Visualization_Line_Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 71,
   "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": 72,
   "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": 73,
   "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": 74,
   "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": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.drop(['Type','Coverage','REG','AREA','DEV'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.rename(columns= {\"OdName\":\"Country\", \"AreaName\":\"Continent\", \"RegName\":\"Region\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n",
       "2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n",
       "3     0     0     0     0  ...     0     0     1     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     0     1     1     0     0     0   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "2  4325  3774  4331  \n",
       "3     0     0     0  \n",
       "4     0     1     1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can[\"Total\"]=df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.index.name=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Integer Column names into Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.columns= list(map(str, df_can.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Visualization using Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    1666\n",
       "1981    3692\n",
       "1982    3498\n",
       "1983    2860\n",
       "1984    1418\n",
       "Name: Haiti, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haiti= df_can.loc[\"Haiti\", years]\n",
    "haiti.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(haiti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d36f18188>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988',\n",
       "       '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',\n",
       "       '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006',\n",
       "       '2007', '2008', '2009', '2010', '2011', '2012', '2013'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haiti.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.index= haiti.index.map(int)\n",
    "haiti.plot(kind=\"line\")\n",
    "\n",
    "plt.title(\"Immigration from Haiti\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haiti.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting using the plt.text() method\n",
    "haiti.plot(kind=\"line\")\n",
    "\n",
    "plt.title(\"Immigration from Haiti\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "\n",
    "# annotate the 2010 Earthquake. \n",
    "# syntax: plt.text(x, y, label)\n",
    "plt.text(2000, 6000, '2010 Earthquake') # see note below\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Immigration from India and China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>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>China</th>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>2643</td>\n",
       "      <td>2758</td>\n",
       "      <td>4323</td>\n",
       "      <td>...</td>\n",
       "      <td>36619</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>10189</td>\n",
       "      <td>11522</td>\n",
       "      <td>10343</td>\n",
       "      <td>...</td>\n",
       "      <td>28235</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1980  1981  1982  1983  1984  1985  1986   1987   1988   1989  ...  \\\n",
       "China  5123  6682  3308  1863  1527  1816  1960   2643   2758   4323  ...   \n",
       "India  8880  8670  8147  7338  5704  4211  7150  10189  11522  10343  ...   \n",
       "\n",
       "        2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  \n",
       "China  36619  42584  33518  27642  30037  29622  30391  28502  33024  34129  \n",
       "India  28235  36210  33848  28742  28261  29456  34235  27509  30933  33087  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ci= df_can.loc[['China','India'],years]\n",
    "df_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d3682f488>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ci.plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above diagram is not correct because we had to observe that the graph is plotted with indices on the x-axis. Here, the indices are: [\"India\",\"China\"]; but it was supposed to be years. So, we will have to take the transpose of the dataframe df_ci and then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1816</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1960</td>\n",
       "      <td>7150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>2643</td>\n",
       "      <td>10189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>2758</td>\n",
       "      <td>11522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>4323</td>\n",
       "      <td>10343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>8076</td>\n",
       "      <td>12041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>14255</td>\n",
       "      <td>13734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>10846</td>\n",
       "      <td>13673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>9817</td>\n",
       "      <td>21496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>13128</td>\n",
       "      <td>18620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>14398</td>\n",
       "      <td>18489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>19415</td>\n",
       "      <td>23859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>20475</td>\n",
       "      <td>22268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>21049</td>\n",
       "      <td>17241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>30069</td>\n",
       "      <td>18974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>35529</td>\n",
       "      <td>28572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>36434</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>31961</td>\n",
       "      <td>31889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>36439</td>\n",
       "      <td>27155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>36619</td>\n",
       "      <td>28235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>42584</td>\n",
       "      <td>36210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>33518</td>\n",
       "      <td>33848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>27642</td>\n",
       "      <td>28742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>30037</td>\n",
       "      <td>28261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>29622</td>\n",
       "      <td>29456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>30391</td>\n",
       "      <td>34235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28502</td>\n",
       "      <td>27509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>33024</td>\n",
       "      <td>30933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34129</td>\n",
       "      <td>33087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      China  India\n",
       "1980   5123   8880\n",
       "1981   6682   8670\n",
       "1982   3308   8147\n",
       "1983   1863   7338\n",
       "1984   1527   5704\n",
       "1985   1816   4211\n",
       "1986   1960   7150\n",
       "1987   2643  10189\n",
       "1988   2758  11522\n",
       "1989   4323  10343\n",
       "1990   8076  12041\n",
       "1991  14255  13734\n",
       "1992  10846  13673\n",
       "1993   9817  21496\n",
       "1994  13128  18620\n",
       "1995  14398  18489\n",
       "1996  19415  23859\n",
       "1997  20475  22268\n",
       "1998  21049  17241\n",
       "1999  30069  18974\n",
       "2000  35529  28572\n",
       "2001  36434  31223\n",
       "2002  31961  31889\n",
       "2003  36439  27155\n",
       "2004  36619  28235\n",
       "2005  42584  36210\n",
       "2006  33518  33848\n",
       "2007  27642  28742\n",
       "2008  30037  28261\n",
       "2009  29622  29456\n",
       "2010  30391  34235\n",
       "2011  28502  27509\n",
       "2012  33024  30933\n",
       "2013  34129  33087"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ci= df_ci.transpose()\n",
    "df_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we will plot the graph\n",
    "df_ci.index=df_ci.index.map(int)\n",
    "df_ci.plot(kind=\"line\", figsize=(12,8))\n",
    "\n",
    "\n",
    "plt.title(\"Comparison of Immigration from China and India\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Number of Immigrants\")\n",
    "\n",
    "plt.savefig(\"Comparison_immigration_india_china.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}