Visualization_Bubble_Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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": 120,
   "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": 121,
   "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": 122,
   "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": 123,
   "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": 124,
   "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": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": 126,
   "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": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "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": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.rename(columns= {\"OdName\":\"Country\", \"AreaName\":\"Continent\", \"RegName\":\"Region\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>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": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can[\"Total\"]=df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": 134,
   "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": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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": 137,
     "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": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 138,
     "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": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.columns= list(map(str, df_can.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 142,
     "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 Bubble Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0  1980           16        1       80               0        0       1   \n",
       "1  1981           39        0       67               1        0       3   \n",
       "2  1982           39        0       71               0        0       6   \n",
       "3  1983           47        0       69               0        0       6   \n",
       "4  1984           71        0       63               0        0       4   \n",
       "\n",
       "   Antigua and Barbuda  Argentina  Armenia  ...  United States of America  \\\n",
       "0                    0        368        0  ...                      9378   \n",
       "1                    0        426        0  ...                     10030   \n",
       "2                    0        626        0  ...                      9074   \n",
       "3                    0        241        0  ...                      7100   \n",
       "4                   42        237        0  ...                      6661   \n",
       "\n",
       "   Uruguay  Uzbekistan  Vanuatu  Venezuela (Bolivarian Republic of)  Viet Nam  \\\n",
       "0      128           0        0                                 103      1191   \n",
       "1      132           0        0                                 117      1829   \n",
       "2      146           0        0                                 174      2162   \n",
       "3      105           0        0                                 124      3404   \n",
       "4       90           0        0                                 142      7583   \n",
       "\n",
       "   Western Sahara  Yemen  Zambia  Zimbabwe  \n",
       "0               0      1      11        72  \n",
       "1               0      2      17       114  \n",
       "2               0      1      11       102  \n",
       "3               0      6       7        44  \n",
       "4               0      0      16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t= df_can[years].transpose()\n",
    "\n",
    "df_can_t.index= df_can_t.index.map(int)\n",
    "\n",
    "df_can_t.index.name='Year'\n",
    "\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to plot the data: \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}