Location Entropy.ipynb

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   "source": [
    "#Objective: Use location entropy to find popular country across year 2000-2017\n",
    "#location entropy:http://www-scf.usc.edu/~hto/resources/dple.pdf\n",
    "#data source: https://data.worldbank.org/indicator/ST.INT.ARVL?view=map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modules\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\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>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",
       "      <th>Unnamed: 62</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>International tourism, number of arrivals</td>\n",
       "      <td>ST.INT.ARVL</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>813000.0</td>\n",
       "      <td>824000.0</td>\n",
       "      <td>869000.0</td>\n",
       "      <td>904000.0</td>\n",
       "      <td>979000.0</td>\n",
       "      <td>1072000.0</td>\n",
       "      <td>1225000.0</td>\n",
       "      <td>1102000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>International tourism, number of arrivals</td>\n",
       "      <td>ST.INT.ARVL</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>International tourism, number of arrivals</td>\n",
       "      <td>ST.INT.ARVL</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>366000.0</td>\n",
       "      <td>425000.0</td>\n",
       "      <td>481000.0</td>\n",
       "      <td>528000.0</td>\n",
       "      <td>650000.0</td>\n",
       "      <td>595000.0</td>\n",
       "      <td>592000.0</td>\n",
       "      <td>397000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>International tourism, number of arrivals</td>\n",
       "      <td>ST.INT.ARVL</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>1711000.0</td>\n",
       "      <td>2191000.0</td>\n",
       "      <td>2469000.0</td>\n",
       "      <td>3156000.0</td>\n",
       "      <td>2857000.0</td>\n",
       "      <td>3341000.0</td>\n",
       "      <td>3784000.0</td>\n",
       "      <td>4070000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>International tourism, number of arrivals</td>\n",
       "      <td>ST.INT.ARVL</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>1830000.0</td>\n",
       "      <td>1808000.0</td>\n",
       "      <td>2242000.0</td>\n",
       "      <td>2238000.0</td>\n",
       "      <td>2328000.0</td>\n",
       "      <td>2363000.0</td>\n",
       "      <td>2663000.0</td>\n",
       "      <td>2831000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code                             Indicator Name  \\\n",
       "0        Aruba          ABW  International tourism, number of arrivals   \n",
       "1  Afghanistan          AFG  International tourism, number of arrivals   \n",
       "2       Angola          AGO  International tourism, number of arrivals   \n",
       "3      Albania          ALB  International tourism, number of arrivals   \n",
       "4      Andorra          AND  International tourism, number of arrivals   \n",
       "\n",
       "  Indicator Code  1960  1961  1962  1963  1964  1965     ...            2009  \\\n",
       "0    ST.INT.ARVL   NaN   NaN   NaN   NaN   NaN   NaN     ...        813000.0   \n",
       "1    ST.INT.ARVL   NaN   NaN   NaN   NaN   NaN   NaN     ...             NaN   \n",
       "2    ST.INT.ARVL   NaN   NaN   NaN   NaN   NaN   NaN     ...        366000.0   \n",
       "3    ST.INT.ARVL   NaN   NaN   NaN   NaN   NaN   NaN     ...       1711000.0   \n",
       "4    ST.INT.ARVL   NaN   NaN   NaN   NaN   NaN   NaN     ...       1830000.0   \n",
       "\n",
       "        2010       2011       2012       2013       2014       2015  \\\n",
       "0   824000.0   869000.0   904000.0   979000.0  1072000.0  1225000.0   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2   425000.0   481000.0   528000.0   650000.0   595000.0   592000.0   \n",
       "3  2191000.0  2469000.0  3156000.0  2857000.0  3341000.0  3784000.0   \n",
       "4  1808000.0  2242000.0  2238000.0  2328000.0  2363000.0  2663000.0   \n",
       "\n",
       "        2016  2017  Unnamed: 62  \n",
       "0  1102000.0   NaN          NaN  \n",
       "1        NaN   NaN          NaN  \n",
       "2   397000.0   NaN          NaN  \n",
       "3  4070000.0   NaN          NaN  \n",
       "4  2831000.0   NaN          NaN  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data\n",
    "df = pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling missing value with 0\n",
    "df_final = df[['Country Name'] + [str(i) for i in np.arange(2000,2018)]].copy()\n",
    "\n",
    "df_final.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def locationEntropy(country,series):\n",
    "    total = series.sum()\n",
    "    prob = np.array(series/total)\n",
    "    prob_nonzero = prob[np.where(prob!=0)]\n",
    "    entropy = -1*np.sum(np.multiply(prob_nonzero,np.log10(2*prob_nonzero)))\n",
    "    return (country,entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "for country in df_final['Country Name'].unique():\n",
    "    country_series = pd.Series([i for i in df_final[df_final['Country Name'] == country].values[0] if i!=country],\n",
    "                      index = [str(i) for i in np.arange(2000,2018)])\n",
    "    result.append(locationEntropy(country,country_series))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 popular country based on location entropy \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['France',\n",
       " 'New Caledonia',\n",
       " 'Bahamas, The',\n",
       " 'Barbados',\n",
       " 'Antigua and Barbuda',\n",
       " 'Belgium',\n",
       " 'Liechtenstein',\n",
       " 'Luxembourg',\n",
       " 'Bermuda',\n",
       " 'Austria']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Top 10 popular country based on location entropy \\n')\n",
    "[i[0] for i in sorted(result,key = lambda x:x[1],reverse = True) if str(i[1])!='nan'][:10]"
   ]
  }
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