python-data/solutions/ex03a_xr_groupby.ipynb

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   "source": [
    "# Exercise 3a: Grouping"
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   "source": [
    "## Aim: Work with data which has been grouped."
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   "source": [
    "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray.html"
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   "source": [
    "### Issues Covered:\n",
    "- Grouping data with `.groupby()`\n",
    "- Finding the mean of grouped data."
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   "source": [
    "## GroupBy processing"
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   "source": [
    "Our ocean model dataset has no time dimension, so for this exercise we are going to use the NOAA ERSSST dataset from the tutorial. Load it using the command below."
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   "source": [
    "import xarray as xr\n",
    "ds = xr.tutorial.load_dataset(\"ersstv5\")"
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   "source": [
    "Have a quick explore of the dataset and see what it contains."
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 40MB\n",
       "Dimensions:    (lat: 89, lon: 180, time: 624, nbnds: 2)\n",
       "Coordinates:\n",
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       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science &gt; Oceans &gt; Ocean Temperature &gt; S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-06-07</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-b90b6eba-ea9a-49f5-b2e0-3a6ffc948c1d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b90b6eba-ea9a-49f5-b2e0-3a6ffc948c1d' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 624</li><li><span>nbnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4ca52ae-1a52-4279-a68e-1f76d19ae2f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4ca52ae-1a52-4279-a68e-1f76d19ae2f8' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-f482ff62-4c17-4d96-8e10-b521baa9ba40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f482ff62-4c17-4d96-8e10-b521baa9ba40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d05679cb-042c-4bbe-acd1-7881d540c5b3' class='xr-var-data-in' type='checkbox'><label for='data-d05679cb-042c-4bbe-acd1-7881d540c5b3' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-8facd188-9160-4355-a983-b83ec5209bec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8facd188-9160-4355-a983-b83ec5209bec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2750101f-0f50-4077-bb6c-815e73d5e6a8' class='xr-var-data-in' type='checkbox'><label for='data-2750101f-0f50-4077-bb6c-815e73d5e6a8' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</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'>1970-01-01 ... 2021-12-01</div><input id='attrs-9beb84a9-6ed6-48f6-abf7-d34e58c4fc21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9beb84a9-6ed6-48f6-abf7-d34e58c4fc21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08817f26-59a5-4bf6-946a-2815d5d317f1' class='xr-var-data-in' type='checkbox'><label for='data-08817f26-59a5-4bf6-946a-2815d5d317f1' 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>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81204.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1970-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37af4082-0585-4be5-8705-615fd9224b63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37af4082-0585-4be5-8705-615fd9224b63' class='xr-section-summary' >Data variables: <span>(2)</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>time_bnds</span></div><div class='xr-var-dims'>(time, nbnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... 9.969e+36</div><input id='attrs-6e774aec-c304-4d02-bafb-949dbd5b598c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e774aec-c304-4d02-bafb-949dbd5b598c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24467016-0c81-44ef-b5fe-be4ce91792f1' class='xr-var-data-in' type='checkbox'><label for='data-24467016-0c81-44ef-b5fe-be4ce91792f1' 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>long_name :</span></dt><dd>Time Boundaries</dd></dl></div><div class='xr-var-data'><pre>array([[9.96920997e+36, 9.96920997e+36],\n",
       "       [9.96920997e+36, 9.96920997e+36],\n",
       "       [9.96920997e+36, 9.96920997e+36],\n",
       "       ...,\n",
       "       [9.96920997e+36, 9.96920997e+36],\n",
       "       [9.96920997e+36, 9.96920997e+36],\n",
       "       [9.96920997e+36, 9.96920997e+36]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-b9eab59d-0cde-433d-8c0d-7872cad329fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9eab59d-0cde-433d-8c0d-7872cad329fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-301c7109-a231-48ea-bd58-9a8c0d8a3cc7' class='xr-var-data-in' type='checkbox'><label for='data-301c7109-a231-48ea-bd58-9a8c0d8a3cc7' 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>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac4a601e-dd50-41ff-aae1-27528cf4fed5' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac4a601e-dd50-41ff-aae1-27528cf4fed5' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdcb2001-266b-4a29-ba62-99186f4ca63e' class='xr-index-data-in' type='checkbox'/><label for='index-cdcb2001-266b-4a29-ba62-99186f4ca63e' 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([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#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-e9e15e90-ce58-4ea6-b799-b11b923a93c7' class='xr-index-data-in' type='checkbox'/><label for='index-e9e15e90-ce58-4ea6-b799-b11b923a93c7' 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,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</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-d77f5dcf-4499-4d03-a9f7-2c0b593654c2' class='xr-index-data-in' type='checkbox'/><label for='index-d77f5dcf-4499-4d03-a9f7-2c0b593654c2' 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;1970-01-01&#x27;, &#x27;1970-02-01&#x27;, &#x27;1970-03-01&#x27;, &#x27;1970-04-01&#x27;,\n",
       "               &#x27;1970-05-01&#x27;, &#x27;1970-06-01&#x27;, &#x27;1970-07-01&#x27;, &#x27;1970-08-01&#x27;,\n",
       "               &#x27;1970-09-01&#x27;, &#x27;1970-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;, &#x27;2021-06-01&#x27;,\n",
       "               &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;, &#x27;2021-10-01&#x27;,\n",
       "               &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=624, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e04caa4a-01c4-4c31-879d-d72480a5654e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e04caa4a-01c4-4c31-879d-d72480a5654e' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2022-06-07</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 40MB\n",
       "Dimensions:    (lat: 89, lon: 180, time: 624, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 356B 88.0 86.0 84.0 82.0 ... -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 720B 0.0 2.0 4.0 6.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 5kB 1970-01-01 1970-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 10kB 9.969e+36 9.969e+36 ... 9.969e+36\n",
       "    sst        (time, lat, lon) float32 40MB -1.8 -1.8 -1.8 -1.8 ... nan nan nan\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science > Oceans > Ocean Temperature > S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-06-07"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda96f3c-3fef-4aa4-b801-53e34c647f71",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Q1. First, lets group our dataset by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "976836af-ec0b-49b8-8dbc-349139e5db77",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-11-08T14:55:19.035872Z",
     "iopub.status.busy": "2024-11-08T14:55:19.035560Z",
     "iopub.status.idle": "2024-11-08T14:55:19.042702Z",
     "shell.execute_reply": "2024-11-08T14:55:19.041969Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "grouped = ds.groupby(\"time.year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c47c5a9-7c59-470b-b54c-00fb328dc1f0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Q2. Lets take the mean of each group, to give the annual mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef15b52c-8cd8-46cf-87e6-814936ce280c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-11-08T14:55:19.045489Z",
     "iopub.status.busy": "2024-11-08T14:55:19.044962Z",
     "iopub.status.idle": "2024-11-08T14:55:19.178623Z",
     "shell.execute_reply": "2024-11-08T14:55:19.177989Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "annual_means = grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7da30c-5f09-4173-b306-10e26def81e0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Q3. To see what we've done, lets plot the mean for the year 1960."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c4dfcd3-1d9e-4f2c-843b-ebd934fe324a",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-11-08T14:55:19.181782Z",
     "iopub.status.busy": "2024-11-08T14:55:19.181491Z",
     "iopub.status.idle": "2024-11-08T14:55:19.707238Z",
     "shell.execute_reply": "2024-11-08T14:55:19.706705Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efdb40b03d0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "annual_means.sel(year=1970).sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6924bf-ebef-4b91-bfb7-95d286b2972e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Q4. Next, lets plot an annual mean time seties for the point in the Atlantic ocean latitude=-50.625, longitude=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8797acf-2e92-48ea-9a77-0118cd6e552f",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-11-08T14:55:19.710082Z",
     "iopub.status.busy": "2024-11-08T14:55:19.709796Z",
     "iopub.status.idle": "2024-11-08T14:55:19.923161Z",
     "shell.execute_reply": "2024-11-08T14:55:19.922620Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efdb3f6d610>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual_means.sel(lat=-50.625, lon=0, method=\"nearest\").sst.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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