2BoxPlot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a985b6c1-0b81-480f-b133-959992c738fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.5.3\n"
     ]
    }
   ],
   "source": [
    "#Import primary modules.\n",
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library\n",
    "\n",
    "#Importing Matplotlib\n",
    "#%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8c64e58-f194-459d-86bc-863e0081cb3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read into a pandas dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.csv')\n",
    "\n",
    "print('Data read into a pandas dataframe!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb93c026-33a2-4821-ae93-f3a5d179cec6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</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>...</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
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       "      <td>340</td>\n",
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       "      <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",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</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",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "3     0     0     0     0  ...     0     1     0     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     1     1     0     0     0     0   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "3     0     0      6  \n",
       "4     1     1     15  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f949d28-c6de-4972-a072-1916d4ccf0ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 39)\n"
     ]
    }
   ],
   "source": [
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2ccfd9d-6a36-4cec-97f4-873d8aac4c40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_can.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f057324-ac42-4e2e-b45f-26b6cb83c8c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Continent</th>\n",
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       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
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       "      <td>58639</td>\n",
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       "      <td>Southern Europe</td>\n",
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       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</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",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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>6</td>\n",
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       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9c7b2a5-b7aa-4a13-998b-f1630520455b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7de97cb3-5454-4abc-bef0-e5efb92abe6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = list(map(str, range(1980, 2014)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09e40dea-2b6b-4bf9-99d5-12ac13165b9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
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       "      <th>1989</th>\n",
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       "      <th>2005</th>\n",
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       "      <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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 16,
   "id": "479a0e44-ebaf-4ecb-a45f-2441c164e1f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e402c4b9-46db-4fce-a4d4-5209e868b74b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 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": "code",
   "execution_count": 18,
   "id": "699c7a58-4c0b-4b6d-83b9-07a7aac5e6b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "feae1deb-e887-4338-8e8a-cfe59c2a7f24",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013.\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c07be094-f12a-438f-a1f9-561d4797df0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's view the percentiles associated with both countries using the describe() method\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ef5d794-fe3f-4ce3-9389-c4a219e7d7fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data.\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11c5b211-ca13-44c2-a582-a56f060cb631",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to create horizontal box plots, you can pass the vert parameter in the plot function and assign it to False.\n",
    "# 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": "code",
   "execution_count": 23,
   "id": "9265ff96-c412-4f2b-9521-f3cb905653e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "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": "code",
   "execution_count": 24,
   "id": "bbff17f8-8e0b-4f01-9fad-b86189ede36a",
   "metadata": {
    "tags": []
   },
   "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": [
    "# Get the top 15 countries based on Total immigrant population. Name the dataframe df_top15.\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8983b4e-9c85-4621-a570-24532d2e4582",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_top15' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_707/733124261.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0myears_00s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2010\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# slice the original dataframe df_can to create a series for each decade\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdf_80s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_top15\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myears_80s\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mdf_90s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_top15\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myears_90s\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mdf_00s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_top15\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myears_00s\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df_top15' is not defined"
     ]
    }
   ],
   "source": [
    "#Create a new dataframe which contains the aggregate for each decade\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",
    "# 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",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "# display dataframe\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61de1d26-4b00-4999-ad1b-f6159cf097a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_707/2156746082.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# dataframe using the describe() method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'new_df' is not defined"
     ]
    }
   ],
   "source": [
    "# dataframe using the describe() method\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41c1f4d1-5864-4685-bd3d-19a97cab44e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the box plots for Immigration from top 15 countries for decades 80s, 90s and 2000s\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "79cbaffa-5fb1-483e-9c4f-72ffb351f5c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  1980s   1990s   2000s\n",
       "0   India  82154  180395  303591\n",
       "1   China  32003  161528  340385"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how many entries fall above the outlier threshold \n",
    "#any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
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   "execution_count": null,
   "id": "e7b22324-84a5-4459-9b57-dc350536ba7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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