3_S2-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots.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",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "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_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Numpy**](http://www.numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\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_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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    }
   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we'll do is install **openpyxl** (formerly **xlrd**), a module that *pandas* requires to read Excel files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: mamba\r\n"
     ]
    }
   ],
   "source": [
    "!mamba install openpyxl==3.0.9 -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import primary modules.\n"
   ]
  },
  {
   "cell_type": "code",
   "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Download the Canadian Immigration dataset and read it into a *pandas* dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel(\n",
    "    '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,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "data": {
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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",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <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]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's find out how many entries there are in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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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    "new_sheet": false,
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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": 6,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vh/sx834cy56mg5tz4b7lfnkh5r0000gn/T/ipykernel_16364/3015018611.py:14: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_can['Total'] = df_can.sum(axis=1)\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": 7,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.4.3\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circular 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,
    "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/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
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  {
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    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <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": 8,
     "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": 9,
   "metadata": {
    "button": 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 Caribbean) by passing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": 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": [
    "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": 11,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\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",
    "    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",
    "*   **Minimum:** The 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:** The largest 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 `boxplot`, 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 subset of 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": 12,
   "metadata": {
    "button": 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": 12,
     "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": 13,
   "metadata": {
    "button": 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 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": 14,
   "metadata": {
    "button": 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": 14,
     "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 analyze 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": 16,
   "metadata": {
    "button": 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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "#The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.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",
    "    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 percentiles associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": 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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "#The correct answer is:\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": 18,
   "metadata": {
    "button": 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",
    "#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()"
   ]
  },
  {
   "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": 19,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RQw89pISEBO/F7EeOHLno+jl58qQk+fRVQY0fP17NmzdX48aNFRgYqF69eql///6S5H0fbt++XQMHDtTQoUO1YcMGLV26VIGBgerRo4fPBe75yW9727dvn8LCwrx/jz/+eIHa/Pbbb+uVV17RP/7xD1WrVq1AdV/JjTXjxo3TV199pc8++0ySdN999+Va3pCQEO/6R/4IYMVQaGioEhISlJCQoMaNG+uPf/yjOnTooNGjR190OjPzecMdP35cmzdvlr+/v3bu3HnF7Tn3AZaf/N7kF3vzh4WFaePGjZo3b55q1aqlSZMmKSEhQZs2bbqsui5c5qsRGBiYq568xuXk5BTKdOe7cBnyW6ayZcv6DM+ZM0dPPPGE7r33Xn366af66quv9Pzzz+e6yD0oKCjP+V+sTdLPoWHTpk267bbbtGLFCtWvX1+TJ0++6DR58ff3V0hIyCXL5dWfV7t+o6OjFRERUeCgnte6utR7wM/PzydAX9imgvbT+U6cOKHbb79dHo9HU6dO1fr167VhwwZ5PJ5cN2Dk1eZzdRfme+Ra1dWtWzft27dPEydO1Nq1a5WYmKhKlSpd9EaTcwcUhw4duuz6IiMjNXv2bJ08eVJ79uzRnj17vHdMx8XFSZJeeeUVNWjQQKNGjVLjxo3VqVMnffDBB1q1apWWLVsmSapatap+/PHHXPPfv39/vjcAxMTEeA+wExMT9ec///mS7f3rX/+q4cOHa8GCBbkuos+rDeeG82vDxVSpUkW1a9fWL3/5S/3zn//U0qVL9cUXX/iUyczM9DmgQ94IYCVEQECA91b5evXqSfr5duvzrVq1yvuaJA0cOFD+/v5aunSpZs2apY8++uiS9Rw4cMDnbsudO3fq4MGDuvnmm/MsX69ePa1YscJn3IoVK+TxeFS3bl1JP++w8zpi9Pf3V4cOHfTnP/9ZmzZtUtWqVfXBBx/kWU9CQoKCg4Nz1bVy5UqfZS6O6tatqw0bNvisozVr1hRo2pUrV6pJkyYaNmyYmjVrppo1a17Rs8/Ofajm1U/169fXsGHD9Nlnn6l///7629/+dtnzv1BBt+HC4Ofnp/vvv1/vv/++UlJScr1uZjpy5Eih1nmhK+mnb775RgcOHNDo0aPVqVMn3XzzzTp06NBl311Xr149HThwQLt27fKOy8jIuKqDssKs6+DBg9q2bZtGjBihrl27qm7dugoJCbnkoxyaNGkij8dzVWfAg4KCVK1aNfn5+enDDz9Uhw4dvMHip59+ynWn8rmQfa4P2rVrp5SUFJ/l/eabb5SamqpbbrklzzoDAgK8B9gJCQmqVKnSRdv4/PPP6//+7//06aef5nkHY7t27fTFF1/4HIAsWrRIZcqUUZMmTQqwFvJ3bp6nT5/2Gf/111+refPmVzXv0oAAVgydOXNGP/74o3788UclJyfr7bff1ueff64ePXpIkmrUqKFevXrp97//vT7//HNt375dTz31lLZu3arhw4dL+vm5TnPmzNFHH32kDh066JVXXtFjjz2W5wfQ+cqUKaNHHnlEmzZt0saNG9W3b181aNAg31uXhw8frs2bN2vYsGHavn27Fi1apMGDB+uBBx7wfl0SFxen7du3KykpSRkZGTp9+rTmz5+vcePGadOmTdq3b58++eQTpaamekNbXu168sknNWrUKM2ZM0e7du3SK6+8ovnz5+vZZ5+90lV9Xfj973+v/fv3a+DAgfrmm2+0bNky7y3klzqbULt2bX399deaP3++kpOTNX78eM2dO/ey23DuqH/BggU6cOCAjh8/rt27d+uZZ57R6tWrtXfvXq1Zs0arVq3Kt48uR0G24cI0evRo1axZU61bt9bf/vY3/fe//1VKSormzZunjh07es9oXCtX0k/Vq1dXcHCw3nzzTSUnJ2vJkiV66qmnLvsMU+fOndWoUSM9+OCDWr9+vRITE/XAAw9ck4fJXkldERERio6O1pQpU7Rz506tWbNGffr0UWho6EXrqlixolq2bJnroOz48ePes0vn9qWJiYk+z+/asGGD5syZo+TkZK1Zs0Y9e/ZUYmKiJkyY4C3TvXt3LVq0SOPGjVNycrI2btyofv36KSYmRq1atZIkdenSRU2bNvUu77p16/TQQw+pdevW6tix45WsQh9DhgzRa6+9ppkzZ6p27drez4XzDxgGDhyoI0eO6NFHH1VSUpIWLFigUaNGafDgwT5nzM+tk8zMTJ91dM4///lP/f3vf9fWrVu1d+9eLV68WL1791a1atX0i1/8wlvu2LFj2rRpk8/jOJCPIrnyDFesb9++Prfxh4aGWt26de21117zuc34yJEj3lv4g4KCfG7h37Vrl5UrV84mTJjgLZ+Tk2O//OUvrWXLlvneLXbucQUzZ8606tWrW1BQkHXq1Mn7uAKzSz+GIioqyh5//HGf258PHjxod9xxh4WHh3sfQ7FixQrr1KmT9xEECQkJNmbMGO/dlnm51GMozq2/K70I//wLx1NTU3NdgJ6WluZzQ0Jej5NYtWqVSbKUlBTvuHMXZJ+7uSG/x1DUq1fPgoKCrEGDBt6L8M9dzJ9XG8+tk9/97ncWERFh5cqVsz59+tibb77pc0H9uX49X17tfOqpp6xSpUrm8Xisb9++9sMPP1iPHj0sNjbWgoKCrGrVqjZgwICL3kmb30X4F24zZhffhvObV0H6JT/Hjx+3F1980erVq2chISFWoUIFa9mypb311lveC7nz2n4uvEEhv8dQnO/Cdhakn/IyZ84cS0hIsODgYGvcuLEtX77c/P39vY9yyW+7qFGjhr3wwgve4ZSUFLvtttssODjYYmNj7Y033rjix1AURl0XDi9fvtwaNmxowcHBVqtWLfvHP/6Ra755mT59utWsWdNnXF6PVdEFj6VZtWqVdzsIDw+3bt262ZYtW3LNf9KkSdagQQMrU6aMRUdH21133ZXrZo8ffvjBevbsaWFhYVauXDnr3bu37d+//6LtLqi8lkPnPSbmnDVr1libNm0sODjYKleubCNGjPC5qedi8zpn4cKF1qpVKytfvrwFBwdbfHy8Pf7447Zv3z6f+UydOtVq165dKMtX0nnMCuFpcCgVXnzxRc2aNSvXk57h3sqVK9WxY0dt2bJFDRo0KOrmANels2fPqmHDhhozZoy6d+9e1M0p8XJyctSoUSM999xzuvfee4u6Ode94v+DZUAp8M4776hRo0aKiYnRtm3bNHToULVq1YrwBVxEYGCgZsyY4XMNFq6d77//Xv369SN8FRABDCgG9u7dqzFjxnjvnrrtttv0//7f/yvqZgHXvZYtWxboETu4ejfccIP+8Ic/FHUzig2+ggQAAHCMuyABAAAcI4ABAAA4VuyuAfvhhx9yjYuKirroz9SgZKG/Sw/6unShv0uX0tDfMTEx+b7GGTAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAsYCibgBQ2OrVq6LDhzm2KDliiroBKKAKFXKUlPRjUTcDKBYIYChxDh/20/ff/1DUzUAhiIqKUkZGhiQpNjaGfr3OxcYSloGC4jQBAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAuw5xJxEAoCD4vCi+CGAAAACOEcAAAAAcI4ABAAA4dtkB7KGHHrqs8klJSXr11VclSRs3btQnn3xyuVUCAACUKE5/iqh58+Zq3ry5yyoBAACuO1ccwJKSkjRnzhyVK1dOqampio+P1+DBg+XxeJSYmKjp06erXLlyiouL806zfPlyJScnq3///tq4caPmzp2rrKwslStXToMHD1aFChUKY5kAAACua1d1BiwlJUVjx45VRESERo0apR07dig+Pl6TJ0/W888/rypVqmjcuHF5TlunTh2NHj1aHo9HS5Ys0YIFC/Twww/nKrd48WItXrxYkvTqq68qKioq90IEBOQ5vjjj1uJLufj6KWnbQ2l14Xubfr3+Xf2+i33f5Squ74uS+Nl9Oa4qgCUkJKhixYqSpJtuuknp6ekKCQlRpUqVVLVqVUlShw4dvAHqfJmZmXrjjTd06NAhZWVlqVKlSnnW0aVLF3Xp0sU7nJGRkatMVFRUnuOLrxh9//0PRd2I69al+js2NqaEbQ+ll29f06/Xv6vbd5W8ffm1V5z3d6Whv2Ni8j+guKoAFhgY6P3fz89POTk5BZ526tSp6tatm5o3b+79OhMAAKA0KPTHUMTExCg9PV0//vijJGn16tV5ljtx4oQiIyMlSStWrCjsZgAAAFy3Cv0uyKCgID322GN69dVXVa5cOdWpU0epqam5yvXq1Utjx45VZGSkatasqfT09MJuCgAAwHXJY2ZW1I24HD/8kPv6gpL2PXJsLNeAXUxBrgFj/ZUM5/c1/Xr9u9o+Kmn7cheK8/uiNPT3xa4B40n4AAAAjhHAAAAAHCOAAQAAOEYAuw4V1+/zAQBu8XlRfBHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwLFC/yki4HoQG5v/04dR3PyvL+nX61uFCjlF3QSg2CCAocThtuySozT8VAmA0omvIAEAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwKKugEofPXqVdHhwyU9W8cUdQPgDH1duhROf1eokKOkpB8LZV7AtUAAK4EOH/bT99//UNTNuGaioqKUkZFR1M2AA7GxMSV6W4avwnxvx8YS3HF9K+mnSQAAAK47BDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAB2AW5dBgCgZLsePusJYAAAAI4RwAAAABwjgAEAADhWoJ8iOnz4sKZPn67k5GQFBASoUqVKatGihTZu3KgRI0bkKj9p0iR169ZN1apVK/QGAwAAFHeXDGBmptdee00dO3bUkCFDJEl79uzRxo0b853m8ccfL7QGAgAAlDSXDGBJSUkKCAjQ7bff7h1300036aefftLWrVv1+uuvKzU1VfHx8Ro8eLA8Ho9efPFFPfTQQ6pRo4Yeeugh3Xnnndq8ebOCgoI0fPhwVahQQRs3btTcuXOVlZWlcuXKafDgwapQocK1XFYAAIDrwiUD2L59+xQXF5fnaykpKRo7dqwiIiI0atQo7dixQ3Xq1PEpc/r0adWsWVN9+vTRrFmztGTJEt1zzz2qU6eORo8eLY/HoyVLlmjBggV6+OGHc9WxePFiLV68WJL06quvKioqKvdCBATkOf5KXQ+3p16twlwf15vC7m9c3+jr0oN9eWlUdH1U1PuWAl0Dlp+EhARVrFhR0s9nxdLT03MFsICAADVr1kySFB8fry1btkiSMjMz9cYbb+jQoUPKyspSpUqV8qyjS5cu6tKli3c4IyMjV5moqKg8x1+ZGH3//Q+FNK+iERsbU4jr4/pTuP2N61vJ3pbhi3156VKU+3JXn5MxMfkHzEsGsBtuuEHr1q3L87XAwEDv/35+fsrJyclVxt/fXx6Px1smOztbkjR16lR169ZNzZs3V1JSkubMmXOppgAAAJQIl3wMRf369XX27Fnv14CStHv3bm3btu2qKj5x4oQiIyMlSStWrLiqeQEAABQnlzwD5vF49PTTT2v69OmaP3++AgMDFR0drRYtWlxVxb169dLYsWMVGRmpmjVrKj09/armBwAAUFx4zMyKuhGX44cfcn+nX5jfI8fGFv/rBkrCMlwM14CVHiV9W4Yv9uWlS1FfA+Zi+7jYNWA8CR8AAMAxAhgAAIBjBLALcMoaAICS7Xr4rCeAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGNX9WPcuH7FxhbdL8y7UdKXD+eU/G0ZvgqnvytUyP3bxMD1hABWAl0Pt9deSzwJv/Sgr0sX+hulCV9BAgAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwAAAABwjgAEAADhGAAMAAHCMAAYAAOAYAQwAAMAxAhgAAIBjBDAAAADHCGAAAACOEcAAAAAcI4ABAAA4RgADAABwjAAGAADgGAEMAADAMQIYAACAYwQwAAAAxwhgAAAAjhHAAAAAHCOAAQAAOEYAAwAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI55zMyKuhEAAAClSYk4AzZixIiibgIcor9LD/q6dKG/S5fS3t8lIoABAAAUJwQwAAAAx0pEAOvSpUtRNwEO0d+lB31dutDfpUtp728uwgcAAHCsRJwBAwAAKE4IYAAAAI4FFHUDrlZiYqKmTZumnJwcde7cWd27dy/qJqEA3n77bW3evFnly5fX66+/Lkk6fvy4xo0bpwMHDig6OlpDhw5VWFiYJGnevHlaunSp/Pz89Mgjj6hx48aSpG+//VYTJ07UmTNn1KRJEz3yyCPyeDw6e/as3nrrLX377bcqV66chgwZokqVKhXV4pZqGRkZmjhxog4fPiyPx6MuXbrozjvvpL9LqDNnzuiFF15QVlaWsrOz1bp1a/Xu3Zv+LsFycnI0YsQIRUZGasSIEfR1QVkxlp2dbYMGDbIff/zRzp49a08//bSlpqYWdbNQAElJSZacnGzDhg3zjps5c6bNmzfPzMzmzZtnM2fONDOz1NRUe/rpp+3MmTO2f/9+GzRokGVnZ5uZ2YgRI2zHjh2Wk5Njo0ePts2bN5uZ2aJFi2zy5MlmZrZ69WobO3asw6XD+TIzMy05OdnMzE6cOGFPPvmkpaam0t8lVE5Ojp08edLMzM6ePWsjR460HTt20N8l2MKFC+2NN96wMWPGmBn78oIq1l9B7t69W1WqVFHlypUVEBCgtm3basOGDUXdLBRA3bp1vUdE52zYsEEdO3aUJHXs2NHblxs2bFDbtm0VGBioSpUqqUqVKtq9e7cOHTqkkydPqlatWvJ4POrQoYN3mo0bN+rWW2+VJLVu3Vpbt26Vcb9JkYiIiFB8fLwkKTQ0VLGxscrMzKS/SyiPx6OQkBBJUnZ2trKzs+XxeOjvEurgwYPavHmzOnfu7B1HXxdMsQ5gmZmZqlixone4YsWKyszMLMIW4WocOXJEERERkn7+0D569Kik3P0cGRmpzMzMi/b/+a/5+/urTJkyOnbsmKtFQT7S09OVkpKihIQE+rsEy8nJ0fDhwzVgwAA1aNBANWvWpL9LqOnTp+vBBx+Ux+PxjqOvC6ZYB7C8UvD5GwFKhvyOdi52FMS2cf05deqUXn/9dfXr109lypTJtxz9Xfz5+fnptdde06RJk5ScnKx9+/blW5b+Lr42bdqk8uXLe89wXwp97atYX4RfsWJFHTx40Dt88OBBb+pG8VO+fHkdOnRIEREROnTokMLDwyXl7ufMzExFRkbm2f+RkZE+01SsWFHZ2dk6ceJErq884U5WVpZef/11tW/fXq1atZJEf5cGZcuWVd26dZWYmEh/l0A7duzQxo0b9dVXX+nMmTM6efKkJkyYQF8XULE+A1ajRg2lpaUpPT1dWVlZ+vLLL9W8efOibhauUPPmzbVixQpJ0ooVK9SiRQvv+C+//FJnz55Venq60tLSlJCQoIiICIWGhmrnzp0yM61cudLb/82aNdPy5cslSWvXrlW9evVKzFFTcWNmmjRpkmJjY9WtWzfvePq7ZDp69Kh++uknST/fEfn1118rNjaW/i6B7r//fk2aNEkTJ07UkCFDVL9+fT355JP0dQEV+yfhb968WTNmzFBOTo46deqk3/zmN0XdJBTAG2+8oW3btunYsWMqX768evfurRYtWmjcuHHKyMhQVFSUhg0b5j3SmTt3rpYtWyY/Pz/169dPTZo0kSQlJyfr7bff1pkzZ9S4cWP99re/lcfj0ZkzZ/TWW28pJSVFYWFhGjJkiCpXrlyUi1xqbd++Xc8//7xuvPFG746zT58+qlmzJv1dAu3du1cTJ05UTk6OzExt2rRRz549dezYMfq7BEtKStLChQs1YsQI+rqAin0AAwAAKG6K9VeQAAAAxREBDAAAwDECGAAAgGMEMAAAAMcIYAAAAI4RwABccxMnTtRHH31UJHWbmd5++2098sgjGjlyZJG04ZxVq1bp5ZdfLtI2ALg+EMCAUuiJJ57Qo48+qlOnTnnHLVmyRC+++GLRNeoa2b59u7Zs2aJ33nlHY8aMyfX68uXLNWrUKCdtad++vZ577jkndV0oKSlJjz/+eJHUDSA3AhhQSmVnZ+vTTz8t6mZctpycnMsqf+DAAUVHRyskJOQatciN7Ozsom4CgEJUrH8LEsCV+/Wvf6358+era9euKlu2rM9r6enpGjRokD788EP5+/tLkl588UW1b99enTt31vLly7VkyRLVqFFDy5cvV1hYmAYPHqy0tDR9/PHHOnv2rB588EHdeuut3nkePXpUL730knbt2qW4uDgNGjRI0dHRkqTvv/9eU6dO1bfffqvw8HDde++9atu2raSfv74MCgpSRkaGtm3bpuHDh6thw4Y+7c3MzNSUKVO0fft2hYWF6e6771aXLl20dOlSvffee8rKytJDDz2ku+66S717977oenniiSfUtWtXrVy5Uvv371fbtm3Vp08fvf3229q+fbtq1qypoUOHKiwszLueBg4cqNmzZ+vUqVPq06eP4uPjNWnSJGVkZKh9+/bq37+/JHnX20svvSRJ+u9//6upU6fq8OHDat++vVJTU9WhQ4dc63jFihXq2rWrbr31Vk2ePFl79+6Vx+NRo0aN1L9/f2//nd/2AwcOqHHjxnriiSeUk5OjV155xbseJGn8+PHKzMzUu+++q7S0NAUFBemWW25R3759r2RzAnCZOAMGlFLx8fGqV6+eFi5ceEXT79q1S9WrV9fUqVN1yy236I033tDu3bs1YcIEDR48WFOnTvX5inP16tW655579N577+mmm27ShAkTJEmnTp3Syy+/rFtuuUXvvvuunnrqKb333ntKTU31mbZHjx6aMWOG6tSpk6st48ePV8WKFTV58mT94Q9/0Icffqivv/5av/jFL/Too4+qVq1amjlz5iXD1znr1q3Tc889p/Hjx2vTpk0aM2aM+vTpo/fee085OTn67LPPcq2L8ePHa8iQIZoxY4bmzp2rUaNGaezYsVqzZo22bduWq46jR49q7Nixuv/++zV16lTFxMRo586dueZbuXJlvfvuu96fWevRo4cmT56scePG6eDBg5ozZ47PNGvWrNGzzz6riRMnat++fVq+fLlCQkL07LPPKiIiQjNnztTMmTMVGRmpadOm6c4779SMGTP05ptvqk2bNgVaPwCuHgEMKMV69+6tzz77TEePHr3saStVqqROnTrJz89Pbdu21cGDB9WzZ08FBgaqUaNGCggI0I8//ugt37RpU9WtW1eBgYHq06ePdu7cqYyMDG3evFnR0dHq1KmT/P39FR8fr1atWmnt2rXeaVu0aKE6derIz89PQUFBPu3IyMjQ9u3b9cADDygoKEg33XSTOnfurJUrV17xevnlL3+pChUqKDIyUnXq1FFCQoLi4uIUGBioli1bKiUlxad8z549FRQUpEaNGik4OFi33HKLypcv753+wvKS9NVXX6latWpq1aqV/P39dccdd6hChQo+ZSIiInTHHXfI399fQUFBqlKliho2bKjAwECFh4frV7/6Va5wd8cddygyMlJhYWFq1qyZ9uzZk+9ynuujo0ePKiQkRLVq1bridQbg8vAVJFCK3XjjjWrWrJk++eQTxcbGXta05cuX9/5/LhSdHyCCgoJ8zoBVrFjR+39ISIjCwsJ06NAhHThwQLt27VK/fv28r2dnZ6tDhw55TnuhQ4cOKSwsTKGhod5xUVFRSk5OvqzlOd+Fy3bh8OnTpy+r/Pnr4fx2n79cHo9HkZGRPmWioqJ8ho8cOaJp06bpm2++0alTp5STk+P9keNzLuyDzMzMfJfz8ccf18cff6yhQ4eqUqVK6tmzp5o1a5ZveQCFhwAGlHK9e/fWM888o27dunnHnbtg/fTp0ypTpowk6fDhw1dVz8GDB73/nzp1SsePH1dERIQqVqyounXrXvRORI/Hk+9rEREROn78uE6ePOkNYRkZGbnCzPWmQoUKPuHIzC4aliTpgw8+kCT99a9/Vbly5bR+/XpNnTq1QPXltQ6rVq2qIUOGKCcnR+vXr9fYsWP13nvvFfsbFoDigK8ggVKuSpUqatOmjc91TeHh4YqMjNSqVauUk5OjpUuXav/+/VdVz1dffaXt27crKytLH330kWrWrKmoqCg1a9ZMaWlpWrlypbKyspSVlaXdu3fru+++K9B8o6KiVLt2bX3wwQc6c+aM9u7dq2XLlql9+/ZX1d5rrWnTptq3b5/Wr1+v7Oxsff7555cMuSdPnlRISIjKli2rzMzMy7p+r3z58jp27JhOnDjhHbdy5UodPXpUfn5+3qDt58fHAuACZ8AAqGfPnlq1apXPuMcee0zvvvuuPvzwQ/3iF7+46uuD2rVrpzlz5mjnzp2Kj4/Xk08+KUkKDQ3Vc889pxkzZmjGjBkyM1WvXv2y7sZ76qmnNGXKFD322GMKCwtTr169ct0peb0JDw/XsGHDNG3aNE2cOFHt27dXfHy8AgMD852mV69eeuutt9S3b19VqVJFHTp00L/+9a8C1RcbG6t27dpp0KBBysnJ0dixY5WYmKi///3vOn36tKKjo/XUU0/lusYOwLXhMTMr6kYAQGmXk5OjgQMHavDgwapfv35RNwfANca5ZgAoIomJifrpp5909uxZzZs3T2bGnYhAKcFXkABQRHbu3KkJEyYoKytL1apV0/Dhw/kKECgl+AoSAADAMb6CBAAAcIwABgAA4BgBDAAAwDECGAAAgGMEMAAAAMf+P2NdNdprDbzLAAAAAElFTkSuQmCC\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": 20,
   "metadata": {
    "button": 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 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": 22,
   "metadata": {
    "button": 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "#The correct answer is:\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": 23,
   "metadata": {
    "button": 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\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()"
   ]
  },
  {
   "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": 24,
   "metadata": {
    "button": 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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "#The correct answer is:    \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": 25,
   "metadata": {
    "button": 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",
    "\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()"
   ]
  },
  {
   "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": 26,
   "metadata": {
    "button": 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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "\n",
    "#The correct answer is:    \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_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#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 data points 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": 27,
   "metadata": {
    "button": 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": 27,
     "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": 28,
   "metadata": {
    "button": 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 data  points 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": 29,
   "metadata": {
    "button": 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": 29,
     "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": 30,
   "metadata": {
    "button": 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 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 30,
     "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 actual from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01), 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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": 32,
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   "outputs": [
    {
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DYu3AgQPSr18/USgU0rNnT5k6dap8/PHHer1JycnJMnToUBER2bx5s3Tv3l1mzpwpzz33nIhc7TFqOLYh7fnw4cMCQDZv3qx7/MqVK+Ln56drB635ubjeiRMnBIBs2bJFb/3IkSN17aHB8uXLxdnZudmenZkzZ4q7u3uzx2rJ+vXrxdHRUdfWG75/8vPzddvk5uYKADl48GCz+7m+vdxxxx3y8MMP620zd+7cGxYTTz/9tMTExLQY8/V/30SuvgfR0dF66yZPnqxrMw3S09NFoVDI0aNHdfu6mb+NLWmuWDt79qx4enrKY489JuXl5VJVVSWLFi0SAHo95NXV1TJz5kwBIPb29qJQKBp97zk4OMjbb7+tt27fvn0CQH7++WeD4jTUhQsXZNCgQXqv/4cffhAAemc/RETS0tKkQ4cOTe6npWLt/Pnz4urqKg4ODqJUKhsVos1pl2PW+vTpA0dHR92yr68vevXqBScnJ711149jGjRokN6yr68vBg4c2Ghdc+OfCgsLUV1djeHDh+utHzFiRJMxurm56a0rKirClClTEBwcDHd3d7i7u6O8vBzHjh1r4dU2VlBQgOHDh+vlYNCgQfDw8EBBQYFunZ+fH7y9vXXL/v7+EJGbGt91/espKChA3759odFodOs6deqEXr166cWgUCgwYMAA3bKvry8A6OXd19cX586dazTo1FDyf+PnFAoFCgoKcPnyZfzpT3+Cm5ub7t+MGTNQXl6Os2fP6p4XEhKitx9/f3+98RbAzbWZ3bt3449//CO6deuGjh07IjAwEAAavc/Dhg1r8vWMHz8eAJCRkQG1Wn2jl9+ipUuX4uDBg01e1VtQUAAvLy/07dtXt87JyQm333673nvYVKxjxoxBSUkJ9u3bh8zMTIwcORJjx47Fli1bICLIzMxEdHS0bvuzZ89i1qxZ6N27Nzw9PeHm5oaCggK9nMyYMQNffPEFysrKAADvvvsupkyZAmdnZ6Ne87vvvovbb78dnTp1gpubG1544YVGub/RZ2P//v2444479J4zcuTIGx67d+/e2Lt3L/Lz8xEfH4/q6mo8/vjjGD58uO5CmOjoaOzcuRPl5eXIzMzEmDFjMHr0aGRmZgKAXu4Mac/79+8HcPVCpAaOjo4YOnSobrm1PxeGKCgoQGRkpN66qKgoVFVV4bfffmvyOQ2fZUOsX78ekZGR8PPzg5ubGx555BFUV1fj9OnTum0UCoXeZ9jf3x8A9F7PjdrL/v379XILNG4L9fX1WLp0KUJCQqDRaODm5oa33nrL6O/2Btd/3prLpYjo3n/g5v82Gkuj0WD9+vXYvn07PD094erqih9//BHjx4/XG3e3evVqfPvtt/jiiy+wc+dOvP/++1i2bBneeecdg46jUCiaXP/qq6/qteNt27bdcF8XL15EbGws7O3t8d57793S8VvSsWNH7N69Gzt27MCqVauQmpqKNWvW3PB59kYfqQ1wcHDQW264QvD6dfX19a3yvOsZ8ga6uro2WnfPPfdAo9Fg1apVCAgIgKOjI0aOHHlTA0ibi+Ha9dd+aK997EavrylNvZ6mYhARvfVKpVLvw9vw2LV5b1hnzBf1tfbt2wdPT094eXnhyJEjAIDPPvsMPXv2bLTttcVPU/m51TZz6dIl3HXXXRg5ciTef/99XXHar1+/Ru9zUzkFgNjYWKxduxa5ubl6Bc/N6NKlC+bMmYMFCxZg0qRJjR435D1sKtaAgAAEBQXh+++/R05ODqKjoxEaGora2lrs2bMHW7Zswauvvqrbftq0aTh+/Dhef/11dOvWDS4uLvjzn/+sl5Nx48ahU6dOWLduHSIjI5Gfn48PP/zQqNf72WefYfbs2Vi6dCmioqLg7u6Ozz77rNFA6Rt9NprKgaEUCgUGDx6MwYMHIyEhAdu3b0dERAQ+/fRTPProoxg+fDicnJywdetWZGZmYs6cORg9ejQeeughHD9+HDt27MDixYv14mmpPRvyuTFkPw0M+VwY6vocXvvDqim9evVCRUUFTp48ic6dOze7359++gkPPPAAXnjhBfz973+HSqXCjz/+iEcffVSvTTX3/XNtPgxpLzdqCykpKViyZAmWLVuGIUOGoGPHjkhNTcXXX3/d4vOaY+j37fXrW+tvnCFGjx6NQ4cOoaysDPX19fDy8sKwYcMQFBQEAKiqqsLzzz+Pjz76CBMnTgQADBgwACdOnMDLL7+MJ598EgBw22236RXYwP+K6Ybvz+s99dRTet9nDUV4c8rLy/GHP/wBNTU1yMjIgIeHh+6x2267DQBw+vRpvc/GmTNnmj1+S5RKJYKDgwFc7ZQoKyvDggULbjgFWLvsWbOU4OBgODo6Ijc3V2/9jz/+eMPnnjt3Dvv370dSUhLuvvtu9O3bF87Ozo1+4Tg6Ot6wh6lfv37Izc3V+1L65ZdfUF5ejn79+hnxim5ev379UFBQgNLSUt26M2fO4NdffzVbDMDVaQP++c9/4v7774dSqUS/fv3g7OyMI0eOIDg4uNG/pq62ak0HDhzA2bNnsXjxYowePRp9+vRBWVmZUYVoUlISXnrpJdxzzz2NLh+/GS+88ALq6+vx2muv6a3v168fSktL9X6ZX7lyBT///LNB72F0dDS+//57bN26FWPGjIFSqURkZCTeeOMNnDlzRq/QzM7OxqxZsxAbG4sBAwbgtttu0xXWDZRKJR5//HG8++67ePfddxEeHm50W8rOzsbgwYORmJiI0NBQ9OjR46bmrurXrx9++OEHvXXXLxuqT58+AKD7rDs6OuKOO+7Ahg0bsHPnTkRHR0Oj0aBfv354+eWXYWdnp+vJMaQ9N+QoJydHd8zq6mrk5eXpvR5TfS4airvrv7f69euHrKwsvXXZ2dlwcXFB9+7dm9zXAw88ACcnJ7zyyitNPt7Q67p9+3ZoNBq88soruP3229GzZ8+bmvPMkPbSt2/fG7aF7OxsjB07FnFxcRg8eDCCg4Nx+PBho+NpTlO5zMrKgkKh0OsZtwSVSgUvLy8cOnQI+fn5usKspqYGNTU1jWYxsLOz0/s+vOOOO7Bp0ya9bb799lt06dKl2YJdrVbrtd+mZi1oUFpaitGjRwMAvvvuO3h6euo93rVrV/j5+TUZgyG96TdSX1+PK1eu3HC7dtmzZimurq6YMWMGFixYgE6dOqFnz5748MMPceDAAb1TKk1RqVTw9vbGu+++i6CgIJw7dw7PP/98o0bWrVs3bNmyBePGjYOjo6PeacYG8fHxWLFiBaZNm4Z58+bh/PnzmDVrFkaOHImIiIhWfc3Nefjhh/Hyyy/jwQcfxN///neICJ599ln4+/ubbG62y5cv4/Tp06ivr0dpaSmys7OxZMkS+Pv7Y8mSJQAANzc3zJs3D/PmzQMA3HnnnaitrcXevXuxa9euRgVLa+vSpQucnJzwxhtvYO7cuTh69CiSkpKM7qV59tln4ejoiPvuuw+ff/45/vCHP9x0TB07dsSiRYsaTXsQHR2NYcOG4eGHH8aqVavg4eGBRYsWoaqqCjNnzrzhfqOjozFlyhS4urpiyJAhunXPPvssunXrhq5du+q27dWrF/75z39i5MiRqKurw8KFC5v8URIXF4eXXnoJv/76q8GnSq7Vq1cvvPfee/jyyy/Rv39/fPXVV1i/fr3R+5kzZw6GDh2K+fPn49FHH0VBQQFSUlJu+Lw//elPCA8PR3h4OPz8/PD777/jlVdegYODg957GB0djb/97W/o3bs3fHx8dOvS0tIQERGhO/VrSHsODg5GbGwsZs+ejbfffhudOnXC0qVLceHCBd3xTPm5aDjtt3nzZvTr1w9OTk5QqVR44YUXcO+992Lp0qWYOHGiblqGuXPnNuq9a+Dv74+0tDTMmDED58+fxxNPPIGgoCBotVp8+eWX2LJlC7Kzs9GrVy+cPXsW7733HkaPHo3t27dj9erVRsduSHuZO3cuHnjgAQwbNgzjx4/H9u3bsW7dukb7WbduHbZs2QJ/f3989NFH+Omnn6BSqYyOqSnPPfcchgwZgsTERDz55JM4evQoEhIS8Mgjj+iGWbSm48ePQ6vVorCwEMDVU8GlpaUIDAzU9cJ+8MEH6NGjB2677Tbs2rULf/nLXxAZGYmpU6cCuPq9M3r0aMyfPx+enp7o0aMH8vPzkZKSgocfflh3rDlz5iA8PBzz58/HlClT8PPPP+ONN95AamrqLb+O4uJijBkzBi4uLvjwww9x6dIlXLp0CcDVgs/R0REKhQLPPfcc5s2bhz59+iAsLAwffPABfvnlF7z77ru6fWm1Whw/flwvR7t374Zarda9B8nJyYiIiED37t1RU1OD7OxsvPbaa5g+ffqNgzVoZJsVa27qjmvFxcU1urx/xowZcscdd+iWmxqgPmbMmEaX5t599916g5ybm7qjY8eO4uHhITNnzpRnnnlGb9B5UzGKiGzdulUGDhwoTk5O0rNnT/n8888lKChIkpOTddv897//ld69e4ujo6PBU3d4eHg0O3XHtbZt2yYApKioqFFsDZq7wKCp13Pw4EEZN26cbuqOP/zhD01O3XGtdevWyfXN8pNPPhEALV7SHxUVpbvE3d7eXry8vCQyMlJSUlLk0qVLjbZfs2aNDBo0SJycnMTT01OGDRumdwVUly5dZNGiRXrPub4d3Wyb+eyzzyQ4OFicnJwkJCREtm7dqnfhSMMFBtcPVG/qfV69erU4OTlJenq6iFx9X2/0sW7q/aqrq5OBAwfecOqOyMjIJqfuaGog9ZkzZ0ShUOhND7Jnzx4B0Chve/bskREjRoizs7N06dJFVq1a1WQuRa5e2ejh4dHk+wpA7/NyverqannyySdFpVLprphuuDCggaGfjU8++US6d+8ujo6OMmzYMElPT7/hBQbvvPOOxMTEiK+vrzg6Ooqfn5/cd999kpOTo7fdzz//LADk6aef1q3buHGjAGjULkVu3J5LS0vlgQcekA4dOohGo5GkpKQmp+5ojc9FUz788EPp2rWr2NvbN5q6o3fv3uLg4CB+fn4yb968Fj/nDbZt2yb33XefeHt7i6Ojo3Tr1k2mTJmid7HSggULxMfHRzp06CDjxo2T//f//p/ee9jU98/1F0MY0l5Erl4Y4efnJ87OzjJmzBjdVeANn4vz58/LAw88IB07dhS1Wi2zZs2SBQsW6OWiKc1dYNBUG7h26g6NRiNPPfVUk1N3XMuQv43NxdXwfXvtv2svfps/f77cdttt4uDgIIGBgfL888/rpp9pUFJSIk888YR07txZnJycpHv37pKUlNTos/3VV1/JwIEDxdHRUQIDAyUlJaXF+AzVcJFJU/+uvyDmtddek4CAAHF0dJRBgwbprs6+0b6u/Q77y1/+IkFBQboptYYMGSJpaWktTn3TQCFigdlLbUx0dDRUKhW++OILS4dC7dzUqVNx+vTpVjk9aq2GDRuG22+/HW+88Ybe+oZTeNu2bWs0+J+IqC3jadBWtnfvXuzcuRMjRoxAdXW1ruv7m2++sXRo1M7V19fj+++/x5YtWywdikmUlJTgyy+/xM6dO/HJJ580evyrr77C1KlTWagRUbvDnrVWtm/fPjz++OM4cOAA6uvr0bt3b8yfPx8TJkywdGhEbZpCoYBKpcKiRYswe/ZsS4dDRGQ2ZinWTp06pTcYsKSkBJMmTUJUVBRSU1Nx9uxZeHt7Y86cObq5ujZs2IDMzEwolUpMnz690dw+RERERLbA7D1r9fX1mDFjBl599VVs2rQJbm5umDBhAtLT01FZWYnJkyfj5MmTWLFiBV599VWUlZVh0aJFWLFihVluVE5ERERkTcxe/ezduxe+vr7w9vZGXl4eoqKiAFydbblh3p+8vDyEh4fDwcEBPj4+8PX11V0iTERERGRLzH6BwQ8//KAbAFxeXq6bZ0alUqGiogLA1flKevTooXuOWq2GVqtttK+MjAxkZGQAuHrbHCIiIqL2xqzFWm1tLfLz8/UmvGuKoWdmY2JiEBMTo1s+derULcVnCI1GozcrP90Yc2Yc5st4zJnxmDPjMWfGY86a5+fnZ/C2Zj0NumvXLnTr1k13OwcPDw/d7UHKysrg7u4OAPDy8sK5c+d0z9Nqtbd8w2oiIiKitsisxdq1p0ABICwsTHc/s6ysLAwdOlS3PicnBzU1NSgpKUFxcbHuxqdEREREtsRsp0GvXLmCPXv24Mknn9StmzBhAlJTU5GZmQmNRoPExEQAQEBAAEaMGIHExEQolUrExcXxSlAiIiKySe1qUlyOWbNOzJlxmC/jMWfGY86Mx5wZjzlrntWOWSMiIiIi47BYIyIiIrJiLNaIiIiIrBiLNSIiIiIrxmKNiIiIyIqxWCMiIiKyYma/N6gtOnbsAhIScqHVVkGtdkZaWjgCA90sHRYRERG1AexZM4OEhFzk55eiqKgS+fmliI/PsXRIRERE1EawWDMDrbaqxWUiIiKi5rBYMwO12rnFZSIiIqLmsFgzg7S0cISGatCtmxtCQzVISwu3dEhERETURvACAzMIDHTDxo13WToMIiIiaoNYrBmo4YrO8vIaeHg48IpOIiIiMgueBjVQwxWdhYXlvKKTiIiIzIbFmoF4RScRERFZAos1A/GKTiIiIrIEFmsGariiMzjYg1d0EhERkdnwAgMDNVzRqdFoUFpaaulwiIiIyEawZ42IiIjIirFYIyIiIrJiLNaIiIiIrBiLNSIiIiIrxmKNiIiIyIqxWCMiIiKyYizWiIiIiKwYizUiIiIiK8ZijYiIiMiKsVgjIiIismIs1oiIiIisGIs1IiIiIivGYo2IiIjIirFYIyIiIrJiLNaIiIiIrBiLNSIiIiIrxmKNiIiIyIqxWCMiIiKyYizWiIiIiKwYizUiIiIiK8ZijYiIiMiK2Vs6AGrZsWMXkJCQC622Cmq1M9LSwhEY6GbpsIiIiMhM2LNm5RIScpGfX4qiokrk55ciPj7H0iERERGRGbFYs3JabVWLy0RERNS+sVizcmq1c4vLRERE1L6xWLNyaWnhCA3VoFs3N4SGapCWFm7pkIiIiMiMeIGBlQsMdMPGjXdZOgwiIiKyEPasEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMVYrBERERFZMRZrRERERFaMxRoRERGRFWOxRkRERGTFWKwRERERWTEWa0RERERWzGy3m7p48SLeeustnDhxAgqFAjNnzoSfnx9SU1Nx9uxZeHt7Y86cOXBzcwMAbNiwAZmZmVAqlZg+fTpCQkLMFSoRERGR1TBbsbZ27VqEhIRg7ty5qK2txZUrV7BhwwYMGDAAEyZMQHp6OtLT0zF58mScPHkSOTk5WLZsGcrKyrBo0SKsWLECSiU7AomIiMi2mKX6uXTpEg4cOIDo6GgAgL29PVxdXZGXl4eoqCgAQFRUFPLy8gAAeXl5CA8Ph4ODA3x8fODr64vCwkJzhEpERERkVczSs1ZSUgJ3d3esXr0ax44dQ/fu3TFt2jSUl5dDpVIBAFQqFSoqKgAAWq0WPXr00D1frVZDq9WaI1QiIiIiq2KWYq2urg5FRUV47LHH0KNHD6xduxbp6enNbi8iBu03IyMDGRkZAIClS5dCo9G0Rrgtsre3N8tx2hPmzDjMl/GYM+MxZ8ZjzozHnLUOsxRrXl5e8PLy0vWWDR8+HOnp6fDw8EBZWRlUKhXKysrg7u6u2/7cuXO652u1WqjV6kb7jYmJQUxMjG65tLTUxK8E0Gg0ZjlOe8KcGYf5Mh5zZjzmzHjMmfGYs+b5+fkZvK1Zxqx5enrCy8sLp06dAgDs3bsXnTt3RlhYGLKysgAAWVlZGDp0KAAgLCwMOTk5qKmpQUlJCYqLixEcHGyOUImIiIisitmuBn3sscewcuVK1NbWwsfHB7NmzYKIIDU1FZmZmdBoNEhMTAQABAQEYMSIEUhMTIRSqURcXByvBCUiIiKbpBBDB4i1AQ09d6bELl3jMWfGYb6Mx5wZjzkzHnNmPOaseVZ3GpSIiIiIbg6LNSIiIiIrxmKNiIiIyIqxWCMiIiKyYizWiIiIiKwYizUiIiIiK8ZijYiIiMiKsVgjIiIismIs1oiIiIisGIs1IiIiIivGYo2IiIjIirFYIyIiIrJiLNaIiIiIrBiLNSIiIiIrxmKNiIiIyIqxWCMiIiKyYizWiIiIiKwYizUiIiIiK8ZijYiIiMiKsVgjIiIismIs1oiIiIisGIs1IiIiIivGYo2IiIjIirFYIyIiIrJiLNaIiIiIrBiLNSIiIiIrdlPFWnV1NWpra1s7FiIiIiK6jkHF2kcffYTCwkIAwM6dOzF9+nRMmzYNO3bsMGlwRERERLbOoGJt+/btCAgIAAB8/vnnSEhIwPPPP49PPvnEpMERERER2Tp7Qza6cuUKnJyccOHCBZw5cwbDhw8HAJSWlpo0OCIiIiJbZ1Cx5ufnh23btuH06dMYOHAgAKCiogKOjo4mDY6IiIjI1hl0GjQuLg6bNm1CQUEBHnzwQQDAL7/8oivciIiIiMg0DOpZ02g0eOWVV/TWRUREYMCAASYJioiIiIiuMqhn7Zlnnmly/Zw5c1o1GCIiIiLSZ1CxJiKN1l26dAlKJefUJSIiIjKlFk+Dzpw5E8DVSXAb/t+gsrISd9xxh+kiIyIiIqKWi7WEhASICJYsWYKEhAS9xzw9PeHn52fS4IiIiIhsXYvFWt++fQEA7733HpycnMwSEBERERH9j0FXg9rZ2SEjIwNHjx5FVVWV3mPx8fEmCYyIiIiIDCzW0tLScOzYMYSGhsLDw8PUMRERERHR/zGoWPvll1+QlpYGV1dXU8dDRERERNcwaO4NjUaDmpoaU8dCRERERNcxqGctMjISf//73zFu3Dh4enrqPda/f39TxEVEREREMLBY+/bbbwEAn3zyid56hUKBtLS01o+KiIiIiAAYWKytWrXK1HEQERERURN4vygiIiIiK2ZQz9qlS5fw2WefYf/+/bhw4YLevULffPNNkwVHREREZOsM6llbs2YNioqKcP/996OyshKPPfYYNBoN/vCHP5g6PiIiIiKbZlCxtmfPHsydOxdDhw6FUqnE0KFDMWfOHGzbts3U8RERERHZNIOKNRFBhw4dAADOzs64ePEiPD09cfr0aZMGR0RERGTrDBqz1qVLF+zfvx8DBgxA79698d5778HZ2Rm33XabqeMjIiIismkG9azNmDED3t7eAIDHHnsMjo6OuHjxIm/iTkRERGRiN+xZq6+vx9atWzFx4kQAgLu7O5566imTB0ZEREREBvSsKZVKbNq0CXZ2duaIh4iIiIiuYdBp0KioKHz33XemjoWIiIiIrmPQBQaFhYX49ttvsXHjRnh5eUGhUOgee+mll0wWHBEREZGtM6hYGzNmDMaMGXNLB5o9ezacnZ2hVCphZ2eHpUuXorKyEqmpqTh79iy8vb0xZ84cuLm5AQA2bNiAzMxMKJVKTJ8+HSEhIbd0fCIiIqK2yKBibdSoUa1ysOTkZLi7u+uW09PTMWDAAEyYMAHp6elIT0/H5MmTcfLkSeTk5GDZsmUoKyvDokWLsGLFCiiVvJUpERER2RaDirXMzMwm1zs4OMDLyws9evSAg4OD0QfPy8vDiy++CODquLgXX3wRkydPRl5eHsLDw+Hg4AAfHx/4+vqisLAQPXv2NPoYRERERG2ZQcVadnY2fv31V3h4eMDLywvnzp1DeXk5goKCUFJSAgB4/vnnERQU1OJ+Fi9eDAC48847ERMTg/LycqhUKgCASqVCRUUFAECr1aJHjx6656nVami12kb7y8jIQEZGBgBg6dKl0Gg0hrycW2Jvb2+W47QnzJlxmC/jMWfGY86Mx5wZjzlrHQYVa507d8awYcMwfvx43bpvv/0Wv//+O15++WWsX78e77//vq4Ya8qiRYugVqtRXl6OV155BX5+fs1uKyIGBR8TE4OYmBjdcmlpqUHPuxUajcYsx2lPmDPjMF/GY86Mx5wZjzkzHnPWvJbqoOsZNAjshx9+wNixY/XW3XXXXdi+fTsUCgViY2Nx8uTJFvehVqsBAB4eHhg6dCgKCwvh4eGBsrIyAEBZWZluPFtD710DrVarez4RERGRLTGoWPPw8EB+fr7eup07d+qKq5qaGtjbN99JV1VVhcuXL+v+v2fPHgQGBiIsLAxZWVkAgKysLAwdOhQAEBYWhpycHNTU1KCkpATFxcUIDg42/tURERERtXEGnQadPn06li1bhsDAQF2v1/Hjx5GYmAgAOHz4cKOet2uVl5fjH//4BwCgrq4OI0eOREhICIKCgpCamorMzExoNBrd/gICAjBixAgkJiZCqVQiLi6OV4ISERGRTVKIgQPEKioqsHv3bmi1WqhUKgwZMgQdO3Y0dXxGOXXqlMmPwfPvxmPOjMN8GY85Mx5zZjzmzHjMWfOMGbNmUM8acPUG7pGRkTcVEBERERHdnGaLtcWLF2P+/PkAgIULF+rdYupavN0UERERkek0W6xFRUXp/h8dHW2WYIiIiIhIX7PF2siRI3X/b63bTRERERGRcQwes3bgwAEUFRWhqqpKb/3EiRNbPSgialuOHbuAhIRcaLVVUKudkZYWjsBAN0uHRUTULhhUrL3//vvIzc1F79694ejoqFvf3Dg2IrItCQm5yM+/esVXUVEl4uNzsHHjXRaOioiofTCoWNu2bRtSUlJ4FwEiapJWW9XiMhER3TyDZprVaDRwcHAwdSxE1Eap1c4tLhMR0c0zqGftqaeewttvv4077rgDHh4eeo/17dvXJIERUduRlhaO+PgcvTFrRETUOgwq1o4cOYJdu3bhwIEDemPWAODNN980SWBE1HYEBrpxjBoRkYkYVKx98skn+Otf/4qBAweaOh4iIiIiuoZBY9acnJx4upOIiIjIAgwq1h588EF88MEHOH/+POrr6/X+EREREZHpGHQatGFc2nfffdfosX//+9+tGxERERER6RhUrKWlpZk6DiIiIiJqgkHFmre3t6njICIiIqImGFSsXbp0Cd988w2OHj3a6N6gCxYsMElgRERERGRgsbZs2TLU19dj2LBhjeZZIyIiIiLTMahYO3z4MN577z3Y2xu0ORERERG1EoOm7ujduzd+//13U8dCRERERNcxqKts1qxZWLJkCYKDg+Hp6an32P3332+KuIiIiIgIRtxu6ty5c/D29sbly5d16xUKhckCIyIiIiIDi7WcnBysWLECKpXK1PEQERER0TUMGrPWqVMn2NnZmToWIiIiIrqOQT1rEREReP311zF27NhGY9b69+9viriIiIiICAYWa5s2bQJwdezatRQKBW9FRURERGRCBhVrq1atMnUcRERERNQEg8asEREREZFltNiztnDhwhtOz/HSSy+1akBERERE9D8tFmvR0dHmioOIiIiImtBisTZq1CgzhUFERERETeGYNSIiIiIrxmKNiIiIyIqxWCMiIiKyYs0Wa/Pnz9f9/7PPPjNLMERERESkr9li7dSpU6iurgYAfPXVV2YLiIiIiIj+p9mrQYcOHYpnnnkGPj4+qK6uRnJycpPbcZ41IiIiItNptlibNWsWDh48iJKSEhQWFmL06NHmjIuIiIiIcIN51nr37o3evXujtraWc64RERERWYBBN3KPjo7Gvn37kJ2djbKyMqhUKkRGRqJ///6mjo+IiIjIphk0dcf333+P5cuXw9PTE8OGDYNKpcKKFSuQkZFh6viIiIiIbJpBPWsbN27EggUL0LVrV9268PBwpKSkICYmxlSxEREREdk8g3rWLly4gM6dO+ut8/PzQ2VlpUmCIiIiIqKrDCrWevfujY8++ghXrlwBAFRVVWHdunXo2bOnSYMjIiIisnUGnQZ94oknsHz5ckybNg1ubm6orKxEz5498cwzz5g6PiIiIiKbZlCxplKp8NJLL+HcuXO6q0G9vLxMHRsRERGRzTOoWGvg5eXFIo2IiIjIjAwas0ZERERElsFijYiIiMiK3bBYq6+vx759+1BbW2uOeIjIAo4du4DY2M0YOXIjYmM34/hxTstDRGQtblisKZVKvP7667C3N2p4GxG1IQkJucjPL0VRUSXy80sRH59j6ZCIiOj/GHQatE+fPvj1119NHQsRWYhWW9XiMhERWY5B3WXe3t5YsmQJwsLC4OXlBYVCoXvswQcfNFlwRGQearUziooq9ZaJiMg6GFSsVVdXY+jQoQAArVZr0oCIyPzS0sIRH58DrbYKarUz0tLCLR0SERH9H4OKtVmzZpk6DiKyoMBAN2zceJelwyAioiYYfNXAyZMn8eOPP6K8vBxxcXE4deoUampq0KVLF4MPVl9fj6SkJKjVaiQlJaGyshKpqak4e/YsvL29MWfOHLi5uQEANmzYgMzMTCiVSkyfPh0hISFGvzgiIiKits6gCwxyc3ORnJwMrVaL7OxsAMDly5fx0UcfGXWwb775Bv7+/rrl9PR0DBgwACtXrsSAAQOQnp4O4GphmJOTg2XLlmH+/Pl47733UF9fb9SxiIiIiNoDg4q1Tz/9FH/729/w5JNPQqm8+pQuXbrg6NGjBh/o3Llz2LlzJ8aMGaNbl5eXh6ioKABAVFQU8vLydOvDw8Ph4OAAHx8f+Pr6orCw0OBjEREREbUXBp0GLS8vb3S6U6FQ6F0VeiMffPABJk+ejMuXL+vtV6VSAbh6s/iKigoAVy9i6NGjh247tVrd5IUNGRkZyMjIAAAsXboUGo3G4Hhulr29vVmO054wZ8ZhvozHnBmPOTMec2Y85qx1GFSsde/eHdnZ2bpeMAD44YcfEBwcbNBB8vPz4eHhge7du6OgoOCG24uIQfuNiYlBTEyMbrm0tNSg590KjUZjluO0J8yZcZgv4zFnxmPOjMecGY85a56fn5/B2xpUrE2fPh2vvPIKMjMzceXKFSxevBinTp3CggULDDrIoUOHsGPHDuzatQvV1dW4fPkyVq5cCQ8PD5SVlUGlUqGsrAzu7u4AAC8vL5w7d073fK1WC7VabfCLIiIiImovDCrW/P39sXz5cuTn5yM0NBReXl4IDQ2Fs7NhE2c+/PDDePjhhwEABQUF+M9//oOnn34a69atQ1ZWFiZMmICsrCzdXG5hYWFYuXIl7rnnHpSVlaG4uNjgXjwiIiKi9sTgqTucnJzQu3dvXS+XoYVaSyZMmIDU1FRkZmZCo9EgMTERABAQEIARI0YgMTERSqUScXFxugsbiIiIiGyJQgwYIFZaWoqVK1fi8OHDcHV1xcWLFxEcHIynn34a3t7e5ojTIKdOnTL5MXj+3XjMmXGYL+MxZ8ZjzozHnBmPOWueMWPWDOquWrVqFbp37461a9dizZo1WLt2LYKCgrBq1aqbDpKIiIiIbsygYu3IkSOYPHmy7tSns7MzJk+ejCNHjpg0OKKbcezYBcTGbsbIkRsRG7sZx49X3vhJ7eDYRETUPhlUrPXo0aPRpLS//fYbevbsaZKgiG5FQkIu8vNLUVRUifz8UsTH59jEsYmIqH1q9gKDf//737r/d+rUCUuWLMGQIUN002rs2rULI0eONEuQRMbQaqtaXG6vxyYiovap2WLt2nnOAOD2228HAFRUVMDBwQHDhg1DdXW1aaMjuglqtTOKiir1lm3h2MeOXUBCQi602iqo1c5ISwtHYKCb2Y5PRESm0WyxNmvWLHPGQdRq0tLCER+fo1e02MKxG07BAkBRUSXi43OwceNdZjs+ERGZhsHzrF25cgWnT59GVZX+aZ1evXq1elBEtyIw0M1iRYolj81TsERE7ZNBxVpWVhbef/992Nvbw9HRUe+xN9980ySBEZFxLHkKloiITMegYu3jjz/G3LlzMXDgQFPHQ0Q3yZKnYImIyHQMKtbs7e3Rt29fU8dCRLfAkqdgiYjIdAyaZ+3BBx/ERx99hIqKClPHQ2RxnNiWiIisiUE9a35+fvj000+xadOmRo9dOx8bUXvAqyqJiMiaGFSsvfHGG4iMjER4eHijCwyI2hteVUlERNbEoGKtsrISDz74IBQKhanjIbI4XlVJRETWxKAxa6NGjUJ2drapYyGyCmlp4QgN1aBbNzeEhmp4VSUREVmUQT1rhYWF+Pbbb7F+/Xp4enrqPfbSSy+ZIi4ii+FVlUREZE0MKtbGjBmDMWPGmDoWIiIiugbv+UuAgcXaqFGjTBwGERERXY9XpxNgYLGWmZnZ7GPR0dGtFgwRERH9D69OJ8DAYm3btm16y+fPn8fp06fRu3dvFmtEREQmwqvTCTCwWEtOTm60LjMzE7///nurB0RERERX8Z6/BBhYrDVl1KhRiIuLw5QpU1ozHiIiIvo/vDqdAAOLtfr6er3l6upqZGdnw9XV1SRBEREREdFVBhVrDz30UKN1arUaM2bMaPWAiIgM1TCtQXl5DTw8HDitARG1SwYVa2lpaXrLTk5OcHd3N0lARESGunZaAwCc1oCI2iWDijVvb29Tx0FEZDROa0BEtqDFYu1Gt5JSKBRYuHBhqwZERGQoTmtARLagxWItIiKiyfVarRb//e9/ceXKFZMERURkiIZpDa4ds0ZE1N60WKxdP+HthQsXsGHDBnz//fcIDw/H/fffb9LgiKj9u5V7HzZMa6DRaFBaWnrjJxARtUEGjVm7dOkSNm7ciE2bNmHIkCF47bXX4Ovra+rYiMgG8N6HbQdvKk5kGS0Wa9XV1fj666/x1VdfoW/fvnj55ZcREBBgrtiIyAbwIoG2g4U1kWW0WKzNnj0b9fX1iI2NRVBQEMrLy1FeXq63Tf/+/U0aIBG1b7xIoO1gYU1kGS0Wa46OjgCAzZs3N/m4QqFoNAcbEZEx2uq9D23xlCAL67bFFttoe6UQEbF0EK3l1KlTJj8GBzIb72ZyZstfMmxjxrNEzmJjN+tNyBsaqmlTpwRvJmfHj1c2Kqxt5XMJtL3PpjW00baWM3Py8/MzeNubvpE7kSlxbAxZO1s8JcibircttthG2yulpQMg0zp27AJiYzdj5MiNiI3djOPHK2/8JCvALxmydtefAuQpQbI2bKPtB4u1dq6hh6qoqBL5+aWIj8+xdEgG4ZcMWbu0tHCEhmrQrZsbQkM1bWasHdkOttH2g6dB27m22kPVVged34qGcXrXzsZvS+OB2hqeEiRrxzbafrBYa+fa6tVbtvglc+04PQAcp0dERAB4GrTdYzd429FWe0GJzKGtjr8lag3sWWvnbLGHqq1qq72gRObAK8TJlrFYI7ISDeP0rh2zRkRXWbLnmeNJydJYrBFZiYZeUE4iSdSYJXuebXE8qS1PTG6NOGaNiIisniXH39rieNK2Ou1Te8WeNSIisnqWHH9ri+NJbbFAtWbsWSMiImpBQ69ecLCHzVxVz4nJrQt71oiIiFpgi+NJbXFicmvGYo2IiIj0cNon68LToERERERWjMUaERERkRVjsUZERERkxVisEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMXMMs9adXU1kpOTUVtbi7q6OgwfPhyTJk1CZWUlUlNTcfbsWXh7e2POnDlwc7t6o9gNGzYgMzMTSqUS06dPR0hIiDlCJSIiIrIqZinWHBwckJycDGdnZ9TW1mLhwoUICQnBzz//jAEDBmDChAlIT09Heno6Jk+ejJMnTyInJwfLli1DWVkZFi1ahBUrVkCpZEcgEbWeY8cuICEhV2+W9sBAN0uHRUSkxyzVj0KhgLPz1fuK1dXVoa6uDgqFAnl5eYiKigIAREVFIS8vDwCQl5eH8PBwODg4wMfHB76+vigsLDRHqERkQxIScpGfX4qiokrk55ciPj7H0iERETVitttN1dfX469//StOnz6Nu+++Gz169EB5eTlUKhUAQKVSoaKiAgCg1WrRo0cP3XPVajW0Wq25QiUiG6HVVrW4TERkDcxWrCmVSvz973/HxYsX8Y9//APHjx9vdlsRMWifGRkZyMjIAAAsXboUGo2mVWJtib29vVmO054wZ8Zhvox3sznr1MkNRUWVesu2knu2M+MxZ4YrKjqPadM2o7S0ChqNMz744G506+Zh6bDaLLPfyN3V1RV9+/bF7t274eHhgbKyMqhUKpSVlcHd3R0A4OXlhXPnzumeo9VqoVarG+0rJiYGMTExuuXS0lKTx6/RaMxynPaEOTMO82W8m81ZauowxMfn6MaspaYOs5ncs50Zjzkz3COPbEZ+/tVcFRYCjzzyNW8Mfx0/Pz+DtzXLmLWKigpcvHgRwNUrQ/fu3Qt/f3+EhYUhKysLAJCVlYWhQ4cCAMLCwpCTk4OamhqUlJSguLgYwcHB5giViGxIYKAbNm68C9u3x2Ljxrt4cYGJHTt2AbGxmzFy5EbExm7G8eOVN35SG2eLrxngEIPWZpaetbKyMqxatQr19fUQEYwYMQKhoaHo2bMnUlNTkZmZCY1Gg8TERABAQEAARowYgcTERCiVSsTFxfFKUCKiNq7hgg4AKCqqRHx8jtl6Wyx15a8lX7MlqdXOekMM1GpnC0bT9pmlWOvSpQtef/31Rus7duyIhQsXNvmciRMnYuLEiaYOjYiIzMSSvS2WKppstYcpLS0c8fE5KC+vgYeHA9LSwi0dUptm9jFrRERkmyzZ22KposlWe5gahhhwnF/r4LlFIqI25lbGQTU8t1+/D80+hiotLRyhoRp06+aG0FCNWXtbri+SzFU0WfI126r2OE5QIYbOk9EGnDp1yuTH4K8EwzWMEbm2G5wDuG+Mbcx4tpaz2Nj/XWkHAKGhGoNP6d3Kc9uy48cr9a78vZnvI1trZ63BEjlrK23cmKtBeRqUTObaMSIAbGZgLZGp3copPVsdQ9VwWo7av/bYxnkalEymPX5giKzBrZzSs9TpQCJzaY9tnMUamUx7/MAQWYNbGQfV8NzgYA+OoaJ26VY+H9Y63o1j1ozEMQuGaxgjwjFrxmEbMx5zZjzmzHjMmfHaWs7MOd6NY9bIKvDSbSIiakusdfgOT4MSERGR1bDkqUhrHb7DYo2IiIisRsNMAkVFlcjPL0V8fI7Zjm2t8+LxNCgRERFZDUueirTWKV7Ys0ZERERWw1pPRVoSizUiIiKyGtZ6KtKSeBqUiIiIrIa1noq0JPasEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMVYrBERERFZMRZrRERERFaMxRoRERGRFWOxRkRERGTFWKwRERERWTHewYCadezYBSQk5EKrrYJa7Yy0tHAEBrpZOiwiIiKbwp41alZCQi7y80tRVFSJ/PxSxMfnWDokIiIim8NijZql1Va1uExERESmx2KNmqVWO7e4TERERKbHYo2alZYWjtBQDbp1c0NoqAZpaeGWDomIiMjm8AIDalZgoBs2brzL0mEQERHZNPasEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMVYrBERERFZMRZrRERERFaMxRoRERGRFWOxRkRERGTFWKwRERERWTEWa0RERERWjMUaERERkRVjsUZERERkxVisEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMVYrBERERFZMXtLB0BEZGuOHbuAhIRcaLVVUKudkZYWjsBAN0uHRURWij1rRERmlpCQi/z8UhQVVSI/vxTx8TmWDomIrBiLNSIiM9Nqq1pcJiK6Fos1IiIzU6udW1wmIroWizUiIjNLSwtHaKgG3bq5ITRUg7S0cEuHRERWjBcYEBGZWWCgGzZuvMvSYRBRG8GeNSIiIiIrxmKNiIiIyIqxWCMiIiKyYmYZs1ZaWopVq1bh/PnzUCgUiImJwfjx41FZWYnU1FScPXsW3t7emDNnDtzcrk4MuWHDBmRmZkKpVGL69OkICQkxR6hEREREVsUsxZqdnR2mTJmC7t274/Lly0hKSsLAgQOxdetWDBgwABMmTEB6ejrS09MxefJknDx5Ejk5OVi2bBnKysqwaNEirFixAkolOwKJiIjItpil+lGpVOjevTsAwMXFBf7+/tBqtcjLy0NUVBQAICoqCnl5eQCAvLw8hIeHw8HBAT4+PvD19UVhYaE5QiUiIiKyKmbvqiopKUFRURGCg4NRXl4OlUoF4GpBV1FRAQDQarXw8vLSPUetVkOr1Zo7VCIiIiKLM+s8a1VVVUhJScG0adPQoUOHZrcTEYP2l5GRgYyMDADA0qVLodFoWiXOltjb25vlOO0Jc2Yc5st4zJnxmDPjMWfGY85ah9mKtdraWqSkpCAiIgK33347AMDDwwNlZWVQqVQoKyuDu7s7AMDLywvnzp3TPVer1UKtVjfaZ0xMDGJiYnTLpaWlJn4VgEajMctx2hPmzDjMl/GYM+MxZ8ZjzozHnDXPz8/P4G3NchpURPDWW2/B398f99xzj259WFgYsrKyAABZWVkYOnSobn1OTg5qampQUlKC4uJiBAcHmyNUIiIiIqtilp61Q4cOITs7G4GBgXjuuecAAA899BAmTJiA1NRUZGZmQqPRIDExEQAQEBCAESNGIDExEUqlEnFxcbwSlIiIiGySQgwdIEZEREREZsfuKiMlJSVZOoQ2hzkzDvNlPObMeMyZ8Zgz4zFnrYPFGhEREZEVY7FGREREZMVYrBnp2qlCyDDMmXGYL+MxZ8ZjzozHnBmPOWsdvMCAiIiIyIqxZ42IiIjIipn1dlPWaPXq1di5cyc8PDyQkpICADh69CjeffddVFVVwdvbG08//TQ6dOiA2tpavPXWWygqKkJ9fT0iIyPxxz/+EQBw5MgRrFq1CtXV1Rg8eDCmT58OhUJhyZdmMq2VsxdffBFlZWVwdHQEACxYsAAeHh4We12mZGzO3nnnHfz2229QKpWYNm0a+vXrB4Dt7GZyZivtrLS0FKtWrcL58+ehUCgQExOD8ePHo7KyEqmpqTh79iy8vb0xZ84cuLm5AQA2bNiAzMxMKJVKTJ8+HSEhIQBsp521Zs7YzprO2YULF7Bs2TIUFhZi1KhRiIuL0+3LVtpZqxAbV1BQIL/99pskJibq1iUlJUlBQYGIiHz//ffyySefiIjItm3bJDU1VUREqqqqZNasWXLmzBndcw4dOiT19fWyePFi2blzp3lfiBm1Vs6Sk5OlsLDQvMFbiDE5++9//yurVq0SEZHz58/L888/L3V1dbrnsJ0ZlzNbaWdarVZ+++03ERG5dOmSPP3003LixAlZt26dbNiwQURENmzYIOvWrRMRkRMnTsizzz4r1dXVcubMGYmPj7e5dtaaOWM7azpnly9flgMHDsimTZtkzZo1evuylXbWGmz+NGjfvn11v5ganDp1Cn369AEADBw4ED/99JPusaqqKtTV1aG6uhr29vbo0KEDysrKcPnyZfTs2RMKhQKRkZHIy8sz6+swp9bIma0xJmcnT55E//79AVy9f66rqyuOHDnCdgbjc2ZLVCoVunfvDgBwcXGBv78/tFot8vLyEBUVBQCIiorStZm8vDyEh4fDwcEBPj4+8PX1RWFhoU21s9bKmS0xNmfOzs7o3bu3rsexgS21s9Zg88VaUwICArBjxw4AwI8//qi7qfzw4cPh7OyMJ598ErNmzcK9994LNzc3aLVaeHl56Z7v5eUFrVZrkdgtxdicNVi9ejWee+45fP755xAbu9aluZx17doVO3bsQF1dHUpKSnDkyBGUlpayncH4nDWwtXZWUlKCoqIiBAcHo7y8HCqVCsDVP7QVFRUA0Kg9qdVqaLVam21nt5KzBmxnjXPWHFttZzfL5sesNWXmzJlYu3YtPv/8c4SFhcHe/mqaCgsLoVQq8fbbb+PixYtYuHAhBgwYYBMfyhsxNmedOnXC008/DbVajcuXLyMlJQXZ2dm6X2a2oLmcjR49GidPnkRSUhK8vb3Rq1cv2NnZsZ3B+JwBsLl2VlVVhZSUFEybNq3FXuzm2pMttrNbzRnAdmYsW2xnt4LFWhP8/f2xYMECAFdPu+zcuRMAsH37doSEhMDe3h4eHh7o1asXfvvtN/Tp00f3Cx8Azp07B7VabZHYLcXYnHXq1EmXIxcXF4wcORKFhYXt+svtes3lzM7ODtOmTdNtt2DBAtx2221wdXVlOzMyZwBsqp3V1tYiJSUFERERuP322wFcPS1cVlYGlUqFsrIyuLu7A7jak3Fte9JqtVCr1Y3Wt/d21ho5A9jOmstZc2ytnd0qngZtQnl5OQCgvr4e69evx5133gkA0Gg02LdvH0QEVVVVOHz4MPz9/aFSqeDi4oJff/0VIoLs7GyEhYVZ8iWYnbE5q6ur03WT19bWIj8/HwEBARaL3xKay9mVK1dQVVUFANizZw/s7OzQuXNntjMYnzNbamcigrfeegv+/v645557dOvDwsKQlZUFAMjKysLQoUN163NyclBTU4OSkhIUFxcjODjYptpZa+WM7az5nDXHltpZa7D5SXGXL1+O/fv348KFC/Dw8MCkSZNQVVWFTZs2AQCGDRuGhx9+GAqFAlVVVVi9ejVOnjwJEcHo0aMRGxsLAPjtt9+wevVqVFdXIyQkBI899li7vQS5NXJWVVWF5ORk1NXVob6+HgMGDMCjjz4KpbJ9/n4wJmclJSVYvHgxlEol1Go1nnrqKXh7ewNgOzM2Z7bUzg4ePIiFCxciMDBQ1yYeeugh9OjRA6mpqSgtLYVGo0FiYqJu3Oj69euxZcsW3XQngwcPBmA77ay1csZ21nLOZs+ejUuXLqG2thaurq5YsGABOnfubDPtrDXYfLFGREREZM3aX9lPRERE1I6wWCMiIiKyYizWiIiIiKwYizUiIiIiK8ZijYiIiMiKsVgjIiIismIs1ojIpqxcuRKrV6/WW7d//3489thjKCsrs1BURETNY7FGRDZl+vTp2LVrF/bs2QMAqK6uxttvv42pU6fqbkR9K+rq6m55H0RE1+KkuERkc3Jzc/Hxxx8jJSUF69evx9GjR3H//ffjo48+wsmTJ+Ht7Y1p06ahX79+AIAtW7Zg48aNOHfuHNzd3XHffffpbnVVUFCAN954A2PHjsXXX3+NgQMHIiEhwZIvj4jaGd7InYhszogRI5CTk4MVK1bg0KFDeO211/DXv/4V8fHxCAkJwb59+5CSkoLly5fD3d0dHh4e+Otf/4pOnTrhwIEDePXVVxEUFITu3bsDAM6fP4/KykqsXr0a/P1LRK2Np0GJyCbFxcVh3759uP/++/HDDz9g8ODBGDJkCJRKJQYOHIigoCDs3LkTADBkyBD4+vpCoVCgb9++GDhwIA4ePKjbl0KhwKRJk+Dg4ABHR0dLvSQiaqfYs0ZENsnT0xPu7u7o3Lkzfv75Z/z444/Iz8/XPV5XV6c7Dbpr1y58/vnnOHXqFEQEV65cQWBgoG5bd3d3FmlEZDIs1ojI5nl5eSEiIgJPPfVUo8dqamqQkpKC+Ph4hIWFwd7eHq+//rreNgqFwlyhEpEN4mlQIrJ5ERERyM/Px+7du1FfX4/q6moUFBTg3LlzqK2tRU1NDdzd3WFnZ6d3JSkRkTmwZ42IbJ5Go8Hzzz+Pjz/+GCtWrIBSqURwcDCeeOIJuLi4YPr06UhNTUVNTQ1CQ0MRFhZm6ZCJyIZw6g4iIiIiK8bToERERERWjMUaERERkRVjsUZERERkxVisEREREVkxFmtEREREVozFGhEREZEVY7FGREREZMVYrBERERFZMRZrRERERFbs/wNLG3duWQy8/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\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"
   ]
  },
  {
   "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 data points 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 parameter `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 to 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 include it in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transposed dataframe\n",
    "df_can_t = df_can[years].transpose()\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_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) 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 the original value, $X'$ is the corresponding normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the data points are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "button": 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 $\\times 2000$).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe830a2a580>"
      ]
     },
     "execution_count": 35,
     "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 to 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 is, the more immigrants are 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 to 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": 37,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "#The correct answer is:  \n",
    "\n",
    "# normalized Chinese data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "# normalized Indian data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())"
   ]
  },
  {
   "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",
    "    # normalized Chinese data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalized Indian 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": 38,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe8309afb80>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1K0uWLOm3rz2dd3/n3NNrjuzP//pf/4vHH3+cn/70p8yePZtgMMjXvva1Pot/rFmzho9//OPceuut/PjHPyY/P5/Vq1fzmc98pstn3tt71NHno9/TI/cZqOE41v79+7n44ov51Kc+xXe+8x2Kioqoqqri/PPPH9R3e7DfNej5e9XTtsH8Bo5UUFDAAw88QDwep66ujvLycn79618DMGXKFAD+4z/+gwULFvDv//7vna/761//ysSJE3nxxRc5//zzKSsr44UXXujWfm1tba8FQMrLy9m4cWPn43SKBP3kJz/hu9/9Lk888US3IhplZWVdbggAnY9760NfSktLO2+KLVq0iPLycp5//nk++MEPDrgtIUTmSAZMCHFMmj59Oh6Ph9dff73L9tWrV/f72oaGBrZt28Y3v/lNLrzwQubOnYvP5+t2J97j8fR7t3jevHm8/vrrXS5uN23aREtLC/PmzRvAGQ3evHnz2Lp1a5c5OLW1tVRWVma8D4sWLWLBggX813/9V+dcpSOFQqEet2fSyy+/zCc/+UmuuuoqFi1axNSpU6msrOzzNa+++ipFRUX88Ic/5JRTTmHmzJmDKsk9b9481qxZ0+V7MRzzgwZ7rHXr1hGJRLjjjjs4/fTTmTVrVr8Zx4H26fDhw2zbtq1zWywWY+3atSP2fe+Nx+PpXMfsb3/7G8uXL+/MOre3t3erXNoR/HYE96effjp79uxhx44dnfu8/fbbHDhwoNfMo2VZTJ8+vfNfSUlJn338zne+w/e//32efvrpHisYnn766Tz//PNdgtJnnnmGQCCQ1g2PvnS0GYvFhtSOEGLoJAATQhyTgsEgN954I//n//wfnnrqKSorK/n2t7/N22+/3W+WID8/n+LiYn77299SWVnJ66+/zjXXXNOtzPuUKVN48cUXqa6u7lZgoMO//uu/0traynXXXceWLVt49dVX+dSnPsUZZ5zBmWeembHz7csnPvEJiouLueqqq1i/fj1vvvkmV199NRUVFRlfO6xjQeiqqipOOeUUHnvsMXbs2MH27du5++67WbhwIaFQKKPHPNqsWbN4/PHHWbt2Ldu2beMLX/gC1dXV/b6mvr6e3/3ud+zevZs//elPPRZs6c9NN91EfX09X/jCF3j77bf5n//5ny4lwjNpMMeaMWMGSil++tOfsmfPHh577DH+7//9vxnr07nnnsvJJ5/MJz7xCV577TW2bNnCpz/9aaLRKDfddFPGjtMhFAqxceNGNm7c2DlcdOPGjV3W71q3bh0PPvggu3bt4vXXX+djH/sYGzdu5Oc//3nnPpdffjnPPPMMt99+O7t27eKNN97guuuuo7y8nFNOOQWA888/nxNOOIFrr72WtWvXsmbNGj71qU9x6qmnctZZZw35XG6++WZ+/OMfc9999zFr1iwOHTrEoUOHumRub7rpJlpaWvj85z/P1q1beeKJJ/j3f/93vvzlL3fJRna8J42NjV3eow4PP/wwf/rTn9iyZQv79u3jhRde4Morr2T8+PGce+65Qz4XIcTQSAAmhDhm/dd//Rcf/vCH+cQnPsHJJ59MU1MT1113Xb9rIhmG0XnBtnDhQq677jpuvvlmysrKuuz305/+lDfffJMpU6Z0mb91pHHjxvHcc89RVVXFSSedxCWXXML8+fM7q8qNBL/fz3PPPYfX62X58uWcddZZBINBnnnmmW7D+TLhhBNOYMOGDSxdupSbb76ZefPmce655/Loo49yxx13DHuVtdtvv51JkyZxzjnncN5551FRUdHvQsSXXHIJ3/72t/nWt77FggUL+Pvf/86Pf/zjAR+7oqKCJ598krVr17J48WL+7d/+bdgWBx/MsRYuXMgvfvEL7r77bubOnctPfvIT7rjjjoz1SSnFY489xuzZs/nQhz7ESSedxKFDh3j++ee7zEHMlDfeeIMlS5awZMkSampq+OUvf8mSJUv43Oc+17lPLBbj+9//PvPnz+eDH/wgsViMVatWdZmD9qlPfYq77rqLP/zhDyxcuJCLL74Yn8/Hs88+2zls0DAMnnrqKSZOnMh5553HBz7wAaZNm8bjjz8+6GGmR/rZz35GNBrlIx/5CGVlZZ3/jqxsOWHCBJ577jnefvttTjzxRL7whS/whS98gf/3//5fl7Y63pMnn3ySNWvWdD7u4PV6ueuuuzjjjDOYNWsWN954IwsXLmTVqlVkZ2cP+VyEEEOjdG8D/YUQ4hh07rnnkp+fP6IBkBBCCCFEuqQIhxDimLV582bWr1/PsmXLiMfj3Hfffbz44os8/fTTo901IYQQQogeSQAmhDhmKaX41a9+xVe+8hVc12X27Nk8+uijXHTRRaPdNSGEEEKIHskQRCGEEEIIIYQYIVKEQwghhBBCCCFGiARgQgghhBBCCDFCJAATQgghhBBCiBFy3Bfh6G/xTjH8ioqKel3kVowM+QzGBvkcxgb5HEaffAaZ47gOL+x/gbW1a/Gb/gGtaRYIBAiHw2nt62qXmBPjtLLTOHvC2RhK7vFnivweRl95eXlG2zvuAzAhhBBCiPejaDLKvdvupSHaQMAKDOuxDGXgt/ysqlnF7pbdfHrup/GYmV8IXoj3A7k9IYQQQgjxPhNJRvjN5t/QHGvGZ/pG7Lh+y099pJ57ttxDNBkdseMKcSyRDNgw0FrTlmjjQOsBdjbvJJwM42oXQxkErADT86YzMWciWXbWgIYCCCGEEEL0J+bEuGfzPUSSEWzDHvHje0wPrfFW7t12LzfMv2FU+iDEWCYBWAZFk1Fer3mdtw6/RUusBTT4bX+XcdCudtlUvwmlFDneHBYWLWRZ2TJ81sjdnRJCCCHE+5PWmr9u/yvtyfZRDXxsw6Y52sz979zPJ2d/Um44C3EECcAywHEdnt33LJvqN+FqF5/lI2gHe9zXUAZBT+q5pJtkVfUqVtesZnHxYi6YdAGmYY5k14UQQgjxPrK6ZjUHQwfxW/7R7gq2abOnZQ8b6zayZNyS0e6OEGOGBGB9cByHaDQ1frm3OzfV7dU8uvtRwskwXtOLiUkikUj7GCYmaFhbvZYtdVv4yNSPUB7MbKWVsUxrjeM4AJimBJ9CCCHEYLXEWlhRtWJMBF8dAnaAZ/c/y8yCmb3enBbieCMBWC8cxyESiRAMBnsNvjbVb+KJXU/gt/xkWVlDOp5t22it+duuv3HZtMtYWLxwSO0dS0zTpKWlBb/fL0GYEEIIMQhaax6ofGBMzrdSKB7a8RCfmfuZ0e6KEGOCVEHsRTQa7Tf4enLXkwTsQMbGNSulCNgBntj1BG/Vv5WRNo8FSimCwWBntlEIIYQQA7OreRfV7dWYauzdyLQMi30t+zjYdnC0uyLEmCABWB96C6wOhg6mMl/28KT4/bafx3c9zsHQ8fOHSibnCiGEEIP3SvUrBK2xO8TPb/t56eBLo90NIcYECcB60VtA4LgOD1U+NOzjq/2Wn4d3PIzjOsN6nLFEgjAhhBBi4NribVSFqsb0/48aymBf6z4iychod0WIUScB2AA9t+852pPtw/5HTilFKBHiuf3PDetxhBBCCHFse+nAS2Ny7ldPXqt+bbS7IMSokwBsAKLJKBvrN+I1vSNyPK/pZWPdxmFbSf5jH/sYX//61/vc56c//Smnn376sBxfCCGEEEO3t3XvMRGAeU0vlU2Vo90NIUadBGAD8HrN67jaHdFjutplzaE1g3ptY2MjP/zhDznzzDOZOnUqCxcu5KMf/SgPPvggyWQyrTa++MUv8uSTTw7q+EIIIYQYXnEnTmu8dbS7kbaWWMuIX0sJMdZIGfo0aa156/Bb+CzfiB7XZ/nYWL+R5RXLBzTssbq6mssvvxzLsvj617/O/PnzsSyLN954g7vvvps5c+ak1U4wGCQYHLuTeoUQQojjWW24loSbwMfIXp8MVtyJ0xBpoDhQPNpdEWLUSAYsTW2JNlpiLaNy7NZYK6FEaECvufXWW4nH4zzzzDN89KMfZebMmUydOpUrr7ySf/7zn0ydOrVz39tvv53Fixczb948br75ZsLhcOdzRw9B7Hj87LPPsnz5cqZPn87HPvYx9u7d27lPc3MzX/7ylznppJOYNm0aZ555Jr/+9a/RWg/+TRBCCCFEN5VNlfjMYyP4glRJ+p3NO0e7G0KMKgnA0nSg9QCMUvygteZA24G0929qamLFihVcd9115OTkdHvetm0CgQAA//jHP2hubuahhx7izjvv5Nlnn+Wuu+7qs/3a2lr+9Kc/ceedd/L444/T2trK1772tc7n4/E4s2fP5ve//z0vvvgiN998Mz/96U954IEH0j4HIYQQQvSvLlyHbY79+V8dvKb3uFpmR4ieyBDENO1s3jls6371x2/72dm8k7mFc9Paf+/evbiuy8yZM/vdt6Kigu9///sATJ8+nUsvvZSVK1f2WZwjHo/z85//nMLCQgC+9KUv8aUvfYloNIrP56OkpIQvfelLnftPnDiRTZs28eijj3LVVVeldQ5CCCGE6F/STW9O91ihlCKhE6PdDSFGlQRgaQonwxhqdBKGhjJoT7SnvX/HUL905ozNnds1qCstLeXll1/u8zXjxo3rDL46XqO1pqGhgYqKClzX5a677uLxxx+npqaGWCxGMpmkoqIi7XMQQgghRP9cjr2CFq577PVZiEySIYhpGu2KPQM5/pQpUzAMg3feeafffT0eT5fHSql+/zD29Bp47w/q3XffzZ133slnP/tZ/v73v/Pcc89xzTXXkEjIHS8hhBAikxRjd/Hl3ozWDW0hxgr5BaRptP9YDOT4+fn5nHPOOdx77720tnYvTZtIJLoU2si01atXc/bZZ3PNNdcwf/58pkyZwp49e4bteEIIIcTxarSvTwbDVOZod0GIUXXs/WpHScAKjFoWzNUuQXtgpeB/9KMfYVkWF110EY8++iiVlZXs2bOHhx9+mIsuuojdu3cPU29h2rRpvP7667z22mvs2rWL//qv/2LDhg3DdjwhhBDieJXjyRn1UToDkXST5PnyRrsbQowqmQOWpul509lUv4mgZ+TXxIokIkzPmz6g11RUVPDss89y55138tOf/pTq6mqysrKYMWMGN910E7Nnzx6m3sLNN9/MwYMH+exnP4tlWVx22WV89rOf5eGHHx62YwohhBDHo+l509lQt4EsT9ZodyUt4WSYGXkzRrsbQowqpY/zxZmqq6t73B4OhztLtQO0xlu5Y/0dA85EZUI4EebmE24m25M94sceCZZlkUwmu73nYuQUFRVx+PDh0e7GcU8+h7FBPofRJ59B+tribdyx/g4Cdub//zMQCGR8ykIkGeHrJ34dn3XsrF022uT3MPrKy8sz2p5kwNKUbWeT681Nq9yr1hAP+2ipy6fxQAmJqBetFUppbF+Mggl15I5rxOOPkUahQnK8OWTZx8adLSGEEEKMnCw7i4B17Ny8zLKzJPgSxz0JwNKklGJh0UJWVa/q9Q9HMm5xYPM06naNJxLyo7TC8sZRxntJRu0qandMBEPjC0YomVbFhAW7sDw9B3bRZJQzKs5Iq6S8EEIIIY4vSimKAkXUh+vH/LWCq13Kg5nNJAhxLJIiHAOwrGxZj9WGXEexY9V8Xv/7BziweRpO0sTji2P7Y12CLwBlaGx/DNsbx0maHNg8jdf//gF2vD4P1+n+h9NQBqeUnjJs5ySEEEKIY9uZFWfSnkx/vdDREk6GOWv8WaPdDSFGnQRgA+CzfCwqXkTMiXVua6vPZe1D51KzYyKm5fSayeqN5UliWg41lZNY+9C5tNXndj4Xc2IsLlksqXohhBBC9GpKzhTyvfmj3Y1+lfhLKM+SDJgQEoAN0IWTLiRoBdFac2hHBRueOgMnaWLZAwu8jmbZSZykyYanzqB2ZwVaa7LsLC6YeEGGei6EEEKI9yOlFIuLFxNLxvrfeZREkhGWjls62t0QYkyQAGyATMPkYzM/xv7txVS+thjLm0irkEY6lALLm+CdVxez/50irphxBaYhixUKIYQQom+nlJ0yZq8ZtNb4LT9LSpaMdleEGBMkABuMlgmw7SqwIsPTvhWBrVenjiOEEEII0Q+v6eXiyRcTTmS2bHwmRJIRPjz1w1iG1H4TAiQAGzDHgYceCjCxoJiZ+TNJuImMtp9wE8zKn8XEgmIeftiP42S0eSGEEEK8Ty0oXsDU3KkZvzYZirgTZ3bBbGbmzxztrggxZkgANkDPPeelvT01XLA0WMqSkiUYysDRQ4uUHO1gKpMlJUsYFxyHUhAKKZ57zpuhngshhBDi/e6jMz6KQqG17n/nYaa1xjZsLp126Wh3RYgxRQKwAYhGYeNGD17ve5O+cjw5nFx6MqWBUhztpLVQ85GSbhJHO5QGSzmp9CRyPDmdz3m9io0bPUSjGTuFAVm1ahUVFRVUV1f3+FgIIYQQY4vf8vPxmR8n6ozSxcO7tNbEnBhXz7oaryk3k4U4kgRgA/D6615ct/t2QxnMyJ/BsrJlTMyeiKEMEm6ChJNA0/UOlEaTcBIk3ASGMpiYM5FlZcuYkTej5zXGXFizZuB/uG6++WauuuqqAb+uL0uXLmXDhg2UlpZmtF0hhBBCZM7U3KlcMeMKwsnRmQ+mtSbqRLlm9jWMzx4/Kn0QYiyT2ZBp0hreesvG18eSXJZhMTl3MpNyJhF347TEWmiKNhF342itUUrhMTwU+ArI8ebgMTz9rlrv88HGjTbLl8cyVm1xsDweDyUlJaPbCSGEEEL0a07BHK6ceSUPVj6Iz/T1e72RKR3B1ydnf5IpuVNG5JhCHGskA5amtjZFS0t6b5dSCq/ppSRQwqyCWSwoWsDC4oUsKFrArIJZFAeK8ZretP8YtrYahEKD/8PZkQ3785//zMknn8ysWbO4/vrraWho6LLf73//e0488USmTZvGJz7xCQ4ePNjl+aOHIGqt+V//639x2mmnMW3aNJYtW8aPfvQjYrGxuw6JEEIIcbyYlT+L6+ddj2VYI7JGWMyJ4TE9fH7+5yX4EqIPEoCl6cABExidCa1a63ePP3ibNm1i1apV/OlPf+LPf/4zW7du5f/+3//b+fyzzz7L9773Pb7whS/w3HPP8eEPf5gf/vCH/farqKiIO++8k5deeonvf//7PPDAA/ziF78YUl+FEEIIkRkVWRX86+J/ZWHxQsKJ8LAU59BaE06GObHkRL606EuMC47L+DGEeD+RIYhp2rnTwu8fnWP7/anjz507sAIfR7Jtm9tvvx2vNzWf7NOf/jS/+93vOp//1a9+xaWXXsqNN94IwLRp09ixYwd33313r20ahsE3vvGNzscTJkxg7969/PGPf+TrX//6oPsqhBBCiMyxDItLpl7CouJFPL7rcRqiDQSsQI9zzwfC1S6RZIRifzGfmP0JyrPKM9RjId7fJABLUzisMEYpX2gY0N4+tLHbM2bM6Ay+AEpLS6mvr+98vGPHDi6//PIurzn55JP7DMAA/vKXv/C3v/2NAwcOEA6HcRwHt6dKJUIIIYQYVROyJ/ClRV+iKlTFS1UvcaD1ACgGXKUwmoxiKINJOZM4q+IsKrIrhqnHQrw/jWgA5rou3/zmNykoKOCb3/wmoVCI22+/nfr6eoqLi/nqV79KVlYWAI8++igrVqzAMAyuv/56Fi9eDMDu3bv55S9/STweZ8mSJVx//fUopUgkEtx5553s3r2b7Oxsbr755owWjBjtmGKox7dtu8tjpYa+RsiTTz7Jt7/9bW699VaWLVtGVlYWTz31FP/1X/81pHaFEEIIMTyUUkzInsCn5nyKcCLMutp1HAgdoCnSRGu8lYSbQKEwlIFOaNrj7Wg0HtNDjieHPG8ek3ImsXTcUvzWKA0NEuIYN6IB2NNPP01FRQWRSASAxx57jAULFnD55Zfz2GOP8dhjj3HttddSVVXFqlWruO2222hqauIHP/gBP/vZzzAMg9/+9rfceOONzJgxgx/96Eds3LiRJUuWsGLFCoLBIL/4xS947bXX+Mtf/sJXv/rVjPV9tLJfI3X8GTNm8MYbb3Ddddd1blu3bl2fr1mzZg3z58/vHLYIUFVVNVxdFEIIIUQGBewAZ40/q/Nx3IlTF66jLdFGLBkjLy+PSChCjieHkkAJtmH30ZoQIl0jFlY0NDSwfv16zjvvvM5t69at46yzUj/8s846q/OCf926dZx22mnYtk1JSQmlpaXs3LmTpqYmIpEIM2fORCnF8uXLO1/zxhtvcPbZZwNw6qmnsmXLloxONA0E9KhlwVwXgsHhLQBy44038sQTT3DPPfewe/du7r//fh5++OE+XzNt2jTefvttnn32Wfbu3cs999zD008/Paz9FEIIIcTw8JgexmePZ07BHBaXLGZp+VLmFMyhIqtCgi8hMmjEArB7772Xa6+9tkvp9ZaWFvLz8wHIz8+ntbUVgMbGRgoLCzv3KygooLGxsdv2wsJCGhsbu73GNE0CgQBtbW0Z6//06UneTdyNuEgkdfzhdNFFF/Gd73yHX/3qV3zgAx/gkUce4Vvf+lafr7n22mu54ooruOWWW7jwwgvZsGEDX/va14a1n0IIIYQQQhzLRmQI4ptvvklubi5Tp05l69at/e7fW+aqr4xWT8/1tM7WCy+8wAsvvADAf/7nf1JUVNRje7W1tVjWe2/PlCkKwzBGZSiiYaSOf2R/+nPnnXf2+N8drrrqKq666qou2774xS/yxS9+scu2T3ziE53/vXz5cmprazsfW5bFbbfdxm233dblNZ///OfT7ueRbXm93l4/DzG8LMuS934MkM9hbJDPYfTJZzA2yOcwNsjn8P4zIgHYO++8wxtvvMGGDRuIx+NEIhF+/vOfk5ubS1NTE/n5+TQ1NZGTkwOkMltHLhLc2NhIQUFBt+0NDQ0UFBR0eU1hYSGO4xAOhzsLehzp/PPP5/zzz+98fPjw4R77HIvFMM331t7y+yE72yGZHJmV5I+Una3x+RIkhzcJNmosyyKZTBKLxXr9PMTwKioqkvd+DJDPYWyQz2H0yWcwNsjnMDbI5zD6ysszu8TCiORzPvGJT/DrX/+aX/7yl9x8883Mnz+fr3zlKyxdupSVK1cCsHLlSk466SQAli5dyqpVq0gkEtTV1VFTU8P06dPJz8/H7/dTWVmJ1pqXX36ZpUuXAnDiiSfy0ksvAbB69WrmzZvXYwZssJSChQsTRKMZazIt0SgsXpwgg6cihBBCCCGEGCWjug7Y5Zdfzu23386KFSsoKirilltuAVIL+i5btoxbbrkFwzC44YYbMN4d+/e5z32Ou+66i3g8zuLFi1myZAkA5557LnfeeSdf/vKXycrK4uabb854f5cti7F6tSfj7fbFMOCUU2IjekwhhBBCCCHE8FA6k6UCj0HV1dU9bg+HwwQCgW7bn37ay8aNNl7v8KekYjHNkiUJLrro/R2AdQxB7O09F8NPhjeMDfI5jA3yOYw++QzGBvkcxgb5HEbfMTkE8f3kwgtjBIMw3GGr1pCVpbnggvd38CWEEEIIIcTxRAKwXvSWGDRN+NjHwsNekj4SgSuuiHBEHZD3veM8GSuEEEIIIY4DEoD1obeAoKLC5bLLIsMWhEUicNllESoqRmnl51EgwZcQQgghhDgeSADWC5/PR3t7e6+BwcKFST784SjhcOaGI2oN4TBcemmEhQvfpzXne6C1pr29HZ/PN9pdEUIIIYQQYliNahXEscw0Tfx+P+FwGOh5UecZM+Caa0I8+mg27e0GXu/gjxeLQVaWyzXXtFFe7vDuYd/3tNb4fD78fn+XddeEEEIIIYR4P5IArA+maRIMBvvcZ/p0uOWWOM8952XjRg+uCwNJ5ESjqVLzJ58c54IL4pjmEKK4Y5RU9xFCCCGEEMcLCcAywDThootinHNOjNWrvWzaZNPaaqC1xu9PBVgdXDc1x0spRU6OyxlnJDjllNiAgjYhhBBCHL8SboK6cB07mnZwMHSQ1kQrjuvgahdDGViGRa4nlwnZE5ieN51ifzGmIaNMhBgrJADLIJ8Pzj47xllnxQiFFPv3m+zaZdHernDdVCAWDGqmT08yYYJDVpamh5GNQgghhMgQV7soVI9TCUaL1hpHOyTdJEopLGVhKKPPPmqtOdB2gJcOvkRVWxVxJ47X9OIxPd32jTkxQvEQe1r38ML+F/BZPibnTObs8WdTGiwdzlMTQqRBArBhoBRkZ2vmzUsyb97xU0xDCCGEGC2RZIS9rXvZ2bST+kg9TbEm4k4cV7ugwMAgaAfJ9+VTGihlZv5MxmeNH5HMUHuinR1NO9jVsovGaCNN0SbibrzzeYXCZ/nI9+ZT5C9iRv4MpuZOxWt60VrzRu0brDm0hsZoIwErgNf04u1nyoJSqst+B9oO8JvNv6HYX8zyiuXMK5rXtY/tioMHDXbssAiHFa6ryM42aG/3kZWlmTEjSUWFi98vVYuFGCoJwIQQQghxTNJaUxWqYmXVSva37ifhJgjYAQyVGvt/dHaoY+jewdBBXj34KlmeLGbnz+asCWeR48nJeN/2tu7l5YMvc6DtAK52CVgBlFKYhonf8Hd7TVOsiYZoAxvqNuC1vJQGSmmKNtGebMdv+Qnafc9L74uhUgFoOBnmkZ2P8GbtBhY417BpfTaNjUbnaB2/n841SAMBRThs4ziwdq0Hw0jdYC4ocDnllBgzZzoykkeIQZAATAghhBDHnHca3+G5/c/RFG3Cb/nxWl68pFfIyjZsbK8NwNbGrWw8vJGJ2RO5bOpl5Pnyhty3txvf5vl9z9McbSZgB/Bb3YOt3nQESgdDB1lVvQqtNVmeLGblzyLbkz3kviWiHqo3zWX9rjIejFQye9wkSrPGEQj0/hrThKys1H87jqKuzuT++wNkZ2sWLkxw2mlxyYwJMQASgAkhhBDimBFzYjy+83G2N20naAeHlBWCd4Mxw6YuXMddm+5i+fjlnF5++qDmjEWTUR7f9TiVTZUE7ABBz8D7prWmsqmS2nAttpEKEuNOnPV166nIqmBq7tTODN+A2nUVezfM5MDm6SjlYtoOHn+Sd5q3E3UiTM6dnHZbSkEwCK6rWLPGw5o1Hs48M8oZZyQkIyZEGiQAE0IIIcQxYWfTTh7d9SiO6ww58DqaoQy8lpeXql5iW8M2rpl1Ddne9DNOR/YtYPeRTuqD1prtTds5HD6MZXS9RLMNm5r2GhoiDcwvmj+g8w83B9n6PycRaQtgeRLd2t3fth8Hh2m50wbc5441UFeu9LFtm4crrwyTny/ZMCH6MvBbKEIIIYQQI2xj3Ub+Vvk3FArbtIftOH7LT3O8mV9v/jUNkYa0XrOpbhN/q/wbhjKG1LcdzTuoD9f3WhjEVCaOdlhft57WWGtabe5/axpvPHo28YgHy9NzYTDLsDjYdpD9rfsH3Xe/H1pbDX71qyzWrRu+z0eI9wMJwIQQQggxpm2s28hTe57qLGIx3EyVCoB+u+W3NEWb+tx3U/0mntj9BAFrcFmvDvWRemraa7plvnrr38b6jbTGew/CtIadq+ey9805WN4E/Y1atAyLPa17aIu3DbTrnQwjlRF75hkfK1d2L48vhEiRAEwIIYQQY1ZlUyVP7XlqQIUsMsFQBqYy+d2W3xFJRnrcZ1fzLp7c/eSghxx2SLpJKhsrO+d8pcMyLDbVb+q9b2vmUv32FCxvvMfne2IbNlsbtqZK9w9BIACvvOLjpZckCBOiJxKACSGEEGLMcLVL0k2ite4sajHSwVcHQxk42uHByge7PRdzYjy681F8pm/Ix9neuH1QQY+hDLY2bEXrrnOu9m+aTvXbU7G8iV5e2buEm2BXy64Bv+5ofr/mlVe8MhxRiB5IEQ4hhBBCjDitNTXtNVQ2VVIVqqIl1kJrvBVXu/j8PqLRKJVNlUQTUYKeIAW+Agp8Bf0uQJxplmGxr3Ufm+o3sah4Uef2x3c9TsJNdFtrbKBaY60cjh7GYwy8HYUinAizv20/k3ImARBqyGbv+lkDynwdyVQm1aFqJmZPJMDQMnuBADz/vI/p05NSmEOII0gAJoQQQogRE01Geb3mdd6qf4umWBNe09sZxHT8b8AOUN9aT3O0GcuwaIm1cDhyGIBsTzYTsiZQ5C8akflgHf15Zu8zzMibQcAOUNlUyfbG7RmpxLi3dS+2GnyWqCNALAmU4DMCbF2xFNMz8MzXkUxlsrdlL/nZ+UNqB8C24f77A9x4Y7uUqBfiXRKACSGEEGLYxZ04/9jzD95ufButNT7L1+vCwlprdjfv7lKQomN+VDQZZVvjNjymh0nZkygLlo1YIPbcvue4fPrlPLfvuSEX3YDUcL+WWEuvVQ/TZRomO5p3ENx9FbF2f6/VDtNlKIPD0cNDngsGqcIchw8bvPqqhzPPHFxWToj3G5kDJoQQQohhtaNpB7/Y8Au2N27Ha3rxWX3Pm2qMNBJ1or0+bxs2Wmt2NO9gU/0m4s7wX9hbhkVlUyV7W/bSGGnMSNB3oO0AmqEPzVMoGpocDmydNOTgq0PSTVITqslIW34/vPyyl3BYUmBCgARgQgghhBgmjuvw8I6H+ds7f0Oj054vtadlD5bqf5CObdiEEiHWHFrDofZDQ+1uv+JunL9s/wt+OzNFQRqjjWmVnU9Hy7ZTaHP6Lpk/ELZhcyiUuffUMODVV6UqohAgAZgQQgghhkHCTfCnbX+isqmSoB1MO2MUd+I0R5vT3r+jXPw7Te+wv23wCwmnwzIsNtRvwOhvUa00dFR5zATtmMSqZxBxB7+GV08iiZ5L3A+GxwNbtti4Qx/VKMQxTwIwIYQQQmRU0k3yx61/pDZcO+AqgY2Rxm5l1dNhGzZ7WvZwoO3AgF+brpZoC+2JdpLu0If5RZIRHO1koFcQ2jcbN+nBcZ2M9K1D1IlmtL1QSPHOO0Ob7ybE+4EEYEIIIYTIGK01D1Q+QF24DtsceHW/xljjgBYkPpJt2Oxu2U1tuHZQr+9PQ6wBE5NQPDTktppimRsu2LZnHoYnikYTc2IZa9fVbkbOtUMgAKtXj+wyAkKMRRKACSGEECJj3qx7k90tu/Fag7vQjiajQypwYRs2O5p2kHCGVoq9J+FEGNu0aYg2DLmtUCKU1jy3/mgNyVAekCofH0lmbtigqUxCicwFYEpBY6PBIBKcQryvSAAmhBBCiIxoT7Tz/P7n8VuDK1Khtc5YALGtcVtG2jlS1IliKCMjQYmr3YxUUky25+Im3hvmmckhg0qpjA2T7BAOK0IhqYYojm8SgAkhhBAiIx6sfBBjCJcWGp2RC35DGTTHmjNeGbEjuMlkkDNU0boJoN6rbJGJtbuONJj5eH1xXaiqkstPcXyThZiFEEIIMWTVoWr2te0jy84adBuZDB5sw2Zv617GBcZlJNN0ZCCSqbW7tNZD7lu0bjyG/d68r0z0rbMtrYe8SPTRAgHYudNmzpzMZtaOdfE41Naa7NhhUVNj0NJikEyC6yqCQYN4PEgwqCkpcZgxI8n48S5+v4zlPFZJACaEEEKIIVtZtZKAFRjtbnQRc2I0x5rJ9+WPdle68Vt+HO0MeR6Ym/CQgfiy57a1O+jhpL0xDGRB5ndpDbt3m7z8spfqapN4XOH3a+yjatAkk4po1CAahbo6k3XrPBgGFBW5nHRSnMWLE1hyRX9MkY9LCCGEEEMSTUbZ07oHrzm0CneZWF/rSLZhs691X0YCMKUUilTgMJRhlh3yvfnsYc+Q29Fu1wxVRx8zQaHI8eRkrL0OznGe/HIceO01Dxs2eGhpUQQC4POBz9d/Rss0IevdJHM4bPDMMz5WrPAya1aSCy6IHXNZMa01USdKwk3gahdLWfgsX8YWKB+r3t9nJ4QQQohht/bQ2ozMFVKojF94tcZbSTiJQZXEP5pt2jiuk5G2gp5gZgLOo952w8hcEOsxPQNex60nR341lOK4roJ46JDBAw/4aWsz8PkgGBxae34/gOLtt222b7f44AdjLFqU+QqgmZJwE2xv3M6O5h00RhppjDYSc2Odfz8MZWBgkOPNocBXQHmwnIVFCynwF4xyzzNLAjAhhBBCDMne1r34LN+Q21FK4bf8JMlckQtXuzTHmyn2Fw+5Lb/pp8VpyUhWyFQmPtM39IIexnvz5jQajzH0gKnDQIcfap0aXtjYqGhpMYhEFPG4wnVTzymVGoJYXGximjBxYpLp0x2Ki91hG0Y5VjgOvPCCl7VrPfj9qYxXJqWGLSqeeMLPW2/ZfOxjkTGVDWuMNLLy4Ep2NO0g5sTwWT4MZWAaJgGj+9DlmBOjpr2Gfa37eOXgK5QGSzmt/DTmFMzJeKZ8NEgAJoQQQoghaYw2Zqwtv+WnNdmasfZs06Yx0piRACzLk8XhyOGMzSnLtrNpiDYM6YLStOLE3w1uXO1mbB6eox3y/emdZzIJBw6Y1NYaRKMGhqExzVSflEoNm+ugdaqwRFWVxa5dFs8/r8jLc1m4MMGyZbGMByZjQSwGf/xjkPp6g8AwT5MMBDTV1Sa/+EUWn/1sO0VFma2KOVDhRJiHdzzM3ta9eEwPlmH1GHD1piML2xJr4ZEdjxCwA1w0+SLmFs4dxl4Pv2M/hBRCCCHEqIkmoxldrLfQV0jCzdwQKoUinAxnpK1CXyFaa4L2EMeNvWtSzqQhl933FlWjk6msl0JlZMhgh0m5k/p8Ph6HLVssXn/dw4EDJq6r8Hg0lkWvGa1kMhVwKZXKAmVlaZJJxapVHm67LZv77/fT3v7+SYdFo3DPPUEaG40RCy4tK5Vp/O1vg1RXj86lvtaaDbUb+PnGn1PdXk3ADgxpeLFSioCdCtwe2fkIf9v+N6LJaKa6O+IkABNCCCHEoNWF6zIaMOX78jNe+jxTF2pZdhZ5vjxMlZn+BewAWXbWkObP+cYdQDupOWm2aWdkeJZGk+fN63Wum9ZQU2Owdq2H5mYD02QAVfg0eXndszI+H3i9sHevxc9/nsWbb9rH/FyxeBz+8IcgbW1Gt8qGw02p1LDEP/0pQEPDyAa0STfJX7f/laf2PIVt2Bmf1+m3/Oxr28fPN/ycfa37Mtr2SJEATAghhBCD1p5sz0hVwA6WYVHoL8zommCZaivqRFlesTyjAef47PEk9eDngdnZzRhWDFe7GatYmHATTMruOfuVSMCmTTY7dlgYRirT0h+NxtUumlR2zNtHsUzLAo8Hnn7axx//GDims2EPP+ynuXnkg68OSoFlKf74xyCJEarLEXfi/H7r79nftr8zYzUcbCN1s+HPb/+Zd5reGbbjDBcJwIQQQggxaAk3kZGFjo80LW8ajpu5WuUumQnAsj3ZXDP7mowGYCX+EgJWYNBZMGW4WMFWDGVkZGikq13yvHnkeLsHc5EIrF3rIRRSvWa8Em6CllgLdeE6qkPV7G/bz/7W/RxoO8D+1v1Ux3fxRu06Nh/ezL7WfYQTPQ8PDQRSCxPfdVfWiGdwMmHzZoudO208mRsROihKpeag/eMfwz/+MekmuXfbvTRGGjM6FLY3Sil8lo+HKh9id8vuYT9eJkkAJoQQQohBMzHRR9dCH6IsbxZBTzAjpe0hM2tjxZIxFhQtINeby8TsiRnLqimlmFc4D4fBB5z+Cdvx6byMrQE2r2Bet23hMLz5Zuqi+uisl0YTSoSobq/mYOggLfEWYk4MRzsoVKq0uDLAtckpbCXhJmiLt7G/bT9ra9fyRu0b1LTXdHtPO4K83/wmeEwFYZGI4umnfQQCY2MMpcejeOstmz17Mju092gP73iYhkhDRpZpGAiv6eVv7/yNlljLiB53KCQAE0IIIcSg+S0/2s38heas/FlDLlDRYajzorTWBOwAZ48/G4DLpl5GwslcFixgB5iQNWHQJelzp2+hIDD04YdJN8m03GndLqCjUdiwwdNZ1fBIbfE2qtqqaIg04LgOpjJ7DQQN0yGr8L0Kl6Yy8Rge4k6cyqZKXq95naq2qi6Bt2GAbSt+97ssWlqOjSDs0Ud9aD22+hoIwKOP+klmboWHLrYc3sKOph0Dynw5CZOW2nwOvj2J/W9NY9/GGVRtmUL93lJi7b605wAqpbANmwcqH8jYTZvhJmXohRBCCDFoxYHijBfNgNRwv7JgGTXhGiw1tMsVr9nHpKM0RJIRPjPvM53FBPJ8eZw1/ixerHpxwGtl9WZyzmQao42EE+EBvZ8JN8HCcXOorjhMU00RhjG4C1BHOxT4CijLKuuyXWvYvDkVkB0ZfDnaoS5cR9yJv5fh6oPWikBeCKV67p9tpI6xq2UXdZE65hXMw2t5O4+rFPz5zwFuuqk9rXlno6WlRbFrlzXs5eYHSqnUGm0bN9osXZrZCWGRZIR/7PkHfrvv34LrKGp3jqd+TzmRtiCxdh+uY6IMN/WP1PdEOyYoje2N48sOk13UwsRFO/AGey+mYyqT2nAtq2tWs6x8WUbPbziM4a+wEEIIIca6LDtr2CbbT8ubhtfwDvmu9lAWiY4lYywuWczE7Ildtp9Wfhol/pKMZemUUiwuXozX8qY9vNHRDiWBEvJ9+Uw+cTvJ+OCGfjnaIcvO6nFtpb17TSIR1SX4ak+0c7DtIEk3mXZ2UTsGeaWH+93PNmwiyQhra9dSG67t3G4Y0NRk8NJLQwumh9tLL3lHfd5Xb/z+1By+oYo5MVpiLTRGG2mONfNg5YN9/kZj7T4qX13I6r9/gMrXFhJqzMVJWFieJB5/DNubwLIdTNvB8iSx/TFsXxwUREMBandWsPqB89jw1OkcPlDca2bMb/l5sepF2hPtQz7H4SYZMCGEEEIMmlKKPG8ezbHmjLdtKIP5RfNZX7ceQxuDKvaRcBMU+AoGdfyEm6DAX8BFky/q9pxSimtmXcOvN/8aV7sZKf9uGiYnlJzAhroNRJPRPjNhrnbxWT5m5s8EIJjXTum0Kur3lWFa6QeFjnbItrNZWLyw2zm0t6cWWD6y4EZbvI3GaOOAztd1DbKLmrE86Y1/UyhMZfJO4zsk3ATjs8YDqQBi1SoPCxcmRn2B4Z4kk/DOO9YASvKPvPp6g5oag7Ky9N+/unAdG+s3UtteS0O0gfZEO4520GiSTpI3697EZ/nwW378lp8iXxFF/iJcx2THqgXU7RyPYTkYlovtG3j2zbBcDMsl3BJk6/On4MsOM/ecN8gu6r5gu4HBy1Uvc9GU7r/ZsUQyYEIIIYQYknGBcYOev9SfoB1kYfHC1AXfIDJhHWtaDVTCTZBtZ3P9vOt7Xcco25vNZ+d9Fkc7GSvKYRkWJ4w7gTxfXq/VFl3tYhs2S4qXdFmTbMbpb2HZybTnziTcBMX+YhYVL+oxoNq8GcwjYsBQIjTg4AvAtBzyx9cP6DWQei92N++mJlTTuc3rTZV3H4s2b7aJxcbW3K+jBQLw8sv9ZxEd12FD3Qbufutufv3Wr1lft566SB2OdvBZPoJ2kCw7i8PRw3gMDwpFNBmlMdrItsZtrHhrH0/ft4hDu0uxfAkMa+i/D6XA9sVJxi02PHkmO1fPw3W6vt+2abOtYVtGl7EYDhKACSGEEGJITi8/nbgTH7b2cz25LClZgos74CAsaAUHPE8r7sTJ8+bxuQWf63f+WKG/kBsX3IhCZSwINZXJgqIFzMmfg4vb5WLS0Q5ey8uJ407sFhialsvss9bjxPoeitgxbHJh0UJmF8zuMbPY2qoIhd4behhzYjREGgYcfLmOScmU6l7nfvXHMiwqmys7K9wZBtTWGhw8OPYuYbdvt/CPzdiwU8f715fN9Zu5ff3tPLX7KUKJEEE72DlH70haa+rCdV0ztVrR+ta51K38CKFYhJroXg5HDme0UqpSYHkTVL8ziXUPn0M01HWIcXuyna0NWzN2vOEw9r69QgghhDim5PvyKQuWDWsFsmxPNktLluK1vGkHOgk3QUVWRdrH0FoTToaZUzCHG+bfkHbxjnxfPl9a/CXKgmW9rms1GCXBEk4pPYU8bx5JN0k0GaXYX8wJJSf0mpXLL2+gbPY+kvHuzyfdJI52KPYXc0rZKeT78ns99r597w091GjqI/WDCL4Mckoa8QR6L56QDtuw2db4XlbD74eVK8feXLDmZqNblcixqK3NIBbrvj2SjPDnt//MY7seQynV79zO1nhrlxsvWsPhNR8ktGsBhjeKoVJLEISTYaraqogmh/Y9OJplJ0nGbd587GzCze+tgRewAqyvW5/RY2WaBGBCCCGEGLLTy08nnMxc8NETv+3nxJITmZwzGcftf0iipSxKg6VptR134iil+NTsT3H59Mt7vOPfZ98sP5+e+2kunXYpSTeZsWyYbdjMyJvBsrJlXD3rauYUzCGcCPfZ/vRlWyicWIsTt3C121mpcGLORJaVLWN2wewuQxePlkh0DSYao4247sCGdLmOQVZBG/nl/RfeSEfSSbKjaQeQyuLs3WsRiWSk6YxwnNR7diyIx1OLXB9pf+t+frb+Z1SFqtIuqtMQbejMfmkNh9deSLh6Koana3TXsSxBbbiWxmhjBs7giLYNDcpl/ZNnEmlLpR+VUsMyJzWTxvA0QSGEEEIcK2YVzCLXk0vCTQyqWEa6lFJMzJlIsb+YnS07aYo1gaZbRiipk1QEK/rM2nRkvIJWkMUlizl/4vkDDryOtqh4ETPyZvDcvueobKok7sYHVaq+o29ZdhZLS5dyzoRzsA0bV7vsbd3LO03vUBeuoynaRCgRSg3x0qn3R6FYdO5m9ryaQ6h6IhW5hWR7stP+XKqrjc55ZHEnTigeGljRDccgmN9G4cRDAz7v3piGyaHwIcqzysn2ZKM1rFvnYfny4Rv6OhCHDxvE4wqPZ+yvQ+X3w44dFhMnpoai7mzayf2V9+M1vQP67Ybioc5Avumt0wkfnIZh95Bae5ehDNribbjapchfNLSTOIJSgHLZ8OQZnPyxF7E8SVpjrUSSkYwtE5FpEoAJIYQQYsgMZfCxGR/jD9v+MCIXPX7bz4KiBSTcBFVtVdSGa4k5qYs/S1l4DS9Tcqd0e13CTRBJRrANmxJ/CRdOvpC5BXMzupZZwA5w+fTLSbgJNtRt4M3aN2mINpBwEwSsQK/DB+NOnJgTw2t6KfYXc/GUi5ldMLtL8GMog6m5U5maO7XLOSXdZGohZMPENmwsw0KfAE8/7WP9+oEFlS0tRufww6ZY0wCDL5Oc4ibyKwZedKM/lmGxp2UPC4sX4vPB/v2ZX39usJqbVdrFT0abbaf6C7CvdR/3V94/qKUaIslUCjLWUELbzsUYnv6HGBrKoD3RjlKKQl/hgI/ZG2WAk7TY/vJi5p//Bo52qA5VMy1vWsaOkUkSgAkhhBAiIyqyKzrLqA9l7a2BsA2bKblTmJwzmbgbpyXWwqHwIZYUL8Fn+joLTpiGSdAKMi44jhl5MygLlg17H23D5uTSkzm59GSiySg17TXsaN5BXbiOSDKCo53Okut+y09FVgUz8mdQEigZUCbONuwe91cKPvShKHPmJHj0Uf+7GZr+24tGUxfnrnaJObHOIWR90a6BYTqMm34AX9bwjA1UKFriLSTcBLZh09Q0dob8xWLGmF4g+miOowgnwvx1+18HvVB53I1juB7q11yEstOf32Uog1A8hMfwkO3JHtSxe2JaDg0HSqnfO46cCTGq2yUAE0IIIcRx4MJJF7KzeSdRJ9rnPKNMU0rhNb3keHI4b8J5XDD5ghE7djp8lo8puVN6zMoNt6lTHb785RBPPeVj82abQIBegwXHgVhMYZrQHG3ut22tFdoxyCpspWBC7aCrHQ7EgbYDTM2dSlubQTzOmFj4WGuOiQIcR3pox0MYanDr62mt0WgaN52BG/Wj7IGt72Uog6ZYEwE7kNG/E7Y3TuVriznhikPDWpl1qI6hWF0IIYQQY51pmFw39zoUasTX4ok6UabnTecDkz6QsTaTydScqLVrbV55xcOKFV5eecVmwwabhgaDAdamGDUeD3z0o1G++MV2JkxIEo9DtIekRTiscN5dWymcCPea/XIdE+0q/NlhyufspXDioREJvkxl0hBpAFKBYn392LiU9XrdY+a7AFDVvo99rft6HQ6bDifupX3f3AEHXx0Uivpw5oequkmD/Rtnj+m1wCQDJoQQQoiMyvHmcMP8G/jdlt/haGdEMmGRZIRpedP4+MyPD6kIiNawe7fJ2rUeGhsNahviNLS3EXKa0SqJAjRgaC+5dh5F2dmUFJoUFzssXx6nqGjsXvQBjBvncvXVEaJRWLvWw+bNHlpaFPG4wuvVJBLvZTeOrLToOgZaGxiGi+VJkFPSRE5xM8oY+fONOlFc7aKUSXv72Eg7ZWfrY2YOWDzpsDf0BjPTrHbYE6UUbdtPQqPTGKDau5gToz3RTtAO9r9zmkzboX53BR4jlLE2M00CMCGEEEJkXIGvgC8u+CL3bruX1kQrPnN45ltprYkkIywqXsSHp3540MFXNApr1njZuNFiz6EW6p13iDjvlXu3DZsj15LVaBoi+9kTVXgaPAR35bBmw2Smjg9y6qlx5s9PYI6dGhHd+HywfHmc5cvjJJNQV2ewc6fFmjU2gYAFZhwjEcUEDMPFmxXBn92O7Y+PSKarL47rEE6GMYxsYrGxEYCVlLjHRAVEgH2H68laVjWkNrSriFbNwbAGl/3qYCiD1nhrRgMwgEjYovXAJJiQ0WYzRgIwIYQQQgyLbG82/7LoX3hh/wusrV2L3/RntER93IljGzZXzbqKmfkzB9WG1vDGGzbPvWCzt7mKJqe6s12F6rUYhkLhMVOTjzSaNqeZxtA69u4IsnnPJKaNK+KKKyJMnDi2M2IAlgXl5S7l5XGKix3CYZMmXYUbrkI7Yy+oMJRBU7SJfJU9ZoJc24acHJdYbGwMiexLfayGyaVDWxS54UAJRHPB0zbk/sScWGdhlUyxvQn2vTUFTnMy1mYmSQAmhBBCiGFjGiYXTr6QRcWLeGjHQzRGGwlYgSEFYkk3SdyJM7tgNpdOu3TQVdxCIcWDD/rZtivEnsgGkm6ys4z7QCkUHsNDUic4ENtOQ3WQht/P5+QTDC66KIqduWvLYeX1gutCe7Idy7BIOEPLcAwHy7AIxUPkecDrHTsBYn6+y6FDYzsAa421krSbsL1D+1xrd07AH3BpT+q0qmT2xVQmzbFmiv3FQ2rnSB7Tpq3Jj+OExkyQfiQJwIQQQggx7EqDpXxp0ZfY3bKblw++TFVbVap8upleZKK1JuJE8BpeZufP5uwJZ5PrzR10fyorTR540Meeth3Uxw5hG3bG1gKzDIuYG2VL61qaVk9l9+5yrruunby8sRMs9CYvz8Uw9JguYACprKNSmvz8sfOeTp6cZM8eC//YXPsXgOpQDdn5Q8t+AURagwRsl7ZEa0bmeHas4ZcpPstHLGZw+LDBuHFj77ssAZgQQgghRoRSiml505iWN422eBura1ZT3V5NU7SJUCJEwk2gtSZhJAjHwpjKxGN6yPPlke/JZ1bBLBYULRhS5TaAzZstHnnMS2XbRtoT7Rkd+tShY32vA5GdxOtC/PrXM7nhhjDFxWPvYvBI+fk6FUBk9np4WPh8qazTWHHiiQlWrhyZ9e8GqzmUYOYpu4fUhnYV0ZAfn+kMaJHuvjiug6vdjLSXdJMU+YrweDQ7dpgSgAkhhBBCAGR7sruUi48kIzRFm0i4CXJyc2hvbcdreinwFaSdmYrFoKbGpLLSoq7OJBZTOE5qfSbDgEDARSmXjRtt9ibXE3XDGct69cY2bOoitWg0v/vdLL74xbGdCVMqFdQYLWN7KJ2hDPLy3DG19pbPB1OnJjlwwBq7izL7m8ivODykJiKtQZJxG4/fxW/5iSQjQx6GqNHEnXhGFkc3lEFFVgWGggMHLGAMDqMd7Q4IIYQQQvgtP/6s1NitosIiDuv0LhITCVi/3ubNNz00NRkkEqkL4Z7mXB08aLJ6tU1DvB6HCXgDMfJKG/BlhzN5Kt1YhkV9pA6P4eH3v5/Cl78cGtNzwvLzHQJVQZoSTaPdlR4l3SQBK0hBwdgrsHD22THuvtsiK2u0e9Jda3uCvCk7hhy0RtoCnSX387x5qfXihtiooQzCyfCQAzCNJt+X33ljJRIZm5GwBGBCCCGEOOaEw4pnn/WyY4dFPK7w+1OBl6+X6zetYetWi5hqwVFhDGWQiHqo3Tkey5Mkq7CZ3HFNMEwl1i3Doqr9AHmeYv7xDx+XXz70eTjDZeHCBG9sKsLR+zAYexewrnYJ6EIWLBh7mY3SUpeyMoeWFnPsZcGMBKVzK4GhBTnJuNW5FIFt2HhMT5c14wZDoXD00APqpJtkSs6UzsfO2IvRAQnAhBBCCHGM2bDB5tlnfSiVKqGeTtGDPXtM2sMOLfHmLvNMDMvBdRXNhwoJNeZSPKUaj394JkDZhs2O1q143zqFhQtNpk4dm1eHM2Y4jCsIsvuQjeOOvT6ahklRnpdZs4Y3czlYH/94hLvuyur1ZsBoCIfhzPMbeZUEAw3AtKsItwRpPFhMS20h9XvGUbtjIqblgtK4eiIh9zCmP4Kd1Yzpb0cZg/jeDPHeh+M6lAZLCQxhgemRIgGYEEIIIY4J4XCqbPyBA+aAKs2Fw3DggEljoqbXSf6G6eI6BjXvTCKnpIn8ssPDkg1LuAmqYzt57LHpfOUrIawxeCWmFCxcmGRddRCH1tHuTjeWG2TRQmdMzf86Un6+ZvnyGCtXesdERcRkEiZNcliwKMqrm9J/XTTkZ8+bs2iqKiEe8WJYDqadJBn34DoGytCAAgwsJ59oU5BYQxlKuZj+dryF1djZzekfcIifp23azMib0WXbmMtCvmuMdksIIYQQ4j2NjYpf/jKLQ4cGFnwB7N5t4ahYWqWuDdOhtS6PQzvHo3Xmr/BNZVIXqaUtlCoGMladdlqcwkDRkIeWZZqjHQq8hZx+eny0u9KnM86IU1zsjvoQuNRcLc3HPhZJLS6eRtTaUpvH+ifOYM0D59GwvxQUeAIxLE8SpcC0u38nfKYP01AoK44yHZyYj3DVTFp3LCZ6uCytvg6lkEfCTTCnYE63Gywez9gseCMBmBBCCCHGtIYGxW9+k6pqMNCMUTIJzc0GLfHmtNcrMkyXeNjHoR0ThiUIc7VLo3uQdes8GW87U/x+zeXnVOAkx1aKzklYfPC0Ivz+sXlh3UEpuPbacGpNtVGsgh6NpoZEBgKaXE9un0suuI5B5WsL2PT0GUTaAti+OIbZvfMefxxldn//szxZnUGUUqDMJGiDWP142nYtwI33vmC6q91BL6iecBNMzZ3abV3ARALGjRt7Q2hBAjAhhBBCjGGtrYrf/z4L0xzccKKDB02SrkMsObB5XcpwSUS81O6sgAwHYZZhcSh0iLo6g0OHxu6l2BWXW4zLDeLqsRHsuFpTlBPg0g+NvXWdehIMaj73uXa0ZlSCsEgErrgi0jnX0DRM8rx5Pe7bWpfL2ofOpXbnBCxvvM/hnYahMa3uWTCFIstO3SjRR0zoUqaDm7Rp27WQaH1Fj21q9KAqICbcBJNzJjMhe0K356JRmDlzbGVwO4zdX70QQgghjmtaw9/+lip5Pdi5HLW1BhEnNKj5JcpwibX7aT5UOLiD9yHshMEO8/LLg7vrPxIsC/7107nEomPjcjEeNfnXT+eOyXlzvcnL09x4YwjD0CRHKBbQOhV8XXllmDlzuh4035uPPiqgrt9bysZ/nIGTNHocXtgT29vzEFBDGWTZqUxYlyBMpQKx2OFywgend7upYShjwAuiJ9wE03KnMSlnUs99tKG0VDJgQgghhBBpe/VVm/p6A3OQayUnkxCNKmJObNDzSwzTpaW2gEQ0s8MFDQya4g0cPjy2L8WWzCzktJNT7+NoikUVpyzVnDgr88HwcMvN1fzLv7RTUZEkEhne9zEaBa/X5fOfb2fmzO7Bx9S8qUST7y2BUL+3lG0vnojlTQyoqIkvO4zr9FLQRhnkeHLwGJ5uwZ4yHRKt+bRXTe+yfSDBl6MdTGVyYsmJjM8e3+t+ubl6TFWiPNIxdA9BCCGEEMeLpibFK6/4hlRFrr1d4bqpO+VDYRgO9XsqKJ+9N2OVES3DojXWSnPzBJLJgc9tG0nf/NQcbqx9ncNVhViekR/SlYxbjJvYwK2fOXXEj50pfr/m2msjbNqU5JlnvIDq8pm72qUl1kJDtIFQIkQ0GcXRDlprPLYHJ+ngtbwErAAFvgLyffldghatU8HXySfHOe+8WK83LZYUL+HF/S8C0FRTwNsvnojtHfjvI7uomZZ+MsN+y49t2oQTYbTWnQVAlOmQDOURrp5KoHw3rnY7hy72xdUujnaoyKpgau7UXiuaAkSjmpNOGruFWsbwz10IIYQQx6snnvBjD7FI4OHDBoapcVwnrepvvVKQiFm01ueRU9I0tE4dIeJEiMcVdXUG5eVjd16TZZp876YpfOPn22mvLR/RICwZtwiOq+Z7X5yNbR77l62LFiWYOTPJU0952bnTJpKIURPbQ0OkgbgbxzbszsBCoVAq9U+jiSajRJIRattrMZRBrjeXisAk/CqXigqHiy+OUlra9/fIY3qYmjeVnQ0HePvFpZiewd2cMEyNP6edSFugz8yZpSxyPDkk3ARRJ4rruvDucMRESxGJrCbsnCayPL0HYAk3gW3YlARKmJQzKa1iHYahOOWUsRuAje28txBCCCGOO62tsH+/OeQ1fMJhBcrpMhdlsAzTpe1w3pDbOVLCSeDxaPbsGeQYyxE0IaeCb3yhmLyJezM+HLM3iaiH3Il7+d+fL2JCbs/FG45Ffr/mQ5c3UnrR3Rwq+QuNiUMkIn5MJ4jq59JcoTC0l2Q0SGs8xMHgPwmccwcfvHJnv8FXh3PHn8u2lfNxEuaQ1lLLLT2MdtL77tqGTbadTbYnuzPIVGaS9urJmHhJukkSboK4EyfuxHG0g2VYZHuymZU/i1PLTmVm/sy0gi/HgalTk3jH7vRKyYAJIYQQIn0JN0Fdex2VzZUcDB0klozh4KBQmIZJrie3c2J8njdvUJmn555TQ85+QWoOWCaCrw6JmE2s3Y83GMlIey4upgmh0LFxP3xRyUJu/ozDH198jf2rT8Z1FaaV+cydkzQwDM3U5a/x6bMXs3jcooy2r7WmKdbE/tb9tMRaOjNPWXYWk3ImUegv7HN421BtrNvIM3ufQSnFrJMs9NL9hJuzaawqpqW2kEhrgFgogOsq0AqlLRzHxeOP4c8Ok1XYTOGEOrKLmzFMF601v9vyO5aWLuWCiRdgGn0HRU0Hx+HUzkVZjQwlF+PxJ/AGI8QjHtJ9uwxlELACnY+TjktZ6AJmTAjhahfLsPCYHoJ2MO1lI44Wj8P550f733EUSQAmhBBCiD5prdnbupeXD75MVaiKhJPAa3rxmN0zIY2RRrY1bEOjybKzmFMwh+XjlxO0g2kdy3Fg27bMBGCDKn3YB9N0aK4pZNz0qoy1aRgQj49ugYuBWFKyBN8HfDxc8TSVL59Ic1U5ljeW9gV4X7QLyZiX3PHVzFz+BlfMuZi5hXOH3jAQTUZZXbOayuZKmqPNRJKRzsp7Sim01iR1Etd18Zge8rx5TMqdxPKK9L+7/XFchwd3PMiOph0E7PeCEKUgmN9GML+NCQt2d27XrsJ1FVlZfiLR9l7bVUoRsANsqNvAzqadXD/v+l6H9GkNzzzjZ2HZDNYeWjPkcyqeUs3BrVNgEDc6XO1SFCgg0ZZNwA2SlTX0myWRyLuLiBeOjaUTeiMBmBBCCCF6tal+EyurVtIcbSZgB/CZPnxm76XFOi4GIZV92lS/iTfr3mRSziQum3oZOd6cPo9XXW3S1papohR60NUPe6QgHs5cWTUDA63Bssb2xeLR5hTM4ZZTJvFgwYNs2bmVus2LaD5UiFIa0x542W8nYaK1Iq+0gZIFm5g3PcjHZ9yUkcCnPlzPC/tfYE/rHrROrTVlGmaPAYrNe1F/e7KdTXWbeLP2TSZkT+CcCecwMXvioPuRdJPc9/Z9HGo/1CX46osyNKahUUZ63w+v6SXiRPj1W7/m8ws+321hYkgN7W1sNMjKspmeN53KpkosY/A/NtN2yKs4TNPB4h4Xbe6vv9mebLSGvXtN5s8f2txC14X8fJezzx7Ymn+jQQIwIYQQQnQTToR5aMdD7GvdR8AOEPQM7mLYNm1sbGraa7hz052cO+FcTik9pdehiZWVFj6fysiaSYaRWnx2SAU4juI6Bsm4jTXI4gVHsg0b12VMz1XpTcAO8Ok5n2Zj4UZWVKygORSnYdtCGqvGEW0L4CRsDCuJaSe7zDPSGpyEheuYmFYSX1aEkmmHKJ63mbwsD2eNP4sTSk4Y8mfmuA7P73+edbXr8Jm+tOYOHa3ju1sfruferfcyr3AeH5764R4zv31xtctft/+VQ+2HBvzagTKViatd7tlyD19c+MVuQezKlR6CwVRAVxospSHaQEO0AUsNPiTILmom3JxNLOzFSDNYBCgOFAOpDGBTkzHkaqCxGFx/fWTIc0dHggRgQgghhOhie+N2Ht/1OEDad+v7YyoT0zR5Yd8LbGvYxidmfwKf1T2bVFNj4PGQkQDM69WEQgaWsnB05hZkjYb8ZBUMPQDz236iUZg4ceRLu2eCUool45awqGQRbze+zaqCVdS0vwmuiRsqoungONoO5+E6BtpVKENjGC7Zxc3kl9diZB8Gw6E0WMppZZcwt3BuRuZeHWo/xAOVD9CWaOsy32go5xm0g1Q2VfKLjb/g8mmXMy1vWufzWmtCiRC1ocOEYwk0Gr/HpjCQR4Evn+f3Pc+BtgM9ft+Hg6EMHNfhL9v/wufnf74zmA2HFQcOWF3WxppbMJe3Dr9Fa7x10HOulIKSqVVUb5+EmzT7HY6q0ZQFy7ocT2s4eNBk0qTB/U4jEfjoRyMUFY3daqJHkgBMCCGEEJ021m3kqd1P4beHsABXH/y2n7pIHb/e/Gu+MP8L3QK8piYjY2tiFRRoDh1KrbnlOJkJwAzLIdIaJKugdUjtJN0kud5cTAMqKo6Ni8beGMpgXuE85hXOoznWzI6mHexp3UNj6SFaY5UkdCI1RUiBrWxyPDkU+AuYlLOYWfmzyPPmZawvO5t2cn/l/XhNb59DZQfDY6YWFv7r9r9yVtl57NxtsGW7S12doq3FJh7xpKYdaoVWUSy7CU/WZhqNfZRMNJkzK05OcGQqSJqGSV24jlXVqzi94nQAduywcBzFkfO1lFIsKFrA1oatNEWbBj0c0TA1ZbP2U/NORxDWeyasLFjWbeFly4LGRmNQAVgkAh/6UJS5c4+dGxkSgAkhhBACgC2Ht/Dk7iczlvXqjW3YxJNxfrvlt9y44MYumYFIxCA7OzPHyc11UQq8lrez6EImuMmhl413tUuBt4Bsn8bvP7bmgPUlz5vHSaUncVLpSZ3btNY42sFUmR0OerSdTTv5e+Xf8Zm+YTtObVOMTavKeWR/A7mqnPzs1LA7S0HXZJuBxqb6cATHncCufTPYuSpObmkd8049wOQKz7C+F5BaCHll1UrmFc4jz5fHrl0mgUD375qhDOYXzmdP6x6q2qoGHYSZlkv57H0c2jGBRNTTZU6Yq1PFTUoCJb1m2qLRgb0frpsadnjFFRHmzDl2gi+QdcCEEEIIARyOHOaxXY8Ne/DVwTRMIskIf93+V7R+76IwE0MPO3g8YNuQbWdn9GJX66G3FbAD+K0AeXnHdvYrHUopLMMa1oCjrr2O+yvvH7bgq71d8fSD43nhzyfTtH88tmkSUjXE3HCvr2mLt5F0k6niJL4Ilu0Qqi9i1cMn89h906ivG/7132zT5qk9TwHQ2Nj7ul9KKabmTuXEcSdiGRZJd3A/RMN0KZu1j+ziJpykidapIYf5vvxuww6PFo+n/qUjEoGsLJebbgodc8EXSAAmhBBCHPdc7XL/O/dnfMhWf2zDpjpUzZoMlMPuiVKkAhxtjPi59SXpJikPlhMOw5IlQ59LdrxztcsDOx7Aa3ozHnxpDVs3ZPHEvfNoqc/B9iZQZmqYnKEMDkcO4+qeg+i2eFu3rKsyNJYvTizs5bm/z+P1lwpwMzc9sRtDGexr3UckGaGxsf/3JmgHOWncSUzOmYypTBLOwL+fSkFO2SFKZ+4m6PVQ4q0gx9N39VNIvdetrX33MZmEaBTOPDPGF7/YPubLzfdGhiAKIYQQx7mXDrxEU6wJvzU887764rf9rDiwgrkFc8nx5mS8gtnkyQ719Qa53lxq2msGXWjgSEO9xjeVSVlWGabSzJ8vAdhQ/c/+/6El1pLxIhfJhGLlk1M5VJWF5e05NaPRNEQbKPYXd9kec2Ik3ESv3zelwPIm2L25gvr9JZz/0V0Esobnu2Aog5UHXiGRuAIzja+/UoqJOROZkD2BlngL+1r3EUqEOrNiR8/fAnC0g+M6mIaJz/QxIXsC5eXlGNNNqqs1Bw9qIhGFZfX++zFNiES6zlHrEImAx6OZOTPJOefEyMs7NgOvDhKACSGEEMex9kQ7r9e8PirBVwfLsHhi9xNcO+da/P7MDskLBDRZWZpo1IvX9A56aNWRDHPwKYukm6QiqwI3aTJvQTytC2LRu8ZII6trVmd86GwybvHCQ7NpaKTX4AtAoQgnwkTsSJffUEusJa1g3/IkaW9XPPPXeXzwyncI5EUy0v8jeUwPW+veQQ8wZlFKkefNI684D601MSdGc6yZplgTSTeJ1hqlFAYGWZ4sCnwFBO1gt6xfRYVLRYVLW5ti716TUMggHk9lvGz7vYBMKXAchdapuV2JhMLn0+TnuyxfHmfx4kSGFmgffRKACSGEEMexVw6+krHiFINlKpO9rXtpT7STlxcg3Pu0mkGZMCHJ22/blPhLONh+cEiLM7uOiTdr8BfJXsvL1NypxGKwfHmaE15Er/7nwP/gtTK7kJqTNHjxoXk0NiWw7P6jFkMZNMeauwRgCTf9bJZhQNSN8vyDc7nwE1vwBTO/kHBbshXtJvAzuAhGKYXP8lFqlVIaLB1UG9nZmgULUjdA4nFoaVE0NhrEYqmgK5GAwkKHiROTjB+fZPp0h8JC9315k0ICMCGEEOI45WqXrQ1bh31x2HSYyuTlgy9TUnI5O3dmtu3iYk11tSYUMsnz5tEUbRp00Km1wp/dPqjXJtwEC4sWEo0qTjstTnb2sT2MaiQ0NSn27bPYtcuksdEkHFY4Tip7gpFkXcMEgrlF5JU1kF9xGF92+5CGiGoN659dTF1THMtK//OJO3GSbhLLsHC1S9JNDug7ZipFe6Kd1x5dwjnXrO1SQTAjlENbvI2cYEFm2x0kjyf1uywufi+bHI3CuedGWbbs/T8sVwIwIYQQ4jj1TuM7tCfaCdrB0e4KtvKyfs8+zi9wqKvT2LbCtnWXIUqDpRTMnZtgzRoPOZ4cIskIsWRsUAUbDMPB9g78AjHpJpmcM5mAlUUgx+XsszOf5Xi/SCRg/XqbN9/0cPiwgVIKv193+x7sb6kh0u4lHg5Sv68MXIUvt52ymfupmLMX0x74UNHqdyawe4/CGuBrlVK0xFoo9BeScFOLMQ+UaSrqmtt5+7U5zFu+dcCv70vA4+OQqgPGRgDWk0QCysre/1VBQQIwIYQQ4ri1rXEbAWtkys4fzUka1O6qoPHAOKJtQSJtAWIxl5YCg3e2KlzXg1KpAMzn0wSDmtJSh5yc7hfi6fB4YNq0JDt3WpQESjjUfoiEmxjwcETbHwc1sIvrpJukLFjGpJxJRCJw/fWRjBcbeT9wHHj+eS9vvWUTjyv8fggGoaeiDAANkYbONas6gmInbrN3/Wz2bZzBuKnVTDt1C6aV3kV9POJhwyvjca1GjAEWClcook4UIBXcD3KYq7ITbN+cS8XMfPJKmwbVRk9MZWIEmjPW3nCwLCgtHcaSkGOIBGBCCCHEcaox2jjsi8EeLdLmZ+/62TQcGIeTMLE8CZQC03KwjQT4mykvz+fQId0ZpESjikhEceiQid+vKStLUl4+8LkhZWUura0udXUGpcFSattribmxtC+23aRJ7sTaAR0z4SYYnzWeqblTiUTg4oujFBUdH3f5B+LgQYOHHgrQ3g5ebyr46k8k2fNcPMuTCsZqd42n4UAJc85eT15ZY7/tvf3SCbQn2jDMwUXHHYUpXNxB/64UipjRzJYXF3H6VStRxtCHqWqtUxUZc+oJtUSwLQPbsEd97ufRsrNdfGNntYhhNSIBWDwe57vf/S7JZBLHcTj11FO58sorCYVC3H777dTX11NcXMxXv/pVsrKyAHj00UdZsWIFhmFw/fXXs3jxYgB2797NL3/5S+LxOEuWLOH6669HKUUikeDOO+9k9+7dZGdnc/PNN1NSUjISpyeEEEIcc7TWNMWaMlKWPR3JhMk7ryzm8N4yTDuBYboY3q6BiGVYNEYbmTV1CgcP0iVLpBTYtiaZhD17LPbtg6lTk5SVuWlnxJSCWbOSOI5FQ4PBuOA4mqJNPa7X1BPTThLIDaV1LFe7aDQz82ZSllVGJALnnBPlhBPe//NbBkLrVNZr7VoPPh9406ynEXfiJNxEjyXRO5h2Etc12PTP0yibuZ8Zp23uEtC4jsJ1TFzHIB7xcHBfANdoGnD2q/Nc0MTd+KCGHx7JMBSHmxIc3j+O4smHBt4PrWmMNlIbriXiRIgmoiR1EtfTyI59xXgCMQyVWhvPZ/lSiyQHyjCN0at2oTUUFBw/cyJHJACzbZvvfve7+Hw+kskk3/nOd1i8eDFr165lwYIFXH755Tz22GM89thjXHvttVRVVbFq1Spuu+02mpqa+MEPfsDPfvYzDMPgt7/9LTfeeCMzZszgRz/6ERs3bmTJkiWsWLGCYDDIL37xC1577TX+8pe/8NWvfnUkTk8IIYQ45oQSIWLJWMbLd/ek4UAJ77yyGCdhYvv6LukdTUbJyoJgUBOLqR6DK+vdq5edOy1qazXz5iXwpFlHJDUfLMn27Rb19QYFvgKy7CzqInU4rtNrIOY6BjklzWkdI+EmyPXmMrdgLrbhIRyGiy6KsnRpZoIv14XGRoNdu0z27rVoaTFIJFIXsUqlhlvm5TlMm+YwebJDfn76QepI0hoeecTH22/baWW8jtQab0WnUVddKbB9cQ7tHE/r4VwKJxyirSGfaGuQWLsP7RqAovFAMfUNGuUpxrBjmP527JxGTF/6JTkNZRBJRjAwOku0D4ZCETUa2bdp2oACsISboKqtitpwLVEniq1slFIopbCVTaCsgVAAjHeD1oSbIBFP0BRtYm/LXvJ9+UzJmTIifxOOFonAokXHz82JEQnAlFL43s0pOo6D4zgopVi3bh3f+973ADjrrLP43ve+x7XXXsu6des47bTTsG2bkpISSktL2blzJ8XFxUQiEWbOnAnA8uXLWbduHUuWLOGNN97g4x//OACnnnoqv//974f05RdCCCHezxJuApfhHQqnNex8fQE170zE9CTSKorQcVE9Y0aSTZvszmCrJ5YF4bBizRoP8+cnyM9P7w66UjB7dpKcHIPduy0s5aEiq4LWWGvngrNHB2Km5ZJXerjPfid1kiw7ixl5MyjyF5FIgGFpPvWpMJMnD/29bm1VvPSSl3fesQiHFaYJPl/3IiXRKLS0GGzf7sFxNLm5qSD1jDPiBAJjI8swlOALUhmwdLKWWkO4KYvW+kKiIR++7AhlMw6A0u9+Hx20q4iGfWizBVwLJ2aRjGQRPVyO6Y3gya/Fm38YVN+foULhaIeAFRhyFszFoak+SKTVT6CfeEhrzcHQQfa07kFrjWVYeIyudyQ0Gq/XwDuhlob9JRjme/3rmEfXHGtmXe06iv3FzMqfNaIZsUAg9R09XozYHDDXdfnGN77BoUOHuPDCC5kxYwYtLS3k5+cDkJ+fT2trKwCNjY3MmDGj87UFBQU0NjZimiaFhYWd2wsLC2lsbOx8TcdzpmkSCARoa2sjJydnpE5RCCGEEO/SGravXEL93jKsAVQN7Lhwzc1NFd2orTX7nOulFJgmbN5sM3duMu35VUqlFogtKoqzbZtNW5si15tLrjeXaDJKS7yFhJPA0Q5O0qB0enXX4WvaTc2rUSZe00u2N5spOVPw2360Tt3RX7gwwcUXR4e8eGxDg+KJJ/xUVZnYdmrx2ndnbPR5fh3Blusq3nzTw7p1HiZPTnLZZVGyskY3EPuf//EOOvgCcHT/wXx7UxZNB0tIJiwM08HyJImHvTQcKKHwiLl8kdZAKoOI7iyeoQyNMpJoxyZaO5lY/QS8xVV4C/qeA6i1xmt6h7TWHKSyaTFaqdoyjcLS3b3uF01G2da4jVA8lAqkejls0k1S4CsgZ8l26nZXYJjdM9EKhW3YNEQbWHNoDbPyZ1HoL+yhtcxKJGDBgsT7cr2v3oxYAGYYBj/+8Y9pb2/nJz/5Cfv37+91395Syn2lmnt6rqfs1wsvvMALL7wAwH/+539SVFTUX9fFMLMsSz6HUSafwdggn8PYcLx8DlbUIuAPEPBkfriR1rBt5TyaD5bjDyZhAIu/+mwfhmEQCARYuBBeew0cp+ehiEeybaistMnK0hQMoNJ2IACnnQY1NbBvH4RCiiyfTbY/G4B4wiGYH6JgYixVZOHdi3Sv5aXIX0S2N7szg5BIQCKhmTwZLrrIZdIkL9BPpNQH14UXXlC89JLC61UM5WvZkUVpaoJ77snh4otdli3ru6LkcP0Wqqth0yaDwsLBBymBZAArbPU4B8x1DOr2lBBuCWJaLh4vQOrq3jQh3JxPXkkEX3aqiEe8PRdtxjFco8fASVmpYC9WN4lkWzFZE3Zi2N1vKmg0lmXh9XjxWJ6hZ8HMOIn2IgxjL4Ee0mCtsVY21m1EofB7+45ktaMpyyvDZymKyttpbw72WuDDfvf3ur1lO9ON6UzJnzKk8+hPJKL56Ee9ZGdnD+txxpIRr4IYDAaZO3cuGzduJDc3l6amJvLz82lqaurMVhUWFtLQ0ND5msbGRgoKCrptb2hooODdv7IdzxUWFuI4DuFwuLOgx5HOP/98zj///M7Hhw/3PpxAjIyioiL5HEaZfAZjg3wOY8Px8jkk3ASJWIJwMv05Lumq3j6R/VvHYXsjJAY4qshVLq7rEg6n+jVzpmLDBhvTTG89sHXr4NRT4wPOOuXlpf6FQoq9e02amw3icfB4NPNnWFjWxO59daDxcBzTTA3tmzUrwVlnxTuzS0P5GrW2Kv7ylwANDQZ+f2pYYSbdfz+89prD1VeHe81CDcdvwXHg7ruDaG0QHsJXTyc08Xi8I67qFG33UrdrAlqDYSRwekqUKYeanUVUzN2DMjTRdpOkmwBN30GTkSQZ9dBcuYBAxU7s7K5l4jUaTEgkEiitSLrJwZ8gEHNjtDTYXX4PHdribWyo24CpTJRSOPSdEVQonJhDOB5m6mmrWffwOVievvunULxT/w6RaITJuZOHdC69iUTgtNPixGIxYmN4abzy8vKMtjci9SdbW1tpb0+tGh+Px9m8eTMVFRUsXbqUlStXArBy5UpOOukkAJYuXcqqVatIJBLU1dVRU1PD9OnTyc/Px+/3U1lZidaal19+maVLlwJw4okn8tJLLwGwevVq5s2bJ/O/hBBCiF7Yhk2OJ/PD9OMRD7vWzMP29l5sozeOdrr1KTtbM39+6kI6jZoLKAVbtw5+zF9Wlmb+/CSnnhrntNPi/O//HWL69AT5+Q45OS5ZWS65uS6FhQ6zZiW48sowN98c4pZbQnzoQ7GMDO1raFDcfXcWoZAx6CF6/QkEoK7O5O67s2hvH7nrpZde8tLaagy5KEi2J7vbdV6kzU/tjomAxuinfLvrmDRWpaplJ2N22tkqpUAZDuGqGcSbu2YHXe3it1IfWI4nB1cPbd6fRhOPeIhHun6fI4kIG+s3dgZf/XG1S74vv3Nff3aESYt3kIz1/zuxDIt9bfuoDlUP7iT66pcL+fnH56LkI5IBa2pq4pe//CWu66K1ZtmyZZx44onMnDmT22+/nRUrVlBUVMQtt9wCwIQJE1i2bBm33HILhmFwww03YLxbi/Zzn/scd911F/F4nMWLF7NkyRIAzj33XO68806+/OUvk5WVxc033zwSpyaEEEIcs/J9+RxqH3iZ695oDVtXLEUZg7vwdFyHAl/38YMFBakgbMuW/jNhhpHKHh06ZFBaOrh+JBIQCLh87nPhES9a0dysuOeeLAyDYV+s2bIgHlf89rdBbryxHb9/eM/VcWDDBjsjaz15DE+X4YfRdh91u8ajjPQqPirDpb0pm/zywzjOwCcfKTNJpGYqynCwc1KZMIXqLH4RsAOYUXNowxA1aMcgGvJjBVtSm7Rma8NWDIy0Ew2OdpicPbnLtomLdlC/p5xou6/fYNU2bHa27KTAV4DPytxCXbHY8bsoudLp1PB8H6uuznxELwbmeBnuM5bJZzA2yOcwNhxPn8PLVS/zavWreM00F1/qx+F949j6wknY/oFnvyB1oXh6+elkBbO6DbkCaGtTbN1qEY+rPqsjQioYXLYsPuCLu3BYMXVqgiuuiIz4orDxONx5ZxaOo0b0ojSZhJwclxtvbO9y3Ez/FjZtsnn8cR/B4MBe52qXSDJCY7SRtnhb57z//W2pegJeFaR2+ywU6Q1T7WzXMckpaaC1roCw2zqojJV2LLKnbcLwpNbWqsiq6HyuMdpIKB4afDl6pShWMzj9qnV4clLXq/ta97GvdV/nvMM++6Yh2Z6D2ziZ8uRpOIlUoGkYGn9uiGBBM9tXLkUpl/4KSmo0PtPHCSUnZGSEWTgMH/rQsbMuXqaHII74HDAhhBBCjA1Lxy3llYOvZKy9/ZtmYPWxzld/cjw5fZYWz87WnHxygl27TGpqzD6zYY4Dhw4ZlJend1GdSIBSmo98JML8+UObuzNYTz7pIxZTaa9plimWBQ0NBi++6OW884ZvONjq1Z60gy+tNXWROqraqogkIyR1EgOjW+DRFGuiff843Fgrpqnwmt5uJdh7Y5gOocZcgMFXLTSStB+YSdbUt/BaXW9k5HnzaE+0D67dd/uktaYjLow5sX6DL+0atB+YQdvORSRCuSTjNiWBYg77VWeQpTU4e8twkxauo2jYV4Y/r4280iYMs+e5ZApFe6Kdg6GDjM8eP+hzgtS8r/PPP3aCr+EgAZgQQghxnArYASbkTKCuvW7Id7UjbX7aDuf1udByXxJugsk5k/vdzzBgxgyH0lKXykqLUCi1HtbRGSPLgoMHzX4DsGg09drp0xNcckl02OZc9WfXLpNt2+x+13waLn4/vP66h4ULExQXZ359uPZ2RV2d0e/5udplb+teDrUfIuEmsA0bQxl4VPegKseTQ329idueh2E5aK2JJCJEVRTbsDvnY/UlGbdBK0zTJOkkBxyIKQVu3Ef0cDnlk7s+ZyiDQn8h9eH6tNYsO5plWCjAsFKfx56WPb0vFJ7w0LR5GZGD00jGAhieCCiH3GwfWb6u56QUWLYD767LVzL9ANXbJ9NSW0AgJ0x+RT0ef/dA3DIsDoYOUpFVMai/Fx3LM2RyUfJj1XE46lIIIYQQHc6uODsjlRD3bZiJ2UNp7nT5TN+AioJkZ2tOPDHBySfHKSx00BpiMdWl6l04rAiFul4opi4CFaEQ2LbmzDNj3HJLGx//+OgFX44Djz3mH7Xjd/D54MEH/WkVOxmoAwcM3H7iurZ4G2sPreVg6CBAjyXmu9AKp246ynovY9kRGMTdOK3x1n7XC1NK4yRMbMPuc7mjPtswHZKNEzDc7kN5A1YAv+UfcNua1HpihuHiDcRxtUtDtKHHAKy9aipVT3+G0L7ZaBSmN4JSqSWgeppTeTTbm2TC/N0E80JEWwPUvDOJxqoStO4eZEWSEZpjzQM6F4BYTGNZmuuvbz/ugy+QDJgQQghxXJuQPYGyYBlNsSZMNfiVUFvrCjDMwV3AJtwE0wumD+quut8Pc+Y4uK5DKKRoajJoaVHEYgqtFTU1BtOnJzHN1MLEBQUOU6Y4TJ3qkJ+fXsGGodIaDh40eOcdm5oag6Ymg3DYwHFS2Yi6OsW+fRY5OZqcHJeiIk0wOPJT9JWCw4dNDhwwmDgxs1mwHTt6X3RZa82e1j0caDuAbdhpfw/bm3LwkEVCN3dbgLgjkxWKh/CYnl6zYablkIh4UhUFBzkMUevU/KiW2gLyK+q7PV/kL6KmvQZHO2kfo6Oiom3E8QajVNUfIukmuwSlbsLm8NoLiByajOGJdil+42qXskBZ2sczTJeSqdWEm4M07C+lpS6PSGsWxVMOdsmGeUwP+9r2ke/LT+88XIhGFUuWxPngB2P9zt08XgzqbYjH4xiGgSXvohBCCHFMU0px5cwr+eWmX2KagwvAXMcgFvZjWgOfO+Vql3xvPiWBkkEdu4Nh8G4A0zXjUVLi8KlPZX6ts3REIvD66142bbJpaTHw+XTn+mSWRefF6KFDFkop2toUzc0Ge/akgsXycoeyMpdBfiyDEghoXnrJy6c/Hclou/X1Ro/nobWmsqmS2nBt/xmvo7TW5WPZGm/SS8yJ9RjAK6WIO6kMUtDueQKaJhUkm4Y54EIcGo1lWHgsg/amHPLLD4PqGjwbyqAsWEZ1qBoXN62gyFQmHtODLzucCowjh7u8P07MR82Kq3BiXgxP10XiXO0yLjAOjznwyYSBvHZ82XtoPlRAe2MOB7dOYdyMAwRy35vL1h5vR2vd5w2TjjW9pkxJcs45sUFXJH2/SiuC+tOf/sRpp53G9OnTWb9+PT/96U9RSnHzzTd3rsMlhBBCiGNTrjeXc8afw/8c+J+05s0crb0pCyduDioAA5hTOGdQr0tHY+PIrwmaTMIzz/jYvNlGa/B6U0Mme9LermhvV52BmWmm/iWTit27Lfbtg/HjHSZOdEYkW6cUHDhgEQ5n9mCRSM/t7WjeQW24Nq2qfkeKR2wSUS+G5eCzfCTcRK8l35VKLYocToYJWN0noZmWg+uYeE0v4UR4QJlYhSJgp9p0Eibh1iCB3FC3/QxlUJ5VnlYmTKNTwaKr8Oek2oo47wXETtxLzfPX4DpWt6IZGk1psHRIlU0N06Wg4jD55YcJNwdpPlgMgO1JpIJVO0TUiXb+rdA6VcEzkVDYtiYvz+XEE5Occkps1IfVjlVpfdtfffVVrrrqKgAeeughvvzlLxMIBPjjH/8oAZgQQgjxPnBq2ansbdvLnpY9A754a6ouxrAHHnwl3STzCucNOPMxEKGQQSyWCoJGwv79Bo88EiAcTu+YVVU9Z4bgvQzZvn0m9fUG8+YlRuyCduNGi4kTM9ee08NUrJpQDTXtNYP6/EONuXDEkLuAHeiz5LtSioSTIKZi3b7ftjcVWNiGPaDgS2tNwA50BlOG5dDemNNjAAbvBWEd5el7K6ihtSbPm0ci6qFizh6SbpKYE8NSFtoxOfTix3GTFsrsOuTQa3opDhQPaSjxkZSCYH47gbx2knGbeeetw4nbHNqfiyevmvKsSQCYpqaszGX69CTFxa4MM0xDWm9RLBbD6/XS1tZGbW0tp556KsBxs06KEEII8X6nlOLKGVfy57f/zMH2gwMKwmLtPgxzYEOMkm6SWfmzKPQXDrSrA+K6qcWGvd7hnVOlNaxY4WXVKg9+f/oBXzjc/5pflpUqMLJunYeZM5PDPpzL64V9+4b3KjrhJNjVsmvQwXci7O2ygLCpTIJ2kPZEe59BWDQZ7ays2Lnd1Hj9EWLtfjymh1iy5+GMR9JavztHq2v/E7G+z0ehKPQVkmVnUR+px3GdLn3RpNo1lYkvr52ckhbCiSSu64IJjRvPxGnPQr1b8MbVLkopCnwFZHuy+zz2YCkFlifBrjULOOmjKyiZVsXUvAgfnT60YcPHs7SqIJaXl/PKK6/wzDPPsHDhQgBaW1vxjPRCFUIIIYQYNqZhcu2ca5mSM4VIIv05QG7SQqn0AhyNJukmmVMwh3HBcYPtatpcNzUkcDhpDU895eP11z0EAukvBpyqyJheQWqlUoFYZaVFVdXwFrFWCpqaMnuMI7N8Wmu2Nm4dUnuJePdrUMuwCNrBPisOKqW6Vf1UaPJKD+M6Jj7T12/J+I7gq6c5Vh1l7fvjNb2MzxpPkb8Iy7Bwtds5hLLIX0QyZlM2a2+qTTeJRhNtKCG0dx7KTuDoVOCW581jQtaEYQu+OigFsbCX3evmpoZ0OqOzVt77RVq/rhtuuIFnn32WrVu3dg5F3LRpU2cwJoQQQoj3B9MwuXrW1Zw36TxiTqzfMt5AaihUGhedSTeJx/CwdNxSigPFmehuvwyDYS9i8cwzqUIbAx0emEik/g2EZcGuXRY1NcMbhLW0GD0OGxysQOC9oKgt3kZztHlQa2MBuI7CSfScobMMiyxPFkCvc8KSbpKE+94bb1oO3mAcjy+G1qSCuB5e27EtaAd7LXChHZNEPP2sXtAOUhYsoyKrAr/pZ3zW+FRJfDNGzrRthBIhQvEQiaTD4TUX4fE6BKwApcFSKrIqyPXmDnkNv3RZdpLqtyfT1pA94GIloqu08stFRUX88Ic/7LLtzDPPZMGCBcPSKSGEEEKMHqUUy8qWMbdgLg/ueJCDoYMErECvF8y2N47rGphGz1fsjk4tkjshewKTcyaP2AVjZ/+Gb4oZb71l8cYbnkEtoByPK1xXQS+BQm9sO5UJy85OkJU1PEMrHSe1SHWmFBc71NebmCbsbd2LbQ7+Q9Gu0WfAbyqTHE8OkWSEuBPv9n0zlEHMiXUOH7S8qcXDiybVUFM5GcNMrUsXTUY7X6u1xjZTizv3WUBDg3YGEVgqmJE/g7mFcwmH4cKLW5k1fzKOdgibYX5Q+RQ1xizswOAWOs8U006w5425zJ1cPar9ONal9Q35t3/7tx63f/WrX81oZ4QQQggxduR6c7lh3g18dt5nKQuWEUvGiDmxbvvllR3GiXe9p6vRxN3UxW95sJxlZcuYkjtlxIOvYFDj9w9PkBKJKP75T9+ggi/g3YWJB9c324atW61hWTQZUoHEQLNzfZk5M0kkklrzrSXWMug1t1J9S++1fstPlicLU5lodJehiY7r4GoXN2niy0kNSfQE4mQXN+I6Bl7Ti23auNrFUAZBT5CAFei331rzblCdPkc75HpymVMwh2QSJk50OPlERa43lwJfAeNzx3P4nfmdgeJoUgY01uTjddNbB0z0LK0MWE9jacPhMEZ/s0aFEEIIcUxTSjEhewLXzrmWcCLMm3VvUtVW9f/bu/Mwuetq3/fv31Bzz2OSzjxPhIwMgZAwiKigiAwOqIAzils95+ztc7ZXr+de7x7OgygC6lbErSgbQVDkKAgECKMEAsQMkHkeOj1311y/3+/+UaShSXenurqqp3xez8NDUl3169X9TXXXqrW+60tLooX2VDuO62CUJ3CMBBnXwTItQnaIkB2iNlRLdbB6yJOud6qoKF6r1O9/H+SEE4AHYLDflmTSYNcuixkzCtgr+A6FfJk3caKLacKhrkN9tgbmzPBy6XgF3h7O4eGRdJKk3XT3fqu4Eyfg+QhEurLJmOcSqT9EtD2C6QSoDdXi4fU7sfCE0AwwzNy/voyboTJQyYKaBYCB63pcdVXP/ZduVzXJ1hoCoQJmxIOQcdN0vbkc5g53JKNXvwnYl770JSB78PLxPx/X1dXFOeecU7zIREREZEQJ+8KsaljV/ffjZytl3Aw/W1+L69gDHuVdTK4LtbXFScD27rXYtcsm0vvZvjkxTd76XuWXkNg2HDxoMXmyU5Q2S9vOnu9UCOGwR329y45dHQM+8+vdTNPDGOD3zMAgaAUJWkGA7j1MVeEgE6tqsAwTv+WnIlCBURvi1Q3Z+xlGth2xOdHcfZ2TfKIe0xn74uHhuA6TSyczpWxKdkJjAq69NtZjvxzAM8+YlIQs0oyMBMzv92jaVw8Mf0VutOr3GXDTTTfheR7/8i//wk033dTjYxUVFUyYMKGowYmIiMjIZZs2Zf4yACZP8HPggD0khwXnKhaDefOK86L16afz2/f1ToGAl9OL9f4YRjYZnDmzsFWwQMAjFMp+Dwtl5cokf/m7gznIIdqm5WLZTs6tiL1ewzBxHZsF08uYWFHS84M+WLo0xYYN/u6hHCE7xLH4MeKZeL/nbBmme9JWwbSbJmyHWVC7gLAvjOdl99tddVWMKVNOfMOgsdGgJBCiJRkfVOtmofgtP7GuAJlMSmd+5anfb9v8+fMBuPPOOwkM1QmGIiIiMuqce26Su+4aXEWo0MrKvKK058ViBvv32wSDg7uObWcTHcfJ/0W1ZcGxY9k2xEImv1VVXsGT6dlzE7i+DszcdsD0ywqkySQGl8l5Rpra+iRwYvkwFIJly1K8+qqPTMbAskzqw/UkMgnaU+3dAzrenRD5/Olej2TwPI+Ml6HEV8KMihnUheqy49wz4Loe117be/IF0NwMk0oncTR2tM/pi0PF8Rxqw7WkUgbHjpmMH69piPnI6RlgWRaPP/44e/bsIfGukThf+cpXihKYiIiIjB4TJ7pUVbkkkyNjf3gyCWeemS5KRe7ZZ/0F2x8VDHpEo4MLMpWCpiaD2trCTORwXaiqKnzimvFS1M/ewdFNC7EDgztHyh9IkY75yXOSPZ5rEq5sxjXK6S0BAwgG4Ywz0uzYYXHkiIVtQ9AOErSDOJ5De7KdlJPqsa/MsrOTFz08DAx8lo+gFSTiizC5dDJh39tl01gsO3DjqqviJ7QdHheNGnR1QYm/hBJ/CSlneNv+PM9jSukUvIzHjh2WErA85ZSA3Xbbbezdu5dly5ZRXl5e7JhERERklDGMbMXgiSeCAz4Pq1hWrjxxYmMh7N9v4S9QIaKszKO9nUG1cvl80NRkUVtbmMNx43GYNavwB+1mvAwTF71Jx74ZpOK+vJMngEhlB53N5X0efXAypu1QPuEorjep3/tZFsyZ41Bf77J1q006bWBZ2eEeVcGq7vs5nkMimaFhUhe1NWXYhp1N1qzgCXsi43Hw+TwuuyzJ4sX9t8h2dho4TvbfR0NJA9tat3WPzx9qHh7lgXL8lh/PhOZmC0bIvrTRJqen++uvv85tt91GZCT1FYiIiMiIcsYZaV56KUA6bQzrXrBEAlasSBUlEfQ8aGsrXJVv4kSH/fsHf1J0PF64b3go5DFvXuETMMuwME1YcOFLvPzgGuxA/i/eAyVxbH86eybYALmOxbgZ+zBML+fphhUVHmedlebYMYMDByw6Okxs++1JkZZhEQ5YzJliYFmlJ35ON5t4VVW5rFyZYvnydE6DUxzH6D5qYFx4HPs795NxC782uXBch1kVs4DsGy6FPKj7VJPzQczpQh4GISIiImOOZWUHCfziF5Fhq4J5HpSVuVx0UXGqX11dBrGY2WfL2ED5fNlR+Z2d5qCS1ng8+0J9sIlvJgPz5mWKMlzBb/kxTZNARZTJi7ex7/XZ2P78Xl8aBpRUt9N+pBrTyr0NznVMSmtaCYQTpF2j34EavX3OujqPurrsmWYHDlh0dRkkEibJZPbfXVdX9s0Hz/Ow7WwyW1XlUVXlcMYZKSZMcAe4Rm//OzMMgwVVC3i58eUhr4Jl3AyTSif1aKGU/OX09DrvvPP43//7f/O+972PioqKHh9buHBhMeISERGRUWjCBJelS1O89pqf4ZjflR3lHS/oGVbv1Nho4jiFPf14yhSHV1+18Pvzv24mkz04ebCtkakUrF5dnOTVZ/oo9ZWSclNMWbydZDTE0R0Tsf35VXTKatvoOFqd8/1dxyRcHqVq4rHuePIdahEKwaxZb5eA2toMPvaxKJFI9k0An8+jpMSjrGxww0x8vp7nsUX8ESaWTORg18FBj/PPlYdHyA4xtWzq27d5FOXog1NFTiv3yCOPAHDPPff0uN0wDG677bbCRyUiIiKj1nvfm+TQIZumJnNIX6TF43D++QnGjSveYIBEwsA08z+7qzdlZR5lZS6xmJF34uh5g28JS6dh9uwM5eWFTTDfqTJYydHYUQwDZp+zEYCjOyblVQkzLZeyuhbaj1adtAp2PPmqnXq4+7aQXZgybSoFixenmD+/8D15FRUutk13GyLA9PLpRNNR2lPtA6rg5eP4odmLahb12MsWjxtMnqwexHzllIDdfvvtxY5DRERExgjLgk99Ksqdd0ZoazMLNrCiP/G4wdlnJznnnOJumRjMyPi+GAbMn59m/frBfqMGlxjatsfll8cHGUP/JkQmcKDzAD7Lh2HAnHM3EojE2ffabCxfesCDOcrHNRNtLcHJ9H0GnetYlNW2UtlwrPs2D4+wPfh2Os/LHkx82WWJk985D34/VFRAa+vbtxmGwcKahWxs2khnqrNoSdjx5Gtp3VICds9ytud5TJkyPHvRxoKRMStWRERExhS/Hz7zmSi1tS6J4rw2BbIvgKNRWL06wYUXFqd17p38fhe3CAW2YBCmTcuQyfM1rWHQ6/lTuYrF4NJL40VvG10+bjkZr+cXOXXJdpZ/+Cn8oTSZ5MDa6gwDaqcfwnNPTEJc18QwPcbP3tcj+YLsnqaJpRMH/gW8SywGH/5wvKhvMlRVnXibaZicXnM61cFq0m7h33TIeBls02Z53fJeK4XBoEdFRfEqpWNdTv/KY7EY9913H1u2bKGzsxPvHXXQH//4x0ULTkREREYvvx+uvz7KE08EeOklP8Hg4IdEvFMqla0+fPzjcWbOHJp2qLKy4r3obGhwaWnxaG/PjjofCMPIf09OPA6nn55m7tzifw/L/GU0lDTQHG/u0dIWroiy/MNPsfe1WRx6YwrpRAA7kMrp34s/mKZq4lFaDtRjWg5uxsK0Hcpq2qiY0NRrYhrxRYj4BjfdOx6Hc89NMX16cb9v06d7bN7MCQd/G4bB/Or5HIsfY1vrNjwv96mO/Um7aRpKGphePr3X63ledprjcE46He1yWqWf//zn7N69myuvvJKuri5uuOEGampq+MAHPlDs+ERERGQUsyy4+OIkn/1slEDAJRYb/DVdN1v1mj07zU03dQ1Z8gVQV+cOalhGfwwDFi5ME4l4A97PFQh4A07aIJtEzJyZKVoLXW/OmXAOscyJ/xAM02Pq0m2c/dHHmbt6A8GSOJmkj1QsiOv2/Wrfcw0CkQSlNa0Yhkv15KNMXLiLyoZjvSZfaTfNxJLBVb/icVi6NMUFFxS/6rpyZf+DPGpDtZw57kyqglU4npPXmHrP80i7aQJWgGV1y5hZMbPPZC4ezx45IfnLqQK2ceNGbrnlFkpLSzFNkxUrVjBjxgz+7d/+jUsvvbTYMYqIiMgoN26cy403Rtm40ceLL/ppbDQJhxnQ0InkW691p07NsGZNkgkTijdsoy+2DeXlLolEcXZxmCYsXpxm40YfHR1GzuPgQ6GBJ4XxOMybl+aKKxJDWs2YVTGL2lAtXemuXl/kG6ZH/YxD1M84RCrup6OxkpYDdUTbSknH/XieCR4YposvmCZc0UXVxKOU17XStK+e7S8sor+9cAErQF24Lq/YPS/bdrhyZapoRx282/H21AMH7D6fL7ZpM796Phk3w4HOAxyJHSHlpHA9N7vfjhMXOONmcHHxGT4qg5VMK5tGyHfywSShkMeCBUrABiOnp7XneYTD2Y2KwWCQaDRKRUUFR44cKWpwIiIiMnZYFixZkmbJkjRHj5qsW+fn2DGLtjaTVMrAtrNVHJ8vm2yl09mhEsfPUpo+PcPZZyeH7Yyx4yorXQ4dGty5Xf0xzWxL4M6dFocOWVhW/62brgulpbknYK6bbd9csybBOeekh7yVzDAMrpl9DT/e+GOCdrDf+/pDKWqmHKVmytGcrj1h7j5Ka9rZvHY5qXgA29ezGpR20yytW5pXq1625RU+9rFYjxH0Q2HNmiQ//alN6YlnPPdgmzZTy6cytXwqKSdFe6qdlngLcSfevYXIMAx8po+KQAWVgUpCdqhHO2h/0mlYtCidV7VV3pZTAjZlyhS2bNnCaaedxty5c7nzzjsJBoOMHz++2PGJiIjIGFRf73LVVdm2t0wGjh0zOXDAIh43CIeDxOMJyss9Jk92KC8f3FlKhbZ8eYr/+i+byOC2EPXLMGDmTIf6epctW2ySyb6rYY4DEybklhDEYtnv/dVXx4Z1iEJVqIpVDatYd2BdTlWXgSitaeeMj6xl5/r5HNoyDcvOYNouGS9DQ0kDpf6TZDHv4jjZ8+Xmz0/zwQ8mhmSq57uNH+8yc2aGgwftnKuifstPbaiW2lBtweIwTY/zzx+ayt9YltMSfuELX+jOmm+44QZ++9vfEo1G+cpXvlLU4ERERGTss+3sC8zx47MthTU1Hk1NI7fFadYsh7Iyrygj6d+ttNRjxYo0Bw+aHD5sEY+b2HbPhLSszOt3eqHrvp14nX9+iiVLhr7q1ZtVDas40HmAPZ17CFiFHb9oWh6zztrMpIU72bNhDo17awl4VUwvn57zNRKJbNV2+vQ0F1yQoqZm6Fte3+kjH0lw660R6KWdcCjE43DFFYlhr0CPBSdNwFzX5amnnuKKK64AoKysjC9+8YtFD0xERERkJDKMbBvWiy/6iz62HbItiZMmuUyc6NLRYbB3r0UsZr7VppkdouF52biOH8gcj2cT29LSbGK7enWS+vrhTSDezTAMrplzDb954zfs79pP0Oq/HTEfwZIE089Zz/IL6lic/izb33Bpa4P29mzbazaOtw869vs9Skvdt1pe0yxfnh6WildvQiGP970vwR//GCYcHtrqZToNM2ZkmD9fZ38VwkkTMNM0efTRR7nqqquGIh4RERGREe/ss5OsX+9jKKsRhgHl5R6LFmVfBKdS2SrNWWelSCQMXDdbsQmHPWbOzDB+vDPiqxWWaXHtvGt5cMeDbGneQtg3+MOR3ymWjjGjYgZXz74a2/Q4c3n2oOl0GpqaTGIxg3Q6u+8wGPSoqXGHJKnO16JFGXbvTvH3v/uGbG0dB0Ihlw9/uLiHdJ9KcmpBXL16NY899hjvfe97ix2PiIiIyIgXCsEllyR56KHQkFcjjnMcuPHG6IirbA2UaZh8ZNZHmFc1j4d3PZydzGfmeajZWzJuBs/zuHTapSyuW3zCkAmfj+6W19Hmgx9MkEwabNtmFz0Jy2SyVcHPfS52wjlkkr+cErAdO3bwyCOP8NBDD1FdXd3jH/F3v/vdogUnIiIiMlKdfnp2XPyhQ1bOgxEKJZGAM85Ijfrk653mV89nWvk0/rTrT+xo2wEw4L1hx0evTyufxuUzLi94RW0kMAy46qo4Dz0UZONGf9HeAEgmPcrLPW64IZbXMQfSt5x+XFx44YVceOGFxY5FREREZFS58so4t91WgusO7EyzwUinoarKHbJzqIZSyA5x9eyriWfivHDoBTY2baQz1YnneYR8oRPGx7ueSzwTxzAMSnwlLK1byrkN5xLxFXFE5QhgGPChDyWYOtXhkUcCQO5nxp2M50E8brBkSYpLLkkO+ZsLp4KcvqVr1qwpchgiIiIio08o5HHDDV38x39E8PmMok8XzGSy+3Guvz46ZAnfcAjZIS6YfAHnTzqfznQn+zv2s71tO+2pdhwvO3LfMixK/aXMrJjJ5NLJlPvLcz7Paqw4/fQ0s2dn+P3vg+zcaQ/4cPN3On7IdHm5x0c/GmXixLFTXR1pckrA1q5d2+vtPp+P6upqZs2ahc83uF5dERGR0cp14cgRk507bfbvt2hvN3Gc7Asa08wORaitdZk1K83kya7aecaY6mqPz38+yp13lgAU7ZDaVArKylxuuCF6yuzHMQyDMn8ZC2oWsKBmwXCHMyKFQh7XXhvn0CGTp58OsHu3jeeR87+R4+ecjRvn8t73pliwID2mk/uRIKcEbN26dWzbto3y8nKqq6tpbm6mvb2dGTNm0NjYCMA//uM/MmPGjKIGKyIiMpJEowbr1vnZutVHR4eJ3+/h93NCFSSRgGPHLF55xYdtw6RJGVatSjF1qjMizmOSwauu9vjSl7q4994QR45YBR2OcLwyMXdumssvH56DgGXkmzDB5WMfi5NIwPr1fvbutWhtNensNEmnDVw3+8aPYRhYlkc47FFZ6VJX53L22Smqq1XxGio5JWATJ07kjDPO4P3vf3/3bY888ggHDx7kf/2v/8UDDzzAL37xC773ve8VLVAREZGRIpWCP/0pyNat2YTK58semNsfy4KSbIGExkabX//aR0WFwxVXxNXqM0aUlnp85jMx/vY3H2vXBrEsBr1/JpXK/vv66EdjzJ7tFCZQGdOCQVi1KsWqVdm/p1LQ1maSyYDnGdi2RyTiUVKiSvxwyanA+Nxzz3HJJZf0uO3iiy/m2WefxTAMPvjBD3LgwIGiBCgiIjKSbN9u8aMflbJtW/Ycnnw68A0DIhGPVMrkrrtKePjhABmdbzomGAacdVaam27qYs6cNI7jER/g8UmeB9EomKbHsmUpbrqpU8mX5M3vh7o6lwkTXBoaHOrrXSVfwyyn92XKy8t55ZVXWLFiRfdtGzZsoKysDIB0Oo2tESkiIjKGeR489liAv/3NTyhEQdrADCO7P2zjRh+7d9t85jOxYTtTSgqrtNTj8ssTpNPw6qs+Nm7009JiEI8bmKZBIOB177NxHEgms61hoZBLdbXL+9+fZO5cR3txRMagnLKm66+/nu9///tMnjy5ew/Yvn37+MY3vgHA9u3bT6iQiYiIjCV//nOAV1/1Ey7CsUKBQPaF+U9+EuELX4gSiSgJGyt8PjjjjDRnnJHG86CtzWDvXpvDh7P7cgwje9DtxIkOkyc7lJR42hcoMsYZnufl9FO+o6OD1157jZaWFiorK1m6dCmlpaXFjq/oDh06NNwhnPJqampoamoa7jBOaVqDkUHrMDL0tg5r1wZ4/nl/QQcr9MZxIBBw+eIXowQGdv7smKLnwsigdRgZtA7Db8KECQW9Xs59g2VlZZx33nkF/eQiIiIj3cGDJs89V5zK17tZFsRiJg89FOSqqxLF/4QiIjLk+kzAvve97/HP//zPAHz729/u82C77373u8WJTEREZJg5Dtx3X7jola938vth61Yf27enmTVLgxdERMaaPhOw1atXd//5ggsuGJJgRERERpLHHgsQjeZ+oGmhhMPw0EMh/uEfugY9xlxEREaWPn+sn3vuud1/XrNmzVDEIiIiMmJkMrBxo49gcOgnIhgGxGIGr7/uY9my9JB/fhERKZ6c31fbunUru3fvJpHo2ZN+xRVXFDwoERGR4fbaaz5SKWNI2w/fKRyGl17yKwETERljckrAfvGLX/DCCy8wd+5c/O84+KSvfWEiIiKj3fr1xZ96eDKNjSZHj5rU17vDG4iIiBRMTgnYM888w80330xVVVWx4xERERl2ySS0tJhDvvfr3QKBbBvke96THN5ARESkYHI6X72mpgafz1fsWEREREaEo0ct0iOg88/ny8YiIiJjR04VsC9+8Yv89Kc/5ZxzzqG8vLzHx+bPn1+UwERERIbL9u32sFe/jmttVbu/iMhYklMCtmvXLl599VW2bt3aYw8YwI9//OOiBCYiIjJcjh0zGc7GD8eBjg6DlhaT9nYbnw88Lzsd0eeDigqHGTMcpk7NUFHhDV+gIiIyYDklYPfccw//9E//xKJFi4odj4iIyLBzhun842jUYM8ei5YWE8cB2wbXhc5OE/MdmwZaW022bPHjuh61tS5nnJFi8eK0zgwTERkFcvpRHQgE1GooIiJSJIkEbNnio7PTwLLo/g+yCdi7GQaEw9nKVzxu8sgjQZ54IsCFFyZZtiyNhhSLiIxcOQ3huOaaa/jlL39JW1sbruv2+E9ERGSsGapKkufBgQMm69f7icUMfD56VLogm2ydLKEKhcC2DR55JMgvfxmmq0sZmIjISJXTr5jj+7wee+yxEz527733FjYiERGRYVZT47J7N0XdB+Z5sGWLTVNT//vN/H4v54pWKATHjlncdluET30qxoQJeqNURGSkySkBu+2224odh4iIyIgxe3aGZ57xFy0B8zzYtMmmre3kwz5CoYEN2bAsME2Du+6KcP31USVhIiIjTE4JWG1tbbHjEBERGTHq652iVr/efNOitdU8aauj60JJycCnHBpG9hDn//zPMF/5SpTSUk1KFBEZKXJKwGKxGH/+85/Zs2cPiUSix8e+9a1vFSUwEREZm46PUx/J/H6ornaJRnPaKj0gLS0GR49aOSV4mQzU1eVXwTIMsCyD3/0uxA03xEb891xE5FSRUwL2/e9/H9d1OeOMM044B0xERKQ3ngeHDpls3OijsdGirc0gFjNx3eMVGo+KCo/qaoe5c9PMnu2cMIBiOC1fnuIvfwkSDhfumo4Db7xh5zzkIxLx8qqAHWdZcPiwxUsv+TjzzHTe1xERkcLJ6VfA9u3bufPOO7F1wIiIiJxEOg3r1/vYsCFAc7NJMOh1Jxw938MzaGvLHjb8+us+Sko85s9Pc955KSKR4W+ZW7w4zdq1AaBwpaNduywcx+geMd+fdBqmTRv8gWShEDz1VIAlS9LoPVQRkeGX03uNc+fO5eDBg8WORURERrlt2yxuvbWUtWuDJJMGJSXeSas9pgklJQAGr7/u54c/LOGZZ3x4w5yD2XY2CUsmC3M9181OKMwl+YLsBMb6+sIM0EinDV55pYib2kREJGc5lbRuvPFG/uVf/oWZM2dSUVHR42NXXnllMeISEZFRJJ2GP/whyBtv+AiFslWXfBzfF/X000E2b/bzsY/FKC8fvkzswguTbN1qk06bg95D1dhokk7nNto+nYbTTksXrCUzFIJXXvFz1lk6pFlEZLjl9KP9nnvuobm5mfb2dg4fPtz935EjR4odn4iIjHCJBPz85xF27PARDhdmwEYoBJ2dJj/5SQnHjg3fxjDLgiuvjPOu+VN5OXTo5FMPIbtPrLbWpaqqsIlnc7NJc/MI2mQnInKKyqkC9vzzz/PDH/6QysrKYscjIiKjSCoFd90Vob3dLPj+ItPMJnN33hnm85+PFjwhyVVDg8u55yZ59tlA3pU9z4NE4uRVNM/LHrw8Z04mv0/UB9dzibsxfv/8Zspmv07KTeF5HqZhUuYvY2bFTCaXTabMX1bQzysiIifKKQGrr6/HyrVpXUREThn33x+ira3wyddxx0ep/+pXEb7yla6cpwcW2po1KeJxg1de8eeVhKVS2f/6+z65bnbf2dKl6Zz3iZ1MPB1nd8duWhOtpJw0Bze3s7jhUI/7HI0dZWPTRgwMakI1rKhfweK6xdimBm+JiBRDTj9dV61axb//+79zySWXnLAHbOHChcWIS0RERrhNm2x27bLzrgrlyjQhFoO//CXIZZcVoBcwT+97XxKfD55/3j/gVsvOzv7v7DjZsfxLlqQLcgB02k2ztXkrrclWbMPGMAwCtp9MtPSE+5qGScQXASCWifHI3kdYu38tF0+5mMV1iwcfjIiI9JBTAvboo48C2b1g72QYBrfddlvhoxIRkREtHoc//zlY9OTruEDA4LXXfCxenGLSpMJMBszHRRclmT49w4MPhkiljJwrf8mk0edAjXQaJkxwmDGjMOegNcYa2da6DQCf2TObczInL62F7Oyi/mnXn3i96XWunn11920iIjJ4OSVgt99+e7HjEBGRUWTdukDO51kVSigEf/1rkM98JjZ0n7QX06c73HRTFw8/HGTrVh+m2X9rIWTbC98tnTYIhVwWLsxQVlaY/W172vewt3PvCYnX23Iv24V9YY5Ej3DH63fw2YWfpTxQXpAYRUROdRqHJCIiA+K6sHmzb8gP9TUMOHTIor19+Oeo+/1wxRUJvv71LpYtS2GaHp2dBokEvZ5fZlnZ71sqlW03LClxWbw4xYoV6YIlX3s79p4k+QLDGFj10DZtXM/lP/7+H0TT0cGGKCIinKQC9u1vfxvjJE3u3/3udwsakIiIjGxvvmnR1WUQiQz95/b74amnAnzoQ8O3F+ydwmGPiy9OctFFSY4dM9m502LfPpv2dpNMJpuM2TZMnOjS1GQycaJDaal30n1eGTfDsfgxWhItxDNxUk4Kj2yiZhkWITtEia+EunAdpf5SWhIt7OnY02/yBeALpgf8NZqGieM5/GrLr/jioi+e9HWBiIj0r98E7IILLhiqOEREZJTYuDE7hGI42DYcPDjypvKaJtTXu9TXu6xceWKSk0rB//7fJsFg/9eJpqPsbt9Na7IVz/N6TCI03mofdD2XaDpKZ6qT/V37CVkh2pJtlPhK+r225xqEyrsG/sWRTfqaE82sO7CO1ZNW53UNERHJ6jcBW7NmzRCFISIio0VLy8nPsyqm1tZsdWm4RtLnw++HsjKXVKr3zn/Xc9neup0jsSPYpo1lWCfdrmUaJn7Dz9HYUbpSXbSn2qkN1RKwAr3eP530Uz3paN5fQ8gO8eyhZzm99nQqghV5X0dE5FSnPWAiIpIz14X29uH91ZFOGxw7Nvp+fdXXu70O4+hKdfG3I3+jMdaIz/R1V7py4XousUwMy7TwPI8j0SO0Jlp7va9lO1SMb843fCC7J+zJA08O6hoiIqe60fcbTEREhk00apBOD+8eIMuCxsaR14Z4MuedlyT2rgGOHckOXj32Kp7nYZkD/5o6Uh1475j6YRomHakOjsWP9bif50FZXQu+wMD3gL2Tbdpsb9tO2h3cdURETmVKwEREJGfZwRKFmdqXL8PwSCaHNYS8jBuX3SN2XDQd5fWm17PthnmKpqOYRs9f5aZhEkvHaI6/Xe1KJ/1MWbIt78/zTiknxaamTQW5lojIqajPBOyf//mfu/983333DUkwIiIyshXioODBG9rzxwrp3HOzVTDXc9nUvGlQyZfruWTcTK8fMw2TrnQXsUwMz4VIeRfl9S15f653ClpBtrdtL8i1RERORX3+Kj106BCpVAqAhx9+eMgCEhGRkSsQ8IY9CctkPEpLB3ae1UixcGGGKVMcdjTvIekMroyXdtPdo+l7YxomzfFm0mmLBReuL9jgFMMw+txnJiIiJ9fnDKkVK1bwD//wD9TV1ZFKpfjOd77T6/10DpiIyKkjGISSEg/HGb59YJYFEyaMzgQM4L0fbOR36w8R8A9ujGM8E8c8yU4CJ+3HnL2OcEVhD1FuT7YX9HoiIqeSPn/633jjjbzxxhs0NjayY8cOzj///KGMS0RERqiKCpfm5uHrAQyHPUpKhncf2mC83Po0889v5M21Z2EHem8hzIXruf2OqvcyNsHqI5gzH8PxzhxUu+O7pb00nufpUGYRkTz0+/bb3LlzmTt3LplMRmeCiYgIABMmOBw5YuHzDf3n9jyoqhrZ1a+0m6Yp3kRnqhPHcwhaQWpDtZT4S3Bchy3NW6ifYsJ5r/HmusV5J2EeXp8j672MD195E/XnPoRjZDjUdYhJpZMG82WJiEiB5NT/cMEFF7Bp0ybWrVtHa2srlZWVnHfeeSxcuLDY8YmIyAhz9tkp/vY3/7AkYLEYvPe9I28Eeku8hacOPMXh6GHaU+2knOweagMD13OxTZuwLwzA3o69TC2bSv2MQ1i+DFufWoZhuJjWwKp6lmH1WoVyUwFC4/ZSe/ZfMEwXG5tj8WMFTcBsw1b1S0QkTzklYE888QT33HMPF1xwAbNmzaKpqYkf/vCHXHPNNVx00UXFjlFEREaQ0lKPiRMdmputgg12yFUk4rFgwchJwA51HeLh3Q9zNHqUgBXAMi0CVoCAFej1/jvbdrK3cy9HokeYUDKByZM8zrr6CbasXUbb0Wp8gVTOnztoBWmjDYtsa6HnWGBA9fLHKZnSc+R8IpPI/4vsRVmgrKDXExE5leSUgD300EN861vfYurUqd23rVy5kptvvlkJmIjIKWj16iR33x0mEhm6z5lMeixfnh72KYwAjuvw131/5eUjLxOyQ93VrZOJpqMErSAeHns79tIYa2RB9QJOf/8LHNnewL6Ns4m1RfAFUhgn+Tr9lj9bYUv7MUyH0Lg9VC19Gst/4nTFtJsm5aTwW/58vtwePM+jKlA16OuIiJyqckrAOjs7mThxYo/bJkyYQFdXV1GCEhGRkW36dIcZMzLs328PSSui50E4DBdcMPwnMCcyCX655Ze0JFpyTryOS7lvV7hs0yblpnj56MvMr57PuFkwbtZBOhrL2fvaHLpaykjHA7iuiWk5GKYLnoHrmOCZWP40gXCS0MTXKZ25EdPuey+Z67kknERBErCEk2Bq+VSiUYP9+03277dJJLLTKcvKXKZPd6irc7EHN+RRRGTMyunH49y5c/nVr37FJz7xCQKBAIlEgt/+9rfMnj272PGJiMgIdcUVcW69tYR+R/EVSDwO110XG/YX9UknyZ2b7qQz3dlnm2F/PK/nPi8DA9u02dy8mQXVC6gJ1VBW185pF78EQDrpo/NYBV0tZThpG9Ny8YUSVNS3EiqLsrNjO4e7DmOaJ59w+O7PnY9EV5Bdryxk3XNr+GvUxnEgFMoe0O15kMnA448b+P0eVVUuK1akOP309LCvm4jISJLTj8TPfe5z/OAHP+C6666jpKSErq4uZs+ezT/8wz/k9Emampq4/fbbaWtrwzAMLrroIt7//vfT1dXFLbfcwrFjx6itreXrX/86JSUlADz44IOsXbsW0zS5/vrrWbx4MQC7du3i9ttvJ5VKsWTJEq6//noMwyCdTnPbbbexa9cuSktL+drXvkZdXV1+3xURETmpUAg+9KEE998fIhQq3ueJx+Gss1JMmjS80w89z+OeN++hM9WJz8qv7NfX4Aqf6WNL8xaW1y/vUVXzBdJUTTxG1cRjvT5uSukUDkUPDepz5yIVC7D1qaW0HqmiMhLGrLcJ91L8s6zsYd0A0ajJn/8cZO3aAGedleLcc1NDvmdQRGQkyikBq6ys5Lvf/S7Nzc3dUxCrq6tz/iSWZfHJT36S6dOnE4/H+eY3v8miRYt46qmnOO2007j88sv5wx/+wB/+8AeuvfZaDhw4wPPPP8/3v/99Wltb+X/+n/+HH/7wh5imyc9+9jO+8IUvMGvWLP7lX/6F1157jSVLlrB27VoikQg/+tGPeO655/jNb37D17/+9by/MSIicnJz52Z4//sT/PnPwaIkYYkEnH56mosuGv7Ww1caX2F/537C9sDaDt/JNmxS9D5owzIttjRvYVn9spyTJZ/loypQRVuyDbOfTWOmYeI3B95+6HlwaOtUdq2fh2G6GP4YM6vm5/z4bJJm8PTTATZt8nH11TGqq0fvGW4iIoUwoK3M1dXVzJw5c0DJF2QTuOnTpwMQCoVoaGigpaWF9evXs3r1agBWr17N+vXrAVi/fj0rV67E5/NRV1fHuHHj2LFjB62trcTjcWbPno1hGJx33nndj3n55Ze7zyo766yz2LRpU0HaLUREpH9Ll6b50IfixOPgFqhI5XkQjcKyZSkuvTTRXTlpbDRZuzbA3XeHuO22CLfcUsItt5Rw660R7rorzMMPB9ixwypYHMfF0jEe2/vYoJIvgIgvkj1AuRcGBrFMjH2d+wZ0zTlVc06asNmGPeCWSc+DrU8vYeeLC7B8Dp6ZYULJBCK+gU9eCYWgq8vkJz8pYdeu4TvEW0RkJBjyruzGxkZ2797NzJkzaW9vp7KyEsgmaR0dHQC0tLQwa9as7sdUVVXR0tKCZVk9kr/q6mpaWlq6H3P8Y5ZlEQ6H6ezspKxMo3JFRIrttNMyTJzYxb33hmlqMgdVDUulwO/3+MQn4syY4eA48OqrPtav99PYaOL3g/9dxRzXNWhpgWPHLF55xU9pqcf8+WnOOy9FONz3m3GOA01NJjt3Whw+nE3cysoMIMDs2RnGj3cIBmHdwXV9Hno8EDWhGg52HexzGIZt2hzqOsTk0sm5V8FMHzPLZ/JGyxt9tkaG7NCAWhA9D7Y8uYzmffXYwTQeHj7Tx4zyGTlf491MEwIB+O1vw3ziE1GmTRvZB2qLiBTLkCZgiUSCm2++meuuu45wb83jb+mrctVfRau3j/X2y+bxxx/n8ccfB+Bf//VfqampOVnYUmS2bWsdhpnWYGQY7etQUwP//M/w1FMGzz5r0NUFkYiR876feNzDtg2WLnX50Ic8/P4g+/fD3XebtLdnr5XL1t7S0uz/33wT3ngD3v9+l7PP9rrj8DzYtw/++leTffsMkkkPn88gEADDgJYWk1Sqgtde87BtqK722BbJ0DCvEtMaXNIQCAbY1rGt33bBtJOmy+uiPlKf83WnhaeRNJIc6DyAz+yZhGXcDOPKx/X7e/fddr40k/ZDkwhF0oAPx3M4q+GsAU997E04DH/4Q5hvftPpXqt3G+3PhbFC6zAyaB3GnpMmYK7rsmXLFubOnYs9iDFGmUyGm2++mVWrVnHmmWcCUF5e3r2nrLW1tbtaVV1dTXNzc/djW1paqKqqOuH25uZmqqqqejymuroax3GIxWLdAz3e6aKLLupxdllTU1PeX5MURk1NjdZhmGkNRoaxsg6nnQYLFsAbb1g8/3yA5mbrrTHl2el4xyfmpdOQSmVvq6hwOeusFMuXp/H7ob0dHnsswEsv+QkGs4lRLJZfPPfeC8895/Cxj8Vobze5//4QTU0W4XA2FsvKtk7G49n7h8NhUqm3Jy5uP9jCqwcm88bz05iy+E0aFuwe1DCJUquUlkRLv0nYjqYdlJp9ZCd9mBSaRDwZ52j0KLb59u9rx3Oo9dcSy/EbGGuLsH39JOxAjFTaw/VcltQugXS2FbMQHAd+/GOH66+P9fq9HCvPhdFO6zAyaB2G34QJEwp6vZNmVKZp8u///u/86le/yvuTeJ7HT37yExoaGrj00ku7b1++fDlPP/00l19+OU8//TQrVqzovv3WW2/l0ksvpbW1lcOHDzNz5kxM0yQUCrFt2zZmzZrFunXruOSSSwBYtmwZTz31FLNnz+bFF19kwYIFg5r4JCIi+TNNmD/fYf78GK4Lra0mu3dbNDaapFIGtu1RXu4xY0bmhDOjPA8eeCDI1q2+ggz2CIfhyBGLf/zHciorPUpLPUpKct8j3BRvIhQGA5edL82ncddE5l/4EsFIfoNBppZNpTHeiN/oeyhGIpMY8HUNw2B2xWxCVoi9HXuzCZ4BFYGKE6piffE82PzECixfmoybIWSHWFizkJBd2AkrlgWHDlm88oqP5cvTBb22iMhIl1NJa968eWzbti3vc7/efPNN1q1bx+TJk/kf/+N/APCxj32Myy+/nFtuuYW1a9dSU1PDN77xDQAmTZrE2WefzTe+8Q1M0+Qzn/kMppl9p/Czn/0sd9xxB6lUisWLF7NkyRIALrjgAm677TZuuukmSkpK+NrXvpZXrCIiUlimCdXVLtXVubXvPfxwkDfeKEzyBdnq1tatPtraDI4e9Vi2LI01gDkQ0XS0e/+XL5gm1hHm5QfOZ8mlzxCpjA44nrAvTG2olpZ4C1Yf53el3BQpJzXgg5MNw2By2WRqQ7VsbtlMR6qDWeWzTv7At7QdrqazNYwdSjCldMqA9qINVCgEzz8fYNmytMbTi8gpxfByGBX485//nOeee47ly5dTXV3d44fxNddcU9QAi+3QodzOT5HiUWl9+GkNRgatA2zdanH//eFez5jKh+fBxo0+OjoMLCvb+lZT4zJvXqbPx4TD4R7ten87/Dcczznhup5rsuxD6wiVDbwtz3Ed/nbkb3ie12uCk3SSLK1bSnmgfMDXPi6WirGgdgGdqU72d+7HdV1CvlCvrY8pJ0XSSbLr8UuIJKczpXxyzlWzwejqMvjkJ6NMn97z+6vnwsigdRgZtA7Db8hbEAFSqVR3e+DxqYMiIiKFlEjAn/5UuOQLYOdOi/Z2o7vF0bKyo+zHjTOorMytDbG3sfGGARgurz9yNmd8ZC2mNbBjTyzTYm7VXDY1bcI2TvxVbGCQdvNvzUs5KaaUT+HDMz6MYRjE0jF2t+9mZ/tOmuJNpNwUeNmKWYmvhEmlk2gIzuSeJ2YTqhy6clQk4vHcc36mT48P2ecUERluOSVgN954Y7HjEBGRU9xf/xrEdRlQe2B/OjsNDh608L2rkOPzwbZtNmeckVvrm2EYvU/aNSEVD7Bz/XxmnbV5wPFVBauYUzmHN1vf7DE047j+hnT0J+WkqA3X8vG5H++uroV9YRbULGBBzYI+H7d9u4XjDG0voGFAc3N+X6eIyGiV80+9AwcOcP/993PnnXcC2da9vXv3Fi0wERE5dWQy8MYb9gnJUr48D7ZutelreG88btLenluyYRl9Z4S2L8PhrdOItp44dTcX9ZF65lfPx3GdEyptfnNg+78gO6VwStkUrpt/Xa9JXX+2bbMLtu9uIDo7ze4JlCIip4KcErAXXniB73znO7S0tLBu3ToA4vH4oCYjioiIHPfaaz5SqcJVX9raDGIxs88Kl9/vsXdvbqW2oB3s9+OmL82eDXMHGmK3mlANZ44/k4gvQtpN43kelmkN6MyttJsm7aS5bPplfHzuxwecfAE0N1sFqz4ORCYDjY3D8IlFRIZJTj+hf/e73/F//V//F1OnTuWFF14AYMqUKezZs6eYsYmIyCli8+bCTT0E2LfPwufrf19WV5eJ53HSNsQyfxltybY+K2Gm6dFysJZM2sL2Ob3e52T8lp/FtYtpjDWyv2s/SSeZbXvsJzbP84g7cQJmgHmV83jPlPcM6qDkTGZ4RhEahqEKmIicUnJKwNrb25kyZUqP2wzD0DlbIiJSEG1thft94rrQ0WGetJqTTmcHf5ws8asP17OnY0+/rYieY9G4s4EJc/flEXGWYRjUR+qpDlUzPjyeimAFx+LHaE20knAS3QmZZVhUBCqoCFQwp3IOp9eenlfFa6QwjGzLqIjIqSKnn9jTp09n3bp1rF69uvu25557jpkzZxYtMBEROTUkEtl9QIWqgEWjBo5jYJ1kMqFhGLS1mYRC/Z9PFrSDlPpKSTh9H45s+VO0HqwdVAJ2XMpJcdmMy6gMVgLZSlfSSeJ4DqZh4jf9fZ4fNhgn+34Vi+t6w7L3TERkuOSUgF1//fX8v//v/8vatWtJJpN873vf49ChQ3zrW98qdnwiIjLGRaMmbm5nNOekpcXMKZmwLI9oNLfK28TSiWxt2drn2ViGAYnOyIDi7I3neYwvGd+dfGWvbZx0H9rJRKMGGzfa7Ntn09JikkoZeF72e1BS4lFb65DJZPdj9TW4pFhsG+rqCvgPQERkhMvpx2xDQwM/+MEPeOWVV1i2bBnV1dUsW7aMYHBwvxBEREQcB1zXAApTgenqMnIaJmEY5Jz41YZq2e/bT8JJYPSxMSsZDwwgyt4lnASXTbts0Nc5bu9ek6eeCrB/f/bXfTDYc8+b4xi0tMCxYxYHDhjs2WMzcaLLlCnOkCViJSUe4bB6EEXk1JHzj9dAIMDcuXNpaWmhqqpKyZeIiBSEZWUHWRRKrvuJshWg3O5rGAYLqhew/sj6Ptv/PGdw51klMgnOGHcG9ZH6QV0Hsm2df/xjiDfesIlETr7PzbJgwgSPvXsNDh2yOHLEYvbsDLW1xa1MeR5UVan6JSKnlpwSsKamJm699Va2b99OJBIhGo0yc+ZMvvrVr1JbW1vsGEVEZAwrLXULOv481/lQjmNQUpJ74he0g0yvmM6Oth29tiIaZv6JRNpNUxuu5aLJF+V9jeMOHDC5554IjgMlAziezOfLVqPicQPDgC1bbGpqXObPz+T8PR2oWAzOOitZnIuLiIxQOb1dd/vttzN9+nTuuusufv7zn3PXXXcxY8YMbr/99mLHJyIiY5zfn03CCsXn83JsLfQoLx/Y520oaWBq2VTSbvqEj1l5jqBPOSnK/GV8ev6nMY3BVdH27jX5z/+MYBjkdaj15MkZMpnsn32+7H66jRt9RZtSWFrqMXt2ft83EZHRKqef9Lt27eLaa6/tbjsMBoNce+217Nq1q6jBiYjIqaGqyi3Yi/yqKrc7ieiPbUMgj21bU8qmMKtiFo7r4L1j31qoNDbga8XSMRpKGvjsws8SsAa3h6y11eDuuyMEArlXAd+tutojGPS618KyoKPDYOvWwm8Ii8fhzDOTRauuiYiMVDklYLNmzWLHjh09btu5cyezZ88uSlAiInJqWbw4TWzg+Uuvyso8zJP8dvM8KC93837xP6FkAivGrSBoBUm7aZyMSUl1W86PTzkp0k6aS6ddyifnfRK/5c8vkLd4Htx7bxi/P//kC7KPnT8/g/OOopRlwbFjJseODa46906uC7W1LmeffWIlUURkrOvzLa177723+8/19fX8y7/8C0uXLqW6uprm5mZeffVVzj333CEJUkRExrb58zP89a8enjf4cojfD4GAh+P0fa10GqZOHVzrW9AOsrRuKU3xJnY0HqF0ynYSmQQBK4DRSxaUcTPEM3HKA+Usq1/GqoZVhOzCHID17LN+mpoKc5ZaaanHhAkOhw5Z3ZMQfT7Yts2msjJVkOmIqRRcfXVM1S8ROSX1+WO0ubm5x9/PPPNMADo6OvD5fJxxxhmkUqniRiciIqcE04SFC9O8/LKPQGDwr8rHj3fYs8fuM1mIRLwBDeDoi2EY1IZrmbawmmsuqGBL8xb2deyjNdVKykmBB6ZhUhYooy5Ux5yqOUwtmzrovV7v5Djw4ov+gh5mPGOGQyxm0tZmdH8PXRf277eYNm1wiWssBlddFaOyUqPnReTU1GcCduONNw5lHCIicoq74IIkW7b4yGQG10YH0NDgsm9f7x/LZGDRohw2ieUoGoWLLkpREahg5YSVrJywsmDXzsWmTT7icYPI4M+B7mYY2YR4y5bswc22nW1FPHLEYupUJ6/18bzsePwrr4wzd64Gb4jIqSvnRoJkMsmRI0dIJBI9bp8zZ07BgxIRkVOPbcNHPhLjl7+MEA4P7lqWBZMmOezda/WogmUy0NDgFKT6BW/vZTrttOHby7R+va+gyddxpgkLFmTYu9di3z4Ly8q2Dra1GQOuXiUS2UmX114bZ9w4nfslIqe2nBKwp59+ml/84hfYto3f33Oj8I9//OOiBCYiIqeeSZNcVq1K8uyzgUG31E2e7HDsmEkymT3XynWz+5sG20L3Tsf3Mp1s6EexeF52VHwhz1F7J8PI7pWrr3fYssVHKmXQ2GhSWZnb9zCR8LAsg+XLU1x0UbJocYqIjCY5JWB33303/+2//TcWLVpU7HhEROQUt2ZNikzGGPS+JsOABQvSvPyyH8/LHjK8aFG6YMlSPA4XXpigunr49jK1thokEoVtP+xNKATLlqXp6jJobjZwnOyBzaZpEAp53S2JmUz2+2JZUFnpccYZKc44I5XXuH8RkbEqpwTMtm3mz59f7FhEREQAuOiiJGVlLo89FsTnI+/KSTAIs2enOXrUYsGCTMEqMPE4nH12atjHqDc2mriuAQxNElhS4lFd7fDlL0dpbTXYs8dm3z6re2x9JOIxa1aGCRNcQiEN2RAR6U1OCdg111zDr371K6688krKysqKHZOIiAhnnJFmzpwMv/tdmEOHLCIRb0DDH+JxCIU8Pve5GOXlHnffHSaZBL8//wkfrgvJJJx/foJzzhn+M6ySyeK1H/bFdbPVxaoqj6qqNEuXDv/3QURkNMkpAZswYQK/+93vePTRR0/42DvPCxMRESmk8nKPz342yp49Fs8842f/fptMBsJhTmglPD5lz3Ggrs5l9eoUixenu4dw3HRTF3/+c5DXX/cRCp34+JOJxaC62uX66+PU1IyMQRKm6eGp0CQiMqrklID96Ec/4rzzzmPlypUnDOEQEREpJsOAadMcpk2LE4sZ7N1rsn27j+Zmk3TawPOyLYplZQ7TpjnMmJGhqurErMTngw99KMGKFSmefDLA3r02rpttU+yrspbJZJO6ujqXNWtSLFlSuD1khVBV5Xa3/w0V7ecSERmcnBKwrq4urrnmGgwdWS8iIsMoHPaYN89h3rz8s44JE1w+8Yk48bjBa6/Z7N1r09pq0tlp4HkGjgO27VJZ6VFf73D66ekROzq9ttbF7x+6EpjnkfMERBER6V1OCdiaNWtYt24dq1evLnY8IiIiQyIU8jj77PQJgzRqagI0NUWHKaqB8fuhrMwllRqaslw8bjB9uhIwEZHByCkB27FjB4888ggPPPAAFRUVPT723e9+txhxiYiISA6mTnXYvNnE5yv+57IsjwULMsX/RCIiY1hOCdiFF17IhRdeWOxYREREZIBWr07y2mu+oidgrguTJ2c0Xl5EZJBybkEUERGRkaeszGPy5AxHj9pFHUkfj8P55yeL9wlERE4ROSVga9eu7fNjF1xwQcGCERERkYH78IcT3HZbSdESsFQKFixI09AwMoeRiIiMJjklYM8880yPv7e1tXHkyBHmzp2rBExERGSYlZV5XHBBgsceCxIOF/bangc+n8dllyUKe2ERkVNUTgnYd77znRNuW7t2LQcPHix4QCIiIjJwZ56Z5tAhiy1bsgdNF4LnQTIJN9wQQ8eAiogURt5za9esWdNva6KIiIgMHcPItiLOn58mFhv89Rwn23p43XVRxo9X66GISKHkVAFz3Z4/eFOpFOvWrSMSiRQlKBERERm440lYQ4PDE08EsW3y2hcWj0Ndncs118QoL9fUQxGRQsopAfvYxz52wm1VVVV84QtfKHhAIiIikj/DyLYjzpuX4cEHg+zbZ+PzcdIx9Z4HsRiUlHhccEGSs89OYxhDE7OIyKkkpwTstttu6/H3QCBAWVlZUQISERGRwSsr8/j0p+N0dhqsWxdgzx6Ljg6TZNLAMLKJmuuCaXqEwx5VVS6XXJJi/vwMZt4bFERE5GRySsBqa2uLHYeIiMiQi6ajbG3eyu6O3XSkOsi4GUpLSvFSHhMiE5hfNZ/6SP1whzkopaUeH/hAdoJhOg2NjSadnQaZjEEw6FFb61JW5qnaJSIyRPpNwL773e/2+2DDMPj2t79d0IBERCQ/nufRnmqnMdZIwkngN/3UheuoDFRi6NV1Dwc7D/LE/ifY37Ufz/MIWsHu75GbdInFYuzv2M+6A+uoDlWzYtwKVtSvwDRGd2nI50NneYmIDLN+E7BVq1b1entLSwt/+ctfSCaTRQlKRERy43keuzt288zBZzgaPUrCSeB6LqZh4nouhmEQskJUh6pZOX4lc6rmjPokYjDSbpo/7/4zrx97nbAdJmgF+7yvz/Lhs3wknSSP7XmMV468wjVzrqE6VD2EEYuIyFjTbwL27kOWOzs7efDBB3niiSdYuXIlV155ZVGDExGRvh3qOsQDOx6gOdFMxI5gmRYRs/fptK2JVu7bfh+l/lI+NONDTC+fPsTRDr9oOsqdm+4kmo4S8Q1sim/IFyKaiXLH63dw5awrmVc9r0hRiojIWJfTHrBYLMZDDz3Eo48+ytKlS/m3f/s3xo0bV+zYRESkF57n8fj+x3nx0IuE7BAlvpKTPsYwDCK+CI7rcPfWu1lUs4gPzvjgKVMNi2fi/OzvPyPlpPBb+Z0obBomYV+Y+3fcz1XGVcytmlvgKEVE5FTQbwKWSqX4P//n//Dwww8zf/58/tf/+l9MmjRpqGITEZF38TyPP+78I5uaNxH2hQf8+OOJ2OaWzXSlu/j43I+P+STM8zzu3XYvSSeJbeb0vmO/wnaYB3Y8wFcXf5US/8mTXxERkXfq9zfRl7/8ZVzX5YMf/CAzZsygvb2d9vb2HvdZuHBhUQMUEZG3Pbn/STY1byJkh/q8j+uYuI6JabmYVu8DF4JWkL2de3lo50NcPvPyIkU7MrzS+AoHOg/0+z0bKNuwuW/7fVw3/zoNOBERkQHpNwHz+7NtGn/96197/bhhGCecESYiIsVxNHqU5w8/f0Ii4WRMDr85haa940h0hUnFgoAHgD+YIlgao7KhkYYFu7F9TvfjglaQvzf9ndNqTmNGxYwe1/Q8aG832LfPornZAqC01GXaNIfKSnfUnBPlei5PH3i6oMkXgGVa7OvYx+6O3afkfjoREclfvwnY7bffPlRxiIjISTyw44EeU/tcx2TX+rkc3T4Zx7Gw/WmA7v8DuK5JrL2EzqYK9m+cRfWUw8xe+XestxKxkB3ijzv/yNeWfg3TMGlvN3jqqQDbt9vEYgZg4Pdnk7l02sB1PUIhj8mTHdasSTJu3Mgeab6leUteQzdyEfFFeObgM0rARERkQAbfDC8iIkV3sPMgx+LHuhOJzqZyNj+xnFQ8iO1PY/fRanic5csA0LRnPK0H6pi7+lWqJh7DMAy60l1sanyDfeuXsmGDj0AAbBsiETheSQPw+Y7/2WD/fpuf/tRm5swMV1wRJ1TYAlPBrD+6nrA98L1yuTAMg4NdB0k6SQJWoCifQ0RExp5R0kQiInJqW3dwXXcbXcuBWl7907k4GatHtSsXls/BAzY9diZHtk8EwIhX8b9/YLJxo00kkk2+TsY0oaQE9u+3ufXWEvbsGZm/TloTrUXdo5V0khyNHi3a9UVEZOwZmb8xRUSkh6Oxo5iGSWdzKZsePwPLnybfvMIwwA6k2PbsYg6+MZENfzyfzngSfx7T2X0+sCyDu++OsGuXlV9ARRJNR4mmo0X9HCE7xPa27UX9HCIiMrYoARMRGeHimTidqU5cx2DL2jOwfPknX+9k2mme/dWleB44ZIhn4nldxzAgGIT/+q8wXV0jZyJgLB3D8ZyT33EQfKaP9lT7ye8oIiLyFu0BExEZ4dqSbWS8DLtfmUsyFsB+az/XYLUerMNJ27QcqKdi8j660l15nS0Gb1XVbLjvvhDXXRcrSILYm7Sb5vXG19netp22ZBvxTBwPD7/ppyJQwYSSCZw57kxK/CV4eBgUPyH0PO/kdxIREXmLEjARkRHOcR2cDBzdPqlgyVcmZdPZXI5lO8TbSyh3rEFXiywruyds+3aL2bMLW3lKOSke3fMoW1u2knJTBK1gj71dKTdFY7yRg10HeeHwC0wqncR5DecV/ZBpx3WKNuRDRETGJrUgioiMcJZp0bJnCumkr2DXbDtcjWlmJyd6HnQ0VmEZg9/DFQ57PPtsYScC7mzbyY9e+xGbmjdhmRYhO9TnYA2f5SNkh2iMNfKbrb/hSPRIUStUcSfOrMpZRbu+iIiMPUrARERGuMpAJe37puALDmziYV88DxIdEQwzm5iYlkuiK0KJr2TQ1zYMOHTIeusMscF7+ejL3PPmPXieh9/KfUqIaZiEfCGOxY+xpWVL0ZIwE5OGkoaiXFtERMYmJWAiIiNc0A7ixqoKdj0nbZPJ9OxAd1MBgnZhDvPKZGD//sH/etnaspVHdj/Sb8XrZCZEJnA0dpQ3W98cdDzv5nke9eH67uMBREREcqEETERkFPCna/AoTBUnGQ3x7ktZXhAnU5iqVTgM27cPrl0ynonz0M6HCPkGl9xMKJ2A3/RzNHaU5njzoK71bvFMnLMnnF3Qa4qIyNinBExEZBSYGJ5C2ilMC2I64cN4a/8XgOu5lPpLKVSXnmUx6BbEh3Y+VJCE0zIsxkfGY2DwZuubuJ578gflwPM8ygPlLKheUJDriYjIqUMJmIjIKFAWimSTpAJVwd7JMi1CVqhoo+MHKpqOsr1tOz6zMENHppdPJ2AHSDtpjkSPFOSacSfO1bOvLvqURRERGXv0m0NEZBQoLXWZVzUPxx38eHfLn8Hzsj/+Xc+lNlSLz5c9x6sQXBf8/vwTxWcPPluQiYzHGYbBwuqFmKbJwa6Dg75ePBNnzcQ1jIuMK0B0IiJyqlECJiIyClRWuoTsMFPLppJxB3cWWLAkDmSTrxJ/CQErQCjkFawCFosZzJyZf4z7O/cPaOJhLiK+CItrFxNNR0m5qbyvE01HWTl+JasaVhUwOhEROZUoARMRGQUWLMgQj8PkssmMj4wfVBJm+9MYZpqwL0x1sJpMJpvgFYphwOTJ+VXqPM+jJdlSsFjeqdRfysLqhdiGTTwTH9Bj026ajJvhyllXcsHkC4oSn4iInBoK1HAiIiIDFY0a7N5tsWOHTSxmUFpqkskEmTEjw/TpDiUlb7fxLViQ5rHHAoDBrMpZ+C0/ezv2YpkWBgMrXWW8NPXVfvypWiCbME2cOPjWxuPq6hzKyvJrQUw4CVJOqmij3SP+CGsa1uDg8Lcjf6M92U7YDvc55j6eieMzfcyqmMX7p71fI+dFRGTQlICJiAyx/ftN1q4Nsn+/heMYhMMepgnt7QZdXT7+/nc/punR0OCwZk2SadMcLAuWLEnzwgt+gkGYUjaF2lAtW1q20JXuwmf6TpqIHU9sFtYtxKosZ/367MTCmhq3YPu/olG44IL8W/xczy3aockABgYZL8PZE87mzHFnsrtjN682vkpLooWOZAcZL4PruQStIJXBSqaXT2dp3dKCt0SKiMipSwmYiMgQSafhz38OsnGjj1AIQiF494FcpgmRSPa25maLu+8OM39+mssuS7BmTZLNm22SSRPDgLAvzPL65XSmOtnTsYdoOkrSSfYYtW4aJj7TR8QXYX71fMr95dlqj9+jqsqlvd1k9uzB7Sk7zvOgutrl9NPzH5fvM31FnSzoeA5hXxjIDueYXj6d6eXTe9ynpqaGpqamosUgIiKnNiVgIiJDIJGAu+6K0NZmEA7n9hjDyB5qvG2bj5/+1OKzn41xzTVxfvazCIEA3UMzSv2lnFZzGpDdqxTPxHFcB8uwCNrBPqs306ZlSCQMzALlO/E4XHddfFDX81t+Svwlgx400hcPj0mlk4pybRERkVxoCIeISJG5Lvznf0Zobzfx+wc+atDvh1jM5Be/CFNV5fLxj0dJJun14GSf6aPMX0ZlsJKyQFmvyZfnZZOlj3wkzo03dvV5rYGIx+EDH0hQUzP4YR4VgYpBX6MvIStEZaCyaNcXERE5GSVgIjLmuS40N5vs3Wuxf79FS4sx6IRjINauDdDUZOIbxLnCtp3dI/boowGmTXP57Gej+P0uyeTArpNKeRiGx7XXRjnttAyTJ7tce22UdBoyeRSdPA9iMXj/+xMsXZp/6+E7La7JjosvNNdzaSht6HPghoiIyFBQC6KIjEmuC2++afH88wGamiwSCXAcA8PIDrwIhTxqa11WrUoyY4ZTsDOw3q293eCFF/w5tx32JxAw2LDBz1lnpamvd/nyl6OsXRvg1Vd9JJP9tzbG4+DzwcKFGS65JNEjGZwyxeWrX+3ivvtC7NtnEw7ndiZYLAZVVS7XXRcvSOXruIU1C3ls/2MFu95x8XScNQ1rCn5dERGRgVACJiJjzpEjJvfdF6K11SQczlaPSkqg58ALg+Zmi9/8JkJdncPVV8eori58WeyppwL4CzhALxCAJ57wc/XVCSwL3vOeJOefn+T1131s3WrT2mrS2Wniutk9YpGIS2Wlx6xZaZYvT/cZSyTi8elPx9i50+KZZwIcPGjhONlBIZaVvY/rZhM5w4C6OpeLLkpx2mnpgu0hO84yLc4edzZPHniyYGPfHddhUtkkGkobCnI9ERGRfCkBE5ExZf16H488EiQUgkik//saBpSUeHR1mfz4xyV88INxFi0q3PAHz4Nt2+yCjXiHbDK0e7cP1010Jz62DcuWpVm2LN39eR0nO1FxIMmRYcDMmQ4zZ8aIxw0OHDDZvt2mqyt7kWDQY8aMDJMnO5SWFreHc+WElWxq3kR7qh3LsAZ9PcdzuGrWVQWITEREZHCUgInImPHyyz4efTQ44Ha/bEsiPPRQCNuOM39+YZKw9naDeNw4aSI4UImEQXOzSW1t721/hsGgk75QyGPWLIdZswp3QPNAGIbBJ+Z+gh9v/DGu5w5qNH0sE+OqWVdR4i8pYIQiIiL50RAOERkTWloM/vrX4Ftna+UnFII//jFENFqYDWH791twksOR82FZHrt2Db4qNNKV+Ev4/GmfxzZsUs7AD3d2PZdEJsGVM69kbtXcIkQoIiIycErARGRMuP/+0KCmDB5nmvD73wcHfyGgo8PAtgvfqufzQWfnqfHjuzxQzleWfIW5VXPpSnf1OGS6L57nEU1HKfGXcOPpNzKvet4QRCoiIpIbtSCKyKh3+LDJ4cPWW4M2Bse2Ye9em/Z2g/LywSVPpjn487V643kD29uVj/ZkO5ubN7OvYx8xJwYeBOwAEyITWFi9kNpwbXEDeAef6ePDMz/MWePO4qkDT7G7YzdJJ0nIDuEzs1m34znE03FM02RceBzvnfpeFlYv1Mh5EREZcZSAicio98wzgYKMeT/O74d16wJcdlliUNcZP94lnYZgYQpq3ZJJmDChcMNC3ulg50Ee2fsIh7oOYRomASvwdhKThH0d+1h3YB114TrWTFwzpNWl8SXj+djcj5FyUhyJHmFb2zbaEm14eITsEDMrZjKpdBIRX4E33YmIiBSQEjARGfUaG82CVoRsGw4fHvweq/HjnYJOQDzONGHixMKduwXZ/VJ/2fMXXm18laAVJOzrPaP1W378lp9YJsbvt/+eGcdmcMWsKwhYgYLG0x+/5Wdy2WQml00ess8pIiJSKKfGJgIRGbMyGejoKPyPsra2wbeuBQIU9IDi4yorPUpKCtfb6Houd2+9m9eOvUbIDuXcthfyhdjbuZf/2PgfJDKDqxaKiIicKpSAiciolkoZOEWYlJ5OG7gFyJ1WrEgRiw3+OsfF47BkSbJwFwT+sPMPHOg6QNAaeK+kz/QRzUT51dZf4RVjw5uIiMgYowRMREY1w/AoxpwFw6Df6x4/7PhkOcfixWlKS72CDOPwvOz5XGeckR78xd6yrXUbm5s2D6qF0Gf6aIw28uyhZwsWl4iIyFilPWAiMqoFg+D3exT6vK1w2D0hATt61GTdOj9NTRZtbSaum92PVV7uUlfncN55KerqepbNLAs+8pEYv/xlCeHw4LKwWAw+8Yl4QcbtQ3Zc+6N7HiVkD+LwtLeEfCGePfQsZ447E7/lL0B0IiIiY5MSMBEZcTIZOHbMZN8+i0TCIBLxmDLFobraPWHYhmFAVZVHe3thE7DKyreTpY4Og/vvD7F/v0U4nE26/O/IMeJxk127TLZs8TFpksNVV8V77NGaNMnl/PPjPPlkMO9pjfE4rFqVYsaMwvVb7u3cS2uilYi/MFMDXdflpSMvcW7DuQW5noiIyFikBExERozmZoMnngiya5dNMpk9xNg0s61+jmMQDrvMnp3hwguTPRKcmTMzPPecv2Dj3mMxWLAg2+a3bZvFffeF8fno95wxy4JIBI4ds/jhD0u4+uoYs2a9nSyde24ay4InnggSDPbf3vhOnpdNvtasSbJqVWowX9YJ1h9Z3+e0w3wE7SBvtLyhBExERKQf2gMmIsPO82Dt2gA//nEJe/bYbyU73lvthRAKZf9umgZvvOHjRz8qYf36t/vwzjorWdB9YMGgx+LFaXbtsvjd78IEg9kEKxeWlW2LvPfeMLt39/wRe/bZab7whSjhsEtXl9HvvjDPg2gUAgGXz3wmWvDkC6Al0VLwg4pbEi0axiEiItIPJWAiMqw8D+69N8Tzz/sJhU6e6Nh2Nil75JEgjzySHRwRDMLy5SkSBZiEHo/DypVJHAceeCCcd1UtGITf/z5C6l15U22ty5e+FOVTn4pSX58BPGIx6Ow06OjwiMUMwKO21uHjH4/xla9EmTCh8KPsAbpSXQW/ZspNkXB6XwjXc2lJtHA4ephjsWNk3OIcJi0iIjKSqQVRRIbVX/4SYOdOm9AA50CEw/Dyy34qKlzOOivNRRcl2b7dJho1c65WvVsmA+PGuaxcmeahh4Kk0z33eg2EYUAqBY8+GuSyyxInfGzaNIdp0+IAdHUZRKMGlZV+kslOSkqKM9nx3VxczCK8D+e4To8/b2raxPqj62lONJNwEnieh2EY+AwfFYEK5lfP56zxZxG0C9RDKiIiMoKpAiYiw+bgQZMNG/LfuxUKwdq1QdrbDUwTPv3pGJbl5XUuWDoNoZDLJz4RJZOBN9/05Z18Hef3w9atNumTTI0vKfGor3eZMAFKS4cm+QKKMq3QwOi+7r7Ofdz62q38cdcfaU+1Y5s2Jb4SSv2llPhKCNgB4k6c5w49xw82/ID1R9arfVFERMY8JWAiMmwefTQ46MEZtg2PPZZtRSwp8fjiF6NUVrrE47lfIxaD8eMdPv/5KKEQbNtmD+jx/YnHDXbsyLMkV2QV/oqCX7PEV4Lf8vPswWf55eZf4rgOEV//UxaDdhCf5eORvY9w3/b7cL3itFyKiIiMBErARGRYdHQYHDxoDbraY1mwc6evu8oUiXh89rNRLrwwgWl6RKO9H5bsutnWP9v2uPTSBJ/6VKw7Gdy+3c57XPy7hcOwY0eBDu4qsAklE0g7hTvU2fM8qkPVrD+ynif3P0nEFxnQkI+wHWZ763Ye3PFgwWISEREZabQHTESGxfbt9lsvzgffchaPGzQ2mjQ0ZCsnhpGdOHjWWWm2bbPYtMlHa6vVPaQjFILqaodFi9JMm+ackAR2dponnDeWL9Ok4GeUFcrKCSv525G/4aMwCWI0HWVRzSIe3vVw3uPtg3aQrc1b2VK1hfnV8wsSl4iIyEgyJAnYHXfcwYYNGygvL+fmm28GoKuri1tuuYVjx45RW1vL17/+dUreOmTnwQcfZO3atZimyfXXX8/ixYsB2LVrF7fffjupVIolS5Zw/fXXYxgG6XSa2267jV27dlFaWsrXvvY16urqhuJLE5E87dljEQwWZr9PIOCxc6dNQ0PPkYOGAXPmOMyZM7BNYYXehuR5IzMBi/gizCifwb7Ofdjm4H4deJ5HTaiGDY0b8FmDS+hCvhB/2f0X5lTOwTJHZvumiIhIvoakBXHNmjX8z//5P3vc9oc//IHTTjuNW2+9ldNOO40//OEPABw4cIDnn3+e73//+/zzP/8zd955J66bfVf7Zz/7GV/4whe49dZbOXLkCK+99hoAa9euJRKJ8KMf/YgPfOAD/OY3vxmKL0tEBiGVMgo2bMKysu2EhRIMegVLwjwPgsGRu6fp8pmXF+Q6CSfBxVMuZl/XPkxj8L9aYpkYm5o3FSAyERGRkWVIErD58+d3V7eOW79+PatXrwZg9erVrF+/vvv2lStX4vP5qKurY9y4cezYsYPW1lbi8TizZ8/GMAzOO++87se8/PLLrFmzBoCzzjqLTZs2aZKWyAhnWYWrNLkuBAKFuRbAlCmZgpwpBpBIGEyblsdYxiESskN8aMaHiKfznzoSz8RZ3bCafZ378BuFmawYskO8duy1glxLRERkJBm2PWDt7e1UVlYCUFlZSUdHBwAtLS3MmjWr+35VVVW0tLRgWRbV1dXdt1dXV9PS0tL9mOMfsyyLcDhMZ2cnZWVlJ3zexx9/nMcffxyAf/3Xf6WmpqY4X6DkzLZtrcMwG441mDvX4NAhsyCJU0eHx5IlAWpqSgd/MeC88+DZZ03C4UJU1TzOPdfPu96D6tVwPRfOrTmXSFmE3235HUE7mHMFy/M8ouko75v5Pt474738fMPPKS8tL1hcSTM5LN8P/UwaflqDkUHrMDJoHcaeETeEo6/KVX8Vrd4+1tfkrYsuuoiLLrqo++9NTU0DjFAKraamRuswzIZjDcaNM2lvj+SUmJxMJgOhUCeF/BJqasI0NlqDGsbhulBfnyGRiOdUURvO58IkexKfnvVpfvfm72hMNBKx+59gGM/ECdthrpx5JdPKp9HU1MThtsPE0rGCxZTIJGg81liQlsaB0M+k4ac1GBm0DiOD1mH4TZgwoaDXG7YErLy8nNbWViorK2ltbe2uVlVXV9Pc3Nx9v5aWFqqqqk64vbm5maqqqh6Pqa6uxnEcYrHYCS2PMnIkk7B+vY89e2zSaYOKChPXDXLuuSnq60fuXhkprNpal+pql2RycC+uPQ8mTnQIhQoU2FsuvTTOj39cMqjrplLwgQ8UqJdxCNSEavjS6V/ijZY3eP7w8xyLHyPpJLsHVXp4BKwAlcFKVjesZnHd4p7DO4rQ+e167pAnYCIiIsU0bAnY8uXLefrpp7n88st5+umnWbFiRfftt956K5deeimtra0cPnyYmTNnYpomoVCIbdu2MWvWLNatW8cll1wCwLJly3jqqaeYPXs2L774IgsWLBjQ2TMyNNraDP761wC7dvlwHLrPXEokDLq6fGza5KO+3uXss1MsWpQu2IAGGZkMA845J8XDDwcHdeZWPA4XXFD4JKe62mP16iRPPx3IKwmLx2H16iTV1aNrP6phGMyrnse86nk4rkNzopmWRAseHmX+MurCdfjM3qcc+i0/sUzhKmC2aWMZmoIoIiJjy5AkYD/4wQ/YsmULnZ2dfPGLX+Tqq6/m8ssv55ZbbmHt2rXU1NTwjW98A4BJkyZx9tln841vfAPTNPnMZz6D+VYP0Gc/+1nuuOMOUqkUixcvZsmSJQBccMEF3Hbbbdx0002UlJTwta99bSi+LBmAvXtNfvvbCJYFPl/2v3cyTSgpgWjU5KGHguzaZfGhDyVyav9Kp2HLFh9Hj5qkUlBa6jFvXoa6OlXTRrrFi9O89pqPxkYLO4+fRsmkx2mnZZg0qThrfe65Kbq6DF5+OUA4nHsiFYsZrFiRZNWq1MnvPIJZpkVduI66cG7HelQFq2hJtBSsYlUZrNSbaSIiMuYY3ik+LvDQoUPDHcKYd/iwyZ13RggG6bWqFQ6HicV6vmsej8PChWkuv7zvykZrq8FTTwXYts1HKgV+fzaRy2SySVl9vcsZZ2SraZbeRO/XcPaXx+PwH/8RIZEwB5SEpVIedXUen/50NK/kbSBef93HI48EcByj36EhySRYlscllyQ5/fT0gD/PaO/z39G6g9+++Vsivsigr5V205xeczrvm/a+AkQ2MKN9HcYCrcHIoHUYGbQOw2/M7AGTU4PnwT33hPtMvvoSCsHGjX5mzcqwYEHmhI+//rqPhx4K4veDbdPjBbjfn/2vq8vkT38Ksn69n099Ktrd8igjSygEn/98lHvuCXPwoJVTO2IsBrNnO3zkI/GiJ18Ap5+eZvbsNM89F2DzZh8dHQaOk23Xc10P24ayMo/Fi9OsXJks+H600WJGxQzK/GU43uDH7mfcDKsaVhUgKhERkZFFCZgU1RtvWHR1GUTyeEM8HPZ4/vnACQnY66/7+OMfgzldMxKBlpZsBe7zn4+e0PooI0MoBNdfH+OVV3w8/3yA1laTQMDrsV6pVPa/2lqX978/wbx5Q3u2VigEF12U5KKLksRiBo2NJvF49vb6epdQ6JRuJgCyCenqhtU8vPthwr78N/alnBTzq+dT4tcwJRERGXuUgElRPf98IO8BC4aRbV9sbjaprs7u8WltNfjTn3JLvo7z+aCjw+TBB0NcfXX+h81KcRkGLF+eZtmyNAcOmGzd6uPYMRPXNbBtj/HjXebPT4+IvX3hsMfUqSP3cOXhtLhuMX9v/juHug7hswb+jofnefgtP5dOu7QI0YmIiAw/JWBSNPG4weHD1qDasYJBeO45Px/8YHYv2FNPBfKqYvn9sH27TSxmDGiYggw9w4BJk1wmTUoOdyhDIuNmONx1mPZUOxWZCtyYy/iS8X1OGhzpDMPgo3M+yp2b7qQt2Ybf8uf8WNdzcVyHz532uQE9TkREZDRRAiZFE40auIMsVlhW9jqQHayxfbud954f04Rnn/Vz8cV9v7A/etTkqacCHD5skUplHxMKeSxalOaMM1L9DmAQGYi2ZBtP7X+Kba3biGaiWIZFKBSiK9ZF2A4zs2Im5088n6pQ1XCHOmB+y89nFn6GB3Y8wPa27YTtk5fB45k41cFqPjrno1QGK4cgShERkeGhBEyKxnHA8wwGezqr42QTsC1bfCSTRt4tjX4/vPGGr9cErK3N4L/+K8zRoyahUDbxOz45MZEwWLcuwLPPBli6NMXFFyd1RpnkzfM8Ht71MK8dew2/5cc2bcr82YPow/4wZiY7wn1n2062NG9hfvV8Pjzzw6PuMGK/5eejcz7KGy1v8PSBpzkSO4JlWAStYPdo+ZSTIuWkqAhWcP7E81k5YaXGzouIyJinBEyKJhDwMIzBt/v5/dlrHD1q4h9kV1IiceKLu8ZGkzvvDGPbBiV97Pk/PkFx/Xo/ra0m11wTVxImA+Z5Hv/15n+xs33nSYdUWKZF2AzzZuub/Hrrr/nkvE+OuiQMYG7VXOZWzaUl0cKbLW+yv2s/GTeDiUl9pJ45lXMYHxmvxEtERE4ZSsCkaMrKPEpKvLeqYPmJRmHWrOwUxOMtgYPhvGtuQjwO//mfEXy+3Mbkh0KwY4fNX/4S4P3vPzX2KEnhPLzrYXa17yJk574xMmAFONh1kAd3PMhHZn2kiNEVV1WwirMnnM3ZnD3coYiIiAyr0fd2qowapgnz56dJpfK/xvH9V5BN6DInHgk2ID5fz4rcM88EyGQGfkbZa6/5iWugogxAZ6qT15teJ2gP/EC6gBVgc/NmWuItRYhMREREhpISMCmqVatSJ1SdcpVOw9y5me69WHPnZkin84/F86Cm5u2pIK4Lmzf78m5rfOEFTeSQ3D194GlsM/+mg4AV4MkDTxYwIhERERkOSsCkqCIRj2XLUgOuFnledgjGhRe+3eZXV+dSX5//WMVYzGDVqrevt3OnRUdHfu2RgQD8/e+jc0y4DD3HddjasnVQo+Vt02ZH2w7S7iDehRAREZFhpwRMiu6SS5LMnp3JOQlzXUinPa67LnrCmV1nnZUiGs0vjvJyl+nT3y7HHTxoD2qsfDRq4OlIMcnBsfgxutJdg75OLBPjSNeRAkQkIiIiw0UJmBSdYcBVV8VZvjxFKgXJPmZXuC50dRmEwy5f+lKU2toTq12nnZZm/Hh3wK2I8Ti8972JHnu9UqmB7f16N88jp3POOjoMtm2z2LjRZs8ea9D72GT0iWfigz2NAQDTMOlIdQz+QiIiIjJsNAVRhoRhZCthq1cnef75ABs3+ujqylaQPM8jlYLJkzOsXp1k4kS3z8TINOGTn4zyi19EaG/PbSx9PA6XXJJg7tyemU9JiUsmQ94HO9v222eF9Wb7dot16wIcPGjhugamCY7jEYl4zJ6d4fzzk5SXq4R2qjAozJh1jWsXEREZ3ZSAyZAKhbL7ui64IEk8bpBIQH19gFisE1+O22OCQfjc56I8+GCQHTt8GAYnJGKel93zVV7ucuWVJyZfAPPnZ3jiify+Ds+Durrep4t4HvzpT0FefdVPJOIRiUDP8ofBm2/62LzZxzXXxJg5M88pJSOM52XPVGttNbAsqK52qapSgglkz/wqQN7kuA6lvtLBX0hERESGjRIwGRaGAeGwRzgM5eUMuKXQ54Orr04Qjyd59lk/W7f6SCQMHCc7ar6mxmXVqiTTpzt9VtMqKjzGj3doa7MG3IoYi8EHP9h7L+Vf/pKt8JWU9J18HK+e3XtvmOuui9LQkP9wkeGWTsOLL/p59VU/LS1vdzUbhse4cS5nnZVi0aL0KX1wdW2oljJ/Gd4g+xBLfCWMLxlfoKhERERkOCgBk1EtFPJ4z3uSvOc9+R2KfM45SX73u0i/ydK7eR6UlnrMmnVi5aqlxeDllwNEIie/nmFkpyk+9FCIL30pz8kiwyweN/jFL8K0tZkEg9nvyzt1dZn88Y9BNm3y8dGPxvpt2RzLTMNkfvV8Xm18Fb+V37kHGTfD3Mq5gxplLyIiIsNPQzjklDZ3rsP8+WkSidzu73mQSMBVV8V6reg8+WSAQCD3ZM4wsm17R4+OvqdiOg2/+EWYrq5s8tWXSAT27jW5777Q0AU3Aq1qWIXj5d9umnJSrJm0pnABiYiIyLDQW6kyqqXdNBsaN/Ba42vE0jEcz8Fn+RgfGc+aiWuoC9f1+3jDgI98JM7994d44w37rf1avXOcbNLx0Y/GmDjxxJZBz4OdO+0BD/UIh+GZZ/xceWWOWeAI8dJLPlpbTUI55FWBgMGbb9ocPGiO6nbLwYj4IqyoX8HLR18maPeTsfYikUmwuG4x5YHyIkUnIiIiQ0UJmIxKnufx5P4nebnxZVJOipCdzQIMwyDjZtjTsYefbPwJ9eF6rpx1JdWh6j6vZZrZMfkbN/p48UU/R45kKzrH2+USieyesxkz0lx4YZLKyt4rXIkEpFJGTgnJuz9/Z+foqoB5HmzYEBjQ1xoOw1NPBfjEJwZ4KvcYcvGUi+lKd7G1ZWv3v9mTiWfizKiYwaXTLi1ydCIiIjIUlIDJqON5Hr/f/nveaH2DkB3q9YWsZVhEfBE6Uh389O8/5dPzP01DSUOf1zQMOP30NKefnubYMZNXX/URjWan+Y0f73D66ekcRt7nfzDzaDvQuanJpKXFoKQk98eYJuzbZ+M4/Y/vH8sMw+CKmVfw171/5eWjL2MaZp97wtJumoybYXHtYj4w7QMaPy8iIjJGKAGTUefRvY92J18nYxomftPPr7f8mhtPv5GyQNlJH1Nb63LxxQMf6hEIeDmP0n8nz8sOExlN2tsNPM9goKcLZzKQTBqEw6Pr6y0kwzB479T3sqphFc8cfIbNzZvpSHXgeR5pM00ilaDEV8JpNaexumE1Jf4BZLkiIiIy4ikBk1Elnomz4eiGnNu3IPuC1zAMHt//OFfMvKJosZlm9jDpgwdtzAF0FMZiBmedlSpaXMVgWflV7QwjO55esmeDvXfqe3nPlPfQFG8inolTWVlJvDNOTbAGyzxFy4QiIiJj3OjaeCKnvOcOPZfXgba2abOjdQdpd4AHjg3Q6tVJ4gPc4lRZ6TJ16ug6jLmqys2rjdDv9wgECh/PaGYaJnXhOqaUTWF65XTqw/VKvkRERMYwJWAyqmxu3kzAyu8VfMpN8Vrja4UN6F0aGlxmzsyQSuVW5YnH4aKLEqPukOLyco9x4zIDqoJlMjB7dmZA1UERERGRsUYvhWTUcFyHrlRX3o8PWkH2d+0vYES9u/rqOOPHu/1Wwjwvm3y9970J5s3LFD2mYjjnnCSxWO73TyRgzZr8DswWERERGSuUgMmokXbTeAMc+vBOhmGQcYqf7Ng2fOpTMVavTmLbHtGoQToNrgvJJMRiUF3tcO21UVasKG5LZDHNn+8wZ04mp0OsYzE4//wkFRXa/yUiIiKnNg3hkFHDb/kHNYrb87w+R34XmmnCqlUpzj03xe7dFrt22SQS2da9009PU1Y2+hMRw8ien/bAA0E2bfIRiXBCe2E6DalUNvlatWp0DRoRERERKQYlYDJqmIZJZaCSeCa/g3xjmRhzKucUOKr+GQZMn+4wffroGrKRK9OEK69McO65KZ56KsCePTbpdPbrDgQ8FizIsHp1ckwknCIiIiKFoARMRpUV9St4dO+jAxpDf1ypv5Q5VUObgJ0qxo1z+ehH491tltkEjFE3XERERESk2JSAyaiypG4Ja/evHfDjkk6SFfUrMI3Rve3RcWDTJh9791qYpseSJWkaGtzhDqubaUJo4LmxiIiIyClDCZiMKrZp894p7+Xh3Q/nXAVzXIdSXymrJ64ucnTF9eqrPh5/PEA8bhAOZ4d6bNjgp7ra5aMfjVFdrTY/ERERkZFudJcD5JS0uG4xF066kHgmjneSg6jSTpqgHeSGhTcM2QCOYnj1VR8PPxzENA0ikWxrn2VBJAKxmMnPflZCe7v6/URERERGOiVgMiqdPeFsrp59NWWBMqLpKI7Xc8hFPBPHcR3mVs3li4u+SMQXGaZIB891Ye3aAOFw7x83zex/jz4aHNrARERERGTA1IIoQ87zPA50HeClIy8Rz8SpKKug0qhkRf2KAVWpZlfOZnblbJpiTaw7uI7OVCcODn7Tz5zKOSypW4Jtjv5/4lu3WkSj2cpXXywLdu2ySaXAP3oLfSIiIiJj3uh/dSqjyputb/LY3sdoSbQQskOYhkknnbze8TrPHHiGeVXz+MD0DwwocaoJ13DFrCuKGPXw2rvXJphDcSuZhK4uk6qqkTOUQ0RERER6UguiDJlXG1/ld2/+jqSTJOKL9JhIGLSD+Cwfm5s3c9fmu8i4mWGMdGTx+bJtiCdjGGCaGsQhIiIiMpIpAZMhcSR6hId3PUzY18dGprcE7ABN8SYe3PHgEEU28i1ZkiaVOvn9yss9ysuVgImIiIiMZErAZEis3b+WoJ3bkAi/5Wdb6zai6WiRoxodampcxo1z+q2CxeOwZElKBx+LiIiIjHBKwKTo4pk4ezv2DugQZNMweebgM0WManT56EfjAGR66cyMx2HqVIdzz82hTCYiIiIiw0oJmBTdoa5DJJzEgB7jt/wc7DpYpIhGn7Iyjy9/uYvZs9M4jkcsBrEY+Hweq1cn+fjHY5h6NouIiIiMeJqCKEUXz8QHVP06ToM4egqHPT784QSZDESjxlsHMXtqOxQREREZRZSASdGV+ktxvYGPRveZviJEM/rZNhq2Mcw8z6Mz3UkykyRoByn1lw53SCIiIjJKKAGTomsoaSBi93OKcC/imThnjT+rSBGJ5MdxHV468hKvNL5Ca6IVFxcTk8pgJcvrl7OifgWWaQ13mCIiIjKCadeIFJ1t2syunD2glkKf6WNF/YoiRiUyMGk3zV2b7+KJ/U+QdJKEfWFKfCWEfWGSTpLH9z3OL7f8krSbHu5QRUREZARTAiZD4oJJF+B6Lp538ta5eCbOsvpl+C3/EEQmkpt737yXY/FjhOxQrx8P2SGOxY5x35v3DXFkIiIiMpooAZMhURYo45PzPknKTfW7HyyWibGgegEXTrpwCKMT6V9TrIld7btO+qaAz/Kxo30HzfHmIYpMRERERhslYDJkJpZO5AunfYGGkgZSTop4Jk7GzZByUkTTUQJWgEumXMLlMy7H0Gg/GUGePPBkzgeJB60gTx14qrgBiYiIyKilIRwypKpD1Xx87seJZ+JsbtpMa7KV2spaao1aJpRMUOI1xDwPXnjBR1OTyfz5FjNnOsMd0ojUkmjBMnIbrmGZFi2JliJHJCIiIqOVEjAZFiE7xPJxywGoqamhqalpmCM6NW3davPYYyFqaw02bgzzjW90EQ5rxP27eQzse+J4SmRFRESkd2pBFDmFNTWZ2Hb2MOdMBuJxVSB7E/FFchogA9kzwkp8JUWOSEREREYrJWAip7AzzkhRVeWSTMJpp6Wpqhr4gdmngnMmnEM0E83pvrF0jHMbzi1yRCIiIjJaqQVR5BQWDMKXvhSlujpEc3NiuMMZsaaVTaM6WE08E8c0+n7fyvVcqkPVTCmdMoTRiYiIyGiiCphILzpSHexu383BroMDOkB6tNLsk/4ZhsEn530SAMftfX/X8ds/Oe+TGiYjIiIifVIFTOQdGqONPLz74e7Ey8Oj1F/KaTWncfGUi/utfsjYVh4o58bTb+RPu/7E7vbdpN00tmnjuA62aTO9fDqXTr+UiC8y3KGKiIjICKYETOQtR6NH+fnmnxMwA4R94R4f29C4gaZYEx+f93ElYaewiC/CR+d8lEQmwba2bXQmOykNlDK7YnbO54SJiIjIqU0JmMhbHtjxAAEz0Gv7WMAKsLN9J5uaN7GoZtEwRCcjSdAO6t+BiIiI5EVv5YsAjbFGGmON/e7difgi/O3w34YwKhEREREZa5SAiQD7OvZhmVa/9zEMg7Zk29AEJCIiIiJjkloQRQDLtHI6aNdA0+1Gq7SbzlY5MagP15804RYREREpBiVgIsCsilknHR3ueR71kfohikgKxfM8/rr3r7ze9DrRVBQMKPGVsKJ+BasnrtbIeBERERlSSsBEgBJ/CVPLpnKw6yC22fvTIpqOsrph9RBHdmpKu2me3P8kW5q3kHASROwIi+sWc86EcwY8hfKhnQ/x9+a/E7JDlAXKum9/9uCzpN0075nynkKHLyIiItIn7QETectHZn2EkB0i6SR73O55HtF0lPMmnsfkssnDFN2pI+NmuGvzXbx85GUcz8Fn+ki5KZ4+8DT3brs3p1bR47pSXWxs2kjIDp3wsZAvxPqj60lkEoUMX0RERKRfSsBE3hKyQ3xh0RdYXr8c0zCJZ+IkMgkqghV8dM5HOX/S+cMd4ilh/ZH1NEYbCdiBHreH7BDbWrexu2N3ztfa0Lih371eGTfDGy1v5B2riIiIyECpBVHkHQJWgIunXMzFUy4m42YwDVMHLw+xvzf/nZDvxIoVQMSO8OLhF5lePj2nayWcBJbRdwJmGRYJRxUwERERGTp6ZSnSB9u0lXwNg7ST7vNjhmH0+/F3m1M5p98WQ8dzmFk+c0DxiYiIiAyGXl2KyIhSEazA9dxeP5Z20tSF63K+1uTSydSEanq9XsbN0FDSQE24Ju9YRURERAZKCZiIjCjnTzyfeCbe68cyXoZVDatyvpZhGHxy3ifxmT6i6Sie53UPVSnxlfDxuR8vVNgiIiIiOdEeMBEpGM/zaE+1Yxs2Jf6SvK4xoWQC75v6Pv66968YhkHACpDIJLBMi6tmXzXg65YFyvjqkq+ypXkLm5s3Y2Bwet3pzK6YrTPAREREZMgpARMZIp7njekX/C2JFn77xm9pSbRgYDCpdBIfm/sxAlbg5A9+lxXjVrCwZiEvHn6RlkQL4yPjWV6/HL/lzys20zBZWLOQhTUL83q8iIiISKEoARMpsg2NG3j6wNPE0jHqwnVcNesqKoIVwx0WkE0KNzdvJtmepNasZXJp/uec/deb/0U8EyfiiwBwJHaEh3Y+xFWzr8rreiE7pNH/IiIiMuZoD5hIER2LHeNPO/+E67kE7SBtyTZ+8+ZvhjssIJt83bvtXh7Y8QDP7n+WX27+Jc8fej6va3WlumiON/eYGukzfezr3FeocEVERETGBCVgIkX00pGXCNlvn2llGiYt8Ra6Ul3DGFXWoeghtrZsJeKL4Lf8RHwRnj7wdJ8TCPtjmVavI/ttU0V2ERERkXdSAiZSRFXBKtJuz3OrLNPCZ/mGKaK3daW7MOi5J83xHDJuZsDXCtkhZpTPIOWkum+LZ+KcXnP6oOMUERERGUuUgIkU0dL6pYTsECkn1T3+fFHNorwGUxy3u303P934U3699dd9jmvPxcSSifhNP57nAeC4DtXB6rwHXVw5+0oWVi/EMiz8lp/VE1ezeuLqvOMTERERGYvUHyRSRAErwJdO/xLPHHyGlngLC2oWsLB6cJP4HtjxAJ7n0ZZs4+FdD+c95CLii3DtvGv5464/4nkeNeEarpl9Td5x2abNZTMuy/vxIiIiIqcCJWAiRRayQ1w85eKCXc/1XEzDxMDA8ZxBXWty2WRuWnwTNTU1NDU1FShCEREREemLWhBFRpnLpl9GwApQFajiA1M/MNzhiIiIiMgAqAImMsrMrZrL3Kq5wx2GiIiIiORBFTAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIbImDqI+bXXXuOuu+7CdV0uvPBCLr/88uEOSUREREREpNuYqYC5rsudd97J//yf/5NbbrmF5557jgMHDgx3WCIiIiIiIt3GTAK2Y8cOxo0bR319PbZts3LlStavXz/cYYmIiIiIiHQbMwlYS0sL1dXV3X+vrq6mpaVlGCMSERERERHpaczsAfM874TbDMM44bbHH3+cxx9/HIB//dd/paampuixSf9s29Y6DDOtwcigdRgZtA7DT2swMmgdRgatw9gzZhKw6upqmpubu//e3NxMZWXlCfe76KKLuOiii7r/3tTUNCTxSd9qamq0DsNMazAyaB1GBq3D8NMajAxah5FB6zD8JkyYUNDrjZkWxBkzZnD48GEaGxvJZDI8//zzLF++fLjDEhERERER6TZmKmCWZXHDDTfwve99D9d1Of/885k0adJwhyUiIiIiItJtzCRgAEuXLmXp0qXDHYaIiIiIiEivDK+36RUiIiIiIiJScGNmD1g+vvnNbw53CILWYSTQGowMWoeRQesw/LQGI4PWYWTQOgy/Qq/BKZ2AiYiIiIiIDCUlYCIiIiIiIkPklE7A3nkemAwfrcPw0xqMDFqHkUHrMPy0BiOD1mFk0DoMv0KvgYZwiIiIiIiIDJFTugImIiIiIiIylMbUOWB33HEHGzZsoLy8nJtvvhmAPXv28LOf/YxEIkFtbS1f/epXCYfDPPPMMzz00EPdj923bx//9m//xtSpU/m//+//m9bWVvx+PwDf+ta3KC8vH5avaTQayDpkMhl+8pOfsHv3blzX5bzzzuPDH/4wALt27eL2228nlUqxZMkSrr/+egzDGM4vbVQp1Dro+ZC/ga7Bf/zHf7Bz505M0+S6665jwYIFgJ4Lg1WoddBzIX9NTU3cfvvttLW1YRgGF110Ee9///vp6urilltu4dixY9TW1vL1r3+dkpISAB588EHWrl2LaZpcf/31LF68GNDzYTAKuQ56PuRvoOvQ2dnJ97//fXbs2MGaNWv4zGc+030tPR/yU8g1yOu54I0hmzdv9nbu3Ol94xvf6L7tm9/8prd582bP8zzviSee8O65554THrd3717vy1/+cvffv/Od73g7duwofsBj1EDW4ZlnnvFuueUWz/M8L5FIeDfeeKN39OjR7se8+eabnuu63ve+9z1vw4YNQ/uFjHKFWgc9H/I3kDX4y1/+4t1+++2e53leW1ub94//+I+e4zjdj9FzIX+FWgc9F/LX0tLi7dy50/M8z4vFYt5Xv/pVb//+/d6vf/1r78EHH/Q8z/MefPBB79e//rXneZ63f/9+77//9//upVIp7+jRo95XvvIVPR8KoJDroOdD/ga6DvF43Nu6dav36KOPej//+c97XEvPh/wUcg3yeS6MqRbE+fPnd79jc9yhQ4eYN28eAIsWLeJvf/vbCY979tlnOeecc4YkxlPBQNchkUjgOA6pVArbtgmHw7S2thKPx5k9ezaGYXDeeeexfv36If06RrtCrIMMzkDW4MCBAyxcuBCA8vJyIpEIu3bt0nOhAAqxDjI4lZWVTJ8+HYBQKERDQwMtLS2sX7+e1atXA7B69eruf9vr169n5cqV+Hw+6urqGDduHDt27NDzYZAKtQ4yOANdh2AwyNy5c7srLMfp+ZC/Qq1BvsZUAtabSZMm8fLLLwPw4osv0tzcfMJ9XnjhhRMSsDvuuIP/8T/+B/fffz+e5pQMWl/rcNZZZxEMBvn85z/PjTfeyGWXXUZJSQktLS1UV1d3P766upqWlpZhiX0sGeg6HKfnQ+H0tQZTp07l5ZdfxnEcGhsb2bVrF01NTXouFMlA1+E4PRcGr7Gxkd27dzNz5kza29uprKwEsi+IOjo6AE74d19VVUVLS4ueDwU0mHU4Ts+HwctlHfqi50NhDGYNjhvoc2FM7QHrzZe+9CXuuusu7r//fpYvX45t9/ySt2/fjt/vZ/Lkyd23ffWrX6Wqqop4PM7NN9/MunXrurNhyU9f67Bjxw5M0+SnP/0p0WiUb3/725x22mn6QV4kA12H+vp6PR8KrK81OP/88zlw4ADf/OY3qa2tZc6cOViWpedCkQx0HUC/GwohkUhw8803c9111/VbZe/r372eD4Ux2HUAPR8KIdd16IueD4M32DWA/J4LYz4Ba2ho4Fvf+haQbTnZsGFDj48/99xzJ1S/qqqqgGxJ8txzz2XHjh36oTJIfa3Ds88+y+LFi7Ftm/LycubMmcPOnTuZN29ej2plc3Nz97pI/ga6DvX19Xo+FFhfa2BZFtddd133/b71rW8xfvx4IpGIngtFMNB1AP1uGKxMJsPNN9/MqlWrOPPMM4Fsm2drayuVlZW0trZSVlYGZN/Jf+e/+5aWFqqqqk64Xc+HgSvEOoCeD4M1kHXoi54Pg1OINYD8ngtjvgWxvb0dANd1eeCBB3jPe97T/THXdXnxxRd7JGCO43SXGzOZDK+88gqTJk0a2qDHoL7Woaamhk2bNuF5HolEgu3bt9PQ0EBlZSWhUIht27bheR7r1q1j+fLlw/kljAkDXQc9HwqvrzVIJpMkEgkANm7ciGVZTJw4Uc+FIhnoOui5MDie5/GTn/yEhoYGLr300u7bly9fztNPPw3A008/zYoVK7pvf/7550mn0zQ2NnL48GFmzpyp58MgFWod9HwYnIGuQ1/0fMhfodYg3+fCmDqI+Qc/+AFbtmyhs7OT8vJyrr76ahKJBI8++igAZ5xxBh//+Me7x3Nu3ryZ3/72t3zve9/rvkYikeA73/kOjuPgui6nnXYan/70pzHNMZ+rFsxA1iGRSHDHHXdw4MABPM/j/PPP54Mf/CAAO3fu5I477iCVSrF48WJuuOEGjVYdgEKsg54PgzOQNWhsbOR73/sepmlSVVXFF7/4RWprawE9FwarEOug58LgvPHGG3z7299m8uTJ3f92P/axjzFr1ixuueUWmpqaqKmp4Rvf+Eb3/tMHHniAJ598svs4gCVLlgB6PgxGodZBz4fByWcdvvzlLxOLxchkMkQiEb71rW8xceJEPR/yVKg1qKmpyeu5MKYSMBERERERkZFMb1WIiIiIiIgMESVgIiIiIiIiQ0QJmIiIiIiIyBBRAiYiIiIiIjJElICJiIiIiIgMESVgIiIiIiIiQ0QJmIiIjFm33nord9xxR4/btmzZwg033EBra+swRSUiIqcyJWAiIjJmXX/99bz66qts3LgRgFQqxU9/+lM+9alPUVlZOejrO44z6GuIiMipRQcxi4jImPbCCy9w9913c/PNN/PAAw+wZ88errzySn71q19x4MABamtrue6661iwYAEATz75JA899BDNzc2UlZXxoQ99iPe85z0AbN68mR/96Edccskl/J//839YtGgRN91003B+eSIiMsrYwx2AiIhIMZ199tk8//zz/PCHP+TNN9/k3/7t3/inf/onvvKVr7B48WI2bdrEzTffzA9+8APKysooLy/nn/7pn6ivr2fr1q38f//f/8eMGTOYPn06AG1tbXR1dXHHHXeg9zBFRGSg1IIoIiJj3mc+8xk2bdrEwmwDcgAAAZlJREFUlVdeyXPPPceSJUtYunQppmmyaNEiZsyYwYYNGwBYunQp48aNwzAM5s+fz6JFi3jjjTe6r2UYBldffTU+nw+/3z9cX5KIiIxSqoCJiMiYV1FRQVlZGRMnTuSll17ixRdf5JVXXun+uOM43S2Ir776Kvfffz+HDh3C8zySySSTJ0/uvm9ZWZkSLxERyZsSMBEROaVUV1ezatUqvvjFL57wsXQ6zc0338xXvvIVli9fjm3b/Pu//3uP+xiGMVShiojIGKQWRBEROaWsWrWKV155hddeew3XdUmlUmzevJnm5mYymQzpdJqysjIsy+oxQVFERKQQVAETEZFTSk1NDf/4j//I3XffzQ9/+ENM02TmzJl87nOfIxQKcf3113PLLbeQTqdZtmwZy5cvH+6QRURkDNEYehERERERkSGiFkQREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEh8v8D7Meqr1iA6iwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\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"
   ]
  },
  {
   "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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2021-05-29        | 2.6     | Weiqing Wang | Fixed typos and code smells.       |\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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