Visualization_Pie_Chart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function    #adds compatibility to Python2\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the inline backend\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version: 3.1.3\n"
     ]
    }
   ],
   "source": [
    "print(\"Matplotlib version:\", mpl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n",
    "mpl.style.use(['ggplot'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can = pd.read_excel(\"Canada.xlsx\", sheet_name= \"Canada by Citizenship\", skiprows= range(20), skipfooter=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Operations and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 43 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Type      195 non-null    object\n",
      " 1   Coverage  195 non-null    object\n",
      " 2   OdName    195 non-null    object\n",
      " 3   AREA      195 non-null    int64 \n",
      " 4   AreaName  195 non-null    object\n",
      " 5   REG       195 non-null    int64 \n",
      " 6   RegName   195 non-null    object\n",
      " 7   DEV       195 non-null    int64 \n",
      " 8   DevName   195 non-null    object\n",
      " 9   1980      195 non-null    int64 \n",
      " 10  1981      195 non-null    int64 \n",
      " 11  1982      195 non-null    int64 \n",
      " 12  1983      195 non-null    int64 \n",
      " 13  1984      195 non-null    int64 \n",
      " 14  1985      195 non-null    int64 \n",
      " 15  1986      195 non-null    int64 \n",
      " 16  1987      195 non-null    int64 \n",
      " 17  1988      195 non-null    int64 \n",
      " 18  1989      195 non-null    int64 \n",
      " 19  1990      195 non-null    int64 \n",
      " 20  1991      195 non-null    int64 \n",
      " 21  1992      195 non-null    int64 \n",
      " 22  1993      195 non-null    int64 \n",
      " 23  1994      195 non-null    int64 \n",
      " 24  1995      195 non-null    int64 \n",
      " 25  1996      195 non-null    int64 \n",
      " 26  1997      195 non-null    int64 \n",
      " 27  1998      195 non-null    int64 \n",
      " 28  1999      195 non-null    int64 \n",
      " 29  2000      195 non-null    int64 \n",
      " 30  2001      195 non-null    int64 \n",
      " 31  2002      195 non-null    int64 \n",
      " 32  2003      195 non-null    int64 \n",
      " 33  2004      195 non-null    int64 \n",
      " 34  2005      195 non-null    int64 \n",
      " 35  2006      195 non-null    int64 \n",
      " 36  2007      195 non-null    int64 \n",
      " 37  2008      195 non-null    int64 \n",
      " 38  2009      195 non-null    int64 \n",
      " 39  2010      195 non-null    int64 \n",
      " 40  2011      195 non-null    int64 \n",
      " 41  2012      195 non-null    int64 \n",
      " 42  2013      195 non-null    int64 \n",
      "dtypes: int64(37), object(6)\n",
      "memory usage: 65.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_can.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
       "       'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean the dataset to remove some columns which are not required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.drop(['Type','Coverage','REG','AREA','DEV'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename some columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.rename(columns= {\"OdName\":\"Country\", \"AreaName\":\"Continent\", \"RegName\":\"Region\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>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>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",
       "    </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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 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  ...  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n",
       "2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n",
       "3     0     0     0     0  ...     0     0     1     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     0     1     1     0     0     0   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "2  4325  3774  4331  \n",
       "3     0     0     0  \n",
       "4     0     1     1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can[\"Total\"]=df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country      0\n",
       "Continent    0\n",
       "Region       0\n",
       "DevName      0\n",
       "1980         0\n",
       "1981         0\n",
       "1982         0\n",
       "1983         0\n",
       "1984         0\n",
       "1985         0\n",
       "1986         0\n",
       "1987         0\n",
       "1988         0\n",
       "1989         0\n",
       "1990         0\n",
       "1991         0\n",
       "1992         0\n",
       "1993         0\n",
       "1994         0\n",
       "1995         0\n",
       "1996         0\n",
       "1997         0\n",
       "1998         0\n",
       "1999         0\n",
       "2000         0\n",
       "2001         0\n",
       "2002         0\n",
       "2003         0\n",
       "2004         0\n",
       "2005         0\n",
       "2006         0\n",
       "2007         0\n",
       "2008         0\n",
       "2009         0\n",
       "2010         0\n",
       "2011         0\n",
       "2012         0\n",
       "2013         0\n",
       "Total        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.set_index(\"Country\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Opposite of the set_index() function is reset_index() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "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",
       "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",
       "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra',\n",
       "       'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       ...\n",
       "       'United States of America', 'Uruguay', 'Uzbekistan', 'Vanuatu',\n",
       "       'Venezuela (Bolivarian Republic of)', 'Viet Nam', 'Western Sahara',\n",
       "       'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype='object', length=195)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For consistency, ensure that all column labels of type string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(isinstance(column, str) for column in df_can.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Integer Column names into Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.columns= list(map(str, df_can.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
       "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
       "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
       "       '2011', '2012', '2013', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']\n"
     ]
    }
   ],
   "source": [
    "years= list(map(str, range(1980,2014)))\n",
    "print(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check the column labels types now\n",
    "all(isinstance(column, str) for column in df_can.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization using Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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>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",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1942</td>\n",
       "      <td>1839</td>\n",
       "      <td>1675</td>\n",
       "      <td>1018</td>\n",
       "      <td>878</td>\n",
       "      <td>920</td>\n",
       "      <td>904</td>\n",
       "      <td>1200</td>\n",
       "      <td>1181</td>\n",
       "      <td>1539</td>\n",
       "      <td>...</td>\n",
       "      <td>1585</td>\n",
       "      <td>1473</td>\n",
       "      <td>1693</td>\n",
       "      <td>1834</td>\n",
       "      <td>1860</td>\n",
       "      <td>1834</td>\n",
       "      <td>1548</td>\n",
       "      <td>1679</td>\n",
       "      <td>1775</td>\n",
       "      <td>55174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 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",
       "Oceania                           1942   1839   1675   1018    878    920   \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",
       "Oceania                            904   1200   1181   1539  ...    1585   \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",
       "Oceania                            1473    1693    1834    1860    1834   \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",
       "Oceania                            1548    1679    1775    55174  \n",
       "\n",
       "[6 rows x 35 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the data according to the Continent column\n",
    "df_continents= df_can.groupby('Continent', axis=0).sum()\n",
    "df_continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_continents['Total'].plot(kind='pie')\n",
    "\n",
    "plt.title(\"Immigration to Cananda by Continent (1980-2013)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.05) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.savefig(\"Pie_Chart_Immigration_Continentwise.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explode_list=[0,0,0,0.1,0.1,0.1]\n",
    "\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                           figsize=(15,6),\n",
    "                           startangle=0,\n",
    "                           shadow=True,\n",
    "                           autopct='%1.1f%%',\n",
    "                           pctdistance=1.12,\n",
    "                           labels=None,\n",
    "                           explode=explode_list,\n",
    "                          )\n",
    "\n",
    "plt.title(\"Immigration to Canada by Continent [2013]\",y=1.05)\n",
    "\n",
    "\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "plt.legend(labels=df_continents.index, loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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 "nbformat": 4,
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