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DV0101EN-1-1-1-Introduction-to-Matplotlib-and-Line-Plots-py-v2.0.ipynb
{
"cells": [
{
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},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
]
},
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"source": [
"## Introduction\n",
"\n",
"The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
"Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem _and_ audience.\n",
"\n",
"Please make sure that you have completed the prerequisites for this course, namely <a href='http://cocl.us/PY0101EN_DV0101EN_LAB1_Coursera'>**Python for Data Science**</a> and <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which are part of this specialization. \n",
"\n",
"**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course <a href='http://cocl.us/DA0101EN_DV0101EN_LAB1_Coursera'>**Data Analysis with Python**</a>, which is also part of this specialization. \n",
"\n",
"------------"
]
},
{
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
"1.2 [*pandas* Basics](#4) <br>\n",
"1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
"2. [Visualizing Data using Matplotlib](#8) <br>\n",
"2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
"3. [Line Plots](#12)\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
"\n",
"*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
"\n",
"The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
]
},
{
"cell_type": "markdown",
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},
"source": [
"## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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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/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",
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"source": [
"## *pandas* Basics<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 1,
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"button": false,
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},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"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": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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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": {
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}
},
"source": [
"Let's view the top 5 rows of the dataset using the `head()` function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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" <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",
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" <td>902</td>\n",
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" <td>2978</td>\n",
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" <td>Immigrants</td>\n",
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" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
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" <td>Immigrants</td>\n",
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" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
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" <td>5393</td>\n",
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" <td>4325</td>\n",
" <td>3774</td>\n",
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" </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",
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" <td>0</td>\n",
" <td>0</td>\n",
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"<p>5 rows × 43 columns</p>\n",
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"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": 3,
"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": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"outputs": [
{
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" }\n",
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" <th>190</th>\n",
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" <th>193</th>\n",
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" <td>Africa</td>\n",
" <td>Eastern Africa</td>\n",
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" <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>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>Eastern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>72</td>\n",
" <td>114</td>\n",
" <td>102</td>\n",
" <td>44</td>\n",
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],
"text/plain": [
" OdName AreaName RegName DevName 1980 \\\n",
"190 Viet Nam Asia South-Eastern Asia Developing regions 1191 \n",
"191 Western Sahara Africa Northern Africa Developing regions 0 \n",
"192 Yemen Asia Western Asia Developing regions 1 \n",
"193 Zambia Africa Eastern Africa Developing regions 11 \n",
"194 Zimbabwe Africa Eastern Africa Developing regions 72 \n",
"\n",
" 1981 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 \\\n",
"190 1829 2162 3404 7583 5907 ... 1816 1852 3153 2574 1784 2171 \n",
"191 0 0 0 0 0 ... 0 0 1 0 0 0 \n",
"192 2 1 6 0 18 ... 124 161 140 122 133 128 \n",
"193 17 11 7 16 9 ... 56 91 77 71 64 60 \n",
"194 114 102 44 32 29 ... 1450 615 454 663 611 508 \n",
"\n",
" 2010 2011 2012 2013 \n",
"190 1942 1723 1731 2112 \n",
"191 0 0 0 0 \n",
"192 211 160 174 217 \n",
"193 102 69 46 59 \n",
"194 494 434 437 407 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 30,
"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": 5,
"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": 10,
"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": 10,
"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": 13,
"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": 13,
"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": 14,
"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": 21,
"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",
"print (type(df_can.columns.tolist()))\n",
"print (type(df_can.index.tolist()))\n"
]
},
{
"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": 16,
"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": 16,
"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": 31,
"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": 31,
"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": 32,
"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": 32,
"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": 33,
"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": 34,
"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": 34,
"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": 35,
"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": 35,
"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": 36,
"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": 36,
"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": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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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>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": 37,
"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": 38,
"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": 39,
"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": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"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
}
},
"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": 41,
"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": 42,
"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": [
"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": 46,
"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",
"1985 198\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, 1985]])\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": 51,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n",
"<class 'str'>\n"
]
},
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"[print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"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": 52,
"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": 53,
"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": 54,
"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",
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"</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",
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" <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",
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" <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",
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" <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",
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" <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",
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" <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",
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" <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>1879</td>\n",
" <td>1075</td>\n",
" <td>487</td>\n",
" <td>5876</td>\n",
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" <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",
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" <tr>\n",
" <th>Cambodia</th>\n",
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" <td>8</td>\n",
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" <td>529</td>\n",
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" <td>203</td>\n",
" <td>200</td>\n",
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" <td>233</td>\n",
" <td>288</td>\n",
" <td>6538</td>\n",
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" <tr>\n",
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" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
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" <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": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. pass this condition into the dataFrame\n",
"df_can[condition]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"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",
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" <th>2013</th>\n",
" <th>Total</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",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <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",
" <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>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": 55,
"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": 56,
"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",
" 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",
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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",
" <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",
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" <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",
" </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": 56,
"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": 58,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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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": 59,
"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": 60,
"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": [
"['Solarize_Light2', 'seaborn-whitegrid', 'seaborn-muted', 'classic', 'seaborn-deep', 'seaborn-dark', 'seaborn', '_classic_test', 'seaborn-dark-palette', 'seaborn-colorblind', 'dark_background', 'seaborn-bright', 'seaborn-pastel', 'seaborn-ticks', 'fivethirtyeight', 'fast', 'seaborn-darkgrid', 'seaborn-white', 'seaborn-paper', 'ggplot', 'seaborn-notebook', 'seaborn-talk', 'seaborn-poster', 'tableau-colorblind10', 'grayscale', 'bmh']\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": 61,
"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": 61,
"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": 62,
"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 0x7f918d18b128>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haiti.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n",
"\n",
"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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": 64,
"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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ZAsAPP/zAb7/9xlVXXQVAWVkZOp2uwXvUkFNZQr22rioqp+hG90VuT0ZOmur033kOa3GsX7+emJgYVqxYwcsvv0znzp1JSkqiX79+rFq1in79+pGUlARARkYGaWlpLFu2jHnz5rFu3TqMlVUi33jjDWbNmsWqVavIyspiz549jnoJiuKSysvLueeee/i///s/8y9AAJ1Ox5kzpl9oZ86cISAgoN7rhIaGkpmZaf46MzPT3GKxxqeffkpubi5ff/01mzdvRqfTcf78ecDUPVaf6t1mWq2WiooKpJR1/oJ1c3Mz/04BzPc5ceIE//znP/nwww9JTk5m/PjxlJWVmZ83aNAgtmzZYk6SUkpuvPFG81jNtm3bePTRR61+zWY5pwHLNRwXEyPiTTOvDh+o8zmO4pDEce7cOQ4ePGjuX3Rzc8Pb25v09HTi4uIAiIuLIz3dNGsgPT2d4cOH4+7uTlBQECEhIRw5coS8vDxKS0uJjo5GCMHo0aPN5yiK0nhSSh599FEiIyOZNWuWxWNXXHEFGzZsAGDDhg1MnDix3msFBwfToUMHfvnlF6SUfPzxxw2eU11RURE6nQ53d3e2b99ORkZGnc91d3envLz+6am+vr74+Piwc+dOwNSLUSUiIoL9+/djNBo5deqU+Q/QoqIivLy88PHxIScnp0YL5bHHHsPPz48nn3wSgJEjR/Lll19y9uxZwDQTrb646yKzK7v06ksclw+Hdl4tYiW5Q7qqsrOz8fHxYc2aNfz555/06NGD6dOnU1BQgJ+fHwB+fn4UFhYCpul+UVFR5vP9/f3R6/VotVqLv3oCAgLqnB6XnJxMcrLpDV6yZEnTmo+YklxTz3UmFbdjtda4d+zYwSeffELfvn2ZNGkSAM8++yyTJk1i/vz53HbbbXz00UdERETwwQcf4O/vD0B0dDSFhYUYDAY2b97Mf//7Xy699FJee+01Zs6cSWlpKRMnTuSmm26q8Vd/x44d+fLLL9m1a5f52KpVq5g5cyZTpkzhmmuuYcCAAVxyySXm3w9ardbi/b3nnnuYOHEiAwcOZOHChRaPt2/fHiklOp2ON998k3vvvZf27dszYcIE8/MmTZrEhx9+yBVXXEGfPn247LLL8PX1JS4ujtjYWOLj4+nevTsjRoygY8eO6HQ6tFot/v7+JCYmcu+997J06VIWL17Mc889xx133IHRaMTd3Z2VK1fW+7NQ289KYVEeZd4dCezavd7vV+GoCZRu/Rb/B55A4+Xd0LfXboSsrWPSxo4ePcq8efNYtGgRUVFRrF+/Hi8vL7755hveeust8/MSEhJYv349a9euJTo6mtGjRwPw2muvMXDgQHQ6HR988IF5GuDBgwf57LPPeOKJJxqM4fTp002KXafTmf+aaE1U3I6l4naspsR98uRJ7rrrLlJSnDvAXFvsFcsXQEkR2qeW1XuuPPo/jEvmIu58AM2oK+wZJgBhYWG1HndIV1VAQAABAQHmVsTQoUM5fvw4vr6+5OXlAaYmno+Pj/n51Wdw6PV6/P39axzPzc01/wWkKIrSauVk1ju+YdbjEgiNcHp3lUMSR6dOnQgICDD/1f/bb78RHh5ObGwsqampAKSmpjJo0CAAYmNjSUtLo7y8nOzsbDIzM4mMjMTPzw8vLy8OHTqElJKtW7cSGxvriJegKEorFxER4fTWRm3khQuQm13njKrqhBCmQfKj/0NmNn4sxVYcNh337rvvZtWqVVy4cIGgoCBmz56NlJLly5eTkpKCTqdjzpw5gOkbPGzYMObMmYNGo2HGjBloNKYcN3PmTNasWYPBYCAmJsY851xRFKVV0ueA0VjvwHh1YtgY5Kf/Rm5PRkydbt/Y6orBEWMcLYEa42gdVNyOpeJ2vItjl/t3Y1yxAM1jLyCi+1p1jYqVz0BuDtpnE+0UpYlTxzgURVGU2klzVVzrWhwAonNXyMlEGptY3qSZVOJQFEVxppxM8PAAXz/rzwkKgwsXIM+O1XrroRKHoiiKE8mcLNCFIDTW/zo2z8DKbloXfHOpxKEoiuJM2ZlWzaiyUJk4zN1cDqYSh6IoipNIoxHOZlm3hqO6TgHg7vHXroEOphKHoiiKsxTkgcHQqIFxwNStFRiiWhyKoihtTo7pF3+jWxxg6q5qTWMcBoOBCxcu2DoWRVGUNkXmVFbFbewYB5XJJifL1N3lYFYljrfffpsjR44AsGvXLhISEpg+fTo///yzXYNTFEVxadmZoNGAf2Djzw0MhXKDaedAB7Mqcfzwww9EREQA8PHHH/Pggw8yd+5cPvjgA7sGpyiK4tJysiAgCOHW+OpPIrhyVXeO48c5rIr2/PnzeHp6UlRUxJkzZxg6dChAq13yryiK0hLI7MxGD4ybVU3JPXMacUk/G0bVMKsSR1hYGNu2bSMrK4v+/fsDUFhYiIeHh12DUxRFcWk5mYjuUQ0/rzZ+AeDm5pQpuVZ1Vc2YMYNNmzaxf/9+br75ZgD27t1rTiKKoihK48iSIjhX0uQWh9BoQReCbKldVTqdjueee87i2KhRo+jXz7HNI0VRFJeRXTUVt/EzqsyCw+CM46fkWtXiePjhh2s9/sgjj9g0GEVRlLaiKVVxLyYCQ01Vch28O4ZViaO2oM6dO2feXElRFEVppKo1HLpmtDiCQk0rzwscOyW33q6q++67DzAt+Kv6vEpxcTEjRoywX2SKoiiuLDsTOvkjPD2bfAkRFIo0XyvAZqE1pN7E8eCDDyKlZPHixTz44IMWj3Xq1KnO3aEURVGU+smcLKu3i61T9Sm5Vu4eaAv1Jo7evXsDsG7dOjybkRUVRVGUi+RkIfoObN41/ANB6+bwRYBWzarSarUkJyfzxx9/UFZWZvHYAw88YJfAFEVRXJU8X2Yal2jGwDiA0GpBF+zwKrlWJY7Vq1fz559/cvnll+Pr62vvmBRFUVxb1cB4c7uqqq7REhPH3r17Wb16Nd7e3vaOR1EUxfVVreFoQlXci4mgUOShfUgpEUI0+3rWsGo+rU6no7y83N6xKIqitAl/lVO3UYvjfBkU5jf/WlayqsUxevRoXn75ZSZNmkSnTp0sHuvb13Ej+YqiKC4hJxO8OyK8OzT7UhZTcn39mn09a1iVOL755huAGmXUhRCsXr3a9lEpiqK4MFNV3OZ3UwF/TcnNzkRE9bbNNRtgVeJITEy0dxyKoihtR04Wonu0ba4VEAxarUO3kVU1QxRFURxIlpdDbo5tZlRROSU3IMihM6usanGcO3eODRs2cODAAYqKiixqV7322mt2C05RFMXVVORkgTTaZmC8SlCoQ9dyWNXiWLt2LcePH2fq1KkUFxdz9913o9PpuPrqq+0dn6IoikupyDoF2GYqbhVHV8m1qsXx66+/snz5cjp27IhGo2HQoEH07NmTF198kcmTJ1t1o/vvv5927dqh0WjQarUsWbKE4uJili9fTk5ODoGBgTzyyCN06GCaZbBx40ZSUlLQaDQkJCQQExMDwLFjx0hMTMRgMDBw4EASEhIcNndZURSluaoSh626qgDTvhyl56CoAHw6Nfz8ZrIqcUgpad++PQDt2rWjpKSETp06kZWV1aibLViwAB8fH/PXSUlJ9OvXj+uvv56kpCSSkpKYNm0aGRkZpKWlsWzZMvLy8li0aBErV65Eo9HwxhtvMGvWLKKioli8eDF79uxh4MBm1ntRFEVxkIqsDPDwtOnUWYspuQ5IHFZ1VXXt2pUDBw4A0KtXL9atW8fatWsJDW1exkxPTycuLg6AuLg40tPTzceHDx+Ou7s7QUFBhISEcOTIEfLy8igtLSU6OhohBKNHjzafoyiK0hpcyDoFgSG27SkJ/GtKriNY1eKYNWuWue/s7rvv5v3336ekpKTRBQ6ff/55ACZMmEB8fDwFBQX4+Zmyrp+fH4WFhQDo9Xqiov7awN3f3x+9Xo9WqyUg4K+a8wEBAej1tW9gkpycTHJyMgBLlixBp9M1KtYqbm5uTT7XmVTcjqXidqzWGjdAbtYpPMO70smG8UtfX7I1GtoXF9DBAe9Lg4nDaDTy/fffM2XKFAB8fHz429/+1ugbLVq0CH9/fwoKCnjuuefq3cujrgGexgz8xMfHEx8fb/767Nmz1gdbjU6na/K5zqTidiwVt2O11ril0Ygx6xQVlw6wffwBQZz74whlNrxuXb+nG+yq0mg0bNq0Ca1W26wA/P39AfD19WXQoEEcOXIEX19f8vLyAMjLyzOPfwQEBJCbm2s+V6/X4+/vX+N4bm6u+bqKoigtXr4eyg22nYpbJdBxU3KtGuOIi4tj8+bNTb5JWVkZpaWl5s9//fVXunTpQmxsLKmpqQCkpqYyaNAgAGJjY0lLS6O8vJzs7GwyMzOJjIzEz88PLy8vDh06hJSSrVu3Ehsb2+S4FEVRHKpywyURZLupuFVEZXl1R0zJtWqM48iRI3zzzTd8/vnnBAQEWAzqLFy4sMHzCwoKeOWVVwCoqKhg5MiRxMTE0LNnT5YvX05KSgo6nY45c+YAEBERwbBhw5gzZw4ajYYZM2ag0Zhy3MyZM1mzZg0Gg4GYmBg1o0pRlFbD3CKwR4sjKBRKS6CkCDr4NPz8ZrAqcYwfP57x48c3+SbBwcG8/PLLNY537NiR+fPn13rOlClTzOMq1fXs2ZOlS5c2ORZFURSnycky1ZXyD7T5pUVQmGlK7pnTLSNxjBkzxq5BKIqitAnZmWiDQk3Jw9aqquTmZCJ69rL99auxKnGkpKTUetzd3Z2AgACioqJwd3e3aWCKoiiuRuZkoQ3pTIU9Lq4LBiEcUuzQqsSxdetWDh06hK+vr3lmU0FBAT179iQ7OxuAuXPn0rNnT7sGqyiK0lpJKSEnE22fGLskDuHubuoCaymJIzw8nMGDB3PVVVeZj33zzTecOnWKZ599lk8//ZQ333zTvMBPURRFuUhxEZSeQxvS2X73cFCVXKum427fvp0rr7zS4tgVV1zBDz/8gBCCa6+9loyMDLsEqCiK4hLyTAvztIHBdrtF1ZRce7Mqcfj6+vLLL79YHNu1a5d5wV55eTlublY1XhRFUdqmQtNiZ02ngAae2AxBoVBShCwpst89sLKrKiEhgWXLltGlSxfzGMeJEyfM6y4OHz5co0WiKIqi/EUW5gOg6WS/ahd/VcnNgu4d7XYfqxLHgAEDePXVV9mzZw96vZ6BAwdy2WWX0bFjR/PjAwYMsFuQiqIorV71xFFyzj73CDLVlpLZpxHdoxp4ctNZ3b/k4+PD6NGj7RaIoiiKSyvIBw9PNF7t7Zc4AkMcMiW3zsTx/PPPM2/ePADmz59fZ+14a0qOKIqitHmF+XbfZEm4e4BfgPMSR9UGSwDjxo2zaxCKoiiuThbl23TXvzoFhiJznJQ4Ro4caf5clRxRFEVppoI88xiEPYngMOSuH+16D6vHOA4ePMjx48cpKyuzOF5bIUJFURTlIoX5iKje9r9PUCgUFyLPFSPad7DLLaxKHG+++SY//vgjvXr1wsPDw3zcpnvmKoqiuCh54QIUF9p9jANABFZOyc3Jgq6RdrmHVYlj27ZtLF26VO22pyiK0hTFBaZ/HZA4zFVyszMRdkocVq0c1+l0qvqtoihKU1Wu4RA+jhkcB0z7ctiJVS2Ov/3tb/zzn/9kxIgR+Pr6WjzWu7cD+uwURVFaswJT4nBIV5WnJ3Sy75RcqxLHsWPH2L17NwcPHrQY4wB47bXX7BKYoiiKq6gqN+KQriowVcm145RcqxLHBx98wOOPP07//v3tFoiiKIrLcnDiEEGhyF/T7XZ9q8Y4PD09VZeUoihKUxXmgWc7RDsvx9wvKAwK85Gl9iltYlXiuPnmm3nrrbfIz8/HaDRafCiKoigNcEC5kepE5cwq7NRdZVVXVdU4xubNm2s89uGHH9o2IkVRFBcjHZw4qqbkkp0JXWy/pbdViWP16tU2v7GiKEqbUZAH9twy9mKBIUDlWg47XN6qxBEYGGiHWyuKorQRRfmI6D4Ou51o5wW+/pBtn7UcViWOc+fO8dVXX/HHH3/UqFX11FNP2SUwRVEUV2AqN1IEjlj8V11QCNJOazmsShzLli3DaDQyePDgGus4FEVRlHoUObDcSDUiKBS5b7ddrm1V4jh8+N47O6UAACAASURBVDDr1q3Dzc3qYrqKoigKVCs34tjEQWAoFHyHPF+G8Gxn00tblQl69erFqVOn6Nq1q01vriiK4vIK80z/OmITp2rE0LGIPgPBzfZ1Bq1KHLNnz2bx4sVERkbSqZNl1pw6darNg1IURXEVDi83UkkEBEKAfSY2WV1yJDc3l8DAQEpLS/8KrJH7cRiNRp544gn8/f154oknKC4uZvny5eTk5BAYGMgjjzxChw6mjUc2btxISkoKGo2GhIQEYmJiAFPdrMTERAwGAwMHDiQhIUHtC6IoSsvlpMRhT1YljrS0NFauXImfX/OaWl999RWdO3c2J5+kpCT69evH9ddfT1JSEklJSUybNo2MjAzS0tJYtmwZeXl5LFq0iJUrV6LRaHjjjTeYNWsWUVFRLF68mD179jBw4MBmxaUoimI3BXng6WXzcQZnsqrkSHBwMFqttlk3ys3NZdeuXYwfP958LD09nbi4OADi4uJIT083Hx8+fDju7u4EBQUREhLCkSNHyMvLo7S0lOjoaIQQjB492nyOoihKi1SYDz6+DT+vFbGqxTFq1CheeuklrrzyyhpjHH379rXqRm+99RbTpk2z6OoqKCgwt2L8/PwoLCwEQK/XExUVZX6ev78/er0erVZLQECA+XhAQAB6vb7W+yUnJ5OcnAzAkiVL0Ol0VsV5MTc3tyaf60wqbsdScTtWa4pbX1oCAUH4V8bbmmKvi1WJY9OmTYBprKM6IYRV5Uh++eUXfH196dGjB/v372/w+VLKRh2vTXx8PPHx8eavz549a/W51el0uiaf60wqbsdScTtWa4q7IjcHQsPN8bam2MPCwmo9blXiSExMbNbNf//9d37++Wd2796NwWCgtLSUVatW4evrS15eHn5+fuTl5eHj4wOYWhK5ubnm8/V6Pf7+/jWO5+bmqn3QFUVp2QrzEZdY1zPTWlg1xtFct912G6+//jqJiYn8/e9/p2/fvjz00EPExsaSmpoKQGpqKoMGDQIgNjaWtLQ0ysvLyc7OJjMzk8jISPz8/PDy8uLQoUNIKdm6dSuxsbGOeAmKoiiNJi+UQ4kTyo3YWb0tjvnz5zc41XXhwoVNvvn111/P8uXLSUlJQafTMWfOHAAiIiIYNmwYc+bMQaPRMGPGDDQaU46bOXMma9aswWAwEBMTo2ZUKYrSchU6p9yIvdWbOMaNG2fzG/bp04c+fUxVIjt27Mj8+fNrfd6UKVOYMmVKjeM9e/Zk6dKlNo9LURTF5oqcVG7EzupNHGPGjHFQGIqiKC7IBRf/gYPGOBRFUdoiWVBZp0olDkVRFMUq5haHaw2Oq8ShKIpiL4X50M4L4enp7Ehsqs7EMW/ePPPnGzZscEgwiqIoLqUw3+W6qaCexHH69GkMBgMAX375pcMCam3krjSMb61ElhQ5OxRFUVoY6aKJo85ZVYMGDeLhhx8mKCgIg8HAggULan1ec9ZxuALjj9/Dnh3I3/ehmf0PRER3Z4ekWEke+x0CghAO3mBHaUMK8iCsi7OjsLk6E8fs2bP53//+R3Z2NkeOHGHs2LGOjKv10GdDaASUnsO45DHEXQ+hGTza2VEpDZAVFRiXPY0YNhZx+33ODkdxVYX5iF79nR2FzdW7jqNXr1706tWLCxcuqDUdddHnIC4fgbjmVoyvv4h84xWMfx5FTLkT0cxS9IodZWXA+TLk6RPOjkRxUbK8HM4Vg28b6qqqbty4cezbt4+tW7eaixKOHj3a6pLqrkqeL4PiInN3h+bRRciP1iG/3Yg8eQzNvY8hOvg4O0ylFvLPo6ZPMjOcG4jiuopcc/EfWDkd97vvvmPFihV06tSJwYMH4+fnx8qVK837XbRZudmmf/1N+/oKN3c0t/0NMf0hOHwA43NzkCeOOTFApU4nK78vRQVqYoNiH4WuWW4ErGxxfP755zz11FN069bNfGz48OEsXbrUYs+LNkefA1RuCl+NZkQ8MqwrxtcWY3xxLuLOB9EMiXNGhEod5ImjoNGA0WhqdURe6uyQFFfjoov/wMoWR1FREeHh4RbHwsLCKC4utktQrYXMNSUO/INqPCa6R6F5ail0i0KuXYpxw/pGbUSl2I80GuHkcbh0gOnrLNVdpdieq5YbASsTR69evXj77bc5f/48AGVlZbzzzjtER0fbNbgWLzcbtFroVPtfFMLHD80jixCjr0R+uxEONbz7oeIAZ7Og9Bxi4DBwczcNlCuKrblogUOwsqvqnnvuYcWKFUyfPp0OHTpQXFxMdHQ0Dz/8sL3ja9lyc6BTAEJT9+wp4eYGN81A7kxFbk92uZ3AWqXKcSfRLRIZHIbMOuXceBTXVFQAXu0RHq5VbgSsTBx+fn4sXLiQ3Nxc86yqgIAAe8fW4kl9DgTU7Ka6mPD0RAwahfwpFXnbvYh27R0QnVIXeeKoqaUY1hUREo48edzZISmuqCAPOrpeawMaWeQwICCAyMhIlTSq6LNrDIzXRYyIB8N5ZPoPdg5KaYg8cQxCuyDc3SE0HM5mmebcK4oNuWq5EVDVcZtMXrgAeXrzVNwG9bgEQsKRad/ZNzClXlJKOHEM0bWH6UBIuGlmVU6mcwNTXE9hvksu/gOVOJquQA/SaFVXFYAQAjFiPBw5qGbxOFO+3tT3HNETABFSOVtQfU8UWyvMc8k1HGBF4jAajezbt48LFy44Ip7Wo3Lxn7VdVQBi6FjQaFSrw5lOmFaMm1scwWEASLWCXLEhU7mRkrbbVaXRaHjppZdwc7NqHL3NqG8NR11EJ3/oezkybQuyosJOkSn1kSeOgRAQbqpiLNp5gb8O1MwqxZZcePEfWNlVdemll3Lo0CF7x9K66KsSh65Rp2lGxJu6uQ7stkNQSkPkiaMQHGZKGFVCwlX3oWJbLlxuBKycjhsYGMjixYuJjY0lICAAIYT5sZtvvtluwbVoudnQ0bfxc7T7x0IHH4w/JKPtF2uf2JS6nTiKiOxtcUhUTlqQUlr8bCtKk1W1OFx0rxerEofBYGDQoEEA6PV6uwbUWshc69ZwXEy4uSOGjkVu+S+yqBDRUVXPdRRZVAj6s9Clh+UDIeFQVmpqCXZSU82V5pOFrltuBKxMHLNnz7Z3HK2PPhvCujbpVDFiPDL5M+RP3yPir7VxYEqdTlYOjHfpaXFYhHRGgqnYoUocii24cLkRaMR03IyMDD7++GPWrVsHmPYk//PPP+0WWEsmpTRt4NSIGVXVifBu0DUSuT1ZFT50IHOJ+4tbHKGmKbmq9IhiM4X54OWNcPdwdiR2YVXi+PHHH1mwYAF6vZ6tW7cCUFpayttvv23X4Fqs4kIwGJrUVVVFjIiHjD/MdZMUBzhxzLTplndHy+O+/tDOS63lUGynIM9lWxtgZeL46KOPePrpp7n33nvRaEyndO3alT/++MOesbVcVWs4rF01XgsxeDS4uSO3t/HNsBxInjgGET1qHBdCqJlVik3Jonzw8XV2GHZjVeIoKCiga1fL/nwhRNudgVI1FbeJXVUAwrsDYuBQU+HDcoONAlPqIsvOwZlTfy38u4gICVctDsV2CvMRLrqGA6wcHO/Rowdbt24lLu6vXey2b99OZGSkVTcxGAwsWLCACxcuUFFRwdChQ7npppsoLi5m+fLl5OTkEBgYyCOPPEKHDh0A2LhxIykpKWg0GhISEoiJiQHg2LFjJCYmYjAYGDhwIAkJCQ5PYObFf83oqgIQI+OR6duQe3YiBo20QWRKnU7+AYCI6Fn74yGdYccWZFmp5RoPRWmKgny4tI13VSUkJPCf//yHBQsWcP78eZ5//nk+/PBD7rrrLqtu4u7uzoIFC3j55Zd56aWX2LNnD4cOHSIpKYl+/fqxatUq+vXrR1JSEmAaiE9LS2PZsmXMmzePdevWYTQaAXjjjTeYNWsWq1atIisriz179jTxpTdDbjZ4ekH7Ds27Tq/+4K9Dbt9sm7iUOsnKUiPU1eKoHCDnzGkHRaS4KllugFLXLTcCViaOzp07s2LFCiZOnMgtt9zCmDFjWLp0KaGhoVbdRAhBu3btAKioqKCiogIhBOnp6eZWTFxcHOnp6QCkp6czfPhw3N3dCQoKIiQkhCNHjpCXl0dpaSnR0dEIIRg9erT5HEeSuTngr2t2S0dotIhh4+DAHtPeHor9nDgGHX1NA+G1CamaWaW6q5RmKiww/euii//Ayq4qAE9PT3r16oVer8ff39+cCKxlNBp5/PHHycrKYuLEiURFRVFQUICfn+nN9fPzo7CwEDAtMoyKijKf6+/vj16vR6vVWuwFEhAQUOeCxOTkZJKTTQPPS5YsQadrXGmQKm5ubjXOzS3MQxMajl8Tr1ndhaunkvvfj/Da+xMdbpze7OtVqS3u1sBeceee/hNN5KX4BdY+LiV9fcjWaGlfoKdDE+6v3m/Haslxl+uz0QO+4V3wrCXGlhy7taxKHGfPnmXVqlUcPnwYb29vSkpKiIyM5KGHHiKwjv+IF9NoNLz88suUlJTwyiuvcOLEiTqfW9fahsaseYiPjyc+Pt7iNTSFTqercW5F9mlERPcmX9OCezuI7kvJ5s8pHXO1zcZraou7NbBH3LLcgPHkccSlMfVfWxfMuWOHKGvC/dX77VgtOW5ZOZ5WiAZRS4wtOfaLhYWF1Xrcqq6qxMREevTowfr161m7di3r16+nZ8+eJCYmNjoQb29vevfuzZ49e/D19SUvz7Q0Py8vDx8fU/mNgIAAcnNzzedUtXIuPp6bm4u/fx1dD3Yiz5dBcZH1GzhZQYyIh5wsOLzfZtdUqjn1J1RUILrWMTBeJVRNyVWaTxZUlRtx3a4qqxLHsWPHmDZtmrl7ql27dkybNo1jx6xbvFZYWEhJSQlgmmH122+/0blzZ2JjY0lNTQUgNTXVXA8rNjaWtLQ0ysvLyc7OJjMzk8jISPz8/PDy8uLQoUNIKdm6dSuxsQ4uFKi3zYyq6sTlw6GdF3K72qfDHv5aMV5/4hAhneHMaaRRlbxXmsFcbsR113FY1VUVFRXFkSNH6NWrl/nY0aNHiY6OtuomeXl5JCYmYjQakVIybNgwLr/8cqKjo1m+fDkpKSnodDrmzJkDQEREBMOGDWPOnDloNBpmzJhhXng4c+ZM1qxZg8FgICYmhoEDBzb2NTdPEzZwaojwbIcYNMq0puPWexDt2tvs2gqmzZu8vEEXXP/zQsLhQjnk5kBgiGNiU1yPi5cbgXoSx4cffmj+PDg4mMWLF3PZZZeZu4t2797NyJHWrT3o2rUrL730Uo3jHTt2ZP78+bWeM2XKFKZMmVLjeM+ePVm6dKlV97WHpmzgZA0xbBxy27fI33apNR0NkEYjQmP9rsfyxDHo0qPB8SMREm4qdpiVoRKH0mSyMM9l9xqvUuf/vtzcXPNHeXk5Q4YMwd3dncLCQtzd3Rk8eDAGQxtc8azPAa0WOtm4/7JbFGg0pvpVSp2M6dswzr3b6oKEsqICMv5A1FJqpIaQzqZz1DaySnMU5rv0Gg6op8WhSqnXITcbOgUgNFqbXla4u0NQKPJ03bPN2jppOI/csB4K9Bg/Xo/2gacaPikrA8oNdS78q0508DGt9VAD5EpzFBYgIro7Owq7snodx/nz58nKyqKsrMzi+CWXXGLzoFqypm7gZJWwLpDRNkvVW0OmfAl5ZyFmKOzZgTy4F3HpgPrPqRwYr7PUyMVCOquZVUrzFOaBj4PHXh3MqsSRmprKm2++iZubGx4elgM+r732ml0Ca7H02Yjofna5tAjrgtz9E7Lc4NIDa00hS4qQX38M/WLR3Pv/MD49G+NHb6J5eln9rb8TR8HDw9wN1RAREo7c85ONolbaGlO5kXNtt6uqunfffZdHH32U/v372zueFk1WVECevllVcesV1hWk0bQT3cWbDbVx8uuPofQcmil3INw9EDfchfzXy8jt3yFGXVH3eSeOQeduCK2VXYsh4VD0LbK40NR1pSiN4eI7/1WxamqKm5sbvXv3tncsLV9+rukXu526qkRYFwA1znERqc9BfvclYuhYRLip71jEjoSevZCfvWcqmV7beUYjnDzW8MK/aszFDtVugEpTVC7+c+WS6mBl4rj55pt5++23zbWk2iwbbOBUr+BQ04yt02qcozr52fsAiOtuNx8TQqC5aQYU5CG//rT2E8+eMXUbWDOjqooqdqg0RxtpcVjVVRUWFsZHH33Epk2bajxWfb2Hq5M22MCpPsLNHYLCkKdP2uX6rZHM+AP5YwpiwnU1Fl2KHpcgBschNychR0+suSizspR6Y1ocBASCm7uaWaU0iaxKHC6+jsOqxPHqq68yevRohg8fXmNwvE0xL/6zU4uDygHyqr0jFIwb34F27RGTptb6uJhyJ3L3j8hP30bc86jFY/LEMVMLLqxrrefWej2NFoLDrF4noigWCivrVHVUiYPi4mJuvvnmtrtVbJXcbOjoi/DwtN89wrrArjTk+fMITzvepxWQh/bBr+mIKXfVOVAtAgIRE65HfvURcvxkRI+/pofLE0chtItpjUwjiJBw5MnjzYpdaaMK86G9d6N/5lobq8Y4xowZw9atW+0dS4tn2sDJfq0NANG5C0gJWW27u0pKifGTf5sWW46fXO9zxaQbwNcP40frzKX3pZRw4lide4zXKzQczmYhy8ubErrShsnCfJeuilvFqhbHkSNH+Oabb/j000/p1MmyCbZw4UK7BNYi6XNMLQJ7quxWkadOILpat6e7S9r9Ixz7HXHnAw228EQ7L8R1tyPfXo38+QfEoFGQr4eiArB24V91IeFgNEJOpv2/34prKXD9ciNgZeIYP34848ePt3csLZqU0rT4r9/l9r1RUCi4uUEbnpIrKyowfvoOhEYghlv3cydGjEdu+S/yk38jY4aYtoqFJrU4LIodqsTR5snz56HsHMKarWAL8xFtYA2WVYljzJgxdg6jFSguBIPB/l1VWi0Ed27Taznk9s1w5hSa++dZvXBPaLRobrwb47KnkZs/g4oKEALCm1AzKNi065nMzKCNj+opgPz4TeTOrWgWrWl4fUZhnkvvNV7FqsSRkpJS52Pjxo2zWTAtWuVUXGGvOlXViLAuyGO/2/0+LZEsK0V+/h+IvBQGDG7UueLSATBgMPKrjyG8KwSHIdp5NToG0c4L/HRqEWAlKWWbnRgjpUT+mg7nSkwz96Y/XPdzDeehrNRUKNPFWZU4tm3bZvF1fn4+WVlZ9OrVq+0kjsrFf3YrN1JdWBdI34YsK23SL77WrOSLD6FAj+Zvc5v0y0ozNQHjMw/A0f+ZxjqaShU7BEDm52Jc8jhi4v+hGXu1s8NxvDOnQH8WAkOQ279Djr7SYuaeBfMaDtXiAGDBggU1jqWkpHDqVNv5i8y8gZOjWhwAmSehu3W7LLoCWVTIuY3vQswQRGTTStyIkM6IMVchv/sCGrPwr8Z1wpE/prTpv7YB5O4dkJuNfP+fGD290AxvI38oVpL79wCgmf0kxhULMX7wLzRPvlz7RmLmciNqcLxOY8aMYcaMGdxxxx22jKfl0ueAZzto38H+96pWs0q4SOIwvrkcuW+XaaqxlKaaX1KCsfrnFSAlmv9r3s+UuOZWKCpADBzW9IuEhpu6HQr00CmgWfG0ZnLPTtOEjYAg5FurkO28EJc1431tZeSB3RAYggjvjph6F3LdcuT25NoLaxa1jXIjYGXiMBqNFl8bDAa2bt2Kt7e3XYJqiWRuNvgHOuavz6AQU9kLFxkglyeOIX/cAr1jEEFhpkFrjcb0rxAgKj/XCHwHDqGomTOZhHcHxD3/r3nXqJpZlZnRZhOHLD0Hv/+GGD8Zcc2tGJfPx/jGy2gefBrR27X3mwCQF8rh932IYWMAEEPGIFM3IT99G3nZcIS35R+R5nIjah2Hya233lrjmL+/P7NmzbJ5QC2WPTdwuojQaCE03GVmVsnkz8CzHZpZcxENtNg8dTqKzp51UGT1MBc7PNXgZlEu68BuqLiAGDAY0c4LzUMLML78JMbEF9DMWYTo2cvZEdrXsd/hfKk5SQoh0Nx6L8bn5iA/fx9x672Wzy+oShxqcByA1atXW3zt6emJj08b26tAn43oFuWw24mwLshD+x12P3uR+Xrkzm2IuCsbTBotSid/8PRq08UO5d6d4N0Rel4KmFpymkeexfjSExhXLUTz/15w6S1S5f49ppbxJX9t3Ca69EDEXYnc8hVy1ARzmX+gstxIB1OxUhdnVcmRwMBAi4+2ljTk+TIoLnLMjKoqYV0g7yzyXInj7mkHcstXYKxAjL/G2aE0ihCiTc+sksYK5G8/I/pdbrGWRvj6oZmzCDy9MC6fjzxz2olR2pc8uAe6RyPaW3bJi+tvB29vjB/8y1ziBiq7qtrAjCpooMXRUDkRIQTz58+3aUAtkt5xM6qqWMysaqVdAvL8eWTq1zBgCCIo1NnhNJoIDTcVWmyLjv4OxUWIWtbSiIAgU8vj5ScxLnsazdwlNUvat3KypAj+OIyYfEuNx4R3R8T1dyDfXYNM34YYPNr0QGFemxgYhwYSx6hRtc+D1+v1fP3115w/f94uQbU49t7AqTbVZ1a11sSxYwuUFKGZcK2zQ2makHDY8X2bXE8j9/4EWjfoc1mtj4vQcDR/fwbjK/MwLp+PZu5i15qGenAvSInoHVPrw2LUBOTWTcgN65H9B5l+Pgrz20x9uXq7qsaNG2fxMWjQIDIyMvjiiy8YPHgwK1eudFScTmXvDZxqpQsGD49WO7NKGo2mQfGukRDVx9nhNImoHCDHhbtj6iL37oRL+iK82tf5HNGlJ5oH50NeDsYVC5Dnih0YoX3JA3vAq32d66iERovmtlmQn4v86iPTwcK2UeAQrBzjOHfuHP/5z3946KGHKCgo4MUXX2TWrFkEBLSRaYq5OaYNgTr5O+yWQqOB0C6td2bV/l2QdQoRf23rXUDXRreRlVmnTN+7/g2XfBFRvdHc9w84fRLjimdMU3hbOSmlKXH06l9vrTTRsxdi2Fjkt5+Z9m8pK1WJA0zrNTZu3MiDDz7IqVOnePbZZ3nwwQcJCQlxVHwtQ262aV8IjXUF92xFhEU4tcUhS4ow/vcjZEnj/5I0bv7M9J7FjrBDZA4SFGpaY9LWEsevOwEQAwZZ9XzR9zI0s+bCiaOmlkdrTx5nTkNudp3dVNWJG6aDuzvGN1eYDqjEAffffz9ffvkl1157LRMnTqSgoIB9+/ZZfLQFMjfHsd1UVcK6QL6+Sb+4bUF+8C9k0rsY176CNFZYf17GcTi4FzHu6lY9NVG4u0NgMPLPo8gK619/ayf3pkN4N4Qu2OpzxMChaO6dC38ewbiydbc85EFTmRFrFjkKXz9TpYKM4+av24J6B8er9hf/9ttva31cCFFjjYdL0ucgovs6/LbmmVWnT0BU02o3NZX87RfkT6mmKrX7diGT3kNMudO6czd/Dh6eiNET7Ryl/YlL+iG3fYtxbgJi0CjEkDjoFtV6u98aIEuK4MgBxJW17/FeH3HZMDT3PobxXy9jXPkMmr8/g2hX9xhJSyX37wZdsNUzAcW4ycgfNptmQLaRFke9iSMxMdEmNzl79iyJiYnk5+cjhCA+Pp6rrrqK4uJili9fTk5ODoGBgTzyyCN06GBaJLZx40ZSUlLQaDQkJCQQE2NqNh47dozExEQMBgMDBw4kISHBrv+JZcUFyM91XouDyplVDkwcsqwU47trIDQCzZznkP/5F/Lrj5FdeiBiR9Z/bkEecmcqYuQVCO+ODorYfsStsxB9L8P4Uyoy9WtT8cSgUMTgOMSQ0X8NoLsI+dsvYDQiYhpX0r6KuGw4mnsew/ivlzCuaH3JQ164YCqzMjjO6nOEmxuaO+/HuPFdCO5sx+haDqsGx5tLq9Vyxx13sHz5cp5//nk2bdpERkYGSUlJ9OvXj1WrVtGvXz+SkpIAyMjIIC0tjWXLljFv3jzWrVtnrpf1xhtvMGvWLFatWkVWVhZ79uyxa+zG3BzTNqKOnIpbxT/QVFjRweMcMuldyDuL5s4HEO7uiFvuhZ69MK5ficz4o/5zv/8KKlrfgr+6CHd3xGXD0d73JJqlbyPuetBU8O+/H2J8ejYVz83B+G0SFVXVk1u7vTtNi9iaMa1UXD7c1G11/BDGlQuRZa2o2+r4ISgrRfRpeHyjOhHZG+1jL7SZadsOSRx+fn706GHaTtHLy4vOnTuj1+tJT08nLs6U2ePi4khPTwcgPT2d4cOH4+7uTlBQECEhIRw5coS8vDxKS0uJjo5GCMHo0aPN59hLxdkzgGM2cLqYaWZVhENnVsmj/0OmfIkYMwkRWVlqwt0dzd+eAC9vjGteMHVn1Hau4Tzy+6+h/yBEiOv95SXad0AzcgLaOYvQvPQm4qYZAMgNb3J25nVUPHUfxrdXY/xxy19l+FsReaEcue8XRP9BtZcNbwRx+XA09/w/OPY7xpXPtprkIQ/sNk2I6NXf2aG0aE0uq95U2dnZHD9+nMjISAoKCvDzMw0m+fn5UVhYCJgWGEZF/VUXyt/fH71ej1artZgCHBAQgF6vr/U+ycnJJCcnA7BkyRJ0Ol2T4jUc3G2KLzIatyZeozkKekRj2PVjo+N3c3Nr9DmyvJzc915D4x9IwD2PoPGqVmpBp8Pw5BLynroft7dW0umppTWmKp779jOKigvxm3onHk18r5oSt1PodBB5Cdw6gwun/qT8lzTK9u2mfNePyG3fIgFNYAgefWJw7x2DR+8YtGERLW5spPr7fX5vOvllpfiOisfTFt+DK6+nrGNHCpY9g3bNYjo9vRRNPetCGsNePyf6Q/sg6lL8u3Sz+bWrtJqf8Xo4NHGUlZWxdOlSpk+fTvv2df8AVa//Ys3x2sTHxxMfH2/++mwTK662q9w+NE+4IZxQtdUYEITM15PzxzFEB+trhOl0uka/ZuOX/0GePI7mgafRl5RCSanlEwJCELfNwvD2anLeWI5m6nTzmX20/gAAFJdJREFUQ1JKjEnvQ0R3CoIjmvxeNSVup/P0RnftrZQMn4AwGhGn/0T+vh/j4X2U/fIjZd9/Y3qev860j3qXpm8wZWvV32/j1s3g7kFhWHfb/axfMgAxcw7la5eSs+AhNA8tsEl3jj1+TmRJMcbDBxFX32TXn8HW9DMeFhZW63GHdFUBXLhwgaVLlzJq1CiGDBkCgK+vL3l5pl2z8vLyzMUTAwICyM3NNZ+r1+vx9/evcTw3Nxd/f/suyjOePQMdfREenna9T11E1d4Udu6ukpknkf/9yDRzqJ75+5pRV5iqg276FGN6tS2F9++CzJOICde3uL+qHUloNIjw7mjGT0b7tyfQLH0bzaI1iDvuBwnG15bU2dXnTFJK02rx3jEIT9v+rGsGjULMfBSO/A/j6ueQ5eU2vb7N/O9XkEar1m+0dQ5JHFJKXn/9dTp37szkyZPNx2NjY0lNTQUgNTWVQYMGmY+npaVRXl5OdnY2mZmZREZG4ufnh5eXF4cOHUJKydatW4mNjbVr7BU5Wc4ZGK9SNbPqlP0ShzQaMb69Gjy9ELfc0+DzxS33QOSlph3hTprmrxs3fwa+/ohB9c+6amuEEIiQcDSjJ6K57wnIz8W4dmmj1sU4xKk/TYve+lu36K+xNINGIRIeht9/M/3cNKL3wFHkgT3QzqtNbdfcVA7pqvr999/ZunUrXbp04bHHHgNMm0Ndf/31LF++nJSUFHQ6HXPmzAEgIiKCYcOGMWfOHDQaDTNmzEBTOVg3c+ZM1qxZg8FgICYmhoED7bsTWUV2lnOn2PnpTDVz7NjikFu/gSMHEQkPW1WoTriZBsuNz83BuOYFNNMfhgN7ENdPa9UL/uxNdI9G3HKvqarqFx8irrvN2SGZyb2Vq8XtlDgANMPGYtTnmGbt6YIQzdwi2JaklMj9u+CSfgg3hw/9tjoOeYd69erFRx99VOtjdZVlnzJlClOmTKlxvGfPnixdutSm8dVFSokxJwtxqfOarkIIu86skvqzyE/+DZcOQAwbZ31cvn5o7nvCVFp7xXzw8EDEXWmXGF2JGD0Rjv+O/PI/yG5RVpf1sDe5d6dpYaOd67GJq26E3GzkVxswBgSiGd1CfmZyMk0tron/5+xIWgWHjXG0SsVFYDjvnMV/1YiwLnZpcUgpMb7/OhiNaO64v9FjE6LHJYjb74MLFxBDxzVq8L6tEkIgbvsbdOmJcd0yZLbzK+/Kgjw4fqjWvTdszfz6+16GfO9104LDFkAesL7MiKISR/30lftwOGENh4WwLlBcaNphzIbkz9th707EdbcjAptWuFIzcgKax14wr2lQGiY8PE3jHRoNxjWLkU7e10b+aloL1dTV4o0l3NxMRRE7d8X4zxeRfx51yH3rI/fvMW3U1go3HHMGlTjqU7mBk1MHxwHR2fYzq2RJEfKDf0LXyGav8hbRfW0+E8fVCV2waYHc6RPId1Y7dbBY7t1p+hnv3M1h9xTt2qN5aD54d8D46rPIqv9rTiArKuD3XxG9Y9r0jMDGUImjHk7ZwKk21WpW2Yrc+I5pd767Hqx3zwHFfkSfgYjrbkf+lIpM+a9TYpDnz8PBPYgBgx3+S1N0CkDz0DNgMJhKkzhrI6jjh6D0HKKP6qaylkoc9cnNMS1WcnaxPl9/aO8NNpqSK0+dQG79FjHmKkREd5tcU2kaMWkqDBiM3LAOefiAw+9v+PVnMBgcMr5RG9G5i6nbLjvT1G13wfFrPExlRoQqM9IIKnHUQ+ZmowkMcXrzVQgBYbbbDdD48Zvg5YW45habXE9pOvH/27v/qKjrPY/jz88XMH46MoCYIKlXUDL8UfgjM4U92T11rbveTe8x20J0yyy5m8cMd1l02/X04xzCbRdWS3O7erVbXij3Xs2b5UpWFopXUsME7CRbV+SnKAw4fD/7x8TcXIUYGGaGeD/O8RzPMDO8eJ/vmff3+5nP9/MxDIz0v4eIoY7x/obrL6HTV1qLP3Tcu+CFbQM6qMSJjsUjT3+Ofv3frztsp1tbHTepfn4U83/2cunNrejPj7hlDSx96k+OGWXePkHsR2TCcldqL+AXGY0v3Kqlhsehj3yE1rpXjUyfOAonSlDz02UWlI9QwaEYj6/BfM6xHLmx8l89ci+BNk1aj3yMGn+rY9MqLzJuT8WsrUa/8xtQBjrM4pi2W1vt+K6xqfGq5192vtCAUQmocRNQ4ybAT8ahAgZ1+/fq5kuOGWX3uL7/yEAmjaMLavJ0bogZgU+s6zk8Dpr3QWN9j/c+1+3tmG9thahhqNSfuTmg6A0VOxL18JPozTnod36D+ptH+v6XnjmFWV+DmugbN+Kpny2Ahlr0wXchYJDju0XrUFTcaMeMp4ihjhmOkUOJjI2jpvhjdFmp49/eXeg/vOl43ZjEvzSS6BgIDun8ZKvsc8f+I7LMiEukcXTBmPtLgiMjafaBBcmu2g2wp43j0HvwzdcYj2d6/QxTXMuYNhvz9OfofQXopGRUwvg++1265BPM1zZgDLFCH94t7gqlFOqh5ehfPAxBXXzYAyowyDHElTgRAN18Gc6cQpcddzSSwm04B7z8/CDM8t2/IajBf/m/LiuFG4Jg9Ni+/wN/RKRx9Bcx39sNsAdnR7ql2TEMEH8zTL7d3emEm6gFS9BlpZiv5WKsfRnlpmXIO2izHf3ODvSet2BUAtZ/eJF6H/uqUwWH9uA1ITBxivNOfN3UCF+eRNdfgIuN0NTouA+qqRF9/n+hqQHa2hwvvm2GLJXjImkc/UXYEAgN6/G9HHrvLmhqxMjI9vqX/aJzKjAII/0pzBfXoH+7GZWW4bb31pcuYr6aA6eOoe68G7XwMfwih4IPXFG7mwqzOBpCF8/RrTa42NDjK/iBTBpHP9GbmVW6thr93juo6amokfE/+HzhXWpMIuqeB9B73kRPnIqaPL3X76m/rsDMfw4a61B/+wTGrJ+6IWn/pm4IhB6umDDQ+dY1quhSx5pVrt5lrAt+DYZCzXuoj5IJd1P3/RLiRmP++j/QF+t79V7m4QOYzz/jWJNs9fPSNESvSePoT4bHQUsz1Nf+8HO/oyvK0J8Voe6eh/Ly0imi+5R/AMaSlWBrwXy9Z0uSaLsdc+cr6C25MCoBI+sllOw1IdxAGkc/4upugFprzLdeA0s46qfXLlEvfJsaHueYllta7JgR5wLdUIuZk4X+4PeoOT/HWPkv3dprRYjukMbRn3SsWVV2vFs7yOkjH0FFmWODJTfs8yw8T/3VXEiciP7tZnT1tz/4fG23Y773DuY/LYevK1B/twpjwRJZj0y4lTSOfkSFWWBUAnpfIeaaRzF//wa6k2Er3daK/t1/Qewo1Izub9AkfIsyDIy0DDD8MF/Ldazk2gn9xXHMZ3+FfnMLjEnEyP43jKmzPJhWDBQyq6qfMVY/B3/6FLNon2M+/u43YEKy4wvPW25FGY4zy+Y/vAW11Y4hCkPONvszZY1CLVrmuKv83d857rD+Hl17Af3Wa+ijH0FkNMYT/wheWO1WDBzSOPoZ5R8AyTPxS56Jrv4WfeiP6EP7MY9/BtZI1My7UROncHnX644Pj+/urBX9m5o6C45/hv7vneik21BxP0FfaUP/8W30njdBg/r5g45JEINkbxTRt6Rx9GNq6I2oXzyCvv9BOF6MWfQuevcO9O4d4OeH8UCatyMKN1FKwaJl6DMnMTe/hPHXD2Hu2goX/gy33o4xPx0VGe3tmGKAkMbxI6D8A+C2GfjdNgN94c/oQ/sJvWkUzcNivR1NuJEKCcNI+xXmhrWY//kcDIvFeOqfZZ9s4XHSOH5kVNQw1LyHfGZxRuFeavxk1KJlYLejUu6RNZaEV0jjEKKfMVLu9XYEMcDJdFwhhBAukcYhhBDCJdI4hBBCuEQahxBCCJdI4xBCCOESaRxCCCFcIo1DCCGES6RxCCGEcInSPdlaTAghxIAlVxw/IDMz09sRekRye5bk9qz+mhv6d/YO0jiEEEK4RBqHEEIIl/itW7dunbdD+LrRo0d7O0KPSG7Pktye1V9zQ//ODvLluBBCCBfJUJUQQgiXSOMQQgjhkgG3kVN+fj4lJSVYLBZycnIA+Oqrr3j11Vex2WxERUWRkZFBcHAwdrudjRs3cvbsWUzTZNasWcybNw+AyspK8vLyaGtrY/LkySxevNixL7SP5163bh319fUMGjQIgKysLCwWS5/l7kn2V155hYqKCgzDIC0tjfHjxwO+X/POcnuy5jU1NeTl5dHQ0IBSirvuuot7772XS5cukZuby4ULF4iKiuKpp54iNDQUgMLCQj744AMMw2Dx4sVMmjQJ8Gy93Znb08e4q9mbmpp46aWXKC8vJyUlhSVLljjfy9PHeI/pAebkyZO6oqJCr1y50vlYZmamPnnypNZa6/fff1/v3LlTa631hx9+qHNzc7XWWttsNr18+XJ9/vx552tOnz6tTdPU69ev1yUlJf0i99q1a3V5eXmfZu1N9r179+q8vDyttdYNDQ169erVur293fkaX615V7k9WfO6ujpdUVGhtda6ublZZ2Rk6HPnzult27bpwsJCrbXWhYWFetu2bVprrc+dO6dXrVql29ra9Pnz5/WTTz7plXq7M7enj3FXs7e0tOgvvvhC79u3T2/evPmq9/L0Md5TA26o6uabb3aesXT45ptvSExMBGDChAl8+umnzp/ZbDba29tpa2vD39+f4OBg6uvraWlpISEhAaUUs2bNori42Odze4sr2auqqrjlllsAsFgshISEUFlZ6fM17yy3p4WHhztn7AQFBRETE0NdXR3FxcXMnj0bgNmzZztrV1xczIwZMwgICGDo0KEMGzaM8vJyj9fbXbm9wdXsgYGBjBs3znlF1MEbx3hPDbjGcT0jRozgyJEjABw+fJja2loApk+fTmBgII8++ijLly/nvvvuIzQ0lLq6OiIiIpyvj4iIoK6uzudzd8jPz+fpp59m165daC9Nquss+8iRIzly5Ajt7e1UV1dTWVlJTU2Nz9e8s9wdvFHz6upqzp49y5gxY2hsbCQ8PBxwfNBdvHgR4Jq6Wq1W6urqvFrv3uTu4K1jvDvZO+Mrx3h3DLjvOK7n8ccfZ+vWrezatYvk5GT8/R1lKS8vxzAMNm3axOXLl8nOziYpKclrH7b/n6u5o6OjycjIwGq10tLSQk5ODkVFRc6zIl/InpqaSlVVFZmZmURFRTF27Fj8/Px8vuad5Qa8UnObzUZOTg5paWldXm12Vldv1bu3ucE79YbuZ++Mrxzj3SGNA4iJiSErKwtwDEWUlJQAcOjQISZNmoS/vz8Wi4WxY8dSUVFBYmKi80wToLa2FqvV6vO5o6OjnTmDgoKYOXMm5eXlXmkcnWX38/MjLS3N+bysrCxuvPFGQkJCfLrmneUGPF5zu91OTk4Od955J9OmTQMcw2f19fWEh4dTX1/P4MGDAcdZ7ffrWldXh9VqveZxT9TbHbnB8/V2NXtnvFHznpKhKqCxsREA0zQpKChgzpw5AERGRnLixAm01thsNs6cOUNMTAzh4eEEBQXx5ZdforWmqKiI5ORkn8/d3t7uvFy22+0cPXqUESNGeDx3V9lbW1ux2WwAlJaW4ufnR2xsrM/XvLPcnq651pqNGzcSExPD3LlznY8nJydz8OBBAA4ePMiUKVOcj3/88cdcuXKF6upqvv32W8aMGePxersrtzeOcVezd8ZXjvHuGHB3jm/YsIFTp07R1NSExWJhwYIF2Gw29u3bB8DUqVN58MEHUUphs9nIz8+nqqoKrTWpqancf//9AFRUVJCfn09bWxuTJk0iPT29T6fNuSO3zWZj7dq1tLe3Y5omSUlJPPLIIxhG354/uJK9urqa9evXYxgGVquVZcuWERUVBfh2zTvL7emal5WVkZ2dTVxcnLM2CxcuJD4+ntzcXGpqaoiMjGTlypXO770KCgo4cOCAcxrx5MmTAc/W2125vXGM9yT7E088QXNzM3a7nZCQELKysoiNjfX4Md5TA65xCCGE6B0ZqhJCCOESaRxCCCFcIo1DCCGES6RxCCGEcIk0DiGEEC6RxiGEEMIl0jiE6KGXX36Z/Pz8qx47deoU6enp1NfXeymVEH1PGocQPbR48WKOHTtGaWkpAG1tbWzatImHH37YubidO5im6bb3EsIdZK0qIXooLCyM9PR0Nm3aRE5ODgUFBURHR5OSkoJpmrz99tscOHCA5uZmkpKSWLp0KaGhoZimSW5uLmVlZVy5coWRI0eydOlSYmNjAceVTHBwMOfPn6esrIzMzExsNhvbt2+ntraW4OBg5s6de9XyFkJ4ktw5LkQv5eTkYLfbOX36NC+++CKRkZHs3r2b4uJi565vW7ZswW63s2LFCkzTpKioiGnTpuHn58e2bds4c+YMzz//POBoHMeOHWPNmjXO9ZeWLVvG6tWrGTt2LJcuXaK6utq5B4QQniZDVUL00pIlSzhx4gQPPPAAkZGRAOzfv5+FCxditVoZNGgQ8+fP55NPPsE0TQzDICUlhaCgIOfPKisrnYskAkyZMoWEhAQMwyAgIAB/f3+qqqpoaWkhNDRUmobwKhmqEqKXhgwZwuDBg51DTeDYh/qFF164aoE6pRQXL15k8ODB7Nixg8OHD9PU1OR8TlNTE4GBgQDOBtRh1apVFBQUsH37dm666SYWLVpEfHy8B/46Ia4ljUOIPhAREUFGRsZ1P9wPHDjAsWPHyM7OJioqiqamJpYuXdrlRj7x8fE888wz2O129uzZw4YNG8jLy+vLP0GITslQlRB9YM6cOezcudO5fWxjY6Nzy9mWlhb8/f0JCwujtbWVN954o8v3amtr49ChQzQ3N+Pv709QUFCfL4UvRFfkikOIPtAx4+nZZ5+loaEBi8XCHXfcQXJyMqmpqZSWlvLYY48RFhbG/Pnz2b9/f5fvd/DgQbZs2YJpmgwfPpwVK1Z44s8Q4rpkVpUQQgiXyPWuEEIIl0jjEEII4RJpHEIIIVwijUMIIYRLpHEIIYRwiTQOIYQQLpHGIYQQwiXSOIQQQrjk/wAH+JF84yWJWwAAAABJRU5ErkJggg==\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": 65,
"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>India</th>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>10189</td>\n",
" <td>11522</td>\n",
" <td>10343</td>\n",
" <td>...</td>\n",
" <td>28235</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>2643</td>\n",
" <td>2758</td>\n",
" <td>4323</td>\n",
" <td>...</td>\n",
" <td>36619</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"India 8880 8670 8147 7338 5704 4211 7150 10189 11522 10343 ... \n",
"China 5123 6682 3308 1863 1527 1816 1960 2643 2758 4323 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"India 28235 36210 33848 28742 28261 29456 34235 27509 30933 33087 \n",
"China 36619 42584 33518 27642 30037 29622 30391 28502 33024 34129 \n",
"\n",
"[2 rows x 34 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI = df_can.loc[['India', 'China'], years]\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 69,
"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 0x7f918cf7d908>"
]
},
"execution_count": 69,
"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",
"\n",
"df_CI.plot(kind='line')"
]
},
{
"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": 70,
"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>India</th>\n",
" <th>China</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8880</td>\n",
" <td>5123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>8670</td>\n",
" <td>6682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>8147</td>\n",
" <td>3308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>7338</td>\n",
" <td>1863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>5704</td>\n",
" <td>1527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" India China\n",
"1980 8880 5123\n",
"1981 8670 6682\n",
"1982 8147 3308\n",
"1983 7338 1863\n",
"1984 5704 1527"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI = df_CI.transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes."
]
},
{
"cell_type": "code",
"execution_count": 71,
"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",
"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",
"plt.title('Immigrants 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": 77,
"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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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"df_top5 = df_can.head(5)\n",
"df_top5 = df_top5[years].transpose()\n",
"print(df_top5)\n",
"df_top5.index = df_top5.index.map(int) # 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",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
"\\\\ inplace = True paramemter saves the changes to the original df_can dataframe\n",
"df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head(5)\n",
"-->\n",
"\n",
"<!--\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"-->\n",
"\n",
"<!--\n",
"print(df_top5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"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,
"run_control": {
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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 **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week1_LAB1)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 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"
},
"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"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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