W2-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots.ipynb

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    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Explore Matplotlib library further\n",
    "-   Create pie charts, box plots, scatter plots and bubble charts\n"
   ]
  },
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    "## 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>\n"
   ]
  },
  {
   "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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data.\n"
   ]
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   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Import primary modules.\n"
   ]
  },
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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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",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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    }
   },
   "source": [
    "Download the dataset and read it into a _pandas_ dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
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   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
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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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       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\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",
       "      <td>5501</td>\n",
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       "      <td>Developing regions</td>\n",
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       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>...</td>\n",
       "      <td>1450</td>\n",
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       "      <td>620</td>\n",
       "      <td>603</td>\n",
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       "    <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",
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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",
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       "      <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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       "         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",
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     },
     "execution_count": 3,
     "metadata": {},
     "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to _Introduction to Matplotlib and Line Plots_ and _Area Plots, Histograms, and Bar Plots_ for a detailed description of this preprocessing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Import `Matplotlib`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.3.2\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": {
    "button": false,
    "new_sheet": false,
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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. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Step 1: Gather data. \n",
    "\n",
    "We will use _pandas_ `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1.  **Split:** Splitting the data into groups based on some criteria.\n",
    "2.  **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3.  **Combine:** Combining the results into a data structure.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas.core.groupby.generic.DataFrameGroupBy\n"
     ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "\n",
    "-   `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "-   `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "-   `shadow` - Draws a shadow beneath the pie (to give a 3D feel).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "-   Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "-   Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "-   Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "-   **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='2013'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "\n",
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].plot(kind='pie',\n",
    "                                figsize=(15, 6),\n",
    "                                autopct='%1.1f%%', \n",
    "                                startangle=90,    \n",
    "                                shadow=True,       \n",
    "                                labels=None,                 # turn off labels on pie chart\n",
    "                                pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                                explode=explode_list         # 'explode' lowest 3 continents\n",
    "                                )\n",
    "\n",
    "    # scale the title up by 12% to match pctdistance\n",
    "    plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the _distribution_ of the data through five main dimensions: \n",
    "\n",
    "-   **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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\" width=440, align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "\n",
    "1.  The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2.  25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "3.  75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to _False_. You can also specify a different color in case you are not a big fan of the default red color.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "\n",
    "Where\n",
    "\n",
    "-   `nrows` and `ncols` are used to notionally split the figure into (`nrows` * `ncols`) sub-axes,  \n",
    "-   `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that 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",
    "\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the _decades_ `1980s`, `1990s`, and `2000s`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "run_control": {
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    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "\n",
    "1.  Create a list of all years in decades 80's, 90's, and 00's.\n",
    "2.  Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "3.  Merge the three series into a new data frame. Call your dataframe **new_df**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "# slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "# display dataframe\n",
    "new_df.head()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a list of all years in decades 80's, 90's, and 00's\n",
    "    years_80s = list(map(str, range(1980, 1990))) \n",
    "    years_90s = list(map(str, range(1990, 2000))) \n",
    "    years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "    # slice the original dataframe df_can to create a series for each decade\n",
    "    df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "    df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "    df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "jupyter": {
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    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "Step 3: Plot the box plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "\n",
    "-   larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "-   smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "\n",
    "-   Q1 (25%) = 36,101.5 <br>\n",
    "-   Q3 (75%) = 105,505.5 <br>\n",
    "-   IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  1980s   1990s   2000s\n",
       "0   India  82154  180395  303591\n",
       "1   China  32003  161528  340385"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) on box plots for more information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the 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). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "\n",
    "-   `x`: x-coordinates of the data. \n",
    "-   `y`: y-coordinates of the data. \n",
    "-   `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 33,
     "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]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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   },
   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Step 1: Get the data:\n",
    "\n",
    "1.  Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "2.  Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "3.  Reset the index in place.\n",
    "4.  Rename the columns to **year** and **total**.\n",
    "5.  Display the resulting dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "# create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "# reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "# change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "# show resulting dataframe\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # create df_countries dataframe\n",
    "    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "    # create df_total by summing across three countries for each year\n",
    "    df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "    # reset index in place\n",
    "    df_total.reset_index(inplace=True)\n",
    "\n",
    "    # rename columns\n",
    "    df_total.columns = ['year', 'total']\n",
    "\n",
    "    # change column year from string to int to create scatter plot\n",
    "    df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "# add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# show plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "    # add title and label to axes\n",
    "    plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
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    "# 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.\n"
   ]
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   "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.\n"
   ]
  },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
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       "      <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",
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       "      <td>6661</td>\n",
       "      <td>90</td>\n",
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       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
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       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
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       "<p>5 rows × 196 columns</p>\n",
       "</div>"
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       "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]"
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   "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()"
   ]
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    "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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.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"
   ]
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   "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())"
   ]
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    "Step 3: Plot the data. \n",
    "\n",
    "-   To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "-   We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    -   multiply weights by 2000 to scale it up on the graph, and,\n",
    "    -   add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000).\n"
   ]
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "button": false,
    "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",
    "# 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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # 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",
    "    # 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",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1654290>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2020-11-12        | 2.3     | LakshmiHolla | Added example code for outliers    |\n",
    "| 2020-11-03        | 2.2     | LakshmiHolla | Changed URL of excel file          |\n",
    "| 2020-09-29        | 2.1     | LakshmiHolla | Made fix to a boxplot label        |\n",
    "| 2020-08-27        | 2.0     | Lavanya      | Moved lab to course repo in GitLab |\n",
    "|                   |         |              |                                    |\n",
    "|                   |         |              |                                    |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
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