Biodiversity for food and agriculture.ipynb

{
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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"><li><span><a href=\"#Biodiversity-for-food-and-agriculture---Dec-2017\" data-toc-modified-id=\"Biodiversity-for-food-and-agriculture---Dec-2017-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Biodiversity for food and agriculture - Dec 2017</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pre-analysis-discussions\" data-toc-modified-id=\"Pre-analysis-discussions-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Pre-analysis discussions</a></span></li><li><span><a href=\"#Data,-pre-processing-and-scripts\" data-toc-modified-id=\"Data,-pre-processing-and-scripts-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Data, pre-processing and scripts</a></span><ul class=\"toc-item\"><li><span><a href=\"#Red-List\" data-toc-modified-id=\"Red-List-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Red List</a></span></li><li><span><a href=\"#Hexagon-Grid\" data-toc-modified-id=\"Hexagon-Grid-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Hexagon Grid</a></span></li><li><span><a href=\"#WDPA\" data-toc-modified-id=\"WDPA-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>WDPA</a></span></li><li><span><a href=\"#Script\" data-toc-modified-id=\"Script-1.2.4\"><span class=\"toc-item-num\">1.2.4&nbsp;&nbsp;</span>Script</a></span></li></ul></li><li><span><a href=\"#Proposed-methodology-thinking\" data-toc-modified-id=\"Proposed-methodology-thinking-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Proposed methodology thinking</a></span></li><li><span><a href=\"#Understanding-the-data\" data-toc-modified-id=\"Understanding-the-data-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Understanding the data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Spatial-data-for-species-for-food-and-agriculture\" data-toc-modified-id=\"Spatial-data-for-species-for-food-and-agriculture-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Spatial data for species for food and agriculture</a></span></li><li><span><a href=\"#Restricted-range-species\" data-toc-modified-id=\"Restricted-range-species-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Restricted range species</a></span></li></ul></li><li><span><a href=\"#Datasets\" data-toc-modified-id=\"Datasets-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Datasets</a></span></li><li><span><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Big-data-challenge\" data-toc-modified-id=\"Big-data-challenge-1.6.1\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>Big data challenge</a></span></li><li><span><a href=\"#Load-result-and-lookup-table\" data-toc-modified-id=\"Load-result-and-lookup-table-1.6.2\"><span class=\"toc-item-num\">1.6.2&nbsp;&nbsp;</span>Load result and lookup table</a></span></li><li><span><a href=\"#Filtering\" data-toc-modified-id=\"Filtering-1.6.3\"><span class=\"toc-item-num\">1.6.3&nbsp;&nbsp;</span>Filtering</a></span></li><li><span><a href=\"#Filtering-by-comprehensive-species-list\" data-toc-modified-id=\"Filtering-by-comprehensive-species-list-1.6.4\"><span class=\"toc-item-num\">1.6.4&nbsp;&nbsp;</span>Filtering by comprehensive species list</a></span></li><li><span><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-1.6.5\"><span class=\"toc-item-num\">1.6.5&nbsp;&nbsp;</span>Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Gap-species\" data-toc-modified-id=\"Gap-species-1.6.5.1\"><span class=\"toc-item-num\">1.6.5.1&nbsp;&nbsp;</span>Gap species</a></span></li><li><span><a href=\"#Top-protected-areas\" data-toc-modified-id=\"Top-protected-areas-1.6.5.2\"><span class=\"toc-item-num\">1.6.5.2&nbsp;&nbsp;</span>Top protected areas</a></span></li><li><span><a href=\"#Country-level-statistics\" data-toc-modified-id=\"Country-level-statistics-1.6.5.3\"><span class=\"toc-item-num\">1.6.5.3&nbsp;&nbsp;</span>Country level statistics</a></span></li></ul></li></ul></li><li><span><a href=\"#TEST---DO-NOT-USE\" data-toc-modified-id=\"TEST---DO-NOT-USE-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>TEST - DO NOT USE</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Check-failed-species\" data-toc-modified-id=\"Check-failed-species-1.7.0.1\"><span class=\"toc-item-num\">1.7.0.1&nbsp;&nbsp;</span>Check failed species</a></span></li></ul></li></ul></li></ul></li></ul></div>"
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    "# Biodiversity for food and agriculture - Dec 2017\n",
    "\n",
    "The notebook assesses the contribution of biodiversity for food and agriculture through an analysis of overlay between IUCN's Red List of Threatened Species and the World Database on Protected Areas"
   ]
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    "## Pre-analysis discussions\n",
    "- all species for food and agriculture to be included, regardless of their comprehensiveness\n",
    "\n",
    "## Data, pre-processing and scripts\n",
    "\n",
    "### Red List\n",
    "- Presence: 1 (including 2 for old range distributions); Origin: 1,2; Seasonality: all\n",
    "- create index on `id_no`\n",
    "- decisions to be deferred: RedList categories (threatened etc)\n",
    "\n",
    "### Hexagon Grid\n",
    "- an equal area global hex grid using the binary in [Discrete Global Grids for R](https://github.com/r-barnes/dggridR/)\n",
    "- convert string ids to int ids\n",
    "- create index on `id`\n",
    "- additionally the grid has the following characteristics\n",
    ">dggs_type ISEA3H \n",
    ">\n",
    ">verbosity 1 \n",
    ">\n",
    ">dggs_res_spec 12\n",
    ">\n",
    ">densification 5\n",
    ">\n",
    ">max_cells_per_output_file 1000000\n",
    ">\n",
    ">cell_output_type SHAPEFILE\n",
    ">\n",
    ">cell_output_file_name ../output/hex_globe10k\n",
    ">\n",
    ">point_output_type SHAPEFILE\n",
    ">\n",
    ">point_output_file_name ../output/hex_globe_centre10k\n",
    "\n",
    "**Filtering of the Hexagon Grid**\n",
    "\n",
    "It is important to note that during the creation of the Hexagon Grid, cells around the 180/-180 longitude have wrapped around the globe due to a bug. These cells need to be excluded from the calculation. Fortunately, there is an easy way to identify - they all have a negative `shape_area` value in the attribute tables. These can be identified in the spatial data.\n",
    "\n",
    "Out of 5,314,412 records, 2,427 are invalid and thus should be removed from the calculation.\n",
    "\n",
    "![hex_grid](./hex.png)\n",
    "\n",
    "\n",
    "### WDPA\n",
    "- Nov 2017 release public version\n",
    "- point data in the WDPA are **not** considered for reasons: a) the points do not represent the extent of PAs, leading to unquantifiable error; b) result less than 2% difference globally, with area potentially affected in Russia, Ukrain, Belarus, China and India ; c) siginifcant time required to process\n",
    "- apart from the point, the pre-processing of the WDPA uses the same methodology in calculating global statistics and it can be found on the [protectedplanet](https://www.protectedplanet.net/c/calculating-protected-area-coverage).\n",
    "\n",
    "### Script\n",
    "A script is developed to distribute the workload across multiple cores. It requires the `arcpy` site package to programmatically apply `SelectByLocation`. After each iteration, it appends the output to a compressed csv file. The script is shared on [github](https://github.com/Yichuans/geoprocessing/blob/master/species_richness/FAO_BFA.py)\n"
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    "## Proposed methodology thinking\n",
    "\n",
    "In short the methodology\n",
    "- use a 10km by 10km global grid (hex or regular)\n",
    "- use the above grid to bin each and every species \n",
    "- use the above grid to bin each and every protected area\n",
    "\n",
    "result will enable further interogation of species and protected area at 100sqkm resolution, without undertaking any additional spatial analysis\n",
    "\n",
    "The above result will produce the following metrics:\n",
    "- Species range (at 100 sqkm reso)\n",
    "- Range of each species protected. Per species level, area (number of cell x cellsize) and how much of that *is protected* (number of cell shared by species + pa/total number of cells by species)\n",
    "- Combining the two, we will be able to produce the *Rodrigues 2004* graph, i.e. species with different targets\n",
    "- Heatmap of number of species protected?"
   ]
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   "source": [
    "\n",
    "**THINKING: **\n",
    "\n",
    "**Can we justify the methodology (grid approach 10km by 10km) by saying the species has a *protected status* if it overlaps with a protected areas or within 10km of a protected area? Also considering the inaccruacy of the RedList EOO data and the inaccruacy of global protected areas network. **\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sis = pd.read_csv('sis_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa = pd.read_csv('Food_FAO_2017_2.csv', delimiter=';')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>compiler</th>\n",
       "      <th>year</th>\n",
       "      <th>citation</th>\n",
       "      <th>source</th>\n",
       "      <th>...</th>\n",
       "      <th>tax_comm</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>shape_length</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10143</td>\n",
       "      <td>190868.0</td>\n",
       "      <td>Nihonogomphus thomassoni</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kate Saunders</td>\n",
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       "      <td>Red List assessment</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>INSECTA</td>\n",
       "      <td>ODONATA</td>\n",
       "      <td>GOMPHIDAE</td>\n",
       "      <td>Nihonogomphus</td>\n",
       "      <td>LC</td>\n",
       "      <td>99.744070</td>\n",
       "      <td>27.275351</td>\n",
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       "      <th>1</th>\n",
       "      <td>10144</td>\n",
       "      <td>190868.0</td>\n",
       "      <td>Nihonogomphus thomassoni</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>FBU</td>\n",
       "      <td>2011</td>\n",
       "      <td>IUCN (International Union for Conservation of ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Nihonogomphus</td>\n",
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       "      <td>10145</td>\n",
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       "      <td>Nihonogomphus thomassoni</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DO Manh Cuong</td>\n",
       "      <td>2010</td>\n",
       "      <td>Asia freshwater biodiversity assessments, IUCN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>INSECTA</td>\n",
       "      <td>ODONATA</td>\n",
       "      <td>GOMPHIDAE</td>\n",
       "      <td>Nihonogomphus</td>\n",
       "      <td>LC</td>\n",
       "      <td>4.357552</td>\n",
       "      <td>0.301940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10146</td>\n",
       "      <td>190868.0</td>\n",
       "      <td>Nihonogomphus thomassoni</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DO Manh Cuong</td>\n",
       "      <td>2010</td>\n",
       "      <td>Asia freshwater biodiversity assessments, IUCN</td>\n",
       "      <td>Red List assessment</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ANIMALIA</td>\n",
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       "      <td>INSECTA</td>\n",
       "      <td>ODONATA</td>\n",
       "      <td>GOMPHIDAE</td>\n",
       "      <td>Nihonogomphus</td>\n",
       "      <td>LC</td>\n",
       "      <td>6.100119</td>\n",
       "      <td>0.527099</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10147</td>\n",
       "      <td>63940.0</td>\n",
       "      <td>Tantilla coronadoi</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NatureServe and IUCN</td>\n",
       "      <td>2007</td>\n",
       "      <td>NatureServe and IUCN (International Union for ...</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ANIMALIA</td>\n",
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       "      <td>COLUBRIDAE</td>\n",
       "      <td>Tantilla</td>\n",
       "      <td>LC</td>\n",
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       "   objectid     id_no                  binomial  presence  origin  seasonal  \\\n",
       "0     10143  190868.0  Nihonogomphus thomassoni         2       1         1   \n",
       "1     10144  190868.0  Nihonogomphus thomassoni         1       1         1   \n",
       "2     10145  190868.0  Nihonogomphus thomassoni         1       1         1   \n",
       "3     10146  190868.0  Nihonogomphus thomassoni         1       1         1   \n",
       "4     10147   63940.0        Tantilla coronadoi         1       1         1   \n",
       "\n",
       "               compiler  year  \\\n",
       "0         Kate Saunders  2011   \n",
       "1                   FBU  2011   \n",
       "2         DO Manh Cuong  2010   \n",
       "3         DO Manh Cuong  2010   \n",
       "4  NatureServe and IUCN  2007   \n",
       "\n",
       "                                            citation               source  \\\n",
       "0     Asia freshwater biodiversity assessments, IUCN  Red List assessment   \n",
       "1  IUCN (International Union for Conservation of ...                  NaN   \n",
       "2     Asia freshwater biodiversity assessments, IUCN              unknown   \n",
       "3     Asia freshwater biodiversity assessments, IUCN  Red List assessment   \n",
       "4  NatureServe and IUCN (International Union for ...                  NaN   \n",
       "\n",
       "     ...     tax_comm   kingdom      phylum     class    order_      family  \\\n",
       "0    ...          NaN  ANIMALIA  ARTHROPODA   INSECTA   ODONATA   GOMPHIDAE   \n",
       "1    ...          NaN  ANIMALIA  ARTHROPODA   INSECTA   ODONATA   GOMPHIDAE   \n",
       "2    ...          NaN  ANIMALIA  ARTHROPODA   INSECTA   ODONATA   GOMPHIDAE   \n",
       "3    ...          NaN  ANIMALIA  ARTHROPODA   INSECTA   ODONATA   GOMPHIDAE   \n",
       "4    ...          NaN  ANIMALIA    CHORDATA  REPTILIA  SQUAMATA  COLUBRIDAE   \n",
       "\n",
       "           genus code shape_length shape_area  \n",
       "0  Nihonogomphus   LC    99.744070  27.275351  \n",
       "1  Nihonogomphus   LC     2.133768   0.093543  \n",
       "2  Nihonogomphus   LC     4.357552   0.301940  \n",
       "3  Nihonogomphus   LC     6.100119   0.527099  \n",
       "4       Tantilla   LC     0.789530   0.049604  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of database rows of species id 137: 13\n"
     ]
    }
   ],
   "source": [
    "print('The number of database rows of species id 137: {}'.format(sis[sis.id_no==137].id_no.count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to understand that the `sis` table, i.e., the attribute table of the spatial data, contain potentially both multi-part polygons as well as single-part polygons (probably due to different sources + range polygons with different presence, origin and seasonality). \n",
    "\n",
    "There may also be a possiblity that these rows may be overlapping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the indices for both datasets. Make sure the ids are integer. Also clean the tables, to reduce size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 78042 entries, 0 to 78041\n",
      "Data columns (total 25 columns):\n",
      "objectid        78042 non-null int64\n",
      "id_no           78042 non-null float64\n",
      "binomial        78042 non-null object\n",
      "presence        78042 non-null int64\n",
      "origin          78042 non-null int64\n",
      "seasonal        78042 non-null int64\n",
      "compiler        76165 non-null object\n",
      "year            78042 non-null int64\n",
      "citation        78038 non-null object\n",
      "source          24156 non-null object\n",
      "dist_comm       947 non-null object\n",
      "island          7320 non-null object\n",
      "subspecies      1197 non-null object\n",
      "subpop          186 non-null object\n",
      "legend          78042 non-null object\n",
      "tax_comm        426 non-null object\n",
      "kingdom         78042 non-null object\n",
      "phylum          78042 non-null object\n",
      "class           78042 non-null object\n",
      "order_          78042 non-null object\n",
      "family          78042 non-null object\n",
      "genus           78042 non-null object\n",
      "code            78042 non-null object\n",
      "shape_length    78042 non-null float64\n",
      "shape_area      78042 non-null float64\n",
      "dtypes: float64(3), int64(5), object(17)\n",
      "memory usage: 14.9+ MB\n"
     ]
    }
   ],
   "source": [
    "sis.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "117582065.0"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sis.id_no.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose 4 bytes for the ID field. The largest `id_no` is less than the largest value of `uint32`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine parameters for uint8\n",
      "---------------------------------------------------------------\n",
      "min = 0\n",
      "max = 255\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for int8\n",
      "---------------------------------------------------------------\n",
      "min = -128\n",
      "max = 127\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for int16\n",
      "---------------------------------------------------------------\n",
      "min = -32768\n",
      "max = 32767\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for uint16\n",
      "---------------------------------------------------------------\n",
      "min = 0\n",
      "max = 65535\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for uint32\n",
      "---------------------------------------------------------------\n",
      "min = 0\n",
      "max = 4294967295\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for int32\n",
      "---------------------------------------------------------------\n",
      "min = -2147483648\n",
      "max = 2147483647\n",
      "---------------------------------------------------------------\n",
      "\n",
      "Machine parameters for int64\n",
      "---------------------------------------------------------------\n",
      "min = -9223372036854775808\n",
      "max = 9223372036854775807\n",
      "---------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "int_types = [\"uint8\", \"int8\", \"int16\", \"uint16\", \"uint32\", \"int32\", 'int64']\n",
    "for it in int_types:\n",
    "    print(np.iinfo(it))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sis_columns=['id_no', 'kingdom', 'phylum', 'class', 'order_', 'family', 'genus', 'code', 'shape_area']\n",
    "sis_clean = pd.DataFrame(sis[sis_columns])\n",
    "sis_clean = sis_clean.astype(dtype={\"id_no\": \"uint32\",\n",
    "                                    'kingdom': \"category\",\n",
    "                                    'phylum': \"category\",\n",
    "                                     'class': \"category\",\n",
    "                                     'order_': \"category\",\n",
    "                                     'family': \"category\",\n",
    "                                     'genus': \"category\",\n",
    "                                     'code': \"category\"})\n",
    "# sis_clean = sis_clean.set_index('id_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 78042 entries, 0 to 78041\n",
      "Data columns (total 9 columns):\n",
      "id_no         78042 non-null uint32\n",
      "kingdom       78042 non-null category\n",
      "phylum        78042 non-null category\n",
      "class         78042 non-null category\n",
      "order_        78042 non-null category\n",
      "family        78042 non-null category\n",
      "genus         78042 non-null category\n",
      "code          78042 non-null category\n",
      "shape_area    78042 non-null float64\n",
      "dtypes: category(7), float64(1), uint32(1)\n",
      "memory usage: 2.1 MB\n"
     ]
    }
   ],
   "source": [
    "sis_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the data cleaning process, the size of the `sis` table has reduced from 14.9+MB to 2.1MB. All essential information is retained and the number of rows stays the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sis.index.size == sis_clean.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117582065.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sis.id_no.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>kingdom_name</th>\n",
       "      <th>phylum_name</th>\n",
       "      <th>class_name</th>\n",
       "      <th>order_name</th>\n",
       "      <th>family_name</th>\n",
       "      <th>genus_name</th>\n",
       "      <th>species_name</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>authority</th>\n",
       "      <th>infra_rank</th>\n",
       "      <th>infra_name</th>\n",
       "      <th>infra_authority</th>\n",
       "      <th>category</th>\n",
       "      <th>criteria</th>\n",
       "      <th>publicationyear</th>\n",
       "      <th>main_common_name</th>\n",
       "      <th>value</th>\n",
       "      <th>REF</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Aaptosyax</td>\n",
       "      <td>grypus</td>\n",
       "      <td>Aaptosyax grypus</td>\n",
       "      <td>Rainboth, 1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CR</td>\n",
       "      <td>A2acd</td>\n",
       "      <td>2011</td>\n",
       "      <td>Mekong Giant Salmon Carp</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food - human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>137</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>celebensis</td>\n",
       "      <td>Acerodon celebensis</td>\n",
       "      <td>Peters, 1867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VU</td>\n",
       "      <td>A2d</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sulawesi Fruit Bat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food - human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>humilis</td>\n",
       "      <td>Acerodon humilis</td>\n",
       "      <td>K. Andersen, 1909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EN</td>\n",
       "      <td>B1ab(iii,v)</td>\n",
       "      <td>2016</td>\n",
       "      <td>Talaud Fruit Bat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food - human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>jubatus</td>\n",
       "      <td>Acerodon jubatus</td>\n",
       "      <td>(Eschscholtz, 1831)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EN</td>\n",
       "      <td>A2cd</td>\n",
       "      <td>2016</td>\n",
       "      <td>Golden-capped Fruit Bat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food - human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>leucotis</td>\n",
       "      <td>Acerodon leucotis</td>\n",
       "      <td>(Sanborn, 1950)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VU</td>\n",
       "      <td>A4cd</td>\n",
       "      <td>2008</td>\n",
       "      <td>Palawan Fruit Bat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food - human</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id kingdom_name phylum_name      class_name     order_name   family_name  \\\n",
       "0    9     ANIMALIA    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE   \n",
       "1  137     ANIMALIA    CHORDATA        MAMMALIA     CHIROPTERA  PTEROPODIDAE   \n",
       "2  138     ANIMALIA    CHORDATA        MAMMALIA     CHIROPTERA  PTEROPODIDAE   \n",
       "3  139     ANIMALIA    CHORDATA        MAMMALIA     CHIROPTERA  PTEROPODIDAE   \n",
       "4  140     ANIMALIA    CHORDATA        MAMMALIA     CHIROPTERA  PTEROPODIDAE   \n",
       "\n",
       "  genus_name species_name      scientific_name            authority  \\\n",
       "0  Aaptosyax       grypus     Aaptosyax grypus       Rainboth, 1991   \n",
       "1   Acerodon   celebensis  Acerodon celebensis         Peters, 1867   \n",
       "2   Acerodon      humilis     Acerodon humilis    K. Andersen, 1909   \n",
       "3   Acerodon      jubatus     Acerodon jubatus  (Eschscholtz, 1831)   \n",
       "4   Acerodon     leucotis    Acerodon leucotis      (Sanborn, 1950)   \n",
       "\n",
       "  infra_rank infra_name  infra_authority category     criteria  \\\n",
       "0        NaN        NaN              NaN       CR        A2acd   \n",
       "1        NaN        NaN              NaN       VU          A2d   \n",
       "2        NaN        NaN              NaN       EN  B1ab(iii,v)   \n",
       "3        NaN        NaN              NaN       EN         A2cd   \n",
       "4        NaN        NaN              NaN       VU         A4cd   \n",
       "\n",
       "   publicationyear          main_common_name  value  REF   DESCRIPTION  \n",
       "0             2011  Mekong Giant Salmon Carp      1    1  Food - human  \n",
       "1             2016        Sulawesi Fruit Bat      1    1  Food - human  \n",
       "2             2016          Talaud Fruit Bat      1    1  Food - human  \n",
       "3             2016   Golden-capped Fruit Bat      1    1  Food - human  \n",
       "4             2008         Palawan Fruit Bat      1    1  Food - human  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11396 entries, 0 to 11395\n",
      "Data columns (total 20 columns):\n",
      "id                  11396 non-null int64\n",
      "kingdom_name        11396 non-null object\n",
      "phylum_name         11396 non-null object\n",
      "class_name          11396 non-null object\n",
      "order_name          11396 non-null object\n",
      "family_name         11396 non-null object\n",
      "genus_name          11396 non-null object\n",
      "species_name        11396 non-null object\n",
      "scientific_name     11396 non-null object\n",
      "authority           11380 non-null object\n",
      "infra_rank          126 non-null object\n",
      "infra_name          126 non-null object\n",
      "infra_authority     0 non-null float64\n",
      "category            11396 non-null object\n",
      "criteria            1899 non-null object\n",
      "publicationyear     11396 non-null int64\n",
      "main_common_name    8649 non-null object\n",
      "value               11396 non-null int64\n",
      "REF                 11396 non-null int64\n",
      "DESCRIPTION         11396 non-null object\n",
      "dtypes: float64(1), int64(4), object(15)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "bfa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117582065"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa.id.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['id', 'kingdom_name', 'phylum_name', 'class_name', 'order_name', 'family_name', 'genus_name', 'category']\n",
    "bfa_clean = pd.DataFrame(bfa[columns])\n",
    "bfa_clean = bfa_clean.astype(dtype={\"id\": \"uint32\",\n",
    "                                    'kingdom_name': \"category\",\n",
    "                                    'phylum_name': \"category\",\n",
    "                                     'class_name': \"category\",\n",
    "                                     'order_name': \"category\",\n",
    "                                     'family_name': \"category\",\n",
    "                                     'genus_name': \"category\",\n",
    "                                     'category': \"category\"})\n",
    "# bfa_clean = bfa_clean.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11396 entries, 0 to 11395\n",
      "Data columns (total 8 columns):\n",
      "id              11396 non-null uint32\n",
      "kingdom_name    11396 non-null category\n",
      "phylum_name     11396 non-null category\n",
      "class_name      11396 non-null category\n",
      "order_name      11396 non-null category\n",
      "family_name     11396 non-null category\n",
      "genus_name      11396 non-null category\n",
      "category        11396 non-null category\n",
      "dtypes: category(7), uint32(1)\n",
      "memory usage: 294.3 KB\n"
     ]
    }
   ],
   "source": [
    "bfa_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since both `sis` and `bfa` tables contains multiple rows per species, it is imperative that they must not counted multiple times. The below function is to achieve that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_unique(array_like):\n",
    "    return np.count_nonzero(np.unique(array_like))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9627"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(bfa_clean.id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9627"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_unique(bfa_clean.id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Spatial data for species for food and agriculture\n",
    "\n",
    "This is to find out how many species in the non spatial database have spatial data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Count of species by class and red list categories from the `bfa` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>category</th>\n",
       "      <th>CR</th>\n",
       "      <th>DD</th>\n",
       "      <th>EN</th>\n",
       "      <th>EW</th>\n",
       "      <th>EX</th>\n",
       "      <th>LC</th>\n",
       "      <th>LR/lc</th>\n",
       "      <th>LR/nt</th>\n",
       "      <th>NT</th>\n",
       "      <th>VU</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_name</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>ACTINOPTERYGII</th>\n",
       "      <td>133</td>\n",
       "      <td>799</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>233</td>\n",
       "      <td>4611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTHOZOA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>1021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>205</td>\n",
       "      <td>1646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALOPODA</th>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\n",
       "      <td>6</td>\n",
       "      <td>83</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYCADOPSIDA</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNETOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSECTA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>97</td>\n",
       "      <td>69</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>210</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\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",
       "      <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>All</th>\n",
       "      <td>359</td>\n",
       "      <td>1218</td>\n",
       "      <td>575</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>5946</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>609</td>\n",
       "      <td>849</td>\n",
       "      <td>9627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category             CR    DD   EN  EW  EX    LC  LR/lc  LR/nt   NT   VU   All\n",
       "class_name                                                                    \n",
       "ACTINOPTERYGII      133   799  148   1   9  3129      1      1  157  233  4611\n",
       "AGARICOMYCETES        1     0    2   0   0     0      0      0    1    4     8\n",
       "AMPHIBIA              9    12   20   0   0   141      0      0   22   34   238\n",
       "ANTHOZOA              0     1    0   0   0     1      0      0    0    0     2\n",
       "AVES                 63     5  113   2  46  1021      0      0  191  205  1646\n",
       "BIVALVIA              6    23    8   0   0    50      0      0    1    7    95\n",
       "CEPHALASPIDOMORPHI    0     0    0   0   1     3      0      0    1    0     5\n",
       "CEPHALOPODA           0    62    0   0   0    32      0      0    1    1    96\n",
       "CHONDRICHTHYES        6    83   28   0   0    62      0      0   51   57   287\n",
       "CLITELLATA            0     2    0   0   1     0      0      0    0    0     3\n",
       "CYCADOPSIDA           1     1    3   0   0     3      0      0    3    4    15\n",
       "GASTROPODA            1    17    8   0   0    71      0      0    5    7   109\n",
       "GNETOPSIDA            0     0    0   0   0    21      0      0    3    0    24\n",
       "HOLOTHUROIDEA         0    27    7   0   0    34      0      0    0    7    75\n",
       "INSECTA               0     0    0   0   0     1      0      0    0    0     1\n",
       "LILIOPSIDA           10    19   12   0   0   158      0      0   16   10   225\n",
       "MAGNOLIOPSIDA        11    38   19   1   0   370      1      0   26   35   501\n",
       "MALACOSTRACA          1    56    8   0   0   179      0      0    7   12   263\n",
       "MAMMALIA             97    69  181   0   3   560      0      0  117  210  1237\n",
       "MYXINI                0     2    0   0   0     0      0      0    1    1     4\n",
       "PINOPSIDA             1     0    7   0   0    26      1      0    1    3    39\n",
       "POLYPODIOPSIDA        0     0    0   0   0    10      0      0    0    0    10\n",
       "REPTILIA             19     2   11   0   2    68      0      1    5   19   127\n",
       "SARCOPTERYGII         0     0    0   0   0     6      0      0    0    0     6\n",
       "All                 359  1218  575   4  62  5946      3      2  609  849  9627"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_clean.pivot_table(index='class_name',  columns='category', values='id', aggfunc=count_unique, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of which those have spatial data that have been mapped and have spatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>category</th>\n",
       "      <th>CR</th>\n",
       "      <th>DD</th>\n",
       "      <th>EN</th>\n",
       "      <th>EW</th>\n",
       "      <th>EX</th>\n",
       "      <th>LC</th>\n",
       "      <th>LR/lc</th>\n",
       "      <th>LR/nt</th>\n",
       "      <th>NT</th>\n",
       "      <th>VU</th>\n",
       "      <th>All</th>\n",
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       "    <tr>\n",
       "      <th>class_name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACTINOPTERYGII</th>\n",
       "      <td>60</td>\n",
       "      <td>563</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2719</td>\n",
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       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>201</td>\n",
       "      <td>3782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>4</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTHOZOA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>205</td>\n",
       "      <td>1588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALOPODA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\n",
       "      <td>6</td>\n",
       "      <td>82</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYCADOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNETOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSECTA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>198</td>\n",
       "      <td>1172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>239</td>\n",
       "      <td>860</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>754</td>\n",
       "      <td>7867.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category             CR   DD   EN  EW  EX    LC  LR/lc  LR/nt   NT   VU  \\\n",
       "class_name                                                                \n",
       "ACTINOPTERYGII       60  563  114   0   0  2719      0      0  125  201   \n",
       "AGARICOMYCETES        1    0    2   0   0     0      0      0    1    4   \n",
       "AMPHIBIA              9   12   20   0   0   141      0      0   22   34   \n",
       "ANTHOZOA              0    0    0   0   0     0      0      0    0    0   \n",
       "AVES                 55    5  113   0   0  1019      0      0  191  205   \n",
       "BIVALVIA              5   16    8   0   0    35      0      0    1    2   \n",
       "CEPHALASPIDOMORPHI    0    0    0   0   0     3      0      0    1    0   \n",
       "CEPHALOPODA           0    0    0   0   0     0      0      0    0    0   \n",
       "CHONDRICHTHYES        6   82   27   0   0    62      0      0   51   57   \n",
       "CLITELLATA            0    2    0   0   0     0      0      0    0    0   \n",
       "CYCADOPSIDA           0    0    0   0   0     0      0      0    0    0   \n",
       "GASTROPODA            1   13    7   0   0    45      0      0    3    3   \n",
       "GNETOPSIDA            0    0    0   0   0     0      0      0    0    0   \n",
       "HOLOTHUROIDEA         0   27    7   0   0    34      0      0    0    7   \n",
       "INSECTA               0    0    0   0   0     0      0      0    0    0   \n",
       "LILIOPSIDA            6    2    8   0   0    61      0      0    8    7   \n",
       "MAGNOLIOPSIDA         2   13    3   0   0   101      0      0    9   10   \n",
       "MALACOSTRACA          0   54    4   0   0   158      0      0    6    9   \n",
       "MAMMALIA             81   67  169   0   0   545      0      0  112  198   \n",
       "MYXINI                0    2    0   0   0     0      0      0    1    1   \n",
       "PINOPSIDA             0    0    0   0   0     1      0      0    0    0   \n",
       "POLYPODIOPSIDA        0    0    0   0   0     5      0      0    0    0   \n",
       "REPTILIA             13    2    9   0   0    53      0      0    4   16   \n",
       "SARCOPTERYGII         0    0    0   0   0     6      0      0    0    0   \n",
       "All                 239  860  491   0   0  4988      0      0  535  754   \n",
       "\n",
       "category               All  \n",
       "class_name                  \n",
       "ACTINOPTERYGII      3782.0  \n",
       "AGARICOMYCETES         8.0  \n",
       "AMPHIBIA             238.0  \n",
       "ANTHOZOA               NaN  \n",
       "AVES                1588.0  \n",
       "BIVALVIA              67.0  \n",
       "CEPHALASPIDOMORPHI     4.0  \n",
       "CEPHALOPODA            NaN  \n",
       "CHONDRICHTHYES       285.0  \n",
       "CLITELLATA             2.0  \n",
       "CYCADOPSIDA            NaN  \n",
       "GASTROPODA            72.0  \n",
       "GNETOPSIDA             NaN  \n",
       "HOLOTHUROIDEA         75.0  \n",
       "INSECTA                NaN  \n",
       "LILIOPSIDA            92.0  \n",
       "MAGNOLIOPSIDA        138.0  \n",
       "MALACOSTRACA         231.0  \n",
       "MAMMALIA            1172.0  \n",
       "MYXINI                 4.0  \n",
       "PINOPSIDA              1.0  \n",
       "POLYPODIOPSIDA         5.0  \n",
       "REPTILIA              97.0  \n",
       "SARCOPTERYGII          6.0  \n",
       "All                 7867.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_clean[bfa_clean.id.isin(sis_clean.id_no.unique())].pivot_table(\n",
    "    index='class_name',  columns='category', values='id', aggfunc=count_unique, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7867"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.intersect1d(sis_clean.id_no.unique(), bfa.id.unique()).size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7,867 species in the `bfa` table have spatial data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restricted range species\n",
    "\n",
    "This impacts the resolution and accuracy of analysis. We propose to use a 10km resolution global grid to map the indicative presence of species. It works for wide ranging species but may encounter difficult if the distribution of species is way below this threshold. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A rough rule of thumb calculation between square degrees is made, i.e., 1 degree equates to 111km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree2_to_km2 = 111*111"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join the two tables to migrate attribute `shape_area`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sis = pd.merge(bfa_clean, sis_clean, how='inner', left_on='id', right_on='id_no')[sis_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double check the species that have spatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7867"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_sis.id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save this table for filtering the spatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(bfa_sis.id_no.unique(),columns=['bfa']).to_csv('bfa_sis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sis_area_dist = bfa_sis.pivot_table(values='shape_area', index='id_no', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the number of species that have less than 100 sqkm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_sis_area_dist[bfa_sis_area_dist['shape_area']*degree2_to_km2 < 100].size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above calculation indicates at least **64** species will be less than 100 sqkm (thus affected by the 10x10 resolution grid). This assumes they all occur near the equator. In reality, more species could be affected. \n",
    "\n",
    "Below is a table of what these 64 species are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>category</th>\n",
       "      <th>CR</th>\n",
       "      <th>DD</th>\n",
       "      <th>EN</th>\n",
       "      <th>EW</th>\n",
       "      <th>EX</th>\n",
       "      <th>LC</th>\n",
       "      <th>LR/lc</th>\n",
       "      <th>LR/nt</th>\n",
       "      <th>NT</th>\n",
       "      <th>VU</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_name</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>ACTINOPTERYGII</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>4</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTHOZOA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</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>5</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALOPODA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYCADOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNETOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSECTA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\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",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category            CR  DD  EN  EW  EX  LC  LR/lc  LR/nt  NT  VU   All\n",
       "class_name                                                            \n",
       "ACTINOPTERYGII       4   1   0   0   0   3      0      0   0   5  13.0\n",
       "AGARICOMYCETES       0   0   0   0   0   0      0      0   0   0   NaN\n",
       "AMPHIBIA             4   1   1   0   0   0      0      0   0   0   6.0\n",
       "ANTHOZOA             0   0   0   0   0   0      0      0   0   0   NaN\n",
       "AVES                10   0  10   0   0   0      0      0   0   5  25.0\n",
       "BIVALVIA             0   0   0   0   0   0      0      0   0   0   NaN\n",
       "CEPHALASPIDOMORPHI   0   0   0   0   0   0      0      0   0   0   NaN\n",
       "CEPHALOPODA          0   0   0   0   0   0      0      0   0   0   NaN\n",
       "CHONDRICHTHYES       0   0   0   0   0   0      0      0   0   0   NaN\n",
       "CLITELLATA           0   0   0   0   0   0      0      0   0   0   NaN\n",
       "CYCADOPSIDA          0   0   0   0   0   0      0      0   0   0   NaN\n",
       "GASTROPODA           0   1   1   0   0   0      0      0   0   0   2.0\n",
       "GNETOPSIDA           0   0   0   0   0   0      0      0   0   0   NaN\n",
       "HOLOTHUROIDEA        0   0   0   0   0   0      0      0   0   0   NaN\n",
       "INSECTA              0   0   0   0   0   0      0      0   0   0   NaN\n",
       "LILIOPSIDA           0   0   1   0   0   0      0      0   0   0   1.0\n",
       "MAGNOLIOPSIDA        0   1   0   0   0   1      0      0   0   0   2.0\n",
       "MALACOSTRACA         0   0   0   0   0   0      0      0   0   0   NaN\n",
       "MAMMALIA            10   2   2   0   0   0      0      0   0   0  14.0\n",
       "MYXINI               0   0   0   0   0   0      0      0   0   0   NaN\n",
       "PINOPSIDA            0   0   0   0   0   0      0      0   0   0   NaN\n",
       "POLYPODIOPSIDA       0   0   0   0   0   0      0      0   0   0   NaN\n",
       "REPTILIA             1   0   0   0   0   0      0      0   0   0   1.0\n",
       "SARCOPTERYGII        0   0   0   0   0   0      0      0   0   0   NaN\n",
       "All                 29   6  15   0   0   4      0      0   0  10  64.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_clean[bfa_clean.id.isin(bfa_sis_area_dist[bfa_sis_area_dist['shape_area']*degree2_to_km2 < 100].index)].pivot_table(\n",
    "    index='class_name',  columns='category', values='id', aggfunc=count_unique, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the 64 species represent a small proportion (out of 7,867), they account for almost 10% of critically endangered species (29 out of 239)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets\n",
    "- Protected areas data: the November 2017 public release of the WDPA, excluding point data\n",
    "- Species data: 2017.2 RedList data, only with those labeled for **food**, and **does not** include wildcrop relatives. This due to time constraint and the rush to run the analysis. This does not include cacti species due to heterogenous nature of both the polygon and attributes, which require additional time pre-processing and post-processing\n",
    "- Hexagon data: discrete global grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "The binning exercise for species (7,867, or 13,000+ rows) and hex (5 millions) took about 22 hours to finish, on a machine with 10 running cores in parallel. The same technique has also been applied for the WDPA (200k+ records). The end result is a **common** mapping of species and protected areas to a hex grid.\n",
    "\n",
    "Notably, the two species failed on two processes (row_id: 8840 and 8841, i.e. Guttera pucherani, Hipposideros grandis, LC bird and mammal)\n",
    "\n",
    "The main benefit of this approach is to avoid any additional spatial analysis which are costly and that any filtering, for example, by presence/origin/seasonality, or in the WDPA, could be done without undertaking any additional spatial analysis. This requires the spatial analysis done at the granularity that allows separation at this level. In the case of WDPA, as `status` and `desgination_type` could be identified at per `WDPAID` level, no further processing is needed. In the case of RL species, the same species may have mulitple polygons with different `presence` codes, thus it cannot be done at the `ID_NO` level - so the analysis must be done at a row level, requiring an additional `row_id`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Big data challenge\n",
    "The end result of the analyses is a set of two-column tables containing the mapping of two datasets by IDs (the names `ID_NO`, `WDPAID` are for historical references but also for ease of remembering the what gets mapped to what). This will be fixed in the next version of the script.\n",
    "\n",
    "| first column (id_no)| second column (wdpaid)|\n",
    "|----------------------|----------------------|\n",
    "| wdpaid(WDPA) or row_id(RL)|hexid            |\n",
    "\n",
    "The resulting tables are huge in size. By configuring the data types, it can be read in the memory.\n",
    "\n",
    "**!the below is outdated!**\n",
    "\n",
    "(For example, the csv for hex and RL would be 1.2TB uncompressed. This means the data cannot be read as a big lump in memory in one go - it requires a piecemeal approach, albeit slower. The idea is to process the data chunk by chunk, but distribute the workload of processing chunks to different processes/cores using the `ipyparallel` library. This requires additional packages to be install and configured elsewhere.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load result and lookup table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HEX tables**\n",
    "\n",
    "The problem with the DGGrid hexagons is that near the 180/-180 longitudes some cells wrap around the world. These cells will intersect all species occupying the same latitude, an artefact that inflate the numbers. Therefore, these cells must be removed. \n",
    "\n",
    "An inspection of the hexagon layer reveals that they could be easily identified as `shape_area` with negative values. The `hexid` of these cells have been identified and stored in the `invalid_hex.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "invalid_hex = pd.read_csv('invalid_hex.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table contains all `hexid` that should be excluded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2427 entries, 0 to 2426\n",
      "Data columns (total 5 columns):\n",
      "OBJECTID        2427 non-null int64\n",
      "global_id       2427 non-null int64\n",
      "Shape_Length    2427 non-null float64\n",
      "Shape_Area      2427 non-null float64\n",
      "hexid           2427 non-null int64\n",
      "dtypes: float64(2), int64(3)\n",
      "memory usage: 94.9 KB\n"
     ]
    }
   ],
   "source": [
    "invalid_hex.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BFA tables**\n",
    "\n",
    "This is the largest table of all, with more than 480 million rows. After cleaning and adjustment of data type, it still takes about 3.6GB in the memory. This is however smaller than the workstation memory and thus no special treatment (such as processing by chunks) is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex = pd.read_csv(\"hex_bfa.csv.gz\",\n",
    "                        skipinitialspace=True,\n",
    "                        dtype={'ID_NO': np.uint32, 'WDPAID': np.uint32})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The old style column name needs to be changed. This does not apply to future analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex.columns = ['row_id', 'hexid']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add below re-runs, failed species identified in the result, fix them in the beginning now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_add_failed_dice = pd.read_csv('hex_bfa_add_dice.csv.gz', skipinitialspace=True, \n",
    "                                     dtype={'row_id': np.uint32, 'hexid': np.uint32})\n",
    "\n",
    "bfa_hex_add_failed_dice_still_fail = pd.read_csv('hex_bfa_add_dice_still_fail.csv.gz', skipinitialspace=True, \n",
    "                                                dtype={'row_id': np.uint32, 'hexid': np.uint32})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concat into a single df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex = pd.concat([bfa_hex, bfa_hex_add_failed_dice, bfa_hex_add_failed_dice_still_fail], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exclude those invalid hex passing 180/-180 longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex = bfa_hex[~bfa_hex.hexid.isin(invalid_hex.hexid)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex = bfa_hex.astype('uint32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 500265827 entries, 0 to 503361761\n",
      "Data columns (total 2 columns):\n",
      "row_id    uint32\n",
      "hexid     uint32\n",
      "dtypes: uint32(2)\n",
      "memory usage: 7.5 GB\n"
     ]
    }
   ],
   "source": [
    "bfa_hex.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "536"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare lookup tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The marine, terr, and freshwater fields are found in the `Food_FAO_2017_2_v2.csv` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_marine = pd.read_csv('Food_FAO_2017_2_v2.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**!! IMPORTANT !!**\n",
    "\n",
    "Due to the nature of floating `id_no` field in csv file, for example `pd_read_csv` interprets `117582065.0` as `117582064.999999`. This leads to a serious problem when calling `astype('unit32')`, which will convert `117582064.999999` to `117582064`. \n",
    "\n",
    "This could be avoided by using the option argument, `float_precision=high`, this ensures in the case of `117582065.0` it will be converted correctly to `117582065` when the data type chanegs to `uint32`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14723 entries, 0 to 14722\n",
      "Data columns (total 28 columns):\n",
      "OID             14723 non-null int64\n",
      "id_no           14723 non-null float64\n",
      "binomial        14723 non-null object\n",
      "presence        14723 non-null int64\n",
      "origin          14723 non-null int64\n",
      "seasonal        14723 non-null int64\n",
      "compiler        14651 non-null object\n",
      "year            14723 non-null int64\n",
      "citation        14723 non-null object\n",
      "source          4735 non-null object\n",
      "dist_comm       255 non-null object\n",
      "island          2435 non-null object\n",
      "subspecies      611 non-null object\n",
      "subpop          73 non-null object\n",
      "legend          14723 non-null object\n",
      "tax_comm        29 non-null object\n",
      "kingdom         14723 non-null object\n",
      "phylum          14723 non-null object\n",
      "class           14723 non-null object\n",
      "order_          14723 non-null object\n",
      "family          14723 non-null object\n",
      "genus           14723 non-null object\n",
      "code            14723 non-null object\n",
      "OBJECTID_1      14723 non-null int64\n",
      "bfa             14723 non-null int64\n",
      "Shape_Length    14723 non-null float64\n",
      "Shape_Area      14723 non-null float64\n",
      "row_id          14723 non-null int64\n",
      "dtypes: float64(3), int64(8), object(17)\n",
      "memory usage: 3.1+ MB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14723 entries, 0 to 14722\n",
      "Data columns (total 13 columns):\n",
      "row_id      14723 non-null uint16\n",
      "id_no       14723 non-null uint32\n",
      "binomial    14723 non-null category\n",
      "presence    14723 non-null uint8\n",
      "origin      14723 non-null uint8\n",
      "seasonal    14723 non-null uint8\n",
      "kingdom     14723 non-null category\n",
      "phylum      14723 non-null category\n",
      "class       14723 non-null category\n",
      "order_      14723 non-null category\n",
      "family      14723 non-null category\n",
      "genus       14723 non-null category\n",
      "code        14723 non-null category\n",
      "dtypes: category(8), uint16(1), uint32(1), uint8(3)\n",
      "memory usage: 817.3 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "bfa_hex_lu = pd.read_csv('bfa_species_lookup.csv', float_precision='high')\n",
    "\n",
    "print(bfa_hex_lu.info())\n",
    "\n",
    "# convert to 'category' and other numeric data types that take less space\n",
    "bfa_hex_lu = bfa_hex_lu[['row_id', 'id_no', 'binomial', 'presence', 'origin', 'seasonal', 'kingdom', 'phylum', 'class', 'order_', 'family', 'genus', 'code']]\n",
    "bfa_hex_lu['binomial'] = bfa_hex_lu['binomial'].astype('category')\n",
    "bfa_hex_lu['kingdom'] = bfa_hex_lu['kingdom'].astype('category')\n",
    "bfa_hex_lu['phylum'] = bfa_hex_lu['phylum'].astype('category')\n",
    "bfa_hex_lu['class'] = bfa_hex_lu['class'].astype('category')\n",
    "bfa_hex_lu['order_'] = bfa_hex_lu['order_'].astype('category')\n",
    "bfa_hex_lu['family'] = bfa_hex_lu['family'].astype('category')\n",
    "bfa_hex_lu['genus'] = bfa_hex_lu['genus'].astype('category')\n",
    "bfa_hex_lu['code'] = bfa_hex_lu['code'].astype('category')\n",
    "\n",
    "bfa_hex_lu['row_id'] = bfa_hex_lu['row_id'].astype('uint16')\n",
    "bfa_hex_lu['id_no'] = bfa_hex_lu['id_no'].astype('uint32')\n",
    "bfa_hex_lu[['presence', 'origin', 'seasonal']] = bfa_hex_lu[['presence', 'origin', 'seasonal']].astype('uint8')\n",
    "\n",
    "print(bfa_hex_lu.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce the amount of memory space needed to store the look up table. It is reduced from 3.1+MB to 817.4KB, a sixfold reduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total number of species with spatial data in the analysis is **7,867**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7867"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checks are requires to ascertain that the number of `id_no` in the `bfa_hex_lu` (from the spatial data) match attritbutes (from the non-spatial table). The former should be a subset of the latter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9627, 7867)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_marine.id.unique().size, bfa_hex_lu.id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below checks the total number of unique `id_no` that are in the spatial table but **not** in the attribute look up table (non spatial). If the above holds, the number should be zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~pd.Series(bfa_hex_lu.id_no.unique()).isin(pd.Series(bfa_marine.id.unique()))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>row_id</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>166467</td>\n",
       "      <td>Rasbora daniconius</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Rasbora</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170066</td>\n",
       "      <td>Panulirus longipes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>MALACOSTRACA</td>\n",
       "      <td>DECAPODA</td>\n",
       "      <td>PALINURIDAE</td>\n",
       "      <td>Panulirus</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>178108</td>\n",
       "      <td>Sphyraena lucasana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>SPHYRAENIDAE</td>\n",
       "      <td>Sphyraena</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id   id_no            binomial  presence  origin  seasonal   kingdom  \\\n",
       "0       1  182562   Labeo cyclopinnis         2       1         1  ANIMALIA   \n",
       "1       2  182562   Labeo cyclopinnis         1       1         1  ANIMALIA   \n",
       "2       3  166467  Rasbora daniconius         1       1         1  ANIMALIA   \n",
       "3       4  170066  Panulirus longipes         1       1         1  ANIMALIA   \n",
       "4       5  178108  Sphyraena lucasana         1       1         1  ANIMALIA   \n",
       "\n",
       "       phylum           class         order_        family      genus code  \n",
       "0    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC  \n",
       "1    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC  \n",
       "2    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE    Rasbora   LC  \n",
       "3  ARTHROPODA    MALACOSTRACA       DECAPODA   PALINURIDAE  Panulirus   LC  \n",
       "4    CHORDATA  ACTINOPTERYGII    PERCIFORMES  SPHYRAENIDAE  Sphyraena   DD  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_marine = bfa_marine[['id', 'biome_marine', 'biome_freshwater', 'biome_terrestrial']]\n",
    "bfa_marine['biome_marine'] = bfa_marine['biome_marine'].astype('category')\n",
    "bfa_marine['biome_freshwater'] = bfa_marine['biome_freshwater'].astype('category')\n",
    "bfa_marine['biome_terrestrial'] = bfa_marine['biome_terrestrial'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure the joined table does not have any duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_marine = bfa_marine.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14723"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu = pd.merge(bfa_hex_lu, bfa_marine, left_on='id_no', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14723"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>row_id</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>166467</td>\n",
       "      <td>Rasbora daniconius</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Rasbora</td>\n",
       "      <td>LC</td>\n",
       "      <td>166467</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170066</td>\n",
       "      <td>Panulirus longipes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>MALACOSTRACA</td>\n",
       "      <td>DECAPODA</td>\n",
       "      <td>PALINURIDAE</td>\n",
       "      <td>Panulirus</td>\n",
       "      <td>LC</td>\n",
       "      <td>170066</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>178108</td>\n",
       "      <td>Sphyraena lucasana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>SPHYRAENIDAE</td>\n",
       "      <td>Sphyraena</td>\n",
       "      <td>DD</td>\n",
       "      <td>178108</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id   id_no            binomial  presence  origin  seasonal   kingdom  \\\n",
       "0       1  182562   Labeo cyclopinnis         2       1         1  ANIMALIA   \n",
       "1       2  182562   Labeo cyclopinnis         1       1         1  ANIMALIA   \n",
       "2       3  166467  Rasbora daniconius         1       1         1  ANIMALIA   \n",
       "3       4  170066  Panulirus longipes         1       1         1  ANIMALIA   \n",
       "4       5  178108  Sphyraena lucasana         1       1         1  ANIMALIA   \n",
       "\n",
       "       phylum           class         order_        family      genus code  \\\n",
       "0    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "1    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "2    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE    Rasbora   LC   \n",
       "3  ARTHROPODA    MALACOSTRACA       DECAPODA   PALINURIDAE  Panulirus   LC   \n",
       "4    CHORDATA  ACTINOPTERYGII    PERCIFORMES  SPHYRAENIDAE  Sphyraena   DD   \n",
       "\n",
       "       id biome_marine biome_freshwater biome_terrestrial  \n",
       "0  182562            f                t                 f  \n",
       "1  182562            f                t                 f  \n",
       "2  166467            f                t                 f  \n",
       "3  170066            t                f                 f  \n",
       "4  178108            t                f                 f  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of species in the `bfa_hex` is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7867"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu[bfa_hex_lu.row_id.isin(bfa_hex.row_id.unique())].id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WDPA related tables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex = pd.read_csv('hex_wdpa_poly.csv.gz',\n",
    "                        skipinitialspace=True,\n",
    "                        dtype={'ID_NO': np.uint32, 'WDPAID': np.uint32})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1499915 entries, 0 to 1499914\n",
      "Data columns (total 2 columns):\n",
      "ID_NO     1499915 non-null uint32\n",
      "WDPAID    1499915 non-null uint32\n",
      "dtypes: uint32(2)\n",
      "memory usage: 11.4 MB\n"
     ]
    }
   ],
   "source": [
    "wdpa_hex.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex.columns = ['wdpaid', 'hexid']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering the hexagon layer, drop all cells with invalid hexid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1499915"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdpa_hex.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex = wdpa_hex[~wdpa_hex.hexid.isin(invalid_hex.hexid)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and clean the look up table\n",
    "\n",
    "Note the dump of the `wdpa_lookup.csv` is ill-formed due to the inclusions of `\\n` and `\"` (when convering to csv, esri does not add additional `\"` to wrap around the string) in string fields in the WDPA, causing `pd.read_csv` failures to read and convert to dataframes correctly. The WDPA needs to be fixed before dumping. The below script looks for fields of data type string and use `.replace` and `.strip()` function to clear `\\n`\n",
    "\n",
    "```python\n",
    "for each in [a.name for a in arcpy.ListFields('WDPA_poly_Nov2017') if a.type=='String']:\n",
    "     exp = r\"!{}!.replace('\\n', ' ').replace('\\\"', '\\'').strip()\".format(each)\n",
    "     arcpy.CalculateField_management(\"WDPA_poly_Nov2017\", each, exp, \"PYTHON_9.3\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above does not work and it's too complicated to identify and fix the source of the problem. Use personal database (mdb) and export to an excel sheet for import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install xlrd to read excel tables\n",
    "# !conda install -y xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex_lu = pd.read_excel('wdpa_lookup.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217544 entries, 0 to 217543\n",
      "Data columns (total 31 columns):\n",
      "OBJECTID        217544 non-null int64\n",
      "WDPAID          217544 non-null int64\n",
      "WDPA_PID        217544 non-null object\n",
      "PA_DEF          217544 non-null int64\n",
      "NAME            217544 non-null object\n",
      "ORIG_NAME       217544 non-null object\n",
      "DESIG           217544 non-null object\n",
      "DESIG_ENG       217544 non-null object\n",
      "DESIG_TYPE      217544 non-null object\n",
      "IUCN_CAT        217544 non-null object\n",
      "INT_CRIT        217544 non-null object\n",
      "MARINE          217544 non-null int64\n",
      "REP_M_AREA      217544 non-null float64\n",
      "GIS_M_AREA      217544 non-null float64\n",
      "REP_AREA        217544 non-null float64\n",
      "GIS_AREA        217544 non-null float64\n",
      "NO_TAKE         217544 non-null object\n",
      "NO_TK_AREA      217544 non-null float64\n",
      "STATUS          217544 non-null object\n",
      "STATUS_YR       217544 non-null int64\n",
      "GOV_TYPE        217544 non-null object\n",
      "OWN_TYPE        217544 non-null object\n",
      "MANG_AUTH       217544 non-null object\n",
      "MANG_PLAN       217544 non-null object\n",
      "VERIF           217544 non-null object\n",
      "METADATAID      217544 non-null int64\n",
      "SUB_LOC         217544 non-null object\n",
      "ISO3            217544 non-null object\n",
      "PARENT_ISO3     217544 non-null object\n",
      "Shape_Length    217544 non-null float64\n",
      "Shape_Area      217544 non-null float64\n",
      "dtypes: float64(7), int64(6), object(18)\n",
      "memory usage: 51.5+ MB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217544 entries, 0 to 217543\n",
      "Data columns (total 8 columns):\n",
      "WDPAID         217544 non-null uint32\n",
      "NAME           217544 non-null object\n",
      "DESIG_ENG      217544 non-null category\n",
      "DESIG_TYPE     217544 non-null category\n",
      "IUCN_CAT       217544 non-null category\n",
      "INT_CRIT       217544 non-null category\n",
      "STATUS         217544 non-null category\n",
      "PARENT_ISO3    217544 non-null category\n",
      "dtypes: category(6), object(1), uint32(1)\n",
      "memory usage: 4.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(wdpa_hex_lu.info())\n",
    "\n",
    "# change dtypes to make it smaller in memory\n",
    "wdpa_hex_lu['WDPAID'] = wdpa_hex_lu['WDPAID'].astype('uint32')\n",
    "wdpa_hex_lu['STATUS_YR'] = wdpa_hex_lu['STATUS_YR'].astype('uint16')\n",
    "wdpa_hex_lu['MARINE'] = wdpa_hex_lu['MARINE'].astype('uint8')\n",
    "wdpa_hex_lu[['REP_AREA', 'GIS_AREA']] = wdpa_hex_lu[['REP_AREA', 'GIS_AREA']].astype('float16')\n",
    "\n",
    "# wdpa_hex_lu = wdpa_hex_lu.drop('OID', axis=1)\n",
    "\n",
    "wdpa_hex_lu['DESIG_ENG'] = wdpa_hex_lu['DESIG_ENG'].astype('category')\n",
    "wdpa_hex_lu['DESIG_TYPE'] = wdpa_hex_lu['DESIG_TYPE'].astype('category')\n",
    "wdpa_hex_lu['IUCN_CAT'] = wdpa_hex_lu['IUCN_CAT'].astype('category')\n",
    "wdpa_hex_lu['INT_CRIT'] = wdpa_hex_lu['INT_CRIT'].astype('category')\n",
    "wdpa_hex_lu['STATUS'] = wdpa_hex_lu['STATUS'].astype('category')\n",
    "wdpa_hex_lu['PARENT_ISO3'] = wdpa_hex_lu['PARENT_ISO3'].astype('category')\n",
    "\n",
    "wdpa_hex_lu = wdpa_hex_lu[['WDPAID', 'NAME', 'DESIG_ENG', 'DESIG_TYPE', 'IUCN_CAT', 'INT_CRIT', 'STATUS', 'PARENT_ISO3']]\n",
    "\n",
    "print(wdpa_hex_lu.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex_lu_filter = wdpa_hex_lu_filter.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering\n",
    "The idea is to re-use the same set of rules for the analysis, and, depending on the input, reflect different narratives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The filter works through the use of a fitlered look up table**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filters needed:\n",
    "1. presence (1 or 2), origin (1 or 2) and seasonality (no filtering) (following discussions with Craig and Barbara)\n",
    "2. remove all invalid hexagons that wrap around the globe\n",
    "3. the filtering of protected areas in the WDPA is according to the [methodology for calculating global statistics](https://www.protectedplanet.net/c/calculating-protected-area-coverage), more specifically:\n",
    "    - Only sites with Status = designated, inscribed, and established are included.\n",
    "    - Exclude sites with Status = Proposed, Not Reported;\n",
    "    - Exclude 'UNESCO Man and Biosphere Reserves' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering the species lookup table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>row_id</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>166467</td>\n",
       "      <td>Rasbora daniconius</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Rasbora</td>\n",
       "      <td>LC</td>\n",
       "      <td>166467</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170066</td>\n",
       "      <td>Panulirus longipes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>MALACOSTRACA</td>\n",
       "      <td>DECAPODA</td>\n",
       "      <td>PALINURIDAE</td>\n",
       "      <td>Panulirus</td>\n",
       "      <td>LC</td>\n",
       "      <td>170066</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>178108</td>\n",
       "      <td>Sphyraena lucasana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>SPHYRAENIDAE</td>\n",
       "      <td>Sphyraena</td>\n",
       "      <td>DD</td>\n",
       "      <td>178108</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id   id_no            binomial  presence  origin  seasonal   kingdom  \\\n",
       "0       1  182562   Labeo cyclopinnis         2       1         1  ANIMALIA   \n",
       "1       2  182562   Labeo cyclopinnis         1       1         1  ANIMALIA   \n",
       "2       3  166467  Rasbora daniconius         1       1         1  ANIMALIA   \n",
       "3       4  170066  Panulirus longipes         1       1         1  ANIMALIA   \n",
       "4       5  178108  Sphyraena lucasana         1       1         1  ANIMALIA   \n",
       "\n",
       "       phylum           class         order_        family      genus code  \\\n",
       "0    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "1    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "2    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE    Rasbora   LC   \n",
       "3  ARTHROPODA    MALACOSTRACA       DECAPODA   PALINURIDAE  Panulirus   LC   \n",
       "4    CHORDATA  ACTINOPTERYGII    PERCIFORMES  SPHYRAENIDAE  Sphyraena   DD   \n",
       "\n",
       "       id biome_marine biome_freshwater biome_terrestrial  \n",
       "0  182562            f                t                 f  \n",
       "1  182562            f                t                 f  \n",
       "2  166467            f                t                 f  \n",
       "3  170066            t                f                 f  \n",
       "4  178108            t                f                 f  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "BFA_HEX_LU_FILTER = (bfa_hex_lu.presence.isin([1,2])) & (bfa_hex_lu.origin.isin([1,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter = bfa_hex_lu[BFA_HEX_LU_FILTER]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records in the lookup table:\n",
      "After filtering: 13410\n",
      "Original: 14723\n"
     ]
    }
   ],
   "source": [
    "print('Number of records in the lookup table:\\nAfter filtering: {}\\nOriginal: {}'.format(bfa_hex_lu_filter.index.size, bfa_hex_lu.index.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of species in the lookup table:\n",
      "After filtering: 7867\n",
      "Original: 7867\n"
     ]
    }
   ],
   "source": [
    "print('Number of species in the lookup table:\\nAfter filtering: {}\\nOriginal: {}'.format(bfa_hex_lu_filter.id_no.unique().size, bfa_hex_lu.id_no.unique().size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering the WDPA lookup table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>WDPAID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>DESIG_ENG</th>\n",
       "      <th>DESIG_TYPE</th>\n",
       "      <th>IUCN_CAT</th>\n",
       "      <th>INT_CRIT</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>PARENT_ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123</td>\n",
       "      <td>Bosque Fray Jorge</td>\n",
       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
       "      <td>International</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Designated</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10707</td>\n",
       "      <td>La Campana - Peñuelas</td>\n",
       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
       "      <td>International</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Designated</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>Torres del Paine</td>\n",
       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
       "      <td>International</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Designated</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2548</td>\n",
       "      <td>Laguna San Rafael</td>\n",
       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
       "      <td>International</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Designated</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3013</td>\n",
       "      <td>Lauca</td>\n",
       "      <td>UNESCO-MAB Biosphere Reserve</td>\n",
       "      <td>International</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Designated</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WDPAID                   NAME                     DESIG_ENG     DESIG_TYPE  \\\n",
       "0     123      Bosque Fray Jorge  UNESCO-MAB Biosphere Reserve  International   \n",
       "1   10707  La Campana - Peñuelas  UNESCO-MAB Biosphere Reserve  International   \n",
       "2     120       Torres del Paine  UNESCO-MAB Biosphere Reserve  International   \n",
       "3    2548      Laguna San Rafael  UNESCO-MAB Biosphere Reserve  International   \n",
       "4    3013                  Lauca  UNESCO-MAB Biosphere Reserve  International   \n",
       "\n",
       "         IUCN_CAT        INT_CRIT      STATUS PARENT_ISO3  \n",
       "0  Not Applicable  Not Applicable  Designated         CHL  \n",
       "1  Not Applicable  Not Applicable  Designated         CHL  \n",
       "2  Not Applicable  Not Applicable  Designated         CHL  \n",
       "3  Not Applicable  Not Applicable  Designated         CHL  \n",
       "4  Not Applicable  Not Applicable  Designated         CHL  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdpa_hex_lu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the category names are correct, for filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([Designated, Proposed, Not Reported, Adopted, Inscribed, Established]\n",
       " Categories (6, object): [Designated, Proposed, Not Reported, Adopted, Inscribed, Established],\n",
       " [UNESCO-MAB Biosphere Reserve, Natural Monument, National Park, National Reserve, Forest Reserve, ..., Geological Polygon, Nature Monument (project), Nature Park (project), Natural-ethnic Territory, Nature park (project)]\n",
       " Length: 1565\n",
       " Categories (1565, object): [UNESCO-MAB Biosphere Reserve, Natural Monument, National Park, National Reserve, ..., Nature Monument (project), Nature Park (project), Natural-ethnic Territory, Nature park (project)])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdpa_hex_lu.STATUS.unique(), wdpa_hex_lu.DESIG_ENG.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "WDPA_HEX_LU_FILTER = (wdpa_hex_lu.STATUS.isin(['Designated', 'Inscribed', 'Established', 'Adopted'])) & (wdpa_hex_lu.DESIG_ENG != 'UNESCO-MAB Biosphere Reserve')\n",
    "wdpa_hex_lu_filter = wdpa_hex_lu[WDPA_HEX_LU_FILTER]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records in the lookup table:\n",
      "After filtering: 215586\n",
      "Original: 217544\n"
     ]
    }
   ],
   "source": [
    "print('Number of records in the lookup table:\\nAfter filtering: {}\\nOriginal: {}'.format(wdpa_hex_lu_filter.index.size, wdpa_hex_lu.index.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of protected areas in the lookup table:\n",
      "After filtering: 214879\n",
      "Original: 216829\n"
     ]
    }
   ],
   "source": [
    "print('Number of protected areas in the lookup table:\\nAfter filtering: {}\\nOriginal: {}'.format(wdpa_hex_lu_filter.WDPAID.unique().size, wdpa_hex_lu.WDPAID.unique().size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convinent list of hexid for the WDPA to use**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "protected_hexid = wdpa_hex[wdpa_hex.wdpaid.isin(wdpa_hex_lu_filter.WDPAID)].hexid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only consider those PA that are valid for consideration\n",
    "wdpa_hex = wdpa_hex[wdpa_hex.hexid.isin(protected_hexid)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering by comprehensive species list\n",
    "Only a subset of the analysis to aid narratives. No need to aggregate hexids, as selections occur at the species level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "xl = pd.ExcelFile('refs/Comprehensively assessed groups species lists.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cycads',\n",
       " 'Amphibians',\n",
       " 'Selected dicots',\n",
       " 'Selected reptiles',\n",
       " 'Conifers',\n",
       " 'Reef-forming corals',\n",
       " 'Sharks and rays',\n",
       " 'Selected crustaceans',\n",
       " 'Mammals',\n",
       " 'Birds',\n",
       " 'Selected gastropods',\n",
       " 'Selected bony fishes',\n",
       " 'Hagfishes',\n",
       " 'Mangroves',\n",
       " 'Seagrasses',\n",
       " 'Gnetopsida']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xl.sheet_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = xl.parse('Cycads')\n",
    "a = xl.parse('Seagrasses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use two dictionaries to hold ids, names (becuase seagrass and mangroves do not have `id_no` in the source table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# host all ids\n",
    "id_no_dict = {}\n",
    "binomial_dict = {}\n",
    "\n",
    "for sheet_name in xl.sheet_names:\n",
    "    df = xl.parse(sheet_name)\n",
    "    if df.columns.size > 1:\n",
    "        # has taxon_id\n",
    "        id_no_dict[sheet_name] = df['taxon id']\n",
    "    else:\n",
    "        binomial_dict[sheet_name] = df['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict_keys(['Cycads', 'Amphibians', 'Selected dicots', 'Selected reptiles', 'Conifers', 'Reef-forming corals', 'Sharks and rays', 'Selected crustaceans', 'Mammals', 'Birds', 'Selected gastropods', 'Selected bony fishes', 'Hagfishes', 'Gnetopsida']),\n",
       " dict_keys(['Mangroves', 'Seagrasses']))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_no_dict.keys(), binomial_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "xl.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>row_id</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>presence</th>\n",
       "      <th>origin</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>182562</td>\n",
       "      <td>Labeo cyclopinnis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Labeo</td>\n",
       "      <td>LC</td>\n",
       "      <td>182562</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>166467</td>\n",
       "      <td>Rasbora daniconius</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>CYPRINIFORMES</td>\n",
       "      <td>CYPRINIDAE</td>\n",
       "      <td>Rasbora</td>\n",
       "      <td>LC</td>\n",
       "      <td>166467</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170066</td>\n",
       "      <td>Panulirus longipes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>ARTHROPODA</td>\n",
       "      <td>MALACOSTRACA</td>\n",
       "      <td>DECAPODA</td>\n",
       "      <td>PALINURIDAE</td>\n",
       "      <td>Panulirus</td>\n",
       "      <td>LC</td>\n",
       "      <td>170066</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>178108</td>\n",
       "      <td>Sphyraena lucasana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>SPHYRAENIDAE</td>\n",
       "      <td>Sphyraena</td>\n",
       "      <td>DD</td>\n",
       "      <td>178108</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id   id_no            binomial  presence  origin  seasonal   kingdom  \\\n",
       "0       1  182562   Labeo cyclopinnis         2       1         1  ANIMALIA   \n",
       "1       2  182562   Labeo cyclopinnis         1       1         1  ANIMALIA   \n",
       "2       3  166467  Rasbora daniconius         1       1         1  ANIMALIA   \n",
       "3       4  170066  Panulirus longipes         1       1         1  ANIMALIA   \n",
       "4       5  178108  Sphyraena lucasana         1       1         1  ANIMALIA   \n",
       "\n",
       "       phylum           class         order_        family      genus code  \\\n",
       "0    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "1    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE      Labeo   LC   \n",
       "2    CHORDATA  ACTINOPTERYGII  CYPRINIFORMES    CYPRINIDAE    Rasbora   LC   \n",
       "3  ARTHROPODA    MALACOSTRACA       DECAPODA   PALINURIDAE  Panulirus   LC   \n",
       "4    CHORDATA  ACTINOPTERYGII    PERCIFORMES  SPHYRAENIDAE  Sphyraena   DD   \n",
       "\n",
       "       id biome_marine biome_freshwater biome_terrestrial  \n",
       "0  182562            f                t                 f  \n",
       "1  182562            f                t                 f  \n",
       "2  166467            f                t                 f  \n",
       "3  170066            t                f                 f  \n",
       "4  178108            t                f                 f  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_hex_lu_filter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find `id_no` for mangroves and seagrasses and push them into `id_no_dict`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_no_dict['Mangroves'] = bfa_hex_lu_filter[bfa_hex_lu_filter.binomial.isin(binomial_dict['Mangroves'])].id_no\n",
    "id_no_dict['Seagrasses'] = bfa_hex_lu_filter[bfa_hex_lu_filter.binomial.isin(binomial_dict['Seagrasses'])].id_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 10)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of mangroves in the comprehensively assessed taqxon, the number of mangroves in the spatial data, based on binomial\n",
    "binomial_dict['Mangroves'].index.size, id_no_dict['Mangroves'].index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71, 4)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binomial_dict['Seagrasses'].index.size, id_no_dict['Seagrasses'].index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of mangroves and seagrasses species appears to be smaller, this could potentially be due to:\n",
    "1. without spatial data\n",
    "2. not utilitised for food and agriculture\n",
    "3. differences in the binomial name\n",
    "\n",
    "We need to understand systematically the number of comprehensively assessed species by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "comprehensive_assessed_groups = []\n",
    "for group in id_no_dict:\n",
    "    comprehensive_assessed_groups.append([group, np.intersect1d(id_no_dict[group], bfa_hex_lu_filter.id_no.unique()).size])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cycads: 0\n",
      "Amphibians: 238\n",
      "Selected dicots: 4\n",
      "Selected reptiles: 20\n",
      "Conifers: 1\n",
      "Reef-forming corals: 0\n",
      "Sharks and rays: 285\n",
      "Selected crustaceans: 231\n",
      "Mammals: 1169\n",
      "Birds: 1588\n",
      "Selected gastropods: 3\n",
      "Selected bony fishes: 686\n",
      "Hagfishes: 4\n",
      "Gnetopsida: 0\n",
      "Mangroves: 10\n",
      "Seagrasses: 4\n"
     ]
    }
   ],
   "source": [
    "for group in comprehensive_assessed_groups:\n",
    "    print('{}: {}'.format(group[0], group[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below is the table of all BFA species with spatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>category</th>\n",
       "      <th>CR</th>\n",
       "      <th>DD</th>\n",
       "      <th>EN</th>\n",
       "      <th>EW</th>\n",
       "      <th>EX</th>\n",
       "      <th>LC</th>\n",
       "      <th>LR/lc</th>\n",
       "      <th>LR/nt</th>\n",
       "      <th>NT</th>\n",
       "      <th>VU</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_name</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>ACTINOPTERYGII</th>\n",
       "      <td>60</td>\n",
       "      <td>563</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2719</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>201</td>\n",
       "      <td>3782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>4</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTHOZOA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>205</td>\n",
       "      <td>1588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALOPODA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\n",
       "      <td>6</td>\n",
       "      <td>82</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYCADOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNETOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSECTA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>198</td>\n",
       "      <td>1172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>239</td>\n",
       "      <td>860</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>754</td>\n",
       "      <td>7867.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category             CR   DD   EN  EW  EX    LC  LR/lc  LR/nt   NT   VU  \\\n",
       "class_name                                                                \n",
       "ACTINOPTERYGII       60  563  114   0   0  2719      0      0  125  201   \n",
       "AGARICOMYCETES        1    0    2   0   0     0      0      0    1    4   \n",
       "AMPHIBIA              9   12   20   0   0   141      0      0   22   34   \n",
       "ANTHOZOA              0    0    0   0   0     0      0      0    0    0   \n",
       "AVES                 55    5  113   0   0  1019      0      0  191  205   \n",
       "BIVALVIA              5   16    8   0   0    35      0      0    1    2   \n",
       "CEPHALASPIDOMORPHI    0    0    0   0   0     3      0      0    1    0   \n",
       "CEPHALOPODA           0    0    0   0   0     0      0      0    0    0   \n",
       "CHONDRICHTHYES        6   82   27   0   0    62      0      0   51   57   \n",
       "CLITELLATA            0    2    0   0   0     0      0      0    0    0   \n",
       "CYCADOPSIDA           0    0    0   0   0     0      0      0    0    0   \n",
       "GASTROPODA            1   13    7   0   0    45      0      0    3    3   \n",
       "GNETOPSIDA            0    0    0   0   0     0      0      0    0    0   \n",
       "HOLOTHUROIDEA         0   27    7   0   0    34      0      0    0    7   \n",
       "INSECTA               0    0    0   0   0     0      0      0    0    0   \n",
       "LILIOPSIDA            6    2    8   0   0    61      0      0    8    7   \n",
       "MAGNOLIOPSIDA         2   13    3   0   0   101      0      0    9   10   \n",
       "MALACOSTRACA          0   54    4   0   0   158      0      0    6    9   \n",
       "MAMMALIA             81   67  169   0   0   545      0      0  112  198   \n",
       "MYXINI                0    2    0   0   0     0      0      0    1    1   \n",
       "PINOPSIDA             0    0    0   0   0     1      0      0    0    0   \n",
       "POLYPODIOPSIDA        0    0    0   0   0     5      0      0    0    0   \n",
       "REPTILIA             13    2    9   0   0    53      0      0    4   16   \n",
       "SARCOPTERYGII         0    0    0   0   0     6      0      0    0    0   \n",
       "All                 239  860  491   0   0  4988      0      0  535  754   \n",
       "\n",
       "category               All  \n",
       "class_name                  \n",
       "ACTINOPTERYGII      3782.0  \n",
       "AGARICOMYCETES         8.0  \n",
       "AMPHIBIA             238.0  \n",
       "ANTHOZOA               NaN  \n",
       "AVES                1588.0  \n",
       "BIVALVIA              67.0  \n",
       "CEPHALASPIDOMORPHI     4.0  \n",
       "CEPHALOPODA            NaN  \n",
       "CHONDRICHTHYES       285.0  \n",
       "CLITELLATA             2.0  \n",
       "CYCADOPSIDA            NaN  \n",
       "GASTROPODA            72.0  \n",
       "GNETOPSIDA             NaN  \n",
       "HOLOTHUROIDEA         75.0  \n",
       "INSECTA                NaN  \n",
       "LILIOPSIDA            92.0  \n",
       "MAGNOLIOPSIDA        138.0  \n",
       "MALACOSTRACA         231.0  \n",
       "MAMMALIA            1172.0  \n",
       "MYXINI                 4.0  \n",
       "PINOPSIDA              1.0  \n",
       "POLYPODIOPSIDA         5.0  \n",
       "REPTILIA              97.0  \n",
       "SARCOPTERYGII          6.0  \n",
       "All                 7867.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfa_clean[bfa_clean.id.isin(sis_clean.id_no.unique())].pivot_table(\n",
    "    index='class_name',  columns='category', values='id', aggfunc=count_unique, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The actual analysis**  (this may take a very long time)\n",
    "\n",
    "1. Remove invalid hexid, using boolean index to include only those valid hexid (this has been done previously)\n",
    "2. Calculate the unique number of hexagon cells per species\n",
    "3. Calculate the unique number of *protected* hexagon cells per species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_hex(df):\n",
    "    # df in the form of id, hex_id\n",
    "    return pd.merge(df, bfa_hex_lu_filter, on='row_id').groupby('id_no').hexid.nunique()\n",
    "    \n",
    "def count_protected_hex(df):\n",
    "    # only count those protectd, this is already filtered\n",
    "    df_protected = df[df.hexid.isin(protected_hexid)]\n",
    "    return count_hex(df_protected)\n",
    "\n",
    "# MapReduce if needs to distribute on multiple cores\n",
    "def reduce_hex(a, b):\n",
    "    return a.append(b).groupby('id_no').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The below step takes a long time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "663"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it appear to help with the overflowing memory issue\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_count = count_hex(bfa_hex)\n",
    "protected_species_count = count_protected_hex(bfa_hex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result = pd.concat([species_count, protected_species_count], axis=1)\n",
    "\n",
    "species_result.columns = ['count', 'count_protected']\n",
    "\n",
    "species_result = species_result.fillna(0)\n",
    "\n",
    "species_result['per'] = species_result['count_protected']/species_result['count']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sanity checks on min and max of coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_result.per.min(), species_result.per.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a lookup table on species attributes to facilite further interrogation if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_attr = bfa_hex_lu_filter[['id_no', 'binomial','kingdom', 'phylum', 'class', 'order_', 'family', 'genus', 'code', 'biome_marine', 'biome_freshwater', 'biome_terrestrial']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr = pd.merge(species_result, species_attr, left_index=True, right_on='id_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>count</th>\n",
       "      <th>count_protected</th>\n",
       "      <th>per</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>2285</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.219694</td>\n",
       "      <td>137</td>\n",
       "      <td>Acerodon celebensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9902</th>\n",
       "      <td>24</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>138</td>\n",
       "      <td>Acerodon humilis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11984</th>\n",
       "      <td>2078</td>\n",
       "      <td>622.0</td>\n",
       "      <td>0.299326</td>\n",
       "      <td>139</td>\n",
       "      <td>Acerodon jubatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7337</th>\n",
       "      <td>220</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>140</td>\n",
       "      <td>Acerodon leucotis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13884</th>\n",
       "      <td>1053</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.353276</td>\n",
       "      <td>142</td>\n",
       "      <td>Acerodon mackloti</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>Acerodon</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  count_protected       per  id_no             binomial   kingdom  \\\n",
       "9913    2285            502.0  0.219694    137  Acerodon celebensis  ANIMALIA   \n",
       "9902      24             12.0  0.500000    138     Acerodon humilis  ANIMALIA   \n",
       "11984   2078            622.0  0.299326    139     Acerodon jubatus  ANIMALIA   \n",
       "7337     220            190.0  0.863636    140    Acerodon leucotis  ANIMALIA   \n",
       "13884   1053            372.0  0.353276    142    Acerodon mackloti  ANIMALIA   \n",
       "\n",
       "         phylum     class      order_        family     genus code  \\\n",
       "9913   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE  Acerodon   VU   \n",
       "9902   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE  Acerodon   EN   \n",
       "11984  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE  Acerodon   EN   \n",
       "7337   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE  Acerodon   VU   \n",
       "13884  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE  Acerodon   VU   \n",
       "\n",
       "      biome_marine biome_freshwater biome_terrestrial  \n",
       "9913             f                f                 t  \n",
       "9902             f                f                 t  \n",
       "11984            f                f                 t  \n",
       "7337             f                f                 t  \n",
       "13884            f                f                 t  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_result_attr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the comprehensive group tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr['comprehensive_groups'] = 'Unassigned'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Cycads', 0],\n",
       " ['Amphibians', 238],\n",
       " ['Selected dicots', 4],\n",
       " ['Selected reptiles', 20],\n",
       " ['Conifers', 1],\n",
       " ['Reef-forming corals', 0],\n",
       " ['Sharks and rays', 285],\n",
       " ['Selected crustaceans', 231],\n",
       " ['Mammals', 1169],\n",
       " ['Birds', 1588],\n",
       " ['Selected gastropods', 3],\n",
       " ['Selected bony fishes', 686],\n",
       " ['Hagfishes', 4],\n",
       " ['Gnetopsida', 0],\n",
       " ['Mangroves', 10],\n",
       " ['Seagrasses', 4]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comprehensive_assessed_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is wrong as the value is set on the copy\n",
    "# species_result_attr[species_result_attr.id_no.isin(id_no_dict['Mammals'])].comprehensive_groups = 'Mammals'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "for group in id_no_dict:\n",
    "    species_result_attr.loc[species_result_attr.id_no.isin(id_no_dict[group]), 'comprehensive_groups'] = group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr.comprehensive_groups = species_result_attr.comprehensive_groups.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gap species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition of gaps species: \n",
    "1. if geographic range < 1,000 km2, `per` < 100%\n",
    "2. if geographic range >= 250,000 km2, `per` < 10%\n",
    "\n",
    "covered species\n",
    "1. if geographic range < 1,000 km2, `per` = 100%\n",
    "2. if geographic range >= 250,000 km2, `per` > 10%\n",
    "\n",
    "in-between, partly gap species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions of interest:\n",
    "1. Gaps. Proportions (discrete) of species range covered by protected, by taxonmy (e.g. classes)\n",
    "2. Relationship between the (a) size of species range and (b) protection percentage, by taxonmy\n",
    "3. Distribution of protection proportion, by taxonmy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# if seaborn not installed\n",
    "# !conda install -y seaborn\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "\n",
    "print(plt.style.available)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prepare the data for plotting**\n",
    "1. add discrete levels to the percentage overlap\n",
    "2. add threatened flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the distribution discrete, i.e., to generate the *Rodrigues 2004* graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The `find_target` function is a crude simplification that vastly under-estimate real world complexity**. At the moment, it is an linear intepolation between the two extreme cases, i.e., 1 for range size less than 1000 km2, and 0.1 for range size above 250,000 km2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threat(code):\n",
    "    if code in ['VU', 'EN', 'CR']:\n",
    "        return 'Threatened'\n",
    "    elif code == 'DD':\n",
    "        return 'Data deficient'\n",
    "    else:\n",
    "        return 'Not Threatened'\n",
    "\n",
    "def discrete_per(per):\n",
    "    if isinstance(per, float):\n",
    "        return int(np.ceil(per*10)*10)\n",
    "    elif isinstance(per, pd.Series):\n",
    "        return np.ceil(per*10).astype('int')*10\n",
    "\n",
    "    \n",
    "## find gaps\n",
    "def find_target(range_size):\n",
    "    # two defined targets\n",
    "    if range_size <= 1000:\n",
    "        target = 1.0\n",
    "    elif range_size >= 250_000:\n",
    "        target = 0.1\n",
    "\n",
    "    # interpolate function (linear)\n",
    "    else:\n",
    "        a, b = -3.614457831325301e-06, 1.0036144578313253\n",
    "        target = a*range_size + b \n",
    "    return target\n",
    "\n",
    "def gap_status(per, range_size):         \n",
    "    # if per == 0, gap\n",
    "    if per == 0:\n",
    "        return 'Gap'\n",
    "    else:\n",
    "        target = find_target(range_size)\n",
    "        # debug\n",
    "        # print(target)\n",
    "        if per >= target:\n",
    "            return 'Covered'\n",
    "        else:\n",
    "            return 'Partial gap'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all correct\n"
     ]
    }
   ],
   "source": [
    "# test it\n",
    "if gap_status(1, 1000) == 'Covered' and \\\n",
    "gap_status(0.89, 1000) == 'Partial gap' and \\\n",
    "gap_status(0.1, 250_000) == 'Covered' and \\\n",
    "gap_status(0.09, 250_000) == 'Partial gap' and \\\n",
    "gap_status(0.1, 300_000) == 'Covered' and \\\n",
    "gap_status(0, 10000) == 'Gap' and \\\n",
    "gap_status(0, 100) == 'Gap' and \\\n",
    "gap_status(1, 100) == 'Covered' and \\\n",
    "gap_status(1, 100) == 'Covered' and \\\n",
    "gap_status(0.5, 100_000) == 'Partial gap':\n",
    "    print('all correct')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vectorize for column calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "threat_vec = np.vectorize(threat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "gap_status_vec = np.vectorize(gap_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "find_target_vec = np.vectorize(find_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add threat and gap columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`count` multiply by 100 (hexagon cell size) would return the range size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda\\envs\\pydata36\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "species_result_attr['target'] = find_target_vec(species_result_attr['count'] * 100)\n",
    "species_result_attr['threaten'] = threat_vec(species_result_attr.code)\n",
    "species_result_attr['threaten'] = species_result_attr['threaten'].astype('category')\n",
    "\n",
    "# ordered categorical\n",
    "species_result_attr['code'] = species_result_attr.code.cat.set_categories(['DD', 'LC', 'NT', 'VU', 'EN', 'CR' ], ordered=True)\n",
    "\n",
    "# ordered categorical\n",
    "species_result_attr['gap_status'] = gap_status_vec(species_result_attr['per'], species_result_attr['count'] * 100)\n",
    "species_result_attr['gap_status'] = species_result_attr['gap_status'].astype('category', ordered=True, categories=['Gap', 'Partial gap', 'Covered'])\n",
    "\n",
    "# discrete for plotting\n",
    "species_result_attr['discrete_per'] = discrete_per(species_result_attr.per)\n",
    "species_result_attr['discrete_target'] = discrete_per(species_result_attr.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>count</th>\n",
       "      <th>count_protected</th>\n",
       "      <th>per</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>...</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "      <th>comprehensive_groups</th>\n",
       "      <th>target</th>\n",
       "      <th>threaten</th>\n",
       "      <th>gap_status</th>\n",
       "      <th>discrete_per</th>\n",
       "      <th>discrete_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>2285</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.219694</td>\n",
       "      <td>137</td>\n",
       "      <td>Acerodon celebensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.177711</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9902</th>\n",
       "      <td>24</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>138</td>\n",
       "      <td>Acerodon humilis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.994940</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11984</th>\n",
       "      <td>2078</td>\n",
       "      <td>622.0</td>\n",
       "      <td>0.299326</td>\n",
       "      <td>139</td>\n",
       "      <td>Acerodon jubatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.252530</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7337</th>\n",
       "      <td>220</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>140</td>\n",
       "      <td>Acerodon leucotis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.924096</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13884</th>\n",
       "      <td>1053</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.353276</td>\n",
       "      <td>142</td>\n",
       "      <td>Acerodon mackloti</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.623012</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  count_protected       per  id_no             binomial   kingdom  \\\n",
       "9913    2285            502.0  0.219694    137  Acerodon celebensis  ANIMALIA   \n",
       "9902      24             12.0  0.500000    138     Acerodon humilis  ANIMALIA   \n",
       "11984   2078            622.0  0.299326    139     Acerodon jubatus  ANIMALIA   \n",
       "7337     220            190.0  0.863636    140    Acerodon leucotis  ANIMALIA   \n",
       "13884   1053            372.0  0.353276    142    Acerodon mackloti  ANIMALIA   \n",
       "\n",
       "         phylum     class      order_        family       ...        code  \\\n",
       "9913   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "9902   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          EN   \n",
       "11984  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          EN   \n",
       "7337   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "13884  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "\n",
       "      biome_marine biome_freshwater biome_terrestrial comprehensive_groups  \\\n",
       "9913             f                f                 t              Mammals   \n",
       "9902             f                f                 t              Mammals   \n",
       "11984            f                f                 t              Mammals   \n",
       "7337             f                f                 t              Mammals   \n",
       "13884            f                f                 t              Mammals   \n",
       "\n",
       "         target    threaten   gap_status discrete_per  discrete_target  \n",
       "9913   0.177711  Threatened      Covered           30               20  \n",
       "9902   0.994940  Threatened  Partial gap           50              100  \n",
       "11984  0.252530  Threatened      Covered           30               30  \n",
       "7337   0.924096  Threatened  Partial gap           90              100  \n",
       "13884  0.623012  Threatened  Partial gap           40               70  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_result_attr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above function maps the numeric datatype percentage protection to a discrete range of values 0-100 (step 10). As is seen above, it uses a ceiling function, e.g. 1.2 will be mapped 20\n",
    "\n",
    "**In terms of intepretation, 20 means between 10%-20%, 100 means between 90%-100% (including 100%)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009DF1588>]], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z+UXgqSnFa4HJV/A3AGf3lN+YmU1m3gccHhFHAb8J3J2ZT2XmHuBuXhgqkqRFNNs5gOWZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e0524a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_result_attr.hist('discrete_per')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_result = species_result_attr.pivot_table(values='id_no', \n",
    "                                columns='discrete_per', aggfunc=np.count_nonzero, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e1efd0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fW2ufdmjGcwNnlGCMaYb/Af85sBm4vZqMFdlvBzKttb7A+EhjTNPAGT3dgffP\nNp+19hFrbSdrbVf8j69Ma+1op+QDMMa0MMacU/E1/vvnUxxyP1tr9wPfBs7MAf8x+M+cku8HRvHv\nw0IVWZyQ8RugnzGmeeD3uuJnGLbHoWOLIHDsawr+H/Tn/iH7f+HavjFmBfAu8BNjzHfGmHHA74Ab\njTFfAzcGLgP8L/BP/C/OvAhMCnwPecAT+EvtA+DxwFgwJAI/B5KNMX8LfNzssIzxwGZjzMeBdb9p\nrX0DeBh4wBjzD/zHNpcEll8CtAuMP0DgEEPgfrf4fzk2ApOttWVBylgVJ+WLA7YZY/6O/5d6vbV2\nI866n6cCrwbu58uBpxyWD2NM80CO1ScMOyJjYG8qHf8pop/g/7ucRhgfh3pnsYiIyzl2j0BERMJD\nRSAi4nIqAhERl1MRiIi4nIpARMTlVAQiIi6nIhARcTkVgYiIy/1/NRHKVjMND6YAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9df8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_result.plot.barh(stacked=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>count_protected</th>\n",
       "      <th>per</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>...</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "      <th>comprehensive_groups</th>\n",
       "      <th>target</th>\n",
       "      <th>threaten</th>\n",
       "      <th>gap_status</th>\n",
       "      <th>discrete_per</th>\n",
       "      <th>discrete_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>2285</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.219694</td>\n",
       "      <td>137</td>\n",
       "      <td>Acerodon celebensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.177711</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9902</th>\n",
       "      <td>24</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>138</td>\n",
       "      <td>Acerodon humilis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.994940</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11984</th>\n",
       "      <td>2078</td>\n",
       "      <td>622.0</td>\n",
       "      <td>0.299326</td>\n",
       "      <td>139</td>\n",
       "      <td>Acerodon jubatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.252530</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7337</th>\n",
       "      <td>220</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>140</td>\n",
       "      <td>Acerodon leucotis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.924096</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13884</th>\n",
       "      <td>1053</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.353276</td>\n",
       "      <td>142</td>\n",
       "      <td>Acerodon mackloti</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CHIROPTERA</td>\n",
       "      <td>PTEROPODIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.623012</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  count_protected       per  id_no             binomial   kingdom  \\\n",
       "9913    2285            502.0  0.219694    137  Acerodon celebensis  ANIMALIA   \n",
       "9902      24             12.0  0.500000    138     Acerodon humilis  ANIMALIA   \n",
       "11984   2078            622.0  0.299326    139     Acerodon jubatus  ANIMALIA   \n",
       "7337     220            190.0  0.863636    140    Acerodon leucotis  ANIMALIA   \n",
       "13884   1053            372.0  0.353276    142    Acerodon mackloti  ANIMALIA   \n",
       "\n",
       "         phylum     class      order_        family       ...        code  \\\n",
       "9913   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "9902   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          EN   \n",
       "11984  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          EN   \n",
       "7337   CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "13884  CHORDATA  MAMMALIA  CHIROPTERA  PTEROPODIDAE       ...          VU   \n",
       "\n",
       "      biome_marine biome_freshwater biome_terrestrial comprehensive_groups  \\\n",
       "9913             f                f                 t              Mammals   \n",
       "9902             f                f                 t              Mammals   \n",
       "11984            f                f                 t              Mammals   \n",
       "7337             f                f                 t              Mammals   \n",
       "13884            f                f                 t              Mammals   \n",
       "\n",
       "         target    threaten   gap_status discrete_per  discrete_target  \n",
       "9913   0.177711  Threatened      Covered           30               20  \n",
       "9902   0.994940  Threatened  Partial gap           50              100  \n",
       "11984  0.252530  Threatened      Covered           30               30  \n",
       "7337   0.924096  Threatened  Partial gap           90              100  \n",
       "13884  0.623012  Threatened  Partial gap           40               70  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_result_attr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assess the multiple system species**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "biome_systems = species_result_attr.loc[:, ['biome_marine', 'biome_freshwater', 'biome_terrestrial']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marine+Freshwater: 236\n",
      "Marine+Terr: 157\n",
      "Freshwater+Terr: 626\n",
      "Marine+Freshwater+Terr: 216\n"
     ]
    }
   ],
   "source": [
    "print('Marine+Freshwater: {}'.format((biome_systems == 'ttf').sum()))\n",
    "print('Marine+Terr: {}'.format((biome_systems == 'tft').sum()))\n",
    "print('Freshwater+Terr: {}'.format((biome_systems == 'ftt').sum()))\n",
    "print('Marine+Freshwater+Terr: {}'.format((biome_systems == 'ttt').sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>count</th>\n",
       "      <th>count_protected</th>\n",
       "      <th>per</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>...</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "      <th>comprehensive_groups</th>\n",
       "      <th>target</th>\n",
       "      <th>threaten</th>\n",
       "      <th>gap_status</th>\n",
       "      <th>discrete_per</th>\n",
       "      <th>discrete_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10058</th>\n",
       "      <td>125675</td>\n",
       "      <td>37712.0</td>\n",
       "      <td>0.300076</td>\n",
       "      <td>1793</td>\n",
       "      <td>Aonyx capensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MUSTELIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4250</th>\n",
       "      <td>16894</td>\n",
       "      <td>13199.0</td>\n",
       "      <td>0.781283</td>\n",
       "      <td>10103</td>\n",
       "      <td>Hippopotamus amphibius</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>HIPPOPOTAMIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12790</th>\n",
       "      <td>289352</td>\n",
       "      <td>75003.0</td>\n",
       "      <td>0.259210</td>\n",
       "      <td>12419</td>\n",
       "      <td>Lutra lutra</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MUSTELIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>9782</td>\n",
       "      <td>1813.0</td>\n",
       "      <td>0.185340</td>\n",
       "      <td>12421</td>\n",
       "      <td>Lutra sumatrana</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MUSTELIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>52877</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>0.166405</td>\n",
       "      <td>12427</td>\n",
       "      <td>Lutrogale perspicillata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MUSTELIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13228</th>\n",
       "      <td>82002</td>\n",
       "      <td>22718.0</td>\n",
       "      <td>0.277042</td>\n",
       "      <td>17013</td>\n",
       "      <td>Phoca vitulina</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>PHOCIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>201340</td>\n",
       "      <td>14034.0</td>\n",
       "      <td>0.069703</td>\n",
       "      <td>41672</td>\n",
       "      <td>Pusa hispida</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>PHOCIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>52548</td>\n",
       "      <td>10354.0</td>\n",
       "      <td>0.197039</td>\n",
       "      <td>44166</td>\n",
       "      <td>Aonyx cinereus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MUSTELIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3105</th>\n",
       "      <td>83906</td>\n",
       "      <td>12580.0</td>\n",
       "      <td>0.149930</td>\n",
       "      <td>177110</td>\n",
       "      <td>Acrostichum aureum</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>POLYPODIOPSIDA</td>\n",
       "      <td>POLYPODIALES</td>\n",
       "      <td>PTERIDACEAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mangroves</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136</th>\n",
       "      <td>71904</td>\n",
       "      <td>7572.0</td>\n",
       "      <td>0.105307</td>\n",
       "      <td>178796</td>\n",
       "      <td>Sonneratia caseolaris</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>MAGNOLIOPSIDA</td>\n",
       "      <td>MYRTALES</td>\n",
       "      <td>LYTHRACEAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mangroves</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>20688</td>\n",
       "      <td>2738.0</td>\n",
       "      <td>0.132347</td>\n",
       "      <td>178827</td>\n",
       "      <td>Sonneratia lanceolata</td>\n",
       "      <td>PLANTAE</td>\n",
       "      <td>TRACHEOPHYTA</td>\n",
       "      <td>MAGNOLIOPSIDA</td>\n",
       "      <td>MYRTALES</td>\n",
       "      <td>LYTHRACEAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Mangroves</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8407</th>\n",
       "      <td>1846</td>\n",
       "      <td>428.0</td>\n",
       "      <td>0.231853</td>\n",
       "      <td>22678576</td>\n",
       "      <td>Macrocephalon maleo</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GALLIFORMES</td>\n",
       "      <td>MEGAPODIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.336386</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8419</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22678625</td>\n",
       "      <td>Megapodius pritchardii</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GALLIFORMES</td>\n",
       "      <td>MEGAPODIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Gap</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9151</th>\n",
       "      <td>56494</td>\n",
       "      <td>14919.0</td>\n",
       "      <td>0.264081</td>\n",
       "      <td>22679753</td>\n",
       "      <td>Dendrocygna arcuata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>78623</td>\n",
       "      <td>12492.0</td>\n",
       "      <td>0.158885</td>\n",
       "      <td>22679758</td>\n",
       "      <td>Dendrocygna javanica</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6921</th>\n",
       "      <td>1607</td>\n",
       "      <td>696.0</td>\n",
       "      <td>0.433105</td>\n",
       "      <td>22679770</td>\n",
       "      <td>Dendrocygna arborea</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.422771</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3895</th>\n",
       "      <td>29606</td>\n",
       "      <td>11725.0</td>\n",
       "      <td>0.396035</td>\n",
       "      <td>22679830</td>\n",
       "      <td>Biziura lobata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10529</th>\n",
       "      <td>67297</td>\n",
       "      <td>26555.0</td>\n",
       "      <td>0.394594</td>\n",
       "      <td>22679839</td>\n",
       "      <td>Cygnus olor</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6864</th>\n",
       "      <td>71364</td>\n",
       "      <td>22144.0</td>\n",
       "      <td>0.310297</td>\n",
       "      <td>22679843</td>\n",
       "      <td>Cygnus atratus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9231</th>\n",
       "      <td>39959</td>\n",
       "      <td>6774.0</td>\n",
       "      <td>0.169524</td>\n",
       "      <td>22679846</td>\n",
       "      <td>Cygnus melancoryphus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12033</th>\n",
       "      <td>75773</td>\n",
       "      <td>10672.0</td>\n",
       "      <td>0.140842</td>\n",
       "      <td>22679869</td>\n",
       "      <td>Anser cygnoid</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7552</th>\n",
       "      <td>3362</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>0.677573</td>\n",
       "      <td>22679872</td>\n",
       "      <td>Anser brachyrhynchus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10066</th>\n",
       "      <td>118753</td>\n",
       "      <td>31787.0</td>\n",
       "      <td>0.267673</td>\n",
       "      <td>22679881</td>\n",
       "      <td>Anser albifrons</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>3756</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>0.409212</td>\n",
       "      <td>22679919</td>\n",
       "      <td>Anser canagicus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12607</th>\n",
       "      <td>3258</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>0.659300</td>\n",
       "      <td>22679943</td>\n",
       "      <td>Branta leucopsis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12932</th>\n",
       "      <td>61364</td>\n",
       "      <td>28125.0</td>\n",
       "      <td>0.458331</td>\n",
       "      <td>22679946</td>\n",
       "      <td>Branta bernicla</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8425</th>\n",
       "      <td>32224</td>\n",
       "      <td>4236.0</td>\n",
       "      <td>0.131455</td>\n",
       "      <td>22679954</td>\n",
       "      <td>Branta ruficollis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4824</th>\n",
       "      <td>124271</td>\n",
       "      <td>27851.0</td>\n",
       "      <td>0.224115</td>\n",
       "      <td>22680024</td>\n",
       "      <td>Tadorna tadorna</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>25176</td>\n",
       "      <td>7006.0</td>\n",
       "      <td>0.278281</td>\n",
       "      <td>22680029</td>\n",
       "      <td>Radjah radjah</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>11748</td>\n",
       "      <td>3487.0</td>\n",
       "      <td>0.296816</td>\n",
       "      <td>22680052</td>\n",
       "      <td>Tachyeres patachonicus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>ANSERIFORMES</td>\n",
       "      <td>ANATIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12463</th>\n",
       "      <td>212492</td>\n",
       "      <td>57838.0</td>\n",
       "      <td>0.272189</td>\n",
       "      <td>22713491</td>\n",
       "      <td>Cisticola juncidis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>CISTICOLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>127709</td>\n",
       "      <td>21162.0</td>\n",
       "      <td>0.165705</td>\n",
       "      <td>22717424</td>\n",
       "      <td>Alauda gulgula</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>ALAUDIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>125814</td>\n",
       "      <td>53252.0</td>\n",
       "      <td>0.423260</td>\n",
       "      <td>22718556</td>\n",
       "      <td>Anthus pratensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>MOTACILLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>24638</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.311592</td>\n",
       "      <td>22725033</td>\n",
       "      <td>Charadrius nivosus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>CHARADRIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11177</th>\n",
       "      <td>75260</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>0.158119</td>\n",
       "      <td>22725846</td>\n",
       "      <td>Halcyon smyrnensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CORACIIFORMES</td>\n",
       "      <td>ALCEDINIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7621</th>\n",
       "      <td>3959</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>0.289214</td>\n",
       "      <td>22725856</td>\n",
       "      <td>Halcyon gularis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CORACIIFORMES</td>\n",
       "      <td>ALCEDINIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13994</th>\n",
       "      <td>43548</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>0.162763</td>\n",
       "      <td>22727255</td>\n",
       "      <td>Ciconia episcopus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CICONIIFORMES</td>\n",
       "      <td>CICONIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>VU</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>153432</td>\n",
       "      <td>41140.0</td>\n",
       "      <td>0.268132</td>\n",
       "      <td>22727265</td>\n",
       "      <td>Ciconia microscelis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CICONIIFORMES</td>\n",
       "      <td>CICONIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>250349</td>\n",
       "      <td>43073.0</td>\n",
       "      <td>0.172052</td>\n",
       "      <td>22727487</td>\n",
       "      <td>Charadrius alexandrinus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>CHARADRIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>99390</td>\n",
       "      <td>19445.0</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>22727668</td>\n",
       "      <td>Ardea intermedia</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>ARDEIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8818</th>\n",
       "      <td>146019</td>\n",
       "      <td>40372.0</td>\n",
       "      <td>0.276485</td>\n",
       "      <td>22727677</td>\n",
       "      <td>Ardea brachyrhyncha</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>ARDEIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>60236</td>\n",
       "      <td>18898.0</td>\n",
       "      <td>0.313733</td>\n",
       "      <td>22727683</td>\n",
       "      <td>Ardea plumifera</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>ARDEIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>640151</td>\n",
       "      <td>155235.0</td>\n",
       "      <td>0.242497</td>\n",
       "      <td>22727969</td>\n",
       "      <td>Himantopus himantopus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>RECURVIROSTRIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6226</th>\n",
       "      <td>586249</td>\n",
       "      <td>168475.0</td>\n",
       "      <td>0.287378</td>\n",
       "      <td>22728182</td>\n",
       "      <td>Butorides striata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>ARDEIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>1052</td>\n",
       "      <td>605.0</td>\n",
       "      <td>0.575095</td>\n",
       "      <td>22731577</td>\n",
       "      <td>Rallus obsoletus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.623373</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>647</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.285935</td>\n",
       "      <td>22731855</td>\n",
       "      <td>Threskiornis bernieri</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>THRESKIORNITHIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.769759</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11124</th>\n",
       "      <td>71904</td>\n",
       "      <td>12505.0</td>\n",
       "      <td>0.173912</td>\n",
       "      <td>22733989</td>\n",
       "      <td>Pelecanus occidentalis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PELECANIFORMES</td>\n",
       "      <td>PELECANIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2897</th>\n",
       "      <td>11216</td>\n",
       "      <td>2449.0</td>\n",
       "      <td>0.218349</td>\n",
       "      <td>22735615</td>\n",
       "      <td>Charadrius dealbatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>CHARADRIIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>DD</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Data deficient</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>1003893</td>\n",
       "      <td>266143.0</td>\n",
       "      <td>0.265111</td>\n",
       "      <td>45354964</td>\n",
       "      <td>Falco peregrinus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>FALCONIFORMES</td>\n",
       "      <td>FALCONIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12152</th>\n",
       "      <td>277606</td>\n",
       "      <td>81930.0</td>\n",
       "      <td>0.295131</td>\n",
       "      <td>62030590</td>\n",
       "      <td>Larus smithsonianus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>LARIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12452</th>\n",
       "      <td>38904</td>\n",
       "      <td>20970.0</td>\n",
       "      <td>0.539019</td>\n",
       "      <td>62030608</td>\n",
       "      <td>Larus argentatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>CHARADRIIFORMES</td>\n",
       "      <td>LARIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3685</th>\n",
       "      <td>425388</td>\n",
       "      <td>112977.0</td>\n",
       "      <td>0.265586</td>\n",
       "      <td>62120190</td>\n",
       "      <td>Gallinula chloropus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>159808</td>\n",
       "      <td>47312.0</td>\n",
       "      <td>0.296055</td>\n",
       "      <td>62120280</td>\n",
       "      <td>Gallinula galeata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9117</th>\n",
       "      <td>5152</td>\n",
       "      <td>2629.0</td>\n",
       "      <td>0.510287</td>\n",
       "      <td>62154828</td>\n",
       "      <td>Rallus longirostris</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>4695</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>0.684132</td>\n",
       "      <td>62155296</td>\n",
       "      <td>Rallus crepitans</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14075</th>\n",
       "      <td>148827</td>\n",
       "      <td>58501.0</td>\n",
       "      <td>0.393081</td>\n",
       "      <td>62169677</td>\n",
       "      <td>Fulica americana</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>GRUIFORMES</td>\n",
       "      <td>RALLIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10515</th>\n",
       "      <td>365325</td>\n",
       "      <td>89989.0</td>\n",
       "      <td>0.246326</td>\n",
       "      <td>102998555</td>\n",
       "      <td>Alauda arvensis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>ALAUDIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12014</th>\n",
       "      <td>145119</td>\n",
       "      <td>27983.0</td>\n",
       "      <td>0.192828</td>\n",
       "      <td>103727590</td>\n",
       "      <td>Corvus macrorhynchos</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>CORVIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>422629</td>\n",
       "      <td>104517.0</td>\n",
       "      <td>0.247302</td>\n",
       "      <td>103773898</td>\n",
       "      <td>Oenanthe oenanthe</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>MUSCICAPIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>13744</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>0.142389</td>\n",
       "      <td>103773966</td>\n",
       "      <td>Oenanthe seebohmi</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>AVES</td>\n",
       "      <td>PASSERIFORMES</td>\n",
       "      <td>MUSCICAPIDAE</td>\n",
       "      <td>...</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Birds</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Covered</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  count_protected       per      id_no                 binomial  \\\n",
       "10058   125675          37712.0  0.300076       1793           Aonyx capensis   \n",
       "4250     16894          13199.0  0.781283      10103   Hippopotamus amphibius   \n",
       "12790   289352          75003.0  0.259210      12419              Lutra lutra   \n",
       "9937      9782           1813.0  0.185340      12421          Lutra sumatrana   \n",
       "192      52877           8799.0  0.166405      12427  Lutrogale perspicillata   \n",
       "13228    82002          22718.0  0.277042      17013           Phoca vitulina   \n",
       "515     201340          14034.0  0.069703      41672             Pusa hispida   \n",
       "2234     52548          10354.0  0.197039      44166           Aonyx cinereus   \n",
       "3105     83906          12580.0  0.149930     177110       Acrostichum aureum   \n",
       "6136     71904           7572.0  0.105307     178796    Sonneratia caseolaris   \n",
       "698      20688           2738.0  0.132347     178827    Sonneratia lanceolata   \n",
       "8407      1846            428.0  0.231853   22678576      Macrocephalon maleo   \n",
       "8419         7              0.0  0.000000   22678625   Megapodius pritchardii   \n",
       "9151     56494          14919.0  0.264081   22679753      Dendrocygna arcuata   \n",
       "2253     78623          12492.0  0.158885   22679758     Dendrocygna javanica   \n",
       "6921      1607            696.0  0.433105   22679770      Dendrocygna arborea   \n",
       "3895     29606          11725.0  0.396035   22679830           Biziura lobata   \n",
       "10529    67297          26555.0  0.394594   22679839              Cygnus olor   \n",
       "6864     71364          22144.0  0.310297   22679843           Cygnus atratus   \n",
       "9231     39959           6774.0  0.169524   22679846     Cygnus melancoryphus   \n",
       "12033    75773          10672.0  0.140842   22679869            Anser cygnoid   \n",
       "7552      3362           2278.0  0.677573   22679872     Anser brachyrhynchus   \n",
       "10066   118753          31787.0  0.267673   22679881          Anser albifrons   \n",
       "732       3756           1537.0  0.409212   22679919          Anser canagicus   \n",
       "12607     3258           2148.0  0.659300   22679943         Branta leucopsis   \n",
       "12932    61364          28125.0  0.458331   22679946          Branta bernicla   \n",
       "8425     32224           4236.0  0.131455   22679954        Branta ruficollis   \n",
       "4824    124271          27851.0  0.224115   22680024          Tadorna tadorna   \n",
       "3651     25176           7006.0  0.278281   22680029            Radjah radjah   \n",
       "7272     11748           3487.0  0.296816   22680052   Tachyeres patachonicus   \n",
       "...        ...              ...       ...        ...                      ...   \n",
       "12463   212492          57838.0  0.272189   22713491       Cisticola juncidis   \n",
       "1201    127709          21162.0  0.165705   22717424           Alauda gulgula   \n",
       "1441    125814          53252.0  0.423260   22718556         Anthus pratensis   \n",
       "3992     24638           7677.0  0.311592   22725033       Charadrius nivosus   \n",
       "11177    75260          11900.0  0.158119   22725846       Halcyon smyrnensis   \n",
       "7621      3959           1145.0  0.289214   22725856          Halcyon gularis   \n",
       "13994    43548           7088.0  0.162763   22727255        Ciconia episcopus   \n",
       "1705    153432          41140.0  0.268132   22727265      Ciconia microscelis   \n",
       "2979    250349          43073.0  0.172052   22727487  Charadrius alexandrinus   \n",
       "2011     99390          19445.0  0.195643   22727668         Ardea intermedia   \n",
       "8818    146019          40372.0  0.276485   22727677      Ardea brachyrhyncha   \n",
       "3646     60236          18898.0  0.313733   22727683          Ardea plumifera   \n",
       "2187    640151         155235.0  0.242497   22727969    Himantopus himantopus   \n",
       "6226    586249         168475.0  0.287378   22728182        Butorides striata   \n",
       "982       1052            605.0  0.575095   22731577         Rallus obsoletus   \n",
       "1519       647            185.0  0.285935   22731855    Threskiornis bernieri   \n",
       "11124    71904          12505.0  0.173912   22733989   Pelecanus occidentalis   \n",
       "2897     11216           2449.0  0.218349   22735615     Charadrius dealbatus   \n",
       "757    1003893         266143.0  0.265111   45354964         Falco peregrinus   \n",
       "12152   277606          81930.0  0.295131   62030590      Larus smithsonianus   \n",
       "12452    38904          20970.0  0.539019   62030608         Larus argentatus   \n",
       "3685    425388         112977.0  0.265586   62120190      Gallinula chloropus   \n",
       "497     159808          47312.0  0.296055   62120280        Gallinula galeata   \n",
       "9117      5152           2629.0  0.510287   62154828      Rallus longirostris   \n",
       "3617      4695           3212.0  0.684132   62155296         Rallus crepitans   \n",
       "14075   148827          58501.0  0.393081   62169677         Fulica americana   \n",
       "10515   365325          89989.0  0.246326  102998555          Alauda arvensis   \n",
       "12014   145119          27983.0  0.192828  103727590     Corvus macrorhynchos   \n",
       "12622   422629         104517.0  0.247302  103773898        Oenanthe oenanthe   \n",
       "1629     13744           1957.0  0.142389  103773966        Oenanthe seebohmi   \n",
       "\n",
       "        kingdom        phylum           class           order_  \\\n",
       "10058  ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "4250   ANIMALIA      CHORDATA        MAMMALIA  CETARTIODACTYLA   \n",
       "12790  ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "9937   ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "192    ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "13228  ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "515    ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "2234   ANIMALIA      CHORDATA        MAMMALIA        CARNIVORA   \n",
       "3105    PLANTAE  TRACHEOPHYTA  POLYPODIOPSIDA     POLYPODIALES   \n",
       "6136    PLANTAE  TRACHEOPHYTA   MAGNOLIOPSIDA         MYRTALES   \n",
       "698     PLANTAE  TRACHEOPHYTA   MAGNOLIOPSIDA         MYRTALES   \n",
       "8407   ANIMALIA      CHORDATA            AVES      GALLIFORMES   \n",
       "8419   ANIMALIA      CHORDATA            AVES      GALLIFORMES   \n",
       "9151   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "2253   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "6921   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "3895   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "10529  ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "6864   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "9231   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "12033  ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "7552   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "10066  ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "732    ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "12607  ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "12932  ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "8425   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "4824   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "3651   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "7272   ANIMALIA      CHORDATA            AVES     ANSERIFORMES   \n",
       "...         ...           ...             ...              ...   \n",
       "12463  ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "1201   ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "1441   ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "3992   ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "11177  ANIMALIA      CHORDATA            AVES    CORACIIFORMES   \n",
       "7621   ANIMALIA      CHORDATA            AVES    CORACIIFORMES   \n",
       "13994  ANIMALIA      CHORDATA            AVES    CICONIIFORMES   \n",
       "1705   ANIMALIA      CHORDATA            AVES    CICONIIFORMES   \n",
       "2979   ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "2011   ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "8818   ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "3646   ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "2187   ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "6226   ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "982    ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "1519   ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "11124  ANIMALIA      CHORDATA            AVES   PELECANIFORMES   \n",
       "2897   ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "757    ANIMALIA      CHORDATA            AVES    FALCONIFORMES   \n",
       "12152  ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "12452  ANIMALIA      CHORDATA            AVES  CHARADRIIFORMES   \n",
       "3685   ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "497    ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "9117   ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "3617   ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "14075  ANIMALIA      CHORDATA            AVES       GRUIFORMES   \n",
       "10515  ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "12014  ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "12622  ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "1629   ANIMALIA      CHORDATA            AVES    PASSERIFORMES   \n",
       "\n",
       "                  family       ...        code biome_marine biome_freshwater  \\\n",
       "10058         MUSTELIDAE       ...          NT            t                t   \n",
       "4250      HIPPOPOTAMIDAE       ...          VU            t                t   \n",
       "12790         MUSTELIDAE       ...          NT            t                t   \n",
       "9937          MUSTELIDAE       ...          EN            t                t   \n",
       "192           MUSTELIDAE       ...          VU            t                t   \n",
       "13228           PHOCIDAE       ...          LC            t                t   \n",
       "515             PHOCIDAE       ...          LC            t                t   \n",
       "2234          MUSTELIDAE       ...          VU            t                t   \n",
       "3105         PTERIDACEAE       ...          LC            t                t   \n",
       "6136          LYTHRACEAE       ...          LC            t                t   \n",
       "698           LYTHRACEAE       ...          LC            t                t   \n",
       "8407        MEGAPODIIDAE       ...          EN            t                t   \n",
       "8419        MEGAPODIIDAE       ...          EN            t                t   \n",
       "9151            ANATIDAE       ...          LC            t                t   \n",
       "2253            ANATIDAE       ...          LC            t                t   \n",
       "6921            ANATIDAE       ...          VU            t                t   \n",
       "3895            ANATIDAE       ...          LC            t                t   \n",
       "10529           ANATIDAE       ...          LC            t                t   \n",
       "6864            ANATIDAE       ...          LC            t                t   \n",
       "9231            ANATIDAE       ...          LC            t                t   \n",
       "12033           ANATIDAE       ...          VU            t                t   \n",
       "7552            ANATIDAE       ...          LC            t                t   \n",
       "10066           ANATIDAE       ...          LC            t                t   \n",
       "732             ANATIDAE       ...          NT            t                t   \n",
       "12607           ANATIDAE       ...          LC            t                t   \n",
       "12932           ANATIDAE       ...          LC            t                t   \n",
       "8425            ANATIDAE       ...          VU            t                t   \n",
       "4824            ANATIDAE       ...          LC            t                t   \n",
       "3651            ANATIDAE       ...          LC            t                t   \n",
       "7272            ANATIDAE       ...          LC            t                t   \n",
       "...                  ...       ...         ...          ...              ...   \n",
       "12463       CISTICOLIDAE       ...          LC            t                t   \n",
       "1201           ALAUDIDAE       ...          LC            t                t   \n",
       "1441        MOTACILLIDAE       ...          NT            t                t   \n",
       "3992        CHARADRIIDAE       ...          NT            t                t   \n",
       "11177        ALCEDINIDAE       ...          LC            t                t   \n",
       "7621         ALCEDINIDAE       ...          LC            t                t   \n",
       "13994         CICONIIDAE       ...          VU            t                t   \n",
       "1705          CICONIIDAE       ...          LC            t                t   \n",
       "2979        CHARADRIIDAE       ...          LC            t                t   \n",
       "2011            ARDEIDAE       ...          LC            t                t   \n",
       "8818            ARDEIDAE       ...          LC            t                t   \n",
       "3646            ARDEIDAE       ...          LC            t                t   \n",
       "2187    RECURVIROSTRIDAE       ...          LC            t                t   \n",
       "6226            ARDEIDAE       ...          LC            t                t   \n",
       "982             RALLIDAE       ...          NT            t                t   \n",
       "1519   THRESKIORNITHIDAE       ...          EN            t                t   \n",
       "11124        PELECANIDAE       ...          LC            t                t   \n",
       "2897        CHARADRIIDAE       ...          DD            t                t   \n",
       "757           FALCONIDAE       ...          LC            t                t   \n",
       "12152            LARIDAE       ...          LC            t                t   \n",
       "12452            LARIDAE       ...          LC            t                t   \n",
       "3685            RALLIDAE       ...          LC            t                t   \n",
       "497             RALLIDAE       ...          LC            t                t   \n",
       "9117            RALLIDAE       ...          LC            t                t   \n",
       "3617            RALLIDAE       ...          LC            t                t   \n",
       "14075           RALLIDAE       ...          LC            t                t   \n",
       "10515          ALAUDIDAE       ...          LC            t                t   \n",
       "12014           CORVIDAE       ...          LC            t                t   \n",
       "12622       MUSCICAPIDAE       ...          LC            t                t   \n",
       "1629        MUSCICAPIDAE       ...          LC            t                t   \n",
       "\n",
       "      biome_terrestrial comprehensive_groups    target        threaten  \\\n",
       "10058                 t              Mammals  0.100000  Not Threatened   \n",
       "4250                  t              Mammals  0.100000      Threatened   \n",
       "12790                 t              Mammals  0.100000  Not Threatened   \n",
       "9937                  t              Mammals  0.100000      Threatened   \n",
       "192                   t              Mammals  0.100000      Threatened   \n",
       "13228                 t              Mammals  0.100000  Not Threatened   \n",
       "515                   t              Mammals  0.100000  Not Threatened   \n",
       "2234                  t              Mammals  0.100000      Threatened   \n",
       "3105                  t            Mangroves  0.100000  Not Threatened   \n",
       "6136                  t            Mangroves  0.100000  Not Threatened   \n",
       "698                   t            Mangroves  0.100000  Not Threatened   \n",
       "8407                  t                Birds  0.336386      Threatened   \n",
       "8419                  t                Birds  1.000000      Threatened   \n",
       "9151                  t                Birds  0.100000  Not Threatened   \n",
       "2253                  t                Birds  0.100000  Not Threatened   \n",
       "6921                  t                Birds  0.422771      Threatened   \n",
       "3895                  t                Birds  0.100000  Not Threatened   \n",
       "10529                 t                Birds  0.100000  Not Threatened   \n",
       "6864                  t                Birds  0.100000  Not Threatened   \n",
       "9231                  t                Birds  0.100000  Not Threatened   \n",
       "12033                 t                Birds  0.100000      Threatened   \n",
       "7552                  t                Birds  0.100000  Not Threatened   \n",
       "10066                 t                Birds  0.100000  Not Threatened   \n",
       "732                   t                Birds  0.100000  Not Threatened   \n",
       "12607                 t                Birds  0.100000  Not Threatened   \n",
       "12932                 t                Birds  0.100000  Not Threatened   \n",
       "8425                  t                Birds  0.100000      Threatened   \n",
       "4824                  t                Birds  0.100000  Not Threatened   \n",
       "3651                  t                Birds  0.100000  Not Threatened   \n",
       "7272                  t                Birds  0.100000  Not Threatened   \n",
       "...                 ...                  ...       ...             ...   \n",
       "12463                 t                Birds  0.100000  Not Threatened   \n",
       "1201                  t                Birds  0.100000  Not Threatened   \n",
       "1441                  t                Birds  0.100000  Not Threatened   \n",
       "3992                  t                Birds  0.100000  Not Threatened   \n",
       "11177                 t                Birds  0.100000  Not Threatened   \n",
       "7621                  t                Birds  0.100000  Not Threatened   \n",
       "13994                 t                Birds  0.100000      Threatened   \n",
       "1705                  t                Birds  0.100000  Not Threatened   \n",
       "2979                  t                Birds  0.100000  Not Threatened   \n",
       "2011                  t                Birds  0.100000  Not Threatened   \n",
       "8818                  t                Birds  0.100000  Not Threatened   \n",
       "3646                  t                Birds  0.100000  Not Threatened   \n",
       "2187                  t                Birds  0.100000  Not Threatened   \n",
       "6226                  t                Birds  0.100000  Not Threatened   \n",
       "982                   t                Birds  0.623373  Not Threatened   \n",
       "1519                  t                Birds  0.769759      Threatened   \n",
       "11124                 t                Birds  0.100000  Not Threatened   \n",
       "2897                  t                Birds  0.100000  Data deficient   \n",
       "757                   t                Birds  0.100000  Not Threatened   \n",
       "12152                 t                Birds  0.100000  Not Threatened   \n",
       "12452                 t                Birds  0.100000  Not Threatened   \n",
       "3685                  t                Birds  0.100000  Not Threatened   \n",
       "497                   t                Birds  0.100000  Not Threatened   \n",
       "9117                  t                Birds  0.100000  Not Threatened   \n",
       "3617                  t                Birds  0.100000  Not Threatened   \n",
       "14075                 t                Birds  0.100000  Not Threatened   \n",
       "10515                 t                Birds  0.100000  Not Threatened   \n",
       "12014                 t                Birds  0.100000  Not Threatened   \n",
       "12622                 t                Birds  0.100000  Not Threatened   \n",
       "1629                  t                Birds  0.100000  Not Threatened   \n",
       "\n",
       "        gap_status discrete_per  discrete_target  \n",
       "10058      Covered           40               10  \n",
       "4250       Covered           80               10  \n",
       "12790      Covered           30               10  \n",
       "9937       Covered           20               10  \n",
       "192        Covered           20               10  \n",
       "13228      Covered           30               10  \n",
       "515    Partial gap           10               10  \n",
       "2234       Covered           20               10  \n",
       "3105       Covered           20               10  \n",
       "6136       Covered           20               10  \n",
       "698        Covered           20               10  \n",
       "8407   Partial gap           30               40  \n",
       "8419           Gap            0              100  \n",
       "9151       Covered           30               10  \n",
       "2253       Covered           20               10  \n",
       "6921       Covered           50               50  \n",
       "3895       Covered           40               10  \n",
       "10529      Covered           40               10  \n",
       "6864       Covered           40               10  \n",
       "9231       Covered           20               10  \n",
       "12033      Covered           20               10  \n",
       "7552       Covered           70               10  \n",
       "10066      Covered           30               10  \n",
       "732        Covered           50               10  \n",
       "12607      Covered           70               10  \n",
       "12932      Covered           50               10  \n",
       "8425       Covered           20               10  \n",
       "4824       Covered           30               10  \n",
       "3651       Covered           30               10  \n",
       "7272       Covered           30               10  \n",
       "...            ...          ...              ...  \n",
       "12463      Covered           30               10  \n",
       "1201       Covered           20               10  \n",
       "1441       Covered           50               10  \n",
       "3992       Covered           40               10  \n",
       "11177      Covered           20               10  \n",
       "7621       Covered           30               10  \n",
       "13994      Covered           20               10  \n",
       "1705       Covered           30               10  \n",
       "2979       Covered           20               10  \n",
       "2011       Covered           20               10  \n",
       "8818       Covered           30               10  \n",
       "3646       Covered           40               10  \n",
       "2187       Covered           30               10  \n",
       "6226       Covered           30               10  \n",
       "982    Partial gap           60               70  \n",
       "1519   Partial gap           30               80  \n",
       "11124      Covered           20               10  \n",
       "2897       Covered           30               10  \n",
       "757        Covered           30               10  \n",
       "12152      Covered           30               10  \n",
       "12452      Covered           60               10  \n",
       "3685       Covered           30               10  \n",
       "497        Covered           30               10  \n",
       "9117       Covered           60               10  \n",
       "3617       Covered           70               10  \n",
       "14075      Covered           40               10  \n",
       "10515      Covered           30               10  \n",
       "12014      Covered           20               10  \n",
       "12622      Covered           30               10  \n",
       "1629       Covered           20               10  \n",
       "\n",
       "[216 rows x 21 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_result_attr.loc[species_result_attr.index.isin(biome_systems[biome_systems=='ttt'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>gap_status</th>\n",
       "      <th>Gap</th>\n",
       "      <th>Partial gap</th>\n",
       "      <th>Covered</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LC</th>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>1533</td>\n",
       "      <td>1871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NT</th>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>194</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VU</th>\n",
       "      <td>7</td>\n",
       "      <td>279</td>\n",
       "      <td>177</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN</th>\n",
       "      <td>21</td>\n",
       "      <td>202</td>\n",
       "      <td>96</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR</th>\n",
       "      <td>21</td>\n",
       "      <td>97</td>\n",
       "      <td>42</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>66</td>\n",
       "      <td>1098</td>\n",
       "      <td>2072</td>\n",
       "      <td>3236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gap_status  Gap  Partial gap  Covered   All\n",
       "code                                       \n",
       "DD            9           43       30    82\n",
       "LC            4          334     1533  1871\n",
       "NT            4          143      194   341\n",
       "VU            7          279      177   463\n",
       "EN           21          202       96   319\n",
       "CR           21           97       42   160\n",
       "All          66         1098     2072  3236"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = species_result_attr[species_result_attr.biome_terrestrial=='t']\n",
    "a.pivot_table(columns='gap_status', index='code', values='id_no',aggfunc=np.count_nonzero, fill_value=0, margins=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tables and graphs for distributions, gaps and targets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gap_table(df):\n",
    "    # df: the filtered 'species_result_attr'\n",
    "    return df.pivot_table(index='class', values='id_no', \n",
    "                                columns='discrete_per', aggfunc=np.count_nonzero, fill_value=0, margins=True)\n",
    "\n",
    "def gap_table_normalise(df):\n",
    "    return pd.crosstab(df['class'], df['discrete_per'],\n",
    "            values='id_no', aggfunc=np.count_nonzero, normalize='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>discrete_per</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "      <th>100</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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>ACTINOPTERYGII</th>\n",
       "      <td>98</td>\n",
       "      <td>696</td>\n",
       "      <td>1577</td>\n",
       "      <td>716</td>\n",
       "      <td>298</td>\n",
       "      <td>156</td>\n",
       "      <td>71</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "      <td>57</td>\n",
       "      <td>3782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>30</td>\n",
       "      <td>122</td>\n",
       "      <td>434</td>\n",
       "      <td>458</td>\n",
       "      <td>245</td>\n",
       "      <td>123</td>\n",
       "      <td>64</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>1588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>105</td>\n",
       "      <td>70</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>32</td>\n",
       "      <td>113</td>\n",
       "      <td>232</td>\n",
       "      <td>333</td>\n",
       "      <td>173</td>\n",
       "      <td>117</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>181</td>\n",
       "      <td>1096</td>\n",
       "      <td>2614</td>\n",
       "      <td>1811</td>\n",
       "      <td>880</td>\n",
       "      <td>487</td>\n",
       "      <td>238</td>\n",
       "      <td>144</td>\n",
       "      <td>132</td>\n",
       "      <td>120</td>\n",
       "      <td>164</td>\n",
       "      <td>7867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "discrete_per          0    10    20    30   40   50   60   70   80   90  100  \\\n",
       "class                                                                          \n",
       "ACTINOPTERYGII       98   696  1577   716  298  156   71   48   29   36   57   \n",
       "AGARICOMYCETES        0     0     0     0    1    1    0    0    2    3    1   \n",
       "AMPHIBIA              3    19    59    58   36   26    9    6    8    5    9   \n",
       "AVES                 30   122   434   458  245  123   64   22   31   18   41   \n",
       "BIVALVIA              1     3    24    22   13    2    0    0    0    1    1   \n",
       "CEPHALASPIDOMORPHI    0     0     1     1    0    0    0    1    0    1    0   \n",
       "CHONDRICHTHYES        2    65   105    70   23   14    2    1    2    1    0   \n",
       "CLITELLATA            0     0     0     0    0    0    0    0    0    0    2   \n",
       "GASTROPODA            2     1    16    17    7    5    1    3    9    5    6   \n",
       "HOLOTHUROIDEA         0     6    53     8    3    3    0    0    1    0    1   \n",
       "LILIOPSIDA            0    10    10    23   14    8    7    5    8    6    1   \n",
       "MAGNOLIOPSIDA         3     9    18    28   13    7   10    7   14   17   12   \n",
       "MALACOSTRACA         10    35    53    48   40   16    9    6    7    2    5   \n",
       "MAMMALIA             32   113   232   333  173  117   60   45   20   23   24   \n",
       "MYXINI                0     2     2     0    0    0    0    0    0    0    0   \n",
       "PINOPSIDA             0     0     0     0    0    0    0    0    0    1    0   \n",
       "POLYPODIOPSIDA        0     2     1     1    0    0    1    0    0    0    0   \n",
       "REPTILIA              0    13    27    25   13    9    4    0    1    1    4   \n",
       "SARCOPTERYGII         0     0     2     3    1    0    0    0    0    0    0   \n",
       "All                 181  1096  2614  1811  880  487  238  144  132  120  164   \n",
       "\n",
       "discrete_per         All  \n",
       "class                     \n",
       "ACTINOPTERYGII      3782  \n",
       "AGARICOMYCETES         8  \n",
       "AMPHIBIA             238  \n",
       "AVES                1588  \n",
       "BIVALVIA              67  \n",
       "CEPHALASPIDOMORPHI     4  \n",
       "CHONDRICHTHYES       285  \n",
       "CLITELLATA             2  \n",
       "GASTROPODA            72  \n",
       "HOLOTHUROIDEA         75  \n",
       "LILIOPSIDA            92  \n",
       "MAGNOLIOPSIDA        138  \n",
       "MALACOSTRACA         231  \n",
       "MAMMALIA            1172  \n",
       "MYXINI                 4  \n",
       "PINOPSIDA              1  \n",
       "POLYPODIOPSIDA         5  \n",
       "REPTILIA              97  \n",
       "SARCOPTERYGII          6  \n",
       "All                 7867  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gap_table(species_result_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = gap_table_normalise(species_result_attr[species_result_attr.threaten=='Threatened']).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find those rows with 0s and drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.drop(a[a.sum(axis=1)==0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalIndex([], categories=['ACTINOPTERYGII', 'AGARICOMYCETES', 'AMPHIBIA', 'AVES', 'BIVALVIA', 'CEPHALASPIDOMORPHI', 'CHONDRICHTHYES', 'CLITELLATA', ...], ordered=False, name='class', dtype='category')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a.sum(axis=1)==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.index = a.index.remove_unused_categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>discrete_per</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "      <th>80</th>\n",
       "      <th>90</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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>ACTINOPTERYGII</th>\n",
       "      <td>0.025912</td>\n",
       "      <td>0.184030</td>\n",
       "      <td>0.416975</td>\n",
       "      <td>0.189318</td>\n",
       "      <td>0.078794</td>\n",
       "      <td>0.041248</td>\n",
       "      <td>0.018773</td>\n",
       "      <td>0.012692</td>\n",
       "      <td>0.007668</td>\n",
       "      <td>0.009519</td>\n",
       "      <td>0.015071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGARICOMYCETES</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMPHIBIA</th>\n",
       "      <td>0.012605</td>\n",
       "      <td>0.079832</td>\n",
       "      <td>0.247899</td>\n",
       "      <td>0.243697</td>\n",
       "      <td>0.151261</td>\n",
       "      <td>0.109244</td>\n",
       "      <td>0.037815</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>0.021008</td>\n",
       "      <td>0.037815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>0.018892</td>\n",
       "      <td>0.076826</td>\n",
       "      <td>0.273300</td>\n",
       "      <td>0.288413</td>\n",
       "      <td>0.154282</td>\n",
       "      <td>0.077456</td>\n",
       "      <td>0.040302</td>\n",
       "      <td>0.013854</td>\n",
       "      <td>0.019521</td>\n",
       "      <td>0.011335</td>\n",
       "      <td>0.025819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIVALVIA</th>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.358209</td>\n",
       "      <td>0.328358</td>\n",
       "      <td>0.194030</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.014925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEPHALASPIDOMORPHI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHONDRICHTHYES</th>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.228070</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.245614</td>\n",
       "      <td>0.080702</td>\n",
       "      <td>0.049123</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLITELLATA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GASTROPODA</th>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOLOTHUROIDEA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.706667</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LILIOPSIDA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.108696</td>\n",
       "      <td>0.108696</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>0.054348</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.010870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNOLIOPSIDA</th>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.202899</td>\n",
       "      <td>0.094203</td>\n",
       "      <td>0.050725</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.050725</td>\n",
       "      <td>0.101449</td>\n",
       "      <td>0.123188</td>\n",
       "      <td>0.086957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALACOSTRACA</th>\n",
       "      <td>0.043290</td>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.229437</td>\n",
       "      <td>0.207792</td>\n",
       "      <td>0.173160</td>\n",
       "      <td>0.069264</td>\n",
       "      <td>0.038961</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.021645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAMMALIA</th>\n",
       "      <td>0.027304</td>\n",
       "      <td>0.096416</td>\n",
       "      <td>0.197952</td>\n",
       "      <td>0.284130</td>\n",
       "      <td>0.147611</td>\n",
       "      <td>0.099829</td>\n",
       "      <td>0.051195</td>\n",
       "      <td>0.038396</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.019625</td>\n",
       "      <td>0.020478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYXINI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PINOPSIDA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLYPODIOPSIDA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPTILIA</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.134021</td>\n",
       "      <td>0.278351</td>\n",
       "      <td>0.257732</td>\n",
       "      <td>0.134021</td>\n",
       "      <td>0.092784</td>\n",
       "      <td>0.041237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.041237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARCOPTERYGII</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "discrete_per             0         10        20        30        40   \\\n",
       "class                                                                  \n",
       "ACTINOPTERYGII      0.025912  0.184030  0.416975  0.189318  0.078794   \n",
       "AGARICOMYCETES      0.000000  0.000000  0.000000  0.000000  0.125000   \n",
       "AMPHIBIA            0.012605  0.079832  0.247899  0.243697  0.151261   \n",
       "AVES                0.018892  0.076826  0.273300  0.288413  0.154282   \n",
       "BIVALVIA            0.014925  0.044776  0.358209  0.328358  0.194030   \n",
       "CEPHALASPIDOMORPHI  0.000000  0.000000  0.250000  0.250000  0.000000   \n",
       "CHONDRICHTHYES      0.007018  0.228070  0.368421  0.245614  0.080702   \n",
       "CLITELLATA          0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "GASTROPODA          0.027778  0.013889  0.222222  0.236111  0.097222   \n",
       "HOLOTHUROIDEA       0.000000  0.080000  0.706667  0.106667  0.040000   \n",
       "LILIOPSIDA          0.000000  0.108696  0.108696  0.250000  0.152174   \n",
       "MAGNOLIOPSIDA       0.021739  0.065217  0.130435  0.202899  0.094203   \n",
       "MALACOSTRACA        0.043290  0.151515  0.229437  0.207792  0.173160   \n",
       "MAMMALIA            0.027304  0.096416  0.197952  0.284130  0.147611   \n",
       "MYXINI              0.000000  0.500000  0.500000  0.000000  0.000000   \n",
       "PINOPSIDA           0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "POLYPODIOPSIDA      0.000000  0.400000  0.200000  0.200000  0.000000   \n",
       "REPTILIA            0.000000  0.134021  0.278351  0.257732  0.134021   \n",
       "SARCOPTERYGII       0.000000  0.000000  0.333333  0.500000  0.166667   \n",
       "\n",
       "discrete_per             50        60        70        80        90        100  \n",
       "class                                                                           \n",
       "ACTINOPTERYGII      0.041248  0.018773  0.012692  0.007668  0.009519  0.015071  \n",
       "AGARICOMYCETES      0.125000  0.000000  0.000000  0.250000  0.375000  0.125000  \n",
       "AMPHIBIA            0.109244  0.037815  0.025210  0.033613  0.021008  0.037815  \n",
       "AVES                0.077456  0.040302  0.013854  0.019521  0.011335  0.025819  \n",
       "BIVALVIA            0.029851  0.000000  0.000000  0.000000  0.014925  0.014925  \n",
       "CEPHALASPIDOMORPHI  0.000000  0.000000  0.250000  0.000000  0.250000  0.000000  \n",
       "CHONDRICHTHYES      0.049123  0.007018  0.003509  0.007018  0.003509  0.000000  \n",
       "CLITELLATA          0.000000  0.000000  0.000000  0.000000  0.000000  1.000000  \n",
       "GASTROPODA          0.069444  0.013889  0.041667  0.125000  0.069444  0.083333  \n",
       "HOLOTHUROIDEA       0.040000  0.000000  0.000000  0.013333  0.000000  0.013333  \n",
       "LILIOPSIDA          0.086957  0.076087  0.054348  0.086957  0.065217  0.010870  \n",
       "MAGNOLIOPSIDA       0.050725  0.072464  0.050725  0.101449  0.123188  0.086957  \n",
       "MALACOSTRACA        0.069264  0.038961  0.025974  0.030303  0.008658  0.021645  \n",
       "MAMMALIA            0.099829  0.051195  0.038396  0.017065  0.019625  0.020478  \n",
       "MYXINI              0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "PINOPSIDA           0.000000  0.000000  0.000000  0.000000  1.000000  0.000000  \n",
       "POLYPODIOPSIDA      0.000000  0.200000  0.000000  0.000000  0.000000  0.000000  \n",
       "REPTILIA            0.092784  0.041237  0.000000  0.010309  0.010309  0.041237  \n",
       "SARCOPTERYGII       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gap_table_normalise(species_result_attr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Rodrigues 2004* style graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def species_pa_coverage_graph(df, title, drop_empty=False):\n",
    "    # df: the filtered 'species_result_attr'\n",
    "    # 1. pivot table for margin count\n",
    "    # 2. cross tabulation for normalise count\n",
    "    pvt = gap_table(df)\n",
    "    ctb = gap_table_normalise(df)\n",
    "    \n",
    "    if drop_empty:\n",
    "        # drop those rows where every column is zero\n",
    "        ctb = ctb.drop(ctb[ctb.sum(axis=1)==0].index)\n",
    "        # ensure the labels are correct\n",
    "        pvt = pvt.dropna()\n",
    "    \n",
    "    # 3. plot\n",
    "    fig, axes = plt.subplots(1, figsize=(14,14))\n",
    "    ctb.sort_index(ascending=False).plot.barh(stacked=True, cmap='RdYlGn', ax=axes)\n",
    "    \n",
    "    # fine tune graph\n",
    "    axes.legend(loc='lower center', bbox_to_anchor=(0.5,-0.1), ncol=11)\n",
    "    axes.set_title(title)\n",
    "    axes.set_ylabel('')\n",
    "\n",
    "    # use the margin column 'All', i.e. sum\n",
    "    for i, each in enumerate(zip(pvt.index, pvt['All'])):\n",
    "        axes.annotate(s='{:.0f}'.format(each[1]), xy=(1.005, ctb.index.size-1-i), verticalalignment='center')\n",
    "        \n",
    "#     return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ox1aqFk7K7rNnVM27ivLZxk8A58QYz6H8WZS/Asd2dgMppbdjjD8Ezq2YfThw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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3baf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_pa_coverage_graph(species_result_attr, title='Proportions of species covered by protected area across different taxa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HPZ5z+8F/kB4VOJHUiuAV0rPf+1YLFUWxBumLhnmkFljVae8iNYP/CnAy6XGDd5OaXdtB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EJB4AQAAAAAhILACQAAAAAIBYETAAAAABAKAicAAAAAIBQETgAAAABAKAicAAAAAIBQEDgBAAAA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb77de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_pa_coverage_graph(species_result_attr[species_result_attr.code.isin(['VU', 'CR', 'EN'])],\n",
    "        title='Proportions of threatened species covered by protected area across different taxa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XQniW1JT2/jxscXU3we12cYzxuw1xTA0hXAEcwYIvLKqWKUMIewFnhxC+Cvyd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//GP/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\nccMRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8b6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_pa_coverage_graph(species_result_attr[species_result_attr.code.isin(['VU', 'CR', 'EN'])],\n",
    "        title='Proportions of threatened species covered by protected area across different taxa', drop_empty=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EXPORT SPECIES RESULT**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('output'):\n",
    "    os.mkdir('output')\n",
    "    \n",
    "species_result_attr.to_csv('output' + os.sep + 'species_result_attr.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pack for each export. It will include two tables and two graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gap_table_mk2(df, index='class', columns='discrete_per'):\n",
    "    # df: the filtered 'species_result_attr'\n",
    "    return df.pivot_table(index=index, values='id_no', \n",
    "                                columns=columns, aggfunc=np.count_nonzero, fill_value=0, margins=True)\n",
    "\n",
    "def gap_table_normalise_mk2(df, index='class', columns='discrete_per'):\n",
    "    return pd.crosstab(df[index], df[columns],\n",
    "            values='id_no', aggfunc=np.count_nonzero, normalize='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pack_export(df, title, index, columns, drop_empty=False):\n",
    "    \n",
    "    # create table and export\n",
    "    pvt = gap_table_mk2(df, index, columns)\n",
    "    ctb = gap_table_normalise_mk2(df, index, columns)\n",
    "    \n",
    "    if not os.path.exists('output'):\n",
    "        os.mkdir('output')\n",
    "    \n",
    "    pvt_csv = 'output' + os.sep + title + '_count.csv'\n",
    "    ctb_csv = 'output' + os.sep + title + '_normalise.csv'\n",
    "    \n",
    "    pvt.to_csv(pvt_csv)\n",
    "    ctb.to_csv(ctb_csv)\n",
    "    \n",
    "    \n",
    "    if drop_empty:\n",
    "        # ensure the labels are correct\n",
    "        pvt = pvt.dropna()\n",
    "        # drop those rows where every column is zero\n",
    "        ctb = ctb.drop(ctb[ctb.sum(axis=1)==0].index)\n",
    "    \n",
    "    # plotting\n",
    "    fig, axes = plt.subplots(1, figsize=(14,14))\n",
    "    ctb.sort_index(ascending=False).plot.barh(stacked=True, cmap='RdYlGn', ax=axes)\n",
    "    \n",
    "    # fine tune graph\n",
    "    axes.legend(loc='lower center', bbox_to_anchor=(0.5,-0.1), ncol=11)\n",
    "    axes.set_title(title)\n",
    "    axes.set_ylabel('')\n",
    "\n",
    "    # use the margin column 'All', i.e. sum\n",
    "    for i, each in enumerate(zip(pvt.index, pvt['All'])):\n",
    "        axes.annotate(s='{:.0f}'.format(each[1]), xy=(1.005, ctb.index.size-1-i), verticalalignment='center')\n",
    "    \n",
    "    fig_png = 'output' + os.sep + title + '.png'\n",
    "    fig.savefig(fig_png)\n",
    "    \n",
    "    plt.close(fig)\n",
    "#     return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph by classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribution of targets by CLASS\n",
    "pack_export(species_result_attr, \n",
    "            title='Targets of gap, partial gap, and covered species across different taxa', \n",
    "            index='class', columns='discrete_target')\n",
    "\n",
    "pack_export(species_result_attr[species_result_attr.threaten=='Threatened'], \n",
    "            title='Targets of gap, partial gap, and covered species across different taxa (Threatened)', \n",
    "            index='class', columns='discrete_target')\n",
    "\n",
    "# distribution of percentage overlap by pa by CLASS\n",
    "pack_export(species_result_attr, \n",
    "            title='Proportions of species covered by protected areas across different taxa', \n",
    "            index='class', columns='discrete_per')\n",
    "\n",
    "pack_export(species_result_attr[species_result_attr.threaten=='Threatened'], \n",
    "            title='Proportions of species covered by protected areas across different taxa (Threatened)', \n",
    "            index='class', columns='discrete_per')\n",
    "\n",
    "# distribution of gap status by CLASS\n",
    "pack_export(species_result_attr, \n",
    "            title='Proportions of gap, partial gap, and covered species across different taxa', \n",
    "            index='class', columns='gap_status')\n",
    "\n",
    "pack_export(species_result_attr[species_result_attr.threaten=='Threatened'], \n",
    "            title='Proportions of gap, partial gap, and covered species across different taxa (Threatened)', \n",
    "            index='class', columns='gap_status')\n",
    "                                \n",
    "# gap status by IUCN cat\n",
    "pack_export(species_result_attr, \n",
    "            title='Proportions of gap, partial gap, and covered species by IUCN category',\n",
    "            index='code', columns='gap_status')\n",
    "\n",
    "# gap status by IUCN cat for Mammals\n",
    "pack_export(species_result_attr[species_result_attr['class']=='MAMMALIA'], \n",
    "            title='Proportions of gap, partial gap, and covered mammals by IUCN category',\n",
    "            index='code', columns='gap_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs for comprehensively assessed select groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMPREHENSIVE only, distribution of targets\n",
    "pack_export(species_result_attr[species_result_attr.comprehensive_groups!='Unassigned'], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa', \n",
    "            index='comprehensive_groups', columns='discrete_target')\n",
    "\n",
    "pack_export(species_result_attr[(species_result_attr.threaten=='Threatened') & (species_result_attr.comprehensive_groups!='Unassigned')], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Threatened)', \n",
    "            index='comprehensive_groups', columns='discrete_target')\n",
    "\n",
    "\n",
    "# COMPRENENSIVE only, distribution of percentage overlap by pa\n",
    "pack_export(species_result_attr[species_result_attr.comprehensive_groups!='Unassigned'], \n",
    "            title='Proportions of species covered by protected areas across select taxa', \n",
    "            index='comprehensive_groups', columns='discrete_per')\n",
    "\n",
    "pack_export(species_result_attr[(species_result_attr.threaten=='Threatened') & (species_result_attr.comprehensive_groups!='Unassigned')], \n",
    "            title='Proportions of species covered by protected areas across select taxa (Threatened)', \n",
    "            index='comprehensive_groups', columns='discrete_per')\n",
    "\n",
    "\n",
    "# COMPRENENSIVE only distribution of gap status\n",
    "pack_export(species_result_attr[species_result_attr.comprehensive_groups!='Unassigned'], \n",
    "            title='Proportions of gap, partial gap, and covered species across select taxa', \n",
    "            index='comprehensive_groups', columns='gap_status')\n",
    "\n",
    "pack_export(species_result_attr[(species_result_attr.threaten=='Threatened') & (species_result_attr.comprehensive_groups!='Unassigned')], \n",
    "            title='Proportions of gap, partial gap, and covered species across select taxa (Threatened)', \n",
    "            index='comprehensive_groups', columns='gap_status')\n",
    "\n",
    "# COMPRENENSIVE only gap status by IUCN cat\n",
    "pack_export(species_result_attr[species_result_attr.comprehensive_groups!='Unassigned'], \n",
    "            title='Proportions of gap, partial gap, and covered species across select taxa by IUCN category',\n",
    "            index='code', columns='gap_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs for comprehensively assessed select groups, by biome systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMPREHENSIVE only, distribution of targets BY BIOME SYSTEM\n",
    "# Terrestrial\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned')\\\n",
    "            & (species_result_attr.biome_terrestrial=='t')\n",
    "    \n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Terrestrial)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n",
    "\n",
    "\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned') \\\n",
    "            & (species_result_attr.biome_terrestrial=='t') \\\n",
    "            & (species_result_attr.threaten=='Threatened')\n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Terrestrial and threatened)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n",
    "\n",
    "# Marine\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned') \\\n",
    "            & (species_result_attr.biome_marine=='t')\n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Marine)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n",
    "\n",
    "\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned') \\\n",
    "            & (species_result_attr.biome_marine=='t') \\\n",
    "            & (species_result_attr.threaten=='Threatened')\n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Marine and threatened)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n",
    "\n",
    "\n",
    "# Freshwater\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned')\\\n",
    "            & (species_result_attr.biome_freshwater=='t')\n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Freshwater)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n",
    "\n",
    "\n",
    "condition = (species_result_attr.comprehensive_groups!='Unassigned') \\\n",
    "            & (species_result_attr.biome_freshwater=='t') \\\n",
    "            & (species_result_attr.threaten=='Threatened')\n",
    "pack_export(species_result_attr[condition], \n",
    "            title='Targets of gap, partial gap, and covered species across select taxa (Freshwater and threatened)', \n",
    "            index='comprehensive_groups', columns='discrete_target', drop_empty=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top protected areas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a protected area point of view, it would be useful to consider the number of species (richness), and their ranking compared to other protected areas. There are a number of perspectives: groupings, threatened status and gap status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the three tables: 1. wdpa-hex 2. row_id-hex and 3 id_no-row_id. By joining them with appropriate keys, the relationship between PA and species can be established"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result: wdpaid, id_no, uniquecount of hex\n",
    "# input: df in the form of row_id, id\n",
    "\n",
    "# join wdpa with bfa result using hexid\n",
    "pa_species = pd.merge(wdpa_hex, bfa_hex, on='hexid')\n",
    "\n",
    "# join bfa_hex_lu to get id_no\n",
    "pa_species = pd.merge(pa_species, bfa_hex_lu_filter, on='row_id')\n",
    "\n",
    "# group to count number of hex per pa-species pair\n",
    "pa_species = pa_species.groupby(['wdpaid', 'id_no']).hexid.nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_species = pa_species.astype('uint32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_species.columns = ['wdpaid', 'id_no', 'count_hex']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get attributes for both species and PA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34258002 entries, 0 to 34258001\n",
      "Data columns (total 3 columns):\n",
      "wdpaid       uint32\n",
      "id_no        uint32\n",
      "count_hex    uint32\n",
      "dtypes: uint32(3)\n",
      "memory usage: 392.1 MB\n"
     ]
    }
   ],
   "source": [
    "pa_species.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_species = pd.merge(pa_species, species_result_attr, on='id_no')\n",
    "pa_species = pd.merge(pa_species, wdpa_hex_lu_filter, left_on='wdpaid', right_on='WDPAID')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_species_fields = ['WDPAID', 'NAME', 'PARENT_ISO3', 'DESIG_ENG',\n",
    "                     'id_no', 'binomial','kingdom', 'phylum', 'class', 'order_', 'family', 'genus', 'code', 'biome_marine', 'biome_freshwater', 'biome_terrestrial',\n",
    "                     'gap_status', 'threaten', 'comprehensive_groups',\n",
    "                     'count_hex']\n",
    "\n",
    "pa_species = pa_species[pa_species_fields]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34060571 entries, 0 to 34060570\n",
      "Data columns (total 20 columns):\n",
      "WDPAID                  uint32\n",
      "NAME                    object\n",
      "PARENT_ISO3             category\n",
      "DESIG_ENG               category\n",
      "id_no                   uint32\n",
      "binomial                category\n",
      "kingdom                 category\n",
      "phylum                  category\n",
      "class                   category\n",
      "order_                  category\n",
      "family                  category\n",
      "genus                   category\n",
      "code                    category\n",
      "biome_marine            category\n",
      "biome_freshwater        category\n",
      "biome_terrestrial       category\n",
      "gap_status              category\n",
      "threaten                category\n",
      "comprehensive_groups    category\n",
      "count_hex               uint32\n",
      "dtypes: category(16), object(1), uint32(3)\n",
      "memory usage: 1.6+ GB\n"
     ]
    }
   ],
   "source": [
    "pa_species.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>WDPAID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PARENT_ISO3</th>\n",
       "      <th>DESIG_ENG</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "      <th>gap_status</th>\n",
       "      <th>threaten</th>\n",
       "      <th>comprehensive_groups</th>\n",
       "      <th>count_hex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2474</td>\n",
       "      <td>Balaenoptera acutorostrata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2475</td>\n",
       "      <td>Balaenoptera borealis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2476</td>\n",
       "      <td>Balaenoptera edeni</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>DD</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Data deficient</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2539</td>\n",
       "      <td>Balistes vetula</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>TETRAODONTIFORMES</td>\n",
       "      <td>BALISTIDAE</td>\n",
       "      <td>Balistes</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Covered</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>3851</td>\n",
       "      <td>Carcharhinus limbatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>CHONDRICHTHYES</td>\n",
       "      <td>CARCHARHINIFORMES</td>\n",
       "      <td>CARCHARHINIDAE</td>\n",
       "      <td>Carcharhinus</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Covered</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Sharks and rays</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WDPAID           NAME PARENT_ISO3       DESIG_ENG  id_no  \\\n",
       "0       2  Palaster Reef         ATG  Marine Reserve   2474   \n",
       "1       2  Palaster Reef         ATG  Marine Reserve   2475   \n",
       "2       2  Palaster Reef         ATG  Marine Reserve   2476   \n",
       "3       2  Palaster Reef         ATG  Marine Reserve   2539   \n",
       "4       2  Palaster Reef         ATG  Marine Reserve   3851   \n",
       "\n",
       "                     binomial   kingdom    phylum           class  \\\n",
       "0  Balaenoptera acutorostrata  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "1       Balaenoptera borealis  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "2          Balaenoptera edeni  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "3             Balistes vetula  ANIMALIA  CHORDATA  ACTINOPTERYGII   \n",
       "4       Carcharhinus limbatus  ANIMALIA  CHORDATA  CHONDRICHTHYES   \n",
       "\n",
       "              order_           family         genus code biome_marine  \\\n",
       "0    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   LC            t   \n",
       "1    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   EN            t   \n",
       "2    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   DD            t   \n",
       "3  TETRAODONTIFORMES       BALISTIDAE      Balistes   NT            t   \n",
       "4  CARCHARHINIFORMES   CARCHARHINIDAE  Carcharhinus   NT            t   \n",
       "\n",
       "  biome_freshwater biome_terrestrial   gap_status        threaten  \\\n",
       "0                f                 f  Partial gap  Not Threatened   \n",
       "1                f                 f  Partial gap      Threatened   \n",
       "2                f                 f  Partial gap  Data deficient   \n",
       "3                f                 f      Covered  Not Threatened   \n",
       "4                f                 f      Covered  Not Threatened   \n",
       "\n",
       "  comprehensive_groups  count_hex  \n",
       "0              Mammals          2  \n",
       "1              Mammals          2  \n",
       "2              Mammals          2  \n",
       "3           Unassigned          2  \n",
       "4      Sharks and rays          2  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_species.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Richness and ranking**\n",
    "\n",
    "Filter a subset of species and count unique occurrence of `id_no`. The ranking works by looking at each column. If ties are found the highest ranking is shared between all.\n",
    "At the end of the process, joining the attribute information of protected areas to add context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is for testing. In case the notebook dies and has to restart.\n",
    "sample_pa_species = pa_species.head(50000)\n",
    "\n",
    "# pa_species.to_csv('pa_species.csv')\n",
    "# save it for testing, without having to run the whole lot next time a crash happens\n",
    "sample_pa_species.to_csv('sample_pa_species.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of finding the top 100 pa by the number of threatened species. Note all queries such as 'top 100' will have to be individually calculated, as values need to be sorted by each level in the grouping. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_top_100_pa_for_threatened_comprehensive(pa_species=sample_pa_species):\n",
    "    df = pa_species.loc[(pa_species.threaten=='Threatened') & (pa_species.comprehensive_groups!='Unassigned')]\n",
    "    result = df.pivot_table(index='WDPAID', values='id_no', aggfunc=count_unique, fill_value=0)\n",
    "    result = result.sort_values('id_no', ascending=False).head(100)\n",
    "    result.columns=['count_species']\n",
    "    \n",
    "    # append attributes\n",
    "    result = pd.merge(result, wdpa_hex_lu_filter, right_on='WDPAID', left_index=True)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "find_top_100_pa_for_threatened_comprehensive(pa_species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because iteratively finding top pa will produce a large number of individual tables/queries. Perhaps, it is better to enable the selection afterwards (for example to be done in excel).\n",
    "\n",
    "The below creates two pa richness master tables (one with ranking). So for every level, both the richness and the ranking is calculated for each pa. Should one needs to find top pas, it could be easily done in the excel, by using `sort/filter` for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def richness_by_pa(df, columns='comprehensive_groups'):\n",
    "    return df.pivot_table(index='WDPAID', values='id_no', columns=columns, aggfunc=count_unique, fill_value=0)\n",
    "\n",
    "def append_pa_attr(df):\n",
    "    return pd.merge(wdpa_hex_lu_filter, df, left_on='WDPAID', right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprehensive groups master richness tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_pa_result = richness_by_pa(pa_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_pa_result_rank = master_pa_result.rank(ascending=False, method='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "append_pa_attr(master_pa_result).to_csv('output' + os.sep + 'master_pa_result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "append_pa_attr(master_pa_result_rank).to_csv('output' + os.sep + 'master_pa_result_rank.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprehensive groups masters table, but for threatened species only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "thr_master_pa_result = richness_by_pa(pa_species[pa_species.threaten=='Threatened'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "thr_master_pa_result_rank = thr_master_pa_result.rank(ascending=False, method='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "append_pa_attr(thr_master_pa_result).to_csv('output' + os.sep + 'master_pa_thr_result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "append_pa_attr(thr_master_pa_result_rank).to_csv('output' + os.sep + 'master_pa_thr_result_rank.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Country level statistics\n",
    "\n",
    "Because no spatial overlay of country boundaries and the hexagon layer exist, no relationship could be obtained. The only country information is in the WDPA. The narrative would be for each country, how many species/gap species are present in its **protected areas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>WDPAID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>PARENT_ISO3</th>\n",
       "      <th>DESIG_ENG</th>\n",
       "      <th>id_no</th>\n",
       "      <th>binomial</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order_</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>code</th>\n",
       "      <th>biome_marine</th>\n",
       "      <th>biome_freshwater</th>\n",
       "      <th>biome_terrestrial</th>\n",
       "      <th>gap_status</th>\n",
       "      <th>threaten</th>\n",
       "      <th>comprehensive_groups</th>\n",
       "      <th>count_hex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2474</td>\n",
       "      <td>Balaenoptera acutorostrata</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>LC</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2475</td>\n",
       "      <td>Balaenoptera borealis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>EN</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2476</td>\n",
       "      <td>Balaenoptera edeni</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>CETARTIODACTYLA</td>\n",
       "      <td>BALAENOPTERIDAE</td>\n",
       "      <td>Balaenoptera</td>\n",
       "      <td>DD</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Partial gap</td>\n",
       "      <td>Data deficient</td>\n",
       "      <td>Mammals</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>2539</td>\n",
       "      <td>Balistes vetula</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>TETRAODONTIFORMES</td>\n",
       "      <td>BALISTIDAE</td>\n",
       "      <td>Balistes</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Covered</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Unassigned</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Palaster Reef</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Marine Reserve</td>\n",
       "      <td>3851</td>\n",
       "      <td>Carcharhinus limbatus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>CHONDRICHTHYES</td>\n",
       "      <td>CARCHARHINIFORMES</td>\n",
       "      <td>CARCHARHINIDAE</td>\n",
       "      <td>Carcharhinus</td>\n",
       "      <td>NT</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>Covered</td>\n",
       "      <td>Not Threatened</td>\n",
       "      <td>Sharks and rays</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WDPAID           NAME PARENT_ISO3       DESIG_ENG  id_no  \\\n",
       "0       2  Palaster Reef         ATG  Marine Reserve   2474   \n",
       "1       2  Palaster Reef         ATG  Marine Reserve   2475   \n",
       "2       2  Palaster Reef         ATG  Marine Reserve   2476   \n",
       "3       2  Palaster Reef         ATG  Marine Reserve   2539   \n",
       "4       2  Palaster Reef         ATG  Marine Reserve   3851   \n",
       "\n",
       "                     binomial   kingdom    phylum           class  \\\n",
       "0  Balaenoptera acutorostrata  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "1       Balaenoptera borealis  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "2          Balaenoptera edeni  ANIMALIA  CHORDATA        MAMMALIA   \n",
       "3             Balistes vetula  ANIMALIA  CHORDATA  ACTINOPTERYGII   \n",
       "4       Carcharhinus limbatus  ANIMALIA  CHORDATA  CHONDRICHTHYES   \n",
       "\n",
       "              order_           family         genus code biome_marine  \\\n",
       "0    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   LC            t   \n",
       "1    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   EN            t   \n",
       "2    CETARTIODACTYLA  BALAENOPTERIDAE  Balaenoptera   DD            t   \n",
       "3  TETRAODONTIFORMES       BALISTIDAE      Balistes   NT            t   \n",
       "4  CARCHARHINIFORMES   CARCHARHINIDAE  Carcharhinus   NT            t   \n",
       "\n",
       "  biome_freshwater biome_terrestrial   gap_status        threaten  \\\n",
       "0                f                 f  Partial gap  Not Threatened   \n",
       "1                f                 f  Partial gap      Threatened   \n",
       "2                f                 f  Partial gap  Data deficient   \n",
       "3                f                 f      Covered  Not Threatened   \n",
       "4                f                 f      Covered  Not Threatened   \n",
       "\n",
       "  comprehensive_groups  count_hex  \n",
       "0              Mammals          2  \n",
       "1              Mammals          2  \n",
       "2              Mammals          2  \n",
       "3           Unassigned          2  \n",
       "4      Sharks and rays          2  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_species.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARE</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</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>10</td>\n",
       "      <td>12</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARM</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATG</th>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>199</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>254</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>94</td>\n",
       "      <td>466</td>\n",
       "      <td>1038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZE</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BDI</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEL</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEN</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BFA</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BFA;NER;BEN</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGD</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGR</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BHS</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>185</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIH</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLR</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLR;POL</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLZ</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMU</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOL</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRA</th>\n",
       "      <td>8</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>281</td>\n",
       "      <td>623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRB</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>205</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRN</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGO</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THA</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TJK</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKM</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLS</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TON</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTO</th>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>288</td>\n",
       "      <td>496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUN</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUR</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUV</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWN</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TZA</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UGA</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UKR</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URY</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>240</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UZB</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UZB;KGZ;KAZ</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VCT</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEN</th>\n",
       "      <td>3</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>319</td>\n",
       "      <td>597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VGB</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>235</td>\n",
       "      <td>394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VNM</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VUT</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSM</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEM</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAF</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMB</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMB;ZWE</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>18</td>\n",
       "      <td>483</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>396</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>137</td>\n",
       "      <td>1202</td>\n",
       "      <td>2530.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "comprehensive_groups  Amphibians  Birds  Conifers  Hagfishes  Mammals  \\\n",
       "PARENT_ISO3                                                             \n",
       "ABNJ                           0      0         0          0        0   \n",
       "AFG                            0      0         0          0        0   \n",
       "AGO                            5    100         0          0       77   \n",
       "AIA                            0      0         0          0        0   \n",
       "ALB                            0      0         0          0        0   \n",
       "AND                            0      0         0          0        0   \n",
       "ARE                            0      0         0          0        0   \n",
       "ARG                            0     35         0          0       20   \n",
       "ARM                            0      0         0          0        0   \n",
       "ATG                            0     41         0          0       21   \n",
       "AUS                            1    129         0          0       51   \n",
       "AUT                            0      0         0          0        0   \n",
       "AZE                            0      0         0          0        0   \n",
       "BDI                            0      0         0          0        0   \n",
       "BEL                            0      0         0          0        0   \n",
       "BEN                            0      0         0          0        0   \n",
       "BFA                            0      0         0          0        0   \n",
       "BFA;NER;BEN                    0      0         0          0        0   \n",
       "BGD                            0      0         0          0        0   \n",
       "BGR                            0      0         0          0        0   \n",
       "BHS                            0     49         0          0       21   \n",
       "BIH                            0      0         0          0        0   \n",
       "BLR                            0      0         0          0        0   \n",
       "BLR;POL                        0      0         0          0        0   \n",
       "BLZ                            0      0         0          0        0   \n",
       "BMU                            0      0         0          0        0   \n",
       "BOL                            0      0         0          0        0   \n",
       "BRA                            8    147         0          0       74   \n",
       "BRB                            0     56         0          0       21   \n",
       "BRN                            0      0         0          0        0   \n",
       "...                          ...    ...       ...        ...      ...   \n",
       "TGO                            0      0         0          0        0   \n",
       "THA                            0      0         0          0        0   \n",
       "TJK                            0      0         0          0        0   \n",
       "TKM                            0      0         0          0        0   \n",
       "TLS                            0      0         0          0        0   \n",
       "TON                            0      0         0          0        0   \n",
       "TTO                            2     72         0          0       27   \n",
       "TUN                            0      0         0          0        0   \n",
       "TUR                            0      0         0          0        0   \n",
       "TUV                            0      0         0          0        0   \n",
       "TWN                            0      0         0          0        0   \n",
       "TZA                            0      0         0          0        0   \n",
       "UGA                            0      0         0          0        0   \n",
       "UKR                            0      0         0          0        0   \n",
       "URY                            0      0         0          0        0   \n",
       "USA                            0     52         0          0       21   \n",
       "UZB                            0      0         0          0        0   \n",
       "UZB;KGZ;KAZ                    0      0         0          0        0   \n",
       "VCT                            0      0         0          0        0   \n",
       "VEN                            3    111         0          0       45   \n",
       "VGB                            0     43         0          0       21   \n",
       "VNM                            0      0         0          0        0   \n",
       "VUT                            0      0         0          0        0   \n",
       "WSM                            0      0         0          0        0   \n",
       "YEM                            0      0         0          0        0   \n",
       "ZAF                            0      0         0          0        0   \n",
       "ZMB                            0      0         0          0        0   \n",
       "ZMB;ZWE                        0      0         0          0        0   \n",
       "ZWE                            0      0         0          0        0   \n",
       "All                           18    483         0          1      211   \n",
       "\n",
       "comprehensive_groups  Mangroves  Seagrasses  Selected bony fishes  \\\n",
       "PARENT_ISO3                                                         \n",
       "ABNJ                          0           0                     0   \n",
       "AFG                           0           0                     0   \n",
       "AGO                           1           0                    47   \n",
       "AIA                           0           0                     0   \n",
       "ALB                           0           0                     0   \n",
       "AND                           0           0                     0   \n",
       "ARE                           0           0                     0   \n",
       "ARG                           0           0                     0   \n",
       "ARM                           0           0                     0   \n",
       "ATG                           1           0                    61   \n",
       "AUS                           8           3                   254   \n",
       "AUT                           0           0                     0   \n",
       "AZE                           0           0                     0   \n",
       "BDI                           0           0                     0   \n",
       "BEL                           0           0                     0   \n",
       "BEN                           0           0                     0   \n",
       "BFA                           0           0                     0   \n",
       "BFA;NER;BEN                   0           0                     0   \n",
       "BGD                           0           0                     0   \n",
       "BGR                           0           0                     0   \n",
       "BHS                           0           0                    56   \n",
       "BIH                           0           0                     0   \n",
       "BLR                           0           0                     0   \n",
       "BLR;POL                       0           0                     0   \n",
       "BLZ                           0           0                     0   \n",
       "BMU                           0           0                     0   \n",
       "BOL                           0           0                     0   \n",
       "BRA                           0           0                    63   \n",
       "BRB                           1           0                    61   \n",
       "BRN                           0           0                     0   \n",
       "...                         ...         ...                   ...   \n",
       "TGO                           0           0                     0   \n",
       "THA                           0           0                     0   \n",
       "TJK                           0           0                     0   \n",
       "TKM                           0           0                     0   \n",
       "TLS                           0           0                     0   \n",
       "TON                           0           0                     0   \n",
       "TTO                           1           0                    64   \n",
       "TUN                           0           0                     0   \n",
       "TUR                           0           0                     0   \n",
       "TUV                           0           0                     0   \n",
       "TWN                           0           0                     0   \n",
       "TZA                           0           0                     0   \n",
       "UGA                           0           0                     0   \n",
       "UKR                           0           0                     0   \n",
       "URY                           0           0                     0   \n",
       "USA                           1           0                    63   \n",
       "UZB                           0           0                     0   \n",
       "UZB;KGZ;KAZ                   0           0                     0   \n",
       "VCT                           0           0                     0   \n",
       "VEN                           1           0                    69   \n",
       "VGB                           1           0                    63   \n",
       "VNM                           0           0                     0   \n",
       "VUT                           0           0                     0   \n",
       "WSM                           0           0                     0   \n",
       "YEM                           0           0                     0   \n",
       "ZAF                           0           0                     0   \n",
       "ZMB                           0           0                     0   \n",
       "ZMB;ZWE                       0           0                     0   \n",
       "ZWE                           0           0                     0   \n",
       "All                           8           4                   396   \n",
       "\n",
       "comprehensive_groups  Selected crustaceans  Selected dicots  \\\n",
       "PARENT_ISO3                                                   \n",
       "ABNJ                                     0                0   \n",
       "AFG                                      0                0   \n",
       "AGO                                      3                0   \n",
       "AIA                                      0                0   \n",
       "ALB                                      0                0   \n",
       "AND                                      0                0   \n",
       "ARE                                      0                0   \n",
       "ARG                                      0                0   \n",
       "ARM                                      0                0   \n",
       "ATG                                     10                0   \n",
       "AUS                                     26                0   \n",
       "AUT                                      0                0   \n",
       "AZE                                      0                0   \n",
       "BDI                                      0                0   \n",
       "BEL                                      0                0   \n",
       "BEN                                      0                0   \n",
       "BFA                                      0                0   \n",
       "BFA;NER;BEN                              0                0   \n",
       "BGD                                      0                0   \n",
       "BGR                                      0                0   \n",
       "BHS                                      7                0   \n",
       "BIH                                      0                0   \n",
       "BLR                                      0                0   \n",
       "BLR;POL                                  0                0   \n",
       "BLZ                                      0                0   \n",
       "BMU                                      0                0   \n",
       "BOL                                      0                0   \n",
       "BRA                                     16                0   \n",
       "BRB                                      8                0   \n",
       "BRN                                      0                0   \n",
       "...                                    ...              ...   \n",
       "TGO                                      0                0   \n",
       "THA                                      0                0   \n",
       "TJK                                      0                0   \n",
       "TKM                                      0                0   \n",
       "TLS                                      0                0   \n",
       "TON                                      0                0   \n",
       "TTO                                     13                0   \n",
       "TUN                                      0                0   \n",
       "TUR                                      0                0   \n",
       "TUV                                      0                0   \n",
       "TWN                                      0                0   \n",
       "TZA                                      0                0   \n",
       "UGA                                      0                0   \n",
       "UKR                                      0                0   \n",
       "URY                                      0                0   \n",
       "USA                                      9                0   \n",
       "UZB                                      0                0   \n",
       "UZB;KGZ;KAZ                              0                0   \n",
       "VCT                                      0                0   \n",
       "VEN                                     14                0   \n",
       "VGB                                      9                0   \n",
       "VNM                                      0                0   \n",
       "VUT                                      0                0   \n",
       "WSM                                      0                0   \n",
       "YEM                                      0                0   \n",
       "ZAF                                      0                0   \n",
       "ZMB                                      0                0   \n",
       "ZMB;ZWE                                  0                0   \n",
       "ZWE                                      0                0   \n",
       "All                                     64                0   \n",
       "\n",
       "comprehensive_groups  Selected gastropods  Selected reptiles  Sharks and rays  \\\n",
       "PARENT_ISO3                                                                     \n",
       "ABNJ                                    0                  0                0   \n",
       "AFG                                     0                  0                0   \n",
       "AGO                                     0                  1               40   \n",
       "AIA                                     0                  0                0   \n",
       "ALB                                     0                  0                0   \n",
       "AND                                     0                  0                0   \n",
       "ARE                                     0                  0                0   \n",
       "ARG                                     0                  0               10   \n",
       "ARM                                     0                  0                0   \n",
       "ATG                                     0                  2               21   \n",
       "AUS                                     2                  4               94   \n",
       "AUT                                     0                  0                0   \n",
       "AZE                                     0                  0                0   \n",
       "BDI                                     0                  0                0   \n",
       "BEL                                     0                  0                0   \n",
       "BEN                                     0                  0                0   \n",
       "BFA                                     0                  0                0   \n",
       "BFA;NER;BEN                             0                  0                0   \n",
       "BGD                                     0                  0                0   \n",
       "BGR                                     0                  0                0   \n",
       "BHS                                     0                  2               24   \n",
       "BIH                                     0                  0                0   \n",
       "BLR                                     0                  0                0   \n",
       "BLR;POL                                 0                  0                0   \n",
       "BLZ                                     0                  0                0   \n",
       "BMU                                     0                  0                0   \n",
       "BOL                                     0                  0                0   \n",
       "BRA                                     0                  2               32   \n",
       "BRB                                     0                  2               18   \n",
       "BRN                                     0                  0                0   \n",
       "...                                   ...                ...              ...   \n",
       "TGO                                     0                  0                0   \n",
       "THA                                     0                  0                0   \n",
       "TJK                                     0                  0                0   \n",
       "TKM                                     0                  0                0   \n",
       "TLS                                     0                  0                0   \n",
       "TON                                     0                  0                0   \n",
       "TTO                                     0                  2               27   \n",
       "TUN                                     0                  0                0   \n",
       "TUR                                     0                  0                0   \n",
       "TUV                                     0                  0                0   \n",
       "TWN                                     0                  0                0   \n",
       "TZA                                     0                  0                0   \n",
       "UGA                                     0                  0                0   \n",
       "UKR                                     0                  0                0   \n",
       "URY                                     0                  0                0   \n",
       "USA                                     0                  2               20   \n",
       "UZB                                     0                  0                0   \n",
       "UZB;KGZ;KAZ                             0                  0                0   \n",
       "VCT                                     0                  0                0   \n",
       "VEN                                     0                  2               33   \n",
       "VGB                                     0                  2               20   \n",
       "VNM                                     0                  0                0   \n",
       "VUT                                     0                  0                0   \n",
       "WSM                                     0                  0                0   \n",
       "YEM                                     0                  0                0   \n",
       "ZAF                                     0                  0                0   \n",
       "ZMB                                     0                  0                0   \n",
       "ZMB;ZWE                                 0                  0                0   \n",
       "ZWE                                     0                  0                0   \n",
       "All                                     2                  4              137   \n",
       "\n",
       "comprehensive_groups  Unassigned     All  \n",
       "PARENT_ISO3                               \n",
       "ABNJ                           0     NaN  \n",
       "AFG                            0     NaN  \n",
       "AGO                          264   538.0  \n",
       "AIA                            0     NaN  \n",
       "ALB                            0     NaN  \n",
       "AND                            0     NaN  \n",
       "ARE                            0     NaN  \n",
       "ARG                           12    77.0  \n",
       "ARM                            0     NaN  \n",
       "ATG                          199   356.0  \n",
       "AUS                          466  1038.0  \n",
       "AUT                            0     NaN  \n",
       "AZE                            0     NaN  \n",
       "BDI                            0     NaN  \n",
       "BEL                            0     NaN  \n",
       "BEN                            0     NaN  \n",
       "BFA                            0     NaN  \n",
       "BFA;NER;BEN                    0     NaN  \n",
       "BGD                            0     NaN  \n",
       "BGR                            0     NaN  \n",
       "BHS                          185   344.0  \n",
       "BIH                            0     NaN  \n",
       "BLR                            0     NaN  \n",
       "BLR;POL                        0     NaN  \n",
       "BLZ                            0     NaN  \n",
       "BMU                            0     NaN  \n",
       "BOL                            0     NaN  \n",
       "BRA                          281   623.0  \n",
       "BRB                          205   372.0  \n",
       "BRN                            0     NaN  \n",
       "...                          ...     ...  \n",
       "TGO                            0     NaN  \n",
       "THA                            0     NaN  \n",
       "TJK                            0     NaN  \n",
       "TKM                            0     NaN  \n",
       "TLS                            0     NaN  \n",
       "TON                            0     NaN  \n",
       "TTO                          288   496.0  \n",
       "TUN                            0     NaN  \n",
       "TUR                            0     NaN  \n",
       "TUV                            0     NaN  \n",
       "TWN                            0     NaN  \n",
       "TZA                            0     NaN  \n",
       "UGA                            0     NaN  \n",
       "UKR                            0     NaN  \n",
       "URY                            0     NaN  \n",
       "USA                          240   408.0  \n",
       "UZB                            0     NaN  \n",
       "UZB;KGZ;KAZ                    0     NaN  \n",
       "VCT                            0     NaN  \n",
       "VEN                          319   597.0  \n",
       "VGB                          235   394.0  \n",
       "VNM                            0     NaN  \n",
       "VUT                            0     NaN  \n",
       "WSM                            0     NaN  \n",
       "YEM                            0     NaN  \n",
       "ZAF                            0     NaN  \n",
       "ZMB                            0     NaN  \n",
       "ZMB;ZWE                        0     NaN  \n",
       "ZWE                            0     NaN  \n",
       "All                         1202  2530.0  \n",
       "\n",
       "[230 rows x 15 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_pa_species.pivot_table(index='PARENT_ISO3', values='id_no', aggfunc=count_unique, columns='comprehensive_groups',\n",
    "                              margins=True, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is not perfect because ISO3 field sometimes contain multiple countries ISO3 codes. To consider the subset of national designations only may eliminate the effect of transboundary sites..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Species in protected areas by country **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso3_lu = pd.read_csv('iso_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso3_lu = iso3_lu[['alpha_3', 'en_country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_in_pa_by_country = pa_species.loc[pa_species.comprehensive_groups!='Unassigned'].pivot_table(index='PARENT_ISO3', values='id_no', aggfunc=count_unique, columns='comprehensive_groups',\n",
    "                              margins=True, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_in_pa_by_country_thr = pa_species.loc[(pa_species.comprehensive_groups!='Unassigned') & \\\n",
    "                                              (pa_species.threaten=='Threatened')].pivot_table(index='PARENT_ISO3', values='id_no', aggfunc=count_unique, columns='comprehensive_groups',\n",
    "                              margins=True, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_in_pa_by_country = pd.merge(species_in_pa_by_country, iso3_lu, \n",
    "                                    left_index=True, right_on='alpha_3', how='left')\n",
    "species_in_pa_by_country_thr = pd.merge(species_in_pa_by_country_thr, iso3_lu, \n",
    "                                    left_index=True, right_on='alpha_3', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_in_pa_by_country.to_csv('output' + os.sep + 'species_in_country_pas.csv')\n",
    "species_in_pa_by_country_thr.to_csv('output' + os.sep + 'thr_species_in_country_pas.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEST - DO NOT USE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime, time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.datetime.now().strftime('%c')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.strftime('%x-%X')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.strftime('%c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.datetime.fromtimestamp(time.time())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1 = bfa_hex.head(10000)\n",
    "bfa_sample2 = bfa_hex.tail(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid = bfa_sample1[~bfa_sample1.hexid.isin(invalid_hex.hexid)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join2 = pd.merge(bfa_sample1_valid, bfa_hex_lu_filter, on='row_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit bfa_sample1_valid_join2.pivot_table(values='hexid', index=['id_no'],aggfunc=count_unique, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit bfa_sample1_valid_join2.groupby('id_no').hexid.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join = bfa_sample1_valid.join(bfa_hex_lu_filter, on='row_id', rsuffix='r_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_join.binomial.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = bfa_sample1_valid_join.pivot_table(values='hexid', index=['id_no'],aggfunc=count_unique, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex[wdpa_hex.wdpaid.isin(wdpa_hex_lu_filter.WDPAID)].hexid.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_protected = bfa_sample1[(~bfa_sample1.hexid.isin(invalid_hex.hexid)) & (bfa_sample1.hexid.isin(protected_hexid))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_protected_join =  bfa_sample1_valid_protected.join(bfa_hex_lu_filter, on='row_id', rsuffix='r_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_sample1_valid_protected_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = bfa_sample1_valid_protected_join.pivot_table(values='hexid', index=['id_no'],aggfunc=count_unique, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.join(aa, rsuffix='_p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_hex(df):\n",
    "    # df in the form of id, hex_id\n",
    "    df_join = pd.merge(df, bfa_hex_lu_filter, on='row_id')\n",
    "    return df_join.groupby('id_no').hexid.nunique()\n",
    "\n",
    "def count_protected_hex(df):\n",
    "    df_protected = df[df.hexid.isin(protected_hexid)]\n",
    "    return count_hex(df_protected)\n",
    "\n",
    "def reduce_hex(a, b):\n",
    "    return a.append(b).groupby('id_no').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = count_hex(bfa_sample1_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = count_protected_hex(bfa_sample1_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([a, aa], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = count_hex(bfa_sample2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb = count_protected_hex(bfa_sample2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit a.append(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeit pd.concat([a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = a.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa.groupby('id_no').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_hex(a,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_hex(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce(reduce_hex, [a,b,a,a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = species_result_attr.pivot_table(index='class', values='id_no', \n",
    "                                columns='discrete_per', aggfunc=np.count_nonzero, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.crosstab(species_result_attr['class'], species_result_attr['discrete_per'],\n",
    "            values='id_no', aggfunc=np.count_nonzero, normalize='index')\n",
    "a.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gap_graph(species_result_attr, title='Proportions of gap, partial gap, and covered species across different taxa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at mammals for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr[species_result_attr['class']=='MAMMALIA'].plot.scatter('count', 'per')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr[species_result_attr['class']=='MAMMALIA'].plot.hexbin(x='count', y='per',\n",
    "                                                                          gridsize=50, xscale='log',\n",
    "#                                                                           color='red'\n",
    "                                                                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(species_result_attr['count'], species_result_attr['per'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpa_hex.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex[bfa_hex.row_id==8440]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter[bfa_hex_lu_filter.id_no==60121]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.FacetGrid(species_result_attr, row='class', \n",
    "                  hue='threaten', \n",
    "                  size=4, aspect=4\n",
    "                 )\n",
    "g = (g.map(sns.distplot, 'per', bins=10, rug=True)).add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def per_count_relationship(species_class):\n",
    "    species_class = species_result_attr[species_result_attr['class'] ==species_class]\n",
    "\n",
    "# plt.figure(figsize=(15,5))\n",
    "    g = sns.lmplot('count', 'per', data=species_class, hue='threaten', fit_reg=False, size=10, palette='coolwarm')\n",
    "    g.set(xscale=\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_count_relationship('REPTILIA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aves = species_result_attr[species_result_attr['class'] =='AVES']\n",
    "\n",
    "# plt.figure(figsize=(15,5))\n",
    "g = sns.lmplot('count', 'per', data=aves, hue='threaten', fit_reg=False, size=12, palette='coolwarm')\n",
    "g.set(xscale=\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check failed species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of species in the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result.index.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.setdiff1d(bfa_hex_lu.id_no.unique(), species_result.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.setdiff1d(bfa_hex_lu.id_no.unique(), species_result.index).size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "24 species do not have any hex cell - this is not right. Every species **must** have at least one cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.setdiff1d(species_result.index, bfa_hex_lu.id_no.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Potential issue in the analysis, missing species**\n",
    "\n",
    "There is a difference between the unique number of IDs in the hex result and the look up table.\n",
    "\n",
    "**!AP: investigate the reason of failure and remedy!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-run those failed species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_add_failed = pd.read_csv('hex_bfa_add.csv.gz', skipinitialspace=True)\n",
    "bfa_hex_add_failed.row_id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still failed, possibly due to large godzilla polygons, dice them and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_add_failed_dice = pd.read_csv('hex_bfa_add_dice.csv.gz', skipinitialspace=True)\n",
    "bfa_hex_add_failed_dice.row_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter[bfa_hex_lu_filter.row_id.isin([14511, 2601, 13477, 11316,  5953,  6824,  4783, 12144, 14269,\n",
    "        8175,  7120,   594, 12887,  3168,  8440,  2417,  6079,  8176,\n",
    "        5313,  1650,  4732,  9701,  6390,  4862])].id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dice method appears to work - all the missing species have now been added, except one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.setdiff1d(np.setdiff1d(bfa_hex_lu.id_no.unique(), species_result.index),\n",
    "            bfa_hex_lu_filter[bfa_hex_lu_filter.row_id.isin([14511, 2601, 13477, 11316,  5953,  6824,  4783, 12144, 14269,\n",
    "        8175,  7120,   594, 12887,  3168,  8440,  2417,  6079,  8176,\n",
    "        5313,  1650,  4732,  9701,  6390,  4862])].id_no.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Species of id 170349, *Allothunnus fallai* LC wide ranging fish across all oceans.\n",
    "\n",
    "The issue seems to be the 180/-180 or polygon holes. Clipping using natural earth 110m ocean layer seems to resolve the issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_add_failed_dice_still_fail = pd.read_csv('hex_bfa_add_dice_still_fail.csv.gz', skipinitialspace=True)\n",
    "bfa_hex_add_failed_dice_still_fail.row_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_add_failed = bfa_hex_add_failed_dice.append(bfa_hex_add_failed_dice_still_fail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfa_hex_lu_filter[bfa_hex_lu_filter.row_id.isin(bfa_hex_add_failed.row_id.unique())].id_no.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checked, the failed 24 species will need to be join to the big table. The new hexagon layer has already had the invalid hexagon cells remove and therefore there is no need to test validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_count_add = count_hex(bfa_hex_add_failed)\n",
    "protected_species_add = count_protected_hex(bfa_hex_add_failed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_add = pd.concat([species_count_add, protected_species_add], axis=1)\n",
    "species_result_add.columns = ['count', 'count_protected']\n",
    "species_result_add = species_result_add.fillna(0)\n",
    "species_result_add['per'] = species_result_add['count_protected']/species_result_add['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_add_attr = pd.merge(species_result_add, species_attr, left_index=True, right_on='id_no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the failed species to the pool, use `species_result_attr`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr = species_result_attr.append(species_result_add_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_result_attr.id_no.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sis_clean[sis_clean.id_no==176529]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number match now"
   ]
  }
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