Created on Cognitive Class Labs

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

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   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
    "\n",
    "<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
   ]
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Pie Charts](#6) <br>\n",
    "5. [Box Plots](#8) <br>\n",
    "6. [Scatter Plots](#10) <br>\n",
    "7. [Bubble Plots](#12) <br> \n",
    "</div>\n",
    "<hr>"
   ]
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   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
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   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
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   "source": [
    "Import primary modules."
   ]
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
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   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
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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",
       "      <th>2007</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "    <tr>\n",
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       "      <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",
       "      <td>Foreigners</td>\n",
       "      <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>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
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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. "
   ]
  },
  {
   "cell_type": "markdown",
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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>1980</th>\n",
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       "      <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": 15,
   "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",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\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": 16,
   "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": 16,
     "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": 17,
   "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": 18,
   "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": 18,
     "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": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['China','India'], years].transpose()\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
    "\n",
    "df_CI.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Chinese and Indian Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Continent  \\\n",
       "Country                                                        \n",
       "India                                                   Asia   \n",
       "China                                                   Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...    Europe   \n",
       "Philippines                                             Asia   \n",
       "Pakistan                                                Asia   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 24,
     "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.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 26,
   "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",
       "    <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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\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",
    "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",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \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": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Immigrants from top 15 Countries in 80s, 90s and 2000s')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\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": 30,
   "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": 30,
     "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": 31,
   "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": 31,
     "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": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "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": 34,
   "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": 34,
     "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": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_countries = df_can.loc[['Denmark','Norway','Sweden'],years].transpose()\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "df_total.reset_index(inplace=True)\n",
    "df_total.columns = ['year','total']\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show resulting dataframe\n",
    "df_total.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
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   "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."
   ]
  },
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   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
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       "<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",
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       "      <th>New Caledonia</th>\n",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "    <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",
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       "<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]"
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   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
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   "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"
   ]
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  {
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   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
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   "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)."
   ]
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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": 42,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\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": 45,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faffc93ab70>"
      ]
     },
     "execution_count": 45,
     "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",
    "# Brazil\n",
    "ax2 = 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",
    "# Argentina\n",
    "ax3 = 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 = ax2\n",
    "                   )\n",
    "\n",
    "ax2.set_ylabel('Number of Immigrants')\n",
    "ax2.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax2.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This notebook is part of a course on **edX** called *Visualizing Data with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_edX_LAB3)."
   ]
  },
  {
   "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/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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