Created on Cognitive Class Labs

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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  {
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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."
   ]
  },
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   "execution_count": 2,
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     "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>539</td>\n",
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       "      <td>603</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
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       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
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       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
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       "      <td>...</td>\n",
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       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
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       "      <td>Immigrants</td>\n",
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       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
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   "source": [
    "df_can.head()"
   ]
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   },
   "source": [
    "Let's find out how many entries there are in our dataset."
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
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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."
   ]
  },
  {
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   "execution_count": 5,
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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)"
   ]
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   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
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   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Matplotlib version:  3.0.3\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"
   ]
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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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      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
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       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 8,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "colors_list2 = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list2 = [0, 0, 0, 0.1, 0.1, 0.2]\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,\n",
    "                          pctdistance=1.12,\n",
    "                          colors=colors_list2,\n",
    "                          explode=explode_list2)\n",
    "plt.title('Immigration to Canada by continent in 2013', y=1.12)\n",
    "plt.axis('equal')\n",
    "plt.legend(labels=df_continents.index, loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\n",
    "<!--\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://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": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 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": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>India</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  India  China\n",
       "1980      8880   5123\n",
       "1981      8670   6682\n",
       "1982      8147   3308\n",
       "1983      7338   1863\n",
       "1984      5704   1527"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['India', 'China'], years].transpose()\n",
    "df_CI.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_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": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country         India         China\n",
      "count       34.000000     34.000000\n",
      "mean     20350.117647  19410.647059\n",
      "std      10007.342579  13568.230790\n",
      "min       4211.000000   1527.000000\n",
      "25%      10637.750000   5512.750000\n",
      "50%      20235.000000  19945.000000\n",
      "75%      28699.500000  31568.500000\n",
      "max      36210.000000  42584.000000\n"
     ]
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "print(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": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.plot(kind='box', figsize=(8,6))\n",
    "\n",
    "plt.title('Box plot for immigrants from China and India between 1980-2013')\n",
    "plt.ylabel('Number of Immigrants')\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": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\n",
    "- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,  \n",
    "- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://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": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
    "df_top15 = df_can.head(15)\n",
    "df_top15\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
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    "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": {
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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": 40,
   "metadata": {
    "button": false,
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# 1. Create a list of all years in decades 80's, 90's, and 00's\n",
    "l80s = list(map(str, range(1980, 1990)))\n",
    "l90s = list(map(str, range(1990, 2000)))\n",
    "l00s = list(map(str, range(2000, 2010)))\n",
    "\n",
    "# 2. Slice the original dataframe df_can to create a series for \n",
    "#   each decade and sum across all years for each country.\n",
    "df80s = df_top15.loc[:, l80s].sum(axis=1) \n",
    "df90s = df_top15.loc[:, l90s].sum(axis=1)\n",
    "df00s = df_top15.loc[:, l00s].sum(axis=1)\n",
    "\n",
    "# 3. Merge the three series into a new data frame. Call your dataframe new_df.\n",
    "new_df = pd.DataFrame({'1980s':df80s, '1990s':df90s, '2000s':df00s})\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # display dataframe\n",
    "new_df.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()\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": 42,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "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",
    "new_df.plot(kind='box', figsize=(10,6))\n",
    "\n",
    "plt.title('Box plot for top 15 countries of immigrants between in 1980s, 1990s and 2000s')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()\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",
    "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": 43,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1980s   1990s   2000s\n",
       "Country                       \n",
       "India    82154  180395  303591\n",
       "China    32003  161528  340385"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 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": 46,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 47,
     "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": 67,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# Create dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "# Sum and turn result to Dataframe\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_total.index = map(int, df_total.index)\n",
    "# reset index\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "#Rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "#Display\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": 68,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_total.plot(kind='scatter', x='year', y='total')\n",
    "\n",
    "plt.title('Immigration from Denmark, Sweden and Norway to Canada 1980-2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\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",
    "\\\\ # 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": 69,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>India</th>\n",
       "      <th>China</th>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Pakistan</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>Republic of Korea</th>\n",
       "      <th>...</th>\n",
       "      <th>Kiribati</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Sao Tome and Principe</th>\n",
       "      <th>Tuvalu</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>San Marino</th>\n",
       "      <th>New Caledonia</th>\n",
       "      <th>Marshall Islands</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Palau</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "      <td>22045</td>\n",
       "      <td>6051</td>\n",
       "      <td>978</td>\n",
       "      <td>9378</td>\n",
       "      <td>1172</td>\n",
       "      <td>185</td>\n",
       "      <td>1011</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "      <td>24796</td>\n",
       "      <td>5921</td>\n",
       "      <td>972</td>\n",
       "      <td>10030</td>\n",
       "      <td>1429</td>\n",
       "      <td>371</td>\n",
       "      <td>1456</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "      <td>20620</td>\n",
       "      <td>5249</td>\n",
       "      <td>1201</td>\n",
       "      <td>9074</td>\n",
       "      <td>1822</td>\n",
       "      <td>290</td>\n",
       "      <td>1572</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "      <td>10015</td>\n",
       "      <td>4562</td>\n",
       "      <td>900</td>\n",
       "      <td>7100</td>\n",
       "      <td>1592</td>\n",
       "      <td>197</td>\n",
       "      <td>1081</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "      <td>10170</td>\n",
       "      <td>3801</td>\n",
       "      <td>668</td>\n",
       "      <td>6661</td>\n",
       "      <td>1977</td>\n",
       "      <td>1086</td>\n",
       "      <td>847</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  India  China  \\\n",
       "0        1980   8880   5123   \n",
       "1        1981   8670   6682   \n",
       "2        1982   8147   3308   \n",
       "3        1983   7338   1863   \n",
       "4        1984   5704   1527   \n",
       "\n",
       "Country  United Kingdom of Great Britain and Northern Ireland  Philippines  \\\n",
       "0                                                    22045            6051   \n",
       "1                                                    24796            5921   \n",
       "2                                                    20620            5249   \n",
       "3                                                    10015            4562   \n",
       "4                                                    10170            3801   \n",
       "\n",
       "Country  Pakistan  United States of America  Iran (Islamic Republic of)  \\\n",
       "0             978                      9378                        1172   \n",
       "1             972                     10030                        1429   \n",
       "2            1201                      9074                        1822   \n",
       "3             900                      7100                        1592   \n",
       "4             668                      6661                        1977   \n",
       "\n",
       "Country  Sri Lanka  Republic of Korea  ...  Kiribati  Vanuatu  \\\n",
       "0              185               1011  ...         0        0   \n",
       "1              371               1456  ...         0        0   \n",
       "2              290               1572  ...         0        0   \n",
       "3              197               1081  ...         1        0   \n",
       "4             1086                847  ...         0        0   \n",
       "\n",
       "Country  Sao Tome and Principe  Tuvalu  American Samoa  San Marino  \\\n",
       "0                            0       0               0           1   \n",
       "1                            0       1               1           0   \n",
       "2                            0       0               0           0   \n",
       "3                            0       0               0           0   \n",
       "4                            0       1               0           0   \n",
       "\n",
       "Country  New Caledonia  Marshall Islands  Western Sahara  Palau  \n",
       "0                    0                 0               0      0  \n",
       "1                    0                 0               0      0  \n",
       "2                    0                 0               0      0  \n",
       "3                    0                 0               0      0  \n",
       "4                    0                 0               0      0  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://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": 70,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa22678cb00>"
      ]
     },
     "execution_count": 71,
     "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": 72,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "# normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\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",
    "\\\\ # 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": 73,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa2267e90f0>"
      ]
     },
     "execution_count": 73,
     "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",
    "\n",
    "# China\n",
    "ax2 = df_can_t.plot(kind='scatter', \n",
    "                   x = 'Year', \n",
    "                   y = 'China', \n",
    "                   figsize=(14,8), \n",
    "                   alpha=0.5, \n",
    "                   color='red', \n",
    "                   s = norm_china * 2000 + 10, \n",
    "                   xlim = (1975, 2015))\n",
    "\n",
    "# India\n",
    "ax3 = df_can_t.plot(kind='scatter', \n",
    "                   x = 'Year', \n",
    "                   y = 'India', \n",
    "                   alpha = 0.5, \n",
    "                   color = 'green', \n",
    "                   s = norm_india * 2000 + 10, \n",
    "                   ax = ax2)\n",
    "\n",
    "ax2.set_title('Immigration from China and India 1980 to 2013')\n",
    "ax2.set_ylabel('Number of Immigrants')\n",
    "ax2.legend(['China', 'India'], loc='upper left', fontsize='large')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This notebook is part of 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": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
 ],
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