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DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots-py.ipynb
{
"cells": [
{
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"metadata": {
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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 [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python).\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 [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python).\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,
"deletable": true,
"new_sheet": 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,
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"new_sheet": false,
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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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/coursera/Images/Mod1Fig1-Dataset.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,
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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": 2,
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"button": false,
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},
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"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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": 3,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"run_control": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data read into a pandas dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"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,
"collapsed": false,
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" <th>RegName</th>\n",
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" <th>DevName</th>\n",
" <th>1980</th>\n",
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" <th>2007</th>\n",
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" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
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" <td>...</td>\n",
" <td>2978</td>\n",
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" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
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" <td>Southern Europe</td>\n",
" <td>901</td>\n",
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" <td>1450</td>\n",
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" <td>539</td>\n",
" <td>620</td>\n",
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" </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",
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" <td>0</td>\n",
" <td>1</td>\n",
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" </tr>\n",
" </tbody>\n",
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"<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": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"outputs": [
{
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" vertical-align: top;\n",
" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
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" <th>190</th>\n",
" <td>Immigrants</td>\n",
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" <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",
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" <td>1784</td>\n",
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" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
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" <tr>\n",
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" <td>Africa</td>\n",
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" <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",
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" <td>217</td>\n",
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" <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",
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],
"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": 5,
"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": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Type 195 non-null object\n",
" 1 Coverage 195 non-null object\n",
" 2 OdName 195 non-null object\n",
" 3 AREA 195 non-null int64 \n",
" 4 AreaName 195 non-null object\n",
" 5 REG 195 non-null int64 \n",
" 6 RegName 195 non-null object\n",
" 7 DEV 195 non-null int64 \n",
" 8 DevName 195 non-null object\n",
" 9 1980 195 non-null int64 \n",
" 10 1981 195 non-null int64 \n",
" 11 1982 195 non-null int64 \n",
" 12 1983 195 non-null int64 \n",
" 13 1984 195 non-null int64 \n",
" 14 1985 195 non-null int64 \n",
" 15 1986 195 non-null int64 \n",
" 16 1987 195 non-null int64 \n",
" 17 1988 195 non-null int64 \n",
" 18 1989 195 non-null int64 \n",
" 19 1990 195 non-null int64 \n",
" 20 1991 195 non-null int64 \n",
" 21 1992 195 non-null int64 \n",
" 22 1993 195 non-null int64 \n",
" 23 1994 195 non-null int64 \n",
" 24 1995 195 non-null int64 \n",
" 25 1996 195 non-null int64 \n",
" 26 1997 195 non-null int64 \n",
" 27 1998 195 non-null int64 \n",
" 28 1999 195 non-null int64 \n",
" 29 2000 195 non-null int64 \n",
" 30 2001 195 non-null int64 \n",
" 31 2002 195 non-null int64 \n",
" 32 2003 195 non-null int64 \n",
" 33 2004 195 non-null int64 \n",
" 34 2005 195 non-null int64 \n",
" 35 2006 195 non-null int64 \n",
" 36 2007 195 non-null int64 \n",
" 37 2008 195 non-null int64 \n",
" 38 2009 195 non-null int64 \n",
" 39 2010 195 non-null int64 \n",
" 40 2011 195 non-null int64 \n",
" 41 2012 195 non-null int64 \n",
" 42 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": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": 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": 7,
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 8,
"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": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": 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": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": 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": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(195, 43)"
]
},
"execution_count": 11,
"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": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 12,
"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": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": 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": 13,
"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": 14,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": 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": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 15,
"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": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 16,
"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": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
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"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 Afghanistan\n",
"1 Albania\n",
"2 Algeria\n",
"3 American Samoa\n",
"4 Andorra\n",
" ... \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": 17,
"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": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>...</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>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 1983 1984 1985\n",
"0 Afghanistan 16 39 39 47 71 340\n",
"1 Albania 1 0 0 0 0 0\n",
"2 Algeria 80 67 71 69 63 44\n",
"3 American Samoa 0 1 0 0 0 0\n",
"4 Andorra 0 0 0 0 0 0\n",
".. ... ... ... ... ... ... ...\n",
"190 Viet Nam 1191 1829 2162 3404 7583 5907\n",
"191 Western Sahara 0 0 0 0 0 0\n",
"192 Yemen 1 2 1 6 0 18\n",
"193 Zambia 11 17 11 7 16 9\n",
"194 Zimbabwe 72 114 102 44 32 29\n",
"\n",
"[195 rows x 7 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"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": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"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",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>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>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",
" <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",
"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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# optional: to remove the name of the index\n",
"df_can.index.name = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
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},
"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": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"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": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 25,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"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": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"new_sheet": false,
"run_control": {
"read_only": 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": 26,
"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": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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",
" ... \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": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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"run_control": {
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}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
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" <td>Developing regions</td>\n",
" <td>16</td>\n",
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" <td>58639</td>\n",
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" <td>Asia</td>\n",
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" <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",
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" <td>865</td>\n",
" <td>1464</td>\n",
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" <td>1075</td>\n",
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" <td>5876</td>\n",
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" <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>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>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>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>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>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",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 \\\n",
"Afghanistan Asia Southern Asia Developing regions 16 \n",
"Bangladesh Asia Southern Asia Developing regions 83 \n",
"Bhutan Asia Southern Asia Developing regions 0 \n",
"India Asia Southern Asia Developing regions 8880 \n",
"Iran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \n",
"Maldives Asia Southern Asia Developing regions 0 \n",
"Nepal Asia Southern Asia Developing regions 1 \n",
"Pakistan Asia Southern Asia Developing regions 978 \n",
"Sri Lanka Asia Southern Asia Developing regions 185 \n",
"\n",
" 1981 1982 1983 1984 1985 1986 ... 2005 \\\n",
"Afghanistan 39 39 47 71 340 496 ... 3436 \n",
"Bangladesh 84 86 81 98 92 486 ... 4171 \n",
"Bhutan 0 0 0 1 0 0 ... 5 \n",
"India 8670 8147 7338 5704 4211 7150 ... 36210 \n",
"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \n",
"Maldives 0 0 1 0 0 0 ... 0 \n",
"Nepal 1 6 1 2 4 13 ... 607 \n",
"Pakistan 972 1201 900 668 514 691 ... 14314 \n",
"Sri Lanka 371 290 197 1086 845 1838 ... 4930 \n",
"\n",
" 2006 2007 2008 2009 2010 2011 2012 \\\n",
"Afghanistan 3009 2652 2111 1746 1758 2203 2635 \n",
"Bangladesh 4014 2897 2939 2104 4721 2694 2640 \n",
"Bhutan 10 7 36 865 1464 1879 1075 \n",
"India 33848 28742 28261 29456 34235 27509 30933 \n",
"Iran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \n",
"Maldives 0 2 1 7 4 3 1 \n",
"Nepal 540 511 581 561 1392 1129 1185 \n",
"Pakistan 13127 10124 8994 7217 6811 7468 11227 \n",
"Sri Lanka 4714 4123 4756 4547 4422 3309 3338 \n",
"\n",
" 2013 Total \n",
"Afghanistan 2004 58639 \n",
"Bangladesh 3789 65568 \n",
"Bhutan 487 5876 \n",
"India 33087 691904 \n",
"Iran (Islamic Republic of) 11291 175923 \n",
"Maldives 1 30 \n",
"Nepal 1308 10222 \n",
"Pakistan 12603 241600 \n",
"Sri Lanka 2394 148358 \n",
"\n",
"[9 rows x 38 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n",
"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
" '2011', '2012', '2013', 'Total'],\n",
" dtype='object')\n"
]
},
{
"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",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
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" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
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" <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",
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" <td>1758</td>\n",
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" <td>2635</td>\n",
" <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",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <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",
" </tbody>\n",
"</table>\n",
"<p>2 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",
"\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",
"\n",
" 2011 2012 2013 Total \n",
"Afghanistan 2203 2635 2004 58639 \n",
"Albania 539 620 603 15699 \n",
"\n",
"[2 rows x 38 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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},
"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": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\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": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['seaborn-notebook', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-white', 'fivethirtyeight', 'seaborn-talk', 'seaborn-poster', 'seaborn-darkgrid', 'seaborn-ticks', 'fast', '_classic_test', 'tableau-colorblind10', 'dark_background', 'ggplot', 'classic', 'seaborn', 'seaborn-pastel', 'bmh', 'seaborn-bright', 'seaborn-deep', 'seaborn-whitegrid', 'seaborn-colorblind', 'grayscale', 'Solarize_Light2', 'seaborn-paper', 'seaborn-muted']\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": 34,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": 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": 34,
"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": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbb97024198>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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n5wNQU1PDhg0b8Hg8FBQUUFJSglIqKC9MCCFGLExJP5h8HtN/5plnyMrK8v5cXl5OXl4e69evJy8vj/LycgBqa2uprKxk3bp13H777Tz88MOYpgnApk2bWLZsGevXr6e+vp4dO3YE+OUIIUQANbthfAJqfHy4IwkYn5K+0+lk+/btXHLJJd5tVVVVFBUVAVBUVERVVZV3+8KFC4mLiyMtLY309HSqq6txu910dHQwe/ZslFIsWrTIu48QQoxKLU0wOSncUQSUT8M7v/71r7n22mvp6OjwbmtubiY52bq5kZycTEtLCwAul4tZs2Z5H2ez2XC5XMTExJCSkuLdnpKSgsvl6vf5tmzZwpYtWwBYs2YNdrt9mC/LEhsb6/e+4SRxh5bEHVpjKW5XRzukpGHrjXcsxT6QIZP+W2+9RVJSEjNmzGDXrl1DHlBrPazt/SkuLqa4uNj7c2Njo8/7Hstut/u9bzhJ3KElcYfWWIq7x+mAjGxvvGMp9szMzH63D5n09+zZw5tvvsnbb7+Nx+Oho6OD9evXk5SUhNvtJjk5GbfbzeTJkwHrCt7pdHr3d7lc2Gy2E7Y7nU5sNttIX5cQQgRPSxPqlDPCHUVADTmmf8011/Dggw+yYcMGfvCDH3DGGWewfPlyCgsLqaioAKCiooJ58+YBUFhYSGVlJV1dXTQ0NFBXV0dubi7JyckkJCSwd+9etNZs27aNwsLC4L46IYTwk+7ugvYwtGAIMr/r9K+44grKysrYunUrdrud0tJSAKZNm8aCBQsoLS3FMAyWLl2KYVh/W2644QY2btyIx+MhPz+fgoKCwLwKIYQItJbwtGAINqWHM9geJocO+ddtbiyNvx1L4g4tiTu0xkrc+uNqzLtLMW7+L1TBfGDsxA4Dj+lL7x0hhOhPBE7MAkn6QgjRL93c23dHkr4QQkQB75V+ZN3IlaQvhBD9aWmC+ATU+PHhjiSgJOkLIUR/Qr0geohEddLX2ysxf/0Aur013KEIIUYZLUk/8piv/RP96ouYd5eiD+4PdzhiGHTNnk9vtAkRDM3uiBvPhyhP+rgaIGMadHdjrrkV841t4Y5I+ED39GCu+zH6b38KdygikrU0hX6ZxBCI8qTvQM0+HeOOdZCTi970c8wnHkX39IQ7MjGY+lo42ok+dCDckYgIpbu64EgbJEnSjxj6aCe0tUJKGiopGeP/3IW66DL085sxH7gT3dYS7hDFAPTH+6xv6mrDG4iIXK2ROTELojjp42ywvtqsdShVbBzGNd9FLVkOH+62xvkP1IQxQDGgg73/Lq3NchNeBEdvjb4M70QSlwPoXYD4GMZ5xRi3rQHTxPy/t2G+XhGO6MQg9IF90NvET672RVBE6MQsiOKkr51W0seWdsLv1PRZGHeshZNnoR9aa43zj/6+dFFBmyYc3A9z5lo/10vSF4EXqS0YIIqTPs4GiImBKf3/JVeTkzFW3IVa9G/o5zfD3qFXDRMh0FgPHUdQBQsgNs66qStEoEVoszWI6qTvgCkpKCNmwIeo2FjUVUshPgH96pYQBicG1HufRZ2cC1Mz0fWfhDceEZlamyFhAmpcZLVggChO+trlgJQTh3Y+S40fj5p3AfqtV9GdR0IQmRiMPrDP+oSWeRIqPVvG9EVwNLthUuRd5UMUJ31cDSfcxB2IOq8YPEfRVa8EOSgxFH2gBjJyUHFxkJENjfVWTbUQARSpLRggSpO+7u4Gt8tbrjmkGadAeja68sXgBiYGpbWGAzWok2ZYG9KzwTTBURfewETkaWmKyIlZEKVJn2YXaNOn4R0ApRTqvEug+n2pFgmnJpc11jptJoA1vANyM1cEXos7Imv0IVqTfu/ELF+HdwDU/IvAMORqP5wOWDNxvVf6U601QLWM64sAslowtMvwTiQZrEZ/IGqKDc44G135kvTmCRN9oAaUguzpAKj4BLDZQSp4RCBF8MQsiNKk3zcbF5t9WLsZ5xVbQ0O73w5CUGIo+sA+mJppJfs+6dky5CYCK4JbMEC0Jn1nA0xKGn4N7pmFkDgZ8xWp2Q+LA/tQOTOP26TSs6G+VmZMi8Dpu9JPkiv9iKGdvtXof5aKjbPG9ne+gW6VLpyhpFtbwNUIOTOO/0V6NnR2WJ/AhAgA3RK5LRggSpM+rgbfyzU/Q513CfR0o1//Z2BjEoM72HsT94Qr/SzrG7mZKwIlglswQBQmfa21tXjKMCp3jqWyT4aTctGvbpEhhRDytrn+7JV+hlW2Ke0YRMC0NEHCRFTcuHBHEhRRl/RpawGPx6/hnT7qvGKo/cjbB0aEwIEaa8GbiZOO355kg/gEqdUXgdPsjtirfIjGpN9Xo+/n8A6AOmcRxMZJE7YQ0gdqYNqME7YrpaSCRwSUbm2CyUnhDiNooi/p95Vr+jm8A6AmJqIK5qNfr0B3eQIUmBiI7jwChz/5dFLWZ/RV8AgREC1NqAit0QeIHeoBHo+H1atX093dTU9PD/Pnz+eqq66ira2NsrIyHA4HqamprFixgsTERAA2b97M1q1bMQyDkpIS8vPzAaipqWHDhg14PB4KCgooKSmxrtRCyDsxawTDOwDq/GJ01cvoHW+g5p0fgMjEgA5+BICaNrP/36dnwb9eQnd2HF/DL4Q/mptgThQP78TFxbF69Wruu+8+7r33Xnbs2MHevXspLy8nLy+P9evXk5eXR3l5OQC1tbVUVlaybt06br/9dh5++GFM0wRg06ZNLFu2jPXr11NfX8+OHTuC++r642yA8QkwIXFkxzn1TLDZ0a++EJi4xIB0b/sFBrrS772Zy+FDIYpIRCrd5YGOyG3BAD4kfaUU8fHxAPT09NDT04NSiqqqKoqKigAoKiqiqqoKgKqqKhYuXEhcXBxpaWmkp6dTXV2N2+2mo6OD2bNno5Ri0aJF3n1CSTsdYLOP+BOGMmJQCy6G3Tus3vwieA7UwKQk66Ztf9L7KnhkiEeMUEuz9TVCJ2aBD8M7AKZp8p//+Z/U19fz+c9/nlmzZtHc3ExysnVikpOTaWmxJiu5XC5mzZrl3ddms+FyuYiJiSElJcW7PSUlBZer/wk1W7ZsYcsW6ybpmjVrsNuH1y7B++JiY0/Y19nixsjIJtnPYx6r+4tX4vz74yTsfJ3Ery8Z8fH69Bf3WBCsuJ2HPsbInUNyav/3YXTSZBqMGCY0u0j04/nlfIfWaI67y9WAC0jKzmF8PzGO5th95VPSNwyD++67j/b2dn7+859z4MCBAR87UO36cGrai4uLKS4u9v7c2Njo877HstvtJ+zb03AINW2638c8Tlw8zD6D9hf+SseFXwzY/Yn+4h4LghG37vJgHtyPmpM/+LHtUzlSs5dOP55fzndojea4de/9oxYMVD8xjubYPyszM7Pf7cOq3pk4cSKnnXYaO3bsICkpCbfbmq7sdruZPHkyYF3BO51O7z4ulwubzXbCdqfTic02wMf1INFHO6Gt1e/ZuP1R5xWDox4+lIXTg+KTj6GnB3XSADdx+2RI2aYYOd3c14Ihcod3hkz6LS0ttLe3A1Ylz7vvvktWVhaFhYVUVFQAUFFRwbx58wAoLCyksrKSrq4uGhoaqKurIzc3l+TkZBISEti7dy9aa7Zt20ZhYWEQX1o/XIGp3DmWOnth78Lp0mc/GD6diTt40lfpWXD4ENqUttdiBLwtGCK3Tn/I4R23282GDRswTROtNQsWLODss89m9uzZlJWVsXXrVux2O6WlpQBMmzaNBQsWUFpaimEYLF26FMOw/rbccMMNbNy4EY/HQ35+PgUFBcF9dZ/lx+IpQ1Hj462F01+vQF/9HVT8hIAdW2AtnJIwEexTB39cejZ0d4HTAanpoYlNRJ4Ib8EAPiT9k046iXvvvfeE7ZMmTWLVqlX97rN48WIWL158wvaZM2eydu1aP8IMDH8WT/GFWnAx+uXn0e9ul5r9IWjTRBm+jyrqAzWQM2PI+yUqPRsN1iQtSfrCT7rFHbFr4/aJrhm5LgfExMCUAI/XnTwLDMPqxyMGZFa9jHnb9T43R9M9PVD7Eaqf9gsn6O22KUsnihFpaYroGn2ItqTvbIApKSgjJqCHVXFxkJaBPjRwVVO0056j6CcehWYX5pOP+rZTfS10eQaclHUslTjZquWXm7liJFqaI7oFA0RZ0vd38RSfZOaAJP0B6a1/A3cj5M+3FqF5f+fQ+/TexB2w/cJnpWdJBY8YmZbI7rAJUZb0cTWMqLvmYFRmDjjqpQFbP3R7K/rZJyGvEOPGH0JKGubjjwxdaXNgH4wb5x26GYrVeE366gv/WC0YjkjSjxS6pwfcrhF11xxU5kmgTVnBqR/62Seh4wjG4m+h4sahvnYd1O4fssxVH6iBrJNRMT4Ox6VnQ2szuk2WshR+iPAVs/pETdKnyWkl5SAN76jMHAAZ1/8M7XKgX/wbav5FqOzpAKjC82Hmqeinf2+1Te5vP9OEgzVDT8o6hrfxmlztC3/0TsySMf1IEYDFUwY1NcOqDDr0cXCOP0bpp/8AgPrKN73blFIYVy2FZjf62af637HxsPVR25fKnT7SeE2MhFzpRxYdgMVTBqNi4yAtE33oYFCOPxbp2o/Qr21FXfzFEybEqRmnoM4pQr9Q/un8iWP1tlMezpU+KakQGycVPMIvui/pS51+hPBOzArSlT69QzwyvONlbn4M4iegvnBlv79Xi78NgH7qtyf8Th+osT45ZZ7k8/MpIwamZsoi6cI/Lb19dyZJ0o8MzgaYlIQaNz54z5GZA42H0UePBu85xgi99z14pwr1hSutGvp+qJRU1KVXoN+oQNfsOX7/A/sgI8eaAzEMKj1bbqYL/7Q0wYSJw37PjTVRk/StxVOCd5UPoLJyQGuoj+4hHq015l9+Y02Eu+TyQR+rvvA1SErGfPxhb/ttrTUcqBlwTdxBZWRDYz26q8uf0EUU0y1NEd1ds0/UJH1cQZyY1ad3KEJ/EuVDPG+/BjV7UF++eshPVio+wbrJu+8D9JuvWBubXNDaDL5OyjpWejaYJjjq/AhcRLXmyG/BAFGS9LXW1sSsYNXo90nLgNjYqB7X1z09mE89BhnTUAsv8Wkfdd4lMG06+i+/sSbI9M3E9eNKX6X3lW3KEI8AffTopz3yh9LShJKkHyHaWsDjCf7wTkwMTM2K6lp9/eoLcPgTjMXf9nlSlTJiML5+PTgb0C88bY3nKwW9df3DMtVaLUgarwkA/eQjmKtutrpnDqXFHdFr4/aJjqTfW66pgj28Q3RX8OjODvRf/wS5c2DuOcPaV82ZC3PPQT/zJHrXdpiaiYpPGHYMKj4Bku0yQavXcJYpjTRaa/Q7VXCkvd8KseMe6zkKnR1W074IFx1Jv3diVtBaMBwrM8e6Yu3sCP5zjTLt//tnaHZhfO06v9YLNq4sgW4P7PvAt3bKA5HGawDoJifmj76D+dLfwx1KeBz+BFyNkJqOfvXFEyrEjuOt0Zcr/YjgnfwTqit9gLroquDRrS0c2fw7yD8XlXuaX8dQ6VmoCy+zfhjOpKwTjpMN9bVRfZULoN/+l3UB8odfYlZuDXc4Iad37QDAuPlHkGTD/OOvrPYe/fG2YIj8Mf0hV86KCC4HjI+HCYnBf65jevCo6bOD/3whYD5Shn5vu1WOqrXVw0hrMI/9vge0xvjqt0b0XOpLV0NrM6pggf8Hyci2Pqo3u2BKyojiGcv0jjes4oKUNPSv16PjE1BnjeC8jjF699uQmo7Kno668jr0w2XoV7egLvjciQ9ujY4WDBAlSV87G8CW6teQw7ClpVutACJkXF8fqEG/9hKclo9Ky7RusBqG9VUpUL3fG4qkgnNp7fuk4yc1MRH1nR+O7Bh9SyfW1UZt0tcdR2DPu6hLLkd96WrMslWYm+7D+I8fo04L8drUYaC7u2DPe6gFFwKgzr0QXfEc+qnfos9aiJp4/AWgtwVDFNTpR0XSJ5iLp3yGMmIgIztiKnj0lqdhfDzGsttQQ3xSGm+309rYGKLIBuFtvPaJdYM4Gu1+G3q6UXPPQcUnYCxfjXnfjzA3/BSj9C7UzFPDHWFw1eyBox3eP3BKKYyrb8S8uxT91z+grr7x+Mc39yV9uZEbGYK4eEp/VGYORMAELd3kQr/xMuq84iET/latQ3oAABjnSURBVKgyxQbjE6K6Vl/vfAMmToKZcwDrE5Sx4icwxYa5/r/RB/eHOcLg0rt2WJ9IT8nzblM5M1BF/4Z+6Rl07Wdef0sTTEi0GidGuIhP+vpoJ7S1hqZyp09mDrgb0UfaQ/ecQaBfegbMHtQlXwp3KMOilIrqCh5t9qDffROVd/ZxcyVUUjJG6V0wPgGzbBX68KEwRhlc+v0dMH02asLE47arK74JEydaN3WPudGvW5qionIHoiDp4wpd5U6fSKjg0UePoiuehbnnotIywh3OsKmM7Oi90t+3B9paUf3MlVApadYVP2Cu+3H/ba3HON3eCh992O+9CzVxEuqKb8HeXeiqlz/9RRSsjdsn8pN+sBdP6U8ErKKl//UStLdiXPrlcIfin/RscDVG5XwJvfN1iImF08/q9/cqIxvjB3dCR7t1xd93EzNSvL8TtEadlt/vr9UFl0LOTPQTj376/oiSFgwQBUk/2Iun9Ms+1VrQe4wmfW2a1g3ck3Jh1unhDscv3h48ETyEMRC98w045QxUwoQBH6NyZmL8xypwOzDvX40+0hbCCINL794BCRNggJJpZcRgXLMMmpzoZx63NrZER7M1iIKkj9NhLcYxxRayp1SGARk5Y/dKf9d2qP8EVfzl0JS5BkOULp2o6z+x/u3OHLoNhpp1GsZN/wWHDmLef6dV5jnGaa2tpH/qmYP2flIzT0UtuAj9/NPWTe3ODkn6EcPZYPV1N3xr/hUoKnNaWK/0dXsr5t8fR7cP/wrOfOFp65wVnheEyEIkLcOaQxBtSf+dNwBQc+f59Hh1xlkYy26DA/usK/6xnvgPHwJnw4BDO8dSX1sCcXGYj9xvbZCkHxm00xHaoZ0+mTnQ5PIr6QaC/uOv0OW/w3zo52izx/f9avfD+zutdW3HcPmaiouD1Knoj/ehe3x//WOd3lkF2Sej7FN93kcVzMe48Tb4uBrzgbF9xa/ft1ov+DIBTSUlWzPAe8s3lVTvRAiXA2ULXeVOH28FTxiu9vW7b6Ffr7C6Xb63HV3+e9/3feGvMG48atHngxhhaKhT8uDdNzFvK8H80yb0/r0R3Y9Ht7dC9W6fhnY+S521AOPGW+GjD63E3zk2E7/e9TbYp/pccaYuvhwyplk/RMmV/pAzchsbG9mwYQNNTU0opSguLuayyy6jra2NsrIyHA4HqamprFixgsREawLP5s2b2bp1K4ZhUFJSQn6+9VGrpqaGDRs24PF4KCgooKSkJKhjxrqnG5qc4bvSp7cHzyz/GpD5Q3d2YP5uI2RMwyi9G/2nX6GffRKdMwNVeP7g+za70W9UoM7/HGripBBFHDzq6mWoM87CfL0CXfEs+sX/hbQM1DlFqHMXfXqzN0Lod98C00TlDz/pA6izFmJ851bMX92Lef+dGD+4ExU/8M3g0UZ3d1utJ84p8nkfFRuL8e1bMDf/DqZmBTG60WPIK/2YmBi+9a1vUVZWxj333MNzzz1HbW0t5eXl5OXlsX79evLy8igvLwegtraWyspK1q1bx+23387DDz+M2dvZbtOmTSxbtoz169dTX1/Pjh07gvriTKfDWjovlOWafWypVpO3EF/p6/LfgbsR49vfQ8XFob5xI8w8FfPRB9C1Hw2+7z+fgZ6xNxlrICouDnXWQmJu+hHG2t+irvsPq/nY3/+M+eOb6bm7FPP5cnoipVZ95xvWBKOTcv0+hDp7oTXUs38v5gP/Pbau+Pfvhc4O1OlDj+cfS+WeRsytP/Vr/YaxaMikn5yczIwZVm/zhIQEsrKycLlcVFVVUVRk/UUtKiqiqqoKgKqqKhYuXEhcXBxpaWmkp6dTXV2N2+2mo6OD2bNno5Ri0aJF3n2CpafxMBCaxVM+y6rgmRbSCh697wP01r+hLvwCKrd3+n1cHMZ3V0LCRMyNP7WGAPrb13MU/c9n4cx5qPTIu+JRExIxzr+UmNK7MO59BHXVUgD0E4/QeMNX6LnjJszf/gLztZfG5IQl3d2Ffu8t1JnzrPfeCKizF2J854dQswfzgZ+MmcSvd79t3bw/9cxwhzKqDavhWkNDA/v37yc3N5fm5maSk60bH8nJybS0tADgcrmYNWuWdx+bzYbL5SImJoaUlE87HqakpOByufp9ni1btrBlyxYA1qxZg91uH96r6uV5/20rvtzZxPp5jJFonjEbz/bXhh1/bGzssPfRXV04f/8/GLZUUr6zAiPhmOnndjueH63BfcctxP76AabcsfaEcrYjzz9Na1sLyVd+m3F+nit/4g4Lux1yT4Grl9L9ycd0vVVJ53tv07X9NfTLz6MBIzWdcafnE3daPuNOyycmc9qoK1899nwf3VlFU2cHSRcUMz4Q/wb/dgWdkybRvO5OYjb+jCk/XosxSN3/cATrfeLa+x7MmoMt5+SAH7vPmHmPD8LnpN/Z2cnatWtZsmQJEyYM/I8/0I2y4dxAKy4upri42Ptzo5+dG+N7l8xzq1hUGLo/milp6CYXjo9qUImTfd7PbrcP+zWbf/sT+uB+jO/9GFd7B7R/ZiZqSjrqmmV4fvsLHJvKMK5c4v2V1hqz/A8wbTrNU6f5fa78iTvsxk/E/uWraV94Kco0UYc+Ru/Zhfnhe3S+9Rqd//yH9TibHeOW21E5/i/uEmjHnm9z2wsQN46WzOmBe6+fMhd1QyldD63FsXo5xvLVARkCCcb7RLe3YX74PuqLVwX1PTiW3uOZmZn9bvfpc2B3dzdr167lggsu4NxzzwUgKSkJt9tabcbtdjN5spXUUlJScDqd3n1dLhc2m+2E7U6nE5stuBOmzMbDMCkJNW58UJ9nIKGq4NF1B9F/fxw174JB67ONCz5ndRl87inMY/uO7NoOdQdRl14x6q5mQ0kZBip7OsYllxPz3ZUYa3+LcddG1LduAQ3m/6wZcHgsnLTW1izc0/JR4wP7XjfmXYC64f9A9QeYv7gb3dUV0OMHzAfvgDZ9qs+PdkMmfa01Dz74IFlZWVx++eXe7YWFhVRUVABQUVHBvHnzvNsrKyvp6uqioaGBuro6cnNzSU5OJiEhgb17rbK5bdu2UVhYGKSXZelx1IfnJm6fvgqeILZZ1qaJ+dtfwPgE1De+M+Tj1Te+A7lzrJWUetvrmi88DUk21LzBq3uijVIKlZ6NsejzGDethCYn5kNrhzXvISQ++diakHSmbxOyhsuYdwGq5Puw513rfTMKy1717h0QnzBg6wXxqSGHd/bs2cO2bdvIycnh1ltvBeDqq6/miiuuoKysjK1bt2K32yktLQVg2rRpLFiwgNLSUgzDYOnSpRi9N5ZuuOEGNm7ciMfjIT8/n4KC4K7g09NQH94yrGS71QMkiFf6ets/oPp9VMn3fWoYpWKtG7vm3aWYG3+KseT7sHsH6oprx/RkrGBT02ejvnEj+ncb0f/7Z9RXrgl3SF56Z+8s3CAlfQBjwUWYLodVHWZPQ41wWcxA0lqjd22HU/JQsdGxLtRIDHmGTj31VB5//PF+f7dq1ap+ty9evJjFixefsH3mzJmsXbt2mCH6R2uN6ahHzQnfxz2lVFAreLSrEf2X38CcuagFF/seV1Iyxk0rrZWU7l8F48ahiv4tKDFGErXo87B/D/pvf0KfPMvnVgfBpne+ASfPQgW5v5S67OvWQuvPPIGZkoqxaJS8Zxx11iedz3813JGMCZE7I7etFTxHwzMx6xgqMycoV/paa8w/PAimifGtW4Y9Fq9mnIL65k3Q3Y2af/GwbjRHK6UU6prvQs5MzIfXoRvC38FTN7th/95+e+cHmvf1n3EW+vcPWpPBRgG92/fWCyKSk76rt49+GGr0j5OZA20tAe9Zrt98FXa+gfrKN1Gp6X4dwzj/Uoxbf+qtWRdDU+PGW+P7hoG58Wfoo0fDGo9+x5rr4u8s3OFSsbFWg7askzB/+X/RH+8LyfMORu/aYS2SNAYX+wmHyE36vYunhPVGLqCyAl/Bo9tb0X/8JZyUO+LZs2r2GQGv+Ih0yj7Vmrx06AD6sV+E9cam3vmG9R7POjlkz6niJ2AsXwUTEzH/30/Qff+vhYHu6YE976BOy4/qyrPhiNikH5bFU/oThFW09ObHrFWtrvuPQXuGi+BRpxegvvJN9OsV6K1/D0sM+uhReH8Hau45IU94akoKxvI7weOx2jWEaxGW/Xuh4wjqdBna8VXEJn2cDmsiSbgbhyXZYMJECFDZpv7kAHrb86gLL0NNmx6QYwr/qC9cCXPPQT/xMPrD3SF/fs87b4LHE5Lx/P6orBxrqKuhzhrq6g59Db/VekFJ64VhiNikr50NGKnpYf/Ip5SCzMCtomU++QgkJKC+9I2AHE/4TxkGxvU/gJQ0a3y7qf+2IsFytOplqzZ99hkhfd5jqTlzrUZ2e95F/+b/9TvUpY8etSYQvvsW5j+fpe3xR9HvvhmQnj569w6rcincF3djSOQWtTodxNinMhqm0ajMHPSbr6K1HtEfIf3eW/DedtTXr5dqm1FCTUjEuOlHmD+zWhIbpXeHpFZcmyZH36xEnX6WtWBMGBkLLsJ0NqCf/j0oAz0pySrtdDZY99Zam497fLt3RwOmz0adeibq1DNh5qmouHE+P68+0mZVLn3hysC9mCgQsUlfFcxnfNY0RkV/wMwcOPIcNLv9XqtX9/RgPvEopKajLvpigAMUI6GyT0Z9+3voh9ain/496mvXBf9JP9yN6W5EzR0dk6TUF6+yFhqv+AfEjbPupdnSUDkzrMqalDSrks6ehj07h8aqSvQH71j/Pfsk+u+PW/vlzvn0j8DULJgwceALpQ/etdYPkNYLwxKxSd+4/N+ZYLdzZBQ0R1KZOWiwKnj8TfqvvACHDmDctDLsV3biRMa5RZh73kU/9xQ6rxA1+/SgPZfe/hrmI/djTLFBEGfhDodSCnXtzejF34aEQRI1oOITrGGhOXMB0Efa4cPd6A92Wn8ENj+Gd5AoJgYmJfX+NwU1+dPv9QfvwPgEmHFK8F9gBInYpD+qZB2zipYfVyW644j10XnWaVCwINDRiQBRVy1Ff/AO5iNlGKvXowLUiriPNnvQT/8B/cwTMH02tv+6F/couy2nJiT6sc9EmDvPO8NZtzbD3l1otwNamqG12Zrn0tqMPvwJtDaBx2PtfPZCaR8yTJL0Q2HSFEic5Hetvn72SWhtxli+Kuw3psXAVHwCxvUrMO/9EfrPD6GWLA/YsXVbC+amtbD7bdQFn0NdvYwYexqMgk+ygaYmJVnJfJDH6KOd0NLk9yfnaCZJPwRGUsGjnQ3oF55Gzb8IdfKsIR8vwkvlzkF94Ur0M4+j556DKpg/4mPqA/swN/4Mml2ob92CEQGL1o+UGh8Pfs5Ej3aj67NhBOvrwTPc2Zv6qd+CoVBfvTZIkYlAU1/6d8iZgfnbX6Bb3CM6lvmvlzDX/KfVY+m2NZLwxYhJ0g+VzBzoOAJu59CP7aX3fYB+Yxvqc19FhbmdhPCdio3DWFoKnR2Yv/GvTYPu7sb846/QD5fB9NkYd6xDSa94EQCS9ENkuKtoaa0xn3gEkpJRnz+xTbUY3VRmjlW6+U6VVXk1DLrJibn2DmuR+0u/glF6l09rJQjhC0n6odLXg+eDnT6tvKTffBX2fWAtbhKAdUlF6KmLL4c5c9F/fgjdUDfk43V3N+YLT2P++GY4sA/1nR9iXLVU+iuJgJKkHyJqUhJMn41+bjPmj260FjIfYKhHe46i//JryJ6OWuj74ihidFGGgbFkORgxmI+UWR0hB6Df34n5k++jH38YcudgrHoA45xFIYxWRAup3gkh47afwY7XMbc9Z9Vb//VPcGahdXPujLNQhnVFd+TvT4CzwfpYb8hV3limbKmob37Xmq37j79YM1ePoZ0O9BOPoN96FexTMW65HcLQNVNED0n6IaRi46DwfGIKz0c31KFfeR79yhbMnW+AzY46/3OoufNof/I31v/4vTMWxdimzlkEO99A/+8f0Xlno3Jmors86OfL0c88DhrUV66xbtiPk7UNRHBJ0g8TlZaBWnwd+svXwM4qzG3/QP/1D+i//gFiYjCuXBLuEEWAKKXgm99Ff7gL86F1GFdci/nko+Coh7MWYHz9epR9arjDFFFCkn6Yqdg4OHshMWcvRDvq0a9sIfGk6RxJzw53aCKA1MRJGEu+j3n/asz/+RmkZ2Os+G9Z11WEnCT9UUSlpqO+eu2oaRQnAkudXoD65netxegv/IL0jBFhIUlfiBAyLrws3CGIKCclm0IIEUUk6QshRBSRpC+EEFFEkr4QQkQRSfpCCBFFJOkLIUQUkaQvhBBRRJK+EEJEEaX9WdZHCCHEmBTRV/orV64Mdwh+kbhDS+IOrbEaN4zt2PtEdNIXQghxPEn6QggRRWLuvPPOO8MdRDDNmDEj3CH4ReIOLYk7tMZq3DC2Ywe5kSuEEFFFhneEECKKSNIXQogoMqYWUdm4cSPbt28nKSmJtWvXAvDRRx+xadMmOjs7SU1NZfny5UyYMIHu7m4efPBB9u/fj2maLFq0iK9+9asA1NTUsGHDBjweDwUFBZSUlFjrmI7yuO+8807cbjfjxo0D4I477iApKSlocfsT+69+9Sv27duHYRgsWbKE008/HRj953yguEN5zhsbG9mwYQNNTU0opSguLuayyy6jra2NsrIyHA4HqamprFixgsTERAA2b97M1q1bMQyDkpIS8vPzgdCe70DGHer3+HBjb21tZd26dVRXV3PhhReydOlS77FC/R73mx5Ddu3apfft26dLS0u921auXKl37dqltdb6xRdf1H/84x+11lq//PLLuqysTGutdWdnp7755pv14cOHvfvs2bNHm6ap77nnHr19+/YxEffq1at1dXV1UGMdSezPPvus3rBhg9Za66amJn3bbbfpnp4e7z6j9ZwPFncoz7nL5dL79u3TWmt95MgRvXz5cn3w4EH92GOP6c2bN2uttd68ebN+7LHHtNZaHzx4UP/whz/UHo9HHz58WH/ve98Ly/kOZNyhfo8PN/aOjg79/vvv6+eee04/9NBDxx0r1O9xf42p4Z3TTjvNe6XQ59ChQ8yZMweAM888k9dff937u87OTnp6evB4PMTGxjJhwgTcbjcdHR3Mnj0bpRSLFi2iqqpq1McdLsOJvba2ljPOOAOApKQkJk6cSE1Nzag/5wPFHWrJycneypCEhASysrJwuVxUVVVRVFQEQFFRkffcVVVVsXDhQuLi4khLSyM9PZ3q6uqQn+9AxR0Ow409Pj6eU0891ftJpE843uP+GlNJvz/Tpk3jzTffBOBf//oXTqcTgPnz5xMfH8+NN97IzTffzJe+9CUSExNxuVykpKR4909JScHlco36uPts3LiRW2+9lSeffBIdpsKrgWI/+eSTefPNN+np6aGhoYGamhoaGxtH/TkfKO4+4TjnDQ0N7N+/n9zcXJqbm0lOTgasJNXS0gJwwnm12Wy4XK6wnu+RxN0nXO9xX2IfyGh5j/tiTI3p9+emm27i0Ucf5cknn6SwsJDYWOslVVdXYxgGv/zlL2lvb2fVqlXk5eWFLVF+1nDjnjp1KsuXL8dms9HR0cHatWvZtm2b92pkNMR+0UUXUVtby8qVK0lNTeWUU04hJiZm1J/zgeIGwnLOOzs7Wbt2LUuWLBn0U95A5zVc53ukcUN4zjf4HvtARst73BdjPulnZWVxxx13ANbH9+3btwPwyiuvkJ+fT2xsLElJSZxyyins27ePOXPmeK/wAJxOJzabbdTHPXXqVG+cCQkJnH/++VRXV4cl6Q8Ue0xMDEuWLPE+7o477iAjI4OJEyeO6nM+UNxAyM95d3c3a9eu5YILLuDcc88FrCEnt9tNcnIybrebyZMnA9bV5LHn1eVyYbPZTtgeivMdiLgh9Od7uLEPJBzn3F9jfninubkZANM0eeqpp7j00ksBsNvtvPfee2it6ezs5MMPPyQrK4vk5GQSEhLYu3cvWmu2bdtGYWHhqI+7p6fH+xGzu7ubt956i2nTpoU87sFiP3r0KJ2dnQC88847xMTEkJ2dPerP+UBxh/qca6158MEHycrK4vLLL/duLywspKKiAoCKigrmzZvn3V5ZWUlXVxcNDQ3U1dWRm5sb8vMdqLjD8R4fbuwDGS3vcV+MqRm5999/P7t376a1tZWkpCSuuuoqOjs7ee655wA455xzuOaaa1BK0dnZycaNG6mtrUVrzUUXXcSXv/xlAPbt28fGjRvxeDzk5+dz/fXXB7W0KhBxd3Z2snr1anp6ejBNk7y8PK677joMI7h/t4cTe0NDA/fccw+GYWCz2fjud79LamoqMLrP+UBxh/qcf/DBB6xatYqcnBzvubn66quZNWsWZWVlNDY2YrfbKS0t9d7neeqpp3jppZe8paYFBQVAaM93oOIOx3vcn9hvueUWjhw5Qnd3NxMnTuSOO+4gOzs75O9xf42ppC+EEGJkxvzwjhBCCN9J0hdCiCgiSV8IIaKIJH0hhIgikvSFECKKSNIXQogoIklfCCGiyP8HnqqISm5Att8AAAAASUVORK5CYII=\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": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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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": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 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(2000, 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": 40,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>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",
" <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",
" </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",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_china_India = df_can.loc[['China','India'], years]\n",
"df_china_India.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['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": 41,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbb96e8c358>"
]
},
"execution_count": 41,
"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": [
"### type your answer here\n",
"df_china_India.plot(kind='line')\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='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": 42,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_china_India = df_china_India.transpose()\n",
"df_china_India.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": 43,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_china_India.plot(kind='line')\n",
"\n",
"plt.title('Immigration from China and India')\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",
"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": 44,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": {
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777Njxw4A5syZw+rVqzl37hyRkZF899132NjY0KxZsxLHKoSo/iSZEkKISuBf//oXTzzxBM8++ywBAQEkJyfz/PPP37dbkVqtZs2aNURGRuLv78/zzz/P9OnTqVu3rkW5kJAQjh07RpMmTSzGO+Xl4eHB9u3buX79Op06dSI4OBhfX1/Wrl1basdZUmq1mq1btzJo0CD+/ve/07JlS4KDg9m+fft9xzvlpdFo2LhxIzY2NnTt2pUhQ4YwYsSI+7Yu1a9fn23btnHx4kU6dOjAk08+SceOHfnpp58e9tAK9OOPP9K5c2eGDRtG+/btOXfuHNu2bcvXCvfZZ58xd+5c2rRpQ2hoKKtXry7ywbSZmZm89tprtGnThkcffZSff/6Zb775hrfffrvIeFq2bMmJEyfo1asXb731Fv7+/nTr1o0VK1YwZ84c8xi5BQsWMHLkSCZPnoyfnx+bNm1i3bp1Fgnn4sWLqV+/Pj169GDo0KEMHz6cFi1alPgcvf3226jVanx8fHBzc+P06dPF3tbR0ZEDBw7g7e3NqFGjaNGiBSNHjuTMmTPUr18fuHtz48MPP6RTp060a9eOvXv3EhoaWuYPtxZCVE0q5UE6WgshhChzvXr1wsnJqUKTGVG5nDhxgnbt2nH69GmLKcGFEEJUDJmAQgghKoHTp09z/PhxHn30UfR6PcuXL2fPnj1s2bKlokMTQgghRCEkmRJCiEpApVLxxRdf8Morr2AymWjVqhXr169nwIABFR2aEEIIIQoh3fyEEEIIIYQQ4gHIBBRCCCGEEEII8QAkmRJCCCGEEEKIByDJlBBCCCGEEEI8gBo/AUXep7FXNFdX10IfpikqF7lWVYtcr6pDrlXVIterapHrVXXItapcinrOnLRMCSGEEEIIIcQDkGRKCCGEEEIIIR6AJFNCCCGEEEII8QBq/JipeymKQlZWFiaTCZVKVa51x8bGkp2dXa51igcj16pqqe7XS1EU1Go1Op2u3D+3hBBCiJpMkql7ZGVlYWVlhVZb/qdGq9Wi0WjKvV5RcnKtqpaacL0MBgNZWVnY2tpWdChCCCFEjSHd/O5hMpkqJJESQoiHodVqMZlMFR2GEEIIUaNIMnUP6SIjhKiq5PNLCCGEKF+STFVCzZs3L1H5sLAwxo4dC8D27dtZtGhRWYQlhBBCCCGEyEP6s1Uzffv2pW/fvhUdhhBCCCGEENWeJFOVWFhYGPPmzcPJyYnz58/j7+/P559/jkqlYs+ePcyePRtnZ2f8/PzM26xatYpTp07x4Ycfsn37dhYuXIher8fJyYlFixbh5uZWgUckhBBCCCFE9SHJVBFMP32NEh1VqvtUNWiC+plJxS4fERHB7t278fT0ZMiQIRw9ehR/f3/eeOMNVq9eTZMmTXjxxRcL3DYgIIDQ0FBUKhU//PADS5YsYfbs2aV1KEIIIYQQQtRokkxVcm3btsXLywsAHx8foqOjsbOzo2HDhjRt2hSAYcOGsWLFinzbxsTEMHXqVOLi4tDr9TRs2LBcYxdCCCGEEKI6k2SqCCVpQSor1tbW5p81Gg0GgwEo3qxd7777LpMnT6Zv377mLoNCCCGEEEKI0iGz+VVB3t7eXLt2jStXrgCwYcOGAsulpqbi6ekJwJo1a8orPCGEEEIIIWoESaaqIJ1Ox6effsrYsWMZOnQo9evXL7DcjBkzmDJlCk8++STOzs7lHKUQQgghhBDVm0pRFKWig6hIN2/etPg9IyMDOzu7ColFq9Wau/GJyk2uVdVSU65XRX5+lRZXV1cSEhIqOgxRTHK9qha5XlWHXKvKJXf+goJIy5QQQgghRDVXw++dC1FmJJkSQgghhKjGYtP0jFp9kaPXbld0KEJUO5JMCSGEEEJUYzsupZBpMPF7ZGJFhyJEtSPJlBBCCCFENWU0Key+nALA8espFRyNENWPJFNCCCGEENXUiZh0EjMNtHTVcSUpg9uZ1X8yHiHKkyRTQgghhBDV1M7LKdS20fB8O3cAIuIyKjgiIaoXSaYqqbi4OKZOnUqXLl3o0aMHY8aMYcWKFYwdO7bA8q+//joXLlwo5yiFEEIIUVmlZhk4cv0OPZrUpqWrLXbWGk7HSjIlRGnSVnQAIj9FUZg4cSIjRozgiy++ACAiIoIdO3YUus3cuXPLKzwhhBBCVAF7r6RiMEFQszpo1CraeNXmdGx6RYclRLUiLVOV0IEDB7CysrJohfL19SUwMJCMjAwmTZpEt27dePnll83PjRg+fDgnT54EoHnz5nzyyScEBQURHBxMfHw8ANu3byc4OJi+ffvy9NNPm5cLIYQQonpRFIUdkSk0d9HRqI4NAO3rO3IjVU+SjJsSotRIy1QRlobHEpWcVar7bOKk44WOHkWWOX/+PH5+fgWui4iIYPfu3Xh6ejJkyBCOHj1KQECARZmMjAzat2/PzJkz+eCDD1i5ciXTp08nICCA0NBQVCoVP/zwA0uWLGH27NmldmxCCCGEqBwuJWVx9XY2UwP++s7Rrr4jABGxGXRrXLuiQhOiWpFkqopp27YtXl5eAPj4+BAdHZ0vmbK2tqZPnz4A+Pn5sW/fPgBiYmKYOnUqcXFx6PV6GjZsWL7BCyGEEKJc7IpMwVqj4vFGfyVNLdxqYW+l5nRsuiRTQpQSSaaKcL8WpLLSokULNm/eXOA6a2tr888ajQaDIX9TvVarRaVS5Svz7rvvMnnyZPr27UtYWBjz5s0rg+iFEEIIUZGyDSZ+v5JKlwYO2FtrzMs1ahWt3e2IkEkohCg1MmaqEuratSt6vZ6VK1eal504cYJDhw491H5TU1Px9PQEYM2aNQ+1LyGEEEJUTgej75CeYyLI2zHfOj8PO27eySExI6cCIhOi+pFkqhJSqVQsXbqU33//nS5dutCzZ09CQkLw8Hi4lrIZM2YwZcoUnnzySZydnUspWiGEEEJUJrsiU/CsZYWPu12+dX4ed5fJFOlClA6VkjsdXA118+ZNi98zMjKws8v/4VMetFptgd32ROUj16pqqSnXqyI/v0qLq6srCQkJFR2GKCa5XpVPbJqeyb9cZrS/KyP9XC3Wubq6Ehcfz5ifL9K5gQN/71y3gqIU9yPvrcold76CgkjLlBBCCCFENbEzMgUV0LNp/i5+AGqVCh8ZNyVEqZFkSgghhBCiGjCaFHZfTqFdXXvc7K0KLefnYcettBzi02XclBAPS5IpIYQQQohq4FRsBgkZBoKaFdwqlUvGTQlReiSZEkIIIYSoBnZcuo2DjYaA+rWKLNewjg0ONhpJpoQoBZJMCSGEEEJUcanZRg5fT6NH49pYaYr+eqdWqfB1t5VxU0KUAkmmhBBCCCGquN+vpGAwKfS+Txe/XH4e9sSl5xCbpi/jyISo3iSZqmSio6Pp1auXxbKQkBC+/PLLIrc7efIk7777LgBhYWEcPXq0xHUHBgaSlJRU5PJTp07RuXNnIiIi2L59O4sWLSpxPQUJCwtj7NixpbKv4rh06RJ9+vShb9++XLlyxWJdeno6M2fOpEuXLvTt25f+/ftbPED5YX399ddkZmYWuG748OE8/vjj9OnTh+7du7NixYpC9/P6669z4cIFABYuXFisuseMGUNKSkrJg35Ahb12Q0JCaNasmcW0r82bNy/x/vMed0HvnbJS2HulKMOHD+fkyZMVVr8QonrbGZlCM2cdTZx0xSrvK+OmhCgV2vKszGQyMXPmTJydnZk5cyZpaWnMnz+f+Ph43NzcePXVV6lV624/3/Xr17N7927UajXjx4+nbdu2AFy+fJnFixej1+tp164d48ePR6VSkZOTw6JFi7h8+TIODg5Mnz4dd3f38jy8CtWmTRvatGkDwMGDB7G3t6dTp06lWsfZs2eZPHkyX3zxBb6+vvj6+tK3b99SraO8/Prrr/Tr14/XX38937rXX3+dhg0bsn//ftRqNYmJifz000/5yhmNRjQaTYnrXrp0KcOGDcPW1rbA9YsWLaJNmzYkJyfz2GOPMXLkSKytrfPVPXfuXPPvn3/+Oa+88sp9616+fHmJ4y0rzs7OfPXVV8yaNavE2yqKgqIoxT7u4jAYDGi1D/6RmBuTWi33qIQQ5SsyKYuo5GymdPIo9jYNHa1xtNEQEZtBULM6ZRidENVbuSZTW7ZsoV69eua78hs2bMDPz4+hQ4eyYcMGNmzYwHPPPcf169cJCwtj3rx5JCcnM2fOHBYsWIBarebrr79mypQpNG/enI8//pgTJ07Qrl07du/ejb29PZ9//jkHDhxg5cqVvPrqq+V5eOVi+PDhtGvXjrCwMFJSUggJCSEwMJCwsDC+/PJLPvzwQ5YvX45Go2Ht2rV88MEHeHt7M3PmTG7cuAHA+++/T6dOnUhKSuKll14iMTGRtm3bUtTzmy9evMj06dNZuHAh7dq1A2DVqlWcOnWKDz/8kOnTp+Pg4MDJkyeJj49n1qxZBAcHYzKZmDVrFocOHaJBgwYoisLTTz9NcHAwe/bsYfbs2Tg7O+Pn52euKzk5mRkzZnDt2jV0Oh2ffvoprVu3JiQkhGvXrhEXF8fly5eZPXs2x48fZ8+ePXh6evLtt99iZWU5FWxERAQzZ84kKyuLRo0aERISwrFjx1i6dCkajYZDhw7x888/m8tfuXKFEydOsHjxYvOXYhcXF1566SUA8+vS09OTiIgIfvvtN9auXcuyZcvMCf7HH3+MRqNh5syZnDx5kqysLAYNGsTrr7/ON998Q2xsLCNGjMDJycmi7ntlZGRga2trTtiaN2/O5MmT2bt3L++99x6ffvop7777Lps3byYrK4s+ffrQsmVLFi1axIQJE7h58ybZ2dlMnDiR5557DrjborF161bS09N57rnnCAgIIDw8HE9PT5YtW5Yvwdu+fTsLFy5Er9fj5OTEokWLcHNzIyQkhBs3bnDt2jVu3LjBCy+8wMSJEwFYsGABP//8M15eXri4uODv71/g8T3zzDOsXr2av/3tbzg5OVms++qrr1i1ahUAo0aNYtKkSURHR/Pcc8/RpUsXjh07ho+Pj8Vxv/XWWxiNRt544418x3TlyhXeeustEhMTsbW15bPPPsPb25vp06dTp04dIiIi8PPzo1atWoUeV0HujWnZsmVERkYyd+5c9Ho9jRo1Yv78+djb21tsV9BrI/f6jBgxgh07dmAwGPjqq6/w9vYu0XtVCFHz7Iy8jbVGRbfGtYu9jUqlwtfDjtOxGSiKgkqlKsMIhai+yu0WamJiIsePH6d3797mZUePHqV79+4AdO/e3dw17ejRo3Tp0gUrKyvc3d3x9PTk0qVLJCcnk5mZSYsWLVCpVHTr1s28TXh4OD169AAwd0N72C8cEcczCNt9p1T/RRx/+OZ0g8HA5s2bef/995k3b57FugYNGjBmzBgmTZrEjh07CAwM5L333mPSpEls2bKFr7/+2vzFbf78+QQEBLB9+3b69u1rTrYKMmHCBD744AMCAgIKLRMbG8uGDRv47rvv+Pjjj4G7CfT169fZtWsXc+fO5dixYwBkZWXxxhtv8O2337J+/Xri4uLM+wkJCcHX15edO3cyc+ZMpk2bZl539epVvv/+e5YtW8bf//53unTpwq5du9DpdOzatStfTNOnT2fWrFns3LmTVq1aMW/ePHr37m0+R/cmMxcuXKB169ZFti6cOHGCf/zjH/z2229cvHiRjRs3smHDBnbs2IFGo2HdunUAvPXWW2zdupWdO3dy6NAhzp49y8SJE/Hw8GDNmjWFJlIvv/wyQUFBdOvWjenTp5uTqYyMDFq2bMmmTZssrsPbb7+NTqdjx44d5m6XISEh/Prrr2zZsoVly5YV2CUsKiqKcePGsWfPHmrXrs2WLVvylQkICCA0NJTt27czZMgQlixZYl536dIlVq5cyebNm5k3bx45OTmcOnWKjRs3sn37dpYuXVpktzZ7e3ueeeYZvvnmG4vlp06dYvXq1WzatInQ0FB++OEHIiIiAIiMjGT48OFs376d+fPn5zvuwo5pxowZzJkzh19//ZV3332Xf/zjH+b6Ll++zKpVq5g9e3ahx1WUvDHZ2dmxYMECVq1axbZt22jTpg3/+c9/8m1T0Gsjl7OzM9u2bWPMmDHmLpIlea8KIWoWvdHE3tx4x+cAACAASURBVCupdG7gQC3rkvWW8PWwIyHDwK00ed6UEA+q3Fqmvv32W5577jmLsSIpKSnmO9JOTk6kpqYCkJSUZDF+wtnZmaSkJDQaDS4uLublLi4u5i+JSUlJ5nUajQY7Ozvu3LlD7drFv0tTGRTnztDAgQMB8Pf35/r16/ctv2/fPvPYGoC0tDTS0tI4dOgQS5cuBSAoKIg6dQpv5u/atSs//vgjPXr0KLRrW//+/VGr1bRo0YL4+HgAjhw5QnBwMGq1Gnd3d7p06QLc/cLasGFDmjZtCsCwYcPM44OOHDnC119/ba43OTnZ/Nro2bMnVlZWPPLII5hMJnr27AlAq1atiI6OtognNTWVlJQUHn30UQBGjBjBlClT7nu+8lqwYAGbNm0y3wwAaNu2LY0aNcJgMLB//35Onz5tviZZWVm4uroCEBoaysqVKzEajcTGxnLx4kVat2593zpzu/klJiYyePBgevbsSf369dFoNAwaNKhYcS9btoytW7cCcPPmTaKionB2drYo06BBA3x9fYG7r6V7zx9ATEwMU6dOJS4uDr1eT8OGDc3revfujY2NDTY2Nri6uhIfH8/hw4fp37+/uYWrT58+RcY5YcIE+vbta3Fdjhw5Qv/+/bGzu9uff8CAARw+fJi+fftSv359OnToUOj+Cjqm9PR0wsPDLerQ6/8acB0cHGzxmi7ouLy8vAqtM29Mx44d48KFCwwZMgSAnJycAuMt6rUxYMAAc/y517Ak71UhRM1yKDqNdL3pvs+WKkju86YiYjOo62B9n9JCiIKUSzJ17NgxHB0dadq0KWfOnLlv+cJalIpqaSpoXUGJyc6dO9m5cycAn3zyifmLb67Y2FjzuIm2AeWfiLm5uZGSkmIxdiMlJYXGjRuj1WpRqVTY2tqi1WqxtrbGaDSi1WrRaDSoVCq0Wi1qtRq1Wm3eh6IobNmyJV8XrtzyeevSaDT5xo2oVCo++eQT3nzzTWbNmmUeq6PRaMz1qNVqc1y5debGmzcWlUplriPvcrVabY7n3jhUKhVWVlb56tBqteZufVqt1lxnrtz685Yv7BzleuSRRzh79qx5/YwZM5gxYwZNmjQxn+fcLlu5+xs5ciTvvPOOxX6uXr3KV199xbZt26hTpw6vvPIKOTk55m0KOs/3nh8PDw/8/f05efIkjRs3Nn/BL6hsbjwABw4cYP/+/WzZsgU7OzuefPJJ83ig3G00Gg02NjbmbaysrNDr9flieu+995gyZQr9+/fnwIEDzJ07t8DrnZuMqNVqi5gKO8+5y11cXHjqqafMY7kKes3kls29SXLvvvLGUNAxqdVqateuzZ49e/Kdb7VajYODg0VdBR1XQe+J3POYNya1Wk337t356quvCr22N27cKPK1kbs/a2trTCaTxev2fu/V3ASwKtNqtVX+GGoSuV4Vb+/vt6hb24aePg1R3+eG7L3Xy8VFwdnuOhduGxkl17FSkfdW1VEuydT58+cJDw/njz/+QK/Xk5mZycKFC3F0dCQ5ORknJyeSk5PNrUguLi4kJiaat09KSsLZ2Tnf8sTERPPd9tx1Li4uGI1GMjIyzJNZ5BUUFERQUJD597yziQFkZ2c/0KQCpUGr1WJjY4O7uzt79uzh8ccfJzk5md27dzNhwgQMBgOKomA0GjEYDBiNRhRFyfezra0tqampGAwGALp168bSpUuZOnUqcHccka+vL4GBgaxZs4bp06eze/dubt++bd53XrkD6xctWsTo0aP5+OOPeeONNzAajZhMJgwGAyaTKd+2BoOBjh07smbNGoYNG0ZiYiJhYWEMGTKExo0bc/XqVS5dukTjxo1Zt26dOf7cuF599VXCwsJwcnLC1tYWk8lkri9vHUCB6+zs7KhduzYHDhwgMDCQVatWERgYaI733vJwt2XD39+fDz/8kDfffBONRkNWVla+85xbd5cuXRg/fjwvvPACrq6uJCcnk56eTkpKCra2ttjZ2RETE8OuXbvMddvb25OSkoKjY/67iHmvb2ZmJqdPn2bq1KnmOPPGm7eslZUVmZmZWFlZcfv2bWrXro21tTV//vknx44dM5fL3cZoNN73/MHdRN7d3R2DwcBPP/1kPg8FlTcajQQEBPDqq68ydepUjEajubvavfvNu/2kSZMYOHCgOcbcffztb38z3whYuHBhvpgBi+Mu7JhsbW1p2LAh69ev54knnkBRFM6ePYuPj0++121hx1XQe6Kg89i2bVtmzpzJxYsXadKkCZmZmdy8eZNmzZqZt7l9+3ahr43C3t/Ffa9mZ2fn+0yralxdXav8MdQkcr0qVmyanmPRt3nG35WkPN+PClPQ9WrtpiP8WjLx8fEybqoSkfdW5VJUD5VySaaeffZZnn32WQDOnDlDaGgor7zyCsuXL2fv3r0MHTqUvXv3mmef69ixIwsXLiQ4OJjk5GRiYmLw9vY23zW+cOECzZs35/fff6d///4AdOjQgd9++40WLVpw6NAhfHx8quyHwoIFC3j77bf5v//7PwBee+01GjduXOzt+/Tpw5QpU9i2bRsffPABc+bM4e233yYoKMj8xexf//oXr776Ki+99BL9+vWjc+fO1KtXr8j92tjYsGzZMoYNG4abm1uhs9HlNWjQIPbv30+vXr1o2rQp7dq1o3bt2uaJJcaOHYuzszMBAQH8+eef5uN97bXXCAoKQqfT8e9//7vYx36vf//73+YJKBo2bJhvjFlB5s6dy5w5c3jssceoU6cOOp2u0BnnWrRowZtvvsmoUaPMLWMffvghHTp0wNfXl549e9KwYUOLmRVHjx7Nc889h7u7e4Hjpl5++WV0Oh16vZ6RI0cWOoFDXqNHjyYoKAg/Pz9CQkJYvnw5QUFBNG3alPbt2993+8LMmDGDKVOm4OnpSfv27QvsCpiXn58fTzzxhLlLXmBg4H3rcHZ2pn///uaunX5+fowYMcLcpXHUqFH4+voWWHfe437rrbcKrWPJkiW8+eabLFiwAIPBwJAhQ/Dx8blvbCXl4uLC/Pnzeemll8xdCd98802aNWtmLuPj41Poa6MwJX2vCiFqhj2X73aB79Wk5F38cvl52LH/6h1i7uTgVVu6+glRUiqlnKeFyk2mZs6cyZ07d5g/fz4JCQm4urry2muvmVuT1q1bx549e1Cr1Tz//PPmGeQiIyNZsmQJer2etm3bMmHCBFQqFXq9nkWLFhEVFUWtWrWYPn06Hh73nyL05s2bFr9nZGSYx2qUN61Wm+9Oc3WQnp6Ovb09SUlJBAcHs2HDhio/bX11vVbVVU25XhX5+VVa5G5s1SLXq+KYFIUpv0Ti5WDN+70b3n8DCr5eN1L1/C30Mn8L8KRfcxmPWVnIe6tyKaplqtyTqcpGkqmyN3z4cFJSUsjJyWHq1Kk8/fTTFR3SQ6uu16q6qinXS5IpUd7kelWcEzHpzN4dzeuPefF4MadEL+h6KYrC+PWR+Lrb8npXafWuLOS9VblUeDc/UbMV9SwlIYQQQpTcrsgUalmrCWyQf3x4SahUKvw87Dh9K12eNyXEAyi350wJIYSoPm5G67mdVP1b+4SojNKyjRyMvkP3xrWx1jz8Vzk/DzuSs4zcSNXfv7AQwoIkU0IIIUokK9PE8UMZnDuVVdGhCFEj7b2SSo5JIahZ6Yxxyn3e1OnYjFLZnxA1iSRTQgghSuTaZT2KCZITDJiMNXrYrRAVYtfl2zR1sqGps65U9udZywoXO60kU0I8AEmmhBBCFJvJpHA1MhutFRiNcDvZWNEhCVGjXE7KIjIpu9RapeCvcVMRcRnU8HnJhCgxSaYqoQYNGtCnTx969erF5MmTyczMJDo6ml69ehVY/rPPPuP3338H7s6cd/LkSQDGjBlDSkrKA8Xw/fffs2bNmgc7ACFEtRV7M4esTAWftnefM5cQJ+OmhChPOy+nYKVW0a2YM/gVl5+HHSlZRqJTZNyUECUhs/lVQjqdjh07dgB3H+D6/fffM3DgwELLv/HGGwUuX758+QPHMHbs2AfeVghRfV25pEdnp6J+Y2uiLmSTGGeA1hUdlRA1g95oYm9UCp0b1MLBRlOq+847bqphHZtS3bcQ1Zm0TFVyAQEBXLlyBQCj0cgbb7xBz549GTVqFJmZmQBMnz6dTZs25ds2MDCQpKQkoqOj6datG9OmTSMoKIhJkyaZtw0MDOTDDz9k0KBBDBo0iKioKABCQkL48ssvgbutXbllunbtyuHDh83xzJkzh4EDBxIUFGRO3mJjY3nqqafMrWu55YUQVVtaqpGEWAONmtmgVqtwcdeSlGDAKOOmhCgXR66nkaY3lWoXv1wetaxxt5dxU5XBtWvXWL9+vfm7mqjcpGWqCL///jvx8fGluk83Nze6detWrLIGg4E9e/bQo0cPAKKioli8eDGfffYZU6ZMYcuWLQwbNqxY+4qMjCQkJIROnTrx2muv8d133/Hiiy8CUKtWLTZv3syaNWuYPXs233//fYGxbN68mV27djFv3jxWrVrFjz/+iIODA1u2bCE7O5uhQ4fSvXt3tmzZQvfu3Zk2bRpGo1E+DISoJq5cykalhkZNrQFwcdcSdVHP7SQjLm7y50SIsrYjMgU3O625Fam0+XrYcfRGOiZFQS3Pm6owp06d4vLly1y6dIng4GDc3NwqOiRRBGmZqoSysrLo06cPAwYMoF69eowaNQq4O5bK19cXAH9/f6Kjo4u9Ty8vLzp16gTAU089xZEjR8zrhg4dav7/2LFjBW6f283Q39+f69evA7B3715+/vln+vTpQ3BwMMnJyURFRdG2bVtWr15NSEgI586do1ath3ugoBCi4hkMCtFX9HjVt8JGd/dPR24ClSjjpoQoc/HpOZyMSadXM0c06rJJdPw87LmTbeTa7ewy2b+4P0VRiImJoVGjRiiKwpo1a7hw4UJFhyWKILcSi1DcFqTSlnfMVF42Nn/1YdZoNGRlFf8ZL/c+0Tzv74X9nJe1tbW5XoPhry9OH3zwgbnlLK+1a9eya9cupk2bxosvvsiIESOKHasQovK5cVWPIQcaef/1OWRto6Z2Hc3dZMqnAoMTogbYfTkFBejd1LHM6vB1/2vcVGOn0pl2XZRMSkoKmZmZtGnTBjc3N7Zs2cKvv/5KQkICnTt3Rq2WdpDKRq5IDXHjxg3Cw8MB+OWXX8ytVAAbN240/9+hQ4di77N79+58//335OTkAHe7EmZkZHD9+nVcXV0ZPXo0zzzzDKdPny7FIxFClDdFUbhySY+DoxpnV8tB7y7uWpISZdyUEGXJpCjsupyCv6cdHrWsy6we91pWeNSyknFTFejmzZsANGzYEHt7e5566il8fX0JDw9n06ZNZGdLq2FlIy1TNUTz5s1Zs2YNM2fOpEmTJowbN868Tq/XExwcjMlkYvHixcXe57PPPkt0dDT9+/dHURScnZ1ZtmwZYWFhfPnll2i1Wuzt7VmwYEFZHJIQopwkJxpJvW3Er4NtvtZrV3ctUReyuZ1oxMVd/qQIURYiYjOITcthtL9rmdfl52HHoeg7Mm6qgsTExGBjY4OrqytJSUloNBp69eqFm5sbe/fuZdWqVQQHB+Ps7FzRoYr/USk1/OlsuXcAcmVkZGBnVzYDO+9Hq9VadKErLdHR0YwbN47du3fnWxcYGMjWrVvlTVlCZXWtRNmoKderrD6/jh9KJ/ZmDn2ecERrZfnlSq83sW19Ki18dLT0ffhuQa6uriQkJDz0fkT5kOtVPuYduEn4jTT++5Q3NtoH71RUnOv1W1QK88NimD+gMU2dpatfeVuxYgUODg5MnDgx37W6ceMGW7ZswWg00q9fP5o0aVJBUdY8Xl5eha6Tbn5CCCEKlZ1lIiY6hwaNrfMlUgDW1mocnTQkxlf/ZFWIipCmN3Iw+g7dGtd+qESquHzzPG9KlK+srCySkpIK/eJer149nnnmGRwdHQkNDeXo0aPU8DaRSkGSqRqgQYMGBbZKARw+fFhapYQQhboWpcdkspx44l4ublqS5XlTQpSJfVdS0RuVMnm2VEFc7ayo62BFRJwkU+Xt1q1bAHh6ehZaxsHBgREjRtCyZUsOHjzI1q1b0ev15RWiKIAkU0IIIQqkmBSuXsrGxV2LQ21NoeVcPbSYTJCcIK1TQpS2nZEpNK5jQzPnwm9olDY/DzvOxGZgNMkNkvIUExODSqUqMpmCu13X+/btS9euXYmMjGTNmjWkpKSUU5TiXpJMCSGEKFBsjIHMDIXG3kXPHubsqgUV0tVPiFJ2JTmLS0lZBDVzLPTRJWXB192O9BwTUckyc1x5iomJwc3NDSsrq/uWValUtG/fnsGDB5OWlsaqVatK9PxRUXokmRJCCFGgK5ey0dmq8KxX9B92K2sVjnU0JMjDe4UoVTsvp6BVq+jepOyeLVUQP097ACLi0su13prMZDJx69Yt6tatW6LtGjVqxNNPP42dnR0bNmzgjz/+kHFU5UySKSGEEPmk3zESf8tAw6Y2qNX3vyPu6qHldqIRg0H+iAtRGnKMJn6LSiWwfi1q2xTezbYsONtqqVfbmtO3ZNxUeUlISMBgMJQ4mQKoU6cOI0eOpEmTJuzbt4+dO3fWiBlsKwtJpiqhBg0a0KdPH3r16sXkyZPJzMwssnzz5s3zLbt16xaTJk0qdJuUlBS+/fbbhw1VCFFNXYnUo1JBo2bFe0Coi9v/xk0lyh9wIUrDkRtp3Mk2EtSsfFulcvm623EmLlPGTZWTmJgYgAdKpgCsra0ZNGgQgYGBnDt3jrVr15KWllaaIYpCSDJVCel0Onbs2MHu3buxtrbm+++/L/E+PD09+frrrwtdn5qa+kD7FUJUfwaDQnSUHs/6Vuhsi/dnwtlNi0oFidLVT4hSsSsyBRc7LW3+1+WuvPl52JFpMHE5OatC6q9pYmJiqFWrFg4ODg+8D5VKRWBgIIMGDSIpKYmffvrJnKSJsiPJVCUXEBDAlStXAJgwYQL9+/enZ8+erFixIl/ZpKQknnjiCXbu3El0dDS9evUC4Pz58wwaNIg+ffoQFBTE5cuX+eijj7h69Sp9+vRhzpw5pKenM3LkSPr160fv3r3Ztm0bcPeBv927d+eNN96gZ8+ejBo16r4tZUKIqu3mNT05eoXGRUyHfi8rKxWOTjJuSojSkJCRwx8x6fRu6oimGN1sy4Jf7vOmpKtfubh58+YDt0rdq1mzZowcORIrKyvWrl1LREREqexXFExb0QFUZrXiQ9Fml25Gb7CpS5rbE8UrazCwZ88eevToAUBISAhOTk5kZmYyaNAgBg4caH5GVHx8POPHj+fNN9+kW7duFjO6LF++nIkTJ/LUU0+h1+sxGo28/fbbnD9/nh07dpjr+uabb3BwcDAnZX379gUgKiqKxYsX89lnnzFlyhS2bNnCsGHDSvGsCCEqkyuX9NSqrcbFrWTjNFzdtUReyMZgUNBqK+YLoBDVwe7LKZgU6N20Yrr4AdSx1VK/tjWnYzN4yselwuKoCe7cuUNaWlqpJVMALi4uPP300/z666/s3r2b+Ph4unXrhkZTvuPvagJJpiqhrKws+vTpA0BgYCCjRo0CYNmyZWzduhW4ewcjKioKZ2dnDAYDTz/9NB9++CGPPvpovv116NCBhQsXEhMTw4ABA2jatGm+Moqi8Mknn3D48GFUKhW3bt0iPj4euDuGy9fXFwB/f3+ZelOIaiw50UBKshHf9rYlnorZxV3LpT+zSU4w4OZ5/6l9hRD5mRSFXZEp+HnY4elQvDGLZcXPw449UakYTAraCmohqwkedrxUYXQ6HYMHDyYsLIzjx4+TmJjIwIEDsbOzK9V6ajpJpopQ3Bak0pY7ZiqvsLAw9u3bR2hoKLa2tgwfPpzs7LvPf9BoNPj5+fHbb78VmEw9+eSTtGvXjl27djF69Gg+++wzGjVqZFFm3bp1JCYmsnXrVqysrAgMDDTv38bmr64+Go2GrCzpPy1EdXXlUjYaLdRvXPIvcc6ud8dNJcRJMiXEgzobl8mttBxG+btWdCj4edqx9eJtIpOyaOlqW9HhVFsxMTFotVpcXUv/mqvVarp27Yqbmxs7d+5kw4YNDBs2zOK7nXg4kkxVEXfu3MHR0RFbW1suXbrE8ePHzetUKhXz5s1jypQpLFq0iJdfftli26tXr9KoUSMmTpzI1atXOXfuHK1bt7aY5eXOnTu4urpiZWXFgQMHuH79erkdmxCicsjONnHzWg4NmlhjZVXyu9BaKxV1nDUyCYUQeeQYTaRmG7mTbST1f/9SsnJ/N5iX5V1nZ6Xm0QYPPhFBafF1/2vclCRTZScmJgYPD48y7YLXsmVLdDodoaGhbNy4kaFDhxbr4cDi/iSZqiJ69OjB8uXLCQoKomnTprRv395ivUajYcmSJTz//PPUqlWL3r17m9dt3LiRdevWodVqcXd359VXX8XJyYlOnTrRq1cvevbsyUsvvcS4ceMYMGAAPj4+eHt7l/chCiEqWHSUHpOJEk08cS8Xdy2Rf2ZjyFHQPkBCJmo2g0nh2u1smjjZlLibaUU4E5vBjTt6UrMKToxSs4xkGkyFbl/LWk1tGw0ONlpc7axo6qTDUafBz8MOG23FzxHmqNPSyNGG03EZDEfGTZWFnJwc4uPj6dChQ5nX1ahRI/r168evv/7K5s2beeKJJ2QMVSlQKTX8Mck3b960+D0jI6PC+pJqtVp5yFoVIdeqaqkp1+thPr8URWH35jvo7FQ81uvB74jH3crh8N50ArvZ41635Hc9XV1dSUhIeOD6RfkqresVc0fP9ku32X05hdtZRqY9WpdeFTj5QnEcjL7DJ7/fMP+u06rMiVFtGw2ONhocdBpq2+T9d3ddbZ0GB2tNuc/U9yDX6z/hsey8dJuVI1pgpan8CW5Vc/36ddatW8fgwYNp3LixeXlZfhaeOXOGXbt24e3tTf/+/VGrKz5xr+y8vLwKXSctU0IIIYiLMZCRbqKV/8PdTModN5UYb3igZErUHDlGEwej09hx6TanYjNQq6BjvVrE3NGz6nQC3RrXrrSTHpgUhR9PJlCvtjXv92pAbRtNpWhJKoqS9WCPNfFzt2Pz+WQuJWbyiLtMXFDacief8PT0LLc6fXx8yM7OZv/+/ezevZvevXtXiZbgykqSKSGEEFy5lI2NTkXdeg+XAGm1d8dNJcRW/5ZA8WCiU7LZfuk2e6JSuZNtxN3eitFtXOnd1BEXOyuOXk/jg73X2X05hb7edSo63ALtv3qHqynZzHjMCzf7yn/TQIm7iemfr3Cn31CUwaNL9MXZx8MOFXA6LkOSqTIQExODk5MTOp2uXOtt37492dnZHD16FBsbG7p27SoJ1QOSZEoIIWq4jDQjcTEGmre2QV0K3XhcPbRcOifjpsRfsg0mDly7w/ZLtzkXn4lWDYH1HejrXQd/TzvUeb7EdaxnTwsXHatOJ9CzSW2sNJWrxcdoUvjpdAKNHG3o2qjiJ4koDuXEEcjRk7FpNaocAwwbV+wvzrVtNDR2suF0bAYjfcs40BpGURRiYmIqbJx6586dyc7O5o8//kCn09GpU6cKiaOqk2TqHjV8CJkQogp70M+vK5F6VCpo1Kx0psp1cdNy8Ww2iQkGPKSrX40WlZzF9ku32RuVSnqOCS8Ha55v50bPpo7U0RX8FUSlUvFsGzf+uTua7ZdSGNTSqZyjLtreK6ncSNUz8/F6FklgZaZEHIO6DbBt05HMX9eBtTWqwc8We3tfdzu2XbpNjtFU6ZLbqiw5OZns7OxSf75UcalUKrp37052djYHDx7E2tqaNm3aVEgsVZkkU/dQq9UYDAa0Wjk1Qoiqw2AwPNAgYqNR4dplPR71rLC1K50vSU6uWlRqSIyTZKomysgxsv/q3Vaoi4lZWKlVdGl4txXKx714D4Nu62lHazdb1pxJJKiZY6UZj2QwKaw6nUBTJxs6N6hV0eEUi5KVCRfOoOr9BA6TZpB1JxUl9CdMWivUA0cUax9+HnaEnk/mQmIWPtLVr9TkToJWUckU3E2ogoKC0Ov17N27FxsbG1q1alVh8VRFkjHcQ6fTkZWVRXZ2drn3HbWxsTE/KFdUbnKtqpbqfr0URUGtVj9Qn/ub13LI0Ss09i75Q3oLo9WqcJLnTdUoiqJwKeluK9TvV+6QZTDRyNGGFzq406OJIw42JZt+WaVSMbqNG7N2XuPXi7cZ8ohzGUVeMrsvp3ArLYd3utevOuNL/jwJRgMq3/ao1GpUY1+GHAPK+uV3E6q+Q++7Cx/3/42bis2QZKoU3bp1C51OR506FTs2UKPRMGDAADZu3MiOHTuwtramadOmFRpTVSLJ1D1UKhW2thXzYDqZErjqkGtVtcj1KtyVS9nYO6hxdS/dPwcu7lounssmJ0d5oAcAi6rhTraBzeeT2RF5m6jkbGw0Kro2qk2/5nVo4aJ7qITD18MOf0871p5JpK93HWytKrZ1KsdoYvXpBJq76OhYz75CYykJ5fRxsLGF5q0BUKk1MGE6ijEHZc0yTFZWqHsOKnIftWw0NPnfuKln/Moj6pohJiaGunXrVorEXKvVEhwczPr169m6dSuDBw+mQYMGFR1WlVA52s2FEEKUu9tJBm4nGWnsXfoPSHV114ICSfHSOlVd/XIuiSFLj/Cf8FjUKnixkwffDvPmlUfr0tK1eN357me0vxsp2UY2X0guhYgfzs7IFOIzDDzr71opvvwWh6IoKBHh8EgbVNq/utyqNBrUL7wObQJQfvgK077t992Xn4cd5+Mz0RsLfwixKL7MzEySk5MrtIvfvaytrRk8eDCOjo5s2rSJ2NjYig6pSpBkSgghaqgrl/RoNNCgcemPa3Jy0aL+37gpUf2kZhtZcTIeX08HQvo3Zt6AJgxo4YSdVcm6891PKzdbOnjZs+FsIhk5xlLdd0nojSbWRCTyiJst7epWnVYpbkZDUgIqvw75Vqm0WtRT3gLfdMcugwAAIABJREFU9ijLF2M6tKfIXfl52JNjUjif8GDPqxKWcp8vVZmSKQBbW1uGDh2Kra0tv/zyC4mJiRUdUqUnyZQQQtRAer2JG9f01GtkjZV16f8p0GhV1HHRkCDJVLW0M/I2eqPCtO5N8XYp2+fjjPJ35Y7exMY/K651atvF2yRmVq1WKeBuqxSg8s2fTAGorKxQT/0HtPBFWbYAJXx/oftq7W6LWgURsRllEmtNExMTg1qtxsPDo6JDyadWrVoMHToUtVrNhg0bSElJqeiQKjVJpoQQogaKjtJjMlKqE0/cy9VdS8ptIzl66RZUnRhNCr9evI2Puy3NXMu+laa5iy2B9Wux8VwSadnl3zqVbTDx85lE/Dzs8PesQq1SgHL6GNRrhMrZtdAyKmsb1H9/F5q1wrQ0BOXEoQLL2VtraOqk47QkU6UiJiYGNze3Sjt7dJ06dXjyyScxGAysX7+e9PT0ig6p0pJkSgghahhFUbh6SY+TiwZHp7L7Q+7ibgUKJMZXXPcsUfr+iEknNi2HgS3K7/lPz/q7kp5jYsO5pHKrM9fmC8nczjIy2r/whKQyUjIz4NLZQlul8lLZ6FC/8h40bIbpy09RTocXWM7Pw47zCVlkG+QGycMwGo3ExsZWui5+93JxcWHIkCFkZmayYcMGsrKyKjqkSkmSKSGEqGHiYw2kp5lo3Lx0HtJbGCcXjYybqoY2n0/GyVZL5wYO5VZnYycdjzV0IPR8MqlZ5fd6ysgxsu5sEu3q2vNIVZsS/NxJMBpR+XUsVnGVrR3q6f+Eeo0wLfkY5dzJfGV8PewwyLiphxYfH4/RaMTLy6uiQ7kvT09PgoODSU5O5pdffkGv11d0SJXO/7N3n4FxVXfCxp9zp6mNNNKod1tykwsuErhiMDYYTHGoSTa76W3fkADpDZJAygJJNsRkd0OygTRYSmJIMMYVYzAYyVWSe5FkS6M+qiNpyj3vh7GMjWVbsqbK5/eFZEZz75El3bln/k1tphRFUS4zNYcHMFsEWbnBHahrMAiSU42qbmoMcXS72enoZXmxDaMW2tqhD89IZcCr87d9oYtO/fOgk+4BHx+NsqgUgKzaAbFxUDT8AawiLgHt/h9CRjb6qoeRh6rOen6wbkql+o3OYPOJzMzMMK9kePLy8rjxxhtpbm7m1VdfxetV1/Qzqc2UoijKZcTVq9Pk8JI/3ozBEPyb4dR0I10dPtyqbmpMeO2QE4OA6yeEfshofpKFqwsTefWQE2df8G/metw+Vu9vpywngYmp4Zk/eamklP56qSkzESOsyREJiWgP/AhS0tGfeBh59MDp5+JMBopTVN3UaDkcDhITE0lISAj3UoatqKiIpUuXcuLECdauXYuuq2v6ILWZUhRFuYzUHh0AoKAouCl+g+ynhgG3q7qpqDfg1dlwrJN5+VZSYsNTNP/h6al4dclL1cFv1/zKgXZ63XpURqWor4GONsS02Zf0cpGYjPbVhyExCf1XP0TWHjn93LSMOA639dGv6qYuiZTy9LDeaDNlyhQWL17MsWPH2LhxI1LKcC8pIqjNlKIoymXC55PUHXOTkWUkLj40l39bigHNAK1NnpCcTwmeLTVd9Lr1kDae+KDsRDNLxiex9nAHra7g/U51Dfh4Zb+TeXlWxqcEt/V7MMjKnQBDzpcaLmGzo331xxAXj/7Lh5AnjgP+JhReHQ60qLqpS9Hd3U1vb29UbqYArrjiCubOncv+/ft588031YYKtZlSFEW5bDhOenAPSAqLQxOVAn/dVEqqkbYWlWMfzaSUrDnkpMBmoSQtvClvd0+zI5G8WBW86NTqfW30e6M0KsWp+VK54xA2+6iOI+xpaF99BMwW9F8+iGyoY0paHAZVN3XJGhoagMgb1jsSZWVlzJw5kz179rB9+/ZwLyfs1GZKURTlMlFzeID4BI20zNCmaNnTjHR16LgHVFpQtDrQ0sdx5wArJiaHfWhtRoKZpUU21h/toLkn8NGpjn4v/zzoZFFBIvm20H3wECjS1QtH9o8qKnUmkZaJ9sDDoGnov/g+Me2NFNtj1WbqEjkcDkwmE3b76Da64SSEYNGiRZSUlPDee++xe/fucC8prNRmSlEU5TLQ6fTibPNRUGwO+c1w6qm6KRWdil5rDnUQb9JYPC4x3EsB4K5pdgSC/6tqDfix/1bdhkeX3DMjSm929+8GXR/WfKnhEpk5/g2Vz4f+8+8x3apzpK2PPo/6gGSkHA4HmZmZaNr5b8Fl7VFcr70UwlWNnBCCJUuWUFRUxNatW+no6Aj3ksJGbaYURVEiRH2tm6MH+mlu9NDfpwc0F73miBvNAHnjzAE75nDZUgwYDGreVLRy9nnZdqKLJeOTiDFGxm1DapyJGybY2HSsE0d34ObetPd5ee1wB4sLE8lNjL6oFODv4hcbP6KW6MMhsvP9G6qBfqZu/BM+CftbVHRqJNxuN21tbRdM8ZNSov9xFd2//TmyrTmEqxs5TdO45ppr0DSNioqhBz1filaXh6cqmtjZ0BOwYwZTZFwVFUVRLnM+n2TXey727eln+5Ze1r/Sxeuru9i2uYeqnS5qjw7gbPXi9Yx8g+Vx69TXusnJN2M2h/6yr6l5U1Ft3ZEOvDrcGMbGE0O5c6odoyZ4bm/golMvVrfh1SX3TI/SWikpkVU7ESUzEQZDwI8v8sahPfAjJrcdxih9VNaFbubXWNDY2IiU8sL1UtU7oe4oALLirRCt7NLFx8dTUlLCgQMH6O7uHtWxGrvdPLndwedfPsprh5zUOAcCtMrgCk9vU0VRFOUsXR0+pA5XlMUSF6/R1anT3emjq8NH3XE3vjP2IXHxGtYkjUSbAWuSgcQkA/FWDe08Q1RP1Hjw+aCwOPRRqUGp6UYOVPYz0K9jiVGf40ULry55/XAHM7PiyUkM3+/PUJJjjdw0MZmX97dz5zQ7eUmjiyS19Hp4/XAH141PIssaWd/rsJ04Dp3tML00aKcQBcXE3vtdJqyrpbLKgixJRCSGfu5YNBrOsF791RcgJRWjLQVv+Vtww+2hWt4lmzNnDtXV1ezcuZPFixeP+PV1nQO8VNXGm7VdGIRgWZGN20vspCcEd7B8oKjNlKIoSgRwtvnnMKVlmoiN00jNeP85KSWuXp3uTp2uDp9/k9Xpo9nhZTATUNMgIVE7vbmy2vz/jYkV1BwZwJZiwJYSvku+/Yy6qey8KL1RvQxtP9lNW5+Xz1+ZcfEvDoPbS1JYe9jJs3tb+cainFEd64WqNkBy97TojEoByEp/qtWlzpcaLlE0memTdV6sl/Q8+R8kfOsnYW9MEg0cDgepqalYLENv/OWhajiyD/HhzxETY6bn6VXI5gZEenaIVzoyiYmJTJo0ierqasrKyoiLixvW64619/N8VRvvnujGbBDcOjmF26akhG2O3aWKrtUqiqKMUR3tXmJiBbFx50ZthBDEJxiITzCQmfP+J3U+n6Sny3dWFKut2Ut97fsdzowm8Hpg5pXDe3MLFluKAYPRXzelNlPRY82hDtLjjZRmJ4R7KUNKijFy86QUXqxuo8bZT2Hypc2Eaupxs+FoB9cX26Lm0/ChyKqdkF+ESAp+Sub0KQU833CC/R0+yg7shSlXBP2c0UzXdRobG5k0adL5v2bN82BNQixaRozZ6N9Mlb+FWHF3CFd6aUpLS9m/fz+7du1iwYIFF/zaAy19PF/Vyo6GXuJMGndNs3PLpGQSY6JzWxKdq1YURRljOtp8I44cGQyCpGQjH7xvcg+cimJ1+qNYPq8kOz+8N4iadmrelKqbihq1HQNUNbn4t5lpGM6TQhoJVk5JYc0hJ3/d28p3Fude0jH+r7INTQjumhalHfwA2dsDRw8gbrwzJOeblBqLUYOqjBLmrFuNQW2mLqi9vR23233eFD9ZcxiqdyFu/zeE2YIhNRWKJiPLt0IUbKaSk5OZMGECe/fuZc6cOcTEnP3BhpSSyiYXz1e1Udnkwmox8LErUrlpYjLx5sDX94WSSlxXFEUJM/eATm+Pjs0emDcUs0XDnm5k3AQLM0rjmDU3HoMh/DfD9nQj3V06A/2qnXI0eO2QE5MmWFaUFO6lXJDVYuC2ySlsP9nDkbb+Eb++ocvN5uOdLJ9owx4XxVGpfbtB6gGbL3UxFqPG5NRYKrNmQNUOZENdSM4brQbrpbKzh07Z0197EeLiEdfcdPoxUbYI6muRjhMhWeNolZaW4vF42Lt37+nHpJRU1PfwzXV1fH/jCU52ufnU7HR+t7KIu6alRv1GCtRmSlEUJew62v31Uskp0f+mciFq3lT06HX72Hy8k0WF1qhIvbllcjIJZo2/7m0Z8Wufq2zFpAnuLIneqBQAlRUQb4XxE0N2yitzrRzT46hLykWufzlk541GDoeDuLg4EhPPndUmG+pg5zuIJTcjYt9PyRZzFoAQ/uhUFEhLS6OwsJDdu3fTPzDA23Vd3P9aDQ+/cRJnn4cvlGXw29vGc9uUlIgZsxAIY+c7URRFiVKDzSfC2SAiFJKS/XVTrU1qMxXpNh/vpN8ruSnC2qGfT7zZwIem2NnR0MuBlr5hv66uc4A3a7pYMSkZW5QVvZ9J6jqy+lRLdC10H8pcMy4RowYbZ30I+e5mZJczZOeONg0NDWRlZQ3ZqEO+9iKYLYglt5z1uLClwMRpyPK3Ajp3MJjmlJbR39/PIy+8yaNbGxjwSr48N5P/urWIGycmYzaMva3H2PuOFEVRokxHuxdroobRFP5UvGDSNIE9zagiUxFOSslrhzqYYI9hgj023MsZthWTkkmyGEYUnXpubysWo8aHpqQEcWUhcOIYdHUEtSX6UJJijFyZa2WLKR+PDnLzmpCeP1r09vbS1dU15Hwp2dKIfO9NxOLlCOu5UStRuhAaT0J9TQhWeuk8Pp3XD3fwo3IX7cYUUjqP8cDcdFbdPI7rimwYR1h3Kb3R8z6hNlOKoihhJKXE2ebDZo/eT8VHwp5mpKdLp79P1U1Fqr1NLk52uaMmKjUo1qRxx1Q7expdVDe5Lvr1x539vF3XHdVdxAbJyh0AiKmzQn7uZUVJdHkk5bNvQb6xBumOjkGrodTY2Agw9GZq7d9A0xDXrxzytWLOfNA05HuRmern8en840A7n3/5GL95rxGrxcCCuWUYfQOkuOovqXmN7O5C/9k30LeuC8KKA09tphRFUcLI1avjcUtsY7xeapCqm4p8rx50kmgxsLDAGu6ljNjyCTaSY438ZW/LRdOint3bSrxJY2W0R6UAWbUDCieEZXjuFZnxpMUZ2ZB9FfR0I9/ZHPI1RLqGhgYMBgNpaWlnPS6dbchtGxDzlyJsQ9fsCWsSTJ6BrIjMVL//3dnM73Y0k2U18cMleTx2QwFLZ00kIyODHTt24PP5RnQ86WxDf+zb0FAXkhb/gaA2U4qiKGHUcapeKjlAnfwiXWKyAaMJ1SI9QrX0eiiv72FZUVJU1jZYjBp3TbVT3dzHnsbzR6eOtPWz/WQPt05JIcES3X97sqcLjh1CTAtNF78PMmiCpUU29nRpNBfNRK5/GamryPOZHA4H6enpGI1nR0Dl+tWg64jlt1/w9aJ0IbQ0Qu2RYC5zxJp7PKw70sH1xUn8eFkBM7PiEUIghKCsrIyuri4OHTo07OPJlkb0R78F7a1oX/kBYkZZEFcfONF3pVQURRlDnG1eNANYk6L7hm64BudNtarNVERae7gDgOUTouMT4aFcX5xEapyRv14gOvXXvS1YzRq3To7e73OQrN4V0pboQ7nuVPv8zVfcBk31/s6CCgBer5fm5uZzUvxkdxdyy1rElYsRaUPPnhokZs8DgxFZ/lYwlzpiL1a3AYK7p6We89y4ceNITU2loqJiWBE1WV+H/h/fgj4X2lcfQUyaFoQVB4faTCmKooRRR7sPW7IBLYKHogZaarqR3m5VNxVpPD6d9Uc6KM1JID0heuctmQwad09L5WBrPzsaes95/kBLHzsaellZYifONAY+xKjaCQlWKCwO2xLS4k3MzIpnY38SvpQ09HWrw7aWSNPc3Iyu6+dupja+Au4BxI13XPQYIt4KJTMjKtWvqcfNhqP+qFRa/LnXCyEEpaWlOJ1Ojhy5cERN1hxGf/zbAGhf/yli3ISgrDlY1GZKURQlTHSfpNN5+TSfGGQfrJtS0amI8nZdN50DPlZEWeOJoVxXlERGgmnI6NRf97aQZDGMie/zdEv0qbND2hJ9KMuKk2h1edm76B44VIWMsJS0cBkc1nvmZkr2uZCbXoXZ8xDZ+cM6jihbBO0tcOxgUNY5Us9XtaEJwZ1Tzz+frbi4GJvNdsHolDxYhf7z70FMHNo3f4bIGd6/RyRRmylFUZQw6er0oetjf1jvByXZ/HVTKtUvsrx60Em21cyMzLiLf3GEM2qCe6bZOdo+wLsne04/Xt3kYk+jizum2ok1jYFboNqj0N0JYaqXOtOVOVYSLQY2xE+CmFjkOjXEF/ybqaSkJOLi3v+7km+sgb5etJvuGvZxxMyrwGiKiAG+jm43m451csMEG/a480exNU2jtLSUlpYWamtrz3leVlag/+oHkJyK9o2fXTTdMVKNgSuJoihKdBpsPnG5RabE4LwptZmKGEfa+jnU1s9NE21oQwwVjUbXjEsi22rm2T2t6FIipeQve1tIjjWyfELou94Fg6ysACEQU2eHeymYDIJrxyXynqOPzoUrkBVbke3Dn/k1FkkpcTgcZGdnv//YwABy/cswdRaiYPipmSI2DqbNQVa8jdRH1iEv0J6vasWoCe64QFRq0KRJk7BarZSXl58VndLLt6I/+WPIzven9iVf/FiRSm2mFEVRwsTZ7sVsEcTGjY2b15Gwpxvp7dHpc6m6qUiw5pCTGKPg2vFJ4V5KwBg0wYen26ntHODt2m7//KnmPu6aasdiHBu3P6dbog8x7DUclhbb8EnYUnQNAHLjP8O7oDDr7Oykr6/v7BS/t9ZDdyfaTXeP+HiibCF0tsPh/YFc5ojUd7l543gXyyfYSIm9+AeBBoOB2bNn43A4qK+vB0Dfug751OMwfhLaAw9HzO/vpRobVxNFUZQo1NHmI9luQIyRSMBIpKq6qYjRNeBja20XiwuTSDCPrZTThQWJ5CWZea6ylb/ubSE1zsj1xWNjwyi7O6HmMGJ6abiXclp+koXJqbFscHhhzgLk1teRfRcfoDxWfbBeSno9yNf/BhNKEBOnjvh44oorwWxBVoQv1e/5ylNRqZLhR5KmTp1KXFwc5eXl6OtWI/+4CqbOQvvKDxFx8UFcbWiozZSiKEoYeNySnm4dW8rlleI3KNFmwGQWajMVATYc7cDtk9w0cWykvp3JoAk+MiOVk11uDrb2c/e0VExROD9rKP6W6DJs86XOZ1lxkv/f+6rboM/lj8RcphwOB2azmZQU/2Bo+c5mcLaOqFbqTMISg5hRhtyxDTnCYbiBcKJzgDdru1gxMRnbMKJSg4xGIzNnzuTEiRM0/vNFxJwFaP/vuwiLJYirDZ2xcUVRFEWJMh3t/k3E5TKs94OEEKSkGWhtUZupcPLpkrWHO5iaHkthcky4lxMUc83djDf2k2HST89DGhMqd4A1CQqKwr2SsyzITyTGqLHeZYUJJciN/wjLjX8kcDgcZGVlIYRA6j7k2pegoBhGUeMmyhb6m44crAzgSofn/ypbMRsEHypJGdHrpK4z7dBOLF4POyZfifjc1xDG6B2/8EFqM6UoihIGzvZTzScus05+Z0pNN+FSdVNhtbOhl6YeDzeNgTbhg6THg6zehf7cU/i++3n4/hd5cMvP+Mk7P8fg6g738gJC6j7kvsGW6JF1Kxdr0ri60MrbtV30X7cS2pqRO7eFe1khNzAwQFtb2/spfhVvQ7MD7aY7R5faPW0OWGJD3tWvrmOAt2q7WTExmaSY4UelpM+HfObXmDb/kxlJcRz3Qlu7M4grDb3I+gtUFEUJsKYGDz3dnnAv4xwdbV7irRom8+V7Gban+d+QVYv08FlzyElyrJG5edZwL2VUZHsL+ptr8a16BP3+f0H/z4eQb74O6dmIj3wO2xe/TnJPy9hpiHD8MPR0w/TISvEbtKzIxoBPstU6EdKzketWR8yw2VA5s15K6jpyzQuQlQcz547quMJsQcy8ErnzHaQ3dO9tz1W2YjFqrBxBrZT0eNB/+xhy20bELR9h5kc/gclkoqKiIogrDb3LM1lfUZTLgntA5723emlr1iiZGTkRICklHe0+0jIv70twok07XTeVV2gO93IuOw1dbnY6evnI9FSMWnQ1QZE+Hxw9gKyqQFbugJM1/ifs6Yh5SxDT58CkGWfXZMyai9z0D+Sy26K+6F1W7QChIabOCvdShjTBHkOBzcKGY11cv+xW5F/+G47shwkl4V5ayDgcDoQQZGRkQGUF1NciPnV/QCKJomwRcvsW2L8HQtCApMbZz9t13dw11U6iZXjvpXJgAP2/fgLVuxD3fBpt6W3EAtOnT2fXrl3MnTsXm21s1Gle3u/kiqKMac2NXpDQcMLFlCsSIqZrXp9LMtAvL9vmE4OE8M+bUpGp8HjtsBODgOujZOaS7O5EVu2Eygpk9U5w9YLBAMUliDs/4e9ql5V33r9zbcXd6LveRb6xBnGJDQAihazcAeMnIuIjM6IohGBZURK/29FMzdKFFMT/BX3dagyX2WYqNTUVk8mE/urz/o3+lVcH5uAlsyAuHlm+NSTdHJ+rbCXOpHHblOHVSklXL/qvH4ajBxAfvxdt4bLTz82aNYs9e/ZQUVHB0qVLg7XkkLq838kVRRnTmhv8KRB9Lh9dHTpJyZERnbrcm0+cKTXdSGO9B1evj7h49e8RKv1enY3HOpmXbx3WrJhwkLoOJ44h91b4h9PWHAYpIdGGmDXXfxM5Zeawo0yioNg/9HT9y8jrbkFYorPhhuxyQu0RxG3/Eu6lXNDicUk8vauFDSf6+MziG5GvvYBsakBkZF/8xVFO13WampqYMmUKHNgLxw8h/uWLCENgrnHCZELMmutP9fO4EabgRfaPtffzzokePjzdjnUYUSnZ3Yn+nw9BfR3a576GKF141vPx8fGUlJRQXV3NVVddhdUamR8IjMTlm6yvKMqYJnVJc6OX1Az/jWJLY+TUTXW0+dA0SExSmwe7mjcVFm/WdNHr1iOu8YSUErnrXfSnf4X+jU+iP/IA8h/PAiBu+Qja936B9tjTaJ/4CmLOghGn62kr7oaeLn89VZSSVbsAImq+1FASLQbm5SWw5XgnnsU3gcGA3PBKuJcVEq2trXg8HrKzs9HXvABJKYgF1wX0HKJ0EfS5oHpnQI/7Qc9VthJv0rhl8sWjUrK9Ff3Rb4PjpL/1+Qc2UoPmzPHX+u3cGdy1h4raTCmKMiY523143JL88WaS7WZamiLnZt3Z7iUp2YBmiIy0w3CyJmmYLUKl+oWQlJI1h5wU2CyUpMWGezlnkav/jP6bnyB3vYuYOA3xqfvRHn8Gw3ceR7vlw4iC4lHVnIjiKTBpOnLd35EedwBXHkJVOyDRBnnjwr2Si1pWbKPHrbO9y4i4ajFy2wZkT1e4lxV0g80nMt29cGAv4vqVgY8eTZ4BCVbke8Hr6nekrZ/tJ3u4bUrKRQd6y+YG9Ee/BR1taPf9wF+3eB6JiYlMmjSJ6upqXK7oH+qsNlOKooxJzQ4PQkBappGcvDjaW7x4veHvJqXrks5232XdEv1Mg3VTbc3ey67bV7gcaOnjuHOAFROTI6aOEEB/az1yzQuIRdej/eLPaJ/7Otq8axGJga3p0lbcDR3tyLc3BvS4oSB9PmT1LsS0ORHXEn0o0zPiyEgwsf5IB2LZSnC7kVvWhntZQedwOIiPjydu0z8h3oq4+oaAn0MYjYjZ85F7y5EDAwE/PsCze1tIMGvcMvnCEWxZX+uPSA30oX3tx4iJ0y567NLSUrxeL7t27QrUcsMm8v8SFUVRLkFTg5fkVANms0ZOfhy6HhmpZN2dOj4f2OyRWacSDvZ0I30uSV+vmjcVCq8echJv0lg8LjHcSzlN7tuF/PNvoGQW4qNfCFhtyZAmz4Dxk5BrX0J6w39NGJHjB8HV4581FAU0IVg6Pom9TS4aE7OgZBZy86tIT+SkXQeDw+Egy5aEqCxHLL0FEROcCLAoXQgD/VBZHvBjH2rto6Khl5VTUogznf/vUdYdRX/sOyAE2td/6q9NHIbk5GQmTJjA3r176e/vD9Syw0JtphRFGXP6+3S6OnykZ/knrKdnxaAZIqNuSjWfOFdqupo3FSrOPi/vnOhmSVESMcbIuAWQ9bXo//0fkJmL9oVvIozB/aBBCOGPTrU1+9tLRxFZuRM0DVEyM9xLGbYlRUloAjYc7US7fiV0OpHvvRnuZQVNd3c33d3dZDXVQUws4tqbg3eySdMg0YZe/lbAD/3s3lasFgMrJp0/KiXdA+i/fRzMFrRv/AyRnT+ic5SWluLxeNi7d+9olxtWkXElVRRFCaBmh3/TlHFqM2U0atjTjLQ0hv9mvaPNh8ksiItXl99BCYn+uqlIiByOdeuOdODV4aYJkdF4Qna0oz/xQzDHoH35QURsXGhOPL0U8sYh17yA1H2hOWcAyKoKGD8ZEZ8Q7qUMW2qcidlZ8Ww61olv8hWQU4BcP3aH+DY2NgKQcXAn4pqbgvqzEpoBMWeBf1xAf+Bqjw609LHT0cuHLhaVevkv0FSP9okvI9IyR3yetLQ0CgsL2b17N253lNYwojZTiqKMQU0NXmJiBdak9y9x6ZlGerp1XGFOJXO2e0m2GyKqViXchBDY0/3zpsbqDVYk8OqStYc7mJkVT3Zi+Icky4F+9FWPQG8P2r3fR6Skhezc/ujUPdDcgNyxLWTnHQ3Z0Q51xy5Y2B+plhXbaO/zssvh8tdO1dfCvt3hXlZQOBwOjECqZwCx7Nagn0+ULQKPG7kncKl+z+5tIcliuGC3T3lkP3L9y4irl49s/TgbAAAgAElEQVQqUlpWVkZ/fz9VVVWXfIxwU5spRVHGFN0naWnykJFtOmvDknYqShXOVD+vR9LdqV/2w3qHkppupL9P0t2lolPBsv1kN+19XlZMDP+QXqn70J96HOqOoX3264iCotAvYtZcyMpDvvq8f6ZVhJOnWmCLKKmXOlNpTgK2GAPrj3b4B9cmJaOvWx3uZQVFw4k60nudGBctQySGIAJcNBlsdmR5YLr67Wt2sbvRxe1TU4g1Db1NkAMD6H/4FaSkIe76xKjOl5WVRW5uLjt37sQbbTWMp6jNlKIoY0pbqxefl9P1UoMSrBoxsSKsqX4dTv+5bape6hyD86Ya66O/TW6kWnPQSXq8kTnZ4U8Rk8//L+x5D/GRzyKuKAvLGoSmIW660x8l2fteWNYwIpU7ICklKlqif5BREywZn0R5fQ9Or0BcuwL27ULW14Z7aQHl8XhobWsny9WJuP5Dl3YMn6Sivoc3j7YNK1IvNM3fiKJqJ9LVc0nnPNOze1uxxRi48QKpwHL1n6G5Ae3j9yJiRp+aW1ZWhsvlYv/+/aM+VjiE5ONRt9vNQw89hNfrxefzMXfuXO6++256enr45S9/SUtLC2lpadx///0kJPgv8n//+9/ZtGkTmqbxyU9+kpkz/SHEY8eO8eSTT+J2u5k1axaf/OQnEULg8XhYtWoVx44dw2q1ct9995Genh6Kb09RlAjS3OBF0zg9rHeQEIL0TBOOkx50XaJpoU+z62jz12Ykq7bo50iwalhiBI76PlLSVeQu0Go7Bqhq7uPfZqZhCMPv/pn0Da8gN/4DsfQ2tGtXhHUtouxq5CvPov/zebQrrorY9Fvp8yH37UbMnhexa7yYpUU2/ravnc3HOrl98XLkmueR61cjPvGVcC8tYJqOH0UHsvILEfbhp616dcnexl7equ3m3ZPd9Lr9kdJrxiXy/67KxGy4cOxDlC1EbngZuWv7qIYDVzb1srfJxafnpGM5T4MaeXgfcuMriGtuREy54pLPdabc3FwyMjLYsWMHJSUlGILZzTMIQhKZMplMPPTQQzz22GM8+uij7N69m0OHDrF69WqmT5/OE088wfTp01m92h/yPXnyJNu2beMXv/gF3/3ud/n973+PfioE/9RTT/H5z3+eJ554gsbGRnbv9ufcbtq0ifj4eH7961+zYsUK/vKXv4TiW1MUJcI0OTzY040YjefecKRlGfF4JB3t4Sk4d7b7iEvQMFtUUsAHDdZNNdb3qbqpIHjtkBOTJlhWlBTWdcjd7yKf/z3Mmjvq9KBAEAYD4sY7ofYIVEfwvJujB6CvFzG9NNwruWQ5iWampsey/miHf/bS/KXI7VuQnc5wLy1gGrb5u0Nm3XDxWimvLtnl6OXX7zr4xEuH+eHmk7xzopsrcxL43uJcPjM3nzeOd/Hd9XW0910ko2LcRLCnIysuPdVPSsmze1tJjjVyQ/HQqcByYAD96VPpfXd84pLP9UFCCMrKyujq6uLQoUMBO26ohOQdXQhBTEwMAD6fD5/PhxCC8vJyFi9eDMDixYspL/cXz5WXlzN//nxMJhPp6elkZmZy5MgRnE4nfX19TJw4ESEEV1999enXVFRUcM011wAwd+5cqqqq1Buyolxment89Hbr56T4DUpNN4IIX91UR5tXRaUuIC3DiKvXx5bXuzmyv58+V+TXsUSDXrePzcc7WVRoJTEmfFE/WXMY/amfQ0Ex2qe/itAi429BzLsWklPRX30+3Es5L1lVAQYDBCgSEC5Li2w4uj1UN/f5mzP4fMhNr4bk3FJKOvqD1+RG9vbgcDhIFjqx+YVDfo1Pl+x29PLkdgef+NsRfrDpBG/XdjMnO4HvLs7hj3cUc9/8bMpyE/jkVfl8a1EOtR0DfO21Go60nX8WkxDC34hi/x5kT9clrb+yyUV1cx93Tk05f1Rq9Z+g2eHv3hfg2Vnjxo0jNTWVioqKqLt/D9lVVdd1vvnNb9LY2MgNN9zAhAkT6OzsJDnZn5OZnJxMV5f/F6C9vZ0JEyacfm1KSgrt7e0YDAbsdvvpx+12O+3t7adfM/icwWAgLi6O7u5uEhMjZyihoijB1ezwf3qXkTX0pc1s0bAlG2hp9DLp4gPaA6rPpdPfJ9Ww3gvIG2cmLi6BA1Xt7N/bz/69/djTjeQWmMjKNWMyR2d6U7hV1PfQ75Vcf55Pm0NBtjWj//phsCah3fs9hMUStrV8kDCaEMtvRz77W+ShKsTEEF8chkFW7oSiKYi4+HAvZVQW5Ft5qqKJ9Uc6mLYgG664CrnlNeRNdyIsMUE774BX5+E3TlLZ5MJq1ihMjqHQZqEw2UKhLYa8JPN5NxDDpW/8B42WBIo+sJHy6ZLqZhdv1XbzzoluugZ8xBg1rsxNYGG+lVnZ8edN45uXbyXTWsCP3zjJt9fX8uW5WSwqHPq+VpQt9A+i3vkO4uobRrR2KSV/3duKPdZ43uuEPFTtT8+99ibE5BkjOv5wCCEoLS1l7dq1HDly5Kx9QKQL2bu6pmk89thj9Pb28vjjj1NXV3ferz3fjvRCO9Whnhsqr3jDhg1s2LABgJ/97GekpqZebOkhYzQaI2o9yvmpn1Vk2vlOA4lJJgrGZZz1+Jk/r8Lxgj07nFgTkrHEhO6T8dpjPUAXhePtpKYG76Yh2mVlGZk8LYmuTg/HDnVz9GA3e8r7qNrZT15hPEWTrOTkx2EwqI3VcB3e7cRqMTB/Ul7A66WGcy3Ue3tof/LHCK+XlEeexBiBDRTkbR+h9bUXMa77O8nzrwn3cs7ia2uh9eRxEv71i8SP8n0nEt67bpjcxav7mvmW1Yblro/j/O4Xid+7nbgb7wjK+dxenW/9cx9VTS4+NieXrgEPR1pdrD/aSb/XH/3WBOTZYilOi6fIHk9xajzFafGkJ5iHVaOm97k4/NZGBnJnMHHWbJJT7Oxp6GTToVbeONKGs89DrEljwbgUlkxIZW5hMhbjhd9/Bn9Wqanwh5x0vvPqAR5/u4EWj4HPzM1H+8C6pN1OW1Yeht3vknz7v4zo32h7rZP9LX187doisjPO7Tcg+/to+9MqDOlZpHz2AbQgzYNLSUmhvLyc3bt3M3fu3KipDwz5R6Tx8fGUlJSwe/dukpKScDqdJCcn43Q6T0eR7HY7bW1tp1/T3t5OSkrKOY+3tbWRkpJy1mvsdjs+nw+Xy3W6mcWZli5dytKlS0///9bW1mB9qyOWmpoaUetRzk/9rCKP1ytxnHRRUGw552dz5s8rPsmLlHBwfxPZeaGbtVNX04fQQIpuWltH33FprDrzZ5U7DnIK4+ho91Ff66a+rpeaoz2YzILsPBO5hWY1s2sYymvbmZIWi7O97eJfPEIXuxZKr9c/lLehDu0rP6Aj1goReu2US2/F/eLTtLz3NmL8pHAv5zR963oAXOOn0DfKf7tIeO9alBvD3yt1/r7jODdOyIbCCXSv/iu9cxYhtMBWn3h1yaNb69l+sod752aytGjwvjAZXUoauz3UdPRz3Dngb9LS0MnGQ+//+ySYNQptFgpORbLGJVvIT7KcE8XS1/2dk6duqV893s/3y9+lo9+HxSAozUlgYUEac7ITTr+uu8NJ90XW/sGf1YNXZ/Lf5YJn3jvBQYeT++Zln9O6XJ89D9+aF2k5dnjYbdmllPzPW7WkxhmZm2EY8vdDf+4pZGM92td+THuvC3qD13V11qxZbNiwgR07dlBYWBi084xUdnb2eZ8LyWaqq6sLg8FAfHw8brebyspKbrvtNkpLS9myZQsrV65ky5YtlJX526OWlpbyxBNPcPPNN+N0OnE4HBQXF6NpGrGxsRw6dIgJEybw5ptvsnz5cgDmzJnDG2+8wcSJE3n33XeZOnWqeoNVlMtIW7MXXT9/it8gW4oBowlaGr0h3Uw523wkJhlURGWEhBAk240k242UzJS0NHqpr3VzosZN7VE3cfEaOQX+jVWCNTJqcCJJS6+Hxh4PKyaFYN7NB0gpkX/+Dezfg/jkVwLW+StYxOLlyNdeQl/zAoYvfS/cyzlNVu2A5FTIKQj3UgKiKCWG8ckW1h/p4KaJyYjrVyJ/+5i/Pf3MuQE7j0+X/OodB9tP9vDZ0nSWFp2dvqYJQXaimexEM/Pz33+81+2jrmOAmo4Bjjv9/914tIN+rz8DSgBZVjPjki0U2ixkxWlU7+ulYfy1JHna2dqgU5prZWG+lTk5CcSMMn1wkMmg8aWrMimwWfjDzma+ta6W7y7OJT3h/RphUbbIPzdtxzZ/+/lh2NnQy8HWfv79ykxMQ6QbyoNVp9L7ViAmTQ/I93IhkyZNYvv27ZSXl1NQUBAV9/Ih2Uw5nU6efPJJdF1HSsm8efOYM2cOEydO5Je//CWbNm0iNTWVBx54AIC8vDzmzZvHAw88gKZpfPrTn0Y79WnFZz7zGX7zm9/gdruZOXMms2bNAmDJkiWsWrWKe++9l4SEBO67775QfGuKokSIpgYPBiOkpF34sqZpgtQME82NHqSUIblQS13S0e4lrzB0m7exSNMEGdkmMrJNeD0Sx0kPJ2vdHN4/wOF9A9hSDOQUmMnJN2GJUR0TwV9UDjA9IzhpORci17yAfHsD4uZ70OZfervmUBExcYiltyBf/iuy7hgif3y4l4T0emHfbkTZoqi4qRyupUU2flvRxNH2fsbPno+0p6OvW40hQJspKSX/9V4jb9Z08a8z07h5UsqwXxtvNjAlPY4p6e//zehS0tTjoaZjgBpnPzUdAxxt7+ftOn98yZxyBQt6t2NLz+CPt08877Db0RJCcOvkFHITzTz+VgNfW1vDt67OoeTUWkVOAWTn+wf4DmMzJaXk2cpW0uONLBl/bqdPOdCP/swTkJaJuOPjAf9+hmIwGJg9ezZbtmyhvr6e3NzckJx3NEKymSooKODRRx8953Gr1cqDDz445Gtuv/12br/99nMeLyoq4uc///k5j5vN5tObMUVRLi9SSpodHlIzjMOK/KRnGmk86aGnW8eaGPxoRk+3js8LthTVfCJQjCZB3jgzeePM9Pfp1Ne5OVnjoXpXH/t295GWaSSnwExmjmnINvmXi8omF1aLgQJbaBs+6Nu3IFf/GXHVYsStHw3puUdDLLkZuW41cs0LiC98M9zLgaP7ob8vqluiD2VxYSJP72pm/ZEOvnBlJuK6W5DP/x55/DBi3OgaD0gp+d2OZtYf7eTuaXbunGq/+IsuQhOCLKuZLKuZeXnW04+7jh/j5DO/I8UCf43PZFrRzKBtpM40OzuBR5f7G1N8f2MdXyjLZNmpxhGibCHylWeRzjZE8oW/94r6Xg639fOlqzIxDfHeKV96Bloa0b7+k6A2CPmgqVOnUl5ezsmTJ6NiM6U+ulMUJer1dOn0uSQZ52mJ/kFpmf5NTUvjRWZ3BIizzX+eZLtKQwuGmFiNokkxLL7ByjXLrRRNttDd6WPXuy7WvdzJrnd76ekOz2yxcKtq6mVaeuw5xerBJA9VI5/+FUycivj4l6MqoiLiEhDXrkDu3IZ0nAz3cpCVO8BghCmB754WTgkWA/PzrLxZ08WAV0csXAaxccj1q0d97D/vaeWfB53cOjmZj84ITrMN2ViP/tvHsPzkPoqcx+la5O+el5WVFZTzDSU30cJjNxQyLT2OVdsb+V1FEz5dIkoXgZTIHW9d8PX+qFQLmQkmrh0qKnWwErn5VcR1t4S8w6XRaORjH/sYc+cGLu0zmNRmSlGUqNfU4J8bdb75Uh8UF28g3qqFbN5UR7sPownireqSG2zWJANTZsRy3c2JzL82gZx8M456D7veDV7BdKRq6nHT3Otlekbo2mnLxnr03/wE7Blo//4dhGl4f5ORRCy9FUxm5GsvhHsp/nqpCSWImNCnaQbbsmIbvR6dbXXdiNg4xKIbkDveRrY1X/Ixn69q5cXqNm4otvGp2ekB38jLthb0Z36N/tD/Q+4tR9x0N9pPn8IhjGiaRkZGxsUPEkAJFgMPXpvHLZOT+cdBJz/afILe5EzIG4csv/Bm6r2TPRxtH+DuaXaMH+jyKfv70J8+ld73oX8N5rdwXoPzaaOBemdXFCXqNTs8JNo0YuOGf0lLzzTS2uzF5wv+cEBnmw9bijGqPqGPdkII7OlGriiLY+LUGDrafZdddCrU9VKyuwv91z8CIdC+/CAi3nrxF0UgYU1CXL0cuX0LsqUxbOuQ7S1QX4uYNidsawimqemxZFlNrD/aAfhTLAHkxn9c0vFeOdDOX/a0cs24RL5wZUZAr7eyy4n+3FPo3/s88t3NiCU3o/3kt2gf+hgiLgGHw0FaWhpGY+hTuQ2a4DNzMvjSVZlUNbv4+uu1NMy+Do4dRLY2Dfka/VStVJbVxDXjhohK/e0ZaGtG+8RXQpreF63UZkpRlKjmceu0t/qGHZUalJZpQvdBe2twU/28Xkl3p0+l+IVRTr6/8Ud9rTvMKwmtykYXSTEG8pKC3/hEetzoTz4C7a1oX/oeIj106U7BIG5YCZqGXPtS2NYgq3b41zJ9bG6mhBAsLbJR3dxHfZcbYU9DlC5Ebl2HdPWO6FjrjnTw+x3NzMuz8uW5WQFLa5W9Peh/+yP6tz/nT3mbtwTtx/+Dds9nEIn+GiWfz0dTU1NIU/yGsqzYxsPX5dPr9vGN3onsSp6IrBg6OvXuiW6OOwf48PTUc2bPyf17kJvXnErvmxqKpUc9tZlSFCWqtTT550YNt15qkD3diNCCXzfV6fQhpWo+EU6xcRqp6UZO1nouOPx9LJFSUtnkYlp6XNAjolLXkX/4FRw9gPbp+xFFk4N6vlAQNjti4TLk2xuR7eGZyyQrd0JKGmTlheX8obBkfBKagA2D0anrV0J/H/KtdcM+xhvHO/nN9kbmZMfz1QXZARlMLfv70F99Hv3bn0WufQkx8yq0H/0G7d++hEhJO+trW1pa8Pl8Yd9MAZSkx/H48kLSEyz8eManeOVQ5znXPF1KntvbRk6imUUFiWc9J/v70J/5NaRnI1aGJ70vGqnNlKIoUa2pwYPJLLCNMPJjNApSUo20OIJbN9Whmk9EhJwCE64enY62yyPVz9Htoa3PG5IUP7n6T8jyrYg7Po4oXRj084WKuOF2QCLX/T3k55Zej38+1/Q5Yzo9OCXWSFlOApuOdeLVJaKgGCZOQ278h78t/EW8U9fNr95xMD0jjm8uyhmyI91ISI8bfcPL6N/5HHL1n2HiVLQH/xPts19DZAw9tNXhcAChbT5xIekJJn52fQFlll7+YJ/Pr984hsenn35+W103tZ3niUq99DS0t6B98ssIS2g7gEYztZlSFCVq+Vuie0nPNKJdwqeR6ZlGujp1+vv0i3/xJepo9xEbJ9TcozDLyjOjGeDkZZLqd7peKjO4mynXupeRr72EuPqGU5uPsUOkZiDmXoPc+jqyyxnakx/eBwN9Y7Ze6kxLi5Lo6PdRUd8DgHb9SmhvRf/RV9CffgJ9y1pk7dFzNlcV9T08/nY9E+2xfGdxLpZRDMeVXi/61nXo3/0C8v9+D7mFaN9+DMOXvofIHXfB1zocDqxWKwkJCZd8/kCLNWl8c0kBd9VsYGODh+9vPEFHnxefLnmuspW8JDML8s+uaZT79yDfeA1x3a2I4pIwrTw6XVLeidvtRtO0sBTaKYqiDOp0+nAPyBHXSw1KyzSyf68/1S9vXHDqSpxtXpLt6loZbiaTIDPbRH2dh6kzJdooP8GOdJVNvSTHGsmxBq9eSu7bRff/PA5TZyE++oUxGUERy+9EbtuMXP9KyIaWwql6KaMRJo+tluhDmZOdQHKskfVHOpibZ4XppYh7PoOs3oXcsx3e3oAEMJkhfzyicAKVGVP5j8YU8pMsfP/a3Eue7SR1HVm+FfnKX6HZAeMnoX3yK4gpVwzv9VLicDgichaSwZ7OR4wnyHO8zirDcr66toYl45M40enm6wvPToeU/S5/el9GDmLlx8K46ug0rHf4P/7xj8yfP5/i4mJ27tzJz3/+c4QQ3HfffZSWjq1BcoqiRI+mBv8nlWlZl7ZZSbQZsMQIWho9QdlMDfT751+Nm6BS/CJBbqGZhhMemhu9ZOZEX8vu4Rqsl5qRGR/UDY7+0jMYsnKQn/8mwjA2f8dFZg6idAFy8xrk8ttD1qFQVu6ACVMRMbEhOV84GTTBdeOT+Nu+NlpdHlLjTP729Etv9df7tDYhaw7D8UPI44c5sGsfP5l6JRn9jTxY8SdiD+aij5uAKJwA4yYikpIvek4pJex5D331n6G+1h+J+tL3YUbpiP5mampq6O3tJScnZxT/AsEjShex8Lnfkn3Lbfy02svzVW0UJFmY/8Go1AtP+9P7vvEzld53CYZ1B/LWW29xzz33APDiiy9y7733EhcXxzPPPKM2U4qihE2zw0Oy3YDFcmmfSgohSM0w0tLoRUoZ8BtP56n6HJuKTEWEtEwjJrOgvtY9pjdTJ7vcdPT7glovJV29cOI4Mfd8iv7YsTcD6Uziprv80YuN/0Dc+tGgn0+2NYPjBGLR9UE/V6RYWpTEi9VtbDrayd3T3x+0K4TwzzpKy4SyRRxt7+fhDXUkGyU/yPeSFDMLefww8rUXkfqpdO2UVCiciBjcYBUUI874HZX796D//U9w/JC/0cLnvo6YswChjex9xOPx8MYbb5CSksKUKVMC8c8QcGLOfOT/PUXRgW08fuPdPLOzmRsm2M7qdij37Ua+uRZx/UpEcWR+H5FuWO/wAwMDWCwWuru7aWpqOj2RuLU1PB1uFEVRBvp1Otp9TJo2uhkY6Zkm6ms9dDp9Ae+419HuRQhISh6bn9pHG00T5OSbqDvuxuOWmMxjLy0NQjRf6ugBkBLzlCvoD95ZIoLILYSZc/1NEZatPOvGPBhk5amW6JdBvdSgLKuZ6RlxbDjWyZ3T7EO2Nq/rGOChTSeIN2k8fH0BqfGTgKUAyIEBOHEUefxUBKvmMHLnNn96oBCQmYsYN9G/UT1YCSmpiH/7EmL+dZccVd2+fTvd3d3ccccdGCI0MitsKf6GHhVbSb71I9y/4OwmGrLvVHpfZg7itn8J0yqj37DuHLKzs9m6dSuNjY3MmOHP3+3q6sJsDv7sCkVRlKE0O/wpfumXmOI3KC3T//qWRm/AN1PONh/WJANG49i8aY9GuQVmao64cZx0kz9+bKazVDa5SI0zkpkQvOibPFwNBgOmSdOguydo54kU2oq70He/6y/Qv/GOoJxDtrci169Gvvk6pGdDZmSmjgXLsqIkfrHNQWWTiysy4896ztHt5sGNdRg1wcNL80mLP/t3W1gsUFxyVuME2d0FtYf9kavjh5CVFaBpiA9/FnH1coTp0v8+Wltb2bVrFyUlJRGb4jdIlC1C/vk3cLIG8s5upiFf/AM429C++TOEeWxeD0NhWHcOn/70p3n66acxGo184QtfAGDPnj2nN1aKoiih1uzwYIkRo476WGI0Em0GWho9TCgJ3KR3KSWd7T6y88duOlk0stkNxCdonKz1jMnNlC4lVU0u5mQHt15KHt4H+UUIS8xlsZkShRNg2mz/ZmfJzQGtK5GNJ5Fr/4Z89w2QOuKqxYib7xmTDT0uZF6+lYSKJtYf6ThrM9Xc4+H7G+rwSfjx0jyyhtlURVgTYdqc0xG+wXlLo/13lVKyadMmLBYLCxYsGNWxQkHMno/863/7xxecsZmS1buQb76OuOFDY2I2XDgNazOVmprKI488ctZjixYtYvr06UFZlKIoyoXouqS50UNWrjkgNxzpmUaOHhzA65EYTYG5gent1vF4JLaUyEz/uFwJIcgpMHOoup8+l05s3NhqWV/XMUDXQJDrpTxuqDmEWHJL0M4RibSb7kZ/9FvIra/7GySMkqw9gr7mRdj1DphMiMXL/XUr9vQArDb6mA0ai8cl8frhDroGfCRaDLT3eXlwUx0ur84j1+WTn3Tpm9hAbU6rqqpobGxk2bJlxMZGfoMQYU2EyVcgK95CfuhfEUIgXb3of/y1P/1RpfeN2rDeRb7yla8M+fj9998f0MUoiqIMh7PVh9cz+hS/QWmZRqSE1uaLD4kcrsHmE6oteuTJLfBHC+vH4Myp9+ul4i/ylaNw/BB4vYgJl9csGjGhxF9/8vrfkJ5LG/YtpUTu34PvF99Hf+QB/2DeG+9C++nv0D7yuct2IzXo+qIkvLpky/FOOvu9PLixDmefj4euzWN8SuAyBy6Vy+Vi27Zt5ObmMnly9ERzRNlCaGmEmiPAYHpfu78NvEmV7IzWsN7lB0OjZ3K5XGgj7HyiKIoSCM0OD/4mT4FJoUtONWIwQkujJ2Bd3jravRiMkGBV18lIE281kGw3cLLWTfGU8N+gBVJlk4uMBBPpQa2X2uf/H5dh5y9txd3ov3wQuW0jYvHyYb9O6jrs3o6+9iX/ZjQpGXHHxxGLbwx6Q4toUpgcQ3FKDGsPd7DpWCdNPR4eujaPSamREQHaunUrHo+Ha6+9NqrSMMWsecg//xeyYiv0diO3rkPccDti/KRwL21MuOBm6otf/CLgH9I7+L8H9fT0REWuqKIoY0+Tw0NKmhFTgFLyDAaBPc3fIj1QnG3+7oBCi5433MtJboGZyp19dDp9Y6bboi4l1c0u/+DTIJKHqyGnAJGQGNTzRKQpV8C4if5W3AuWIowX/kxaer3I97Yg1/4NHCf8bb4/9u+I+UtUROA8lhUn8V/vNWHU4LuLc5kWzK6UI1BXV8fBgwe58sorSU6++CyrSCLiE2DqLOR7W5Hlb0FWHuK24Lf5v1xc8Cpw7733IqXkpz/9Kffee+9Zz9lsNrKzs8/zSkVRlOBw9ep0d+qUXBHYG5H0TBNVjj56e3zEJ4zu5trnk3R1+iiaNPYaHIwVWfkmqnb1UV/rJik5Mj71Hq0a5wA9bj249VK6D44eQMy9JmjniGRCCLQV96Cvehj53puI+UuG/Do5MIB8ax1y3d+hvRVyCxGf/Zp/np8FgwsAACAASURBVFGEttGOFFcXJlJR38uy4iRmZyeEezkAeL1eNm/ejM1mi9r5qqJsIXJvOQgN7duPqs18AF1wM1VS4s+H/v3vf49FTURWFCUCNDv8tQrp2YFNY0rLMsIuf4v0+OLR3ex0OX1IHdV8IoJZLBrpWUbq69xMmREzJiKIIZkvdaIG+vug+PKqlzrLjFLIHYd87QXk3MUI7f2/c9nbg9z8KnLjP6CnC4pL0D727/6uclGUFhZOcSYD37smN9zLOEtFRQWdnZ2sXLkS40WikZFKXHEVMt6KuPYmxLiJ4V7OmDKs3wiDwcCGDRuoqamhv//s8Xxf+tKXgrIwRVEik88n2bqum/wiC+Mnhv5DlmaHh7h4LeC1SPEJGrHxGi2NXgqLR/d9OdtV84lokFtgpqnBRWuLl7SM6G9hX9nUS7bVhD0uyPOlADFhatDOEen80am70P/nUeSOd/yf+He0I9e/jNyyFgb6YHop2o13XnZNOsai9vZ2KioqmDRpEvn5+eFeziUTsXFoj/0BjNF/rYs0w3qnX7VqFbW1tcyZM4ekpKRgr0lRlAjWWO+hu0tn/54+0rOMJFhDF33x+SStTV7yxgWmJfqZhBCkZRhpqHOj6xJtFJGKjjYvMbGCmFjVfCKSZWSbMJqgvsYT9Zspny6pbu5jUUFw65jk4X1gT0ekpAb1PBFv9jzIzEX+8zn0A3uQ2zaCT0eULULceDsid9zFj6FEPCklmzdvxmQysWjRonAvZ9RUal9wDGsztWfPHlatWkV8fBBbrSqKEhVOHHcTEyvweWFPuYv51yaELH2lrdmLzxf4FL9B6VlG6o65cbb5sKddelTJ2e7DpqJSEc9gFGTlmmk44WbanFiMxuhNwzrm7Mfl0YNarC+lhMPViKmzg3aOaCE0A+Kmu5D/+0tkswOxYKm/O1paZriXpgTQgQMHqK+vZ8mSJcTFRUYjDCXyDHtor+cSZyooijJ29Ll0Whq9TCixEBevsae8j7pjbgqKQpPu1+zwoBkgdRQbnQtJTTchhL9F+qVuptwDOq4enYLx6hPAaJBbYOLEcTdNDR5y8qP3Z1bZGIJ6qaYG6O4ElboGgLhqMcJshuISRFJ0dXdTLq6vr4+tW7eSlZXF1KmXb1qrcnHDulu4+uqreeyxx7jxxhux2WxnPTdt2rSgLExRlMhzosY/5DRvnJm4eI36Wg/79vSRnmUiNi64KW1SSpocXlLTjRiCFEEwmQU2u4GWRi+Tp1/aMQbrpWx21XwiGtjTjcTECk7WuKN7M9XkIjfRTHJs8CKiql7qbELTYI4aETNWvf3227jd7qibKaWE3rCuumvXrgXg2WefPetxIQSrVq0K/KoURYk4UkpOHndjTzOcbh0+ozSWN17vpnKni7IF8UF9w+nt8Ud8ioLc9CI908TBqn4GBnQslpFvEDvafCDAlqzS/KKBEIKcAjPHDg4w0K9j+f/s3Xd8XNWZ+P/PvdNVRqNebXVZLnJvGFwwhGJIQtgkJITdDbC7YdndJIRNstkQ9st3lx+72SUQAinfkLJppOwCAZJginA3uFuyJdmqltXbqI6m3vP7Y5DAuI1k3SnSeb9eemFmRvc89rR77nnO81hjb5+bXxPU9Lq4tlDnPc31NZCYBFm5+o4jSRHW3t5OTU0Nq1atIi1tju8PlC4rpG/7Z555Ru84JEmKcgN9AcZGNUoXvZdGFJ9oYMESK7XH3XS2+ciZp9+V/e6OiZLo+k5S0rOMnDoBfd3+aa1UDA74SbSrGGeoobCkv7x8M411HjrO+igsjb02IA39btx+QUWWvns6RP1JKFkor9JLs1ogEKCyspLExETWrl0b6XCkGBB7l+AkSYqIs01ejEbInndu8YeiMgtJyQZOHBnH69V0G7+n00+CXSUuXt/0OUeyAZNZobfTP+XfFULg7A+QnCJXpWKJ3WHA7lBpezeNNdZUd48BUJGhY/EJZz/0dcsUP2nWO3LkCE6nky1btmAyxXaVTyk8QvrGd7lc/O53v6OmpoaRkZFgRZ93fe9739MtOEmSooPfJ+ho85I7z3xexTNVVVi2xsbu10epOeZm+dqZP6Hz+wT9vf6w9LVS1GCJ9N5uH0KIKV2Fd41p+LxC7peKQXn5ZmqOuxkdCYS13P9MqO52ke+wYLeGY7+ULD4hzV6Dg4McOHCA4uJiCgtleXspNCGtTD377LM0Nzfz8Y9/nNHRUe655x7S0tK45ZZb9I5PkqQo0HHWS8AP8y5SoS4p2UjxAgtnm730ds985c/ebh9CC5YuD4f0LCPuccHI0NRW2pz97xafkCtTMSfn3ZTO9jOxtTrlC2jU9o7rW8UPgvulLDaYV6TvOJIUIUIIduzYgaqqbN68OdLhSDEkpMlUVVUVDz74IGvWrEFVVdasWcMDDzzA7t279Y5PkqQocLbZS3yiSvIlVlzKFluJT1CpOjiO3y8u+rjp6On0YzRBSlq4JlPB1I7erqlNDAf7/RgMkJgkM6hjjS1OJS3TSFuL75zsi2h3ut+NNyB0n0yJ+pNQXI5iiK1VO0kKVX19Pa2trVx11VUkJCREOhwphoT0jS+EmGxWZrVaGRsbw+Fw0NXVpWtwkiRF3uhIgIG+APMKzZdMeTMYFZauseEa0zh90j1j4wsh6On0kZ5pQlXDs/HdFqeSYFfp6ZravqnBgQBJKYawxSnNrLx8M64xbXKFMRZUd7tQgCV67pcaG4WOVpniJ81aHo+HXbt2kZGRwdKlSyMdjhRjQppM5efnU1NTA0B5eTk/+tGPePbZZ8nOztY1OEmSIu9ssxcUmFdw+cp2aRkm5heZaTzlYXBg6gUcLmR4UMM9LsKW4jchPcvEQK+fQIirbFpAMOSUxSdiWVaeCdVATBWiqO52UZhsIcGi44pRQy0IIYtPSLPWvn37GB8fZ+vWraiqzCyQpiakV8znPvc50tPTAbjnnnswm82MjY3x93//97oGJ0lSZAlN0NbiJSPLiNUW2hfMomVWLBaF4wfH0bQrT5fq6Xy3JHp2eKsqZWQZ0TTo7w1tUjg8FEDTZLPeWGYyKWTlmug460MLRH+qnzegcSoM+6VE/UkwGKGwVNdxJCkSurq6qK6uZtmyZWRkZEQ6HCkGXfbsSNM0duzYQWpqKgB2u5377ruPBx54gLy8PN0DlCQpcnq7/bjHBfMKQ++3ZDKrVKyyMTwYoPGU54pj6O70kZRsCHkyN1NS0o2oKvSGmOo3KItPzAp5+WZ8XjHlFM9IqOsdx6cJKjLjdR1HNNRAQQmKOfZ6cEnSpWiaRmVlJfHx8axfvz7S4Ugx6rJnJ6qqsn37dgxy06kkzTlnm72YzAqZOVNbFcrOM5OVZ+L0iWCp6enyeoL7VzJ1btR7IUajQkq6kZ4Qi1A4B/xYrAq2OLlfKpalZxkxWxTaYqCqX3W3C1WBRRk23cYQXg+0NMgUP2lWOnbsGH19fWzevBmzWb+m89LsFtKl3s2bN/P666/rHYskSVHE69HoaveRl2/CYJj6BKFipQ3VAFUHXdOujtbb5QcR/hS/CRlZRkaHNcZdly+R7uwP4EgxTKkvlRR9VFUhd76J7nYfPh2bUM+EE90uilOsxJt1vNjZfBoCfll8Qpp1RkZGeOeddygoKKC4uDjS4UgxLKTLvQ0NDbz66qu89NJLpKamnnOy8Mgjj+gWnCRJkdPe6kPTmFKK3/tZbSqLltmoOjROa5OX/OKppwh1d/owWxQcKZFZGU/PMsFxN71dPuYXXTx+n1djbEQLqUiHFP1y880013vpbLv08x5JHr/G6f5xPlKeous44vRJUBQoWajrOJIUbjt37kQIwZYtW+RFMOmKhDSZuu6667juuuv0jkWSpChyttmL3WEgKXn6KXbzi8y0t/qoOT5OZo5pSvuehCbo6fSTmW2M2BddYpKK1abQ2+W/5En14MDEfimZDj0bOFIMxCeotJ2J3slUbe84fo3wFJ/IzUeJk313pNmjsbGRpqYmrr76aux2e6TDkWJcSGdJW7Zs0TkMSZKiyZAzwJAzwJIVV7YXQ1EUlq22sWP7CNWHx1lzTegb5Z0DAXxeQcYU92vNJEVRSM800dXhQ2gC5SL9o5wDsvjEbKIoCnkFZk6dcOMa04iLj75SydXdLgwKLEzXsb9UIABNp1A2bNVtDEkKN6/Xy86dO0lNTWX58uWRDkeaBUL65q+srLzg7SaTidTUVEpLSzGZInfCI0nSzDrb7EFVITf/yt/X8YkGFiy2UlvlpuOsl5x5oaXC9XT6UJRgQYBISs8ycrbFy6AzQHLqhWMZ7PeTkKhiMstUkdkiN9/EqRNuOlq9lCy0Rjqc81R3j1GSasNm0nGi19oEHjfI4hPSLPLOO+8wOjrKzTffLIurSTMipLOUXbt2cfr0aZKSkkhNTaW/v5+hoSGKi4vp6ekB4Ctf+YrcwCdJs4AWELSd8ZGZa8JsmZkTtaIFFtpbfZw4Mk5aphGz+fLH7e7wk5xmCOmxekp7dzLX2+W/4GRKCMHgQCDikz5pZsUnGEhONdDW4qW43BJVeypcvgD1/W5uX5Sq6zii/iSALD4hzRq9vb0cO3aMJUuWkJ2dHelwpFkipG//vLw81q5dy7Zt2yZve/XVV2lvb+f//t//y/PPP8+Pf/xjHn30Ud0ClSQpPLo6fPi8U+stdTmqqrBsjY09b4xSe8zNsrWXTk1yj2sMDwYoXxr5FQGLRSUp2UBPl4+yxefHM+4SeNyCZJniN+vkFZipPjzO8GDgivYOzrTannE0EY79UjWQnoXi0HfSJknhMNFTymq1smHDhkiHI80iIV3y3bt3LzfddNM5t91www3s2bMHRVH4yEc+Qltbmy4BSpIUXmebvVhtChmZM3vy6EgxUrTAQmuzl77uS/du6ukM3p8ZoZLoH5SRbWSwP7iH64MGB4LNXR2pMl1ktsmZZ0JRoO1MaL3GwqW624VRhYXpOvaXEgIaamR/KWnWOHnyJN3d3WzatAmrNfIX6qTZI6TJVFJSEocPHz7ntiNHjkxWQPH5fBiN0XPVTgqqqx7nyNtj0+7xI8097nGNni4/eQXmixZbuBILFluJS1A5fmgcv//ir8vuTj9Wm0JiUnRs/E/PNCEE9PWcf1Lt7A+gqmB3yMnUbGO2qGRkG2k/40Vo0fM5Wt3toizVhsWo4/ujqw1Gh0Gm+EmzgKZpHDp0iOzsbMrKyiIdjjTLhDQDuvvuu/nWt77F/PnzJ/dMtba28qUvfQmA+vr681aupMhqrvdQX+MBIDvPR3ae7H8jXV5bixfE9HtLXY7BGKzut3/HGKdPulm07Pwr61pA0NflIzffHDX7VJLTDBiNwX1TH3wvDfb7SUo2oOow+ZQiL6/ATHeHi74ef7DvWISNeQM0Od18Ykm49kvJlSkp9jU0NDAyMsLmzZuj5ntFmj1CmkwtW7aM73znOxw7doyBgQFWrFjBypUrSUxMnLx/2bJlugYqha63y8fJo+Nk5hgZG9WoPe4mM8ckT/akSxJC0NrsJSXNQEKifqssaZkm5heZaTzlIWee6bxy4gN9fvx+yIiSFD8I7vlKzTTS0+VHCDH5ZaxpgkFnYFoNiaXYkJljwmiCtjPeqJhMnexxhWW/FPU1YHdAhtykL8U2IQSHDx8mOTmZwsLCSIcjzUIh5wjY7XY2bdrEbbfdxubNmycnUlJ0GR0OcGjfGAl2lZXr41m0zMbYqEZrozfSoUlRztkfYGxE021V6v0WLrNisSgcPziO9oH0qe5OP6oKaTO8Z+tKZWSZGB/TGBvVJm8bGQqgBSBZNuudtQwGhZw8M51tvkumpoZLdbcLk6qwIE2//VLwbvGJ0kXyKr4U89ra2ujt7WXFihXy9Szp4qJnK48++ihf//rXAXj44Ycv+gJ85JFH9IlMmjKvR+PA7jFUVWHtxniMJoWMbCOp6QZOnXSTV2DGaJIfJNKFnW32YjAQch+oK2E2q1SssnFor4umU55z+vj0dPhIzTBiNEbXazX9fSXSJ1buBiea9criE7NaboGJ1mYv3e3B9NNIqu52UZ5uw2zQb7+U6O+F/h6UD31UtzEkKVyOHj2KzWajvLw80qFIs9RFJ1ObN2+e/PPWrbL7ebTTNMHh/S5cLo2rtiQQFx88uVMUhUXLbOx+Y5SGOjflFfpezZRik98v6GgNNtQN14Q7O89MVq6PUyfdZOWZSEg0MDYaYHREI78k+tLm4hMMxCWo9Hb5KCwNxufsD2C2KMTFR0ehDEkfqelGrHEKbWe8EZ1MjXgCtDg9fHppmq7jyP5S0mzR399PS0sL69evl4XSJN1c9JV1zTXXTP55y5Yt4YhFugInj47T1+1n+VobqennPq2OVCM58000nvJQUGLBapMnftK5Os/68Pv1KzxxMRWrbLz1Jx9VB11cdW0CPZ3BMuOZ2dH5pZeRZeRsixctIFANCoMDfhwpBpk6MsspikJevpnGOg8et4bFGpnP0BM9LgRh2i9li4O8An3HkSSdHT16FKPRSEVFRaRDkWaxkM9YamtraW5uxu12n3P77bffPuNBSVPTUu+hpcFLcbmFeYUXvqJfXmGls83HqRNulq3R+YtYijlnW7zEJaikpIc3Xc1qU1m0zEbVoXFam7z0dPqIT1CJ17EAxpVIzzLR0uBloM+PI8XIyJAWlrRIKfLy8s001Hpob/VRVBaZldPqbhcWg0Jpqt77pU5CcTmKGp3vQ0kKxdjYGHV1dSxZsgSbTWblSPoJaTL14x//mP3791NeXo7Z/N6Jg7waG3m9XT5OvFu5b2HFxZvQxScYKCyx0FTvoajMQmKS/JKUgsZGA/T3+FlQYY3Ie3p+kZn2Vh81x8fRNKK6Ml5qhhFFgd5u/+RtDll8IuoJvx8MV7aCmJhkwO4w0H7GG7HJ1IkuFwvTbZgM+r1PxegwdJ5FWbf58g+WpCh2/PhxNE1j+fLlkQ5FmuVCmkzt3r2bxx9/nJSUFL3jkaZgdCTA4X2uycp9l2uyWrrIQmuzh5rj46zblBCmKKVod7Y5WOlxXkFkVlgURWHpahs7t4+gBaI3xQ/AZFJITjPQ0+nH9O7eMjmZim5CCLT/+Cp4Paj3fgllftG0j5WXb6LmuJvR4QAJ9vA+70NuP2eGPGwqSNd3oIYaQPaXkmKb1+ulurqa4uJiHA5HpMORZrmQEr/T0tIwmSLfX0N6j9cbrNynqExW7rscs0WldKGVnk4/fd2+MEQpRTshBG0tXtKzjNjiIreXLiHRwKKlNuITVFLSo3cyBcES6cODAbo6gimJZovcgxjV6qqgpR76utD+v39E+9P/IrTAtA6Vm28GJdhzKtxOdLsAqMjSN01b1NeA0QiFpbqOI0l6qq2txePxsHLlykiHIs0BIZ0F3HffffzgBz9g//791NTUnPMjhZ+mCQ7vc+Ea01h9dfxk5b5QFJZZsMUp1Bx3I0Tke6ZIkdXX7WfcJcJeeOJCCsssbL3FjkHHFKaZMFEi3dkXkCXRY4D25suQmIT66A9g2VrE8/+N9p9fR/R2TflYVptKWoaR9jO+sH9+Vne7sBpVilMuns49E0R9DRSWoZgi/5kgSdOhaRpHjx4lOzub7GzZdFrSX0iXgJuamjh69Ci1tbXn7JkC+N73vqdLYNLFTVTuW7bm/Mp9l2MwKCyosHHsHRcdrZHvmSJF1tlmLyaTQlauXHkOVVKyAbNFwesRJKdE9yraXCd6u6DqIMrNn0BxpKLe91XE2zsQz/0A7ZEvoHzqr1Cuvn5Ke6ny8s0cO+DC2RcI6ypqdbeLxRk2jJdJ574SwuOG1kaUG2VhKSl2NTQ0MDw8zMaNGyMdijRHhPRN8Nxzz/HVr36VpUuX6h2PdBktDcHKfUULLMwvmt4m6Lx8E02nDNRWB/v7RPtKgKQPn1ejs93H/EKzfA1MgaIopGcaaW/1yZWpKCd2/AkUBWXLzUDwuVOuuhZRtgTtJ08i/vs7iOMHUP/871Dsoe2ryM4zUXU4mOoXrsnUwLiftmEv1xUn6TtQ0ykIBGR/KSlmCSE4evQoDoeDwsLCSIcjzREhpflZLBYWLZIfrpHW2+3jxJFxMrKNLFo6/VSPYCNfK+NjGi0NnhmMUIol7a0+tED4e0vNBvOKzKSmB6u7SdFJeDyIPa+jrNyAkpx6zn1Kajrql/4V5RP3wInDaP/nHxDHD4R0XKNJITvXRMdZH1ogPKl+k/uldO4vJepPgqJC8UJdx5EkvXR0dNDd3c2KFStQVbmfVQqPkF5pd9xxBz/96U8ZHBxE07RzfqTwmKzcl6iy8qrLV+67nPQsE+lZRuprPHi98nmci842e0lMUklKlhOCqUrPNLFha6Jc0Yti4p0d4BpF2XrrBe9XVBX1httQH3oCklLQnv43tJ89jXCPX/bYuQVmfF5BT5f/so+dCdXdY8SbVIqSdd4vdfokzCtAsclehFJsOnLkCFarlYUL5QUBKXxCylGY2Bf1+uuvn3ffb37zm5mNSDqPb6JynxKs3GcKoXJfKBYtC5ajbqjxsGi5bGg3lwwPBhgcCLB4eWR6S0mSnoQQiMpXYF4hlFz6pErJzUf95/9CvPQrxPbnEXVVqPc8gHKJ30vPNGK2KLS1eMOy37C628WijDgMeu6X8vug+RTKxht1G0OS9DQwMEBzczPr1q3DaJT7WaXwCenV9vTTT+sdh3QRmiY4vD9Yue+qLQnEJczcKoLdYWBegZnmeg8FpeYpVQWUYtvZZi+KgixAIs1Op09A+xmUv/yHkC4WKCYTyp/9JaJiNdqPn0D75tdQbv4zlA9/CsV4/mRJVRVy55s40+jF59UwmfVLJ+pz+egc8XFzabJuYwBwphG8XrlfSopZR48exWAwUFFREelQpDkmpG+A9PT0i/5I+qo5Nk5vl5+lq6ZeuS8UCyqsoEBdtXvGjy1FJ00TtJ3xkpljwmKVOeXS7KNVvgIJiShrN03p95Syxaj/8hTKhq2IP/4O7bEvIzpaL/jYrFwTmgbO/un1rApVdVeY9ku926wXOZmSYpDL5aKuro5FixYRFyfTVKXwCuns3OVy8cc//pGWlhbc7nNPuh966CFdApOClfua670UlU2/ct/l2OJUisosNNR6KCrz45Clnme9nk4/Xo9gfpFclZJmH9HfC0ffQbnpYyjmqX9uKrY4lM9+HrFsLdrPnkb71wdQ/uwvUbbeivK+De0TxUeGhwJkZOuX6lfd7SLRrFKQrM93wARRXwMZOSh2nVfAJEkHVVVVBAIBli9fHulQpDkopDPnb33rW2iaxtq1a8/rMyXpo+/9lfuW6bvpuKTcyplGL7XH3azfEi/30MxyrU0eLFZlsvmsJM0mYucfAVA2b7ui4ygr1qMWL0D776cRv3kWUXUQ9bOfR0kJZmSYLSpWm8LwoM4rU90uFmfGoer4uSw0DeprUFas120MSdKLz+ejqqqKoqIikpPlxQAp/EI6m6qvr+dHP/qR3NAXJmMjAQ7tcxE/Q5X7LsdkVihbbOXk0XF6uvxk6niVVYosj1ujp9NP0QILqs6vK0kKN+H1IHa9BivWoaReeRq6Yk9G/fuHEHteR/zmWbT/83mUz9yHum4zEFyd0nMy1T3qpWfMx0cX6nyC2HkWXKNQuljfcSRJBzU1NbjdblauXBnpUKQ5KqQNE+Xl5bS3t+sdi8R7lftgZiv3XU5BsZm4BJXa4+MILTy9U6Twa2vxIoTsLSXNTuLALhgbQb1IOfTpUBQFdeMNqA9/G3LmIZ59HO3//SdibAS7w8DosEZAp35T1ZP9peJ1Of4EUX8SQBafkGKOpmkcPXqUrKwssrOzIx2ONEeFtNR0//3389hjj1FSUoLDcW6X+I9//OO6BDYXTVTuGxvVWL8lgfgZrNx3OapBYeFSK4f3uTjb4tVtj5YUOUIIWpu9JKcaSLTLyo3S7DJZDj03H8qWzPjxlYxs1C8/hnj1fxEvP4eoP4n9Y/+MEBmMDgdISp75zI3qbhdJFgPzk3S++FFfA0kpkJ6l7ziSNMOampoYHh7mmmuukVsUpIgJaWXqueeeo7+/n6GhITo7Oyd/urq69I5vTjmwty9YuW+1jbSM8KdUZueZcKQYOHXCjd8vV6dmm8GBAKPDmlyVkmanhlo42xwsFKHTSZViMKDe8knUr/0X2OJJ+O23ABgenPnG50IIqrtdLMmM0/UkUQiBqK9BKVssT0almCKE4PDhw9jtdoqKiiIdjjSHhXTGvm/fPr797W/LjX06OtPoobZqXNfKfZejKAqLltvYVzlK82kPpYv0LXwhhdfZZi+qAXLmycmUNPuIylcgLgFl3Rbdx1Lyi1G/9p/E/dNfowq/LvumukZ99Lv8updEp78HnH2yJLoUczo7O+nu7mbz5s2oqmzzIUVOSK++zMxMDAaZFqQXZ7+f6sPj5M6P071y3+WkphvJzDXSUOvG4575q61SZAT8gvZWL9l5JkxmefVZml3EQB/iyD6Uaz6EYgnPxSjFFodh8w0kjJxluNc148d/b7+Uzv2lTsv9UlJsOnLkCFarlUWL5GtXiqyQJlMbN27km9/8Jnv27OHEiRPn/EhXLslhoHSRhS03ZOpeuS8UC5faCATg9EnZyHe26Gz34ffBfJniJ81CYuerIATKlpvDOq6y9cPYR9sYHvAhxMymRld3uUi2Gsi16/yebaiBuHjIydd3HEmaQU6nk6amJpYuXYrJJCsQS5EVUprf9u3bgeDeqfdTFIWnn3565qOaY1SDwoIlNswWA4xEOhpItBuYX2TmTKOXwjILCYlyVTLWnW32YotXSY3AXjxJ0pPweRG7t8PSNShhLqCgOFKwp1loU6y4+4expSXNyHGD+6XGqMjUv++fqD8JxQvPaUgsSdHu2LFjGAwGli5dGulQJCm0ydQzzzyjdxxSlClbbKXtjJe6Kjerr9a3LK+kL9eYRl+3n7LFVrnBU+g2VgAAIABJREFUXJp1xME9MDI0o+XQp8K+ajFUw/Ceg9huu35Gjtk+7MXpDlCRpXOK3/AgdLWjbJiZuCUpHFwuFzU1NZSXlxMXp/OeQkkKgbwUJV2Q1aZSUm6ls83HQJ8/0uFIV6CtxQvAvEKZCiHNLpPl0LPnwcJlEYkhqTjY22a4vg3h9czIMcO1X4qGGkDul5JiS1VVFYFAgBUrVkQ6FEkCLrMy9fDDD1/2SvYjjzwyowFJ0aOozEJLg4ea4+NcvTVBrmrEICEEZ5u9pGUaiYuX6ZpzkTh9Eu1/foJ631dRUtIjHc7MajoFZxpQPnNfxD6fzBYVq8nPsCkd8fZbKJtuuuJjVne7SI0zkpWg7wUQUV8DJjMUlOg6jiTNFJ/PR1VVFYWFhaSkpEQ6HEkCLjOZ2rp1a7jikKKQ0aSwYImVqkPjdLX7yM6TxQtiTX+PH9eYxoIlssz9XCX2vgHNp9G++xjqV/8dxTR73sei8hWwxaOsvzaicdjTrIyMliBeexJxzQ1XtP9ICMGJbhcrcsKxX6oGCstQjHLVWooNdXV1uN1uVq5cGelQJGnSJSdTW7ZsCVMYUrSaV2im6bSH2io3mTkm1CioNiiFrqvdh2qArDx5sjQXCU1DVB+CzFw404D45ffgLz8/K1aZxeAA4vBelGtvQbHaIhqL3WGg15JBoKcb9fgBWLF+2sdqHfIy5AnoXxLd7YLWJpRtH9d1HEmaKZqmcfToUTIzM8nJyYl0OJI0Se6Zki5JVRUWLrUxNqLR2uSNdDjSFA0NBkhyGDAaY//kWZqGM40wMoRyyydRbv0UYu+biJ1/inRUM0LsehU0DeXabZEOBbvDgEBhLHcx2msvXNGxqrvHgDDsl2o8BUJDKV2s7ziSNEOam5sZHBxkxYoVs+KCkDR7yMmUdFmZOUZS0w2cOuHG75vZXiqSfoQQDDkDJCXLvVJzlag+CIqCsmQlyoc/BUvXIH79w2B6VwwTfh9i13ZYsgolI/JXqO2O4HtsZPWHoaEW0Vg35WP0u3zsbB7ijcYhMuJNZCbom44p6k+CokLxAl3HkaSZcuTIEex2OyUlco+fFF3kZEq6LEVRWLjMhtcjaKiTjXxjxdioRsCPnEzNYaLqUHBPTGISiqqi3vsApGaiff/fEc7+SIc3beLwPhhyRqwc+gfFJ6ioBhjJXAhxCSGtTvW7fOxqGea773Txty81cc8LjXxrXyc9oz4+vjhV95hFfQ3ML0KxytLSUvTr7Oyks7OT5cuXo8qeaFKUuegr8utf//rkn3/3u9+FJRgpeiWnGsmZZ6LplAf3uBbpcKQQDDkDgJxMzVViyBmsdFexevI2JS4B9f5/Bo87OKHy+SIY4fSJN18O7gNbtDzSoQDBdOhEu4HhUQVlyzY4+jaiu+OcxwyM+8+bPD2+t4M9Z4bJtZu5Z2UGT9xcwM8/XsqNpQ5d4xU+HzSflil+Usw4cuQIFouFRYtkGX8p+ly0AEVHRwderxez2cwrr7zCJz7xiXDGJUWh8qVWOtt9nDrhZtkaeTUz2g07AygqJNrlZGouEieOAKAsXX3O7UrufNS7vxicTP36/6H8+d9FIrxpE831wYnAp/7miqrmzTS7w0B3hw+23gKvPU//63+k5ppPcqLbxYkeF+3DwT2ncSaVxRk2bixNoiIzngKHBUO4C/ucqQefV/aXkmLC4OAgjY2NrF69GrN59lQjlWaPi06m1qxZwxe+8AUyMjLwer38y7/8ywUfJ/tMzR3xCQYKSiw013soKrOQmCRP0qPZ0GCARLsB1SA36s5FovogOFJgXtF59ymrNqDc/HHEn/4HLb8EddONEYhwekTlK2CxoWyIrtYdxnjwegQ/POnh+NVfp12Jh70dxJlUFqXbuKEkiSUZ8RQmR2Dy9AGTe+bkZEqKAceOHUNVVZYti0xjbkm6nItOpu6//37q6uro6emhoaGBa6+NbB8PKTqULrTQfNpDZ7tPTqai2ETxicwcWRJ9LhJ+P9QcQ1l9DZqm0dnZSXZ2NgbDe+9Z5bbPIFobEc/9AJGbj1JcHsGIQyOGBxGHdqNsvBHFFtnVcY9f41D7KFXdLk50uwiMwC2GFOrOuslOs3Pdgd9TUVFC8Uc/GvHJ0weJ+hrIykNJTIp0KJJ0SePj49TU1FBeXk58fHykw5GkC7pkn6ny8nLKy8vx+/2y55QEgMWqEp+oMtjvj3Qo0iW4xwVej5D7peaqhhoYd6FUrObosWPs3buXuLg4lixZwuLFi0lMTERRDah//Y9ojz6I9v1/R33oCZSk5EhHfkli13bw+1G23hKxGEY9Af5Y7+SVU06G3AFsRpVFGTaW5MdBLdy/NIvShVYCDW7Y9TvUbTeDxRKxeD9IaAFoqEVZfXWkQ5Gky6qursbv97NixYpIhyJJF3XJydSErVu3cuLECXbt2oXT6SQ5OZlNmzaxZMmSkAbp6+vjmWeeYXBwEEVRuP7669m2bRujo6M88cQT9Pb2kp6ezgMPPEBCQgIAL7zwApWVlaiqyt13383y5cGNxk1NTTzzzDN4vV5WrFjB3XffjaIo+Hw+nn76aZqamkhMTOSLX/wiGRkZ0/xnkS4lOdVAT6cfIYTs9RClZPGJuU1UHwKjERYu49TzL5CcnExSUhIHDhzg4MGDFBYWsnTpUubNm4d6/9fQHvsK2vf/A/XBf0UxRudqpvD7gz2yFq9AycoL+/i9Yz5eqhvgtYZB3H7Bqpx4ProwhSUZcZMrT6+3DDEyFHzvqTd8DO0/DyD2vxksShEt2lthfAxk8Qkpyvn9fo4fP05BQQGpqfpXuJSk6Qpp9+6bb77Jk08+icPhYO3atSQnJ/Ptb3+bN954I6RBDAYDf/7nf84TTzzBo48+yvbt22lra+PFF1+koqKCp556ioqKCl588UUA2tra2LdvH9/61rf4+te/zo9+9CM0LVhB7oc//CGf+9zneOqpp+jq6uLYsWMAVFZWEh8fz3e+8x1uueUWfvnLX07n30MKQXKqEa9HMD4mq/pFq4nJlF2mYs5JouoQlC1hcNxNX18fS5Ys4SMf+Qif/exnWblyJR0dHbz44ov8/Oc/51jvIJ7P/C001CB++6NIh35R4uh+GBwIezn0M4MentzXwed+38grp5ysz0vk29sKePjaeSzLij8nhc/uMDA8GHzvUboICssQr70YXA2KEqL+JIAsPiFFvbq6OsbHx+WqlBT1QppMvfTSSzz00EPceeedfOhDH+LTn/40Dz30EC+99FJIgyQnJ1NUFNwEbbPZyM3NZWBggIMHD7J582YANm/ezMGDBwE4ePAgGzZswGQykZGRQVZWFg0NDTidTsbHxykrK0NRFDZt2jT5O4cOHZpMRVy/fj0nTpxACNlgVg+OlOAJunMgek4QpHMNOf0kJKoYTXLlcK4RvV3Q1YZSsZqGhgaAySaXdrudq6++mnvuuYcbbrgBm83G7t27+emxOirX3kT327vR9oZ2kSzcROUrkJ4FS1bpP5YQnOxx8a9vneXzf2hmX+sI28qS+cFHinng6hwKkq0X/D27w8DosEYgEFy1V2/8GPR2wdF3dI85ZPU1kJwGqTJzQ4peQgiOHj1Keno6eXnhX4mWpKkIKc1vZGTkvBdzTk4Oo6OjUx6wp6eH5uZmSkpKGBoaIjk5mKOfnJzM8PAwAAMDA5SWlk7+TkpKCgMDAxgMhnOWelNTUxkYGJj8nYn7DAYDcXFxjIyMYLfbpxyjdGl2hwHVAM4+P7nzZZnSaDQ0GCA1LaS3tzTLiKpDQLAkev0bO8jOziYxMfGcxxiNxsk9sb29vVRXV3Oqro7aknVk7HmbpQEDZRs2YjRGx2tItDYG9/l88l5dy6FrQnCgbZTna/o51efGbjFw59I0bi5Lxm65/Cqv3WFACBgd1oIptivWQ3oW2vbnUVdeFfG0aCEEor4GZcGSiMciSZfS3NyM0+nkxhtvlK9VKeqF9E1ZXl7Oz372Mz7zmc9gsVhwu9386le/oqysbEqDud1uHn/8cT772c8SF3fxSkwXW1G61ErThe670BvwjTfemExP/Pd//3fS0tIuF3bYGI3GqIrnUtIzPIwOi5iJd6ZF83PlHg/gdg2SnWcnLS26CwqESzQ/XzPNeeo4gZz5iPRs+vr6uPnmmy/5d09LS2PhwoW43W6Ovr2ft1/7E28cq2ZP7WlWrlrFmjVrwrpf4ULP1dBzP8BtsZL2kU+ixide5Denz+vX2F7Xw6+OtNPqHCfHbuHBLcVsW5SB1RR6qqxR9XJkfysiYCMtLXghz3XbZxj54eMk9XZgXhTZ0s7+zjb6hwZIWLGWuBl6P8yl99ZsEAvPlxCC3//+9yQlJbF+/fpzqpDOJbHwXElBIU2m/vqv/5onn3ySz372syQkJDA6OkpZWRlf+MIXQh7I7/fz+OOPs3HjRtatWwdAUlLSZEELp9M5uYqUmppKf3//5O8ODAyQkpJy3u39/f2kpKSc8zupqakEAgFcLtdkMYv3u/7667n++usn/7+vry/kv4Pe0tLSoiqeS4m3C1rqPfR0987JPkbR/Fz1dvkAMJrdURtjuEXz8zWThMeNVn0EZcs2Dr2bAp2dnR3y3710SQXFiTbavvMYJ3LLePvtt9m3bx/z58+noqKCwsJCVJ0b5X7wuRIjQ2i7XkO55noGxj0w7pmxsca8AbbXD/LSKSfOcT9FyRb+8eocNsxPxKAqjA45mUr+haYJVAO0tw3hSAs26RXL1kNCIs7f/RTD3319xmKfDu3AXgDGsvNxzdD7Ya68t2aLaH++BgcH2bFjB62trWzatAmn0xnpkCIm2p+ruSYnJ+ei94U0mUpOTuaRRx6hv79/cvIzlSuVQgi+//3vk5uby623vrd5ePXq1ezcuZPbbruNnTt3smbNmsnbn3rqKW699VacTiednZ2UlJSgqio2m43Tp09TWlrKrl27uOmmmwBYtWoVO3bsoKwseAKwePFiuTSso+RUA02nYHgwgCM1OlKBpKDJSn6OuXk1b06rPQ5+H0rFKuqPniQnJ+eCF5UuRc0vIe/jd5H7oydwXfthakuWUV1dzR/+8AcSEhKoqKhg8eLFl8wumEli92vBv9O1M1cOvd/l45VTTl6tH8Tl01ieFccXr8pmWVbcFX1vqKpCov19RSgAxWJB2XIL4g+/QXS1RaQS4aT6kxCXANnzIheDJF1AIBDgyJEjHDhwAFVV2bx5M0uXLo10WJIUkimdBaempk4r3ePUqVPs2rWL+fPn8+UvfxmAT3/609x222088cQTVFZWkpaWxpe+9CUA5s2bx1VXXcWXvvQlVFXl3nvvnbwa+ld/9Vd897vfxev1snz58skqL1u3buXpp5/mH/7hH0hISOCLX/zilOOUQpf87gTK2S8nU9FmyBnAFqdgtui7giBFH1F9CCw2BtKy6e/fNVngZ6rU9deitTQQ9+bLrC4qYfXdd9Pc3ExVVRX79+/nnXfeoaSkhHXr1k3ue9WDCASC5dAXLkPJmX/Fx2sb8vBC7QA7mofRhODq+YncviiVopQLF5SYDrvDQHeH75zblGu3IbY/j3jtRZS/+PsZG2uqRP1JKF2k674zSZqqzs5OKisr6e/vp7i4mM2bN0/5IpAkRVJYzoLLy8v57W9/e8H7Hn744Qvefvvtt3P77befd3txcTGPP/74ebebzebJyZikP6tNwWJVcA74KSR6GlJKweITSclygjvXCCEQ1Ydh0TIamluA96r4TYfy8bsRZ5sRP3sGNWc+xcXFFBcX43Q6qa6upqamhp6eHu688079ClUcewcG+lA//TdXdJgWp5tfVfVxoG0Uk0HhhpIkbluYQmbCzBfQsTsMnG324h7XsNqCkxbF7kC5aiti35uI2z6DYg//XkYx5ISeTpRNN4V9bEm6EI/Hw759+6iuriYhIYFbb711svKzJMUSeXlKmhZFUUhONTLYL8ujRxO/TzA2oslmvXNRWws4+1AqVlNfX09ubi7x8fHTPpxiNKJ+7iuQYEf77mOIkWC11Ymm7TfffDODg4McPnx4hv4C59MqXwmW8F66ZtrH8Pg1Hn7zLDU9Lj5ZkcqztxXzuTVZukykAOyO4Nfq+1P9AJQPfRQCfkTlH3QZ97JkfykpSgghqK+v5+c//zknTpxg+fLl3HXXXXIiJcWsy06mNE3jxIkT+P3+cMQjxZDkVANjoxoej2zeGy2G3j2Bk5OpuUdUBQtODMwrOa+9xHQpdgfq334NhpxoP/xPROC9CUJ+fj5lZWUcPHhQl03ioq0ZTp9AuXYbijr913Nl0xBDngBf25THnUvTSbLqu2o70Sh7eOgDk6msXFi2DrHjTwiPW9cYLkTU14DZAvOLwz62JE0YHh7m5Zdf5k9/+hPx8fHccccdbNq0CbNZtlmRYtdlJ1OqqvLNb34zavqNSNHDkRo8aZCrU9Fj+N3iE3ZZfGLOEdWHYH4xDV09KIpCcfHMnDQrhaUod90PtccRL/zsnPs2btyIyWTirbfemvEm6aLyD2A2o1zzoWkfI6AJXqwdoDTVyqIM2wxGd3Fmi4rVppy3MgUEm/iOjSAi0BhZ1J+EogUo8rtcigBN0zhy5Ai/+MUvaG9vZ+PGjdxxxx1kZMjm0VLsCynNb+HChZw+fVrvWKQY40g2ggKDA3LVMloMOQOYLQpWm6xkOZeI0WFoOg0Vq2Ykxe+D1Kuve7eIwgtoB3dP3h4fH8/VV19NW1sbdXV1MzaeGBtBvLMDZd0WlCvoK/VO2whdoz4+tiglrNVd7Q7DBSdTSslCKC5HvP77c1b59CZcY9DWIlP8pIjo7u7m17/+NXv27GHevHncddddrFixQvc2C5IULiFdokpPT+exxx5j9erVpKamnvOldMcdd+gWnBTdjCaFRLuKU65MRY2hQT9JyQbZFmCOESeOgNAYKCjHuWsfy5cvn/ExlE/eGyxI8dOnENl5KHmFACxevJja2lp2795NQUEBNtuVrwCJPa+D14uy9dbLP/hixxCCF2oGyEowsT5v5hv9XordYaC3y08gIDB8oA+fesPH0L73GBzdD6uv0T0W4XYhfvFdEAJlQYXu40nSBK/Xy/79+6mqqiIuLo5t27ZRXFwsv5+kWSekywJer5c1a9agKAoDAwP09/dP/khz20QRiplO8ZGmLhAQjAzJ4hNzUvVhSEyi3uWd0RS/91OMJtT7/gni4oMFKcZGgrcrCtdeey1er5e9e/de8TgiEEC89UcoW4KSVzDt49T0jnO6381HF6ZgUMN78mZ3GBACRocvsJ90+VrIyEHb/oLun5uirQXt3x5EHNqLcttdULZE1/EkaUJjYyM///nPOX78OBUVFdx1112UlJTIiZQ0K4W0MnX//ffrHYcUo5JTDbQ2eRkb0Uiwy5P4SBoZCiCELD4x1wgtgDh5BCpW09DQQF5enm4NdZWkZNT7/gntP/8Z7Yf/hfr5h1FUA2lpaaxYsYLDhw9TXl5OXt70G9N6Du2F/h7UT9xzRbG+UDNAosXAdUVJV3Sc6ZjYszg8GDjv/aioBpQPfRTxy+/B6ZOwQJ8Jjrb3DcSvvg+2eNQH/1WuSklhMTIyws6dO2lqaiItLY1bbrmFrKysSIclSboKeSdqW1sbb7/9NkNDQ9x77710dHTg8/nIz8/XMz4pyjlS3mveKydTkTX0bvGJJFl8Ym5pOgVjI/QVL2awqo6VK1fqOpxSXI5y5+cQP38G7aG/hcQksMWx2hpPvWrlrd+/wKfmp2GwxYMtHsVmA1s82OLAGgdxccH/N5oueJV6/A+/g5Q0WL5u2jG2DXk42D7KHRWpWIzh35cRn6CiGs6v6DdB2bAV8ftfor32AoYZnkwJjwfxq+8j9r0JCypQ//ofUZLC39dKmls0TZts6i2E4Oqrr2b58uUYDPL7SJr9QppM7d+/n2effZZ169axd+9e7r33XsbHx/nVr37FN77xDb1jlKJYol3FaAwWoZhXKEubRtKQM4DRBHEJclPvXCKqDoGq0qCYdUvx+yB1041oPh/i9Alwu8A1hrG/h03CxCsZJRx++23W9DQH47vYQQzG4ARr8iceLFYC1YdRbv8LlCs4CXuxdgCzQeGWsshMIlRVIdF+4SIUAIrZgnLtLYiXn0N0tKLkzJ+RcUXnWbTv/wd0nkW59Q6UD3/qisrKS1Ioenp6qKyspKenh/z8fLZs2UJSUvhXhCUpUkKaTP32t7/lG9/4BgUFBezfvx8I9hhpaWnRMzYpBiiqgiPFKItQRIHhwQB2hyw+MdeI6kOIkkU0tJxh3rx5M1IAIhTqdbfCdecWiCgCyl59lcMGA2V/92WSTUZwj8P4GIy7EOMuGHcF/9898eeJ28dgoA9jyUK0jTdMOy7nuJ+3moe5vjhJ955Sl2J3GOju8F30fuXaWxDb/xfx2oson/38FY+nvbMT8fNnwGRG/cL/QVm84oqPKUkTvF4vIyMjjIyMMDo6yujo6OT/t7e3Y7PZuOmmmygtLZXfQdKcE9I3zdDQ0HnpfIqiyDeMBAT7TTXWeQj4BQajfE1EgtAEQ4MB8ostkQ5FCiMx0AttLfTdeidDZ3pYvXp1pENi48aNtLS0sOPtA3zsYx8753silE+H1LQ0+vr6pj3+K6ecBDTBR8tTpn2MmWB3GDjb7MU9rmG1nb9arCTaUTZcj9jzGuK2u1Ac04tX+LyIXz+L2PUqlCxC/ZsvoySnXmn40hzi9/snJ0cfnChN/Nnr9Z73e/Hx8SQkJLBs2TLWrl2L1WqNQPSSFHkhTaaKiorYtWsXmzdvnrxt7969lJSU6BaYFDuSU40I4WHIGSAlXTaEjITREQ0tIPdLzTWi+jAADdYkVLWPoqKiCEf0Xu+pt956i7q6OhYuXBi2scd9Gq/WO1k/L4Ece2TTju2O4ARqeChwwckUgPKhjyB2/glR+QrK7X8x5TFET0cwre9sM8qNt6PcdpdsyitdVGtrK3V1dXR3d58zURofHz/vsVarlcTEROx2O7m5uSQmJpKQkDD53/j4eLkfSpLeFdKn7t13382//du/UVlZicfj4dFHH6Wjo4OHHnpI7/ikGOBICX6gOvv9cjIVIZPFJ2QlvzlFVB9CpGbQ0Nkd1hS/y1myZMmM954KxRuNg4x6NW5bGPmVGXvSexX9MrJMF3yMkpEDK64KTqi2fQLFGvq/kzi8F+2nT4FqQP37b6AsWzMjcUuzU2NjI3/4wx8AMJvNkxOj9PR0EhMTz5ssGeWkXJJCFtK7JTc3lyeffJLDhw+zatUqUlNTWbVqlVzSlQCw2lRscQrOAblvKlKGnAFUAyTYZfGJuUL4vFB7nN611zI8NMzatWsjHdIkRVHYunUrv/71r9m7dy/XX3+97mMGNMFLdQMsTLdRnh75SaXZomK1KRctQjFBveE2tCP7EHteR7n+I5c9rvD7EP/zU8SbL0NhGernvoKSmjFTYUuzUCAQYO/evSQnJ3PfffcxNjYW6ZAkaVYJ+czLYrFQXl7OokWLWLhwoZxISecINu/1RzqMOWtoMIA9yYAa5uakUgSdqgavhwZ7BqqqRkWK3/tN9J6qqamhvb1d9/H2to7QM+bnYwsju1fq/eyOi1f0m6AUl0PJIsQbLyECl36s6OtG+49/Qrz5Msr1H0H9ymNyIiVd1okTJxgcHOSaa66JmtVrSZpNQlqZ6uvr46mnnqK+vp74+HjGxsYoKSnh85//POnp6XrHKMUAR6qBjrO+i262lvQjhGDYGSBn/oVTiaTZSVQdQpjNNDiHmT9/flRe4Fq7di319fVUVlby6U9/WrfUISEEL9b2k2s3syYvQZcxpsPuMNDb5ScQEBgMF7/Qod74MbRnHkUc3ouydtMFHyOOvYP2kydBCNT7/gll1Qa9wpZmEY/HwzvvvENeXh4FBQWRDkeSZqWQznqfeeYZioqK+MlPfsKzzz7LT37yE4qLi3nmmWf0jk+KEcmpE8175epUuI2Pafh8ArssPjFnCCEQ1YfoXrCSkdFRSktLIx3SBZlMJrZs2YLT6eTIkSO6jVPd7aJxwMNtC1NQo6jKrN1hQAgYHdYu/cClayArF7H9BYQ4tzOX8PvR/ucnaM88CmmZqA89ISdSUsgOHTqE2+3mmmuukRWYJUknIU2mmpqauOuuuyavfFqtVu666y6ampp0DU6KHUkOA4oCg3LfVNgNDcriE3NOVxv0ddOYlheVKX7vV1BQQGlpKQcPHmRwcFCXMV6oGSDJamBLoV2X40/XxAWOy6b6qSrKh26D1kaoq5q8XQz0of3XPyO2v4Cy5WbUf/omSka2rjFLs8fIyAjHjh2jvLycjAyZDipJeglpMlVaWkpDQ8M5tzU2NlJWVqZLUFLsMRgV7A6DbN4bAUPOAIryXvUwafYTVYcQQIPLS35+PhZLdPcX27RpEwaDgbfeeuu8lZcr1eJ0c6RzjFvLkjEboivFOD5BRVWD5dEvR7nqWkhMQnvtRQDEicNo//pFaDuD8lcPon7mb1FMkS33LsWW/fv3A3DVVVdFOBJJmt0umsD+m9/8ZvLPmZmZPPbYY6xcuZLU1FT6+/s5evQo11xzTViClGJDcqqBsy1ehCZQZCGEsBlyBkiwq7Jh8hwiqg/Rnb+AUZeLDVGa4vd+8fHxbNiwgR07dnDq1CnKy8tn7Ngv1g5gMSjcVJY8Y8ecKaqqkJh0+SIUAIrJjLL1VsTvf4n2s6cRe16HnPmo930VJSsvDNFKs0lPTw91dXWsWrWKxMTESIcjSbPaRS/j9ff3T/74fD7WrVuHyWRieHgYk8nE2rVrL9gRW5q7HKlGAn4Yudz+AGlGDTkDMsVvDhGuMWiooSG7GIPBQGFhYaRDCklFRQWZmZns3r0bt9s9I8fsc/nY1TLM9SUO7JbofA+EUtFvgrLlZjBbELtfQ9lwHerX/ktOpKQpE0KwZ88erFYrq1e/gJNvAAAgAElEQVSvjnQ4kjTrXXRl6v777w9nHNIskJz6XvNeWQwhPNzjGh63IEn+e88dNUcRgQANfiUmUvwmKIrCddddx3PPPcfevXu57rrrrviYr9Q5EcBHy6NvVWqC3WHgbLM3pEqnSoId9a8eBC2AsurqMEUozTYtLS20tbWxefPmmPl8kKRYFnKdWo/HQ1dX13lXFBcsWDDjQUmxKT5BxWRWGBwIkF8c6WjmhoniE/Zk2a1+rhBVh+hKyWLM44naKn4Xk5aWxsqVKzl8+DDl5eXk5uZO+1guX4DtDYNsmJ9IZkL07iWyO4ITqOGhQEhtI5QV6/UOSZrFNE1jz549OBwOlixZEulwJGlOCOkMbOfOnfz4xz/GaDRiNp/7pfW9731Pl8Ck2KMoCo4UgyyPHkZDzncr+cmVqTlBaBrixGEaildgIHZS/N5v7dq1nD59msrKSu68804Mhum9drfXD+LyadwWRU16L2SiMMzwYICMLNkLTtLXyZMncTqd3HLLLdN+b0mSNDUhTaZ+8Ytf8OCDD7J06VK945FiXHKqgdMn/fh9AqNJFkTQ27AzQNy7K4LSHHCmEW1kiAbVSsH8/PMubsUCk8nEtddey0svvcSRI0dYs2bNlI/hCwhernOyJDOO0lSbDlHOHLNFxWpTQt43JUnT5fV6efvtt8nJyYnqdgmSNNuEVEfWaDSyaNEivWORZoGJ5r2DA3J1Khxk8Ym5RVQfpCs+GZfPH3Mpfu9XUFBASUkJBw4cmFbvqd1nhukf9/OxKF+VmjCVIhRzmghgG9xLSuuTWIcPwQyX0Z/tDh8+zPj4uGzQK0lhFtJk6o477uBnP/sZw8PDescjxThHykQRCnnioDefV8M1pskUvzlEVB2iYd4CjEYjBQUFkQ7niky395QQghdrB5ifZGZVTryOEc4cu8PA6LCGFpCTgwsSAvNYHSmt3yax7xWUwDj2nv/F3vVLlMBYpKOLCaOjoxw9epSysjKysrIiHY4kzSkhpfnl5OTw29/+lu3bt5933/v7UUmS2aISn6DilCtTupsoPiFXpuYGMeREO9NA4/IiCgoKYjLF7/0SEhKm1XvqaOcYZwY9fH59Vsxcfbc7DAgRbBsh36/nMni7Sej7AxZXPX5TGoPZf4k3rgzb4B4S+l/D1PokIxl/hjd+5nqTzUZvv/02mqaxYcOGSIciSXNOSJOp73znO2zatIkNGzbE/Be4pD9HqoG+bj9CiJg52YlFk8Un5MnZnCBOHKYzPhlXQIvpFL/3W7JkCbW1tezevZuCggKsVutlf+eF2gFSbEY2FSSFIcKZMdEqYnhQpuVOUAJjxA+8gW3oAEI1M5J2C+NJ60EJnpaMJ2/CG1dKUvdvcHT+Ny77OkbTtoEqz0E+qLe3l5qaGlauXIndbo90OJI054Q0mRodHeWOO+6QJ8ZSSJJTjbSf8THuEsTFy9eMXoacAaw2BYs1pGxdKcaJqkM0ZMyfFSl+E1RVZevWrfz6178OqfdU44Cbqi4Xf7k8HZMhdj5b4hNUVDVYHn3OEwFsQ28TP/AGiuZlPGktYynXIwznp2wGLNkM5P0dCQOvYxvcg3m8keHMT+K3zotA4NFJNuiVpMgL6Sxsy5Yt7Nq1S+9YpFlionnvoCyRrqthWXxizhB+H1rNURoT0yksLMRkmj0lttPT01mxYgUnT56ko6Pjko99sWYAm1HlxlJHmKKbGaqqkJg0x4tQfGBflM8yj4F5n2c0/aMXnEhNUk2Mpm1jMOdeFOEjue37xA28CWIO/1u+z5kzZzh79ixr164NaWVXkqSZF9LKVENDA6+++irPP/88Dse5X2KPPPKILoFJscueZEBVg0UocuZHOprZye8XjIxoZOXNnpNq6RLqa+gwWBkXzJoUv/dbt24d9fX1VFZWXrRybM+ojz2tw3x4QTLx5ti7iGB3GOju8EU6jIgweN7dFzX+/n1RC2AK2S6+uGIG5n2BxN6XSBh4A8vYKYYzP0nAnKZj5NFN0zT27t1LUlISFRUVkQ5HkuaskCZT11133WXTLyRpgmpQSEo2yCIUOhoZCoCQ+6XmClF9iIbkbExGI/n5+ZEOZ8aZTCa2bNnCyy+/zN69e1m8ePF5j3mpbgAF+HB5bJRD/yC7w8DZZi/ucQ2rbW6k5p6/L+rWd/dFTe9zSxhsDGfdgWdkIYm9L5Jy9ilG0m7FbV8zpYnZbFFbW0t/fz/btm2TDXolKYJCmkxt2bJF5zCk2SY51UhLowdNE6jq3PuS09t7xSdCegtLMS5QfYjGtAUUFhXNqhS/9yssLKSkpISdO3ficDjIzc2dvG/UE+D1xkE25ttJj4/Nv7/dEZxADQ8FZv9kSvjf3Rf15mX3RU2HJ3EpPls+9u7/wd77Apb/n73zjo+rvPL+997pvalbstx7keVeMC64G2ODKQmkENhN2xTS3n333dTNZrPZZEkBsmkkbDZZIIAL7g1s3Ivk3uRu2epTNX3m3vcP2cLGsq0+I+l+Px99DDNzn3tmbnvOc875neAp/FmPIqst7TJ+V+Bmg97c3Fz69++fanMUFHo0zZqJbdu27a7vzZo1q92MUeg+2F0qpLMN6lV2pzLhb298niQarYDBqDiq3R25uoJr9WEimWK3TPG7lRkzZrBy5UpWrVrFwoULG4U21pd5iCRklg7rmlEpaEh/hoZ7YlZO13QI74ssow2dwVy7FnW8lqhhIPUZi0jqstt9V5LahjfvWQy+PZjrNuC6+gv8WY8SMzWdJtrdKC0tJRgMsnDhQkUcTEEhxTRrlvvBBx/c9v9er5fKykqGDBmiOFMKTfKhCIXiTHUEvhviE8pDtPsjHzvIOVt2t03xuxWj0chzzz3HH/7wB9asWcP8+fMp7NuPNWc8FOWa6OvougX2Wp2I3iB0WxGK2+uiMltVF9ViBJGwfSox4wCsVW9ir/gzYes46jMWI4u6jttvigkGg5SUlDBgwAByc3NTbY6CQo+nWbPc7373u3e8tm3bNq5du9buBil0DwxGEZ1ewONO0Ifu+1BLBZIkE/Al6TtQ+V17AomjBzlvz6Zf//6o1d1/YcJkMvHYY4+xatUq1q9fT87IKXgjJpYN7bpRqZtY7d1Q0S8ewFyzCoNvH7Koa3NdVGtIarPx5H8ek3srRs92tKEL+LKfIGHonosPe/fuJZlMKg16FRTShFYnbs+YMeOe6X8KPRtBELA7VXjqutnEIQ2o90tIElgV8YlujxwJc+1aOVFR3e1T/G5Fp9OxdOlSevXqRcXRXYwWKxidY0y1WW3Galc1XL9JOdWmtAu6wBGEw/+MwbefsG0SdYXfIGyf2qmOVCOCmqBrHt5efw/IOK79BlPdpm4noV5XV8fJkycZNWrUHerKCgoKqaFZzpQkSbf9RSIRtmzZgsnUPsWkCt0Th0tNMCARi0mpNqVb4fM0qCQqSn49gNNHOGfOQKtW0bt3z+ozoNVqyRs7kxpNJhk1xygpKUm1SW3GalchyxDwd4N7ohTDUrMS9Jm4e3+Z+swl7SYw0Rbihj64e3+ZiKUYk+c9HOW/RhWrTrVZ7cbOnTvRarWMHz8+1aYoKCjcoFk5Ix/72MfueM3pdPLZz3623Q1S6D7cWjeVldvN1as6EZ8niUoFZrPym3Z3EkcOcMGWRd9+PSPF76OsOuOnOnsskw3n2bVrF7FYjEmTJnXZWkGr/YYIha/rN9zWBw4jShGkwidIxtIrBVMW9QSylxMzDcFSvQLn1V9R71pA2Da5S0uoX7lyhcuXLzNt2jQMBkOqzVFQULhBs57OL7300m3/r9PpsFqtHWKQQvfBdkN4wutOkpXbTdWrUoDPm8RqVyEokvPdGlmWuXruHNHM/gwaPDjV5nQ6Z2rDnKwJ85niLOYPHsA2jYYDBw4Qi8WYPn16l3SoTGYRUaTr103JMgbfHuLaXFSWgVBXl2qLmiRqHkFcX4il+i0ste+iDZ3Fn/0UsqrrCZlIksTOnTuxWq2MGjUq1eYoKCjcQrOcqczMzI62Q6EbotEIWKwinjqleW97IcsyPk+Sgj7aVJui0NGUX+KcyoBWJVJQUJBqazqdlafcmDQicwbYEEWR2bNno9VqOXz4MPF4nFmzZiGKXSs6K4oCFlvXF6HQRC6hiVXiz3wUc5o7tZLagi/30w0S6rVrcZT/Gl/eJ0lqXKk2rUWcOXOG2tpa5s+f3yOj1AoK6cw9r8jvf//799xYEAS+853vtKtBCt0Lu0tN5bU4six3yZXkdCNYL5FMKPVSPYHEkf1csGXSr0+fHjd5qgjE2Hs1wLKhToyahnNdEAQeeOABtFot+/fvJx6PM3fuXFSqrnUtWO0qqq7HU21GmzD49iCJeiKW0ZhTbUxzEATC9ikktNnYKv+C4+rL+HKeIW7sl2rLmkU8Hmf37t1kZ2f3KCEaBYWuwj2f0A888ECTr7vdbtavX080Gu0QoxS6Dw6XiqsXY4TqJUyWrjXpSUd8noYVbcWZ6v5cOXmCmD6DQcNHpNqUTmfVKTeiAIsGO257XRAEJk2ahEajYdeuXcTjcRYuXNilnE2rveGeGAlL6A1dK7IGICZ86OpPNKj2iV0rQh439seT/wVsFX/Gfv0PBDIfIWKbkGqz7svhw4cJBoPMnz9fWZRUUEhD7vkE+mhD3kAgwIoVK9i6dStTpkxh+fLlHWqcQtfH4Wo4xTx1ScWZagf8niSCCBar8lt2Z+R6P+fCcXRGocel+HnDcbZe8PFgHxsuY9O1lmPHjkWj0fD++++zevVqFi9ejFbbNSb2VnuDA+X3JbukM2Xw7QdkwraJqTalVSS1GXjyP4+16n+x1qxAHauiPmNhauTcm0EoFOLgwYP079+fXr16pdocBQWFJmjWcl4oFGL16tVs3LiR4uJi/v3f/52cnJyOtk2hG2CxiqjU4KlLkK/U+bQZnzeJxapCVCmrk92Z+NGDXLRm0r9XXpdLY2sr7xypIJaUWTrs3gpxo0aNQqPRsGXLFlauXMmSJUvQ69NfWMBqu6Ho502SldPFhHnkBAb/fmLGQV2u5uhWZJUeX+4nMdeux+jbhSpegz/742kpTLFv3z6lQa+CQppzT2cqFouxdu1a1qxZw7Bhw/jBD37Q41ZJFdqGIArYnWq87q5dcJ0O3BSfyM7rYhMwhRZz9WgpMZWGgUXFqTalU4kmJN4+ep1xeSZ623T3/fzQoUPRaDRs2LCBd955h6VLl2I0pndzX61ORG8QuqQIha7+OGKyvkFivKsjqKjPXExCm42lZiWO8lfw5X6SpDYj1ZY14na7OX78OKNGjcLhcNx/AwUFhZRwT2fqi1/8IpIksWTJEvr374/P58Pn8932mREjel4+v0LLcDhVnD8bJZmUUSkRlVYTCcvEorJSL9XNkZNJyuq86CwZFPSgRr1HK4P8qbQabzjBsql5zd5uwIABPPzww6xdu5a3336bpUuXYrFYOtDStmO1d01FP6NvDwmNi5ix+4ggRGzjSWpd2Cr+0uBQ5TxN3Ng/1WYBsGvXLjQaDRMmtL2uS0iG0YQvgK1nLdAoKHQG93Smbuagb9q0qcn3BUG4oweVgsJHsbtUyFKDeIIzo+sUiqcbivhE+nCsKsjbJ9x8c1oeJm37Ho9E2UkuGh0MzMroESl+V31RXiut4cC1ejKNar4/fzAjXC1bdCksLOSRRx5h9erVvP322yxbtgybzdZBFrcdq11FTWUCKSl3mZRddeQamsgVAhmLQeh6tV73Im7oh7vgi9grXsN+/VUCmUuIpLgmrLy8nIsXLzJlypTWN+iVZdSRKxj8B9DXH0WQ48jVf8Nkm0LI/gCySmn8q6DQHtxzZvvyyy93lh0K3ZibIhTeuoTiTLWBm87UzZoLhdSx4qSb0oogr5ZU86VJue069uVD+4ir1AwsHt+u46Yb3kiC/z1ay6ZzXvRqkU8WZbJ4sINeOZnU1ta2eLxevXrx6KOPsmrVKt566y2WLl2Ky5WedT1WuwpZhoBf6jKLIwbfHiRBS8TSPSMbksbZIExR+TrWmpU3hCkWpUSYQpZldu7cicVioaioqMXbC8kw+kApBv8B1LHKG8dtDFHTMGzR45jc72Hw7SFkn07YPgVZvH9KrYKCwt1RZrYKHY7eIKI3CniUuqk24fMkMFtE1JqusZLdXXGHE5RWBHEZ1Gw572NygYVxvdqv207Z9Sr0WhMFAwa025jpRDQhsfq0m7dPuIklJRYMtPPkyAxs+rY/jrKzs3nsscdYsWJFY8pfVlZWO1jdvljtN0QofMku4UwJySD6+iOELWO7dTRDFm8IU9RtwOj9AHWsBl/Oxzv1O8uyzLFjx6iurmbu3LnNl/1vjELtR19/DEGOE9f1wp+5jKhldKPDJBdOxXNtMqa6zZjdmzD6dhN0PEjYOhFEpR5XQaE1KM6UQqfgcKrx1CnOVFvweZO4lMheytl+0Yckw3dm5vOzXdd5eV8lv1rUF7Ou7ZPieFUFF9UGBtstiGL3SqWSZJn3L/r5nyM11IUSTMw388kxmeRb23dV3OVysXz5ct555x3eeecdlixZQl5e82uwOgOTWUQU6TJ1Uwb/QQQ5Qdg2KdWmdDyCSH3GQhLaLCzVtwpTZHb4rqurq9m1axdXr14lNzeXwYMH39/cxijUftSxKiRBR9hSTMQ6noS+aSn1hC4PX96nUEeuYK7bhKV2LUbPBwSds4hYx6WtTLyCQrqizMwUOgWHS0VFeZxoREKn716TxM4gGpWIhGSsXWAVuzsjyzLvXfAzOENPH4eer0zO45sbL/H7Q1V8dUrbJ+yX9u4koVIzcHTLU3vSmSOVQf5YUs1FT5SBLj1fn5LH8OyOU92z2+08/vjjvPPOO6xcuZLFixfTO43EPERRwGLrIiIUsoTBt5eYoR9JXce3RKmurmbr1q1MnjyZPn36dPj+7kbEOo6k5qPCFB0TLfb5fOzZs4ezZ8+i1+uZPn06I0eOvHuD3tuiUEcR5ARxXT7+zEeJWkY1O20voe+Nt9fzaELnMbs3Ya1Zicmzg6BzNhFLUberjVNQ6CgUZ0qhU7Df0rw3p5dyg24pfkV8Ii244Ily2Rfls+oLJL/yT/QVBR7Ln8Xf5KlM/OAvTIhcBbWm4U9z41+1GuHGv7e9p1Lf9jlZpebM+fMYBC29RnYPZ+qKN8qfSqs5dD1IlknN16fmMa3Qgni3SWI7YrFYWL58OStXrmT16tUsXLiQfv36dfh+m4vVrqLqejzVZtwXbfA0qoS3oX6og4lGo6xfvx6fz8eaNWuYM2dOs6IzHUXc0Bd3wRewV/w39ut/pD5zcbvKwofDYQ4cOMDRo0cRRZFx48YxduxYdLqmnaGmo1BjidgmkNC1fjEnbuyPx/A5tKEzmNybsVb/DaNnO0HXQ0RNwxWnSkHhPijOlEKnYHOoEATwuhPk9FLysltKo5KfXXGmUsm2ExVo5ART338NRhUhWB08nghzQPLzm7w5DPVvwpIIQTwOsSiEgpCII8fjkExAIt7wXuLGnyQREdWcduZx3JmPT2dktEnT5VX8POEGcYnN570Y1CKfGtMgLqFVde6kzGQy8dhjj7Fq1SrWrl3LnDlzGDJkSKfacDesdhVXL8aIhCX0hvSdrBp9e0iqbURNQzt0P7Iss23bNvx+P0uWLOHQoUNs3LiRaDTKqFGjOnTf96JBmOJzWCvfwFKzGlWsmvqMxW1KhYvH4xw+fJhDhw4Rj8cZNmwYEydOxGxuovZSltFELqNvrIW6EYXKepSouflRqPsiCMRMQ4gZB6ELnsBUtwVb5V+J6/IIOucQMw6GTlgEUVDoiijOlEKnoFY3pLUodVOtw+dJYjAKaHXpO+nq7sQP7WbHeR3j/ZewfOZLiOOnAaADvuKO8I0Nl/hD0TN8vZk9kqqrqzl65Ahny8pIJBLkZmUyceAABozqulGpSEJi1Sk375ysI56UWTjIwZMjXFjbQVyitej1epYtW8aaNWvYtGkT0WiU0aNHp8yem1jtDdey35dMW2dKFatGGz5HvXNuh9fRnDhxgrKyssb0vvz8fNavX8/7779PNBpl3Lhxd09762AahCk+galuIybvjhvCFE+3WJhCkiROnTrF3r17CQaD9O3blylTpjSpOtkQhSq5ocjXflGo+yKIRM0jiZqGow8cxuTeir3iNWL6QoLOOWnTg0tBIZ1QnCmFTsPhUnHtSgxZllP2UOyq+LxJbA7lck0FcjyG/OarHDx2Cf/ITzProQmIwwpu+0w/p54nRmbwv0drmVJgYXLvppvGJhIJysrKOHr0KFVVVajVaoYMGcLIkSPJzOz4AveOIinJvHfRx1+O1OIOJ5hcYOaTRVnkWbWpNg1o6Jm4ZMkS1q9fz/bt24nFYimdnMOHLQ783iRZOekZrTf49iKjImztWJn+2tpatm/fTu/evRk3bhwAarWahQsXsmXLFvbs2UMkEmHatGmpO2aCSDBjAUltFpbqFTjKX8aX+6lmCVPIssylS5fYtWsXHncdvXtlsHTuBHJcJsTkNUTvWcRkPUIyiJgMIibr0UTLOy4K1RwEkYi1mIhlNHr/IUzurTiu/56YoT/1rrkk9OlTg6igkGqU2ZlCp+Fwqbl8Pka9X8Ki9EpqNom4TDAgkV+YHhPTnoRceQ3pNz+B8ou8P/Nb2LQqiofkN/nZ5cNd7Lsa4Nf7KxmWZbhN6tvn83H8+HFOnDhBJBLB4XDw4IMPMmTIkLvWR3QVDlc0iEtc8jaIS3xzWh7DsjpOXKK1fHRyHo1GmTp1asom51qdiN4gpK0IhSBF0PsPEbGMQla3n/T/R4nFYqxfvx69Xs/cuXNvOx4qlYq5c+ei1+spLS0lGo0ya9aslCpdRqxjG4QpKv8HR/kr+HM+RlxXgJisb3SEhBv/iskgsWAdQU8FfQgzoljGqJEQhEqIHofrH44rIyCLRiS1CUk0EbaOa1Dk68goVHMQVERsE4hYxmDw78PkeR9n+a+JGocQdM1JvX0KCmmA4kwpdBp2V4MD5alLKM5UC/B5FfGJVCDteQ/5L78GjYb6z32bg2ctLOxrRSU2PflWiwJfmZzL1zdc4jcHqvjmtDwuX77M0aNHuXTpEoIg0K9fP0aNGkV+fn6Xj85e8UX546FqSiqCZJk0fOOGuEQ6f6+bk3OtVktJSQnRaJSZM2embHJutaevop/eX4Iox9pVcOGjyLLMe++9h9frZdmyZRiNdzrhgiAwffp09Ho9+/btIxqNMm/evOb3X+oA4oY+uPO/2ChMcTeiSTWJiIyUVIM5Bxw5hNQWJJX5xp/pxp+5IWUwnSXJRQ1h+zQi1vEYvHswerfjvPorIuaRBJ1zOkU6XkEhXVGcKYVOo6HhbIOiX+/0EdVKe24q+VkV8YlOQY6Ekf/6G+Q922DgMMTnv8HOGhUJqYpZ/Wz33LaPQ88TQyzsPHSU35zZSCxUj9FoZMKECQwfPhyLpen0v65GTTDOP266DMCzxZksGuRA08niEq1FEARmzJiBTqfj4MGDjZPzlop+iHE3Js926l3zkFWti8RZ7SpqKhNISRlRlUZOqCxj8O0lrssnoS+4/+dbycmTJzlz5gwTJ04kP7/piC80HLOJEyei0+nYsWMH7777LosWLUKrTV20XtI48OR/Dr1vHyAgqc3IKhOhuJpDR85ScqwMWVBRXFxMcXExglZLMGXWth+yqCPknEHYNhGj9wMM3l1oQ2W4C76EpHGm2jwFhZSgOFMKnYYgCDhcarzuRKpN6VL4PEm0OgG9IY0mW90Uufwi0m/+A6quISx+EmHxUwgqFe/tv0Rfh46+Dv1dt62srOTYsWPUnj3LwGSSWq2DxbPmMnLowC6vzncrkizzy70VJCWZny/sS66l66WfCoLAlClT0Ov17Ny5k3g8zsKFC9Fomlm7JMtYalaiC5UhCyL1mY+0yg6rXYUsQ8AvpVXkWRM+jzpegz/r8Q7bR11dHdu3byc/P5/x45tXk1VUVIROp2PLli2sWLGCJUuWYDC0TASiPZFFHWHHdKBBoa+0tJRDhw6RSCQYPnw4EydOxGQypcy+jkRWGQi65hKxjsVx9SVslX/F0+uzIKZn/Z+CQkeiOFMKnYrdqaLsVIJEQkatVpyD5uDzJm5Iyyu/V0chyzLy9g3Ib/weTGbEF36AMLRB8e2qL0pZXYTPFGfdsV0ikeDs2bMcPXqU6upqNBoNw4YNI7PvEL67x4/KbWJ0Cus7OoK1ZzwcrQzxhQk5XdKRupXi4mK0Wi3btm1j1apVPPzww82qYdOGTqMLlZFUOzD49hO2TiKpy27x/m9Gm/2+ZFo5U0bfHiSViYh5ZIeMH4/HWb9+PRqNhnnz5rUozXLo0KHodDrWr1/P22+/zdKlS5uWFO8kJEni5MmT7Nu3j2AwSP/+/Zk8eTJOZ8+I0iQ1LvxZj2Ov/DPm2rXUZy1NtUkKCp2O4kwpdCoOlxrkKF53kows5fS7H8mkTMAnkZWrrPZ1FHKoHum/X4JDu2HYGMTnXkCw2hvf33bBh4jM5FwdHo+HYDBIKBSiqqqKkydPEo1GcTqdzJgxg8GDBzdOxp8OanntcA0fXA4wvY81VV+vXbnqi/Lfh2sYl2di7oB7pzx2FUaMGIFWq2XTpk288847PPLII03W7jQiJzDXriWhycTb63mcV17EXLsWX96zLe7DYzKLiCJpVTclxj1og6cIOR7ssCjD9u3bcbvdLF26tFWRm379+rFkyRLWrFnDW2+9xdKlS7Hb7fffsB1JJpOcPXuWgwcP4vF4yM3NZcGCBeTl9TxBhph5GEH7A5i8HxA39CFq6brtHRQUWoMym1XoVOzOhtVXb11CcaaaQcCXRJYV8YmOQJZlYmWnqH/tJUKhIOE5TxAaNILwiVOEQiFCoRDBYJCaOj8zpBiv/7d02/aiKDYKSvTq1euOyOEjQ53sLQ/wmwOVjMg24rk1PhwAACAASURBVDR07fM9Icm8uLsCnVrkHybldqtI6aBBg9Bqtaxbt4633nqLZcuW3bW+zejdjTpehzf3WSS1laBzNpbatWhDZ4iZWtYQWBQb+u+lkzNl8O0DIGyd2CHjnzp1ipMnTzJ+/Hh69269vHZBQQGPPvooq1atanSoMjIy2tHSpolGo5w4cYLS0lKCwSAul4tFixbRr1+/bnVNtJSgax6ayFUs1StI6PJIau+M5CsotARt8DQJbTaSxpFqU+5L1366K3Q5dHoRo1nE406fyUM647shPmFTxCdaRSAQ4OrVq8Tjcerq6hqjSqFQiFB9PUlZhuwbE+BKN1TuQBAEDAYDRqORpEpHndpBUYGLgTl2jEYjRqMRk8mE2Wy+Z0qYShT48uRcXlh3iVf2VfL/HrzT4epKvHm8lvPuCP/ngTwcXdwxbIo+ffqwdOlSVq9e3Tg5dzhuf4iLiQBG9zaixiHETIMACNsmYfDtw1y7FrdxYIsV2ax2FVXX4+32PdqEFMfgP0DUNAxJ0/6RHrfbzfvvv09eXh4TJ7bdWcvOzmb58uWsXLmSt99+myVLlpCbm9sOlt5JfX09R44c4dixY8RiMfLz85k9ezaFhYVd+rpuNwQV/pyncF79FbbKv+DO/yKIXTsNWCE1iHE3lto16IKnCNkmU5+5JNUm3Zfu90RUSHscThV1NYoIRXPweZKoNWA0d6+6m44ikUhQUVHB5cuXuXz5MnV1dQC3OUhGrRZ7oA5DzXWM2bmYHpiDyeFsdJT0en1jDcd/7rrOZbGeH8wb0Cq1unyrjmdGZ/JqSTXvX/Qz8z5qgOnK2dowfztex4y+Vqb07h4pi02Rl5d3R7Tj1mbKprqNCHKC+oxFH24kqKnPWIS94jUMvr2E7VNbtE+rXcXVizGiEQmdPrXXub7+KKIU6hA59EQiwfr161GpVMyfP7/d5OidTmejQ7VixQoWLlxInz592mVsaBDKKC0t5fTp08iyzIABAyguLiY7u+U1ct0dSW3Dl/0U9uuvYq1ZgT/riRanvir0YKQ4Ru8OTJ73kRGpd80n1ML7aapQnCmFTsfuUnPtSpxwSMJgVJyEe+H3JrHaFfGJe+H1ehudp/LychKJBKIo0qtXL4YOHUphYSEDBw7E7XYjnzmO9PufQr0f4fHPIMxcdNffNhRPsudqgFn9bG2S/V482MGeqwF+d6iKUTlGXMauVf8WTUi8uLsCp0HN34/r/hPIrKwsli9fzooVKxqjHXl5eagjVzEEDhG0TyepvT2dLGYcTNQwEJN7CxFLEbKq+XVAVlvDueXzJsnKSeH9UJYx+HaT0GYRN7R/74odO3ZQV1fHkiVL2l0wwmq1NjpUa9asYe7cuQwaNKjV48myzPXr1zl06BCXLl1CrVYzYsQIxowZg83WNRdEOou4cQBB52zM7i3E9H2J2Cak2iSFLoA2eAZz7WrUcTcR80jqXQs7JDreUSjOlEKn47ilea/BqKQB3A1ZkvF5kxT2U36jW4nH45SXlzc6UD6fDwCbzcawYcMoLCwkPz//NplrQZaR3n0d+d3XITMH8f9+G6F3/3vuZ/eVALGkfN/eUvdDJQp8eVIuX1l3kZf3VfLtGV2rYe+fSqu5HojxL7MLMGl7Rrqpw+FonJyvXLmSRQsXUqRaR1JlJuSceecGgkB9xiKcV3+Jyb2lRVLpNxX9At4kWTmpc7TVkStootcJZD7S7tGEs2fPcvz4ccaOHduuUaNbMRqNPPbYY7z77rts2LCBWCzGiBEjWjSGJElcuHCBQ4cOUVVVhV6vZ+LEiYwaNSqlEuxdjZBjJprwZSw1q0noepHQ90q1SQppihj33EjpO0lCk4kn7znixgGpNqvFKM6UQqdjtasQRfC6k+R1XD/ILk99QEJKgs3Rsy9TWZZxu92NztO1a9eQJAm1Wk1+fj5FRUUUFhbeVc1L9tbh+cX3kI+XIEx8EOGZzyPo799kddsFH3kWLYNcd+8t1VzyrFo+WZTJ7w9Vs/WCj4f6d40Vt5Lr9aw76+XhIQ5G5XTPfjl349Zox+VDbzJ+tAd/1mPIYtPnQ1KXTdg2ocVS6VqdiN4g4EuxCIXRtwdJ1BGxjGnXcb1eL1u3biU3N5dJkya169gfRafT8cgjj7Bu3Tq2bdtGJBJh3Lhx990ukUhw6tQpSkpK8Pl8WK1WZsyYwdChQ5vfe0zhQwQRf84TOK++1FA/VfAlZJXijCrcghTH6P0Ak+c9QPgwpU/omvOdrmm1QpdGpRKw2lV46pS6qXvRKD7RA5X8otEoV65caXSggsEgAC6Xq9F5ys3NRa2+9y1MPl6C9OqLSLEowqe/jDBldrOiQlX1MU5Uh3lmdEa7RZEW3Uj3+8OhakbnmMg0pfckLRBN8su9lRTYtHxidOb9N+iGGI1Gli9bjP3iTyn3aiiL6hk+/O6fDzofQh84jKV2Dd68zzQ7wmO1p1bRT0wE0NUfJ2ybiCzev89Wc0kkEqxbt66xTqozmldrNBoWL17M5s2b2b17N9FolClTpjR5HYfDYY4dO8aRI0cIh8NkZ2czZcoU+vfv3241XT0VWWXGl/0xHNd+i7X6LXw5zyj1UwrAzZS+d1HH67pkSl9TKM6UQkpwuFRcuRBDkmREUbnBNoXPk0RUgdnaMx7qkUiEo0ePcvnyZSorK5FlGa1WS0FBAYWFhRQWFt5VrvqjyIkE8qq/IG94G3oV4vo/P8JraN62AO9d8CMAM/q2X32EKHyY7vfSvkq+NzO90/3+60Al/kiC78zog07dM87BpnCE9mDSxtlydRgHz2wjGo1RXFzc5GdllYmg8yEstWtaJJVutauoqUwgJWVEVeefE3r/fgSShG3tGznauXMntbW1PPzww82+dtsDlUrFvHnz0Ol0HDp0iEgkwsyZMxsdJL/fT2lpKSdOnCCRSNCnTx+Ki4ubbHGg0HoShkLqMxZgqV2LwbuTsOOBVJukkELEuAdz7Vr0wRMkNBl48j5D3Dgw1Wa1C4ozpZASHC41F8tiBHzJHp/Gdjd83iRWm6pHOJuyLLNu3TrKy8vJyspi3LhxFBYWkp2d3eLVbLmuGul3P4XzpxGmz0N48nnUeb2gtrbZtrx30cfIHGO7R49yLFo+NSaL3xyoYvN5H3MHpOdq3I5LfnZeDvD06Az6Odue5thVEeNujN6dRCxFTJj9GJ7ERnbu3EksFmPixIlNRztuk0of0Ky0FatdhSxDwC91fiRaTmLw7SdqHEhS234RyLKyMo4ePcqYMWPo27dvi7Ztj0U2QRCYMWMGer2eAwcOEI1GKS4u5vDhw5SVlSEIAoMHD6a4uBiXy9WmfSncnbBtKprwJcx1G0joC4gb+qTaJIXORk5g9NxM6YN61zxC9mldNqWvKbrPN1HoUtgbRSgUZ6opZFnG70mSW5DeqWDtRUlJCeXl5cyePZvh98qjug/y4b1If/wlSEmEv/sG4oTpLR7jVE2Yyvo4T43smAag8wfa2XOlId2vKMdEljm9jnFtKM5/HahkcIaex4b17EmmuXZdo0SvWq1mwYIFbNu2jf379xONRpk+ffqdDpWgoj5j4S1S6dPuu5+bIhR+X7LTnSld8CSqpJ+AbWm7jenz+di6dWtj2lxLuH41xuH9IcZONpGd17ZrQxAEJk+ejE6nY+fOnZw7dw6NRsOYMWMYPXp0p0bLeiyCQCBrOeryX2Gt/N+G+il1+6o5KqQvt6X0mUZQn7Goy6f0NYUyi1VICUaTiFYn4K1LQtcTbulwwkGJeFxO23opORICjQ6hHWogqqur2bNnD/3792fYsGGtsyceR377T8hb34Xe/RE/+02ErLxWjbXtgg+9WmRy746ZaImCwD9MyuHLay/xq70VfH92AWKapBZJssyv9lSQSMq8MCUPVQ+Iit4NTegc+uAJ6p1zkdQN6Z6iKDJ79mx0Oh2lpaVEo1EeeuihO+prYsbBRI0DMbm3ErGMua9UusksIoqkpG7K4N1NUu0gZhzcLuPd7CclCAILFixoUWQ54EtyeH+IZAKOHgoxM9OKWtP2c7C4uBiLxUIgEGD48OH3bLat0P7IKj3+nKdxlP8aW9UbePOeBaHnpg73BD6a0ufNe5aYsfXtCtId5WxWSAmCIOBwqfC4FRGKprip7JVuzpQcqkd6/XdIX/k40ne+gLT3fWSp9RPAeDzOxo0bMRgMzJ7dPHGIO2yqvo70428hb30XYfbDiP/4k1Y7UtGExK4rAab0tqDvwDqhbLOWzxRncbQqxMYyb4ftp6WsP+vlcGWIZ4uzyLX0YEl+OYmldg1JtaMhHeUWBEFg2rRpTJo0idOnT7Nnz547t78hlS5IMUzuLffdnSgKWGydL0KhjlagjVwiZJvUbpPb3bt3U11dzUMPPYTV2vwGz/GYxIGdQdRqgbFTjERCMmeOR9rFJoCBAwdSXFysOFIpIqHLI5CxBG34HCb3tlSbo9BRyAmM7vdwXXkRXegs9c55uHt/pVs7UqBEphRSiN2ppup6hHhMQqNV/Ppb8XmSCAJYbenhTMlSEnnnZuQV/wPBAMKkGchXLyH/4T+R1/0N8ZGnYcwkhBYqYO3cuROPx8OyZcvQ61temyPt34H855dBFBG/8E8IY9pWQL+vvJ5QXGJm3+ZPAlvL3AE2dl/x86fSaorzTGSbU+u8lPuj/Km0mrF5JuYP7H5pGC3B4NuPOlaFN+cZEO9MNRMEgQkTJhAMBjl06BAul4shQ24Xm0hqswnbJjak+lknktTl3HOfVruKquvxdv0e98Pg24MsaIhY7y8f3hzOnz/P4cOHGT16NP3737uP263IskzpvhChoMTkmWZcmWpq+iW4UBYlv49GSQXvJkSs49BELmL0bCNu6N3tJ9g9DW3w7I3Gu3VETMOpz1jcLVP6mkKZwSqkjJvNe73u1PZXSUd8niRmq4hKnfo0K7nsJNK/fh35z69ATj7iP/8n4mdeQPz2i4if/RbIMtJ//Rjphy8gHzmALMvNGvfChQscO3aM4uJiCgpa1nBMjkWR/vwy8u9+Cr0KEb/zizY7UtCQ4pdlUjMi+/59qNqKIAj8w6RcREHgl3srkZr5u3UECUnm57sr0KkabOrJimZCMojJvZmYoT8x073TTqdPn05eXh5bt26lqqrqjveDztnIog5L7Vq4z/G12lXEojLRiNQm+5uLkAyjDxwmYilCVrX9fPf7/WzZsoWsrCymTp3aom3PnohSdT3B8CIDrswGx2noaD1arcDRg2FkKXXXhkI7IggEMpeS1GZhrXwTMeFLtUUK7YCQCGCt+Av2ij8C4M19Fn/uMz3GkQLFmVJIIXZnw0PTU6c4Ux/F50lis6c2KiW7a5F+91Okn/wjBPwNgg7f+jeE3g0rzoIoIoybhvj9XyE89wJEwkgv/QvSv30T+WTpPZ2qYDDIli1byMjIaHEjT7niKtKPvoG8YyPCgscQv/EjBFdWm74rQF0ozpHKIDP62jqthinTpOG5sVkcrwqx/mzq0v3eOl5HWV2Ez0/IwWno2VEAk3sLghQlkLH4vn1xVCoVCxcuxGg0snbt2sZ+aDe5KZWuDZ9DGzp9z7GstobHcWc179X7DyLI8XaRQ08mk2zYsAFZlpk/f/59+7/dStX1OGdPRMgv1NBn4IfRWa1WZPgYA153kkvnY222USFNELX4cp4GOY618n9BVp7/XRpZxlr9N3Sh09Q75+Lu/VVipp4XcVScKYWUodEKmK0iXqVu6jYiYYloJHXiE3IsirTmDaRvfx65ZA/C4icR/+UVxAlNKJcBgqhCnDQT8QevIHzyH8DnRnrxu0g//SfksyfuHF+W2bJlC/F4vMUTL2n3VqQffg38XsSvfBfx0U8htGD7e7H9oh9Jhpnt2FuqOczuZ2NsnonXSqupCHT+pLGsLswbx2t5sI+VqYUdn96YzqiilRh8+wjb7p+WdxOj0cjixYuJRCKsW7eOROL2+1nYNomEJhNz7VqQ736vu6noF+gMZ0qWMPr2EtP3IaFrXX3hrezZs4fKykpmz56N3d781ej6QJKSvUGsdhWjxhnvuL/06q0hI1vN6WNhIuHOidgpdDxJbSaBrMfQRi5jrtuQanMU2oAueBxdqIx61wJCzpndSu68JSjOlEJKcTjVeOqSzU4N6wncXJm2dnKdgCzLyCW7kb7zReRVf4ERYxucqEeeRtDdv55JUKsRH5iL+MPfIHzs76HqOtJ//F+SL34X+eLZxs/dbMz7wAMP4HQ6m2dbJIz0hxeR//gL6DsI8Ts/RxgxttXf9Y7xZZltF30MyTCQZ+3c2iVBEPjixBzUosAv91R0arpfNCHx4u4KHAY1fz8+u9P2m5bIMpbad5FFPUHnQy3aNDMzkzlz5lBRUcH7779/+/3shlS6Ol6Hwbf3rmNodSJ6g9ApkSlt6CyqhJuwbXKbxzpz5gwlJSWMHDmSgQOb34AzEZc5sDOIIAiMn2ZsMqVZEARGjTUgJeF4abjNtiqkD1HLKEK2SRi9O9HVH0+1OQqtQJCimGvWENflEbZNTLU5KUVxphRSit3VUCcQCiqrjjfxeW4o+XVimp987TLSf34b6dc/Br0B8Wv/gurz/4iQ0fIJtqDRIM5ajPivv0V4/Fm4ch7pR98g+dIPqT1+mJ07d9KnTx9GjhzZPNuuXkT64deQ921HWPJxxK/9AMHevv2PzrkjXPXFmNWvc6NSN3EZNTw/LpuTNWF+sbuC6/7OiVC9driGa/4YX5mci1mbHmInqUIXPIE2fIGga06raogGDhzI+PHjOXnyJEePHr3tvZhpCFHjIEzurQjJ4F1GaIhOdUZkyuDbQ1JlIWpuXSuCmwQCAVasWEFGRgYPPPBAs7eTZZnD+0PUByTGTjZiNN393DNZVAwcpqfiapyqis4V6FDoWOozFhHX5WOpfgtVrHlN1RXSB5N7K2IyQCDzERB69vOjZ8bjFNKGRhGKuiQmc8++GG/i9yQxmkU02o6v25GDAeRVf0Xevh70RoSP/T3CgwvapX+UoNMhzF2GPH0e8tY1xDevZGNQQGswM3vksPuKHMiyjLx9A/IbvweTBfHr/4IwuHkOWEt576IfjSgwtTB1TTxn9rVy2RtlzRkP2y/5mZBv5pGhToZlGjpEEOJwRZC1Zzw8PNjB6Jx790Hq9khxzLXrSGhzCFsntHqYSZMmUVtby44dO3A6nbcJq9RnLMR55ZeY6jZTn9V0g1yrXUVNVQIpKSOqOub6V8Vqb0gWz251Sk4sFuPkyZOUlpaSSCRYsGBBi9J1z5+OUlEeZ+goPZk592/M23+IjmuXYxw7FMY1X406DYR5FNoBQY0v5+M4r/4Ka+Vf8eR/vkn1TIX0QxWtxODdRcQ6noS+d6rNSTmKM6WQUiw2FSoVeOoS9CrswX1tbsHnSXZ4vZQsJZF3bGxI5wsGER6ch/DI0wjm9q+ZEfRGhEVPsM/gou74CRaXn0D/b+8hTZyO8PBTTfaEkkNB5P9+CfnQLhhRjPjsVxGsHaMMFE/K7LjkZ2KBOaXRGUEQeLY4i6VDnaw942FDmYd95fUMdOl5ZIiTKb0t7dZEtz6a5Jd7Ksi3avlEUWa7jNmVMXp3okp48OQ936YVVkEQmDt3Lm+++Sbr16/nySefxGZriHbeJpVum9RkTZbVrkKWIOCXOuweYPDtRUZFxNrytJxAIMDRo0c5duwYsViMvLw8Hn/8ccxmc7PHqKmMc+pYhNwCDf2HNK/nk0olMHKckT3v1VN2MsLQUYYW266QnkgaB/7sJ7BXvIal9l0CWY+m2iSF+yFLWGpWIosG6l1zU21NWqA4UwopRRQFbE6VIo9+g3hMIhSU6N2v4xxL+cxxpNd/C+WXYNAIxI/9HUJ+3w7bH8Dly5c5fPwEo0ePpu+zn0be+Dbye2uR9+9AmPoQwqInEVwNk3r5YhnSb38C7hqExz6FMHdZi/tXtYRD1+sJRJOdLjxxNxwGNc8UZbJ8hIttF3ysPu3mp7uuk3VYzeLBTuYMsGHUtG2i/ZsDVXgjCf7pwT7oOrA5cVdATPgwed4jYhpO3Nj83kh3Q6fT8fDDD/PGG2+wdu1ali9fjlbbcD0HnbPRBw5jqV2LN+8zd6gF3hSh8Ps6ZkFFkKLoA4eImkcgqZsfha2pqaGkpISysjJkWWbAgAGMGTOGnJwcMjIyqK1tXopWKJjk0J4QFotI0fg7BSfuRUaWmoI+Ws6fjtKrt7bxt1Lo+sRMQwjaH8Tk3U5c34eItTjVJincA32gFG3kMv6sx5BVPTyr4QaKM6WQchwuNRfPRkkmZVQdlNrSVbhZfN4REym5rhr5b39siPY4Mxt6RI2d2uE9hcLhMJs3b8bpdDJ16lQEtRph+bPIc5Yir38Left65D3bEB6YC47MhmiZzYH4rR8j9B9y/x20kW0XfDj0KsbkptdDQa8WWTjIwbwBdg5eq2flKTevllTz+rFa5g6ws3iwg0xTy1NiPrjkZ8dlPx8flcEAV8sbJXc3zLUbAJn6jIXtNqbdbmf+/PmsXr2azZs3s3DhQgRBuCGVPhtL7Rq0odPETENv285kFhFF8HdQ3ZQucBhRihBqhvCELMtcvnyZkpISysvL0Wg0jBo1iqKiIqzWlkewkwmZAztDyLLMuGlm1JqW33eGFumpvB7n6MEQU2ebe3Q/tO5G0DUHTeQKlpqVxHV5zVbTVOhchGQIc+16YvpCIhbF6b1JpzhTr7zyCiUlJdhsNn72s58BUF9fz4svvkhNTQ2ZmZm88MILjakCK1asYNu2bYiiyLPPPktRURHQ0OTz5ZdfJhaLMWbMGJ599lkEQSAej/PSSy9x4cIFLBYLX/3qV8nKanvfGYXOwe5UIUkNEwiHq2f7943iE+3oTMnRaEMkaMM7IIDw8McQ5j2KoGteik2b9i3LbN26lUgkwiOPPHJbXYVgcyA89XfIc5cir30TecdGSCahaBLip7+EYOr4+iV/JMGh6/UsHuxstxS69kYlCkwssDCxwMLZ2jCrTrtZfdrNu6fdTC20snSok/7O5jlFdaE4/3WgkkEuPcuHt6+IR1dEHb6Mvv4wQcdMJE3zlCWbS2FhIVOnTmXnzp0cOHCACRMaarHCtkkYfPsw167FbRx4W92SKArYHCoqy+MMHq5vlcNxV2QZo28PcV3ePWscEokEp0+fprS0FI/Hg8lkYurUqYwYMQJdK+8Zsixz9GAIvzfJhAdMmC2tu7/pdCLDi/Qc3h/myoUYhf07/h6m0EkIKvw5T+G4+itslX/FU/BFZFE5vumGuW4jghS+ITrRs7MabkX1ve9973sdvROTycTMmTM5cOAA8+bNA+DNN9+koKCAF154AY/Hw9GjRxk1ahTl5eW89dZb/OQnP2H8+PH8/Oc/Z/78+QiCwE9+8hOef/55nnnmGTZs2IDFYiE3N5ctW7YQCoX49re/jV6vZ8OGDUye3DzJ10Ag0JFfvUUYjUZCoVCqzeh01BqBC2ejWG2qVjlTsiyTTEA0KhMOSgT8SXyeJJ7aBHXVCRIJGb1eRGzHyXJHHatL56LEYzKDhrdPTYB87hTSz/4fHNmPUDwZ8Yv/jDhmYrv1ZrofJ06coKSkhGnTptG/f9MpVILBhDB6AsLEGQhDRjXUUWnb9yF6t+O1+byXg9eCfH5CNvYu0KzWZdQwtbeVmX0bIgM7LgVYe9bD8eoQVq2KXIvmrqv1sizzk53XqayP871ZvbHp0/P7dtp9UJawVf4FEPDlfLxD1KhycnLw+/0cPnyYjIyMhlYAgkhS48To24MkGkgYCm/bxmRRceFsw30gO6/9ivE1kYuYvDsIuuaS0Pe64/1wOExJSQkbN26krKwMs9nMtGnTmD17Nr169bqrwERzjtelshjnTkcZNFzfZgfIaldRV53g2pU4BX21ihhFC0nneYYs6kjo8jH6dqKKu4maRty3cXZ3Jt2OlTpyBUvNasL2qUR7YCqmxXL3Bd5OeZoOGzaM6urq2147cOAAN/24Bx98kO9973s888wzHDhwgClTpqDRaMjKyiInJ4dz586RmZlJOBxm0KCGzsrTp0/nwIEDjBkzhoMHD/L4448DDWpKr776KrIsKykAXQSDsaG/irs2QW6BhnhMJhaTiTf+ScTj8h2vN/53XEa+j7K6KIIzQ01GtprMbDU2hwohDSMR/nYUn5C9bqRXftQgdf6NHyEMHtEu4zYXj8fDjh07KCgoaIwu3wshMwcyOze1Y9sFP/0cOvo4ula6W7ZZy3Njs3lqZAabznl594yHH24vJ9+qZckQJzP6Wu+ohVpf5qW0Ishnx2fTq5N7aaUj+kAJmug1fNlPgNgxv4cgCMyaNQuPx8OmTZt44okncLlcxEyDG6TSPduIWMcgqz4UcHBlquk/RMf501Gy8zTt5lAZvA3OW8Q8+rbXPR4PpaWlnDp1imQySZ8+fRgzZgz5+fnt8gytq0lw4nCY7Dw1g4a3fZFEEBrEKLZvDHDicJjiSemVnqvQNuLGfgSdczC7NyFIMYLOh5p0/hU6GVnCUrMKSWVpcR++nkDKliZ9Ph8OhwMAh8OB3+8HwO1239b4z+l04na7UalUuFwfpqW4XC7cbnfjNjffU6lUGI1GAoFAq/K6FVKD3aXm+tU416/evY+IRiOg0X74ZzOKt/3/zfe1ja+JqFTg9SSprUxQWxXn9LEIp481jOXKVpOZpSYjR43JLKbc+U4kZAIBiZz8tk+e5GQS6fc/g2gE8ev/itCrc6VLk8kkGzduRK1WM2fOnJT/tk1xxRvlvDvC82O7bkqwSati2TAXDw9xsuuyn1Wn3byyv5K/HKlhwSA7CwY5sOvVXPPH+GNJNWNyTSwY2DGqiF0JQYpgqttITF9I1Hx/R78tqNVqFi1axOuvv867777LU089hV6vpz5jEc4rv8BUt+UOqfTBI/RUV8Q5ciDEjPkWtLo2y34SiQAAIABJREFUpNPICXT1J9AFTxKyTwNRgyzLXLt2jdLSUi5evIhKpWLIkCEUFRXd9pxtK+GQxMFdQYwmkTETTe12H7BYVQwYoqPsZJSCvnEysxU57e5EyPEgAEbvDpzlLxE1DibonE1CX3CfLRU6CoNvL5rodXw5H1fSL5sg7fI8busc34zX7/be3W7aW7ZsYcuWLQD8+Mc/JiMjoxVWdgxqtTqt7OlMJj9g5crFIFqdiFYnotOp0OlFtDoVOp2IRtv6NL1e+cCN9kThUIKKa2GuXw1TUR6isjwMgMmsJi/fQG6+kdx8A0bTvS+NjjhWNZURkH0UFDrIyGi+1HBT1P/1twTPHMP6pX/GMLrzw/GbN2+murqap556ij59+nT6/j9KU8frjdMXUYkCS4v74jB2/cnYY1mZPDquH6XXfLxeco3Xj9Xxzkk384dmUVYTRKdW8d2Fw8g0p/eDsDPug8LltyAZRBj6FTLMHS8Nn5GRwdNPP82rr77Kli1b+MQnPoFKlQGxGRgq30PfZz4Y82/bZtZ8K+/+7SpnjknMmJfZckckXIVQ/QHU7EZIBJB1mWgLF1BWdp3du3dz/fp1jEYjM2bMYMKECS2SN7+Vux2vZFJm7/vlSEmYs6wXDlf7nneTHpCoLL/KydIYjzyVjbqHq1I2ly4zz8h8HBKLkaq2oa3YjK78FWTbcOT8h8HSdtXNrkDaHKuYD+HiFmTbMCyFM7Ck4eJoqkmZM2Wz2fB4PDgcDjweT2MUyeVyUVdX1/g5t9uN0+m84/W6urqG/PNbtnG5XCSTSUKh0F0fDA899BAPPfRhiLK5kq6dQUskZrsjeYUAt6tYxeINf+2J1QFWh8jgkSZCQYmaygS1VQkuXain7HRDDZ3FJpKRrSEzW40rU31HIXhHHKvLl6IACKoQtbWRVo8jnyhFeus1hKmzCY6aQLCTz6ny8nI++OADhg8fTlZWVlqc0x89XklJZv3JKsbmmUiGfNSmT1p6m+mth29NyaZ8uJ3Vpz1sOFVNLCnzjal5CJEAtZH0qRNtio6+D6piNTgrthCxjCUQMUOkc85Pg8HAzJkz2bJlC6tWrWL69OkIhim4xD0kyv4Hb95zd9SHDBqh5/TReo6USOQ3pw/fjSiUwb8fbfgCMiJR01AC9iIOXYhyePOr1NfXY7fbmTlzJkOHDkWtVhOJRIhEWnfPudvxOnIgRE1VjLFTjCTlALW17X/eDR+jZe/2IPt2XmPwCKX3VHPocvMM3USE3kUYfHsxej5APPFjYoYBBJ2ziBs6tqVHqkmXY2WtfANdMobbtoDkLfPwnkZe3p09MW+SMmdq3LhxbN++naVLl7J9+3bGjx/f+Povf/lLFi9ejMfjoaKiggEDBiCKIgaDgbNnzzJw4EB27NjB/PnzARg7dizvv/8+gwYNYu/evQwfPjwt04oU0gtBEDCZVZgGqOgzQIcsy/g8SWqrEtRUJbh8PsrFs1EEAewuFZnZajKyNThcHdPfxOdJotEKGIytP3dlT11Del9uAcLHPteO1jWPSCTCpk2bsNvtTJ8+vdP331yOVAbxhBPMSpPeUh1Bvk3HFybm8PToDK74oozMVmpLAMy165AFNcEUNJscNmwYtbW1jYIUw4YNI+h8CEvtu2hDp4iZht32+QGDdVRdi3P8UBhXphqDsenoiypWjcF/AL2/BFEKkVQ7qHfOpYqBlBw7z8mTHxCPx+nVqxczZsygb9++HfqMvHw+ypULMQYM0ZFX0HH1eZk5Gnr11nDuVEPvKbNV6T3VHZFFHSHHg4Rtk9D79mHyfoDj2m+JGfoRdMwmbuyXahO7LZrQ+RuKp7NIatMgSpamdIoz9fOf/5yTJ08SCAT43Oc+xxNPPMHSpUt58cUX2bZtGxkZGXzta18DoKCggMmTJ/O1r30NURR57rnnEG807Hz++ed55ZVXiMViFBUVMWbMGABmzZrFSy+9xJe+9CXMZjNf/epXO+NrKXQzBEHA7lRjd6oZMLQhTcVT2+BY1VYlOHsyytkTUVRqyMiMIpNApRJQqbnxr4BKBWq18OHrt/y3+uZnPvL5m+mLvhviE62d5MjJJNLv/gPiMcTP/Z9OkT6/bf+yzHvvvUcoFOLxxx9Ho0nf1Ln3Lvgxa0XG9er+DoZNr2Zkmir3dTba4Bl0odMEXAta1LS2PZk2bRp1dXVs27YNh8NBbs7EG1Lp63AbB90mlS6IAmMmNYgtHN4fYtKDt9QdSXF0weMYfAfQRi7eiEINI2wdz4U6PaU7D3Pp0juIosigQYMoKirqlJYhnroEx0vCZGSrGTKy44Vdho8xUF2R4OihMJNntF9dlkL6IYs6wo7pDe0F/PsxenbguP47Yvq+NyJV/Xu0+l+7Iyew1KwiqXYSdMxItTVpjSDfqxipB3D9+vVUm9BIuoR0FZomHpOorW5wrCJhFZFwjERCJplsaEiZTDZItLcUUWxwruJxmf6DdQwral26ivTOa8jr30Z47gXESTNbNUZbOHXqFJs3b2by5MmNkeZ04dZrKxhL8ul3zjG7n43PTVAaQ6YbHXYflJM4r/wCkHD3/uptTktnEw6HeeONN0gmkzz55JM4hWvYK/5EwLWQsOOBOz5/6VyUY4fCjCg2MKDQi8F3AH2gBFEKk9A4iVgnUG8czcmyco4cOUJdXR0Gg4GRI0cycuRITKaOWzS49XhFIxI7NgUQRIHpc8xtE85oATd/n6IJxv/P3n2Hx3Gd9x7/npntu1i0RQcJgL13AhJFkaZIq9iWLFuObMdWorg7iUuSGydOs9Jukpvr6xbbsh3FcVxiy3G3LFGdktgpgk3sJNjR6/Y25/6xINg7gN0F3s/z4NmK2QPMLjC/Oee8hwkNUqnyasbUcYaVxD2wFU/vOsz0AAlXHZHiu0h4po6JUJXtfeXpfRlf91r6qh4l4Z2etXbkipwc5idEvrE7DKpqHVTVOq74R05rjZWG1GCwygSszPXUedeH7j8viFkWTJx8cwcCeve2TJC68+6sBKm+vj5efvllqqurWbx48ai//o1YfyJIIq25a9LYHeInLuXu34gt2Ulf1e9mNUhBZv7U/fffz5NPPslTTz3FQw89hNszDW/vC5eUSgeoa1DYe/dSHd5O6YnTaEzivtlE/Y30psvZvWcPu3c/SSwWIxAIsGbNGqZNm3bFtaFGgmVptm0Ik0holq/2jlqQAqib7ODUsQR7d2ZKsI/ma4ssMuxEi5YR9S/FHdyGp3cdRa3fJumcQLjkLhKe6WMiVGWDkezF2/MiMe9sCVLXQcKUEMNIqXPD+xilUXa6pxPriS9AbT3qPR8enRc9j2VZPPvssyiluOeee4aG5eaql472U+t3MLU0v9aWEjdPpUJ4e14g7pmWOcDKAaWlpdx999089dRTvPjii9y78i2UnvzyBaXSzXgb7oEtuILNVBTH6I+XsKv7LioW3UZHT4Qdr+7g0KGnsSyLSZMmsWDBAmpqarIy1G3vjig9nWkWNnkoLB7dQwulFPOWeHjl2SB7d8ZY0OgZ1dcXWWbYiRbeTtS/FNfA63h7X6ao9TsknTWES1aT8MyQUHWDCrp+jUYRCrwt203JCxKmhMhjOpXC+ua/QiqF8dE/QzlGv+z11q1baWtr4957773qCuG5oDWYYG9nlEcW3ESpaZG3fD3PoawEocBbc+qgavLkyTQ1NbF582bKysq4s/Y23P0bsezFOMN7scdODPZCzSFa2EhLexWvbjtAcv9v6Otvx263M2/ePObPn09hYfZ6Wk8dS9ByKEHDVAe19dkZZucvMpk0PbPY8YQGB6Vlcngz7igbscImYv7FuILNeHteoqj1v0g6qwkX30XCOxNUbp/sywWO8D6c4b2ESu/Fssu6hNdD/toIkcf0z78LR/ajPvy/UJWjv0p8a2srW7ZsYcaMGUybNm3UX/9GvdTSjwLe1CALeo8XZrwN18BWooXLSDtyb4HmxsZGurq6eO211yh/4B7mG834up8hZS8jGHgrsYKFRJMmb7zxBjt3vkwoFMJmFrB0yXIWLZ6Nc5QLzVysuzPOzm0RSsrMm57vOVymzXZx5kSCXdsirLy7AMPMneAsRpGyEfMvJVawCFdwB57elyhq+x4JWyVt9ruIumZmCkXZFLbBkSSGceX1SccVK0FB569IOcqJFN2R7dbkDQlTOUDHouidW0hMqENrAwqLwOND5fhwKZFdeudW9NqfoVbei9E4+mXI4/E4a9eupaCggJUrV476698oS2teOjrA/EoPgTGwSK+4Pr7up9GGk3DJXdluymUppXjzm9/Mj3/8Y379zEsUP/Qe/F4nKddEenp72fnKFvbt20cqlaK2tpbly1dybF8JsX4Tm5ndYguJuMX6F1pxOBRLlnlvemH14WKzKeYu9rDl1TBHDsSZOkuG8o5ryqTPvpB9iZmY3TuZ4nuNia4f8MbxxWw4tQpLnyulrxSZqrtDFXjPBS3bYBXecwFMXfBcm11hs4PdbmB3KByOzOP5GM68vS9jpnrprflw1ueW5hP5TeWCrjb0v3+e3vPvM00oKAJ/ERQWo/yFg6vNnr1ddO62R8rBjje6uwPrP74AEyeh3v2hrLRh3bp1BINBHnrooayfHb8e+zqidISTvG++rJUxXjgiB3FGDhIsfQvazN15NA6Hg7e97W386Ec/4idPb+aOO+5g9+5fcvz4cUzTZPr06SxYsIBAIPPeLfYn2bQuzP7dMWYvzE5vUCxqsW19mEg4zbK7fDhduXHyr6LaTlWtnYN7Y1RPtOP1ydpT400satF6KsmZkwl6OtMAFPhnEy+Yxwz7C8wu30hdRQdHeBfRdCHplM5U5r2oWFQqBcmEJhqxhm6nU5liUdeiFNgdCrtdZS7Pfg3edlzmPrsjE8ZsWQpiZqITT+8rRAsWknTL2l03QsJULqioxfi7r1KoNH0njsFAHwz0wkAfur8P+nvRJ49CsB/SmT8MF9Szt9kyoWowXKnC4qEgpsoqYPIMlDe357KI66dTSaxv/B+w0hgf/QzKPvpnpw8ePMj+/ftpbGy8arnQXPJiSz8um8FtE+SzMC5oC2/X06RtxUSLbs92a66psLCQ++67j5///Of8+te/xuPxcNtttzFnzhw8nguDYFmlnfopDo4ejFNRnVlMfDT1dKbYtiFMMqlZ+eZKfEWxUX39a5mzyE3nb5Lsfj1K0wo52TgeXD5AGUyf46Jqgp2CwQWd0zxAf7Cego6fMNf4Jv1V7yXpmXxDr2VZg6ErpYeCWDKpSSYGv86/ft7tSMgiMXibqyxKpBTY7IryygRzF9uxO0bh/as1BZ2/RBt2QqX3jfzrjTESpnKAstuhagKOQACjcuIVn6ctCyIh6M+ELT3Qlwle/YPBa6AXervQx49AsA8s69zntWoCavKMTLCaMhMqslPxSdw6/ZP/gpaDmYV5y0c3yCSTSZqbm9m2bRuVlZU0NjaO6uvfrFgyzfrjQe6YWIDLlhtn0MXIcgW3Y0+00V/x3rwZrjJhwgTuv/9+4vE4U6ZMwTSv3Ksyc76bzvYUO7ZEWHmPf1QOuLTWHDuU4I0dUdxeg9tW+qif4qOrK7fClMttMGOumz3NUc6cTFIzUdaeGotiUYvWk0nOnLp6gLpYvGAeKWclha3fo+jME4RL7yFStOK6i9MYhsJwcNOfOa3P9XolE9YlASyR0CTimpPHIsSiJk0rfZgjPP/PGdqNI3qYYNkD6CwtaJ7P8uM/jADIzKHy+TNfNRO52kdLWxaEBqD1FPrIPvThfejtG+G15zIBy1cAk2eiJs9ATZ4J9VOyUglO3BjdvAn9/C9Qq96KWjx6k0O11uzfv58NGzYQDoeZPHkyK1euzPky6GetO9JNNGXJ2lLjhZXA2/0cSecE4r652W7NDamvr7+u59lsioVNHta/EGJPc4SFTSO3MC9kzsLv2hbh9PEkFdU2FjZ5sDty9/NfP8XByWMJ3miOUl5py+m2iut32QBVeO0AdbG0o5zeCX9AQcdP8HU/gz12goHy30KbIz/PTimF3Q52uwLvld+X9ZOLeeW5drZvjLBkmQc1QnMSlRXD1/Vrks4aov6mEXmNsU7C1BilDGNw6F8RavocYDBgtZ9GH94HR/ahj+xH79ySCVemLTP/ZvLMTM/V5BmoopKs/gziQrqzDevbX4K6Kajf+sCove7Jkyd57bXX6OzspKKignvvvZeamtGvHHgrnt7XQbnXzqzy7FYbE6PD0/cqZnqAgcr35lQp9OFWXGpjykwnh/bGqaxJUFU7Mj0w4WCarevDBPstps9xMXWWM+dHNihDMW+Jm1efD7FvV4x5S3J3zpy4uuEKUBfThpOBiveSdE3E1/U0xaf+jf7K95F2Vg1n82/a5GkFdHcN8EZzlF2vR5m3xD0inztv9/MY6RD9VY9I6fibJGFqHFGGkRnuVzUB7rwbAB0cgKP7z/VerXsa/fwvMt8QqBgcGjgYsGomogyZzJsNOjk4TwoG50mN/ByJnp4e1q9fT0tLCz6fj7vvvpvp06fn/EHUxboiSbad6OPhuaUYedZ2ceOMVBBP7yvEvHNIuuuz3ZwRN222i47WFLu2RSkJ2Ia9EET7mSTbN4VRStG0wkt5Vf5UwiwqsdEw1UnLwTgT6h0UB+SQJ1+MVIC6hFJEi5aTctbib/sBJae+zkD5O4gXLBye7d+iSdOcxGMWh/fFcboUM+YO7wlBW/wM7v4NRP2NpFwThnXb44n8ZRnnVIEf5jei5mfmvuhUEk4czfRaHd6H3r8LNq/L9F653DBpeqb3auFtUFufdwfW+Ur/z7fh+GGMj38WVVY5oq8ViUTYsmULu3fvxmazsWzZMhYsWIDNlp9/Ll5uGUADqxpkiN944O15HqVThEvvyXZTRoVhZIb7vfJskJ1bIyxdPjwFF7SlOfBGjEN74/iLTJbe4cGTh5XxZsxx0Xoys/bUnXcXZL18u7i6gb40h/fHOHMiidYjFKAuI+mup3fCJ/C3/TeF7U8SiR4nVPa2nJhvOWOui3hMc2hvHKfLoGHqME3J0BYFnb9Am55x8/dypGT/XSJyirLZM4Fp0nR489vRWkNXO/rI/szQwMP70b/+IfpX/53p5WpaiWpcMeIH+OOZfn09+sVfo1bfj1o0clXJUqkUO3fuZOvWrSSTSebMmUNTU9MllcTySdrSvHi0n3nVfqoKZBL6WGcm2gcX6L2dtGP8lMAvKDSZMc/F3h0xTrYkmDjp1g62EnGL7ZsidLalmFDvYO5iN6YtP0OIza6Ys8jNtvURDr4RY/ocl5wEzEE9nSkO74/RfiaFaYOGqU4mTnaMaIC6mGUroK/mg3i71+LtexV7/DT9le/DsheNWhsuR6nMkNVE3GLP9ihOp6J6GIqquIKvnzdXTIbA3woJU+KqlFJQVpkJS7e9CQAdGkBvW4/esg798++hf/69zByrppWoJctRBdIDMFx0RyvWd74CDdNQ73p0ZF5Daw4dOsT69esJBoPU19dzxx13UFpaOiKvN5rWHu7j9ECCjy2XNTPGA19Xbi/QO5ImTXPSfibFnuYogXLbTfci9fem2Lo+QixqMXexm7rJjrwPH5U1dqom2Dm0N05Ha4qZ812UjXI5eXEprTUdrSkO74vR05XG7lBMn+OifooDhzNLc3eUSTjwFlKuiRS0/w8lJ79Cf+V7SHqmZqc9gwxDsfh2L5vWhdi+OYLdqW7pPazSYXxdT5Nw1RPLkSGN+Uxpra9S7X7sO3PmTLabMCQQCNDV1ZXtZtwQ3d2B3vIqevPLcPo4GAbMWohqWoFacBvKNTbPdozGvtLJBNY/fwa62jH++ouoQMWwv8aZM2d49dVXaW9vJxAIsHz5ciZOvHJ5/nzSF03x+786ypRSF199eCHd3d3ZbpK4Djf72bJHDlN85glCpfcRKV4xAi3LfZGwxbq1A/iLTJa9yXfD1b9OtiTY9XoEh0Ox5A4vxaXXPt+aL/+3tNacPp5k/+4o0YimrNLGrPlu/EX5N3TxVuTC/rIszZmTSQ7vixHst3B7FJOnu5gwyYEth3pAzUQnhW3fx0x0EC55M5HilaNaoOFy+yqRsNjwYohI2GLZKh9FJTfXJ1LQ8VNcA6/TM+ETpJ0ysuh6XG1NTemZErdElZaj7nsI7nsIfepYprdq8yvoJ76AdjhQ85tQTW+C2QsyQwjFddNP/gecOIrxB3857EGqr6+PDRs2cPjwYbxeL2vWrGHGjBl5U+r8eny7uYN4WvPRpZV5f2ZdXIO28HX9hrStiEhh7i/QO1I8XoM5Cz3s2BLh6ME4k2dcX5nndFrzRnOU40cSlJbbWHy7Z9gLWWSbUoraegdVE+wcOxTn0L4469YGqa23M32OG89VSlSL4ZFOaU4eS3Bkf5xI2MLnN1jQ6KGmzp6Tc9nSjjJ6an8ff8dP8fU8mxkSV/FwVofEORwGTSt8rH8hyOZXwixf7cNbcGMnBGzR47gHthIuWiFBaphImBLDRtXWo2rr0Q8+Akf2Z4LVttfQW18FbwFqyR2oxpUwZWamsqC4Imvrq+iXf4O6+0HUguFb9yEWi7F161Z27tyJYRg0NTWxaNEi7KNQHXA07WmP8HLLAA/PKaXGL3OlxjpXcAf2RCv9Fe8GY2y9l29Ubb2dttN29u+OUVZpv2bPSzRisW19mL6eNJNnOJkx15WTB7bDxTQVk2dkekEO74vTcjDOmRNJGqY5mTrTKetRjYBkwuLY4QRHD8ZJxDVFJSazF3qpqLbl/okuw8FAxbsHy6f/JjPsr+r9pJxX7qUYaW6PQdNKH+tfCLFpXZg7Vvtwua/zfavTFHT+grStkMg4HA49UiRMiWGnDAOmzkJNnYV+94dhb3Omt2rjS+h1z0BJWaZoRdMKVG1Dtpubc3T7GfR//VtmHto7fmdYtplOp9m9ezdbtmwhFosxa9YsbrvtNnw+37BsP5ck05qvb2mjwmfnXbPzf96XuAYrgbfnWZLOWuK+edluTdadnaz+8jMpmjdHuHOND8O8/AFrV0eS1zdESKc1i5d5qJ4wfk48OBwGs+a7qZ/i5MCeKEf2xzlxNMHUWU7qpzgxr/A7G0laa/p60rSdThKLWLi9Bm6Pce7SY+TUMLhriUUtWg7GOXYkTioJZZU2psx0UVpm5n6IOp9SRIuWkXTWUNj2A4pPfZ1g2duJ+ZdkrUkFfpOmFV42vhxi8yshlq0qwO649u/U3b8pc+Kp8n1oY5iqAgoJU2JkKZsN5i1FzVuKjkUziwRvXod+9mfoZ34CNXWZYNW4YkTmBOUbnYhjPf4vYNowPvKnmd/frWxPa44ePcprr71Gf38/EyZMYPny5ZSVlQ1Ti3PPL/f3cGogwV+/qRanTc4yj3WevvWYqX4GKt4tC04OcroM5i/1sPW1MAfeiDFz3oXDkrTWHD0QZ9+uGF6fwZLlvlGtmpZLPF6DhU1eJk1LsW9XjL07YrQcSjBjjouaOvuIH/RrS9PdlabtVILWU0liUY1S4HQr4ic0F89qdzjVUMDyDAWtzH0er4HdobIeVMKhNEf2xznZksDSUF1rZ8pMJ4XF+X3ImXLX0TPhExS2/xB/x0+wx04QDNyftd7w4lIbS5Z52fJqmK2vhWha6bv0JIDWmIl2nJEDOMIHsMeOE/dMI+6dnZU2j1X5/c4WeUW53KimldC0Eh3sP1cR8GffRf/suzB7IcYjf4gqHbsH+teif/TvcKoF45N/gyq59d/D+vXr2b59O8XFxTzwwAPU1dVl/R/tSOoIJfnR7i5um+BjSc3Y63UTF1KpIJ7el4l7Z5F0Sy/3+Spr7ExocHB4f5yKajslgwvWppKaHVsjtJ5MUlVrZ0GjB5t97P5NuF6FxTZuW+mjsy3J3p0xmjdHOHLAZNaC4a/8Z6U1XR0pWk8laTudJBHXGGam52ZGrYOKahsOh4FlaWJRTTRiEQ1bRCMWkcHLUH+ajtYkVvrCbZs2hnqxPJfp2Ur6LbTWI/J/oL83xeH9cc6cTGIoqK13MGWG84bn9OQybfPRV/0BvD3P4e19GVv8zGD59OKstKe8KvMZbt4cYfumCEtu92CQwB45nAlQkYOYqX4Ako5KIkV3EileDmP4OCAbJEyJrFAFhahVb4FVb0F3tWd6q57+CdbffhL1yO9jLL0z200cddbmdehX1qLufQg199aHDxw/fpzt27cze/ZsVq1aNaaKS1zJv7/eDsCHFksv53jg7XkBpVOESu/NdlNy0uyFbrrak+zYHGHFPQVD86NCQYuZ811Mnu4c0ydXbkZZpZ0VFbahyn+bXg5TVmlj5jw3hcU3HwpSKU1nW5LWU0nazyRJJTPBp6LaTlWtnfJK+yWh1jAUHq/KFMe4zLk1rTWJhL4oaJ273deTJJm4uGDzAAA2W2YNLtOmsNkUNrsauu/c7avdz9Dt3q7MQrsdrSlsNpg83cmkac7rn8eTb5RBuPQeks4J+DuepOTkV4gVzCflqCLlqCDtrBzVIXS1dXbM+ACx1n3YDxyj2H4SRRpLOUl4phAuXk3COw3LJsvWjBQJUyLrVKAC9daH0UvvxHri/6G/+a9Yu7ehfvujKFf+Lhh7I3TrSfR3vwpTZqEefP8tby8SifDcc89RUlLCypUrx0WQ2noqxOZTIX53QRll3vFdhGA8MBMduAe2Ei1sJO0Yv73ZV2O3KxY0edn4Uoitr4Xp605hmIrbV3oJyDpLV3S5yn+vPHvjlf+SCU37mSStp5NDvUh2h6Kq1kFVrZ1Ahe2W5mYppXA6FU6nQVHJ5Z+TSmqi0XM9Ww67h/7+EKkUpJOaVEqTHLyMhjWpVCb4pZIay7r+tjicihlzM2tEjZciHgnfLHodf0hB5y9wDTRj6E1Dj6VtxaSclaQcg1/OStL2UlDD00unrDj26BGc4UzvU7nZB7XQEw1wSjXhmzibpHsiKDnMHw3yWxaQ106AAAAgAElEQVQ5Q5VXYfzpP6Gf+hH6qR+jD+/D+NCfoCZNz3bTRpSORrC+9r/B4czMkzJv7Y+t1prnn3+eeDzOgw8+iO0W513lg3jK4pvb2plQ6OD+GVc4qhBjiq/rGbSyEy5Zne2m5LRAuY1J050cPRCnqMRkyR1e3J7xcbB7q65W+W/KTCeOy4SGeMyi7XRm+F5newptgdOlmNiQCVAlZbZRrZZosysK7ObQnLhAoJiurvQ1vivDSmdCViZcnQtZ5y4zYc3pUtTWOTDzqDjGcEk7AvTVfBC0xkj1YUu0You3D106wgdQZFKpVjZSjvKhgJV2VpByVGGZvmsPu9MaM9mJI3wAZ+Qg9mjLYO+Tg6RnCuHiVcQ9U2ludnJyb4K5ppv6qWP/f3+ukN+0yCnKZkO9/X3omQuwnvh/WP/yZ6j734t6y7tQxtgZd32W1hrrP78MHa0Yf/z3qOJbrz63a9cujh07xsqVKwkEAsPQytz34z3ddIST/OOaidizUIVLjC575CjOyD5CpfegTZkbdy0z57ooLbNRVnlrPSHj1bUq/yXimrZTmR6o7s4U6Exhi0lTnVTW2ikuzbPqdYMMU+EwFQ4p+nZtSmHZi0nYi0l4Z52730piS3Zii7dhS7RhxttwRA7hDm4/9xTDO9iLVXFeb1YFpOM4wvtwRA7iDB/ATPUCkHKUEylaRsIznaS77oLep3lLNIm4xe7tURwuNa4qdGaThCmRk9S02Rif+xL6+4+jf/F99BvNGB/6Y1RpebabNqz0cz+H7RtQ73oUNX3uLW+vu7ub1157jfr6eubNGx9lok8NxPnZvm5WNfiZUzE+hoWOa9rC1/2bzDophXdkuzV5wTAVlTUyrO9WXa7y36G98aF5SQV+g6kznVTVZtb3yscAJYaZYSflrL5kXSqVDg8FLFuiDVu8DffAVpROAqBR0GJQpM/2Pk0mXLyShGfaVYtdGIZi0e1eNq0L0bwpgsOhZEjvKJAwNUZZWrP2UB+JtObtM/Nz2JPy+OBDfwJzF6O//zjW334K9f6PYzSuyHbThoU+sAf9k+/AottRd7/jlreXSqV45plncDqdrFmzZlz8I9da842t7ThtBo8uHFtBW1yeM7QTe/w0/RUPj/sFekV2nF/578TRBP4ik6paO75xWl5e3Dhtekl6JpP0TD7vTgsz2YOZyAwT9Dpt9Kpaku76G5r7ZLMpGu/0suGFzFzJ21f5KCqRw/2RJL/dMag9lODLm9rY0x4BoLLATlNtQZZbdXOUUqjbVqEnz8wUp/jW/8Xa/XqmOIU7f3shdG831jf+BcqrMB791LAEn/Xr19Pd3c0DDzyAx5O/v5sb8erxILvaInxsaQVFbvlzNuZZSXzdz5J0VhP3zc92a8Q4V1Zpp6xSAr0YJsog7QiQdgRIMBtPIECyq+umNuVwGDSt9LH+hSCbXwmzfLVvTJWozzUyC3UM0Vrz3OE+PvXUMQ53x/j9xkoaip18bXMbA7FUtpt3S1RZJcaf/hPq/vegN6/D+vtPo4/sz3azbopOJTNBKhHH+NhnhyUUtrS0sHPnThYsWEB9ff2tNzIPhBNp/uP1dqaUuLh7SlG2myNGgad/A2aqj1DpW2SBXiGEuAq3JxOotIZN68LEojdQnlHcEPlvNEZ0R5L8/cun+LfNbUwudfHlt9Zzz9QiPn17FaFEmse3tme7ibdMmSbGA7+N8Zn/DZaF9X/+HOtXP0Snr68yUa7Q//OfcGQ/6nc/gaqZeMvbC4fDPPfccwQCAZYtW3brDcwTP9jVRV8szccaKzBHsTqWyA6VDuHpfYm4Z+aFQ2OEEEJcVoHfpGmFl3jMYvMrocusOyaGg4yLyXNaa149HuQbW9tIpDUfWlzOW6cXYwwOG6svdvGeuQG+t7OLV48NcGe9P8stvnVqyiyMvxksTvHLH6D3NmN88I9RgdxfqNXavA79wq9Qax4YloWJtdY899xzJJNJ7rnnnnFRBh3gaE+M3xzs5b5pRUwtdWe7OWIUeHteRFlJQgFZoFcIIa5XcamNJXd42fJqmK3rw8xe4ALUBdXYh66r8y6uel1d8H2mjcsuFTBejI8jrzFqIJbi8a3trD8RZFqpi08tq6LWf2kN03fOKmXzqRDf2NrGnAoPxWNgbonyeFEf/hOsuYvR3/861t99CvW+j2M0rcx2065Inz6O/q9/yyzM+9Cjw7LNHTt2cOLECVatWkVp6a2XVc8HltZ8fUsbBU6T982XxVrHAzPRibt/M1H/UtIOKTQihBA3orzKzoJGD82bI7zybGhEXsPhVPj8BgV+E5/fHLrucqsxXxAr/4+qx4BgMMjatWvx+TLrpTidThwOxwVfF9+3pzvJt5p7CCU1j8wv4x2zSq441Mk0FJ++vYo/evoYX93cyl+urB0zb2zjtjehJ8/IFKf4989j7Xkd9dsfy7niFDoSxvr6P4Pbg/HRz6CGoQeps7OT9evXM2nSJObMmTMMrcwPzx/p52B3jD9aVoXPIRNqxwNf9zNoZZMFeoUQ4ibV1jvw+Q2ikcFFhM8f8adBX3xdX/DwBU84/3u1zizeHApaBAfSnDmZJJlIDD1uszEUrnx+czBsGXi8xqguYD2SJEzlgHQ6jVKKYDBIJBIhHo+TSCSwrKtPFlwMGKZJ3yYn399++eDl9/upq6ujpriY988v4z+2d/Di0X5WTx47E/bPFqfQTz2J/vWP0If3ZYb9TZmZ7aYBZxfm/RJ0tmL8yT+iim69VH0ymeSZZ57B7XazevXqMROOr6U/luI7zR3MKXezcgwMWRXXZo+24AzvJVRyN9qWn1VJhRAiFxSV2BiGQ5Cr0lqTiGuCA2lCAxahgTTBAYuu9hSnjiWHnmcY4C04G7Ayl76CTNDKt8XFJUzlgKKiIh566CECgQBd55XBTKVSJBKJoa83Wvv55Z52YvE4iytczAnYSSXPPX42hPX19V1w+9VXX8Xv91NfX89Cj5cntsG8Si9l3rFT0lWZJuqB96JnLcD6989j/etnUW99N+qtD6PM7PZe6LU/heZNqIc/iJo2e1i2+dprr9Hb28uDDz6I2z1+5gx9p7mTaNLio42V4yZAjmvawtf1G9Kmn0iRLNArhBC5TimF06VwugwCF43KTiY0oWB6KGCFBtL096ZpPZU81/OlMgtkF/gNausdVE9wjPrPcKMkTOUwm82GzWbDcLj4UXMHvzmYpsZfzadvr2J64PoOoAcGBjh27BjHjh3jjTfeoCSdZqky+c8nd3Hvkpk0NDQMDS8cC9SUmZniFP/9DfSv/jtTnOIDf4Qqr8pKe/S+neiffhe1ZDlqzQPDss0jR46we/duFi1axMSJt14NMF/s7YjwwtF+HppVwsTCS+cGirHHGdqFPX6KgfJ3gZH7/1CFEEJcmd2hKC61UVx6YfxIpzXhwWGCocEereBAmlgkP8q5S5jKcfs6InxxYyvtoSQPzMgM1XParr9iit/vZ968ecybN49kMsmpU6d4ZecB2k6d4KWXXuKll14iEAjQ0NBAfX09FRUVGEZ+V2RRHi/qg3+MNWewOMXffgL14COo1W9DGaPXS6V7urC+9X+hsiZTBn0YelJCoRAvvPACZWVl3H777cPQyvyQsjSPb2mnzGPj4bmBbDdHjAYria97LUlHFbGChdlujRBCiBFimgp/kYm/KD/nQUuYylGJtMV/7+riZ3t7KPPa+Yc1E5lTcWtFFex2+1BoeuzFk+xr7eB3G9J0t55k27ZtbN26FZfLRX19PfX19dTV1eF05m8PgNG0Ej11Ntb3v45+8gn0ttcwfvcTqOqR7805tzBvAuPjn0W5bn0ontaaZ599llQqxb333ouZ5eGLo+mpA70c74/zFytqcN3AyQSRvzz9GzFTfQxUPyQL9AohhMhZEqZy0JGeGF/ccIYT/QnumVLEo4vK8NiH78BZKcUf3lbFp56K8WzYyT+8s5FkIs7x48eHhgTu378fpRRVVVVDAaykpCTv5qmokgDGH/4Vessr6B9+E+vvP52ZR3Xvu4alot6V6CefgKMHMD72Z6iq2mHZ5vbt2zl16hSrV6+muLh4WLaZD7oiSX6wq4ulNV4aa8fOkFRxZSodHlygdzpJz5RsN0cIIYS4IglTOSSVtvjhri6e3NNFocvG51bVsqh6ZA4ey7x2PrSkgi9tbOXXB3p5+8wSpk+fzvTp07Esi/b2dlpaWjh27Bjr169n/fr1Q5UBGxoaqK2tzZsFYpVSqKaV6Jnz0T/8FvoXP0C/vgHj0U+i6ob/QM3a9BL6pd+g7n4HavHwTJpvb29n48aNTJ48mVmzZg3LNvPFE693YGnNh5dU5F2YFzcns0BvnFDpfdluihBCCHFV+XE0PA6c6I/zmed2caAjxMp6Px9ZUoHPObLDuFY1+Nl4Msh3d3SyqNrLhMFJ/YZhUFVVRVVVFcuWLSMYDHL8+HFaWlrYt28fu3fvxmazMXHixKFeK6/XO6JtHQ7KX4T6yJ+iG+/E+t7jWP/7f2UCz/3vQTmGZzijPtWC/u5XYdoc1Dt/Z1i2mUgkWLt2LR6PZ1yVQQfYfibEhhNB3jc/QIVPChCMC9F23P2biPmXknZWZLs1QgghxFVJmMoBB7qi/OVzJ/A6Tf7szmqWTRyd9XOUUvx+YyWfeKqFL21s5V/urrvswr8FBQXMmTOHOXPmkEqlOHXqFC0tLbS0tHD06FEAKioqaGhooKGhgUAgkNMH/GrBbRjT5qB//G30Mz9BN2/KzKWaems9PjoSwvraP4HHh/HRPx22kuyvvPIKfX19vPOd78Tlcg3LNvNBIm3xja3t1PgdvGPmCC+MIXKGOvnTwQV612S7KUIIIcQ1mY899thj2W5ENgWDwWw3gWKXjaSl+bu3zqb21mpM3DC33aDCa+dXB3qxmYrZ5VdvgGEYFBUV0dDQwIIFC5gyZQo+n4/e3l4OHDjAnj172Lt3L319fSil8Pl8OVkdUNkdqAVNqCkz0c2b0C/8EoIDMHU2ynbt9bc8Hg+RSGTotrYsrG/+Kxw/jPHJz6GqJgxLOw8dOsTGjRtZsmQJs2cPzxpV+eLHe7rZdCrEny6vptp/az2HF+8vkQO0hZHqxR4/iSNyEFdwJ+7+DdgG9hAuWUXCOyPbLRTXQT5b+UX2V/6QfZVbCgquvGi89EzlANNQPLKgjGKPg64sfG7uqPNz58kgP9rdxZJqH5NKrq/3QylFIBAgEAiwdOlSIpEIx44do6Wlhf379+fFcEA1awHGY19B//x76Bd/jd61FeORP0DNvrFSzPrp/4GdW1Dv+QhqysxhaVswGOTFF1+koqKCpqamYdlmvmgNJvifN7pZUednfmVuvWfEDdAWRqofM9mFLdmFmejGTJ796kGRPvdUZSdlL0WXLSdScGcWGy2EEEJcPwlTAoCPLK1kT3uEL21s5f/eW4fdvPHeJI/Hw6xZs5g1a1ZeDQdULjfqPR9GL7kD6ztfwfri51DLVqMe/iDKe+0CIHpvM/oXP0A1rkDd9dZhaZNlWaxduxbLsrjnnnvGVRl0rTXf2NqOzVD83uLya3+DuDydxh47jjO0F1v8NCgbluFEGw604USrs9cvvu0cvH3+Y/YrlycfCkzdg4GpCzPZg5nsumxgSttLSTnKiXtnkrYHSDtKSdtLsUw/DJ6goatrlH5JQgghxK2RMCUA8DtNfr+pkn9cd5of7u7mkQVlt7Q9m802tF7Vm970Jrq7u4dC1aZNm9i0aRM+n28oWOVCdUA1ZRbG33wJ/asfotf+FP3Gdozf/hhq0ZUXx9XdnZmFeasnoH7nD4ctHL7++uucOXOGN7/5zRQVFQ3LNvPFhpNBmlvDfGhxOSVu+RN1I5QVxxE5hCO8F2d4P4YVRWOSdNWirDi2dBBlxVFWInN5XtC5FkudDVqOobBlpCOYye6LApPtosBUOhSaLLNA1owSQggxpsiRihjSWFvA6kmF/HRvN421PqYHbn2hWbj+4YB1dXVMmTKFhoYGHI7sVG5Tdgfqnb+DXnwH1ne+jPX1f0ItvgP12x9B+S9c20knk1iP/zOk0xgf+3OUc3iKQ7S1tbFp0yamTZvGjBnja95IJJnmiW0dTCp28pZp42ctrVthpII4wvtwhvfiiB5B6RSW4SbhnU7cO4uEZxrauMKcM51CWclMsNLxC4OWTqCsBIZ19v6z9517TtoRIO6dMRiYSkk7AhKYhBBCjCsSpsQFPri4nJ1tYb60sZUv3FeP0zb8B0VXGg549OhRjhw5gmmaFwQrp3N4ypbfCFU3GeMvPp/pofr1D9H7d6He8yFU05uGep/0D78Fxw5hfPyzqMqaYXndeDzOM888g8/nY9WqVTkxDHI0/Wh3Nz3RFH+2ouaylSUFoDVmsgNnKBOg7PGTAKRtxUT9TcS9s0i660Bdx9BQZUObNrQ5PCdOhBBCiPFGwpS4gNdh8onbqvjciyf53s5OPrh4ZNd5uXg4YGtrK4cPH+bQoUMcPXo0q8FK2Wyotz6MXnQ71ne+gn7iC+gtr2K8/+NEd21Gv/IM6t6HrjoM8EatW7eOYDDIQw89lJUQmU0Hu6L8cn8Pd08pGrZe0TFDW5n5T+F9OMJ7sSW7AUg6awiVvJm4dxZpRwWMs/AthBBCZJuEKXGJBVVe7ptaxK/293JbbQGzK0anXrtSiurqaqqrq7nzzjtpa2vj0KFDFwSriRMnMnXq1FENVqpqAsZn/gn94lPon30X63N/yICVhhnzUA++f9he58CBA+zfv5/Gxkaqq6uHbbu5Lm1pfr6vhx/s6qLYZeP9tzhfb8ywEjgih3CG9w3OfwqjMUl4JhEtWk7cOxPLVpjtVgohhBDjmoQpcVm/u7Cc5tYwX9rUypfe0oDbPrpzIJRSVFVVUVVVdUGwOnz4MC0tLRiGMdRjNWnSpBEPVsowUWseQM9vxPre1zB7OtEf/l/DsjBvX18fO3bs4I033qCqqorGxsZhaHF+aA0m+OKGVvZ3Rbl9QgEfb6zA7xw/lQsvptKhTHgK7cMRPTQ4/8lFwjM4/8k7DW2Mn4WbhRBCiFwnYUpclttu8Knbq/iL507wn80dfLyxMmttuZ5gdbbHaqSDlSqrxPyjv6O0tJTu7u6b3o7WmjNnztDc3MzRo0cxDIPp06ezbNmynFzkeLhprXn6UB//ub0Dm6n4o2VVrKz3j7s5YmfZYqfw9K/HGdyNIk3aVkTUv3Rw/lPD9c1/EkIIIcSokzAlrmhWuYe3zyzh5/t6aKr1saj62msujbTLBauzc6yOHTs2asHqZg/60+k0hw4dorm5mc7OTlwuF0uXLmXevHk5t6DxSOmKJPnKxlZ2tEVYWOXlE7dVUuqxZ7tZo0+ncYbewNO/HnvsBJZyEC1sJOZfQspRJfOfhBBCiDwgYUpc1fvmB9h2OsS/bWrjy29rwOfInTPk5wer5cuX097ePtRjdX6wamhooL6+noKCgqy1NRaLsWfPHnbu3Ek4HKa4uJi77rqLGTNmZH19rdGitebllgG+ta2dtNZ8bGkF904tGne9USodxt2/FffAJsxUPyl7CcHA24j5F8sQPiGEECLPjI+jOHHTHKbBp5dV8Zm1x3ni9XY+dXtuFkZQSlFZWUllZeUFwerIkSMcO3YMgOLiYurr66mrq6O6unpUQkxvby87duxg3759pFIpJkyYwOrVq6mrqxtXIaIvluLrW9rYdDLErDI3n7y9iqqC7Kwlli1mvA1P/wZcwWaUTpFwTyZY9nYSnumyLpMQQgiRpyRMiWuaWurmXbNLeXJPN7fVFtA0IXs9PNfj4mDV29vLsWPHOH78ODt37qS5uRm73U5tbS11dXXU1dVRWDh8VdG01pw+fZrm5uahOV0zZsxgwYIFBAKBYXudfLHxZJCvb24jnLR4dGEZD8woGT9rSGkLR2Q/nr4NOKJH0MpGrGAhkcJlpJ3Zm4cohBBCiOEhYUpcl4fnBNh6OsRXt7Qxs8yN35Ufbx2lFCUlJZSUlLBo0SKSySSnTp0aClctLS1AptfqbLCqqam5qV6rdDrNwYMHaW5upqurC5fLRWNjI/PmzcPjGZ3y8rkklEjz79vaeallgMklTv7h9momFo2PtbNUOoYruA1P30bMVA9pWyGh0nuJ+pegzfExN04IIYQYD/LjiFhknd1UfPr2Kv7kmWM8vrWdz9xZk+0m3RS73U5DQwMNDQ1orenr6+P48eMcO3aM3bt3s2PHDmw22wW9VkVFRVfdZjQaZc+ePezatYtwOExJScm4mw91sR2tYb68qZXeaIp3zy3l4TkBbOOgN8pMdOHu34Br4HUMnSDhqiMUuJe4d5ZU5BNCCCHGoPF5pCduSn2xi/fOLeO7Ozv55rZ2HplfNurrTw0npRTFxcUUFxezYMECkskkp0+fHuq1OjvXqrCwcGiuVU1NDXZ7pvLcxfOhJk6cyJo1a5g4ceK4mg91vljK4j+3d/D0oT5q/Q4+e08dU0vd2W7WyNIaR/QQ7r4NOCMH0JjECuYRLbyDlCs/TzoIIYQQ4vpImBI35B2zSuiOJvnNgV62nAzy0aWVLK3Nfsn04WC326mvr6e+vh7ggl6rs5X4TNOkpqYGp9PJoUOHME2T6dOns3DhQkpLS7P7A2TZvs4IX9rYSlswyQMzinn//DKctvwN29dkJXAFm/H0bcCW7CBt+giVrCbmb8Ky5fa8QiGEEEIMDwlT4oaYhuKjSytZWV/I1za38Q/rTnHHxAI+tKSCEvfYejsVFRVRVFTE/PnzSaVSnD59eihcpdNpmpqamDt37ricD3W+ZNriB7u6+Pm+HgIeO/+wZiJzKsbm78RI9eOIHMEePYwzvA/DipF01jBQ/lvECuaBGlufASGEEEJcnfznFzdlRpmbz99Xz8/3dfOj3d3saA3zyIIy7plahDEGh7jZbLahOVQrVqwgEAjQ1dWV7WZl3dGeGF/c2MrxvjhvnlzIBxaX47GPnblBKh3DHjuKI3IYR+QItmQHAJbpJeGdQdTfRNJVJwvsCiGEEOOUhClx0+ym4rfmBLhjop+vb2nj8a3tvNwywB80VY6bqm3jVdrS/GRvNz/a3UWBw+Sv31TLkpoxMNxTp7DHTmbCU/QwttgpFBZa2Um4G4j6l5D0TCHlqJC1oYQQQgghYUrcumq/g79bPYGXWgb4j+0d/NHTLbxzVim/NacUhykHnLlIa008rYkmrcxXKnMZS1lEBi8vfuz8y65wkrZQkjvrCvjI0kr8zjztjdIaM9GOI3oYR+Qw9mgLhk6gUaSctUSKV5LwTCHpmihD+IQQQghxCTk6EMNCKcVdkwpZUu3lP7Z38OSebl47PsDHGyuZVynr6oy2tKXZ3xll6+kQR3piRAZDUOzsZcrC0te3LYepcNsN3DZj6LLW7+CRBWUsr/OP7A8yAoxk33nh6QhmOgRAyl5GzL+IhHsqSXcD2hzjVQiFEEIIccskTIlh5XfZ+PSyat7UUMjXt7Tx1y+cZPWkQh5dVJ6/vRd5IhRPs701zNbTIbafCRFKWNgMmFziotBlUmGzXxKK3PbMl+sy97ltmfvNfF4fSluodBh6TuDrbMYROYwtmZnrljZ9JN1TCHumkHBPxrJffT0xIYQQQoiLSZgSI2JBlZcvv7WBJ/d087O93Ww7HeIDi8tZWe8ft2swDTetNacHEmw9HWLr6RD7OqNYGgqdJo21BTTW+Jhf5RlTBSGATECyohipEEY6hJEOZi6vcFthAeBSDpLuBqKFTSTcU0g7KqRwhBBCCCFuiYQpMWKcNoNHFpRxZ10BX9vSxhc2tPJSywAfX1pBZYEj283LS8m0Zm9nJBOgToVoCyUBaCh28tCsUpbW+pha6srPiorawkx2YaSC50LRNQLSBd+OiWXzYZk+LLOAlKN66La3fBZd8QKZ9ySEEEKIYSVHFmLE1Re7+Kc317H2cB//1dzJJ55q4T1zA7x9Zgm2fB5CNkr6YyleP5MZvtd8Jkw0ZWE3FPMqPTw4s4QlNT7KvPZsN/OmGakgroFtuAe2YKb6LnhMY2TCka1gMCBVDV4/F5rOBiZtuK/Y0+T1B0BK2QshhBBimEmYEqPCNBRvmVZMU62Pb25r5792dPLKsUwZ9WkBmeh/Pq01x/vibDsdZsvpEAe7omig2G3jzvoCltT4mF/pxWXL40qJ2sIePYq7fzPO8F4UFgn3ZMIlq0nbii8KSHn8cwohhBBiTJMwJUZVqcfOZ1fUsulkkG9ubecza4/zlunFvH9+YOzN7bkKrTWxlCacTBNOWIQTafpjaXa1h9l2OkRHOAVkike8Z26AJTU+JpU483P43nlUOoxr4HXcA1uwJbuxDA+RojuI+RtJOwLZbp4QQgghxA2RMCWy4rYJBcyr9PC9nV385kAvm04Eec+8AA3FTqoKHPgcuR2stNbEUxahRJpwMhOGwonB2wnrgpB0yeODty9XmtxhKhZUefmtOT4WV3sp9eTv8L0hWmOPHcv0QoX2oEiTcNXTX7KGuHc2GGPgZxRCCCHEuCRhSmSNx27ykSUVrKz387XNbXx1c9vQY4VOk2q/g+qCwS+/neoCB1UFDpyjMLwtbWl6oik6Qknaw8lzl+EkHaEEPdEDpK6xUJPDVHgdJl67gc9hUujK/Exeu5G535G5//zbEwudo/LzjQaVjuIKbsfdvwVbsgPLcBEtbCLqbyTtrMh284QQQgghbpmEKZF10wNu/t999ZweSHAmOPg1kKA1mGB7a5gXjvZf8PxSj42aAscFYavKb6fC68BuXt8wOEtreqMp2kNnA9K50NQRTtIZTpI+LyspoMRto8JnZ1aZh9pSP0Y6nglDDmMoNJ0NRV67gd0cG6HohmiNLX4Sd/8WXKGdKJ0i6ZzAQPlDxHzzwJAqjkIIIYQYOyRMiZxgGoqJRU4mFjkveSySTNMWTHJ6MGCdDmYu1x8fIJg4V4VXS9cAABEcSURBVCLbUFDutVMzGLKqChxU+uyEkxbtocQFoakznLqkZ6nYbaPca2daqZvldX4qfHbKvXYqfHYCHtsF4SgQCNAl1eGGKCuOM7gDd/9m7IlWLOUgVrCIaGETKWd1tpsnhBBCCDEiJEyJnOexm0wqMZlU4rrksYF4mtbBnqzze7Xe6IgQS10YlgpdJuVeO5NLXNw+4VxQKvfZKfPYx8zwutFki53GPbAFZ3AHhk6QdFQxUPYg8YIFaOPSYCyEEEIIMZZImBJ5ze808TvdTL+ovLrWmt5YmvZgAq8zE6LyupR4DlDpGEaqDzPVh5nsxhXcgT1+Cq3sxHzzBnuhaq+41pMQQgghxFgjYUpcP61RVgwjHcqsAWTm7vpQSilK3DZK3PIWvy5WEjPVj5Hqx0z1DYam824n+zF0/IJvSTnKCQbuJ1awMKffC0IIIYQQI0WONMV5IWkAIxXETA1gpIMY512evU/p1NC3WYaLtL2EtK2YtL0E67zraXsxKHl75QSdHtyvfYPhqP+8sDR4mQ5f8m2W6SNtKyRlL8NyTyFtK8SyFWUu7UVYpl96oYQQQggxrsnRbi7QaZQVg6QTlQ6TqR3HeZeD1xWX3K8vfs7Q1cx1ZSVvOCSdZRlOLNOPZSsg6aojbSvAsvnRhgeVDmOmejCTPdgSHTgjBy7YhkZh2fykbSVD4SoTvEqw7MVYZoEciA83K4kt2YmZ6MCWaMeW6MBMtGMme1FYFz7VcGHZCjNhyVlL2lY0dNuyF5E2/bL+kxBCCCHENUiYygFmoovSk1+EFigbhde7MCRNJG3zY9n8WGbB0GXa5r+xMtbawkgHMZM9mMlezGQPRipz3RE5hJkeuPDpyp4JWEM9WZleLctWMNQ2VG4v3Js1Q6EpE5jOhaYeFJmiGxqDtD1AylFF3DfvvLBUhGUvRBuXFvMQQgghhBA3RsJUDrBsPoKB+/H6vIRDofMeOa8andaX3K8ufs7Z6/rcda1smYA0FJYKRqbKmjKwbIVYtkKS7oZLH7eSmKnewbDVM3TdSPZgj7Zg6MQFT9cotOklfTbgnRfyMtf9g8HLB2qMFpa4JDS1YyY6Lg1NjrOhaQEpZwUpezlpR6kMsxRCCCGEGGFytJUDtOklWrQMbyBAdKyuXWTYSTvKSTvKL31Ma5QVwUz2XTQkMXPdSA9gi5/BSIeGQsTQt6KGQqJl85M+G7LOD1yGB8v0gLLnztBCrVFWHMMKo9JhjHQEIx3GTHYNhqazw/MuCk3O6nOhyVFO2i6hSQghhBAiW+QoTGSfyvRCpUwvUHPl5+k0RjqUmfOVCmKmB4auG+kBjGQf9tiJyxZTANCYaNONZbjRhgvL9KANN5bpvvyl4R58vufq84e0Ruk4ajAQnQ1Gyjp3e+gxKzIUni6ex3S2jZnQVEOsYCFpx9nQFJBhj0IIIYQQOUbClMgfyhwaSnhVOoWRCmGkMwU2VDo6GGKiGFYUZUUx0tFMMEt0oqwoyopd0ut1wSaVbTCEZcKWandSEu8fDEkRFOnLfx8G2sz0jFmGl5Q9gHbVDd72oE1v5rrpRZse0rYiCU1CCCGEEHlCwpQYe5QtU7rbXsSlNQqvQFsoKz4UtDKXkcylFT0XxAYvwSJtL8VyTRwcRug9F5pML/rsfYZz7M7pEkIIIYQY58ZUmNqxYwff/va3sSyL1atX8+CDD2a7SSJfKANtDg7ru46K4IFAgP6xOr9NCCGEEEJclzFzytyyLJ544gn+4i/+gi984QusX7+eU6dOZbtZQgghhBBCiDFqzISpw4cPU1lZSUVFBTabjWXLlrF169ZsN0sIIYQQQggxRo2ZMNXT00NpaenQ7dLSUnp6erLYIiGEEEIIIcRYNmbmTGl9aSU2dZk1hZ5//nmef/55AP75n/+ZQCAw4m27XjabLafaI65M9lV+kf2VP2Rf5RfZX/lF9lf+kH2VP8ZMmCotLaW7u3vodnd3N8XFxZc8b82aNaxZs2bodlcOFREIBAI51R5xZbKv8ovsr/wh+yq/yP7KL7K/8ofsq9xSXV19xcfGzDC/yZMn09raSkdHB6lUig0bNrBkyZJsN0sIIYQQQggxRo2ZninTNPnABz7AP/7jP2JZFqtWrWLChAnZbpYQQgghhBBijBozYQpg0aJFLFq0KNvNEEIIIYQQQowDY2aYnxBCCCGEEEKMJglTQgghhBBCCHETJEwJIYQQQgghxE2QMCWEEEIIIYQQN0HClBBCCCGEEELcBAlTQgghhBBCCHETJEwJIYQQQgghxE2QMCWEEEIIIYQQN0HClBBCCCGEEELcBAlTQgghhBBCCHETlNZaZ7sRQgghhBBCCJFvpGcqh/z5n/95tpsgrpPsq/wi+yt/yL7KL7K/8ovsr/wh+yp/SJgSQgghhBBCiJsgYUoIIYQQQgghboL52GOPPZbtRohzJk2alO0miOsk+yq/yP7KH7Kv8ovsr/wi+yt/yL7KD1KAQgghhBBCCCFuggzzE0IIIYQQQoibYMt2A8ayr33ta2zfvp3CwkI+//nPA3Ds2DG+9a1vEYvFKCsr45Of/CQej4dUKsXjjz9OS0sLlmWxYsUK3vGOdwBw9OhRvvrVr5JIJFi4cCG/93u/h1Iqmz/amDRc++uxxx6jt7cXh8MBwF/91V9RWFiYtZ9rLLrRffXNb36TI0eOYBgGjz76KLNnzwbkszVahmt//f/27j+krvqP4/jzXu/Cq1ftXjUrXY3Q2SLDwLXo1xTaP2H91YIV1Oak1mrCxloG4iAYVHBzBEpCoz8crT+GRn8Ug5VooxbaBNmWa1f7Y1J406vuLu/Z3b338/1j7PKNvo7u7d5z/c7X48+j93iOL17q+5zPPapb2Tc7O0t3dzcLCws4HA6efvppnnnmGa5cuUJXVxd//PEH5eXl7N27F4/HA8DAwADffvstTqeTHTt2UF9fD6hfdshkXupXdqWaVTgc5sMPPyQQCNDY2MjOnTuT+1K3VhgjWXPu3DkzOTlp9u3bl9zW3t5uzp07Z4wx5ptvvjHHjh0zxhjz3Xffma6uLmOMMZZlmd27d5uZmZnkay5cuGASiYQ5dOiQOXPmjM1nsjpkKq+DBw+aQCBg89GvLqlk9fXXX5vu7m5jjDELCwvmwIEDJh6PJ1+jbmVfpvJSt7IvFAqZyclJY4wxS0tLpq2tzVy6dMn09fWZgYEBY4wxAwMDpq+vzxhjzKVLl8z+/ftNNBo1MzMz5s0331S/bJTJvNSv7Eo1q0gkYn7++Wdz4sQJ88knn/xlX+rWyqJlfln0wAMPJK8E3fDbb7+xYcMGAB566CF+/PHH5McsyyIejxONRnG5XBQUFDA/P08kEmH9+vU4HA6eeuopRkZGbD2P1SITeYk9UslqenqaBx98EICSkhIKCwuZmppSt2yUibzEHl6vN/mmd7fbTWVlJaFQiJGRETZv3gzA5s2bk10ZGRnhscceY82aNdxxxx3ceeedBAIB9csmmcpLsi/VrPLz87n//vuTdwpvULdWHg1TNlu7di2jo6MAnD59mrm5OQAeffRR8vPzefXVV9m9ezfPPvssHo+HUChEaWlp8vWlpaWEQqGcHPtqlGpeN/T09PDWW29x/PhxjJ7xYovlslq3bh2jo6PE43GCwSBTU1PMzs6qWzmWal43qFv2CQaD/Prrr1RXV7O4uIjX6wWu/1F4+fJlgL/1yOfzEQqF1K8c+Dd53aB+2eOfZLUcdWvl0XumbPb666/z6aefcvz4cRoaGnC5rkcQCARwOp309vby559/0tnZSV1dnX6Y5ViqeVVUVNDW1obP5yMSieD3+xkeHk5edZLsWS6rpqYmpqenaW9vp7y8nNraWvLy8tStHEs1L0DdspFlWfj9frZv337Tu+7L9Uj9ste/zQvUL7v806yWo26tPBqmbFZZWUlHRwdwfZnLmTNnADh16hT19fW4XC5KSkqora1lcnKSDRs2JK/YAszNzeHz+XJy7KtRqnlVVFQk83G73TzxxBMEAgH9QrLBclnl5eWxffv25Od1dHRw1113UVhYqG7lUKp5AeqWTWKxGH6/nyeffJJNmzYB15dczs/P4/V6mZ+fp7i4GLh+Vfy/exQKhfD5fH/brn5lTybyAvXLDqlktRx1a+XRMj+bLS4uApBIJOjv72fLli0AlJWVcfbsWYwxWJbFxYsXqaysxOv14na7+eWXXzDGMDw8TENDQy5PYVVJNa94PJ68RR+Lxfjpp59Yu3Ztzo5/NVkuq6tXr2JZFgDj4+Pk5eVRVVWlbuVYqnmpW/YwxvDxxx9TWVlJc3NzcntDQwNDQ0MADA0NsXHjxuT277//nmvXrhEMBvn999+prq5Wv2ySqbzUr+xLNavlqFsrj/5pbxYdPnyY8+fPEw6HKSkp4YUXXsCyLE6cOAHAI488wosvvojD4cCyLHp6epiensYYQ1NTE8899xwAk5OT9PT0EI1Gqa+vp6WlRY/AzIJM5GVZFgcPHiQej5NIJKirq+OVV17B6dR1i0xKJatgMMihQ4dwOp34fD527dpFeXk5oG7ZJRN5qVv2mJiYoLOzk3vuuSfZhW3btlFTU0NXVxezs7OUlZWxb9++5PtE+/v7GRwcTD7K/uGHHwbULztkKi/1K/vSyeqNN95gaWmJWCxGYWEhHR0dVFVVqVsrjIYpERERERGRNOiSg4iIiIiISBo0TImIiIiIiKRBw5SIiIiIiEgaNEyJiIiIiIikQcOUiIiIiIhIGjRMiYiIiIiIpEHDlIiI3DI++ugjenp6/rLt/PnztLS0MD8/n6OjEhGRW5WGKRERuWXs2LGDsbExxsfHAYhGo/T29vLyyy/j9Xoz9nUSiUTG9iUiIv+/XLk+ABERkUwpKiqipaWF3t5e/H4//f39VFRU0NjYSCKR4IsvvmBwcJClpSXq6upobW3F4/GQSCTo6upiYmKCa9eusW7dOlpbW6mqqgKu3/EqKChgZmaGiYkJ2tvbsSyLo0ePMjc3R0FBAc3NzTQ3N+f4OyAiInZyGGNMrg9CREQkk/x+P7FYjAsXLvDBBx9QVlbGl19+ycjICHv37sXj8XDkyBFisRh79uwhkUgwPDzMpk2byMvLo6+vj4sXL/Lee+8B14epsbEx3nnnHaqrq4nH4+zatYsDBw5QW1vLlStXCAaD3HfffTk+cxERsZOW+YmIyC1n586dnD17lueff56ysjIATp48ybZt2/D5fNx2221s3bqVH374gUQigdPppLGxEbfbnfzY1NQUlmUl97lx40bWr1+P0+lkzZo1uFwupqeniUQieDweDVIiIquQlvmJiMgt5/bbb6e4uDi5TA9gdnaW999/H4fDkdzmcDi4fPkyxcXFfPbZZ5w+fZpwOJz8nHA4TH5+PkByKLth//799Pf3c/ToUe69915eeuklampqbDg7ERFZKTRMiYjIqlBaWkpbW9v/HHgGBwcZGxujs7OT8vJywuEwra2t3GwlfE1NDW+//TaxWIyvvvqKw4cP093dnc1TEBGRFUbL/EREZFXYsmULx44dY3Z2FoDFxUVGR0cBiEQiuFwuioqKuHr1Kp9//vlN9xWNRjl16hRLS0u4XC7cbjdOp36lioisNrozJSIiq8KNJ+29++67LCwsUFJSwuOPP05DQwNNTU2Mj4/z2muvUVRUxNatWzl58uRN9zc0NMSRI0dIJBLcfffd7Nmzx47TEBGRFURP8xMREREREUmD1iSIiIiIiIikQcOUiIiIiIhIGjRMiYiIiIiIpEHDlIiIiIiISBo0TImIiIiIiKRBw5SIiIiIiEgaNEyJiIiIiIikQcOUiIiIiIhIGjRMiYiIiIiIpOE/IsOTHZjgcsYAAAAASUVORK5CYII=\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",
"\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",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\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,
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"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,
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},
"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": {
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}
},
"source": [
"This notebook is part of a course on **edX** called *Visualizing Data with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_edX_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
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},
"nbformat": 4,
"nbformat_minor": 4
}
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