Visualization_Scatter_Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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": 188,
   "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": 189,
   "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": 190,
   "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": 191,
   "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": 192,
   "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": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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": 194,
   "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": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 195,
     "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": 196,
   "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": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.rename(columns= {\"OdName\":\"Country\", \"AreaName\":\"Continent\", \"RegName\":\"Region\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can[\"Total\"]=df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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": 202,
   "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": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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": 205,
     "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": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 206,
     "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": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.columns= list(map(str, df_can.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 210,
     "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 Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "1980   99137\n",
       "1981  110563\n",
       "1982  104271\n",
       "1983   75550\n",
       "1984   73417"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot=pd.DataFrame(df_can[years].sum(axis=0))\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,\n",
      "            1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
      "            2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,\n",
      "            2013],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "df_tot.index=map(int, df_tot.index)\n",
    "print(df_tot.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reseting the index to obtain years back into a column \n",
    "df_tot.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>Year</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename the columns\n",
    "df_tot.columns=['Year','Total']\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the scatter plot\n",
    "df_tot.plot(kind=\"scatter\", x=\"Year\", y=\"Total\", figsize=(10,6), color=\"darkblue\")\n",
    "\n",
    "plt.title(\"Total Immigration to Canada from 1980-2013\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to plot a linear line of best fit, and use it to predict the number of immigrants in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= df_tot[\"Year\"]\n",
    "y= df_tot[\"Total\"]\n",
    "\n",
    "fit=np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression y= a*x + b, our output has 2 elements *[5.56709228e+03, -1.09261952e+07]* with the the slope in position 0 and intercept in position 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Immigrants= y=5567 x + -10926195\n"
     ]
    }
   ],
   "source": [
    "#Plotting the Regression Line on the Scatterplot\n",
    "df_tot.plot(kind=\"scatter\", x=\"Year\", y=\"Total\", figsize=(10,6), color=\"darkblue\")\n",
    "\n",
    "plt.title(\"Total Immigration to Canada from 1980-2013\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "#Plotting the line using the coefficients stored in the 'fit' object\n",
    "plt.plot(x, fit[0]*x+fit[1], color='red')\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#print out the line of best fit\n",
    "print(\"Number of Immigrants= y={0:.0f} x + {1:.0f}\".format(fit[0], fit[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_countries= pd.DataFrame(df_can.loc[['Denmark','Norway','Sweden'],years].sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_countries.index= map(int, df_countries.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_countries.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>Year</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_countries.columns=['Year','Total']\n",
    "df_countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_countries.plot(kind='scatter', x='Year', y='Total', color='darkblue', figsize=(10,6))\n",
    "\n",
    "plt.title(\"Total Immigration to Canada from Denmark, Norway and Sweden\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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