patrickthoreson/DV0101EN-Exercise-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py.ipynb

## DV0101EN-Exercise-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py.ipynb
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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>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
   ]
  },
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Pie Charts](#6) <br>\n",
    "5. [Box Plots](#8) <br>\n",
    "6. [Scatter Plots](#10) <br>\n",
    "7. [Bubble Plots](#12) <br> \n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Import primary modules."
   ]
  },
  {
   "cell_type": "code",
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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,
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   },
   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset."
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
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       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>...</td>\n",
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       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
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       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
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     },
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
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   },
   "source": [
    "Let's find out how many entries there are in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   },
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.0.2\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Gather data. \n",
    "\n",
    "We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1. **Split:** Splitting the data into groups based on some criteria.\n",
    "2. **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3. **Combine:** Combining the results into a data structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/tkfhxqkehfzpclco8f0eazhie33uxj9j.png\" height=400 align=\"center\">"
   ]
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     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
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       "      <th></th>\n",
       "      <th>1980</th>\n",
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       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_2013 = df_continents[['2013']]\n",
    "df_2013.head()\n",
    "\n",
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_2013['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\n",
    "<!--\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/9nkxsfihu8mgt1go2kfasf61sywlu123.png\" width=440, align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['China','India'], years].transpose()\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[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.describe()"
   ]
  },
  {
   "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.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.plot(kind='box', figsize=(8, 6))\n",
    "plt.title('China and India Immigration to Canada (1980-2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\n",
    "- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,  \n",
    "- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://ibm.box.com/shared/static/03rhrfcealyoi83tigscovgglfchfyor.png\" width=500 align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "   1. Create a list of all years in decades 80's, 90's, and 00's.\n",
    "   2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "   3. Merge the three series into a new data frame. Call your dataframe **new_df**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>429355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>466431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>110363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>312145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>165707.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s     2000s\n",
       "Country                                                                     \n",
       "India                                                82154  180395  429355.0\n",
       "China                                                32003  161528  466431.0\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966  110363.0\n",
       "Philippines                                          60764  138482  312145.0\n",
       "Pakistan                                             10591   65302  165707.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1)\n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1)\n",
    "years_00s = list(map(str, range(2000, 2100))) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s})\n",
    "new_df.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",
    "\\\\ # create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # display dataframe\n",
    "new_df.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>138333.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>145288.871956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>46754.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>88133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>138035.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>466431.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667  138333.266667\n",
       "std     44190.676455   68237.560246  145288.871956\n",
       "min      7613.000000   30028.000000   16775.000000\n",
       "25%     16698.000000   39259.000000   46754.500000\n",
       "50%     30638.000000   56915.000000   88133.000000\n",
       "75%     59183.000000  104451.500000  138035.000000\n",
       "max    179171.000000  261966.000000  466431.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()"
   ]
  },
  {
   "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",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.plot(kind='box', vert=False)\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\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",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "* larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "* smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "* Q1 (25%) = 36,101.5 <br>\n",
    "* Q3 (75%) = 105,505.5 <br>\n",
    "* IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>429355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>466431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>312145.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1980s   1990s     2000s\n",
       "Country                             \n",
       "India        82154  180395  429355.0\n",
       "China        32003  161528  466431.0\n",
       "Philippines  60764  138482  312145.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) on box plots for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
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   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years."
   ]
  },
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   },
   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Step 1: Get the data:\n",
    "   1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "   2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "   3. Reset the index in place.\n",
    "   4. Rename the columns to **year** and **total**.\n",
    "   5. Display the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
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    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Before starting Step 1:                 1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  \\\n",
      "Country                                                                      \n",
      "Afghanistan       16    39    39    47    71   340   496   741   828  1076   \n",
      "Albania            1     0     0     0     0     0     1     2     2     3   \n",
      "Algeria           80    67    71    69    63    44    69   132   242   434   \n",
      "American Samoa     0     1     0     0     0     0     0     1     0     1   \n",
      "Andorra            0     0     0     0     0     0     2     0     0     0   \n",
      "\n",
      "                ...  2004  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
      "Country         ...                                                         \n",
      "Afghanistan     ...  2978  3436  3009  2652  2111  1746  1758  2203  2635   \n",
      "Albania         ...  1450  1223   856   702   560   716   561   539   620   \n",
      "Algeria         ...  3616  3626  4807  3623  4005  5393  4752  4325  3774   \n",
      "American Samoa  ...     0     0     1     0     0     0     0     0     0   \n",
      "Andorra         ...     0     0     1     1     0     0     0     0     1   \n",
      "\n",
      "                2013  \n",
      "Country               \n",
      "Afghanistan     2004  \n",
      "Albania          603  \n",
      "Algeria         4331  \n",
      "American Samoa     0  \n",
      "Andorra            1  \n",
      "\n",
      "[5 rows x 34 columns]\n",
      "\n",
      "Step 1 result: Country  Denmark  Norway  Sweden\n",
      "1980         272     116     281\n",
      "1981         293      77     308\n",
      "1982         299     106     222\n",
      "1983         106      51     176\n",
      "1984          93      31     128\n",
      "\n",
      "Step 2 result:         0\n",
      "1980  669\n",
      "1981  678\n",
      "1982  627\n",
      "1983  333\n",
      "1984  252\n",
      "\n",
      "Step 3 result:   index    0\n",
      "0  1980  669\n",
      "1  1981  678\n",
      "2  1982  627\n",
      "3  1983  333\n",
      "4  1984  252\n",
      "\n",
      "Step 4 result:    year  total\n",
      "0  1980    669\n",
      "1  1981    678\n",
      "2  1982    627\n",
      "3  1983    333\n",
      "4  1984    252\n",
      "\n",
      "Step 5 result:    year  total\n",
      "0  1980    669\n",
      "1  1981    678\n",
      "2  1982    627\n",
      "3  1983    333\n",
      "4  1984    252\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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# Step 1: create dataframe subset of 3 countries only\n",
    "print(\"\\nBefore starting Step 1:\", df_can[years].head())\n",
    "df_countries = df_can.loc[['Denmark','Norway','Sweden'], years].transpose()\n",
    "print(\"\\nStep 1 result:\", df_countries.head())\n",
    "\n",
    "# Step 2: sum the totals for by country\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "print(\"\\nStep 2 result:\", df_total.head())\n",
    "\n",
    "# Step 3: reset the index (so wwe can use the 'year' column correctly as a regular column)\n",
    "df_total.reset_index(inplace=True)\n",
    "print(\"\\nStep 3 result:\", df_total.head())\n",
    "\n",
    "# Step 4: rename the columns correctly ('year' and 'total'), and display the final result\n",
    "df_total.columns = ['year','total']\n",
    "print(\"\\nStep 4 result:\", df_total.head())\n",
    "\n",
    "# Step 5: convert column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "print(\"\\nStep 5 result:\", df_total.head())\n",
    "\n",
    "# Display resulting dataframe data set:\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show resulting dataframe\n",
    "df_total.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
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    "deletable": true,
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   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
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   "outputs": [
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XEtuR+/Iw8O781T4hhF2IX7gP9xdLQZ4gNom8r6x8rxrGUPJFYt+BUm3Kn4n9d57MsuyJsteCXudSnL2IzRdXp5q7p4j9ACr1V+KJ5N+A6dVe0Zf2tYuJ/edG5coXEptretqG/yglM324ndhUeygxKSqV7Zte+S/1vYD7syz7XpZl92VZ9jix6arcxcAuIYRPp3kP9J4/2xObtr6UZdlvsyx7lNgkO9B1+DDwthDCWzUnIYQ2BrYdgXiVKHEfyB/rtxPX+/vpvu72I/6Szq+7SvbnR4hXYuWVDw/lcdHTeethYMe03gAIIWxEXIc9niOyLHuZ2L/wpBBC+Q+I0hWuaw3gfNqfSvaXh4Ed8pdphxB2pPsPnH8m9jU6Ncuyu7Msm02uFqpaBRyrQyaL5mVZtojYvAora3XXSn+Xl022nJXreA4xmTmwbJwPsGotZ365L5Ttw8t6GzeEsBWxT+MjxObYJWWj3Ef8PjswN81qxOOz1xgGYDVi83S/IzWNtMF/AnwjhPChEMI2IYRvEg+q/n6dLSBWXX4qTfdu4i/I8sva/wHsE0Joz59IyvyQ2E/j8hDCTiGE9xJ/mdyVZdnvB/fpBiYdgN8Hvh5CODSEMDGE8J/EmpFzhmq5IYTx6bVVCOGQEMJNxEsoj8+yrCuNdg5xm/wshLBHqlrfJ4RwYQhhy6GKLWc2MDktewfiVYntA5lBlmUPE09+BwOXpYOzGmcSL339SFn5t4DPpeasiSGEE4iXlFayDf9I/CV7FCu/wO8g/rrfje5f6rOBnUMIk9O2O4WY8HWTmgluJFar35FvsqrQ08QT2+fScvZL8xpobc9txKaH0j60K/GqyF5PugAhhBNCCD8JIRwYQtg6hLB9COGLxEuO8zUetxO/eA9h5Xq6PY23Id3XXSX783eBj4cQTknb8TjidmGA8xmsfwC7p3XeFkJYnXjRx4vA1SGE3VITyVXEppW+7m/2WWKNxn0hhMNDCDukdXkkMambSOXn0/5Usr9cSWwC+VkIYZcQwp7AT8uW9Xia5rS0Xj9CPOaKVM2xOmBpve9K6iQfYtPirmU/xL+Q1sl26Zj+AfFqsVLtyCzidro87QNbhxBOJyYa18JblzJ/h9isdGSa17nEH0TTivgcxGRoNrFT/Qa575ERKYaFxAtczknf7TsSt/EaxO/80rzWL62HVLR5Gt48N87XQgjvD/H+S9uGED5F/NE+s79YmyohSr5IvCz0SuIXwnrEzoqL+5ooVaEdSryS4aE0zQWsrKorOY14NcY/iDtST/OaDxxArJL9E7Hq+G/Eqsla+hLxF/0FxF9RRxI7md3W51SDN4K4vjqI/QLOIdZU7ZJl2f+URkq/8t5DvIrjJuKvgouJO/crQxRb3ueJJ9rfEr9YnwOuGehM0q/M9xFrW64IIYwIIUwIg7iDeOrH8Q3iOsi7iHji/k/ievoicEaWZZdWMM9lxD4NI0hNQamm4UHil0y+r91PiEn7ZcTmo3cRO2v2ZDqxJmt6+Rsh3lhyTh8xdRL3w/2J++T5xA7qA7qHVzpJf4TYT+RO4jF2A/HKs778kfhL8EfE43wW8Uq2U8l9Qabmg0eIX7T3p+KHiPvnk/n+I5Xsz1mWXUs8d5ye5nMEcVsykPlU4bvEZqIHieetSVm8h9kBxH3hTmJT6SLgA301g6bPvhuxluEs4jqfRWxK/Q7wtwGcT/tUyf6SfvwdTOw0/EdiYjyN2A+nNM5DxP40JxDX678Tt3mRBn2sDtINxH3zBGCz9P/9dP9xV7r1wYPETvQnZ1n2ldKbWZa9RNwHIO5zDxD3zeOzLLsmN94FxG19TprXB4BDUg17tYx4/7oDibVs83KvzXLjfYF4frqEWGM0kXhRS36fOoSV6wFik//9xGbSkrWJydXDxHPgp4n9lfq9wXOovntE/YXYb2JBlmVNfXNAaXwhhH2JV1XumGXZU/WOZyiEED5LPMFsUl61HWI/m0ezLDuhLsGJiAyRprsFewhhZ+Kvlz8Qf8UeRezvcXA945KW8SHgvOGYDIV4tdrWxF/XP+whGVqPeO+vf61DeCIiQ6rpaohCvCz2EmJb/GrEq4i+mW+yEZGBC/FZWIcTO8d/LOvhsTEiIsNV0yVEw5WZbUv3jo5bEturZ6byCcSrAczdF5hZIHY+PJh487Vj3b2//hUiIiLSg2bsVD0suftsd9/V3Xcldup+nXgVwBnAbe4+kdhB+Iw0SelBfhOJNxK8qPZRi4iIDA9KiBrTfsCT7v408TL6Gal8Bisv254MzHT3zN3vAdY1s437mW+ml1566aXXoF4yzDVdp+oWcRjxnh4AG7n7PAB3n2dmpRufbUK8O2zJ3FTW7bJXM5tCrEHC3Vm6tOKbDnczcuRIli3r8xYwDUlx11azxg3NG7viHnqjRg3mZtfSbJQQNRgzG0W810J/z3np6c6/q/yKcffprLyfTNbZ2Vk+SkXa2toY7LT1pLhrq1njhuaNXXEPvfb2Ad3XVZqUmswaz0HAX9x9fhqeX2oKS39LNyObS/ebWm1KP8+SERERkZ6phqjx/D9WNpdBfAL5McQHQh7DymfUXA+cZGZXEe86/GqpaU1EREQGRglRAzGzNYm3Ys/fBfhcwM3seOJTgA9N5TcQL7l/gnhF2nE1DFVERGRYUULUQNz9deLzevJlLxGvOisfNwNOrFFoIiIiw5r6EImIiEjLUw2RVG1e11Kmzeqga8lyxo0ewdRJ7Ywfq8tURUSkeaiGSKo2bVYHszsX09H1JrM7FzPtbl3sJiIizUUJkVSta8nybsMLy4ZFREQanRIiqdq40SP6HBYREWl06kMkfSr1D1q0bA5rjaTH/kFTJ7Uz7e4OFub6EImIiDQTJUTSp1L/oLeG7+7gvAMndBtn/NhRq5SJiIg0EzWZSZ/UP0hERFqBEiLpk/oHiYhIK1BCJH2aOqmd7drGsOm6Y9i2bYz6B4mIyLCkPkTSp1L/oLa2Njo7O+sdjoiIyJBQDZGIiIi0PCVEIiIi0vKUEImIiEjLU0IkIiIiLU8JkYiIiLQ8JUQiIiLS8pQQiYiISMtTQiQiIiItTwmRiIiItDwlRCIiItLylBCJiIhIy1NCJCIiIi1PD3eVmpnXtZRpszroWrKccaNHMHVSO+PHjqp3WCIiIqohktqZNquD2Z2L6eh6k9mdi5l2d0e9QxIREQGUEEkNdS1Z3m14YdmwiIhIvSghkpoZN3pEn8MiIiL1ooRIambqpHa2axtD+7jV2bZtDFMntdc7JBEREUCdqqWGxo8dxXkHTqh3GCIiIqtQDZGIiIi0PCVEIiIi0vKUEImIiEjLU0IkIiIiLU8JkYiIiLQ8XWXWQMxsXeASYCcgAz4BzAauBiYAcwBz9wVmFoALgYOB14Fj3f0vdQhbRESk6amGaIiY2RpmNtAHdV0I3Oju2wG7AI8CZwC3uftE4LY0DHAQMDG9pgAXFRK4iIhIC1JCVBAzO9/M9kj/fxB4GXjFzD5c4fRrA3sBlwK4+1J3fwWYDMxIo80APpL+nwzMdPfM3e8B1jWzjQv7QCIiIi1ETWbFOQI4M/1/JnAk8CowDfh1BdNvCbwIXGZmuwD3AacAG7n7PAB3n2dmG6bxNwGezU0/N5XNy8/UzKYQa5Bwd9ra2gb+yYCRI0cOetp6Uty11axxQ/PGrrhFiqGEqDhruvvrZrYBsKW7/zeAmb29wulHArsBn3P3e83sQlY2j/Uk9FCWlRe4+3Rgeun9zs7OCsPprq2tjcFOW0+Ku7aaNW5o3tgV99Brb9djhlqBmsyK85iZHQGcBNwCYGZtwBsVTj8XmOvu96bha4gJ0vxSU1j6+0Ju/M1y028KdFT1CURERFqUEqLifBY4EdgH+EoqOxC4uZKJ3f154Fkz2zYV7Qc8AlwPHJPKjgGuS/9fDxxtZsHM9gReLTWtiYiIyMCoyaw4z7r7e/IF7v5zM7ttAPP4HPDzdHXaU8BxxKTVzex44Bng0DTuDcRL7p8gXnZ/XJXxi4iItCwlRMV5DFi7h/JHgPUrmYG7PwC8s4e39uth3IxYIyUiIiJVUpNZcVbp5JwupV9Rh1hERERkAFRDVCUze5Z4ddcaZvZM2dsbAL+ofVQiIiIyEEqIqncksXboBuCoXHkGzHf32XWJSkRERCqmhKhK7v47iJfYu/vr9Y5HREREBk4JUXGWpbtC7wqMzb/h7kfXJyQRERGphBKi4swE/on4mI75dY5FREREBkAJUXEOBLZID2QVERGRJqLL7ovzDDC63kGIiIjIwKmGqDgzgevSQ1m7NZm5++31CUlEREQqoYSoOCelv+eUlWfAljWORURERAZACVFB3H2LescgIiIig6M+RCIiItLyVENUkPTcsrOA9wFt5J5t5u6b1yksERERqYBqiIrzX8BuwNnEp9t/jnjl2bR6BiUiIiL9U0JUnAOAj7r7dcDy9PfjdH++mYiIiDQgJUTFWQ14Nf3/mpmtC8wDtq5fSCIiIlIJ9SEqzoPE/kO3Ab8HfgS8BjxWz6BERESkf6ohKs6ngDnp/5OBN4B1AT3YVUREpMGphqgAZjYCOBb4JoC7vwh8sp4xiYiISOVUQ1QAd18OnAi8We9YREREZOCUEBVnBvDpegchIiIiA6cms+LsAXzOzE4HniU+wwwAd9+rblGJiIhIv5QQFefi9BIREZEmo4SoIO4+o94xiIiIyOAoISqImX2il7eWAHOBe9x9SQ1DEhERkQopISrO0cC7gfnEBGhTYCPgz8AEADOb7O5/rleAIiIi0jMlRMV5GPiVu3+/VGBmJwHbAe8FvgT8gJg0iYiISAPRZffFORz4YVnZRcAR7p4B3wF2qHlUIiIi0i8lRMWZD3y4rOyDwAvp/zHoxo0iIiINSU1mxTkZ+KWZ/Y14H6LNgJ2AQ9P77yI2mYmIiEiDUUJUEHe/2cy2Ag4C2oEbgP9z95dK7wM31zFEERER6YUSogK5eydwRb3jEBERkYFRQlQFM7vR3T+Q/v89ucd15OnRHSIiIo1NCVF1Zub+v6TamZnZHKALWA4sc/d3mtn6wNXEexnNAczdF5hZAC4EDgZeB451979UG4OIiEgrUkJUBXe/Mvd/UY/u2Cc1vZWcAdzm7uea2Rlp+IvEvkoT0+tdxEv831VQDCIiIi1FCVGBzOxfgHcAY/Pl7n5OFbOdDOyd/p8B3EFMiCYDM9M9ju4xs3XNbGN3n1fFskRERFqS7kNUEDP7AXANsBewfe613QBmkwE3m9l9ZjYllW1USnLS3w1T+SbEy/tL5qYyERERGSDVEBXnCGAnd++oYh6T3L3DzDYEbjGzv/cxbuihbJVO3SmxmgLg7rS1tQ0qsJEjRw562npS3LXVrHFD88auuEWKoYSoOM8Sn2w/aKVkyt1fMLNrgT2A+aWmMDPbmJV3vp5LvPljyabAKsmYu08HpqfBrLOzs3yUirS1tTHYaetJcddWs8YNzRu74h567e3t9Q5BakAJUXGOBy42s18QH+PxFne/s7+JzWwtYDV370r/HwCcDVwPHAOcm/5elya5HjjJzK4idqZ+Vf2HREREBkcJUXF2J175tRfwRq48AzavYPqNgGvNDOJ2udLdbzSzPwFuZscDz7DyUSA3EC+5f4J42f1xRXwIERGRVqSEqDjnAB9291sHM7G7PwXs0kP5S8B+PZRnwImDWZaIiIh0p6vMirMI6LdpTERERBqPaoiKcyZwgZmdzcqOzwC4+4r6hCTSt3ldS5k2q4OuJcsZN3oEUye1M37sqHqHJSJSc6ohKs5PgU8DzwFvptey9FekIU2b1cHszsV0dL3J7M7FTLu7mrtGiIg0L9UQFWeLegcgMlBdS5Z3G15YNiwi0iqUEBXE3Z+udwwiAzVu9AjoerP7sIhIC1JCVBAzWwc4mZ6fZXZAXYIS6cfUSe1Mu7uDhbk+RCIirUgJUXF+CYwArqX7fYhEGtb4saM478AJ9Q5DRKTulBAVZ09gA3dXJ2oREZEmo6vMinMX8en2IiIi0mRUQ1ScY4EbzOxeVn2W2dl1iUhEREQqooSoON8kPn1+DrB2rjyrSzQiIiJSMSVExTkM2EZPnBcREWk+6kNUnKfQXalFRESakmqIinNxkGT6AAAWKklEQVQFcL2Z/YBV+xDdXp+QREREpBJKiIpzYvp7Tll5BmxZ41hERERkAJQQFcTd9SwzERGRJqU+RCIiItLyVENUJTP7Pf1cWu/ue9UoHBERERkEJUTVu6TeAYiIiEh1lBBVyd1n1DsGERERqY76EImIiEjLU0IkIiIiLU8JkYiIiLQ8JURVMLN7cv9/tZ6xiIiIyOApIarONmY2Jv1/Wl0jERERkUHTVWbVuQ54zMzmAGuY2Z09jaT7EImIiDQ2JURVcPfjzOy9wATgn4FL6xuRiIiIDIYSoiq5+13AXWY2SvckEhERaU5KiAri7j81s32Ao4BNgOeAn7n77fWNTERERPqjTtUFMbNPAlcDzwO/AuYBV5rZp+oamIiIiPRLNUTFOR3Y390fLBWY2dXAfwMX1y0qERER6ZcSouJsADxSVjYbWL8OsYgwr2sp02Z10LVkOeNGj2DqpHbGjx1V77BERBqSmsyKcxfwPTNbE8DM1gK+A8yqa1TSsqbN6mB252I6ut5kdudipt3dUe+QREQalhKi4nwa+CfgVTObD7wC7AKcUNeopGV1LVnebXhh2bCIiKykJrOCuPs84H1mtinQDnS4+9yBzsfMRgB/Bp5z9w+Z2RbAVcSmt78AR7n7UjMbDcwEdgdeAj7u7nOK+TQyHIwbPQK63uw+LCIiPVINUcHcfa67/3EwyVByCvBobvg8YJq7TwQWAMen8uOBBe6+NTAtjSfylqmT2tmubQzt41Zn27YxTJ3UXu+QREQalhKiBpJqlz4IXJKGA7AvcE0aZQbwkfT/5DRMen+/NL4IAOPHjuK8Aydw0SFb8e0DJ6hDtYhIH9Rk1lguIF6+Py4NbwC84u7L0vBc4k0fSX+fBXD3ZWb2ahq/Mz9DM5sCTEnj0dbWNqjARo4cOehp60lx11azxg3NG7viFimGEqICmNlqwN7AXe6+dJDz+BDwgrvfZ2Z7p+KeanyyCt57i7tPB6aX3u/s7CwfpSJtbW0Mdtp6Uty11axxQ/PGrriHXnu7mptbgZrMCuDuK4DrBpsMJZOAQ8xsDrET9b7EGqN1zayUuG4KlK6dngtsBpDeXwd4uYrli4iItCwlRMW508z2HOzE7v4f7r6pu08ADgNud/cjgN8CH0ujHQNcl/6/Pg2T3r/d3VepIRIREZH+qcmsOE8DvzGz64h9e95KTtz9zCrm+0XgKjP7BnA/cGkqvxS4wsyeINYMHVbFMqQPRd3xWXeOFhFpXEqIirMG8D/p/02rmZG73wHckf5/Ctijh3EWA4dWsxypTOmOzwB0vcm0uzs478AJdZuPiIgUTwlRQdz9uHrHIEOjqDs+687RIiKNSwlRgcxse2J/no3c/SQz2xYY7e4P1Tk0qUJRd3wu8s7Ran4TESmWOlUXxMwOBe4k3h/o6FQ8Dvhe3YKSQhR1x+ci7xytB7eKiBRLNUTFORvY390fMLOPp7IHiQ94lSZWuuNzo8wH1PwmIlI0JUTF2ZCYAMHKK8wyerhZogw/tW7C0oNbRUSKpSaz4twHHFVWdhjwxzrEIjVW6yYsPbhVRKRYqiEqzsnAzWZ2PLCWmd0EbAMcUN+wpBZq3YRVZPObiIiohqgw7v53YDvgR8CXgcuAnd398boGJjVR3mSlJiwRkeaihKhA7v46cDfxpoq/d/fX6huR1IqasKRZzetayuk3zeEz1z/J6TfN4fnXqnkko0jzUpNZQcxsc+DnwJ7AAmA9M7sXOMLdn65rcDLk1IQlzUp3UBeJVENUnBnEjtXruvuGwHrAn1K5iEhD0i0cRCIlRMXZHfiCuy8CSM1lX0zlIiINSf3fRCIlRMW5h1UfwvpO4A91iEVEpCLq/yYSqQ9RFczs7Nzgk8ANZvZ/wLPAZsDBwJX1iE2klko3ply0bA5rjUTPVmsi6v8mEqmGqDqb5V5jgF8BS4h3rV4CXJvKRYa1Usfcua8s1rPVRKQpqYaoCu5+XL1jEGkE6pgrIs1OCVGBzGxNYGtgbL7c3WfVJyKR6lXynDY9W01Emp0SooKY2dHAD4GlwBu5tzJg87oEJVKASu5TM3VSO9Pu7uC1ZbzVh2g4qfXDe0Wk9pQQFefbwEfd/ZZ6ByJSpEqaw0odc9va2ujs7KxVaDWjmxeKDH9KiIqzlPjIDpFhpdbNYY1YG9PqfaQacZuIFE1XmRXnK8D3zKyt3oFIZfQMp8rU+j41pdqYjq43G+aKtVa/eWEjbhORoqmGqDiPAWcDnzWzUlkAMndvrbNnFWr5S1TNIJWp9X1qGrE2ptRHamFuv2wljbhNRIqmhKg4VwAzgavp3qlaBqCWSUojnuR1g8PGvGKt1W9e2IjbRKRoSoiKswFwprtn9Q6kmdUySWnEk3y3hBBastaq1WtjGpG2ibQCJUTFuQw4ilhLJINUyySlEU/yjVhrVWvDuTamWTsnD+dtIlKihKg4ewAnmdmXgPn5N9x9r/qE1HxqmaQ04km+EWutpDhFNgk3YvNqsyZ8IqCEqEgXp5dUoRGTlFoa7jc4bHVF1gA2YvOqLlSQZqaEqCDuPqPeMUjzG+43OGx1RdYANmLzaiPGJFIpJUQFMbNP9Paeu/+0lrGISGMqskm4EZtXGzEmkUopISrOUWXD44GtgLsBJUQiUmiTcCM2rzbihQoilVJCVBB336e8LNUabV+HcERkmGvE5tVW7wMozU2P7hhalwPH1zsIERER6ZtqiApiZuXJ5ZrAkcArdQhHREREBkAJUXGWAeV3qX4O+FQdYhEREZEBUEJUnC3Khhe5e8UN+2Y2BrgTGE3cLte4+1fNbAvgKmB94C/AUe6+1MxGE++KvTvwEvBxd59T/ccQERFpPUqICuLuT1c5iyXAvu7+mpmtDtxlZr8BpgLT3P0qM/sxsU/SRenvAnff2swOA84DPl5lDCJDSncyFpFGpYSoSmb2W1ZtKsvL3H2//uaTHgr7WhpcPb0yYF/g8FQ+AziLmBBNTv8DXAP80MyCHi4rjUx3MhaRRqWEqHo/66V8E+BkYufqipjZCOA+YGvgR8CTwCvuviyNMjfNtzT/ZwHcfZmZvQpsAHSWzXMKMCWNR1tbW6XhdDNy5MhBT1tPiru2+ot70bI53YZfW0bDfM7hus4bzXOvvMHZNz3Gq4ufZp0xI/nqB7ahfZ016h2WiBKiarn7pflhM9sA+A9iZ+qrgbMHMK/lwK5mti5wLT3fw6hUAxT6eC8/z+nA9NL7g71fSSPd62QgFHdt9Rf3WiNXHW6Uz1ntOq9Xc2BfcTdiE+VXbprzVi3hs8BX/vfhhq8lbG/XDSZbge5DVBAzW9vMvg48AWwE7ObuU9x97kDn5e6vAHcAewLrmlnpa2RToCP9PxfYLC17JLAO8HJVH0JkiE2d1M52bWNoH7c627aNGVZ3Mi41B3Z0vcnszsVMu7uj/4l6MK9rKaffNIfPXP8kp980h+dfW1r3mIqk551Jo1INUZXMbA3gVOA0YhLzXnd/eBDzeRvwpru/kub5fmJH6d8CHyNeaXYMcF2a5Po0/If0/u3qPySNbjjfybioL/oi+1k1YvKh551Jo1JCVL1/ACOAbwN/BjYys43yI7j77RXMZ2NgRupHtFqczP/XzB4BrjKzbwD3A6UmukuBK8zsCWLN0GGFfBqRYaLS5qLSeIuWzXnrmWCDaVYq6ou+yCSmEZOPop7B1ojNgdLcQpapUqEaZjaH/q8y27JG4fQn6+gYXJV5s/VpWfklR1VfcvXSbOu7pJHiPj3XVwVgu7YxPda0VDpef55/bekqDzYdzD5XHs+2bWP4dh/x9LXOi4ppKFS7rxS13SqR+hD11G9ThhHVEFXJ3SfUOwZZVbdmB9Dl3S2o0pqWompkimoOLPKJ8WqiFKmcEiIZlnSylEqbixqtWWk4JzFFqmS7qVlNBkJXmcmwVH5yrPeXnNRepVe0lcbbdN0xw+7Kt+Gsku3biFfZSeNSDZEMS0V13JTmVWlNS2m8Rur/1OoqqdmpZPuqplgGQgmRDEv6khNpXkXdeqDRmkOlsanJTEREGkpRNTvD+UagUjzVEImISEMpqmZHHdRlIFRDJCIiDUU1O1IPqiESEZGGopodqQfVEImIiEjLU0IkIiIiLU8JkYiIiLQ8JUQiIiLS8tSpWpqOnk8kIiJFUw2RNB09n0hERIqmhEiajp5PJCIiRVNCJE1HT7IXEZGiKSGSpqO72IqISNHUqVqaju5iKyIiRVMNkYiIiLQ8JUQiIiLS8pQQiYiISMtTQiQiIiItTwmRiIiItDwlRCIiItLylBCJiIhIy1NCJCIiIi1PCZGIiIi0PN2pWkSazryupUyb1UHXkuWMGz2CqZPaGT92VL3DEpEmphoiEWk602Z1MLtzMR1dbzK7czHT7u6od0gi0uSUEIlI0+lasrzb8MKyYRGRgVJCJCJNZ9zoEX0Oi4gMlBIiEWk6Uye1s13bGNrHrc62bWOYOqm93iGJSJNTp2oRaTrjx47ivAMn1DsMERlGlBA1CDPbDJgJjAdWANPd/UIzWx+4GpgAzAHM3ReYWQAuBA4GXgeOdfe/1CN2ERGRZqcms8axDDjN3bcH9gRONLMdgDOA29x9InBbGgY4CJiYXlOAi2ofsoiIyPCghKhBuPu8Ug2Pu3cBjwKbAJOBGWm0GcBH0v+TgZnunrn7PcC6ZrZxjcMWEREZFtRk1oDMbALwDuBeYCN3nwcxaTKzDdNomwDP5iabm8rmlc1rCrEGCXenra1tUDGNHDly0NPWk+KurWaNG5o3dsUtUgwlRA3GzMYC/w2c6u4Lzay3UUMPZVl5gbtPB6aX3u/s7BxUXG1tbQx22npS3LXVrHFD88auuIdee7uuYmwFajJrIGa2OjEZ+rm7/yoVzy81haW/L6TyucBmuck3BXS7XhERkUFQDVGDSFeNXQo86u7fy711PXAMcG76e12u/CQzuwp4F/BqqWmtL9X80mnWX0mKu7aaNW5o3tgVt0j1VEPUOCYBRwH7mtkD6XUwMRHa38weB/ZPwwA3AE8BTwAXA5+tYBlhsC8zu6+a6ev1UtyKe7jHrrhr9pJhTjVEDcLd76L3g26/HsbPgBOHNCgREZEWoRoiERERaXlKiKRS0/sfpSEp7tpq1riheWNX3CIFCFm2ypXaIiIiIi1FNUQiIiLS8pQQiYiISMvTVWYtzMx+CnwIeMHdd0pluwA/BsYCc4Aj0h2zVwcuAXYj7jcz3f1baZoPABcCI4BL3P3c8mU1aNxzgC5gObDM3d/ZQHGPAn4CvBNYAZzi7nekaXYHLgfWIN5+4ZR01WGjx30HsDHwRpr1Ae7+AkPEzDYDZgLjUyzT3f1CM1sfuBqYkGI3d1+Q7gV2IXAw8DpwbOn5gmZ2DPDlNOtvuPuMJol7OfDXNOtn3P2QBop7O+Ay4rH5JXc/Pzevmp5TREA1RK3ucuADZWWXAGe4+87AtcAXUvmhwOhUvjtwgplNMLMRwI+Ag4AdgP9nZjs0ety56fZx912HOhlKLqfyuD8FkMr3B75rZqXj9SLi8+kmplf5PIt2eQ/LGEzcEBOnXdNryJKhZBlwmrtvD+wJnJj2zTOA29x9InBbGoa4D5fW6RTieiZ9oX+VeAPUPYCvmtl6jR538kZufQ9ZMjTIuF8GTgbOz8+kTucUESVErczd7ySelPK2Be5M/98CfDT9nwFrmdlIYs3EUmAh8QviCXd/yt2XAlcBk5sg7pobYNw7EL88SInDK8A70+Nb1nb3P6RaoZnARxo97qGMrzfuPq9UU+LuXcCjxAcgTwZKNTwzWLn+JhNrEDN3vwdYN63vA4Fb3P1ld19A/LxDloQWGHdNDTRud3/B3f8EvFk2q5qfU0RACZGs6m9A6Zfkoax8Xto1wCJgHvAMcL67v0w84T2bm35uKqu1gcYNMVm62czuM7MptQw2p7e4HwQmm9lIM9uCWLu1GXHdzs1N32jru7e4Sy5Ld2H/SmrqqYlUK/gO4F5go9JjbtLfDdNove3LddvHq4wbYIyZ/dnM7jGzIU2c8yqMuzeNck6RFqOESMp9gljVfR8wjlijAvFX23KgHdgCOM3MtqTnu2vX414OA40bYJK770asmj/RzPaqcczQe9w/JX4R/Bm4AJhFbJJo9PXdW9wQm8t2Bv4lvY6qRaBmNpb40ORT3b2v2sHe1m1d1nkBcQNsnpqDDwcuMLOtCg5zFQOIuzeNso9Li1GnaunG3f8OHABgZtsAH0xvHQ7c6O5vAi+Y2d3EppBn6V4DsCnQUbuIo0HE/ZS7d6RpXzCza4nJ052rzLwOcbv7MuDzpfHMbBbwOLCAuI5LGmp99xE37v5c+ttlZlcS1/fMoYwzdar/b+Dn7v6rVDzfzDZ293mpaanUl2kuPe/Lc4G9y8rvaIK4ye3jT6VO7e8AnmyQuHvT6+cRGUqqIZJuzGzD9Hc14lU1P05vPUN88Gwws7WInSb/DvwJmGhmW6QrjA4Drm/0uM1sLTMbl6ZZi/jl/rdGidvM1kxxYWb7E6+CeyQ1OXSZ2Z6pyelo4LpGjzs1obWl8tWJV60N6fpO6+dS4FF3/17ureuBY9L/x7By/V0PHJ32lT2BV9P6vgk4wMzWS52pD0hlDR13ind0mmcb8QHSjzRQ3L1piHOKtB7VELUwM/sF8Zdvm5nNJV5JM9bMSg+N/RXxsliIV31cRvwSC8Bl7v5Qms9JxC+IEcBP3f3hRo87NZtda2YQj4Mr3f3GBop7Q+AmM1sBPEf35qXPsPKy+9+kV6PHPTqVr07cT24FLh7KuIkJwFHAX83sgVT2n8C5gJvZ8cSE+dD03g3ES9efIF6+fhyAu79sZl8nflEDnJ3rh9awcQPbAz9J22I14Fx3H7KEaKBxm9l4YtPq2sAKMzsV2CHdvqGm5xQR0KM7RERERNRkJiIiIqKESERERFqeEiIRERFpeUqIREREpOUpIRIREZGWp4RIREREWp7uQyQihTKznwNL3P0TubL3Ee9XtFPpuVYiIo1ENUQiUrSTgYPTnaoxszHEmzCeVmQyZGYjipqXiIhuzCgihTOzQ4FvAzsRH+2xq7sflB71cQZwPLAO8Y7Vn3H3Bek9B94LjAEeSO89mub5M+BVYCviw2E/6O531PSDiciwpRoiESmcu/8SuA/4BTAFOCG9NZX4INi9iA/tXAR8Pzfp/wITgfHEx61cUTbrw4GvAeOAPwxR+CLSglRDJCJDwsw2Ij5Z/UvufmEqexz4pLv/Lg1vRnwG1xruvqJs+jbgRWCsuy9KNURL832TRESKok7VIjIk3H2+mXUC+Qdzbg78Oj1wtCQDNjSzF4FvAR8D2oDSOG3EmiSAZ4c2ahFpVUqIRKSW5gKHu/u95W+Y2XHEp7bvCzwNbECsIQq50VSlLSJDQn2IRKSWfgycY2abA5jZhmZ2SHpvHLAEeAlYE/hmfUIUkVakhEhEaul7wI3AbWbWBcwC/jm9dxnQkV4Pp/dERGpCnapFRESk5amGSERERFqeEiIRERFpeUqIREREpOUpIRIREZGWp4RIREREWp4SIhEREWl5SohERESk5SkhEhERkZb3/wHylHJI3QMBfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_total.plot(kind='scatter',x='year',y='total')\n",
    "\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://ibm.box.com/shared/static/3e43kt5j9wj4326x1lh8z2jeqzgpk3jv.png\" align=\"center\">\n",
    "\n",
    "where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9a3a8626a0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n"
   ]
  },
  {
   "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",
    "\\\\ # normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "-->\n",
    "\n",
    "<!--\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9a3a7c47f0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='red',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"yellow\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n"
   ]
  },
  {
   "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",
    "\\\\ # China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was 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 revamped 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,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This notebook is part of the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course online by clicking [here](https://cocl.us/DV0101EN_Lab3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
 ],
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   "display_name": "Python 3",
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