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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb
{
"cells": [
{
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
},
{
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely <a href='http://cocl.us/PY0101EN_DV0101EN_LAB1_Coursera'>**Python for Data Science**</a> and <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which are part of this specialization. \n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which is also part of this specialization. \n",
"\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 *pandas*](#0)<br>\n",
"1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
"1.2 [*pandas* Basics](#4) <br>\n",
"1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
"2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
"\n",
"*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
]
},
{
"cell_type": "markdown",
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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},
"source": [
"Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
"\n",
"The dataset contains annual data on the flows of international immigrants 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. The current version presents data pertaining to 45 countries.\n",
"\n",
"In this lab, we will focus on the Canadian immigration data.\n",
"\n",
"<img src = \"https://ibm.box.com/shared/static/mb48k9fiylkd7z3a21cq38xxfy1wni2y.png\" align=\"center\" width=900>\n",
"\n",
"For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"new_sheet": false,
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},
"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"collapsed": true,
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"run_control": {
"read_only": false
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"scrolled": false
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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,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we are ready to read in our data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print ('Data read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
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{
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" <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",
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" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" 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()\n",
"# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
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" 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>Type</th>\n",
" <th>Coverage</th>\n",
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" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
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" <th>2013</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>190</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Viet Nam</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>920</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>...</td>\n",
" <td>1816</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",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Immigrants</td>\n",
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" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
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" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>922</td>\n",
" <td>Western Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>124</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zambia</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>91</td>\n",
" <td>77</td>\n",
" <td>71</td>\n",
" <td>64</td>\n",
" <td>60</td>\n",
" <td>102</td>\n",
" <td>69</td>\n",
" <td>46</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Zimbabwe</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>910</td>\n",
" <td>Eastern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>615</td>\n",
" <td>454</td>\n",
" <td>663</td>\n",
" <td>611</td>\n",
" <td>508</td>\n",
" <td>494</td>\n",
" <td>434</td>\n",
" <td>437</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"190 Immigrants Foreigners Viet Nam 935 Asia 920 \n",
"191 Immigrants Foreigners Western Sahara 903 Africa 912 \n",
"192 Immigrants Foreigners Yemen 935 Asia 922 \n",
"193 Immigrants Foreigners Zambia 903 Africa 910 \n",
"194 Immigrants Foreigners Zimbabwe 903 Africa 910 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"190 South-Eastern Asia 902 Developing regions 1191 ... 1816 1852 3153 \n",
"191 Northern Africa 902 Developing regions 0 ... 0 0 1 \n",
"192 Western Asia 902 Developing regions 1 ... 124 161 140 \n",
"193 Eastern Africa 902 Developing regions 11 ... 56 91 77 \n",
"194 Eastern Africa 902 Developing regions 72 ... 1450 615 454 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"190 2574 1784 2171 1942 1723 1731 2112 \n",
"191 0 0 0 0 0 0 0 \n",
"192 122 133 128 211 160 174 217 \n",
"193 71 64 60 102 69 46 59 \n",
"194 663 611 508 494 434 437 407 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 195 entries, 0 to 194\n",
"Data columns (total 43 columns):\n",
"Type 195 non-null object\n",
"Coverage 195 non-null object\n",
"OdName 195 non-null object\n",
"AREA 195 non-null int64\n",
"AreaName 195 non-null object\n",
"REG 195 non-null int64\n",
"RegName 195 non-null object\n",
"DEV 195 non-null int64\n",
"DevName 195 non-null object\n",
"1980 195 non-null int64\n",
"1981 195 non-null int64\n",
"1982 195 non-null int64\n",
"1983 195 non-null int64\n",
"1984 195 non-null int64\n",
"1985 195 non-null int64\n",
"1986 195 non-null int64\n",
"1987 195 non-null int64\n",
"1988 195 non-null int64\n",
"1989 195 non-null int64\n",
"1990 195 non-null int64\n",
"1991 195 non-null int64\n",
"1992 195 non-null int64\n",
"1993 195 non-null int64\n",
"1994 195 non-null int64\n",
"1995 195 non-null int64\n",
"1996 195 non-null int64\n",
"1997 195 non-null int64\n",
"1998 195 non-null int64\n",
"1999 195 non-null int64\n",
"2000 195 non-null int64\n",
"2001 195 non-null int64\n",
"2002 195 non-null int64\n",
"2003 195 non-null int64\n",
"2004 195 non-null int64\n",
"2005 195 non-null int64\n",
"2006 195 non-null int64\n",
"2007 195 non-null int64\n",
"2008 195 non-null int64\n",
"2009 195 non-null int64\n",
"2010 195 non-null int64\n",
"2011 195 non-null int64\n",
"2012 195 non-null int64\n",
"2013 195 non-null int64\n",
"dtypes: int64(37), object(6)\n",
"memory usage: 65.6+ KB\n"
]
}
],
"source": [
"df_can.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the list of column headers we can call upon the dataframe's `.columns` parameter."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
" 'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
" 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
" 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
" 2010, 2011, 2012, 2013], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns.values "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Similarly, to get the list of indicies we use the `.index` parameter."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.index.values"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The default type of index and columns is NOT list."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.indexes.base.Index'>\n",
"<class 'pandas.core.indexes.range.RangeIndex'>\n"
]
}
],
"source": [
"print(type(df_can.columns))\n",
"print(type(df_can.index))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To get the index and columns as lists, we can use the `tolist()` method."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"<class 'list'>\n"
]
}
],
"source": [
"df_can.columns.tolist()\n",
"df_can.index.tolist()\n",
"\n",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To view the dimensions of the dataframe, we use the `.shape` parameter."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# size of dataframe (rows, columns)\n",
"df_can.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: The main types stored in *pandas* objects are *float*, *int*, *bool*, *datetime64[ns]* and *datetime64[ns, tz] (in >= 0.17.0)*, *timedelta[ns]*, *category (in >= 0.15.0)*, and *object* (string). In addition these dtypes have item sizes, e.g. int64 and int32. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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>OdName</th>\n",
" <th>AreaName</th>\n",
" <th>RegName</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>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>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</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>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>...</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",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 'Country', 'Continent', 'Region', 'DevName', 1980,\n",
" 1981, 1982, 1983, 1984, 1985,\n",
" 1986, 1987, 1988, 1989, 1990,\n",
" 1991, 1992, 1993, 1994, 1995,\n",
" 1996, 1997, 1998, 1999, 2000,\n",
" 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010,\n",
" 2011, 2012, 2013],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
"df_can.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"df_can['Total'] = df_can.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can check to see how many null objects we have in the dataset as follows:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Country 0\n",
"Continent 0\n",
"Region 0\n",
"DevName 0\n",
"1980 0\n",
"1981 0\n",
"1982 0\n",
"1983 0\n",
"1984 0\n",
"1985 0\n",
"1986 0\n",
"1987 0\n",
"1988 0\n",
"1989 0\n",
"1990 0\n",
"1991 0\n",
"1992 0\n",
"1993 0\n",
"1994 0\n",
"1995 0\n",
"1996 0\n",
"1997 0\n",
"1998 0\n",
"1999 0\n",
"2000 0\n",
"2001 0\n",
"2002 0\n",
"2003 0\n",
"2004 0\n",
"2005 0\n",
"2006 0\n",
"2007 0\n",
"2008 0\n",
"2009 0\n",
"2010 0\n",
"2011 0\n",
"2012 0\n",
"2013 0\n",
"Total 0\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Finally, let's view a quick summary of each column in our dataframe using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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>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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>...</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" <td>195.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>508.394872</td>\n",
" <td>566.989744</td>\n",
" <td>534.723077</td>\n",
" <td>387.435897</td>\n",
" <td>376.497436</td>\n",
" <td>358.861538</td>\n",
" <td>441.271795</td>\n",
" <td>691.133333</td>\n",
" <td>714.389744</td>\n",
" <td>843.241026</td>\n",
" <td>...</td>\n",
" <td>1320.292308</td>\n",
" <td>1266.958974</td>\n",
" <td>1191.820513</td>\n",
" <td>1246.394872</td>\n",
" <td>1275.733333</td>\n",
" <td>1420.287179</td>\n",
" <td>1262.533333</td>\n",
" <td>1313.958974</td>\n",
" <td>1320.702564</td>\n",
" <td>32867.451282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1949.588546</td>\n",
" <td>2152.643752</td>\n",
" <td>1866.997511</td>\n",
" <td>1204.333597</td>\n",
" <td>1198.246371</td>\n",
" <td>1079.309600</td>\n",
" <td>1225.576630</td>\n",
" <td>2109.205607</td>\n",
" <td>2443.606788</td>\n",
" <td>2555.048874</td>\n",
" <td>...</td>\n",
" <td>4425.957828</td>\n",
" <td>3926.717747</td>\n",
" <td>3443.542409</td>\n",
" <td>3694.573544</td>\n",
" <td>3829.630424</td>\n",
" <td>4462.946328</td>\n",
" <td>4030.084313</td>\n",
" <td>4247.555161</td>\n",
" <td>4237.951988</td>\n",
" <td>91785.498686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>28.500000</td>\n",
" <td>25.000000</td>\n",
" <td>31.000000</td>\n",
" <td>31.000000</td>\n",
" <td>36.000000</td>\n",
" <td>40.500000</td>\n",
" <td>37.500000</td>\n",
" <td>42.500000</td>\n",
" <td>45.000000</td>\n",
" <td>952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.000000</td>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" <td>26.000000</td>\n",
" <td>34.000000</td>\n",
" <td>44.000000</td>\n",
" <td>...</td>\n",
" <td>210.000000</td>\n",
" <td>218.000000</td>\n",
" <td>198.000000</td>\n",
" <td>205.000000</td>\n",
" <td>214.000000</td>\n",
" <td>211.000000</td>\n",
" <td>179.000000</td>\n",
" <td>233.000000</td>\n",
" <td>213.000000</td>\n",
" <td>5018.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>251.500000</td>\n",
" <td>295.500000</td>\n",
" <td>275.000000</td>\n",
" <td>173.000000</td>\n",
" <td>181.000000</td>\n",
" <td>197.000000</td>\n",
" <td>254.000000</td>\n",
" <td>434.000000</td>\n",
" <td>409.000000</td>\n",
" <td>508.500000</td>\n",
" <td>...</td>\n",
" <td>832.000000</td>\n",
" <td>842.000000</td>\n",
" <td>899.000000</td>\n",
" <td>934.500000</td>\n",
" <td>888.000000</td>\n",
" <td>932.000000</td>\n",
" <td>772.000000</td>\n",
" <td>783.000000</td>\n",
" <td>796.000000</td>\n",
" <td>22239.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22045.000000</td>\n",
" <td>24796.000000</td>\n",
" <td>20620.000000</td>\n",
" <td>10015.000000</td>\n",
" <td>10170.000000</td>\n",
" <td>9564.000000</td>\n",
" <td>9470.000000</td>\n",
" <td>21337.000000</td>\n",
" <td>27359.000000</td>\n",
" <td>23795.000000</td>\n",
" <td>...</td>\n",
" <td>42584.000000</td>\n",
" <td>33848.000000</td>\n",
" <td>28742.000000</td>\n",
" <td>30037.000000</td>\n",
" <td>29622.000000</td>\n",
" <td>38617.000000</td>\n",
" <td>36765.000000</td>\n",
" <td>34315.000000</td>\n",
" <td>34129.000000</td>\n",
" <td>691904.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 508.394872 566.989744 534.723077 387.435897 376.497436 \n",
"std 1949.588546 2152.643752 1866.997511 1204.333597 1198.246371 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 13.000000 10.000000 11.000000 12.000000 13.000000 \n",
"75% 251.500000 295.500000 275.000000 173.000000 181.000000 \n",
"max 22045.000000 24796.000000 20620.000000 10015.000000 10170.000000 \n",
"\n",
" 1985 1986 1987 1988 1989 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 358.861538 441.271795 691.133333 714.389744 843.241026 \n",
"std 1079.309600 1225.576630 2109.205607 2443.606788 2555.048874 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.500000 0.500000 1.000000 1.000000 \n",
"50% 17.000000 18.000000 26.000000 34.000000 44.000000 \n",
"75% 197.000000 254.000000 434.000000 409.000000 508.500000 \n",
"max 9564.000000 9470.000000 21337.000000 27359.000000 23795.000000 \n",
"\n",
" ... 2005 2006 2007 2008 \\\n",
"count ... 195.000000 195.000000 195.000000 195.000000 \n",
"mean ... 1320.292308 1266.958974 1191.820513 1246.394872 \n",
"std ... 4425.957828 3926.717747 3443.542409 3694.573544 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 28.500000 25.000000 31.000000 31.000000 \n",
"50% ... 210.000000 218.000000 198.000000 205.000000 \n",
"75% ... 832.000000 842.000000 899.000000 934.500000 \n",
"max ... 42584.000000 33848.000000 28742.000000 30037.000000 \n",
"\n",
" 2009 2010 2011 2012 2013 \\\n",
"count 195.000000 195.000000 195.000000 195.000000 195.000000 \n",
"mean 1275.733333 1420.287179 1262.533333 1313.958974 1320.702564 \n",
"std 3829.630424 4462.946328 4030.084313 4247.555161 4237.951988 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 40.500000 37.500000 42.500000 45.000000 \n",
"50% 214.000000 211.000000 179.000000 233.000000 213.000000 \n",
"75% 888.000000 932.000000 772.000000 783.000000 796.000000 \n",
"max 29622.000000 38617.000000 36765.000000 34315.000000 34129.000000 \n",
"\n",
" Total \n",
"count 195.000000 \n",
"mean 32867.451282 \n",
"std 91785.498686 \n",
"min 1.000000 \n",
"25% 952.000000 \n",
"50% 5018.000000 \n",
"75% 22239.500000 \n",
"max 691904.000000 \n",
"\n",
"[8 rows x 35 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Select Column\n",
"**There are two ways to filter on a column name:**\n",
"\n",
"Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
"```python\n",
" df.column_name \n",
" (returns series)\n",
"```\n",
"\n",
"Method 2: More robust, and can filter on multiple columns.\n",
"\n",
"```python\n",
" df['column'] \n",
" (returns series)\n",
"```\n",
"\n",
"```python \n",
" df[['column 1', 'column 2']] \n",
" (returns dataframe)\n",
"```\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's try filtering on the list of countries ('Country')."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
"5 Angola\n",
"6 Antigua and Barbuda\n",
"7 Argentina\n",
"8 Armenia\n",
"9 Australia\n",
"10 Austria\n",
"11 Azerbaijan\n",
"12 Bahamas\n",
"13 Bahrain\n",
"14 Bangladesh\n",
"15 Barbados\n",
"16 Belarus\n",
"17 Belgium\n",
"18 Belize\n",
"19 Benin\n",
"20 Bhutan\n",
"21 Bolivia (Plurinational State of)\n",
"22 Bosnia and Herzegovina\n",
"23 Botswana\n",
"24 Brazil\n",
"25 Brunei Darussalam\n",
"26 Bulgaria\n",
"27 Burkina Faso\n",
"28 Burundi\n",
"29 Cabo Verde\n",
" ... \n",
"165 Suriname\n",
"166 Swaziland\n",
"167 Sweden\n",
"168 Switzerland\n",
"169 Syrian Arab Republic\n",
"170 Tajikistan\n",
"171 Thailand\n",
"172 The former Yugoslav Republic of Macedonia\n",
"173 Togo\n",
"174 Tonga\n",
"175 Trinidad and Tobago\n",
"176 Tunisia\n",
"177 Turkey\n",
"178 Turkmenistan\n",
"179 Tuvalu\n",
"180 Uganda\n",
"181 Ukraine\n",
"182 United Arab Emirates\n",
"183 United Kingdom of Great Britain and Northern I...\n",
"184 United Republic of Tanzania\n",
"185 United States of America\n",
"186 Uruguay\n",
"187 Uzbekistan\n",
"188 Vanuatu\n",
"189 Venezuela (Bolivarian Republic of)\n",
"190 Viet Nam\n",
"191 Western Sahara\n",
"192 Yemen\n",
"193 Zambia\n",
"194 Zimbabwe\n",
"Name: Country, Length: 195, dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.Country # returns a series"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's try filtering on the list of countries ('OdName') and the data for years: 1980 - 1985."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</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",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Angola</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Argentina</td>\n",
" <td>368</td>\n",
" <td>426</td>\n",
" <td>626</td>\n",
" <td>241</td>\n",
" <td>237</td>\n",
" <td>196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Armenia</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Australia</td>\n",
" <td>702</td>\n",
" <td>639</td>\n",
" <td>484</td>\n",
" <td>317</td>\n",
" <td>317</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Austria</td>\n",
" <td>234</td>\n",
" <td>238</td>\n",
" <td>201</td>\n",
" <td>117</td>\n",
" <td>127</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Azerbaijan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Bahamas</td>\n",
" <td>26</td>\n",
" <td>23</td>\n",
" <td>38</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Bahrain</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Bangladesh</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Barbados</td>\n",
" <td>372</td>\n",
" <td>376</td>\n",
" <td>299</td>\n",
" <td>244</td>\n",
" <td>265</td>\n",
" <td>285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Belarus</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Belgium</td>\n",
" <td>511</td>\n",
" <td>540</td>\n",
" <td>519</td>\n",
" <td>297</td>\n",
" <td>183</td>\n",
" <td>181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Belize</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>13</td>\n",
" <td>21</td>\n",
" <td>37</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Benin</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bhutan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Bolivia (Plurinational State of)</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>42</td>\n",
" <td>49</td>\n",
" <td>38</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Bosnia and Herzegovina</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Botswana</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Brazil</td>\n",
" <td>211</td>\n",
" <td>220</td>\n",
" <td>192</td>\n",
" <td>139</td>\n",
" <td>145</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Brunei Darussalam</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Bulgaria</td>\n",
" <td>24</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>33</td>\n",
" <td>11</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Burkina Faso</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Burundi</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Cabo Verde</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>Suriname</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Swaziland</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>Sweden</td>\n",
" <td>281</td>\n",
" <td>308</td>\n",
" <td>222</td>\n",
" <td>176</td>\n",
" <td>128</td>\n",
" <td>158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Switzerland</td>\n",
" <td>806</td>\n",
" <td>811</td>\n",
" <td>634</td>\n",
" <td>370</td>\n",
" <td>326</td>\n",
" <td>314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>Syrian Arab Republic</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Tajikistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>Thailand</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>The former Yugoslav Republic of Macedonia</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>Togo</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>Tonga</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>Trinidad and Tobago</td>\n",
" <td>958</td>\n",
" <td>947</td>\n",
" <td>972</td>\n",
" <td>766</td>\n",
" <td>606</td>\n",
" <td>699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Tunisia</td>\n",
" <td>58</td>\n",
" <td>51</td>\n",
" <td>55</td>\n",
" <td>46</td>\n",
" <td>51</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>Turkey</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>Turkmenistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>Tuvalu</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>Uganda</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>38</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>Ukraine</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>United Kingdom of Great Britain and Northern I...</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",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>United Republic of Tanzania</td>\n",
" <td>635</td>\n",
" <td>832</td>\n",
" <td>621</td>\n",
" <td>474</td>\n",
" <td>473</td>\n",
" <td>460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>United States of America</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",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>Uruguay</td>\n",
" <td>128</td>\n",
" <td>132</td>\n",
" <td>146</td>\n",
" <td>105</td>\n",
" <td>90</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>Uzbekistan</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>Vanuatu</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>Venezuela (Bolivarian Republic of)</td>\n",
" <td>103</td>\n",
" <td>117</td>\n",
" <td>174</td>\n",
" <td>124</td>\n",
" <td>142</td>\n",
" <td>165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>Viet Nam</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",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>Western Sahara</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>Yemen</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Zambia</td>\n",
" <td>11</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>Zimbabwe</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
" <td>32</td>\n",
" <td>29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>195 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Country 1980 1981 1982 \\\n",
"0 Afghanistan 16 39 39 \n",
"1 Albania 1 0 0 \n",
"2 Algeria 80 67 71 \n",
"3 American Samoa 0 1 0 \n",
"4 Andorra 0 0 0 \n",
"5 Angola 1 3 6 \n",
"6 Antigua and Barbuda 0 0 0 \n",
"7 Argentina 368 426 626 \n",
"8 Armenia 0 0 0 \n",
"9 Australia 702 639 484 \n",
"10 Austria 234 238 201 \n",
"11 Azerbaijan 0 0 0 \n",
"12 Bahamas 26 23 38 \n",
"13 Bahrain 0 2 1 \n",
"14 Bangladesh 83 84 86 \n",
"15 Barbados 372 376 299 \n",
"16 Belarus 0 0 0 \n",
"17 Belgium 511 540 519 \n",
"18 Belize 16 27 13 \n",
"19 Benin 2 5 4 \n",
"20 Bhutan 0 0 0 \n",
"21 Bolivia (Plurinational State of) 44 52 42 \n",
"22 Bosnia and Herzegovina 0 0 0 \n",
"23 Botswana 10 1 3 \n",
"24 Brazil 211 220 192 \n",
"25 Brunei Darussalam 79 6 8 \n",
"26 Bulgaria 24 20 12 \n",
"27 Burkina Faso 2 1 3 \n",
"28 Burundi 0 0 0 \n",
"29 Cabo Verde 1 1 2 \n",
".. ... ... ... ... \n",
"165 Suriname 15 10 21 \n",
"166 Swaziland 4 1 1 \n",
"167 Sweden 281 308 222 \n",
"168 Switzerland 806 811 634 \n",
"169 Syrian Arab Republic 315 419 409 \n",
"170 Tajikistan 0 0 0 \n",
"171 Thailand 56 53 113 \n",
"172 The former Yugoslav Republic of Macedonia 0 0 0 \n",
"173 Togo 5 5 2 \n",
"174 Tonga 2 4 7 \n",
"175 Trinidad and Tobago 958 947 972 \n",
"176 Tunisia 58 51 55 \n",
"177 Turkey 481 874 706 \n",
"178 Turkmenistan 0 0 0 \n",
"179 Tuvalu 0 1 0 \n",
"180 Uganda 13 16 17 \n",
"181 Ukraine 0 0 0 \n",
"182 United Arab Emirates 0 2 2 \n",
"183 United Kingdom of Great Britain and Northern I... 22045 24796 20620 \n",
"184 United Republic of Tanzania 635 832 621 \n",
"185 United States of America 9378 10030 9074 \n",
"186 Uruguay 128 132 146 \n",
"187 Uzbekistan 0 0 0 \n",
"188 Vanuatu 0 0 0 \n",
"189 Venezuela (Bolivarian Republic of) 103 117 174 \n",
"190 Viet Nam 1191 1829 2162 \n",
"191 Western Sahara 0 0 0 \n",
"192 Yemen 1 2 1 \n",
"193 Zambia 11 17 11 \n",
"194 Zimbabwe 72 114 102 \n",
"\n",
" 1983 1984 1985 \n",
"0 47 71 340 \n",
"1 0 0 0 \n",
"2 69 63 44 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 6 4 3 \n",
"6 0 42 52 \n",
"7 241 237 196 \n",
"8 0 0 0 \n",
"9 317 317 319 \n",
"10 117 127 165 \n",
"11 0 0 0 \n",
"12 12 21 28 \n",
"13 1 1 3 \n",
"14 81 98 92 \n",
"15 244 265 285 \n",
"16 0 0 0 \n",
"17 297 183 181 \n",
"18 21 37 26 \n",
"19 3 4 3 \n",
"20 0 1 0 \n",
"21 49 38 44 \n",
"22 0 0 0 \n",
"23 3 7 4 \n",
"24 139 145 130 \n",
"25 2 2 4 \n",
"26 33 11 24 \n",
"27 2 3 2 \n",
"28 0 1 2 \n",
"29 0 11 1 \n",
".. ... ... ... \n",
"165 12 5 16 \n",
"166 0 10 7 \n",
"167 176 128 158 \n",
"168 370 326 314 \n",
"169 269 264 385 \n",
"170 0 0 0 \n",
"171 65 82 66 \n",
"172 0 0 0 \n",
"173 3 6 5 \n",
"174 1 2 5 \n",
"175 766 606 699 \n",
"176 46 51 57 \n",
"177 280 338 202 \n",
"178 0 0 0 \n",
"179 0 1 0 \n",
"180 38 32 29 \n",
"181 0 0 0 \n",
"182 1 2 0 \n",
"183 10015 10170 9564 \n",
"184 474 473 460 \n",
"185 7100 6661 6543 \n",
"186 105 90 92 \n",
"187 0 0 0 \n",
"188 0 0 0 \n",
"189 124 142 165 \n",
"190 3404 7583 5907 \n",
"191 0 0 0 \n",
"192 6 0 18 \n",
"193 7 16 9 \n",
"194 44 32 29 \n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 16,
"metadata": {},
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],
"source": [
"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\n",
"# notice that 'Country' is string, and the years are integers. \n",
"# for the sake of consistency, we will convert all column names to string later on."
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"### Select Row\n",
"\n",
"There are main 3 ways to select rows:\n",
"\n",
"```python\n",
" df.loc[label] \n",
" #filters by the labels of the index/column\n",
" df.iloc[index] \n",
" #filters by the positions of the index/column\n",
"```"
]
},
{
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"source": [
"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
"\n",
"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
]
},
{
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"source": [
"df_can.set_index('Country', inplace=True)\n",
"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()"
]
},
{
"cell_type": "code",
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" <th>Total</th>\n",
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" <tr>\n",
" <th>Country</th>\n",
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" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
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" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
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" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Country ... \n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
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{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</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",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 1982 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 39 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 71 \n",
"\n",
" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\n",
"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \n",
"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \n",
"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"Algeria 4325 3774 4331 69439 \n",
"\n",
"[3 rows x 38 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None\n",
"df_can.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
" 1. The full row data (all columns)\n",
" 2. For year 2013\n",
" 3. For years 1980 to 1985"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n",
"Continent Asia\n",
"Region Eastern Asia\n",
"DevName Developed regions\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"1986 248\n",
"1987 422\n",
"1988 324\n",
"1989 494\n",
"1990 379\n",
"1991 506\n",
"1992 605\n",
"1993 907\n",
"1994 956\n",
"1995 826\n",
"1996 994\n",
"1997 924\n",
"1998 897\n",
"1999 1083\n",
"2000 1010\n",
"2001 1092\n",
"2002 806\n",
"2003 817\n",
"2004 973\n",
"2005 1067\n",
"2006 1212\n",
"2007 1250\n",
"2008 1284\n",
"2009 1194\n",
"2010 1168\n",
"2011 1265\n",
"2012 1214\n",
"2013 982\n",
"Total 27707\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 1. the full row data (all columns)\n",
"print(df_can.loc['Japan'])\n",
"\n",
"# alternate methods\n",
"print(df_can.iloc[87])\n",
"print(df_can[df_can.index == 'Japan'].T.squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"982\n",
"982\n"
]
}
],
"source": [
"# 2. for year 2013\n",
"print(df_can.loc['Japan', 2013])\n",
"\n",
"# alternate method\n",
"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1984 246\n",
"Name: Japan, dtype: object\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246\n",
"1985 198\n",
"Name: Japan, dtype: object\n"
]
}
],
"source": [
"# 3. for years 1980 to 1985\n",
"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\n",
"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
"\n",
"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n"
]
},
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"[print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Filtering based on a criteria\n",
"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
"\n",
"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Afghanistan True\n",
"Albania False\n",
"Algeria False\n",
"American Samoa False\n",
"Andorra False\n",
"Angola False\n",
"Antigua and Barbuda False\n",
"Argentina False\n",
"Armenia True\n",
"Australia False\n",
"Austria False\n",
"Azerbaijan True\n",
"Bahamas False\n",
"Bahrain True\n",
"Bangladesh True\n",
"Barbados False\n",
"Belarus False\n",
"Belgium False\n",
"Belize False\n",
"Benin False\n",
"Bhutan True\n",
"Bolivia (Plurinational State of) False\n",
"Bosnia and Herzegovina False\n",
"Botswana False\n",
"Brazil False\n",
"Brunei Darussalam True\n",
"Bulgaria False\n",
"Burkina Faso False\n",
"Burundi False\n",
"Cabo Verde False\n",
" ... \n",
"Suriname False\n",
"Swaziland False\n",
"Sweden False\n",
"Switzerland False\n",
"Syrian Arab Republic True\n",
"Tajikistan True\n",
"Thailand True\n",
"The former Yugoslav Republic of Macedonia False\n",
"Togo False\n",
"Tonga False\n",
"Trinidad and Tobago False\n",
"Tunisia False\n",
"Turkey True\n",
"Turkmenistan True\n",
"Tuvalu False\n",
"Uganda False\n",
"Ukraine False\n",
"United Arab Emirates True\n",
"United Kingdom of Great Britain and Northern Ireland False\n",
"United Republic of Tanzania False\n",
"United States of America False\n",
"Uruguay False\n",
"Uzbekistan True\n",
"Vanuatu False\n",
"Venezuela (Bolivarian Republic of) False\n",
"Viet Nam True\n",
"Western Sahara False\n",
"Yemen True\n",
"Zambia False\n",
"Zimbabwe False\n",
"Name: Continent, Length: 195, dtype: bool\n"
]
}
],
"source": [
"# 1. create the condition boolean series\n",
"condition = df_can['Continent'] == 'Asia'\n",
"print (condition)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</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",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>224</td>\n",
" <td>218</td>\n",
" <td>198</td>\n",
" <td>205</td>\n",
" <td>267</td>\n",
" <td>252</td>\n",
" <td>236</td>\n",
" <td>258</td>\n",
" <td>207</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>359</td>\n",
" <td>236</td>\n",
" <td>203</td>\n",
" <td>125</td>\n",
" <td>165</td>\n",
" <td>209</td>\n",
" <td>138</td>\n",
" <td>161</td>\n",
" <td>57</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>35</td>\n",
" <td>28</td>\n",
" <td>21</td>\n",
" <td>39</td>\n",
" <td>32</td>\n",
" <td>475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>83</td>\n",
" <td>84</td>\n",
" <td>86</td>\n",
" <td>81</td>\n",
" <td>98</td>\n",
" <td>92</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>4171</td>\n",
" <td>4014</td>\n",
" <td>2897</td>\n",
" <td>2939</td>\n",
" <td>2104</td>\n",
" <td>4721</td>\n",
" <td>2694</td>\n",
" <td>2640</td>\n",
" <td>3789</td>\n",
" <td>65568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>36</td>\n",
" <td>865</td>\n",
" <td>1464</td>\n",
" <td>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei Darussalam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>79</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>33</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>370</td>\n",
" <td>529</td>\n",
" <td>460</td>\n",
" <td>354</td>\n",
" <td>203</td>\n",
" <td>200</td>\n",
" <td>196</td>\n",
" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</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>China, Hong Kong Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>729</td>\n",
" <td>712</td>\n",
" <td>674</td>\n",
" <td>897</td>\n",
" <td>657</td>\n",
" <td>623</td>\n",
" <td>591</td>\n",
" <td>728</td>\n",
" <td>774</td>\n",
" <td>9327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China, Macao Special Administrative Region</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>21</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>33</td>\n",
" <td>29</td>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>132</td>\n",
" <td>128</td>\n",
" <td>84</td>\n",
" <td>46</td>\n",
" <td>46</td>\n",
" <td>43</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic People's Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>45</td>\n",
" <td>97</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>114</td>\n",
" <td>125</td>\n",
" <td>132</td>\n",
" <td>112</td>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>139</td>\n",
" <td>147</td>\n",
" <td>125</td>\n",
" <td>2068</td>\n",
" </tr>\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>Indonesia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>186</td>\n",
" <td>178</td>\n",
" <td>252</td>\n",
" <td>115</td>\n",
" <td>123</td>\n",
" <td>100</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>632</td>\n",
" <td>613</td>\n",
" <td>657</td>\n",
" <td>661</td>\n",
" <td>504</td>\n",
" <td>712</td>\n",
" <td>390</td>\n",
" <td>395</td>\n",
" <td>387</td>\n",
" <td>13150</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>Iraq</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>262</td>\n",
" <td>245</td>\n",
" <td>260</td>\n",
" <td>380</td>\n",
" <td>428</td>\n",
" <td>231</td>\n",
" <td>265</td>\n",
" <td>...</td>\n",
" <td>2226</td>\n",
" <td>1788</td>\n",
" <td>2406</td>\n",
" <td>3543</td>\n",
" <td>5450</td>\n",
" <td>5941</td>\n",
" <td>6196</td>\n",
" <td>4041</td>\n",
" <td>4918</td>\n",
" <td>69789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1403</td>\n",
" <td>1711</td>\n",
" <td>1334</td>\n",
" <td>541</td>\n",
" <td>446</td>\n",
" <td>680</td>\n",
" <td>1212</td>\n",
" <td>...</td>\n",
" <td>2446</td>\n",
" <td>2625</td>\n",
" <td>2401</td>\n",
" <td>2562</td>\n",
" <td>2316</td>\n",
" <td>2755</td>\n",
" <td>1970</td>\n",
" <td>2134</td>\n",
" <td>1945</td>\n",
" <td>66508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developed regions</td>\n",
" <td>701</td>\n",
" <td>756</td>\n",
" <td>598</td>\n",
" <td>309</td>\n",
" <td>246</td>\n",
" <td>198</td>\n",
" <td>248</td>\n",
" <td>...</td>\n",
" <td>1067</td>\n",
" <td>1212</td>\n",
" <td>1250</td>\n",
" <td>1284</td>\n",
" <td>1194</td>\n",
" <td>1168</td>\n",
" <td>1265</td>\n",
" <td>1214</td>\n",
" <td>982</td>\n",
" <td>27707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>177</td>\n",
" <td>160</td>\n",
" <td>155</td>\n",
" <td>113</td>\n",
" <td>102</td>\n",
" <td>179</td>\n",
" <td>181</td>\n",
" <td>...</td>\n",
" <td>1940</td>\n",
" <td>1827</td>\n",
" <td>1421</td>\n",
" <td>1581</td>\n",
" <td>1235</td>\n",
" <td>1831</td>\n",
" <td>1635</td>\n",
" <td>1206</td>\n",
" <td>1255</td>\n",
" <td>35406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>506</td>\n",
" <td>408</td>\n",
" <td>436</td>\n",
" <td>394</td>\n",
" <td>431</td>\n",
" <td>377</td>\n",
" <td>381</td>\n",
" <td>462</td>\n",
" <td>348</td>\n",
" <td>8490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>66</td>\n",
" <td>35</td>\n",
" <td>62</td>\n",
" <td>53</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>58</td>\n",
" <td>73</td>\n",
" <td>48</td>\n",
" <td>2025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>173</td>\n",
" <td>161</td>\n",
" <td>135</td>\n",
" <td>168</td>\n",
" <td>173</td>\n",
" <td>157</td>\n",
" <td>159</td>\n",
" <td>278</td>\n",
" <td>123</td>\n",
" <td>2353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lao People's Democratic Republic</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>32</td>\n",
" <td>39</td>\n",
" <td>54</td>\n",
" <td>22</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>1089</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>Malaysia</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>786</td>\n",
" <td>816</td>\n",
" <td>813</td>\n",
" <td>448</td>\n",
" <td>384</td>\n",
" <td>374</td>\n",
" <td>425</td>\n",
" <td>...</td>\n",
" <td>593</td>\n",
" <td>580</td>\n",
" <td>600</td>\n",
" <td>658</td>\n",
" <td>640</td>\n",
" <td>802</td>\n",
" <td>409</td>\n",
" <td>358</td>\n",
" <td>204</td>\n",
" <td>24417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</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>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>82</td>\n",
" <td>59</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>103</td>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>62</td>\n",
" <td>46</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>210</td>\n",
" <td>953</td>\n",
" <td>1887</td>\n",
" <td>975</td>\n",
" <td>1153</td>\n",
" <td>556</td>\n",
" <td>368</td>\n",
" <td>193</td>\n",
" <td>262</td>\n",
" <td>9245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>607</td>\n",
" <td>540</td>\n",
" <td>511</td>\n",
" <td>581</td>\n",
" <td>561</td>\n",
" <td>1392</td>\n",
" <td>1129</td>\n",
" <td>1185</td>\n",
" <td>1308</td>\n",
" <td>10222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>224</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>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>Qatar</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>157</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>Saudi Arabia</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>198</td>\n",
" <td>252</td>\n",
" <td>188</td>\n",
" <td>249</td>\n",
" <td>246</td>\n",
" <td>330</td>\n",
" <td>278</td>\n",
" <td>286</td>\n",
" <td>267</td>\n",
" <td>3425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>241</td>\n",
" <td>301</td>\n",
" <td>337</td>\n",
" <td>169</td>\n",
" <td>128</td>\n",
" <td>139</td>\n",
" <td>205</td>\n",
" <td>...</td>\n",
" <td>392</td>\n",
" <td>298</td>\n",
" <td>690</td>\n",
" <td>734</td>\n",
" <td>366</td>\n",
" <td>805</td>\n",
" <td>219</td>\n",
" <td>146</td>\n",
" <td>141</td>\n",
" <td>14579</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>State of Palestine</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>453</td>\n",
" <td>627</td>\n",
" <td>441</td>\n",
" <td>481</td>\n",
" <td>400</td>\n",
" <td>654</td>\n",
" <td>555</td>\n",
" <td>533</td>\n",
" <td>462</td>\n",
" <td>6512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syrian Arab Republic</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>315</td>\n",
" <td>419</td>\n",
" <td>409</td>\n",
" <td>269</td>\n",
" <td>264</td>\n",
" <td>385</td>\n",
" <td>493</td>\n",
" <td>...</td>\n",
" <td>1458</td>\n",
" <td>1145</td>\n",
" <td>1056</td>\n",
" <td>919</td>\n",
" <td>917</td>\n",
" <td>1039</td>\n",
" <td>1005</td>\n",
" <td>650</td>\n",
" <td>1009</td>\n",
" <td>31485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>46</td>\n",
" <td>44</td>\n",
" <td>15</td>\n",
" <td>50</td>\n",
" <td>52</td>\n",
" <td>47</td>\n",
" <td>34</td>\n",
" <td>39</td>\n",
" <td>503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>56</td>\n",
" <td>53</td>\n",
" <td>113</td>\n",
" <td>65</td>\n",
" <td>82</td>\n",
" <td>66</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>575</td>\n",
" <td>500</td>\n",
" <td>487</td>\n",
" <td>519</td>\n",
" <td>512</td>\n",
" <td>499</td>\n",
" <td>396</td>\n",
" <td>296</td>\n",
" <td>400</td>\n",
" <td>9174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>481</td>\n",
" <td>874</td>\n",
" <td>706</td>\n",
" <td>280</td>\n",
" <td>338</td>\n",
" <td>202</td>\n",
" <td>257</td>\n",
" <td>...</td>\n",
" <td>2065</td>\n",
" <td>1638</td>\n",
" <td>1463</td>\n",
" <td>1122</td>\n",
" <td>1238</td>\n",
" <td>1492</td>\n",
" <td>1257</td>\n",
" <td>1068</td>\n",
" <td>729</td>\n",
" <td>31781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkmenistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>26</td>\n",
" <td>37</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>14</td>\n",
" <td>310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>31</td>\n",
" <td>42</td>\n",
" <td>37</td>\n",
" <td>33</td>\n",
" <td>37</td>\n",
" <td>86</td>\n",
" <td>60</td>\n",
" <td>54</td>\n",
" <td>46</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>Asia</td>\n",
" <td>Central Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>330</td>\n",
" <td>262</td>\n",
" <td>284</td>\n",
" <td>215</td>\n",
" <td>288</td>\n",
" <td>289</td>\n",
" <td>162</td>\n",
" <td>235</td>\n",
" <td>167</td>\n",
" <td>3368</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>Yemen</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>161</td>\n",
" <td>140</td>\n",
" <td>122</td>\n",
" <td>133</td>\n",
" <td>128</td>\n",
" <td>211</td>\n",
" <td>160</td>\n",
" <td>174</td>\n",
" <td>217</td>\n",
" <td>2985</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region \\\n",
"Afghanistan Asia Southern Asia \n",
"Armenia Asia Western Asia \n",
"Azerbaijan Asia Western Asia \n",
"Bahrain Asia Western Asia \n",
"Bangladesh Asia Southern Asia \n",
"Bhutan Asia Southern Asia \n",
"Brunei Darussalam Asia South-Eastern Asia \n",
"Cambodia Asia South-Eastern Asia \n",
"China Asia Eastern Asia \n",
"China, Hong Kong Special Administrative Region Asia Eastern Asia \n",
"China, Macao Special Administrative Region Asia Eastern Asia \n",
"Cyprus Asia Western Asia \n",
"Democratic People's Republic of Korea Asia Eastern Asia \n",
"Georgia Asia Western Asia \n",
"India Asia Southern Asia \n",
"Indonesia Asia South-Eastern Asia \n",
"Iran (Islamic Republic of) Asia Southern Asia \n",
"Iraq Asia Western Asia \n",
"Israel Asia Western Asia \n",
"Japan Asia Eastern Asia \n",
"Jordan Asia Western Asia \n",
"Kazakhstan Asia Central Asia \n",
"Kuwait Asia Western Asia \n",
"Kyrgyzstan Asia Central Asia \n",
"Lao People's Democratic Republic Asia South-Eastern Asia \n",
"Lebanon Asia Western Asia \n",
"Malaysia Asia South-Eastern Asia \n",
"Maldives Asia Southern Asia \n",
"Mongolia Asia Eastern Asia \n",
"Myanmar Asia South-Eastern Asia \n",
"Nepal Asia Southern Asia \n",
"Oman Asia Western Asia \n",
"Pakistan Asia Southern Asia \n",
"Philippines Asia South-Eastern Asia \n",
"Qatar Asia Western Asia \n",
"Republic of Korea Asia Eastern Asia \n",
"Saudi Arabia Asia Western Asia \n",
"Singapore Asia South-Eastern Asia \n",
"Sri Lanka Asia Southern Asia \n",
"State of Palestine Asia Western Asia \n",
"Syrian Arab Republic Asia Western Asia \n",
"Tajikistan Asia Central Asia \n",
"Thailand Asia South-Eastern Asia \n",
"Turkey Asia Western Asia \n",
"Turkmenistan Asia Central Asia \n",
"United Arab Emirates Asia Western Asia \n",
"Uzbekistan Asia Central Asia \n",
"Viet Nam Asia South-Eastern Asia \n",
"Yemen Asia Western Asia \n",
"\n",
" DevName 1980 \\\n",
"Afghanistan Developing regions 16 \n",
"Armenia Developing regions 0 \n",
"Azerbaijan Developing regions 0 \n",
"Bahrain Developing regions 0 \n",
"Bangladesh Developing regions 83 \n",
"Bhutan Developing regions 0 \n",
"Brunei Darussalam Developing regions 79 \n",
"Cambodia Developing regions 12 \n",
"China Developing regions 5123 \n",
"China, Hong Kong Special Administrative Region Developing regions 0 \n",
"China, Macao Special Administrative Region Developing regions 0 \n",
"Cyprus Developing regions 132 \n",
"Democratic People's Republic of Korea Developing regions 1 \n",
"Georgia Developing regions 0 \n",
"India Developing regions 8880 \n",
"Indonesia Developing regions 186 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Iraq Developing regions 262 \n",
"Israel Developing regions 1403 \n",
"Japan Developed regions 701 \n",
"Jordan Developing regions 177 \n",
"Kazakhstan Developing regions 0 \n",
"Kuwait Developing regions 1 \n",
"Kyrgyzstan Developing regions 0 \n",
"Lao People's Democratic Republic Developing regions 11 \n",
"Lebanon Developing regions 1409 \n",
"Malaysia Developing regions 786 \n",
"Maldives Developing regions 0 \n",
"Mongolia Developing regions 0 \n",
"Myanmar Developing regions 80 \n",
"Nepal Developing regions 1 \n",
"Oman Developing regions 0 \n",
"Pakistan Developing regions 978 \n",
"Philippines Developing regions 6051 \n",
"Qatar Developing regions 0 \n",
"Republic of Korea Developing regions 1011 \n",
"Saudi Arabia Developing regions 0 \n",
"Singapore Developing regions 241 \n",
"Sri Lanka Developing regions 185 \n",
"State of Palestine Developing regions 0 \n",
"Syrian Arab Republic Developing regions 315 \n",
"Tajikistan Developing regions 0 \n",
"Thailand Developing regions 56 \n",
"Turkey Developing regions 481 \n",
"Turkmenistan Developing regions 0 \n",
"United Arab Emirates Developing regions 0 \n",
"Uzbekistan Developing regions 0 \n",
"Viet Nam Developing regions 1191 \n",
"Yemen Developing regions 1 \n",
"\n",
" 1981 1982 1983 1984 1985 \\\n",
"Afghanistan 39 39 47 71 340 \n",
"Armenia 0 0 0 0 0 \n",
"Azerbaijan 0 0 0 0 0 \n",
"Bahrain 2 1 1 1 3 \n",
"Bangladesh 84 86 81 98 92 \n",
"Bhutan 0 0 0 1 0 \n",
"Brunei Darussalam 6 8 2 2 4 \n",
"Cambodia 19 26 33 10 7 \n",
"China 6682 3308 1863 1527 1816 \n",
"China, Hong Kong Special Administrative Region 0 0 0 0 0 \n",
"China, Macao Special Administrative Region 0 0 0 0 0 \n",
"Cyprus 128 84 46 46 43 \n",
"Democratic People's Republic of Korea 1 3 1 4 3 \n",
"Georgia 0 0 0 0 0 \n",
"India 8670 8147 7338 5704 4211 \n",
"Indonesia 178 252 115 123 100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \n",
"Iraq 245 260 380 428 231 \n",
"Israel 1711 1334 541 446 680 \n",
"Japan 756 598 309 246 198 \n",
"Jordan 160 155 113 102 179 \n",
"Kazakhstan 0 0 0 0 0 \n",
"Kuwait 0 8 2 1 4 \n",
"Kyrgyzstan 0 0 0 0 0 \n",
"Lao People's Democratic Republic 6 16 16 7 17 \n",
"Lebanon 1119 1159 789 1253 1683 \n",
"Malaysia 816 813 448 384 374 \n",
"Maldives 0 0 1 0 0 \n",
"Mongolia 0 0 0 0 0 \n",
"Myanmar 62 46 31 41 23 \n",
"Nepal 1 6 1 2 4 \n",
"Oman 0 0 8 0 0 \n",
"Pakistan 972 1201 900 668 514 \n",
"Philippines 5921 5249 4562 3801 3150 \n",
"Qatar 0 0 0 0 0 \n",
"Republic of Korea 1456 1572 1081 847 962 \n",
"Saudi Arabia 0 1 4 1 2 \n",
"Singapore 301 337 169 128 139 \n",
"Sri Lanka 371 290 197 1086 845 \n",
"State of Palestine 0 0 0 0 0 \n",
"Syrian Arab Republic 419 409 269 264 385 \n",
"Tajikistan 0 0 0 0 0 \n",
"Thailand 53 113 65 82 66 \n",
"Turkey 874 706 280 338 202 \n",
"Turkmenistan 0 0 0 0 0 \n",
"United Arab Emirates 2 2 1 2 0 \n",
"Uzbekistan 0 0 0 0 0 \n",
"Viet Nam 1829 2162 3404 7583 5907 \n",
"Yemen 2 1 6 0 18 \n",
"\n",
" 1986 ... 2005 2006 \\\n",
"Afghanistan 496 ... 3436 3009 \n",
"Armenia 0 ... 224 218 \n",
"Azerbaijan 0 ... 359 236 \n",
"Bahrain 0 ... 12 12 \n",
"Bangladesh 486 ... 4171 4014 \n",
"Bhutan 0 ... 5 10 \n",
"Brunei Darussalam 12 ... 4 5 \n",
"Cambodia 8 ... 370 529 \n",
"China 1960 ... 42584 33518 \n",
"China, Hong Kong Special Administrative Region 0 ... 729 712 \n",
"China, Macao Special Administrative Region 0 ... 21 32 \n",
"Cyprus 48 ... 7 9 \n",
"Democratic People's Republic of Korea 0 ... 14 10 \n",
"Georgia 0 ... 114 125 \n",
"India 7150 ... 36210 33848 \n",
"Indonesia 127 ... 632 613 \n",
"Iran (Islamic Republic of) 1794 ... 5837 7480 \n",
"Iraq 265 ... 2226 1788 \n",
"Israel 1212 ... 2446 2625 \n",
"Japan 248 ... 1067 1212 \n",
"Jordan 181 ... 1940 1827 \n",
"Kazakhstan 0 ... 506 408 \n",
"Kuwait 4 ... 66 35 \n",
"Kyrgyzstan 0 ... 173 161 \n",
"Lao People's Democratic Republic 21 ... 42 74 \n",
"Lebanon 2576 ... 3709 3802 \n",
"Malaysia 425 ... 593 580 \n",
"Maldives 0 ... 0 0 \n",
"Mongolia 0 ... 59 64 \n",
"Myanmar 18 ... 210 953 \n",
"Nepal 13 ... 607 540 \n",
"Oman 0 ... 14 18 \n",
"Pakistan 691 ... 14314 13127 \n",
"Philippines 4166 ... 18139 18400 \n",
"Qatar 1 ... 11 2 \n",
"Republic of Korea 1208 ... 5832 6215 \n",
"Saudi Arabia 5 ... 198 252 \n",
"Singapore 205 ... 392 298 \n",
"Sri Lanka 1838 ... 4930 4714 \n",
"State of Palestine 0 ... 453 627 \n",
"Syrian Arab Republic 493 ... 1458 1145 \n",
"Tajikistan 0 ... 85 46 \n",
"Thailand 78 ... 575 500 \n",
"Turkey 257 ... 2065 1638 \n",
"Turkmenistan 0 ... 40 26 \n",
"United Arab Emirates 5 ... 31 42 \n",
"Uzbekistan 0 ... 330 262 \n",
"Viet Nam 2741 ... 1852 3153 \n",
"Yemen 7 ... 161 140 \n",
"\n",
" 2007 2008 2009 2010 \\\n",
"Afghanistan 2652 2111 1746 1758 \n",
"Armenia 198 205 267 252 \n",
"Azerbaijan 203 125 165 209 \n",
"Bahrain 22 9 35 28 \n",
"Bangladesh 2897 2939 2104 4721 \n",
"Bhutan 7 36 865 1464 \n",
"Brunei Darussalam 11 10 5 12 \n",
"Cambodia 460 354 203 200 \n",
"China 27642 30037 29622 30391 \n",
"China, Hong Kong Special Administrative Region 674 897 657 623 \n",
"China, Macao Special Administrative Region 16 12 21 21 \n",
"Cyprus 4 7 6 18 \n",
"Democratic People's Republic of Korea 7 19 11 45 \n",
"Georgia 132 112 128 126 \n",
"India 28742 28261 29456 34235 \n",
"Indonesia 657 661 504 712 \n",
"Iran (Islamic Republic of) 6974 6475 6580 7477 \n",
"Iraq 2406 3543 5450 5941 \n",
"Israel 2401 2562 2316 2755 \n",
"Japan 1250 1284 1194 1168 \n",
"Jordan 1421 1581 1235 1831 \n",
"Kazakhstan 436 394 431 377 \n",
"Kuwait 62 53 68 67 \n",
"Kyrgyzstan 135 168 173 157 \n",
"Lao People's Democratic Republic 53 32 39 54 \n",
"Lebanon 3467 3566 3077 3432 \n",
"Malaysia 600 658 640 802 \n",
"Maldives 2 1 7 4 \n",
"Mongolia 82 59 118 169 \n",
"Myanmar 1887 975 1153 556 \n",
"Nepal 511 581 561 1392 \n",
"Oman 16 10 7 14 \n",
"Pakistan 10124 8994 7217 6811 \n",
"Philippines 19837 24887 28573 38617 \n",
"Qatar 5 9 6 18 \n",
"Republic of Korea 5920 7294 5874 5537 \n",
"Saudi Arabia 188 249 246 330 \n",
"Singapore 690 734 366 805 \n",
"Sri Lanka 4123 4756 4547 4422 \n",
"State of Palestine 441 481 400 654 \n",
"Syrian Arab Republic 1056 919 917 1039 \n",
"Tajikistan 44 15 50 52 \n",
"Thailand 487 519 512 499 \n",
"Turkey 1463 1122 1238 1492 \n",
"Turkmenistan 37 13 20 30 \n",
"United Arab Emirates 37 33 37 86 \n",
"Uzbekistan 284 215 288 289 \n",
"Viet Nam 2574 1784 2171 1942 \n",
"Yemen 122 133 128 211 \n",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Armenia 236 258 207 3310 \n",
"Azerbaijan 138 161 57 2649 \n",
"Bahrain 21 39 32 475 \n",
"Bangladesh 2694 2640 3789 65568 \n",
"Bhutan 1879 1075 487 5876 \n",
"Brunei Darussalam 6 3 6 600 \n",
"Cambodia 196 233 288 6538 \n",
"China 28502 33024 34129 659962 \n",
"China, Hong Kong Special Administrative Region 591 728 774 9327 \n",
"China, Macao Special Administrative Region 13 33 29 284 \n",
"Cyprus 6 12 16 1126 \n",
"Democratic People's Republic of Korea 97 66 17 388 \n",
"Georgia 139 147 125 2068 \n",
"India 27509 30933 33087 691904 \n",
"Indonesia 390 395 387 13150 \n",
"Iran (Islamic Republic of) 7479 7534 11291 175923 \n",
"Iraq 6196 4041 4918 69789 \n",
"Israel 1970 2134 1945 66508 \n",
"Japan 1265 1214 982 27707 \n",
"Jordan 1635 1206 1255 35406 \n",
"Kazakhstan 381 462 348 8490 \n",
"Kuwait 58 73 48 2025 \n",
"Kyrgyzstan 159 278 123 2353 \n",
"Lao People's Democratic Republic 22 25 15 1089 \n",
"Lebanon 3072 1614 2172 115359 \n",
"Malaysia 409 358 204 24417 \n",
"Maldives 3 1 1 30 \n",
"Mongolia 103 68 99 952 \n",
"Myanmar 368 193 262 9245 \n",
"Nepal 1129 1185 1308 10222 \n",
"Oman 10 13 11 224 \n",
"Pakistan 7468 11227 12603 241600 \n",
"Philippines 36765 34315 29544 511391 \n",
"Qatar 3 14 6 157 \n",
"Republic of Korea 4588 5316 4509 142581 \n",
"Saudi Arabia 278 286 267 3425 \n",
"Singapore 219 146 141 14579 \n",
"Sri Lanka 3309 3338 2394 148358 \n",
"State of Palestine 555 533 462 6512 \n",
"Syrian Arab Republic 1005 650 1009 31485 \n",
"Tajikistan 47 34 39 503 \n",
"Thailand 396 296 400 9174 \n",
"Turkey 1257 1068 729 31781 \n",
"Turkmenistan 20 20 14 310 \n",
"United Arab Emirates 60 54 46 836 \n",
"Uzbekistan 162 235 167 3368 \n",
"Viet Nam 1723 1731 2112 97146 \n",
"Yemen 160 174 217 2985 \n",
"\n",
"[49 rows x 38 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# we can pass mutliple criteria in the same line. \n",
"# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
"\n",
"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
"\n",
"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
"# don't forget to enclose the two conditions in parentheses"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed: let's review the changes we have made to our dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"print ('data dimensions:', df_can.shape)\n",
"print(df_can.columns)\n",
"df_can.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"---\n",
"# Visualizing Data using Matplotlib<a id=\"8\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
"\n",
"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/). As mentioned on their website: \n",
">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
"\n",
"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Matplotlib.Pyplot\n",
"\n",
"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [],
"source": [
"# we are using the inline backend\n",
"%matplotlib inline \n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: check if Matplotlib is loaded."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.0.2\n"
]
}
],
"source": [
"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*optional: apply a style to Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Solarize_Light2', '_classic_test', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'tableau-colorblind10']\n"
]
}
],
"source": [
"print(plt.style.available)\n",
"mpl.style.use(['ggplot']) # optional: for ggplot-like style"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Plotting in *pandas*\n",
"\n",
"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
"\n",
"Documentation:\n",
"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**What is a line plot and why use it?**\n",
"\n",
"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Let's start with a case study:**\n",
"\n",
"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
"\n",
"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"First, we will extract the data series for Haiti."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"1980 1666\n",
"1981 3692\n",
"1982 3498\n",
"1983 2860\n",
"1984 1418\n",
"Name: Haiti, dtype: object"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\n",
"haiti.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7faaea6c6320>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show() # need this line to show the updates made to the figure"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot(kind='line')\n",
"\n",
"plt.title('Immigration from Haiti')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"# annotate the 2010 Earthquake. \n",
"# syntax: plt.text(x, y, label)\n",
"plt.text(2002, 6000, '2010 Earthquake') # see note below\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
"\n",
"Quick note on x and y values in `plt.text(x, y, label)`:\n",
" \n",
" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
" \n",
"```python\n",
" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
"```python\n",
" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
"```\n",
" We will cover advanced annotation methods in later modules."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \n",
"\n",
"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data set for China and India, and display dataframe."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>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>India</th>\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>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</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",
" </tr>\n",
" <tr>\n",
" <th>China</th>\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>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"#haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='line')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That doesn't look right...\n",
"\n",
"Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>India</th>\n",
" <th>China</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8880</td>\n",
" <td>5123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>8670</td>\n",
" <td>6682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>8147</td>\n",
" <td>3308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>7338</td>\n",
" <td>1863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>5704</td>\n",
" <td>1527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" India China\n",
"1980 8880 5123\n",
"1981 8670 6682\n",
"1982 8147 3308\n",
"1983 7338 1863\n",
"1984 5704 1527"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"\n",
"df_CI.plot(kind='line')\n",
"\n",
"plt.title('Immigration from India and China')\n",
"plt.ylabel('Number of immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
"df_CI.plot(kind='line')\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigrants from China and India')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"--> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"From the above plot, we can observe that the China and India have very similar immigration trends through the years. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
"\n",
"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
"```python\n",
"print(type(haiti))\n",
"print(haiti.head(5))\n",
"```\n",
">class 'pandas.core.series.Series' <br>\n",
">1980 1666 <br>\n",
">1981 3692 <br>\n",
">1982 3498 <br>\n",
">1983 2860 <br>\n",
">1984 1418 <br>\n",
">Name: Haiti, dtype: int64 <br>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"1985 4211 1816 9564 \n",
"1986 7150 1960 9470 \n",
"1987 10189 2643 21337 \n",
"1988 11522 2758 27359 \n",
"1989 10343 4323 23795 \n",
"1990 12041 8076 31668 \n",
"1991 13734 14255 23380 \n",
"1992 13673 10846 34123 \n",
"1993 21496 9817 33720 \n",
"1994 18620 13128 39231 \n",
"1995 18489 14398 30145 \n",
"1996 23859 19415 29322 \n",
"1997 22268 20475 22965 \n",
"1998 17241 21049 10367 \n",
"1999 18974 30069 7045 \n",
"2000 28572 35529 8840 \n",
"2001 31223 36434 11728 \n",
"2002 31889 31961 8046 \n",
"2003 27155 36439 6797 \n",
"2004 28235 36619 7533 \n",
"2005 36210 42584 7258 \n",
"2006 33848 33518 7140 \n",
"2007 28742 27642 8216 \n",
"2008 28261 30037 8979 \n",
"2009 29456 29622 8876 \n",
"2010 34235 30391 8724 \n",
"2011 27509 28502 6204 \n",
"2012 30933 33024 6195 \n",
"2013 33087 34129 5827 \n",
"\n",
" Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 \n",
"1985 3150 514 \n",
"1986 4166 691 \n",
"1987 7360 1072 \n",
"1988 8639 1334 \n",
"1989 11865 2261 \n",
"1990 12509 2470 \n",
"1991 12718 3079 \n",
"1992 13670 4071 \n",
"1993 20479 4777 \n",
"1994 19532 4666 \n",
"1995 15864 4994 \n",
"1996 13692 9125 \n",
"1997 11549 13073 \n",
"1998 8735 9068 \n",
"1999 9734 9979 \n",
"2000 10763 15400 \n",
"2001 13836 16708 \n",
"2002 11707 15110 \n",
"2003 12758 13205 \n",
"2004 14004 13399 \n",
"2005 18139 14314 \n",
"2006 18400 13127 \n",
"2007 19837 10124 \n",
"2008 24887 8994 \n",
"2009 28573 7217 \n",
"2010 38617 6811 \n",
"2011 36765 7468 \n",
"2012 34315 11227 \n",
"2013 29544 12603 \n"
]
},
{
"data": {
"image/png": 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bAZg1axbLly/n9OnTXLhwgR9//BFLS0saN25837EKIWoWSayEEKIK+OSTT3jyySd59tlnCQgIIDk5meeff/6eXY/UajUrVqzgwoULtGrViueff54pU6ZQp04ds3JhYWEcOnQIb29vs/FRBbm7u7Nlyxbi4uJo3749oaGh+Pn5sWrVqjI7zvulVqvZtGkT/fv359VXX6VZs2aEhoayZcuWe46PKkij0bB27VosLS3p0qULAwYMYNiwYfdsdfLy8mLz5s2cO3eOtm3bMmjQINq1a8evv/76sIdWpKVLl9KhQweGDBnC448/zunTp9m8eXOh1rnPPvuM2bNn07p1a9atW8fy5ctLfAhuVlYWb7zxBq1bt6Zjx46sXLmSH374gXfffbfEeJo1a0ZUVBQ9e/bk7bffplWrVnTt2pWff/6ZWbNmmcbUzZ07l+HDhzN+/HhatmzJ+vXrWb16tVny+dVXX+Hl5UX37t0ZOHAgQ4cOpWnTpvd9jt59913UajW+vr64urpy/PjxUm/r4ODAnj178PHxYcSIETRt2pThw4dz8uRJvLy8gDs3Oj766CPat2+Pv78/O3fuZN26deX+IG0hRPWnUh6kY7YQQohy17NnTxwdHSs1sRFVS1RUFP7+/hw/ftxsmnEhhBCVTyavEEKIKuD48eMcPnyYjh07otfrWbx4MeHh4WzcuLGyQxNCCCFEKUhiJYQQVYBKpeKbb77htddew2g00rx5c9asWUPfvn0rOzQhhBBClIJ0BRRCCCGEEEKIhySTVwghhBBCCCHEQ5LESgghhBBCCCEekiRWQgghhBBCCPGQavzkFQWfAi8eDS4uLsU+AFVUX3JdH11ybR9Ncl0fTXJdH11ybYtX2ufYSYuVEEIIIYQQQjwkSayEEEIIIYQQ4iFJYiWEEEIIIYQQD6nGj7G6m6IoZGdnYzQaUalUlR2OeAA3btwgJyenssMQZUyua/EURUGtVmNlZSWfW0IIIUQlkcTqLtnZ2VhYWKDVyqmprrRaLRqNprLDEGVMrmvJDAYD2dnZWFtbV3YoQgghRI0kXQHvYjQaJakSQlQ7Wq0Wo9FY2WEIIYQQNZYkVneRbjRCiOpKPr+EEEKIyiOJVRXUpEmT+yofERHB6NGjAdiyZQvz588vj7CEEEIIIYQQxZA+b4+YkJAQQkJCKjsMIYQQQgghahRJrKqwiIgI5syZg6OjI2fPnqVVq1Z8+eWXqFQqwsPDmTlzJk5OTrRs2dK0zbJlyzh27BgfffQRW7ZsYd68eej1ehwdHZk/fz6urq6VeERCCCGEEEI8miSxKoHx1+9RYmPKtE5VPW/Uz4wrdfkTJ06wfft2PDw8GDBgAAcOHKBVq1ZMmzaN5cuX4+3tzcsvv1zktgEBAaxbtw6VSsUvv/zC119/zcyZM8vqUIQQQgghhBB/k8SqimvTpg2enp4A+Pr6Ehsbi42NDfXr16dRo0YADBkyhJ9//rnQtvHx8UycOJGEhAT0ej3169ev0NiFEEIIIYSoKSSxKsH9tCyVF51OZ/pZo9FgMBiA0s3+9f777zN+/HhCQkJM3QqFEEIIIYQQZU9mBayGfHx8uHLlCpcuXQLgt99+K7JcWloaHh4eAKxYsaKiwhNCCCGEEKLGkcSqGrKysuLTTz9l9OjRDBw4EC8vryLLTZ06lQkTJjBo0CCcnJwqOEohhBBCCCFqDpWiKEplB1GZrl27ZvZ7ZmYmNjY2lRSNKAtardbUZVI8OuS63lt1/fxycXHh5s2blR2GKGNyXR9Ncl0fXXJti5c/38G9SIuVEEIIIYQolRp+P16IEkliJYQQQggh7ml/3G1Cvo0kKTO3skMRokqSxEoIIYQQQtzT6lO3yNTncfBqRmWHIkSVJImVEEIIIYQo0cVb2ZxOzALgSHx6JUcjRNUkiZUQQgghhCjRhuhkLDUquvs4c+x6JnlGGWslxN0ksRJCCCGEEMW6nZPHX5fS6O7tQFBTVzJyjUTfzKrssISociSxqoISEhKYOHEinTp1onv37owaNYqff/6Z0aNHF1n+zTffJDo6uoKjFEIIIURNsPVCCvo8hX5Na9O2Xm3UKjgcL+OshLibtrIDEOYURWHs2LEMGzaMb775BoATJ06wdevWYreZPXt2RYUnhBBCiBokz6iwKToFPzdrGjpaYW+lpYmzNUfiMxjZ2rWywxOiSpEWqypmz549WFhYmLVO+fn5ERgYSGZmJuPGjaNr16688sorpmdJDB06lKNHjwLQpEkT/vOf/xAUFERoaCiJiYkAbNmyhdDQUEJCQnj66adNy4UQQgghinPoWjoJGbn0a+ZoWvZ4HVvOJ2WTli0PbReiIGmxKsGCgzeISc4u0zq9Ha14qZ17sevPnj1Ly5Yti1x34sQJtm/fjoeHBwMGDODAgQMEBASYlcnMzOTxxx9n+vTpfPjhhyxZsoQpU6YQEBDAunXrUKlU/PLLL3z99dfMnDmzTI9NCCGEEI+WDdEpOFtrCfSqZVrm72nL0uM3ibqeSdeG9pUYnRBViyRW1UibNm3w9PQEwNfXl9jY2EKJlU6nIzg4GICWLVuya9cuAOLj45k4cSIJCQno9Xrq169fscELIYQQolqJS8shKj6Dka1c0KpVpuU+TlbY6dQcic+QxEqIAiSxKkFJLUvlpWnTpmzYsKHIdTqdzvSzRqPBYCjcBK/ValGpVIXKvP/++4wfP56QkBAiIiKYM2dOOUQvhBBCiEfFpugUtGoVIT61zZZr1Cpae9gSFZ+Boiim7x1C1HQyxqqK6dKlC3q9niVLlpiWRUVFERkZ+VD1pqWl4eHhAcCKFSseqi4hhBBCPNoyc/PYfjGVLvVrUdu68H14/zq23MoycDklpxKiE6JqksSqilGpVCxYsIC//vqLTp060aNHD8LCwnB3f7jWs6lTpzJhwgQGDRqEk5NTGUUrhBBCiEfRzpg0MnONZpNWFOTvaQvAEZl2XQgTlZI/tVwNde3aNbPfMzMzsbGxqaRoRFnQarVFdpMU1Ztc13urrp9fLi4u3Lx5s7LDEGVMrmv1pSgKr26IwVKjZnafBmZd/Qpe11fXX8TRWsu/esm47UeBvGeLlz/Hwb1Ii5UQQgghhDA5fiOT2FQ9/Zs5ljh+yr+OLScTssg2GCswOiGqLkmshBBCCCGEyYboZGpZaujSoFaJ5fw97TAYFU7cyKygyISo2iSxEkIIIYQQACRm5LI/Lp2Qxg7oNCV/TWzhao1Oo5JxVkL8TRIrIYQQQggBwB/nUgDo06ToSSsKstSq8XWzkcRKiL9JYiWEEEIIIdDnGdlyPoX2de1ws7Mo1Tb+dWy5mqYnIT23nKMTouqTxEoIIYQQQrD78m3ScvLoX8wU60V5XKZdF8JEEqsqJjY2lp49e5otCwsL49tvvy1xu6NHj/L+++8DEBERwYEDB+5734GBgdy6davE5ceOHaNDhw6cOHGCLVu2MH/+/PveT1EiIiIYPXp0mdRVGufPnyc4OJiQkBAuXbpkti4jI4Pp06fTqVMnQkJC6NOnj9kDmx/W999/T1ZWVpHrhg4dyhNPPEFwcDDdunXj559/LraeN998k+joaADmzZtXqn2PGjWK1NTU+w/6ARX32g0LC6Nx48Zm07o2adLkvusveNxFvXfKS3HvlZIMHTqUo0ePVtr+hRDiXjZGJ+Nlr6OVe+kf2+Blr8PFRsuR+PRyjEyI6qHwo7TLkdFoZPr06Tg5OTF9+nQSEhL44osvSE9Px9vbm1dffRWtVktubi7z58/n4sWL1KpViylTpuDm5gbAmjVr2L59O2q1mhdeeIE2bdoAEBUVxf/+9z+MRiO9evVi4MCBFXlola5169a0bt0agL1792Jra0v79u3LdB+nTp1i/PjxfPPNN/j5+eHn50dISEiZ7qOi/PHHH/Tu3Zs333yz0Lo333yT+vXrs3v3btRqNUlJSfz666+FyuXl5aHRaO573wsWLGDIkCFYW1sXuX7+/Pm0bt2a5ORkOnfuzPDhw9HpdIX2PXv2bNPvX375Ja+99to997148eL7jre8ODk58d133zFjxoz73lZRFBRFKfVxl4bBYECrffCPxPyY1Gq5XyWEqH6ib2ZxLimbCe3dS5xi/W4qlQr/OrbsuXIbg1FBqy79tkI8aio0sdq4cSN169Y13a3/+eef6d+/P507d+a///0v27dvJyQkhO3bt2Nra8uXX37Jnj17WLJkCa+//jpxcXFEREQwZ84ckpOTmTVrFnPnzgXghx9+4L333sPZ2Zl33nmHdu3a4eXlVZGHVyGGDh2Kv78/ERERpKamEhYWRmBgIBEREXz77bd89NFHLF68GI1Gw6pVq/jwww/x8fFh+vTpXL16FYB//vOftG/fnlu3bjFp0iSSkpJo06YNJT0r+ty5c0yZMoV58+bh7+8PwLJlyzh27BgfffQRU6ZMoVatWhw9epTExERmzJhBaGgoRqORGTNmEBkZSb169VAUhaeffprQ0FDCw8OZOXMmTk5OtGzZ0rSv5ORkpk6dypUrV7CysuLTTz+lRYsWhIWFceXKFRISErh48SIzZ87k8OHDhIeH4+HhwaJFi7CwMO8TfuLECaZPn052djYNGjQgLCyMQ4cOsWDBAjQaDZGRkaxcudJU/tKlS0RFRfHVV1+ZviA7OzszadIkANPrz93dnZMnT7Jjxw5WrVrFwoUL0ev1+Pv78/HHH6PRaJg+fTpHjx4lOzub/v378+abb/LDDz9w48YNhg0bhqOjo9m+75aZmYm1tbUpeWvSpAnjx49n586dfPDBB3z66ae8//77bNiwgezsbIKDg2nWrBnz58/nxRdf5Nq1a+Tk5DB27Fiee+454E5Lx6ZNm8jIyOC5554jICCAgwcP4uHhwcKFCwsle1u2bGHevHno9XocHR2ZP38+rq6uhIWFcfXqVa5cucLVq1d56aWXGDt2LABz585l5cqVeHp64uzsTKtWrYo8vmeeeYbly5fzj3/8A0dH824n3333HcuWLQNgxIgRjBs3jitXrjBixAg6derEoUOH8PX1NTvut99+m7y8PKZNm1bomC5dusSMGTNISkrC2tqazz77DB8fH6ZMmULt2rU5ceIELVu2xM7OrtjjKkpsbCzPPfecKaaFCxdy4cIFZs+ejV6vp0GDBnz++efY2tqabVfUayP/+gwbNoytW7diMBj47rvv8PHxua/3qhBCPIgNZ5Ox1qrp7m1/39v6e9qy9UIq0TezaOFW/R5SLkRZqbBbq0lJSRw+fJhevXoBd+7unjx5kg4dOgDQvXt3U/e1gwcP0r17dwBTtzNFUThw4ACdOnXCwsICNzc3PDw8OH/+POfPn8fDwwN3d3e0Wi2dOnV6oK5wdztxOJOI7bfL9N+Jww//rAeDwcCGDRv45z//yZw5c8zW1atXj1GjRjFu3Di2bt1KYGAgH3zwAePGjWPjxo18//33pi9xn3/+OQEBAWzZsoWQkBBT4lWUF198kQ8//JCAgIBiy9y4cYPffvuNH3/8kY8//hi4k0zHxcWxbds2Zs+ezaFDhwDIzs5m2rRpLFq0iDVr1pCQkGCqJywsDD8/P/7880+mT5/O5MmTTesuX77MTz/9xMKFC3n11Vfp1KkT27Ztw8rKim3bthWKacqUKcyYMYM///yT5s2bM2fOHHr16mU6R3cnNtHR0bRo0aLEVoeoqCjefvttduzYwblz51i7di2//fYbW7duRaPRsHr1agDefvttNm3axJ9//klkZCSnTp1i7NixuLu7s2LFimKTqldeeYWgoCC6du3KlClTTIlVZmYmzZo1Y/369WbX4d1338XKyoqtW7eaumaGhYXxxx9/sHHjRhYuXFhkt7GYmBjGjBlDeHg49vb2bNy4sVCZgIAA1q1bx5YtWxgwYABff/21ad358+dZsmQJGzZsYM6cOeTm5nLs2DHWrl3Lli1bWLBgQYld32xtbXnmmWf44YcfzJYfO3aM5cuXs379etatW8cvv/zCiRMnALhw4QJDhw5ly5YtfP7554WOu7hjeuutt5g1axZ//PEH77//Pu+8845pfxcvXmTZsmXMnDmz2OMqScGYbGxsmDt3LsuWLWPz5s20bt2a//73v4W2KerZrvYTAAAgAElEQVS1kc/JyYnNmzczatQoUzfK+3mvCiHE/UrJMrD7ym16NnbAxuL+e2K0drdFrZJxVkJUWIvVokWLeO6550ytVbdv38bGxsb0pdHJycn05e/WrVs4OzsDoNFosLGx4fbt29y6dctsHEbBbfLL5/987ty5Cjmuslaa5vd+/foB0KpVK+Li4u5ZfteuXaaxOADp6emkp6cTGRnJggULAAgKCqJ27drF1tGlSxeWLl1K9+7di+3+1qdPH9RqNU2bNiUxMRGA/fv3Exoailqtxs3NjU6dOgF3vrzWr1+fRo0aATBkyBDTeKL9+/fz/fffm/abnJxMWloaAD169MDCwoLHHnsMo9FIjx49AGjevDmxsbFm8aSlpZGamkrHjh0BGDZsGBMmTLjn+Spo7ty5rF+/3nRjAKBNmzbUr18fgN27d3P8+HHTNcnOzsbFxQWAdevWsWTJEvLy8rhx4wbnzp2jRYsW99xnflfApKQknnrqKXr06IGXlxcajYb+/fuXKu6FCxeyadMmAK5du0ZMTAxOTk5mZerVq4efnx9w57V09/kDiI+PZ+LEiSQkJKDX603HDdCrVy8sLS2xtLTExcWFxMRE9u3bR58+fUwtX8HBwSXG+eKLLxISEmJ2Xfbv30+fPn2wsblz17Nv377s27ePvn374uXlRdu2bYutr6hjysjI4NChQ2b70Ov1pp9DQ0PNXtNFHZenp2ex+ywY06FDh4iOjmbAgAEA5ObmFhlvSa+Nvn37muLPv4b3814VQoj7teVCCgajQr8mD/bZYmepoYmzNUfiMxjZ2rWMoxOi+qiQxOrQoUM4ODjQqFEjTp48ec/yRXVzUalUxXZ/Ka58Uf7880/+/PNPAP7zn/+YvgTnu3HjhmmcRZuA+28Of1iurq6kpqaajfVITU2lYcOGaLVaVCoV1tbWaLVadDodeXl5aLVaNBoNKpUKrVaLWq1GrVab6lAUhY0bNxbq5pVfvuC+NBpNoXEmKpWK//znP7z11lvMmDHDNLZHo9GY9qNWq01x5e8zP96CsahUKtM+Ci5Xq9WmeO6OQ6VSYWFhUWgfWq3W1PVPq9Wa9pn/e/7+Cy4r7hzle+yxxzh16pRp/dSpU5k6dSre3t6m82xra2sW2/Dhw3nvvffM6rl8+TLfffcdmzdvpnbt2rz22mvk5uaaYijqPN99ftzd3WnVqhVHjx6lYcOGpi/7RZXNPz6APXv2sHv3bjZu3IiNjQ2DBg0yjR/K30aj0WBpaWnaxsLCAr1eXyimDz74gAkTJtCnTx/27NnD7Nmzi7ze+YmJWq02i6m485y/3NnZmcGDB5vGfhX1mskvC2BjY1OoroIxFHVMarUae3t7wsPDC51vtVpNrVq1zPZV1HEV9Z7IP48FY1Kr1XTr1o3vvvuu2Gt79erVEl8b+fXpdDqMRqPZ6/Ze79X8ZLC60Wq11TJuUTK5rtWDwaiw5cJF2tevTZvGde9Zvrjr2qVxJgv3XUFr60Bt69JN1S6qFnnPPrwKSazOnj3LwYMHOXLkCHq9nqysLBYtWkRmZqZpAoBbt26Z7qg7OzuTlJSEs7MzeXl5ZGZmYmdnZ1qer+A2BZcnJSUVGrORLygoiKCgINPvBWclA8jJyXmgCQnKiqWlJW5uboSHh/PEE0+QnJzM9u3befHFFzEYDCiKQl5eHgaDgby8PBRFKfSztbU1aWlpGAwGALp27cqCBQuYOHEicGfckZ+fH4GBgaxYsYIpU6awfft2UlJSTHUXlD8of/78+YwcOZKPP/6YadOmkZeXh9FoxGAwYDQaC21rMBho164dK1asYMiQISQlJREREcGAAQNo2LAhly9f5vz58zRs2JDVq1eb4s+P6/XXXyciIgJHR0esra0xGo2m/RXcB2C2TqvVYjAYsLGxwd7enj179hAYGMiyZcsIDAw0xXt3XXCnxaNVq1Z89NFHvPXWW2g0GrKzs4s8zwCdOnXihRde4KWXXsLFxYXk5GQyMjJITU3F2toaGxsb4uPj2bZtm2nftra2pKam4uDgUOj6F7y+WVlZHD9+nIkTJ5r2VzDegmUtLCzIysrCwsKClJQU7O3t0el0nDlzhkOHDpnK5W+Tl5dX7PkrKDU1FTc3NwwGA7/++qvp2Isqn5eXR0BAAK+//joTJ04kLy/P1KXt7noLbj9u3Dj69etnijG/jn/84x+mmwIFZ/8rWFfB4y7umKytralXrx5r1qzhySefRFEUTp06ha+vb6HXbXHHVdR7oqjz2KZNG6ZPn865c+fw9vYmKyuLa9eu0bhxY9M2KSkpxb42int/l/a9mpOTU+gzrTpwcXGplnGLksl1rR4irqSRmK5nXFvXUl2v4q5rs9oqFGD7yVi6Nqz4G9Pi4cl7tngl9VwpqEISq2effZZnn30WgJMnT7Ju3Tpee+015syZQ2RkJJ07d2bHjh20a9cOgLZt27Jjxw6aNm1KZGQkvr6+qFQq2rVrx7x58wgNDSU5OZn4+Hh8fHxQFIX4+HgSEhJwcnIiIiKizGYKqwxz587l3Xff5V//+hcAb7zxBg0bNiz19sHBwUyYMIHNmzfz4YcfMmvWLN59912CgoJMX9I++eQTXn/9dSZNmkTv3r3p0KEDdeuWfKfK0tKShQsXMmTIEFxdXYud1a6g/v37s3v3bnr27EmjRo3w9/fH3t7eNCnF6NGjcXJyIiAggDNnzpiO94033iAoKAgrKyu++OKLUh/73b744gvT5BX169cvNCatKLNnz2bWrFl07tyZ2rVrY2VlVezMdU2bNuWtt95ixIgRphazjz76iLZt2+Ln50ePHj2oX7++2QyNI0eO5LnnnsPNza3IcVavvPIKVlZW6PV6hg8fXuzkDwWNHDmSoKAgWrZsSVhYGIsXLyYoKIhGjRrx+OOP33P74kydOpUJEybg4eHB448/XmR3wYJatmzJk08+SUhICF5eXgQGBt5zH05OTvTp08fU/bNly5YMGzbM1O1xxIgR+Pn5ce3atULbFjzut99+u9h9zJ8/n3feeYe5c+diMBgYMGAAvr6+94ztfjk7O/P5558zadIkU3fDt956i8aNG5vK+Pr6FvvaKM79vleFEKK0NkSn4GarpZ2n3UPV4+NkhZ1OzZH4DEmsRI2lUip4eqn8xGr69OncuHGj0HTr+d135s+fT0xMDHZ2dkyZMgV3d3cAVq9eTXh4OGq1mueff940Q93hw4f58ccfTeNuBg8eXKp47v6ylpmZaRrbIcpGRkYGtra23Lp1i9DQUH777TfT9PnlIb/FSjxa5LreW3X9/JK7pI8mua5V3+WUHF7bEMOYNq4M9nW+9waUfF0/3XWV04lZLBzU+L6mbBdVg7xni1faFqsKT6yqGkmsyt/QoUNJTU0lNzeXiRMn8vTTT5fr/uQL+KNJruu9VdfPL/lj/miS61r1fbP/OtsvpvLDIB/sLUs3DKKk6/rnhRS+jLzO3H4NaehoVZahigog79niVamugKJmK+lZTUIIIYSoeOn6PMIvptK1oX2pk6p7aVPnzjP7DsdnSGIlaqQKe46VEEKI6ifxei43b5T8LC8hRPWz/WIqOXkK/ZoWPdnXg3CxsaC+g06eZyVqLEmshBBCFMmQq3BobybHDmZVdihCiDJkVBQ2RifT3MWaxk5l27LkX8eWUwlZZBuMZVqvENWBJFZCCCGKdCVGT65eISPdSEZ6XmWHI4QoI1HxGcTfzqV/s7Jrrcrn72mHwahw4kZmmdctRFUniZUQQohCjEaFi9E52Njd+TOReF0mDhHiUbHhbDK1rTR0rFerzOv2dbNGp1FJd0BRI0liVQXVq1eP4OBgevbsyfjx48nKyiI2NpaePXsWWf6zzz7jr7/+Au7MwHf06FEARo0aRWpq6gPF8NNPP7FixYoHOwAhRLUXH5dLVoYR3zbWWNuqSbgu46yEeBTE39Zz6FoGvZvUxkJT9lOi6zRq/NxsJLESNZIkVlWQlZUVW7duZfv27eh0On766acSy0+bNo2uXbsWWr548WIcHBweKIbRo0czbNiwB9pWCFG9KYrChTM52NZS4+6pxc1DS9INA0ZjjX46hxCPhD/OpaBWQW+f2uW2D39PW66m6UlIlxsyomaRxKqKCwgI4NKlSwDk5eUxbdo0evTowYgRI8jKujOgfMqUKaxfv77QtoGBgdy6dYvY2Fi6du3K5MmTCQoKYty4caZtAwMD+eijj+jfvz/9+/cnJiYGgLCwML799lvgTitYfpkuXbqwb98+UzyzZs2iX79+BAUFsXjxYgBu3LjB4MGDTa1u+eWFENVDUoKB1OQ8GjezRKVS4eqhxWCA5JsyzkqI6izHYGTrhRQ61KuFs41Fue3H/+9p16XVqnpQFIXw8HAOHDhADX+87UOT51iV4K+//iIxMbFM63R1dS2ydakoBoOB8PBwunfvDkBMTAxfffUVn332GRMmTGDjxo0MGTKkVHVduHCBsLAw2rdvzxtvvMGPP/7Iyy+/DICdnR0bNmxgxYoVzJw5s8gWMoPBwIYNG9i2bRtz5sxh2bJlLF26lFq1arFx40ZycnIYOHAg3bp1Y+PGjXTr1o3JkyeTl5dnSuKEENXD+TM5WFqp8GqoA8DFzQKVChKu5+LsJn82hKiudl5KI0NvJLQcJq0oyMteh4uNlsPx6fRuUn4tY6JspKSkcPz4cY4fP07jxo3p1asXVlbyHLIHIS1WVVB2djbBwcH07duXunXrMmLECODO2Cs/Pz8AWrVqRWxsbKnr9PT0pH379gAMHjyY/fv3m9YNHDjQ9P+hQ4eK3L5fv36m/cbFxQGwc+dOVq5cSXBwMKGhoSQnJxMTE0ObNm1Yvnw5YWFhnD59Gjs7u/s8A0KIypKWkkfidQMNm1ii+Xv8hYVORW1njUxgIUQ1pigKG84m4+1oyWOu1uW6L5VKhX8dW45dz8QgXYirvPzvdZ06dSImJoalS5dy7dq1So6qepJbjyUobctSWcsfY3U3S0tL088ajYbs7OxS16lSqYr9vbifC9LpdKb9Ggz//8vVhx9+aGpRK2jVqlVs27aNyZMn8/LLL8t4LSGqiQtns9FooWFjndlyNw8Lzp7IJifbiKWV3JMToro5lZjFpZQcJgV6FPu3viz5e9qy9UIq0TezaOFmU+77Ew/u6tWr2Nra0rt3b+rVq8emTZtYtWoVHTp0oG3btqjV8plfWnKmaoirV69y8OBBAH7//XdT6xXA2rVrTf+3bdu21HV269aNn376idzcO4NTL1y4QGZmJnFxcbi4uDBy5EieeeYZjh8/XoZHIoQoL1mZRq5ezqW+tw6dpfmfB1ePO/fhEm9Iq5UQ1dGGs8nY6tR0a2hfIftr7W6LWiXjrKo6RVGIi4vDy8sLlUqFu7s7I0aMoEmTJuzdu5fff/+djAy5hqUlLVY1RJMmTVixYgXTp0/H29ubMWPGmNbp9XpCQ0MxGo189dVXpa7z2WefJTY2lj59+qAoCk5OTixcuJCIiAi+/fZbtFottra2zJ07tzwOSQhRxmKicwBo1Myy0LrajhosdCoSr+fi1UBXaL0QoupKyswlMvY2TzZ3wlJbMffU7Sw1NHW25kh8BiNbu1bIPsX9S0lJITMzk7p165qWWVpamlqvdu7cyS+//EJISAgNGjSoxEirB5VSw6f/uLsPaWZmJjY2j1aTdWxsLGPGjGH79u2F1gUGBrJp0yacnJwqIbLyodVqzborikeDXNd7e5jPr1y9wp/rUnHztKBtR9siyxyKyCAp0UDwU/Zl2pXIxcWFmzdvlll9omqQ61p1/HIskeXHk/jmqUbUqfVwN0bu57r+evwmvx67yU9DfLC3knv5VdHx48cJDw9n9OjR+Pj4FLq2SUlJ/PHHHyQlJdG2bVs6dOiARqOppGgrj6enZ6nKSVdAIYQQXL6Yg8EAjYtorcrn6qElJ1shLcVYgZEJIR5Gbp7C5nMptPW0feik6n7517FFAaKuZ1bofkXpxcXFYWdnV+xzT52dnXn66adp2bIlhw4dYuXKlaSmplZwlNWHJFY1QL169YpsrQLYt2/fI9VaJYS4f8Y8hZjoHFzctNR2Kv6usqvHnefeJN6Qh34KUV3sjb1NSnYe/ct5ivWi+DhZUUunlnFWVZSiKFy9epW6deuW2AtBq9XSo0cP+vbtS3JyMkuXLuXcuXMVGGn1IYmVEELUcFev5JKdpdC4efGtVQDWNmpqOahl2nUhqpENZ5PxrGVBmzpFd/EtTxq1ilYethyJz5AHz1ZBycnJZGZm4uXlVaryTZo04dlnn8XJyYlNmzaxfft20wRm4g5JrIQQogZTFIULZ7Kp5aA2zfxXElcPC24lGjAY5EuSEFXdhVvZnLmZRb+mjqgrYIr1ojzuaUtyloHLKTmVsn9RvKtXrwKYTVxxL/b29gwZMoS2bdty4sQJli1bRlJSUnmFWO1IYiWEEDVYQryB22lGGjezKtWEFG4eWoxGSEqQVishqroNZ5Ox0qro0ajo8TMVIb+l7LB0B6xy7jW+qjgajYbOnTszYMAAsrKyWLZsGSdOnJBWSSSxEkKIGu3C2RysrFXUrW9RqvJOrlrUGki8Lt0/hKjK0nLy+OtSGt29HbDTVd4sbi42FjRwsJRxVlXM3c+vehANGjTg2WefpU6dOmzfvp0//viDnJya3TIpiVUVVK9ePYKDg+nZsyfjx48nKyurxPJNmjQptOz69euMGzeu2G1SU1NZtGjRw4YqhKjGUm4ZSEow0KipJWpN6f6wajQqnF21JMg4KyGqtD/Pp5BrVOjXtOInrbibv6ctpxKyyDbIjKJVRXJyMllZWffVDbAotra2DBw4kI4dO3L+/HmWLl3K9evXyyjK6kcSqyrIysqKrVu3sn37dnQ6HT/99NN91+Hh4cH3339f7Pq0tLQHqlcI8ei4cCYHrQXUb1zypBV3c/PQknHbSGaGfEkSoirKMypsOpeMn7sNDWrf3/u7PPjXscVgVDhxQ6Zdryri4uIASj1xRUlUKhXt27dn6NChKIrCypUrOXz4cI3sGiiJVRUXEBDApUuXAHjxxRfp06cPPXr04Oeffy5U9tatWzz55JP8+eefxMbG0rNnTwDOnj1L//79CQ4OJigoiIsXL/Lvf/+by5cvExwczKxZs8jIyGD48OH07t2bXr16sXnzZuDOw4W7devGtGnT6NGjByNGjLhnC5oQourLSM/jWlwuDRpbYmFxf91AXOv8Pe26dAcUoko6eC2dhAwD/ZvWruxQAGjhZo1Oo5LugFXI1atXsbOzw97evszqrFOnDiNGjMDb25vdu3ezdu1aMjNrVjItj8EugV3iOrQ58WVap8GyDumuT5aurMFAeHg43bt3ByAsLAxHR0eysrLo378//fr1Mz2DKjExkRdeeIG33nqLrl27Ehsba6pn8eLFjB07lsGDB6PX68nLy+Pdd9/l7NmzbN261bSvH374gVq1apkStJCQEABiYmL46quv+Oyzz5gwYQIbN25kyJAhZXhWhBAV7eLZHFQq8G5y/3ez7WqpsbJRkXDdQIP7bO0SQpS/jWeTcbbREuhVq7JDAUCnUePnZiOJVRWRP76qQYMGDzy+qjhWVlb069eP48ePs2vXLpYuXUpISAj16tUr0/1UVZJYVUHZ2dkEBwcDEBgYyIgRIwBYuHAhmzZtAuDatWvExMTg5OSEwWDg6aef5qOPPqJjx46F6mvbti3z5s0jPj6evn370qhRo0JlFEXhP//5D/v27UOlUnH9+nUSExOBO2O+/Pz8AGjVqpVZ0iaEqH70OUZiY/TUrW+Btc39d1xQqVS4eVhwLVaP0aigVlfONM5CiMLiUnOIup7JyNYuaKrQe9Pf05YfDiVwI12Pu52ussOp0W7dukVWVlaZdAMsikqlolWrVtSpU4c//viDNWvW0L59ezp06FDmiVxVI4lVCUrbslTW8sdYFRQREcGuXbtYt24d1tbWDB061DTzikajoWXLluzYsaPIxGrQoEH4+/uzbds2Ro4cyWeffUaDBg3MyqxevZqkpCQ2bdqEhYUFgYGBpvotLf//HWmNRkN2dnZZH7IQogJdOq8nLw8aN7N64DpcPbRcuagnJSkPJ1f5UyJEVbExOhmtWkWIT9XoBpjP/+9p14/EZ9CniSRWlelBnl/1IFxdXXnmmWfYsWMHBw4c4Pbt2wQFBaFWP7ojkeSvYTVx+/ZtHBwcsLa25vz58xw+fNi0TqVSMWfOHCZMmMD8+fN55ZVXzLa9fPkyDRo0YOzYsVy+fJnTp0/TokUL0tPTzep3cXHBwsKCPXv2mAY1CiEeLXkGhZhzObjV0WJf+8GnYHZx14IKEq7nSmIlRDkwKgqZeiO39Xnczskj/e//b+vzSM8x/v1/XhHrjXT3tqe2VdV6X3rZ63C10f6dWFX+TIU1WVxcHLVq1SrT8VXFsbCwIDg4GAcHByIjIzEajQQHB6PRVN4jAMpT1XrXiWJ1796dxYsXExQURKNGjXj88cfN1ms0Gr7++muef/557Ozs6NWrl2nd2rVrWb16NVqtFjc3N15//XUcHR1p3749PXv2pEePHkyaNIkxY8bQt29ffH198fHxqehDFEJUgNhLevQ5Co2bPdzYKJ1OjaOThsTrBpq3LKPghCgFo6JwKTmHeg6WWJTyMQFVSWq2gaj4DNL1xgKJ0l0Jkt5Ihj4PYwmTqtlaqLGz1GCn01DLUoO7nQV2Og32Vpoq11oFd24C+3vasvvybQxGBW0V6qZYkyiKwtWrV8tlfFVJAgIC0Gg07Nmzh7y8PPr06fNIJlcqpSbOhVjAtWvXzH7PzMzExsamkqIRZUGr1WIwyDN2HjVyXe/tXp9filEhfNNttBYqngi2e+g/qmdPZBN9MpveA+3RWT541w4XFxdu3rz5ULGIqqesr+uVlBx2xKSy81IaNzMN9G/myPh27mVWf0UwGBUmb4ghLk1vWpafINXSaf7+X21Klmr9nTjZ//2/naX6TjmdptLGTz3MdY24ksYnu67xcXB9WrjJd63KkJSUxJIlSwgKCqJFixZm6yriszgqKoq//vqLhg0b0q9fP7Ta6tHG4+npWapy1eNohBBCPLTr13LJSDfyeEebMrlT6eahJfokJN4wULe+jJkQZS8pM5ddl9PYEZNGTHIOatWdsToNaluyKTqZfk1q4+VQfWam3HI+hbg0PZM71qGdpy22lZggPShj1oPP7NfKwxa16s44K0msKkdZPr/qQbRp0waNRkN4eDjr168nNDS02iRXpfHojh4TQghh5sKZHKxt1dTxsiiT+mo7abDQqUi8Li2Jouxk5uax/WIqH2y7wtg1F/jf4US0ahXj2rnxv8E+fNCjHq91rIOVVs2iIwmVHW6ppevzWHrsJi3dbejhbY+9lbbaJVXK4QgSn+uNccfGB9reTqehqbO1TLteia5evVph46uK07JlS3r16sWVK1dYu3YtubmPzjMRH50UUQghRLFuJRpITsrDz9+6zKZHV6lVuLhrSbyei6Ioj/w0uqL8GIwKUfEZ7IhJZV9cOvo8BQ87C4a3dKZbQwfq2pu3iNa20jLM15kfoxKJis+gzd8zzlVlK08kcTsnjxcfd6u27xXj3h1gNKIs+RZjVibqvkPvuw5/T1t+PXaTtGwD9lVsgo1HXf7zq7y9vSs7FHx9fdFoNGzdupXff/+dp556Cp2u+vd8kFf0XWr4kDMhRDVW0ufX+bPZWOhU1GtUtn+43Dy0xMfmcjvV+FCzDIqaR1EUopOy2RmTyq7Lt0nLyaOWpYZejRzo7u1AMxerEhOQ0OaObDqXwv8OJzCnb8Mq3fpz/baedWeT6dnIgUZOD/6Yg8qk5OTAqcNYhwwkO+UWyuqfMGZmoBo8+r4SRf86tiw9dpOo65l0bVh5rSY10a1bt8jOzi73adZLq3nz5qjVajZv3sxvv/3GgAEDzB7xUx1JYnUXtVqNwWB4pPp7CiEefQaDodhng6Sn5XHjqoEmLSzRasv2y6erhwWQReL1XEmsRKnE39azMyaNHZdSib+di06jon1dO7p72+Nfx67UM/3pNGrG+Lvy2e5rbL+YSnAVnAkv309RiWhUMLK1S2WH8uBOHga9HsvOPcnxqAfWNih/rIKsDHj2ZVSlfDaRj5MVtXRqjsSnS2JVwSp7fFVRmjZtikajYdOmTaxZs4aBAwdiZVU9bz6AJFaFWFlZkZ2dTU5OTrVtqq/pLC0tTQ83Fo8Oua7FUxQFtVpd7B+jC2dzUGvAu0nZ3wm0tlFjZ68m4bqBxs3LvHrxiEjNNrD78m12Xkrl7M1sVEBLdxuG+jrTsV4tbHUPlpR3rl+LdS7WLDmaSOcGtbCxqHrJ/emETPZcuc2IVi4425TN+MbKoByJBBs7dC3aoEpJgZETwcYWZdMqyMqCFyajKsVNaY1aRes6thyJz5QuxBUsLi4Oe3v7Sh1fVZTGjRvTv39/Nm7cyOrVqxk4cGC1naFbEqu7qFQqrK2tKzsM8RBk6uZHk1zXB5OTbSTukp563josrcpnviI3Dwsunc/BYFDKvEVMVF/ZuXnsupTGzkupHL6WQZ4CDWtbMsbfla4N7XEpgyRDpVLxYls33tp8mdUnb/FcG9cyiLzsGBWFHw4n4GytZeBjTpUdzgNTDAaUY/tRtQ4wJU8qlQrV4DEYrW1RVv+Ekp2JesJbqHT3voHjX+fO86wup+TQ0LH6tk5UJ/nPr6oK46uK4u3tTWhoKOvXr2f16tUMGjQIW9uqP3bybjIroBBCPMJizuVgNEKjh3wgcElcPbQYjXcmyBACYMPZZJ5csJ/Ze65x8VYOAx5zYm6/hszt783gFs5lklTla+ZiTdeG9vx+5haJGVVrdrFdl9I4l5TNc21csdJW469c0ScgMwOVf8dCq9R9h6IaORGOH8Q4718o2Zn3rM7/78lGDsvsgBkibIsAACAASURBVBUmf3xVVeoGeLcGDRowYMAAbt++zapVq0hPT6/skO5bNX6XCyGEKIkhV+HSeT0edS2wq1V+XaScXbWoNZAg064LIC3bwKIjCTR1tWVWr3osGNSYMf5u5doyMfrvlqrFUYnlto/7lWMw8lNUIo2dLOnuXbW6Xt0vJSoSdDpo4V/kenX3vqjGvgHnTmIMex8lPa3E+pxtLGjgYCnTrleg/PFVVWXiiuJ4eXkxYMAAMjIyWLVqFbdv367skO6LJFZCCPGIuhKjJ1ev0Lh5+c6ypNGqcHa9M+26EBujU9DnKUzr6fP3A2HLv3uoq60FTzV3YuelNKJvZpX7/kpj7Zlb3Mw08OLj7hVyDsqLYjSiHNkHvo+jKmHGNnVgN9T/eBfiLmH89B2UlKQS6/X3tOVUQhbZBmNZhyyKUFXHVxXF09OTQYMGkZWVxcqVK0lNTa3skEpNEishhHgEGY0KF6NzcHTR4ORS/sNpXT20pKcZycqUL0k1WbbByProZAK87GjoVLGDz4f4OlHbSsPCwwmV/uiU5CwDK0/eokM9O/zcq+cgfJPL5yElqchugHdTtQ5APeX/4NZNjJ9MR0m8XmxZ/zq2GIwKJ27cu+ugeDj546uqcjfAu3l4eDB48GByc3NZuXIlycnJlR1SqUhiJYQQj6D4uFyyMoz4NK+YgeFuHnfGzCTES6tVTbbtQiq3c/IYXAkTNdhYaBjZ2pXTiVlEXKnc7kO/HEvEYDQypo1bpcZRFpQje0GtRtWqfanK/z/27jw+qvpc/Pjne2Ym+zrZJhs7hB0SdlDZFQVFwEqrVq3e9tfNVu1uF6+9tlptq9fae28XbdVadxBRcQFcEAhbEkLYIZCQPZPJvsx2vr8/hiBIgCyzJXzfr1df9ZXMnPMMSWbOc77P93lE1gS0HzwM7W2e5Kq8tMvHjU0OJ8Qg1D4rP6irqwuq+VXdlZyczMqVK9F1nTfeeIO6uouvggYDlVgpiqIMMFJKjh+yExmtkZLmn+avUTEaYeGCWrXP6rLl1iVvHrQxOjGcMcmBWaVZOCyWwXGhPFdQi8MdmNXTk/UdbDzeyHWj4kmL8e5A7kCQ+bmQNQERGdXt54ihI9F+9AgA+uM/Q544et5jQgwaE1IiyK9QiZWvlZeXA8G/v6oriYmJrFy5EoA1a9YEfXdglVgpiqIMMHU1Lhrr3QzPCvXbjBghBEkWE9ZqF7oe2DIsJTC2ljZT0+pk5djAtRU3aIK7cpKpbnHy9mH/lw5JKXk2r4ZIk8bq8f14GPBpsvIUVJUjsmf2+LkifRDaTx6F8Aj0P/wCeXjfeY/JTo2kotlBdYvDG+EqF9Cf9ld1JSEhgVWrVqFpGmvWrKGmpibQIV2QSqwURVEGmGOH7ISGCTKG+PdueZLFiNMpabC5/XpeJfCklKw9UEd6TAjTMrq/suELk1MjmZoWyWtFdTR2+HcFdU9FK3ur2lg9IZGo0OAbVtxTMm87AGJyzxMrAJFk8SRX5kT0J/8TuXfXOd/vbLuuugP6Tn/cX9WV+Ph4brrpJkwmE2vXrqWq6sL79wJJJVaKoigDSFODm9oqF0NGhmIw+LcTWVKKp+xQdQe8/OytaqO43s6KMeag6IB3Z04yHS6dlwr9Vzbk0iX/yKshLTqEa0fF++28viQLdsDQUYj4hF4fQ8QloP34EUgfjP6/v0Xf8cmZ76XHhJAUYVSJlQ/11/1VXYmNjWXVqlWEhYWxdu1aKioqAh3SeVRipSiKMoAcP9yBwQhDhvt/b0dIqEac2aD2WV2G1h6oIz7MEDTzmjJjQ7l2ZBzvH2ugtNHul3N+cKyBsiYHd+YkYdQCn1z2lbRZ4eTRXpUBfpGIivE0tBg+BvnMH9E/3uD5uhBkp0VSWNWGS5UQ+0Tn/Kr+vmLVKSYmhlWrVhEVFcW6devOvL5goRIrRVGUAaK9Tae8xMmgoSGEhAbm7T3JYqTe5sbhUG3XLxfFtg4Kqtq4frQZkyF4Liu+PCGRcKPGP/N8vx+jxeHmpUIrE1IimJ4e2FJIb5EFuQBeSawARHgE2vcfhAlTkS/+L/qGNwBPOWCbUw+a+WMDTXl5OTExMURHRwc6FK+Jiopi5cqVREdH89Zbb1Fa2nXnyUAInndARVEUpU9OHPHcmR+W5duBwBeTbDGBBGu1WrW6XKw9YCPcqHHNyLhAh3KOmDAjN09IYE9FK3kVLT491+tFdTTb3dyVk+y3hjG+JvNzITUTYfHeSocICUX71s8Q069CrnkOfc1zTEiJQBNqn5UvDJT9VV2JjIxk5cqVxMXFsX79ek6ePBnokACVWCmKogwIToek5Lid1EwTEZGB2zQfl2DAaILaSpVYXQ6qWxx8VtrEkpFxRIUEX7OGpaPisUSZ+EdeDW4flZpVNTtYf7ieBcNiGWb2z9w4X5MtTXCkCDF5htePLYxGxN33IeYuQW54g8jX/sqohDDyVNt1r7Nard3aXyWlRP9wHa6S436KzDsiIiJYuXIlZrOZ9957j46OjkCHpBIrRVEUX5JScvxwB8VH7NRWOWlv05HS+xd4JcV2XC4YHsDVKgBNEySmmKipcvrkdSrBZd1BG5qA60cHZ7MGk0HjjuwkShsdbDze6JNzPF9Qi0HArZP6f3v1TrJwF+g6InuWT44vNAPi1m8hlqxCfvIe2RV7OW7roMnPXRwHus75VZdcsdqfh3z1GVpe/IsfovKusLAwrr76ahwOB3v27PH68auaHfx9T3W3H++fyZGKoiiXqcZ6NwcKzr2LZjRCVIyBqBiNqBgD0af/OyJSQ+vFpnfdLTlxxE5ispE4c+Df1pMtRqrKnLQ06UTHBt8qhuIdTR0uPjzeyNwhsSREmAIdzgXNyoxmbFI4LxbWcuWQaCJM3vudPFjTxtbSZr4yMTGo/w16SubnQnwiDBnhs3MIIRCr7kCPiGTyhx/w0pQs8submDs8cHPQBpqysjJiY2Mvur9K6jr62hcAsOdtR2tqQMQEV1nvpSQkJJCVlUVBQQGTJk0iKqpv+xyllOyvaeetQzZ2lrWgCfjV9d17buA/gRVFUQYwm9Uz0+nKxVG4nJKWJp3mJjctzTrWahdlJz9vTa5pEBmlnUm6OhOuqGgDBuOFE67yUicd7ZJJ0wK7WtUpyWIC2qmpcqrEagB790gDDrfkxgAOBO4OIQR3TUnmh++V8HpRHbdnJ3vluLqUPJNXQ0K4kRvHBPe/QU9Iux0O5CPmLPbLfjHt2psYHhZBVEkr+Zu3c1X6fERYhM/PO9B17q8aPnz4xR+3ZxuUFiOuXYXc8AZy5yeIRcv9FKX3zJw5k6NHj7Jr1y7mz5/fq2M43TpbSppZf8hGcb2d6BCNVeMSuG5U9xNNlVgpiqL4kM3qIjxCnFlJSkw59/tOh6Sl2U1Lk5vmJp2WJjdNDW4qy51wViVdRKR21grX58mXySQ4friD6FiNJEtwvKVHRGpERmvUVrkYnhXoaBRfsLt03j5Sz7T0KAbFBkdCfzEjE8KZNzSGtw7Vc83IOFKi+j6OYMvJJo7WdfD9WamEGQfQzor9eeBweK0bYHeY5l/H5HUFFNQl4f6fRzDc9+sB0wQkUKxWK3a7/aJlgNLtRq57EdIGIW68DcPRA7i2boZ+mFjFxsYyfvx4ioqKyM7OJi6u+8lQQ4eL9442sOFIPQ0dbjJjQ/jODAtzh8QQ2sO/7eD4FFYURRmApJTYal0kplz4rdYUIohPMBKfcO5j3G5Ja7N+TsLV0uTGWu1C1899vtMhmTw9IqguRJItRkqKHbhd8qKrbUr/tPF4I812NyuDfLXqbLdNSmJbaTMvFNTywyv6NizV7tJ5vqCW4ebQoJnd5S0yPxcio2HUeL+eN3v8ED7LraKktJphh/fB6Il+Pf9A0znf6WKNK+S2TVBdjvadBxCagfAF19H81z8gS4sRg4b5K1SvmTZtGgcOHCA3N5clS5Zc8vEn6ztYf7ieT0404dQlU9IiuX60mcmW3n+eqsRKURTFR9padewdEnNiz99qDQZBTJyBmLhzS+mkLmlr02lp+jzpklKSPii49nckWUycOOqgzurytGBXBgy3Lll3yEZWYjhjksIDHU63JUWauHGMmVeL6liW1c7oPsT+1iEb1jYX981OQwuiGxp9JV0uZOFOxKQZCIN/y3izUyMBKEibzJD1L2NQiVWflJeXX3R/lXQ6kOtfhmFZMMnT/THsisU0P/vfyG2b+mViFRkZyeTJk9m9ezdTpkwhKSnpvMfoUrKnvJW3DtsorGojxCBYNDyWZVnxZHhh9X0ArV0riqIEF1utZ39VbxKrCxGaIDLKQEqaieGjw5g8PYLsGZFohuC6uEtINqJpqu36QLSttJnqFicrx5qDapW0O1aOTSA+3MizedW97lpZ3+7i9f02ZmZGMT5lgO0FOlIEba1+LQPslBBhYnBsKHlDZsKRIuThIr/HMFB0Z36V/OhdqLeirfjqmb9jLToGMWkGcscnSJfzgs8NZjk5OYSGhrJ9+/Zzvt7u1HnncD3fWV/Mw5+UUd7o4PbJSTy7YgTfnG7xSlIFKrFSFEXxGZvVhckkiI69/N5qjUaBOclIbVX//HBWuialZO3BOtJjQpie0bfOW4EQbtK4bVIih60dbClp7tUxXtxbi0vXuWOyd5pgBBOZnwshITA2OyDnnzM4miJnBOVJw9DfeSUgMQwEtbW12O32C5YByvY25IbXYOxkxBdWBsXsBdDSBPu837rcH8LCwpgyZQonT56koqKC2lYn/8ir4e61x/jr7moiQwz8YE4af71xOKvGJRAd6t2V2cvv015RFMVPbFYX8YmGfndX31uSLEaam3Ta2/RLP1jpF/ZWtXHcZmfFGHO/LYGbPzSWofGhPJ9fg93Vs9/NE/UdbDzeyHWj4kmL6XsDjGAidR1ZkAvjchChgWlIcs2IOIyaYMOU1XBwL/LYgYDE0d91zq+6YGL14ZvQ0oy24qvnf3NcDsTEoW/b5MsQfWrixImEhIXz6oaP+cabx3jrkI3stEgeu2Ywv18yhKuGxGDsxWiT7lCJlaIoig/Y7Z59UOaky3cra+feKrVqNXCsPVBHfJihXzdsMGiCu3KSqW1zsf5wfbefJ6Xk2bwaokI0Vo8fOMOAzzh5FBpsPhsK3B1x4UauGBzNZpeZttgk9PVq1ao3Lja/SjY3Ij9YBzmzEUNGnvd9YTAgZs6HfbuRzb4Zqu0rLl3y6ckmHthcQaFhCFqrlaUWO39dPpwfXZFOVmLv9lVKt7vbj1WJlaIoig/UW72/v6q/iY7VCA0T1FapfVYDQbGtg4KqNq4fbcZk6N+XDxMtkUzPiOL1ojoa2rv3+7mnopXCqjZWT0gkysvlQ8FAFuSCwYCYOC2gcSzLiqfDJfnoiq/CgXzk8UMBjae/0XWdioqKC+6vkhteB4cd7cZbL3gMMXsBuN3IHZ/4KkyvanW4eX1/Hd948zh/2FpBq0Nn2ZxsomNiiKg+QGJE7z+HZWM9+u9+0u3H9+93RkVRlCBls7rQNIgzD7wLsO4SQpBkMVJb7ULqvWsUoASPtQdshBs1rhnZ/fkwwezO7GQcbp1/F1ov+ViXLvlHXg1p0SFcOyreD9H5n8zPhVHjEZGB3Ts3MiGcrMRw3iUDPSoa/Z1XAxpPf3Ox+VXSVov86F3E7PmI1MwLHkOkD4bBIzzt2IOclJJHPy3nhYJaMmND+OW8DP58/VCuG53IrJkzsVqtHD16tHfHLi9Bf+RHUF7S7eeoxEpRFMUHbLUuYs0GDEHWrc/fki0mnA5JQ333SymU4FPd4uCz0iauGRlHVMjAuFmQHhPCdaPi+fB4AyUN9os+9v2jDZQ1ObgzJ8lnezMCSVaegqrygJYBnm1ZVjyVrS4K5n3VU5J2oncXxpeji82vkm+/AkjE9bdc8jhizkI4dQJZWuztEL1qZ3kLhdVtfH1qMg8tHMTU9Kgz+z+zsrJISEhg+/btuHtQzgcg9+d7VqpcLrQfP9Lt56nESlEUxcvcLk8ikXAZlwF2SrR4/g1UOWD/tu5QPZqA60cPrNWa1RMSiTBpPJtXc8H26y0ONy/tszIhJYLp6f2vE2J3yDxPa2oxeUaAI/GYlRlNfLiRdyNHQ2S06hDYA+Xl5cTFxREVde7vqqwqR27diJh7LSLh/PlOXySmXQlGI3L7Zl+F2mcuXfJcfi3pMSEsGXn+e5MQglmzZtHY2MjBgwe7fVz9k/fQn3oIEpLRHngcMXhEt5+rEitFURQva7C5kTqXdeOKTqGhGrHxBmoqVQOL/qqpw8WHxxq4akgsiREDa9hzdKiB1RMSKahsJa+itcvHvFZUR4vdzV05yQO2w6fMz4WhoxDxCYEOBQCTQXDtyDjyqjuomP8l2LsTWXo80GEFPV3XKS8v73q1at2LYApBXPelbh1LRMXApOnI3I+DdqbV+0cbKG9ycGf2hVeShw4disViYceOHbhcF7/BJ3U3+mvPIv/1PzAuB+0njyLMl05Cz6YSK0VRFC+zWT1v3vEJA6Nkqq+SU4002Nw4Harten/07tEGHG7JijHmQIfiE9eOjCc12sSzeTW4v7AXsLLZwduH61kwLJZh5rAARehb0lYLJccCMhT4Yjpbr7+bPBXCI1WHwG6wWq04HI7z9lfJkuPI3Z8hFt2AiOn+Hklt1kLPTKui4Jtp1epw8/LpleRpF1lJFkIwe/ZsWltbKSwsvODjpL0D/X9/h/zgTcT8pWjf+TkirOcDwFVipSiK4mU2q4voGI2QUPUWC5BkMSElWGtUOWB/Y3fpvHO4nmnpkQyKC8xsI18zGQR3ZidT1uTgg2MN53zv+YJaDAJunTQA26ufJvN3AARdYnWm9XppG+0Ll0NBLvLUiUCHFdQutL9Kf/MFiIxGXL2iZwccl+2ZabU1+MoBX99fR7Pdzde6sZKckZHB4MGD2b17N3b7+fspZUMd+uMPwN6diC9/He2W/4cw9O7GqPrUVxRF8SKpS2xWlyoDPEt8ggGjEWoqVWLV32w83kiT3c3KscFRIuYrMzKiGJ8czr8LrbQ6PJvcD9S0sa20mZXjEkgYYCWQZ5MFuZCaibB03Z47kDyt13U+GjoXwiPUXqtLKCsrO29/lTxSBEV5iGtXISIie3Q8YTQiZs6DfbuCaqZVdYuD9YfqmTc0huHdXEmeNWsWHR0d5OXlnfN1WXYC/bc/gqoytO/8HG3h9X2KTSVWiqIoXtTcpONyXt7zq75I0wSJKSZqq5wXbBCgBB+3Lll3yEZWYjhjkno3WLO/EEJw15QUmu1uXiuqQz89DDgh3MiNA7QEEkC2NMGRoqBbrerU2Xr9nZNtyPnLYM82ZA9aX19OuppfJaVEX/M8xJkR85f26rhi1umZVjs/9VaoffavvVaEgFsndX//U3JyMiNHjqSgoIC2tjYA5L7d6I/+FKRE+/EjiEl9n+GmEitFURQvstV6VmXMSWp/1dmSLEba2yStzWqfVX+xrbSZ6hYnK8eaB2zThrMNN4cxf1gs6w/X81pRHUfrOrhtchJhxoF7qSQLd4GuB21iBadbrzc7yZ94NYSGI9Vcqy517q86pwywcDccP4RY9mVESO9KeUXGkKCaaXXE2s6nJ5tYPtpMUmTPVpJnzpyJy+Vi165d6B+9g/6nhyElFe2B3yMGDfdKfAP33UJRFCUAbFYXYeGC8Aj19nq25NNt12tU2/V+QUrJ2oN1pEWHMD1jYLYY78ptkxIxCPh3oZXh5lDmDY0JdEg+JfNzIT4RetBO2t9mD/K0Xn+nxI5YsBS5+zPP3C3lHJ37qzpXrKSue/ZWJaci5izq07HF7AVQWhzwPW5SegZ1x4YZWDmu5yvJ8fHxjBkzhn1799L42nMwcSrajx7xajdM9cmvKIriRXVWF+ZE42Vxh78nIqIMREZp1FYFZ9te5VyF1W0ct9lZMdZ8Ztjm5cDsbGFlTBMCydcmJw7o1y7tHbA/HzF5RlC/Xxk1T+v1/MpWKmYtg5BQtWrVhbKyMuLj44mM9Oyjkru2QNlJxA23IIx9K00X068CgxG5LbBNLHLLWjhQ284tExOJMPW8KkR2tDPt4A6E7mbX5Llo3/4ZIsy7Zc4qsVIURfGStladjjapGldcQJLFSF2NC7db7bMKdmsO2IgPMwz8FZv2NuTenegv/w33g99F/+Ed3LT2v/jf7Y8w7tj2QIfnW/vzwOkI6jLATmdar5c5EPOuRe7cgqwqD3RYQaNzf1VnGaB0uTxzqzKGegb99tGZmVY7PkZeYhaUrzjdkufya8iMDWHx8O63jO8k6+vQH/8ZUft2MSE5gcN2HVt9w6Wf2EMqsVIUJejZO3ROHGsJdBiX1Dm/ypyo9ld1JTnVhNv9+b+TEpyKbR0UVLaybLSZEMPAukyQLifySBH6un/jfvTH6Pfegv70w8hP3/ds8F95B4af/4HktGTk+peRjvNbMw8UMj8XIqNh1PhAh3JJceFGrhwczabiJtrmLweTEfmuWrXqVFtbe878KvnZh1BbhbbiNoTmnb9hbfYCaG4M2Eyr947WU9ns5M7sZAwXGAZ8IbL0OPpvfwDVlWj3/JJpK76E0WgkNzfX63Gq26qKogS9owftnDjSxLxro4mOCd6kxVbrwmiEmNjgjTGQEpKMCA1qq1wkpQzc9tX93dqDNsKMGktG9vyucLCRug7lJciDBciDhXCkCBx2EBoMGYFYsgoxZhIMH40whZx5nrbqDvTf/xz58bs9n/3TD0iXC1m4CzFpRq/n9fjbsiwzH51o4qNaWDr3WuSm9chlqxHJaYEOLeDKyz2rd+np6Ui7Hfn2KzBiDEyY6r2TjMuB6Fj07ZsxTJ7hveN2Q4vdzSv7rEyyRDAlrWct4+Xeneh/+z1ERqH99FFExlDCgZycHHbs2EFVVRUWi8VrsarESlGUoCalpKrMAUB1hTO4Eyuri/hEI6KHd9MuF0aTwJxopLbSCZMGdvvu/qq6xcFnJU3cMNpMVEjw/q1djLRWIw/uhYN7kYcKPXfZASwZiDkLEWMmQ9Z4RMSFm3KIrAkwLhv57uvIK67u8fyfoHekCNpaETnBXwbYaURCmKf1+pF6rlt8I3y8Afnua4g7vx/o0ALu7P1V+ntvQKMN7Rs/8ureuc6ZVnLzO8jmJkS0/8qEX9tfR4tD79Yw4E5SSk/y/eqzMGgY2nd/gYj7vOFFdnY2hYWFbN++nRUrvHfzRCVWiqIEtcZ6N+1tEiGgutzJiNHdGwbobw6HTnOjTlpmyKUffBlLshg5VNhBR7tOWPjAKjMbCNYdqkcTcP3o+ECH0m2ypQkOFSIP7vUkVLVVnm/EmhHjcmDMRMToSQhzYo+Oq628Hf2/7kO+vxax4jYfRB44Mj8XQkJhbHagQ+mRZVnx/GFrBfltoeRcdQ3yo3eQS1cjkry34tDf6LpOeXk5o0ePRra1IDe8AeOnIEaN8/q5xOwFyA/XIXd+ili4zOvH70pVs4O3D9ezYFgsQ+O79/kv3W7kK39DfvQuTJ6J9h/3I0LPfW5ISAhTp05ly5YtnDp1iszMTK/EqxIrRVGCWmWZEyFg9PhYDhY1Yu/QCQ0LvgvyeqsbUPOrLiXZYuRQoaccMHOoSkKDSVOHiw+PNXDVkFgSI4K7VFMeO4As2OEp7ztVDFJCWDhkTUAsvAExZiKkZvbpjr0YNBwx7UrkxnXIBUsRsf0n2bwYqevIglwYn9Pr2UaBMntQNP/IM/L24XqmLFmJ/OQ95IbXEbd/N9ChBUxtbS1Op9NTBvj+m9DWguajGwEiYygMGu6ZaeWnxOr5gloMAm6d1L0bI7KjDf0vj0PRHsTVNyJW3YHQuv5cnjBhAvn5+Wzbto2bb77ZKyt8wXd1oiiKcpaqMicJyUZGjIkGCTWVwdn4wGZ1IQTEmdX9qouJiTMQEipU2/Ug9O7RBhxuyYoxPZ8P40/6lg/Qf/dT5Mb1EBaOuOEWtJ8+hvbkvzF89xdoC5ch0gZ55SJJ3HgruF3Id17xQuRB4uRRaLAhJvefMsBOZ7deL9eiEFcuRm7bhKyrCXRoAdM5vyotJgq56S3EtCu9Nuy2K2L2Qig9jizz/UyrQ7XtbC1t5saxZhK6cbNH2mrRf/dTOJCPuO3baF+664JJFYDRaGTGjBlUV1dz/Phxr8SsEitFUYJWc5Oblmad1HQTCYmhhIULqiqC84LcVusiNt6A0aj2V12MEIIki5HaahdSqrbrwcLu0nnncD3T0iMZFBe8qxj6zk+RL/wZxuegPfkihh/9Fm3ZasTw0T5pwiCS0xBXLEZ++j6ys8Swn5P5uWAwICZOC3QovXL1yNOt1w/XI5asAgRyw+uBDitgysvLiY+PJ+Kjtz3t85ff6tPz+WumlZSSZ/NqiA8zsGLMpQf4ypJj6L/9EVir0e75FdrcJd06z5gxY4iPj2f79u3out7XsFVipShK8Kos8yRRlgwTQghS0kzUVjmDbg6S2y1psLnV/KpuSraYcNgljfXuQIeinLapuJEmu5sVYy99ARMocu9O5LNPwMhxaN/0/mDPCxHLvgwGg2cuUD8npfQkVqPGIyIv3LwjmMWFndV6PcqMuGIR8rONSFttoEPzu879VRmJZuQn7yHmLEKk+LZLooiOgUnTkLm+nWm1rbSZw9Z2bpmURLjp4umKLNqD/tjPwGBA++ljiPE53T6PpmnMmjWL+vp6Dh061NewVWKlKErwqipzEp9gONPkICXdhNsFdTXBVQ7YaHOj62p+VXclWTwJaE1VcP0cL1duXbLuoI2sxDDGJgVnt0Z5oAD9/34Hg4aj3fMLRKj/VtVEnBmx8Abkzk+Rp3xf/uRTlaeguhyRPSvQkfTJsiwzHS6dzcWNiGtvAiTyvTWBDsvvampqcDqdpJUeBSE8NwH8QJt1eqbV/jyfHN/p1nm+oJbBcaEsHBZ70cfK2ir0vz4OKWlo7SIKAAAAIABJREFUD/wekT64x+cbPnw4ycnJ7NixA1cfk0WVWCmKEpTaWt001rtJzfi8rjox2YjBCFXlwVUO+PlgYLVi1R2hYRoxcQa1zypIbD/VTFWLkxVjE7zantlb5LED6H/+DVjS0b7/ICIswu8xiCUrITwSfe0Lfj+3N8l8z0BU4ec5RN42IiGM0adbr0tzEmL2QuSWD5ANdYEOza8651elFWxFzL+ux50ve238FM9MKx+VA757pIGqFidfy7n4MGDpcnpmVCHQvv1ArxvMCCGYPXs2zc3NFBUV9TJqD5VYKYoSlKrOKgPsZDAIkiwmqiucQbU/x2Z1ERmtBWW3wmCVnGqk3urG6Qyen+PlSErJmgM20qJDmJ4efKVhsuQ4+lO/hvhEtPseQkRGByQOERGFuHYV7NuNPNK3C69Akvm5MHQUIj54Sz67a2lWPJXNTvIqWj2rVrr7slu1KisrIx6dCKOGuPZLfjuvMBoRM+bB3p2ecQde1Gx380qRlezUSLJTLz4/Tr75LzhxBO2O7yISU/p03szMTDIyMti1axcOh6PXx1FXAYqiBKXKcicxsRqRUeeW11nSTHS0B8/+HCklNqubBLVa1SNJFiNSgrVarVoF0r7qNo7bOlgx1nzRO8OBIMtL0Z/8FUREod3/a0RMYNudiwXLIM6Mvub5oLqx013SVgslx/p9GWCn2YOiMYd7Wq+LJAti1nxPk5HG+kCH5he6rlNRVkZ6XTli8Y1+HdgLIOYs8HTM3PmpV4/7SpGVdqdnGPDFyH17PDPm5i5BTJnT5/MKIZg1axbt7e0UFBT0+jgqsVIUJejYO3RstW4sGefPOUpO9SQw1UHSHbClScfpkGp+VQ+ZEzxlnbVqn1VArTlgIz7MwLyh/r0ouxRZU4n+xK/AYEK7/78Q5qRAh4QICUVc/xU4fgj27gx0OD0m83cAILL7dxlgp7Nbr5c12hHXfQlcLuT7l8eqVU1NDU63m3RXB+Lq5X06VmWzgw+ONVBa397t53hmWg3zanfAymYHG47Us3BYLIMv0p1UNtShP/sEpA9G3Hy3186fmprKsGHDyMvLo729+/8WZ/PLLVaHw8GDDz6Iy+XC7XYzc+ZMbr75ZmpqanjyySdpaWlh6NCh3HPPPRiNRpxOJ08//TTFxcVER0dz7733kpzsyVzXrl3L5s2b0TSNr33ta0yePBmAgoIC/vGPf6DrOgsXLuTGG2/0x0tTFMUHOvdQnb2/qlNomEZ8ooHqChdZ4/0d2fnqatX+qt7QDILEZCO1VarteqAU2zrIr2zlq5OTCDEEz31WaatF/+Mvwe1E+9EjiOTUQId0hpizCPnBm+hrX0CbOPWiM3KCjczf7hmabMkIdChec/XIOF4pquOdI/X8v2lpiBlzkZ9sQC5ZhYiJC3R4PlWWtwuA9Cvm93jfoS4lR+s62FnWws6yZkobPaVvz+yp4ZvTUph/iYYRncTshciX/4YsO4nIGNKjGLryXH4tRk1wy6QL30iRuhv9mSfAYUf7fz/2+pDrWbNm8eKLL7Jnzx6uuOKKHj/fL++kJpOJBx98kMcff5zHHnuMgoICjhw5wr/+9S+WLl3KU089RWRkJJs3e7LezZs3ExkZyZ/+9CeWLl3Kiy96WpyWlZWxbds2/vjHP/Lzn/+cZ555Bl3X0XWdZ555hgceeIAnnniCrVu3nhmYpihK/1NZ5iQySiM6tuu3KEuaicZ6N+1tfZ850Vc2q4vQMEFEVPBcmPYXSRYTba06TY3Bsfp4uVl70EaYUWPJyOC5AJVN9eh//BW0taDd+2tE2qBAh3QOYTCg3XgrVJQicz8JdDjdJlua4Oh+RHb/Gwp8MZ2t1zcXN9HqcCOWfgmcLuQHbwY6NJ+SUlJ26ADxznYiFy3r1nPsLp0dZc38KbeSr605xo/fL2HNgTpiw4zcPSWZx68ZzJiUKJ7cXsmfciuxuy79+Sqmz/XMtNre91WrAzVtbD/VzIqxCZjDL3yjUr77OhwqRHzlG4jUzD6f94sSEhIYPXo0e/fupaWlpcfP98uVgBCCsLAwANxuN263GyEE+/fvZ+ZMzx/5vHnz2LXLk33v3r2befPmATBz5kyKioqQUrJr1y5mz56NyWQiOTkZi8XCsWPHOHbsGBaLhZSUFIxGI7Nnzz5zLEVR+henQ8da7Tozu6orKemelaxgKAe0Wd2YE41B2U0t2CWnGkHAO2+UUbCzjepKJ3qQzSgbqKpbHHxW0sSSkXFEhQTHqotsbUZ/4kGot6J971eIwcMDHVLXcmbD4BHIt/6NdAb+Pag75N5doOsDLrGCL7Ret2Qgpl2J/PhdZLN3myp0R0OHC7fu+/cwd952KoWJjNRUhOn8kvkz8bS7+PBYA7/5pIzbXj/Kbz8pZ1tpM+NTIrh/diovrBrJw4sGccNoM6MSw3ly5QRuHp/ApuON/PC9k5xqtF80DhEdAxOnemZauXu/77lzGLA53MiNY8wXftyR/ci3XkJMn4uYs6jX57uUmTNnIqVk586el/z6rXZF13V+8pOfUFVVxTXXXENKSgoREREYTk9KN5vN2Gw2AGw2GwkJno41BoOBiIgImpubsdlsjBw58swxz35O5+M7//vo0aP+emmKonhRdYULKbsuA+wUFa0RGaVRVe5kyAj/zbP5ovY2nfZWnWEjL/zBplxYZJSBmXMjqakQlBa3cOqEA6PJsyKZmhlCksWIwaASVl/45EQTuoRlWYFtCNFJdrSh//dDUFWGds+vECPGBjqkCxKahrbydvQnfoX8ZANi0Q2BDumSZP52MCfC4BGBDsXrOluvv324nqVZ8YhlNyN3fYr88E3Eytv9EoMuJX/fXc07RxowaoL06BAy40LIjA0lM9bz/6lRIZi88H4mdTfV77yBM24w6VPO3S8npeRUo8NT4lfezBFrBxJIijCyeEQc09OjGJccccE4jJrg1klJjE2O4ImtFfxgw0m+Nd1y0dJAbfYC9Pxcz0yridN69Zq2lDRztK6D7820EGbses1HtjSh//0PkJiMuO1bPr2ZGRMTw/jx49m3bx85OTnExXV/Vd9viZWmaTz++OO0trby+9///kzv/a50VW8vhLhgHf6FHt+VjRs3snHjRgAeffRREhP91PNf8Ruj0ah+rv3Y3l2VREQaGDEq5Zy/4y/+XIcMFxzc10BsjBlTSGDK8IqPNgNNDBuZSGJiWEBi6O8SE2HCJCN2u5OKU22cPN5C6YlWykpaMZoEmUMiGTI8ioxBERhNqtzSW/ZZK8hKjmLMYN/tX+rue7G0d1D/5IPoJceJ/clvCZt+pc9i8pqrFlG/6S2cG17HvHw1WvjF20IHkuxop+ZgAeGLbyAmqe9NQILxM/Yr0yQPbjjMsRYDsydk0zBnAY6P3sX8lf9A83G3PJcu+d3Go7x7pIFl41KICzNxwtZGsa2NrSXNdF6hGjRBZlwYQ80RDDFHMCQhgqHmCDLjwgm5QDLRlfaPNlDR7oA4mDhpEmERkRRWNPJZsY3Pim2UN3YAMDo5irtnDuKKYWZGJEZ2KxHp/NkuTkxk8lALD713mCe3V3KsUee+ecMIM52/ui3nLaH2hf/BtPsz4hZc2+3X0cnu0nmx8AQjEiO5adrwLruTSilp/Otj2JsbMD/yF0yZvi8RXrJkCQcPHiQvL4+bb76528/z+27ryMhIxo4dy9GjR2lra8PtdmMwGLDZbJjNnuW/hIQE6urqSEhIwO1209bWRlRU1Jmvdzr7OWd/va6ujvj4ru/CLVq0iEWLPl8+tFqtvniZSgAlJiaqn2s/5XJJykpaGTQ05Jy/aTj/5xpjdqHrcOhAFalddA/0h5LiNgxG0GnGau15LbbikZiYSH19HeFRMGaSgawJ0VhrXFSeclJe2sqJoy0YDJCcaiI100RKqgmjSa1k9Varw01RZRMrxyb49L2yO+/F0ulE/5/fwIECxN330zJsDC395P1bLvsK8rc/wPrSs2g3fCXQ4VyQzNsGDgf20ZO98vMOxs/Y8XFgDjfy4q4SRkXryEU3Ij/bhPXVf6Atv9Vn53W6JU9sq2BraTO3TEzk5vFxpxMYz0w4u0unvMlBaaOdU40OTjXaOVzdxCfH6+isGNQEWKJCGBQXQmbM5ytc6TEhhH4h4ZIuJ/qLf6E0eRRh0XH8dlMxeypaaHHomDTBREsEN2SlMC09ioSIzqqPDurqOrr1es7+2Qrgl1el8vI+E68VVVNYXs+Pr0wnM7aLKpHpV2H/+F1qTxYjonqWyK45UEdVs51fT0+m3tb1gGd909vIXZ8hVt9NY1wS+On3b9KkSezevZvx48efaZZ3KX5JrJqamjAYDERGRuJwONi3bx/Lly9n3Lhx5ObmMmfOHD7++GOmTp0KwJQpU/j4448ZNWoUubm5jBs3DiEEU6dO5amnnmLZsmXU19dTWVnJiBEjkFJSWVlJTU0NZrOZbdu28b3vfc8fL01RFC+qrXKiuy9eBtjJnGjAFCKoKncGLLGyWd3EJxjRgmz+T3+naYJki4lki4kJUyS2WheVZc4z/9M0zxys1MwQLGnGgK1Y9leFVW3oEnLSArvKIt1u9L//HoryELd/F23G3IDG01Ni6EjImY384E3k/OsQ0d3rpOZvMj8XIqNh5LhAh+Izna3XXyy0UtZoJyN9sOdns2k9ctFyRKT3h1/bXTq/21LOnopW7spJZnkXe4NCjRrDzGEMM59b0eBw61Q0OSg9nWydOp147SxrOZNwCSAlysSguFAyYzzJVuv+QnalLSOOcioc0VRVtjI9I5rpGVFMtkQS7uVVfUM3SwPF7IXIjW8hd21BzF/a7eM3dbh4vaiOKWmRTLJ0/X4kS44jX38WJk5DLPRv2e2UKVPYt28f27dvD67Eqr6+nj//+c/ouo6UklmzZjFlyhQyMjJ48sknefnllxk6dCgLFiwAYMGCBTz99NPcc889REVFce+99wKeqcizZs3i/vvvR9M07r77bjTN80t011138Zvf/AZd15k/fz6Zmd7vFKIoim9VljkxhQjMSZd+a9I0QXKq0bMnS5cIPyc3ToekqdHNqLGqBNCXNE2QmGIiMcXE+GyJrc5N5SkHlWVOqivaEBokpRhJzTBhSTcREqqSrEvJq2wh3KiRlRgesBikriP/+RTkbUesvhvtyqsDFktfaCtuQy/IRb7zKuLLXw90OOeRLheycBdi0gyEITialPjKua3XLWjLVqPnbUNuWo/w8opim9PNbz8pp6i6jW9Pt3BNDztrhhg0hsSHMST+3M8Pp1tS2exJts5e5cqraMHTpC+J4RF2EtrcXDd1FFdlj/DLYO/s1EieuG4If9xawZPbKymqaeMbU1POrKiJzKGQORS5dRP0ILF6uaiOdpfOnRcYBiw72tD/+jhExaLd+X2/N4kKDQ1l6tSpbN26tdvP8UtiNXjwYB577LHzvp6SksIjjzxy3tdDQkK4//77uzzWypUrWbly5Xlfz8nJIScnp+/BKooSELpbUl3hWX3q7gqQJd1EeYkTW52bhG4kY95UX+cCiRoM7EdCEyQkGUlIMjIuW9Jgc1N5yrOKtXdXO4W720lI9iRZqRkmQsNUkvVFUkryK1qZlBqBMUArrVJK5Et/QeZ+hFh+K9qivg03DSRhyfDMtvpkA3LRDYjElECHdK4j+6CtFZEz8LoBflFcmJGrhkSzubiR2yYlEZk5FCbPRG56y/OzifDOCm2L3c2vPz7F0boO7p2dyryh3lupNBkEg+JCGRQXypyzvu5saqRizesY8j6jcunNbD8MM8YM9UtS1SkhwsSvFw7i5X1WXiuq44i1/ZzSQDF7IfKVvyPLSxDpgy95vPImB+8dqefqEXEM6qq8EJAv/gVqq9B+8LCnA2EATJw4kYKCgm4/Xn3qKIoSFKw1LlzO7pUBdkqymBBaYNqu26wuhIB4sxoMHAhCCOITjIydHM6CpdFcuTiK4aNDaW/V2bennQ/eamLb5mZOnXAEOtSgUtbkoLbNRXZqYMoApZTIN/6J/HgD4pqViKXd3xQerMSyLwMC+dZLgQ7lPDI/F0JCYWx2oEPxi6WjzHS4JJuKGwHQlq2Gtlbk5re9cvzGDhe/2FTKcZudn1yZ7tWkqiuyvg79lWfQHvg6GVvXkTZzJuXtdhISEoiI6NlQYG/oLA18cEEmjR1ufrDhJB+d/rcWM+aCwYDc1r2ZVs/l12AyaHxlQteNUPRtmzw3X5atRmSN99pr6CmTycQtt9zS7cerxEpRlKBQWebEYITElO4nKiaTZwWjqjwQiZWbmDiDaqIQBIQQxJmNjJkYzvzropl7TTSjxoZi75AU7Gyj3uoKdIhBI7+yFSBwidU7ryDfX4uYdx1i1R0DYv6bMCciFixD5n6ELC8JdDhnSF1H5u+A8TmIkMCNpfCnztbr7xyuR5fSMwtt4jTkh+uQHW19OnZdm5MHPiylvMnBL+ZlMDMz2ktRn09WV6A//zT6z76O3LwekT0T7T//hFz9H1RUVJCenu6zc3dHZ2ngyISwMwOFHeHRMGGa5+/gEjOtiqrb2FHWwk3jzMR1MQxYVpUhX/w/GDUesSzwN1/Cw7tfNq0SK0VRAk7qkqpyJympph7PLbKkm2ht1mlp7v1wwp7S3ZL6Ole39oIp/iWEICbOQNb4cK5cHI3BCCXFatWqU15FK+kxIaRE+b/hi75xHXLdvxGz5iO+8o0BkVR1EtfdBGER6GtfCHQonzt5FBptA3Io8MUsy4qnqsVJXoXnJoK27MvQ1oL86N1eH7O6xcHPPiylrs3Ffy7I9NmNCXnqBPpfH0f/5beR2z9CXLkY7eH/Q7v7fkT6YGpqanC5XGRkZPjk/D3RWRp49kDh8qmLoKkBDuRf8Hn66WHACRFGbhh9fsMP6XSg/+VxCAlB+48fILT+VW6vEitFUQLOVufGYZc9KgPslJLmeU61H1etGhvc6G5PZ0IleBlNgvRBIVSUOnA6u56DeDmxu3T217SRE4DVKn3LB8hXnoGc2Yg7vofQBtblh4iMRlyzAvbuRB47GOhwgNNlgAYDYkLvhrb2V7MGRWMON7L+cD1wunvj+CnID9YiO9p7fLyyRjs/+6CUNoeb/1qUybhk75fgyaMHcD/1a/Rffx+5bzfi6hvRHv072q3fQiRZPo+lrAyAtLQ0r8fQG18sDfzhiVg+HjTH08TiAj492cRxWwdfnZR0Xjt5APnas1B2Au1r9yLiE3wZvk8MrHc2RVH6pc4W2smpPU+sIiI1YuI0qvy4z8pW6yktMyeqFatgN2hYCG43lJeoVav9NW043NLvZYD6jk+QL/wZxk9B+/oPBmx3OrHoBoiJQ1/zHFIGNpGXUnoSq6wJPmk1HsyMmuDaUXEUVLZS1mgHTu+1amlGfrKhR8cqtnXwwIeluKXkN4sHMzLBe500pZTIoj24H/sp+mM/hRNHPM1cHn0G7aY7EbHnz2MtLy8P2P6qizm7NPCpYct5ujWdjsbG8x5nd+m8UFDLcHMoc4ee34xC5m1HfvQuYvFyxMT+eUNAJVaKogSUlJKqMgdJFmOv9yulpJmwWd047LqXo+uazeomMkojLFy9hQa7OLOBmDiNUlUOSH5lKyZNMD7FfxdlsiAX+ewTMHIc2rd+ijD2/OZJfyFCwzyNLI4egKK8wAZTeQqqyxGTL68ywE5Xj4jDqAneOXJ61Wr4aBibjXx/LdJu79YxDlvb+cWmUkwGwSOLBzM4zjv71KTuRt/1GfrD96H/90NgrUF8+eueFaplqy+YCLvdbioqKoKiDLArnaWBX8rU2JySww/fK+FU47n/1usP1WNtc/G1nGS0L5QCy7oa9OeegsEjECtv92foXqWuChRFCajGejftbb0rA+xkSTOBhOpK3zcpkFJis7rUalU/IYRg0LBQGuvdNNgu7yYWeRWtjEsO77L8xhfsBTvR//IYDB6Bds8vLosGCuLKxZBkQV/zPFL3z42ersj8XE88k2cELIZAOrv1eqvDs/9Wu341NDeiP/gd3P/3KPo7ryL37UE21p/3/MKqVn61qZSYUAOPLB5MWkzf9yRKlxN9ywfov/wO8q+PgcOOuPN7aL/9C9rC6xGhF5+J2Lm/KtCNKy7GoAluu2oUv6x4h0b7uV0DGzpcvL6/jukZUUxIOXfVXLpc6H/7Peg62jd+1K9vwPTqysDhcKBpGkajurBQFKVvKsucCPH5XqneiDUbCA0TVFc4yRzi2035rc06DrtU86v6kYzBJg7sbae02EHcZdoev7bVSVmTg6tH9GyQaW/JYwdpePJBsGSgff8/EWHBVbrkK8JoQiy/Ffn3PyB3bfG0oA4AmZ8LQ0f1yz0q3rJ0lJnNxU1sKm7khtFmxIixiDu/hyzcDaXFyD3bOFOwGRsPmcMQg4axx5zFYxUxWKJDeGjhIMxddK3rCWnvQG55H/nBOqi3wqDhaN/8CWTP7FFjhs79VcGcWHXKzhnFH9f+gSeu/dWZgcIAdrfOHZOTznu8XP8SHD+E+PoPEcmp/g7Xq7r12/L8888ze/ZsRowYQV5eHn/4wx8QQnDvvfcydepUX8eoKMoAVlXmJCHZSEho7++iCyFISTNRUerA7ZY97izYEzar2l/V35hCNNIyTZSVOBg7KfyybJHv7zbr+qvPoMXGw30PXXZ7fMS0K5HvrUGuexE5Zbbf777LulooOYZYeYdfzxtsRiSEMSbJ03p9WVY8mhBocxbBnEUAyLZWOHUCeeq4J9EqLWZrjZMnR2czuLWMX+16kegDKeiDhp9JukjNRHRzUUG2tiA/ehu5aT20NMOo8Wi3fxfGZfe4I6bdbqeoqIjk5OQetf4OFDF9LubX/8lD7Vt5dfwyXiuqQwLXjYoj4wvDgOWBAuSG1xFXLEabflVgAvaibv12fPbZZ6xevRqA119/nXvuuYeIiAiee+45lVgpitJrzU1uWpp1ho7se4mQJd1EabGDuloXyRbfXcjYat2EhAoio1UldX8yeFgoZSedVJxyMGjYwC9J+6K8ilYSIoxkxvq+zbpsa4GTxwi/6Q46Ys7fgD/QCU1DW3k7+lMPIbd8iJh/nV/PLwtOlwFeZm3Wu7J0VDy/31pBXkUrU9PPTfBFRCRkjT8zfHZzcSN/yq0kKxp+PkgSGT0DeaoYueUDcNg9q1tGI6QN9iRZg4YhModBxhBE2OfJjmywITeuQ378HtjbYeI0tGtvQowY0+vXsWXLFlpaWliyZEmvj+FPIiYOJkxF2/EJt6y8nbHJEWw83nDeMGDZVI/+zB/BkoH48tcDFK13dSuxstvthIaG0tzcTHV1NTNnev5YrVarT4NTFGVgqyzzdPKz9GF/VafEZCMGg6ftuk8Tq9P7qwbSDJ7LQXyigagYjZLjl19i5dYlhVWtzBoU7Z/f2yNFIHVCJk6lw/dnC07jc2DUOOTbLyNnL7jk/hlvkvm5npUVS/CXjPnarEHRmPM8rde/mFid7Z3D9fx1dzWTLRH8bG4GYcbRwGLA02yC6kpk6XE45VnZkgW58NmHnmTLU8vuSbJMIcidn4LbjZh2BeLaVYiMoX16DSdOnODAgQNMmTKF1NT+UyanzV6IXrADDhSQPWHKeavlUtfRn3kS2tvQ7vu1X/9GfKlbiVVaWhpbtmyhqqqKiRMnAtDU1ERIiP8HDCqKMnBUlTmJTzB4pbuewShItBipqnAyPkf65AKyo12ntUVn8HD13tffCCEYPCyE/QUdNDW4iYm7fPbIHbG20+rUyUnzTxmgPLAXQkIxZY2Dxia/nDPYCCHQVt6B/uiPkRvfQiy92afnky4XMj8XuXk9HDuIuP7LPj1ff9HZev3FvVZONdrJjD3/psob++t4vqCWGRlR/PCKNEIM534eCc0AqRmI1Aw4vWdOSunZL3W6hFCeKkYWH4amBsTsBYhrVnplr1B7ezubNm0iMTGRGTP6WSOSCVMgKhq5bRNiwpTzvi3fXwsH8hG3fRuRMcT/8flItxKru+++m3/+858YjUa++c1vArB3794zSZaiKEpPtbXqNNa7GTPJe3epLGkmqstdNDW4iY33/h6oM/urktT+qv4oY0gIBws7KDluZ8KUy6OZAkBeZSuagEkpfkqsDhXCyLEI0+V9A0IMHw2TZyDfX4OcuwQRdf7cnr6SzY3IT99HfrwBGuogyYJYfTdirn/LD4PZNSPieHVfHe8crueb0z8ftiul5N+FVl4tquOqITF8f1YqRq17N+SEEGBOAnPSOZ0XpfTuTb2PP/6Yjo4Oli9f3u8axgmjCTFjHvKTDcjWlnP2Wsrjh5BvvgBTZiOuuiaAUXpft35KiYmJPPzww+d87corr2TChAk+CUpRlIGvqswzVyg13Xtle57Ogu1UV7h8lFi50QwQexmtdgwkIaEaqRmeJhZjJoVjNF4e5Zz5la2MSggnKtT3v7eyoQ4qTyHmLPT5ufoD7cavoj90D3LDG4gvfc1rx5Ulx5Gb3/aUnbmcMDYb7bZvw4ScHnWauxzEhhm5ckg0H51o5KuTk4gMMSCl5Jm8GtYfqmfx8Fi+Nd2CoZtJ1cV4M6k6cuQIR48eZdasWSQlnd9Jrz8QsxcgN61H7voUMc+T7Mu2Fk9r9fhEtNu/O+DK6rtVf/P973+/y6/fd999Xg1GUZTLR2W5k5hYjcho710EhIZpxCcYqCp3eu2YZ7PVuohPMKL5sOug4luDhofickLlKd/8jgSbpg4Xx+o6yPZXGeChQgDE6El+OV+wE+mDEDPne5IgW9/2pUuXC33XZ7h/9xP0h+9D7tnq6aT26z9juO8hxKRpKqm6gKWjzHS4JJuKG3Hrkj/vqGL9oXquHx3Pd2Z4J6nyppaWFj766CMsFgtTppxfRtdvnG7uIbdtBjwrevpzT0NDHdrXf4iIGHgdQ7t1S1dKed7X2tra0DTVFUtRlJ6zd+jYat2MGuf9zar7B4OPAAAgAElEQVQp6SYOFXbQ3qYTHuG99yiXU9LU4GbEmMur8cFAk5BkIDJao6TYTubQgV+qVlDVhgRy/NRmnYOFEBkNmX3bsD+QiOW3IHd9ilz/EuKOe3r8/AuW+81eOCAvTH3h7NbrR60dfFrSxM3jE7hlYmLQrZhIKdm0aRNut5vFixf362ttIQRi9kLkq88gK08hDxdB3jbEqjs8pbID0EUTq29961uAZyBw5393amlpYc6cOb6LTFGUAatzRSnVC90Av8iS5kmsqiucDBnhvSSovs6FlGp+VX/X2cTiwN4OmhvdRMcO7Dv8eRUtRIdoDDf7vuOWlBJ5aC+MnoDoxxeD3iYSkhHzrkNueht59QpPE4RukKXHPc9R5X5esSwrnsc/q6Cqxckd2UmsHBucw5P3799PSUkJc+fOJT6+/48rEDPmIt/4J/qa56EozzPH6+oVgQ7LZy56hXDPPfcgpeSRRx7hnnvOvcsSFxdHWlqaT4NTFGVgqixzEhmlER3r/YuvqBiNiEjN64mVzeoCAfEqser3MoaEcHBfByXFDsZnB/+wzd7SpSS/spXJqZH+KXWqrgCbFXHtl3x/rn5GXPcl5JYP0d/8F4Zv/fSCj/u8u9/bcOwAhIYhrliMWLAUkZrpx4gHnpmZ0czKjCYnLZKrR8QFOpwuNTY2smXLFjIyMgZMgzgREwfjp0DBDoiNR7vrvgF94+WiVwhjx44F4JlnniE0VJW/KIrSd06HjrXaxbCsUJ+UYAghSEk3UXLMjsslvdagwGZ1ExNrwGQKrrIRpedCwzRS002UnXQwZmIYhgG6Z+5kvZ2GDvd582N8RR7aC4AYo/ZXfZGIjkVcfSNy/UvIE0cRQ0ee831V7ud7Rk3w06uCd7aXlJIPP/wQIQSLFy8OuhLFvtAWLEU/UoR29/2eRGsA69atV4PBwMaNGzl58iQdHeeO+/vud7/rk8AURfGuvNxWNE0waVp4QN+wqys8JXW+KAPsZEkzcuKIndoqJ6kZfd9Ho+uS+joXmUMG/p6cy8WgYSFUnHJSWeYkY/DA/LnmV7YCkJ3mnwtzebAQzInghfk9A5G4ejny43fR1zyHdv9/IYTwlPttfhu5o7Pcb7Iq97tM5efnU1FRwaJFi4iOjg50OF4lxmaj/fFfiH7WMr43uvUKn376aUpKSpgyZQqxsbG+jklRFC9ranBTXuLZ1xSfYGDw8MCtQFeWOQkLF8SZfXfRYE4yYjIJqitcXkmsmhrcuF1qftVAkphiJCJSo7TYMWATq7zKVobEhWIO9/3vrdR1OLwPMWn6gLrT7k0iLAKx9Gbky39DvvUS+qFCVe6nAFBXV8f27dv/P3t3HldneSZ8/Hc/Z2PfdxJIIBDITnYTs5tU21qttbVT7Yzd3va1Mzq270zbacfRTh077dhV6ywuM85U61LHWmujUWMSI1khhCQsYQkJAQKcw3o4+3O/fxCIaUI4gbNB7u/nw0c/B3ieK3A457me676um9mzZ1NaWhrucILiWkiqwM/Eqqqqiscee4zY2BBNFVIUJaBONbjQNEhKMXC80kFqhpG4AI4595fXK+ns8JA32xzUiy9NE2RkGznX5kHqEjHJ/hJb1/mNgVV/1bQhhCCvwExttZPBAV9Y/h6Cacjjo7ZriE+UpITmhGeawT4AahngFYn1NyJ3/A75+m+Gl/t95kuItWq537XM5/OxY8cOTCYTW7ZsUTcmpji/usfS0tLweK6NPT8UZbrxeCStLW5y8kwsvS4WzSCoKB9C1y/dRiHYujo86L7gLgMckZlrwu2S9Nh8kz6WrdtHdKwW0PHtSvjNnG1GCDjd5A53KAFXfW4Ir07o+qtqjgAgSqZHw32wCJMJ7d4H0O57EO0HT6BtvUUlVde4Q4cO0dnZyebNm4mJiQl3OMok+XX7df369fz4xz/mpptuIinp4qazBQsWBCUwRVECo/WUG58XZhdZiI7RWLQ8msMfDFF3zEnpotBORGtv9WAyi5AsqcvIMiIEnDvrmVSlSUqJrdtLWqaqVk03UdEamTkmzjS7KVkQNa02fq5ssxNlFJSmh+ZvXNYcheyZiKQQVcimMJGTBzl54Q5DiQCdnZ0cPHiQuXPnMmfOnHCHowSAX1cK27dvB+D555+/6HEhBI899ljgo1IUJSCklJxqcJGUYiApZfjPPWemmc7ZXhpqXGRkm0gNUd+Q7pOcaxseJqGFYPSzyayRmmGko81D6eKJX1wODeq4nFItA5ym8grNdJz10NHmIWfm9Om1qmy3szAzBpMh+FVW6fFAw3HE9duCfi5FmS68Xi9vvfUW0dHRbNiwIdzhKAHi15XC448/Huw4FEUJAmunl8F+nSUrL15esKAsGmuXl8p9djZ8JAGTOfiJTnenF68nNMsAR2TmmDhe6cA+4CN2gj00tu7h/qpQJaBKaGVkGomOEbQ0uqdNYtU+4KZj0BO6/qqmOnC7EaVqGaCi+Ku8vBybzcYtt9xCVFTwN/BWQkM1DCjKNNbc4MZsEeTkXZzMGE2CpaticDok1RVDIYmlvdWDwUhIl9Rl5Qyfq6Nt4j2iti4fJrMgLkG9XE5HQhPkFVjoPufFPjj5frxIUNE2PGZ9aU4I968SGhQvDMn5FGWqO3v2LJWVlSxcuJD8/Pxwh6MEkF9XOENDQ7z00kucOHGCgYEBpLzQ9P7EE08ELThFUSbOMaTTcdbDnLmWy26AmpxmpGheFPXHnWRmu8kN4shpqUs6znrIzDaFdDPWmDgD8Yka59q8FM6d2DGs3V5S0gxqUtM0NnO2mbrjTk43uUPedxgMle2DZMWZyI4PTQVO1lTBrDmIGDU5WFHG43a72bFjBwkJCaxduzbc4SgB5tct2CeffJLm5mZuv/12BgcH+eIXv0haWhof+9jHgh2foigT1NLoAgn5c8a+uCqaZyE51cDRw0MM2fWgxWKz+nC7ZEiXAY7IzDFh6/Lidl/9v8/l1LEP6Kq/apqLjtHIzDZyptkdlmmZgeTx6VSfGwrdNEDHEDTXI9SYdUXxy/vvv09/fz/btm3DbJ4ey4+VC/xKrI4ePco3v/lNVqxYgaZprFixgvvvv589e/YEOz5FUSbA55O0NLrJzDESEzt2b5GmCcpWxyAlHNlvRwbporK91YOmQUZ26BOrrFwTUkJnu/eqv3ekv0olVtNfXoEFl3N4wMpUVtPlwOmVIVsGSP1x0HU1Zl1R/HDq1CmOHTvG0qVLycnJCXc4ShD4lVhJKUdn60dFRWG320lKSqKjoyOowSmKMjHtrR7cLsmsOZZxvzY2zsDCpdFYu3w01rkCHouUko5WN+lZRoym0C+nS0oxYIkSnDt79RfMtm4fmgaJKdNr81jlUhnZRqKixZTf06qy3Y5Rg4WZIeyvMplhTmlIzqcoU5XT6eSdd94hJSWF1atXhzscJUj8Sqzy8/M5ceIEACUlJTz11FM8+eSTZGdnBzU4RVEm5tRJF7FxGulZ/lVaZswykz3DRO0xJ722q6/sXElfjw/HUHiWAcLwthCZ2SY6OzzovquryNm6vCSlGELaF6aEh6YJZs4209nuDeqy2GCraLNTmh5DtCk0w1ZkTRXMKUWY1JImRbmSXbt24XA42LZtG0ajWgUxXfn1yvvVr36V9PR0AL74xS9iNpux2+385V/+ZVCDUxTl6vX1eOmx+sifY/Z74IIQgkXLo7FYBBX7hvB6A7cksL3VgxDDvU7hkplrwusBa5f/SaPXK+nr8YVkM2MlMuQVDCcHZ5oDX7kNBeuQh1O9rtD1V/X3wNkW1V+lKOM4efIkdXV1rFy5koyMjHCHowTRuImVruu89957pKamApCQkMDXvvY17r//fmbMmBH0ABVFuTqnGtxohuFJZ1fDbNFYsjIG+4DOiSOOgMXTcdZDaoYRsyV848rTMo1oBq6qf6bX5kVK1V91LYmJNZCeZeR0kzto/YbBdKQ9xGPWa44CIEpUYqUoY7Hb7ezcuZPMzEyWL18e7nCUIBv3SkfTNN58800MBtVjoCiRzu3WaW1xMyPfjNl89YlMepaJgmILLY3ugDTxD/T7GOzXyc4NX7UKwGgUpGca6WjzXrRdxJXYuob3NEpOU69915L8QjNOh6SzI7BLYkOhst1OcpSBWUnj91YGRO1RiImF/ILQnE9RphgpJe+++y4ej4etW7eiaWo/xOnOr9/whg0b2LFjR7BjURRlks40u9F9MOsKI9bHU7IoioREjSMHhnA5J9dr0tE6nJxlham/6sMyc0w47DoDff79m2zdXuITtQklqMrUlZljwhIlhrcrmEJ8uuRIu52ynNiQ7bkma6qgeCFCUzcfFOVyampqaG5uZs2aNaSkpIQ7HCUE/Frj0tDQwPbt23nttddITU296EX7oYceClpwiqL4T0pJS4Ob5DQDickTX75mMAiWXhfL7rcGOHJgiJXrJn6h1t7qITnVQFR0+JOT4R4vBx1tHhKSrnwhKHVJT7c3qJsmK5FpZIhFY60Lp0OPiOeuPxptTgbcOmXZcSE5n+zqAGsnYtutITmfokw1/f397Nq1i9zcXJYsWRLucJQQ8evqa8uWLWzZsiXYsSiKMgldHV7sgzpzF8RM+ljxiQZKF0dzvNJBS4ObWUVXv7RoyK7T1+OjdHHUpOMJhKhojaQUA+fOeiied+WY+vt8eL2owRXXqLwCMw01Lk43u8d9rkSKinY7AliSNfm/f3/ImioARKm6YFSUPyWl5O233wZg69atIasiK+Hn11XDxo0bgxyGoiiTdarBhSVKBGys+ewiM53tHo5XOUjNNBKfcHXLfTpah/cDCnd/1Ydl5pqoq3aOW4mwdQ/3V6nBFdem2DgDaZnDQyyKSi1T4qKoss3OnNQoEqJC9JytqYKkFMjKDc35FGUKqaqqorW1lS1btpCQkBDucJQQ8usV+N13373s4yaTidTUVIqKijCZIufiSVGuNUODPs61eSmaZ0EL0J5LQgiWrIxh15sDVJQPse6GuKs6dvtZDwmJGrHxkdN/kZUznFida/OQXzh2Fc7W5SUqRhATOzWWgSmBl1dgpqJ8iK5zXjKyIvv9bdDlo97q4Pb5qSE5n9R1ZO1RxIJlUyLpVJRQ6unpYe/evcyaNYt58+aFOxwlxPxKrHbv3k19fT2JiYmkpqZitVrp6+ujsLCQzs5OAP72b/+WwsLCoAarKMrlnWp0IwRXTBYmIipaY/GKGA6+b6f2mJN5i6P9+j6XU8fW5aN4fmQto4pP1IiO1a6YWEkpsXV7SVXLAK9pWbkmTGbB6UZ3xCdWVefs6BKWhmj/Ks62wGA/lC4KzfkUZYrQdZ233noLk8nEli1b1I2Ha5BfVw4zZsxg5cqVfPSjHx19bPv27Zw9e5bvf//7vPLKKzz99NM8/PDDQQtUUZTL8/kkp5vcZOaaiI4JfIUlK9dEXsFwM39GtpG0jPEvMjvODk8DDNSyxEARQpCVY6SlyY3XKzEaL33TcwzpOB1SLQO8xhkMw0MsmutduJw6lqjIrV5WtNmJNWkUp/l342OyRvur1P5VinKRw4cPc+7cOW688UZiY0N0o0OJKH69U+zdu5cbb7zxose2bdvG+++/jxCCT3ziE7S2tgYlQEVRrqzttAePWzJ7EiPWxzO/LJrYOI3KfUO43eOPK29v9RAbpxGfGHkXo5k5JnQfdJ+7/D5FI/tXqcEVSl6BGSmHtzGIVFJKKtvtLMqKxaCFaMx67VHIykWkpIXkfIoyFVitVvbv309xcTHFxcXhDkcJE7+uehITEzl8+PBFj1VUVIw25Hk8HoxGdREyovmki/L3BvF6/duIVFEm41SDi7gEjdSM4P0NGo2CpatjcDkl1YccV9xk1+PW6T7nJWuGKSKXQaSmGzGa4NzZy2+AbOv2YjRBfELkJYVKaMUnGEhJN3C6ye33xtKhdqbPjXXIy9Kc0Nwdl14P1B9T1SpF+RPl5eWYTCY2bNgQ7lCUMPLrSuwLX/gCP/nJT8jLyxvtsTp9+jTf+MY3ADh58uQlFa1r1ZlmF8cqHAA01DgpWRiapRnKtanX6qXX5mPB0uigJzFJqUaKF0RRV+0kM8fDjFmXr5Cda/MiZeQtAxyhGQQZWSbOtXuQUl7yc7N1eUlJMyJCdPdfiWz5BRYq9w9h7fSSlhl5z+nKdjsAZaHqr2o+CS4nQvVXKcqozs5OmpqaWLVqFdHR6rrvWuZXYrV48WJ++ctfcuTIEWw2G2VlZSxdupT4+PjRzy9erO5edZz1UHXQQVqmEbNF0FjrYsYsM3ERNBVNmV6aG1wYjIyZ5ARaUYmFrnYP1RVDpKQbiIm99LndftZDVLQgKSVyn/eZuSbaznjotflITr3wMuh26Qz062pjYGVU9gwTxyoFLU3uiEysKtoGmZloJj02NLHJmioQAuYuDMn5FGUqOHDgAGazWW0ErPi3FBAgISGB9evXc+utt7Jhw4bRpEoZZu3ycrjcTmKygRVrY5m/JBrNAMcqrrxsSlEmyuXSaTvtYUa+GZMpNNUVoQnKVseAhMp9Q0j94ue21yvpbPeQlRuZywBHZGQbEeLCkI0RPVa1f5VyMYNRMCPfREerB5dr/P7CUHJ5dY53OkJXrQJkbRXkFSJi1TWAogB0dXXR1NREWVkZFktgJ/MqU8+YVw8PP/ww3/3udwF44IEHxrxIeuihh4IT2RTS1+PjwJ5BomM0Vq6PxWgSGE2CkgXRHKt00N7qIWemugOuBNaZJje6DrOLQvtCHhNrYMGyGI7sH+JkrYvieRdGqnd1eNB9kbsMcITZrJGSbuRcm4fSRReWbdi6vAiNiK62KaGXV2Ch+aSb1lNuCudGzhYCx84N4dFlyBIr6XRAUz1i6y0hOZ+iTAWqWqV82JiJ1Yeb7zZv3hySYKYi+6CP/bsHMRoFqzfEYbFcKALmzzFzutnN8UoHGVkmjCGqKijTn9QlpxrdpKYbiE8MfRIwI99EZ5uJ+mNOMjKNJJ1fTtfe6sFkFlNiol5mjpETR5zYB33Exg3/DK3dXpKSDRguM4ZduXYlJBlITjVwutFNQbElYqqxle12zAbB/IyY0Jzw5AnweVV/laKc19XVRWNjIytXrlTVKgW4QmJ1/fXXj/7/xo0bQxHLlON06OzbZUfXYe3mOGJiL15ZqWmChcui2fvOICdPOCn1c3NVRRlPZ4cXh11n3uIQXVD9CSEEC5dHY7N6qdg3xPpt8WganGvzkD3DjDYFBj9k5Zo4ccTJuTYvBcUGfD5Jn83H7GL15qhcKr/QzJEDDmzdvojZPLqi3c78jBgsxtBMsJS1VWA0QuG8kJxPUSLdSLWqrKws3KEoEcLvd4eamhqam5txOp0XPX7bbbcFPKipwOOW7N9tx+XQuW5j3JhVg5Q0IzNnm2msczFjtpn4BLXESJm85pMuoqIFWbnhW3JnNmuUrYqhfKed40ccZM8w4fVE/jLAEbFxBuISNM6d9VBQbKHX5kPXVX+VcnnZM80cq3RwutEVEYnVuUE3Z/vd3FiUFLJzypoqKCxFqDvziqKqVcpl+fXu8PTTT1NeXk5JSQlm84VeoUhZDhFqPp/k4PuDDPT5WLkuluRxLsRKF0XR0erh2GEHqzfGXrM/NyUwBgd8dHV4mbsgKuyVobQME4UlFhprXfRYvRiMkJYZ/otOf2XlmmisdeFx69i6hjcMTk5TNz+mE+n1IgKwz6LRKMjNM3PmlJv5bh2zObz7nIV6zLoc6IczzYhb7gzJ+RQl0qneKuVy/Hq32bNnD48++igpKSnBjifi6bqkonwIa5ePpatjyMge/+68JUqjZFEU1YcdtJ3xkJunBlkoE9fS4EYIyCuIjOdRyYIoujq89Pf6yJlpwmCYOjcOMnNMNNS46OzwYuv2EpegXdQnqUxt+vs7kL9+AnHT7YibPo0wTa6aml9opqXRzdlTnrAvGa1st5MeY2RGQoheB+qOAiBK1dYqitLd3T1arYqKipyBNkr4+XUFkZaWhmmSb0jTgZSS6kMOOs56WFAWfVV73eQXmElMNnC80oHXo8avKxPj9UrONLvJnmEiKjoyEgDNIFh6XQxR0YKZsyMj2fNXcooBs0XQ0erB1u1VywCnEen1Il9/AcwW5O9/g/79e5H1xyZ1zMRkI4nJBlqaXGHdRsOrS6rahyjLCd0KCFlzFKJjYFZRSM6nKJFs//79qlqlXJZfV2Zf+9rX+Ld/+zfKy8s5ceLERR/XktpqJ6eb3RTNs1z13UpxfpCFyympO+4c/xsU5TLOtrjxeCSzQjxifTzxCQa2fiLRrwpuJBGaIDPHRHurB69H9VdNJ/LQHrB2on3xG2h//RB4veg//jv0Zx9D2gcnfNz8QjMDfTq95/c8C4e6bgcOr87S7LiQnVPWHIHiBQiDWiqrXNtGqlVLlixR1SrlEn5dRTQ1NVFZWUlNTc1FPVYATzzxRFACizSNdU4aalzkF5qZu2Bif0jJqUbyCsw017uYOctMQpJ6g1L8J6XkVIOb+ESNFNUHFDCZOUbONLsBSElXP9fpQOo68o+/hdx8WLgMoWloDz6G/P3zyB2vIo/sR3z2K4gV66664pObZ+b4EQctTe5x+2uDpaLNjiZgUVZopoJKayd0dSA2fzwk51OUSKaqVcqV+PWu8Pzzz/Otb32LRYuuzb0rzjS7OXHESfYMEwuXRk9q6UXpoijaWz1UVwyxZlOcGmSh+K3H6qO/18fCZZN7DioXS88yoWlgMotLtkxQpqjqw9B2GvGl+xHa8O9UWCyI2+9GrlyP/t+PI//jX5DlO9Hu/BoiLdPvQxtNw0Mszra4mV8WjSkM+xNWttspSYsm1hyaGwGypgpQ/VWKonqrlPH4dRVhsViYN+/a3LfiXJuHqoNDpGUYKVsdg5jkFDazRaN0URS2Lh9nWzwBilK5Fpw66cJoghlX0dunjM9oFMwqspBfaFYJ6zShb38ZUjMQy9dd8jmRV4D2nR8hPvsVOHkC/R/+Ev2t/0X6/F/al19gxucbXpobar1OL402Z8imAQJQcxQSkiAnL3TnVJQIpCYBKuPxK7G64447+M///E96e3vRdf2ij+nM1uXl0Ad2EpIMrLg+NmDTzvIKzCSlGDhR5cDjVoMslPG5nDptrR5mzjJjDMMd8ulu/pJo5i5QG3hPB/LkCWioQWy7dcwx60IzoG25Ge37j0HpYuRLz6D/0zeRLQ1+nSMxxUBCkoGWxtAnVkdGxqznhGjMupTI2ipEyWJ140G5pnV3d9PQ0MDixYtVtUoZk19LAUf6qHbs2HHJ51544YXARhQh+nt9HNhjJzpGY9X62IBezAoxPMhiz45B6o45WLA0NOvklamrpcmN1GHWnMgaWqEokUb/48sQl4BYu3XcrxUp6Whf/y5UlKM//+/oD/8/xJabEbd8DhE1dqIthCC/wEx1hYNem5eklND1WlW22UmwGChMCdGFXdtp6O+F0muzFUBRRhw4cACTyURZWVm4Q1EimF/vBo899liw44goQ3Yf+3YNYjDC6g1xWKIC33eRlGIkv9BMc4ObmbMtJCarpnnl8nRd0tLgIi3TSFyCep4oylhkazNUH0LccifC4t9NCCEELFuDVroY+b/PIt/+HbLig+Heq0Urxvy+3Hwzx6sctDS6Q5ZY6VJS2WFnSXYsWsjGrKv+KkWxWq00NDSwYsUKVa1Srsivd4P09PRgxxExXE6dfe/Z0XVYuzkuqM3sJR8aZLF2sxpkoVzeuTYPTodk4TJVrVKUK5F/fAUs0YhNH7vq7xUxsYg7/y9y1Qb0Zx9H/+U/IpZfPzw9MDH5kq83mQWZ2Sa6z3kDEbpfmntc9Dl9Ie2vkrVHIT0LkZoRsnMqSqTZv3+/qlYpfvErsRoaGuKNN97g1KlTOJ0X78H0ve99LyiBhYPHI9m/247DoXPdxjjiE4NbHTCbhwdZVB100HpquHKlKH/q1Ek30TGCzGy1x5KijEV2dSAP7kFsvQURO/H9ncSceWgP/Az55v8iX38BeaIS8am/QFy/bXTC4IjEFAPtrR7cbh2zOfgTJSvbzvdXhSixkj4f1B9DrLh0CIiiXCtUtUq5Gn5dqf3kJz9B13VWrlx5yT5W04XPJzn4vp3+Xh8r1sWGbKPQmbPNnG5yc6LKSWauKSRvzsrUMdDvo7vTS8nCqElPpFSU6Uy+9SoYNMTWT0z6WMJoQnzsM8hla9H/51fI//4Vsvw9tD//OiJ75ujXJZ1fwt1n85GeFfzX7or2QWYnW0iODtFNllMnwTGklgEq17SR3io1CVDxh1+vzidPnuSpp57COMaEpalO6pKKfUNYO72UrYohM9sUsnOPDLLYvWOQumonC5epQRbKBadOutC04UmSiqJcnuzvQe59G3HdZkRSasCOK7Jy0b75A+QH7yJfehr9ofsQH70dcdOnESbTaG9sX4+P9Kzgvm8MeXzUdjm4tTQlqOf5sJH+KuaqwRXKtclqtXLy5EmWL19OdLSaHKuMz69bbCUlJZw9ezbYsYSFlJKjhx10tHqYXxbNjFmhv4BNTDYye46ZUw1uem2hW6+vRDavR9J6yk32TFNQBqgoynQh33kdvB7Etk8G/NhCCLS1W9C+/zhi+Vrk73+D/v17kXXHMFs0YmI1env83wNroo52DOGToRuzDuf7q2bORsQnhOycihJJ1CRA5Wr5VYK65557eOSRR5gzZw5JSUkXfe72228PSmChUnfMyekmN3NKLRQUh6/Hae6CKNrOeKg+7OD6G9QgCwVaW9x4vTBbjVhXlDFJxxBy5xuw9DpEVm7QziMSkhBf/iZy9Sb0Xz+B/i9/h7h+K4mzP0+fLWinHVXZbifKqFGSFppVDdLlgsYaxOaPh+R8ihJpVLVKmQi/Eqvnn38eq9VKeno6Dodj9PHpcPF/8oSLvAIzJQvD25BoMmuULo7myP4hTje5yS9UF9PXMiklp8q9AWUAACAASURBVE66SEw2kJSqRqwryljk7u3gsKPd+KmQnE8sWIr24GPI3z+P3PEqCfZc2lM3B3WAhZSSijY7i7JiMAVoo/pxNZ4ArxdRovqrlGuTqlYpE+FXYvXBBx/w85//nOTkS0fOTnVZM0wsWhYdEUnijHwTp5sM1Bx1kj3DhNmiln9dq6xdPgb6dRaviIznpqJEIulxI3f8DkoXI2YVhey8wmJB3H43emYOCa/vhNTNQR1g0TbgodPu4bZ5oeyvOgoGIxTNC9k5FSVSqGqVMlF+vQtkZmZiMEzPu+ZLV8dEzLQ1IQQLl8bg9UhqjjrH/wZl2jrV4MJkFuTkqaEVijIWWb4T+nrQbgrPknRx3WYSzUMA9NqC12dV0TYIhG7MOpwfXFFQjIhSF5XKtefgwYOqWqVMiF8Vq3Xr1vGjH/2IG2+88ZIeqwULFgQlsFAxhGpZhZ8SkgzMLrLQVD+8RDE5dXpOYlTG5nTodLR6mF1swWiMrOenokQKqfuQb74C+XOgJDxT64TRiOXGm4mu76TvVBTMywvKeSrb7eTEm8iKD82NFmkfgNONiI9/NiTnU5RIYrVaqa+vV9UqZUL8ump/8803geFeqw8TQvDYY48FPqprXPGCKM6edlN92MG6G+IipqKmhEZLowspYdYcVa1SlLHIw+XQ2Y72tW+HdbmsWLWRxGMf0GedgZQy4LG4fTrV54bYWpgY0ONeUV01SKn2r1KuSapapUyGX4nV448/Huw4lA8xmQTzl0RTsW+IliY3s9RUuGuG7pO0NLrJyDYSGzc9l98qymRJKZHbX4asXChbHdZYhNFIYl4KHf0puCsOYVm2IqDHP9HpwO2TLM2JC+hxr0TWHAVLFMwOXd+aokQCm81GfX09y5YtU9UqZULUdIQIlZNnIjXDSG21E5dTD3c4Soi0n/XgckqVTCvKlZw4AqebEB+5DaGF/20safFwAtL7XjlSyoAeu7LdjlETLMgM3ebxsrYKihcgjMHd9FhRIs3IJMClS5eGOxRlirpixeqBBx4Yd1nDQw89FNCAlGHDgyyi2fXmADVHnSxZGbo3VSV8TjW4iInVyMhSvXWKf2T9MfTXnkf74l8jUtLDHU5I6H98GZJSEas2hjsUAJJSTYCDPoeFjKr9sCRwVbTKNjvzMqKJMoYmgZS2bug4i1j3kZCcT1EihapWKYFwxau3zZs3hyoO5TLiEw0UzLXQWDs8yCIlTV1sT2f9vT5sXT7mLY5SfXWK3/S3X4O6avRffB/tb3+IiAnd5LhwkE11UFeN+PQXEabIqKiYLRoxsYK+9JLhJHfxqoD0WnUPeWjpc/EXBaFLmGVtFYDqr1KuOWrfKiUQrnilvnHjxhCFoYyleF4UZ1vOD7LYGoemLrinrbYzboSAmbPV0ArFP9LlhOMVUFgCp06i/+sP0e59YFov4dK3/xZi4hDrt4U7lIskJhvpc5bAoWYIUNXqSLsdgKUhHLNOzVGIS4Dc/NCdU1HC7MPVqpgYtUJImbjwL05XrshoEswvi6a/10dLozvc4ShB1GP1EZ9oUBtDK/47XgFuN9otdyL+/K+gpgr57GMB7/OJFLL9DFTuQ2z+GCIqsi5+ElMMDPkseLJmo7/2fEB+BxVtdlKijeQnhabnUkqJrK1ClCyKiN41RQmVgwcPYjQaVbVKmTT1yjkFZM8wkZZppLbaoQZZTFNSSnptXpJT1SRAxX/ycDnExUPxArQ1mxG3fA5ZvhP52vPjf/MUJN98BcxmxOaPhzuUSyQlD//t9m+6E840w5H9EzqOxyep63bwuxoble12yrJjQzdOvuMs9NqgNDz7gilKONhsNurq6li8eLGqVimTppp2poCRQRbvvTnAiSoHZaumdw/Ftcg+oOP1QFKKSqwU/0iPB1l9ELF0DcIw/LwRH7sDujuRr/8GPTUd7fqtYY4ycKStC7lvF2LDjYj4EO7p5KfE84lVX+YCUjOy0X//PNqS8Xut+pxearsd1HYNfzTYnLh9w9WurDgTH5ubHPTYR1zor1oSsnMqSripapUSSGMmVt/97nd5+OGHAXjppZf49Kc/HbKglEvFJRgonGuhocZFXoGX1HSVE08nPVYfAEkp6veq+Km2ChxDiGVrRh8SQsBd9yB7rcj/fhyZlIpYMD3GBssdvwOpI7bdGu5QLmt4gIVGX59EfOwO5DM/G65afWifLV1KWvvd1HY5qDmfSLUNDC/xNmpQmBLFTUVJlKRHU5IeQ0p0aF8P5IkqSM1ApGeF9LyKEi4jvVVlZWWqWqUExJiv2m1tbbjdbsxmM6+//rpKrCJA0eggiyHWb4tXgyymkV6bF4MR4hPU6lzFP7KiHKKioeTi6W3CaET72rfQf/Qd9H/9Z7S/fQSRVxCmKANDDvYjd7+JWLkBkZoR7nDGlJhsoM/mQ9y0AfmHFxh6/SUasxdeqEh1O7C7h5dzJ1gMlKRHs7UwkZL0aApTorCEaKT65UjdB/XViKVrxv9iRZkmDh48iMFgUPtWKQEzZmK1YsUK7rvvPjIyMnC73fzDP/zDZb9O7WMVOkbj8CCLQ3uHONXgpqBYbSI7XfTafCSlGNWYdcUv0udDHtmHWLTisiPHRVQM2r0PoD/yN8Nj2L/zY0Tq1N3jSr77B3C7EDd+KtyhXJExDoZadZ481E3Nkq/T7DKgv3MGgLxEM2vz4ilJi6Y0PYbseFPoeqf80dIEQ3YoUf1VyrWhp6dHVauUgBszsbrnnnuora2ls7OThoYGNm3aFMq4lDFk5ZpISjFwtkUlVtOFzyfp6/VRqH6fir9OHofBAcTSNXi9Xjo6OsjOzsZguNCjJ5JS0e79B/R//hb6Lx5C+9YPETFxYQx6YqTLiXz3dVi8EpGbF+5wLnK2301F2yC13cNL+6IcGjcZUqhuHiIhNZHbjr9Hiaebkr+6l/ioyB6Bf6G/SiVWyrXhwIEDqlqlBNwVF3CXlJRQUlKC1+tVe1pFCCEEaZlGGmtdeL0SozGC7ngqE9Lf60PqkKQmAip+khXlYDbDgqUcOnSIAwcOEBUVxdy5cyktLSU9PR0hBCI3H+3/fgf95w+h/+oRtL9+cMrtcSX3vAX2AbSbbg93KAD4dMmhs4O8Ud/DkY4hANJjjMxLj2ZucjQcg79clE3xvCj0uDzk079FO7EGll4X5sivTNZUQW4+IiF0wzIUJVxUtUoJFr86Yzdv3syxY8fYvXs3PT09JCcns379ehYsWODXSbq7u3n88cfp7e1FCMENN9zARz/6UQYHB/npT39KV1cX6enp3H///cTFxSGl5JlnnqGyshKLxcI999xDQcFwj8B7773HK6+8AsBtt902mvA1NTXx+OOP43a7KSsr4wtf+EJkLbMIoNR0Iw01LnqsXtIzp9ZFknKpXjW4QrkKUteRleUwfymYLdTU1JCRkUFiYiLV1dVUVVWRlpZGaWkpc+fOJaZ0MeLuv0I+9VPkf/0Svnj/lHltlF4PcserUDwfUVgS1lj6nV52NPax/WQPnXYvqTFG7lycxqbZiaTHXngdfqe5n/7e4b9psXI98vUXLkwIjNC9oaTHDQ01iA03hjsURQkJVa1SgsWvK7l33nmH559/ns2bN1NUVER3dzc///nPueOOO7jhhhvG/X6DwcDnP/95CgoKcDgcfPvb32bRokW89957LFy4kFtvvZVXX32VV199lbvuuovKyko6Ojr4xS9+wcmTJ3nyySf5p3/6JwYHB3n55Zf54Q9/CMC3v/1tli9fTlxcHP/xH//BV7/6VYqKinjkkUc4cuTItB2dmZxmBAG2LpVYTQc9Ni9R0YLomMi86FIiTHM99NoQS6+jtbWVgYEB1qxZw9y5c3E6ndTX11NTU8OePXvYu3cvs2bNorS0lLxb7kT73a+Hp77dele4/xV+kQd2g60b7a6vhy2Gk1YHb9T3sOfUAB5dsjAzhi8uzWTljDgMl+mJHBlgASAMBsTH70A+/VM4sg8idTBEQw143Ig/GYSiKNORqlYpweRXYvXaa6/xve99j1mzZo0+tmbNGh599FG/Eqvk5GSSk4eXF0RHR5Obm4vNZuPgwYM8+OCDAGzYsIEHH3yQu+66i0OHDrF+/XqEEBQXF2O32+np6eH48eMsWrSIuLjhPoFFixZx5MgR5s+fj8PhoLi4GID169dz8ODBaZtYmUyChEQDti5fuENRAqDX6lPVKsVvsqIcDEbEohXU7i3HbDZTWFgIQFRUFIsWLWLRokVYrVZqamqora2lqalpeKng8hsoeecN0lMz0NZtC/O/5MqkriO3vwIzZkGIR8Z7fDrvtwzwh/oeTlqdRBk1bihM5KPFyeQlXbkXMjHFQHurB7dbx2zWhqtWf3gR/fe/QVuyOiKrVrL2KGgaFM8PdyiKEnRqEqASTH5dzQ0MDDBjxoyLHsvJyWFwcPCqT9jZ2UlzczNz5syhr69vNOFKTk6mv78fGN5XIC0tbfR7UlNTsdls2Gw2UlNTRx9PSUm57OMjXz+dpaYbON3kRtelGrs+hbldOvZBnZmzzeEORZkCpJTDywBLF+E1WWhoaKCoqAij8dKX8tTUVK6//nrWrFnD6dOnOXHiBNVNTVQVrSbtg4OUDriYu3FL5N6xPXoA2s8gvvzNkC1d7LJ72H6ylx0NvfS5fOQmmPk/yzPZVJBAjMm/HsikkY2CbT7Ss7TzVavPIJ+K3KqVrKmC2cWI6Ah9LihKgPT09FBXV8eSJUsi97VPmdL8SqxKSkp49tlnufPOO7FYLDidTp577rnRCpG/nE4njz76KHffffcVn9BSykseG+uNVQhx2a8fy9tvv83bb78NwA9/+MOLEripJL9gkOaTHQgZT1paVLjDiShGo3HK/F7PnrYD/eQXpJCWpl7kr2Qq/V6DxdNcj62rg/jb/4L6c+fweDysXr163J9LRkYGy5cvZ2hoiKMVhzn0x9fZ09DM3qanmTt3LmVlZRQVFV00VTCU/vR3K6WkZ8fvEJk5pN54C8IQvIqulJKK1j5ermrn/SYrANcXpHDbohyWz0y86qQuLs7Hvl3NeNxRpKUN3ziUN30S6x9/i3jjJVJu+HhEVa10+yBdLQ3EfuoviAvw35f6m52epvLvddeuXRiNRrZu3Tq6+km5YCr/biOFX+9WX/nKV/jZz37G3XffTVxcHIODgxQXF3Pffff5fSKv18ujjz7KunXrWLVqFQCJiYmjwzB6enpISEgAhu+0dnd3j36v1WolOTmZlJQUTpw4Mfq4zWZj3rx5pKamYrVaL/r6lJSUy8Zxww03XLR88cPnmUpMluFNJptOdiMMKrH6sLS0tCnze21pdgIgNDvd3UNhjiayTaXfa7Do7/4RhIZ9znwOvrOThIQEYmNjr+rnMqeklMKsDLp+/AC10YnUNZuoqakhOjqauXPnMm/evJC/sf7p71bWVaPXH0fc+TWsPb1BOeeQx8fOpn7eqO+htd9NvMXAJ0tTuLEomYw4E+C96H3lasTEarS19pOTd2G5tn7T7cinfkL3jtcRyyKnaiWP7Addx5FfhDPAf1/qb3Z6moq/V5vNxp49e2hpaWHp0qU4nU6cTme4w4o4U/F3Gyo5OTl+fZ1fiVVycjIPPfQQVqt1NBH68NK78Ugp+dd//Vdyc3P5+Mc/Pvr48uXL2bVrF7feeiu7du1ixYoVo49v376dtWvXcvLkSWJiYkhOTmbJkiU8//zzo0sQq6qq+NznPkdcXBzR0dHU19dTVFTE7t27ufHG6T3dKCpaIzZOw9rtpTDcwSgT1mvzEpegYTKr5ZzK+GRFORTNY1AYOHPmDKtWrZrQMjmRlEr6Pd8i9Z+/xWp3H62f+zo1jc0cPXqUI0eOkJ6ezrx58yguLiY6OjoI/5Ir0//4MsQnItZsCfixW/tcvFHfw7tN/Ti8OkWpUdx3XTbX58djNgSmkvThARYjxMp1FyYElkVOr5WsqRoe3V8wN9yhKErAORwODhw4wNGjRzGZTFx//fUsXqyGtCjBc1XrK1JTU68qoRpRV1fH7t27ycvL42/+5m8A+LM/+zNuvfVWfvrTn/Luu++SlpbGN77xDQDKysqoqKjg3nvvxWw2c8899wAQFxfHpz71Kb7zne8AcPvtt4+Wcr/85S/zq1/9CrfbzZIlS6bt4IoPS0k30nHWg5RyyoxPVi6QUtJj9ZGZrSY7KuOTHa3Qdhrx2f9DbW0tMLxMe6JEbh7aPd+Bnz1I/uu/ZvZ9D+L0eqmvr+fEiRPs2rWLPXv2MHv2bJYvX05mZmag/ilXJE83wvFKxCc/jzAHZtNsny45eHaQP9T3cLRjCKMmWJcfz0eLkylOC3zi+KcDLACEdn5C4FM/gcp9ECFVK1lTBXPmI0zqdUiZPnw+H9XV1ezfvx+3282CBQtYtWqV6qtSgi4ko8hKSkp48cUXL/u5Bx544JLHhBB8+ctfvuzXb968mc2bN1/yeGFhIY8++ujkAp1iUtIMnGl2MzigE5+gNpedahxDOm6XVBsDK36RFeXD/7NkFbVvbCcnJ4fExMRJHVOULELcfS/yqZ8g/+sXRH3pGyxevJjFixfT3d3NiRMnqK2tpbW1lTvvvDMkPQly+ysQHYPY+NFJH8vt03mttoft9T10DXlJizHy+cXpbJ2TSGJU8N7+RgdY9PhIz7xQmRIr1yH/EDlVK9lrGx4QsubS91RFmYqklLS0tLBnzx56enqYOXMm69atU31DSshExloEZUJS0ocvDGxd3jBHokzEhY2BVWKljE9WlMPsYjo9Pnp6eigtLQ3IcbXVGxGf/Dxy/y7kq/8z+nhaWhrr16/n05/+ND6fj3feeeeqBgVNhOxsQx7ai9hwEyImdtLH+3VVN/99pIvsBDPfWZ/Lv99SyO0LUoOaVMHwUkDg0uWAmgHx8c/C2RaoLA9qDP6QtUcBEKVLwhyJokye1Wrld7/7Ha+99hpSSm6++WZuvfVWlVQpITVuYqXrOseOHcPrVRfvkSY2TsMSJbCqxGpK6rH60DRISFKJlXJl0toJLQ2IpddRU1ODwWBgzpw5ATu+uOl2xPqPIN94CX339os+l5yczNq1a2lpaeHYsWMBO+flyDdfBYMBseXmSR+rx+HljfoeNs1O4B+35LF6ZvxlN/QNBrNFIyZWo7fn0r0GxYrrISsX/fe/Qep6SOIZU20VxMbDzNnhjUNRJsHhcPDee+/x3HPPce7cOdatW8edd97J7NmzVZuEEnLjJlaapvGjH/3osvukKOElhCAlzagqVlNUr81LYrJB7UOmjGtkGaC+eCX19fUUFhZisQSm/wiGX0vE574GC5cjf/2vyOpDF31+0aJFzJw5kz179tDbG5wpfbLXhvzgbcSaLYiky091vRr/e8KKV5fcsTA8d6svN8ACIqdqJaUc7q+auzDsSxIVZSJ8Ph+VlZU8++yzVFdXs2DBAv78z/+csrKysG0doSh+vZqWlpZSX18f7FiUCUhJN+IYkjiGwnznU7kqui7p7fGpZYCKX2RFOcyYxSm7C5fLFbBlgB8mDAa0//M3MGM2+r/9CNnScOFzQnDDDTegaRo7duxAD0KlRb7ze/DpiI/cOulj9Ti8/PFkLxtnJ5AdH57NtxNTDAzZddzuS39Ww1WrGeGtWnW2g60bUbooPOdXlAmSUtLc3Myvf/1r9uzZQ0ZGBp/73OfYtGlTWKaYKsqH+VWGSk9P55FHHmH58uWkpqZeVFq94447ghacMr7U9OELc1uXl9z88FxAKFdvoM+H7oPkVFUJVq5M9vVAYw3i45+lpqaG2NhYZs6cGZRziahotHsfQH/kb9B/+Y9o3/4RIm14GmB8fDwbN27krbfeoqKiguXLlwfsvLp9ELnrj4jlaxEZ/u0VciWvnK9WfWZB+HorxhpgAR+aEPjko1DxASy/PqSxya4O9P/8xXAs81R/lTJ1WK1Wdu/ezZkzZ0hKSuLmm29m1qxZasmfEjH8qli53W5WrFiBEAKbzYbVah39UMIrIdGA0Yjqs5pies8vEVITAZXxyMp9ICXOBctoaWlh7ty5aEFcuiUSk9HufQA8bvRffB9pHxz93Ny5cyksLGTfvn0B3UTSsf0VcAwhbrxt0seyObxsP9nLxtmJYatWwdgDLEaEo2olpUTf8xb6Q/fB2VOIL90fkERWUYLN4XCwc+dOnnvuOTo7O1m/fr3qo1Iikl+3y0f2kVIij9AEyWlGbN0qsZpKeqw+TGZBTKzqbVCuTFaWQ0YOdX12dF0PyjLAPyVy8tDu+S76zx5A/9U/of31QwiTCSEEmzZtor29nbfeeovPfOYzk+6/lW4XQ79/AeaXIfImv935hWrV1e+5GEhmi0b0GAMs4OKqlTz8wXCiFUSyrwf92cfg6EEoWYR2932I1PSgnlNRJsvn83H06FH279+Px+Nh4cKFrFq1Si35UyKW3++Ira2t7Nu3j76+Pr70pS/R1taGx+MhPz8/mPEpfkhJN1JX7cTt0jFb1IX6VNBr85KcalB32pQrkvYBqKtGbLuV2tpaMjIyJrRJ+0SIuQsQd9+HfPJR9O99FeKTIDoGS1Q0m4wx/KF7iP3PPsV1mckQFQNR0YioaIiOBkv06GNERw9/Trt8dVZ+8A6yrwftK/9v0jHbHF7ePNnLpjBXq0YkjTHAYoRYcT3yDy8if/88ctmaoA2RkIf3ov/Pr8DlQnz2K4hNH1MDK5SINtJH9f7779Pb20teXh7r1q0L2eufokyUX4lVeXk5Tz75JKtWrWLv3r186UtfwuFw8Nxzz/H3f//3wY5RGUdq2vn9rLp9ZOWqN8tI5/VIBvp0smeYwh2KEuFk1QHw+bAWLqRr1/usX78+pOfXVm1AlzpUHUQ6HeAcgu5+ZjkdlMZlUiHTya/6PdlDfcPxXulgZvOFZCvqQ4lXcz2m4vn4ihdMOt5XjkdGtWpEYoqB9lYPbreO2Xzpa/No1eo//iUoVStpH0Q+/2/I/bsgfw7al76ByJ4R0HMoSqB1d3ezZ88ezpw5Q3JyMp/4xCeYNWtWuMNSFL/4lVi9+OKL/P3f/z2zZs2ivHx4PGx+fj6nTp0KZmyKn5JSDWga2Lq9ZOWqi/VI19szvGwzKUUNrlCuTFaUQ0oatf12NE2juLg45DFoqzfB6k2XPL7e5aL1ued4p2wLn/3EzZh9HnA5wOEAp+NCIuZ0fOhj6OLP9VohLoG4O79K/ySrt9YhD9tP9rK5IJGsCKhWwZUHWIwQy9ciX38h4FUreaIS/ZlfQH8P4hOfG96rTG2bokQAr9fL0NAQdrudwcHBS/7b1taG2Wxmw4YNLFiwQI1OV6YUv15l+/r6LlnyJ4RQy5gihMEgSEw2qP2spoheqxpcoYxPOofgeCVy/Ueoq6tj1qxZxMTEhDusURaLha1bt/LKK6/wQdVRNm26OPm6mncHc1oaTHIYxisnbOhS8un5kVGtgosHWKRnXv6m18VVq72IFesmdU7pciF/+5/InX+ArBloX/87xKyiSR1TUfwhpcThcIwmSWMlTk6n85Lv1TSNuLg4YmNjKSsrY/ny5URFRYXhX6Eok+NXYlVQUMDu3bvZsGHD6GN79+5lzpw5QQtMuTqp6UYa6134vBKDUSW8kazX5iMmVsOi+uGUK5DVh8HroTVvLkOHqygpKQl3SJeYMWMGZWVlVFZWUlBQELaeW+uQZ7i3KoKqVTD+AIsRF6pWvzlftZrYTRfZVIf+9M/g3FnEDZ9AfPLzCHPgNpJWFJ/Px7Fjx2htbb0oYRr5uNwedzExMcTGxhIfH09WVtZoAhUbGzv6/1FRUepmvTIt+JVYfeELX+AHP/gB7777Li6Xi4cffpi2tja+973vBTs+xU8p6UYaal302LykZajlgJGsx+olNV0tyVHGUVEO8YnU9A8RFRUVsT0G1113HS0tLbz99tvceeedYbnL/NvjVnQZOb1VHzbeAAs4X7W6+bPIf//x+V6rq6taSa9nODF742VITkX75g8QJWrjXyXwysvLqaioAMBsNo8mR7m5uRclSiP/jYmJUUv5lGuKX1d3ubm5/OxnP+Pw4cMsW7aM1NRUli1bpsq0ESQlbWSjYJ9KrCKY06HjdEiSUtQbjTI26XEjqw/hXrmepqYm5s+fP+mx5sFiNBrZtm0bL774Irt27eIjH/lISM9vHfLwZkMfmwsSyYyLnGrViPEGWIwQy9Ygs2deddVKnj2N/vRP4XQjYs0WxB1fRsTEBip8RRlltVqprKykrKyMVatWYTZH3t+booSb32uRLBYLJSUlzJs3j9LSUpVURRiTWSMhUVMbBUe4CxsDR+ZFshIhjleCy0lDVgE+ny8ke1dNRkZGBitWrKCuro76+vqQnvvl41aklHw6AqtVcPEAiysZrlr9GbSfQR7aO+5xpa6jv/Uq+g/uh55utHv+Du0L96mkSgkKKSU7d+7EYrHwkY98RCVVijIGv67uuru7+cUvfsHJkyeJjY3FbrczZ84c7r33XtLT1QaDkSIl3ciZU250XaJpaq1yJOqxehECEpNUxUoZm6woh5hYavsGSU5OJiMjI9whjWv58uWcOnWK9957b3RZULB1D3l4q6GPLYWRWa0C/wZYjBitWr3+AnL52rH3/uo+h/7Mz6H+GCxZhfb5ryMSkgIeu6KMqK2tpa2tjS1bthATE8PQ0FC4Q1KUiORXxerxxx+noKCAZ555hieffJJnnnmGwsJCHn/88WDHp1yFlHQjPi/09175zqgSPr02HwlJBjVgRBmT9HqRVQfoX7iS9vYOSktLp0RTt8FgYOvWrXg8Ht555x2kvOKuVgHx8rHz1ar5aUE/10T5O8ACQGjaFatWUkr093egP3Tv8NK/u+9Du+fvVFKlBJXT6eT9998nOzubefPmhTscRYlofiVWTU1N3HXXXaPL/6KiorjrrrtoamoKanDK1UkZ2ShYLQeMSFJKem1ecPlKDAAAIABJREFU1V+lXFl9NQwNUps2E4C5c+eGOSD/paSksHbtWk6dOsXx48eDeq4uu4cdjX3cUJhERlxk95X6M8BihFi2BnLyhnut9AvfI/t70B9/GPlfvxze7PfBX6Kt3TIlkm5laisvL8fpdLJp0yb1fFOUcfiVWBUVFdHQ0HDRY42NjWHZrFIZW3SMRkyshrVbVawi0eCAjtcDyWr/KuUK5OFypCWKup4BZs6cSXx8fLhDuiqLFy9mxowZ7Nmzh76+vqCd57fHrYDk9gjat2osiSkGhuw6bvelo6j/lNA0tJs/Cx2tyIPvAyArPkD/h7+C45WIO76E9o1/RKRG/vJQZerr6OigurqaxYsXk5YWuZVhRYkUY/ZYvfDCC6P/n5mZySOPPMLSpUtJTU0dnQxz/fXXhyRIxX8p6QY6271IKdWdpQgzujFwihpcoVye1H3II/ton7eC/oEBVl93XbhDumpCCLZu3cqvf/1rduzYwW233YamBXbPtuFqVS9bCiK/WgUXD7BIz/TjZ7H0fNXq9d+gH6tA7ts5XKX64l8jcvKCHK2iDNN1nZ07dxIbG8uqVavCHY6iTAljvsJbrdbRD4/Hw6pVqzCZTPT392MymVi5ciVutzuUsSp+SEkz4nZJ7IPj3xlVQqvX5sVohLh4tTGwMobGOujvpTY5G5PJRGFhYbgjmpD4+Hg2bNhAW1sbR44cCfjxXz5uBYjYSYB/6sMDLPxxoWp1FnlgF+Lmz6J9+0cqqVJCqrq6mq6uLtatW4fFojaaVhR/jHnr/J577gllHEqApKRf6LOKi1dLziJJj9VHUooRoSY2KmOQFR/gMZlp6B2gqKgIkynyqzFjKSkpobGxkQ8++ID8/HxSUwOTBHXZPbzd2MsNhUmkx06Nn8/VDLAYtXQN4tNfQBQtQMwuCl5winIZdrud8vJyZs6cSVGRev4pir/8vnXucrloaWmhrq7uog8lssTFa5gtQu1nFWF8Pkl/r48k1V+ljEFKiawop7lkOR6Ph5KSknCHNClCCDZv3ozFYuHNN9/E5wtM7+dLx4arVVOht+rDrmaABZyvWm37pEqqlLB4//338Xq9bNy4UbUVKMpV8KvZY9euXTz99NMYjcZLNoV74oknghKYMjFCCFLSjNi61ACLSNLf40NK1ERAZWwtDWDrorZ0DfFSIzc3N9wRTVpMTAybN2/mD3/4AwcOHOC6SfaMdQ56eKepl61TqFo1IjHFQHurB7dbx2xWy4GVyNXa2kpdXR0rV64kOTk53OEoypTiV2L1P//zP3zzm99k0aJFwY5HCYCUdAMdZz04HTpR0eoNPBL0nL9TnZyqBlcolycryhk0R9E6MMSKFSumzV3iwsJCSktLOXToELNmzSI7O3vCxxrurRLcPkV6qz7sqgdYKEoY+Hw+du7cSUJCAsuXLw93OIoy5fj16m40GtWmcFNI6vk+K7UcMHL0Wr1ERQuV6CqXNbIMsH7OEqSUU34Z4J9av349cXFx7NixA4/HM6FjnBt083ZjL9vmJJIWM7WqVXD1AyyUMUgfUf2HSW59gtju7aCrIVqBVFlZSU9PDxs2bMBoVDcCFeVq+XWVd8cdd/Dss8/S398f7HiUAEhIMmAwqo2CI0mvzafGrCtjazuDPHeWupgUsrOzSUpKCndEAWWxWLjhhhvo7e1l7969EzrGy8etCCH41BTrrRoxoQEWygVSxzJwlJTTPyeh82U07wCxvbtIPf1zTEMN43+/Mq7+/n4OHDhAQUEBs2fPDnc4ijIl+XWll5OTw4svvsibb755yec+vN+VEhk0TZCcasTWrRKrSOB26dgHdfIKzON/sXJNkpUf0BWdgM3lZnNpabjDCYqZM2eyZMkSjhw5QkFBAXl5/o8OPzfo5p3GPm4sSpqS1aoRVzvAQgGkxDxUS6x1ByZ3O15zBn1Zd+KKnY/J0UR81/+S3PYUjvgyBtM+hjTEhjviKWv37t3AcIVZUZSJ8Sux+uUvf8n69etZs2bNJcMrlMiUmm6k7pgTj1vHpBqlw6rXNrIxsBpcoVyePFxO7ax5GAyGaT3aeM2aNbS0tLBjxw7uuusuv/fGeemYFW0KV6tGqAEWV8c01Eic7S1MztP4jCn0ZX4GV9xiEMM/O09MIbaZ9xHb8y4xPbux2OsZSP8YrrglME16FEOlubmZpqYm1qxZQ8L/Z++846M6r7z/vXd6bxoVkKgSAgECTO/ddAzG2Emc5s1mnc0m2U225d2STfK+mzjZTbIpTnaT7KatYxvjRjem92YEyBTTu+r03u59/xBgME3ASDOS7vfzmY/gztx7z8ydO89znnPO71ituTZHQaHD0irHKhwO88wzz3SaYuqugLOgZRLvbc5Q1E0ZwHPJdcfKpqQCKtwBubGOzJXznBpSQZ8+fTp1I061Ws3jjz/OsmXL2Lp1K48//vh992kIJ9l0NsDsfg5cHThaBYqARWtRxy9i9qxHGztDRm0j6F5M3DochDssTokaIq5ZxM1DsDa+ga1hGYlgDaHCRUgaZ/sb3wFJpVJs3boVh8PBsGHDcm2OgkKHplW/7FOmTLkRIlboGNhdagQBJR0wD/B50lisIhqNsjChcDtyzW4uWAqIZyQGdNI0wJspKipi5MiRnDhxgtOn718bs+x6tKqq40+SFQGLe6NOXMV29Xc4L/8CdbKeUME8PD3+mrht1J2dqpvI6IrxlX6BUMFCNPGLuC7+B0bfNpCVz/p+HDhwgGAwyNSpU1GplMwKBYVHoVVL6KdPn2bdunW88cYbtxVVf+tb32oTwxQeDbVawOZQKQIWOUaWZfzeDEXdOvZKu0LbIR/czYlu5RiNxgeqO+rIjBw5kvPnz7Np0yZKSkooKCi44+vqQy3RqrmdIFoFioDF3VAlGzF5N6AP1yKJesLOWcTsY5HFB4zeCiIx+1gS5iosTW9j9qxFFzpEqPBJ0vrStjG+g+Pz+XjvvfeorKyktFT5jBQUHpVWOVbTp09n+vTpbW2LQpZxudWcO5Ugk5FRqZRoSS6IRSSSCVmpr1K4I7K3mdiFs1yoKmNIZSWi2DXSw1QqFTNnzuSVV15h06ZNfPazn73j61476kEtCjzZCaJV11EELD5ETPkweTeiDx1EFjREHFOJ2iciqwyPdFxJbSNQ/Cl0kaOYm1bguPxzYrZxRFwzH9xZ68TIssyWLVtQq9VMmDAh1+YoKHQKWuVYTZkypY3NUGgLnG41Zz5I4PdmbvS2UmhfPmwMrDhWCrcjH9rDKXsREnSJNMCbcblcjBs3ju3bt7Np0yaqq6tvqeOtuxatmtdJolXXUQQsQEwHMfo2YwjsB0EgZh9PxDEZWWXO3kkEgYR5EElDX0yedzAGdqKLHCXkXkTSVJm983RgTp06xaVLl5g8eTImk6KmqKCQDVo12960adNdn5s2bVrWjFHILjcELJrSimOVI/yeDKIKLDbFsVK4Hfngbj5w96SgoOCu6XCdmaFDh9LU1MTWrVtpbGxk2rRpN2o8Xnv/WrSqgysBfpSuLGAhZCKYfFsxBHaDLBGzjiTqnIqktrXZOWWVgXDhIhKWoVga38Re91vi5mpCBfOR1ZY2O2++k0gk2L59O263m8GDB+faHAWFTkOrZtvbt2+/5f9+v5/6+nr69++vOFZ5jFYnYrGKeJrSdF4B5/zG50ljc6gQRSUVU+FW5FAA74VzNFaMYWIXi1ZdRxAEZs6cSUlJCZs3byYYDDJv3jx8KZHN5wLMq3TgNHSuRaGbBSzcRZ0nEncvhEwco387Bv8OBDlF3DKUiHNGu6r2pQy98Pb4MkbfVkzezWijpwgXzCVuGd4lpdn37t1LJBJh3rx5XSYFWUGhPWjViPUv//Ivt23btGkTV65cybpBCtnF6VZz5WISWZIRlMl9uyJJMgF/hl59lZx+hduRD+3lA1sxgiBQWdl1U5MEQWDq1KloNBo2bNjAa6+9hrd09LXaqs4VrYIuJmCRSWD0bcHo24YoxYibBhFxzSSjLcyNPYKaqHM6CfNgLI1vYm18HX2ohpB7MRlt14kYNzU1cfjwYQYPHkxxcXGuzVFQ6FQ89DLFlClT7pkiqJAfOAvUpFMQDHSBQTzPCAUySBmwK/VVCncgc3A3H7i607NnT4xGY67NyTn9+/dn0aJFhCNRUu9vZGZxutNFq67TFQQstOGjCIf+AbPnHVL6HnjLvkyw5NncOVU3kdEW4u/+eYLuxagTV3Fe+jFG72aQO7+KrizLbN68Gb1ez9ixY3NtjoJCp6NVjpUkSbc84vE4GzZsUIodOwDOa7VV3qbOPYjnIz7PNeEKRRFQ4SPI0QiXL14kotJ0OdGKe1FaWkqi3xQygprU0S2t6nPVEbE5VUQjEsmklGtT2gYpibXxddBY8Xb/AoFunyWt65Zrq25FEInbRuHt8VUSpgGYvetxXvoZ6vjFXFvWphw7doz6+nomTJiAXq/PtTkKCp2OVi0HfvzjH79tm9Pp5Pnnn8+6QQrZxWgSMRgFPM1pevdTUtLaE783g1YnYDAp+esKtyIf2c8H1kJ0ajW9e/fOtTl5w9Vgkq31MvNHPI7l4l7WrFnDhAkTGDZs2C2KgR2dzi5gYQjuR5RiSL2fJZ2w33+HHCKprQSLP0E8chxL09s4Lv8nMdtoIq5ZyGLncjxisRg7d+6kW7du9O/fP9fmKCh0SlrlWP3sZz+75f86nQ6r1domBilkH6dbTXNDGlmWO9XkJN/xe9LYnSrlM1e4jcTB3Zy1FdG/f3/U6s6Z7vYwvPp+M2pRYMmQ7phHPMn69evZsWMHfr+fKVOmdJoi+04tYCFnMPp3kNT3Qm0ph0Rzri1qFUnTALyGPpg86zEEdqOLHCdY9AwpQ+dZ+Ni1axeJRIKpU6cq45KCQhvRqlHK7Xbf8lCcqo6Fs0BNIi4TDXfStJM8JJWSCQUlHC5l0qxwK3IizulLl0mLopIGeBNXgkm2nQ8yt58Du0GNWq1mzpw5DB8+nPfff5+VK1eSSCRybWZW6MwCFvrQYVRpP1HHpFyb8sDIoo6wewG+0i8gC2rsV36N0bcV5I4/dtbV1XH06FGGDRuGy9X5RGEUFPKFe876vvWtb91zZ0EQ+MY3vpFVgxSyz/UeVt7mNCaLUu/THgS8LUXQdqW+SuGjHD3IB5ZC7EaDosh1E8tqW6JViwd8KMEtCALjx4/HZrOxefNmXn/9dRYsWIDF0vH7D3VKAQtZxujfRlpbRNLYcZUu0/oe+Mq+hKXxDcyedWhi5wgWLUVWdcy6ckmS2Lx5MyaTiVGjRuXaHAWFTs09HauJEyfecbvX62Xt2rWdZvWws2O2imi0Ap6mDGWdJ6shr/FfmzApjpXCRwkc2M1Vs4Mxg6uVdJxrXPBF2XYhyML+Tux3UAIcNGgQFouFNWvWsGzZMhYsWEBhYe7V5R4Fm1NF3eUUyaSEVts5Uhy10Q9QJxsIFi4FoWO/J1nUEyz6OCl9b8zNq3Fe+imB4k+Q1vfItWkPzJEjR2hubmbu3Llotdpcm6Og0Km55y/ftGnTbnmMHDmSy5cvs3LlSkaNGsWPf/zj9rJT4REQBAFngQpvU+eXks0XfJ4MJrOIVtexJxcK2UVOpThxpQ6AAVVVObYmf/jt3ktoRIHFVXdvGNuzZ0+WLl2KIAi8/vrrnDt3rh0tzD43C1h0Foy+rWTUNuKWIbk2JTsIAjH7WHylXwBEHJf/C4N/B8hyri1rNZFIhN27d9OzZ0/69u2ba3MUFDo9rZr1RaNRXnnlFb7yla8QCAT43ve+x/PPP6/k6XYgXG41kbBEPNbxc8U7An5vWolWKdyGfPwwJ8wFlNqtnSKdLRtcDiTYcLKppbZKf++axIKCAp5++mnsdjurVq3i8OHD7WRl9rlZwKIzoI5dQBs/T9Q+AYTO9duX1pfiLfsySVN/LM2rsda/hJCJ5dqsVrF9+3YkSWLy5MlKhFxBoR245yiWTCZZvXo1q1atoqqqim9/+9uUlZW1l20KWcR5U51VtzIlFaAtiUUl4jEZuyJcofARru7fRVBnZNSw4bk2JW949X0PWpV4z2jVzZjNZp566inWrVvH1q1bCQQCTJgwocMpBnY2AQuTfxuSaCBuHZlrU9oEWWUgUPxJDIGdmJvXorn0UwLFz5LWd8+1aXfl0qVLnDx5ktGjR2O357fsvYJCZ+GeM7+/+Iu/QJIkFi5cSN++fQkEAgQCgVteM2jQoDY1UCE72OwqRBV4mxTHqq3xXxOuUBoDK9yMnMlwoq4RjdVNeWXHLezPFme8cV4+0sz+K2GeHd4d232iVTej0WiYN28e27dv59ChQwSDQWbNmoVG07GkyzuLgIUq2Yg2cpyoYyqy2In7JQoCMfsEUroybA0v47j8C8IF84jZxkCeRYPS6TRbtmzBZrMxfHj2FnLElBe8F0EqBlGZSygofJR7jmTXixzXr19/x+cFQbitx5VCfiKqBBwuNd7mjj+I5zt+bwZBAKtDcaw6Cq/UNoMMH6suaLNzpE8c4bTJSV+3q0sXkJ/1xnmltpm9l8OYtCLPVhfwuTE9Cfm9D3QcURSZPHkyNpuN7du331AMNJk6jnKbzdE5BCyMvu0gqInax+balHYhbeiJt+zLWBtew9K8Ak38HKHCJ/OqoXBNTQ0+n4+FCxc+cq88IRNDFz6CPlSDNn4BgALRRNQ+lphtHLLKkA2TFRQ6Bfe821588cX2skOhHXC5VZw8liCVktFo8mt1rTPh92Sw2lWoVMpn3BHwRFO8WtuMJEM3q5ZJvdqmT9+ZfbtJqtT0H9U1Jp8f5bwvzsu1zey5FMakEfl4dQELKh2YtCp0apHQQx536NChWK1W1q1bx7Jly1i4cGGHqf+9XocZ8GVwF3VMx0pMB9CHaojZRiKrzLk2p92QVSYCJZ/G6N+GyfMu6sRVgsXPktaV5No0AoEA+/bto7y8nF69ej3cQeQ02sgH6EM16CInEMiQ1hQSds7CWNif1MV3MHs3YPRtI24bTdQ+AUmt9DhVUFCKQLoQzgI1yAl8zWkKSzpWykxHQZZk/N40pb26bkSio7HhTABJhl52HS/urafcqaebNbvXT5YkTjR6MBsslPXpk9Vj5zvnfXFeqfWw+1IIo0bkY4NdLOjvxKzNXkS3T58+PPXUU6xYsYLXXnuNuXPn0qNH/sti3yxg4S7qmL/JBv9OQCZqv3N7lk6NIBJ1TCGl74m1/mUcl39OqGBBS51ZjlIDk8kkmzdvRhTFu7bMuSuyjDp+EX2oBn34CKIUQ1KZidnGELcMI63rBoKA0V5AIF2MKlGPybcVg38nBv8u4tbHiNonkdG2XeRfQSHfURyrLoTDpUYQWgQsFMeqbQiHJNJpsDuVW6sjkJFk3jntZ2iJiS+NLuav1pzj33de4XuP90Sjyl4EIXK8lkt6K4+VFHYZZa6L/gSv1Daz82IIg1rk6UEunujvxKxrmxTZwsJCnnnmGVasWMGKFSuYOnUqAwcObJNzZYuOLmAhZGIYAvtImAcjaVonPtIZSRl64y37CraGV7E2vYk2fo6Qe1G71pslEgmOHDlCTU0N8XicyZMnt1p5VJXyoAvVoA8dQp3yIAsaEqYq4pZhJI3ld1V5zOiKCRY/g5iaidG3DUPoPfTBAyTMg4g6prQ4YgoKXQxl9teFUGsErHaln1Vbcl24wu5S6qs6AgeuhvFE03x+RBFuk4avjC3hO1uv8NuaJj4/oihr5zmxfw+yIDBg/KSsHTNfuRS45lBdCKFTiywd6OKJAU4sbeRQ3YzFYuGpp55izZo1bNy4kUAgwNixY/Pame3IAhaGwF5EOUHU0b7f60gkwurVq+nbty+PPfZYXlxfWW3G3+05jL7NmLwbUcevEij+BBld9n5H7kQikeDQoUMcOnSIRCJBr169GDVqFMXFxffcT8hE0YVrb9RNyQikDH0IOqaSMA98oHoxSeMkXLiIiHM6Rv9ODME96MO1JIwVRO1TSBl65524h4JCW6E4Vl0Ml1vN+TMJMhlZqQFqA3yeDGoNmC0ds16iq/HOKT9OncDwV14gc+ksI9Rq5vWYxSpGMGjj7xkVuwhqDajVLX9V6hv/FtTqm/6v/vB1Ks2H21Rq0qKKEx4/RWo1zm75K838qFwOJni11sP280F0aoEl1xwqazs4VDej0+lYuHAhW7Zs4cCBAwQCAWbOnPnIBfxtRYcVsJBSGAI7SRgr2jUykU6nWbVqFQ0NDdTX1+P1epk2bRoqVR4sZgkiUed0Uvqe2BpexXn5RULuRcStj2X9VPF4/IZDlUwm6d27N6NHj6awsPDuO92pbkpbRNg1m7h5CJLm0STZZbWFSMFsoo4pGIJ7MPp34rj6K1K6MiLOKSSN/UHoQN9xBYWHID9HGoU2w+lWcfZkS7G0s0C5/NnG781gd6rzYgVV4d7UB+McvBpm6cXNqOrPIYyZDLLMZ9J+jksBftZ9Fj8IbsSdDkE6BZk0xCItf9Np5HT6w+3pa49MquU5oMFg5YSjG6fsRSQ1Bqb3Ls31W24TrgSTLKttZtuFIFqVwOIqJ4sHOLE+gHx6tlGpVEybNg2bzcauXbsIhULMnz8fo9GYM5vuRkcVsNCHDqLKhAnaJ7fbOWVZZtOmTTQ0NDB37lw8Hg979+4lEAgwb948DIb8UKdLGctbVAPrX8Ha+FqLamDBQhAfPQU/Ho9TU1PD4cOHSSaT9O3bl5EjR97dobpD3VTmet2U9THS2pKsR5NklZ6oYwpR23gMofcw+rZhr/sDaW0RUfsk4pYhna6JtILCdZSZdRfjujPlbUorjlWWyaRlgv4Mfft34j4unQS58SrvvL4DwTKQGdYY4p//DMHeUiOiA/4ulOSra87zH+VP8q8zeqASWzfxiEQiHD9+nOPHj+Pz+VCrVPTt2YMBfftQWjmgDd9R+1MXSvJqbTNbzwfRiAJP9HeyuMr5QP2o2hJBEBgxYgQ2m43169ezbNkyFixYkHeKgR1SwEKWMPq3k9KVkjK0nxhLTU0NJ06cYPTo0ZSXl1NeXo7dbmfDhg03FCEdDke72XMvJLUVf/fPYfJuwOTbgiZ+uSU1UOt+qOPFYrEbDlUqlaK8vJyRI0fidt/5eKpkM/rQIXThGtQpb6vrprKKqCFmG0PMOhJd+Agm31asja9h8r5L1D6RmHWE0gtLodORHyOgQruh04uYLSKepjTlnWuel3MC/gyy3CISopCfyJKEvHkNyTf/wIYRf8dIU4rCZ792W4SxxKLli6OL+cHOq/zxSDOfGnr3yVA6neb8+fMcO3aMCxcuIMsyJSUlTJs2jYqKCnS6zuVo14WSLHvfw5ZzAdSiwIJKB09WubAb8vN7X1FRgcViYdWqVXmpGHhdwCLQgQQsdJGjqFMeAsXPtlvtzPnz59m5cyfl5eWMGjXqxvbKykqsViurVq1i2bJlzJ07l7Kysnax6b4IKiKuWaT0vbA2LMNx6WeECp8kYRnS6kNEo1Fqamo4cuQIUjrJwMqejBjaH4dZiyjVIwTOImYiiJkoohRByEQR00E0yfqb6qamkTAPyl3zZkFFwjKMhHkI2ugHGH1bsTSvxOTbRNQ2nphtjNILS6HTkJ8joUKb4nSrqbuUQpZlJWUti/g914QrnEqKQz4iN9Uj/fYncPJ99g5fTFBjYvbo0rveA5N6WTlcH+H1ox4GFxkZWnJr49nGxkaOHz/OBx98QDwex2QyMXz4cAYMGJA3q+bZpCHc4lBtOtviUM2rdLCkyoUjTx2qmykuLr6hGPj2228zZcoUBg8enGuzbmB3qPB3FAELWcbo20pa4yJhqmqXU3q9XtatW4fL5WLmzJm3L4SUlNx2fQcNGtQutrWGpKkSb9mXsTW8jK3hFaKxc0QK5iBIiRZH6GanKBNFzESQkkHC/no08QCTLRnmTQetmAHqIbgXgreeQxJ0yCojksqIpLYStgwlbhmKpLbl5D3fEUEkaRpA0tgfTfw8Rt8WzN71GH1bidlGE7OPV3phKXR48n9EVMg6zgI1F88mCQUkrHbFCcgWfm8GvUFAb+g4dRJdAVmSkLetQ17+WxBFhM9+hXeiFRRH07c5Sx/lz0YU8UFzjB/uusqP5/ZGJyc5efIkx44do7m5GVEU6du3LwMGDKBHjx6IYue79k2RlgbKm84GEAWBuf0cLBnowtkBHKqbua4YuG7dOjZv3ozf72f8+PF5cc06koCFJnYGTeIKQffidhEiSCQSrFq1CpVKxfz589Fo7pwuabVaWbp0KevWrWPTpk34fL68ub4AksaOr/ufYfa8g9G/HWNw711fm5JUhBOQTogIWjMqezFpg4OkynTNeTIhiS1/rztTCB3ofhQEUobeBAy9USeuYvRtbflM/DuJW4cTcUxB0nS+xSmFrkEHuhMVsoXL3eJMeZvSimOVRXyejJIGmGfInkak3/0Ujh+GqmGIn/kSl1VWjq46x2eGuhHvE7HVqUX+elwxL6x8j1+/cghDpAFJkigsLGTKlCn069cPvb71ssQdjWA8zdfXX8AfzzC7ws6SgS5cxg5SB3QHdDodCxYsYPv27dTU1OD3+5k1axZa7cPVeegD+xDIELONfSS7OpKAhcm3lYzKQtwyrM3PJUkSa9euJRgMsnjxYqzWe0czsn19s46gIlwwl4SxAk38IrLKdMNJiiSg5v3T1NSeIpWR6d+/PyNGjMDhcJAAErm2vY1I67oRLP44quRMjP7t6IPvoYscxVv6F4pzpdAhUWaBXRCDSURvEPA0p+lV0bnqP3JFIiERjUj07JsnA3gXR5Zl5O3rkZf9DwDCp76IMHEWgiDwzoEG1CJM73vvFBmPx8OxY8f44IMPGBiNkhS0GEr7sWjS8LwTQGgLJFnmR7vq8MczvPB4DypcnaMGQhRFJk+ejN1uZ9u2bSxfvpwFCxa0upnqddTxS1ia3gIgpetOWv8LPMi5AAAgAElEQVTwdVsdRcBCHb+CNnaasGt2VhTu7sfOnTu5ePEi06ZNo3v31rUqyNb1bUtSxgpSxgoAQqEQ7+17j6NHjyJJEgMGDGDEiBHY7Y8mfd7RyGgLCBUuJmofj+PyL7DX/Q5f6RceqJ+WgkI+oDhWXRBBEHC61Xib0kqdVZa4Xh9hVyJWOUf2NiH97mdwrAb6VyN+5ssIBS1NOhNpiU3nAowrs95RvS4ej3Py5EmOHz9OQ0MDoijSq1cvBgwYwJtXtay6FGFcxkDnd6tg+VEPB+sifGFkUadxqm5myJAh2Gw21q5de0Mx8J49gG5GzmBpfANJZQYELI1v4iv70kMrrXUUAQujfxuSoCNmHd3m5zp+/Dg1NTVUV1c/VL3UkCFDsNvtrF27lldffZX58+fft2luexIKhThw4ABHjx4FuOFQ2Wx5VBOVAzLaQgLFz2K/+hus9a8QKPm00vtKoUOhzAK7KM4CNVcvpohFJIxmJR3wUfF70iC0FKEr5AZZlpF3bUR+9deQySB84gsIk2cj3FRjsf1CkEhSYmYfM8FgkFgsduNx/vx5zp49SyaToaCggIkTJ1JZWXmj99EXe2Q45T3Pv++8yn/M7Y2lnRvftidH6iO8fKSZST2tzK7ovCvnvXr1YunSpaxYsYLly5cze/Zs+vS5v3y40b8dTbIef/EnAbDX/y9G/w6ijofv6ZTvAhaqlAdduJaofSKyqm2jCHV1dWzcuJHS0lImTpz40Mfp2bMnS5cuZeXKlbz++us8/vjjVFRUZNHSB6exsZEjR45w4sQJAKqqqhgxYsR90xy7EiljOSH3QqxNb2FuXkPYPT/XJikotBrFseqiuNwtl97TnFEcqyzg92awWEXUGiX6lwuSjQ1EXvkVsdMniPXsT2LCLGJqLbFdu25xnq54QkzNJNiy/PYJrF6vZ+DAgVRVVeF2u2+L5Bo1Kv5mQje+vv4CP9lTxz9M6t4po73eWJof7LxKt2uS853xPd5MQUEBzzzzDKtWrWLVqlVMmDCBYcOG3fV9q5LNmLwbiZsGkjQPBCBhqmrZZh6MpHE+lB35LmBh8G0HRGL28W16nlAoxOrVqzGbzcyZMweV6tHGJ5fLxdNPP83q1atZu3Ytfr+fESNGtOv3Op1Oc+rUKY4cOUJDQwNqtZqBAwcyYsSIvEpRzCfittGok40YAztJawuJ20bdfycFhbZAltFGTwLdWvVyxbHqolhsIhqNgLcpTVkvpS7oUZBlGZ8nQ0n3/K2N6Mik02nq6uo4ceIETU1NtzhKsViMWCRMWpIBK5RfG3z37gdApVJhMBgwGAzIah1Noo3+3e0M7O64sf36w2Kx3HcSV+Ey8Jlhhfz3e42s+sDHgv4PN4nOVzKSzA92XCGWkvi/03tg0OTfBL8tMJlMPPnkk7z77rvs2LEDv9/P5MmTb/8+yDKWpjeRBTVh98Ibm0PuhTgv/AhL01sESp57qN5O+SxgIaTDGELvEbcOa1M57HQ6zerVq0mlUixevBiDITspqEajkcWLF7Nx40Z2796Nz+dj2rRpqNVtOwUKBoPU1tZy9OhR4vE4DoeDSZMmMWDAgE7X364tCBfMRZVqxtL0NhmNi5Sxb65NUuhiqONXMHvWoo2dgYqprdunjW1SyFMEQcBRoMLblM61KR2eaEQilZSxu5TIXzbIZDLU19dz+fJlLl++TF1dHZIkAR86SkajEb1GjaPRg76pDqPdjnHcNAxFJTeeNxgMaDSaGyvTL+6t40wmyD/NKcekffhrtaDSwZH6KL+taWSA20i5q/MUV//xSDPvN8b4q7El9LB3rYmfRqNhzpw57N69mwMHDhAIBJg7d+4tE2B96D20sbME3YtvcTAktY2I63EszSvRhQ+TsAx94PPns4CFMbAL5AxR+6Q2O4csy2zcuJHGxkbmz5+fdYEYtVrN448/jsPhYM+ePQSDQebNm5c15+06sixz8eJFjhw5wrlz5xAEgd69e1NdXU1ZWVmnjwBnFUFFsOjjOK78Alv9S/hK/5yM9u7N2hUUsoWY8mH2rEcfPoQkGgkVzKe1GpWKY9WFcbnVNNbFScQldPr8WiHtSPg914QrlMbAD4UkSTQ2NnL58mUuXbpEXV0d6XSLw+92uxkyZAhlZWUMHDiQcDgMgLxvG/LLv4REHGHxpxBmLEAQ7/75R5IZtp4LMrGX9ZGcKmhZlPjK2BL+as05/m3HFX40txdGTce/9geuhFl+1MPMvjam9umaBfSCIDBu3DjsdjubNm1i2bJlLFy4EJvNhpgOYW5eQ1Lfi7h1xG37xmxj0IdqsDSvJmnsh6wyPtC581XAQpASGAK7SZiq2nRS+9577/HBBx8wduzYVtW5PQyCIDBq1Cjsdjvvvvsur776KgsXLsTpfPTIczwe59ixY9TW1hIIBDAYDIwcOZJBgwYp6X6PgKzS4y/5DM5LP8dW93t8pV9EVnU+MR2F/EDIRDH5NmPw7wZBIOKYQtQ+GVmlVxwrhfvjLGi5/N7mNCWlSjrgw+LzpBFVYLF1/Ml1eyDLMk1NTTciUleuXCGVSgEt9RADBw6ktLSU7t2739IjymAwEK67gvTSL+DgbujdD/G5v0IoKb3vObecC5LIyMypyE5fFKtOxV+P78Y/bbjIL/Y28LXxJR16JboxnOJHu67S26Hj8yOKcm1OzqmqqsJqtbJ69WqWLVvG/PnzqRQ2I0hJQoV3aYwriIQKn8Rx6WeYPesIFT75wOfNRwELfXA/ohQn6mi7aNW5c+fYtWsXFRUVjBhxu9Oabfr164fVamXVqlUsW7aMuXPn0qPHw8nlXxejOHnyJOl0mpKSEsaMGUPfvn3bPNWwqyBpnARKnsV+5b+x1b+Ev9tzD63AqaBwR6QUxsBujL7NCFKCuGU4EdcMJPWDLzIqd30XxuZUIYrgbcrQirmpwl3wezPYHSpEseNOrNsSWZbxer03HKnLly+TSLS0u7Tb7fTv35/u3btTWlp6Q4HvTsR3bkL6z+9DPIqw5DMIMxchtKKwXZZl3jnlp9ypz2ra3sBCIx+vLuClw81UFxuZWd4x1fNSGZnv77iCJMPfT+yOTq1ErwFKS0t5+umnWbFiBe9vf4khj3kIO2eS0d5dkj2tKyFqn4DJv424ZRgpQ+8HOmfeCVjIaYz+HST1vR+pT9e98Hg8rFu3DrfbzYwZM9ptgaK4uJinn36alStX8vbbbzN58mSqq6tbtW86neb06dMcOXKE+vp61Go1lZWVVFdX43YrqWptQcrQm1DhYqyNyzE3rSTsfuKhahkVFG5BltCHDmHyvosq7SdhrCTsmk1G9/CtGRTHqgujUgnYXSq8zUqd1cMiSTIBX4Ze5V2rHuVeyLKM3++/xZGKxWIAWK1W+vbtS2lpKaWlpZjN5vsfLxRE/uN/EjiwA3qWt0Spurd+kne8KcaFQIIvjc5+D5slVS5qG6L88kADlQWGDlmX9NuaRk554vz9xG6UWJTI9c04HA6eeeoJnBd+SH1IzQGPgZGj7t37L+Kcjj5ci6XxTbw9vgJC64dZW54JWOhDh1GlA4Tci9vk+PF4nFWrVqHRaJg/fz4aTfvWllmtVpYuXcq6devYsmULfr+fCRMmIIp3/uxDodANMYpYLIbdbmfixIlUVVUpYhTtQNw6HFWyCZN/Kxmtu80VKhU6N9roSUzN69Ak60jpuhMsfCorAimKY9XFcbnVnD6eIJ2SFanwhyDozyBJ4OjiwhWyLHPq1CnOnTvH5cuXiUQiQIvaWo8ePSgrK6O0tPSBe7XINXuQ/vAiRCOYPvF5YpPmtipKdTPrTvkxaUQm9sq+mplKFPjauG785ZpzfH/HFX4wu1eHivjsvBBsUTesdDCuh9JH504URLdh0KbZ0TSMPYf34/MHmT59+t3TvEQtIfcT2Ot+i9G3lahzeqvPZc8nAQtZwujfRkpbTNLYL+uHlySJtWvXEgqFWLJkSc7qkLRaLfPnz2fHjh0cOnQIv9/PrFmzbjhK18UoamtrOXfuHIAiRpFDIq7HUaeaMDevJqMpIGmqzLVJCh0MdeIq5ua1aGOnyagdBIqeIWGuzlojasWx6uI4C9TIcgKfJ427OL+UqDoC1+shuroi4OHDh9m2bRsGg+FGNKq0tBS73f5QEw85EkZ+5ZfIe7ZAWW/Er30b89CRxJubH+g4gXianRdDzKqwo28jh8dhUPPVcd345qZL/OpAA18aU9Im58k2V4JJfrqnnsoCPZ8Zdvf0tq6MOnYBQ2APMdsYhvVdQMZ4gN27dxMKhe6pKJc0VRI3V2PybiZhrm616EM+CVhoox+gTjYSKHq6TVKutm/fzqVLl5gxYwYlJQ9+z0gZGVGVHbtEUWTSpEk4HA62bNnC8uXLmTVrFpcuXaK2tha/34/BYGD48OEMHjxYEaPIJYJIsOhp7Jf/C2v9yy1KgTqlLlTh/ogpHybvu+hDh5BFPaGCecRsYx4oq6A1KI5VF8dRoAahRcBCcaweHL8ng1YnYDB2nChFtrl69So7duygT58+zJs375FXcOUj+5F+/yKEAwgLPoYwdymC+uG+mxvPBkhLMrPbuP5pWImJJVVOXj/mpbrYxKQ2iI5lk0Ra4vvbr6AW4W8ndEeTpQlqp0JOY216E0ltJeKahSAIjBw5EpvNxrvvvsuyZctYsGDBXRXlQgXz0UZPYml6C3+3P221c5IvAhZG31YyanvLSm6WOXr0KIcPH2bo0KFUVVU98P5+T5rdW8P0rdTTb2D26iYHDx6MzWZjzZo1/PGPfwSgpKSEUaNGUV5erohR5AmyqCNQ8mkcl3+Ove53eMu+iKy6f1q5QtdEyMQw+jZjDOwGIGqfRNQxuc3UJZVfiS6ORiNgtanwNOV+IO+I+LxpHC5Vh0gHkRMJEEDQZq8WIBKJsGbNGiwWCzNnznykz0GORpCX/Rp550bo3hPxy/+M0PPh852la6IVAwvbp/bpE0PcvN8Y4+d766lw6fO6XumXBxo470/wjSmluE3KgsqdMPq2ok424C/5NLL44fenX79+WCwWVq1axWuvvcbcuXMpKyu7bX9ZbSHsmoO16U30offuKNF+J/JBwEITO482foFQwYKsq69dvXqVzZs306NHDyZMmPDA+0fDGfZuj5BOw8mjcQqL1dhd2ZvK9OjRg6effpoTJ05QXl5OYaESzc1HJI2dQMmncFz5Jba6l/B3/1zWIw8KHRwphSGwB5NvM4IUJ24ZRsQ5E0nTtgutXXeZXeEGLrcKnyeNJMm5NqVDkUrKhIMSdmd+/5jL8SjSyleQ/ubTSH/zWaQ3fo8c9D3ycTOZDGvXriWZTDJv3rxHKt6Wj9UgfevLyLs2I8x5CvEff/hIThXA4foo9eEUs7MksX4/1KLA34zvhijCv+24Siojtct5H5RNZwNsOBPgqYEuhndXVnnvhCrZiMm7mbi5mqRpwG3Pl5SU8Mwzz2AymVi5ciX19fV3PE7cOoKkvhfm5jUImXCrzn2zgEWuMPq2IYlGYq10BltLKBRi9erVWK1WZs+efVeRiLuRTEjs2RZBlmHCdDM6vUDN3iiZdHbHLqfTybhx4xSnKs9J68sIFi5FGz+PpfFNkJU5jAIgS+hCNbgu/hCLZw0pfRnesi8TKlra5k4VKI6VAuB0q5EyuR3IOyIBX4uaYr42BpZTKaQNK5D+4XnkFX+EAUOgagjyuteR/v5PkV76BXLTnSeErWHnzp1cvXqV6dOnU1BQ8HA2xqNIf/g50o/+BbQ6xK9/D/HJTyNkQR1s3SkfNp2KsWXt5zwUmjV8ZUwJZ7xxfneoqd3O21rO++L8Yl89g4qMfKL64a5Zp0eWsDS+iSxqCRXMv+vLrFYrTz75JEajkRUrVuDz3WGxQhAJFS5GkJJYmle36vQ3C1jkAlWiAV30OFH7WBCzF3VNpVKsXLmSTCbD/Pnzb+lR1xoyGZn9OyPEIhIjJ5hwuNQMHWUkHJI4XhvPmp0KHYuEpZqIYzqG0EGM/m25Nkchx2iip3FcfhFbwzIklRFft88R6PYcGV371T7n91K7Qrvgcl9rFNyUxpHFlIrOjs9zTbgizxwrOZNB3rMZecXL4G2CAUMQF38KoXeLspdcfwV5/ZvI299F3voOwsgJCLOXIJS1vufOyZMnOXToEEOGDKGy8uFUmeQTR5B++xPwNiE8vgjhiWezlqboiabYdznMogFONKr2XT8aU2ZhXqWDlSd8DC4yMro0Pwrdo6kM399xFZNG5G/Gd0Ol9F27I/rgfrTx8wQLlyCr733tjEYjixYtYtmyZbz99tssXboUk8l0y2sy2kKijsmYfJuIWR4jZay45zFzLWBh9G9DFjTEbGOzdkxZlnn33Xdpbm5m4cKFd61Lu9f+h/ZF8TZleGys8caY5S7W0Ktcy7mTCYq7qSnItZKiQk6IOKejSjVh8rxDWlNA0jww1yYptDOqZDPm5hXooqfIqO0Eip4mYR6SNaW/B0GJWCmg04uYzCKeJqWf1YPg92YwmUW0uvy4jWRZRj64C+lbX0H+7U/AYkP86rdRfe3/3nCqAITi7oif/hLid3+FMPMJ5MP7kb79l2R+/C3kk+8j3yedwuPxsHHjRkpKSh6qRkJOxJFe/iXSD/4JVCrEv/su4tI/yWrt17unA8gyzMpR097nhrnp49Dx0911NEVSObHhZmRZ5ud766kLJfnrCd1wGJQFlDshpoOYPWtJGvoStwxv1T52u52FCxcSjUZZsWLFjebXNxNxTCGtKcDS9BZI9/8+5ErAQkz50YcOEbOORFaZ7r9DK9m/fz+nT59m/Pjx9OrV64H3P1Eb5+rFFAOq9XTvcWsUbcAQAyazyKF9UVIpJRWsSyIIBAufIq3rjq3hVdSJq7m2SKE9kZLY6n6DJn6JkGsunh5fI2EZlhOnChTHSuEaTrcab3PmvpNqhQ/xe9N5I7MuHz+M9N2/RfrFCyDLiH/+dcR//AFC1dC77iM4XIhLn0P83n8jLPokXDiN9G//gPTC3yEf2oMs3V4jlEgkWLNmDRqNhjlz5qB6wJ5S8uljSN/+S+RNqxCmL0D8xo8Ryh9cFexeZCSZ9af9DCsxUZwjAQmNSuRvJ3QnJcEPdl4lk+P6xbWn/Gy/EOIT1QUMLsrehLmzYW5agSBnCLkXPZDEeHFxMXPnzqW5uZk1a9aQyXzEKRI1hNyLUKe8mHyb7ns8m0NFNCKRTLZvnZ7RvwOAqP3BF0zuxpkzZ9izZw+VlZU89thjD7z/+dMJTh9P0LOvlr79b198UasFho42EovJHK2JZcNkhY6IqCFQ8ikklRFb3e8R08FcW6TQTpg976BOeQmUfIqYYyKIuY1cK46VAgDOAtUNMQaF+xOLSsRjMo4cC1fI506R+eE/I/3wnyHgRfjsVxC/+VOEx8a1WqFPMJkR5z2N+MKvET7xBQj4kF78DtI3v4y0ayNyumWFXZZlNmzYgN/vZ/bs2ZjNra9dkpMJpNf+B+n7/wcyGcS/+VfEj30eQZc9qeTrHLgSxhNLM7siN9Gq63SzavnzUUUcb4rx8pEH67+VTU55Yvz3e40M72ZiyUBXzuzId7Tho+gjR4k4Z5DRPnj9Wa9evZg+fTqXLl1iw4YNty1SpYx9iVmGY/RtQ5W4d21jLgQshEwUfXA/CUs1kiY7gi8NDQ2sX7+eoqIipk+f/sCqoQ1XU9QejFFYombQY4a77u8sUFPeX8elc0nqr+Q+QqyQGyS1lUDJpxEzUWx1f2hVdFihY6OJncUY2EXUNo6UoU+uzQGUGiuFa1zPWfc0pbHY8iMKk8/4vdeEK3IUsZLrLiG99b9wcDeYrQjPfA5h8hwEzcNHaAStDmHqXORJs5AP7EBeuxz5Nz9GfvslhJlPUGMp4syZM0yYMIHS0tLW23r2A6Tf/BjqLyNMno3w1HMI+rbpHwEt0RmXUc2IPFC8m9LbxpH6KMuPesjIMrMr7BSZ2y+KFk5k+P72qzj0Kv5qXDfEDtAWIBcImRiWprdJaUseKVpTVVVFJBJh9+7dmEym21JlwwVz0EVOYG16E1/35++aqnKzgIW7neqGDIE9iHKSiH1yVo4Xi8VYvnw5Wq2WefPmPXAPKL83zXu7I1htKoaPNSHepyaw30A9jVdTHDkQxVFgQZcnKdoK7Uta141A0TPY6l/C2ricYNHH2qTBtULuEaQE1obXSWuchF2zcm3ODRTHSgEAo1lEpxfwNqfpVd72PX86On5PBkEEq719HSvZ04S88o/IuzaDVoew4OMIM59AMBizdg5BpUIYPRl51CR4/z2kda9zadUb7Or9GOUmHUPLWyeDLqdSyCtfRl73BjiciF/9FkLVsKzZeSfqQ0lq6iJ8vLogb8QZ/mxkEfG0xFvHvbx5zMtj3UzMqXDwWDdTm9ooyzI/3lOHN5biOzN7YtUpCyZ3w+xZh5gJEyj59CP3bRoxYgThcJiDBw9iNpsZOvTDdFxZZSJcMBdr42sYgvuI2cbc8RjtLmAhpTAGdpEwVpLRFT/SoaLRKMeOHaO2tpZYLMaTTz75QNFtgGhEYt/2CFqtwOhJJtSa+98nKpXAsDEmtr0bova9GMPHGjtEf0GF7JM0DyTimoXZs4601k3UOSPXJim0ASbPOsS0D3/3z2dVwfRRURwrBQAEQWips1IELFqFz5vBZlehUrXPwC2HAshrXkPesgYAYfoChLlPIVhsbXZOQRBg8AiivSpZ/9L/Yk/Gmbr/HeSajUgTH0eYuQjB5b6zvRfOIP3mP+DKBYTxMxCe/hyCse1re9457UcUYGbftvtcHhS9WuTvJnanKZLi3TN+1p8O8P+2XsZtVPN4hZ2Zfe1tIibx5nEv+y6H+dPhhVQWtF2EsKOjiZ3DENxH1D6BtL71kdi7IQgCkydPJhqNsm3bNoxGI/36fSgeE7cMQx86iMmzjoSpCkltveNx2lPAwhA6gJiJEHU8XLRKlmWuXr1KbW0tp0+fRpIkSktLWbJkCVbrnd/f3UglJfZtC5PJyIydYkFvaH3kyWpXUTlQ3yJ20T1F9575M9lSaF+i9kmoko2YvRvJaApJWKpzbZJCFtFEz2AM7CFqG0/K0HpF4/ZAcawUbuAsUFN3KUU0ImE0KWkUd0OWZPzeNGW92n7QlmNR5HffQl7/NiQTCOOmtUSp7uLQZJt0Os2aNWtIyzDvk59BH1uC/M7ryFvWIG9ZgzBqMsLsJxG69WixN51CXv0a8pplYLUjfuUbCIOz22T0bqQyEhvOBBhVasZlzD/ZZbdJwyeq3Tw9qIB9l0OsO+XnpcPNvHKkmTFlFmZX2BlclJ1V9qONUf5wqImxZRbmV7ZPg+QOiZTC0vgmGbWDsHNm1g4riiKzZs3irbfeYv369RgMBsrKylqeFARC7kU4L/0Yc9NKgiXP3vEYNoeKusspkkkJrbYNf4/lDEbfdlK6MlL6Xg+0ayKR4MSJE9TW1uL1etHpdFRXVzNo0CCcTicFBQU0N7e+vlDKyOzfGSUclhgzyfRQael9++tu1Ga5CtUP5JgpdCIEgVDhYlQpL9bG1/BpHKT1Zbm2SiELCFICa+PrpDUuwq7Hc23ObSiOlcINXO6WQczbnMZoUlb67kY4JJFJg70Ne37JqSTylrXIa16DcBAeG4e46FmEkvYdGLZv305DQwNz58691nvGifAnX0V+4pMtDt/29ci7N8GQUYhjpiCteQ0unUMYMxXhY59HMLVfndPuS2GCiQxzKvLbkVCLAuN6WBnXw8qVYJJ3TvnYeDbAzoshulu1zK6wM623DfNDpu7542n+fcdViswavjymWEmHugcm3xbUqSb8Jc9lPZVErVYzf/58li9fzurVq1myZAlud8uCSEZbQMQxHbP3HeKR4yRNA27b/2YBC3dR2zkHuvD7qNI+QgXzWl2L0tjYyJEjRzh58iTpdJqioiJmzJhBRUUFmods7i3LMof3R/E0phk62vjQPalEsUUlcOs7IQ7tizJ6kkm5B7oqgppAybM4L/0cW90f8JV+EUmTW1EjhUfH1LwWMe3H3/3P8ioF8Drt4lj9/Oc/5+DBg9hsNn7wgx8AEA6H+dGPfkRTUxNut5uvfvWrmM1mZFnmN7/5DTU1Neh0Or74xS/Sp0+L0seWLVt44403AHjyySeZMmUKAGfPnuXFF18kmUwybNgwnnvuOeWH9CGw2lSoNS2NgkuVFIq74vO0pEs62qAxsJzJIO/ehLzyZfA2X2vu+2mE3vduKtoWHD9+nNraWh577DHKy8tveU5wuRE+9nnkec8gb16FvGk10uF9Lb2z/uIfEIbeuXakLVl70kexWUN1cfbqzdqa7lYtfzK8iGeHuNl5sSWK9d/vNfKHQ01M6GlldoWdfi59q3/PMpLMD3deJZzM8I2pPTFplbqqu6FKNGD0bSVuGUrS1O/+OzwEer2eJ554gmXLlrFixQqWLl16IzUu6piIPnwIS9MKvIY+yOKtta12hwpBgPOnkxQUqttmTJNljL6tpDXuOzp3N5NKpTh58iS1tbU0NjaiVquprKxk8ODBFBYWPrIpJ4/GuXwhReUg/SNnA5gtKqqGGHj/YIwLZ5JK3XAXRlaZ8Zd8BseVX2Cr+z3+0udvu9cUOg6a6GmMwb1E7RNJGXrl2pw70i6O1ZQpU5g9ezYvvvjijW1vvfUWgwcPZtGiRbz11lu89dZbfPKTn6Smpob6+np+8pOfcOrUKX7961/zne98h3A4zPLly3nhhRcA+PrXv86IESMwm8386le/4vnnn6eiooLvfve7HDp0iGHD2rZIvjMiiAIO16PXWcmyTCYNqZRMOiWTSsqkrv1Np2QQwOFSY7WJCHkiMPAg+L0Z1BowWbK7iixfOI306x9C/WXoVYH42b9EGDAkq+doLU1NTWzatInu3bszbty4u75OsFgRFn4C+fHFcOII9B2AYHmwmopscAzgxOYAACAASURBVNGf4FhTjM8Mc3dI5TudWmRaHxvT+tg454uz9qSfrecDbDoboI9Dx5x+Dib2tGLQ3Ps7t+z9Zg7XR/nS6GJ6O7IvZd9pkCWsja8ji7qWSE0bYrFYWLRoEcuXL+ftt9/mqaeewmAwgKAi6F6M48p/YfK8S9g9/5b9tDqR/tV6jh+Oc/5Ukt79sj8Z1MROo0nWESxccleFQq/XS21tLcePHyeZTOJ0Opk8eTL9+/dHp8uOTRfPJjh5NEFZby0VVdk5Zq9yLfVXUhw7HMNdrMZkVhYZuioZXRHBoo9jq/sd1oZlBIqfzVnzWIWHR5Di11IA3VlN3c427eJYVVVV0djYeMu2/fv3881vfhOAyZMn881vfpNPfvKTHDhwgEmTJiEIAv369SMSieDz+Th69CjV1dU31IWqq6s5dOgQAwcOJBaL3SgOnjRpEvv371ccq4fE5VZzojZONJIBhBtO0c0O0i3OUkomfZPjdP351vQZVmta6rpcbjVOtxq7Q4XYTmIQj4LPk8HuzO4KsuxtQvrJt0GtRvzz/wPDxuQs6hqPx1m9ejV6vZ45c+YgivcfgAS9AYaObgfr7sy6037UosCMPvkjWvGw9Hbo+eLoYj77mJut54KsPeXnxb31/OZgI1N6W5ld4aCn/fbJZ01dhFdrPUztbWVGHol35COGwB40iUsEC5ciq9o+XdXlcjF//nzeeustVq1axeLFi1Gr1aQNPYlZR2EI7CJuGUZa3/2W/fpW6vA2pTl6OIbdpcKR5fRjk28rGZWVuOXWRuKZTIYzZ85QW1vLlStXEEWR8vJyBg8eTLdu3bL629RUn+LIgRgFRWqqR9y9V9WDIggCQ0cZ2bIuyKG9UcZNNXfIhTyF7JA0VRIumIeleRX2K78kap/YEqVVHKwOg7l5LWI6gK/0CzlvAnwvclZjFQgEcDhaaiEcDgfBYEuXbK/XS0HBh80ZXS4XXq8Xr9eLy/Vhc0un03nH7ddfr/BwOK/1s9q4KnTP16nUoNEIaDQCaq2ATi9gtoot/9cIaLTCLc9rbtqWych4mzJ4mtJ4m9I01sUBEFUtkSyXW4XLrcbuUqNW59dAmE7LhAIZygdkb/VYTsSRXvxXSCYQ//r/3RCCyAWyLLN+/XrC4TBLlizBaMz/tLp4WmLz2QDje1iw6jtP2ahRo2JOPwezK+ycaI6x7qSfd08HWHPST5XbwOwKO+N6WNCoRDzRFD/ceZUym5YvjFLqqu6FmPJj8rxDwlBB3NJ+C3Ddu3dn1qxZrFmzhrVr1zJv3jxEUSTimo0ucgxL0xv4Sr94i9z7dedg2/oQ7+2KMOlxC9os9GcSMlH0oYNoY2cIueaA0HLfBINB3n//fY4ePUosFsNqtTJu3Diqqqra5Lcg6M9wYGcEi1VkxPj796p6UAxGkUHDjBzaF+XsyQR9+ytR3K5MzDYOWVBj8m3BXv+/pDUuYvbxxCzD87JWR+FDtNGTGIL7iNgnkdbnbo7UGvJuFvLRbvXAXScJgiDc8fX3YsOGDWzYsAGAF1544RYnTgFcLplMSo8staSiaLQiWl3LQ6dVodGJaLXiIw+APXp++O9YNE1DXZyGqzEa6uKcOpbgpJxAEKHAraOom4HibgYKi/Xo9PdP51Cr1W12XRvqYshygB69nBQUPLp8uCxJBP79n0lcOof9H/8NXfVjWbDy4dmyZQvnz59n3rx5DB48OKe2fJS7XdeV79cTTUl8bGQvCgraPw2xPXC7YeKAHvhjKdYca+Ct2np+uKuO/6lpYl5VEUeuBklJ8MLCQZQ6898Z/ihtec/egiwjfPAyCKCpfI4Cffuoa16noKAAQRBYvXo1e/bsYcGCBS3jm/pZNKf+k4L0ESi5PcVl+lwra964zLFDaabPLXk4x1lKgb8WoWlPy185jWzqiaHXLC6fvcy+ffs4ffo0AJWVlYwcOZK+ffu2KmJ9N+51XSPhNBt3XEKrUzF7URkmc9tMR1wuGW9TPSdqI/Qb4MbhUuprHpV2u1/bAvc8kGcjeQ+iuvoulqYVmH0boWgyctE00HbtaH9eXtt0FOHi28iGEgz9PoYhj6NVkEPHymaz4fP5cDgc+Hy+GwW9LpfrFnlWj8eDw+HA6XRy7NixG9u9Xi9VVVW4XC48Hs8tr29RL7szM2bMYMaMD5vFPYgUbFehsNudtydSLY+2wGwDs02g7wADqeT/Z+/O4+Oq7jvuf86dfdO+b5ZkS95kvMg7GLANxhgCJED2kj2hZGvSJmnaPk3Sp03TJ21J0mzNRpYmJCVNIICxISwGvK/YsrzItizL1r7PPvfOPc8fI8s2xuBF0sxI5/166TWj0ejOsUca3e+c3/kdJ329idms3m6DxtcGaNg7AEBGpkZO/rnywTdqpXulLX6vxMkTw7Nr1iA9PeFrPp75p0eRW19E3P8h/FNq8Sfx57GlpYUXXniB6dOnU11dnXK/G5d6Xh/be5opmQ6KbdGUG/NYWDPFyS0VU9jfEWJDUz+P7jmDKeGvry/BY4bo6Qkle4hXbCx/Z8/n8O8nc2A//tx1hAMCAuP/8zJ16lTq6+vZtWsXFouFJUuWgKwg0z0D26nH6aMS03ZhZ0thIdGMYW+I7a+eYdrMy5x5kRJbpAWnfy+OwH40M0Lc4iWasYR+2wz2NfXRsOH7+P1+PB4PixYtYvbs2fh8PoBrrv641POq65Itz/uJRU2Wr/IRjgwQjlzTQ72pGddZ6GgTvLixjRtu8Y76zNhkM16/r2OrCoo+hi3SgnvgFexnnkGc2UjEN49Q1g3XvFF2ukrF59bX9X84Y/30l/0lRt9g0sZRUnKJk+PXSVqwWrhwIZs2beKee+5h06ZNLFq0aOT2DRs2cP3119PU1ITb7SY7O5t58+bx6KOPEggEAHjttdd473vfi9frxeVycfToUWpqanj55ZdZu3Ztsv5Zyiiw2QWFxTYKixPvSsQNSX9ffCRotZ6McfJYDACP9/ygZRnz/bcGeuO43GJU9kaRu15FPvkoYvlqxK33jMLort7Q0BAbN24kLy+PVatWpU0pWVNvmON9ET6+sDBtxjwaNCGYV+xhXrGH3pBOu1+nrjD9ZqrGk4iH8fY8ie4oJZx16YYs42H58uUEg0G2b9+Ox+Ohrq4Of/7d5Jx6GF/3EwwWf+Ci1ueVNXZ6ewwOH4iQPbw29VIssW6c/r04/fuwGP1IYSPqmU3IO49j3TYatjfS3PwMUkrKy8tZsWIFVVVVWCxj3+DBNCW7twTxD5ksvtFDZvbYP6bDqXHdQhe7NodoaowwvU5tmK0AQqC7Khl0VWKJ9eAa3IxraDcu/26irhrC2TcQc9Vc9jYEyuizB4/gGtpFMOumtNmHbFyC1be+9S0aGxvx+/08+OCDvPOd7+See+7h4Ycf5oUXXiAvL4/Pf/7zAMyfP589e/bwmc98BrvdzkMPPQSA1+vl3nvv5ctf/jIA991330gji49+9KN8//vfJxaLMW/ePNW4YoKxWAV5BVbyChI/rqYpGepPrNHq7THoOKPT2pwIWk6XICsnhmnqWCwCiyXx/drZ6xaBxTp8ecF1hu9z4W0Wi0CznCtHHehLNK64VrLlGOYj34JpMxHvfyipocAwDJ5++mlM02TdunVXvQ9NMmxoGsBpFaysnpglgJcj121LyQ2RU423dz1aPMRgyYcuWMeUDEIIVq9eTTgc5sUXX8Tj8VBVVUUw91Z8PU/jCDYQ9c656HvmLnIz1O9nz9bEeiuH89wbPCIewOnfj9O/F1v0NBJBzDWNYM4t9MgpHDx8jMbGLfj9flwuFwsWLGD27NlkZY3fvj5SSg7sCtPdYTB3kYuCovH7uS0us1NWqdPUGKWw2Dam+xAq6SduzyOQfzfBnFtwDe7ANbiVrLZHMOxFhLJuIOKbO7IWURkfIh7G1/UHDHshwdxb3vobUoSQV7pIaYJpa2tL9hCUaySlJDBkJoJWt0FctxCJ6MTjkng8MeNlDl+/2p92bThk6THJrLnOa1oELQf6MP/lr0HT0P7+PxAZyd2w8M9//jONjY3ceeedI3vGpaLXlygEYnE+9Idj3FyVwSeXFCdxZMq1GuvyE1voONltPyGYdSPBvNvH7HGuVCwW4w9/+AN9fX28/e1vp7iogOzT30cz/PRVfA5puXhmZbA/zqt/9pOTb2XpDXac4cOJRhShJgQmur2YiG8+Yc8cTpzuo6GhgZaWFqSUVFRUMHv2bKqrq8dldur1z+vRxghHDkSomeVgxpzxnzXSYyYvbfBjtQpuXOPDkmLNkdJFKpaLjTpp4PS/hnvgFayxTuIWH+HMZYQzFyMt176+OlWl0nPr6/w9Tv/eRAmgsyzZw0n9UkBFGS1CCHyZFnyZFiqnOd70hcE0E3tsJUKXxBwOXvH4udvOfZ3h+5y7TUoovYbNk2Usivn9r0M4iPalf0t6qGpoaKCxsZFFixaldKh6Iy81DxKLS9bWZL/1nZXJy9Txdf8Rw5ZDMGd1skdzAbvdzl133cVjjz3Gk08+yf333481/x1kn/4ent6NBAouLhHOzBIsm9+O1rOX3OYjWEWMuDWTUNYKIr559EfdHDx4kMbG/yMYDOLxeKivr2f27NlkZiZvYf7pkzGOHIhQOsXG9LrkdOez2TXmLXazbVOQQwci1M1XJYHKJQgrkYx6Ir4F2MNNuPpfxdv3LJ7+Fwln1BPOvJ64PcWaPEwg9uBhXP7dBLNXpkSouhIqWCmTiqYJNDvYGP93KqWUyF98F5qPoj30d4jyqnEfw/k6Ozt56aWXqKioSCygTyNSSjY0DVCT62RqjmqhrFyap/8FrHov/SUfScmWym63m7vvvpvHHnuMxx9/nHe+8504M5fjHtyc2NvKlWihaol24PTvwxnYRwGDGDl2TvROx1ZWj7OomuaTLTS8uo1Tp04BUFlZyc0330xVVdU1dfYbDT2dOvt2hsgtsDJvkTuppc/5RTYqp9lpPhqlqNRKXoEqo1XehBDE3LXE3LVYoh24B17FNbgT1+B2Yp6ZhLJWoDunqHVYo+hcCWARwZxVyR7OFVPBSlHGiXzm98gdmxD3vB8xf2lSxxIOh1m/fj0ej4fbbrst6SdeV6qxK0zrYIxPL52cnZuUy2OJtuPuf5mwrx7dPS3Zw7mkrKws7rrrLv7whz/wxBNPcN/b78QRPEhG9x8J++oT66Zi7Ug0Yu5aArnrCDpmsO1QFz2HjxLWXyEcDuH1elm8ePEFnf2SzT8YZ+fmIB6vxqLr3SmxCfzMuS66Owz2bQ9x09oMbLbkj0lJfXFHEf7C+wjm3oZrcCuuwW1kBxvRHWWEslYQ9c5O+vrNicDb8yRaPEh/8QfScl1b+o1YUdKQ3LcN+cdfIRbfhFh3f1LHYpomGzZsIBQKcd999+FypV85zIamATx2jRVTJm/TCuUtSJOM7seRFheBvHXJHs1bKiws5Pbbb+epp57i6Q3Pc/8td5LT+T/4etejO8rw572NiO86dOnkxIkTHDz4NK2trYAg01fO6juvo7KyMqXeJAkFDba/HMBiESy50YvNnhpjs1oF85a42fxCgMa9YeYuVh01lctnWn0Ec9cQzL4Zl383roHNZHY+itGTRY91OQPOxVisVizWxM/aSBMt1eb/LdmDh3D59xLMXoXhLE32cK7KpA9W8nQz+LLAm4EYh8W8yuQjTzdj/uQ/oaoW8YFPJb0t+Pbt22ltbWX16tUUFhYmdSxXYyBisKV1iNtrsnFYU+NETUk9Tv9ubJFTDBXch7Skx4lzZWUlq1ev5rnnnuOpzS7etuKDmLYc4vZ8+vv7adiyl0OHDhGJRPD5fCxbtows3zQOvyaIBhwpFaoMXfLnF9qJxSTLV3rHfCuMK5WTZ2XaDAfHDkUpKrNRWKJKApUrpNnptS6hMzYPehuZ4txGkXc9lr69vNR8J0PRC/dUFYKRrsPnBy7L+dctAuvZzsSvu/1sULPZBVabwDb8cX7n4nQm4iF8XX9EtxcTzFmZ7OFctUkfrMyvffbcJ24v+DKHPzIQI9czE8ErIwt8GcO72WYgrJP+v095C3JoAPO7/wIuT2Jdld2R1PGcOHGCnTt3Mnv2bGbPnp3UsVyt548PYphwW01yG38oqUvEg3h7niHmrCTiW5Ds4VyRmTNnEgwG2bJlC8+73RQUSBoaNtHW1oamaVRVVVFXV0dFRcXIyVTIH+LYoSg5edaUCAjhkMmerUH6e+MsusEzKltUjIXa2U662nRe2xniprU+HI7UCn9K6pFSMtAXp7NNp/OMztCgCYDHV0O4ZBZRxxFK+RPvqPs5p7W1dJoLhrsTDzfFMs51KzbONsYyJLHoeY2zDDDiEmle3piExkjIstoSwWvkuu3CIGa1MfL18+8jUmA2zdedKAEcLPlgWpYAnpW+Ix8l2oNfQvoHYWgQAoPgH0p83tmGPHYIAn7O/nRf1Kn7TYKYyM6FKdMgJ39CvJOgXDmp65g/+FfwD6B98RuIrJy3/qYxNDAwwLPPPktBQQE33XRTUsdytUwp2XhsgLoCF+WZyQ2pSury9m5AmFH8+Xen5aLy+vp6AoEA+/btAyAzM5Ply5cza9Ys3O6LZ9/qFrgY6Iuzd3uIG9f4kjo71NYaY//OMKaU3HhrIRnZ0aSN5a1YLIL5Sz28/JyfA7vD1C9LbmMNJTUZhqSn00iEqTadaESCgJw8C7PmOiksteH1na14mseAUUVG52NMCT9JkecYQwXvQFq8V/y4F3QxHg5khi7Rhz8MXaLH5BveFgmbI7fHjbd+LIs1EbjmLbSSn4TdS+yBgzgD+wjkrMZwXF5b81Q16YOVqL/+TfvDSTMOwWAidA2Hr8sJYiMhzJcJlTWIymmIqtrEdV/yWt4q40NKifz19+HYIcTHv4iYkryF84ZhcPDgQXbu3Immaaxbtw5rms627msP0hnQ+Yu5+ckeipKirOEWXEO7CGbdSNyRns1NhBDceOONZGdnk5OTQ1lZ2Zue8FssgoXL3bz8rJ/dW4Jcv8o77k0iDF3SsDdMa3OMrBwLC5a6mVLlo6cndYMVQEaWhemznRw+EKGtVL+m7TSUiSMSNkeCVHengRkHqxUKihNlowXFVuyXmOE0rZkMlHwY1+AWvD0byDn1bfwF9xHzTL+iMYxWF2PTPC98nRfEzoYwXU/8/g70G2x7uYfZ85xUTx+/brsiHiSj+3F0ezGh7PQtATwrPc+uxpHQLInyP18GFJcnbnuT+48EsZ4O5MljcLIJ2XwU2bCbkb2YcwugchqisgZRWQNTpiFc6bEGQLk88rknkJufR7zt3WiLbkjKGAzDoLGxkZ07dxIMBikpKeHGG28kIyN9Gz5saBog02lhaXlqdDxTUoyM4+t+PLGvUxq26T2fpmnMnTv3su/v8VmYu9jN7i0hGl8LU7dg/P6m9Pca7NkWIhQ0qZnloHa2M60W6k+d4aCzTefAnjC5BVacLlUSONlIKfEPmnScSYSpgb44AC63YEq1ncISG7n51st/w0JohLNuIOaaSmbn78hq/zmhzGUEcm8HbXzLdTVNYHcI3molgmlKGnYbHNwXBBi3cOXr/hMiHmao5MMToquiClaj7PwgJqpqR26XkRC0nECebEqErZNNyN1bEjNbQkBRGaJy2vDsVg2UVyFs6p2zdCQP7EL+/udQvxxx57vH/fHj8TiHDh1i586d+P1+iouLWbNmzVu+653quvxRdp4J8I5ZudhSoGWzknpcg1uxxToYLHofUpt8paIl5Xb6agyam2Lk5FspKR/bvyHSlDQdjnK0IYLTJVi+0ktufvqdVmhaokvgyxv97NsRYsmNnrR+rVQujxmX9HYbI2EqHEq8+Z2VY2H6HCdFJTZ8mdo1/SzEHcX0lX0Sb+9G3IObsYeOM1T0rpQsd9M0wc1ritj41CkO7osAYx+uHIEGnIH9BHJuJe5IQg3iGEi/V8A0JZxumF6HmF43cpv0D0HLcMhqbkIe3AtbX0yELYsVyiq5IGyVlCeCm5KyZNspzB//O5RXon3orxDj2KXLNE0OHz7Mjh07GBoaorCwkFWrVl2wyD2d/amhAylhzTRVSqtcTDMG8fQ+R9RdS9STno1ZRsOsuS76e+O8tjNEZpYFj29s/maEgiZ7twfp645TUmHjunpXyrRTvxpen4WZc1007Alz6kSMKVMnXzCfDGJRk872xHqp7nYdwwDNAvmFVmpn2ygoto3+jKVmI5B/J1HPdDI6HyO79fsEc28llLUi0XkihWgWQf0yN7u3hsY8XIl4AF/34+iOUkLZ6bnu+42oYJVEwpcBdfWIunogMRVNf8+5Ga2Tx5A7XoFNGxJhy+6AiqmIqhrE9Otg1lw1q5VCZGAI87v/DDY72if/HuEYn2l00zQ5cuQIO3bsYHBwkIKCAm6++WamTJkyIQIVgGFK/nSwkwUlHgq96mdeuZi3Zz0Ck0D+XWnZsGK0aBZB/XI3Lz8bYNeWEDfc4sUyyjO8Z07F2L8rBBLmLXFTNsU2IV5rKqfZ6Tijc3BfmLxCKx6veiNzohjoNTjRFKW9Vcc0weEUlFTYKSq1kVdgxWId+59f3V1DX8Vn8XX9EW/vBuyhowwV3I9pS60Ot5o2PuEqUQIYYajkvglRAniWClYpRAgBOfmJToILlgMgTRO62i8sIXzpGeRzT4DDhbhuIcxfhpizIDErpiSFNAzMH/4b9Pei/c2/IHLGvrmCaZo0NTWxY8cO+vv7ycvL484776SqqmpCnOScb1urn95gjAcXqqYVysVsoabhcpJbiNtykz2cpHN7LMxf4mbHK0EO7g1z3cLR+dug65KG3SFOt+hk51qYv9Q9ocKHEIJ5i928tGGIvdtCLFrhUS3Y05gZl7S16jQ3RRnoi2O1wpSpdsoq7WRmW5Lyd1JaPAwVvY+Yfzfe7ifJaf02/vx7iPoufz3leBjrcOUIHMAZOEAg57a0bTJ0KSpYpTihaVBUiigqhaU3AyANHY40IPdsRe7dCjtfQVptMHs+YsEyxNzFCI9a3D9epJTIR38ERw4gPvI5xNQZY/54x44dY/v27fT19ZGbm8u6deuYOnXqhAtUAIFonJ/u7qI61019yZW3rFUmOGng634Cw5ZLKOvGZI8mZRSW2Jg6w8Hxw4n9rcoqr22mt6/HYO+2EKGQSe1sBzWz0qtBxeVyuTWuq3ezZ1uI558aomqag+rpDhxOFbDSRSRs0nI8SsvxGNGIxOPTqJvvoqzKjs2WAj+zQhDJWIjuqiKj43dkdv6WSPAw/vy7kZbx68b3VsYqXAkjgK/rCXRHGaHsFdd8vFSjglUaEmdD1Oz5yPd9Ao4dRu7dmghar+1AahpMn5MIWfOWJn3/pIlOvrQe+fIGxO33oi0du1ahUkpOnDjBtm3b6O3tJTs7m7Vr11JTUzMhA9VZP9ndyUDE4N/vqcOiRZI9HCXFuPs3YdV7GSj50Lh320p1M+Y46e8x2L87RGaOBV/Glc8umabk2KEoRw9GcLo1rl/pJScNG1RcidIpdjKyLDQ1Rjh2OEpzU5TKaQ6mzlABK1VJKRnojdPcFKXttI40oaDYSlWNg/wia0r+jYzbcukv+wTu/hfx9L2ILXKSocJ3oruqkj20EaMerqTE1/0EwowwVDixSgDPEnKkB/jk1NbWluwhjBopJbQcSwSsPVuh80xirUH1dMSC5Yj5SxH5E2vK9Y3k5eXR09MzLo8lG/difvtrMGch2kN/NybNKqSUNDc3s337drq7u8nKymLJkiXU1NSgjWNzjGTYftrP1zed4V1zcvnMqpnj9rwq4+tqf2ctei85p75F1DOToaL3jsHI0l84ZPLys37sDsGKW31Yr2AtSSgQZ8+2EP29cUqn2JizwI3NfvnfP56vxWPFPxSnqTHCmVM6mgaVUxMBazK3ZE+l5zV+ttzvaJTB/jhWG5RXOaiaZh+zxi1jwRo5RUbn77Do/YSybyKYsxrE+L+Bcann1jQlu7eG6DitX9M+Vw7/fjI7HyWQuzbtGlaUlFxeJ0cVrCZQsDqflBLaW5F7tiRCVmtz4gsV1Yj5yxD1yxHD+3JNNOP1oi87zmD+699Adh7a3/7bqK9xk1LS0tLCtm3b6OrqIjMzk8WLFzN9+vQJH6gAhqJxPvPUCbJcVr55WyXFhfkp88dcGV1X9TsrJZntv8AWbqZvyucxrapb5KV0d+hs2xSkbIqNeUvcl/Xu/emTMQ7sCQEwp95N2VVsnJtKJ+DXKuCP03QwwunhgDVlqoNpkzRgpcLzGg6dK/eLRSVen0ZVjYOySjvWVCj3uwrCjOLteQrX0C50RylDhe8kbi8Y1zG82XN7reFKM/zknHqYuC2P/rJPpN1s1eUGq4k9nz+JCSGgpAJRUgF3vhvZ3XGuXPCJXyOf+HVi76wFyxALliW6DabgVHmqksFAogOgZkH71D+MaqiSUtLa2sq2bdvo6OggIyOD1atXM2PGDCyW9HohuhY/3tXJUDTOP64sV/tWKRexBxtxhI7gz7tDhaq3kF9ko3a2g6MHo+QWxKiovnQrcT0mObA7xJlTOtl5FhYsdeP2TJ7XnUvx+izMX+qhZnacY41RTjZFaTkeZUq1nWkznSkRsKJRk+52g54uAyES68WcLpG4dGu4XFrahg5I/G3s70mU+7Wf1pESCksS5X55halZ7nclpObAX3AvMfd0fF1/IKf1u/jz7iCSsTglOp1eU1mglPi6H0dIfcKWAJ6lgtUkIfKLEGveDmvejhzoRe7dnghaG/4Puf6x4U6EyxLdCKdOV/tlvQkZj2P+6JvQ04n2+f8XkVc4asdubW1l+/bttLW14fV6WbVqFTNnzpxUgQpga6ufl08O8Z7r8qjOSZ3FvEpqEGYUX8+T6PYiwpnLkj2ctFA7y0lfT5wDe8JkZlvJzL74NaWv22DPtiCRsGR6nZNpMx0TskHFtfD6KQbXeAAAIABJREFULMxb4qZmtoOmxignj8VoOR6jYjhgudzjF7CklAwNxOlsN+hq0+nvi4MEm10gBMSiFxckWW3gdGnDoUvD5RY4Xdp5t4nh70+d5z0el7SdinHiaIyhgUS5X1WNg8oa+4TqSnlW1FuH7qwgo/MxMrofxxE8zFDBvUhr8ps3XUm4EvEg9vAJbKHj2MPHsOq9+HNvH/dZuPGmSgEnaCng5ZKBoUTDiz1boXEvGAZkZiPueg/ihjXjusHtaBnrMgXztz9GPv8k4oFPoa1YM2rH3bZtGzt27MDj8bBo0SJmzZqF1Tr53vsYihh86ulmcl1Wvrm2EuvwiV0qlJ8oY+NKn1tPzzN4Bl6mv/QT6K7KsRvYBBONmGza6MdqFaxY4xvpkGaakqbGCEcbo7jdGguWusnOu/bXnsnwOxsKxGk6FKW1OYYQjHnAMnRJd6dOV7tBV7tOJJw4hcvMtlBYYqWg2EZWTqKVeDwuiYZNwmFJJGQSDpvDl4nPI2Fz5PvPp1nA5To7yyVGZrucw8GrpDSPIX8/FgtjGsDCIZOTx6KcOjFc7pcxXO43JX3L/a6INHENbsXbuwGpORkquJeYZ2y7Dl/u7+wFZYHzXVTXOhBmFFv4JPbwcWyhY1hjHQgkpnCgu6qIeaYTzliccpsiXy61xuoyTfZgdT4ZDiEbdiNfegaONsD0OWgPfBJRcHk/TKliLP+Ymy9vRP7qe4hb7kZ710dG7bjHjx/n6aefZsaMGaxatWpSBqqzvvnqGba1+vmPtZVUZp97J2wynKRNVlfy3FqineS0foeIbwH+wnvHeGQTT2+XwZaXAhSX2ahf5iYUNNk73KCirNJG3QL3qLWknky/s6FgnKbGKK0nYwBUVCUClttz7SeRAX+crnaDzjadvm4D0wSrNVHiWVCcCFNXW4pompJoRBIeDlrhkEkkJBPXh4NYJCy51JmixQpWq8BqFVisAquV4cuzt4HVJs677dzXL75/4np/b5zmo1E6zgyX+5Vaqa5xkFuQ/uV+V8MS7SCz83dYYx1EPTPRHaXE7QUYtnzi9rxRbXJxJb+zZlyneVcTjuhxphW34hNnEJhILOiuKcRc04i5qjGcZROi9E+tsVKumHC5EYtWIBfegHz1OeRjj2B+9TOIu9+HuOUuxCQrR3s9eeg15G9+CHULEPd9cNSO29/fz7PPPkthYeGkD1WbW4Z4tcXP++bmXRCqFAUYadUrNQeBvLXJHk1ayi2wMmOOk8P7I+zTQrSf0RECFixzU1pxbXtdTWZuj4W5i9zUzHJy7FCEU80xTjXHKK+0UzPLcUXr1My4pLfbGCnxCwZMALw+jcoaB4XFVnLyrGijsPZU0wQut3jTGTYpE+HrbPCyWT0MDPiJGxJDB8OQieuGJG4k1umFQ+bwbRA3JKZ5ZeOy2QXVtQ4qp9lxT8ByvysRdxTRV/YQnr7ncQb2Yw8eRpBIuhKNuC3nvKBVgGEvIG7PR2qXXkt5VaSJNdqOPXwsUd4XOUlRro6Ugu5QEX73ctyl09GdUyb11heT9wxOuSQhBGLFGuScesxf/xD5+0eQO19B++CnEWWps7/CeJLtpzF/+A0oKEH72BdGLWTGYjGeeuoprFYr69atm9ShaiBi8MOdnUzNcXLvrNxkD0dJQU7/XuyRZoby3460eJI9nLQ1bYaDvm6D0y06OfkW5i/xjMrMigJuj8Z1C88LWCditA4HrGmzHJdcExQJm3S2JUr8ujt14gZoWiIIV9U4KCixJm09kRACpyuxFisrB/LyMunp0a/oGGZcYsQTwcswJHE9EcTOBi/jvGDmdAlKKuxXtDXAhKfZCOatJZi3FswYVr0HS6wLa6wLS6wba6xrOHCdS7BxaxaGPZ+47WzYKsCw51/+a6eUWPRu7KHj2MLHsYdPoJlhAAx7IeGMhcRc04g6Ktm1XdJxWGe2dFFdO3lDFahgpbwJkZWL9tDfwe7NmL/5b8x//jzi9vsQ696JsE2eXxzpH8T8ztfAYkX7zD8i3KNzQiel5LnnnmNgYIB77rkHn883KsdNR1JKfrijk5Bu8lfLirGoBfPK64h4GG/venRHOZGMhckeTloTQlC/zENPl0FhsRWhft9GncutMafezbSZTo4fjtByPEbryRhlZ2ew3BoDfXE623U62wyGBuJAIlSUTbFTUGwjr9A6YcKFZhHYLQLUpOi10+wYjhIMRwnR82+XcSx670jQssa6sOhd2MM7EPJcEDYtHgzbuZktw15I3J6PacmAaB/Ood3Ds1InsMSHgERIi3pmE3NPRXdNxbSeO18RQP1yye4tIQ7uTQSv6tpRni1LIypYKW9KCAELb0CbcR3ydz9FPvU75O4taB/4NGLq2C6iTAVSj2F+719gsB/tC18f1Q6Ae/bs4fjx49xwww2Ul0/MPcUu16stfra2+nlgXj4VWZP3BVm5NE/vRkQ8hL/kw2m7+DmVWG2CotLJ8wZZsrjcGnULEgHr2KEILSdinD4Zw2oT6DEJAnJyLcy4zklhsQ1fpjYp1xEpo0BYiA/PTMWYfe52aaIZg8OzW11Y9cQslzOwf2QGCkAKG0LqZACmxUvMVU3QNZWYexqmLedNH1rTBPXL3SpcoYLVqJNS8kqLn+b+CO+fmz9h3nkX3gzERz6HXHIj5q++j/lvX0KsuhNxz/sRTleyhzcmpGkiH/k2HD+M9uDfIqpqR+3Yp06dYsuWLdTU1DB//vxRO2466g8b/PfODmpzndwz881fvJXJyRppxTW0g3DmMgxHejXTURRItDg/G7BOHI0SjZgUFNnIL7Jid6g3CpQxJDRMWzYxWzZ4pp+7XUq0eOC8sNWDM6ucfrOIuL3wivfOUuEqQQWrUXRmKMYPd3awvyOxW73NInjvdflJHtXoEnX1aF/7L+Qffol8/knkvu2JzoGzJl44kE/8GrnzFcS9H0DULx+14w4NDbFhwways7NZvXr1pH53UkrJD3Z0EDEkn1UlgMobkSa+7icwLV6CubcmezSKck2cLo1Zcyfmm5FKmhEC0+rDtPrQmQqAMy+P+DV08lThCtTbJKMgFjf57f4ePvN0M8d6I3xiUSErqzL43wO9vNYRTPbwRp1wutHe+yDaF/4VrDbMh7+C+fPvIIOBZA9t1Jib/4xc/xhixRrEbe8YteMahsH69esxTZM777wTu31yF5xvOjnE9tMB3jc3j7LMyfXiq1we19B2bNEzBPLuQGqqU6SiKEoqOxuuikptHNwb5sTR6Ft/0wSigtU1eq0jyGefPsmjB3pYVu7le2+rZl1tNg8uLqI0w85/bm6jP2wke5hjQtTORvvKtxG334fc+gLmVz6J3LMl2cO6ZvLQa8hffQ9mzUO898FRm1GSUvLSSy/R1dXFmjVryMrKGpXjpqu+sMGPd3UyPc/FXTNUCaByMWH48fQ+S8w1laj3umQPR1EURbkMkzlcqVLAqzQQNvjZni42nRyiyGvjq6vKmV98rluc06rxxRWl/M2Gk/znlja+urJ8QpY5CZsd8Y4HkAuvx/zFf2H+4BuwYDnaez+ByMxO9vCu2AVt1T/xJcQotj9vaGigsbGRxYsXU11dPWrHTUdSSr6/vYNYXJUAKpfm7X0GYer48+++4np/RVEUJXleXxZoxiX5RVZAXPRyPvK5OO/iTa+LC7+PREOe0drc/FqoYHWFTCl59tgAv9zXTdQweWddLvfNzsVhvXjyb0qWg08sKuS/tnXw2MFe3j0nLwkjHh+iYiral/8d+dzjyD89inl4P+JdH0EsW5U2a4jGqq06QHt7O5s2bWLKlCksXrx41I6brl5sHmLnmQAfqS+gNGNyl0Mqb8wWOoHLv5dg9kri9om1VlVRFGUyOD9cHdof4dD+sX08u0Pg9Wl4fBY8Pg2PV8Prs+D2auO2dcGkD1bPPPMMdrsdm82G3W4f+Tj/87PXO0ImP3utnyO9MeqKPPzlosK3XBeyujqTAx0hfnegh9kFLuYUTtxNLYXVmigLnL8U8xffRT7ybeT2l9H+4qFRbVM+FsayrXooFGL9+vV4vV5uu+02NG1yV+D2hnR+squTWfku7pyefrOayjiQBr7uJ4hbswlmr0z2aBRFUZSrdDZc9XQaxOMSAClfdycJ8vXX5QVfvuAO53//2et6TBIMmAT8cbradaLNFz6I0yXw+CyJ4OU9L3x5NDTL6IWuSR+suru7icVi6LqOrr/1TuJlQJkQOIJ2nj11cfg6++F2uykrK6OwsJAHFxfR1BfhPza38611lWQ5J/Z/uygqQ/vC15GbNiD/7xeYX/004u0PIFauQ6RgqBjLturxeJz169cTjUa5//77cTon9+J7KSXf296Bbko+s6wYLU1mM5Xx5R7YjFXvYqD4AdDUXkuKoijpTNMEBcXj+1pu6JJgIE7QbxLwmyPX21r1xB5yZwlwu7ULZrg8vsTnLreGdoVLFSb2Gf5leOCBB0aum6aJrusjQSsWi7H/zCDPHO4mFI1Rl2tjQaEDYRojXz/7oes6wWBw5PZoNLFQz263U1payv15xTxyXPDw5ja+sqp8wp9QCk1DrFyHvG4R5v98D/nbHyF3vpzYWLg4tTbDHau26gCbN2+mra2NNWvWkJ+vypmePzHI7rYgH1tYQLFPlQAqF9P0fjx9zxP1zCLmmZns4SiKoihpyGoTZGZbeaPl/rGoSTBgDoeu+Mj1/p4Yxnn95oQGbo+G16dxz7su83FHZ/gTg6ZpOBwOHA4H3UGdnx3oZPtpyZTMUv5qcSEzC9yXfaxwOMzp06dpbW3l1KlTNDc3sxCIDDj4aW8JK66roby8HI9n4pYGAojcfLTPfAW5/SXkb3+C+U+fRdzxLsRt70DYkv9O9Fi1VQc4cuQI+/btY968ecyYMWNUj52OuoM6P93dRV2Bi3W1qgRQeWO+nqcA8OfdmeSRKIqiKBOR3aFhd2hk5154u5SSWFQmZriGA9fZ65dLBavXMUzJk4f7eHR/YoO0D8zP564ZOVivcCrQ5XJRU1NDTU0NAIODg5w6dYo/7z3KUOdpnn22GYDc3FzKy8upqKigpKRkQu5rJIRALF2JnDUf+eiPEjNEW19Ee/fHEHPqkzausWqrDtDT08Pzzz9PSUkJ119//agdN11JKfnu9g5MKfn0UlUCqLwxe/AQjmAjgdy1mDYVvhVFUZTxI4TA4RQ4nBq5+VcXkVSwOs/h7jA/2NHByYEoi0o9fHxhEQXe0ZlVyczMZM6cOUydMYvPr2/GEh7kPZVxutvPcODAAfbt24emaRQVFVFRUUF5eTmFhYUTqtGByMhCfOKLyBtuxfztjxId+OYuRnvXRxH5ReM6lrFsqx6JRHjqqadwOBzcfvvtWCyWUTt2unru+CD72oM8uKiQIlUCqLwRM4av+0kMWwGhLPVmhKIoipJ+VLACAtE4v9zXzcZjA+S6rfztjaUsLfOOSZtwt83CF1eU8cWNcZ4Pufl/7lmEGY/T1tZGa2srra2tbNu2jW3btmG32ykrK6O8vJzy8nKys7PTpnX5mxGz56N95TvI559EPvk7zH/8JGLtOxBr70M43rzL4mgYy7bqUko2btxIIBDg3nvvnfClnpejK5AoAbyuyM1tNZN7U2Tl0jz9L2Ex+ukv+RgI9adJURRFST+T/q/XS82D/GxPF/5onLtnZPPu6/Jw28Z2hqE6x8lH6gv44c5O/tjYx72zc6moqKCiogK4eH3WiRMnAPB6vSMhq6KiArf78td8pRphtSFuewdyyU3I3/8c+dTvkFteQHvXR2D+sjELkGPZVh1g+/bttLS0cPPNN1NcXDyqx05HppT817Z2AD69RJUAKpcQ7sDd/zIR3zx09+TePFtRFEVJX5M+WD28pZ2aXCdfXVlOdc74tcJeW5PFgc4Q//NaN7PyXRc0xnij9Vnnh6xDhw4BkJeXNxLISkpKsI5iOdt4EVm5iI/+NfLG2zAf/RHmD74BM+eivefjo949cCzbqgM0NzezY8cOZs6cyZw5c0b12OlqY9MA+ztDPLR49MpqlQlGSkTzb5CaDX/uumSPRlEURVGuWvqdiY+yBxcVsmZaFpYrbE5xrYQQfHJJEcf7InxzcxvfWldFhuONZ8oyMzPJzMykrq4O0zTp7u7m1KlTnDp1in379rFnzx4sFgulpaUjQSs3NzetygZFbR3aPzyM3PQM8olfY37tM4jVdyHufBfCNTozc2PZVn1gYICNGzeSn5/PypUr0+r/fqx0BmL8fG8X84rcrJmWmezhKCnKEdiPGDpEIP8upNWX7OEoiqIoylWb9MHq9iS2ffbYLXzhhlK+9GwL39naxt/fVPaWJ+SaplFYWEhhYSGLFi0iFovR1tbGqVOnaGlp4dVXXwXA7XaPhKx0KRsUFgti1Z3IRSuQf/wV8rnHkdtfQtz3QcSSm68prIxlW/VYLMbTTz+NpmnccccdaTlzONpMKfnOtg40IfjU0mIVNBUwdSxGPxa9H4veh8XoR9P7sIePIz1TCGcsSfYIFUVRFOWaqDPAJJuW6+RDC/L58a4unjjcxz0zc9/6m85jt9uprKyksrISAL/fP1I2ePLkSQ4fPgykV9mg8GUiHvgUcsUazN/8N/KnDyM3bUyUB1Zc+fqLsWyrLqXk+eefp6+vj7vvvpuMjIxRO3Y6e+boAA2dIT69tIh8jyoBnBSkiWYMYTH6zgtPfWj6cJiKD114d2Elbs1Gd1Zhm/puCE2cDqiKoijK5JS6Z9eTyB212TR0hvjl3m5m5ruZnue66mP5fD5mzZrFrFmzkFLS3d1NS0tLWpYNiqpatC9/E7n5z8g//BLznz+PuGkt4p73ITyXVzIk21vHrK06wN69e2lqamL58uUjzUcmu3Z/jF/s7aK+xMPqalUCOFaEGcUWPoE9dBRrtAOEFanZkcI+fGlLXGqOc9fPfu2C+527DSxwqdcCKRFmKBGSjOHgpA8HJ6MPiz6A4NwmihKBac0kbs0m5q4hbssmbsvBtA5fWryJbe2BPHcehHrG4X9NURRFUcaOClYpQAyXS31u/Um++coZHl5Xhe8S662u9LgFBQUUFBRcdtlgeXl5SrUIF5qGWLEGuWA58k+/Qb64HrnrFcTbH0DccAtCu/T/kxwawPzOP41JW3WA1tZWNm/ezNSpU6mvT95Gx6nElJLvbG3HqgkeWlKUkoE9bUmJJdaBI3QUe6gJW/gkgjhS2NAdpQgZQ9MDCDMGMoYwYwipI5CX/xBo54Ut23AosyPMaGL2yYxecH9TcxO3ZWM4Soh66l4XnrJU23RFURRlUlF/9VKE127hCzeU8OXnWvjOtnb+7sbSUT8pvZKywcrKSqqqqigqSo2TY+HxIt7zceSKWzEf/RHyV99DvjxcHjh1xkX3l3oM8/tfH7O26n6/nw0bNpCVlcWtt96aEv9HqeCpI/00dof57LJi8tyqBPBaiXgIe+gY9uEwdbaczrAXEcq6npi7Ft015dIBRkqQOuJs0DJjw9d1hIwO33b2+hvdL3FpWjPQXZXEz8422XKI27KR2vh1UlUURVGUVKeCVQqpzXPxgfkF/HR3F08e6eeuGTlj+niXKhtsaWlh9+7d7Nq1C5fLRVVVFVVVVVRUVGCzJfdkWZRVof3N15E7Xkb+/hHMb3wRcf1qxDseQGQkGpGMdVt1wzBYv349hmFwxx13YLfbR/X46erMUIxf7etmUamHlVVqrdlVkSbW6GkcwaPDJX6nEUhMzUXMPY2gu5aYuwbTepkllkKAsCOxI8d2ez5FURRFmfRUsEoxb5ueWG/1i71dzMx3UZN79eutrsTrywYjkQgtLS2cOHGCY8eO0djYiMVioby8nKqqKiorK/H5ktMaWQiBWHITcu4i5FP/i/zzn5B7tiLuei9i5R0Ef/OjMWurDrBp0yY6Ozu54447yMkZ2/CbLkJ6nG9vbcduEfzl4tSY5UwXmjE0MiNlDzWhmWEkAsNRRih7FVFPLYajbGQ9kqIoiqIoqUlIKS+/AH8CamtrS/YQLuKPxvnc+maEEDy8rhKvPblvNcfjcdra2mhububEiRMMDSXKkfLz86mqqqK6upr8/PyknUzLjtOYj/4YGvdCbgH0diXaqv/FJ0d9TA0NDbzwwgssXLiQ5ctHP7SlGyklW1v9/HhXF/1hg89fX8KNlWMzW5WXl0dPzwRocCANbOGW4TB1FFusA4C4xUfMXTv8MRVpSZ21jmNtwjy3ygXU8zoxqed14lLP7aWVlJRc1v1UsErBYAVwpCfMl59tYXGZly+tGP31VldLSklfXx/Nzc00NzfT3t4OgMfjGSkZLC8vH/d27lJK2Lcd87GfYa+owvjoF0atA6CUkjNnztDQ0MCxY8coLS3l7rvvRtMm9wxCZyDGj3Z2sqstSFW2g4cWF1F7DR0t30o6v+Breh+O0BHswSZs4eNoMobEgu6aQsxdS9RdS9xedOmOfBNcOj+3yqWp53ViUs/rxKWe20u73GClSgFT1PQ8F38xL5+f7+3m6aP93Dk9NUrOhBDk5uaSm5vLwoULCYVCnDx5kubmZo4cOUJDQwNWq5WKioqRoDUemxMLIWD+UrR5S8jKy6O3t/eajxmJRDh06BANDQ309/fjcDiYM2cOS5YsmdShyjAlfzrUx6MHetAEfHhBAXdOz8aiTc5QcEmmjiPYgGtwJ/ZIMwBxazaRjAXDTSeqkZojyYNUFEVRFGW0qGCVwu6emUNDZ4hH9nQzI8/NtNzU68DldrtHGmAYhsHp06dHZrNOnDgBQGFhIdXV1VRVVY35nllCiGs6vpSS9vZ2GhoaaGpqIh6PU1RUxK233sq0adOS3rwj2Q51h/jBjk5aBqIsKfPysYWFagPg17FEO3AN7cDp34dmhjFsOQRybxtuR547aWelFEVRFGWiU6WAKVoKeNZQNM5frW/Gpgn+8/ZKPEleb3W5pJT09PSMhKzOzk4AMjIyqKyspLy8nNLSUpzO0Q+LVzOVHY1GOXz4MA0NDfT29mKz2ZgxYwZz5swhLy9v1MeYbgLROL/c183GYwPkua18fFEhS8rGt3lJKpcoCDOKI7Af1+BObNFWJBai3jrCGYvQXVWq8cRbSOXnVrl66nmdmNTzOnGp5/bSVCngBJHhSOxv9XfPneJ72zv4wg0lKbPe6s0IIcjPzyc/P5/FixcTCARGSgYbGxvZv38/AAUFBZSVlVFWVkZpaem4zghJKens7KShoYGjR49iGAYFBQWsWrWK2tpa1UadxP/RppND/GxPF/5onHtm5vDuOXm4bCooICXW6GlcQztx+F9DkzEMewH+vDuI+OZPquYTiqIoiqKoYJUWZua7ef/cfH65r5s5TQPcXpud7CFdMa/XS11dHXV1dcTjcTo6Ojh9+jStra3s27ePPXv2oGkaRUVFI0GrqKhoTJpgRKPRkfVgPT09I7NTdXV1FBQUjPrjpau2oRg/3NnBax0hanKdfHVlOdU5qVeOOt5EPIzTvxfn0C5ssXaksBHxXkc4YxGGs0KV+imKoijKJKWCVZp4+6wcDnaF+OnuLqbnudL6BNdisVBaWkppaSlLlixB13Xa29tpbW3l9OnT7Ny5kx07dmCxWCgpKaGsrIzy8nIKCgquqWlEV1cXBw4c4OjRo+i6Tl5eHitXrqS2thaHQzUROEuPm/xfYx+/b+jFZhF8YlEht03LmtzNKaTEFjmJc2gnzsABhDTQHSUM5d9D1DsXaUnf30dFURRFUUaHClZpQhOCzy4r5q/Wn+T/e/UM/3l7JW5beqy3eis2m42KigoqKiqAxIzSmTNnRma0tm7dytatW7Hb7ZSWlo4ErctphBGLxTh69CgNDQ10dXVhtVqpra2lrq6OwsLCtCirHE/7O4L8cGcnZ4Zi3DDFx0fqC8lxTd6XCREP4Bzag2toF1a9G1NzEPbVE8lYhOEsTfbwFEVRFEVJIZP3jCkNZTqt/M31JfzD86f46gutfGJREVPTeObqUhwOB9XV1VRXVwMQCoU4ffr0SNBqbk60rnY6nSMhq7y8nMzMzJGg1N3dTUNDA4cPH0bXdXJycrjpppuYMWOGmp16A4MRg0f2dPFi8xBFXhtfWVnGghJvsoeVHNLEFj6eWDsVaEQQJ+acwlD2fUS8c0BTa+8URVEURbmYClZpZnahm88tL+Enuzr562dOsnpqJu+fm0/2BJ5VcLvd1NbWUltbC4Df7x8JWa2trRw7dgxIrOMqKysjEAhw+vRpLBYLNTU11NXVUVxcrGan3oApJc8fH+QXe7sIGyb3zc7lnXW5OKyTrzmFZgwOz07txGL0Y2puwplLCWcsIu4oTPbwFEVRFEVJcRP3bHwCu7Eyg/oSD//b0MtTR/rY3OLn/rpc7pqRjc0y8U+IfT4fM2fOZObMmUgpGRgYGAlaLS0teL1eVqxYwcyZM8eknftEcWogyg92dNDYHWZWvou/XFJEReYkms0zdWzRVmzhZuzhZmzhEwgkMdfUxL5T3tkg1EukoiiKoiiXR501pCmP3cKHFhRw27Qsfrani1/u6+bZYwN8cEEBS8u8k2Z2RghBdnY22dnZzJkzB1D7MLyVqGHyvw29/LGxF7dN49NLi1hVnYk2wX9mhBnBFm7BFjmJLdyMLXIaQRyJwLAXEcq+iYivnrhd7VumKIqiKMqVU8EqzZVk2PmHm8vY1x7kp7s7+cbLZ5hT6Oaj9QVUZqvZGuVCe9oC/HBnJ50BnVXVGXxwfgGZzon5MiDiQWzhk4nZqMhJrNE2BBKJhuEoJZR1PbqrCt05BWlxJXu4iqIoiqKkuYl5RjUJzSv28K11VWxoGuDR/d187pmT3Do1i/fNzZuwJ86TSdyURAxz+OO863riMmyYRA1J+Oxt8XNfO/s9gVic5v4opRl2/vmWcuYUTqwNbDVjEFs4MRtlj5zEGusEQAorurOCUPZKYq4qdGeFakChKIo7g+zGAAAXyUlEQVSiKMqoU2fcE4hFE9wxPZsbKzP43YEe1h/t55WWId41J5c7anOwWSZ2qVe6ihomjd1h9rUHOdEfuSgQRQyTWFxe9vE0AQ6LhtOm4bIKHFYNl1Ujy2nlL+ZmcPfMCbAWT0osRl+ipG94Vspi9AFgCge6awoR3zx0ZyW6s0ytlVIURVEUZcyps40JyOew8NGFhaytSay/emRPNxubBvjQggIWlU6e9VepypSSk/1R9rUH2dcRpLErjG5KrJqgKtuB124h123DaRU4rRoumzYSjpxWDYdVJK7bEp8nPsTIdbtFTLzn2IxB6Ayuwb0jYcoSH0p8SXMTc1URylqG7qzCcBSBmBh7vCmKoiiKkj5UsJrAyjId/OPKcnafCfCzPV38y6YzzCty85H6QiqyJlH3txTQG9KHg1SI19qDDEbjAFRk2rm9Not5RR5mF7pxTpY252YMLR5EiweGLxMfYuR64MLbZAwAHxC3ZKC7qgi6qtBdlcRtBTDRgqSiKIqiKGlHBatJoL7Uy9xiD88c7efRAz18dn0za2uyeM91+WQ41Dv7YyFimDR0hkZmpVoHE8Eg02lhXrGHecUe5ha5yXXbkjzS0aMZQ2jGwAVBKRGMAhffJvU3PIYUVkyLZ+Qjbs/DtHgxLR7cWWX0GTmY1mwVpBRFURRFSTkqWE0SVk3wthk53FSZwW/297ChaYBNJ4d4z5w8bq/NxqqpE9VrETclJ/ojI7NSh7tDGCbYLYJZ+S5WV2cyr9jDlCzHhGprLuJhHIH9uPx7sEVOXfT1RFDyjgQlw16APO/zcx9epMWDFPZLhiZ3Xh6maqOvKIqiKEqKUsFqkslwWnlwcRG312bz092d/GR3FxuaBvjwggLqS73JHl5a6Qro7OsIsq89yP6OIP6YCUBVtoO3Tc9hXrGHmfkuHBOtvE/GsYeO4hzagyN4CEEcw15AIPc2DHvhZQclRVEURVGUiUQFq0lqSpaDr60qZ+eZAI/s6eKfXjpNfYmHDy8ooCxTrb8CMExJSDcJxeKJS91kMGLQ0BViX3uINn+ivC/HZWVRmZd5RR7mFnnIck3AXyspsUbbcPr34Ay8hhYPYmoewplLiPgWYDhKVIBSFEVRFGVSm4BngMrlEkKwuMzH/GIvTx/t43cHevnM082src1mYYmHQq+dAo817VpzSykJ6yYh/VwgOhuQgrpJWDcJ6nFCsbNfO3e/YMwkrCfud6kW5w6LoK7QnWg6UeyhPMM+8brwDdOMQZz+fTj9e7HGOpFYiHpmEslYQMxdq7rvKYqiKIqiDFPBSsFmEdwzM5ebqzL5zWs9PHO0n6eP9AMggDy3lUKfnSKvjUKvjSKvffjSRobDMq6hIm5KBiIG3UGDnpBOT0g/dz1o0B3SCUQPcznbPrmsGm67hsem4bJZ8NktFHptuG0abptl+HYNj90yfJuG126hPNOedmHzipgxHMGDuIb2YAsfRyDRnRUM5d9D1DsHaXEne4SKoiiKoigpRwUrZUSW08pDS4p439w82oZidAR0OgM67YEYnQGd3W1B+sPGBd/jsmoU+S4OXFcz2yWlxB8z6QnqdA8HpZ7zLruDOn1h46LQ5LRq5Hus5LltVGU7KM7xIfQobnsiILltifDkPi8guWzahGoicc2kiS3cjNO/F0fgAJqMEbdmEcpeScQ3n7g9L9kjVBRFURRFSWkqWCkXyXRayXRamVlw8deihklnUKfTr9MROBu+YpwZirGnLXhB+dylZru8do3ekHHhbFPIoDuoX1R+Z9Ugz20jz21ldoGbPE/iev7wZZ7HhsemXTBrlpeXR4/qHndZLLFunP69OP17sRgDmMJB1HsdkYwF6M4pICbwzJyiKIqi/P/t3X9sVXf9x/Hn+XFLe3vbS3tvv2X82r7jV0QhSIpbcDLqWMxXmZmLU6dGGRKdnSPZMkenhJG5JmisXUxoxgwhhmXzjwXUPzQm1QnOOYXxKxuh0MIS6hilv1t6b2/vOZ/vH7dc2tEi9Zbecvt6JDf3ntNzTz+fvXe553U/n/upyARSsJJxmeHazA/PYP4oC1z4xtAV9/iw92rgujLqNdpolwWUFLhEgy63z5xBxezCjwSnAOF8RyNLE8zy+snvO05+z1ECA+cxWCSCi+iLfI6BwqVg52W7iSIiIiK3HAUrmTC2ZVFa4FJa4LJ0rNGuvkH6Eh6RoEtpQYCAo9B00xiD5cewkz04yW7sZA95/Y3MuHxqaIn0cnoj/8dA0Qp8tzjbrRURERG5pSlY3UqMwTIJ7GRv6g+vusW31FStGa7N/Jlayn1C+IM4Xg928upt+LaT7MH2erDMyFFC3wkRC99NvHglybzbtES6iIiIyARRsMq2YWHJ9vqGbr3Yyb6r28N+ZpnBq0/FxnfDeO5MvMBMfHcmnjsTP5C699yZmtZ1qzH+sJp3pwLSlZvXc3Xbj137VCuA5xbjO8UM5s9LPR7a9t3ioe2wlkgXERERuQmmfbCyvMvDt4Zuw7ev3F3db/jop/zDtodGACzj/VdhafjvME4Q3wnhO0UMBiL4bmhoOwQmiZPswhnswk52kRc7h53swcIfcR7fLsQbClr+iABWghcIY+xCjVpMNmOwvV6cRBvO4CXcxCWcwbahW+c1NTRY+E5RKhwFIiQK/ndkWBp6bOx81VJEREQkS6Z9sCo798Kk/a7rh6Wi1H16u3D8IwtDYc5JdmIPhS4nmQpebuISTv/pa0JcapRj5EhXKngVp9tj7OAtNeVwyvATuINtOIlLQwFqKDwl2rDNQPowYwVIBiIkZ8xmILQsFZicML5bhO+G/7v/F0RERERkUk37YNUbfWDo0fBlvoc9NiP3W6Mdc+WxGfbYctKjSxmFpfGwHPxAKiCNyhgsvz89yuUM3ezBLpxkJ4HLF7C9vmufho3vFKYu9EcNgUVDj4swdsH0GjUx/tXgOhSi0mHK67l6GBa+GyYZKGOweCVeIEoyrwwvUHbLfVdORERERK417YNVbObqbDdh8lgWxikk6RQCc0Y/xh9Mha0RUxZ7R0xfdBMXsZN9WHjXPN3gjJiyeG0YK8J3CjF2Ab6dD5Y7dYOYSWJ7MSy/f9j9ZZxEO85gWypADbaPWCDCt/PxAmUkggvwAmV4eVGSgTK8QATsQBY7IyIiIiI307QPVvIRdgAvrwyPsusfd2Up72vC17DvlHm9uAMfYHuXr/neUPo0OBinAN+egbELMHY+vp0/tC9/aN8MfCf1s9TPhx47+Rgr7/qjPcaAGcT2+rH9GFb6Pobt91//3iTGaLONF4jg5UUZCC7BC0TTAco4+s6aiIiIyHSkYCX/HSv1fTHPCeLljfJHq4Yzfmq0Z/jiHX4cy4tj+fFU0PGHHnsx3GT30P74qAt7jDg11rDAlbq3LjqUDvSkwpHXP+rI2tXnO/hOcCjIBfEDM0nas9Pb6f1OAWboXivriYiIiMhHKVjJzWfZGCeE54SuE3HGYJJDgSuO5cew/IGhEafRQlnqGCyHZOB/MPkjw1HqviC1gIhdgO8EwQpohElEREREMpZTwerYsWPs2bMH3/e57777ePDBB7PdJMmU5aZD2Y2KRqP0tLXdxEaJiIiIiIyUM0uR+b7P7t27+dGPfkRdXR1///vfaWlpyXazRERERERkGsiZYNXU1MSsWbMoLy/HdV1Wr17NoUOHst0sERERERGZBnImWHV0dBCJRNLbkUiEjo6OLLZIRERERESmi5z5jpUZ8Yd8U6xRFiVoaGigoaEBgB07dhCNRm9622Ryua6ruuYg1TV3qba5SXXNTapr7lJtM5czwSoSidDe3p7ebm9vp6Sk5Jrj1q1bx7p169LbbVrkIOdEo1HVNQeprrlLtc1NqmtuUl1zl2o7ttmzZ9/QcTkzFXDBggVcuHCB1tZWkskkb731FhUVFdluloiIiIiITAM5M2LlOA4bN26kpqYG3/eprKxk3rx52W6WiIiIiIhMAzkTrABWrlzJypUrs90MERERERGZZnJmKqCIiIiIiEi2KFiJiIiIiIhkSMFKREREREQkQwpWIiIiIiIiGVKwEhERERERyZCClYiIiIiISIYUrERERERERDKkYCUiIiIiIpIhBSsREREREZEMKViJiIiIiIhkyDLGmGw3QkRERERE5FY2rUesqqurs90EuQlU19ykuuYu1TY3qa65SXXNXapt5qZ1sBIREREREZkIClYiIiIiIiIZcrZv3749243IpjvvvDPbTZCbQHXNTapr7lJtc5PqmptU19yl2mZGi1eIiIiIiIhkSFMBRUREREREMuRmuwETqb6+niNHjhAOh6mtrQXg/fff51e/+hXxeJyysjI2b95MMBgkmUzy0ksvce7cOXzfZ82aNXzpS18C4NixY+zZswff97nvvvt48MEHs9mtaW+i6vr444+Tn5+Pbds4jsOOHTuy2S1h/LV9+eWXaW5uxrZtNmzYwMc//nEAzp49y86dO0kkEnzyk5/k0UcfxbKsbHZtWpuoum7fvp3Ozk7y8vIA2Lp1K+FwOGv9mu7a2trYuXMnXV1dWJbFunXr+PznP09fXx91dXVcunSJsrIynnzySUKhEMYY9uzZw9GjR5kxYwZVVVXpaUZ//etf2bdvHwAPPfQQa9euzWLPZCJr+9WvfpX58+cDEI1G2bJlSza7Nq2Nt67//ve/qa+v59y5c3zta1/ji1/8Yvpcuja+QSaHvPfee6a5udk89dRT6X3V1dXmvffeM8YY8+c//9m89tprxhhj/va3v5m6ujpjjDHxeNxUVVWZixcvGs/zzA9+8APz4YcfmsHBQfP000+b8+fPT35nJG0i6mqMMVVVVaa7u3uSWy/XM57a/vGPfzQ7d+40xhjT1dVlnnnmGeN5Xvo5jY2Nxvd9U1NTY44cOTLJPZHhJqquzz33nGlqaprk1stYOjo6THNzszHGmP7+frN582Zz/vx5s3fvXrN//35jjDH79+83e/fuNcYY884775iamhrj+75pbGw0zz77rDHGmN7eXvP444+b3t7eEY8leyaqtsYY881vfnPyOyCjGm9du7q6zJkzZ8yrr75qfve736XPo2vjG5dTUwGXLl1KKBQase+DDz7gYx/7GADLly/nn//8Z/pn8Xgcz/NIJBK4rkswGKSpqYlZs2ZRXl6O67qsXr2aQ4cOTWo/ZKSJqKtMTeOpbUtLC5/4xCcACIfDFBYWcvbsWTo7O4nFYixevBjLslizZo1es1k2EXWVqaekpCQ9KlFQUMCcOXPo6Ojg0KFD3HvvvQDce++96dff4cOHWbNmDZZlsXjxYi5fvkxnZyfHjh1j+fLlhEIhQqEQy5cv59ixY1nrl0xcbWVqGW9dw+EwCxcuxHGcEefRtfGNy6lgNZp58+Zx+PBhAN5++23a29sBuPvuu8nPz+e73/0uVVVVPPDAA4RCITo6OohEIunnRyIROjo6stJ2Gdt463pFTU0NW7ZsoaGhISvtlv9srNrecccdHD58GM/zaG1t5ezZs7S1tek1e4sYb12vqK+v54c//CGvv/46RmstTRmtra2cO3eOhQsX0t3dTUlJCZC6kOvp6QGgo6ODaDSafs6V1+ZHX7OlpaV6zU4hmdQWYHBwkOrqan784x/zr3/9a/I7IKO6kbqORe+zNy6nvmM1mu9///vs2bOH119/nYqKClw31eWmpiZs22bXrl1cvnyZbdu2sWzZslHfuPVdjalnvHUtLy/nJz/5CaWlpXR3d/PCCy8we/Zsli5dmuWeyEeNVdvKykpaWlqorq6mrKyMJUuW4DiOLrZvEeOtK8DmzZspLS0lFotRW1vLwYMH05+ySvbE43Fqa2vZsGHDdWcEjOf9VO+zU8NE1La+vp7S0lIuXrzI888/z/z585k1a9ZNa7P8Zzda17Ho2vjG5XywmjNnDlu3bgVSU1GOHDkCwJtvvsmKFStwXZdwOMySJUtobm4mGo2mP0kFaG9vT6d6mTrGW9fy8nJKS0uB1FD3qlWraGpqUrCagsaqreM4bNiwIX3c1q1bue222ygsLLzmNXul1jJ1jLeuQLqOBQUF3HPPPTQ1NSlYZVkymaS2tpbPfOYz3HXXXUDq39TOzk5KSkro7OykuLgYSH2qPXz08cr7aWlpKSdPnkzv7+jo0L/FU8BE1Bauvm7Ly8tZunQp77//voJVFo2nrmOJRCK6Nr5BOT8VsLu7GwDf99m3bx/3338/kFqp5t1338UYQzwe58yZM8yZM4cFCxZw4cIFWltbSSaTvPXWW1RUVGSzCzKK8dY1Ho8Ti8WA1Cc3J06cSK9aJFPLWLUdGBggHo8DcOLECRzHYe7cuZSUlFBQUMDp06cxxnDw4EG9Zqeg8dbV87z09JRkMsk777zDvHnzstN4AVKfWr/00kvMmTOH9evXp/dXVFRw4MABAA4cOMCqVavS+w8ePIgxhtOnTxMMBikpKWHFihUcP36cvr4++vr6OH78OCtWrMhKnyRlomrb19fH4OAgAD09PTQ2NjJ37tzJ75AA46/rWHRtfONy6g8Ev/jii5w8eZLe3l7C4TBf+cpXiMfj/OlPfwLgU5/6FF//+texLIt4PE59fT0tLS0YY6isrEwvK3nkyBF+/etf4/s+lZWVPPTQQ9ns1rQ3EXW9ePEiP//5zwHwPI977rlHdZ0CxlPb1tZWampqsG2b0tJSHnvsMcrKygBobm6mvr6eRCLBihUr2Lhxo6YpZNFE1DUej/Pcc8/heR6+77Ns2TK+/e1vY9s5/3nglHXq1Cm2bdvG/Pnz06+vRx55hEWLFlFXV0dbWxvRaJSnnnoqvST37t27OX78OHl5eVRVVbFgwQIA/vKXv7B//34gtdx6ZWVl1volE1fbxsZGXn75ZWzbxvd9vvCFL/DZz342y72bvsZb166uLqqrq4nFYliWRX5+Pr/4xS8IBoO6Nr5BORWsREREREREskEf/YmIiIiIiGRIwUpERERERCRDClYiIiIiIiIZUrASERERERHJkIKViIiIiIhIhhSsREREREREMqRgJSIiOeGXv/wl9fX1I/adPHmSjRs30tnZmaVWiYjIdKFgJSIiOeHRRx/l6NGjnDhxAoBEIsGuXbv41re+RUlJyYT9Ht/3J+xcIiKSO9xsN0BERGQiFBUVsXHjRnbt2kVtbS379u2jvLyctWvX4vs+v/3tb3njjTfo7+9n2bJlbNq0iVAohO/71NXVcerUKQYHB7njjjvYtGkTc+fOBVIjYcFgkIsXL3Lq1Cmqq6uJx+O88sortLe3EwwGWb9+PevXr8/yfwEREckmyxhjst0IERGRiVJbW0symaSxsZGf/exnRKNRfv/733Po0CGefPJJQqEQu3fvJplM8sQTT+D7PgcPHuSuu+7CcRz27t3LmTNn2LFjB5AKVkePHuXZZ59l4cKFeJ7HY489xjPPPMOSJUvo6+ujtbWVO++8M8s9FxGRbNJUQBERySnf+c53ePfdd/nyl79MNBoFoKGhgUceeYTS0lLy8vJ4+OGH+cc//oHv+9i2zdq1aykoKEj/7OzZs8Tj8fQ5V61axeLFi7Ftm0AggOu6tLS0EIvFCIVCClUiIqKpgCIikltmzpxJcXFxeiofQFtbGz/96U+xLCu9z7Isenp6KC4u5tVXX+Xtt9+mt7c3fUxvby/5+fkA6YB2xdNPP82+fft45ZVXuP322/nGN77BokWLJqF3IiIyVSlYiYhIzotEImzevHnU8PPGG29w9OhRtm3bRllZGb29vWzatInrzZRftGgRW7ZsIZlM8oc//IEXX3yRnTt33swuiIjIFKepgCIikvPuv/9+XnvtNdra2gDo7u7m8OHDAMRiMVzXpaioiIGBAX7zm99c91yJRII333yT/v5+XNeloKAA29bbqYjIdKcRKxERyXlXVux7/vnn6erqIhwO8+lPf5qKigoqKys5ceIE3/ve9ygqKuLhhx+moaHhuuc7cOAAu3fvxvd9Zs+ezRNPPDEZ3RARkSlMqwKKiIiIiIhkSHMXREREREREMqRgJSIiIiIikiEFKxERERERkQwpWImIiIiIiGRIwUpERERERCRDClYiIiIiIiIZUrASERERERHJkIKViIiIiIhIhhSsREREREREMvT/j1/dBy/655YAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"df_top5 = df_can.head(5)\n",
"df_top5 = df_top5[years].transpose()\n",
"print(df_top5)\n",
"df_top5.index = df_top5.index.map(int)\n",
"df_top5.plot(kind='line', figsize=(14, 8))\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\\\\ inplace = True paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Other Plots\n",
"\n",
"Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
"\n",
"* `bar` for vertical bar plots\n",
"* `barh` for horizontal bar plots\n",
"* `hist` for histogram\n",
"* `box` for boxplot\n",
"* `kde` or `density` for density plots\n",
"* `area` for area plots\n",
"* `pie` for pie plots\n",
"* `scatter` for scatter plots\n",
"* `hexbin` for hexbin plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week1_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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