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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "377c1939-0449-4a4a-b2e1-5927a438e282",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pystac_client\n",
    "import planetary_computer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8843db71-467d-4537-837c-844b30f0cf68",
   "metadata": {},
   "source": [
    "To access the data stored in Azure Blob Storage, we'll use the Planetary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1/docs). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f456e340-2e97-4534-9548-28e94b03806b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gbif-2022-10-01',\n",
       " 'gbif-2022-09-01',\n",
       " 'gbif-2022-08-01',\n",
       " 'gbif-2022-07-01',\n",
       " 'gbif-2022-06-01',\n",
       " 'gbif-2022-05-01',\n",
       " 'gbif-2022-04-01',\n",
       " 'gbif-2022-03-01',\n",
       " 'gbif-2022-02-01',\n",
       " 'gbif-2022-01-01',\n",
       " 'gbif-2021-12-01',\n",
       " 'gbif-2021-11-01',\n",
       " 'gbif-2021-10-01',\n",
       " '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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\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": [
    "https://sasweb.microsoft.com/Member/Silo/16477We'll take the most recent item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a786e42-a134-48a9-b8d4-1046b8c78556",
   "metadata": {},
   "outputs": [],
   "source": [
    "item = list(items.values())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435ec017-8d3c-49a1-8c03-748ec2ba2613",
   "metadata": {},
   "source": [
    "We'll use [Dask](https://docs.dask.org/en/latest/) to read the partitioned Parquet Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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=1960</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
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       "      <td>object</td>\n",
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       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int32</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
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       "      <td>datetime64[ns]</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: read-parquet, 1 graph layer</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=1960                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \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  datetime64[ns]  int32  int32  int32    int32      int32        object          object         object        object       object       object  datetime64[ns]  object       object     object     object             object  datetime64[ns]    object  object\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...             ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...             ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "...                 ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...             ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...             ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...             ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...             ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "                    ...        ...          ...     ...     ...     ...     ...     ...     ...     ...                  ...       ...            ...                    ...                              ...         ...      ...           ...              ...             ...              ...             ...              ...                           ...                 ...       ...               ...      ...           ...             ...    ...    ...    ...      ...        ...           ...             ...            ...           ...          ...          ...             ...     ...          ...        ...        ...                ...             ...       ...     ...\n",
       "Dask Name: read-parquet, 1 graph layer"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "\n",
    "asset = item.assets[\"data\"]\n",
    "df = dd.read_parquet(\n",
    "    asset.href,\n",
    "    storage_options=asset.extra_fields[\"table:storage_options\"],\n",
    "    parquet_file_extension=None,\n",
    "    arrow_to_pandas=dict(timestamp_as_object=True),\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": 11,
   "id": "4cc8cfbc-23db-4b55-9a94-2840f2f0f0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>2141788029</td>\n",
       "      <td>4fa7b334-ce0d-4e88-aaae-2e0c138d049e</td>\n",
       "      <td>URN:catalog:CLO:EBIRD:OBS590769735</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Aves</td>\n",
       "      <td>Passeriformes</td>\n",
       "      <td>Polioptilidae</td>\n",
       "      <td>Polioptila</td>\n",
       "      <td>Polioptila caerulea</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'obsr233099'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-08 14:29:55.344000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2142511629</td>\n",
       "      <td>4fa7b334-ce0d-4e88-aaae-2e0c138d049e</td>\n",
       "      <td>URN:catalog:CLO:EBIRD:OBS591457037</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Aves</td>\n",
       "      <td>Columbiformes</td>\n",
       "      <td>Columbidae</td>\n",
       "      <td>Patagioenas</td>\n",
       "      <td>Patagioenas cayennensis</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'obsr767103'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-08 14:29:56.715000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2126155116</td>\n",
       "      <td>4fa7b334-ce0d-4e88-aaae-2e0c138d049e</td>\n",
       "      <td>URN:catalog:CLO:EBIRD:OBS592106103</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Aves</td>\n",
       "      <td>Accipitriformes</td>\n",
       "      <td>Pandionidae</td>\n",
       "      <td>Pandion</td>\n",
       "      <td>Pandion haliaetus</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'obsr370369'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-08 14:29:58.118000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2143587663</td>\n",
       "      <td>4fa7b334-ce0d-4e88-aaae-2e0c138d049e</td>\n",
       "      <td>URN:catalog:CLO:EBIRD:OBS592831487</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Aves</td>\n",
       "      <td>Passeriformes</td>\n",
       "      <td>Icteridae</td>\n",
       "      <td>Quiscalus</td>\n",
       "      <td>Quiscalus quiscula</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'obsr29404'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-08 14:29:59.965000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2152055267</td>\n",
       "      <td>4fa7b334-ce0d-4e88-aaae-2e0c138d049e</td>\n",
       "      <td>URN:catalog:CLO:EBIRD:OBS593603038</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Aves</td>\n",
       "      <td>Passeriformes</td>\n",
       "      <td>Regulidae</td>\n",
       "      <td>Regulus</td>\n",
       "      <td>Regulus calendula</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'obsr383648'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-08 14:30:01.862000</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>1947843</th>\n",
       "      <td>1674906857</td>\n",
       "      <td>9e932f70-0c61-11dd-84ce-b8a03c50a862</td>\n",
       "      <td>urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Actinopterygii</td>\n",
       "      <td>Perciformes</td>\n",
       "      <td>Percidae</td>\n",
       "      <td>Perca</td>\n",
       "      <td>Perca fluviatilis</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'Fiskeriverkets utredningsk...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-25 05:23:40.367000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947844</th>\n",
       "      <td>2012826837</td>\n",
       "      <td>9e932f70-0c61-11dd-84ce-b8a03c50a862</td>\n",
       "      <td>urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Arthropoda</td>\n",
       "      <td>Malacostraca</td>\n",
       "      <td>Decapoda</td>\n",
       "      <td>Astacidae</td>\n",
       "      <td>Astacus</td>\n",
       "      <td>Astacus astacus</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'Fiskeriverkets utredningsk...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-25 05:23:40.367000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947845</th>\n",
       "      <td>1674906890</td>\n",
       "      <td>9e932f70-0c61-11dd-84ce-b8a03c50a862</td>\n",
       "      <td>urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Actinopterygii</td>\n",
       "      <td>Salmoniformes</td>\n",
       "      <td>Salmonidae</td>\n",
       "      <td>Salmo</td>\n",
       "      <td>Salmo trutta</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'Fiskeriverkets utredningsk...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-25 05:23:40.367000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947846</th>\n",
       "      <td>1675104176</td>\n",
       "      <td>9e932f70-0c61-11dd-84ce-b8a03c50a862</td>\n",
       "      <td>urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Actinopterygii</td>\n",
       "      <td>Salmoniformes</td>\n",
       "      <td>Salmonidae</td>\n",
       "      <td>Thymallus</td>\n",
       "      <td>Thymallus thymallus</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'Fiskeriverkets utredningsk...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-25 05:23:40.367000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947847</th>\n",
       "      <td>1674906836</td>\n",
       "      <td>9e932f70-0c61-11dd-84ce-b8a03c50a862</td>\n",
       "      <td>urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...</td>\n",
       "      <td>Animalia</td>\n",
       "      <td>Chordata</td>\n",
       "      <td>Actinopterygii</td>\n",
       "      <td>Perciformes</td>\n",
       "      <td>Percidae</td>\n",
       "      <td>Perca</td>\n",
       "      <td>Perca fluviatilis</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>CC0_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'array_element': 'Konsult'}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-09-25 05:23:40.367000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'array_element': 'COORDINATE_ROUNDED'}]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1947848 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbifid                            datasetkey  \\\n",
       "0        2141788029  4fa7b334-ce0d-4e88-aaae-2e0c138d049e   \n",
       "1        2142511629  4fa7b334-ce0d-4e88-aaae-2e0c138d049e   \n",
       "2        2126155116  4fa7b334-ce0d-4e88-aaae-2e0c138d049e   \n",
       "3        2143587663  4fa7b334-ce0d-4e88-aaae-2e0c138d049e   \n",
       "4        2152055267  4fa7b334-ce0d-4e88-aaae-2e0c138d049e   \n",
       "...             ...                                   ...   \n",
       "1947843  1674906857  9e932f70-0c61-11dd-84ce-b8a03c50a862   \n",
       "1947844  2012826837  9e932f70-0c61-11dd-84ce-b8a03c50a862   \n",
       "1947845  1674906890  9e932f70-0c61-11dd-84ce-b8a03c50a862   \n",
       "1947846  1675104176  9e932f70-0c61-11dd-84ce-b8a03c50a862   \n",
       "1947847  1674906836  9e932f70-0c61-11dd-84ce-b8a03c50a862   \n",
       "\n",
       "                                              occurrenceid   kingdom  \\\n",
       "0                       URN:catalog:CLO:EBIRD:OBS590769735  Animalia   \n",
       "1                       URN:catalog:CLO:EBIRD:OBS591457037  Animalia   \n",
       "2                       URN:catalog:CLO:EBIRD:OBS592106103  Animalia   \n",
       "3                       URN:catalog:CLO:EBIRD:OBS592831487  Animalia   \n",
       "4                       URN:catalog:CLO:EBIRD:OBS593603038  Animalia   \n",
       "...                                                    ...       ...   \n",
       "1947843  urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...  Animalia   \n",
       "1947844  urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...  Animalia   \n",
       "1947845  urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...  Animalia   \n",
       "1947846  urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...  Animalia   \n",
       "1947847  urn:lsid:slu.aqua.rom.sers:ObservedProperty:14...  Animalia   \n",
       "\n",
       "             phylum           class            order         family  \\\n",
       "0          Chordata            Aves    Passeriformes  Polioptilidae   \n",
       "1          Chordata            Aves    Columbiformes     Columbidae   \n",
       "2          Chordata            Aves  Accipitriformes    Pandionidae   \n",
       "3          Chordata            Aves    Passeriformes      Icteridae   \n",
       "4          Chordata            Aves    Passeriformes      Regulidae   \n",
       "...             ...             ...              ...            ...   \n",
       "1947843    Chordata  Actinopterygii      Perciformes       Percidae   \n",
       "1947844  Arthropoda    Malacostraca         Decapoda      Astacidae   \n",
       "1947845    Chordata  Actinopterygii    Salmoniformes     Salmonidae   \n",
       "1947846    Chordata  Actinopterygii    Salmoniformes     Salmonidae   \n",
       "1947847    Chordata  Actinopterygii      Perciformes       Percidae   \n",
       "\n",
       "               genus                  species  ... identifiedby  \\\n",
       "0         Polioptila      Polioptila caerulea  ...           []   \n",
       "1        Patagioenas  Patagioenas cayennensis  ...           []   \n",
       "2            Pandion        Pandion haliaetus  ...           []   \n",
       "3          Quiscalus       Quiscalus quiscula  ...           []   \n",
       "4            Regulus        Regulus calendula  ...           []   \n",
       "...              ...                      ...  ...          ...   \n",
       "1947843        Perca        Perca fluviatilis  ...           []   \n",
       "1947844      Astacus          Astacus astacus  ...           []   \n",
       "1947845        Salmo             Salmo trutta  ...           []   \n",
       "1947846    Thymallus      Thymallus thymallus  ...           []   \n",
       "1947847        Perca        Perca fluviatilis  ...           []   \n",
       "\n",
       "        dateidentified    license rightsholder  \\\n",
       "0                 None  CC_BY_4_0         None   \n",
       "1                 None  CC_BY_4_0         None   \n",
       "2                 None  CC_BY_4_0         None   \n",
       "3                 None  CC_BY_4_0         None   \n",
       "4                 None  CC_BY_4_0         None   \n",
       "...                ...        ...          ...   \n",
       "1947843           None    CC0_1_0         None   \n",
       "1947844           None    CC0_1_0         None   \n",
       "1947845           None    CC0_1_0         None   \n",
       "1947846           None    CC0_1_0         None   \n",
       "1947847           None    CC0_1_0         None   \n",
       "\n",
       "                                                recordedby typestatus  \\\n",
       "0                        [{'array_element': 'obsr233099'}]         []   \n",
       "1                        [{'array_element': 'obsr767103'}]         []   \n",
       "2                        [{'array_element': 'obsr370369'}]         []   \n",
       "3                         [{'array_element': 'obsr29404'}]         []   \n",
       "4                        [{'array_element': 'obsr383648'}]         []   \n",
       "...                                                    ...        ...   \n",
       "1947843  [{'array_element': 'Fiskeriverkets utredningsk...         []   \n",
       "1947844  [{'array_element': 'Fiskeriverkets utredningsk...         []   \n",
       "1947845  [{'array_element': 'Fiskeriverkets utredningsk...         []   \n",
       "1947846  [{'array_element': 'Fiskeriverkets utredningsk...         []   \n",
       "1947847                     [{'array_element': 'Konsult'}]         []   \n",
       "\n",
       "        establishmentmeans             lastinterpreted mediatype  \\\n",
       "0                     None  2022-09-08 14:29:55.344000        []   \n",
       "1                     None  2022-09-08 14:29:56.715000        []   \n",
       "2                     None  2022-09-08 14:29:58.118000        []   \n",
       "3                     None  2022-09-08 14:29:59.965000        []   \n",
       "4                     None  2022-09-08 14:30:01.862000        []   \n",
       "...                    ...                         ...       ...   \n",
       "1947843               None  2022-09-25 05:23:40.367000        []   \n",
       "1947844               None  2022-09-25 05:23:40.367000        []   \n",
       "1947845               None  2022-09-25 05:23:40.367000        []   \n",
       "1947846               None  2022-09-25 05:23:40.367000        []   \n",
       "1947847               None  2022-09-25 05:23:40.367000        []   \n",
       "\n",
       "                                             issue  \n",
       "0                                               []  \n",
       "1        [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "2                                               []  \n",
       "3                                               []  \n",
       "4                                               []  \n",
       "...                                            ...  \n",
       "1947843  [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "1947844  [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "1947845  [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "1947846  [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "1947847  [{'array_element': 'COORDINATE_ROUNDED'}]  \n",
       "\n",
       "[1947848 rows x 50 columns]"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "31d9b0f4-2d61-40dc-8465-2ae039401fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kingdom   phylum    class           family        genus       species              \n",
       "Animalia  Chordata  Actinopterygii  Salmonidae    Salmo       Salmo trutta             36867\n",
       "                                    Cyprinidae    Phoxinus    Phoxinus phoxinus        16818\n",
       "                    Aves            Sturnidae     Sturnus     Sturnus vulgaris         12594\n",
       "                    Actinopterygii  Salmonidae    Salmo       Salmo salar              12313\n",
       "                    Aves            Passeridae    Passer      Passer domesticus        12047\n",
       "                                    Turdidae      Turdus      Turdus migratorius       12024\n",
       "                                    Corvidae      Corvus      Corvus brachyrhynchos    11941\n",
       "                    Actinopterygii  Cottidae      Cottus      Cottus gobio             11923\n",
       "                    Aves            Columbidae    Zenaida     Zenaida macroura         11370\n",
       "                    Actinopterygii  Lotidae       Lota        Lota lota                11167\n",
       "                    Aves            Cardinalidae  Cardinalis  Cardinalis cardinalis    10779\n",
       "                                    Anatidae      Anas        Anas platyrhynchos       10139\n",
       "                                    Corvidae      Cyanocitta  Cyanocitta cristata      10009\n",
       "                                    Emberizidae   Melospiza   Melospiza melodia         9357\n",
       "                                    Anatidae      Branta      Branta canadensis         8980\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "e06d8774-c648-44d4-9994-32acc129d2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "countrycode\n",
       "AD      4\n",
       "AE    208\n",
       "AF     10\n",
       "AG     18\n",
       "AI     11\n",
       "     ... \n",
       "YT      1\n",
       "ZA    482\n",
       "ZM    111\n",
       "ZW    117\n",
       "ZZ     15\n",
       "Name: species, Length: 239, dtype: int64"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "6af8d4a4-e491-422f-b33d-c4b64b2f2c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-gbif-gbif-example.ipynb/14.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {
    "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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