datasets/gbif/gbif-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61a040ce-418a-4a97-9850-0813cfb02422",
   "metadata": {},
   "source": [
    "## Accessing GBIF data with the Planetary Computer STAC API\n",
    "\n",
    "This notebook provides an example of accessing [Global Biodiversity Information Facility](https://planetarycomputer.microsoft.com/dataset/gbif) (GBIF) occurrence data from the Planetary Computer STAC API. Periodic snapshots of the data are stored in Parquet format.\n",
    "\n",
    "We'll use [Dask](https://docs.dask.org/en/latest/) to read the partitioned Parquet Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1560fea5-d64f-4778-8372-c2b24aef8193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gbif-2021-09-01',\n",
       " 'gbif-2021-08-01',\n",
       " 'gbif-2021-07-01',\n",
       " 'gbif-2021-06-01',\n",
       " 'gbif-2021-04-13']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pystac_client\n",
    "\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    ")\n",
    "search = catalog.search(collections=[\"gbif\"])\n",
    "items = search.get_all_items()\n",
    "items = {x.id: x for x in items}\n",
    "list(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a7e8ac-5128-49f7-8781-96c578044b99",
   "metadata": {},
   "source": [
    "We'll take the most recent item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a786e42-a134-48a9-b8d4-1046b8c78556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Item id=gbif-2021-09-01>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item = list(items.values())[0]\n",
    "item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb01b837-715b-43a5-9055-6e9a9b1d1881",
   "metadata": {},
   "source": [
    "As usual, you should sign the item before trying to load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c98dd84-79c1-4b75-9af0-e3d4fcb8d700",
   "metadata": {},
   "outputs": [],
   "source": [
    "import planetary_computer\n",
    "\n",
    "signed_asset = planetary_computer.sign(item).assets[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aeee8190-2183-4600-8820-c3d323270488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>gbifid</th>\n",
       "      <th>datasetkey</th>\n",
       "      <th>occurrenceid</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>species</th>\n",
       "      <th>infraspecificepithet</th>\n",
       "      <th>taxonrank</th>\n",
       "      <th>scientificname</th>\n",
       "      <th>verbatimscientificname</th>\n",
       "      <th>verbatimscientificnameauthorship</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>locality</th>\n",
       "      <th>stateprovince</th>\n",
       "      <th>occurrencestatus</th>\n",
       "      <th>individualcount</th>\n",
       "      <th>publishingorgkey</th>\n",
       "      <th>decimallatitude</th>\n",
       "      <th>decimallongitude</th>\n",
       "      <th>coordinateuncertaintyinmeters</th>\n",
       "      <th>coordinateprecision</th>\n",
       "      <th>elevation</th>\n",
       "      <th>elevationaccuracy</th>\n",
       "      <th>depth</th>\n",
       "      <th>depthaccuracy</th>\n",
       "      <th>eventdate</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>taxonkey</th>\n",
       "      <th>specieskey</th>\n",
       "      <th>basisofrecord</th>\n",
       "      <th>institutioncode</th>\n",
       "      <th>collectioncode</th>\n",
       "      <th>catalognumber</th>\n",
       "      <th>recordnumber</th>\n",
       "      <th>identifiedby</th>\n",
       "      <th>dateidentified</th>\n",
       "      <th>license</th>\n",
       "      <th>rightsholder</th>\n",
       "      <th>recordedby</th>\n",
       "      <th>typestatus</th>\n",
       "      <th>establishmentmeans</th>\n",
       "      <th>lastinterpreted</th>\n",
       "      <th>mediatype</th>\n",
       "      <th>issue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1034</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: read-parquet, 1034 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                 gbifid datasetkey occurrenceid kingdom  phylum   class   order  family   genus species infraspecificepithet taxonrank scientificname verbatimscientificname verbatimscientificnameauthorship countrycode locality stateprovince occurrencestatus individualcount publishingorgkey decimallatitude decimallongitude coordinateuncertaintyinmeters coordinateprecision elevation elevationaccuracy    depth depthaccuracy eventdate    day  month   year taxonkey specieskey basisofrecord institutioncode collectioncode catalognumber recordnumber identifiedby dateidentified license rightsholder recordedby typestatus establishmentmeans lastinterpreted mediatype   issue\n",
       "npartitions=1034                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               \n",
       "                  int64     object       object  object  object  object  object  object  object  object               object    object         object                 object                           object      object   object        object           object           int32           object         float64          float64                       float64             float64   float64           float64  float64       float64    object  int32  int32  int32    int32      int32        object          object         object        object       object       object         object  object       object     object     object             object          object    object  object\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...       ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...            ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "...                 ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...       ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...            ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...       ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...            ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...       ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...            ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "Dask Name: read-parquet, 1034 tasks"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "\n",
    "df = dd.read_parquet(\n",
    "    signed_asset.href,\n",
    "    storage_options=signed_asset.extra_fields[\"table:storage_options\"],\n",
    "    dataset={\"require_extension\": None},\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2141fc59-b96c-4e87-a594-3ce15e86fa9d",
   "metadata": {},
   "source": [
    "As indicated by `npartitions`, this Parquet dataset is made up of many individual parquet files. We can read in a specific partition with `.get_partition`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cc8cfbc-23db-4b55-9a94-2840f2f0f0e0",
   "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>gbifid</th>\n",
       "      <th>datasetkey</th>\n",
       "      <th>occurrenceid</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>species</th>\n",
       "      <th>...</th>\n",
       "      <th>identifiedby</th>\n",
       "      <th>dateidentified</th>\n",
       "      <th>license</th>\n",
       "      <th>rightsholder</th>\n",
       "      <th>recordedby</th>\n",
       "      <th>typestatus</th>\n",
       "      <th>establishmentmeans</th>\n",
       "      <th>lastinterpreted</th>\n",
       "      <th>mediatype</th>\n",
       "      <th>issue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>321355870</td>\n",
       "      <td>67fb29a4-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>None</td>\n",
       "      <td>Chromista</td>\n",
       "      <td>Foraminifera</td>\n",
       "      <td>Globothalamea</td>\n",
       "      <td>Rotaliida</td>\n",
       "      <td>Globigerinidae</td>\n",
       "      <td>Globigerina</td>\n",
       "      <td>Globigerina bulloides</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schiebel, Ralf</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-14T23:58:35.923Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321355956</td>\n",
       "      <td>67fb29a4-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>None</td>\n",
       "      <td>Chromista</td>\n",
       "      <td>Foraminifera</td>\n",
       "      <td>Globothalamea</td>\n",
       "      <td>Rotaliida</td>\n",
       "      <td>Globorotaliidae</td>\n",
       "      <td>Neogloboquadrina</td>\n",
       "      <td>Neogloboquadrina pachyderma</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schiebel, Ralf</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-14T23:58:35.924Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321355949</td>\n",
       "      <td>67fb29a4-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>None</td>\n",
       "      <td>Chromista</td>\n",
       "      <td>Foraminifera</td>\n",
       "      <td>Globothalamea</td>\n",
       "      <td>Rotaliida</td>\n",
       "      <td>Globigerinitidae</td>\n",
       "      <td>Globigerinita</td>\n",
       "      <td>Globigerinita minuta</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schiebel, Ralf</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-14T23:58:35.926Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321355912</td>\n",
       "      <td>67fb29a4-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>None</td>\n",
       "      <td>Chromista</td>\n",
       "      <td>Foraminifera</td>\n",
       "      <td>Globothalamea</td>\n",
       "      <td>Rotaliida</td>\n",
       "      <td>Globigerinitidae</td>\n",
       "      <td>Globigerinita</td>\n",
       "      <td>Globigerinita glutinata</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schiebel, Ralf</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-14T23:58:35.927Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321355905</td>\n",
       "      <td>67fb29a4-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>None</td>\n",
       "      <td>Chromista</td>\n",
       "      <td>Foraminifera</td>\n",
       "      <td>Globothalamea</td>\n",
       "      <td>Rotaliida</td>\n",
       "      <td>Globigerinidae</td>\n",
       "      <td>Globigerina</td>\n",
       "      <td>Globigerina falconensis</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schiebel, Ralf</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-14T23:58:35.928Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</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>1667009</th>\n",
       "      <td>1322591709</td>\n",
       "      <td>821cc27a-e3bb-4bc5-ac34-89ada245069d</td>\n",
       "      <td>http://n2t.net/ark:/65665/3ee636308-1443-45be-...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Arthropoda</td>\n",
       "      <td>Malacostraca</td>\n",
       "      <td>Decapoda</td>\n",
       "      <td>Processidae</td>\n",
       "      <td>Processa</td>\n",
       "      <td>Processa bermudensis</td>\n",
       "      <td>...</td>\n",
       "      <td>Mclaughlin, P.</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Continental Shelf Associates for BLM/ MMS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-27T10:49:36.284Z</td>\n",
       "      <td>[StillImage]</td>\n",
       "      <td>[OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667010</th>\n",
       "      <td>1322595622</td>\n",
       "      <td>821cc27a-e3bb-4bc5-ac34-89ada245069d</td>\n",
       "      <td>http://n2t.net/ark:/65665/3ee8fc491-fa05-4edc-...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Mammalia</td>\n",
       "      <td>Rodentia</td>\n",
       "      <td>Muridae</td>\n",
       "      <td>Hybomys</td>\n",
       "      <td>Hybomys univittatus</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>J. Malcolm</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-27T10:50:21.046Z</td>\n",
       "      <td>[StillImage]</td>\n",
       "      <td>[OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667011</th>\n",
       "      <td>1322597346</td>\n",
       "      <td>821cc27a-e3bb-4bc5-ac34-89ada245069d</td>\n",
       "      <td>http://n2t.net/ark:/65665/3eea331a2-d225-4e88-...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Amphibia</td>\n",
       "      <td>Caudata</td>\n",
       "      <td>Plethodontidae</td>\n",
       "      <td>Plethodon</td>\n",
       "      <td>Plethodon montanus</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-27T10:49:36.308Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667012</th>\n",
       "      <td>1322605552</td>\n",
       "      <td>821cc27a-e3bb-4bc5-ac34-89ada245069d</td>\n",
       "      <td>http://n2t.net/ark:/65665/3eeff553c-637b-4742-...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Thaliacea</td>\n",
       "      <td>Salpida</td>\n",
       "      <td>Salpidae</td>\n",
       "      <td>Salpa</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>Cole, Linda L., (IZ), Smithsonian Institution ...</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>Schuyler</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-27T10:50:57.439Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>[OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667013</th>\n",
       "      <td>1456448120</td>\n",
       "      <td>821cc27a-e3bb-4bc5-ac34-89ada245069d</td>\n",
       "      <td>http://n2t.net/ark:/65665/3ef2a984e-e0aa-4544-...</td>\n",
       "      <td>Plantae</td>\n",
       "      <td>Tracheophyta</td>\n",
       "      <td>Magnoliopsida</td>\n",
       "      <td>Fabales</td>\n",
       "      <td>Fabaceae</td>\n",
       "      <td>Swartzia</td>\n",
       "      <td>Swartzia polyphylla</td>\n",
       "      <td>...</td>\n",
       "      <td>Torke, B. M., (MO)</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>R. Oldeman</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-06-27T10:49:36.344Z</td>\n",
       "      <td>[StillImage]</td>\n",
       "      <td>[OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1667014 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbifid                            datasetkey  \\\n",
       "0         321355870  67fb29a4-f762-11e1-a439-00145eb45e9a   \n",
       "1         321355956  67fb29a4-f762-11e1-a439-00145eb45e9a   \n",
       "2         321355949  67fb29a4-f762-11e1-a439-00145eb45e9a   \n",
       "3         321355912  67fb29a4-f762-11e1-a439-00145eb45e9a   \n",
       "4         321355905  67fb29a4-f762-11e1-a439-00145eb45e9a   \n",
       "...             ...                                   ...   \n",
       "1667009  1322591709  821cc27a-e3bb-4bc5-ac34-89ada245069d   \n",
       "1667010  1322595622  821cc27a-e3bb-4bc5-ac34-89ada245069d   \n",
       "1667011  1322597346  821cc27a-e3bb-4bc5-ac34-89ada245069d   \n",
       "1667012  1322605552  821cc27a-e3bb-4bc5-ac34-89ada245069d   \n",
       "1667013  1456448120  821cc27a-e3bb-4bc5-ac34-89ada245069d   \n",
       "\n",
       "                                              occurrenceid    kingdom  \\\n",
       "0                                                     None  Chromista   \n",
       "1                                                     None  Chromista   \n",
       "2                                                     None  Chromista   \n",
       "3                                                     None  Chromista   \n",
       "4                                                     None  Chromista   \n",
       "...                                                    ...        ...   \n",
       "1667009  http://n2t.net/ark:/65665/3ee636308-1443-45be-...   Animalia   \n",
       "1667010  http://n2t.net/ark:/65665/3ee8fc491-fa05-4edc-...   Animalia   \n",
       "1667011  http://n2t.net/ark:/65665/3eea331a2-d225-4e88-...   Animalia   \n",
       "1667012  http://n2t.net/ark:/65665/3eeff553c-637b-4742-...   Animalia   \n",
       "1667013  http://n2t.net/ark:/65665/3ef2a984e-e0aa-4544-...    Plantae   \n",
       "\n",
       "               phylum          class      order            family  \\\n",
       "0        Foraminifera  Globothalamea  Rotaliida    Globigerinidae   \n",
       "1        Foraminifera  Globothalamea  Rotaliida   Globorotaliidae   \n",
       "2        Foraminifera  Globothalamea  Rotaliida  Globigerinitidae   \n",
       "3        Foraminifera  Globothalamea  Rotaliida  Globigerinitidae   \n",
       "4        Foraminifera  Globothalamea  Rotaliida    Globigerinidae   \n",
       "...               ...            ...        ...               ...   \n",
       "1667009    Arthropoda   Malacostraca   Decapoda       Processidae   \n",
       "1667010      Chordata       Mammalia   Rodentia           Muridae   \n",
       "1667011      Chordata       Amphibia    Caudata    Plethodontidae   \n",
       "1667012      Chordata      Thaliacea    Salpida          Salpidae   \n",
       "1667013  Tracheophyta  Magnoliopsida    Fabales          Fabaceae   \n",
       "\n",
       "                    genus                      species  ...  \\\n",
       "0             Globigerina        Globigerina bulloides  ...   \n",
       "1        Neogloboquadrina  Neogloboquadrina pachyderma  ...   \n",
       "2           Globigerinita         Globigerinita minuta  ...   \n",
       "3           Globigerinita      Globigerinita glutinata  ...   \n",
       "4             Globigerina      Globigerina falconensis  ...   \n",
       "...                   ...                          ...  ...   \n",
       "1667009          Processa         Processa bermudensis  ...   \n",
       "1667010           Hybomys          Hybomys univittatus  ...   \n",
       "1667011         Plethodon           Plethodon montanus  ...   \n",
       "1667012             Salpa                         None  ...   \n",
       "1667013          Swartzia          Swartzia polyphylla  ...   \n",
       "\n",
       "                                              identifiedby dateidentified  \\\n",
       "0                                                     None           None   \n",
       "1                                                     None           None   \n",
       "2                                                     None           None   \n",
       "3                                                     None           None   \n",
       "4                                                     None           None   \n",
       "...                                                    ...            ...   \n",
       "1667009                                     Mclaughlin, P.           None   \n",
       "1667010                                               None           None   \n",
       "1667011                                               None           None   \n",
       "1667012  Cole, Linda L., (IZ), Smithsonian Institution ...           None   \n",
       "1667013                                 Torke, B. M., (MO)           None   \n",
       "\n",
       "           license rightsholder                                 recordedby  \\\n",
       "0        CC_BY_4_0         None                             Schiebel, Ralf   \n",
       "1        CC_BY_4_0         None                             Schiebel, Ralf   \n",
       "2        CC_BY_4_0         None                             Schiebel, Ralf   \n",
       "3        CC_BY_4_0         None                             Schiebel, Ralf   \n",
       "4        CC_BY_4_0         None                             Schiebel, Ralf   \n",
       "...            ...          ...                                        ...   \n",
       "1667009    CC0_1_0         None  Continental Shelf Associates for BLM/ MMS   \n",
       "1667010    CC0_1_0         None                                 J. Malcolm   \n",
       "1667011    CC0_1_0         None                                       None   \n",
       "1667012    CC0_1_0         None                                   Schuyler   \n",
       "1667013    CC0_1_0         None                                 R. Oldeman   \n",
       "\n",
       "        typestatus establishmentmeans           lastinterpreted     mediatype  \\\n",
       "0             None               None  2021-06-14T23:58:35.923Z            []   \n",
       "1             None               None  2021-06-14T23:58:35.924Z            []   \n",
       "2             None               None  2021-06-14T23:58:35.926Z            []   \n",
       "3             None               None  2021-06-14T23:58:35.927Z            []   \n",
       "4             None               None  2021-06-14T23:58:35.928Z            []   \n",
       "...            ...                ...                       ...           ...   \n",
       "1667009       None               None  2021-06-27T10:49:36.284Z  [StillImage]   \n",
       "1667010       None               None  2021-06-27T10:50:21.046Z  [StillImage]   \n",
       "1667011       None               None  2021-06-27T10:49:36.308Z            []   \n",
       "1667012       None               None  2021-06-27T10:50:57.439Z            []   \n",
       "1667013       None               None  2021-06-27T10:49:36.344Z  [StillImage]   \n",
       "\n",
       "                                                     issue  \n",
       "0                                                       []  \n",
       "1                                                       []  \n",
       "2                                                       []  \n",
       "3                                                       []  \n",
       "4                                                       []  \n",
       "...                                                    ...  \n",
       "1667009  [OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...  \n",
       "1667010  [OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...  \n",
       "1667011  [OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...  \n",
       "1667012  [OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...  \n",
       "1667013  [OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_CO...  \n",
       "\n",
       "[1667014 rows x 50 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk = df.get_partition(0).compute()\n",
    "chunk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3a79a9-8ec1-4e17-9844-50b19a12be61",
   "metadata": {},
   "source": [
    "To get a sense for the most commonly observed species, we'll group the dataset and get the count of each species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31d9b0f4-2d61-40dc-8465-2ae039401fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kingdom   phylum    class           family       genus          species                    \n",
       "Animalia  Chordata  Aves            Sulidae      Sula           Sula variegata                 143568\n",
       "                    Actinopterygii  Percidae     Perca          Perca fluviatilis               48700\n",
       "                                    Cyprinidae   Rutilus        Rutilus rutilus                 39743\n",
       "                                    Esocidae     Esox           Esox lucius                     37786\n",
       "                                    Percidae     Gymnocephalus  Gymnocephalus cernua            23030\n",
       "                                    Cyprinidae   Abramis        Abramis brama                   17225\n",
       "                                    Lotidae      Lota           Lota lota                       15668\n",
       "                                    Cyprinidae   Alburnus       Alburnus alburnus               14580\n",
       "                                    Salmonidae   Coregonus      Coregonus maraena               14381\n",
       "                                                                Coregonus albula                13104\n",
       "                    Mammalia        Phocoenidae  Phocoena       Phocoena phocoena               12250\n",
       "                    Actinopterygii  Osmeridae    Osmerus        Osmerus eperlanus               11782\n",
       "                                    Salmonidae   Salmo          Salmo trutta                    10384\n",
       "                                                 Salvelinus     Salvelinus alpinus              10340\n",
       "                                    Cyprinidae   Scardinius     Scardinius erythrophthalmus      9120\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk.groupby([\"kingdom\", \"phylum\", \"class\", \"family\", \"genus\"])[\n",
    "    \"species\"\n",
    "].value_counts().sort_values(ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100d4a53-a1cf-4812-899c-247e754f3bf3",
   "metadata": {},
   "source": [
    "Let's create a map with the number of unique species per country. First, we'll group by country code and compute the number of unique species (per country)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e06d8774-c648-44d4-9994-32acc129d2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "countrycode\n",
       "AD      6\n",
       "AE     55\n",
       "AF     32\n",
       "AG     51\n",
       "AI     37\n",
       "     ... \n",
       "YE     19\n",
       "YT      5\n",
       "ZA    688\n",
       "ZM     82\n",
       "ZW     91\n",
       "Name: species, Length: 243, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_per_country = chunk.groupby(\"countrycode\").species.nunique()\n",
    "species_per_country"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e1c79c-1fb6-4c98-b250-d0af3eea69ba",
   "metadata": {},
   "source": [
    "Finally, we can plot the counts on a map using geopandas, by joining `species_per_country` to a dataset with country boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6af8d4a4-e491-422f-b33d-c4b64b2f2c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas\n",
    "import cartopy\n",
    "\n",
    "countries = geopandas.read_file(\n",
    "    \"https://raw.githubusercontent.com/datasets/geo-countries/master/data/countries.geojson\"\n",
    ")\n",
    "gdf = geopandas.GeoDataFrame(\n",
    "    countries.merge(species_per_country, left_on=\"ISO_A2\", right_index=True)\n",
    ")\n",
    "crs = cartopy.crs.Robinson()\n",
    "ax = gdf.to_crs(crs.proj4_init).plot(\n",
    "    column=\"species\", legend=True, scheme=\"natural_breaks\", k=5, figsize=(15, 15)\n",
    ")\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91946b8e-db90-4bfa-8960-09b91c6e3b73",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Working with the full dataset\n",
    "\n",
    "Thus far, we've just used a single partition from the full GBIF dataset. All of the examples shown in this notebook work on the entire dataset using `dask.dataframe` to read in the Parquet dataset.\n",
    "\n",
    "You might want create a cluster to process the data in parallel on many machines.\n",
    "\n",
    "```python\n",
    "from dask_gateway import GatewayCluster\n",
    "\n",
    "cluster = GatewayCluster()\n",
    "cluster.scale(16)\n",
    "client = cluster.get_client()\n",
    "```\n",
    "\n",
    "Then use `dask.dataframe.read_parquet` to read in the files. To speed things up even more, we'll specify a subset of files to read in.\n",
    "\n",
    "```python\n",
    "df = dd.read_parquet(\n",
    "    signed_asset.href,\n",
    "    columns=[\"countrycode\", \"species\"],\n",
    "    storage_options=signed_asset.extra_fields[\"table:storage_options\"],\n",
    ")\n",
    "```\n",
    "\n",
    "Now you can repeat the computations above, replacing `chunk` with `df`.\n",
    "\n",
    "### Next Steps\n",
    "\n",
    "Now that you've an introduction to the Forest Inventory and Analysis dataset, learn more with\n",
    "\n",
    "* The [Reading tabular data quickstart](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-tabular-data/) for an introduction to tabular data on the Planeatry Computer\n",
    "* [Scale with Dask](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/) for more on using Dask to work with large datasets"
   ]
  }
 ],
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