hi5san/DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb

## DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb
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   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
    "\n",
    "<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
   ]
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Pie Charts](#6) <br>\n",
    "5. [Box Plots](#8) <br>\n",
    "6. [Scatter Plots](#10) <br>\n",
    "7. [Bubble Plots](#12) <br> \n",
    "</div>\n",
    "<hr>"
   ]
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   },
   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
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   },
   "source": [
    "Import primary modules."
   ]
  },
  {
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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",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
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   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Collecting xlrd\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/16/63576a1a001752e34bf8ea62e367997530dc553b689356b9879339cf45a4/xlrd-1.2.0-py2.py3-none-any.whl (103kB)\n",
      "\u001b[K     |████████████████████████████████| 112kB 3.2MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: xlrd\n",
      "Successfully installed xlrd-1.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\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."
   ]
  },
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
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      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
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   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.1.1\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,
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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. "
   ]
  },
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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."
   ]
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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\">"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>Africa</td>\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",
       "      <td>Asia</td>\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",
       "      <td>Europe</td>\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",
       "      <td>Latin America and the Caribbean</td>\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",
       "      <td>Northern America</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa                              38543\n",
       "Asia                               155075\n",
       "Europe                              28691\n",
       "Latin America and the Caribbean     24950\n",
       "Northern America                     8503\n",
       "Oceania                              1775\n",
       "Name: 2013, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents['2013']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "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",
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 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 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 in the year 2013', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\n",
    "<!--\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\" width=440, align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1982</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1983</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1984</td>\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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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",
       "      <td>1980</td>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981</td>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1982</td>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1983</td>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1984</td>\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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.plot.box( figsize=(10, 7))\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NneugberrbTrj7BtC+KcQwt+FEJ4AnEsJu93TgV8D/jmEcHjd/o+ntGBeDvy6s74rKe/jR4VyYcvHgNNyzlePUv4RhXJvulMprZ4RuKVzbNi0M+qHgN1CCMfWffALKRfQvLczrw0622RTYGgbdb8AHRFCOCiEsENd32cAn6T0If55Z7ztqWctxrN+c4EBbOZ5PGWncj3lTf9K4E2U2xIMOQI4m3JZ+88oV8Hsn3O+sv7/Q5Rvby8DqP0xDgFeHkJ4JiN7B+XKxJ9RdkZvzjmfPsL4I5alfpt8JWUHci3lwwzlG+SHKTf1O7+W92k557473ZzzjZR70iykdJ49q0574CjrMxP8f0rn3lMooeQBlM7sI/Zvqdv2YOChlIPCyZRtev0YyvB2SivFryihbhvK6ZijKP3Rfk7p7H5gbYkZl1rPB1AuBvgGpU63AvbNk3CPrZzzbyn9Ws6gdJq/jNLZ+KWULwu/mOhl9ngn5RYhZ1C25wOAj4w0QT3V9GrK5/iXlFPBr1vbBeec/0Tpv7Ql5f31Bco+4qa1nddkLKvW96GUlqMllC+KR7Nm61PvdPdQvrwc1vOvZ1L2M0P7mmPr83d0xrk/5bNyBaVD+XJg95zz8s4476bcPuUtlO1/GuX9+oyhVrLaF+vJlFOlFwGnU06n9l4tPRYLufeK3PO597hwPWX7UMvwE8pnaX/KPvidlNbE7qnN+dy7TR5N+Sz/lHKKe8jdlK4uF3NvPZxJ+Ux26+JQysUjv5mAdZzVwjDHM2kNIYQMHJZzHq0vliZZCOG7lPvvzIaAKU2Keqrzfyn3pFrb21FoLdWWt6WUK5NHuiWNgLX5eQ1JUyCE8EhK68xFlG/Sh1HusTNjf4JKaiHnvKqeRt56qssyR2xHuX2I4WsABjBp+suUq6s+Quk2cCXllw/sYyGNIud8zlSXYa7IOf+cTn8wjcxTkJIkSY3ZCV+SJKmxmXYK0uY6SZI0k/S9Z91MC2CsWLFijWHz5s1j5coJvzJd05T1PXdY13OL9T23zIX6nj9//rD/8xSkJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1Nh6U10AaaItWrQVq1b53WL2mD/VBdCANt/8HpYsuWGqiyHNCAYwzTqrVq3D8uUrproYmgDz5s1j5cqVACxYMN96neYWLDAsS4OymUCSJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BNQ15JJEkahMeLmcsAJkmS1JgBTJIkqTEDmCRJUmNrHcBijKvXcvy9Y4xn1efPjDG+aW2XKUmSNJs0/SmilNKZwJktlylJkjTdjDmAxRj3Bo4BVgI7AZcCh6aUcoxxP+DD9X+XdaZ5EfCYlNKrYozPAN4GbADcAhySUrpxrOWRJEmaKcbbArYLsAhYAVwI7BljvAT4JPAkYClw2jDTXgDsXgPbEcAbgdf3jhRjPBI4EiClxLx589ZcifXW6zt8JvPS4tGMvH1m2/thrur9bFuv09/4913u+9bWTP1czMZj99oYbwD7cUrpOoAY42JgW2A1cHVK6ao6/PPUANVjIXBajHFrSivY1f0WkFI6ETixvswrV65cY5x58+bRb/jMNZ/ly1dMdSGmrdHqe8GC+bPs/TB33beurdfpb3z7rtm3L598M3l/Nxfqe/784b9QjPcqyDs7z+/m3kCXB5j2o8AJKaVHAi8DNhpnWSRJkmaEybgNxZXAdjHGh9bXzx9mvM2A5fX54ZNQDkmSpGlpwgNYSukOyinHb8QYLwCuGWbUY4AvxRh/QOmsL0mSNCeEnAc5Wzht5BUr1uxfMNvOIy9YYB+wkQzSB8ztNzt069p6nf7GW0ezbV/ewkz+XMyF+q59wEK//3knfEmSpMYMYJIkSY0ZwCRJkhozgE1DM/V8viSpLY8XM5cBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhpbb/RRpJlnwYLhf4FeM829dWm9Tm+bb37PVBdBmjEMYJp1vCx79pgLP1UiaW7yFKQkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1Nh6U10ATbxFi7Zi1arZnq3nT3UB1Ix1PbdMTH1vvvk9LFlyw4TMS5oMBrBZaNWqdVi+fMVUF2PSzJs3j5UrV051MdTAggXzZ/V7Wfc1kZ/tBQsM7preZnsziSRJ0rRjAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGA9vHRZkqTZbToc6w1gkiRJjRnAJEmSGjOASZIkNTbQTxHFGLcCPgzsCtwJLAO+BjwzpbR/n/FPAj6YUvrlxBVVkiRpdhg1gMUYA/BV4LMppefVYTsDzxhumpTSERNWQkmSpFlmkBawJwJ/Til9YmhASmlxjHFzYJ8Y4+nATsClwKEppRxjPA84OqV0SYxxNXA8sD9wO/CslNKNMcZnAG8DNgBuAQ5JKd04kSsnSZI0HQ0SwIbCVT+7AIuAFcCFwJ7ABT3jbAL8KKX01hjj+4CXAu+q4+1eA9sRwBuB1/cuIMZ4JHAkQEqJefPmrbkS663Xd/hYTYfLU8drIrfHdDPR9a3pzbqeO9yXz0VTV0dTvW8ZqA/YCH6cUroOIMa4GNiWNQPYXcBZ9fmlwL71+ULgtBjj1pRWsKv7LSCldCJwYn2ZV65cucY48+bNo9/wsZnP8uUrJmheU2PBgvkTuD2mn4mtb01vs/u9rPtyXz63TOW+vNVxcv784QPmIFdBLgEePcz/7uw8v5v+ge7PKaXcZ5yPAieklB4JvAzYaICySJIkzXiDBLDvAhvGGF86NCDGuCvwhHEuezNgeX1++DjnJUmSNGOMGsBq69WzgX1jjL+OMS4BjqH0+xqPY4AvxRh/AHiOQZIkzRkh5zz6WNNHXrFizdw3keeRFyyY+f0GZsM6jMQ+YHPHbH8v677cl88tU90HrMX7o/YBC/3+553wJUmSGjOASZIkNWYA62GTtSRJs9t0ONYbwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKmx8f4Yt6apBQum7hfm25jt66chs/+9rPuamPrefPN7JmQ+0mQxgM1C0+Hy2snknfDnDut6brG+NZd4ClKSJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqzAAmSZLUmAFMkiSpMQOYJElSYwYwSZKkxgxgkiRJjRnAJEmSGjOASZIkNWYAkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMYMYJIkSY0ZwCRJkhozgEmSJDVmAJMkSWrMACZJktSYAUySJKkxA5gkSVJjBjBJkqTGDGCSJEmNGcAkSZIaM4BJkiQ1ZgCTJElqLOScp7oMa2NGFVaSJM15od/AmdYCFvo9YoyXDvc/H7PvYX3PnYd1Pbce1vfcesyh+u5rpgUwSZKkGc8AJkmS1NhsCWAnTnUB1JT1PXdY13OL9T23zOn6nmmd8CVJkma82dICJkmSNGMYwCRJkhpbb6oLMF4xxv2A44F1gZNSSu+d4iJpADHGTwP7AzellHaqw7YATgO2BZYBMaV0W/3fm4GXAHcDr0kpnV2HPxo4GdgY+B/gtSmlHGPcEPhv4NHALcBzU0rLGq2eOmKMD6bUxVbAPcCJKaXjre/ZKca4EXA+sCHlGHN6Sunt1vfsFWNcF7gEWJ5S2t+6HsyMbgGrlf4x4GnAjsDzY4w7Tm2pNKCTgf16hr0J+E5KaXvgO/U1tU6fByyq03y81j3AfwJHAtvXx9A8XwLcllJ6GPAh4LhJWxON5i/A61NKOwC7A6+sdWp9z053Ak9KKT0K2BnYL8a4O9b3bPZa4IrOa+t6ADM6gAG7AUtTSr9JKd0FnAo8a9WR7xIAAAgdSURBVIrLpAGklM4Hbu0Z/Czgs/X5Z4EDOsNPTSndmVK6GlgK7BZj3Bq4f0rpopRSpnxLOqDPvE4H9okxDntDPE2elNL1KaXL6vM/UHbUC7C+Z6WUUk4pra4v16+PjPU9K8UYFwL/BJzUGWxdD2CmB7AFwLWd19fVYZqZ/jaldD2UgzbwoDp8uHpeUJ/3Dr/PNCmlvwC/A7actJJrIDHGbYFdgIuxvmetGOO6McbFwE3AuSkl63v2+jDwRkr3giHW9QBmegDrl4K9r8bsM1w9j1T/vjemmRjjpsCXgdellH4/wqjW9wyXUro7pbQzsJDSwrHTCKNb3zNUjHGoH++lA05iXXfM9AB2HfDgzuuFwIopKovG78baFE39e1MdPlw9X1ef9w6/zzQxxvWAzVjzlKcaiTGuTwlfX0gpfaUOtr5nuZTSKuA8Sn8e63v22RN4ZoxxGaUL0JNijJ/Huh7ITA9gPwG2jzFuF2PcgNK578wpLpPG7kzg8Pr8cOCMzvDnxRg3jDFuR+mg+ePatP2HGOPutU/AC3umGZrXQcB3a98CNVbr5lPAFSmlD3b+ZX3PQjHGB8YYN6/PNwaeDFyJ9T3rpJTenFJamFLalnL8/W5K6VCs64HM6ABWzwe/Cjib0rE3pZSWTG2pNIgY4xeBi4D/F2O8Lsb4EuC9wL4xxquAfetrap0m4JfAt4BXppTurrP6F0rnz6XAr4Fv1uGfAraMMS4FjqJehaMpsSdwGOXb8eL6eDrW92y1NfC9GOPllC/J56aUzsL6nkus6wH4U0SSJEmNzegWMEmSpJnIACZJktSYAUySJKkxA5gkSVJjBjBJkqTG1pvqAkia/WKMJwPXpZTeNgXLDsCnKb8td1VKabfWZeiU5RDg8JTSU6aqDJKmBwOYNAfVO1dvDPxdSumPddgRwKEppb2nsGiTYS/KvYgWDq1rV4zxRcARKaW9JrsgKaUvAF+Y7OX0E2PcG/h8SmnhaONKmnyegpTmrvWA1051IdZWjHHdtZzkIcCyfuFrJqk/wyJplvADLc1d/wG8Mcb48fqbfX8VY9wWuBpYv/7iBDHG8ygtKCfVVqOXAj8G/pny22yHAg8H3glsCLwhpfTZzmznxRjPBXYHLgNemFK6ps77EcBHgUcDNwP/mlJK9X8nA7dTgtQTgGcB3+4p73zgE5TWrluB41JKn6y/sPAxYP0Y42rgAymlt4+0UWrr4Mcod+9/KOU37t4CnFznfzFwcErpts52ejHwDmBT4M3ApZQ7eG9Tt9mr6rxfRKe1Lcb4lLreW1FaxhYBn+uzjQ8HPh5j/AzwSeBRlB8kPptyN/FVnbKfQPkpl4dQ7jZ+OLAu5c7iG9btAKWuFgIfr89vp/xW51EjbR9JE8MWMGnuuoTyQ8lHj3H6xwKXA1sCp1CCyq7Awyhh7IQY46ad8Q+hhLN5wGLqqbgY4ybAuXUeDwKeTwkbizrTvgA4FrgfcEGfsnyR8qO98ym/F/fuGOM+KaVPAS8HLkopbTpa+Oo4kHLa8uHAMyjh5S217OsAr+mzLbYHngt8GHgr5TcQF5VVjE/oXUCMcR5wOiWwbQn8Cnhcn/n+hrJdjgUC8J66njtQfqT4mN5ZU378ejvg74EX1da/pwEr6nbYNKW0AjgeOD6ldH9K2EwDbR1J42YLmDS3/RtwYYzx+DFMe3VK6TMAMcbTKKHjHSmlO4FzYox3UcLY4jr+N1JK59fx3wr8Lsb4YEroWDY0L+CyGOOXKUFq6Lddz0gpXVif39EtRJ3HXsD+KaU7gMUxxpMoLVjfGcN6AXw0pXRjnf8PgJtSSj+tr78K7NMz/jvrss+JMf4R+GJK6abO9LsA3++Z5unAkpTSV+p4H2HNMLwipfTR+vwvlN/JW1pf3xxj/CDQGyo/UsMVMcavAzuPsJ5/Bh4WY5yXUloJ/GiEcSVNIAOYNIellH4RYzyL8gO3V6zl5Dd2nt9e59c7rNsCdm1nuatjjLdSWnIeAjw2xtg9Dboe8Ll+0/YxH7g1pfSHzrBrgMcMshLD6F2PkdZrLONDKXd3m+QY43U949xnvWOMDwI+Ajye0hq4DnBbzzQ3dJ7/qS5nOC+hnDq9MsZ4NfDv9YezJU0yA5ikt1P6ZH2gM2yow/rfAL+vz7ca53IePPSknprcAlhBCRnfTyntO8K0eYT/rQC2iDHerxPCtgGWj7O8k+16Sh8s4K+3y+i9QrF3vd9Th/19SumWGOMBlD5fg1hjG6aUrgKeH2NcB3gOcHqMccuZfsGCNBPYB0ya41JKS4HT6PRrSindTAkwh8YY140xvpjSR2g8nh5j3CvGuAGlL9jFKaVrgbOAh8cYD4sxrl8fu8YYdxiw/NcCPwTeE2PcKMb495SWnSm53cNa+AbwyBjjAfUKx1cyesi9H7AaWBVjXAC8YS2WdyOwZYxxs6EBMcZDY4wPTCndAwy1QN69FvOUNEYGMElQTkNt0jPspZQD/C2UzuQ/HOcyTqG0tt1KudrxEIDaavUU4HmU1qwbgOMoV1IO6vnAtnX6rwJvTymdO87yTqra5+pg4H2Ubbwj5cKIO0eY7N+BfwB+RwlwX1mL5V1JuVjhNzHGVfXK0f2AJfXKyOOB59W+bJImWch5pJZ9SVIL9TTgdcAhKaXvTXV5JE0u+4BJ0hSJMT6Vcl+x2ymtjQGvRJTmBE9BStLU2QP4NbCScr+xA1JKt09tkSS14ClISZKkxmwBkyRJaswAJkmS1JgBTJIkqTEDmCRJUmMGMEmSpMb+D9jldrrqdMopAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\n",
    "- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,  \n",
    "- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <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",
       "      <td>India</td>\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",
       "      <td>China</td>\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",
       "      <td>United Kingdom of Great Britain and Northern Ireland</td>\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",
       "      <td>Philippines</td>\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",
       "      <td>Pakistan</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_top15=df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "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",
    "   1. Create a list of all years in decades 80's, 90's, and 00's.\n",
    "   2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "   3. Merge the three series into a new data frame. Call your dataframe **new_df**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country\n",
      "India                                                   303591\n",
      "China                                                   340385\n",
      "United Kingdom of Great Britain and Northern Ireland     83413\n",
      "Philippines                                             172904\n",
      "Pakistan                                                127598\n",
      "United States of America                                 75173\n",
      "Iran (Islamic Republic of)                               65794\n",
      "Sri Lanka                                                49678\n",
      "Republic of Korea                                        68183\n",
      "Poland                                                   13629\n",
      "Lebanon                                                  30906\n",
      "France                                                   41297\n",
      "Jamaica                                                  22733\n",
      "Viet Nam                                                 21274\n",
      "Romania                                                  45515\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom of Great Britain and Northern Ireland</td>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Philippines</td>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pakistan</td>\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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\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",
    "#years_00s\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",
    "print(df_00s)\n",
    "new_df = pd.DataFrame({'1980s':df_80s, '1990s':df_90s, '2000s':df_00s})\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # display dataframe\n",
    "new_df.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.plot.box(figsize=(10,6))\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "* larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "* smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "* Q1 (25%) = 36,101.5 <br>\n",
    "* Q3 (75%) = 105,505.5 <br>\n",
    "* IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\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": [
       "         1980s   1990s   2000s\n",
       "Country                       \n",
       "India    82154  180395  303591\n",
       "China    32003  161528  340385"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) on box plots for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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1bNGTJEmL1tDQOkZGxrn11s2MjIwzOLi20yF1FRM9SZK0aE1MbJtxWTMz0ZMkSYtWb+/yGZc1MxM9SZK0aA0Pr2JgoI9DDlnBwEAfw8OrOh1SV3EwhiRJWrT6+3tYs+Y4+vr6GB8f73Q4XccWPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqqGWdDkCSpKVibGwLQ0Pr2Lz5flas2JXh4VX09/d0Oiy1weRnPTGxjd7e5R37rG3RkyRpgQwNrWNkZJxbb93MyMg4g4NrOx2S2mTysx4d3drRz9oWPUmSFsjExLYZl7W4zKdVbrF81rboSZK0QHp7l8+4rMVlPq1yi+WzNtGTJGmBDA+vYmCgj0MOWcHAQB/Dw6s6HZJmMJ9WucnPeuXKno5+1l66lSRpgfT397BmzXH09fUxPj7e6XA0i97e5YyObt1huVGTn3Wn2aInSZI0hcXSKjcftuhJkiRNYbG0ys2HLXqSJEkVZaInSZJUUQty6TbGeBBwMbAv8BBwfkrpAzHGfwBeBfwyb/qWlNKVeZ83A2cADwJnp5SuyuUDwIXA7sCVwGtTSkWMcbf8GgPAr4CTU0rr8z6nA2/Nr/GOlNJFbX3DkiRJi8BCteg9ALwhpfQE4GjgrBjjYXnd+1NKR+bHZJJ3GHAKcDjwfOBDMcZd8vbnAWcCh+bH83P5GcBdKaVDgPcD78nH6gXOAZ4KHAWcE2Pcq63vVpIkaRFYkEQvpbQppfSd/HwL8EPggBl2OQG4NKV0b0ppFLgVOCrGuB+wZ0ppXUqpoGzBe3HNPpMtdZcBz40xBuB44JqU0kRK6S7gGrYnh5IkSZW14KNuY4wHA08Cvgk8HRiMMZ4GfJuy1e8uyiTwhprdNuSy+/Pz+nLyz9sBUkoPxBg3A3vXlk+xT21cZ1K2FJJSoq+vb17vsxHLli1bkNepEuusedZZc6yv5llnzVtqdTY6+j/8+Z9fzfj4PfT17c6FFx7PypUrmjrGUquzVlnQRC/G2AN8FnhdSunXMcbzgLcDRf75r8ArgDDF7sUM5cxxn99KKZ0PnD+5fiEmsnTCzOZZZ82zzppjfTXPOmveUquzU0+9mpGR8v3eeutmTj31i01PW7LU6qwZ+++//7TrFizRizHuSpnkfSKldDlASumOmvUfBr6QFzcAB9XsfiCwMZcfOEV57T4bYozLgBXARC5/Vt0+17fiPUmStFDGxrYwNLSOiYlt9PYuZ3h4Ff39PZ0OqyHzuZWY5mdB+ujlvnIfBX6YUnpfTfl+NZv9CXBTfr4GOCXGuFuMcSXloIsbU0qbgC0xxqPzMU8DrqjZ5/T8/ETgutyP7yrguBjjXnkQxnG5TJKkrjE0tI6RkXFGR7cyMjLO4ODaTofUsPpbhzVzKzHNz0K16D0deDnwgxjjd3PZW4CXxhiPpLyUuh74S4CU0s0xxgTcQjli96yU0oN5v9ewfXqVL+UHlInkJTHGWylb8k7Jx5qIMb4d+Fbe7tyU0kSb3qckSW3Rza1iw8OrGBxcu0Nr5ELo5lbQVglFsVN3NUGxcePG2beaJ/sbNM86a5511hzrq3nWWfPmUmerV2/v5wYwMNDX9bfnaoZ1Nr3cR2+qMQneGUOSpG4wPLyKgYE+Vq7sYWCgb8FaxbpZN7eCtsqCT68iSZKa19/fU8nWqHbq7V3O6OjWHZaXGlv0JEnSjMbGtrB69dUcc8waVq++mttu2zr7TouAraC26EmSOsSO8t1jcsQvwOjoVgYH13ZF66KtoLboSZI6pJunC1lq7OvWvUz0JEkdYfLQPZwHr3uZ6EmSOsLkoXvY16172UdPktQRnZpEV82zr1v3MtGTJHWEyYPUfl66lSRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIpywmRJkpaAsbEtDA2t2+FOJP39PZ0OS21mi54kSU0YG9vC6tVXc8wxa1i9+mpuu21rp0NqyNDQOkZGxhkd3crIyDiDg2s7HZIWgImeJElN6NaEaWJi24zLqiYTPUmSmtCtCVNv7/IZl1VNJnqSJDWhWxOm4eFVDAz0sXJlDwMDfQwPr+p0SFoADsaQJKkJw8OrGBxcu8Oghm7Q39/DmjXHdToMLTATPUmSmmDCpG7ipVtJkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKch49SVLXGRvbwtDQuh0mLe7v7+l0WNKiY4uepEVlbGwLq1dfzTHHrGH16qu57batnQ5Ji9DQ0DpGRsYZHd3KyMg4g4NrOx2StCiZ6Elqqfkman6BqxETE9tmXJZUMtGT1FLzTdT8AlcjenuXz7gsqWSiJ6ml5puo+QWuRgwPr2JgoI+VK3sYGOhjeHhVp0OSFiUHY0hqqd7e5YyObt1huRnDw6sYHFy7Qyd7qV5/fw9r1hzX6TCkRW9OiV6McXfgwZTSfS2OR1KXm2+i5he4JLVOQ4lejPFfgJRSujHG+ELgMqCIMZ6cUvp8WyOU1FVM1CRp8Wi0j96pwE35+duAPwNWA+9qR1CSJLWLU/hoKWk00dsjpfSbGOPewGNSSp9NKf0X8DttjE2StIhNJkyHH35RVyVMTuGjpaTRPno/iTGeChwCXAMQY+wD7mlXYJKkxW0yYZo0OLi2Ky7bO4WPlpJGE72/Aj4A3AeckcuOB65uR1CSpMWvWxOm+Y4Ml7pJo4ne7SmlHYbOpZQ+EWO8tg0xSZK6QLcmTE7ho6Wk4Uu3wJ5TlN8C9LYuHElSt5hMmDZvvp8VK3btmoTJkeFaShpN9EJ9QYxxT+Ch1oYjSeoWkwlTX18f4+Pjs+8gacHNmOjFGG8HCmD3GONtdav3Bj7VrsAkSZI0P7O16P0ZZWvelcDLa8oL4I6U0o/bFZgkSZLmZ8ZEL6X0FSinUkkp/WZhQpIkSVIrNNpH74EY45nAkUBP7YqU0mktj0qSJEnz1miidzHw+8DngTvaF44kSZJapdFE73hgZUrpf9oZjCRJklqn0Xvd3gbs1s5AJEmS1FrNXLq9Isb4Aeou3aaUrmt5VJI6amxsC0ND63a4c0B/f8/sO0qSFpVGE73B/PNddeUF8JjWhSNpMai9Wf3o6NauuVm9JGlHDSV6KaWV7Q5E0uLRrTerlyTtqNE+epKWkPqb03fLzeolSTtqqEUv39f2H4BnAn3U3Ps2pdTflsgkdczkzepr++hJkrpPo330PgQcCJwLfJzy1mh/A3y2TXFJ6qDJm9VLkrpbo5dujwNeklK6Angw/zyZHe9/K0mSpEWk0UTvYcDm/HxrjPFRwCbgkLZEJUmSpHlr9NLt9yj7510LfA34D2Ar8JM2xSVJkqR5ajTRexXbB2CcDfwT8CjgtEZ2jjEeRDnp8r7AQ8D5KaUPxBh7gU8DBwPrgZhSuivv82bgDOBB4OyU0lW5fAC4ENgduBJ4bUqpiDHull9jAPgVcHJKaX3e53TgrTmcd6SULmrwfUuSJHWtWS/dxhh3Af4c2ACQUvplSumVKaWTU0q3NPg6DwBvSCk9ATgaOCvGeBjwJuDalNKhlK2Fb8qveRhwCnA48HzgQzkOgPOAM4FD8+P5ufwM4K6U0iHA+4H35GP1AucATwWOAs6JMe7VYNySJElda9ZEL6X0IHAWcP9cXySltCml9J38fAvwQ+AA4ARgsnXtIuDF+fkJwKUppXtTSqPArcBRMcb9gD1TSutSSgVlC17tPpPHugx4bowxAMcD16SUJnJr4TVsTw4lqSXGxrawevXVHHPMGlavvprbbtva6ZAkqeHBGBcBr27FC8YYDwaeBHwTeHRKaROUySCwT97sAOD2mt025LID8vP68h32SSk9QDl4ZO8ZjiVJLTN527jR0a2MjIwzOLi20yFJUsN99I4ChmKM/4syaSomV6SUjm30xWKMPZRz770upfTrGON0m4YpyooZyue6T21sZ1JeEialRF9f33SxtcyyZcsW5HWqxDprnnXWnLnW1+bN9++0vFTq3XOsedZZ86yzuWk00ftwfsxZjHFXyiTvEymly3PxHTHG/VJKm/Jl2Ttz+QbgoJrdDwQ25vIDpyiv3WdDjHEZsAKYyOXPqtvn+vr4UkrnA+fnxWJ8fHwO77I5fX19LMTrVIl11jzrrDlzra8VK3bdaXmp1LvnWPOss+ZZZ9Pbf//9p13XUKI331Gqua/cR4EfppTeV7NqDXA68O7884qa8k/GGN8H7E856OLGlNKDMcYtMcajKS/9ngZ8sO5Y64ATgevyaNyrgHfVDMA4DnjzfN6PJNXztnGSFqNG73X7imlW3UvZYnZDSuneGQ7xdMq7aPwgxvjdXPYWygQvxRjPAG4DTgJIKd0cY0zALZQjds/Kg0IAXsP26VW+lB9QJpKXxBhvpWzJOyUfayLG+HbgW3m7c1NKE428b0lqlLeNk7QYhaLYqbvaTmKM1wNPA+5g++XTRwPfppwDD+CElNK32xLlwis2btw4+1bzZDN086yz5llnzbG+mmedNc86a551Nr186XaqMQkN99G7Gbg8pfTvkwUxxkHg8cAxwN9RXkJ92rwilSRJUss0Or3Ky4DhurLzgFPzfHbvBQ5rZWCSJEman0YTvTuAF9WVvZDto2SXM48JlSWpFZy0WJJ21Oil27OBz8QYb6KcR+8g4Ajy4AnK24t9cJp9JWlBTE5aDDA6upXBwbVdMUBibGwLQ0Prdhix29/f0+mwJFVAo9OrXB1j/F3gjyinO7kS+GJK6VeT64Gr2xalJDVgYmLbjMuLVbcmqJIWv0Zb9EgpjQOXtDEWSZqX3t7ljI5u3WG5G3Rrgipp8Zs20Ysxfjml9Pz8/GtMcdswaO4WaJLUTt06aXG3JqiSFr+ZWvQurnn+kXYHIknz1a2TFndrgipp8Zs20UspfbLm+bxugSZJml63JqiSFr+G++jFGJ8BPAnYYShYSuldrQ5KkiRJ89fovW4/CETga8A9Natmv3+apI6YnLJj8+b7WbFiV6fsmIX1JamKGm3ROxU4IqXU/hvASmqJ2ik7AKfsmIX1JamKGr0zxu3Ave0MRFJrOWVHc6wvSVXUaIveGcCHY4yforwd2m+llL7a8qgkzZtTdjTH+pJURY0megOUd8U4lp376PW3OihJ8zc5ZUdtnzNNz/qSVEWNJnrvAl6UUvqvdgYjqXUmp+zo6+tjfHx89h2WOOtLUhU12kfvbsBLtJIkSV2k0Ra9twH/FmM8F7izdkVK6aGWRyVJkqR5azTRuyD//MuaskDZR2+XlkYkSZKklmg00VvZ1igkSZLUcg0leimlsXYHIkmSpNZq9BZoK4Czmfpet04dL0mStAg1eun2M5R98T7HjvPoSZK62OQ9ficmttHbu9x7/EoV02iidzSwd0rp/nYGI0laWLX3+B0d3eo9fqWKaXQeva8DT2hnIJKkhec9fqVqa7RF78+BK2OM32Tne92e2+qgJEkLw3v8StXWaKL3TuAgYD2wZ0150eqAJEkLZ/Iev7V99CRVR6OJ3inAY1NKm9oZjCRpYU3e41dSNTXaR+9ngAMxJEmSukijLXqXAGtijB9k5z5617U8KkmSJM1bo4neWfnnu+rKC+AxrQtHkiRJrdLoLdC8160kSVKXabSPniRJkrrMjC16McavMcsUKimlY1sakSRJklpitku3H1mQKCRJktRyMyZ6KaWLFioQSZIktZZ99CRJkirKRE+SJKmiTPQkSZIqatpEL8Z4Q83zcxYmHEmSJLXKTC16j40xLs/P37AQwUiSJKl1Zhp1ewXwkxjjemD3GONXp9rIefQkSZIWp2kTvZTSX8QYjwEOBv4A+OhCBSVJkqT5m20eva8DX48xPtw59SRJkrrLbHfGACCldEGM8dnAy4EDgJ8DH08pXdfO4KSlbGxsC0ND65iY2EZv73KGh1fR39/T6bAkSV2koelVYoyvBD4N/LtJWYoAABT4SURBVAK4HNgEfDLG+Ko2xiYtaUND6xgZGWd0dCsjI+MMDq7tdEiSpC7TUIse8L+A56WUvjdZEGP8NPBZ4MPtCExa6iYmts24LEnSbBqdMHlv4Ja6sh8Dva0NR9Kk3t7lMy5LkjSbRhO9rwPvizHuARBjfATwXsBrSVKbDA+vYmCgj5UrexgY6GN4eFWnQ5IkdZlGL92+GrgU2BxjnKBsyVsLvLRdgUlLXX9/D2vWHNfpMNQFHLgjaTqNjrrdBDwzxnggsD+wMaW0oa2RSZIaMjlwB2B0dCuDg2v9J0ES0HiLHgA5uTPBk6RFxIE7kqbTaB89SdIi5cAdSdMx0ZOkLufAHUnTmfXSbYzxYcCzgK+nlO5re0SSpKY4cEfSdGZt0UspPQRcYZInSZLUXRq9dPvVGOPRbY1EkiRJLdXoqNsx4EsxxiuA24FickVK6W3tCEySJEnz02iitzvwf/LzA9sUiyRJklqo0QmT/6LdgUiSJKm1Gp4wOcb4BOBE4NEppcEY4+OA3VJK329bdJIkSZqzhgZjxBhPAr4KHACclosfCbyvTXFJkiRpnhoddXsu8LyU0quBB3PZ94AntiUqSZIkzVujl273oUzsYPuI26Lm+YxijBcAfwzcmVI6Ipf9A/Aq4Jd5s7eklK7M694MnEGZVJ6dUroqlw8AF1IODrkSeG1KqYgx7gZcDAwAvwJOTimtz/ucDrw1v8Y7UkoXNfieJUmSulqjLXojwMvryk4Bbmxw/wuB509R/v6U0pH5MZnkHZaPfXje50Mxxl3y9ucBZwKH5sfkMc8A7kopHQK8H3hPPlYvcA7wVOAo4JwY414NxixJktTVGk30zgbeEWP8CvCIGONVwNuB1zeyc0rpq8BEg691AnBpSunelNIocCtwVIxxP2DPlNK6lFJB2YL34pp9JlvqLgOeG2MMwPHANSmliZTSXcA1TJ1wSpIkVU6j06v8KMb4eMrLr1+gnDT5CymlrfN8/cEY42nAt4E35GTsAOCGmm025LL78/P6cvLP23OsD8QYNwN715ZPsc8OYoxnUrYWklKir69vfu+sAcuWLVuQ16kS66x51llzrK/mWWfNs86aZ53NTcPTq6SUfhNj/AYwCmxsQZJ3HmWrYJF//ivwCiBMsW0xQzlz3GcHKaXzgfMntxkfH5828Fbp6+tjIV6nSqyz5llnzbG+mmedNc86a551Nr39999/2nUNJXoxxn7gE8DRwF3AXjHGbwKnppTG5hJUSumOmuN/mLKlEMpWt4NqNj0Q2JjLD5yivHafDTHGZcAKykvFG4Bn1e1z/VzilSRJ6jaN9tG7iHJAxqNSSvsAewHfYnu/uKblPneT/gS4KT9fA5wSY9wtxriSctDFjSmlTcCWGOPRuf/dacAVNfucnp+fCFyX+/FdBRwXY9wrD8I4LpdJkiRVXqOXbgeA41JK9wOklLbGGP+WciqTWcUYP0XZstYXY9xAORL2WTHGIykvpa4H/jIf++YYYwJuAR4AzkopTc7d9xq2T6/ypfwA+ChwSYzxVsqWvFPysSZijG+nTEoBzk0pNTooRJIkqas1mujdQDk9yTdqyp4CrGtk55TSS6co/ugM278TeOcU5d8GjpiifBtw0jTHugC4oJE4JUmSqmTaRC/GeG7N4k+BK2OMX6QcxXoQ8ALgk+0NT5IkSXM1U4veQXXLl+ef+wD3Ap8DlrcjKEmSJM3ftIleSukvFjIQSZIktVbD8+jFGPcADgF6astTSmtbHZQkSZLmr9F59E4DhoH7gHtqVhVAfxvikiRJ0jw12qL3z8BLUkrXtDMYSZIktU6jEybfh3eUkCRJ6iqNJnp/D7wvxujdhCVJkrpEo5dufwKcC/xVjHGyLABFSmmXdgQmVcHY2BaGhtYxMbGN3t7lDA+vor+/Z/YdJUlqgUZb9C4BLgaeCDw2Pw7NPyVNY2hoHSMj44yObmVkZJzBQQepS5IWTqMtensDb0spFe0MRqqaiYltMy5LktROjbbofQx4eTsDkaqot3f5jMuSJLVToy16RwGDMca/A+6oXZFSOrblUUkVMTy8isHBtTv00ZMkaaE0muh9OD8kNaG/v4c1a47rdBiSpCWqoUQvpXRRuwORFiNHzUqSulmjt0B7xXTrUkoXtC4caXGZHDULMDq6lcHBtbbQSZK6RqOXbusHYuwL/C7wDcBET5XlqFlJUjdr9NLts+vLcivfE1oekbSI9PYuZ3R06w7LkiR1i0anV5nKhcAZLYpDWpSGh1cxMNDHypU9DAz0OWpWktRVGu2jV58Q7gH8GfA/LY9IWkQcNStJ6maN9tF7AKi/K8bPgVe1NhxJkiS1SqOJ3sq65btTSuOtDkaSJEmt0+hgjLF2ByJJkqTWmjHRizH+X3a+ZFurSCk9t7UhSZIkqRVma9H7+DTlBwBnUw7KkCRJ0iI0Y6KXUvpo7XKMcW/gzZSDMD4NnNu+0CRJkjQfjU6vsifwN8Ag8AXgySmln7YzMEmSJM3PbH30dgdeB7wBuB44JqV08wLEJUmSpHmarUVvFNgF+Gfg28CjY4yPrt0gpXRdm2KTJEnSPMyW6G2jHHX7mmnWF8BjWhqRJEmSWmK2wRgHL1AckiRJarH6e9hKkiSpIkz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqalmnA1Bzxsa2MDS0jomJbfT2Lmd4eBX9/T2dDkuSJC1Ctuh1maGhdYyMjDM6upWRkXEGB9d2OiRJkrRImeh1mYmJbTMuS5IkTTLR6zK9vctnXJYkSZpkotdlhodXMTDQx8qVPQwM9DE8vKrTIUmSpEXKwRhdpr+/hzVrjut0GJIkqQvYoidJklRRJnqSJEkVZaInSZJUUfbRW2KccFmSpKXDFr0lxgmXJUlaOkz0lhgnXJYkaekw0VtinHBZkqSlw0RviXHCZUmSlo4FGYwRY7wA+GPgzpTSEbmsF/g0cDCwHogppbvyujcDZwAPAmenlK7K5QPAhcDuwJXAa1NKRYxxN+BiYAD4FXBySml93ud04K05lHeklC5q89td1JxwWZKkpWOhWvQuBJ5fV/Ym4NqU0qHAtXmZGONhwCnA4XmfD8UYd8n7nAecCRyaH5PHPAO4K6V0CPB+4D35WL3AOcBTgaOAc2KMe7Xh/UmSJC06C5LopZS+CkzUFZ8ATLauXQS8uKb80pTSvSmlUeBW4KgY437AnimldSmlgrIF78VTHOsy4LkxxgAcD1yTUprIrYXXsHPCueDGxrawevXVHH74RaxefTW33ba10yFJkqQK6uQ8eo9OKW0CSCltijHuk8sPAG6o2W5DLrs/P68vn9zn9nysB2KMm4G9a8un2GcHMcYzKVsLSSnR19c393c2i5e85DpGRsZ/u/z619/IV74S2/Z6VbJs2bK2fjZVZJ01x/pqnnXWPOusedbZ3CzGCZPDFGXFDOVz3WcHKaXzgfMntxkfH59qs5a4446tOy238/WqpK+vz7pqknXWHOuredZZ86yz5lln09t///2nXdfJUbd35Mux5J935vINwEE12x0IbMzlB05RvsM+McZlwArKS8XTHaujnOJEkiQthE4memuA0/Pz04EraspPiTHuFmNcSTno4sZ8mXdLjPHo3P/utLp9Jo91InBd7sd3FXBcjHGvPAjjuFzWUZNTnBxyyAqnOJEkSW2zUNOrfAp4FtAXY9xAORL23UCKMZ4B3AacBJBSujnGmIBbgAeAs1JKD+ZDvYbt06t8KT8APgpcEmO8lbIl75R8rIkY49uBb+Xtzk0p1Q8KWXCTU5zYDC1JktopFMWUXdaWumLjxvZf4TXRa5511jzrrDnWV/Oss+ZZZ82zzqaX++hNNS7BO2NIkiRVlYmeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFmehJkiRVlImeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFLet0AFK7jY1tYWhoHRMT2+jtXc7w8Cr6+3s6HZYkSW1ni54qb2hoHSMj44yObmVkZJzBwbWdDkmSpAVhoqfKm5jYNuOyJElVZaKnyuvtXT7jsiRJVWWip8obHl7FwEAfK1f2MDDQx/Dwqk6HJEnSgnAwhiqvv7+HNWuO63QYkiQtOFv0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKO2OoYWNjWxgaWsfExDZ6e5czPLyK/v6eToclSZKmYYueGjY0tI6RkXFGR7cyMjLO4ODaTockSZJmYKKnhk1MbJtxWZIkLS4mempYb+/yGZdnMja2hdWrr+aYY9awevXV3Hbb1laHJ0mS6pjoqWHDw6sYGOhj5coeBgb6GB5e1fC+XvaVJGnhORhDDevv72HNmuPmtK+XfSVJWni26GlBzOeyryRJmhsTPS2I+Vz2lSRJc+OlWy2I+Vz2he1z+G3efD8rVuzqHH6SJDXAFj11hcnBHLfeutnBHJIkNchET13BwRySJDXPRE9dwcEckiQ1z0RPXWFyMMchh6xwMIckSQ1yMIa6wuRgjr6+PsbHxzsdjiRJXcEWPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkigpFUXQ6hsXISpEkSd0kTFVoi97UwkI8YowjC/VaVXlYZ9aZ9bX4HtaZdWadLYrHlEz0JEmSKspET5IkqaJM9Drr/E4H0IWss+ZZZ82xvppnnTXPOmuedTYHDsaQJEmqKFv0JEmSKmpZpwOokhjjBcAfA3emlI7IZU8E/hPoAdYDp6aUfh1j3BX4CPBkys/h4pTSP+V9BoALgd2BK4HXppQq2fTawjq7HtgPuCcf+riU0p0L+FYWTJN19nDgfwNPAR6iPJeuz/t4njVfZ9ezBM6zGONBwMXAvpR1cH5K6QMxxl7g08DBlHUWU0p35X3eDJwBPAicnVK6KpcvifOsxXV2PZ5nO9VZjHFv4DLgD4ALU0qDNcdaEufZXNii11oXAs+vK/sI8KaU0u8BnwP+JpefBOyWyweAv4wxHpzXnQecCRyaH/XHrJILaU2dQflFfWR+VO6PYo0LabzOXgWQy58H/GuMcfL33vOs+TqDpXGePQC8IaX0BOBo4KwY42HAm4BrU0qHAtfmZfK6U4DDKev5QzHGXfKxlsp51so6A8+zneoM2Ab8PfDGKY61VM6zppnotVBK6avARF3x44Cv5ufXAC/JzwvgETHGZZT/gdwH/DrGuB+wZ0ppXf5v5GLgxW0PvkNaUWcLEedi0mSdHUb5h5L8ZfE/wFM8z4Am62wBwlw0UkqbUkrfyc+3AD8EDgBOAC7Km13E9nPmBODSlNK9KaVR4FbgqKV0nrWqzhY26s5qts5SSnenlL5OmfD91lI6z+bCRK/9bgJW5+cnAQfl55cBdwObgNuAf0kpTVCe5Btq9t+Qy5aSZuts0sdijN+NMf59jHHaySMraro6+x5wQoxxWYxxJWVL6EF4nkHzdTZpSZ1nudX8ScA3gUenlDZB+SUN7JM3OwC4vWa3yfNpSZ5n86yzSZ5n7FRn01mS51mjTPTa7xWUzdEjwCMpW6Gg/M/tQWB/YCXwhhjjY5h6duul1s+g2TqD8jLH7wHPyI+XL2zIHTddnV1A+Ufv28C/AWspL5d4njVfZ7DEzrMYYw/wWeB1KaWZWs+nO5+W3HnWgjoDz7NmLbnzrBkOxmizlNKPgOMAYoyPBV6YV70M+HJK6X7gzhjjNygvD30NOLDmEAcCGxcu4s6bQ539LKX087zvlhjjJymTwosXPPgOma7OUkoPAK+f3C7GuBb4b+AuPM+arTOW0nmWBz99FvhESunyXHxHjHG/lNKmfLlssu/YBnZs9Zw8nzawhM6zFtWZ59n0dTadJXWeNcsWvTaLMe6Tfz4MeCvlKD8oLz0+J8YYYoyPoOyI+qPcTL0lxnh0bq4/DbiiA6F3TLN1li+x9eV9dqUcXXnTwkfeOdPVWYxxj1xXxBifBzyQUrrF86z5OltK51k+Jz4K/DCl9L6aVWuA0/Pz09l+zqwBTokx7pYvdx8K3LiUzrNW1ZnnGTB9nU1pKZ1nc2GLXgvFGD8FPAvoizFuAM4BemKMZ+VNLgc+lp//R35+E2Wz88dSSt/P617D9mHiX8qPSmpFneUv5avyH8VdgP8CPrxw72JhNVln+1DWzUPAz9nxEpDnWXN1thtL5zx7OuX7/kGM8bu57C3Au4EUYzyD8h+vkwBSSjfHGBNwC+Vl7rNSSg/m/ZbKedaSOltif8+aqjOAGON6YE/g4THGF1NOPXMLS+c8a5p3xpAkSaooL91KkiRVlImeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFOY+eJM1BjPETwL0ppVfUlD2Tck6+Iybv1SlJnWSLniTNzdnAC/LdM4gxLqec2PYNrUzyYoy7tOpYkpYeJ0yWpDmKMZ4E/DNwBOVt1I5MKf1Rvq3am4AzgBWUdzd4TUrprrwuAccAy4Hv5nU/zMf8OLAZ+F3KG9q/MKV0/YK+MUmVYYueJM1RSukzwAjwKeBM4C/zqr8GXggcS3mD9buBf6/Z9QuU9zbdl/KWfpfUHfplwD8CjwTWtSl8SUuALXqSNA8xxkcDPwX+LqX0gVz238ArU0pfycsHAbcCu6eUHqrbvw/4JdCTUro7t+jdV9v3T5LmysEYkjQPKaU7YozjwM01xf3A52OMtUldAewTY/wl8E/AiUAfMLlNH2XLH8Dt7Y1a0lJhoidJrbcBeFlK6Zv1K2KMfwG8AHgOMAbsTdmiF2o281KLpJawj54ktd5/Au+KMfYDxBj3iTGuzuseCdwL/ArYA3hnZ0KUtBSY6ElS670P+DJwbYxxC7AW+IO87mPAxvy4Oa+TpLZwMIYkSVJF2aInSZJUUSZ6kiRJFWWiJ0mSVFEmepIkSRVloidJklRRJnqSJEkVZaInSZJUUSZ6kiRJFWWiJ0mSVFH/D44an85jppVhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 103,
     "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,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "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?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data:\n",
    "   1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "   2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "   3. Reset the index in place.\n",
    "   4. Rename the columns to **year** and **total**.\n",
    "   5. Display the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_countries=df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "df_total=pd.DataFrame(df_countries.sum(axis=1))\n",
    "df_total.reset_index(inplace=True)\n",
    "df_total.columns=['year', 'total']\n",
    "df_total['year']=df_total['year'].astype(int)\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show resulting dataframe\n",
    "df_total.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4.38045968e-02  2.62798886e+02 -5.25537539e+05  3.50316632e+08]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_total.plot.scatter(x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from Denmark, Norway, Sweden 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "\n",
    "x = df_total['year']      # year on x-axis\n",
    "y = df_total['total']     # total on y-axis\n",
    "fit2 = np.polyfit(x, y, deg=3)\n",
    "print(fit2)\n",
    "\n",
    "i=0\n",
    "plt.plot(x, fit2[0]*x**3 + fit2[1]*x**2 + fit2[2] * x + fit2[3], 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",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
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    "new_sheet": false,
    "run_control": {
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb5007aaeb8>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalize Brazil data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "-->\n",
    "\n",
    "<!--\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb501135470>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# Brazil\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_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='India',\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 China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
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   },
   "source": [
    "This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week2_LAB2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
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