Forest area by country.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest area by country\n",
    "This notebook prepares data for this visu : https://bl.ocks.org/rhuille/583877d1d5f82d186a87d6145055340f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import *\n",
    "from numpy import *\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code\n",
       "0  AFG\n",
       "1  AGO\n",
       "2  ALB\n",
       "3  ARE\n",
       "4  ARG"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo = {\"code\":[]}\n",
    "with open('countries.json') as data_file:    \n",
    "    for i in json.load(data_file)[\"features\"]:\n",
    "        geo[\"code\"]+=[i[\"id\"]]\n",
    "geo = DataFrame(geo)\n",
    "geo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year = \"2013\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>surface</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.0</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>470.0</td>\n",
       "      <td>AND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>652860.0</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1246700.0</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28750.0</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     surface code\n",
       "0      180.0  ABW\n",
       "1      470.0  AND\n",
       "2   652860.0  AFG\n",
       "3  1246700.0  AGO\n",
       "4    28750.0  ALB"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface = DataFrame.from_csv(\"API_AG.SRF.TOTL.K2_DS2_en_csv_v2.csv\", encoding=\"iso-8859-9\", header = 2)[[year, \"Country Code\"]]\n",
    "surface[year] = surface[year].apply(lambda x: round(x, 0) )\n",
    "surface.index = Series(range(surface.shape[0]))\n",
    "surface.columns = Index([\"surface\", \"code\"])\n",
    "surface.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>land</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.0</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>470.0</td>\n",
       "      <td>AND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>652860.0</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1246700.0</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27400.0</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        land code\n",
       "0      180.0  ABW\n",
       "1      470.0  AND\n",
       "2   652860.0  AFG\n",
       "3  1246700.0  AGO\n",
       "4    27400.0  ALB"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land =DataFrame.from_csv(\"API_AG.LND.TOTL.K2_DS2_en_csv_v2.csv\", encoding=\"iso-8859-9\", header = 2)[[year, \"Country Code\"]]\n",
    "land[year] = land[year].apply(lambda x: round(x, 2) )\n",
    "land.index = Series(range(land.shape[0]))\n",
    "land.columns = Index([\"land\", \"code\"])\n",
    "land.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agricultural area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agri</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.11</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.26</td>\n",
       "      <td>AND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.07</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.48</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.33</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    agri code\n",
       "0  11.11  ABW\n",
       "1  44.26  AND\n",
       "2  58.07  AFG\n",
       "3  47.48  AGO\n",
       "4  43.33  ALB"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agri = DataFrame.from_csv(\"API_AG.LND.AGRI.ZS_DS2_en_csv_v2.csv\", encoding=\"iso-8859-9\", header = 2)[[year, \"Country Code\"]]\n",
    "agri[year] = agri[year].apply(lambda x: round(x, 2) )\n",
    "agri.index = Series(range(agri.shape[0]))\n",
    "agri.columns = Index([\"agri\", \"code\"])\n",
    "agri.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forest area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>forest</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.33</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.04</td>\n",
       "      <td>AND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.07</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.61</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.23</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   forest code\n",
       "0    2.33  ABW\n",
       "1   34.04  AND\n",
       "2    2.07  AFG\n",
       "3   46.61  AGO\n",
       "4   28.23  ALB"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = DataFrame.from_csv(\"API_AG.LND.FRST.ZS_DS2_en_csv_v2.csv\", encoding=\"iso-8859-9\", header = 2)[[year, \"Country Code\"]]\n",
    "forest.index = Series(range(forest.shape[0]))\n",
    "forest[year] = forest[year].apply(lambda x: round(x, 2) )\n",
    "forest.columns = Index([\"forest\", \"code\"])\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>surface</th>\n",
       "      <th>agri</th>\n",
       "      <th>land</th>\n",
       "      <th>forest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>58.07</td>\n",
       "      <td>652860.0</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>1246700.0</td>\n",
       "      <td>47.48</td>\n",
       "      <td>1246700.0</td>\n",
       "      <td>46.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>28750.0</td>\n",
       "      <td>43.33</td>\n",
       "      <td>27400.0</td>\n",
       "      <td>28.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>83600.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>83600.0</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>2780400.0</td>\n",
       "      <td>54.52</td>\n",
       "      <td>2736690.0</td>\n",
       "      <td>10.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code    surface   agri       land  forest\n",
       "0  AFG   652860.0  58.07   652860.0    2.07\n",
       "1  AGO  1246700.0  47.48  1246700.0   46.61\n",
       "2  ALB    28750.0  43.33    27400.0   28.23\n",
       "3  ARE    83600.0   4.57    83600.0    3.83\n",
       "4  ARG  2780400.0  54.52  2736690.0   10.12"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataForest = merge(geo,merge(merge(surface, agri, how=\"inner\"), merge(land, forest, how=\"inner\"), how=\"inner\"), how=\"inner\")\n",
    "dataForest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataForest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAFCCAYAAACwzqo0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81dc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataForest.plot(kind=\"scatter\", x = \"forest\", y =\"agri\", figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAFNCAYAAABWuogoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHZZJREFUeJzt3XuQ3WWd5/H3N+TWclO0QeXSjVw26Bi5SOTm0DFmBcaC\ntaAEBpRlIhVuNay6tboMO2TWdUbH0ZqoxU1bRywIUWe5jOKKIzQIK5AKZIMSJDB0uIh4arhIMJAg\n3/3jnA6dpO/pPr/zdL9fVV19Ls8553v45Rc+eZ7n9zyRmUiSJKkM06ouQJIkSSNneJMkSSqI4U2S\nJKkghjdJkqSCGN4kSZIKYniTJEkqyKQLbxHRHRHPRMTqEbT9SkTcHxH3RcSvI+LZZtQoSZI0VjHZ\n1nmLiGOA9cDVmTl3FK+7EDg4Mz8xYcVJkiRtp0nX85aZdwLP9X8sIt4RET+OiBURcXtEHDjAS08H\nljWlSEmSpDGaXnUBTXIVsDgzH42IecDlwIK+JyNiH6ATuLWa8iRJkkZm0oe3iNgROAr4fkRE4+EZ\nWzU7DfhBTrYxZEmSNOlM+vBGfWj4ucw8dIg2pwHnN6keSZKkMat0zltE7BURt0bEryLigYj4y0Ha\nfTUi1kbEqog4eCRv3fghM18EHouIU/q939x+t+cAb8zMu7fv20iSJE28qi9YeBX4VGa+CzgSuKAR\npjaLiOOB/TLzAGAxcMVQbxgR1wL/FzgwIh6PiLOBM4BFjfD3S+DEfi85Fbhu3L6RJEnSBGqppUIi\n4gbga5n5s36PXQHclpnLG/fXAF2Z+UxFZUqSJFWm6p63zSKiEzgYuGerp/YEnuh3/6nGY5IkSVNO\nS4S3iNgJ+AFwUWaur7oeSZKkVlX51aYRMZ16cPtuZt44QJOngL373d+r8dhA79U6Y8CSJEnDyMwY\nvtWWKg9vwLeABzNz6SDP3wRcACyPiCOA54ea79ZKc/g0ckuWLGHJkiVVl6Ex8viVy2NXNo9f2V5f\nfnZ0Kg1vEXE09StBH4iI+4EELgY6gMzMqzLz5og4ISIeAV4Czq6uYkmSpGpVGt4y8y5ghxG0u7AJ\n5UiSJLW8lrhgQerq6qq6BG0Hj1+5PHZl8/hNTS21ztv2igi3J5UkSUWIiDFdsGDPmyRJUkEMb5Ik\nSQUxvEmSJBXE8CZJklQQw5skSVJBDG+SJEkFMbxJkiQVxPAmSZJUEMObJElSQQxvkiRJBTG8SZIk\nFcTwJkmSVBDDmyRJUkEMb5IkSQUxvEmSJBXE8CZJklQQw5skSVJBDG+SJEkFMbxJkiQVxPAmSZJU\nEMObJElSQQxvkiRJBTG8SZIkFcTwJkmSVBDDmyRJUkEMb5IkSQUxvEmSJBXE8CZJklQQw5skSVJB\nDG+SJEkFMbxJkiQVxPAmSZKaqlarsWLFCmq1WtWlFMnwJkmSmmbZsuV0dMxh4cJz6eiYw7Jly6su\nqTiRmVXXMG4iIifT95EkaTKp1Wp0dMxhw4bbgLnAatra5rNu3UO0t7dXXV7TRQSZGaN9nT1vkiSp\nKXp7e5k5s5N6cAOYy4wZHfT29lZXVIEMb5IkqSk6OzvZuLEXWN14ZDWbNq2js7OzuqIKZHiTJElN\n0d7eTnf3ZbS1zWeXXQ6lrW0+3d2XTckh0+3hnDdJktRUtVqN3t5eOjs7p3RwG+ucN8ObJElSBbxg\nQZIkaQowvEmSJBXE8CZJklQQw5skSVJBDG+SJEkFMbxJkiQVpPLwFhHdEfFMRKwe5PljI+L5iLiv\n8XNJs2uUJElqFdOrLgD4NvA14Ooh2tyRmSc2qR5JkqSWVXnPW2beCTw3TLNRL2AnSZI0GVUe3kbo\nyIhYFRE/ioh3Vl2MJElSVVph2HQ4K4F9MvMPEXE8cANwYMU1SZIkVaLlw1tmru93+8cRcVlE7JaZ\nzw7UfsmSJZtvd3V10dXVNeE1SpIkDaenp4eenp7tfp+W2Jg+IjqBf8nMdw/w3B6Z+Uzj9jzge5nZ\nOcj7uDG9JEkqwlg3pq+85y0irgW6gDdHxOPApcBMIDPzKuCUiDgP2ARsAE6tqlZJkqSqtUTP23ix\n502SJJVirD1vpVxtKkmSJAxvkiRJRTG8SZIkFcTwJkmSVBDDmyRJUkEMb5IkSQUxvEmSJBXE8CZJ\nklQQw5skSVJBDG+SJEkFMbxJkiQVxPAmSZJUEMObJElSQQxvkiRJBTG8SZIkFcTwJkmSVBDDmyRJ\nUkEMb5IkSQUxvEmSJBXE8CZJklQQw5skSVJBDG+SJEkFMbxJkiQVxPAmSZJUEMObJElSQQxvkiRJ\nBTG8SZIkFcTwJkmSVBDDmyRJUkEMb5IkSQUxvEmSJBXE8CZJklQQw5skSVJBDG+SJEkFMbxJkiQV\nxPAmSZJUEMObJElSQQxvkiRJBTG8SZIkFcTwJkmSVBDDmyRJUkEMb5IkSQUxvEmSJBXE8CZJklQQ\nw5skSVJBDG+SJEkFMbxJkiQVpPLwFhHdEfFMRKweos1XI2JtRKyKiIObWZ8kSVIrqTy8Ad8GPjTY\nkxFxPLBfZh4ALAauaFZhkiRJraby8JaZdwLPDdHkJODqRtt7gF0jYo9m1CZJktRqKg9vI7An8ES/\n+081HpMkSZpyplddwHhbsmTJ5ttdXV10dXVVVoskSVKfnp4eenp6tvt9IjO3v5rtLSKiA/iXzJw7\nwHNXALdl5vLG/YeAYzPzmQHaZit8H0mSpOFEBJkZo31dqwybRuNnIDcBHweIiCOA5wcKbpIkSVNB\n5cOmEXEt0AW8OSIeBy4FZgKZmVdl5s0RcUJEPAK8BJxdXbWSJEnVaolh0/HisKkk1dVqNXp7e+ns\n7KS9vb3qciQNoPRhU0nSOFm2bDkdHXNYuPBcOjrmsGzZ8qpLkjSO7HmTpEmkVqvR0TGHDRtuA+YC\nq2lrm8+6dQ/ZAye1GHveJEn09vYyc2Yn9eAGMJcZMzro7e0F6uFuxYoV1Gq1iiqUtL0Mb5I0iXR2\ndrJxYy/Qt130ajZtWkdnZ+c2w6lXXvkNg5xUIIdNJWmSWbZsOYsWnc+MGR1s2rSO7u7L+OAHP7DN\ncCocwc4778+rrz5Fd/dlnH76qRVXLk0tYx02NbxJ0iS09dWmK1asYOHCc3nhhZX9Wr0H+CYwy3lx\nUgXGGt4qX+dNkjT+2tvbtwhiWw6n9vW8PQl0Au2b58UZ3qTW55w3SZoC2tvb6e6+jLa2+ey88yHA\nEcBngHb6z4uT1PocNpWkKaRvOPW++1bxyU9+dot5cc55k5rLOW8Y3iRpNNyFQaqW4Q3DmyRJKoeL\n9EpSQVwsV9JYGd4kqcnce1TS9nDYVJKayL1HJfVx2FSSCjDc3qOSNBzDmyQ10VB7j0rSSAy5w0JE\nPAAMOg6ZmXMHe06StK2+xXIXLZq/xRprDplKGqkh57xFREfj5gWN399t/D4DIDM/O3GljZ5z3iSV\nwjXWJE3oOm8RcX9mHrLVY/dl5qGj/cCJZHiT1AwGL0njYaIvWIiIOLrfnaNG8VpJmjRc5kNS1Uba\n83YY8C1gVyCA54C/yMz7Jra80bHnTdJEcpkPSeNprD1vQ16w0CczVwLviYhdG/dfGO0HSVLp+pb5\n2LBh22U+DG+SmmVE4S0iZgEnA53A9Ih6SMzM/zlhlUlSi9lymY96z5vLfEhqtpHOW7sROAl4FXip\n348kTRl9y3y0tc1nl10Opa1tvst8SGq6kc55+2Vm/kkT6tkuznmT1AxebSppPEz0UiFXAV/LzAfG\nUlyzGN4kSVIpJjq8PQjsDzwGvEL9itNstR0WDG+SJKkUE3q1KXD8aN9YkiRJ42+kS4WsA4iI3YHZ\nE1qRJEmSBjWiq00j4sSIWEt92PR2oBf48QTWJUmSpAGMdKmQzwFHAA9n5r7AAuDuCatKkiRJAxpp\neNuUmf8OTIuIaZl5G/DeCaxLkiRJAxjpBQvPR8ROwB3ANRHxO1ykV5IkqelGulTIjsDL1JcIOYP6\nBvXXNHrjWoZLhUiSpFJM6DpvpTC8SZKkUkzIOm8R8SIwUBrqW6R3l9F+oCRJksZuyPCWmTs3qxBJ\nkiQNb6RXm0qSJKkFGN4kSZIKYniTJEkqiOFNkiSpIIY3SZKkghjeJEmSCmJ4kyRJKojhTZIkqSCG\nN0mSpIIY3iRJkgpSeXiLiOMi4qGIeDgiPjPA88dGxPMRcV/j55Iq6pQkSWoFQ+5tOtEiYhrwdWAB\n8BtgRUTcmJkPbdX0jsw8sekFSpIktZiqe97mAWszc11mbgKuA04aoF00tyxJkqTWVHV42xN4ot/9\nJxuPbe3IiFgVET+KiHc2pzRJkqTWU+mw6QitBPbJzD9ExPHADcCBgzVesmTJ5ttdXV10dXVNdH2S\nJEnD6unpoaenZ7vfJzJz+6sZ64dHHAEsyczjGvc/C2RmfnGI1zwGHJaZzw7wXFb5fSRJkkYqIsjM\nUU8Nq3rYdAWwf0R0RMRM4DTgpv4NImKPfrfnUQ+c2wQ3SZKkqaDSYdPM/GNEXAjcQj1IdmfmmohY\nXH86rwJOiYjzgE3ABuDU6iqWJEmqVqXDpuPNYVNJklSKUodNJUmSNAqGN0mSpIIY3iRJkgpieJMk\nSSqI4U2SJKkghjdJkqSCGN4kSZIKYniTJEkqiOFNkiSpIIY3SZKkghjeJEmSCmJ4kyRJKojhTZIk\nqSCGN0mSpIIY3iRJkgpieJMKUavVWLFiBbVarepSJEkVMrxJBVi2bDkdHXNYuPBcOjrmsGzZ8qpL\nkiRVJDKz6hrGTUTkZPo+mtxqtRq9vb10dnbS3t4+ZLuOjjls2HAbMBdYTVvbfNate2jI10mSWltE\nkJkx2tfZ8yZV4Morv8Hee+/PggWLhu1J6+3tZebMTurBDWAuM2Z00Nvb24RKJUmtxvAmNdmVV36D\nc8+9iFde2ZcXX3yKDRs+w6JF5w86l62zs5ONG3uB1Y1HVrNp0zo6OzubVLEkqZUY3qQmqtVqXHTR\nfwPuBlYBtwFfZIcd3j5oT1p7ezvd3ZfR1jafXXY5lLa2+XR3X+aQqSRNUc55k0ZopHPUhmq7YsUK\nFixYzIsv3tev9XuYNauXJ554ZNi5byP9fElS63POmzSBRnO151BtOzs7efXVdfQfAoW1LF36D8MG\nsvb2dg4//HCDmyRNcfa8ScMYzdWeI2m7bNlyFi06n+nT92Hjxl6WLv17Fi8+p9lfS5JUsbH2vE2f\niGKkyaTvas8NG7a92rO9vX2L4cz6vLU96X9lKLx9c1uA008/lQ9+8AMOgUqSxsRhU2kYQ13tufUQ\n6e23/5wNGx7Zou2GDY+y0047bfGeDoFKksbKYVNpBPqGOmfM6GDTpnVcfPGnOfnkj3DYYcdsMUQ6\na9axROzCyy+vBzqAdcyevQt33PE9Dj/88Gq/hCSppYx12NTwJo1QrVbjyiu/wec//yVmzXoHL7/8\nCNOmdbBhw+rNbXbe+RA2bvw3XnnlRmBH4CXa2k52NwRJ0ja82lSaAFtvBv+3f/tlXn75dl54YSWv\nvHLjNkOkr776OEuX/gNtbSezyy6LaWs72TXZJEnjyp43aRB9Q6UzZ9bnvH34wwv5/vdXAms3t5k9\ne18yn2fWrH3ZtGkd3d2Xcfrpp7ommyRpWA6bYnjT+BloyQ94HzAbuJ3+y4CsXHkn69evN6hJkkbF\npUKkcdDXY/bcc8+x7ZIfewFnA/OpX4zway6++K846KCDqilWkjQl2fMmNfQfJn3llcd4+eWXgBW8\n3vN2BHAtcDTwU2bPvoDHH3/Y3jZJ0pg4bIrhTWM38DDpkcBMYF9gHTNm7Mi0aS8wa9Z+W8xvkyRp\nLBw2lUagVqtx4403ctddd/H2t7+dM888k4MOOmjAXRTgbcB/BQ4DXmL69JNZufJu57dJkiplz5um\njGXLlnPmmYt47bW3Ar8B3gg8z4UXnsNf//Ul2/S8zZz5p0ybFsycua89bZKkceewKYY3bavvAoSN\nGzcyf/4JbNr0c14fFp0P/DNwAg8+uJJVq1ZvsYtCd/dl7kEqSZowDptKW+m7AOG1197GK688Qv1q\n0f7Doh3Ud0HYi3vvvZezzjprwLBmaJMktRLDmyalH/7wh5x55sd57bWrgLOAHuAE6j1ufT1v64CX\ngCeZN28eUA9qhjVJUiszvGnSuOuuu7j++uu5+eZbWLPmEeo9a+cBy4GbgT2Aoxq/nwZ2BU7gwgvP\nca02SVIxDG+aFI466k/5xS/uBd4CPAvczZbrs30H+D31dduOAzZx0UWnsnjxYoObJKkoXrCgYq1Z\ns4brr7+ea65ZxoMPPgocCPQCb6B+NWmfA4AnqAe4g4AjuOKKpSxefE6zS5YkaTOvNsXwNhX0rdP2\nhS98iUcf7QV2A15g2562m4GufvdfZebM/Yl4mqVL/97gJkmqnOENw9tkVqvV+OQnP8U111xDfXP4\nPYGngF2AnYG1/VrPod4DtzfwFO973yF8+9vfdHFdSVJLcakQTUq1Wo2LL76Eb37zm9T/uM5my162\nY4FX2PIq0seBTXzkI+/h05/+J44++uhqipckaQIY3tSS7rrrLs4//0JWr14FBPXQth/wb8Aa6kFt\nLvV9R0+hvg/pW4GnmT//KJYvX2YPmyRpUppWdQERcVxEPBQRD0fEZwZp89WIWBsRqyLi4GbXqOZZ\ns2YNe+65N8cc835Wr+5ly962B4BfUF/+o8bra7UdBbzG+9+/Fw8+uJJbb/1Xg5skadKqNLxFxDTg\n68CHgHcBp0fEnK3aHA/sl5kHAIuBK5peqCbc5Zdfzg47zOSd73wPv/nNv1PvZXsZ2IH6VaT9d0bY\nDTiE+oUIM4j4M770pc9zxx23u+yHJGnSq3rYdB6wNjPXAUTEdcBJwEP92pwEXA2QmfdExK4RsUdm\nPtP0ajUhImYCf2zcm8Xrc9quBS6hvsxH/zltvwE2svvu7Xz+85/jpJNOsqdNkjRlVB3e9qT+f+Y+\nT1IPdEO1earxmOFtEqh3vs4GOqkf/tm83su2EDgHuJT6JvJ7AWvZccfp/OQnP/VCBEnSlFT5nDdN\nXQsWLOD1+WwPN36/DNzQaPE09T+if0d9SZCH+PCHF7J+/e8NbpKkKavqnrengH363d+r8djWbfYe\nps1mS5Ys2Xy7q6uLrq6u7a1RE+TOO++kvv9o//lsewIfbTzet47bCxx33EF85Ss3O6dNklSsnp4e\nenp6tvt9Kl2kNyJ2AH4NLKDezXIvcHpmrunX5gTggsz8s4g4AvjHzDxikPdzkd6CLFiwgFtv/QXb\n7o6wCXgVgC9/+ct87GMfc06bJGnSKXaHhYg4DlhKfXysOzO/EBGLgczMqxptvk59N/GXgLMz875B\n3svwVpjX57z17ZiwEfgjp556Ktddd12ltUmSNJGKDW/jyfBWpnoP3K0AvOUtb6FWq1VckSRJE8/w\nhuFNkiSVY6zhzatNJUmSCmJ4kyRJKojhTZIkqSCGN0mSpIIY3iRJkgpieJMkSSqI4U2SJKkghjdJ\nkqSCGN4kSZIKYniTJEkqiOFNkiSpIIY3SZKkghjeJEmSCmJ4kyRJKojhTZIkqSCGN0mSpIIY3iRJ\nkgpieJMkSSqI4U2SJKkghjdJkqSCGN4kSZIKYniTJEkqiOFNkiSpIIY3SZKkghjeJEmSCmJ4kyRJ\nKojhTZIkqSCGN0mSpIIY3iRJkgpieJMkSSqI4U2SJKkghjdJkqSCGN4kSZIKYniTJEkqiOFNkiSp\nIIY3SZKkghjeJEmSCmJ4kyRJKojhTZIkqSCGN0mSpIIY3iRJkgpieJMkSSqI4U2SJKkghjdJkqSC\nGN4kSZIKYniTJEkqyPSqPjgi3gQsBzqAXuCjmfnCAO16gReA14BNmTmviWVKkiS1lCp73j4L/Gtm\n/gfgVuC/D9LuNaArMw8xuE1ePT09VZeg7eDxK5fHrmwev6mpyvB2EvCdxu3vAP9pkHaBw7uTnn8B\nlc3jVy6PXdk8flNTlaFo98x8BiAzfwvsPki7BH4aESsi4pymVSdJktSCJnTOW0T8FNij/0PUw9gl\nAzTPQd7m6Mx8OiLaqYe4NZl55ziXKkmSVITIHCwzTfAHR6yhPpftmYh4K3BbZh40zGsuBV7MzK8M\n8nw1X0aSJGkMMjNG+5rKrjYFbgL+M/BF4Czgxq0bRMQbgGmZuT4idgT+I/A3g73hWP4DSJIklaTK\nnrfdgO8BewPrqC8V8nxEvA34RmZ+OCL2Ba6nPqQ6HbgmM79QScGSJEktoLLwJkmSpNErdgmOiHhT\nRNwSEb+OiJ9ExK6DtOuNiP8XEfdHxL3NrlNbiojjIuKhiHg4Ij4zSJuvRsTaiFgVEQc3u0YNbLhj\nFxHHRsTzEXFf42egC5NUgYjojohnImL1EG0871rUcMfPc691RcReEXFrRPwqIh6IiL8cpN2ozr9i\nwxsu8luciJgGfB34EPAu4PSImLNVm+OB/TLzAGAxcEXTC9U2RnLsGu7IzEMbP/+rqUVqKN+mfuwG\n5HnX8oY8fg2ee63pVeBTmfku4EjggvH4/17J4c1FfsszD1ibmesycxNwHfXj2N9JwNUAmXkPsGtE\n7IGqNpJjB/XzTS2msbzSc0M08bxrYSM4fuC515Iy87eZuapxez2wBthzq2ajPv9KDjUu8luePYEn\n+t1/km3/EG/d5qkB2qj5RnLsAI5sdPv/KCLe2ZzSNA4878rnudfiIqITOBi4Z6unRn3+VblUyLBc\n5Fcqykpgn8z8Q2MY4AbgwIprkqYCz70WFxE7AT8ALmr0wG2Xlg5vmblwsOcakzf36LfI7+8GeY+n\nG79rEXE99eEfw1s1ngL26Xd/r8ZjW7fZe5g2ar5hj13/v5Ay88cRcVlE7JaZzzapRo2d513BPPda\nW0RMpx7cvpuZ26xpyxjOv5KHTfsW+YUhFvltpF36LfL7y2YVqG2sAPaPiI6ImAmcRv049ncT8HGA\niDgCeL5veFyVGvbY9Z+jERHzqC9F5P88Wkcw+Lwoz7vWN+jx89xred8CHszMpYM8P+rzr6V73obx\nReB7EfEXNBb5Bei/yC/1IdfrG9tm9S3ye0tVBU91mfnHiLgQuIX6Pxy6M3NNRCyuP51XZebNEXFC\nRDwCvAScXWXNqhvJsQNOiYjzgE3ABuDU6ipWfxFxLdAFvDkiHgcuBWbieVeE4Y4fnnstKyKOBs4A\nHoiI+6lP8boY6GA7zj8X6ZUkSSpIycOmkiRJU47hTZIkqSCGN0mSpIIY3iRJkgpieJMkSRqFiOhu\nrDe7egRtvxIR90fEfRHx64jY7mVcvNpUkiRpFCLiGGA9cHVmzh3F6y4EDs7MT2zP59vzJkmDiIhl\njf0iL6q6Fkmto7HN5nP9H4uId0TEjxt7qd8eEQNtUXY6sGx7P7/kRXolaUJExA5AO/DezDyg6nok\nFeEqYHFmPtrY6eJyYEHfkxGxD9AJ3Lq9H2TPm6RJq7FF3g8b801WR8RHI+KxiNit8fxhEXFb4/al\nEXF1RPwcuBr4CbBnY57K0RHxiYi4t/Fe34+I2Y3X7R4R/7vRQ3d/Y3sbIuKMiLin8frLI2Kwrakk\nFa6xBedRwPcbOylcSX2Xp/5OA36Q4zBfzfAmaTI7DngqMw9pzEv5P9S3p+mv//2DgAWZeQZwIvBI\nZh6amXcB/5yZ8zLzEOAhYFHjNV8FejLzYOBQ4FcRMYf6FkVHZeahwGvUt8iRNDlNA55r/H1xSOPn\nT7ZqcxrjMGTa92GSNFk9ACyMiL+LiGMy8/cMvjk7wE2ZuXGQ594dEXc0ri77c+Bdjcc/QH14hKx7\nkfpQyaHAisa/wj8AvGMcvo+k1hGNHxrn/WMRccrmJyPm9rs9B3hjZt49Hh/snDdJk1Zmro2IQ4ET\ngM9FxK3UN+/u+4fr7K1e8tIQb/dPwImZ+cuIOAs4tu9jBmgbwHcy86/GXLyklhUR1wJdwJsj4nHg\nUuq961dExCXU89V1QN9SIqc27o8Lw5ukSSsi3gY8m5nXRsQLwCeAXuC91IdQTx7uLfrd3gn4bUTM\noP6X9JONx38GnA8sjYhpjXY/A26IiH/MzFpEvAnYOTMfH6evJqlCmfnngzx1/CDt/2Y8P9/wJmky\nezfwpYh4DdgInAe8AehuhLmeYV7fv1ftfwD3Ar8D7gF2bjz+X4CrImIR8CpwXmbe0/jX9y2NQLcR\nuAAwvEnabi7SK0mSVBAvWJAkSSqI4U2SJKkghjdJkqSCGN4kSZIKYniTJEkqiOFNkiSpIIY3SZKk\nghjeJEmSCvL/AXxUZ53KeohTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8294400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataForest.plot(kind=\"scatter\", x = \"surface\", y =\"land\", figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataForest.to_csv(\"dataForest.csv\", index = False)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}