNGjEBMTIyrQnfnnXdi1qxZGDRoEI4ePYqJEycCABRFQWRk6Yeuo0eP4s0330RBQQGKi4sxYMAA1zVGjBgBjUaD2NhYt8Sc6q5zOVux6vQzEBrbVfeRhQ2GdmdRsLkhTKZL/x9ISDg1DiM7fA69zn7VY6m82kjIJFkDQ4EZcnAkoPGdt9sCO/DdCRmDYwS6RpR/7yEiouvjO+8Q1eDFF1/EX/7yFwwfPhxbtmy5arXrSkajEUBp0lN2IddLDh06hNOnT7s+uNvtdjRp0sSVYBkMBte+Go3G9Vij0cDpdEIIgaCgIKxbt67C6/v7+1f5Hrds2YKNGzdi2bJl8PPzw7hx42CzuX8ItFqtmD59OlatWoWYmBj85z//cRuCeLX7vZF5BTqdznWcVquF01m6NpCiKFi2bBnM5quv2dO6dWvs27cPI0aMKPfcW2+9hb59+2LOnDlISkrC+PHjXc9da37Zlfdw6XFF24UQiI2NxfLly8ud54knnsDs2bPRvn17zJs3D1u2bHE9d+n1A9yrHVQ3bTv1DfbnfgJJU/nPQq79BKK6NkT2HgsMxtL/N4ptodh9bjh63bK4pkP1KXZR8009pAwDNJIWVi8frlgRRQCrUhSklQgMa6SFTsMhjEREN4pDFssoKChAgwalE6/LdlEMCAhAUVHRDZ934cKFePbZZ7F7927s3r0bBw4cQFpa2lWraVcKDAxE48aNsXhx6QcuIQQOHjxY6XH9+vXD7NmzAQCyLKOwsBAFBQUICQmBn58fTpw4gd27d5c77lKCFhYWhqKiIixdurTSa/Xu3Ru//vorAODIkSM4fPhwhftV9bUcMGAAvvrqK9fjAwcOlNvnwQcfxM8//+x2D/Pnz0d6ejoKCgoQFRUFoHSeXlVt2LABubm5sFgsWL58uauRS0pKCnbu3Amg9N+zZ8+eaNGiBbKzs13bHQ4Hjh49CgAoKipC/fr14XA48Msvv1T5+lR3KIqCBbuewYH8j6uUjF2Srt0Iv/oaKMrlIWOnMzsjOSe2JsL0WY5aqJDZzpX+G8lB3tvQozIHcgR+OCGjyMEvl4iIblSdTcgsFgvi4uJcfz777DM8++yzeOihhzBmzBi3IXzDhg3DsmXLMHDgQGzbtu26r7Vo0SKMHDnSbduIESNc86iq4rPPPsP333+PAQMGoF+/flixYkWlx7z++uvYsmUL4uPjMWTIEBw9ehSDBg2C0+lEfHw83nrrLXTt2rXcccHBwbjvvvsQHx+PKVOmoFOnTpVea+rUqSguLkZ8fDw+/vhjdO7cucL97rvvPtxzzz1uFauKzJw5E/v27UN8fLyr0nWlyMhIfPHFF3jllVfQu3dv9OnTB9u2bUNgYCAee+wxzJw5E6NGjXL74FqZnj174m9/+xsGDRqE0aNHu+69VatW+OmnnxAfH4/c3FxMnToVBoMBX331FV577TUMGDAAgwYNciVn06dPx4gRIzBx4kS0bNmyytenusHutOD7bfchW1Nx1ftaBGQotxyGU7G4bd9xegws9qpXy+u6Gh+yKABNuhGyOQTCcPNdZT1ZaonAbM4rIyK6YZJQYczU+fPn3YbpEZE7u92OmJgYtcOgGlBQkob5iQ9AMWXc1Hnqa/oge7sJRuPlD/sxIccQ37rqFWGfVP8j2P2m4fsvv4bRVDo8eHKLjuV2+zRpOwQA5zBnjbS+1+YZgfWBsNdvC3uD9tV/AQ+klYDhjbToUK/OftdLRHRD+FuTiKiWJGftx7z9k246GQOAdGULgprqIMtO17bzebE4mV5xdZoucygyBAABUWMVMntS6X+dwb43f+xqZAH8niRjZwbXxiMiuh5MyIiIasG+s0ux/PTDkAzF1XZOS4NdEFr37op7zg1HobXiLqJUyjVcUYsKlxioDnKKBorOBMVc9/4t1pxXsCWNSRkRUVUxISMiqmHrDn6E7VmvQNI5Kt/5Olic2Qjukg+b9XKS51QMSDg5Hopg17ursSsXq4o1MFQRALRWHUw2v9JmHjfQfdYXbLqgYN15JmVERFXBhIyIqIYoioIFO5/GSfvs6+qkeD0ynLsQ1kYHh+NypSyrqBGOpPapkev5AlsNN/SQz2shSVKdGq5Yke0ZCv5Ilrm8BxFRJZiQERHVAKfTiR8SHkK2dn2NXys3eCt0fu4ffPenDEBOcVSNX9sbuSpkNTh/TEgayAH1a+YCXmRvloKl52QoTMqIiK6KCRkRUTWz2ayYu/EhWMz7a+V6DqUI5o4XYLNdHroohBYJJ8fDqdTgQlteynaxEUpNLAotyRoY8v0gB0QCWr72AHAoV2DRGRmywqSMiKgidTYhi4yMxL/+9S/X408++QT/+c9/ruscW7ZswY4dO1yPH3/8cSxZsqTaYqzMgAED8Mgjj9ToNZ566ikcO3asRq9B5EsKCvMwZ900yCGVL95enbIdhxAep4XDYXVty7dEYl/SoFqNwxtcSshqokImZRqghbbOD1e80vF8gV9Oy3AwKSMiKscjvr77QfdDtZ5vsnNypfsYjUYsW7YMTz75JOrVq3fd13A6ndiyZQv8/f3Ro0ePGwnTjRACQghoNFXLkY8fPw4hBBISElBcXAx//+pfEFaWZbz//vvVfl4iX1VSUoQfNjwJbfRhVa6fZdoIv5B4yEUC0sVmEsfSeiEm9ASigs+oEpMnsomaS8hsZwVMQGlDD3JzplDgp5MyJt6ihVFbN5udEBFVpM5WyLRaLe677z588cUX5Z5LTk7GhAkTEB8fjwkTJiAlJQVAaQXspZdewvjx4zFt2jTMmTMHX3zxBQYOHIht27YBABISEjBy5Eh069bNrVr28ccfY+jQoYiPj8fbb78NAEhKSkKfPn3w3HPPYfDgwdi2bRv69OmDf/zjH+jXrx8mTpwIi8VSYfwLFizAnXfeiQEDBuCPP/5wbR83bhxeeukl3H777ejTpw/27t2LqVOnomfPnnjzzTdd+82fPx/Dhg3DwIED8fTTT0OWSye5N23aFG+99RaGDx+OnTt3Yty4cUhMTAQArF27FoMHD8aAAQMwYcIEAMCePXswcuRIDBo0CCNHjsTJkydv9J+EyKtZrCX4ds1T0EbXbmWsLFnYoWl9CjZnSZmtEradGgu706haXJ7GKZTSv1R3l0UBaNINkE3BEIbq/5LMF6QUC/x4UobVyUoZEdEldTYhA4AHH3wQv/76KwoKCty2v/DCC5g4cSI2bNiACRMm4MUXX3Q9d/r0afzyyy/45ptvMGXKFDzyyCNYt24devXqBQBIT0/H0qVL8f333+O1114DAKxbtw5nzpzBH3/8gXXr1mH//v1ISEgAAJw8eRKTJk3C2rVr0bBhQ5w+fRoPPPAANm3ahODgYCxdurTC2BctWoRx48Zh/PjxWLhwodtzer0eixcvxpQpU3D//ffj7bffxsaNGzFv3jzk5OTg+PHj+O2337B06VKsW7cOWq0Wv/zyCwCgpKQEbdq0wYoVK1z3BABZWVn4xz/+ga+//hrr16/Hl19+CQBo2bIlFi9ejLVr12L69OmYOXPmzfyTEHklm82K71Y+Cyl6j9qhIN9xGhGdZdjtl7/MKbEHY9fZkSpG5VkclxIybfWeV1tghEEYIXO44jWllZQOX3Ry+CIREQAPGbKolsDAQEycOBGzZs2CyWRybd+1axe++eYbAMDEiRPx6quvup4bM2YMtNqrv4uPGDECGo0GsbGxyMzMBACsX78e69evx6BBpXM5iouLcfr0acTExKBRo0bo1q2b6/jGjRujQ4cOAICOHTsiOTm53DX27t2LevXqoVGjRoiOjsbf//535OXlISQkBAAwbNgwAECbNm0QGxuL+vVLO301adIE58+fx44dO7Bv3z4MHToUAGC1WhEeHg6gtHI4evToctfcvXs3evXqhSZNmgAAQkNLFzstKCjAY489hjNnzkCSJDgc1bvOEpGnczjs+GHVDIiY7R6z5FS6ZhOCGwyBLVOGRlP6++psVkfEhB5Hk3qHVI5Ofc6LHf+EvnoTAkdSadHNGcSErDIpxQK/n5Nxe1Ota3gtEVFdVacTMgB45JFHMGTIENx9991X3afsm0Vlc7WMxsvDgi61oBZC4IknnsCUKVPc9k1KSoKfn99Vj9dqtbBarbjSggULcPLkSXTt2hUAUFhYiKVLl+Lee+91O4dGo3E7n0ajgSyXtsa+66678M9//rPC+CtKOIUQFb5pvvXWW+jbty/mzJmDpKQkjB8/vtw+RL7K6XTihz/+BUfUBkiS53zbL6DA2fQAnBltYMDl31k7T49CRGAS/AyFKkanPgcuVsiq+R3QmayBVmeE4hdWvSf2UUfyBIJTFQyIqeZSJRGRl6nTQxaB0krP7bffjh9+uNxYpHv37q5hgL/++utVm3YEBASgqKio0msMHDgQP/74o2vfCxcuuKpn10tRFCxZsgTr16/H7t27sXv3bsydO7fcsMVr6devH5YsWeKKITc3t8JKXFndunVDQkICzp075zoGKK2QRUWVrnU0b968G7klIq8kyzJ+WvU6bFFramzR55tR5LiAet2ssNkuzyezy2ZsP3W7ilF5BteQxWpMyLQ2HUw2P8hB0fCYUqkX2JahYG+WrHYYRESqqvMJGQA8+uijyMnJcT2eOXMm5s2bh/j4eMyfP/+q86KGDRuGZcuWuTX1qMjAgQNxxx13YNSoUYiPj8eDDz5YpUSuIgkJCWjQoAEaNLjcwat37944duwY0tPTq3SO2NhYvPDCC5g0aRLi4+MxceLESo8NDw/He++9hwceeAADBgzAtGnTAACPPfYYZs6ciVGjRkFRlBu6JyJvoygK5q9+B8XhyyBpPPfnPk3eiuDmWjidl4cSX8hvgeNp3a5xlO+zKxcTgGpMyJTzpUPvnOyueN1WJis4me+5/x8REdU0SVwaV1eLzp8/D4PBUNuXJfIadrsdMTExaodBFRBCYOHqj5EV/D0knefPmTRpQ4F9XaBVLg+P1mocGNHhCwSZs1WMrIbU/wh2v2n4/suvYTSVDtme3KKj2y4/XziATEcJnL2dwPWvelIh+3ojTPnBKG4/FtBWd/tG36fXAH9qqUOUH6uLRFT3sEJGRFRFQggsW/MDMv3me0UyBgBWOReBnXNhsxa7tsmKHgknx0NR6uZbgF1Ub4VMkjUw5JohB0QwGbtBDgWYf8qJfLvnDf8lIqppdfPdmIjoBqzduATnxI/QmIsr39mDZDr2IKytFk6HzbUtuzgGh1L7qRiVehxK9c4h02QZoJV0pfPH6IYVO0uTMq5RRkR1DRMyIqIq2Ll3Mw7n/Ax9vQtqh3JDcoO2QBegoOwo9YMp/ZFdVPeSiOqukFnPlr6mTq4/dtOyrMCCMzJkrlFGRHUIEzIiokqkpiVj68F5MDY+qnYoN8yhlMDY4bxb10UBDbaevANOue4Ms5OFAmd1dlkUgJSmh2wKhjBce1kUqpqkIoFlSey8SER1BxMyIqJrsNosWPjH/2Butdej1hq7ETn2IwjvDNgdl9c3LLTWw96kISpGVbsuDVcUGgFUw/JX2kIDjMIEmd0Vq9WhXIHELHZeJKK6gQkZEdFVCCHw65LZ0DXdDklffpF2b5Rp2ARzPcltmYoT6T2QmtdCxahqj01xlv6lmoYrOpJL30Y5XLH6rTkvI9vq3V+CEBFVRZ1OyFJTU3H//fejZ8+e6N69O2bMmAG73V7j101LS8ODDz5Y49chopuzfsty5JnXQhPoO+3hFeEAWh6Hw2lx277t1O2wOcwqRVV77NWckDmTJAitEYpfWPWckFwcCrD4rJPzyYjI51Xjspg3LuLwuWo9X2bbJpXuI4TAAw88gKlTp2Lu3LmQZRlPP/003njjDbzyyivVGs+VoqKi8PXXX9foNYjo5pw4fRgHUhbC3Oq02qFUuwLHWUR1a4SsXRYYDaVJmNURiB1nRqFfq19Ujq5m2apxUWitTQeT1Q/OsChAqtPfb9aYdAuwPlXB4IbVML6UiMhD1dl3kE2bNsFoNOKee+4BAGi1Wrz22mv48ccfUVxcjJdffhnx8fGIj4/Hl19+CQDYt28fxo4diyFDhmDSpElIT08HAHz77bcYOnQoBgwYgAceeAAlJaWT5h9//HG8+OKLGDlyJLp164YlS5YAAJKSktC/f3/X38eMGYPBgwdj8ODB2LFjR22/FER0hfyCPCzf9A1Mt+xXO5Qak4bNCIzRQJYvN09IzmmHM5kdVIyq5tnl6quQKak6SJLE4Yo1bGemgtMFnE9GRL6rziZkx44dQ1xcnNu2wMBAxMTE4Pvvv0dSUhLWrFmDDRs2YMKECXA4HHjhhRfw1VdfYfXq1Zg8eTLeeOMNAMCoUaOwcuVKrF+/Hq1atcIPP/zgOmd6ejqWLl2K77//Hq+99lq5OMLDwzF//nysWbMGs2bNwowZM2r2xonompxOJ+Yv+R+MLXZB0jrVDqcGCdga7YMi2dy27jo7EsW2IJViqnk2pXRBb6G7+WFwtnMCQtJADoy66XPRtf1+Tkaxg0MXicg3ecSQRTWUXYvnyu0JCQmYMmUKdLrSlyc0NBRHjhzB0aNHMXHiRACAoiiIjIwEABw9ehRvvvkmCgoKUFxcjAEDBrjON2LECGg0GsTGxiIzM7Pc9ZxOJ55//nkcOnQIGo0Gp0/73vAoIm+yfM0vcERsgc5coHYoNa7EmY763ZohOwEwGv0AAA7ZhG2nxmFQm7mQJJUDrAE2uXqGLEqKBEOuGXJgBKCtO8sGqKXYCSxLknFncy0kX/zBJKI6rc4mZLGxsVi6dKnbtsLCQqSmpqJJkyblfuELIRAbG4vly5eXO9cTTzyB2bNno3379pg3bx62bNnies5oNLqd40qff/45IiIisG7dOiiKgkaNGt3srRHRDdqzPwFnctfA3CpF7VBqTbpzG8JbDEPhGQd0utLEIr2gGY6l9UTrBttVjq76OappUWhNlhGQdLAFs919bTlVILArU0H3SM4nIyLfUmeHLPbv3x8WiwU//fQTAECWZbz88su46667MGDAAMyZMwdOZ+lwpdzcXLRo0QLZ2dnYuXMnAMDhcODo0dJFYouKilC/fn04HA788sv1TYgvLCxE/fr1odFoMH/+fLf5HERUe9IyzmPDtsUwNTusdii1rjB8GzRGh9uXRolJQ5BXEqFiVDXDcWlR6JssatnOlb5WziDOH6tN61MVZFg4dJGIfEudTcgkScLs2bOxZMkS9OzZE7169YLRaMSMGTNw7733IiYmBgMGDMCAAQOwYMECGAwGfPXVV3jttdcwYMAADBo0yJWcTZ8+HSNGjMDEiRPRsmXL64rjgQcewE8//YQRI0bg1KlT8PPzq4nbJaJrcDjsWLjsW/g1O+4z641dD5ucj4BOWbDbLrfCV4QOCSfHQ1Z8623CgYsLQ9/kHDJxQQ/FGARhDKiOsKiKZAH8dtYJB1vhE5EPkcTVJlPVoPPnz8NgMNT2ZYm8ht1uR0xMjNph1BnL1/yCk+nrYGq9Te1QVBVpGYK8wxL0epNrW9voTejUeK2KUV2n+h/B7jcN33/5NYym0iHjk1t0dD29JusUjpZkQW4nQzS7sbc/baEBWBMEe2Rr2KM7Vn4AVbuuERrcxlb4ROQjfOurTyKi65Ry4RwOn9gDY7MDaoeiuhz/zdAHuc93PZLaBxkFvjO31XmxQnYzc8icSaVzjJ1BnD+mlj2ZCtJKWCUjIt/AhIyI6ixZlrFs1c/wb3oakrFY7XBU51SsMLQ7B5v98mshoEHCqfFwyL4xqsFeDQtDO5IlCK0Bin+96gmKrpsA8EeyfNWOyURE3oQJGRHVWRu2LocFF6CJOKl2KB4j134cEZ0Bm/3yfLJiWyj2nBuqYlTV52YTMo1dB6PFD86gKEDiW6iaLpQIJGZzwWgi8n58NyGiOikzKw37Du6AuelBSBK/ZS8rXbcR/hEaKMrlD7unMroiJaeVilFVD/vFtvc32tRDpGqhkTSQ2V3RI2xIVVDCBaOJyMupkpDp9Xo4HA41Lk3k8RwOB/R6LjRbk4QQWLpyHkxR6ZACstUOx+MIyBAtjsAhl7ht3376dlgd3t0J9mYrZLZzAgJSaYWMVGeVgXWpXC6GiLybKgtDR0REIDMzE3a7XY3LE3k0vV6PiAjfW//Jk+zetwV5xenwv4WNPK6mwJGMqG5NkLWjBEZjaRJmc/pjx+kx6B/7k8rR3ThXQnYD33lIigR9jh/kwAhA6xtz6rxdiE5Bk30JcOpaQsfOtETkpVRJyCRJQmRkpBqXJqI6zmqzIGHnOvg1Pg1Jb1M7HI+WJrYgrPFtKEl1QqstfbtIyW2NUxmdcEtkorrB3aBLQxZv5N1Pk2WERtLBxu6KqpMUJ3pbktB+4wJoHXZYs04j4MEH1Q6LiOiGcA4ZEdUpK9ctAsx5kMJPqR2KFxCwxOyB0LoPMd99djiKrCHqhHQTHBerYwICuIElrOznSucqOYM5f0xNwZknMX7N54hbMw9aR+lIGzk5GfaDB1WOjIjoxjAhI6I640JaMk6cPgxDw6Ns5FFFFmcWgrvkw2q93ArfqRiRcGocFCGpGFnVlG2L7jZ/7AZCV9J0UIyBEMbA6gmOrou+OBvx237E5ISfUd9SUO556+rVEE6nCpEREd0cJmREVCcIIbBi7a8wh1qhCUpTOxyvkuHcibBYHRyOy/N+Mwub4OiF3ipGVTWyfLnhg025+GH9BoYraosMMMlmONldsdZJDitij67DA2u+QNuMM1fdT+Tnw5aQUIuRERFVDyZkRFQn7Nm/FXkFOdBGHVU7FK+UH5oArZ/7Qrz7kwcht7i+ilFVrmzrfvtNJGTOpNKSmjOY88dqjVAQkXoQ96z+DIOOJ1RplKlt82YoJSWV70hE5EGYkBGRz5NlGTv3boYp2A4pKFXtcLySXSmEf1w6rLbLQxcVocXWk3dAVm5gQlYtUcpWyOQbb+jhSJYgtHoo/uHVFBldiyn/AoZtnoM7dy1GsMNS+QGX2O2wb99ec4EREdUAJmRE5PP2HdwOi6UYmvpHIHn+tCePlWU/gIiOWjgcVte2fEsk9iUPUjGqa5PlyxWyS0MWr3dRaI1dC2OJH5yBDQCJb5s1SWMrRqd9v+OBDd+gee6FGzqHfccOCC6rQ0RehO8sROTTFEXBzsTNMAU7IAWnqB2O18syb4IxxL1ZxtELvZGe31S1mK6lbP5tky92i7zONcjEBT00kgYyhyvWHMWJmHO7cP/qT9D73L6bOpWwWmHftauaAiMiqnlMyIjIp+0/vBMlJUWsjlUTWdigbXMGNntxma0SEk6Ng91pVC2uq5E0l9/mbnQNMts5AQEJzsCoaoyMLgnMPoux62bh9n0rYZarp0uiLSGBHReJyGswISMin6UoCnbs3ghTkBNSCKtj1SXPfhIRXQXststze0rswdh9driKUVWsbBLuuNTg4zoSMkmRoM82l84d03lewunNdJY83LpjPu7d8gOii3Or9dyiqAj2xMRqPScRUU1hQkZEPuvwsb0oKimApj7XHatu6ZpN8G+gcetieCarE5Ky26gYVXlSmTlfTlx/QqbJMUIn6bgYdDWSnHa0OLEJU1d/hri0EzV2HfvWrRBlfj6JiDwVEzIi8klCCGzbvR7mIAEpNEntcHyOgAxns4NwCvcOeDvOjIbFHqBSVOVJZUpkjovz3q6nqYf9XOm+ziDOH7tpQkFY2lFMWvMpbjuyCXpRs1+SKLm5cBw6VKPXICKqDkzIiMgnHTm+DwUFedBEsjpWU4ocqQjraoXNenndJ7vTD9tP365iVO50usst+R03UCFTUnVQDAEQpqBqjqxuMRZmYNDW73HXjgUIs9XeOmG2zZvdGtAQEXkiJmRE5HOEEEjYuQ7mIEAKPat2OD4tXdmK4GZayGWaMaTmtcSJ9K4qRnWZVns5+3JcZ1MPbZEBJtnM4Yo3QWMvQftDKzF17ZeIzU6u9esrGRlwHj9e69clIroeTMiIyOecOH0I+QU50EQeg6Tht+M1rThqF6B1uG3bc24oCixhKkV0mU53OfuyK9eXkDlTSt8iZQ5XvH6KjKjkRPxp1afod2oXNCp2OLVt3qzexYmIqoAJGRH5FCEENm9bBZPZACn0nNrh1AlWOQdBXXNhtV5uhS8rBiScGg9FqLvWgLaChKyqc8gcSYDQ6CH7h9dIbL7KLzcFozZ+jfF7lyFAVn+BZjklBc4kziMlIs/FhIyIfMqpM0eRm58DTUgqJC3XIaotGY7dCGujhcNhc23LLmqIw+f7qhgVoNWXmUN2achiFRaG1ji0MBSZ4QyKAjTayg8gaK0F6L7nN0zZNBeNCzLVDseNfe9etUMgIroqJmRE5FO27lgDs8kMKYzVsdqWF7wVOn/FrYnCgZQByC5Sbw6WTns5+3INWaxKfpWmh1aj5XDFqpAdaHo6AVNWfYpuKZ7Z1dBx+DCEw1H5jkREKmBCRkQ+I78gFxnZaZD0VkgB6WqHU+c4lGKYO6bCZrs8dFFAg4ST4+GUr6O1YTVym0N2HRUy61kBAYnt7isRknESd675AiMOroNRePCaX3Y7HEeOqB0FEVGFmJARkc/YsWcDjAYDpNAkSOpOXaqzsu2HEdFJA7v98vpkBdZwJCYPUSUenb40IZOFAvlS5a6SCpmkSNBnm6D41wN0xhqO0Dvpi7MRv+1H3LPtZ0RYC9QOp0oc+/apHQIRUYXU+cqSiKiaCSFw+txx6HR6aNjMQ1UZxo3wrzcAznwFGk3p937H03ogJuQ4GoScrrU4hBCuCpmroYdGVJqQaXIM0EAPWxDb3V9JcljR6lQC4o8nVGnkpydxnjkDpaAAmiCuKUdEnoUVMiLyCSmpZ1FYVACYcyGZveMbe1+lCAc0sSfhcFrKbJWw7dRY2JymWotDCAGjqfR619Py3n6xIZ8zmMMVXYSCiNSDuGf1ZxjkhckYAEAI2FklIyIPxISMiHzCjr2bYDaZWR3zEPn2MwjvKsNuu5yUWRxB2HVmZK3FIMuyKyGzKRc7blYhIZPP66AY/CFMwTUYnfcw5V/AsE1zcOeuxQh2WCo/wINx2CIReSImZETk9RxOB1IvnINGA0ihXG/IU6RLm+Afo4FyqToF4Fx2B5zNal8r11dkGSazGQBgl6uWkGmL9TA5TXByuCI0tmJ02vc7HtjwDZrnXVA7nGqhZGfDmZKidhhERG44h4yIvN6RY4lwOOzQh+VB0qm/EC2VElDgaLIfckY7aODn2r7zzEhEBCbB31izQ0sVRYHZbAZgrfKQRWeKBjpJglyXhysqTjRMSsSQg2thVnxvLT9HYiJ0DRuqHQYRkQsrZETk9fYf3gmz2Q8Shyt6nGJHGkK7lsBmvdwK3yGbse3UWJRZrqxGKIoCk19phezSkEWhu/ZF7UkShEYH2T+iZoPzUIHZZzF23SyM2b/SJ5MxALAfOgTh9M17IyLvxISMiLxaUXEBMrPSAK0dUpBvDKvyNelyAkJa6OF0Xl6YN72gOY6n9ajR60rQwGAwAABs8sVrX2MNMo1DC2OhGXJgFKDxyrYVN0xnycOtO+bj3i0/ILo4V+1wapbVCsexY2pHQUTkwoSMiLza7n1bodVqIYUkQ9J48MK0dVxhxHZoDA6IMmWxxKQhyC8Jr7FrShoJ+osJmePSosXXGrKYrodWo4UzuO7MH5OcdrQ4vglTV3+GuLQTaodTa5xMyIjIgzAhIyKvJYTAiVOHoNcbIAVzor4ns8l5COicA7utxLVNFnpsPTUeslIzb0UaSYJeX1oSq0pCZj2rQAClFTJfJxSEpR3FpDWf4rajm6Cv6fGjHsZ56pTblwNERGpiQkZEXis9MxV5BbmAJEPyz1Y7HKpEpmMvwtpp4XDYXNtyi6Nx6Hz/GrmeVEFCdtU5ZEKCLssMxa8ehL721kpTg7EwA0O2fI+7dixAWJkEuS4RJSVQ0tLUDoOICAC7LBKRF7u09pjkn8nhil4iJ3AzjIF9ISwCkiQBAA6d74fokJMID6zeKqdGq4FGWzoXzIFrV8i0uQZooYfNh4crauwlaHt8M/qc2gWNpHY06nOcPAltgzrcTZOIPAYrZETktS6kJUGj0UAKyFQ7FKoip2KBsX0SbPbLlRkBDbaeHA+HfI2OGzdAq9W5kj7npeFpV0nI7BcbdMpBPvgBXZERlZyIP636FP1OMxm7xHnqlNohEBEBYEJGRF6qoDAPRcWl61hJARkqR0PXI8d+DBGdALvd4tpWZAvD3nNDq/U6Wv3l7Msurr0OmXxeB0XvB8UcUq0xqM0vNwWjNn6N8XuXIUDmGn1lycnJEHa+JkSkPiZkROSVjp86CI1GA2gcgJ+Pt+n2QRmGjTBHSFCUy0NNT2Z0w/ncltV2Db3ucsXtWgtDa0v0MDlMPtVdUWstQPc9v2HKprloXMAKcoUUBc4zZ9SOgoiICRkReafT547DaDBB8s+CJLFbmrdRhBNocQwO2b2pxPbTt8Pq8KuWa2h1l9cSc1yskAl9+Z8VOUULSZJ8Y7ii7EDT0wmYsupTdEs5pHY0Ho/DFonIEzAhIyKvI4RAVk46JEnicEUvVuBIQnhXJ2xlOv1ZHQHYcXp0tZxfpyszZPEaFTJbEiA0OsgBkdVyXbWEZJzEnWu+wIiD62AUbHJTFUzIiMgTMCEjIq+Tl5+DkpJCAIAUyITMm6VhMwIbayHLTte2lNw2OJ0Zd9Pn1lVhyKLGqYWx0Aw5sD6g0cIb6YuzEb/tR9yz7WdEWAvUDserKDk5UHI55JmI1MWEjIi8ztET+6HXGQCtDTDlqx0O3RQBW8xeKBqb29ZdZ0egyBZ8U2e+NGRRCHH1ph7pemglLZxB3jd/THJY0frIWjyw5gu0zeBcqBvlOHlS7RCIqI5jQkZEXicp5RQMBiOkgExIbOHt9UqcmQjtWgSbtdi1zSkbse3kOCjixv+BdRcTMtei0BDlEjLbWQEBL2t3LxREpB7EPas/w8AT2+CddT3PwcYeRKQ2JmRE5FUuzR8D2O7el6Q7dyC0lQ5Ox+U25BmFTXHsQq8bO6EQ0OkNAAC7cnE45JXVMSFBm2mE4hcGoTfd2HVqmSn/AoZtmoM7dy1GsMNS+QFUKfnCBbVDIKI67iorshAReaas7HRYrCXw9wvggtA+Jr9eAjTne0A49a4FnfclD0JU8CmE+l9f8i3LMkwmE5yy86rzx7R5BmhhgM0LhitqbEWIO7oBvc7tUzsUnyPy8iCsVkgm70jKicj3sEJGRF7l8PFEGAxGQGeBZCpUOxyqRna5EAGdMmG1XR66qAgdEk7dAVm5voF5sizDbPaD3WGH7SoVMvu5i/sGe/BwRcWJhmd34f5VnzIZq0FyerraIRBRHcaEjIi8yvnUc9Dr9JD8ctQOhWpAlmM/Ijpo4bBbXdvySupjf8qA6zqPLMsw+ZnhcDhglyuukMnntVD0Zijm0JuMumYEZp/F2HWzMGb/SpgVZ+UH0A2T09LUDoGI6jAOWSQiryHLMrJzM0rXlzKxvbevyvLbBFNwf4gS4Rq6eCS1D2JCTiAyKKlK55CdlxMym+IA4L4otNaih8luhjPc84Yr6kry0OPgKsSlnVA7lDqDCRkRqYkVMiLyGhlZqbDZShsZSEzIfJYsbNC3OwNbmaGLgISEU+PhcBqqdA4hBEwmU+mQRbn8kEU5RQNJkuAM9pyETHLa0eL4Jkxd8xmTsVrGhIyI1MSEjIi8xtmkk9AbSj+QMyHzbXn2k4joCthtlzsJFttCsPvc8KqdQBLQ6w0osZS4mnoI7eUKmf2cBCFpIQdEVGvcN0QoCEs7iklrPsVtRzdBL0Tlx1C1UjIzIRRF7TCIqI5iQkZEXiM9K7V0QWgogJENPXxdunYj/OpLUMp8UD6d2RnJOa0rP1gAeoMexZZi1zpkDpQOXdQ4NTAUmiEH1gc06o7cNxZmYMiW73HXjgUIs5WoGkudJstQMtm1lYjUwTlkROQ1ioryS+cUGQshafhttq8TkKHcchjOrFgYNP6u7TtOj0Z4QDLMhuJrHA3o9XqUWErgROnPis508S0vwwCtpIVVxeGKGnsJ2h7fjD6ndkHDxc09gpyWBm39+mqHQUR1ECtkROQVhBAoKMgDwOGKdUmh4zzqdbPBVqZ6ZHP6Y/vp2695nBACOr0eJdYSOIUCIQT8AvxKjz8nIADIQSq0u1dkNEhOxL2rPkG/00zGPAnnkRGRWpiQEZFXsFiKYbVfnE/E4Yp1SpqyBUFNdZDly63fU/Na4WR656seo9FqIUkSrDYrHFBgtVsRHBYMCECbYYBiDoXQm2sjfBf/3GSM3vA1xu1dBn/ZUavXpspxLTIiUguHLBKRV8jKyYB8cT0pqZKhauR7LA12QVzohLJvW3vODUf94LMINOWW299gLG3+4nA6AKFAggRzoBnaLCO0wghbLQ5X1FoL0OXwOnRLOVRr16TrxzlkRKQWVsiIyCsknT8Fo8EIAJCMRSpHQ7XN4sxGcJd82KyXk3GnYkDCyfFQRPm3MsPFnxWn0wm7IqNeaD04hBOO5NIxgrUyXFF2oOnpBExZ9SmTMS8giovZaZGIVHHVCll6FUv39TkBlohqQXZOBvT6i2tQsUJWJ2U4dyGyzVDkHbdBry9NuLKKGuFoWj+0bbDBbV+j6XJCZnPY0KRRC5yzJsOZrIFWb4biF1ajsYZknMSQxBWIsHK+o9cQAqKoCFJQkNqREFEdc9WE7IknnqjSCX766adqC4aI6GpKLBerYpIM6C3X3pl8Vm7wVuj9ekLYRWnHTQCHUgehbdMCwLbXtZ/JZAIAOGQHrHYb4trE4VjmKZjsZjjDa646pi/Oxq37V6Jt5pkauwbVHKWoCBomZERUy66akDHRIiJPUlxyMSEzlEBiZ7o6y6EUISguDXmbI2EyBQAAhNAC0d8BZ7u69jNcTMicDif0JhOCA4Nh2yeglSQ4g6p//pjksCL25Fb0P7EN2mo/O9UWUciGQURU+9jUg4g8nhACxSVFMOgNkAycP1bXZdsPoX5cA+QesEJvKE28YGwLRLzp2udShczutCMmogEupGfClqLALGkhB0ZWXzBCQWTqYQzZtxLBTmv1nZdUIYr4+4WIal+VEjJZlvHHH3/g8OHDKLzi26N///vfNRIYEdElRcUFcDodMOgNgJ4fegnIMm2EX2g85CJxeWPok4BtPwDAZCptaZ+Tm4Oh/YYi8fBRGK1myCGRgKZ6vos056ei/74/0DzvQrWcj9SnsEJGRCqoUpfFOXPmYPXq1Wjbti1Onz6Nnj17Ij8/H+3atavp+IiIkF+Q62p5Dw3XbyJAFnZoWp+CzXl5wWhIEmCKAwCY/EorZBqtBo0bNEZqWga0Gm21DFfU2IrQed/vmLphNpMxH8Mhi0Skhip9Tbh9+3bMnDkT4eHh+PnnnzFy5EjExcXhf//7X03HR0SEtIyU0uoYAGic196Z6ox8x2lEdWmMnN3lP0T7BwRACIGWTVuiqMSCvIIC+JnNN9fuXnGiYdJeDDm4DmaFP4e+SOGQRSJSQZUSMrvdjnr16gEADAYDbDYbYmJicPbs2ZqMjYgIQGmFTKfXAwAkLT8I02Xp0kbEdn3EbZvBaESbDh0ghMCI+BHYvf8IdDodZHMIhMHvhq4TmH0GgxNXoEFx+UWoyXdwDhkRqaFKCVlMTAxOnTqFFi1aoHnz5pg/fz7MZjPCwmp2HRciIgCw2qzQSBdHWLNCRmUIKDhvXAyHPBl6rXuyJUkSzCYzTp9LhkGvh/0GhivqSvLQ4+AqxKWdqK6QyYNxDhkRqaFKc8imTp0KjaZ01ylTpuDMmTPYvXs3Hn744RoNjogIAGSnw7XmFBMyulKh/TwSUt6t8Dm73YHM7BwAgDO46gmZ5LSjxfFNmLrmMyZjdYgoLoYQovIdiYiqUZUqZOHh4QgJCQEANGjQAC+99BIAIC8vr6biIiJykcvO1+GQRarAsezf0CQ4Hk1C4t22nzhzFrKiQNGZoJhDKz+RUBCWfhy37VuBMFtJ5fuTb1EUCIsFkt+NDW0lIroRVaqQPfnkkxVuf+qpp6o1GCKiitgdZTorskJGV7Ep6XVYHDlu2w6fOAWzyVjazKOSFcWNhRkYsuV73LVjAZOxukxR1I6AiOqYKiVkFZXvS0pKXMMYiYhqkixfTsIktr2nq7A4c7Ap6XXXY0VRkJaeBUmSrjlcUWMvQfuDKzF17ZdomZNcG6GSJ2NCRkS17JpDFh999FEApV0WL/39kqKiIvTp06fmIiMiusjpLJOEaWX1AiGPdy5/A45mLULr8HG4kJ6BEqsVfv7+kAMiy++syGhw/gBu278K/rIDuHYBjeoKziEjolp2zYTs8ccfhxACb775Jh5//HG350JCQhAdffMLbBIRVcbpLDNMkRUyqsS2lPcQHdgN+46chslkLE3GtHq3ffxzkzEwcQUaFWaqFCV5LFbIiKiWXTMha9u2LQDgq6++gtForJWAiIiu5NbUg3PIqBIOpQTrz76M9AvDoNVoYC0zXFFrLUDXw2vRNeWwihGSJ2OXRSKqbVXqsqjVavHzzz9j48aNyM3NRWhoKPr374877rgDOl2VTkFEdMPcKmTsskhVkF6ciBI/E7T2W0sbesgOND27C4MOb4BRsAJC18AKGRHVsiplU9999x1OnTqFadOmISIiApmZmfj1119RUlKCqVOn1nCIRFTXOZ2O0i9/JBmSxG+vqWrMjXbAqvRAcF4qhiSuQISVi/5SFbBCRkS1rEoJ2bZt2/DOO+8gMDAQABAdHY1mzZrh2WefZUJGRDVKURTIsvNiQsZvrqnqJI2CkJD5uG3TGfhV0vKeyIUVMiKqZTfc9p6IqDY4nQ64fgMpWjVDIS9kC72ArR0PgL05qcqYkBFRLbtmQrZ582YAQO/evfH2228jMTERKSkpSExMxDvvvIPevXvXSpBEVHfZHTYI15wfDYTC9Q/p+uQ0PYudLc+BXy1SVfBLaCKqbdccsjhr1iz07dsX9957L3799Vd89dVXrqYeffr0wYQJE2orTiKqo5Qrv62W9YDGpk4w5LWSWu+Df3EAOqTWUzsU8nSskBFRLbtmQnbpWyKdToe77roLd911V60ERUR0iV5vgFR2xV5FB4AJGV2/I123wN8yBM1z/dQOhYiIyOWaCZmiKDh48OA1T9C+fftqDYiIqCyD3giUbcgg66++M1Eldt+6Bn7rRiCqhEu2UMUkk0ntEIiojrnmO5LD4cDnn39+1fHUkiTh448/rpHAiIiA0nUQtdoyzTwUfpCmGyc0Alv6rsHgdUMR4mDnRSpP8mMFlYhq1zU/2ZhMJiZcRKQ6vfZyVUzIevBjNN0M2WjDhj4bMGTTAPiz/SJdgRUyIqptbFdGRB5Ppy8zTJEVMqoGtsACbOq+E3Zm91SGZDJB0vCjERHVrmv+1mHrVyLyBDodEzKqfgURF7C1w2GuUUYuHK5IRGq4ZkI2d+7c2oqDiOiqdNoySRibelA1ymhyErtbJHONMgIASGaz2iEQUR3EujwRebyyFTLBChlVs7Nt9uJwgxy1wyAPwAoZEamBCRkReTx92SGLrJBRDTjUbTPOhFjVDoNUxgoZEamBCRkReTxt2SGLrJBRDdnVezXSzJxRVpcxISMiNTAhIyKPp2OFjGqB0CnY2m818vScUVZXccgiEamBCRkReTy93uDq+irs/ipHQ77MabRhY5+NKOG7Y53EChkRqYFvOUTk8cwmP8jyxaFktkBwRQ6qSdbAfGzqtgsOrlFW52hYISMiFTAhIyKPF+gfBFl2lj5QdICD32JTzcqvn4qt7Y9CUTsQqlVSaKjaIRBRHcSEjIg8XlT9hnA4Ha7HwhaoYjRUV6Q3PY7dzVPUDoNqkbZePbVDIKI6iAkZEXm80JBw98WhrUHqBUN1ypl2e3A4Kk/tMKgWSP7+kIxGtcMgojqICRkReTyzyQ9G4+VhisIWoGI0VNcc7L4R54K5Rpmv07A6RkQqYUJGRF7B379MEsYhi1TLdty6Gulco8ynacLCAADJyckYOHAg2rRpg3bt2uHDDz8EADz77LNo3bo1OnbsiPHjxyMvL6/C86xYsQKxsbFo0aIF3nrrLdf26dOno2PHjrj//vtd27799lvX+Ymo7mJCRkReIdA/2PV3wSGLVMuETsGWvmuQz2XwfNal+WM6nQ7vvfcejhw5gm3btuGTTz7B4cOHcdttt+HgwYPYv38/WrVqhTfffLPcOWRZxt/+9jcsX74chw8fxo8//ojDhw8jPz8fW7duxf79+yHLMg4cOACLxYLZs2fjr3/9a23fKhF5GCZkROQVQkLqXe606DRDyLprH0BUzZwmKzb23gQL3zl9kiYyEgDQoEEDdOnSBQAQGBiINm3a4Pz58xg6dCh0utLfO7169UJKSvmGLzt27ECLFi3QvHlzGAwG3H333fjtt9+g0Whgt9shhIDFYoFer8c777yDJ554Ano9s3yiuo5vK0TkFRpFN4PVZrm8gcMWSQWW4Fxs6raba5T5IG39+uW2nT17Fnv37kXPnj3dtn/99dcYMWJEuf3Pnz+PRo0auR43bNgQ58+fR2BgICZMmIDOnTujWbNmCA4Oxs6dOzF27NjqvxEi8jpMyIjIK0TUi4JU5lcWW9+TWvLqn0dC2+Nco8yXGI3QBAe7bSoqKsKECRPwwQcfICjo8jDpmTNnQqfT4U9/+lO504gKVq2XpNLs/bnnnkNiYiLee+89vPTSS3j11Vfx5ZdfYtKkSXj99der+YaIyJswISMirxAYEOw2tEdYmZCRetKaH8WeZufVDoOqyZXVMYfDgQkTJuBPf/oT7rjjDtf2OXPmYOnSpfj+++9diVZZDRs2RHJysutxSkoKoqOj3fbZu3cvAKBVq1aYO3cufv75Zxw8eBAnTpyozlsiIi/ChIyIvIJWq4W/f5kkjBUyUtnp9rtxpH6+2mFQNdBenD8GlFa5HnroIbRp0wb/+Mc/XNtXrFiBt99+G4sXL4afn1+F5+nevTtOnDiBM2fOwG63Y968ebj99tvd9rlUHXM4HJDl0s6dGo0GJSUlNXBnROQNmJARkdfw97s8bIhDFskTHOixAUlBNrXDoJukKVMh27JlC7799lusXbsWnTp1QqdOnbBs2TI89thjKCwsxG233YZOnTrhL3/5CwAgNTUVI0eOBFDaofHjjz/GsGHD0KZNG0yaNAnt2rVznXvRokXo3r07oqOjERISgt69e6NDhw6QJAlxcXG1e9NE5DEkUdGAZyIiD/T7qvk4l3zi4lAhAW373yBpnWqHRXWc5NQift1wRFq1aodCNyjg0UfdqmRERLWJFTIi8hoNImNgs1svPpIgiuupGg8RAAidjC1916KAKzF4JcnPD5qICLXDIKI6jAkZEXmN6AZNIDsvV8REMT9EkWdwmC3YeOtmrlHmhXRNm1bYoIOIqLbwrYOIvEZEvSjoDUbXY1EUrmI0RO5KgnOwqcteOPnZ3qtomzZVOwQiquOYkBGR19BqtagXWqYqVhIGoXDeDnmOvAbJSGh7gmuUeREdEzIiUhkTMiLyKpHhDVytogENRHGYqvEQXelC8yPY2/SC2mFQFUgBAdBy/hgRqYwJGRF5ldgWHWGxXl6vh/PIyBOd6rATRyML1A6DKsHqGBF5AiZkRORVohs0hkFvcD0WhWxVTZ5pf/f1SArkGmWejAkZEXkCJmRE5FX0Oj1CQsq0uy+pB+HUqxcQ0dVogB19VyPTxBllnooNPYjIEzAhIyKvExneALJyaR6ZBFHEKhl5JkUnYzPXKPNIUmAgtPW4liERqY8JGRF5nXaxXWAtO4+sMErFaIiuzWEuwaZeW2DlO65H0TVrpnYIREQAmJARkReKadAERoPZ9VgUMCEjz1Ycmo1NXRLhrHxXqiVMyIjIUzAhIyKvo9VqEV6v/uUNTjOEJUi9gIiqILdBEra1Ock1yjyBRgNdbKzaURARAWBCRkReqlnjVrDZL3ew47BF8gapLQ5jX+N0tcOo83QtWkBjNle+IxFRLWBCRkReqW1sJ8jy5QFgSm5jFaMhqroTcdtxLLxQ7TDqNH379mqHQETkwoSMiLxSUGAIAgOCL2+whkBYgq9+AJEH2ddzHVIC7WqHUTfp9dBzuCIReRAmZETktRrUbwRFuTwjR8lpql4wRNdDA2zrsxpZRs4oq2362FhIBkPlOxIR1RImZETktXp06Q+L1eJ6LHIbQyiSihERVZ2id2Jzn/Uo5BpltYrDFYnI0zAhIyKvFRneAKHBYZc3yEY29yCvYvcvwsZeW2Hju3GtkEwm6Fq0UDsMIiI3fAsgIq8lSRJa3dIedkeZbosctkhepjg0C5s67ecaZbVA16YNJK1W7TCIiNwwISMir9Y17lbI8uV5OKKgAYST80PIu+TEnMX21qcg1A7Exxk4XJGIPBATMiLyan5+AYiKjIEQlz7KaiDYAp+80PmWh7CvUYbaYfgsKSAA2mbN1A6DiKgcJmRE5PXi2vWA1Xa5uYeS20TFaIhu3PFO23CiXpHaYfgkfbt2kCQ2/SEiz8OEjIi8XmyLDjDoTZc3WEIhLEHqBUR0E/b2WovzAQ61w/A5hs6d1Q6BiKhCTMiIyOvpdDo0imnqviYZq2TkrTRAQp/VyDZyRll10TVvDm39+mqHQURUISZkROQTenTuD4u1xPVY5DaBEByeRN5JMTiwqc86FLEhYLUw9O6tdghERFfFhIyIfEJU/YYIDgy9vMFpgijkN+Lkvez+RdjYMwE2fq9wUzQREdBz7TEi8mBMyIjIJ0iShFYt2sHhsLu2icyWKkZEdPOK6mVic6cDXKPsJhhZHSMiD8eEjIh8RrdOfSHLsuuxKKoPURymYkRENy+74RnsaHWWa5TdAMnfH/oOHdQOg4jompiQEZHP8PcLRGR4gzJrkgFKehsVIyKqHimx+7G/YabaYXgdQ48ekHQ6tcMgIromJmRE5FN6dRvo3tyjsAFESeg1jiDyDsc6J+BkWLHaYXgPnQ6Gbt3UjoKIqFJMyIjIpzRvGouwkAj3Klkaq2TkG/b0XotUf65RVhWGuDho/PzUDoOIqFJMyIjIp0iShH69hsJis7i2icJoVsnIN2gEEvquRo6BM8oqw1b3ROQtmJARkc+5pVlrhAaHcS4Z+STZ4MCmPuu5Rtk16Fq1grZePbXDICKqEiZkRORzJElC3563wWotUyUraABhCVEvKKJqZAsoxKYe27lG2VUY+/dXOwQioipjQkZEPqll83YIcauSSZxLRj6lMDwdW+MOQq581zpF3749dDExaodBRFRlTMiIyCdJkoRbewy+okoWDWEJVjEqouqV2eg0drY6xzXKLtFqYRo8WO0oiIiuCxMyIvJZsS06IDio7MLQEueSkc9Jit2HgzFZaofhEQw9ekATEqJ2GERE14UJGRH5LEmS0Lv7QFgsZdYly4+BsASpGBVR9TvSZStOhZZUvqMvM5th4twxIvJCTMiIyKe1aRWHoKCQMltYJSPftOfWNbjg51Q7DNWY4uMhmUxqh0FEdN2YkBGRT5MkCb27DYLFWrZK1giiKFzFqIiqn9AIbO23Grl1cI0yKSwMhm7d1A6DiOiGMCEjIp/XNrYTAgPcm3nIKV0hFP4KJN8iG+zYeOsGFNexNcrMQ4ZA0taxmyYin8FPI0Tk80qrZO5zyWALhMiIVS8oohpiCyzApu47YK8ja5RpGzeGvg2HIROR92JCRkR1QrvWXVCvXn0oiuLapmS0hrAGqhgVUc0oiEjD1g6HfX6NMgHANHSo2mEQEd0UJmREVCdIkoQxQ++G3WG/vFFoIad0gah7U26oDshochK7WiT59Bplhg4duAg0EXk9JmREVGeEhYYjrn0P2OzWyxuLIyBymqoWE1FNOtcmEYca5KgdRo0QZjNMw4apHQYR0U1jQkZEdUr/XsNgNvlDlCmLKRc6QjiMKkZFVHMOd9uMMyEWtcOodv6jR0Pj7692GEREN40JGRHVKTqdDsMH3eHWBh+yAUpqR/WCIqphu/qsRpoPrVGmad0a+rZt1Q6DiKhaMCEjojqnSaMWuKVpazidDtc2kdcESmGkilER1RyhEdjSdw3y9N4/o0w2GuF/++1qh0FEVG2YkBFRnTR88AQA7n3BlZQuXJuMfJZstGFjn40o8fLlugLGjYPGbFY7DCKiasNPHkRUJ5mMZvTvPQxWa5m5NfYAKOlcz4h8lzUwHxu77fLaNco0bdvC0Lq12mEQEVUrJmREVGd1bNcd4fWi3NYmExmxEJYgFaMiqlkFkalIaH/E69Yok00mBIwZo3YYRETVjgkZEdVZkiRhzLAr1iaDBnJSDw5dJJ+W3vQEdt+SrHYY1yXwjjsgmUxqh0FEVO34iYOI6rSQ4DB06dgbVluZtcmsIVDOd1ItJqLacLbtXhyKylU7jCrRdOwIfcuWaodBRFQjmJARUZ3Xt+dt8PcLdFubTOQ0h5LbWMWoiGreoe6bcDbEWvmOKpL9/BAwcqTaYRAR1RgmZERU52m1Wowb8SfYbDa37UpKFwhroEpREdWOnb1XI93smTPKZElC8L33QjJy4XYi8l1MyIiIAERGNED/W4fCUrbroqKDfK4XhOLlfcKJrkHoFGzpuwb5erUjKc80ejS0DRqoHQYRUY1iQkZEdFGXjreiaaMWcJRt8mENhpLSWb2giGqB02TFht4bUeJBnwpKYmPh16WL2mEQEdU4D/rVS0SkrktdF40Gs/t8stymULKaqxgZUc2zBudhU7fdcHjAGmWFwcGImjRJ7TCIiGoFEzIiojL0egPuGH0/bPYr5pOd7wRRFK5SVES1I7/+eSS0Owal8l1rjEWnQ/TDD0PS8CMKEdUN/G1HRHSFiPAoxN86HNay88mgKZ1PZjerFhdRbUhrdgy7m6Wocm0ngJApU6Dx81Pl+kREamBCRkRUgS4de6NViw7ulTKnCfLZW9nkg3zemfZ7cKR+Xq1eUwDQjBgBU8OGtXpdIiK1MSEjIrqK4YPuQFhIOGTZeXmjJRRKclf1giKqJQd6bMS54Npbo8zSti1Ce/SotesREXkKJmRERFeh1Wpx5+0PAJLGvclHXmMoaW1UjIyoduy4dQ0yTDW/RllhWBii7ryzxq9DROSJmJARkVdq2rQpOnTogE6dOqFbt24AgPnz56Ndu3bQaDTYtWvXVY99//330a5dO7Rv3x733HMPrNbSKsD06dPRsWNH3H///a59f/1lAXIvOMs3+UhvByWjZQ3cGZHnEDoZm/uurdE1ygqNRkRPmwZJ8oD2jkREKmBCRkRea926dUhMTHQlX+3bt8eCBQvQv3//qx5z/vx5/Pe//8WuXbtw8OBByLKMefPmIT8/H1u3bsX+/fshyzIOHDgAi8WC2bNn4/npL2Jw/9FXNPkAlAtxbIdPPs9ptmBT702w1MAnhnytFpGPPgqNyVT9Jyci8hJMyIjIZ7Rp0waxsbGV7ud0OmGxWOB0OlFSUoLo6GhoNBrY7XYIIWCxWKDX6/HOO+/giSeegF6vR8e23dElrjesNvc5Ncr5zlBymtTULRF5hJLgXGzquqda1ygrkCQEP/AADMHB1XdSIiIvxISMiLySJEkYOnQounbtiv/9739VPi4mJgbPPPMMGjdujAYNGiA4OBhDhw5FYGAgJkyYgM6dO6NZs2YIDg7Gzp07MXbsWNex/XsPR/vWnWFzS8okKMndoOSxMxz5tryoFGxre7xa1igrlCRo7rwTwTEx1XA2IiLvxoSMiLzSli1bsGfPHixfvhyffPIJNm7cWKXjcnNz8dtvv+HMmTNITU1FcXExvvvuOwDAc889h8TERLz33nt46aWX8Oqrr+LLL7/EpEmT8Prrr0OSJAyJH4uWLdrDais7fFGCcq4HlPwGNXCnRJ7jQvOj2Nss9abOUSxJkG+/HTFt21ZTVERE3o0JGRF5pejoaABAZGQkxo8fjx07dlTpuNWrV6NZs2aIiIiAXq/HHXfcga1bt7rts3fvXgBAq1atMHfuXPz88884ePAgTpw4AUmSMHLwnWjetPUVwxc1UM71glJYv1ruj8hTnWq/C0cj82/oWIskwTZiBJp06lS9QREReTEmZETkdYqLi1FYWOj6+8qVK9G+ffsqHdu4cWNs27YNJSUlEEJgzZo1aNPGvYX9peqYw+GALJe2/NZoNCgpKQFQOlxyzNC70SSmufvwRaGFcqY3RFF4Ndwlkefa33MDkoJsle9YhhVA0eDBaNa9e80ERUTkpZiQEZHXSU9PR9++fREXF4cePXpg1KhRGD58OBYuXIiGDRsiISEBo0aNwrBhwwAAqampGDlyJACgZ8+euPPOO9GlSxd06NABiqLg4Ycfdp170aJF6N69O6KjoxESEoLevXujQ4cOkCQJcXFxrv00Gg3GjrwXDaIawV62Jb7QQT7TB6I4tHZeDCKV7OizGpmmqs0oswPI798fLfr0qdmgiIi8kCTKrnZKRETXxel04uffvkJWThr0OsPlJ7R2aG/ZAMl8Y0O7iLyB3uKHweuHIMh59X0cALJ690broUNrLS4iIm/CChkR0U3Q6XSYOPZBhATVg8Nhv/yEbIB8qj+EhS29yXc5zCXY2GszrFf5NGEFkNajB5MxIqJrYEJGRHST9Do97h4/DYEBIXA6HZefkI2QTw6EUhClXnBENawkNAebuiTCecUaZUWShPPdu6P9iBHqBEZE5CWYkBERVQODwYjJEx6ByeQPWS4zfkvRQTnTB0pmC/WCI6phuQ2SsK3NCdcaZTkaDZK7dkWXi3M3iYjo6jiHjIioGlmsJfh+/mcosRRBrze4PSfVOwlNzD5IEn/tkm9qsb87opNjkN+7N7oPHgxJkio/iIiojmNCRkRUzRwOOxYsnYvU9GSYjCa356TANGiabIOkvUYXBCJvdb4ZekU9iw7de6odCRGR12BCRkRUAxRFwcr1i3Dk2D6YTO5JGUz50DbbAslQok5wRNVNSJBOxmFQ3LNo3rq12tEQEXkVJmRERDVo597N2LRtJcwms/sTOiu0TbdA8s9VJzCiaqI4NbDu7IBJY15Bg8aN1Q6HiMjrMCEjIqphJ88cwdI/5sFgMLrPqZFkaBrvhCYkRb3giG6CsJshn+mLO297ChHh9dUOh4jIKzEhIyKqBVnZ6fhp0ZcQQkCr1ZZ5RkATdRCa+sdUi43oRshFITBnDsddox+DyWiu/AAiIqoQEzIiolpSYinGTwtnobAov3wHxtCz0DTcA0mjXOVoIs8gBOBIvQWN9GMx6ra7r/iCgYiIrhcTMiKiWuRwOrB4+Q9ITj0No+GKZh/GfGib7IRkzlMlNqLKCLsfLCc7omvL8ejTg23tiYiqAxMyIqJaJoTAus2/Y9/BHTBd2ewDCjRRhyFFHuN6ZeRR5JxGsJ5pi+ED7kbrlh3VDoeIyGcwISMiUsme/QlYv2U5TEZT+UqDXza0jXdCMhapExzRRULWw3amPQLkDrhj1BQEB4WoHRIRkU9hQkZEpKKs7HQsWv4diooLYTQY3Z/UOKFpsB+a8NPqBEd1nlIUjpLjHdCl7WD06zUMGo1G7ZCIiHwOEzIiIpXJsoz1m3/HvsO7KqyWSYFp0DTaBUlvVSlCqmuEIsGRGgtNTnvcPuxPiGnA9cWIiGoKEzIiIg+RcuEclvzxI+x2Kwz6K6plWjs0MXugCeWaZVSzhDUQJSfi0CyyJ0YOubNcR1AiIqpeTMiIiDyIw2HHynWLcPTUfviZ/Ms9L4UkQROzF5LOoUJ05OvkzKawJbfFbf3vRNtWcWqHQ0RUJzAhIyLyQCfPHMGKNb9CCAU6nd79SZ0Fmsa7oAlMVyc48jmiJBSWs61Rz9gW40fdB3+/QLVDIiKqM5iQERF5KKvNgqUrf0JSymmYy7XHB6TgFGgaHIBkLFYhOvIFwm6GfKEdLGmR6NVtIHp3G8i1xYiIahkTMiIiD3fgyC6s3fQ7tBottFqt+5OSDCniBDSRRyFpneoESF5HyFoombGwpDSBvykE40b8CZERDdQOi4ioTmJCRkTkBYpLCrHw9++QkZkKs9mv/A46KzRRByGFnQULHHQ1QgAitwlsKbGA3Ywucb3Ru9ug8ok+ERHVGiZkREReQgiBw8cSsTHhD9jsFhgNpvI7mXOhaXAAmsCM2g+QPJooCoczpQOs+Wa0bN4OQ+Jvh9lUQXJPRES1igkZEZGXcTqd2LJjNfYe2AatRgudTlduH8k/A5oGByH556gQIXkSYfOHnNoBJWmhiKrfEMMG3YHwsEi1wyIioouYkBEReamSkiKsXLcIp84dg9lkrrAZgxSUWjqU0VygQoSkJiHroaS3hiWlIQL8QzCo72jc0qy12mEREdEVmJAREXm5tIzzWLVuETKyLsBs9qsgMROQQpOgqX8EkrFIlRip9gi7H5SsW2BLj4FWmNGjSzy6deoDjUajdmhERFQBJmRERD7izLnjWLf5d+Tl51Tc+AMCUmAapPCTkALT2fzDx4jielAyW8KZGwW73YG2sZ0wsM9IGI0VzDUkIiKPwYSMiMiHCCFw9MR+bNq2CkXF+Vdv2mAohCb8FKSwc5C0jtoNkqqNUCSI/EZQMltAKQ6B1VqChjHNMHzQBAQHhaodHhERVQETMiIiH6QoCvYf3oltu9ajuKQQZlNFQxkBaJyQQs9BU+8U55l5EeE0QGQ3h5J1CxwWLWRFQeOGt6Bfr9sQGc71xIiIvAkTMiIiH6YoCk6eOYztezYiIzMVRoPpqmtOSf4ZkMJPQQpOhSTxrcETCUsQlKwWELlNYLHYYTSYENuiPW7tPgh+fgFqh0dERDeACRkRUR2Rk5uFzTtW42zScSiKXPE6ZgCgLymtmIWdhaS31W6QVI5QJIjCKIisW6AU1ofl/9u7s98mjgCO47/x+tjNYTtOyAFJOApCgApUCFoV8VSp/3WlPrVFRVRAKeEqBEpOkjh2vN7LO31wEkIVAojApvH3I1u7ltfSWH76ambHga9qeVjffP2tLl64tuvfHgAA/j8IMgDoMVEU6vbdX3XvwS01mvV3L2c0HZnKq+5zcFHGSb78YHuUtUZ244hsfVJ2fVKdyFEUhRofm9T1az9oevKr3X8zAMD/DkEGAD3KWqvnLx7rt1s/a37xpQqF4rtnW0xHZmBJpjzfXdJYCL7sYHuAtZJaI0o3I0yJqzAMZHI5nTp+Vje++1HVSi3rYQIA9hlBBgBQo1nXL7//pCfPZhRFoTzX2+NqK3lrylXmZMpzbAbyCWzqyDZHZRsTso0JKfHUSTsKgkDlwYrOn72sq5dvsHU9ABxiBBkAYFuSJLr34JZmHt/V8ut5xUn87iWNW4obMuU55SpzUv8KG4K8h43d7QCzzTHJOkrTVO3A1+BAWVPHTunKpesaHZlgWSIA9ACCDACwqziJ9ez5Q/05c1sLS6/k+xtyPU9ObvddGiVJTtRd1jiwJOOtSW6zpwPNWiMFZVl/SNavyfo1KahIMpszYW319w1q8ugJXbn0vSbGpogwAOgxBBkA4L3SNNX84j+6c/+m5hZeaH19VYViSYV8Ye8PmkTy6jJ9dRlv7dBHmo287fCyfk1qD0npm/vy4jhSnMSqlGs6Nj6tixeu6eg4EQYAvYwgAwB8tLX6iu7cv6nZl0+1urYsGckt7XXf2Q6HJNJspyDrD0k7Ayx5+16v7lLElvJOUSO1UZ04fkYXz19VebCazaABAAcOQQYA+CR+u6W/Hv6hv2cfqb6+oo1WU51OIrfkffh/ZG1FmtuUKfhSsS0VfJnC5tHpfN4v8R82NVLiSbEnG28eo7dfK+6T9GZmy1qrIGwrTVN5br+qlZqGa6M6ffKcpo6eZGMOAMCuCDIAwL4KwrYWFl/p2YtHWl5Z0Pr6qpqtxsdH2k5OJOXbMvlQykdSPuzer5YPN8/j7nV2K5CMZDePW9G023vWSEnpTXRthVZS0s7Y2k0cRwrjSKVCUZVyTUOVYR2fPq0TU2dUHqyyDBEA8EEIMgDAZxeEbS0uzenp7Ixev15QvbG6PZNWKrnKO/kDGTDWWiWdRHEUKrVWeaeggYGyhio1TYxP6/TJcxqpjclx9tjoBACAPRBkAIBMhGGgxeU5zb58orXGqtrtloLAVxC21Q58dTodddJEOWOUdwrK5wvK5XL7Fm7WWsVxpCiOZIyRk3Pk5PPy3H65JVee2yfP7VelMqSR2piGqiOqlmssPQQA7CuCDABw4KRpqpbfVMtvqrnR0HpjVY1mXX67pSgKFUaBojiUkVH30V2aaIy2g6373ua5MZvXdM+LxaJct0+16hGNDI+pMjik/v5BuSXvQM7UAQAOL4IMAAAAADKSy3oAAAAAANCrCDIAAAAAyAhBBgAAAAAZIcgAAAAAICMEGQAAAABkhCADAAAAgIwQZAAAAACQEYIMAAAAADJCkAEAAABARggyAAAAAMgIQQYAAAAAGSHIAAAAACAjBBkAAAAAZIQgAwAAAICMEGQAAAAAkBGCDAAAAAAyQpABAAAAQEYIMgAAAADICEEGAAAAABn5F6N5e4wOPiC6AAAAAElFTkSuQmCC\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,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "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",
    "\n",
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\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",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12)\n",
    "plt.axis('equal')\n",
    "plt.legend(labels=df_continents.index, loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].plot(kind='pie',\n",
    "                                figsize=(15, 6),\n",
    "                                autopct='%1.1f%%', \n",
    "                                startangle=90,    \n",
    "                                shadow=True,       \n",
    "                                labels=None,                 # turn off labels on pie chart\n",
    "                                pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                                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 in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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 excluding the outliers.\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 excluding the outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/boxplot_complete.png\" width=440, align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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w9ttv49ixYxgxYgQOHTqE/v3746233rIo652y/zpHrVtfu8C9/17ulkajwdy5c3Hp0iWcOnUKpaWl+P3vfw8A6NKlCwBg3bp1+Omnn/D555+jf//+eOCBB7Bs2TI4Ozvjr3/9KwCgffv2AFDnRL5z584pbwLrs2XLFuU5bMnVS7t27cLDDz+M6dOnY+bMmWbj2rdvX++JhHfK0BA3NzcEBQUhMjISq1evhpOTEz799NO7Xk5LxOJvBezs7FBVVQUAyrvirKwss2l2795t9o558uTJ0Ol0yMjIwNq1a/GPf/zjjus5f/682dUDx44dQ1lZGbp27Vrv9N27d0dmZqbZsMzMTGg0GnTr1g3AzRK8cePGHddtybLuRVlZGY4cOYIZM2bgkUceQbdu3dCmTZt6t5T27dtndnvv3r3Kfc/JyUF1dTUWLVqEqKgo3HfffXfccqxPREQETpw4AVdXV+UNXu2Pn5+fMp2Liwv+53/+B8nJyfjhhx+Ql5eHzMxMtGvXDgEBATh69Gid+YOCgtCmTZu7zmQL3bp1Q15eHioqKpRhR48exYULF+447549e2AwGPDee++hX79+CAkJwenTp+86Q+0btPqen507d8aUKVOwYcMGvPvuu1i6dOldL/9WQUFB0Ov1dZ7nWVlZFm/tWotWq4W/vz/atGmDL774Am3btsXDDz8MALh8+TI0Gg202v+/LjQaDXQ6nfLm+IEHHoBer1f2TAI332Clp6cre8bqExgYqDx3AwMDb5vxm2++weOPP445c+bgzTffrDM+KioKhYWFyp4zAMjLy8Mvv/xy2wyWqqmpwdWrVxu9nJbAtp/GQnX8enfl5cuXsX37dmzfvh3vvPMOgJvvwv/4xz9iypQpWL58OQIDA7F06VIcOnQIX3zxBQBg7dq1WL9+Pfbt24fw8HC8//77mDhxIvr164dOnTo1uG4nJyfEx8cjKSkJIoKXXnoJYWFhDV4K9Prrr6N3795ISEjACy+8gJMnT+Kll17C6NGj8bvf/Q4A0KlTJ6xfvx6HDx9Gu3bt0LZtW+j1+nta1r3w8PCAt7c3VqxYgS5duqCsrAzTp0+Ho6NjnWm//vprLFmyBI888gi2bduGL7/8UnnDFBwcDI1Gg8TERIwePRoHDx7Eu+++e9d5Ro8ejaSkJPz+97/HvHnzEBISgnPnziEjI0PZzfrRRx/Bz88P4eHhcHJywrp166DT6RASEgIAmDdvHiZMmAB3d3fExcXB3t4eeXl52Lp1K5YvX37Pj1VzGj16NGbNmoU///nPmDt3Lqqrq/Hqq6/C0dHxjnsC7rvvPpw/fx4rV67E4MGDsWfPnnvaEgsMDIRWq8WWLVswcuRI6PV66HQ6vPHGG/jDH/6ATp064cKFC9i2bVuj3nzWcnJywssvv4y3334b3t7eCA8Px/r167Fp06Y618Q3lQMHDgAAjEYjKisrldu1h6PKy8vxxRdfYPDgwbhx4wY2bNiABQsWYNmyZXB1dQUAPPLII5g+fTomTJiA119/HXZ2dli+fDmOHz+OYcOGAQBcXV0xadIkvPnmm2jfvj06deqEjz76CNXV1Zg4cWKj78f69esxevRozJgxA6NHj1b+Rup0Onh7ewMAYmNj0bt3b4wZMwaffPIJRAR/+ctf0L9/f8TExCjLKigoQGVlpbLbvvYxCQoKgouLCw4dOoStW7fioYcegre3N86dO4elS5eisLAQzzzzTKPvS4tgs7MLqI6xY8eaXRbk6Ogo3bp1k48++kg5aUlEpKKiQrmcz8HBwexyvvz8fGnbtq0kJycr09fU1Mijjz4qffv2Vc6yvlXt5XyfffaZBAYGioODgwwePFgKCgqUae50OZ/BYJBJkyaZXVZWVlYmjz32mLi6ut7V5Xz1LcvSk/sGDhwoEydOVG7v2rVLevToIXq9XkJCQmTDhg3SpUsXs8uM8P+dsPbUU0+Jo6Oj+Pr6yocffmi23CVLlkiHDh2kTZs2EhUVpZz9XHupUEP5dDqd2f0uLS2VSZMmKZd0+fn5SVxcnOTm5oqIyLJly6R3797Stm1bcXZ2loiIiDqXEaWmpkr//v3F0dFR2rZtKz179pR33nnnto8L6jm579e368sqIqLX62XFihUiUv9leb/88kudS6ZqT2qsPTH0dpfzOTg4SFBQkKxfv168vb3NThKsL6OIyFtvvSU+Pj7i5OQkjz32mHzxxRdmJ+rV91ytL+cHH3wgfn5+otVqJSYmRqqrq2XUqFHSsWNH0ev14u3tLSNGjJCff/75No9swyf33fpcsPRyvrv9vdwuV30/tYxGo0RGRkrbtm2lTZs20rdvX0lNTa2znJ07d0pMTIy4u7uLq6ur9OvXTzZt2lTnvr3++uvSrl070ev1EhkZaXYCYGPExMTUez8CAwPNpisqKpLhw4eLi4uLtG3bVkaMGKFceXCnZdU+L/Lz82Xo0KHi4+OjvD6feuop2bt3b5Pcl5ZAI3KHg7ikCnPmzMHatWvv+uSwligkJASjR4/G7NmzLZ5Ho9Hgs88+w5gxY6yYjG7n1KlT6NixIzZv3mzxSVtEdPe4q59+M86ePYtvvvkG+fn5DR6eoJZj7dq18Pf3R6dOnXDq1ClMnz4dgYGBGDp0qK2jEf2msfjpNyMqKgoigoULFyIqKsrWcegOysrKMHv2bJw5cwaenp6IiorC+vXr6z0HhIiaDnf1ExERqQgv5yMiIlIRFj8REZGKsPiJiIhURDUn9xUVFdk6ApHqGAwGlJaW2joGker8+pNAb8UtfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqopov6SGiu+Pv72/rCIozZ87YOgLRbwaLn4jq1RRle+P5YdCt2NwEaYioqXBXPxERkYqw+ImIiFSExU9ERKQiLH4iIiIVYfETERGpCIufiIhIRVj8REREKsLiJyIiUhEWPxERkYqw+ImIiFSExU9ERKQiLH4iIiIVYfETERGpCIufiIhIRVj8REREKsLiJyIiUhEWPxERkYqw+ImIiFTErjlW8umnnyI3Nxdubm5ITEwEAHz22WfYv38/7Ozs0K5dO0yZMgXOzs4AgNTUVGRkZECr1SI+Ph7h4eEAgBMnTiAlJQUmkwm9evVCfHw8NBpNc9wFIiKi34Rm2eIfNGgQ3nzzTbNhPXr0QGJiIj7++GO0b98eqampAIDTp08jOzsbCxcuxMyZM7Fy5UrU1NQAAFasWIGJEyciOTkZZ8+exYEDB5ojPhER0W9GsxR/t27d4OLiYjasZ8+e0Ol0AICQkBAYjUYAQE5ODiIjI2Fvbw8fHx/4+vqioKAA5eXlqK6uRkhICDQaDaKjo5GTk9Mc8YmIiH4zmmVX/51kZGQgMjISAGA0GhEcHKyM8/T0hNFohE6ng5eXlzLcy8tLebNQn/T0dKSnpwMAFixYAIPBYKX0RNSQcwBfe0QtjM2Lf+PGjdDpdBg4cCAAQETqna6h4Q2JjY1FbGyscru0tPTeQxLRPeNrj6j5+fn5NTjOpmf179q1C/v378fLL7+snKTn5eWFsrIyZRqj0QhPT886w8vKyuDp6dnsmYmIiFozmxX/gQMHsGnTJrzxxhvQ6/XK8IiICGRnZ+PatWsoKSlBcXExgoKC4OHhAUdHRxw7dgwigqysLERERNgqPhERUaukkbvdh34PFi1ahCNHjuDSpUtwc3PDiBEjkJqaiuvXrysn/QUHB+OFF14AcHP3/86dO6HVajFu3Dj06tULAHD8+HF8+umnMJlMCA8Px/jx4y2+nK+oqMg6d46IGnTj+WHQrdhs6xhEqnO7Xf3NUvwtAYufqPmx+Ilso8Ue4yciIqLmxeInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRimhERGwdojkUFRXZOgJRs7kx9RmgqtLWMVoGJxfoFn9h6xREzcrPz6/BcXbNmIOImktVJXQrNts6BQwGA0pLS22a4cbzw2y6fqKWhrv6iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVMSuOVby6aefIjc3F25ubkhMTAQAVFZWIikpCefPn4e3tzemTZsGFxcXAEBqaioyMjKg1WoRHx+P8PBwAMCJEyeQkpICk8mEXr16IT4+HhqNpjnuAhER0W9Cs2zxDxo0CG+++abZsLS0NISFhSE5ORlhYWFIS0sDAJw+fRrZ2dlYuHAhZs6ciZUrV6KmpgYAsGLFCkycOBHJyck4e/YsDhw40BzxiYiIfjOapfi7deumbM3XysnJQUxMDAAgJiYGOTk5yvDIyEjY29vDx8cHvr6+KCgoQHl5OaqrqxESEgKNRoPo6GhlHiIiIrJMs+zqr09FRQU8PDwAAB4eHrh48SIAwGg0Ijg4WJnO09MTRqMROp0OXl5eynAvLy8YjcYGl5+eno709HQAwIIFC2AwGKxxN4hapHNAi3jO29nZ2TxHS3ksiFoKmxV/Q0TkroY3JDY2FrGxscrt0tLSRuUiam1awnPeYDC0iBwtIQNRc/Lz82twnM3O6ndzc0N5eTkAoLy8HK6urgBubsmXlZUp0xmNRnh6etYZXlZWBk9Pz+YNTURE1MrZrPgjIiKQmZkJAMjMzESfPn2U4dnZ2bh27RpKSkpQXFyMoKAgeHh4wNHREceOHYOIICsrCxEREbaKT0RE1Crd065+k8kErVYLOzvLZl+0aBGOHDmCS5cuYdKkSRgxYgTi4uKQlJSEjIwMGAwGJCQkAAACAgIwYMAAJCQkQKvVYsKECdBqb74/ee655/Dpp5/CZDIhPDwcvXr1upf4REREqqURCw6er1mzBpGRkQgKCkJubi4SExOh0WjwyiuvtJqt7qKiIltHIGo2N54fBt2KzbaO0SKO8beUx4KoOTX6GP+ePXsQEBAAANiwYQNeeuklTJ8+HevWrWuahERERNQsLNpXf/XqVej1ely6dAnnzp1D//79AfBMWSIiotbGouL38/PD7t27cfbsWfTo0QMAcPHiRTg4OFg1HBERETUti3b1T5gwAdu3b8fhw4cxcuRIAMDBgweVNwFERETUOli0xW8wGPDee++ZDRs4cCDCwsKsEoqIiIisw6It/qlTp9Y7fNq0aU0ahoiIiKzLouKv74q/qqoq5fp6IiIiah1uu6t/8uTJAG5+YE/t/2tVVlYiKirKesmIiIioyd22+F966SWICObPn4+XXnrJbJy7u/ttPyCAiIiIWp7bFn+3bt0AACtXroRer2+WQERERGQ9Fp3Vr9PpkJ6ejpMnT+LKlStm41588UWrBCMiIqKmZ1HxL1myBKdOncIDDzwANzc3a2ciIiIiK7Go+A8ePIglS5bA2dnZ2nmIiIjIiiy6Hs9gMODatWvWzkJERERWZtEWf3R0ND766CM89thjcHd3Nxt3//33WyMXERERWYFFxb9t2zYAqPM1vBqNBkuWLGn6VERERGQVFhV/SkqKtXMQERFRM+Bn7hIREamIRVv8VVVVWL9+PY4cOYJLly6ZfXb/0qVLrRaOiIiImpZFW/x/+9vfUFhYiOHDh6OyshLjx4+HwWDA73//e2vnIyIioiZkUfH/5z//wauvvoo+ffpAq9WiT58+mDZtGnbv3m3tfERERNSELNrVLyJwcnICALRp0waXL1+Gu7s7zp49a9VwRHRvtsSuAb68YOsYAC7YOgAQuwZP2joDUQtiUfEHBgbiyJEjCAsLQ2hoKFauXIk2bdqgffv21s5HRPfg8fQ/Q7dis61jwGAwoLS01KYZbjw/DBhp+8eCqKWwaFf/xIkT4e3tDQAYP348HBwccPnyZX5BDxERUStzxy3+mpoa7Nq1C08//TQAwNXVFZMmTbJ6MCIiImp6d9zi12q12L59O3Q6XXPkISIiIiuyaFd/TEwMduzYYe0sREREZGUWndxXUFCAbdu2YfPmzfDy8oJGo1HGvfPOO1YLR0RERE3LouIfMmQIhgwZYu0sREREZGUWFf+gQYOsHIOIiIiag0XFn5GRUe9we3t7eHl5ITg4GPb29k0ajIiIiJqeRcWflZWFY8eOwc3NDV5eXigrK0NFRQW6dOmCkpISAMD06dPRpUsXq4YlIiKixrGo+Dt06IC+ffvi8ccfV4Zt27YNZ86cwbvvvouNGzfi73//O+bNm2e1oERERNR4Fl3O9+9//xuPPvqo2bChQ4diz5490Gg0GDZsGE6fPm2VgERERNR0LCp+Nzc37N+/32xYbm4uXF1dAQDXrl2DnZ1FOw+IiIjIhixq6/j4eCxcuBC/+93vlGP8P//8MxISEgAA+fn5dfYIEBERUctjUfH37NkTn3zyCQ4cOACj0YhevXqhd+/eaNu2rTK+Z8+eVg1KREREjWfx/nlXV1dER0dbMwsRERFZWYPFP2/ePMycORMAMGvWLLOP6f01fmQvERFR69Fg8cfExCj/f+ihh5olDBEREVlXg8X/4IMPKv/nR/YSERH9Nlh8jD8vLw+FhYW4cuWK2fCnn366yUMRERGRdVhU/H//+9+xd+9ehIaGwsHBQRne0HF/IiIiapksKv7du3cjMTERnp6e1s5DREREVmTRJ/cZDAZ++x4REdFvgEVb/JMmTcLy5csRFRUFNzc3s3HdunVrVICvv/4aGRkZ0Gg0CAgIwJQpU2AymZCUlITz58/D29sb06ZNg4uLCwAgNTUVGRkZ0Gq1iI+PR3h4eKPWT0REpCYWFf+JEyfw448/Ii8vz+wYPwAsXbr0nlduNBqxdetWJCUlwcHBAQsXLkR2djZOnz6NsLAwxMXFIS0tDWlpaRgzZgxOnz6N7OxsLFy4EOXl5Zg7dy4WL14MrdaiHRdERESqZ1Hxr1u3Dm+88QZ69OjR5AFqampgMpmg0+lgMpng4eGB1NRUzJkzB8DNzxOYM2cOxowZg5ycHERGRsLe3h4+Pj7w9fVFQUEBQkJCmjwXERHRb5FFxa/X6xu9S78+np6eePLJJzF58mQ4ODgon/lfUVEBDw8PAICHhwcuXrwI4OYeguDgYLP5jUZjk+ciIiL6rbKo+EeOHInVq1dj+PDhylfx1mrMbvbKykrk5OQgJSUFTk5OWLhwIbKyshqcXkQsXnZ6ejrS09MBAAsWLIDBYLjnnEStzTmgRTzn7ezsbJ6jpTwWRC2FRcVfexx/x44ddcZ9+eWX97zyn376CT4+PsqbiX79+uHYsWNwc3NDeXk5PDw8UF5eroyv/UrgWkajscFLDGNjYxEbG6vcLi0tveecRK1RS3jOGwyGFpGjJWQgak5+fn4NjrOo+JcsWdJkYX7NYDAgPz8fV69ehYODA3766Sd06dIFer0emZmZiIuLQ2ZmJvr06QMAiIiIQHJyMp544gmUl5ejuLgYQUFBVslGRET0W2RR8Xt7e1tl5cHBwejfvz/eeOMN6HQ6dOzYEbGxsbhy5QqSkpKQkZEBg8GAhIQEAEBAQAAGDBiAhIQEaLVaTJgwgWf0ExER3QWNWHDgvKqqClu2bMHJkyfrfFb/W2+9ZbVwTamoqMjWEYiazY3nh0G3YrOtY7SIXf0t5bEgak6N3tW/cOFC1NTUoG/fvnWu4yciIqLWw6Liz8/Px8qVK2FnZ/GX+REREVELZNEB8tDQUJw5c8baWYiIiMjKLNqEnzJlCubPn4+goCC4u7ubjRs+fLg1chEREZEVWPyRvWVlZfD29kZ1dbUyXKPRWC0YERERNT2Lij87OxuLFy9WPkaXiIiIWieLjvG3a9cOOp3O2lmIiIjIyiza4h84cCA+/PBDPProo3WO8d9///3WyEVERERWYFHxb9++HcDNY/2/ptForPZxvkRERNT0LCr+lJQUa+cgIiKiZsAPuiciIlKR227xz5o1646X7L3zzjtNGoiIiIis57bF/9BDDzVXDiIiImoGty3+QYMGNVMMIiIiag48xk9ERKQiLH4iIiIVYfETERGpSIPFP3PmTOX/69evb5YwREREZF0NFn9RURFMJhMA4Ouvv262QERERGQ9DZ7V36dPH0ydOhU+Pj4wmUyYPXt2vdPxOn4iIqLWo8HinzJlCv773/+ipKQEBQUFGDx4cHPmIiIiIiu47XX8oaGhCA0NxfXr13lNPxER0W+ARV/S89BDD+HQoUPIyspCeXk5PDw8EB0dza/kJSIiamUsupzvX//6FxYtWgR3d3f07dsXHh4eWLx4MdLT062dj4iIiJqQRVv8mzdvxltvvYWOHTsqwyIjI5GYmIjY2FhrZSMiIqImZtEW/6VLl9ChQwezYX5+fqisrLRKKCIiIrIOi4o/NDQUa9aswdWrVwEAV65cwWeffYaQkBCrhiMiIqKmZdGu/ueffx6LFi3CuHHj4OLigsrKSoSEhGDq1KnWzkdERERNyKLi9/DwwDvvvIOysjLlrH4vLy9rZyMiIqImZlHx1/Ly8mLhExERtWL8dj4iIiIVYfETERGpyB2Lv6amBocOHcL169ebIw8RERFZ0R2LX6vV4sMPP4Sd3V2dDkBEREQtkEW7+rt27Ypjx45ZOwsRERFZmUWb8d7e3pg/fz4iIiLg5eUFjUajjBs5cqTVwhEREVHTsqj4TSYT+vTpAwAwGo1WDURERETWY1HxT5kyxdo5iIiIqBlYfMbe6dOnsW/fPlRUVGDChAkoKirCtWvXEBgYaM18RERE1IQsOrlv7969mD17NoxGI7KysgAA1dXVWLNmjVXDERERUdOyaIv/q6++wttvv42OHTti7969AIDAwECcPHnSmtmIiIioiVm0xV9RUVFnl75GozE7u5+IiIhaPouKv3Pnzsou/lr//ve/ERQUZJVQREREZB0W7eqPj4/He++9h4yMDFy9ehXz5s1DUVER3nrrLWvnIyIioiZkUfH7+/tj0aJF2L9/Px544AF4eXnhgQceQJs2baydj4iIiJqQxZfz6fV6hIaGwmg0wtPTk6VPRETUCllU/KWlpUhOTkZ+fj6cnZ1x+fJlBAUF4eWXX4a3t3ejAly+fBnLli3DL7/8Ao1Gg8mTJ8PPzw9JSUk4f/48vL29MW3aNLi4uAAAUlNTkZGRAa1Wi/j4eISHhzdq/URERGpi0cl9KSkp6Ny5M1atWoW//e1vWLVqFbp06YKUlJRGB1i1ahXCw8OxaNEifPTRR/D390daWhrCwsKQnJyMsLAwpKWlAbj5IULZ2dlYuHAhZs6ciZUrV6KmpqbRGYiIiNTCouI/ceIExowZo+zeb9OmDcaMGYMTJ040auVVVVXIy8vDQw89BACws7ODs7MzcnJyEBMTAwCIiYlBTk4OACAnJweRkZGwt7eHj48PfH19UVBQ0KgMREREamLRrv7g4GAUFBQgNDRUGXb8+HGEhIQ0auUlJSVwdXXFp59+ilOnTqFz584YN24cKioq4OHhAQDw8PDAxYsXAdz8gqDg4GBlfk9Pzwa/NCg9PR3p6ekAgAULFsBgMDQqK1Frcg5oEc95Ozs7m+doKY8FUUvRYPF/+eWXyv/btWuH+fPno3fv3vDy8kJZWRl+/PFHPPjgg41a+Y0bN1BYWIjx48cjODgYq1atUnbr10dELF52bGwsYmNjldulpaWNiUrU6rSE57zBYGgROVpCBqLm5Ofn1+C4Bnf1l5WVKT/Xrl1Dv379YG9vj4sXL8Le3h59+/aFyWRqVDAvLy94eXkpW/H9+/dHYWEh3NzcUF5eDgAoLy+Hq6urMn1ZWZkyf+0VBkRERGSZBrf4m+OreN3d3eHl5YWioiL4+fnhp59+QocOHdChQwdkZmYiLi4OmZmZ6NOnDwAgIiICycnJeOKJJ1BeXo7i4mJ+eiAREdFdsPg6/qtXr+Ls2bO4cuWK2fD77ruvUQHGjx+P5ORkXL9+HT4+PpgyZQpEBElJScjIyIDBYEBCQgIAICAgAAMGDEBCQgK0Wi0mTJgArdai8xOJiIgIgEYsOHCemZmJv//977Czs4ODg4PZuKVLl1otXFMqKiqydQSiZnPj+WHQrdhs6xgt4hh/S3ksiJrT7Y7xW7TFv3btWrz66qvo0aNHk4UiIiKi5mfRfnI7Ozt069bN2lmIiIjIyiwq/pEjR2LNmjXK9fRERETUOlm0q9/Pzw9fffUVtm/fXmfcr6/3JyIiopbNouL/5JNPEB0djcjIyDon9xEREVHrYVHxV1ZWYuTIkdBoNNbOQ0RERFZk0TH+QYMGISsry9pZiIiIyMos2uIvKCjAtm3bsHHjRri7u5uNe+edd6yRi4iIiKzAouIfMmQIhgwZYu0sREREZGUWFf+gQYOsHIOIiIiag0XFn5GR0eC4hx56qMnCEBERkXVZVPy7d+82u33hwgWcPXsWoaGhLH4iIqJWxKLinz17dp1hGRkZOHPmTJMHIiIiIuu55++0HTRo0G0PARAREVHLY9EWf01Njdltk8mErKwsODs7WyUUETXejeeH2ToCztk6AAA4udg6AVGLYlHxjxo1qs4wT09PTJw4sckDEVHjtZTvn7/x/LAWk4WIbrKo+JcsWWJ2W6/Xw9XV1SqBiIiIyHosKn5vb29r5yAiIqJmcNviv9PH8Wo0GsyaNatJAxEREZH13Lb4Bw4cWO9wo9GIrVu34urVq1YJRURERNZx2+K/9cN5Ll26hNTUVPzrX/9CZGQkhg8fbtVwRERE1LQsOsZfVVWFzZs3Y/v27ejduzc++OAD+Pr6WjsbERERNbHbFr/JZMI333yDr7/+Gt26dcO7776LgICA5spGRERETey2xf+Xv/wFNTU1GDZsGLp06YKKigpUVFSYTXP//fdbNSARERE1ndsWv4ODAwDg22+/rXe8RqOpc40/ERERtVy3Lf6UlJTmykFERETN4J6/pIeIiIhaHxY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSERY/ERGRirD4iYiIVMTO1gEAoKamBjNmzICnpydmzJiByspKJCUl4fz58/D29sa0adPg4uICAEhNTUVGRga0Wi3i4+MRHh5u2/BEREStSIvY4t+yZQv8/f2V22lpaQgLC0NycjLCwsKQlpYGADh9+jSys7OxcOFCzJw5EytXrkRNTY2NUhMREbU+Ni/+srIy5ObmYsiQIcqwnJwcxMTEAABiYmKQk5OjDI+MjIS9vT18fHzg6+uLgoICm+QmIiJqjWxe/KtXr8aYMWOg0WiUYRUVFfDw8AAAeHh44OLFiwAAo9EILy8vZTpPT08YjcbmDUxERNSK2fQY//79++Hm5obOnTvj8OHDd5xeRCxednp6OtLT0wEACxYsgMFguOecRHRvzgF87RG1MDYt/qNHj+KHH37Ajz/+CJPJhOrqaiQnJ8PNzQ3l5eXw8PBAeXk5XF1dAQBeXl4oKytT5jcajfD09Kx32bGxsYiNjVVul5aWWvfOEFG9+Nojan5+fn4NjrPprv5nnnkGy5YtQ0pKCl555RXcf//9ePnllxEREYHMzEwAQGZmJvr06QMAiIiIQHZ2Nq5du4aSkhIUFxcjKCjIlneBiIioVWkRl/PdKi4uDklJScjIyIDBYEBCQgIAICAgAAMGDEBCQgK0Wi0mTJgArdbmpykQERG1Ghq5mwPnrVhRUZGtIxCpzo3nh0G3YrOtYxCpTovd1U9ERETNi8VPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCrC4iciIlIRFj8REZGKsPiJiIhUhMVPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCrC4iciIlIRFj8REZGKsPiJiIhUhMVPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCrC4iciIlIRFj8REZGKsPiJiIhUhMVPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCrC4iciIlIRFj8REZGKsPiJiIhUhMVPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCrC4iciIlIRFj8REZGKsPiJiIhUhMVPRESkIix+IiIiFWHxExERqQiLn4iISEVY/ERERCpiZ8uVl5aWIiUlBRcuXIBGo0FsbCwef/xxVFZWIikpCefPn4e3tzemTZsGFxcXAEBqaioyMjKg1WoRHx+P8PBwW94FIiKiVsWmxa/T6fDss8+ic+fOqK6uxowZM9CjRw/s2rULYWFhiIuLQ1paGtLS0jBmzBicPn0a2dnZWLhwIcrLyzF37lwsXrwYWi13XBAREVnCpo3p4eGBzp07AwAcHR3h7+8Po9GInJwcxMTEAABiYmKQk5MDAMjJyUFkZCTs7e3h4+MDX19fFBQU2Cw/ERFRa2PTLf5fKykpQWFhIYKCglBRUQEPDw8AN98cXLx4EQBgNBoRHByszOPp6Qmj0Vjv8tLT05Geng4AWLBgAQwGg5XvARHd6hzA1x5RC9Miiv/KlStITEzEuHHj4OTk1OB0ImLxMmNjYxEbG6vcLi0tbVRGIrXx9/dvmgXp9Y1exJkzZ5ogCJF6+Pn5NTjO5sV//fp1JCYmYuDAgejXrx8AwM3NDeXl5fDw8EB5eTlcXV0BAF5eXigrK1PmNRqN8PT0tEluot+6pihbg8HAN91ELYxNj/GLCJYtWwZ/f3888cQTyvCIiAhkZmYCADIzM9GnTx9leHZ2Nq5du4aSkhIUFxcjKCjIJtmJiIhaI43czf7zJvbf//4Xs2bNwu9+9ztoNBoAwKhRoxAcHIykpCSUlpbCYDAgISFBuZxv48aN2LlzJ7RaLcaNG4devXpZtK6ioiKr3Q8iqh+3+Ils43a7+m1a/M2JxU/U/Fj8RLZxu+LnBfBEREQqwuInIiJSERY/ERGRirD4iYiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQqwuInIiJSEdV8ZC8RERFxi5+IrGjGjBm2jkBEt2DxExERqQiLn4iISEVY/ERkNbGxsbaOQES34Ml9REREKsItfiIiIhVh8RORxZ599llbRyCiRmLxExERqYidrQMQUety5coVfPjhh7h8+TKuX7+OP/3pT+jTpw9KSkrw/vvvIygoCCdPnkT79u3x4osvQq/XY8OGDdi/fz9MJhNCQkLwwgsvQKPRYM6cOQgKCsLhw4dRVVWFSZMmoWvXrra+i0S/adziJ6K7Ym9vj9deew0ffPABZs+ejTVr1qD2HOGioiLExsbi448/hqOjI7Zv3w4AePTRRzF//nwkJibCZDJh//79yvJqamowf/58jB07Fhs2bLDJfSJSE27xE9FdERGsW7cOeXl50Gg0MBqNqKioAAB4eXkhNDQUABAdHY0tW7Zg2LBhOHToEDZv3oyrV6+isrISAQEBiIiIAAD07dsXANC5c2eUlJTY5k4RqQiLn4juyp49e3Dx4kUsWLAAdnZ2+Mtf/gKTyQQA0Gg0ZtNqNBqYTCasXLkS8+fPh8FgwFdffaVMD9zcgwAAWq0WNTU1zXdHiFSKu/qJ6K5UVVXBzc0NdnZ2OHToEM6fP6+MKy0txbFjxwDcfIMQGhqKa9euAQBcXV1x5coVfPfddzbJTUQ3cYufiCxy48YN2Nvb48EHH8QHH3yAGTNmoGPHjvD391em8ff3x65du/DXv/4Vvr6+GDp0KPR6PYYMGYJXX30VPj4+6NKliw3vBRHxk/uIyCInT57E8uXLMX/+/HrHl5SU4IMPPkBiYmIzJyOiu8EtfiK6o2+//RZbt27FuHHjbB2FiBqJW/xEREQqwpP7iIiIVITFT0REpCIsfiIiIhVh8RMREakIi5+IiEhFWPxEREQq8v8AXTzwuDfy80wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "\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",
    "3.  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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI=df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "plt.title('Box plots of Immigrants from China and India (1980-2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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",
    "\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "\n",
    "Where\n",
    "\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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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,
    "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",
    "\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\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).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_top15=df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "\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**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "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",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "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",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \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",
    "    # 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",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s, and 2000s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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",
    "\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",
    "\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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "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>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  1980s   1990s   2000s\n",
       "0   India  82154  180395  303591\n",
       "1   China  32003  161528  340385"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "\n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740#matplotlib.pyplot.boxplot) on box plots for more information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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sWZUe69Chw0Uv/LhUDdUJCQlRVFSU4uPj9eabbyooKEhLlixx+3XQdBD00Oz07dtXGzdurLRu48aNslgszhGlFi1aVBo5k6StW7fq1KlTWrhwoa677jr16tXL7ZGkxqhPnz5VplhwdfqVtLQ03X333Ro/fryuueYadevWTXv37q20zcV6eaG+ffsqLy+v0ojq6dOn9eWXX7o8IlKdi71/xWumpaVVWr9p06Ya3++ee+7RunXrqp2SomJ6FVf64oq0tDRNnTpVY8aMUb9+/dShQwfl5ORU+Rzp6enOdaWlpZWuKs/Pz1dWVpZmzpypm266SX369FFAQECtR80qREVFqWXLllX+TqWlpdX6u6stHx8fderUSQEBAfrf//1ftW7dWjfeeKMk6aeffpLFYpGPz79/DVosFvn6+joDdGxsrFq2bKnVq1c7tykvL9fatWudo/kXc8UVVygqKkpRUVG64ooraqzxo48+0ujRo/XMM8/ot7/9bZXHr7vuOuXm5mrfvn3Odd98842+/fbbGmtwVXl5uU6fPl3r10HjRdBDs/PEE09o+/btmjFjhnbv3q1Vq1YpOTlZd999tyIjIyVJXbt21e7du7Vr1y7l5eXp9OnT6tGjhywWi1JSUpSbm6sPPvhAzz33nIc/Te395je/0V//+lctXrxY2dnZWrp0qZYuXSrp0iN9vXr10ocffqgvv/xSWVlZevDBB3XkyJFK23Tt2lUZGRnav3+/8vLyLjotyw033KBBgwbprrvu0hdffKGdO3fqvvvuU0lJiR555JFafb6uXbvq2LFj2rx5s/Ly8nTy5El1795dv/zlLzV16lStXr1au3fv1mOPPaadO3fqiSeeqPa1HnvsMY0cOVI33XST/vjHP2rbtm06ePCgVq1apdtvv93ZN1f64opevXrpnXfe0b/+9S9lZmZqwoQJlUJrVFSUxowZo1//+tdav369srKy9MADD6ioqMi5TWhoqCIiIvTaa69p79692rx5syZMmKDAwEC367mYoKAgPfroo/qv//ovvf/++9q3b5/mzZunDz/88KLBpS5UjJY5HA4VFxc7lysUFBQoNTVVWVlZ+te//qU5c+bohRde0Pz58xUcHCxJuummm1RaWqopU6Zo165d2rNnj2bMmKH9+/drzJgxkqTg4GA9/PDD+u1vf6t//OMf2rVrlyZPnqxTp07poYceqvXneP/99/WLX/xCjz/+uO6++24dO3ZMx44d0w8//ODcJjExUQMGDNA999yjL7/8Uv/85z917733avDgwUpISHBul52drczMTOdh2IqeFBcXS5J27typl156SRkZGTp06JC2bt2qyZMnKzc3V3fddVetPwsaMc+dHgg0jEtNrxIeHm4efvjhStNa5Ofnm5/97GcmODi40vQqdrvddO7c2QQEBJjrrrvOeSVfxTQHtZle5XxvvfWWufCv57vvvmskmbKyMmPMxadJudiFB88//7zp1KmTc/liz5s/f77p2LGjCQgIMKNGjTKvvvqqkeS8+rG6E7wPHTpkRo0aZYKCgkz79u3N7NmzzeTJkytdyLJ//34zbNgw06pVK7emVxk+fPhFp1epuECkgq+vr/P7uZjS0lIzYcIEExoaWml6lcLCQuf0Ki1atHBpehVjjCkrKzMLFy40sbGxJigoyLRu3drExMSYuXPnOq/+daUvFdOrnO/C7+/rr782Q4YMMQEBAeaKK64wqampVS5uycvLM7/85S9NUFCQCQ8PNzNnzqwyvcqGDRtMdHS0admypenZs6dZtmyZ6d69+yWnCLnwO6ruO3B1epXzX8uYi393LVu2NK+99tol67rYnwoOh8PEx8eb1q1bm4CAADNo0CCzYsWKKq+zfv16k5CQYNq0aWOCg4PNtddeaz788MMqn+2JJ54w7dq1My1btjTx8fGV9svaSEhIuOjnuPDv8JEjR8zYsWON1Wo1rVu3NuPGjXNeGXyp16r4+7Zv3z4zatQo07ZtW+Pv7286duxofv7zn5vNmzfXyWdB42UxxsWTPAA0G88995xefvll5efne7oUAEAteO4+TAAahbKyMqWkpGj06NFq1aqV1q9fr5deekm//vWvPV0aAKCWGNEDmrkzZ87o1ltvVUZGhoqKitS1a1fdd999euKJJzx6T14AQO0R9AAAALwUV90CAAB4KYIeAACAlyLoAQAAeCnOtK7G5Uxu6q7w8HDl5eXV+/t4E3rmPnrmHvrlPnrmPnrmPnpWvY4dO1b7GCN6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOCluAUaAABotA4eLFJy8mYVFpYpJMRfdnu8IiOtni6ryWBEDwAANFrJyZuVkZGn7OxCZWTkKSkp3dMlNSkEPQAA0Gg5HCU1LqNmBD0AANBo2WwBNS6jZgQ9AADQaNnt8YqNDVdUVIhiY8Nlt8d7uqQmhYsxAABAoxUZadXKlaMUHh6uvLw8T5fT5DCiBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeys/TBQAA0FwcPFik5OTNKiwsU0iIv+z2eEVGWj1dFupBxXftcJTIZgvw2HfNiB4AAA0kOXmzMjLylJ1dqIyMPCUlpXu6JNSTiu86N7fYo981I3oAADQQh6OkxmU0LrUZlWss3zUjegAANBCbLaDGZTQutRmVayzfNUEPAIAGYrfHKzY2XFFRIYqNDZfdHu/pklCD2ozKVXzXXbtaPfpdc+gWAIAGEhlp1cqVoxQeHq68vDxPl4NLsNkClJtbXGnZVRXftacxogcAAHARjWVUrjYY0QMAALiIxjIqVxuM6AEAAHgpgh4AAICXapBDt3l5eUpNTdWJEydksViUmJio0aNH629/+5s+++wzBQcHS5ImTJigAQMGSJJWrFihdevWycfHR5MmTVJMTIwkKScnR6mpqSotLVX//v01adIkWSwWlZWVyW63KycnR61bt9a0adPUtm1bSdKGDRu0fPlySdIdd9yhESNGNMTHBgAA8KgGCXq+vr6699571a1bN506dUozZ85UdHS0JOmWW27RmDFjKm1/+PBhpaena/78+SooKNDvfvc7vfzyy/Lx8dFrr72mhx56SD169NDzzz+vzMxM9e/fX+vWrVOrVq20ePFiffHFF3rnnXc0ffp0FRcXa9myZXrhhRckSTNnzlRcXJysVm45AwAAvFuDHLoNDQ1Vt27dJEmBgYHq1KmTHA5Htdtv3bpV8fHx8vf3V9u2bdW+fXtlZ2eroKBAp06dUs+ePWWxWDR8+HBt3bpVkrRt2zbnSN3gwYO1c+dOGWOUmZmp6OhoWa1WWa1WRUdHKzMzs74/MgAAgMc1+FW3x48fV25urqKiorR7926tXr1aaWlp6tatm+677z5ZrVY5HA716NHD+RybzSaHwyFfX1+FhYU514eFhTkDo8PhcD7m6+uroKAgFRUVVVp//mtdaO3atVq7dq0k6YUXXlB4eHi9fP7z+fn5Ncj7eBN65j565h765T565r7m1rPc3BOaOHGN8vJOKTw8UG++eZO6dg1x6zWaW8/qSoMGvZKSEqWkpGjixIkKCgrSqFGjNHbsWEnSe++9p6VLl2rq1Kkyxlz0+dWtr+4xi8Vy0W0vtj4xMVGJiYnO5YaYyJIJM91Hz9xHz9xDv9xHz9zX3Hp2991rlJFx7vNmZxfq7rs/cnvakubWM3d07Nix2sca7KrbM2fOKCUlRcOGDdO1114rSWrTpo18fHzk4+OjkSNHav/+/ZLOjdTl5+c7n+twOGSz2aqsz8/Pl81mq/Kcs2fP6uTJk7JarbLZbFVeKzQ0tN4/LwAAdengwSKNGbNGQ4eu1Jgxa3ToUPGln9RI1OZWYqidBgl6xhi98sor6tSpk2699Vbn+oKCAufPX375pbp06SJJiouLU3p6usrKynT8+HEdPXpUUVFRCg0NVWBgoPbu3StjjNLS0hQXFydJio2N1YYNGyRJW7ZsUd++fWWxWBQTE6MdO3aouLhYxcXF2rFjh/MKXgAAmork5M3KyMhTbm6xMjLylJSU7umSXHbhrcPcuZUYaqdBDt3u2bNHaWlpioyM1BNPPCHp3FQqX3zxhQ4cOCCLxaKIiAg9+OCDkqQuXbpoyJAhmjFjhnx8fDRlyhT5+JzLpA888ICWLFmi0tJSxcTEqH///pKkG264QXa7XcnJybJarZo2bZokyWq16s4779R//ud/SpLGjh3LFbcAgCanKY+K2e3xSkpKl8NRIpstoMFuJXbwYJGSkzdXet/IyOaVASymphPfmrEjR47U+3twvoH76Jn76Jl76Jf76Jn7LqdnY8b8+zw3SYqNDW/yt+dyBz2rXqM4Rw8AAFw+uz1esbHh6trVqtjY8AYbFWvKmvIoaF1p8OlVAACA+yIjrV45GlWfbLYA5eYWV1pubhjRAwAANWqqV/wyCsqIHgDAQzhRvumouOJXknJzi5WUlN4kRhcZBWVEDwDgIU15upDmhnPdmi6CHgDAIwgPTQfz4DVdBD0AgEcQHpoOznVrujhHDwDgEZ6aRBfu41y3pougBwDwCMIDUP84dAsAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeigmTAQBoBg4eLFJy8uZKdyKJjLR6uizUM0b0AABww8GDRRozZo2GDl2pMWPW6NChYk+X5JLk5M3KyMhTbm6xMjLylJSU7umS0AAIegAAuKGpBiaHo6TGZXgngh4AAG5oqoHJZguocRneiaAHAIAbmmpgstvjFRsbrq5drYqNDZfdHu/pktAAuBgDAAA32O3xSkpKr3RRQ1MQGWnVypWjPF0GGhhBDwAANxCY0JRw6BYAAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLMY8eAKDJOXiwSMnJmytNWhwZafV0WUCjw4gegEbl4MEijRmzRkOHrtSYMWt06FCxp0tCI5ScvFkZGXnKzS1WRkaekpLSPV0S0CgR9ADUqdoGNX6BwxUOR0mNywDOIegBqFO1DWr8AocrbLaAGpcBnEPQA1CnahvU+AUOV9jt8YqNDVfXrlbFxobLbo/3dElAo8TFGADqlM0WoNzc4krL7rDb45WUlF7pJHvgQpGRVq1cOcrTZQCN3mUFvdLSUvn4+MjPj5wIoLLaBjV+gQNA3XEpqS1dulTx8fGKiorS9u3blZKSIovFomnTpikuLq6+awTQhBDUAKDxcOkcvc8//1xdunSRJC1btkzJycl68skn9e6779ZrcQAA1DWm8EFz4tKI3unTp9WyZUsVFRXp+++/1+DBgyVJeXl59VocAKDxqpi0uLCwTCEh/k1m0uKKK8MlKTe3WElJ6YxCw2u5FPQ6duyoTZs26dixY4qOjpYk/fjjj2rRokW9FgcAaLzOD0ySmkxgYgofNCcuHbqdMmWKVq9erV27dmn8+PGSpB07djhDHwCg+WmqgYkpfNCcuDSiFx4ert///veV1g0bNkz9+vWrl6IAAI1fbafS8RSm8EFz4tKI3mOPPXbR9dOnT6/TYgAATUfFpMVRUSFNatLiiivDP/98jFauHNUkzisELpdLI3rGmCrrTp48KR8fbqwBAM1VRWAKDw/n4jygkaox6D3yyCOSzk2QXPFzheLiYl133XX1VxkAAABqpcagl5ycLGOMnn/+eSUnJ1d6rE2bNurYsWO9FgcAAIDLV2PQ69OnjyTp9ddfV8uWLRukIAAAANQNl87R8/X11dq1a3XgwAGVlFS+fD4pKaleCgMAAEDtuBT07Ha7Dh48qNjYWIWEhNR3TQAAAKgDLgW9HTt2yG63q1WrVvVdDwAAAOqIS/OjhIeHq6ysrL5rAQAAQB1yaURv+PDheumll/Szn/1Mbdq0qfTY1VdfXR91AfCgipvVn3/nACaVBYCmx6Wgt2rVKknSu+++W2m9xWKR3W6v+6oAeNT5N6vPzS1uMjerBwBU5lLQS01Nre86ADQiTfVm9QCAyriHGYAqLrw5fVO5WT0AoDKXRvROnjyp999/X1lZWSoqKqp079s//elP9VYcAM+w2+OVlJRe6Rw9AEDT41LQ+/Of/yyHw6GxY8dq8eLFSk5O1sqVK3XttdfWd30APKDiZvUAgKbNpUO3X3/9tX7zm99o4MCB8vHx0cCBAzV9+nRt2rSpvusDAADAZXIp6BljFBQUJEkKCAjQTz/9pDZt2ujYsWP1WhwAAAAun0uHbq+44gplZWWpX79+6t27t15//XUFBASoQ4cO9V0fAAAALpNLQe+hhx5yXoAxefJk/e///q9++uknJSUlufQmeXl5Sk1N1YkTJ2SxWJSYmKjRo0eruLhYCxYs0A8//KCIiAhNnz5dVuu5SVlXrFihdevWycfHR5MmTVJMTIwkKScnR6mpqSotLVX//v01adIkWSwWlZWVyW63KycnR61bt9a0adPUtm1bSdKGDRu0fPlySdIdd9yhESNGuNMjAACAJumSh27Ly8u1YcMGhYWFSZKCg4P18MMPa/r06ercubNLb+Lr66t7771XCxYs0Ny5c7V69WodPnxYH3zwgfr166dFixapX79++uCDDyRJhw8fVnp6uubPn69Zs2bp9ddfV3l5uSTptdde00MPPaRFixbp2LFjyszMlCStW7dOrVq10uLFi3XLLbfonXfekSQVFxdr2bJlmjdvnubNm6dly5apuLjY3T4BAAA0OZcMej4+Plq9erV8fX0v+01CQ0PVrVs3SVJgYKA6deokh8OhrVu3KiEhQZKUkJCgrVu3SpK2bt2q+Ph4+fv7q23btmrfvr2ys7NVUFCgU6dOqWfPnrJYLBo+fLjzOdu2bXOO1A0ePFg7d+6UMUaZmZmKjo6W1WqV1WpVdHS0MxwCQF05eLBIY8as0dChKzVmzBodOsR/KAF4nksXYyQkJOjTTz+tkzc8fvy4cnNzFRUVpcLCQoWGhko6FwZ//PFHSZLD4XCOIEqSzWaTw+Gosj4sLEwOh6PKc3x9fRUUFKSioqJqXwsA6lLFbeNyc4uVkZGnpKR0T5cEAK6do5edna1Vq1Zp5cqVCgsLk8VicT727LPPuvxmJSUlSklJ0cSJE51X8V7M+RMyu7K+usfOr/NS69euXau1a9dKkl544QWFh4dX+151xc/Pr0Hex5vQM/fRM/dcbr8KC8uqLDeXvrOPuY+euY+eXR6Xgt7IkSM1cuTIWr3RmTNnlJKSomHDhjknWg4JCVFBQYFCQ0NVUFCg4OBgSedG6vLz853PdTgcstlsVdbn5+fLZrNVek5YWJjOnj2rkydPymq1ymazKSsrq9Jr9enTp0p9iYmJSkxMdC7n5eXV6vO6Ijw8vEHex5vQM/fRM/dcbr9CQvyrLDeXvrOPuY+euY+eVa9jx47VPuZS0KvtVarGGL3yyivq1KmTbr31Vuf6uLg4bdy4Ubfffrs2btyogQMHOtcvWrRIt956qwoKCnT06FFFRUXJx8dHgYGB2rt3r3r06KG0tDTdfPPNkqTY2Fht2LBBPXv21JYtW9S3b19ZLBbFxMTo3XffdV6AsWPHDt111121+jwAcCFuGwegMbKYmo6H/n/r1q276Hp/f3+FhYWpR48e8vf3v+g2krR7927Nnj1bkZGRzsOmEyZMUI8ePbRgwQLl5eUpPDxcM2bMcE6vsnz5cq1fv14+Pj6aOHGi+vfvL0nav3+/lixZotLSUsXExGjy5MmyWCwqLS2V3W5Xbm6urFarpk2bpnbt2jnrX7FihaRz06tcf/31l2zMkSNHLrlNbfG/E/fRM/fRM/fQL/fRM/fRM/fRs+rVNKLnUtB75plntHfvXoWEhDgPkRYWFqp79+46fvy4JOnJJ59U9+7d665qDyPoNU70zH30zD30y330zH30zH30rHq1PnTbuXNnDRo0SKNHj3auW7Vqlb777js999xzWr58uf7yl79o7ty5ta8WAAAAdcKl6VW++OIL57lwFUaNGqXPP/9cFotFY8aM0eHDh+ulQAAAAFwel4JeSEiIMjIyKq3bvn278yrZsrIy+fm5NDgIAPWGSYsBoDKX0tmkSZM0f/58RUZGOs/RO3TokGbMmCFJ2rdvX5URPwBoaBWTFktSbm6xkpLStXLlKA9XdWkHDxYpOXlzpSt2IyOtni4LgBdwKehdc801Wrx4sTIzM+VwONS/f38NGDBArVu3dj5+zTXX1GuhAHApDkdJjcuNVVMNqAAaP5ePtwYHB2v48OH1WQsA1IrNFqDc3OJKy01BUw2oABq/aoPe3LlzNWvWLEnS7Nmzq72dmDu3QAOA+tRUJy1uqgEVQONXbdBLSEhw/nzDDTc0SDEAUBuRkdYmecizqQZUAI1ftUFv6NChzp9rews0AED1mmpABdD4uXyO3jfffKPc3FyVlFQ+d+SOO+6o86IAAABQey4Fvb/85S/avHmzevfurRYtWjjXV3feHgDPq5iyo7CwTCEh/kzZcQn0C4A3cinobdq0SSkpKbLZbPVdD4A6cv6UHZKYsuMS6BcAb+TSnTHCw8Pl7+9f37UAqENM2eEe+gXAG7k0ovfwww/r1Vdf1XXXXaeQkJBKj/Xp06deCgNQO0zZ4R76BcAbuRT0cnJy9NVXX+mbb76pdI6eJP3pT3+ql8IA1E7FlB3nn3OG6tEvAN7IpaD37rvv6qmnnlJ0dHR91wOgjlRM2REeHq68vLxLP6GZo18AvJFL5+i1bNmSQ7QAAABNjEtBb/z48XrzzTd14sQJlZeXV/oDAACAxsmlQ7cV5+F9+umnVR5777336rYiAAAA1AmXgp7dbq/vOgAAAFDHXAp6ERER9V0HAAAA6phLQe/kyZP6+OOPdeDAgSr3un366afrpTAAAADUjktBb/78+SovL9egQYOqzKMHAGi6Ku7x63CUyGYL4B6/gJdxKejt27dPr7/+uvz8XNocANBEnH+P39zcYu7xC3gZl6ZX6d27t7777rv6rgUA0MC4xy/g3Vwaops6daqef/55RUVFqU2bNpUeGzt2bH3UBQBoANzjF/BuLt8CLT8/XxERETp16pRzvcViqbfCAAD1r+Iev+efowfAe7gU9NLT0/Xyyy8rNDS0vusBADSginv8AvBOLp2j165dO/n6+tZ3LQAAAKhDLo3oDRs2TC+++KJuvvnmKufoXX311fVRFwAAAGrJpaC3evVqSefO1TufxWLh9mgAAACNlEtBLzU1tb7rAAAAQB1z6Rw9AAAAND01jujNnj37klOoPPvss3VaEAAAAOpGjUHvhhtuaKg6AAAAUMdqDHojRoxooDIAAABQ1zhHDwAAwEsR9AAAALwUQQ8AAMBLVRv0Zs2a5fz5/fffb5BiAAAAUHeqDXpHjhxRaWmpJOkf//hHgxUEAACAulHtVbcDBw7UY489prZt26q0tFRz5sy56HbMowcAANA4VRv0pk6dqt27d+v48ePKzs7W9ddf35B1AQAAoJZqnEevd+/e6t27t86cOcOcegAAAE1MjUGvwg033KCdO3cqLS1NBQUFCg0N1fDhw3X11VfXd31As3XwYJGSkzfL4SiRzRYguz1ekZFWT5cFAGhCXJpe5bPPPtPChQvVpk0bDRo0SKGhoXr55Ze1du3a+q4PaLaSkzcrIyNPubnFysjIU1JSuqdLAgA0MS6N6K1cuVJPP/20rrzySue6+Ph4paSkKDExsb5qA5o1h6OkxmUAAC7FpRG9oqIide7cudK6jh07qri4uF6KAiDZbAE1LgMAcCkuBb3evXtr6dKlOn36tCSppKREb731lnr27FmvxQHNmd0er9jYcHXtalVsbLjs9nhPlwQAaGJcOnT7q1/9SgsXLtTEiRNltVpVXFysnj176rHHHqvv+oBmKzLSqpUrR3m6DDQBXLgDoDouBb3Q0FA9++yzys/Pd151GxYWVt+1AQBcUHHhjiTl5hYrKSmd/yQAkORi0KsQFhZGwAOARoYLdwBUx6Vz9AAAjRcX7gCoDkEPAJo4LtwBUJ1LHrotLy9XVlaWevfuLT8/t470AgAaABfuAKjOJUf0fHx89OKLLxLyAAAAmhiXDt1eddVV2rt3b33XAgAAgDrk0jBdRESEnn/+ecXFxSksLEwWi8X52Pjx4+utOAAAAFw+l4JeaWmpBg4cKElyOBz1WhAAAADqhktBb+rUqfVdBwAAAOqYy1dYHD58WFu2bFFhYaGmTJmiI0eOqKysTFdccUV91gcAAIDL5NLFGJs3b9acOXPkcDiUlpYmSTp16pSWLl1ar8UBAADg8rk0ove3v/1N//Vf/6Urr7xSmzdvliRdccUVOnDgQH3WBgAAgFpwKegVFhZWOURrsVgqXX1bkyVLlmj79u0KCQlRSkqKpHPh8bPPPlNwcLAkacKECRowYIAkacWKFVq3bp18fHw0adIkxcTESJJycnKUmpqq0tJS9e/fX5MmTZLFYlFZWZnsdrtycnLUunVrTZs2TW3btpUkbdiwQcuXL5ck3XHHHRoxYoRLNQMAADR1Lh267datm/OQbYUvvvhCUVFRLr3JiBEj9Nvf/rbK+ltuuUUvvfSSXnrpJWfIO3z4sNLT0zV//nzNmjVLr7/+usrLyyVJr732mh566CEtWrRIx44dU2ZmpiRp3bp1atWqlRYvXqxbbrlF77zzjiSpuLhYy5Yt07x58zRv3jwtW7ZMxcXFLtUMAADQ1LkU9CZNmqS//vWvmjNnjk6fPq25c+fqvffe0/333+/Sm/Tp00dWq9Wlbbdu3ar4+Hj5+/urbdu2at++vbKzs1VQUKBTp06pZ8+eslgsGj58uLZu3SpJ2rZtm3OkbvDgwdq5c6eMMcrMzFR0dLSsVqusVquio6Od4RAAAMDbuXTotlOnTlq4cKEyMjIUGxursLAwxcbGKiAgoFZvvnr1aqWlpalbt2667777ZLVa5XA41KNHD+c2NptNDodDvr6+CgsLc64PCwtzzunncDicj/n6+iooKEhFRUWV1p//Whezdu1arV27VpL0wgsvKDw8vFafzRV+fn4N8j7ehJ65j565h365j565j565j55dHpenV2nZsqV69+4th8Mhm81W65A3atQojR07VpL03nvvaenSpZo6daqMMRfdvrr11T1W3fmD1a1PTExUYmKiczkvL6/a96sr4eHhDfI+3oSeuY+euYd+uY+euY+euY+eVa9jx47VPuZS0MvLy9OiRYu0b98+tWrVSj/99JOioqL06KOPKiIi4rKKatOmjfPnkSNH6g9/+IOkcyN1+fn5zscqguWF6/Pz82Wz2So9JywsTGfPntXJkydltVpls9mUlZVV6bX69OlzWfUCAAA0NS6do5eamqpu3brpjTfe0J///Ge98cYb6t69u1JTUy/7jQsKCpw/f/nll+rSpYskKS4uTunp6SorK9Px48d19OhRRUVFKTQ0VIGBgdq7d6+MMUpLS1NcXJwkKTY2Vhs2bJAkbdmyRX379pXFYlFMTIx27Nih4uJiFRcXa8eOHc4reAEAALydSyN6OTk5mjVrlvz8zm0eEBCge+65R5MnT3bpTRYuXKisrCwVFRXp4Ycf1rhx47Rr1y4dOHBAFotFERERevDBByVJXbp00ZAhQzRjxgz5+PhoypQp8vE5l0cfeOABLVmyRKWlpYqJiVH//v0lSTfccIPsdruSk5NltVo1bdo0SZLVatWdd96p//zP/5QkjR071uWLQgAAAJo6i6np5Lf/7/e//73Gjh2r3r17O9ft2bNH77//vp5++ul6LdBTjhw5Uu/vwfkG7qNn7qNn7qFf7qNn7qNn7qNn1busc/Tee+8958/t2rXT888/rwEDBjjPh/vqq680dOjQuq0UAAAAdabaoHf+hQ+SdO2110qSfvzxR/n7+2vQoEEqLS2t3+oAAABw2aoNelOnTm3IOgAAAFDHXJ5H7/Tp0zp27JhKSkoqre/Vq1edFwUAAIDacynobdy4UX/5y1/k5+enFi1aVHrsT3/6U70UBgAAgNpxKei9/fbb+s1vfqPo6Oj6rgcAAAB1xKUJk/38/LijBAAAQBPjUtAbP368li5dqh9//LG+6wEAAEAdcenQbceOHfW3v/1Nq1evrvLY+fPtAajs4MEiJSdvlsNRIpstQHZ7vCIjuTsLAKBhuBT0Fi9erOHDhys+Pr7KxRgAqpecvFkZGedmcs/NLVZSUrpWrhzl4aoAAM2FS0GvuLhY48ePl8Viqe96AK/icJTUuAwAQH1y6Ry9ESNGKC0trb5rAbyOzRZQ4zIAAPXJpRG97OxsrVq1SsuXL1ebNm0qPfbss8/WR12AV7Db45WUlF7pHD0AABqKS0Fv5MiRGjlyZH3XAnidyEgr5+QBADzGpaA3YsSIei4DaJy4ahYA0JS5FPTWrVtX7WM33HBDnRUDNDZcNQsAaMpcCnqbNm2qtHzixAkdO3ZMvXv3JujBq3HVLACgKXMp6M2ZM6fKunXr1um7776r84KAxsRmC1BubnGlZQAAmgqXple5mBEjRtR4SBfwBnZ7vGJjw9W1q1WxseFcNQsAaFJcGtErLy+vtFxaWqq0tDS1atWqXooCGguumgUANGUuBb0JEyZUWWez2fTQQw/VeUEAAACoGy4FPbvdXmm5ZcuWCg4OrpeCAAAAUDdcCnoRERH1XQcAAADqWI1B71K3N7NYLJo9e3adFgQAAIC6UWPQGzZs2EXXOxwOffLJJzp9+nS9FAUAAIDaqzHoXTgZclFRkVasWKHPPvtM8fHxGjt2bL0WBwAAgMvn0jl6J0+e1MqVK7V69WoNGDBAf/jDH9S+ffv6rg0AAAC1UGPQKy0t1UcffaR//OMf6tOnj5577jl16dKloWoDAABALdQY9H7961+rvLxcY8aMUffu3VVYWKjCwsJK21x99dX1WiAAAAAuT41Br0WLFpKkNWvWXPRxi8VSZY49AAAANA41Br3U1NSGqgMAAAB1zMfTBQAAAKB+EPQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALyUn6cLgHsOHixScvJmORwlstkCZLfHKzLS6umyAABAI8SIXhOTnLxZGRl5ys0tVkZGnpKS0j1dEgAAaKQIek2Mw1FS4zIAAEAFgl4TY7MF1LgMAABQgaDXxNjt8YqNDVfXrlbFxobLbo/3dEkAAKCR4mKMJiYy0qqVK0d5ugwAANAEMKIHAADgpQh6AAAAXoqgBwAA4KU4R6+ZYcJlAACaD0b0mhkmXAYAoPkg6DUzTLgMAEDzQdBrZphwGQCA5oOg18ww4TIAAM1Hg1yMsWTJEm3fvl0hISFKSUmRJBUXF2vBggX64YcfFBERoenTp8tqPXdRwIoVK7Ru3Tr5+Pho0qRJiomJkSTl5OQoNTVVpaWl6t+/vyZNmiSLxaKysjLZ7Xbl5OSodevWmjZtmtq2bStJ2rBhg5YvXy5JuuOOOzRixIiG+MiNFhMuAwDQfDTIiN6IESP029/+ttK6Dz74QP369dOiRYvUr18/ffDBB5Kkw4cPKz09XfPnz9esWbP0+uuvq7y8XJL02muv6aGHHtKiRYt07NgxZWZmSpLWrVunVq1aafHixbrlllv0zjvvSDoXJpctW6Z58+Zp3rx5WrZsmYqLixviIwMAAHhcgwS9Pn36OEfrKmzdulUJCQmSpISEBG3dutW5Pj4+Xv7+/mrbtq3at2+v7OxsFRQU6NSpU+rZs6csFouGDx/ufM62bducI3WDBw/Wzp07ZYxRZmamoqOjZbVaZbVaFR0d7QyHnnTwYJHGjFmjvn3/R2PGrNGhQ4RPAABQ9zw2j15hYaFCQ0MlSaGhofrxxx8lSQ6HQz169HBuZ7PZ5HA45Ovrq7CwMOf6sLAwORwO53MqHvP19VVQUJCKiooqrT//tS5m7dq1Wrt2rSTphRdeUHh4eB1+2sruvHOdMjLynMvTp3+pjRvH1dv7eRM/P796/W68ET1zD/1yHz1zHz1zHz27PI1uwmRjjFvrq3vMYrFcdNvq1icmJioxMdG5nJeXd9Ht6sL33xdXWa7P9/Mm4eHh9MpN9Mw99Mt99Mx99Mx99Kx6HTt2rPYxj111GxISooKCAklSQUGBgoODJZ0bqcvPz3du53A4ZLPZqqzPz8+XzWar8pyzZ8/q5MmTslqtstlsVV6rYhTRk5jiBAAANASPBb24uDht3LhRkrRx40YNHDjQuT49PV1lZWU6fvy4jh49qqioKIWGhiowMFB79+6VMUZpaWmKi4uTJMXGxmrDhg2SpC1btqhv376yWCyKiYnRjh07VFxcrOLiYu3YscN5Ba8nVUxxEhUVwhQnAACg3lhMTcdE68jChQuVlZWloqIihYSEaNy4cRo4cKAWLFigvLw8hYeHa8aMGc4LNpYvX67169fLx8dHEydOVP/+/SVJ+/fv15IlS1RaWqqYmBhNnjxZFotFpaWlstvtys3NldVq1bRp09SuXTtJ567IXbFihaRz06tcf/31LtV85MiReuhEZQxDu4+euY+euYd+uY+euY+euY+eVa+mQ7cNEvSaIoJe40TP3EfP3EO/3EfP3EfP3EfPqtcoz9EDAABA/SLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4KYIeAACAlyLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4KT9PFwDUt4MHi5ScvFkOR4lstgDZ7fGKjLR6uiwAAOodI3rwesnJm5WRkafc3GJlZOQpKSnd0yUBANAgCHrweg5HSY3LAAB4K4IevJ7NFlDjMgAA3oqgB69nt8crNjZcXbtaFRsbLrs93tMlAQDQILgYA14vMtKqlStHeboMAAAaHCN6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KO2PAZQcPFik5ebMcjhLZbAGy2+MVGWn1dFkAAKAajOjBZcnJm5WRkafc3GJlZOQpKSnd0yUBAIAaEPTgMoejpMZlAADQuBD04DKbLaDG5ZocPFikMWPWaOjQlRozZo0OHSqu6/IAAMAFCHpwmd0er9jYcHXtalVsbLjs9niXn8thXwAAGh4XY8BlkZFWrVw56rKey2FfAAAaHiN6aBC1OewLAAAuD0EPDaI2h30BAMDl4dAtGkRtDvtK/57Dr7CwTCEh/szhBwCACxjRQ5NQcTFHdnYhF3MAAOAigh6aBC7mAADAfQQ9NAlczAEAgPsIemgSKi7miIoK4WIOAABcxMUYaBIqLuYIDw9XXl6ep8sBAKBJYEQPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLWYwxxtNFAAAAoO4xoudBM2fO9HQJTQ49cx89cw/9ch89cx89cx89uzwEPQAAAC9F0AMAAPBSBD0PSkxM9HQJTQ49cx89cw/9ch89cx89cx89uzxcjAEAAOClGNEDAADwUn6eLsCbLFmyRNu3b1dISIhSUlIkSQcOHNBrr72mkpISRURE6NFHH1VQUJDOnDmjV155Rbm5uSovL9fw4cP1i1/8QpKUk5Oj1NRUlZaWqn///po0aZIsFosnP1q9qauePfPMMyooKFCLFi0kSU8//bRCQkI89rnqk7s9++///m/t379fPj4+mjhxovr27SuJ/exyetZc9rO8vDylpqbqxIkTslgsSkxM1OjRo1VcXKwFCxbohx9+UEREhKZPny6r1SpJWrFihdatWycfHx9NmjRJMTExkprPflaXPWM/u3jPioqKNH/+fGVnZ2vEiBGaMmWK87Way352WQzqzK5du8z+/fvNjBkznOtmzpxpdu3aZYwx5rPPPjPvvvuuMcaYTZs2mQULFhhjjCkpKTFTp04133//vfM5e/bsMeXl5Wbu3Llm+/btDftBGlBd9WzOnDkmOzu7YYv3EHd69sknn5jU1FRjjDEnTpwwTz75pDl79qzzOexn7vWsuexnDofD7N+/3xhjzMmTJ82jjz5qvv32W/PWW2+ZFStWGGOMWbFihXnrrbeMMcZ8++235vHHHzelpaXm+++/N0lJSc1uP6vLnrGfXbxnp06dMt98841ZvXq1+fOf/1zptZrLfnY5OHRbh/r06eP8n1qFI0eO6KqrrpIkRUdH65///KfzsZKSEp09e1alpaXy8/NTUFCQCgoKdOrUKfXs2VMWi0XDhw/X1q1bG/RzNKS66Flz407PDh8+rKuvvlqSFBISolatWiknJ4f9TO73rDkJDQ1Vt27dJEmBgYHq1KmTHA6Htm7dqoSEBElSQkKCc5/ZunWr4uPj5e/vr7Zt26p9+/bKzs5uVvtZXfWsOXG3ZwEBAerdu7dzpLNCc9rPLgdBr5516dJF27ZtkyRt2bJF+fn5kqTBgwcrICBADz74oKZOnarbbrtNVqtVDodDYWFhzueHhYXJ4XB4pHZPcbdnFZYsWaInnnhCy5Ytk2lm1xhV17Mrr7xS27Zt09mzZ3X8+HHl5OQoLy+P/Uzu96xCc9vPjh8/rtzcXEVFRamwsFChoaGSzv2S/vHHHyWpyv5ks9nkcDia7X5Wm55VYD+r2rPqNNf9zFWco1fPHnnkEb3xxhtatmyZ4uLi5Od3ruXZ2dny8fHRq6++qp9++kmzZ89Wv379msVf6Etxt2ft2rXTo48+KpvNplOnTiklJUVpaWnO/xE2B9X17Prrr9fhw4c1c+ZMRUREqFevXvL19WU/k/s9k9Ts9rOSkhKlpKRo4sSJNY6eV7c/Ncf9rLY9k9jP3NUc9zN3EPTqWadOnfT0009LOneoaPv27ZKkzz//XDExMfLz81NISIh69eql/fv366qrrnKOLEhSfn6+bDabR2r3FHd71q5dO2ePAgMDNXToUGVnZ3v1P4wXqq5nvr6+mjhxonO7p59+Wh06dFCrVq3Yz9zsmaRmtZ+dOXNGKSkpGjZsmK699lpJ5w5lFxQUKDQ0VAUFBQoODpZ0bgTl/P3J4XDIZrNVWe/t+1ld9ExiP6uuZ9VpbvuZuzh0W88KCwslSeXl5Vq+fLluvPFGSVJ4eLh27twpY4xKSkq0b98+derUSaGhoQoMDNTevXtljFFaWpri4uI8+REanLs9O3v2rHNo/8yZM8rIyFCXLl08Vr8nVNez06dPq6SkRJL09ddfy9fXV507d2Y/k/s9a077mTFGr7zyijp16qRbb73VuT4uLk4bN26UJG3cuFEDBw50rk9PT1dZWZmOHz+uo0ePKioqqlntZ3XVM/az6ntWnea0n10OJkyuQwsXLlRWVpaKiooUEhKicePGqaSkRKtXr5YkDRo0SHfddZcsFotKSkq0ZMkSHT58WMYYXX/99RozZowkaf/+/VqyZIlKS0sVExOjyZMne+1l4nXRs5KSEs2ZM0dnz55VeXm5+vXrp/vvv18+Pt75/xh3enb8+HHNnTtXPj4+stlsevjhhxURESGJ/czdnjWn/Wz37t2aPXu2IiMjnfvEhAkT1KNHDy1YsEB5eXkKDw/XjBkznOfJLl++XOvXr3dOSdO/f39JzWc/q6uesZ/V3LNf//rXOnnypM6cOaNWrVrp6aefVufOnZvNfnY5CHoAAABeyvv+iwAAAABJBD0AAACvRdADAADwUgQ9AAAAL0XQAwAA8FIEPQAAAC9F0AOAy7Ro0SItWbKk0rqsrCxNnjxZBQUFHqoKAP6NoAcAl2nSpEn66quv9PXXX0uSSktL9eqrr+q+++5z3pS9Ns6ePVvr1wDQvDFhMgDUwubNm/X2228rJSVFy5cv14EDBzR27FgtXbpUhw8fVkREhCZOnKi+fftKktavX6+VK1cqPz9fwcHB+vnPf+68/dquXbu0ePFi3Xzzzfroo48UHR2t5ORkT348AE2cn6cLAICmbMiQIUpPT9fLL7+sPXv26A9/+IOeeuopJSUlKSYmRjt37lRKSooWLlyo4OBghYSE6KmnnlK7du30zTffaN68eerevbu6desmSTpx4oSKi4u1ZMkS8f9wALXFoVsAqKUpU6Zo586dGjt2rL744gv1799fAwYMkI+Pj6Kjo9W9e3dt375dkjRgwAC1b99eFotFffr0UXR0tHbv3u18LYvFonHjxsnf318tWrTw1EcC4CUY0QOAWmrTpo2Cg4PVuXNnffnll9qyZYsyMjKcj589e9Z56Parr77SsmXLdOTIERljdPr0aUVGRjq3DQ4OJuABqDMEPQCoQ2FhYRo2bJgefvjhKo+VlZUpJSVFSUlJiouLk5+fn1588cVK21gsloYqFUAzwKFbAKhDw4YNU0ZGhjIzM1VeXq7S0lLt2rVL+fn5OnPmjMrKyhQcHCxfX99KV+wCQH1gRA8A6lB4eLiefPJJvf3223r55Zfl4+OjqKgo/epXv1JgYKAmTZqkBQsWqKysTLGxsYqLi/N0yQC8GNOrAAAAeCkO3QIAAHgpgh4AAICXIugBAAB4KYIeAACAlyLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4qf8H4o8GdT1enMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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,
    "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). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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",
    "\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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JQefOndGsWbMyX0mqilq2bHnPEAuWDr+SmJiIkSNHYvjw4Wjbti0aN26Mc+fOFVvmfm15t1atWiEjI6PYFdX8/Hz8/vvvFl8RKcn99l+0zcTExGLz9+/fX+r+Ro0ahYSEhBKHpCgaXsWSdrFEYmIiJk6ciIEDB6J169aoXbs2UlNT7zmOpKQk8zyDwVDsqfLMzEycPn0as2bNQt++fdGyZUs4OTk98lWzIgEBAXB0dLznZyoxMfGRv7tHJZVKUbduXTg5OWHdunVQKBTo3bs3ACA3NxcSiQRS6f/+G5RIJJDJZOYAHRQUBEdHR+zcudO8jMlkwu7du81X8++nQYMGCAgIQEBAABo0aFBqjT/++COefvppzJ07F//3f/93z+edO3dGWloazp8/b5535swZ/PPPP6XWYCmTyYT8/PxH3g5VXQx6VOO8+eabOHbsGKZNm4azZ89ix44diIyMxMiRI+Hv7w8AaNSoEc6ePYtTp04hIyMD+fn5aNq0KSQSCaKjo5GWloatW7fi/ffft/LRPLrp06dj/fr1iI2NRUpKCtasWYM1a9YAePCVvmbNmuG7777D77//jtOnT+PVV1/F1atXiy3TqFEjHD16FBcuXEBGRsZ9h2Xp2bMnOnTogBdffBG//vorTp48iZdeegl6vR6vv/76Ix1fo0aNcP36dRw8eBAZGRnIy8tDkyZN8Pzzz2PixInYuXMnzp49i8mTJ+PkyZN48803S9zW5MmT0atXL/Tt2xdRUVE4cuQILl26hB07duDZZ581t5sl7WKJZs2a4auvvsKff/6J5ORkjBgxolhoDQgIwMCBAzFp0iTs2bMHp0+fxvjx46HRaMzLeHp6wsfHB8uXL8e5c+dw8OBBjBgxAs7OzmWu535cXFzwxhtv4N1338U333yD8+fP48MPP8R333133+BSHoqulqnVami1WvN0kaysLMTFxeH06dP4888/MWfOHHz88cdYuHAh3NzcAAB9+/aFwWDAuHHjcOrUKfz111+YNm0aLly4gIEDBwIA3NzcMGHCBPzf//0ffvjhB5w6dQpjx46FTqfDa6+99sjH8c0332Dw4MGYMWMGRo4cievXr+P69eu4efOmeZmwsDC0b98eo0aNwu+//47ffvsNo0ePRseOHdGtWzfzcikpKUhOTjbfhi1qE61WCwA4efIkFixYgKNHj+Lvv//G4cOHMXbsWKSlpeHFF1985GOhKsx63QOJKseDhlfx9vYWEyZMKDasRWZmpujXr59wc3MrNryKSqUS9erVE05OTqJz587mJ/mKhjl4lOFV7vTll1+Ku388v/76awFAGI1GIcT9h0m534MHH330kahbt655+n7rLVy4UNSpU0c4OTmJPn36iKVLlwoA5qcfS+rg/ffff4s+ffoIFxcXUatWLTF79mwxduzYYg+yXLhwQXTt2lW4urqWaXiV0NDQ+w6vUvSASBGZTGb+fu7HYDCIESNGCE9Pz2LDq+Tk5JiHV3FwcLBoeBUhhDAajSImJkYEBQUJFxcXoVAoRGBgoJg3b5756V9L2qVoeJU73f39nThxQnTq1Ek4OTmJBg0aiLi4uHsebsnIyBDPP/+8cHFxEd7e3mLWrFn3DK+yd+9e0aZNG+Ho6Cgee+wxsWnTJtGkSZMHDhFy93dU0ndg6fAqd25LiPt/d46OjmL58uUPrOt+f4qo1WoREhIiFAqFcHJyEh06dBBbtmy5Zzt79uwR3bp1Ex4eHsLNzU08+eST4rvvvrvn2N58803h5+cnHB0dRUhISLHz8lF069btvsdx98/w1atXxdChQ4VcLhcKhUIMGzbM/GTwg7ZV9PN2/vx50adPH+Hr6yvs7e1FnTp1xKBBg8TBgwfL5Vio6pIIYWEnDyKqMd5//30sXrwYmZmZ1i6FiIgegfXew0REVYLRaER0dDSefvppuLq6Ys+ePViwYAEmTZpk7dKIiOgR8YoeUQ1XUFCAZ555BkePHoVGo0GjRo3w0ksv4c0337TqO3mJiOjRMegRERER2Sg+dUtERERkoxj0iIiIiGwUgx4RERGRjWJP6xI8zOCmZeXt7Y2MjIwK348tYZuVHdusbNheZcc2Kzu2WdmxzUpWp06dEj/jFT0iIiIiG8WgR0RERGSjGPSIiIiIbBSDHhEREZGNYtAjIiIislEMekREREQ2ikGPiIiIyEYx6BERERHZKAY9IiIiIhvFoEdERERkoxj0iIiIiGwUgx4RERFRBZBeuQKHAwesWoOdVfdOREREZGOkN25ArlLBde1aFHp7I/3QIUAms0otDHpERERE5UCakQF5XBxc16wBjEbkDR8O7eTJVgt5AIMeERER0SORqNWQL10K188/h0Svh27IEGimTEFhw4bWLo1Bj4iIiKquS5c0iIw8iJwcI9zd7aFShcDfX27tsgAAkpwcyJctg+uKFZDk5kL37LO3A15AgLVLM2PQIyIioiorMvIgjh7NME9HRCRh27Y+VqwIkGg0cF2xAvJlyyC9dQu6/v2hmT4dBc2aWbWu+2HQIyIioipLrdaXOl2ZJLm5cF21CvJPP4U0Oxu6vn1vB7xWraxW04Mw6BEREVGVpVQ6IS1NW2y6skl0OrisWQN5XBxkmZnQ9+wJzZtvwtimTaXXUlYMekRERFRlqVQhiIhIKtZHr9Lo9XBdtw7y2FjI0tOhDw2Fevp0GIODK6+GR8SgR0RERFWWv78c27b1gbe3NzIyMh68QnkwGOCyfj0US5ZAdu0a8jt1Qtann8LQsWPl7L8cMegRERERAYDRCJdNmyCPiYHd5cswBAcjKyYGhs6dAYnE2tU9FAY9IiIiqtkKC+G8ZQsUixbB7uJFGAIDkfnxx8jv3r3aBrwiDHpERERUM5lMcPr+eyiio2F/4QKMrVohc9Uq5PfuXe0DXhEGPSIiIqpZTCY47dhxO+CdPQtj8+ZQL18O/VNPAVKptasrVwx6REREVDMIAcddu+AWFQX7U6dgbNIE6vh46AcMsLmAV4RBj4iIiGybEHDcuxeKqCg4JCejoGFDZC1eDN2zzwJ2th2FbPvoiIiIqEZzOHAAbgsWwOHIERTUq4fsqCjkDR0K2Ntbu7RKwaBHRERENsfht9+gWLAAjgcPorBWLWR/9BHyXngBcHCwdmmVikGPiIiIbIb90aNQREXBKTERhb6+yPngA+S++CLgVPmvTqsKGPSIiIio2rM/cQKKBQvglJCAQi8v5Lz7LvJefhnC2dnapVkVgx4RERFVW3anT0MRHQ3nHTtg8vDArf/8B7nh4RCurtYurUpg0CMiIqJqx+7cudsB74cfYHJzw60ZM5A7fjyEQmHt0qoUBj0iIiKqNmQXLkAREwPnLVsgXFygmTwZ2ldfhfDwsHZpVRKDHhEREVV9qanwmDMHzps2QTg6QjtxInInTIBJqbR2ZVUagx4RERFVWbIrVyBfvBj2GzbA3s4OuePHQztxIkw+PtYurVpg0CMiIqIqR3rtGhSxsXBZtw6QSGB65RXcHD8eplq1rF1atcKgR0RERFWGND0d8rg4uH75JVBYiLwXXoDmjTegbNsWpowMa5dX7TDoERERkdVJ1WrI4+PhsmoVJEYj8p5/HtrJk1Ho72/t0qo1Bj0iIiKyGklWFuRLl8L1888hycuDbvBgaKZORWHjxtYuzSYw6BEREVGlk9y6BdcVKyBftgxSjQa6gQOhmTYNBU2bWrs0m8KgR0RERJVGkpsL15UrIV+6FNLsbOj69YNm+nQUtGhh7dJsEoMeERERVTiJTgeX1ashj4+HTK2GPiwMmhkzYGzd2tql2TQGPSIiokpy6ZIGkZEHkZNjhLu7PVSqEPj7y61dVsXS6+G6di3kKhVkN29C37071NOnw9i+vbUrq1BF37VarYdS6WS175pBj4iIqJJERh7E0aP/GyIkIiIJ27b1sWJFFSg/Hy5ffw1FbCxk168jv3NnZC1bBkOHDtauzGKPEtbu/K7T0rRW+64Z9IiIiCqJWq0vddomGI1w2bgR8sWLYXflCvI7dEBWbCwMISHWrqzMHiWsVZXvmkGPiIiokiiVTkhL0xabthkFBXDevBmKmBjYXboEQ7t2yFywAPmhoYBEYu3qHsqjhLWq8l0z6BEREVUSlSoEERFJxfroVXuFhXDetg2KhQthl5oKQ+vWyPziC+T36lVtA16RRwlrRd/1nbd9rYFBj4iIqJL4+8uxbVsfeHt7I6O6v87LZILTjz9CsXAh7M+dg7FFC6hXroS+b99qH/CKPEpYK/qurY1Bj4iIiCwnBJx+/hmKqCjYnz4NY9OmUH/6KfTPPANIpdaurlxVlbD2KBj0iIiI6MGEgGNCAhRRUXA4cQIFjRohKzYWukGDAJnM2tVRCRj0iIiIqGRCwHH/figWLIDDsWMo8PdH1sKF0A0ZAtgxRlR1lfINZWRkIC4uDtnZ2ZBIJAgLC8PTTz+NjRs34pdffoGbmxsAYMSIEWj/7wCKW7ZsQUJCAqRSKcLDwxEYGAgASE1NRVxcHAwGA9q1a4fw8HBIJBIYjUaoVCqkpqZCoVBgypQp8PX1BQDs3bsXmzdvBgA899xz6N69e2UcNhERUbXmcPAgFAsWwPG331BQpw6yP/kEecOGAQ4O1i6NLFQpQU8mk2H06NFo3LgxdDodZs2ahTZt2gAA+vfvj4EDBxZb/vLly0hKSsLChQuRlZWFDz74AIsXL4ZUKsXy5cvx2muvoWnTpvjoo4+QnJyMdu3aISEhAa6uroiNjcWvv/6Kr776ClOnToVWq8WmTZvw8ccfAwBmzZqF4OBgyOU2PhI5ERHRQ7I/fBhuUVFwPHAAhX5+yJ43D3kjRgCOjtYujcqoUnpNenp6onHjxgAAZ2dn1K1bF2q1usTlDx8+jJCQENjb28PX1xe1atVCSkoKsrKyoNPp8Nhjj0EikSA0NBSHDx8GABw5csR8pa5jx444efIkhBBITk5GmzZtIJfLIZfL0aZNGyQnJ1f0IRMREVU79snJUI4aBZ9nn4Xd2bPImTsXN379FXljxjDkVVOVfnM9PT0daWlpCAgIwNmzZ7Fz504kJiaicePGeOmllyCXy6FWq9G0aVPzOkqlEmq1GjKZDF5eXub5Xl5e5sCoVqvNn8lkMri4uECj0RSbf+e27rZ7927s3r0bAPDxxx/D29u7Qo7/TnZ2dpWyH1vCNis7tlnZsL3Kjm1WdlWtzSTHj0P23nuQ/vgjhJcXCubNg+n11+Hs6grncth+Wlo2xoz5GRkZOnh7O2P16r5o1Mi9TNuoam1WXVRq0NPr9YiOjsaYMWPg4uKCPn36YOjQoQCADRs2YM2aNZg4cSKEEPddv6T5JX0mKWEcn/vNDwsLQ1hYmHm6MsY3solxlCoZ26zs2GZlw/YqO7ZZ2VWVNrM7exaK6Gg4b98Ok7s7bs2cidxx4yDkckCnu/2nHIwc+bP5VWIpKTkYOfLHMg9bUlXarCqqU6dOiZ9V2oA3BQUFiI6ORteuXfHkk08CADw8PCCVSiGVStGrVy9cuHABwO0rdZmZmeZ11Wo1lErlPfMzMzOhVCrvWaewsBB5eXmQy+VQKpX3bMvT07PCj5eIiKg8XbqkwcCBP6NLl20YOPBn/P239sErlcAuJQUeEyfCJywMjomJ0EydihsHD0I7efLtkFfOqsp7X2uiSgl6Qgh89tlnqFu3Lp555hnz/KysLPPff//9d9SvXx8AEBwcjKSkJBiNRqSnp+PatWsICAiAp6cnnJ2dce7cOQghkJiYiODgYABAUFAQ9u7dCwA4dOgQWrVqBYlEgsDAQBw/fhxarRZarRbHjx83P8FLRERUXURGHsTRoxlIS9Pi6NEMREQklXkbsosX4fHGG/Dp0QNOu3ZBO2kSbhw6BM2MGRDuZbuVWhZ3vzrMpt7xW8VVyq3bv/76C4mJifD398ebb74J4PZQKr/++isuXrwIiUQCHx8fvPrqqwCA+vXro1OnTpg2bRqkUinGjRsH6b+jbY8fPx7x8fEwGAwIDAxEu3btAAA9e/aESqVCZGQk5HI5pkyZAgCQy+UYMmQI/vOf/wAAhg4dyiduiYio2nmUq2Kyf/6BfPFiuGzcCGFvj9xXX4V24kSY7ujDXpGs9d7XS5c0iIw8WGy//v41KwNIRGkd32qwq1evVvg+2N+g7NhmZcc2Kxu2V9mxzcruYdps4MD/9XMDgKAg7wf2c5NeuQJFbCxc1q8HpFLkjhoF7aRJMPn5PVTd1lRZbVYdldZHj0NaExERVQNluSomvXEDcpUKrmvXAkIgb8QIaCIjYSolENgi9g1k0CMiIqoW/P3lD76Cl5EBeVwcXNesAYxG5A0fDu3kySisV6+SqqxalEonpKVpi03XNAx6RERE1ZxErYZ86VK4fv45JHo9dEOGQDNlCgobNiyX7VfXvm7W6htYlTDoERGRVVTX8FCVSHJyIF+2DK4rVkCSmwvdoEHQTJ2KwoCAct1P0RO/AJCWpkVERFK16OtmyVVQW8egR0REVlFdw0NVINFo4LpiBeTLlkF66xZ0/ftDM306Cpo1q5D9sa9b9cWgR0REVsHwUHaS3Fy4rloF+aefQpqdDV3fvrcDXqtWFbpf9nWrvhj0iIjIKhgeLCfR6eDyxReQx8dDlpkJfc+e0MyYAWPbtpWyf/Z1q74Y9IiIyCoYHiyg18N13TrIY2MhS09HfteuUM+YAeO/b4WqLOzrVn0x6BERkVUwPJTCYIDL+vVQLFkC2bVryO/UCVmffgpDx47WroyqGQY9IiKiqsJohMumTZDHxMDu8mUYgoKQtWgRDF26ABKJtaujaohBj4iIyNoKC+G8ZQsUixbB7uJFGNq2RebHHyO/e3cGPHokDHpERETWYjLB6fvvoYiOhv2FCzC2bInMVauQ37s3Ax6VCwY9IiKiymYywWn79tsB7+xZGJs1g3rZMuj79QOkUmtXRzaEQY+IiKiyCAHHXbtgFxMD5fHjKGjcGFlxcdANGADIZNaujmwQgx4REVFFEwKOe/dCERUFh+RkiEaNkBUTA93gwYAd/yumisOzi4iIqAI5HDgAtwUL4HDkCArq1UN2VBRcJkyALifH2qVRDcCgR0REVAEcfvsNigUL4HjwIApr1UL2hx8ib8QIwMEBLvb21i6PaggGPSIionJkf/QoFFFRcEpMRKGPD3Lefx+5I0cCTtZ9xdulSxpERh4s9iYSf3+5VWuiisegR0REVAYlBSb7EyegWLAATgkJKFQqkfPuu8h7+WUIZ2drlwwAiIw8iKNHMwAAaWlaREQk8c0kNQCDHhERURncHZhixm7AKv9f4bxzJ0weHrj1n/8gNzwcwtXVypUWp1brS50m28SgR0REVAZFAakFbmAufsawMydguuKGWzNmIHf8eAiFwsoV3p9S6YS0NG2xabJ9DHpERERlEOiSjfexES8iGblwwIpaA9D/l48hPDysXVqpVKoQREQkFbvlTLaPQY+IiMgCskuXoIiJwbdnNkEvtcNyRW9sbPgM5n3WF8Kj6j/U4O8vZ5+8GohBj4iIqBSyK1cgX7wYLhs2AHZ2yB03DtpJkzDAxwcDrF0c0QMw6BEREd2H9No1KGJj4bJuHSCRIHf0aGgjImCqVcvapRFZjEGPiIjoDtL0dMjj4uD65ZdAYSHyXngBmjfegKluXWuXRlRmDHpEREQApGo15PHxcFm1ChKjEXnPPw/t5Mko9Pe3dmlED41Bj4iIajRJVhbkS5fC9fPPIcnLg27wYGimTkVh48bWLo3okTHoERFRjSS5dQvy5cvhunw5pBoNdAMHQjNtGgqaNrV2aUTlhkGPiIhqFIlWC9fPP4d86VJIs7Ohe/rp2wGvRQtrl0ZU7hj0iIio2inpfbOlkeh0cFm9GvL4eMjUaujDwnDrzTdR8PjjlVQ1UeVj0CMiomrn7vfNRkQklTwYsF4P17VrIVepILt5E/ru3aGeMQPGdu0qsWIi62DQI6Jy9TBXWspzfaoZit43W9I0ACA/Hy5ffw1FbCxk168jv3NnZC1fDsMTT1RSlUTWJ32YlQwGAwoKCsq7FiKyAUVXWtLStDh6NAMREUmVuj7VDEqlU8nTRiNcvvoKvl27wuPtt1Hg74+Mb75B5saNDHlU41gU9NasWYOUlBQAwLFjxxAeHo4xY8bgyJEjFVocEVU/Fl1pqcD1qWZQqUIQFOSNRo3kCAryhkoVAhQUwHnDBviGhsJj5kyYfH2R+fXXyNy8GYaQEGuXTGQVFt26PXDgAIYPHw4A2LRpEyIjI+Hi4oIvvvgCwcHBFVogEVUvSqUT0tK0xaYrc32qGfz95f/rk1dYCOdt26AYuRB2qakwtG6NzC++QH6vXoBEYt1CiazMoqCXn58PR0dHaDQa3LhxAx07dgQAZGRkVGhxRFT9qFQhiIhIKtbHrjLXpxrEZILTjz9CsXAh7M+dg7FFC6hXroS+b18GPKJ/WRT06tSpg/379+P69eto06YNAODWrVtwcHCo0OKIqPopdqXFCutTDSAEnH7+GYqoKNifPg1j06ZQf/YZ9P37A9KH6npOZLMsCnrjxo3D6tWrYWdnhwkTJgAAjh8/bg59REREFU4IOCYkQBEVBYcTJ1DQqBGyYmOhGzQIkMks3gyf7KaaxKKg5+3tjf/+97/F5nXt2hWtW7eukKKIiKjqKwpMOTlGuLvbV1xgEgKO+/dDsWABHI4dQ4G/P7IWLoRuyBDAruyjhJVpDD6ias6ia9yTJ0++7/ypU6eWazFERFR9FAWmlJScChsKx+HgQXgNGQKvESMgvX4d2fPnIz0xEbrhwx8q5AF8sptqFot+SoQQ98zLy8uDlH0hiIhqrIoMTPaHD8MtKgqOBw6g0M8P2fPmIW/ECMDR8ZG3zSe7qSYpNei9/vrrAG4PkFz09yJarRadO3euuMqIiKhKq4jAZJ+cDEVUFJz27EGhtzdy5s5F7qhRgLPzI2+7CJ/sppqk1KAXGRkJIQQ++ugjREZGFvvMw8MDderUqdDiiIio6ioKTHf20XtYdidPwi0qCk67dqHQ0xM577yDvJdfhnBxKceKb+OT3VSTlBr0WrZsCQBYuXIlHMvhcjkREdmOosDk7e390OOq2p09C0V0NJy3b4fJ3R23Zs5E7rhxEHI+BUtUHizqoyeTybB7925cvHgRen3xPhgREREVUhgREdkuu5QUyBcuhPO2bRByOTTTpkH7yisQbm7WLo3IplgU9FQqFS5duoSgoCC4u7tXdE1ERGSjZGlpUCxaBOctWyCcnKCNiID2tdcgPD2tXRqRTbIo6B0/fhwqlQqurq4VXQ8REdkg2T//QL54MVw2boSwt0fuq69CO3EiTF5e1i6NyKZZPGCy0Wis6FqIiMjGSK9cgSI2Fi7r1wNSKXLHjIE2IgImX19rl0ZUI1gU9EJDQ7FgwQL069cPHh4exT57/PHHK6IuIiKqxqQ3bkCuUsF17VpACOSNGAFNZCRMHK2BqFJZFPR27NgBAPj666+LzZdIJFCpVOVfFRERVUvSjAzI4+LgumYNYDQib/hwaCdPRmG9etYujahGsijoxcXFVXQdRERUjUnUasiXLoXr559DotdDN2QINFOmoLBhQ2uXRlSjPdyLAonIphW9rP7ONwdUyMvqqdqT5ORAFhcHvyVLIMnNhW7QIGimTkVhQIC1SyMiWBj08vLy8M033+D06dPQaDTF3n376aefVlhxRGQdRS+rB4C0NC0iIpL4JgEqRqLRwHXFCsiXLYP01i3o+veHZvp0FDRrZu3SiOgOUksWWrFiBdLS0jB06FBotVqMHTsW3t7e6N+/f0XXR0RWUJEvq6fqTZKbC7lKBb+OHeEWFYX8Tp1g/P13ZC1bxpBHVAVZdEXvxIkTWLRoERQKBaRSKZ544gk0adIEn3zyCZ555pmKrpGIKllFvKyeqjmdDq5r1kAeFwdZZib0PXtCM2MGjG3bwtvbG3jIV6ARUcWyKOgJIeDy74ulnZyckJubCw8PD1y/fr1CiyMi6yh6Wf2dffSohtLr4bpuHeSxsZClpyO/a1eoZ8yAMTjY2pURkQUsCnoNGjTA6dOn0bp1azRv3hwrV66Ek5MTateuXdH1EZEVFL2snmowgwEu69dDsWQJZNeuIb9TJ2R9+ikMHTtauzIiKgOLgt5rr71mfgBj7NixWLduHXJzcxEREVGhxRERUSUzGuGyaRPkMTGwu3wZhqAgZC1aBEOXLoBEYu3qiKiMHhj0TCYT9u7di+eeew4A4ObmhgkTJlR4YUREVIkKC+G8ZQsUixbB7uJFGNq2RebHHyO/e3cGPKJq7IFP3UqlUuzcuRMymawy6iEiospkMsHpu+/g06MHPCdPhnB1ReaqVcj48Ufk9+jBkEdUzVl067Zbt27YtWsX+vbt+1A7ycjIQFxcHLKzsyGRSBAWFoann34aWq0WixYtws2bN+Hj44OpU6dCLr89KOuWLVuQkJAAqVSK8PBwBAYGAgBSU1MRFxcHg8GAdu3aITw8HBKJBEajESqVCqmpqVAoFJgyZQp8/31p9t69e7F582YAwHPPPYfu3bs/1HEQEdkMkwlOP/0ERXQ07P/6C8ZmzaBetgz6fv0AqUUjbxFRNWBR0EtJScGOHTuwbds2eHl5QXLHb3jvvffeA9eXyWQYPXo0GjduDJ1Oh1mzZqFNmzbYu3cvWrdujWeffRZbt27F1q1bMWrUKFy+fBlJSUlYuHAhsrKy8MEHH2Dx4sWQSqVYvnw5XnvtNTRt2hQfffQRkpOT0a5dOyQkJMDV1RWxsbH49ddf8dVXX2Hq1KnQarXYtGkTPv74YwDArFmzEBwcbA6UREQ1ihBw3LULblFRsD91CsYmTaCOj4d+wAAGPCIbZFHQ69WrF3r16vXQO/H09ISnpycAwNnZGXXr1oVarcbhw4cxd+5cALevGs6dOxejRo3C4cOHERISAnt7e/j6+qJWrVpISUmBj48PdDodHnvsMQBAaGgoDh8+jHbt2uHIkSN4/vnnAQAdO3bE559/DiEEkpOT0aZNG3Owa9OmDZKTk9GlS5eHPh4iortV+dfGCQHHvXuhiIqCQ3IyCho2RNbixdA9+yxgx7dhEtkqi366y/NWZ3p6OtLS0hAQEICcnBxzAPT09MStW7cAAGq1Gk2bNjWvo1QqoVarIZPJ4OXlZZ7v5eUFtVptXqfoM5lMBhcXF2g0mmLz79wWEVF5qsqvjXM4cABuCxbA4cgRFNSrh+yoKOQNHQrY21u7NCKqYBYFvYSEhPvOt7e3h5eXF5o2bQp7C/7B0Ov1iI6OxpgxY8wDMN/Pne/StWR+SZ9JSuhEfL/5u3fvxu7duwEAH3/88e2R3iuYnZ1dpezHlrDNyo5tVjYP2145OcZ7pq3d7pIDByB7/31I9+2DqFsXBbGxMI0ZAxcHB5T8L3DZ8RwrO7ZZ2bHNHo5FQS8xMRHnzp2Du7s7vLy8kJmZiZycHDRp0gTp6ekAgJkzZ6JJkyYlbqOgoADR0dHo2rUrnnzySQCAu7s7srKy4OnpiaysLLi5uQGAeR9F1Go1lErlPfMzMzOhVCqLrePl5YXCwkLk5eVBLpdDqVTi9OnTxbbVsmXLe+oLCwtDWFiYeTqjEl7n4+3tXSn7sSVss7Jjm5XNw7aXu7v9PdPWanf7o0ehiIqCU2IiCn19kfPBB8h98UXAyQn4985JeeI5VnZss7Jjm5WsTp06JX5mUc/bevXqYdSoUfj000/x3//+F59++ileeuklNGzYEJ999hn69OmDzz//vMT1hRD47LPPULdu3WLvxg0ODsa+ffsAAPv27cMTTzxhnp+UlASj0Yj09HRcu3YNAQEB8PT0hLOzM86dOwchBBITExH872t4goKCsHfvXgDAoUOH0KpVK0gkEgQGBuL48ePQarXQarU4fvy4+QleIqLyolKFICjIG40ayREU5G2V18bZnzgB5ejR8Bk4EPanTiHn3XeRnpSE3LFjb4c8IqpxJKK0+6H/Cg8Px8qVKyG944ksk8mEcePGYdWqVTAajRg/fjy++OKL+65/9uxZzJ49G/7+/ubbpiNGjEDTpk2xaNEiZGRkwNvbG9OmTTM/NLF582bs2bMHUqkUY8aMQbt27QAAFy5cQHx8PAwGAwIDAzF27FhIJBIYDAaoVCqkpaVBLpdjypQp8PPzA3D71vOWLVsA3B5epUePHg9smKtXrz5wmUfF307Kjm1WdmyzsqmO7WV3+jQU0dFw3rEDJg8PaF9/Hbnh4RCurpWy/+rYZtbGNis7tlnJSruiZ9GtW3d3dxw9etR8xQ0Ajh07Zr7VajQaYVfKU1vNmzfHxo0b7/vZ7Nmz7zv/ueeeM7+N405NmjRBdHT0PfMdHBwwbdq0+26rZ8+e6NmzZ4n1ERFVR3bnzt0OeD/8AJObG27NmIHc8eMhFAprl0ZEVYRFQS88PBwLFy6Ev7+/uS/c33//bQ5W58+fx1NPPVWhhRIR0W2yCxegWLQIzlu3Qri4QDN5MrSvvgrh4WHt0oioirEo6LVt2xaxsbFITk6GWq1Gu3bt0L59eyj+/a2xbdu2aNu2bYUWSkRU08kuXYIiJgbOmzZBODpCO3EicidMgOnfh9KIiO5m8SiZbm5uCA0NrchaiIgeSZUftPghya5cgXzxYrhs2ADY2SF33DhoJ02CycfH2qURURVXYtCbN28e3n77bQC3+9GVNCadJa9AIyKqDFV50OLSlBRQpdeuQREbC5d16wCJBLmjR0MbEQFTrVrWLpmIqokSg163bt3Mf+eDDERUHajV+lKnq6q7A+qc137Ctx1OwvXLL4HCQuS98AK0b7yBwrp1rVwpEVU3JQa9O98FW56vQCMiqihKpRPS0rTFpquDokDqhVzMxB5E/pkEp1Mm6IYOhWbKFBT6+1u5QiKqrizuo3fmzBmkpaVBry/+G/L9hkAhIrIGlSoEERFJxW6BVgcN3QowFjswGQfgCgN2eHZE0HfzUdi4sbVLI6JqzqKg9/nnn+PgwYNo3rw5HBwczPNL6rdHRGQN/v7yatEnr4jk1i24rliBn1OWwg5a/OAahNUNnsOMlUNRaAMPkRCR9VkU9Pbv34/o6Gjze2WJiOjhSXJz4bpyJeRLl0KanQ1dv35QT5+O9i1aoL21iyMim2JR0PP29oa9vf2DFyQiohJJdDq4rF4NeXw8ZGo19GFh0MyYAWPr1tYujYhslEVBb8KECVi6dCk6d+4Md3f3Yp+1bNmyQgojokdTNGRHTo4R7u72NjOmXEWp0PbS6+G6di3kKhVkN29C37071NOnw9ie1++IqGJZFPRSU1Pxxx9/4MyZM8X66AHAp59+WiGFEdGjuXPIDgDVZkw5a6mQ9srPh8vXX0MRGwvZ9evI79wZWcuWwdChwyNWS0RkGYuC3tdff4233noLbdq0qeh6iKicVNcx5aylXNvLaITLxo2QL14MuytXkN+hA7JiY2EIqR5PAROR7bAo6Dk6OvIWLVE1U13HlLOWcmmvggI4f/stFDExsPv7bxjatUPmggXIDw0FOEoBEVmB1JKFhg8fjtWrVyM7Oxsmk6nYHyKqmlSqEAQFeSMgwB1BQd7VZkw5a3mk9ioshPOWLfDt0QOe06bB5O6OzC++QMb33yO/WzeGPCKyGokQQjxooeHDh5f42YYNG8q1oKri6tWrFb4Pb29vZGRkPHhBMmOblR3brGzK1F4mE5x+/BGKhQthf+4cjC1aQDNjBvR9+9aocMdzrOzYZmXHNitZnTp1SvzMolu3KpWq3IohIqr2hIDTzp1QREXB/swZGJs2hfqzz6Dv3x+QWnSjhIioUlgU9Hx8fCq6DiKiqk8IOP7yCxRRUXD4808UNGqErNhY6AYNAmQya1dHRHQPi4JeXl4etm/fjosXL97zrtt33nmnQgojIqoyhIDj/v1QzJ8Phz/+QIG/P7IWLoRuyBDAzuJXhhMRVTqL/oVauHAhTCYTOnTocM84ekREtswhKQmKqCg4/vYbCurUQfb8+cgbNgzg24KIqBqwKOidP38eK1euhB1/cyWiGsLh8GEoFiyA46+/otDPD9nz5iFvxAjA0dHapRERWcyi5Na8eXNcuXIFDRo0qOh6iIisSnL4MJTvvAOnvXtR6O2NnLlzkTtqFODsbO3SKkTRq9/Uaj2USie+Ko/IxlgU9CZOnIiPPvoIAQEB8PDwKPbZ0KFDK6IuIqJKZXfyJNyiomC/axeknp7Ieecd5L38MoSLi7VLq1B3vvotLU3LV+UR2RiLX4GWmZkJHx8f6HQ683xJDRoniohsk92ZM1AsXAjn7dthcndHwXvv4eYLL0DIa8ZVLb4qj8i2WRT0kpKSsHjxYnh6elZ0PURElcIuJQWK6Gg4ff89hFwOzbRp0L7yCrwaN4aoQYOy8lV5RLbNoqDn5+cHGceIIiIbIEtLg2LRIjhv2QLh5ARtRAS0r70GUUN/kVWpQhARkVSsjx4R2Q6Lgl7Xrl0xf/58PPXUU/f00Xv88ccroi4ionIl++cfyGNi4PLNNxD29sh99VVoJ06EycvL2qVZlb+/nH3yiGyYRUFv586dAG731buTRCLh69GIqEqTXrkCxZIlcFm/HpDJkDtmDLQRETD5+lq7NCKiCmdR0IuLi6voOoiIypX0xg3IVSq4rl0LCIG8F1+EJjISplJe/k1EZGs4AjIR2RRpZubtgLdmDWA0Im/4cGgnT0ZhvXrWLo2IqNKVGvRmz579wCFU3nvvvXItiIjoYUjUasiXLoXr559DotdDN2QINFOmoLBhQ2uXRkRkNaUGvZ49e1ZWHURED0WSkwP5smVwXbECktxc6AYNgmbqVBQGBFi7NCIiqys16HXv3r2SyiAiKhuJRgPXFSsgX7YM0lu3oOvfH5pp01DQvLm1SyMiqjLYR4+IqhVJXh5cV62CPD4e0uxs6Pr2vR3wONQTEdE9GPSIqHrQ6eC6Zg3kcXGQZWZC37MnNDNmwNi2rbUrIyKqshj0iKhqy8+Hy7p1UMTGQnbjBvK7doV6xgwYg4OtXRkRUZUnLemDt99+2/z3b775plKKISIyMxjg8uWX8OvcGR7vvIOCxo2R8e23yFy/niGPiMhCJQa9q1evwmAwAAB++OGHSiuIiGq4ggI4r18P39BQeMyahcI6dZCxfj0yv/kGho4drV0dEVG1UuKt2yeeeAKTJ0+Gr68vDAYD5syZc9/lOI4eEZWLwkI4b90KxcKFsLt4EYa2bZH50UfI794deMB4nkREdH8lBr2JEyfi7NmzSE9PR0pKCnr06FGZdRFRTWEywen776FYuBD2KSkwtmyJzFWrkN+7NwMeEdEjKvVhjObNm6N58+YoKCjgmHpEVL6EgNNPP0ERHQ37s2dhbNYM6mXLoO/XD5CW2KuEiIjKwKKnbnv27ImTJ08iMTERWVlZ8PT0RGhoKB7nuFVEVFZCwHH3biiiouBw8iSMTZpAHR8P/YABDHhEROXMon9Vf/nlF8TExMDDwwMdOnSAp6cnFi9ejN27d1d0fURkK4SA49698B4wAF5jxkCq0SArJgY3ExKgHzSIIY+IqAJYdEVv27ZteOedd9DwjpeDh4SEIDo6GmFhYRVVG1GNdumSBpGRB6FW66FUOkGlCoG/v9zaZT0UhwMHoIiKguPhwyioWxfZCxYg7/nnAXt7a5dGRGTTLAp6Go0G9erVKzavTp060Gq1FVIUEQGRkQdx9GgGACAtTYuIiCRs29bHylWVjcPvv0Mxfz4cDx5EYa1ayP7wQ+SNGAE4OFi7NCKiGsGieyXNmzfHmjVrkJ+fDwDQ6/X48ssv8dhjj1VocUQ1mVqtL3W6KrM/dgzKESPgPXgw7FJSkPP++7jx66/Ie/llhjwiokpk0RW9V155BTExMRgzZgzkcjm0Wi0ee+wxTJ48uaLrI6qxlEonpKVpi01XdfYnTkARFQWnX35BoVKJnHffRd7LL0M4O1u7NCKiGsmioOfp6Yn33nsPmZmZ5qduvby8Kro2ohpNpQpBRERSsT56VZXd6dNQREfDeccOmDw8cGvWLOSGh0PIq2efQiIiW2FR0Cvi5eXFgEdUSfz95VW+T57duXO3A94PP8CkUODW9OnIHT8ews3N2qXVKLb04A4Rla8yBT0iIgCQXbgARUwMnLdsgXBxgeaNN6B97TUIDw9rl1Yj2cKDO0RUMRj0iMhiskuXbge8b7+FcHCA9vXXkfv66zApldYurUarzg/uEFHFemDQM5lMOH36NJo3bw47O+ZCoppIduUK5IsXw2XDBkAmQ+7YsdBOmgSTj4+1SyNUzwd3iKhyPDC5SaVSzJ8/H2vWrKmMeoioCpFeuwaFSgWXdesAAHmjRkETGQlTrVpWrozuVJ0e3CGiymXRJboWLVrg3LlzHDePqIaQ3rwJuUoF1y+/BAoLkTd8OLSTJ6Owbl1rl0b3UR0e3CEi67Ao6Pn4+OCjjz5CcHAwvLy8IJFIzJ8NHz68woojosolVashj4+Hy6pVkBiN0A0ZAs2UKShs0MDapRER0UOwKOgZDAY88cQTAAC1Wl2hBRFR5ZNkZUG+bBlcV66EJC8PusGDoZk6FYWNG1u7NCIiegQWBb2JEydWdB1EZAWSW7fgumIF5MuWQarRQDdgADTTp6OgaVNrl0ZEROXA4sdoL1++jEOHDiEnJwfjxo3D1atXYTQa0YC3dIiqHUluLlxXroR86VJIs7Oh69cPmmnTUNCypbVLIyKiciS1ZKGDBw9izpw5UKvVSExMBADodDo+iUtUzUh0Orh++il8O3aE2yefwBAcjJs7diBrxQqGPLKKKVOmoGPHjujduzd69+6NkydPAgCSkpLQvHlz8/xFixaZ18nJycErr7yC0NBQdOvWDUeOHAEATJgwwbz8k08+id69e1vlmKZNm4Y2bdqgZ8+exeZnZWXhhRdeQOfOndGvXz9kZ2cDuN09aurUqejVqxfCwsKQlJQE4Pb/s6NHj0ZoaCh69OiBDz/8sNj2tm3bhu7du6NHjx6YNGmSef7IkSPRokULvPTSS8WWP3DgAPr27YuePXti8uTJKCgoAFB6W1P1Z9EVvY0bN+Ldd99Fw4YNcfDgQQBAgwYNcPHixYqsjYjKi14P17VrIVepILt5E/pu3aCeMQPG9u2tXRkR3nnnHTzzzDP3zO/QocN9LyjMnj0bPXr0wPLly2EwGKDT6QAAn332mXmZ9957D27l8Cq+DRs24PLly5g+fbrF6wwbNgzh4eGYPHlysflxcXHo0qULIiIisGrVKsTFxeHtt9/Gun+HL/rll1+QkZGBUaNGYfv27QBuh9fOnTvDYDBg+PDhSEhIQM+ePZGamgqVSoWtW7fCw8MDGRkZ5v1MmDABOp0Oa9euNc8zmUyYMmUKNmzYgCZNmmDBggX45ptvMGLECAAltzVVfxZd0cvJybnnFq1EIin29C0RVUH5+ZB+9hn8OneG+5w5KGjaFBlbtkC9bh1DHpWbuXPnYsWKFebpjz/+GCtXrqyQfWk0Gvz222/mgOLg4AB3d/diywgh8P3332PQoEH3rP/TTz9h+PDhEELgxo0b6NKlC9LT08u1xo4dO8LjPq8D3LlzJ55//nkAwKhRo7Bjxw4AwLlz59ClSxcAgLe3N9zc3HD8+HE4Ozujc+fOAG4fZ+vWrXHt2jUAwLp16zBmzBjzfry9vc376dq1K+Ty4u86zsrKgqOjI5o0aQIACA0NNYdJsm0WBb3GjRubb9kW+fXXXxEQEFAhRRHRIzIa4fLVV/Dt2hV2kyejoH59ZGzciMxvvoGhQwdrV0c2ZsyYMfjmm28A3L5ytG3bNgwePBhardZ8O/DuP+fOnTOv/8knnyAsLAxz5sxBfn6+ef7Ro0cRFhaGUaNG4a+//gIAXLp0CV5eXpg6dSr69OmDGTNmIC8vr1g9v/32G3x8fND4Pk+N9+vXD76+vli9ejXefPNNzJgxA76+vhXRLPfIyMiAn58fAKB27drIzMwEALRs2RI7d+5EQUEB/v77b/z555+4evVqsXVzcnKwa9cucyBMTU1FamoqBg0ahGeeeQZ79uwpdd9KpRJGoxHHjx8HAPz444/F9nG/tibbYNGt2/DwcPz3v/9FQkIC8vPzMW/ePFy9ehXvvPOORTuJj4/HsWPH4O7ujujoaAC3bwf/8ssv5kvrI0aMQPt/rzBs2bIFCQkJkEqlCA8PR2BgIIDbJ3ZcXBwMBgPatWuH8PBwSCQSGI1GqFQqpKamQqFQYMqUKeYf3L1792Lz5s0AgOeeew7du3e3uHGIqp2CAjhv3gxFTAzsLl2CoV07GJcuRWZgIMAr8FRBGjZsCE9PT5w8eRI3b95Eq1atoPz3/ce7du0qdd3//Oc/8PX1hcFgwMyZMxEfH4+pU6eidevW+P333+Hq6opffvkFY8eOxa+//orCwkL8+eef+OCDD9C+fXvMnj0bKpUKM2fONG9z69at972aV+SDDz5Ar1690L59ezz77LP3fK5Wq81jxGZnZ8NoNJqvvi1ZsgQtWrQoaxOV6oUXXsD58+fRr18/1KtXD8HBwcVeOVpQUIBJkyZh7Nix5rtrBQUFSEtLw6ZNm3Dt2jUMHjwYCQkJ91zdLCKRSBAfH4+5c+fCYDAgNDQUMpkMAEpsa7INFgW9unXrIiYmBkePHkVQUBC8vLwQFBQEJyfL3qfYvXt3PPXUU4iLiys2v3///hg4cGCxeZcvX0ZSUhIWLlyIrKwsfPDBB1i8eDGkUimWL1+O1157DU2bNsVHH32E5ORktGvXDgkJCXB1dUVsbCx+/fVXfPXVV5g6dSq0Wi02bdqEjz/+GAAwa9YsBAcH33NJm6jaKyyE87ZtUCxcCLvUVBhat0bmF18gv1cvePv4AHf03yGqCCNGjMDGjRuRnp6OF154AQCg1WoxePDg+y4fFxeHxx57zHyFy9HREcOHDzf3s1MoFOZle/Xqhf/7v/+DWq1G7dq1Ubt2bfOFgf79+0OlUpmXLSgowE8//YSffvqpxFqvX78OiUSCmzdvwmQyQSotfnNLqVSaA+qD+uhduXIFY8aMAQCMHj36ngcg7uTt7Y0bN27Az88P165dg5eXFwDAzs4O7733nnm5gQMHolGjRubpmTNnolGjRnjllVfM84rawN7eHv7+/mjSpAnS0tLMF0buJzg4GFu2bAEA7Nu3D6mpqQBKbuuisE7Vm0W3boHbP4TNmzdHy5Yt0aJFC4tDHnD7srSl4erw4cMICQmBvb09fH19UatWLaSkpCArKws6nQ6PPfYYJBIJQkNDcfjwYQDAkSNHzFfqOnbsiJMnT0IIgeTkZLRp0wZyuRxyuRxt2rRBcnKyxXUTVXkmE5y+/x4+YWHwjIiAcHSEesUKZPz0E/LDwngVjypNv379sGfPHhw/ftz877FcLseuXbvu+6folZo3btwAcLtf3Y4dO9C8eXMAQHp6OoQQAIA//vgDJpMJnp6e8PX1RZ06dZCSkgLg9pOkd76ec//+/QgICECdOnXuW2dBQQGmTZuGuLg4NG3aFMuWLXuk465bt675mEoLeQDQp08f8y3utWvXom/fvgBuP11bdPs5MTERdnZ25mP65JNPoNFoigVBAHjqqafMT+eq1WqkpqbC39+/1P0XPbCRn5+PuLg4jB49GkDJbU22waIrehkZGViyZAnOnz8PV1dX5ObmIiAgAG+88QZ8fHweeuc7d+5EYmIiGjdujJdeeglyuRxqtRpN7xisValUQq1WQyaTmX/7AQAvLy/zWzrUarX5M5lMBhcXF2g0mmLz79zW/ezevRu7d+8GcLsj8Z0dWyuKnZ1dpezHlrDN/iUEJN9/D9kHH0B64gRE8+Yo+OoriOeeg1wqxZ2/VrHNyobtVXZ2dnaoU6cOevbsCQ8PD/NVOkuMHDkSN2/ehBACbdu2xfz58yGXy7Fx40YsW7YMdnZ2cHZ2xrp168z/36hUKkyYMAEGgwGNGjXC8uXLzcFk586dGDlyZInf4bx589CtWzf0798foaGhCAkJwZAhQ0q8HatQKODi4lKmc2L06NFITExERkYGOnTogHfffRfh4eGYPXs2XnzxRWzcuBENGjTAV199BaVSiYsXL+KZZ56BVCpFnTp18OWXX8Lb2xuXL1/GkiVL0KxZM/Tv3x8A8Prrr2Ps2LEYOnQofv/9d/Tq1QsymQzz5883h8OePXvir7/+glarRYcOHfDZZ5+hT58+iIqKwvbt22EymfDqq6+ab1uX1tZVCX82H45EFMX4Urz33nto0KABXnjhBTg5OUGv12P9+vW4ePEi5s6da9GO0tPT8cknn5j76GVnZ5v7523YsAFZWVmYOHEiVqxYgcceewyhoaEAgE8//RTt2rWDt7c3vv76a7z77rsAgDNnzuC7777DrFmzMG3aNLz99tvmUBcZGYkPP/wQe/bsgdFoxJAhQwAAmzZtgqOjIwYMGPDAeu/uCFsRvL29iz0STw9W49tMCDgmJEARFQWHEydQ0KgRNNOmQTdoEPBvf5u71fg2KyO2V9l5e3sjPT0dffv2xdKlS+/7EAQVx/Os7NhmJSvpCjZg4a3b1NRUjBo1yny71snJCaNGjTLf338YHh4ekEqlkEql6NWrFy5cuADg9pW6oieRAJj7Cdw9PzMz09x/4M7PCgsLkZeXB7lcDqVSec+2eDmaqiUh4JiYCO+BA+H10kuQZmcja+FCpO/dC91zz5UY8ogqw5kzZ9C5c2d06dKFIY+oirEo6DVt2tTcH6LIhQsXivWLKKusrCzz33///XfUr18fwO3OoklJSTAajUhPT8e1a9cQEBAAT09PODs749y5cxBCIDExEcHBwQCAoKAg7N27FwBw6NAhtGrVChKJBIGBgTh+/Di0Wi20Wi2OHz9eakdVoqrI4eBBeA0ZAq8RIyC9fh3Zn3yC9H37oBs+HLCz+C2GRBWmRYsW5jcoEVHVUuL/Ehs2bDD/3c/PDx999BHat29vvnr2xx9/mMfzeZCYmBicPn0aGo0GEyZMwLBhw3Dq1ClcvHgREokEPj4+ePXVVwEA9evXR6dOnTBt2jRIpVKMGzfO/ETU+PHjER8fD4PBgMDAQLRr1w7A7f4IKpUKkZGRkMvlmDJlCoDbHYGHDBmC//znPwCAoUOH8olbqjbsDx+GW1QUHA8cQKGfH7LnzUPeiBGAo6O1SyMiomqixD568fHxFm1g4sSJ5VpQVcE+elVTTWgz++RkKKKi4LRnDwq9vaGdNAm5o0cDzs4Ptb2a0Gblie1VdmyzsmOblR3brGSl9dEr8YqerQY4oqrK7uRJuEVFwWnXLhR6euLW228jd8wYCBcXa5dGRETVlMUdfPLz83H9+nXo9fpi85s1a1buRRHVJHZnz0IRHQ3n7dthcnfHrZkzkTtuHAS7GRAR0SOyKOjt27cPn3/+Oezs7ODg4FDss08//bRCCiOydbKUFCgWLoTztm0Qcjk006ZBO348RAmvMCIiIiori4Le2rVrMX36dLRp06ai6yGyebK0NChiYuC8eTOEkxO0ERHQvvYaBIf+ISKicmZR0LOzs0PLli0ruhYimya7fBnymBi4bNwIYW+P3FdfhXbiRJjueHsLERFRebIo6A0fPhxr1qzB0KFDzW+zICLLSK9ehWLJErisXw9IpcgdMwbaiAiYfH2tXRoREdk4i4JenTp1sHHjRuzcufOez+4cb4+I/kd64wbkKhVc164FhEDeiy9CExEBUymPwRMREZUni4JebGys+eXPdz+MQUTFSTMyII+Ph+sXXwBGI7Z6dka0Ux/oT9aFqsAN/tYukIiIagyLgp5Wq8Xw4cMhkUgquh6iakuiVkO+dClcP/8cEr0euiFDMOJMMLad/PdNg5czEBGRhG3b+li3UCIiqjEsetdt9+7dkZiYWNG1EFVLkpwcKBYsgF+nTpDHxUHfty/S9+xBdkwM/swt3qdVrdaXsBUiIqLyZ9EVvZSUFOzYsQObN2+Gh4dHsc/ee++9iqiLqMqTaDRwXbEC8mXLIL11C7r+/aGZPh0FdwwirlQ6IS1NW2yaiIioslgU9Hr16oVevXpVdC1E1YIkLw+uq1ZBHh8PaXY2dH373g54rVrds6xKFYKIiCSo1XoolU5QqUKsUDEREdVUFgW97t27V3AZRNWATgfXNWsgj4uDLDMT+p49oZkxA8a2bUtcxd9fzj55RERkNRYFvYSEhBI/69mzZ7kVQ1Ql6fVwXbcO8thYyNLTkd+1K9QzZsAYHGztyoiIiEplUdDbv39/sens7Gxcv34dzZs3Z9Aj22UwQB//BVxiFsPdmIWj8mZwil8Iz0E9rF0ZERGRRSwKenPmzLlnXkJCAq5cuVLuBRFZndEIl02bII+Jgd3ly0hCA4zCUPyibYqglUZsG2TtAomIiCxj0fAq99O9e/dSb+kSVTuFhXDetAm+3bvDY8YMmLy8MMYvAp0xCb/gMQASDo9CRETVikVBz2QyFfuj1+uxe/duuLq6VnR9RBXPZILTd9/Bp0cPeE6eDOHigsxVq5Dx4484WS8IwP8GCufwKEREVJ1YdOt2xIgR98xTKpV47bXXyr0gokpjMsFpxw4ooqNhf/YsjM2aQb1sGfT9+gHS278DcXgUIiKqziwKeiqVqti0o6Mj3NzcSliaqIoTAo67dsEtKgr2p07B2KQJ1PHx0A8YYA54RTg8ChERVWcWBT0fH5+KroOo4gkBx717oYiKgkNyMgoaNEBWTAx0gwcDdhb9KBAREVUrpf7v9qDXm0kkEsyePbtcCyIqd0LA4ddf4bZgARyOHEFBvXrIjopC3tChgL29tasjIiKqMKUGva5du953vlqtxk8//YT8/PwKKYqovDj89hsUCxbA8eBBFNaqheyPPkLeCy8ADg7WLo2IiKjClRr07h4MWaPRYMuWLfjll18QEhKCoUOHVmhxRA/L/uhRKKKi4JSYiEIfH+S8/z5yR44EnPjULBER1RwWdUzKy8vDtm3bsHPnTrRv3x6ffPIJatWqVdG1EZWZ/YkTUCxYAKeEBBQqlch5913kvfwyhLOztUsjIiKqdKUGPYPBgB9//BE//PADWrZsiffffx/169evrNqILGZ36hQU0dFw3rkTJg8P3PrPf5AbHg7BsR6JiKgGKzXoTZo0CSaTCQMHDkSTJk2Qk5ODnJycYss8/vjjFVogUWnszp2DIioKzj/+CJObG27NmIHc8eMhFAprl0ZERGR1pQY9h387rP/888/3/Vwikdwzxh5RZZBduADFokVw3roVwsUFmsmToX31VQgPD2uXRkREVGWUGvTi4uIqqw4iy6SmwmP2bDh/+y2EoyO0r7+O3Ndfh0mptHZlREREVQ5HiaVqQXblCuSLF8N+wwbY29khd9w4aCdNgomDeRMREZWIQY+qNOm1a1DExsJl3TpAIoFp/HjcfOUVmPjUNxER0QMx6FGVJE1PhzwuDq5ffgkUFiJv+HBoJ0+GZ9u2MGVkWLs8IiKiaoFBj6oUqVoNeXw8XFatgsRohG7oUGimTEGhv7+1SyMiIqp2GPSoSpBkZUG+dClcP/8ckrw86AYPhmbqVBQ2bmzt0oiIiKotBj2yKsmtW3BdsQLyZcsg1WigGzAAmunTUdC0qbVLIyIiqvYY9MgqJLm5cF25EvKlSyHNzoauX7/bAa9FC2uXRkREZDMY9KhSSXQ6uHzxBeRxcZCp1dCHhUEzYwaMrVtbuzQiIiKbw6BHlUOvh+vatZCrVJDdvAl99+5QT58OY/v21q6MiIjIZjHoUcXKz4fL119DERsL2fXryO/cGVnLlsHQoYO1KyMiIrJ5DHpUMYxGuHzzDeQxMbC7cgX5TzyBrCVLYOjc2dqVERER1RgMelS+CgrgvHkzFDExsLt0CYZ27ZC5YAHyQ0MBicTa1REREdUoDHpUPgoL4bxtGxQLF8IuNRWG1q2R+cUXyO/ViwGPiIjIShj06NGYTHDavh2K6GjYnzsHY4sWUK9cCX3fvgx4REREVsagRw9HCDj9/DMUUVGwP30axqZNof7sM+j79wekUmtXR0RERGDQo7ISAo4JCVBERcHhxAkUNGqErNhY6AYNAmQya1dHREREd2DQI8sIAcf9+6FYsAAOx46hwN8fWQsXQjdkCGDH04iIiKgq4v/Q9EAOBw9CsWABHH/7DQV16iD7k0+QN3w4YG9v7dKIiIioFAx6VCL7w4fhFhUFxwMHUOjnh+x585A3YgTg6Gjt0oiIiMgCDHrVzKVLGkRGHoRarYdS6QSVKgT+/vJy3Yd9cjIUUVFw2rMHhd7eyJk7F7mjRgHOzuW6HyIiIqpYDHrVTGTkQRw9mgEASEvTIiIiCdu29SmXbdudPAm3qCg47dqFQk9P3Hr7beSOGQPh4lIu2yciIqLKxaBXzajV+lKnH4bd2bNQREfDeft2mNzdcWvmTOSOGwchL98rhURERFS5GPSqGaXSCWlp2mLTD8suJQXyhQvhvG0bhKsrNFOnQvvKKxDu7uVRKhEREVkZg141o1KFICIiqVgfvbKSpaVBsWgRnLdsgXBygnbSJGgnTIDw9KyAiomIiMhaGPSqGX9/+UP3yZP98w/kixfDZeNGCHt75L76KrQTJ8Lk5VXOVRIREVFVwKBXA0ivXoViyRK4rF8PSKXIHTMG2kmTYPLzs3ZpREREVIEY9GyY9MYNyFUquK5dCwiBvBEjoImMhKlOHWuXRkRERJWAQc8GSTMyII+Lg+uaNYDRiLzhw6F94w0U1q9v7dKIiIioEjHo2RCJWg350qVw/fxzSPR66J57DpqpU1HYsKF5mcoYcJmIiIiqBgY9GyDJyYF82TK4rlgBSW4udIMG3Q54AQH3LFuRAy4TERFR1cKgV41JNBq4rlgB+bJlkN66Bd3TT0MzfToKmjcvcZ2KGHCZiIiIqiYGvWpIkpcH11WrII+PhzQ7G7o+fW4HvMcff+C65TngMhEREVVtlRL04uPjcezYMbi7uyM6OhoAoNVqsWjRIty8eRM+Pj6YOnUq5P++cmvLli1ISEiAVCpFeHg4AgMDAQCpqamIi4uDwWBAu3btEB4eDolEAqPRCJVKhdTUVCgUCkyZMgW+vr4AgL1792Lz5s0AgOeeew7du3evjEOuGDodXNesgTwuDrLMTOh79oRm+nQY/20fS5THgMtERERUPUgrYyfdu3fH//3f/xWbt3XrVrRu3RpLlixB69atsXXrVgDA5cuXkZSUhIULF+Ltt9/GypUrYTKZAADLly/Ha6+9hiVLluD69etITk4GACQkJMDV1RWxsbHo378/vvrqKwC3w+SmTZvw4Ycf4sMPP8SmTZug1WpR7ej1cP38c/iFhMD9/fdR0LIlbm7dCvWXX5Yp5AH/G3D5wIGB2LatDx/EICIismGVEvRatmxpvlpX5PDhw+jWrRsAoFu3bjh8+LB5fkhICOzt7eHr64tatWohJSUFWVlZ0Ol0eOyxxyCRSBAaGmpe58iRI+YrdR07dsTJkychhEBycjLatGkDuVwOuVyONm3amMNhtWAwwGXNGvh16QL3d99FQePGyNi0CZnr18P4xBPWro6IiIiqOKv10cvJyYHnv+9W9fT0xK1btwAAarUaTZs2NS+nVCqhVqshk8ngdcerury8vKBWq83rFH0mk8ng4uICjUZTbP6d27K2oiFOcnKMcHe3v3eIE6MRzt9+C0VMDOz++QeGoCBkLVoEQ5cugERivcKJiIioWqlyD2MIIco0v6TPJCUEopLm7969G7t37wYAfPzxx/D29n5QqQ9tyJAE8xAnADB16u/Yt28YUFgI6fr1kP33v5CkpsIUFASjSgX07Qs3BjwAgJ2dXYV+N7aIbVY2bK+yY5uVHdus7NhmD8dqQc/d3R1ZWVnw9PREVlYW3NzcANy+UpeZmWleTq1WQ6lU3jM/MzMTSqWy2DpeXl4oLCxEXl4e5HI5lEolTp8+XWxbLVu2vG89YWFhCAsLM09nZGTcd7nycONG8X6C6ddvQbtyJRQLF8IuJQXGli1xa9Uq5PfuffsK3h3HXdN5e3tX6Hdji9hmZcP2Kju2WdmxzcqObVayOqW82rRS+ujdT3BwMPbt2wcA2LdvH574t89ZcHAwkpKSYDQakZ6ejmvXriEgIACenp5wdnbGuXPnIIRAYmIigoODAQBBQUHYu3cvAODQoUNo1aoVJBIJAgMDcfz4cWi1Wmi1Whw/ftz8BK81FQ1pIoEJg/Endl7/EMqJEwGZDOply3Bz507k9+nD27RERET0SCSitHui5SQmJganT5+GRqOBu7s7hg0bhieeeAKLFi1CRkYGvL29MW3aNPMDG5s3b8aePXsglUoxZswYtGvXDgBw4cIFxMfHw2AwIDAwEGPHjoVEIoHBYIBKpUJaWhrkcjmmTJkCPz8/ALefyN2yZQuA28Or9OjRw6Kar169WgEtcdvflzTYMPpTvPLPZrQ0/AOdf0Po33oTugEDAJmswvZrC/gbXdmxzcqG7VV2bLOyY5uVHdusZKVd0auUoFcdVWTQk+TmwvfJJyH18ED25MnQDR4M2FW57pJVEn/Qy45tVjZsr7Jjm5Ud26zs2GYlKy3oMV1YgXB1ReY338CjY0focnKsXQ4RERHZKKv10avpClq0AOztrV0GERER2TAGPSIiIiIbxaBHREREZKMY9IiIiIhsFIMeERERkY1i0CMiIiKyUQx6RERERDaKQY+IiIjIRjHoEREREdkoBj0iIiIiG8WgR0RERGSj+K5bsnmXLmkQGXkQarUeSqUTVKoQ+PvLrV0WERFRheMVPbJ5kZEHcfRoBtLStDh6NAMREUnWLomIiKhSMOiRzVOr9aVOExER2SoGPbJ5SqVTqdNERES2ikGPbJ5KFYKgIG80aiRHUJA3VKoQa5dERERUKfgwBtk8f385tm3rY+0yiIiIKh2v6BERERHZKAY9IiIiIhvFoEdERERkoxj0iIiIiGwUgx4RERGRjWLQIyIiIrJRDHpERERENopBj4iIiMhGMegRERER2Si+GYMsdumSBpGRB6FW66FUOkGlCoG/v9zaZREREVEJeEWPLBYZeRBHj2YgLU2Lo0czEBGRZO2SiIiIqBQMemQxtVpf6jQRERFVLQx6ZDGl0qnU6dJcuqTBwIE/o0uXbRg48Gf8/be2vMsjIiKiuzDokcVUqhAEBXmjUSM5goK8oVKFWLwub/sSERFVPj6MQRbz95dj27Y+D7Uub/sSERFVPl7Ro0rxKLd9iYiI6OEw6FGleJTbvkRERPRweOuWKsWj3PYF/jeGX06OEe7u9hzDj4iIyAK8okfVQtHDHCkpOXyYg4iIyEIMelQt8GEOIiKismPQo2qBD3MQERGVHYMeVQtFD3MEBLjzYQ4iIiIL8WEMqhaKHubw9vZGRkaGtcshIiKqFnhFj4iIiMhGMegRERER2SgGPSIiIiIbxaBHREREZKMY9IiIiIhsFIMeERERkY1i0CMiIiKyUQx6RERERDaKQY+IiIjIRjHoEREREdkoBj0iIiIiGyURQghrF0FERERE5Y9X9Kxo1qxZ1i6h2mGblR3brGzYXmXHNis7tlnZsc0eDoMeERERkY1i0CMiIiKyUQx6VhQWFmbtEqodtlnZsc3Khu1VdmyzsmOblR3b7OHwYQwiIiIiG8UrekREREQ2ys7aBdiS+Ph4HDt2DO7u7oiOjgYAXLx4EcuXL4der4ePjw/eeOMNuLi4oKCgAJ999hnS0tJgMpkQGhqKwYMHAwBSU1MRFxcHg8GAdu3aITw8HBKJxJqHVmHKq83mzp2LrKwsODg4AADeeecduLu7W+24KlJZ22zZsmW4cOECpFIpxowZg1atWgHgefYwbVZTzrOMjAzExcUhOzsbEokEYWFhePrpp6HVarFo0SLcvHkTPj4+mDp1KuRyOQBgy5YtSEhIgFQqRXh4OAIDAwHUnPOsPNuM59n920yj0WDhwoVISUlB9+7dMW7cOPO2asp59lAElZtTp06JCxcuiGnTppnnzZo1S5w6dUoIIcQvv/wivv76ayGEEPv37xeLFi0SQgih1+vFxIkTxY0bN8zr/PXXX8JkMol58+aJY8eOVe6BVKLyarM5c+aIlJSUyi3eSsrSZj/99JOIi4sTQgiRnZ0tZs6cKQoLC83r8DwrW5vVlPNMrVaLCxcuCCGEyMvLE2+88Yb4559/xJdffim2bNkihBBiy5Yt4ssvvxRCCPHPP/+IGTNmCIPBIG7cuCEiIiJq3HlWnm3G8+z+babT6cSZM2fEzp07xYoVK4ptq6acZw+Dt27LUcuWLc2/qRW5evUqWrRoAQBo06YNfvvtN/Nner0ehYWFMBgMsLOzg4uLC7KysqDT6fDYY49BIpEgNDQUhw8frtTjqEzl0WY1TVna7PLly3j88ccBAO7u7nB1dUVqairPM5S9zWoST09PNG7cGADg7OyMunXrQq1W4/Dhw+jWrRsAoFu3buZz5vDhwwgJCYG9vT18fX1Rq1YtpKSk1KjzrLzarCYpa5s5OTmhefPm5iudRWrSefYwGPQqWP369XHkyBEAwKFDh5CZmQkA6NixI5ycnPDqq69i4sSJGDBgAORyOdRqNby8vMzre3l5Qa1WW6V2aylrmxWJj4/Hm2++iU2bNkHUsGeMSmqzhg0b4siRIygsLER6ejpSU1ORkZHB8wxlb7MiNe08S09PR1paGgICApCTkwNPT08At/+TvnXrFgDccz4plUqo1eoae549SpsV4Xl2b5uVpKaeZ5ZiH70K9vrrr2PVqlXYtGkTgoODYWd3u8lTUlIglUqxdOlS5ObmYvbs2WjdunWN+IF+kLK2mZ+fH9544w0olUrodDpER0cjMTHR/BthTVBSm/Xo0QOXL1/GrFmz4OPjg2bNmkEmk/E8Q9nbDECNO8/0ej2io6MxZsyYUq+el3Q+1cTz7FHbDOB5VlY18TwrCwa9Cla3bl288847AG7fKjp27BgA4MCBAwgMDISdnR3c3d3RrFkzXLhwAS1atDBfWQCAzMxMKJVKq9RuLWVtMz8/P3MbOTs7o0uXLkhJSbHpfxjvVlKbyWQyjBkzxrzcO++8g9q1a8PV1ZXnWRnbDECNOs8KCgoQHR2Nrl274sknnwRw+1Z2VlYWPD09kZWVBTc3NwC3r6DceT6p1Woolcp75tv6eVYebQbwPCupzUpS086zsuKt2wqWk5MDADCZTNi8eTN69+4NAPD29sbJkychhIBer8f58+dRt25deHp6wtnZGefOnYMQAomJiQgODrbmIVS6srZZYWGh+dJ+QUEBjh49ivr161utfmsoqc3y8/Oh1+sBACdOnIBMJkO9evV4nqHsbVaTzjMhBD777DPUrVsXzzzzjHl+cHAw9u3bBwDYt28fnnjiCfP8pKQkGI1GpKen49q1awgICKhR51l5tRnPs5LbrCQ16Tx7GBwwuRzFxMTg9OnT0Gg0cHd3x7Bhw6DX67Fz504AQIcOHfDiiy9CIpFAr9cjPj4ely9fhhACPXr0wMCBAwEAFy5cQHx8PAwGAwIDAzF27FibfUy8PNpMr9djzpw5KCwshMlkQuvWrfHyyy9DKrXN32PK0mbp6emYN28epFIplEolJkyYAB8fHwA8z8raZjXpPDt79ixmz54Nf39/8zkxYsQING3aFIsWLUJGRga8vb0xbdo0cz/ZzZs3Y8+ePeYhadq1aweg5pxn5dVmPM9Kb7NJkyYhLy8PBQUFcHV1xTvvvIN69erVmPPsYTDoEREREdko2/sVgYiIiIgAMOgRERER2SwGPSIiIiIbxaBHREREZKMY9IiIiIhsFIMeERERkY1i0CMiekhLlixBfHx8sXmnT5/G2LFjkZWVZaWqiIj+h0GPiOghhYeH448//sCJEycAAAaDAUuXLsVLL71kfin7oygsLHzkbRBRzcYBk4mIHsHBgwexdu1aREdHY/Pmzbh48SKGDh2KNWvW4PLly/Dx8cGYMWPQqlUrAMCePXuwbds2ZGZmws3NDYMGDTK/fu3UqVOIjY3FU089hR9//BFt2rRBZGSkNQ+PiKo5O2sXQERUnXXq1AlJSUlYvHgx/vrrL3zyySd46623EBERgcDAQJw8eRLR0dGIiYmBm5sb3N3d8dZbb8HPzw9nzpzBhx9+iCZNmqBx48YAgOzsbGi1WsTHx4O/hxPRo+KtWyKiRzRu3DicPHkSQ4cOxa+//op27dqhffv2kEqlaNOmDZo0aYJjx44BANq3b49atWpBIpGgZcuWaNOmDc6ePWvelkQiwbBhw2Bvbw8HBwdrHRIR2Qhe0SMiekQeHh5wc3NDvXr18Pvvv+PQoUM4evSo+fPCwkLzrds//vgDmzZtwtWrVyGEQH5+Pvz9/c3Lurm5MeARUblh0CMiKkdeXl7o2rUrJkyYcM9nRqMR0dHRiIiIQHBwMOzs7DB//vxiy0gkksoqlYhqAN66JSIqR127dsXRo0eRnJwMk8kEg8GAU6dOITMzEwUFBTAajXBzc4NMJiv2xC4RUUXgFT0ionLk7e2NmTNnYu3atVi8eDGkUikCAgLwyiuvwNnZGeHh4Vi0aBGMRiOCgoIQHBxs7ZKJyIZxeBUiIiIiG8Vbt0REREQ2ikGPiIiIyEYx6BERERHZKAY9IiIiIhvFoEdERERkoxj0iIiIiGwUgx4RERGRjWLQIyIiIrJRDHpERERENur/Aeqfx+fyNLkdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
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   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\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?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740), 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.\n"
   ]
  },
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   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\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",
    "# reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "# rename columns \n",
    "df_total.columns = ['year', 'total'] \n",
    "\n",
    "# change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "# show resulting dataframe \n",
    "df_total.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data:\n",
    "\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": "markdown",
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # create df_countries dataframe\n",
    "    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\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",
    "    # reset index in place\n",
    "    df_total.reset_index(inplace=True)\n",
    "\n",
    "    # rename columns\n",
    "    df_total.columns = ['year', 'total']\n",
    "\n",
    "    # change column year from string to int to create scatter plot\n",
    "    df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\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",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<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",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "\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).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7affae1518>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # normalize China data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalize India data\n",
    "    norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "ax0 = df_can_t.plot(kind='scatter', x='Year', y='China', figsize=(14, 8), alpha=0.5, color='green', s=norm_china * 2000 + 10, xlim=(1975, 2015))\n",
    "ax1 = df_can_t.plot(kind='scatter',x='Year', y='India', alpha=0.5, color=\"blue\", s=norm_india * 2000 +10, ax = ax0)\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980-2013')\n",
    "ax0.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\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",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2021-01-20        | 2.5     | LakshmiHolla | Changed TOC markdown section       |\n",
    "| 2021-01-05        | 2.4     | LakshmiHolla | Changed markdown for outliers      |\n",
    "| 2020-11-12        | 2.3     | LakshmiHolla | Added example code for outliers    |\n",
    "| 2020-11-03        | 2.2     | LakshmiHolla | Changed URL of excel file          |\n",
    "| 2020-09-29        | 2.1     | LakshmiHolla | Made fix to a boxplot label        |\n",
    "| 2020-08-27        | 2.0     | Lavanya      | Moved lab to course repo in GitLab |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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