farmOS + JupyterLite: Import a CSV of Animals

animal_csv_import.ipynb

{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "# From https://gist.github.com/bollwyvl/132aaff5cdb2c35ee1f75aed83e87eeb\nasync def get_contents(path):\n    \"\"\"use the IndexedDB API to acess JupyterLite's in-browser (for now) storage\n    \n    for documentation purposes, the full names of the JS API objects are used.\n    \n    see https://developer.mozilla.org/en-US/docs/Web/API/IDBRequest\n    \"\"\"\n    import js, asyncio\n\n    DB_NAME = \"JupyterLite Storage\"\n\n    # we only ever expect one result, either an error _or_ success\n    queue = asyncio.Queue(1)\n    \n    IDBOpenDBRequest = js.self.indexedDB.open(DB_NAME)\n    IDBOpenDBRequest.onsuccess = IDBOpenDBRequest.onerror = queue.put_nowait\n    \n    await queue.get()\n    \n    if IDBOpenDBRequest.result is None:\n        return None\n        \n    IDBTransaction = IDBOpenDBRequest.result.transaction(\"files\", \"readonly\")\n    IDBObjectStore = IDBTransaction.objectStore(\"files\")\n    IDBRequest = IDBObjectStore.get(path, \"key\")\n    IDBRequest.onsuccess = IDBRequest.onerror = queue.put_nowait\n    \n    await queue.get()\n    \n    return IDBRequest.result.to_py() if IDBRequest.result else None\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import io\nimport pandas\n\nanimals = pandas.read_csv(io.StringIO((await get_contents(\"animals.csv\"))[\"content\"]))\n\nanimals",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  animal_name  animal_dob animal_sex\n0       alice  2021/01/18          F\n1         bob  2021/03/12          M\n2        curt  2020/05/01          M\n3       dolly  2021/06/08          F",
            "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>animal_name</th>\n      <th>animal_dob</th>\n      <th>animal_sex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>alice</td>\n      <td>2021/01/18</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>bob</td>\n      <td>2021/03/12</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>curt</td>\n      <td>2020/05/01</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dolly</td>\n      <td>2021/06/08</td>\n      <td>F</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\nfrom js import location\n\nresp = await pyfetch(location.origin + '/session/token', method='POST')\n\nprint(resp.ok, resp.status, resp.status_text)\n\nantiCsrfToken = await resp.string()\n\nheaders = {\n    'Content-type': \"application/vnd.api+json\",\n    'X-CSRF-Token': antiCsrfToken,\n}",
      "metadata": {
        "trusted": true
      },
      "execution_count": 62,
      "outputs": [
        {
          "name": "stdout",
          "text": "True 200 OK\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "resp = await pyfetch(location.origin + '/api/taxonomy_term/animal_type?filter[name]=Sheep', method='GET')\n\ndata = await resp.json()\n\nterms = data['data']\n\nif not len(terms):\n    resp = await pyfetch(location.origin + '/api/taxonomy_term/animal_type', method='POST', body=json.dumps({\n        \"data\": {\n            \"type\": \"taxonomy_term--animal_type\",\n            \"attributes\": {\n                \"name\": \"Sheep\",\n            },\n        },\n    }), headers=headers)\n\n    print(resp.ok, resp.status, resp.status_text)\n\n    data = await resp.json()\n\n    terms = [data['data']]\n\nsheep_animal_type_term = terms[0]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import datetime\nimport json\n\nfor idx, animal in animals.iterrows():\n    resp = await pyfetch(location.origin + '/api/asset/animal', method='POST', body=json.dumps({\n      \"data\": {\n        \"type\": \"asset--animal\",\n        \"attributes\": {\n          \"name\": animal['animal_name'],\n          \"sex\": animal['animal_sex'],\n          \"birthdate\": datetime.datetime.strptime(animal['animal_dob'], \"%Y/%m/%d\").strftime('%Y-%m-%dT%H:%M:%S+00:00'),\n        },\n        \"relationships\": {\n          \"animal_type\": {\n              \"data\": {\n                  \"type\": sheep_animal_type_term['type'],\n                  \"id\": sheep_animal_type_term['id'],\n              },\n          },\n        }\n      }\n    }), headers=headers)\n\n    print(animal['animal_name'], resp.ok, resp.status, resp.status_text)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 70,
      "outputs": [
        {
          "name": "stdout",
          "text": "alice True 201 Created\nbob True 201 Created\ncurt True 201 Created\ndolly True 201 Created\n",
          "output_type": "stream"
        }
      ]
    }
  ]
}

animals.csv