scripts/P_regridding_woa_data.ipynb

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    "# Regridding WOA data\n",
    "In this notebook, we show a basic data analysis worfklow using data from the [FishMIP Input Explorer](https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FishMIP_Input_Explorer/) in Python. You will learn the following:\n",
    "1. How to load a `zarr` file using `xarray`  \n",
    "2. How to subset data temporally and calculate a temporal mean  \n",
    "3. How to create a `Data Array` from a `data frame`  \n",
    "4. How to interpolate data along the `depth` dimension  \n",
    "5. How to regrid a `Data Array` to match the grid of another dataset  \n",
    "6. How to save `Data Arrays` as gridded and tabular data  \n",
    "\n",
    "To use this notebook, you will need to have downloaded GFDL and WOA data from our [shiny app](https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FishMIP_Input_Explorer/). Note that data has to be for the same regional model, but it does not need to be for the same variable as we are only interested in the grids.  \n",
    "\n",
    "Make sure you update the file paths where you have saved the data. Make sure you provide the full path, not only the file name."
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   "source": [
    "# Load libraries"
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  },
  {
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   "execution_count": 1,
   "id": "ac0c5683-616f-4de8-aafd-6879ad2f13ca",
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   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import xesmf as xe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8024b5f-245c-49a7-885c-c675089f3064",
   "metadata": {},
   "source": [
    "# Load GFDL `zarr` file"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "52110aa9-8723-44b4-badc-963964c314b0",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 100MB\n",
       "Dimensions:      (time: 600, depth_bin_m: 35, lat: 36, lon: 33)\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * time         (time) datetime64[ns] 5kB 1961-01-01 1961-02-01 ... 2010-12-01\n",
       "Data variables:\n",
       "    chl          (time, depth_bin_m, lat, lon) float32 100MB dask.array&lt;chunksize=(600, 5, 36, 33), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-092a835f-4459-48ae-86f1-463d54f5ad08' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-092a835f-4459-48ae-86f1-463d54f5ad08' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 600</li><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ca961421-9611-4f80-97c6-a0c1984d2571' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca961421-9611-4f80-97c6-a0c1984d2571' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-3e60e236-7bee-4193-9109-cf25bef1813d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e60e236-7bee-4193-9109-cf25bef1813d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3576583d-ff61-4d08-92f1-697633cd1d45' class='xr-var-data-in' type='checkbox'><label for='data-3576583d-ff61-4d08-92f1-697633cd1d45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>edges :</span></dt><dd>z_i</dd><dt><span>long_name :</span></dt><dd>Depth at cell center</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.38 64.12 63.88 ... 55.88 55.62</div><input id='attrs-bf038733-192e-4ed4-85aa-25539990e690' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf038733-192e-4ed4-85aa-25539990e690' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3d1de95-d118-47ef-bb8f-a7c54f0c0ed2' class='xr-var-data-in' type='checkbox'><label for='data-c3d1de95-d118-47ef-bb8f-a7c54f0c0ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-9c4da3f5-a8a8-479a-a48b-1b7e24869f87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c4da3f5-a8a8-479a-a48b-1b7e24869f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71170679-1846-45b1-a3ad-1c1766b49bc7' class='xr-var-data-in' type='checkbox'><label for='data-71170679-1846-45b1-a3ad-1c1766b49bc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1961-01-01 ... 2010-12-01</div><input id='attrs-c55b79df-8590-40f5-a651-2f71198b83ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c55b79df-8590-40f5-a651-2f71198b83ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bf250b2-c195-406c-817c-bb5aba37c62f' class='xr-var-data-in' type='checkbox'><label for='data-5bf250b2-c195-406c-817c-bb5aba37c62f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1961-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-03-01T00:00:00.000000000&#x27;, ..., &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec33068d-9227-43d8-a242-b8d7defdc830' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec33068d-9227-43d8-a242-b8d7defdc830' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>chl</span></div><div class='xr-var-dims'>(time, depth_bin_m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 5, 36, 33), meta=np.ndarray&gt;</div><input id='attrs-5d2da2a1-142c-47e7-b04a-f07b24b5cbda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d2da2a1-142c-47e7-b04a-f07b24b5cbda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6868be0-a265-4d4b-a97a-008ad39ebb6a' class='xr-var-data-in' type='checkbox'><label for='data-b6868be0-a265-4d4b-a97a-008ad39ebb6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GFDL_variable :</span></dt><dd>chl</dd><dt><span>long_name :</span></dt><dd>Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_phytoplankton_expressed_as_chlorophyll_in_sea_water</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 95.17 MiB </td>\n",
       "                        <td> 13.60 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (600, 35, 36, 33) </td>\n",
       "                        <td> (600, 5, 36, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-827300b5-0974-42f4-a4e0-81f36e6043cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-827300b5-0974-42f4-a4e0-81f36e6043cc' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b2062bc-83fd-412f-ac6a-579b5bbae381' class='xr-index-data-in' type='checkbox'/><label for='index-9b2062bc-83fd-412f-ac6a-579b5bbae381' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c8493916-3ce4-45b2-a210-549d456cf966' class='xr-index-data-in' type='checkbox'/><label for='index-c8493916-3ce4-45b2-a210-549d456cf966' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04b74b36-464e-42eb-8459-6651dbf7fede' class='xr-index-data-in' type='checkbox'/><label for='index-04b74b36-464e-42eb-8459-6651dbf7fede' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf394944-287f-450c-8015-2dcca1c63dfd' class='xr-index-data-in' type='checkbox'/><label for='index-cf394944-287f-450c-8015-2dcca1c63dfd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1961-01-01&#x27;, &#x27;1961-02-01&#x27;, &#x27;1961-03-01&#x27;, &#x27;1961-04-01&#x27;,\n",
       "               &#x27;1961-05-01&#x27;, &#x27;1961-06-01&#x27;, &#x27;1961-07-01&#x27;, &#x27;1961-08-01&#x27;,\n",
       "               &#x27;1961-09-01&#x27;, &#x27;1961-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2010-03-01&#x27;, &#x27;2010-04-01&#x27;, &#x27;2010-05-01&#x27;, &#x27;2010-06-01&#x27;,\n",
       "               &#x27;2010-07-01&#x27;, &#x27;2010-08-01&#x27;, &#x27;2010-09-01&#x27;, &#x27;2010-10-01&#x27;,\n",
       "               &#x27;2010-11-01&#x27;, &#x27;2010-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=600, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f78985d-be8f-4df3-93c7-fa8c0b870f6b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f78985d-be8f-4df3-93c7-fa8c0b870f6b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 100MB\n",
       "Dimensions:      (time: 600, depth_bin_m: 35, lat: 36, lon: 33)\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * time         (time) datetime64[ns] 5kB 1961-01-01 1961-02-01 ... 2010-12-01\n",
       "Data variables:\n",
       "    chl          (time, depth_bin_m, lat, lon) float32 100MB dask.array<chunksize=(600, 5, 36, 33), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define location of GFDL data\n",
    "gfdl_path = 'full_file_path_to_gfdl_zarr_data'\n",
    "gfdl = xr.open_zarr(gfdl_path)\n",
    "gfdl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b5fe46-1584-497c-bdb8-92ff6a0b1a14",
   "metadata": {},
   "source": [
    "# Calculate climatological mean\n",
    "For this example, we will use 10 years of data (1991-2000) to calculate a climatological mean. Note that you will need to know the name of the variable in your `Dataset`. In the example above, under *Data variables*, we can see that there is only one variable: *chl*. We will need to call this variable before we can calculate the mean.\n",
    "\n",
    "**Note:** If you use a different variable, make sure you use the name of the variable you are loading. Check the information provided in the metadata for your file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16b5ddcd-0bbd-462d-b92c-10f062eea8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;chl&#x27; (time: 600, depth_bin_m: 35, lat: 36, lon: 33)&gt; Size: 100MB\n",
       "dask.array&lt;open_dataset-chl, shape=(600, 35, 36, 33), dtype=float32, chunksize=(600, 5, 36, 33), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * time         (time) datetime64[ns] 5kB 1961-01-01 1961-02-01 ... 2010-12-01\n",
       "Attributes:\n",
       "    GFDL_variable:  chl\n",
       "    long_name:      Mass Concentration of Total Phytoplankton expressed as Ch...\n",
       "    standard_name:  mass_concentration_of_phytoplankton_expressed_as_chloroph...\n",
       "    units:          kg m-3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'chl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 600</li><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-912f57e5-288b-40eb-8aac-236676671b8e' class='xr-array-in' type='checkbox' checked><label for='section-912f57e5-288b-40eb-8aac-236676671b8e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(600, 5, 36, 33), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 13.60 MiB </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-50175c78-66dc-45e7-927b-1953a72f065e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50175c78-66dc-45e7-927b-1953a72f065e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-92c9b3f6-0e41-4ce1-8cd0-a1416555f1a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92c9b3f6-0e41-4ce1-8cd0-a1416555f1a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67bd3ee7-bc79-42ce-b093-f6936e8092d3' class='xr-var-data-in' type='checkbox'><label for='data-67bd3ee7-bc79-42ce-b093-f6936e8092d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>edges :</span></dt><dd>z_i</dd><dt><span>long_name :</span></dt><dd>Depth at cell center</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.38 64.12 63.88 ... 55.88 55.62</div><input id='attrs-f697b733-9350-49b1-be62-097d4b8720e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f697b733-9350-49b1-be62-097d4b8720e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68a5662-226e-4e4a-8d17-8679ce620337' class='xr-var-data-in' type='checkbox'><label for='data-e68a5662-226e-4e4a-8d17-8679ce620337' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-ca7f4b36-542a-4152-aa18-acb2e40427f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca7f4b36-542a-4152-aa18-acb2e40427f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12aa0d9d-c400-4e1e-9b0d-cbed1634f91f' class='xr-var-data-in' type='checkbox'><label for='data-12aa0d9d-c400-4e1e-9b0d-cbed1634f91f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1961-01-01 ... 2010-12-01</div><input id='attrs-63749378-e1fd-4035-a1ae-66224fdcf299' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63749378-e1fd-4035-a1ae-66224fdcf299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce7c0d7-7c53-4ef6-95d1-6890288fd868' class='xr-var-data-in' type='checkbox'><label for='data-7ce7c0d7-7c53-4ef6-95d1-6890288fd868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1961-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-03-01T00:00:00.000000000&#x27;, ..., &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ee72deb-c141-41bf-a277-27c149d1ec90' class='xr-section-summary-in' type='checkbox'  ><label for='section-6ee72deb-c141-41bf-a277-27c149d1ec90' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9346d6fd-f742-4ada-bbbd-ee63e503a36c' class='xr-index-data-in' type='checkbox'/><label for='index-9346d6fd-f742-4ada-bbbd-ee63e503a36c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ffc4a9c8-fabe-41bc-8e82-4a7a7aa08644' class='xr-index-data-in' type='checkbox'/><label for='index-ffc4a9c8-fabe-41bc-8e82-4a7a7aa08644' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83cd43df-b086-45e4-b2a3-dae829944e6a' class='xr-index-data-in' type='checkbox'/><label for='index-83cd43df-b086-45e4-b2a3-dae829944e6a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f23fd0e6-d7b6-4b4e-a691-3c8f22834384' class='xr-index-data-in' type='checkbox'/><label for='index-f23fd0e6-d7b6-4b4e-a691-3c8f22834384' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1961-01-01&#x27;, &#x27;1961-02-01&#x27;, &#x27;1961-03-01&#x27;, &#x27;1961-04-01&#x27;,\n",
       "               &#x27;1961-05-01&#x27;, &#x27;1961-06-01&#x27;, &#x27;1961-07-01&#x27;, &#x27;1961-08-01&#x27;,\n",
       "               &#x27;1961-09-01&#x27;, &#x27;1961-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2010-03-01&#x27;, &#x27;2010-04-01&#x27;, &#x27;2010-05-01&#x27;, &#x27;2010-06-01&#x27;,\n",
       "               &#x27;2010-07-01&#x27;, &#x27;2010-08-01&#x27;, &#x27;2010-09-01&#x27;, &#x27;2010-10-01&#x27;,\n",
       "               &#x27;2010-11-01&#x27;, &#x27;2010-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=600, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74362f33-d670-4840-97b2-3a306b97afbc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74362f33-d670-4840-97b2-3a306b97afbc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GFDL_variable :</span></dt><dd>chl</dd><dt><span>long_name :</span></dt><dd>Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_phytoplankton_expressed_as_chlorophyll_in_sea_water</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'chl' (time: 600, depth_bin_m: 35, lat: 36, lon: 33)> Size: 100MB\n",
       "dask.array<open_dataset-chl, shape=(600, 35, 36, 33), dtype=float32, chunksize=(600, 5, 36, 33), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * time         (time) datetime64[ns] 5kB 1961-01-01 1961-02-01 ... 2010-12-01\n",
       "Attributes:\n",
       "    GFDL_variable:  chl\n",
       "    long_name:      Mass Concentration of Total Phytoplankton expressed as Ch...\n",
       "    standard_name:  mass_concentration_of_phytoplankton_expressed_as_chloroph...\n",
       "    units:          kg m-3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdl = gfdl.chl\n",
    "gfdl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097ae6bf-5396-41e7-8947-3e653c414a75",
   "metadata": {},
   "source": [
    "We now have a `Data Array` of the variable we selected (`chl`). Now we can select the years of our interest and calculate the mean across time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b4c31c6-68dd-4fc0-a7d5-de90be0b02a7",
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;chl&#x27; (depth_bin_m: 35, lat: 36, lon: 33)&gt; Size: 166kB\n",
       "dask.array&lt;mean_agg-aggregate, shape=(35, 36, 33), dtype=float32, chunksize=(5, 36, 33), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'chl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b292d130-009b-4bb1-862d-af89bf2c5a6e' class='xr-array-in' type='checkbox' checked><label for='section-b292d130-009b-4bb1-862d-af89bf2c5a6e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(5, 36, 33), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 162.42 kiB </td>\n",
       "                        <td> 23.20 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (35, 36, 33) </td>\n",
       "                        <td> (5, 36, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7 chunks in 5 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"238\" height=\"238\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"49\" y1=\"39\" x2=\"49\" y2=\"159\" />\n",
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       "  <line x1=\"68\" y1=\"58\" x2=\"68\" y2=\"178\" />\n",
       "  <line x1=\"78\" y1=\"68\" x2=\"78\" y2=\"188\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"10.0,0.0 78.62745098039215,68.62745098039215 78.62745098039215,188.62745098039215 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <polygon points=\"10.0,0.0 120.00000000000001,0.0 188.62745098039215,68.62745098039215 78.62745098039215,68.62745098039215\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"78\" y1=\"68\" x2=\"188\" y2=\"68\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"78\" y1=\"188\" x2=\"188\" y2=\"188\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"78\" y1=\"68\" x2=\"78\" y2=\"188\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"188\" y1=\"68\" x2=\"188\" y2=\"188\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"78.62745098039215,68.62745098039215 188.62745098039215,68.62745098039215 188.62745098039215,188.62745098039215 78.62745098039215,188.62745098039215\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"133.627451\" y=\"208.627451\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >33</text>\n",
       "  <text x=\"208.627451\" y=\"128.627451\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,208.627451,128.627451)\">36</text>\n",
       "  <text x=\"34.313725\" y=\"174.313725\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,34.313725,174.313725)\">35</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-455121c5-d120-4ed1-8eb1-71ff3c5cb309' class='xr-section-summary-in' type='checkbox'  checked><label for='section-455121c5-d120-4ed1-8eb1-71ff3c5cb309' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-093b0d7f-6fc0-40a2-b3ee-2b789c060058' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-093b0d7f-6fc0-40a2-b3ee-2b789c060058' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7821cffa-e8cb-4367-9c1b-9c5573d3224d' class='xr-var-data-in' type='checkbox'><label for='data-7821cffa-e8cb-4367-9c1b-9c5573d3224d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>cartesian_axis :</span></dt><dd>Z</dd><dt><span>edges :</span></dt><dd>z_i</dd><dt><span>long_name :</span></dt><dd>Depth at cell center</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.38 64.12 63.88 ... 55.88 55.62</div><input id='attrs-a74fb129-4490-44d5-b919-969c555c94b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a74fb129-4490-44d5-b919-969c555c94b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b70885e-bbac-4ae1-9543-eff2c81799b6' class='xr-var-data-in' type='checkbox'><label for='data-2b70885e-bbac-4ae1-9543-eff2c81799b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-ddeea3dd-841b-48c0-9b4e-44b16cde560e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddeea3dd-841b-48c0-9b4e-44b16cde560e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dbe6aef-dfb2-4a8c-8b57-3f1e180770d9' class='xr-var-data-in' type='checkbox'><label for='data-1dbe6aef-dfb2-4a8c-8b57-3f1e180770d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f24733a0-69be-49d2-94fb-23d74a6309f2' class='xr-section-summary-in' type='checkbox'  ><label for='section-f24733a0-69be-49d2-94fb-23d74a6309f2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a2a5502-0967-421b-980c-94714c76297c' class='xr-index-data-in' type='checkbox'/><label for='index-7a2a5502-0967-421b-980c-94714c76297c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cae3bcda-6755-4e49-b268-41d15f5ef493' class='xr-index-data-in' type='checkbox'/><label for='index-cae3bcda-6755-4e49-b268-41d15f5ef493' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eff131a1-c3d0-47f5-9380-1c70ec14c8e6' class='xr-index-data-in' type='checkbox'/><label for='index-eff131a1-c3d0-47f5-9380-1c70ec14c8e6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f913841c-8b8e-454a-b38a-91507c1d38d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f913841c-8b8e-454a-b38a-91507c1d38d9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'chl' (depth_bin_m: 35, lat: 36, lon: 33)> Size: 166kB\n",
       "dask.array<mean_agg-aggregate, shape=(35, 36, 33), dtype=float32, chunksize=(5, 36, 33), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdl_mean = gfdl.sel(time = slice('1991', '2000')).mean('time')\n",
    "gfdl_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1430a1-1ca8-42d5-98ec-e556ace00c3d",
   "metadata": {},
   "source": [
    "You will notice that the `time` dimension disappeared, but we still have 35 depth levels. We can plot the first depth level to see what our data looks like. Note that this time we will use `isel`, which allows us to use an index to select data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "99fdb816-1640-4c44-b76f-b7a196ea6cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1549d15a6f80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdl_mean.isel(depth_bin_m = 0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd455a6b-1421-4c3f-a3ab-ad379275340a",
   "metadata": {},
   "source": [
    "# Loading WOA data\n",
    "We made the WOA data available as `parquet` files in our shiny app, so we will not be able to load it using the `xarray` package as we did with the `zarr` file. Instead, we will load it as a data frame with `pandas` and transform it to a `Data Array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf5b9bc9-70b0-411f-a37f-3af931acf68b",
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>depth</th>\n",
       "      <th>vals</th>\n",
       "      <th>cell_methods</th>\n",
       "      <th>long_name</th>\n",
       "      <th>standard_name</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.625</td>\n",
       "      <td>15.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.35091</td>\n",
       "      <td>area: mean depth: mean time: mean within years...</td>\n",
       "      <td>Objectively analyzed mean fields for sea_water...</td>\n",
       "      <td>sea_water_practical_salinity</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.625</td>\n",
       "      <td>15.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.36619</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.37479</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38169</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.37289</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lat     lon  depth     vals  \\\n",
       "0  55.625  15.625    0.0  7.35091   \n",
       "1  55.625  15.875    0.0  7.36619   \n",
       "2  55.625  16.125    0.0  7.37479   \n",
       "3  55.625  16.375    0.0  7.38169   \n",
       "4  55.625  16.625    0.0  7.37289   \n",
       "\n",
       "                                        cell_methods  \\\n",
       "0  area: mean depth: mean time: mean within years...   \n",
       "1                                               None   \n",
       "2                                               None   \n",
       "3                                               None   \n",
       "4                                               None   \n",
       "\n",
       "                                           long_name  \\\n",
       "0  Objectively analyzed mean fields for sea_water...   \n",
       "1                                               None   \n",
       "2                                               None   \n",
       "3                                               None   \n",
       "4                                               None   \n",
       "\n",
       "                  standard_name units  \n",
       "0  sea_water_practical_salinity     1  \n",
       "1                          None  None  \n",
       "2                          None  None  \n",
       "3                          None  None  \n",
       "4                          None  None  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define location of GFDL data\n",
    "woa_path = 'full_file_path_to_woa_parquet_data'\n",
    "woa = pd.read_parquet(woa_path)\n",
    "woa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395799b6-6dfd-416b-9e2a-ce79f8d57f89",
   "metadata": {},
   "source": [
    "We will drop the columns after `vals` because we will not need them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2db0edd1-7f43-4991-95cd-8a43b7a4d91f",
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>depth</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.625</td>\n",
       "      <td>15.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.35091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.625</td>\n",
       "      <td>15.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.36619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.37479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.625</td>\n",
       "      <td>16.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.37289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lat     lon  depth     vals\n",
       "0  55.625  15.625    0.0  7.35091\n",
       "1  55.625  15.875    0.0  7.36619\n",
       "2  55.625  16.125    0.0  7.37479\n",
       "3  55.625  16.375    0.0  7.38169\n",
       "4  55.625  16.625    0.0  7.37289"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa = woa[['lat', 'lon', 'depth', 'vals']]\n",
    "woa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0f7bb8-4748-4645-8f45-c493ce91a5ff",
   "metadata": {},
   "source": [
    "We will now \"complete\" our dataset by ensuring we have all possible combinations of `lat` and `lon`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "12708de5-2b5d-4249-b027-0833f5756485",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>55.625</th>\n",
       "      <th>55.875</th>\n",
       "      <th>56.125</th>\n",
       "      <th>56.375</th>\n",
       "      <th>56.625</th>\n",
       "      <th>56.875</th>\n",
       "      <th>57.125</th>\n",
       "      <th>57.375</th>\n",
       "      <th>57.625</th>\n",
       "      <th>57.875</th>\n",
       "      <th>...</th>\n",
       "      <th>62.125</th>\n",
       "      <th>62.375</th>\n",
       "      <th>62.625</th>\n",
       "      <th>62.875</th>\n",
       "      <th>63.125</th>\n",
       "      <th>63.375</th>\n",
       "      <th>63.625</th>\n",
       "      <th>63.875</th>\n",
       "      <th>64.125</th>\n",
       "      <th>64.375</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <th>lon</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th>15.625</th>\n",
       "      <td>7.35091</td>\n",
       "      <td>7.25600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.875</th>\n",
       "      <td>7.36619</td>\n",
       "      <td>7.28809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.125</th>\n",
       "      <td>7.37479</td>\n",
       "      <td>7.30711</td>\n",
       "      <td>7.25431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.375</th>\n",
       "      <td>7.38169</td>\n",
       "      <td>7.30849</td>\n",
       "      <td>7.25761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.625</th>\n",
       "      <td>7.37289</td>\n",
       "      <td>7.29879</td>\n",
       "      <td>7.23289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.60819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "lat            55.625   55.875   56.125  56.375  56.625  56.875   57.125  \\\n",
       "depth lon                                                                  \n",
       "0.0   15.625  7.35091  7.25600      NaN     NaN     NaN     NaN      NaN   \n",
       "      15.875  7.36619  7.28809      NaN     NaN     NaN     NaN      NaN   \n",
       "      16.125  7.37479  7.30711  7.25431     NaN     NaN     NaN      NaN   \n",
       "      16.375  7.38169  7.30849  7.25761     NaN     NaN     NaN      NaN   \n",
       "      16.625  7.37289  7.29879  7.23289     NaN     NaN     NaN  7.60819   \n",
       "\n",
       "lat           57.375  57.625  57.875  ...  62.125  62.375  62.625  62.875  \\\n",
       "depth lon                             ...                                   \n",
       "0.0   15.625     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN   \n",
       "      15.875     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN   \n",
       "      16.125     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN   \n",
       "      16.375     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN   \n",
       "      16.625     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN   \n",
       "\n",
       "lat           63.125  63.375  63.625  63.875  64.125  64.375  \n",
       "depth lon                                                     \n",
       "0.0   15.625     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "      15.875     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "      16.125     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "      16.375     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "      16.625     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa = woa.pivot(index = ['depth', 'lon'], columns = 'lat', values = 'vals')\n",
    "woa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9b7c2e-52e5-4d87-9e9c-e6cbc0147b73",
   "metadata": {},
   "source": [
    "This is looking more like a grid now and we have `NaN` for grid cells outside our area of interest, but now we need to \"complete\" the `depth` dimension. So, we will return the data frame to a long format and complete our data frame along this last dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a842517a-1414-4b53-a328-e3cd4c82e3d3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
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       "      <th>lon</th>\n",
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       "      <td>7.366190</td>\n",
       "      <td>7.374790</td>\n",
       "      <td>7.381690</td>\n",
       "      <td>7.37289</td>\n",
       "      <td>7.36381</td>\n",
       "      <td>7.34959</td>\n",
       "      <td>7.33211</td>\n",
       "      <td>7.30961</td>\n",
       "      <td>7.28469</td>\n",
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       "      <td>4.99969</td>\n",
       "      <td>5.00811</td>\n",
       "      <td>5.00219</td>\n",
       "      <td>5.00149</td>\n",
       "      <td>5.00119</td>\n",
       "      <td>5.00029</td>\n",
       "      <td>5.00019</td>\n",
       "      <td>4.99969</td>\n",
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       "      <th>5.0</th>\n",
       "      <td>7.349290</td>\n",
       "      <td>7.374600</td>\n",
       "      <td>7.395590</td>\n",
       "      <td>7.405100</td>\n",
       "      <td>7.39740</td>\n",
       "      <td>7.38879</td>\n",
       "      <td>7.37421</td>\n",
       "      <td>7.35011</td>\n",
       "      <td>7.32981</td>\n",
       "      <td>7.31021</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.00279</td>\n",
       "      <td>5.00359</td>\n",
       "      <td>5.00300</td>\n",
       "      <td>5.00271</td>\n",
       "      <td>5.00169</td>\n",
       "      <td>5.00521</td>\n",
       "      <td>5.00511</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>7.352490</td>\n",
       "      <td>7.373810</td>\n",
       "      <td>7.400800</td>\n",
       "      <td>7.403790</td>\n",
       "      <td>7.39810</td>\n",
       "      <td>7.39009</td>\n",
       "      <td>7.37619</td>\n",
       "      <td>7.36139</td>\n",
       "      <td>7.32441</td>\n",
       "      <td>7.29481</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00041</td>\n",
       "      <td>5.00449</td>\n",
       "      <td>5.00561</td>\n",
       "      <td>5.00869</td>\n",
       "      <td>5.00849</td>\n",
       "      <td>5.00329</td>\n",
       "      <td>5.00259</td>\n",
       "      <td>5.00220</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>7.335110</td>\n",
       "      <td>7.352700</td>\n",
       "      <td>7.377610</td>\n",
       "      <td>7.392110</td>\n",
       "      <td>7.38841</td>\n",
       "      <td>7.38271</td>\n",
       "      <td>7.37141</td>\n",
       "      <td>7.35341</td>\n",
       "      <td>7.33070</td>\n",
       "      <td>7.29571</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00361</td>\n",
       "      <td>5.01039</td>\n",
       "      <td>5.01529</td>\n",
       "      <td>5.00649</td>\n",
       "      <td>5.00899</td>\n",
       "      <td>5.00750</td>\n",
       "      <td>5.00331</td>\n",
       "      <td>5.00301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>7.360610</td>\n",
       "      <td>7.367490</td>\n",
       "      <td>7.387210</td>\n",
       "      <td>7.397110</td>\n",
       "      <td>7.38509</td>\n",
       "      <td>7.38580</td>\n",
       "      <td>7.38049</td>\n",
       "      <td>7.36111</td>\n",
       "      <td>7.32740</td>\n",
       "      <td>7.28491</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00859</td>\n",
       "      <td>5.01569</td>\n",
       "      <td>5.01730</td>\n",
       "      <td>5.01969</td>\n",
       "      <td>5.01451</td>\n",
       "      <td>5.01241</td>\n",
       "      <td>5.01299</td>\n",
       "      <td>5.00879</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>7.403210</td>\n",
       "      <td>7.383310</td>\n",
       "      <td>7.400600</td>\n",
       "      <td>7.405010</td>\n",
       "      <td>7.39421</td>\n",
       "      <td>7.40720</td>\n",
       "      <td>7.40691</td>\n",
       "      <td>7.38489</td>\n",
       "      <td>7.34729</td>\n",
       "      <td>7.30179</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01541</td>\n",
       "      <td>5.02239</td>\n",
       "      <td>5.02051</td>\n",
       "      <td>5.01929</td>\n",
       "      <td>5.01101</td>\n",
       "      <td>5.01609</td>\n",
       "      <td>5.01530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>7.621500</td>\n",
       "      <td>7.555590</td>\n",
       "      <td>7.520590</td>\n",
       "      <td>7.492110</td>\n",
       "      <td>7.46349</td>\n",
       "      <td>7.44911</td>\n",
       "      <td>7.43089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.44391</td>\n",
       "      <td>7.41571</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01350</td>\n",
       "      <td>5.02131</td>\n",
       "      <td>5.01891</td>\n",
       "      <td>5.01790</td>\n",
       "      <td>5.01081</td>\n",
       "      <td>5.01601</td>\n",
       "      <td>5.01489</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35.0</th>\n",
       "      <td>7.665100</td>\n",
       "      <td>7.579210</td>\n",
       "      <td>7.536910</td>\n",
       "      <td>7.501010</td>\n",
       "      <td>7.48101</td>\n",
       "      <td>7.46891</td>\n",
       "      <td>7.45031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.50060</td>\n",
       "      <td>7.47371</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01339</td>\n",
       "      <td>5.02129</td>\n",
       "      <td>5.01869</td>\n",
       "      <td>5.01850</td>\n",
       "      <td>5.01091</td>\n",
       "      <td>5.01631</td>\n",
       "      <td>5.01501</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>7.797590</td>\n",
       "      <td>7.657990</td>\n",
       "      <td>7.571410</td>\n",
       "      <td>7.518210</td>\n",
       "      <td>7.48951</td>\n",
       "      <td>7.47709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57609</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01519</td>\n",
       "      <td>5.02090</td>\n",
       "      <td>5.01889</td>\n",
       "      <td>5.01899</td>\n",
       "      <td>5.01259</td>\n",
       "      <td>5.01859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>8.241509</td>\n",
       "      <td>7.987690</td>\n",
       "      <td>7.791790</td>\n",
       "      <td>7.640790</td>\n",
       "      <td>7.54879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.73751</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01681</td>\n",
       "      <td>5.02119</td>\n",
       "      <td>5.01929</td>\n",
       "      <td>5.02021</td>\n",
       "      <td>5.01449</td>\n",
       "      <td>5.02009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>9.254910</td>\n",
       "      <td>9.032109</td>\n",
       "      <td>8.752509</td>\n",
       "      <td>8.485009</td>\n",
       "      <td>8.27779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.09749</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01920</td>\n",
       "      <td>5.02129</td>\n",
       "      <td>5.01971</td>\n",
       "      <td>5.02050</td>\n",
       "      <td>5.01441</td>\n",
       "      <td>5.02151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55.0</th>\n",
       "      <td>10.936709</td>\n",
       "      <td>10.519400</td>\n",
       "      <td>9.950590</td>\n",
       "      <td>9.578910</td>\n",
       "      <td>9.28979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.64879</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01729</td>\n",
       "      <td>5.02319</td>\n",
       "      <td>5.02079</td>\n",
       "      <td>5.02100</td>\n",
       "      <td>5.01569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>12.782310</td>\n",
       "      <td>12.226009</td>\n",
       "      <td>11.449209</td>\n",
       "      <td>10.970700</td>\n",
       "      <td>10.52481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.37381</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01909</td>\n",
       "      <td>5.02509</td>\n",
       "      <td>5.02239</td>\n",
       "      <td>5.02191</td>\n",
       "      <td>5.01579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65.0</th>\n",
       "      <td>13.999300</td>\n",
       "      <td>13.426991</td>\n",
       "      <td>12.687791</td>\n",
       "      <td>11.921800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.37161</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01899</td>\n",
       "      <td>5.02511</td>\n",
       "      <td>5.02241</td>\n",
       "      <td>5.02219</td>\n",
       "      <td>5.01640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>15.100309</td>\n",
       "      <td>14.545509</td>\n",
       "      <td>13.811910</td>\n",
       "      <td>13.042690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.03371</td>\n",
       "      <td>5.03590</td>\n",
       "      <td>5.03091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.03540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.480610</td>\n",
       "      <td>14.399091</td>\n",
       "      <td>13.483590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04089</td>\n",
       "      <td>5.04101</td>\n",
       "      <td>5.03651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04179</td>\n",
       "      <td>5.04189</td>\n",
       "      <td>5.03730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04431</td>\n",
       "      <td>5.04571</td>\n",
       "      <td>5.04419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04689</td>\n",
       "      <td>5.04779</td>\n",
       "      <td>5.04909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04699</td>\n",
       "      <td>5.04881</td>\n",
       "      <td>5.04929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04739</td>\n",
       "      <td>5.04941</td>\n",
       "      <td>5.04959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 1188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "lat       55.625                                                               \\\n",
       "lon       15.625     15.875     16.125     16.375    16.625   16.875   17.125   \n",
       "depth                                                                           \n",
       "0.0     7.350910   7.366190   7.374790   7.381690   7.37289  7.36381  7.34959   \n",
       "5.0     7.349290   7.374600   7.395590   7.405100   7.39740  7.38879  7.37421   \n",
       "10.0    7.352490   7.373810   7.400800   7.403790   7.39810  7.39009  7.37619   \n",
       "15.0    7.335110   7.352700   7.377610   7.392110   7.38841  7.38271  7.37141   \n",
       "20.0    7.360610   7.367490   7.387210   7.397110   7.38509  7.38580  7.38049   \n",
       "25.0    7.403210   7.383310   7.400600   7.405010   7.39421  7.40720  7.40691   \n",
       "30.0    7.621500   7.555590   7.520590   7.492110   7.46349  7.44911  7.43089   \n",
       "35.0    7.665100   7.579210   7.536910   7.501010   7.48101  7.46891  7.45031   \n",
       "40.0    7.797590   7.657990   7.571410   7.518210   7.48951  7.47709      NaN   \n",
       "45.0    8.241509   7.987690   7.791790   7.640790   7.54879      NaN      NaN   \n",
       "50.0    9.254910   9.032109   8.752509   8.485009   8.27779      NaN      NaN   \n",
       "55.0   10.936709  10.519400   9.950590   9.578910   9.28979      NaN      NaN   \n",
       "60.0   12.782310  12.226009  11.449209  10.970700  10.52481      NaN      NaN   \n",
       "65.0   13.999300  13.426991  12.687791  11.921800       NaN      NaN      NaN   \n",
       "70.0   15.100309  14.545509  13.811910  13.042690       NaN      NaN      NaN   \n",
       "75.0         NaN  15.480610  14.399091  13.483590       NaN      NaN      NaN   \n",
       "80.0         NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "85.0         NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "90.0         NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "95.0         NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "100.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "125.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "150.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "175.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "200.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "225.0        NaN        NaN        NaN        NaN       NaN      NaN      NaN   \n",
       "\n",
       "lat                                ... 64.375                             \\\n",
       "lon     17.375   17.625    17.875  ... 21.375   21.625   21.875   22.125   \n",
       "depth                              ...                                     \n",
       "0.0    7.33211  7.30961   7.28469  ...    NaN  4.99969  5.00811  5.00219   \n",
       "5.0    7.35011  7.32981   7.31021  ...    NaN  5.00000  5.00279  5.00359   \n",
       "10.0   7.36139  7.32441   7.29481  ...    NaN  5.00041  5.00449  5.00561   \n",
       "15.0   7.35341  7.33070   7.29571  ...    NaN  5.00361  5.01039  5.01529   \n",
       "20.0   7.36111  7.32740   7.28491  ...    NaN  5.00859  5.01569  5.01730   \n",
       "25.0   7.38489  7.34729   7.30179  ...    NaN      NaN  5.01541  5.02239   \n",
       "30.0       NaN  7.44391   7.41571  ...    NaN      NaN  5.01350  5.02131   \n",
       "35.0       NaN  7.50060   7.47371  ...    NaN      NaN  5.01339  5.02129   \n",
       "40.0       NaN      NaN   7.57609  ...    NaN      NaN  5.01519  5.02090   \n",
       "45.0       NaN      NaN   7.73751  ...    NaN      NaN  5.01681  5.02119   \n",
       "50.0       NaN      NaN   8.09749  ...    NaN      NaN  5.01920  5.02129   \n",
       "55.0       NaN      NaN   8.64879  ...    NaN      NaN  5.01729  5.02319   \n",
       "60.0       NaN      NaN   9.37381  ...    NaN      NaN  5.01909  5.02509   \n",
       "65.0       NaN      NaN  10.37161  ...    NaN      NaN  5.01899  5.02511   \n",
       "70.0       NaN      NaN       NaN  ...    NaN      NaN  5.03371  5.03590   \n",
       "75.0       NaN      NaN       NaN  ...    NaN      NaN  5.04089  5.04101   \n",
       "80.0       NaN      NaN       NaN  ...    NaN      NaN  5.04179  5.04189   \n",
       "85.0       NaN      NaN       NaN  ...    NaN      NaN  5.04431  5.04571   \n",
       "90.0       NaN      NaN       NaN  ...    NaN      NaN  5.04689  5.04779   \n",
       "95.0       NaN      NaN       NaN  ...    NaN      NaN  5.04699  5.04881   \n",
       "100.0      NaN      NaN       NaN  ...    NaN      NaN  5.04739  5.04941   \n",
       "125.0      NaN      NaN       NaN  ...    NaN      NaN      NaN      NaN   \n",
       "150.0      NaN      NaN       NaN  ...    NaN      NaN      NaN      NaN   \n",
       "175.0      NaN      NaN       NaN  ...    NaN      NaN      NaN      NaN   \n",
       "200.0      NaN      NaN       NaN  ...    NaN      NaN      NaN      NaN   \n",
       "225.0      NaN      NaN       NaN  ...    NaN      NaN      NaN      NaN   \n",
       "\n",
       "lat                                                        \n",
       "lon     22.375   22.625   22.875   23.125   23.375 23.625  \n",
       "depth                                                      \n",
       "0.0    5.00149  5.00119  5.00029  5.00019  4.99969    NaN  \n",
       "5.0    5.00300  5.00271  5.00169  5.00521  5.00511    NaN  \n",
       "10.0   5.00869  5.00849  5.00329  5.00259  5.00220    NaN  \n",
       "15.0   5.00649  5.00899  5.00750  5.00331  5.00301    NaN  \n",
       "20.0   5.01969  5.01451  5.01241  5.01299  5.00879    NaN  \n",
       "25.0   5.02051  5.01929  5.01101  5.01609  5.01530    NaN  \n",
       "30.0   5.01891  5.01790  5.01081  5.01601  5.01489    NaN  \n",
       "35.0   5.01869  5.01850  5.01091  5.01631  5.01501    NaN  \n",
       "40.0   5.01889  5.01899  5.01259  5.01859      NaN    NaN  \n",
       "45.0   5.01929  5.02021  5.01449  5.02009      NaN    NaN  \n",
       "50.0   5.01971  5.02050  5.01441  5.02151      NaN    NaN  \n",
       "55.0   5.02079  5.02100  5.01569      NaN      NaN    NaN  \n",
       "60.0   5.02239  5.02191  5.01579      NaN      NaN    NaN  \n",
       "65.0   5.02241  5.02219  5.01640      NaN      NaN    NaN  \n",
       "70.0   5.03091      NaN  5.03540      NaN      NaN    NaN  \n",
       "75.0   5.03651      NaN  5.04160      NaN      NaN    NaN  \n",
       "80.0   5.03730      NaN      NaN      NaN      NaN    NaN  \n",
       "85.0   5.04419      NaN      NaN      NaN      NaN    NaN  \n",
       "90.0   5.04909      NaN      NaN      NaN      NaN    NaN  \n",
       "95.0   5.04929      NaN      NaN      NaN      NaN    NaN  \n",
       "100.0  5.04959      NaN      NaN      NaN      NaN    NaN  \n",
       "125.0      NaN      NaN      NaN      NaN      NaN    NaN  \n",
       "150.0      NaN      NaN      NaN      NaN      NaN    NaN  \n",
       "175.0      NaN      NaN      NaN      NaN      NaN    NaN  \n",
       "200.0      NaN      NaN      NaN      NaN      NaN    NaN  \n",
       "225.0      NaN      NaN      NaN      NaN      NaN    NaN  \n",
       "\n",
       "[26 rows x 1188 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa = woa.melt(value_name = 'vals', ignore_index = False).reset_index().sort_values(['depth', 'lat', 'lon'])\n",
    "woa = woa.pivot(index = 'depth', columns = ['lat', 'lon'], values = 'vals')\n",
    "woa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be174e9-285c-40c0-9c00-a1106180c6c1",
   "metadata": {},
   "source": [
    "This is exactly what we needed. The data frame now has all possible combinations of `depth`, `lat` and `lon`. We will turn it back to the long format, and sort them by `depth` and coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "857eeae4-3d00-48b0-a70c-5c3df6d0f5e2",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>0.0</td>\n",
       "      <td>55.625</td>\n",
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       "      <td>7.35091</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>15.875</td>\n",
       "      <td>7.36619</td>\n",
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      "text/plain": [
       "     depth     lat     lon     vals\n",
       "0      0.0  55.625  15.625  7.35091\n",
       "26     0.0  55.625  15.875  7.36619\n",
       "52     0.0  55.625  16.125  7.37479\n",
       "78     0.0  55.625  16.375  7.38169\n",
       "104    0.0  55.625  16.625  7.37289"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa = woa.melt(value_name = 'vals', ignore_index = False).reset_index().sort_values(['depth', 'lat', 'lon'])\n",
    "woa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6feabc-d731-461e-8585-e485527bb6f4",
   "metadata": {},
   "source": [
    "We can get unique values for our dimensions, which we will use when creating the `Data Array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "3916ca93-38cd-4a8c-9160-74ce6f3f9366",
   "metadata": {},
   "outputs": [],
   "source": [
    "depth = pd.unique(woa.depth)\n",
    "lon = pd.unique(woa.lon)\n",
    "lat = pd.unique(woa.lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bfd3dd-8921-44a7-ad51-7361c0eb93fc",
   "metadata": {},
   "source": [
    "We will use the length of these unique values to reshape our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "f0101b44-706b-4f03-9a1a-4596d96b3150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 36, 33)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_vals = woa.vals.values.reshape((len(depth), 36, 33))\n",
    "woa_vals.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42899208-91ec-4b2e-8f28-38effb174073",
   "metadata": {},
   "source": [
    "Now we have everything we need to create our `Data Array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "056f332b-85f1-4bc4-8d5e-b85dc5391c55",
   "metadata": {},
   "outputs": [
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       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (depth_bin_m: 26, lat: 36, lon: 33)&gt; Size: 124kB\n",
       "array([[[7.35091  , 7.36619  , 7.37479  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.256    , 7.2880898, 7.30711  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.25431  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 4.99979  , 4.99979  ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 5.00019  , 4.99969  ,\n",
       "               nan]],\n",
       "\n",
       "       [[7.34929  , 7.3746   , 7.39559  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.25341  , 7.29269  , 7.33349  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.29359  , ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 132B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * lat          (lat) float64 288B 55.62 55.88 56.12 ... 63.88 64.12 64.38\n",
       "  * depth_bin_m  (depth_bin_m) float32 104B 0.0 5.0 10.0 ... 175.0 200.0 225.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth_bin_m</span>: 26</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a174ab2e-f968-425c-b5af-5ec51d3cb187' class='xr-array-in' type='checkbox' checked><label for='section-a174ab2e-f968-425c-b5af-5ec51d3cb187' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>7.351 7.366 7.375 7.382 7.373 7.364 7.35 ... nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[7.35091  , 7.36619  , 7.37479  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.256    , 7.2880898, 7.30711  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.25431  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 4.99979  , 4.99979  ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 5.00019  , 4.99969  ,\n",
       "               nan]],\n",
       "\n",
       "       [[7.34929  , 7.3746   , 7.39559  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.25341  , 7.29269  , 7.33349  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.29359  , ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-dcec593d-44b4-4ae4-af23-b3e5d6354337' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcec593d-44b4-4ae4-af23-b3e5d6354337' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-87b48dde-ffd3-45aa-bac9-52a1eb0be4d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87b48dde-ffd3-45aa-bac9-52a1eb0be4d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea5fef6-80cd-4309-84fb-f168f5da2f7d' class='xr-var-data-in' type='checkbox'><label for='data-eea5fef6-80cd-4309-84fb-f168f5da2f7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.62 55.88 56.12 ... 64.12 64.38</div><input id='attrs-e7b9268e-2b3f-4d7d-a57d-9f358ea3bbc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7b9268e-2b3f-4d7d-a57d-9f358ea3bbc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2b1d0a-9f62-4750-88f6-a5b72e69be59' class='xr-var-data-in' type='checkbox'><label for='data-5f2b1d0a-9f62-4750-88f6-a5b72e69be59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([55.625, 55.875, 56.125, 56.375, 56.625, 56.875, 57.125, 57.375, 57.625,\n",
       "       57.875, 58.125, 58.375, 58.625, 58.875, 59.125, 59.375, 59.625, 59.875,\n",
       "       60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n",
       "       62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 5.0 10.0 ... 175.0 200.0 225.0</div><input id='attrs-eb1c90d2-e776-4714-af31-3780b108d3af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb1c90d2-e776-4714-af31-3780b108d3af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a60d9dbb-2c3e-4ce6-8f96-bd01bc90d2b2' class='xr-var-data-in' type='checkbox'><label for='data-a60d9dbb-2c3e-4ce6-8f96-bd01bc90d2b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.,  55.,\n",
       "        60.,  65.,  70.,  75.,  80.,  85.,  90.,  95., 100., 125., 150., 175.,\n",
       "       200., 225.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c75fd6fb-e43c-47a3-b9d0-68932ce84aa6' class='xr-section-summary-in' type='checkbox'  ><label for='section-c75fd6fb-e43c-47a3-b9d0-68932ce84aa6' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-908e7721-25a6-4447-a39d-a9434d9e3f9a' class='xr-index-data-in' type='checkbox'/><label for='index-908e7721-25a6-4447-a39d-a9434d9e3f9a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4028bd1e-46c5-461e-9607-32525771980f' class='xr-index-data-in' type='checkbox'/><label for='index-4028bd1e-46c5-461e-9607-32525771980f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([55.625, 55.875, 56.125, 56.375, 56.625, 56.875, 57.125, 57.375, 57.625,\n",
       "       57.875, 58.125, 58.375, 58.625, 58.875, 59.125, 59.375, 59.625, 59.875,\n",
       "       60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n",
       "       62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4d3cd46-1767-4964-9f0c-b91b11341645' class='xr-index-data-in' type='checkbox'/><label for='index-f4d3cd46-1767-4964-9f0c-b91b11341645' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   5.0,  10.0,  15.0,  20.0,  25.0,  30.0,  35.0,  40.0,  45.0,\n",
       "        50.0,  55.0,  60.0,  65.0,  70.0,  75.0,  80.0,  85.0,  90.0,  95.0,\n",
       "       100.0, 125.0, 150.0, 175.0, 200.0, 225.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c135a471-28f5-4a27-8db5-673c93775f90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c135a471-28f5-4a27-8db5-673c93775f90' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (depth_bin_m: 26, lat: 36, lon: 33)> Size: 124kB\n",
       "array([[[7.35091  , 7.36619  , 7.37479  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.256    , 7.2880898, 7.30711  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.25431  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 4.99979  , 4.99979  ,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ..., 5.00019  , 4.99969  ,\n",
       "               nan]],\n",
       "\n",
       "       [[7.34929  , 7.3746   , 7.39559  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [7.25341  , 7.29269  , 7.33349  , ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan, 7.29359  , ...,       nan,       nan,\n",
       "               nan],\n",
       "...\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]],\n",
       "\n",
       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        ...,\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan],\n",
       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
       "               nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 132B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * lat          (lat) float64 288B 55.62 55.88 56.12 ... 63.88 64.12 64.38\n",
       "  * depth_bin_m  (depth_bin_m) float32 104B 0.0 5.0 10.0 ... 175.0 200.0 225.0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_da = xr.DataArray(data = woa_vals, dims = ['depth_bin_m', 'lat', 'lon'],\n",
    "                      coords = {'lon': lon, 'lat': lat, 'depth_bin_m': depth})\n",
    "woa_da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b62675b-761c-4eb2-9a60-d41088f205ef",
   "metadata": {},
   "source": [
    "We can check we have created the `Data Array` correctly by plotting the first depth bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e183595a-d5b4-4520-8d70-60a427bdbc18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1549ae984910>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "woa_da.isel(depth_bin_m = 0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5aef868-2eec-4c66-a10c-cab22a414389",
   "metadata": {},
   "source": [
    "This is exactly as we expected for our region of interest. We can now interpolate the data in the WOA data to match the GFDL data. First, we start matching the depth bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "90647066-f6af-434a-9793-e40ee80560ff",
   "metadata": {},
   "outputs": [
    {
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       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (depth_bin_m: 35, lat: 36, lon: 33)&gt; Size: 333kB\n",
       "array([[[7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.2739501 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ..., 4.9998951 ,\n",
       "         4.9998951 ,        nan],\n",
       "        [       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan]],\n",
       "\n",
       "       [[7.35248995, 7.37380981, 7.40080023, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.26500988, 7.28939009, 7.33549976, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.30829   , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 132B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * lat          (lat) float64 288B 55.62 55.88 56.12 ... 63.88 64.12 64.38\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-59b22f32-8415-4585-9915-720053ba41e6' class='xr-array-in' type='checkbox' checked><label for='section-59b22f32-8415-4585-9915-720053ba41e6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>7.35 7.37 7.385 7.393 7.385 7.376 7.362 ... nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.2739501 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ..., 4.9998951 ,\n",
       "         4.9998951 ,        nan],\n",
       "        [       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan]],\n",
       "\n",
       "       [[7.35248995, 7.37380981, 7.40080023, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.26500988, 7.28939009, 7.33549976, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.30829   , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-6db07796-26b6-48e4-b375-09390f9a69a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6db07796-26b6-48e4-b375-09390f9a69a2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-b6e5b360-9193-4aec-8a2f-31ebe4f90fc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6e5b360-9193-4aec-8a2f-31ebe4f90fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b76314f-1b03-4ae3-b227-82461d2ff2b3' class='xr-var-data-in' type='checkbox'><label for='data-1b76314f-1b03-4ae3-b227-82461d2ff2b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>55.62 55.88 56.12 ... 64.12 64.38</div><input id='attrs-39c155f8-65fe-4727-87c7-2c2eb0b5391e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39c155f8-65fe-4727-87c7-2c2eb0b5391e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e01a1d-b43f-43af-8d69-f217accbe9b4' class='xr-var-data-in' type='checkbox'><label for='data-05e01a1d-b43f-43af-8d69-f217accbe9b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([55.625, 55.875, 56.125, 56.375, 56.625, 56.875, 57.125, 57.375, 57.625,\n",
       "       57.875, 58.125, 58.375, 58.625, 58.875, 59.125, 59.375, 59.625, 59.875,\n",
       "       60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n",
       "       62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-8cf6f65d-7383-4179-a86e-aa3bff79bffe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8cf6f65d-7383-4179-a86e-aa3bff79bffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9ddfb5e-b3a7-49ce-b8f1-acc34ba9da8e' class='xr-var-data-in' type='checkbox'><label for='data-f9ddfb5e-b3a7-49ce-b8f1-acc34ba9da8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a3d46e5-a39e-492d-b557-d314432ae4d8' class='xr-section-summary-in' type='checkbox'  ><label for='section-7a3d46e5-a39e-492d-b557-d314432ae4d8' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9d9956cb-8e73-412f-a2f0-394d0f8f19f8' class='xr-index-data-in' type='checkbox'/><label for='index-9d9956cb-8e73-412f-a2f0-394d0f8f19f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5d208cdd-8540-44c3-9ce8-c0418b1dbb53' class='xr-index-data-in' type='checkbox'/><label for='index-5d208cdd-8540-44c3-9ce8-c0418b1dbb53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([55.625, 55.875, 56.125, 56.375, 56.625, 56.875, 57.125, 57.375, 57.625,\n",
       "       57.875, 58.125, 58.375, 58.625, 58.875, 59.125, 59.375, 59.625, 59.875,\n",
       "       60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n",
       "       62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f46327a6-22f0-4714-81f3-4285d37c07ce' class='xr-index-data-in' type='checkbox'/><label for='index-f46327a6-22f0-4714-81f3-4285d37c07ce' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8b4a9d2-0c87-49c2-a794-c3da88b2042d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8b4a9d2-0c87-49c2-a794-c3da88b2042d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (depth_bin_m: 35, lat: 36, lon: 33)> Size: 333kB\n",
       "array([[[7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.2739501 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ..., 4.9998951 ,\n",
       "         4.9998951 ,        nan],\n",
       "        [       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan]],\n",
       "\n",
       "       [[7.35248995, 7.37380981, 7.40080023, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.26500988, 7.28939009, 7.33549976, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan, 7.30829   , ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 132B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "  * lat          (lat) float64 288B 55.62 55.88 56.12 ... 63.88 64.12 64.38\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_da = woa_da.interp({'depth_bin_m': gfdl_mean.depth_bin_m.values})\n",
    "woa_da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b123a066-7523-4e1e-b5b1-079e4d36c5fd",
   "metadata": {},
   "source": [
    "We now have 35 depth bins matching the GFDL data. We can continue with the regridding step. We will use the `xesmf` package to do this. You can refer to the [documentation](https://xesmf.readthedocs.io/en/stable/user_api.html#regridder) to see all available regridding methods.  \n",
    "\n",
    "Remember, in this regridding function, you will need to provide the variable you want to regrid (`ds_in`) and then provide your target grid (`ds_out`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "bca46e27-bb2b-4d13-b8d0-a4a7d3f79725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       bilinear \n",
       "Weight filename:            bilinear_36x33_36x33.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (36, 33) \n",
       "Output grid shape:          (36, 33) \n",
       "Periodic in longitude?      False"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder = xe.Regridder(ds_in = woa_da, ds_out = gfdl_mean, method = 'bilinear')\n",
    "regridder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411eb5e3-18bb-41d0-818e-991cc4305532",
   "metadata": {},
   "source": [
    "We can now apply the regridder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "204eee11-0b1c-4bb7-a722-5aa6da41645f",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "  display: inline-block;\n",
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       "\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (depth_bin_m: 35, lat: 36, lon: 33)&gt; Size: 333kB\n",
       "array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "Attributes:\n",
       "    regrid_method:  bilinear</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-15b25acc-62c1-4d0b-8a6d-5012a220fb0a' class='xr-array-in' type='checkbox' checked><label for='section-15b25acc-62c1-4d0b-8a6d-5012a220fb0a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-e95922e6-086d-4c78-a5a3-4dc857e4d087' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e95922e6-086d-4c78-a5a3-4dc857e4d087' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-3e5bca18-262a-4cef-af83-3af9240c80e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e5bca18-262a-4cef-af83-3af9240c80e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1f6cfd0-5130-46c0-98f1-9f2f43f9884e' class='xr-var-data-in' type='checkbox'><label for='data-f1f6cfd0-5130-46c0-98f1-9f2f43f9884e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.38 64.12 63.88 ... 55.88 55.62</div><input id='attrs-2fb85409-d154-40eb-9054-270d923aa8ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb85409-d154-40eb-9054-270d923aa8ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a857bab5-9baf-4415-a8a3-85d035f14b61' class='xr-var-data-in' type='checkbox'><label for='data-a857bab5-9baf-4415-a8a3-85d035f14b61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-c34c35f5-933c-4613-bb7f-2cd3e0088a29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c34c35f5-933c-4613-bb7f-2cd3e0088a29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54def023-4acf-4040-bf57-4efd53818d93' class='xr-var-data-in' type='checkbox'><label for='data-54def023-4acf-4040-bf57-4efd53818d93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e142a4cb-46fc-4dc8-8201-a37510c767e5' class='xr-section-summary-in' type='checkbox'  ><label for='section-e142a4cb-46fc-4dc8-8201-a37510c767e5' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-46b0ad24-906c-4be4-8870-ca9fcca11846' class='xr-index-data-in' type='checkbox'/><label for='index-46b0ad24-906c-4be4-8870-ca9fcca11846' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-332408d0-d2b6-4b56-a42d-85ca938a76b0' class='xr-index-data-in' type='checkbox'/><label for='index-332408d0-d2b6-4b56-a42d-85ca938a76b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-096e3ab0-85db-4a30-9319-5748b92e2fc4' class='xr-index-data-in' type='checkbox'/><label for='index-096e3ab0-85db-4a30-9319-5748b92e2fc4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2104b18b-bb99-4606-8b9c-fa2fb751a522' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2104b18b-bb99-4606-8b9c-fa2fb751a522' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (depth_bin_m: 35, lat: 36, lon: 33)> Size: 333kB\n",
       "array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_reg = regridder(woa_da)\n",
    "woa_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033c6aec-adc7-48d3-8b24-89c2578730da",
   "metadata": {},
   "source": [
    "We will add a name to our data frame so we know what information is contained in this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4fef975d-3a19-48a2-9e7c-41490c46a5d4",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;salinity&#x27; (depth_bin_m: 35, lat: 36, lon: 33)&gt; Size: 333kB\n",
       "array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "Attributes:\n",
       "    regrid_method:  bilinear</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'salinity'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth_bin_m</span>: 35</li><li><span class='xr-has-index'>lat</span>: 36</li><li><span class='xr-has-index'>lon</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-589ebaa2-2c45-4ceb-a2c8-b102bd17da7c' class='xr-array-in' type='checkbox' checked><label for='section-589ebaa2-2c45-4ceb-a2c8-b102bd17da7c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-bbdc140a-fee6-46e2-af48-2b14c11ba1b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbdc140a-fee6-46e2-af48-2b14c11ba1b7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth_bin_m</span></div><div class='xr-var-dims'>(depth_bin_m)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-35aea4b8-1bcc-4371-b3c0-b3cc15506ced' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35aea4b8-1bcc-4371-b3c0-b3cc15506ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8615e889-62ed-498e-b6ac-27142cbdbe03' class='xr-var-data-in' type='checkbox'><label for='data-8615e889-62ed-498e-b6ac-27142cbdbe03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.38 64.12 63.88 ... 55.88 55.62</div><input id='attrs-09481484-3b4a-41a3-999b-e2b447e08ea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09481484-3b4a-41a3-999b-e2b447e08ea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a986e1b0-09ca-4914-b53d-6093e216f460' class='xr-var-data-in' type='checkbox'><label for='data-a986e1b0-09ca-4914-b53d-6093e216f460' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.62 15.88 16.12 ... 23.38 23.62</div><input id='attrs-92291c33-e5d5-4e56-8c95-68a14ac8b3d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92291c33-e5d5-4e56-8c95-68a14ac8b3d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbf9ba09-e88b-4cef-8bd9-deaf15f9738d' class='xr-var-data-in' type='checkbox'><label for='data-fbf9ba09-e88b-4cef-8bd9-deaf15f9738d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ccee55aa-2742-43f3-94db-8b86c4977f50' class='xr-section-summary-in' type='checkbox'  ><label for='section-ccee55aa-2742-43f3-94db-8b86c4977f50' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth_bin_m</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82b6af4a-0c18-4682-af95-8357d4fbb356' class='xr-index-data-in' type='checkbox'/><label for='index-82b6af4a-0c18-4682-af95-8357d4fbb356' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   32.5,  51.25,   75.0,  100.0,  125.0, 156.25,\n",
       "        200.0,  250.0,  312.5,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1537.5, 1750.0, 2062.5, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth_bin_m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba013845-312c-4b37-a6e4-8cd88d89e966' class='xr-index-data-in' type='checkbox'/><label for='index-ba013845-312c-4b37-a6e4-8cd88d89e966' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([64.375, 64.125, 63.875, 63.625, 63.375, 63.125, 62.875, 62.625, 62.375,\n",
       "       62.125, 61.875, 61.625, 61.375, 61.125, 60.875, 60.625, 60.375, 60.125,\n",
       "       59.875, 59.625, 59.375, 59.125, 58.875, 58.625, 58.375, 58.125, 57.875,\n",
       "       57.625, 57.375, 57.125, 56.875, 56.625, 56.375, 56.125, 55.875, 55.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c140fa29-ccbe-43de-833f-9380130e4213' class='xr-index-data-in' type='checkbox'/><label for='index-c140fa29-ccbe-43de-833f-9380130e4213' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.625, 15.875, 16.125, 16.375, 16.625, 16.875, 17.125, 17.375, 17.625,\n",
       "       17.875, 18.125, 18.375, 18.625, 18.875, 19.125, 19.375, 19.625, 19.875,\n",
       "       20.125, 20.375, 20.625, 20.875, 21.125, 21.375, 21.625, 21.875, 22.125,\n",
       "       22.375, 22.625, 22.875, 23.125, 23.375, 23.625],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60ae44b1-dca5-4871-bd66-5b027f991edc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60ae44b1-dca5-4871-bd66-5b027f991edc' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'salinity' (depth_bin_m: 35, lat: 36, lon: 33)> Size: 333kB\n",
       "array([[[       nan,        nan,        nan, ..., 5.00269985,\n",
       "         5.00239992,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.25470495, 7.29038978, 7.32029986, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [7.35010004, 7.37039495, 7.38519001, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ..., 5.00259018,\n",
       "         5.00220013,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]])\n",
       "Coordinates:\n",
       "  * depth_bin_m  (depth_bin_m) float64 280B 2.5 10.0 20.0 ... 6e+03 6.5e+03\n",
       "  * lat          (lat) float64 288B 64.38 64.12 63.88 ... 56.12 55.88 55.62\n",
       "  * lon          (lon) float64 264B 15.62 15.88 16.12 ... 23.12 23.38 23.62\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_reg.name = 'salinity'\n",
    "woa_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26a6e28-15ea-4adb-a1a1-81b53e638012",
   "metadata": {},
   "source": [
    "We can also check the result by plotting the first depth bin of the regridded `Data Array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "3a98c9d4-9a05-40c8-a8c3-f5f3c65ccd3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1549adc21150>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "woa_reg.isel(depth_bin_m = 0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffb1b8c-76fb-4ec5-9538-ae2b27f9d1e4",
   "metadata": {},
   "source": [
    "You can save the regridded `Data Array` in the file format of your choice. If you want to save this as gridded data, you could simply use:  \n",
    "- For `netcdf`: `woa_reg.to_netcdf('file_path.nc')`  \n",
    "- For `zarr`: `woa_reg.to_zarr('file_path.zarr')`\n",
    "  \n",
    "Make sure you include the correct file extension when saving the variable.  \n",
    "  \n",
    "You can also save the results as a data frame, which requires you to convert the `Data Array` to a `data frame` before saving the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "644583cf-3fe1-496e-b112-0934bc9e9504",
   "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>depth_bin_m</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>salinity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>64.375</td>\n",
       "      <td>15.625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>64.375</td>\n",
       "      <td>15.875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.5</td>\n",
       "      <td>64.375</td>\n",
       "      <td>16.125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>64.375</td>\n",
       "      <td>16.375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>64.375</td>\n",
       "      <td>16.625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depth_bin_m     lat     lon  salinity\n",
       "0          2.5  64.375  15.625       NaN\n",
       "1          2.5  64.375  15.875       NaN\n",
       "2          2.5  64.375  16.125       NaN\n",
       "3          2.5  64.375  16.375       NaN\n",
       "4          2.5  64.375  16.625       NaN"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woa_df = woa_reg.to_dataframe().reset_index()\n",
    "woa_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983ed84e-87b1-4a58-8e92-af253c2a0d9d",
   "metadata": {},
   "source": [
    "Now we can save the result as either:  \n",
    "- `csv`: `woa_df.to_csv('file_path.csv')`  \n",
    "- `parquet`: `woa_df.to_parquet('file_path.parquet')`  \n",
    "\n",
    "Once again, you need to make sure you include the correct file extension when saving the data."
   ]
  }
 ],
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