1PieChart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06dde5fe-18fb-4dc6-aeae-705d0ae7a4cb",
   "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": 3,
   "id": "573ba95f-697b-4502-b6c7-c3b83cd17ddd",
   "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": 3,
   "id": "477e42f1-1fd4-42d8-9f80-e9a8bdda941c",
   "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>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",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</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",
       "      <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",
       "      <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>...</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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f38bf9e-f925-4934-bb84-628a96d85dbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 39)\n"
     ]
    }
   ],
   "source": [
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "009cc69f-ae23-4a00-b619-3fd744eb003a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_can.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f0f3912-1a80-4d5a-a1b9-6c5a4a4d1df7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    .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",
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       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
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       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
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       "  <tbody>\n",
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       "      <th>Afghanistan</th>\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",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</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",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>1</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>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",
       "      <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>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",
       "      <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 × 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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b7c7d19-c38b-409b-8438-5206310e8199",
   "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": 8,
   "id": "aca1c328-aec5-4ee9-9880-b608c54d0906",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = list(map(str, range(1980, 2014)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8995562-6bd7-4476-bef5-60a812eb5aff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
    {
     "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",
       "      <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": 9,
     "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": 13,
   "id": "6a2902f5-34fa-405e-b541-bab06f0f5e67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 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=(10, 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, fontsize = 15) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left', fontsize=7) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "588d6026-b328-47c1-b35e-80b794d7ba5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\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",
    "# 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",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left')\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1fb1531-2de6-48a1-b453-6343e45c8b94",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}