Tourist_Receipts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_Name</th>\n",
       "      <th>Country_Code</th>\n",
       "      <th>Indicator_Name</th>\n",
       "      <th>Indicator_Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</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>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>International tourism, receipts (current US$)</td>\n",
       "      <td>ST.INT.RCPT.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.718900e+10</td>\n",
       "      <td>2.690900e+10</td>\n",
       "      <td>3.106400e+10</td>\n",
       "      <td>3.431500e+10</td>\n",
       "      <td>3.413700e+10</td>\n",
       "      <td>3.288900e+10</td>\n",
       "      <td>3.361900e+10</td>\n",
       "      <td>3.087200e+10</td>\n",
       "      <td>3.447500e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>International tourism, receipts (current US$)</td>\n",
       "      <td>ST.INT.RCPT.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.109000e+09</td>\n",
       "      <td>5.635000e+09</td>\n",
       "      <td>5.522000e+09</td>\n",
       "      <td>6.370000e+09</td>\n",
       "      <td>6.623000e+09</td>\n",
       "      <td>6.784000e+09</td>\n",
       "      <td>7.405000e+09</td>\n",
       "      <td>6.254000e+09</td>\n",
       "      <td>6.613000e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>International tourism, receipts (current US$)</td>\n",
       "      <td>ST.INT.RCPT.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.819100e+10</td>\n",
       "      <td>1.556800e+10</td>\n",
       "      <td>1.843800e+10</td>\n",
       "      <td>1.998900e+10</td>\n",
       "      <td>2.069600e+10</td>\n",
       "      <td>2.094100e+10</td>\n",
       "      <td>2.080200e+10</td>\n",
       "      <td>1.927300e+10</td>\n",
       "      <td>1.827800e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>International tourism, receipts (current US$)</td>\n",
       "      <td>ST.INT.RCPT.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.084300e+10</td>\n",
       "      <td>3.967500e+10</td>\n",
       "      <td>4.581400e+10</td>\n",
       "      <td>4.846400e+10</td>\n",
       "      <td>5.002800e+10</td>\n",
       "      <td>5.166400e+10</td>\n",
       "      <td>4.404400e+10</td>\n",
       "      <td>4.496900e+10</td>\n",
       "      <td>4.443200e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>International tourism, receipts (current US$)</td>\n",
       "      <td>ST.INT.RCPT.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.800100e+10</td>\n",
       "      <td>5.885800e+10</td>\n",
       "      <td>5.618700e+10</td>\n",
       "      <td>6.608700e+10</td>\n",
       "      <td>6.400000e+10</td>\n",
       "      <td>6.606000e+10</td>\n",
       "      <td>6.738200e+10</td>\n",
       "      <td>5.295900e+10</td>\n",
       "      <td>5.088300e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country_Name Country_Code                                 Indicator_Name  \\\n",
       "0    Australia          AUS  International tourism, receipts (current US$)   \n",
       "1       Brazil          BRA  International tourism, receipts (current US$)   \n",
       "2       Canada          CAN  International tourism, receipts (current US$)   \n",
       "3        China          CHN  International tourism, receipts (current US$)   \n",
       "4       France          FRA  International tourism, receipts (current US$)   \n",
       "\n",
       "   Indicator_Code  1960  1961  1962  1963  1964  1965  ...           2008  \\\n",
       "0  ST.INT.RCPT.CD   NaN   NaN   NaN   NaN   NaN   NaN  ...   2.718900e+10   \n",
       "1  ST.INT.RCPT.CD   NaN   NaN   NaN   NaN   NaN   NaN  ...   6.109000e+09   \n",
       "2  ST.INT.RCPT.CD   NaN   NaN   NaN   NaN   NaN   NaN  ...   1.819100e+10   \n",
       "3  ST.INT.RCPT.CD   NaN   NaN   NaN   NaN   NaN   NaN  ...   4.084300e+10   \n",
       "4  ST.INT.RCPT.CD   NaN   NaN   NaN   NaN   NaN   NaN  ...   6.800100e+10   \n",
       "\n",
       "           2009          2010          2011          2012          2013  \\\n",
       "0  2.690900e+10  3.106400e+10  3.431500e+10  3.413700e+10  3.288900e+10   \n",
       "1  5.635000e+09  5.522000e+09  6.370000e+09  6.623000e+09  6.784000e+09   \n",
       "2  1.556800e+10  1.843800e+10  1.998900e+10  2.069600e+10  2.094100e+10   \n",
       "3  3.967500e+10  4.581400e+10  4.846400e+10  5.002800e+10  5.166400e+10   \n",
       "4  5.885800e+10  5.618700e+10  6.608700e+10  6.400000e+10  6.606000e+10   \n",
       "\n",
       "           2014          2015          2016  2017  \n",
       "0  3.361900e+10  3.087200e+10  3.447500e+10   NaN  \n",
       "1  7.405000e+09  6.254000e+09  6.613000e+09   NaN  \n",
       "2  2.080200e+10  1.927300e+10  1.827800e+10   NaN  \n",
       "3  4.404400e+10  4.496900e+10  4.443200e+10   NaN  \n",
       "4  6.738200e+10  5.295900e+10  5.088300e+10   NaN  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Receipts into dataframe\n",
    "receipts= \"Data/tourist_receipt.csv\"\n",
    "receipts_df = pd.read_csv(receipts)\n",
    "\n",
    "receipts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframe for winning countries\n",
    "receipts_winners_df =  receipts_df.loc[receipts_df[\"Country_Name\"].isin(['Brazil','Germany','Italy','Spain'])]\n",
    "\n",
    "\n",
    "# Set the 'Country name' to be our index for easy referencing of rows and drop colmns we dont need\n",
    "receipts_winners_df = receipts_winners_df.set_index(\"Country_Name\")\n",
    "receipts_winners_df.drop([ 'Country_Code', 'Indicator_Code', 'Indicator_Name'], axis=1, inplace=True)\n",
    "\n",
    "receipts_control_df = receipts_df.loc[receipts_df[\"Country_Name\"].isin(['Australia','Canada','China','France','India','Indonesia','Japan','Korea, Rep.','Mexico','Netherlands','Russian Federation','Saudi Arabia','Switzerland','Turkey','United Kingdom','United States'])]\n",
    "#arrivals_Control_df = arrivals_df.loc[(arrivals_df[\"Country Name\"] == \"UK\") | (arrivals_df[\"Country Name\"] == \"France\") | (arrivals_df[\"Country Name\"] == \"Brazil\") ]\n",
    "receipts_control_df = receipts_control_df.set_index(\"Country_Name\")\n",
    "\n",
    "receipts_control_df.drop([ 'Country_Code', 'Indicator_Code', 'Indicator_Name'], axis=1, inplace=True)\n",
    "# receipts_control_df.head()\n",
    "\n",
    "# # find the average of arrivals between control countries\n",
    "mean=receipts_control_df.mean(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
       "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
       "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
       "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
       "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
       "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
       "       '2014', '2015', '2016', '2017'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = receipts_control_df.mean()\n",
    "years = receipts_control_df.keys()\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dfd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of arrivals for each country and add marker X for the winning year\n",
    "plt.plot(years, receipts_winners_df.loc['Germany', years], \n",
    "         color=\"red\", label=\"Germany\")\n",
    "plt.scatter( (receipts_winners_df.loc['Germany'].keys()[52]),receipts_winners_df.loc['Germany', '2012'],  color=\"red\",marker='x',label='_nolegend_')\n",
    "\n",
    "\n",
    "plt.plot(years, receipts_winners_df.loc['Italy', years], \n",
    "         color=\"black\", label=\"Italy\")\n",
    "plt.scatter( (receipts_winners_df.loc['Italy'].keys()[46]),receipts_winners_df.loc['Italy', '2006'],  color=\"black\",marker='x',label='_nolegend_')\n",
    "\n",
    "\n",
    "plt.plot(years, receipts_winners_df.loc['Spain', years], \n",
    "         color=\"green\", label=\"Spain\")\n",
    "plt.scatter( (receipts_winners_df.loc['Italy'].keys()[50]),receipts_winners_df.loc['Spain', '2010'],  color=\"green\",marker='x',label='_nolegend_')\n",
    "\n",
    "plt.plot(years, receipts_winners_df.loc['Brazil', years], \n",
    "         color=\"blue\", label=\"Brazil\")\n",
    "plt.scatter( (receipts_winners_df.loc['Brazil'].keys()[42]),receipts_winners_df.loc['Brazil', '2002'],  color=\"blue\",marker='x',label='_nolegend_')\n",
    "\n",
    "## plot graph for average of control countries\n",
    "plt.plot(years, mean, color='grey',alpha=0.65, label=\"Control countries \")\n",
    "\n",
    "\n",
    "plt.xlim(2000, 2016)\n",
    "\n",
    "## create Legend and place outside of graph\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "plt.title(\"Tourist Receipts ($) 2000-2016\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Tourist Receipts ($)\")\n",
    "\n",
    "# Customize the grid\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "\n",
    "# Show the chart\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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}