Visualization_Box_Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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": 281,
   "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": 282,
   "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": 283,
   "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": 284,
   "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": 285,
   "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": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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": 287,
   "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": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 288,
     "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": 289,
   "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": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.rename(columns= {\"OdName\":\"Country\", \"AreaName\":\"Continent\", \"RegName\":\"Region\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can[\"Total\"]=df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "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": 295,
   "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": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "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": 298,
     "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": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 299,
     "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": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.columns= list(map(str, df_can.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "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": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "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": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 303,
     "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 Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan= df_can.loc[['Japan'],years].transpose()\n",
    "\n",
    "df_japan.plot(kind='box', figsize=(8,6))\n",
    "\n",
    "plt.title(\"Immigration to Canada from Japan [1980,2013]\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "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>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Japan\n",
       "count    34.000000\n",
       "mean    814.911765\n",
       "std     337.219771\n",
       "min     198.000000\n",
       "25%     529.000000\n",
       "50%     902.000000\n",
       "75%    1079.000000\n",
       "max    1284.000000"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparing the Immigration from China and India\n",
    "\n",
    "df_CI= df_can.loc[['China','India'], years].transpose()\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(8,6))\n",
    "\n",
    "plt.title(\"Immigration to Canada from China and India [1980-2913]\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "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>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              China         India\n",
       "count     34.000000     34.000000\n",
       "mean   19410.647059  20350.117647\n",
       "std    13568.230790  10007.342579\n",
       "min     1527.000000   4211.000000\n",
       "25%     5512.750000  10637.750000\n",
       "50%    19945.000000  20235.000000\n",
       "75%    31568.500000  28699.500000\n",
       "max    42584.000000  36210.000000"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the horizontal plot for the previous case\n",
    "df_CI= df_can.loc[['China','India'], years].transpose()\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(8,6), vert=False, color='blue')\n",
    "\n",
    "plt.title(\"Immigration to Canada from China and India [1980-2913]\")\n",
    "plt.xlabel(\"Number of Immigrants\")       #Here we have to swap the ylabel with the xlabel\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure()\n",
    "\n",
    "ax0= fig.add_subplot(1,2,1)\n",
    "ax1= fig.add_subplot(1,2,2)\n",
    "\n",
    "#Subplot1\n",
    "df_CI.plot(kind='box',vert=False, color='blue', ax=ax0, figsize=(20,6))\n",
    "ax0.set_title(\"Box Plot of Immigration from India and China\")\n",
    "ax0.set_xlabel(\"Number of Immigrants\")\n",
    "ax0.set_ylabel(\"Countries\")\n",
    "\n",
    "#Subplot2\n",
    "df_CI.plot(kind='line', figsize=(20,6), ax=ax1)\n",
    "ax1.set_title(\"Line Plot of Immigration from India and China\")\n",
    "ax1.set_xlabel(\"Years\")\n",
    "ax1.set_ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.savefig(\"Comparison-Box-vs-Line.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the immigration to Canada by decade 1980s, 1990s, 2000s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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>India</th>\n",
       "      <th>China</th>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Pakistan</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>Republic of Korea</th>\n",
       "      <th>Poland</th>\n",
       "      <th>Lebanon</th>\n",
       "      <th>France</th>\n",
       "      <th>Jamaica</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Romania</th>\n",
       "      <th>Year_binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "      <td>22045</td>\n",
       "      <td>6051</td>\n",
       "      <td>978</td>\n",
       "      <td>9378</td>\n",
       "      <td>1172</td>\n",
       "      <td>185</td>\n",
       "      <td>1011</td>\n",
       "      <td>863</td>\n",
       "      <td>1409</td>\n",
       "      <td>1729</td>\n",
       "      <td>3198</td>\n",
       "      <td>1191</td>\n",
       "      <td>375</td>\n",
       "      <td>1980s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "      <td>24796</td>\n",
       "      <td>5921</td>\n",
       "      <td>972</td>\n",
       "      <td>10030</td>\n",
       "      <td>1429</td>\n",
       "      <td>371</td>\n",
       "      <td>1456</td>\n",
       "      <td>2930</td>\n",
       "      <td>1119</td>\n",
       "      <td>2027</td>\n",
       "      <td>2634</td>\n",
       "      <td>1829</td>\n",
       "      <td>438</td>\n",
       "      <td>1980s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "      <td>20620</td>\n",
       "      <td>5249</td>\n",
       "      <td>1201</td>\n",
       "      <td>9074</td>\n",
       "      <td>1822</td>\n",
       "      <td>290</td>\n",
       "      <td>1572</td>\n",
       "      <td>5881</td>\n",
       "      <td>1159</td>\n",
       "      <td>2219</td>\n",
       "      <td>2661</td>\n",
       "      <td>2162</td>\n",
       "      <td>583</td>\n",
       "      <td>1980s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "      <td>10015</td>\n",
       "      <td>4562</td>\n",
       "      <td>900</td>\n",
       "      <td>7100</td>\n",
       "      <td>1592</td>\n",
       "      <td>197</td>\n",
       "      <td>1081</td>\n",
       "      <td>4546</td>\n",
       "      <td>789</td>\n",
       "      <td>1490</td>\n",
       "      <td>2455</td>\n",
       "      <td>3404</td>\n",
       "      <td>543</td>\n",
       "      <td>1980s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "      <td>10170</td>\n",
       "      <td>3801</td>\n",
       "      <td>668</td>\n",
       "      <td>6661</td>\n",
       "      <td>1977</td>\n",
       "      <td>1086</td>\n",
       "      <td>847</td>\n",
       "      <td>3588</td>\n",
       "      <td>1253</td>\n",
       "      <td>1169</td>\n",
       "      <td>2508</td>\n",
       "      <td>7583</td>\n",
       "      <td>524</td>\n",
       "      <td>1980s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  India  China  United Kingdom of Great Britain and Northern Ireland  \\\n",
       "0  1980   8880   5123                                              22045      \n",
       "1  1981   8670   6682                                              24796      \n",
       "2  1982   8147   3308                                              20620      \n",
       "3  1983   7338   1863                                              10015      \n",
       "4  1984   5704   1527                                              10170      \n",
       "\n",
       "   Philippines  Pakistan  United States of America  \\\n",
       "0         6051       978                      9378   \n",
       "1         5921       972                     10030   \n",
       "2         5249      1201                      9074   \n",
       "3         4562       900                      7100   \n",
       "4         3801       668                      6661   \n",
       "\n",
       "   Iran (Islamic Republic of)  Sri Lanka  Republic of Korea  Poland  Lebanon  \\\n",
       "0                        1172        185               1011     863     1409   \n",
       "1                        1429        371               1456    2930     1119   \n",
       "2                        1822        290               1572    5881     1159   \n",
       "3                        1592        197               1081    4546      789   \n",
       "4                        1977       1086                847    3588     1253   \n",
       "\n",
       "   France  Jamaica  Viet Nam  Romania Year_binned  \n",
       "0    1729     3198      1191      375       1980s  \n",
       "1    2027     2634      1829      438       1980s  \n",
       "2    2219     2661      2162      583       1980s  \n",
       "3    1490     2455      3404      543       1980s  \n",
       "4    1169     2508      7583      524       1980s  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top15= df_can.sort_values(['Total'], ascending= False).head(15)\n",
    "df_top15=df_top15[years].transpose()\n",
    "df_top15.reset_index(inplace=True)\n",
    "df_top15.rename(columns={\"index\": \"Year\"}, inplace=True)\n",
    "df_top15['Year']=df_top15['Year'].map(int)\n",
    "\n",
    "bins= np.array([1980,1990,2000,2010])\n",
    "group_names= [\"1980s\", \"1990s\", \"2000s\"]\n",
    "\n",
    "df_top15[\"Year_binned\"]=pd.cut(df_top15[\"Year\"], bins, labels= group_names, include_lowest=True)\n",
    "df_top15.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_top15.set_index('Year', inplace=True)\n",
    "df_top15.index.name= None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_top15=df_top15.groupby(['Year_binned']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "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>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>94195</td>\n",
       "      <td>196926</td>\n",
       "      <td>309254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>40079</td>\n",
       "      <td>188981</td>\n",
       "      <td>335247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>210839</td>\n",
       "      <td>239138</td>\n",
       "      <td>83297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>73273</td>\n",
       "      <td>136736</td>\n",
       "      <td>200758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>13061</td>\n",
       "      <td>78232</td>\n",
       "      <td>119009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>82716</td>\n",
       "      <td>56456</td>\n",
       "      <td>77882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>25132</td>\n",
       "      <td>57100</td>\n",
       "      <td>67387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>18321</td>\n",
       "      <td>72977</td>\n",
       "      <td>48019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>18346</td>\n",
       "      <td>43720</td>\n",
       "      <td>66102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>74642</td>\n",
       "      <td>49295</td>\n",
       "      <td>12953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>38486</td>\n",
       "      <td>37580</td>\n",
       "      <td>32435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>19314</td>\n",
       "      <td>31616</td>\n",
       "      <td>42178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>39273</td>\n",
       "      <td>37866</td>\n",
       "      <td>22572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>34439</td>\n",
       "      <td>35728</td>\n",
       "      <td>21413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>9865</td>\n",
       "      <td>35946</td>\n",
       "      <td>42898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "India                                                94195  196926  309254\n",
       "China                                                40079  188981  335247\n",
       "United Kingdom of Great Britain and Northern Ir...  210839  239138   83297\n",
       "Philippines                                          73273  136736  200758\n",
       "Pakistan                                             13061   78232  119009\n",
       "United States of America                             82716   56456   77882\n",
       "Iran (Islamic Republic of)                           25132   57100   67387\n",
       "Sri Lanka                                            18321   72977   48019\n",
       "Republic of Korea                                    18346   43720   66102\n",
       "Poland                                               74642   49295   12953\n",
       "Lebanon                                              38486   37580   32435\n",
       "France                                               19314   31616   42178\n",
       "Jamaica                                              39273   37866   22572\n",
       "Viet Nam                                             34439   35728   21413\n",
       "Romania                                               9865   35946   42898"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top15.index.name= None\n",
    "df_top15=df_top15.transpose()\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_top15.plot(kind='box', figsize=(12,8))\n",
    "\n",
    "plt.title(\"Immigration to Canada by Decade\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "plt.xlabel(\"Decade\")\n",
    "\n",
    "plt.savefig(\"Immigration-to-Canada-by-Decade.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "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>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52798.733333</td>\n",
       "      <td>86553.133333</td>\n",
       "      <td>98760.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51445.663486</td>\n",
       "      <td>69000.513277</td>\n",
       "      <td>102196.937343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9865.000000</td>\n",
       "      <td>31616.000000</td>\n",
       "      <td>12953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18830.000000</td>\n",
       "      <td>37723.000000</td>\n",
       "      <td>37306.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38486.000000</td>\n",
       "      <td>56456.000000</td>\n",
       "      <td>66102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73957.500000</td>\n",
       "      <td>107484.000000</td>\n",
       "      <td>101153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>210839.000000</td>\n",
       "      <td>239138.000000</td>\n",
       "      <td>335247.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    52798.733333   86553.133333   98760.266667\n",
       "std     51445.663486   69000.513277  102196.937343\n",
       "min      9865.000000   31616.000000   12953.000000\n",
       "25%     18830.000000   37723.000000   37306.500000\n",
       "50%     38486.000000   56456.000000   66102.000000\n",
       "75%     73957.500000  107484.000000  101153.000000\n",
       "max    210839.000000  239138.000000  335247.000000"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top15.describe()"
   ]
  }
 ],
 "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
}