Created on Skills Network 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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     "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
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   "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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    {
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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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     "text": [
      "Matplotlib version:  3.1.1\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
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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></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "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",
    "\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\n",
    "<!--\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\" width=440, align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_CI = df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 17,
     "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": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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b45ycHJWUlFTazxMSEirt5xf+bYWFhclqtdq9LkFBQfL09KzxOXbEE088oRdffFGPPPKIPD091bFjRw0ZMkSSZLVaJUlnz55VUlKSOnXqpC1btujLL7/UPffco3vuuUc//PDDJddR0/tR586dbe8DnTt3dqjmbdu2aciQIXr11Vd1zz33OLTMlbwnDhs2TNnZ2dq8ebMSExP14IMPOrS9l0IgcJC3t7eioqIUFRWlmJgYTZw4UfHx8Zo2bVqNy1X1Ibh582ZJ0vHjx694pzFXMAioth/Ite3rhhtuqDbEOMLT07NSW3l5ue3/HTt2tIW2iv/27NmjxYsXS/rlTfjAgQP605/+JE9PTz377LOKiYnRyZMnbf28+eabdst//fXX2r9/v92H04W+/PJLPfjgg4qPj9fq1au1Y8cOLVy4UJJsYUOS3NzcbG9i0v//JlCx3qqezyt9rZyxrh9++EGDBg3S9ddfr7///e/KysrS2rVrJdlvp1Tz61TbbfDw8KjUduG+cOH0inmqartUPY4891artdKgyxdeeEHvvvuuJk+erA0bNig7O1uDBg265HPkSE1+fn7KysrS6tWr1b59ey1cuFBRUVFXNFre19e3yvaLt7uqv5WLn8+q2hzZnkuxWCx6/fXXderUKX3//fc6duyY7rrrLklS27ZtJUkrVqzQf//7X7333nvq2bOnunXrpoULF8rX11d//vOfJUktWrSQpEoDCI8ePVrjAOhPPvnE9j7gyBVlGzdu1G233aaJEydq0qRJdtNatGhR5QDGS9VQncDAQEVFRSkuLk7Lli2Tj4+P3nrrrcvu52IEglpwd3dXcXGxJNkSZEZGht08mzZtskuXu3bt0oQJE7Ro0SLdeeedevjhh3Xu3LlLruviy6K2bt2qjh07VjmvI7VUvCFd+O3qSvty1PDhw5WTk6O///3vVU4vKiq6rP4uFBsbq9zcXAUEBNiCW8V/F16+6Ofnp1//+tdKTk5WVlaW9uzZo/T0dDVv3lwtW7bU3r17Ky0fFRVV7Wj7zZs3KzQ0VG+88YZuueUWtW/f/orun9C5c2d9+eWXdq9HRXCsa1eyrszMTJ09e1bz5s1T7969dcMNN1z2t/6GolOnTpUu43L0ssSMjAwNGzZMQ4cO1U033aQ2bdpo3759l12Dp6dnlfum1WpVfHy8pk6dqu3bt6tFixZ6//33L7v/i0VFRcnLy0vp6el27RkZGZe9n9c1Nzc3RUZGqkmTJnr//ffl7++v2267TZJ05swZWSwWuyuSLBaLrFarLSh269ZNXl5etqO50i8BNTU1tcYjY61atbLt/61ataqxxn//+98aNGiQXn31Vb388suVpvfu3VsHDhywHa2UpD179ujHH3+84iOBFyovL3foc+RS6u/i3wbmwkPAZ86c0fr167V+/Xq99tprkn5JrA8++KCeeuopLVq0SK1atdLbb7+tnTt32nbYn3/+WQ8//LAGDx6skSNH6r777lNMTIyef/55zZ8/v8b1/+tf/1JKSopuv/12rVu3TitXrqz2g9WRWkJDQ+Xn56fPPvtMnTt3lpeXl4KCgq6oL0c98MAD+s1vfqMRI0Zo165dGjRokCIjI3XgwAEtW7ZMQUFBmjNnzmX1WWHYsGGaO3eu7rrrLk2bNk3t27fX0aNHlZaWZjvUOGvWLEVERCgmJkY+Pj5asWKFrFar2rdvL0maNm2aRo4cqaZNm2rIkCHy8PDQnj179Omnn2rRokVVrveGG27QTz/9pCVLlqhfv37avHnzFSX13/3ud5ozZ45++9vf6vnnn9fhw4crfcuoK1eyrnbt2slisWj27NkaNmyYvv76a02dOtUp9dW35557TkOHDlWPHj105513asuWLVq+fLmkSx85uOGGG/Txxx/r/vvvl5+fn+bMmaPDhw+refPml1VD69at9cEHH2jXrl1q3ry5/P39tW7dOuXm5io+Pl5hYWHavn27fvzxR3Xq1OmKt7WCj4+PnnnmGf3hD39QWFiYYmJi9MEHH+jjjz+udE1/XcnOzpYkFRYW6vTp07bHFadTioqK9P7776tfv34qKyvThx9+qBkzZmjhwoUKCAiQJN1+++2aOHGiRo4cqRdeeEHu7u5atGiRvv32Ww0ePFiSFBAQoDFjxujll19WixYt1Lp1a82aNUtnz57V6NGja70dH3zwgYYNG6aXXnpJw4YNs31OWK1W26mqxMRE3XzzzRo+fLjmz58vY4x+//vfq2fPnkpISLD1lZOTo9OnT9sO/1c8J1FRUfLz89POnTv16aefqn///goLC9PRo0f19ttv68CBA3r00UdrvS0MKnTAxZfveHt7m06dOplZs2bZBo4ZY8yJEydsl+d5enpWujxvzJgxpnXr1naj8Ddt2mTc3d3Nxx9/XO36JZm5c+eae++913h7e5vw8HAzc+ZMu3kuvsrgUrUY88slj9dff71xd3ev8bJDR/pyZFBhhWXLlpnevXsbf39/4+PjYzp37mxefPFF2yVs1Q0qvHg0tdVqtV2OZYwxx44dM2PGjLFdNhUREWGGDBliu6Rz4cKF5uabbzb+/v7G19fXxMbGVrpUZ/Xq1aZnz57G29vb+Pv7m5tuusm89tprNW7PK6+8Ypo1a2Z8fHzMnXfead5//30jyRw4cMAYU/UAoaoGdaamppouXboYT09P07lzZ/PFF19c8WWHdbGuix+npKSY6667zjRp0sT07t3bNuK7ol9HX6cr2Ya//e1v5uK3qxUrVhhJtsGRVV0++Prrr1f6254+fbqJjIy0Pa5quTlz5piIiAjTpEkTM3DgQLNo0SIjyTZyvrpBXz/88IMZOHCg8fHxMeHh4Wby5MnmiSeesBsgWNW+cnGdBQUF5s477zQBAQG2yw7T09NNv379bJf/RkVFmenTp1c7SLa6OqsbxOfoZYcX/z1W9fp6eXmZxYsXV1tXRV9V/VehsLDQxMXFGX9/f9OkSRPTo0cPs3r16kr9bNiwwSQkJJimTZuagIAAc8stt1R6Py0pKTEvvPCCad68ufHy8jJxcXF2Aw9rIyEhocrtuPjv7vDhw+aBBx4wfn5+xt/f3zz00EO2KyEu1VfFPrZ//34zcOBA06xZM9t73L333mu2bt1aJ9tiMcbFd6TAZbNYLPrb3/6m4cOH13cpwDVp6tSpevPNN1VQUFDfpQBOwykDALhAaWmpZs+erUGDBsnX11cbNmzQrFmz9Pvf/76+SwOcikAAABeo+I2P2bNn69SpU2rdurVefvllvfDCC/VdGuBUnDIAAABcdggAAAgEAABABAIAACAGFerw4cP1XQKuUGho6BXfyx3AlWPfa7guvHPrxThCAAAACAQAAIBAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAADix41wlYqMjKyTfg4dOlQn/QBAY0cgwFXJkQ/ysicHy7p4rQuqAYDGj1MGAACAQAAAAAgEAABABAIAACACAQAAEIEAAACIQAAAAEQgAAAAIhAAAAARCAAAgAgEAABABAIAACACAQAAEIEAAADIxT9/XF5erpdeeknBwcF66aWXdPr0ac2dO1c//fSTwsLCNH78ePn5+UmSVq9erbS0NLm5uSkpKUkxMTGSpNzcXC1YsEAlJSXq2rWrkpKSZLFYVFpaqpSUFOXm5srf31/jxo1Ts2bNXLl5AAA0WC49QvDJJ58oMjLS9njNmjWKjo5WcnKyoqOjtWbNGknSwYMHtWXLFs2ZM0eTJk3SkiVLVF5eLklavHixRo8ereTkZB05ckTZ2dmSpLS0NPn6+mr+/Pm666679N5777ly0wAAaNBcFggKCgq0Y8cODRgwwNaWmZmphIQESVJCQoIyMzNt7XFxcfLw8FCzZs0UHh6unJwcFRUV6ezZs2rfvr0sFovi4+Nty2RlZalv376SpJ49e2rnzp0yxrhq8wAAaNBcdspg2bJlGj58uM6ePWtrO3HihIKCgiRJQUFBOnnypCSpsLBQ7dq1s80XHByswsJCWa1WhYSE2NpDQkJUWFhoW6ZimtVqlY+Pj06dOqWAgAC7OlJTU5WamipJmjFjhkJDQ52wtXCFoxKvH1AP3N3d2fcaIZcEgu3btyswMFBt2rTRrl27Ljl/dd/sa/rGX9U0i8VSqS0xMVGJiYm2x8eOHbtkPbh68foBrhcaGsq+10BFRERUO80lgWDv3r3KysrSV199pZKSEp09e1bJyckKDAxUUVGRgoKCVFRUZPs2HxISooKCAtvyhYWFCg4OrtReUFCg4OBgu2VCQkJUVlam4uJi2wBFAABQM5eMIXj00Ue1cOFCLViwQOPGjVOXLl30zDPPKDY2Vunp6ZKk9PR0de/eXZIUGxurLVu2qLS0VPn5+crLy1NUVJSCgoLk7e2tffv2yRijjIwMxcbGSpK6deumjRs3SpK2bdumzp07V3mEAAAAVObSyw4vNmTIEM2dO1dpaWkKDQ3VhAkTJEktW7ZUr169NGHCBLm5uWnkyJFyc/slu4waNUpvvfWWSkpKFBMTo65du0qS+vfvr5SUFI0dO1Z+fn4aN25cvW0XAAANjcVc40PxDx8+XN8l4AqVPTlY1sVr67sM4JrDGIKGq6YxBNypEAAAEAgAAACBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAAJLc67sAAMDVJTIystZ9HDp0qA4qgSsRCAAAdi71YV725GBZF691UTVwFU4ZAAAAAgEAACAQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAASHKv7wJwbSp79lGp+HTt+3ly8JUv7OMn65vv17oGAGgMCASoH8WnZV28tlZdhIaG6tixY1e8fK3CBAA0Mld0yqCkpETnz5+v61oAAEA9cSgQLF++XDk5OZKkHTt2KCkpSY8//riysrKcWhwAAHANhwLB5s2b1bJlS0nShx9+qLFjx2rixIlasWKFU4sDAACu4dAYgnPnzsnLy0unTp3S0aNH1bNnT0mq1flbAABw9XAoEERERGjTpk06cuSIbrzxRknSyZMn5enp6dTiAACAazh0ymDkyJFav369du3apaFDh0qSvv76a1s4AAAADZtDRwhCQ0P1xhtv2LX16dNH0dHRDq2kpKREU6ZM0fnz51VWVqaePXvqoYce0unTpzV37lz99NNPCgsL0/jx4+Xn5ydJWr16tdLS0uTm5qakpCTFxMRIknJzc7VgwQKVlJSoa9euSkpKksViUWlpqVJSUpSbmyt/f3+NGzdOzZo1u5znAgCAa5ZDRwieffbZKtvHjx/v0Eo8PDw0ZcoUzZo1SzNnzlR2drb27dunNWvWKDo6WsnJyYqOjtaaNWskSQcPHtSWLVs0Z84cTZo0SUuWLFF5ebkkafHixRo9erSSk5N15MgRZWdnS5LS0tLk6+ur+fPn66677tJ7773nUG0AAMDBQGCMqdRWXFwsNzfHbmNgsVjUpEkTSVJZWZnKyspksViUmZmphIQESVJCQoIyMzMlSZmZmYqLi5OHh4eaNWum8PBw5eTkqKioSGfPnlX79u1lsVgUHx9vWyYrK0t9+/aVJPXs2VM7d+6ssm4AAFBZjacMfve730n65ZB/xb8rnD59Wr1793Z4ReXl5XrxxRd15MgR3X777WrXrp1OnDihoKAgSVJQUJBOnjwpSSosLFS7du1sywYHB6uwsFBWq1UhISG29pCQEBUWFtqWqZhmtVrl4+OjU6dOKSAgwK6O1NRUpaamSpJmzJih0NBQh7cBdeeoVOvn3t3dvVZ91EUNwLWIfadxqjEQjB07VsYYTZ8+XWPHjrWb1rRpU0VERDi8Ijc3N82aNUtnzpzRn/70J/3www/Vztq+m/QAABmcSURBVFvdN/uavvFXNc1isVRqS0xMVGJiou0xl07Wn9o+97W9dXFd1ABcq9h3GqaaPrdrDASdOnWSJC1ZskReXl51Uoyvr686deqk7OxsBQYGqqioSEFBQSoqKrJ9mw8JCVFBQYFtmcLCQgUHB1dqLygoUHBwsN0yISEhKisrU3FxsW2AIgAAqJlDVxlYrValpqbqu+++088//2w37emnn77k8idPnpTVapWvr69KSkr03//+V/fee69iY2OVnp6uIUOGKD09Xd27d5ckxcbGKjk5WXfffbeKioqUl5enqKgoubm5ydvbW/v27VO7du2UkZGhO+64Q5LUrVs3bdy4Ue3bt9e2bdvUuXPnKo8QAACAyhwKBCkpKfr+++/VrVs3BQYGXvZKioqKtGDBApWXl8sYo169eqlbt25q37695s6dq7S0NIWGhmrChAmSpJYtW6pXr16aMGGC3NzcNHLkSNsAxlGjRumtt95SSUmJYmJi1LVrV0lS//79lZKSorFjx8rPz0/jxo277DoBALhWWYwDQ/GTkpKUkpIiX19fV9TkUocPH67vEq5JZU8Ovip+/ri2NQDXIvadhqumMQQOXTcYGhqq0tLSOisIAABcXRw6ZRAfH69Zs2bpzjvvVNOmTe2mdenSxSmFAQAA13EoEKxbt06SKv3cscViUUpKSt1XBQAAXMqhQLBgwQJn1wEAAOqRQ4EAANA4lD37qFR8uvb9PDm4dh34+Mn65vu1rgN1x6FAUFxcrA8++EC7d+/WqVOn7O4K+PbbbzutOABAHSs+Xe9X+Eh1EChQ5xy6yuAvf/mLDhw4oAceeECnT5/WE088odDQUN11113Org8AALiAQ4Hgm2++0XPPPafu3bvLzc1N3bt31/jx47Vp0yZn1wcAAFzA4Z8/9vHxkSQ1adJEZ86cUdOmTXXkyBGnFgcAAFzDoTEErVq10u7duxUdHa0OHTpoyZIlatKkiVq0aOHs+gAAgAs4dIRg9OjRCgsLkyQ98cQT8vT01JkzZxz6YSMAAHD1u+QRgvLycm3cuFH33XefJCkgIEBjxoxxemEAAMB1LnmEwM3NTevXr5fVanVFPQAAoB44dMogISFBn3/+ubNrAQAA9cShQYU5OTlat26d1q5dq5CQEFksFtu01157zWnFAQAA13AoEAwYMEADBgxwdi0AAKCeOBQI+vbt6+QyAABAfXIoEKSlpVXZ7uHhoZCQELVr104eHh51WhgAAHAdhwJBRkaG9u3bp8DAQIWEhKigoEAnTpxQ27ZtlZ+fL0maOHGi2rZt69RiAQCAczgUCK677jr16NFDgwYNsrWtW7dOhw4d0tSpU7Vq1Sq98847mjZtmtMKBQAAzuPQZYf/+c9/dMcdd9i1DRw4UJs3b5bFYtHgwYN18OBBpxQIAACcz6FAEBgYqO3bt9u17dixQwEBAZKk0tJSubs7dLABAABchRz6FE9KStKcOXP0q1/9yjaG4IcfftCECRMkSfv37690BAEAADQcDgWCm266SfPnz1d2drYKCwvVtWtX3XzzzfL397dNv+mmm5xaKAAAcB6Hj/MHBAQoPj7embUAAIB6Um0gmDZtmiZNmiRJmjx5st3tii/ErYsBAGj4qg0ECQkJtn/379/fJcUAAID6UW0guPXWW23/5tbFAAA0bg6PIdizZ48OHDign3/+2a79vvvuq/OiAACAazkUCN555x1t3bpVHTp0kKenp629unEFAACgYXEoEGzatEmzZ89WcHCws+sBAAD1wKE7FYaGhvJrhgAANGIOHSEYM2aMFi1apN69eyswMNBuWqdOnZxSGAAAcB2HAkFubq6++uor7dmzx24MgSS9/fbbTikMAAC4jkOBYMWKFXrxxRd14403OrseAABQDxwaQ+Dl5cWpAQAAGjGHAsHQoUO1bNkyHT9+XOXl5Xb/AQCAhs+hUwYV4wQ+//zzStNWrlxZtxUBAACXcygQpKSkOLsOAABQjxwKBGFhYc6uAwAA1COHAkFxcbE++eQTfffdd5V+y+CVV15xSmEAAMB1HAoEc+bMUXl5uXr06FHpPgQAAKDhcygQ7N+/X0uWLJG7u8M/jggAABoQhy477NChgw4dOuTsWgAAQD1x6Cv/U089penTpysqKkpNmza1m/bAAw84pTAAAOA6Dt+6uKCgQGFhYTp79qyt3WKxOK0wAADgOg4Fgi1btujNN99UUFCQs+sBAAD1wKExBM2bN5fVanV2LQAAoJ44dISgT58+mjlzpu64445KYwi6dOnilMIAAIDrOBQI1q9fL+mXsQQXslgs3NYYAIBGwKFAsGDBAmfXAQAA6pFDYwgAAEDjVuMRgsmTJ1/y0sLXXnutTgsCAACuV2Mg6N+/v6vqAAAA9ajGQNC3b18XlQEAAOoTYwgAAACBAAAAEAgAAIBqCASTJk2y/fuDDz5wSTEAAKB+VBsIDh8+rJKSEknSv/71L5cVBAAAXK/aqwy6d++uZ599Vs2aNVNJSYmmTJlS5XzchwAAgIav2kDw1FNP6X//+5/y8/OVk5Ojfv36ubIuAADgQjXeh6BDhw7q0KGDzp8/zz0JAABoxBz6caP+/ftr586dysjIUFFRkYKCghQfH+/wTx8fO3ZMCxYs0PHjx2WxWJSYmKhBgwbp9OnTmjt3rn766SeFhYVp/Pjx8vPzkyStXr1aaWlpcnNzU1JSkmJiYiRJubm5WrBggUpKStS1a1clJSXJYrGotLRUKSkpys3Nlb+/v8aNG6dmzZpd4dMCAMC1xaHLDr/44gvNmzdPTZs2VY8ePRQUFKQ333xTqampDq3EarXqscce09y5czVt2jStX79eBw8e1Jo1axQdHa3k5GRFR0drzZo1kqSDBw9qy5YtmjNnjiZNmqQlS5aovLxckrR48WKNHj1aycnJOnLkiLKzsyVJaWlp8vX11fz583XXXXfpvffeu5LnAwCAa5JDRwjWrl2rV155Rddff72tLS4uTrNnz1ZiYuIllw8KClJQUJAkydvbW5GRkSosLFRmZqZeffVVSVJCQoJeffVVDR8+XJmZmYqLi5OHh4eaNWum8PBw5eTkKCwsTGfPnlX79u0lSfHx8crMzFTXrl2VlZWlBx98UJLUs2dPvfPOOzLGXPLHmQDgWvJJ4nJp5fFa9lLb5SUlLtc9te8FdcihQHDq1Cldd911dm0RERE6ffr0Za8wPz9fBw4cUFRUlE6cOGELCkFBQTp58qQkqbCwUO3atbMtExwcrMLCQlmtVoWEhNjaQ0JCVFhYaFumYprVapWPj49OnTqlgICAy64RABqrQam/kXXx2lr1ERoaqmPHjtWqj7InB0tDa1cH6pZDgaBDhw5avny5hg0bJi8vL/388896//33bd/UHfXzzz9r9uzZevzxx+Xj41PtfMaYy2qvblpVRwdSU1NtpzpmzJih0NDQS5UNJ1h6NXxLSVyuJF5/XGOOSrV+33N3d691H3VRB+qWQ4HgySef1Lx58/T444/Lz89Pp0+fVvv27fXss886vKLz589r9uzZ6tOnj2655RZJUmBgoG2QYlFRke3bfEhIiAoKCmzLFhYWKjg4uFJ7QUGBgoOD7ZYJCQlRWVmZiouLbQMUL5SYmGh3mqO2KRdX5mr4llL25GAd4xsKrkG1fd+riyMEdVEHLl9ERES10xwKBEFBQXrttddUUFBg+wC/8ND9pRhjtHDhQkVGRuruu++2tcfGxio9PV1DhgxRenq6unfvbmtPTk7W3XffraKiIuXl5SkqKkpubm7y9vbWvn371K5dO2VkZOiOO+6QJHXr1k0bN25U+/bttW3bNnXu3JnxAwAAOMihQFAhJCTksoJAhb179yojI0O/+tWv9MILL0iSHnnkEQ0ZMkRz585VWlqaQkNDNWHCBElSy5Yt1atXL02YMEFubm4aOXKk3Nx+uSBi1KhReuutt1RSUqKYmBh17dpV0i+XRqakpGjs2LHy8/PTuHHjLrtOAACuVRZT04n5a8Dhw4fru4RrUtmTg6+KUwa1rQFoaK6Gfa+u6sDlq+mUAT9/DAAALh0IysvLtXPnTp0/f94V9QAAgHpwyUDg5uammTNnyt39soYbAACABsShUwYdO3bUvn37nF0LAACoJw597Q8LC9P06dMVGxurkJAQu8v5hg4d6rTiAACAazgUCEpKSmz3CKi4VTAAAGg8HAoETz31lLPrAAAA9cjhkYIHDx7Utm3bdOLECY0cOVKHDx9WaWmpWrVq5cz6AACACzg0qHDr1q2aMmWKCgsLlZGRIUk6e/asli9f7tTiAACAazh0hOAf//iH/vCHP+j666/X1q1bJUmtWrXSd99958zaAACAizh0hODEiROVTg1YLBZ+PAgAgEbCoUDQpk0b26mCCv/5z38UFRXllKIAAIBrOXTKICkpSW+88YbS0tJ07tw5TZs2TYcPH9Yrr7zi7PoAAIALOBQIIiMjNW/ePG3fvl3dunVTSEiIunXrpiZNmji7PgAA4AIOX3bo5eWlDh06qLCwUMHBwYQBAAAaEYcCwbFjx5ScnKz9+/fL19dXZ86cUVRUlJ555hmFhYU5u0YAAOBkDg0qXLBggdq0aaOlS5fqL3/5i5YuXaq2bdtqwYIFzq4PAAC4gEOBIDc3V8OHD7edJmjSpImGDx+u3NxcpxYHAABcw6FA0K5dO+Xk5Ni1ffvtt2rfvr1TigIAAK5V7RiClStX2v7dvHlzTZ8+XTfffLNCQkJUUFCgr776SrfeeqtLigQAAM5VbSAoKCiwe3zLLbdIkk6ePCkPDw/16NFDJSUlzq0OAAC4RLWBgJ88BgDg2uHwfQjOnTunI0eO6Oeff7Zrv+GGG+q8KAAA4FoOBYL09HS98847cnd3l6enp920t99+2ymFAQAA13EoELz77rt67rnndOONNzq7HgAAUA8cuuzQ3d1dnTp1cnYtAACgnjgUCIYOHarly5fr5MmTzq4HAADUA4dOGUREROgf//iH1q9fX2nahfcrAAAADZNDgWD+/PmKj49XXFxcpUGFAACg4XMoEJw+fVpDhw6VxWJxdj0AAKAeODSGoG/fvsrIyHB2LQAAoJ44dIQgJydH69at06pVq9S0aVO7aa+99ppTCgMAAK7jUCAYMGCABgwY4OxaAABAPXEoEPTt29fJZQAAgPrkUCBIS0urdlr//v3rrBgAAFA/HAoEmzZtsnt8/PhxHTlyRB06dCAQAADQCDgUCKZMmVKpLS0tTYcOHarzggAAgOs5dNlhVfr27VvjqQQAANBwOHSEoLy83O5xSUmJMjIy5Ovr65SiAACAazkUCB555JFKbcHBwRo9enSdFwQAAFzPoUCQkpJi99jLy0sBAQFOKQgAALieQ4EgLCzM2XUAAIB6VGMguNRtiS0WiyZPnlynBQEAANerMRD06dOnyvbCwkJ9+umnOnfunFOKAgAArlVjILj4pkOnTp3S6tWr9cUXXyguLk4PPPCAU4sDAACu4dAYguLiYq1du1br16/XzTffrP/7v/9TeHi4s2sDAAAuUmMgKCkp0b///W/961//UqdOnTR16lS1bNnSVbUBAAAXqTEQ/P73v1d5ebkGDx6stm3b6sSJEzpx4oTdPF26dHFqgQAAwPlqDASenp6SpM8++6zK6RaLpdI9CgAAQMNTYyBYsGCBq+oAAAD16Ip/3AgAADQeBAIAAEAgAAAABAIAACACAQAAEIEAAACIQAAAAEQgAAAAIhAAAAARCAAAgAgEAABABAIAAKBL/LgRAKDxKXtycK2WP1oXRfj41UUvqEMuCQRvvfWWduzYocDAQM2ePVuSdPr0ac2dO1c//fSTwsLCNH78ePn5/fIHsnr1aqWlpcnNzU1JSUmKiYmRJOXm5mrBggUqKSlR165dlZSUJIvFotLSUqWkpCg3N1f+/v4aN26cmjVr5opNA4AGxbp4ba37KHtycJ30g6uLS04Z9O3bVy+//LJd25o1axQdHa3k5GRFR0drzZo1kqSDBw9qy5YtmjNnjiZNmqQlS5aovLxckrR48WKNHj1aycnJOnLkiLKzsyVJaWlp8vX11fz583XXXXfpvffec8VmAQDQaLgkEHTq1Mn27b9CZmamEhISJEkJCQnKzMy0tcfFxcnDw0PNmjVTeHi4cnJyVFRUpLNnz6p9+/ayWCyKj4+3LZOVlaW+fftKknr27KmdO3fKGOOKTQMAoFGot0GFJ06cUFBQkCQpKChIJ0+elCQVFhYqJCTENl9wcLAKCwsrtYeEhKiwsLDSMlarVT4+Pjp16pSrNgUAgAbvqhtUWN03+5q+8Vc1zWKxVDlvamqqUlNTJUkzZsxQaGjoFVSJ2joq1fq5d3d3r1UfdVEDcC1i32mc6i0QBAYGqqioSEFBQSoqKlJAQICkX775FxQU2OYrLCxUcHBwpfaCggIFBwfbLRMSEqKysjIVFxdXOkVRITExUYmJibbHx44dc8bmwQG1fe5DQ0Nr3QevP3Bl2HcapoiIiGqn1dspg9jYWKWnp0uS0tPT1b17d1v7li1bVFpaqvz8fOXl5SkqKkpBQUHy9vbWvn37ZIxRRkaGYmNjJUndunXTxo0bJUnbtm1T586dqz1CAAAAKnPJEYJ58+Zp9+7dOnXqlMaMGaOHHnpIQ4YM0dy5c5WWlqbQ0FBNmDBBktSyZUv16tVLEyZMkJubm0aOHCk3t19yy6hRo/TWW2+ppKREMTEx6tq1qySpf//+SklJ0dixY+Xn56dx48a5YrMAAGg0LOYaH45/+PDh+i7hmlQX1zHX9pQB11IDV4Z9p+G6Kk8ZAACAqweBAAAAEAgAAACBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAAJDkXt8F4NpV9uTgWi1/tLYF+PjVtgcAaDQIBKgX1sVra91H2ZOD66QfAACnDAAAgAgEAABABAIAACACAQAAEIEAAACIQAAAAEQgAAAAIhAAAABxYyIAwEUiIyMdmanGyYcOHaqjauAqBAIAgJ1LfZiHhobq2LFjLqoGrsIpAwAAQCAAAAAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACAJPf6LgCoSmRkpKMz1jj50KFDdVANADR+BAJclRz5IA8NDdWxY8dcUA0ANH6cMgAAAAQCAABAIAAAACIQAAAAEQgAAIAIBAAAQI3sssPs7GwtXbpU5eXlGjBggIYMGVLfJQEA0CA0miME5eXlWrJkiV5++WXNnTtX//nPf3Tw4MH6LgsAgAah0QSCnJwchYeHq3nz5nJ3d1dcXJwyMzPruywAABqERnPKoLCwUCEhIbbHISEh2r9/f6X5UlNTlZqaKkmaMWOGQkNDXVYj6pa7uzuvH1AP2Pcap0YTCIwxldosFkultsTERCUmJtoec+vbhotbFwP1g32v4YqIiKh2WqM5ZRASEqKCggLb44KCAgUFBdVjRQAANByNJhC0bdtWeXl5ys/P1/nz57VlyxbFxsbWd1kAADQIFlPVsfYGaseOHfrrX/+q8vJy9evXT/fdd199lwQAQIPQqAIBri0vvfSSZsyYUd9lANcc9r3GqdGcMgAAAFeOQAAAAAgEaLguvHwUgOuw7zVOjCEAAAAcIQAAAAQCXCWOHz+uefPmaezYsRo/frymT5+u1NTUakcyL1y4kB+vAurQY489dlnz79q1y7Z/ZmVlac2aNc4oCy7UaG5djIbLGKNZs2YpISFB48aNkyR99913ysrKqnaZMWPGuKo8AJcQGxvLjeAaAQIB6t2uXbvk7u6ugQMH2tquv/56nTlzRjt37tTs2bP1448/qk2bNho7dqwsFoteffVVPfbYY2rbtq0ee+wxDRo0SDt27JCnp6deeOEFNW3aVFlZWVq1apXOnz8vf39/jR07Vk2bNq3HLQWufrt27dIHH3wgf3//Svtddna2li1bJn9/f7Vu3dq2zMaNG/Xtt99q5MiR7HcNGKcMUO9++OEHuzeXCx04cECPP/645syZo6NHj2rv3r2V5jl37pzatWunWbNmqWPHjvriiy8kSR06dNC0adM0c+ZMxcXFae3atU7dDqCxqGq/Kykp0aJFi/Tiiy9q6tSpOn78eJXLst81XBwhwFUtKirK9rPW119/vfLz89WhQwe7edzd3dWtWzdJUps2bfTNN99I+uUnsefNm6eioiKdP39ezZo1c23xQANV1X7XpEkTNWvWTC1atJAkxcfH235K/kLsdw0XgQD1rmXLlvryyy+rnObh4WH7t5ubm8rLyyvNY7VabT917ebmprKyMknSO++8o7vvvluxsbG2w6AALs2R/a467HcNF6cMUO+6dOmi0tJSu28bOTk52r17d636LS4uVnBwsCQpPT29Vn0B17qIiAjl5+fryJEjkqTNmzdXOR/7XcPFEQLUO4vFoueff17Lli3Txx9/LA8PD4WFhal79+616vfBBx/UnDlzFBwcrHbt2ik/P7+OKgauPZ6enho9erRmzJghf39/dejQQT/++GOl+djvGi7uVAgAADhlAAAACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAAEn/HwvWwLkBkT0AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 Cinese 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": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_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": 25,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "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": 25,
     "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",
    "# slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "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": 26,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tDZ1OZ9PuBoMBTk5Od1yvw4cPA0CVOkRERFSpw62vUa9evZT168tkMuHVV19F9+7dERwcjFdeeQUrV67Ehg0b8NNPP1W7TlFREU6fPo0nn3yy2ufvpF3rIjg42Oaxr68vfv755zqte/N1dtXJycnBuXPn0Lx5c+j1euXf7t27kZ+fDwCIjY3Fd999B39/f7z44ov4/PPPYbVa7+gY7uR1rq3Oly5dwk8//YSePXvalD/xxBNVjm3lypU2x9W5c2cAN66Zq+11BGp/vx8+fBgmkwnt27dX1vH29kaHDh2Ux/v374eIICwszKYuixYtUtq4f//+aNu2Ldq0aYNRo0bhL3/5C4qLi2/bDqmpqXj33Xexdu1afPPNN/jss8/wr3/9q9ZJL5XXN1X+Ny4uDosWLUL37t0xc+ZM7Nq167brV2fDhg3w9PRE27Zt4eTkhPj4eGV2q06nu691SUlJQffu3dGiRQvo9XrMnj27yudxbZ+jhw8fRq9evWzWubU/1WbLli2YMGEC1qxZg65du9ZpnbpcE3wny4wYMQJlZWVo3bo1xo4diw8//BCXL1+uU13UhiGtgTk6Oto81mg01ZZVVFTck/VuVZeO7+bmVqXs6aefxo8//ojk5GTs3bsXubm58PHxueMvrdvV4ebyWy+arnyutuOrTnXHU109RKRK2a1tXF1ZXdq9rqqrQ3XL3A+VX/61Be/a6ldbu2o0mirHUFZWVmU71fWBurZzTa95pYqKCnTq1Am5ubk2/77//nukpKQAuBEST548iYSEBDg5OeGVV15BcHAwLl26VKc63E51r3Ntda5ss9rav6KiAjNnzqxybPn5+Rg4cKCy3O22U9v7vS79tPK1ys7OtqnHwYMH8e233wK4MVFl//792Lx5M9q3b4/33nsP/v7++Prrr2vc7quvvopXXnkFY8aMQZcuXTB8+HAsWrQIb7/9Nn755RcAwEMPPYRz587ZrFf5uHICSWxsLH744Qe8+OKLKCwsxMCBA/Hss8/e9phu1bp1a6Snp8NiseDHH39ULmT38PCAyWS6b3X57LPP8NJLL2HkyJHYunUrDhw4gHnz5lV5H9X2OVqX1/F2Pv74Y4wcORJ/+ctfMGbMGJvnqjvusrIymM1m5birWwYAfv75Z5tliouLq8yGvXkZX19fHDlyBH/961/h4+ODBQsWoEOHDjh9+nS9j62hMKQ1Uf7+/nBycsKePXtsyvfu3VvruiUlJTh8+DBmzZqFAQMGoHPnznB2dq4yeuTk5FTrtPLAwEDs2bPHJtzl5eXh4sWLCAwMvIMjqj9/f380a9YMO3futCnftWvXfalDde1SuZ9b/2LevXt3lTrc+hrt2bMHnTp1uuf1PHDgAADg4YcfrvZ5Hx8fPPzww9i2bVu1z9e1XX18fHD27Fnl8a+//lqvkcG69LeahIWFoaCgAB4eHvD397f5d/PtYvR6PX7/+98jMTER+/fvx/fff1/l+G7nTl7n2nh6esLX1xdZWVk25bc+DgsLw6FDh6ocl7+/P/R6fa2vY13e74GBgTh//rwyIgYAxcXFOHbsmPI4NDQUAPDjjz9WqUe7du2U5XQ6HSIiIvDGG2/g66+/xkMPPYS//e1vNbbDlStXoNXafpXpdDrIjct5ANwYbf7yyy9tQv2///1vuLq6IiQkRCl76KGHEBsbi/Xr12PNmjXYuHFjvUK4q6srWrVqBavVitTUVMTExCh1vB912bVrF0JCQjBjxgyEhoYiICCgXvfkCwwMrLU/1SQlJQVjx47FBx98UCWgATeOe8+ePTbHUNkOlaN3vXr1wsmTJ2360ffff4/Tp08rI3q9evVCWVkZMjIylGUuXLiAr776ymbUr1mzZnjqqafw9ttv47vvvsPVq1eRlpZWp2NRE96Co4lyc3PDpEmT8Prrr6NFixZo3749PvjgA3z//fc2w+HVMRgM8Pb2RkpKCtq1a4eSkhL86U9/qjL1uU2bNtixYwcGDhwIJycn5S/Jm7388st45513MHbsWLz22mu4cOECpkyZgieeeAK9e/e+p8dcE1dXV0ybNg1z586Ft7c3goOD8dlnn2HLli348ssv7/n+2rRpg3PnzmHPnj0ICAiAq6sr2rVrh//6r//ClClT8P7776N169Z49913cfDgwSpfUGvWrEHHjh0RFhaGDRs2YM+ePVi5cuVt95mbmwsAMJvNsFgsyuPKU4jr1q2DTqdD165d4ezsjN27d+OPf/wjhg8fjkceeaTG7c6fPx+TJ09GixYtMHz4cFRUVGDHjh0YNWoUTCZTndo1Ojoa7733HiIiIuDu7o6FCxfWa0S2sr+dOHECnp6e8PT0rPO6zzzzDFasWIHBgwdj4cKFaN++PX7++WdkZGSgU6dOiImJwdKlS9GqVSsEBwfD1dUVH330EXQ6nc0pvtrcyetcF6+++irmzp2Ljh07okePHvjiiy+Qnp5us8wbb7yBJ598EtOnT8fzzz8Pd3d35Ofn47PPPkNSUhJcXFxu+zoajcZa3+/9+vXDY489hmeffRarVq2Ck5MTZs6caXNTXX9/f7zwwguYMGEC3n77bYSHh+PKlSv4+uuvcf78ecycORNbtmxBQUEBIiIi4O3tja+//hqnT59WTs9WJyYmBgkJCfD390dISAiOHj2K119/HQMHDlTqOHnyZCQlJWHChAmYMWMGTpw4gblz52Lq1KnKiOXLL7+MQYMGoUOHDvjll1+wadMm+Pn5wd3dvc6vx5dffgmr1YpOnTrh9OnTmDdvHq5du4ZFixYpy9yPunTo0AFr1qzBli1b8Jvf/Ab/+Mc/sGnTpjrXu9L06dPx+OOPY86cOXj++edx6NAhLFu2rNb1VqxYgT/+8Y9ITk5GZGSkMhrm5OSk3PrpD3/4AxYsWIA//OEPWLhwIcxmszL616ZNGwA3Pgu6du2q9CMRwUsvvYQePXogMjISANC+fXv87ne/w+TJk7FmzRp4enritddeg6+vr3K7jzVr1qCiogLdunVD8+bNsX37dly+fPm2/Ui1GuA6uCarpltw3GzcuHHKVP1KkyZNkl69eimPb52lKVL9RdMDBgywuaC8pltwuLu7i6enp0yePFleeeUVmwkINV1onpmZKUFBQdKsWTNp3769pKamSrt27Wwudv3Xv/4lHTt2FCcnpzrfgsPT07PGW3DcbPfu3QJATp48WaVulWqaOFDd8dT3VhHVTY5o1qyZpKSk1Fgvq9Uqo0ePFoPBYHMLjosXLyq3ZnBycrrtLTgiIyOlWbNm0rp1a1m/fn2N+7q57tX9q/TBBx/Ib37zG3FzcxNXV1cJDAyUJUuW2ExgqcmGDRskKChInJycxGg0yqBBg6S0tFQ51tratbCwUJ5++mlxd3eXhx9+WFavXl3txIHK2V2Vbn2vnDhxQnr37i1ubm5VbsFx60XP1ZUXFxfLiy++qNS1VatWEhMTI998842IiLz33nvStWtXcXd3Fzc3NwkLC5O0tLTbtk11/a2ur3NdLtQuLy+X2bNni5eXl7i6usqwYcOqvQXHrl27pF+/fqLX68XV1VU6duwor7zyis1Eidu9jnV5v588eVL69+8vzZo1E19fX1m5cmWVz5zr16/LkiVLpEOHDuLo6CheXl4SERGhXDC/c+dO6du3r3J7En9/f3nrrbduO1nDYrFIfHy8tGnTRpo1ayZ+fn4yefJkZTZtpT179kh4eLg0a9ZMWrRoIbNmzbKZIThlyhQJCAgQZ2dn5fgPHjyoPD9//nyp7SszNTVV/P39lTYcPXq0nDp1qspyd1uXW1mtVpk4caIYDAZltnzlBf83178un6MfffSRtG3bVpycnKRbt26SlpZWa3+s7pZLuOm2M5WOHDki/fv3FxcXFzEajTJx4kTltiOVzp49K8OHDxe9Xi/u7u4yYsQIm+8DkRszaStnGLu4uMiAAQMkPz9fef7zzz+X8PBwad68ubi4uEhgYGCVSXIPCo3IfbqghR5IUVFRMBgM+Pzzzxu6KnSLU6dOoU2bNti9e/cdX8xLRHfnueeew7lz5/Cf//ynoatCTQhPdzZh3333Hb755huEh4fDarXiww8/xI4dO7B169aGrhoRkWpUVFRg+/bt2LFjR0NXhZoYhrQmTKPR4N1338W0adNQUVGBjh07YvPmzTYzvoiImjqtVlvjrWiI7iee7iQiIiJSId6Cg4iIiEiFGNKIiIiIVIghjYiIiEiFGuXEgZvvXk73n8lkqvX39YgedOzn1BSwn9vfzb9qcqtaQ5rVasX8+fNx/fp1lJeXo0ePHhgxYgQ+/fRTbN++HR4eHgCA0aNHKz+munnzZmRkZECr1SI2Nla5q3lBQQGSk5NhtVoREhKC2NhYaDQalJWVISkpCQUFBXB3d0dcXBx8fHwAAJmZmcqdk4cOHYo+ffrcVWMQERERPQhqDWmOjo6YP38+nJ2dcf36dcybN08JXYMHD8aQIUNslj9z5gyys7OxfPlylJaWYsGCBXjnnXeg1WqRkpKCSZMmISAgAG+99RZyc3MREhKCjIwMuLm5YdWqVcjKysLGjRsxffp0WCwWpKamYvHixQCAWbNmISwsDHq9/j40BREREZF61HpNmkajgbOzMwCgvLwc5eXl0Gg0NS6fk5ODnj17wtHRET4+PmjZsiWOHz+O0tJSXLt2De3bt4dGo0FERARycnIAAPv371dGyHr06IGDBw9CRJCbm4ugoCDo9Xro9XoEBQUpvzlIRERE1JjV6Zq0iooKzJw5E+fOncOAAQMQEBCAAwcOYNu2bdi1axfatm2L5557Dnq9HmazGQEBAcq6RqMRZrMZOp0OXl5eSrmXlxfMZjOAGz/6XPmcTqeDq6srLl++bFN+87aIiIiIGrs6hTStVoulS5fiypUrSEhIwI8//ognn3wSw4cPBwB88sknWL9+PaZMmYKa7o17u3vmVvdcTaN11ZWnp6cjPT0dALB48WKYTKZaj4nuHQcHB7Y5NXrs59QUsJ+ryx3N7nRzc0Pnzp2Rm5trcy1av379sGTJEgA3RshKSkqU58xmM4xGY5XykpISGI1Gm3W8vLxQXl6Oq1evQq/Xw2g04vDhwzbb6ty5c5V6RUdHIzo6WnnMmSn2xdlA1BSwn1NTwH5uf7eb3VnrNWmXLl3ClStXANyY6fndd9/B19cXpaWlyjL79u2Dn58fACAsLAzZ2dkoKytDUVERCgsL4e/vD4PBABcXFxw7dgwigl27diEsLAwAEBoaiszMTADA3r17ERgYCI1Gg+DgYOTl5cFiscBisSAvL0+ZtEBERETUmNU6klZaWork5GRUVFRARBAeHo7Q0FCsWrUKp06dgkajgbe3NyZOnAgA8PPzQ3h4OGbMmAGtVotx48ZBq72RBcePH4/Vq1fDarUiODgYISEhAICoqCgkJSVh6tSp0Ov1iIuLAwDo9XoMGzYMs2fPBgAMHz6cMzuJiIioSWiUP7DOm9naF4fHqSlgP6emgP3c/u7qdCcRERE1bmlpaYiKioKLiwuioqKQlpbW0FUiNNKfhSIiIqK6SUtLw5IlS5CQkIBBgwZh69atiI+PBwDExMQ0cO2aNo6kERERNWGJiYlISEhAr1694OjoiF69eiEhIQGJiYkNXbUmjyGNiIioCcvPz0e3bt1syrp164b8/PwGqhFVYkgjIiJqwgICArBv3z6bsn379tn8ehA1DIY0IiKiJmzatGmIj49HVlYWysrKkJWVhfj4eEybNq2hq9bkceIAERFRE1Y5OWDu3LkYNWoUAgICMHPmTE4aUAHeJ43uGu+rQ00B+zk1Bezn9sf7pBERERE9YBjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiIiFSIIY2IiIhIhRjSiIiImri0tDRERUXBxcUFUVFRSEtLa+gqEQCHhq4AERERNZy0tDQsWbrtBokAACAASURBVLIECQkJGDRoELZu3Yr4+HgAQExMTAPXrmnjSBoREVETlpiYiISEBPTq1QuOjo7o1asXEhISkJiY2NBVa/JqHUmzWq2YP38+rl+/jvLycvTo0QMjRoyAxWLBihUrcP78eXh7e2P69OnQ6/UAgM2bNyMjIwNarRaxsbEIDg4GABQUFCA5ORlWqxUhISGIjY2FRqNBWVkZkpKSUFBQAHd3d8TFxcHHxwcAkJmZiU2bNgEAhg4dij59+tynpiAiImp68vPz0a1bN5uybt26IT8/v4FqRJVqHUlzdHTE/PnzsXTpUrz99tvIzc3FsWPHkJaWhi5duiAxMRFdunRRzl+fOXMG2dnZWL58OebMmYM1a9agoqICAJCSkoJJkyYhMTER586dQ25uLgAgIyMDbm5uWLVqFQYPHoyNGzcCACwWC1JTU7Fo0SIsWrQIqampsFgs96stiIiImpyAgADs27fPpmzfvn0ICAhooBpRpVpDmkajgbOzMwCgvLwc5eXl0Gg0yMnJQWRkJAAgMjISOTk5AICcnBz07NkTjo6O8PHxQcuWLXH8+HGUlpbi2rVraN++PTQaDSIiIpR19u/fr4yQ9ejRAwcPHoSIIDc3F0FBQdDr9dDr9QgKClKCHREREd29adOmIT4+HllZWSgrK0NWVhbi4+Mxbdq0hq5ak1eniQMVFRWYOXMmzp07hwEDBiAgIAAXL16EwWAAABgMBly6dAkAYDabbdK30WiE2WyGTqeDl5eXUu7l5QWz2aysU/mcTqeDq6srLl++bFN+87ZulZ6ejvT0dADA4sWLYTKZ7qgR6O44ODiwzanRYz+nxmr8+PFwd3fHf//3f2PUqFHo2LEj3nzzTYwcObKhq9bk1SmkabVaLF26FFeuXEFCQgJ+/PHHGpcVkTsqr+k5jUZT7bLVlUdHRyM6Olp5XFxcXOO+6N4zmUxsc2r02M+pMevXrx/69etn08/Z3+2jVatWNT53R7M73dzc0LlzZ+Tm5sLT0xOlpaUAgNLSUnh4eAC4MUJWUlKirGM2m2E0GquUl5SUwGg0VlmnvLwcV69ehV6vh9ForLKtytE7IiIiosas1pB26dIlXLlyBcCNmZ7fffcdfH19ERYWhp07dwIAdu7ciccffxwAEBYWhuzsbJSVlaGoqAiFhYXw9/eHwWCAi4sLjh07BhHBrl27EBYWBgAIDQ1FZmYmAGDv3r0IDAyERqNBcHAw8vLyYLFYYLFYkJeXp8wUJSIiImrMaj3dWVpaiuTkZFRUVEBEEB4ejtDQULRv3x4rVqxARkYGTCYTZsyYAQDw8/NDeHg4ZsyYAa1Wi3HjxkGrvZEFx48fj9WrV8NqtSI4OBghISEAgKioKCQlJWHq1KnQ6/WIi4sDAOj1egwbNgyzZ88GAAwfPly5zQcRERFRY6aR210s9oA6e/ZsQ1ehSeG1OtQUsJ9TU8B+bn/37Jo0IiIiIrIPhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhhjQiottIS0tDVFQUXFxcEBUVhbS0tIauEhE1EQ4NXQEiIrVKS0vDkiVLkJCQgEGDBmHr1q2Ij48HAMTExDRw7YioseNIGhFRDRITE5GQkIBevXrB0dERvXr1QkJCAhITExu6akTUBDCkERHVID8/H926dbMp69atG/Lz8xuoRkTUlDCkERHVICAgAPv27bMp27dvHwICAhqoRkTUlDCkERHVYNq0aYiPj0dWVhbKysqQlZWF+Ph4TJs2raGrRkRNACcOEBHVoHJywNy5czFq1CgEBARg5syZnDRARHahERFp6Erca2fPnm3oKjQpJpMJxcXFDV0NovuK/ZyaAvZz+2vVqlWNz/F0JxEREZEKMaQRERERqRBDGhEREZEK1TpxoLi4GMnJybhw4QI0Gg2io6MxaNAgfPrpp9i+fTs8PDwAAKNHj0bXrl0BAJs3b0ZGRga0Wi1iY2MRHBwMACgoKEBycjKsVitCQkIQGxsLjUaDsrIyJCUloaCgAO7u7oiLi4OPjw8AIDMzE5s2bQIADB06FH369Lkf7UBERESkKrWGNJ1OhzFjxqBt27a4du0aZs2ahaCgIADA4MGDMWTIEJvlz5w5g+zsbCxfvhylpaVYsGAB3nnnHWi1WqSkpGDSpEkICAjAW2+9hdzcXISEhCAjIwNubm5YtWoVsrKysHHjRkyfPh0WiwWpqalYvHgxAGDWrFkICwuDXq+/D01BREREpB61nu40GAxo27YtAMDFxQW+vr4wm801Lp+Tk4OePXvC0dERPj4+aNmyJY4fP47S0lJcu3YN7du3h0ajQUREBHJycgAA+/fvV0bIevTogYMHD0JEkJubi6CgIOj1euj1egQFBSE3N/ceHDYRERGRut3RfdKKiopw8uRJ+Pv748iRI9i2bRt27dqFtm3b4rnnnoNer4fZbLa5G7fRaITZbIZOp4OXl5dS7uXlpYQ9s9msPKfT6eDq6orLly/blN+8rVulp6cjPT0dALB48WKYTKY7OSy6Sw4ODmxzavTYz6kpYD9XlzqHtF9++QXLli3D2LFj4erqiieffBLDhw8HAHzyySdYv349pkyZgppuu3a727FV95xGo6l22erKo6OjER0drTzmPV7si/fVoaaA/ZyaAvZz+7vr+6Rdv34dy5YtQ+/evdG9e3cAQPPmzaHVaqHVatGvXz+cOHECwI0RspKSEmVds9kMo9FYpbykpARGo7HKOuXl5bh69Sr0ej2MRmOVbRkMhroeNxEREdEDq9aQJiJ477334Ovri6efflopLy0tVf5/37598PPzAwCEhYUhOzsbZWVlKCoqQmFhIfz9/WEwGODi4oJjx45BRLBr1y6EhYUBAEJDQ5GZmQkA2Lt3LwIDA6HRaBAcHIy8vDxYLBZYLBbk5eUpM0WJiIiIGrNafxbqyJEjmDdvHh555BHlVOPo0aORlZWFU6dOQaPRwNvbGxMnTlRGuTZt2oQdO3ZAq9Vi7NixCAkJAQCcOHECq1evhtVqRXBwMF544QVoNBpYrVYkJSXh5MmT0Ov1iIuLQ4sWLQAAGRkZ2Lx5M4Abt+Do27dvrQfFn4WyLw6PU1PAfk5NAfu5/d3udCd/u5PuGt/U1BSwn1NTwH5uf/ztTiIiIqIHDEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpEEMaERERkQoxpBERERGpkENtCxQXFyM5ORkXLlyARqNBdHQ0Bg0aBIvFghUrVuD8+fPw9vbG9OnTodfrAQCbN29GRkYGtFotYmNjERwcDAAoKChAcnIyrFYrQkJCEBsbC41Gg7KyMiQlJaGgoADu7u6Ii4uDj48PACAzMxObNm0CAAwdOhR9+vS5T01BREREpB61jqTpdDqMGTMGK1aswMKFC7Ft2zacOXMGaWlp6NKlCxITE9GlSxekpaUBAM6cOYPs7GwsX74cc+bMwZo1a1BRUQEASElJwaRJk5CYmIhz584hNzcXAJCRkQE3NzesWrUKgwcPxsaNGwEAFosFqampWLRoERYtWoTU1FRYLJb71RZEREREqlFrSDMYDGjbti0AwMXFBb6+vjCbzcjJyUFkZCQAIDIyEjk5OQCAnJwc9OzZE46OjvDx8UHLli1x/PhxlJaW4tq1a2jfvj00Gg0iIiKUdfbv36+MkPXo0QMHDx6EiCA3NxdBQUHQ6/XQ6/UICgpSgh0RERFRY3ZH16QVFRXh5MmT8Pf3x8WLF2EwGADcCHKXLl0CAJjNZnh5eSnrGI1GmM3mKuVeXl4wm81V1tHpdHB1dcXly5dr3BYRERFRY1frNWmVfvnlFyxbtgxjx46Fq6trjcuJyB2V1/ScRqOpdtnqytPT05Geng4AWLx4MUwmU437onvPwcGBbU6NHvs5NQXs5+pSp5B2/fp1LFu2DL1790b37t0BAJ6enigtLYXBYEBpaSk8PDwA3BghKykpUdY1m80wGo1VyktKSmA0Gm3W8fLyQnl5Oa5evQq9Xg+j0YjDhw/bbKtz585V6hcdHY3o6GjlcXFx8Z20Ad0lk8nENqdGj/2cmgL2c/tr1apVjc/VerpTRPDee+/B19cXTz/9tFIeFhaGnTt3AgB27tyJxx9/XCnPzs5GWVkZioqKUFhYCH9/fxgMBri4uODYsWMQEezatQthYWEAgNDQUGRmZgIA9u7di8DAQGg0GgQHByMvLw8WiwUWiwV5eXnKTFEiIiKixkwjtzsPCeDIkSOYN28eHnnkEeVU4+jRoxEQEIAVK1aguLgYJpMJM2bMUG7BsWnTJuzYsQNarRZjx45FSEgIAODEiRNYvXo1rFYrgoOD8cILL0Cj0cBqtSIpKQknT56EXq9HXFwcWrRoAeDGzM/NmzcDuHELjr59+9Z6UGfPnq1/i9Ad419e1BSwn1NTwH5uf7cbSas1pD2IGNLsi29qagrYz6kpYD+3v7s63UlERERE9seQRkRERKRCDGlEREREKsSQRkRERKRCDGlEREREKsSQRkRERKRCDGlEREREKsSQRkRERKRCDGlEREREKsSQRkRERKRCDGlUb2lpaYiKioKLiwuioqKQlpbW0FUiIiJqNBwaugL0YEpLS8OSJUuQkJCAQYMGYevWrYiPjwcAxMTENHDtiIiIHnz8gXWql6ioKCxYsAC9evVSfpA3KysLc+fORUZGRkNXj+i2fH197bq/n376ya77I6ov/sC6/d3uB9Y5kkb1kp+fj27dutmUdevWDfn5+Q1UI6K6q09oKp8wBLqUL+5DbYiIqsdr0qheAgICsG/fPpuyffv2ISAgoIFqRERE1LgwpFG9TJs2DfHx8cjKykJZWRmysrIQHx+PadOmNXTViIiIGgWe7qR6qZwcMHfuXIwaNQoBAQGYOXMmJw0QERHdIwxpVG8xMTGIiYnhhaZERET3AU93EhEREakQR9KIiIgaGXvfZgbgrWbuB4Y0IiKiRqa+gYm3mlEXnu4kIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGH2hZYvXo1vvnmG3h6emLZsmUAgE8//RTbt2+Hh4cHAGD06NHo2rUrAGDz5s3IyMiAVqtFbGwsgoODAQAFBQVITk6G1WpFSEgIYmNjodFoUFZWhqSkJBQUFMDd3R1xcXHw8fEBAGRmZmLTpk0AgKFDh6JPnz73vAGIiIiI1KjWkbQ+ffrgtddeq1I+ePBgLF26FEuXLlUC2pkzZ5CdnY3ly5djzpw5WLNmDSoqKgAAKSkpmDRpEhITE3Hu3Dnk5uYCADIyMuDm5oZVq1Zh8ODB2LhxIwDAYrEgNTUVixYtwqJFi5CamgqLxXLPDpyIiIhIzWoNaZ07d4Zer6/TxnJyctCzZ084OjrCx8cHLVu2xPHjx1FaWopr166hffv20Gg0iIiIQE5ODgBg//79yghZjx49cPDgQYgIcnNzERQUBL1eD71ej6CgICXYERERETV2tZ7urMm2bduwa9cutG3bFs899xz0ej3MZjMCAgKUZYxGI8xmM3Q6Hby8vJRyLy8vmM1mAIDZbFae0+l0cHV1xeXLl23Kb95WddLT05Geng4AWLx4MUwmU30Pi+rBwcGBbU6N3s8A+zk1euzn6lKvkPbkk09i+PDhAIBPPvkE69evx5QpUyAi1S5fU3lNz2k0mmqXrak8Ojoa0dHRyuPi4uIa90f3nslkYptTk8B+Tk0B+7l9tWrVqsbn6jW7s3nz5tBqtdBqtejXrx9OnDgB4MYIWUlJibKc2WyG0WisUl5SUgKj0VhlnfLycly9ehV6vR5Go7HKtgwGQ32qS0RERPTAqVdIKy0tVf5/37598PPzAwCEhYUhOzsbZWVlKCoqQmFhIfz9/WEwGODi4oJjx45BRLBr1y6EhYUBAEJDQ5GZmQkA2Lt3LwIDA6HRaBAcHIy8vDxYLBZYLBbk5eUpM0WJiIiIGrtaT3euXLkShw8fxuXLl/Hiiy9ixIgROHToEE6dOgWNRgNvb29MnDgRAODn54fw8HDMmDEDWq0W48aNg1Z7IweOHz8eq1evhtVqRXBwMEJCQgAAUVFRSEpKwtSpU6HX6xEXFwcA0Ov1GDZsGGbPng0AGD58eJ0nMBARERE96DRyuwvGHlBnz55t6Co0KbwmjZqC8glDoEv5oqGrQXRfsZ/b3z2/Jo2IiIiI7i+GNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVcmjoChAREVHNyl/5A3DVYr/9TRhit33BVQ/dO3+z3/4eMLWGtNWrV+Obb76Bp6cnli1bBgCwWCxYsWIFzp8/D29vb0yfPh16vR4AsHnzZmRkZECr1SI2NhbBwcEAgIKCAiQnJ8NqtSIkJASxsbHQaDQoKytDUlISCgoK4O7ujri4OPj4+AAAMjMzsWnTJgDA0KFD0adPn/vRBkT0gOKXFzUJVy3QpXxhl12ZTCYUFxfbZV+And9TD6BaQ1qfPn3w1FNPITk5WSlLS0tDly5dEBMTg7S0NKSlpeHZZ5/FmTNnkJ2djeXLl6O0tBQLFizAO++8A61Wi5SUFEyaNAkBAQF46623kJubi5CQEGRkZMDNzQ2rVq1CVlYWNm7ciOnTp8NisSA1NRWLFy8GAMyaNQthYWFKGCQi4pcXETVmtV6T1rlz5yrBKCcnB5GRkQCAyMhI5OTkKOU9e/aEo6MjfHx80LJlSxw/fhylpaW4du0a2rdvD41Gg4iICGWd/fv3KyNkPXr0wMGDByEiyM3NRVBQEPR6PfR6PYKCgpCbm3svj52IiIhIteo1ceDixYswGAwAAIPBgEuXLgEAzGYzvLy8lOWMRiPMZnOVci8vL5jN5irr6HQ6uLq64vLlyzVui4iIiKgpuKcTB0Tkjsprek6j0VS7bE3l6enpSE9PBwAsXrwYJpOptqrSPeTg4MA2pwbxM2C3vmfvfm7PYyN1Yz9vuuoV0jw9PVFaWgqDwYDS0lJ4eHgAuDFCVlJSoixnNpthNBqrlJeUlMBoNNqs4+XlhfLycly9ehV6vR5GoxGHDx+22Vbnzp2rrU90dDSio6OVx/a8boTsf60O0c3s1fcaop/zfUWV2M8br1atWtX4XL1Od4aFhWHnzp0AgJ07d+Lxxx9XyrOzs1FWVoaioiIUFhbC398fBoMBLi4uOHbsGEQEu3btQlhYGAAgNDQUmZmZAIC9e/ciMDAQGo0GwcHByMvLg8VigcViQV5enjJTlIiIiKixq3UkbeXKlTh8+DAuX76MF198ESNGjEBMTAxWrFiBjIwMmEwmzJgxAwDg5+eH8PBwzJgxA1qtFuPGjYNWeyMHjh8/HqtXr4bVakVwcDBCQkIAAFFRUUhKSsLUqVOh1+sRFxcHANDr9Rg2bBhmz54NABg+fDhndhIREVGToZHbXTD2gDp79mxDV6FJ4elOaijlE4Y06ltw2OvYSN3Yzxu3e366k4iIiIjuL4Y0IiIiIhViSCMiIiJSIYY0IiIiIhViSCMiIiJSIYY0IiIiIhViSCMiIiJSIYY0IiIiIhViSCMiIiJSIYY0IiIiIhViSCMiIiJSoVp/YJ2aFl9fX7vu76effrLr/oiIiB4UDGlkoz6hiT+QS0REdO/xdCcRERGRCjGkEREREakQQxoRERGRCjGkEREREakQQxoRERGRCjGkEREREakQQxoRERGRCjGkEREREakQQxoRERGRCjGkEREREakQQxoRERGRCjGkEREREakQf2CdiIhIxbZGrwc+uWCnvdlrP/8rej1+a989PlAY0oiIiFRsUPpz0KV8YZd9mUwmFBcX22VfAFA+YQgw0j7H9iDi6U4iIiIiFWJIIyIiIlIhhjQiIiIiFWJIIyIiIlIhThxopMpf+QNw1WK//U0YYrd9wVUP3Tt/s9/+iIiIGgBDWmN11dK4ZwMRERE1cgxpRPTA4v2jiKgxY0gjogcW7x9FRI0ZJw4QERERqdBdjaS99NJLcHZ2hlarhU6nw+LFi2GxWLBixQqcP38e3t7emD59OvR6PQBg8+bNyMjIgFarRWxsLIKDgwEABQUFSE5OhtVqRUhICGJjY6HRaFBWVoakpCQUFBTA3d0dcXFx8PHxufujJiIiIlK5ux5Jmz9/PpYuXYrFixcDANLS0tClSxckJiaiS5cuSEtLAwCcOXMG2dnZWL58OebMmYM1a9agoqICAJCSkoJJkyYhMTER586dQ25uLgAgIyMDbm5uWLVqFQYPHoyNGzfebXWJiIiIHgj3/HRnTk4OIiMjAQCRkZHIyclRynv27AlHR0f4+PigZcuWOH78OEpLS3Ht2jW0b98eGo0GERERyjr79+9Hnz59AAA9evTAwYMHISL3uspEREREqnPXEwcWLlwIAOjfvz+io6Nx8eJFGAwGAIDBYMClS5cAAGazGQEBAcp6RqMRZrMZOp0OXl5eSrmXlxfMZrOyTuVzOp0Orq6uuHz5Mjw8PGzqkJ6ejvT0dADA4sWLYTKZ7vawHng/A3ZrBwcHB7u2uT2PjdSN/ZyaAvbzpuuuQtqCBQtgNBpx8eJFvPnmm2jVqlWNy9Y0Ana7kbHqntNoNFXKoqOjER0drTy25wwsNbNXO9h71hvA15j+D/s5NQXs543X7bLTXZ3uNBqNAABPT088/vjjOH78ODw9PVFaWgoAKC0tVUa9vLy8UFJSoqxrNpthNBqrlJeUlCjbvfm58vJyXL16VZmEQERERNSY1Tuk/fLLL7h27Zry/99++y0eeeQRhIWFYefOnQCAnTt34vHHHwcAhIWFITs7G2VlZSgqKkJhYSH8/f1hMBjg4uKCY8eOQUSwa9cuhIWFAQBCQ0ORmZkJANi7dy8CAwOrHUkjIiIiamzqfbrz4sWLSEhIAHBjlOuJJ55AcHAw2rVrhxUrViAjIwMmkwkzZswAAPj5+SE8PBwzZsyAVqvFuHHjoNXeyIjjx4/H6tWrYbVaERwcjJCQEABAVFQUkpKSMHXqVOj1esTFxd3t8RIRERE9EOod0lq0aIGlS5dWKXd3d8e8efOqXWfo0KEYOnRolfJ27dph2bJlVcqdnJyUkEdERETUlPAXB4iIiIhUiCGNiIiISIUY0oiIiIhUiCGNiIiISIXu+hcHiIgaUvmEIXbZz8922ctNXHlPSKKmjiGNiB5YupQv7Lav8glD7Lo/IiKe7iQiIiJSIY6kERERqRxP6zdNDGlEREQqxtP6TRdPdxIRERGpEEMaERERkQrxdGcjtTV6PfDJBTvtzV77+V/R6/Fb++6RiIjI7hjSGqlB6c/Z7boCk8mE4uJiu+wL+N8LaEfymgmqP19f3/quWK/Vfvrpp/rtj4iaNIY0Impy6hOa7P3HCBERr0kjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIVYkgjIiIiUiGGNCIiIiIV4s9CNWLlE4bYZT8/22UvN3HV23uPREREdseQ1kjZ68fVgRth0J77IyIiagp4upOIiIhIhTiSRkRE1Mj4+vrezcr1Wu2nn36q/z6pWgxpREREjUx9A5PJZEJxcfE9rg3VF093EhEREakQQxoRERGRCjGkEREREakQr0kjG/W+2JQXmhIREd1TDGlkoz6hiReaEhER3Xs83UlERESkQg/ESFpubi7Wrl2LiooK9OvXDzExMQ1dJSIiIqL7SvUjaRUVFVizZg1ee+01rFixAllZWThz5kxDV4uIiIjovlJ9SDt+/DhatmyJFi1awMHBAT179kROTk5DV4uIiIjovlJ9SDObzfDy8lIee3l5wWw2N2CNiIiIiO4/1V+TJiJVyjQajc3j9PR0pKenAwAWL14Mk8lkl7rRDQ4ODmxzavTYz6kpYD9XF9WHNC8vL5SUlCiPS0pKYDAYbJaJjo5G9P9v735Covz2OI6/Z8YW/muYUaOajChNLAkVrShShNpEBG2K2jS6kIoQsjIhqZWbSGzRiNUmcNEmdNEihP5ghRXKLCKj0tFAyZx01P75NOmcu+jeoXvvTy/xI+fp9nnt5jxz4Bz4DHznHM55du6MqHHo+QAABeBJREFUf9Z1EItLV3DIn0A5lz+Bcr74Vq5cOe8z2293rlu3jtHRUcLhMLOzs3R3d1NSUpLoYYmIiIj8UrZfSXO5XFRVVdHY2EgsFqOiooLs7OxED0tERETkl7J9kQZQXFxMcXFxoochIiIismhsv90pIiIi8idSkSYiIiJiQyrSRERERGzIYf7qIjIRERERSSitpMnfVl9fn+ghiPxyyrn8CZRze1GRJiIiImJDKtJEREREbEhFmvxtP76SS+T/lXIufwLl3F50cEBERETEhrSSJiIiImJDv8VroWRxtLS0EAwGcbvdNDU1AfDmzRuuXbuGZVlkZWVRU1NDSkoKs7OztLa2MjQ0RCwWo6ysjH379gEwODhIIBAgGo1SVFREZWUlDocjkVMTifvZnF+9epVQKITT6cTv97Nx40ZAORd7Gx8fJxAIMDU1hcPhYOfOnezevZtPnz7R3NzM+/fvycrK4sSJE6SlpQHQ0dHBvXv3cDqdVFZWUlhYCCjrCWVE/qmvr8+EQiFTW1sbb6uvrzd9fX3GGGPu3r1rbty4YYwx5uHDh6a5udkYY4xlWebYsWNmbGws3ufVq1cmFouZxsZGEwwGF3kmIvP7mZzfvn3bBAIBY4wxU1NTpq6uzszNzcX7KOdiV5FIxIRCIWOMMV++fDE1NTVmeHjYtLW1mY6ODmOMMR0dHaatrc0YY8zw8LA5deqUiUajZmxszBw/flxZtwFtd0rchg0b4v+o/uXt27fk5+cDsGnTJp4+fRp/ZlkWc3NzRKNRkpKSSElJYXJykpmZGdavX4/D4aCsrIyenh4AHj9+zMmTJzl9+jTnz59fvImJ/OBncj4yMkJBQQEAbreb1NRUBgcHlXOxPY/Hw9q1awFITk7G5/MRiUTo6emhvLwcgPLy8nhue3p62LZtG0uWLGHZsmUsX76cgYEBZT3BtN0pC8rOzqa3t5fS0lKePHnCxMQEAFu3bqW3t5fq6mqi0SiHDx8mLS2NUChERkZGvH9GRgaRSASAmzdvcvbsWbxeL58/f07IfET+ynw5X7NmDb29vWzfvp2JiQkGBwcZHx/H4XAo5/LbCIfDDA0NkZOTw/T0NB6PB/heyH348AGASCRCbm5uvI/X6yUSieByuZT1BNJKmizo6NGjdHZ2cubMGWZmZkhK+l7XDwwM4HQ6uXLlCpcvX+bWrVuMjY1hFjgsnJeXRyAQ4M6dO8RiscWagsj/NF/OKyoq8Hq91NfXc/36dfLy8nC5XMq5/DYsy6KpqQm/309KSsq835sv08p6YmklTRbk8/loaGgAvm8JBYNBAB49ekRhYSFJSUm43W7y8vIIhULk5+fHVyEAJiYm8Hq9AFRXV9Pf308wGKSuro4LFy6Qnp6++JMS+Q/z5dzlcuH3++Pfa2hoYMWKFaSmpirnYnuzs7M0NTWxY8cOtmzZAnzftp+cnMTj8TA5OcnSpUuB7ytkP2Y6Eong9Xr/q11ZX1xaSZMFTU9PAxCLxWhvb2fXrl0AZGZm8vz5c4wxWJZFf38/Pp8Pj8dDcnIyr1+/xhjDgwcPKCkpAeDdu3fk5uZy4MAB0tPT/+2HL5JI8+X869evWJYFwLNnz3C5XKxatUo5F9szxtDa2orP52PPnj3x9pKSErq6ugDo6uqitLQ03t7d3c23b98Ih8OMjo6Sk5OjrCeYLrOVuEuXLvHixQs+fvyI2+1m//79WJZFZ2cnAJs3b+bQoUM4HA4sy6KlpYWRkRGMMVRUVLB3714AQqEQLS0tRKNRCgsLqaqqwuFwcPHiRUZHRwEoKCjA7/frGLcsup/JeTgcprGxEafTidfr5ciRI2RlZQHKudjby5cvOXfuHKtXr47n7+DBg+Tm5tLc3Mz4+DiZmZnU1tbGD9K0t7dz//79+HUzRUVFgLKeSCrSRERERGxI250iIiIiNqQiTURERMSGVKSJiIiI2JCKNBEREREbUpEmIiIiYkMq0kRERERsSEWaiIiIiA2pSBMRERGxoX8AsuT1vm32qAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 28,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 28,
     "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": 29,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 29,
     "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": 30,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
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    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 31,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "# create df_total by summing across three countries for each year\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "# reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "# change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "# show resulting dataframe\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 34,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "# add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # add title and label to axes\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>India</th>\n",
       "      <th>China</th>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Pakistan</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>Republic of Korea</th>\n",
       "      <th>...</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Sao Tome and Principe</th>\n",
       "      <th>Tuvalu</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>New Caledonia</th>\n",
       "      <th>San Marino</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Marshall Islands</th>\n",
       "      <th>Palau</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "      <td>22045</td>\n",
       "      <td>6051</td>\n",
       "      <td>978</td>\n",
       "      <td>9378</td>\n",
       "      <td>1172</td>\n",
       "      <td>185</td>\n",
       "      <td>1011</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "      <td>24796</td>\n",
       "      <td>5921</td>\n",
       "      <td>972</td>\n",
       "      <td>10030</td>\n",
       "      <td>1429</td>\n",
       "      <td>371</td>\n",
       "      <td>1456</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "      <td>20620</td>\n",
       "      <td>5249</td>\n",
       "      <td>1201</td>\n",
       "      <td>9074</td>\n",
       "      <td>1822</td>\n",
       "      <td>290</td>\n",
       "      <td>1572</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "      <td>10015</td>\n",
       "      <td>4562</td>\n",
       "      <td>900</td>\n",
       "      <td>7100</td>\n",
       "      <td>1592</td>\n",
       "      <td>197</td>\n",
       "      <td>1081</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "      <td>10170</td>\n",
       "      <td>3801</td>\n",
       "      <td>668</td>\n",
       "      <td>6661</td>\n",
       "      <td>1977</td>\n",
       "      <td>1086</td>\n",
       "      <td>847</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  India  China  \\\n",
       "0        1980   8880   5123   \n",
       "1        1981   8670   6682   \n",
       "2        1982   8147   3308   \n",
       "3        1983   7338   1863   \n",
       "4        1984   5704   1527   \n",
       "\n",
       "Country  United Kingdom of Great Britain and Northern Ireland  Philippines  \\\n",
       "0                                                    22045            6051   \n",
       "1                                                    24796            5921   \n",
       "2                                                    20620            5249   \n",
       "3                                                    10015            4562   \n",
       "4                                                    10170            3801   \n",
       "\n",
       "Country  Pakistan  United States of America  Iran (Islamic Republic of)  \\\n",
       "0             978                      9378                        1172   \n",
       "1             972                     10030                        1429   \n",
       "2            1201                      9074                        1822   \n",
       "3             900                      7100                        1592   \n",
       "4             668                      6661                        1977   \n",
       "\n",
       "Country  Sri Lanka  Republic of Korea  ...  Andorra  Vanuatu  \\\n",
       "0              185               1011  ...        0        0   \n",
       "1              371               1456  ...        0        0   \n",
       "2              290               1572  ...        0        0   \n",
       "3              197               1081  ...        0        0   \n",
       "4             1086                847  ...        0        0   \n",
       "\n",
       "Country  Sao Tome and Principe  Tuvalu  American Samoa  New Caledonia  \\\n",
       "0                            0       0               0              0   \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  San Marino  Western Sahara  Marshall Islands  Palau  \n",
       "0                 1               0                 0      0  \n",
       "1                 0               0                 0      0  \n",
       "2                 0               0                 0      0  \n",
       "3                 0               0                 0      0  \n",
       "4                 0               0                 0      0  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "editable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbbf7971780>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 38,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "# normalize Brazil data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\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": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\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!"
   ]
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   "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)."
   ]
  },
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   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
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