diff --git a/.ipynb_checkpoints/kiss_data_train-checkpoint.ipynb b/.ipynb_checkpoints/kiss_data_train-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..115654f7a699b4892faa125a17b53593cdb394df
--- /dev/null
+++ b/.ipynb_checkpoints/kiss_data_train-checkpoint.ipynb
@@ -0,0 +1,1622 @@
+{
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+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hRVHLnJDZ_5x"
+   },
+   "source": [
+    "# POC Farm Data Train Notebook\n",
+    "Goal of the train: run queries in separated environments without granting the notebook owner access to all the data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ev-naMoKaNAc"
+   },
+   "source": [
+    "## Station 1 Methane data lake\n",
+    "Q Please return all farms in datalake\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
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[0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Gantt Chart\"}, \"xaxis\": {\"rangeselector\": {\"buttons\": [{\"count\": 7, \"label\": \"1w\", \"step\": \"day\", \"stepmode\": \"backward\"}, {\"count\": 1, \"label\": \"1m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 6, \"label\": \"6m\", \"step\": \"month\", \"stepmode\": \"backward\"}, {\"count\": 1, \"label\": \"YTD\", \"step\": \"year\", \"stepmode\": \"todate\"}, {\"count\": 1, \"label\": \"1y\", \"step\": \"year\", \"stepmode\": \"backward\"}, {\"step\": \"all\"}]}, \"showgrid\": false, \"type\": \"date\", \"zeroline\": false}, \"yaxis\": {\"autorange\": false, \"range\": [-1, 20], \"showgrid\": false, \"ticktext\": [\"Zegveld g\", \"Ysselstein s\", \"Ysselstein g\", \"Ysselstein B\", \"Sevenum s\", \"Middenmeer s\", \"Markelo s\", \"Lemelerveld s\", \"Leeuwarden g\", \"Hulst s\", \"Hulst g\", \"Hengelo s\", \"Hengelo g\", \"Hengelo B\", \"Cornwerd s\", \"Bunschoten s\", \"Biddinghuizen s\", \"Bedum s\", \"Bedum g\"], \"tickvals\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], \"zeroline\": false}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('714718b0-531c-4fa6-8677-a5ce0f3ea5da');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # install dependencies\n",
+    "# # !pip3 install azure-storage-blob \n",
+    "# # !pip3 install azure-keyvault\n",
+    "!pip3 install plotly.express\n",
+    "!pip3 install plotly\n",
+    "# # !pip3 install azure-identity\n",
+    "# # string = \"connection string removed after running container\"\n",
+    "\n",
+    "import os, uuid\n",
+    "# # from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, __version__\n",
+    "import pandas as pd\n",
+    "import pandas as pd\n",
+    "\n",
+    "import io\n",
+    "import plotly.figure_factory as ff\n",
+    "\n",
+    "# get simple dataframe from data-lake\n",
+    "def getData():\n",
+    "  # fake data returned by datalake, because persistent storage is not available in poc\n",
+    "  return pd.read_csv('data/station1.csv', sep=';')\n",
+    "\n",
+    "# do some fun stuff with the data\n",
+    "def plot(df):\n",
+    "  df = df.sort_values('Task')\n",
+    "  df = df.to_dict(orient='records')\n",
+    "  fig = ff.create_gantt(df, index_col='Data',  show_colorbar=True, group_tasks=True)\n",
+    "  fig.show()\n",
+    "\n",
+    "df = getData()\n",
+    "\n",
+    "\n",
+    "plot(df)\n",
+    "\n",
+    "# dict = df.to_dict(orient='index')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(df):\n",
+    "  df = df.sort_values('Task')\n",
+    "  df = df.to_dict(orient='records')\n",
+    "  fig = ff.create_gantt(df, index_col='Data',  show_colorbar=True, group_tasks=True)\n",
+    "  fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RK7UWz3fe7XR",
+    "outputId": "a1687828-14c6-4d01-801c-4a58fc203145"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dictionary for Agro data cube\n",
+      "{0: {'Unnamed: 0': 0, 'Unnamed: 0.1': 0, 'Unnamed: 0.1.1': 0, 'Task': '  Bedum s', 'Data': 'sniffer', 'Start': '2019-04-01', 'Finish': '2020-08-31'}, 1: {'Unnamed: 0': 1, 'Unnamed: 0.1': 1, 'Unnamed: 0.1.1': 1, 'Task': '  Biddinghuizen s', 'Data': 'sniffer', 'Start': '2019-01-31', 'Finish': '2020-08-31'}, 2: {'Unnamed: 0': 2, 'Unnamed: 0.1': 2, 'Unnamed: 0.1.1': 2, 'Task': '  Bunschoten s', 'Data': 'sniffer', 'Start': '2018-11-30', 'Finish': '20-08-31'}, 3: {'Unnamed: 0': 3, 'Unnamed: 0.1': 3, 'Unnamed: 0.1.1': 3, 'Task': '  Cornwerd s', 'Data': 'sniffer', 'Start': '2019-02-28', 'Finish': '2020-09-25'}, 4: {'Unnamed: 0': 4, 'Unnamed: 0.1': 4, 'Unnamed: 0.1.1': 4, 'Task': '  Hengelo s', 'Data': 'sniffer', 'Start': '2019-01-22', 'Finish': '2020-09-04'}, 5: {'Unnamed: 0': 5, 'Unnamed: 0.1': 5, 'Unnamed: 0.1.1': 5, 'Task': '  Hulst s', 'Data': 'sniffer', 'Start': '2019-05-31', 'Finish': '2020-09-09'}, 6: {'Unnamed: 0': 6, 'Unnamed: 0.1': 6, 'Unnamed: 0.1.1': 6, 'Task': '  Lemelerveld s', 'Data': 'sniffer', 'Start': '2019-02-28', 'Finish': '2020-08-03'}, 7: {'Unnamed: 0': 7, 'Unnamed: 0.1': 7, 'Unnamed: 0.1.1': 7, 'Task': '  Markelo s', 'Data': 'sniffer', 'Start': '2019-03-01', 'Finish': '2020-06-30'}, 8: {'Unnamed: 0': 8, 'Unnamed: 0.1': 8, 'Unnamed: 0.1.1': 8, 'Task': '  Middenmeer s', 'Data': 'sniffer', 'Start': '2018-12-31', 'Finish': '2021-01-01'}, 9: {'Unnamed: 0': 9, 'Unnamed: 0.1': 9, 'Unnamed: 0.1.1': 9, 'Task': '  Sevenum s', 'Data': 'sniffer', 'Start': '19-02-28', 'Finish': '2019-10-31'}, 10: {'Unnamed: 0': 10, 'Unnamed: 0.1': 10, 'Unnamed: 0.1.1': 10, 'Task': '  Ysselstein s', 'Data': 'sniffer', 'Start': '2019-05-31', 'Finish': '2020-09-03'}, 11: {'Unnamed: 0': 11, 'Unnamed: 0.1': 11, 'Unnamed: 0.1.1': 11, 'Task': '  Bedum g', 'Data': 'greenfeed', 'Start': '2019-05-28', 'Finish': '2019-06-18'}, 12: {'Unnamed: 0': 12, 'Unnamed: 0.1': 12, 'Unnamed: 0.1.1': 12, 'Task': '  Hengelo g', 'Data': 'greenfeed', 'Start': '2020-05-03', 'Finish': '2020-09-15'}, 13: {'Unnamed: 0': 13, 'Unnamed: 0.1': 13, 'Unnamed: 0.1.1': 13, 'Task': '  Hengelo g', 'Data': 'greenfeed', 'Start': '2020-02-20', 'Finish': '2019-04-25'}, 14: {'Unnamed: 0': 14, 'Unnamed: 0.1': 14, 'Unnamed: 0.1.1': 14, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2020-05-04', 'Finish': '2020-08-23'}, 15: {'Unnamed: 0': 15, 'Unnamed: 0.1': 15, 'Unnamed: 0.1.1': 15, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2019-10-22', 'Finish': '2019-11-18'}, 16: {'Unnamed: 0': 16, 'Unnamed: 0.1': 16, 'Unnamed: 0.1.1': 16, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2019-06-20', 'Finish': '2019-07-16'}, 17: {'Unnamed: 0': 17, 'Unnamed: 0.1': 17, 'Unnamed: 0.1.1': 17, 'Task': '  Leeuwarden g', 'Data': 'greenfeed', 'Start': '2018-12-17', 'Finish': '2019-01-14'}, 18: {'Unnamed: 0': 18, 'Unnamed: 0.1': 18, 'Unnamed: 0.1.1': 18, 'Task': '  Leeuwarden g', 'Data': 'greenfeed', 'Start': '2020-02-10', 'Finish': '2020-04-21'}, 19: {'Unnamed: 0': 19, 'Unnamed: 0.1': 19, 'Unnamed: 0.1.1': 19, 'Task': '  Ysselstein g', 'Data': 'greenfeed', 'Start': '2020-02-07', 'Finish': '2020-03-12'}, 20: {'Unnamed: 0': 20, 'Unnamed: 0.1': 20, 'Unnamed: 0.1.1': 20, 'Task': '  Ysselstein g', 'Data': 'greenfeed', 'Start': '2019-05-08', 'Finish': '2019-06-12'}, 21: {'Unnamed: 0': 21, 'Unnamed: 0.1': 21, 'Unnamed: 0.1.1': 21, 'Task': '  Zegveld g', 'Data': 'greenfeed', 'Start': '2019-10-03', 'Finish': '2020-01-14'}, 22: {'Unnamed: 0': 22, 'Unnamed: 0.1': 22, 'Unnamed: 0.1.1': 22, 'Task': '  Hengelo B', 'Data': 'barn', 'Start': '2018-12-10', 'Finish': '2020-11-03'}, 23: {'Unnamed: 0': 23, 'Unnamed: 0.1': 23, 'Unnamed: 0.1.1': 23, 'Task': '  Ysselstein B', 'Data': 'barn', 'Start': '2018-11-13', 'Finish': '2020-11-03'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('dictionary for Agro data cube')\n",
+    "print(dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jHQDrBGtbzRc"
+   },
+   "source": [
+    "## Station 2 Agro data cube\n",
+    "Q please populate average temperature for each farm in the Methane datalake dictionary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "YEQeiOkHb5Xr"
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-25-698d20d86fcf>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-25-698d20d86fcf>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def collect(df)\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "def collect(df)\n",
+    "    #fetch data from AgroDataCube\n",
+    "    return df\n",
+    "\n",
+    "df = collect(df)\n",
+    "df.to_csv('data/station2.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "kiss data train.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..8622aaf70d9e0527dcf858ceebab13edaef94c93
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,6 @@
+# FROM ubuntu:18.04
+from python:3.7.9-buster
+RUN pip3 install jupyter
+RUN pip3 install pandas
+WORKDIR /home
+CMD ["jupyter", "notebook", "--port=8888", "--no-browser", "--ip=0.0.0.0", "--allow-root"]
diff --git a/README.md b/README.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..9b12c0753c675fba43d12f0a94935f9545ebd11a 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,4 @@
+docker build -t fdt .
+
+docker run -p 8888:8888 fdt -v .:/home
+
diff --git a/data/station1.csv b/data/station1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..231452d8ff8c42db6264acf556b7a99bc7a58e23
--- /dev/null
+++ b/data/station1.csv
@@ -0,0 +1,25 @@
+Task;Data;Start;Finish
+Bedum s;sniffer;2019-04-01;2020-08-31
+Biddinghuizen s;sniffer;2019-01-31;2020-08-31
+Bunschoten s;sniffer;2018-11-30;20-08-31
+Cornwerd s;sniffer;2019-02-28;2020-09-25
+Hengelo s;sniffer;2019-01-22;2020-09-04
+Hulst s;sniffer;2019-05-31;2020-09-09
+Lemelerveld s;sniffer;2019-02-28;2020-08-03
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\ No newline at end of file
diff --git a/kiss_data_train.ipynb b/kiss_data_train.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..115654f7a699b4892faa125a17b53593cdb394df
--- /dev/null
+++ b/kiss_data_train.ipynb
@@ -0,0 +1,1622 @@
+{
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+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hRVHLnJDZ_5x"
+   },
+   "source": [
+    "# POC Farm Data Train Notebook\n",
+    "Goal of the train: run queries in separated environments without granting the notebook owner access to all the data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ev-naMoKaNAc"
+   },
+   "source": [
+    "## Station 1 Methane data lake\n",
+    "Q Please return all farms in datalake\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
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+    "id": "4H0j3eDyahlM",
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+      "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from plotly) (1.15.0)\n",
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+       "                            \n",
+       "var gd = document.getElementById('714718b0-531c-4fa6-8677-a5ce0f3ea5da');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # install dependencies\n",
+    "# # !pip3 install azure-storage-blob \n",
+    "# # !pip3 install azure-keyvault\n",
+    "!pip3 install plotly.express\n",
+    "!pip3 install plotly\n",
+    "# # !pip3 install azure-identity\n",
+    "# # string = \"connection string removed after running container\"\n",
+    "\n",
+    "import os, uuid\n",
+    "# # from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, __version__\n",
+    "import pandas as pd\n",
+    "import pandas as pd\n",
+    "\n",
+    "import io\n",
+    "import plotly.figure_factory as ff\n",
+    "\n",
+    "# get simple dataframe from data-lake\n",
+    "def getData():\n",
+    "  # fake data returned by datalake, because persistent storage is not available in poc\n",
+    "  return pd.read_csv('data/station1.csv', sep=';')\n",
+    "\n",
+    "# do some fun stuff with the data\n",
+    "def plot(df):\n",
+    "  df = df.sort_values('Task')\n",
+    "  df = df.to_dict(orient='records')\n",
+    "  fig = ff.create_gantt(df, index_col='Data',  show_colorbar=True, group_tasks=True)\n",
+    "  fig.show()\n",
+    "\n",
+    "df = getData()\n",
+    "\n",
+    "\n",
+    "plot(df)\n",
+    "\n",
+    "# dict = df.to_dict(orient='index')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(df):\n",
+    "  df = df.sort_values('Task')\n",
+    "  df = df.to_dict(orient='records')\n",
+    "  fig = ff.create_gantt(df, index_col='Data',  show_colorbar=True, group_tasks=True)\n",
+    "  fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RK7UWz3fe7XR",
+    "outputId": "a1687828-14c6-4d01-801c-4a58fc203145"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dictionary for Agro data cube\n",
+      "{0: {'Unnamed: 0': 0, 'Unnamed: 0.1': 0, 'Unnamed: 0.1.1': 0, 'Task': '  Bedum s', 'Data': 'sniffer', 'Start': '2019-04-01', 'Finish': '2020-08-31'}, 1: {'Unnamed: 0': 1, 'Unnamed: 0.1': 1, 'Unnamed: 0.1.1': 1, 'Task': '  Biddinghuizen s', 'Data': 'sniffer', 'Start': '2019-01-31', 'Finish': '2020-08-31'}, 2: {'Unnamed: 0': 2, 'Unnamed: 0.1': 2, 'Unnamed: 0.1.1': 2, 'Task': '  Bunschoten s', 'Data': 'sniffer', 'Start': '2018-11-30', 'Finish': '20-08-31'}, 3: {'Unnamed: 0': 3, 'Unnamed: 0.1': 3, 'Unnamed: 0.1.1': 3, 'Task': '  Cornwerd s', 'Data': 'sniffer', 'Start': '2019-02-28', 'Finish': '2020-09-25'}, 4: {'Unnamed: 0': 4, 'Unnamed: 0.1': 4, 'Unnamed: 0.1.1': 4, 'Task': '  Hengelo s', 'Data': 'sniffer', 'Start': '2019-01-22', 'Finish': '2020-09-04'}, 5: {'Unnamed: 0': 5, 'Unnamed: 0.1': 5, 'Unnamed: 0.1.1': 5, 'Task': '  Hulst s', 'Data': 'sniffer', 'Start': '2019-05-31', 'Finish': '2020-09-09'}, 6: {'Unnamed: 0': 6, 'Unnamed: 0.1': 6, 'Unnamed: 0.1.1': 6, 'Task': '  Lemelerveld s', 'Data': 'sniffer', 'Start': '2019-02-28', 'Finish': '2020-08-03'}, 7: {'Unnamed: 0': 7, 'Unnamed: 0.1': 7, 'Unnamed: 0.1.1': 7, 'Task': '  Markelo s', 'Data': 'sniffer', 'Start': '2019-03-01', 'Finish': '2020-06-30'}, 8: {'Unnamed: 0': 8, 'Unnamed: 0.1': 8, 'Unnamed: 0.1.1': 8, 'Task': '  Middenmeer s', 'Data': 'sniffer', 'Start': '2018-12-31', 'Finish': '2021-01-01'}, 9: {'Unnamed: 0': 9, 'Unnamed: 0.1': 9, 'Unnamed: 0.1.1': 9, 'Task': '  Sevenum s', 'Data': 'sniffer', 'Start': '19-02-28', 'Finish': '2019-10-31'}, 10: {'Unnamed: 0': 10, 'Unnamed: 0.1': 10, 'Unnamed: 0.1.1': 10, 'Task': '  Ysselstein s', 'Data': 'sniffer', 'Start': '2019-05-31', 'Finish': '2020-09-03'}, 11: {'Unnamed: 0': 11, 'Unnamed: 0.1': 11, 'Unnamed: 0.1.1': 11, 'Task': '  Bedum g', 'Data': 'greenfeed', 'Start': '2019-05-28', 'Finish': '2019-06-18'}, 12: {'Unnamed: 0': 12, 'Unnamed: 0.1': 12, 'Unnamed: 0.1.1': 12, 'Task': '  Hengelo g', 'Data': 'greenfeed', 'Start': '2020-05-03', 'Finish': '2020-09-15'}, 13: {'Unnamed: 0': 13, 'Unnamed: 0.1': 13, 'Unnamed: 0.1.1': 13, 'Task': '  Hengelo g', 'Data': 'greenfeed', 'Start': '2020-02-20', 'Finish': '2019-04-25'}, 14: {'Unnamed: 0': 14, 'Unnamed: 0.1': 14, 'Unnamed: 0.1.1': 14, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2020-05-04', 'Finish': '2020-08-23'}, 15: {'Unnamed: 0': 15, 'Unnamed: 0.1': 15, 'Unnamed: 0.1.1': 15, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2019-10-22', 'Finish': '2019-11-18'}, 16: {'Unnamed: 0': 16, 'Unnamed: 0.1': 16, 'Unnamed: 0.1.1': 16, 'Task': '  Hulst g', 'Data': 'greenfeed', 'Start': '2019-06-20', 'Finish': '2019-07-16'}, 17: {'Unnamed: 0': 17, 'Unnamed: 0.1': 17, 'Unnamed: 0.1.1': 17, 'Task': '  Leeuwarden g', 'Data': 'greenfeed', 'Start': '2018-12-17', 'Finish': '2019-01-14'}, 18: {'Unnamed: 0': 18, 'Unnamed: 0.1': 18, 'Unnamed: 0.1.1': 18, 'Task': '  Leeuwarden g', 'Data': 'greenfeed', 'Start': '2020-02-10', 'Finish': '2020-04-21'}, 19: {'Unnamed: 0': 19, 'Unnamed: 0.1': 19, 'Unnamed: 0.1.1': 19, 'Task': '  Ysselstein g', 'Data': 'greenfeed', 'Start': '2020-02-07', 'Finish': '2020-03-12'}, 20: {'Unnamed: 0': 20, 'Unnamed: 0.1': 20, 'Unnamed: 0.1.1': 20, 'Task': '  Ysselstein g', 'Data': 'greenfeed', 'Start': '2019-05-08', 'Finish': '2019-06-12'}, 21: {'Unnamed: 0': 21, 'Unnamed: 0.1': 21, 'Unnamed: 0.1.1': 21, 'Task': '  Zegveld g', 'Data': 'greenfeed', 'Start': '2019-10-03', 'Finish': '2020-01-14'}, 22: {'Unnamed: 0': 22, 'Unnamed: 0.1': 22, 'Unnamed: 0.1.1': 22, 'Task': '  Hengelo B', 'Data': 'barn', 'Start': '2018-12-10', 'Finish': '2020-11-03'}, 23: {'Unnamed: 0': 23, 'Unnamed: 0.1': 23, 'Unnamed: 0.1.1': 23, 'Task': '  Ysselstein B', 'Data': 'barn', 'Start': '2018-11-13', 'Finish': '2020-11-03'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('dictionary for Agro data cube')\n",
+    "print(dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jHQDrBGtbzRc"
+   },
+   "source": [
+    "## Station 2 Agro data cube\n",
+    "Q please populate average temperature for each farm in the Methane datalake dictionary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "YEQeiOkHb5Xr"
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-25-698d20d86fcf>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-25-698d20d86fcf>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def collect(df)\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "def collect(df)\n",
+    "    #fetch data from AgroDataCube\n",
+    "    return df\n",
+    "\n",
+    "df = collect(df)\n",
+    "df.to_csv('data/station2.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "kiss data train.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}