deploy: deef167

_sources/intermediate/01-high-level-computation-patterns.ipynb

@@ -928,7 +928,7 @@
     "For instance, grouping data by month (which have varying numbers of days) or the results of an image classification.\n",
     "The GroupBy functions are essentially a generalization of `coarsen`: \n",
     "\n",
-    "- `groupby`: divide data into distinct groups, e.g. climatologies, composites. Works best when the \"group indentifiers\" or \"labels\" are exact and can be determined using equality (`==`), e.g. characters or integers. Remember that floats are not exact values.\n",
+    "- `groupby`: divide data into distinct groups, e.g. climatologies, composites. Works best when the \"group identifiers\" or \"labels\" are exact and can be determined using equality (`==`), e.g. characters or integers. Remember that floats are not exact values.\n",
     "- `groupby_bins`: Use binning operations, e.g. histograms, to group your data.\n",
     "- `resample`: Specialized implementation of GroupBy specifically for time grouping (so far), allows you to change sampling frequency of dataset.\n",
     "\n",

intermediate/01-high-level-computation-patterns.html

@@ -7458,7 +7458,7 @@ <h2>Label space “windows” or bins : GroupBy<a class="headerlink" href="#labe
 For instance, grouping data by month (which have varying numbers of days) or the results of an image classification.
 The GroupBy functions are essentially a generalization of <code class="docutils literal notranslate"><span class="pre">coarsen</span></code>:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">groupby</span></code>: divide data into distinct groups, e.g. climatologies, composites. Works best when the “group indentifiers” or “labels” are exact and can be determined using equality (<code class="docutils literal notranslate"><span class="pre">==</span></code>), e.g. characters or integers. Remember that floats are not exact values.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">groupby</span></code>: divide data into distinct groups, e.g. climatologies, composites. Works best when the “group identifiers” or “labels” are exact and can be determined using equality (<code class="docutils literal notranslate"><span class="pre">==</span></code>), e.g. characters or integers. Remember that floats are not exact values.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">groupby_bins</span></code>: Use binning operations, e.g. histograms, to group your data.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">resample</span></code>: Specialized implementation of GroupBy specifically for time grouping (so far), allows you to change sampling frequency of dataset.</p></li>
 </ul>