for_sids_report.ipynb

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   "source": [
    "# %reset-f\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Pandas display options\n",
    "# pd.set_option('display.max_colwidth', 200)\n",
    "# pd.set_option('display.width', 200)\n",
    "# pd.set_option('display.precision', 10)\n",
    "# pd.set_option('display.max_rows', 500)\n",
    "\n",
    "#import matplotlib.pylab as plt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "font = {'family' : 'serif',\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 15}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "from fancy_plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Names to WB names\n",
    "any_to_wb = pd.read_csv(\"inputs/any_name_to_wb_name.csv\",index_col=\"any\",squeeze=True)\n",
    "\n",
    "#GAR names with SIDS spec\n",
    "gar_name_sids = pd.read_csv(\"inputs/gar_name_sids.csv\")\n",
    "\n",
    "#iso3 to wb country name table\n",
    "iso3_to_wb=pd.read_csv(\"inputs/iso3_to_wb_name.csv\").set_index(\"iso3\").squeeze()\n",
    "\n",
    "#iso2 to iso3 table\n",
    "iso2_iso3 = pd.read_csv(\"inputs/names_to_iso.csv\", usecols=[\"iso2\",\"iso3\"]).drop_duplicates().set_index(\"iso2\").squeeze() #the tables has more lines than countries to account for several ways of writing country names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gar_name_sids['wbcountry'] = gar_name_sids.reset_index().country.replace(any_to_wb)\n",
    "\n",
    "list_of_sids = gar_name_sids[gar_name_sids.isaSID==\"SIDS\"].dropna().reset_index().wbcountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GAR = pd.read_csv(\"inputs/GAR_capital.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "GAR[\"country\"] = GAR.country.replace(any_to_wb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gar_sids = GAR.set_index(\"country\").loc[list_of_sids,:].replace(0, np.nan).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ISO', 'EXPOSED VALUE', 'EARTHQUAKE', 'TROPICAL CYCLONES', 'TSUNAMI',\n",
       "       'FLOODS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gar_aal = pd.read_csv(\"inputs/GAR15 results feb 2016_AALmundo.csv\",index_col=\"Country\")\n",
    "gar_aal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gar_aal[\"total_aal\"] = gar_aal[['EARTHQUAKE', 'TROPICAL CYCLONES', 'TSUNAMI','FLOODS']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gar_asset_losses = gar_aal[\"total_aal\"]/gar_aal['EXPOSED VALUE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.79000000000002"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gar_aal[\"total_aal\"][\"Fiji\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gar_sids[\"asset_losses\"] = gar_asset_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gar_sids.dropna().to_csv(\"GAR_SIDS_for_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "report = pd.read_excel(\"C:\\\\Users\\\\WB451855\\\\OneDrive - WBG\\\\dmu\\\\transport and DRM\\\\SIDS report\\\\data\\\\for_sids_report.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "report[\"GAR_losses\"] = gar_asset_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "report.to_excel(\"C:\\\\Users\\\\WB451855\\\\OneDrive - WBG\\\\dmu\\\\transport and DRM\\\\SIDS report\\\\data\\\\for_sids_report2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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