blocks|key|980755|text|在Wolfram语言中，您可以使用FindClusters标识集群，使用ConvexHullMesh计算集群的凸包，使用ListPlot使用Prolog选项和显示Labeled。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|980756|首先，我们将收集来自美国"AdministrativeDivision"州的一些示例数据。"Name"属性包括县，因此用StringDelete删除它。|980757|data+=+Select[FreeQ[_Missing]]@EntityValue[
++++EntityClass["AdministrativeDivision",+{"ParentRegion"+->+Entity["Country",+"UnitedStates"]}],
++++{"HomeOwnershipRate",+"HealthInsuranceCoverageRate",+"Name"}];
data+=+MapAt[StringDelete[",+United+States"],+{All,+-1}]@data;
First@data|code-block|syntax|javascript|980758|{70.7%25，85.2%25，“阿拉巴马”}|blockquote|980759|对于每个状态，我们得到两个"Percent"+Quantity对象和状态名称。|980760|我们可以在这个列表上使用集群算法中除了每个列表中的最后一个项(+FindClusters+)之外的所有项，但是将包含名称的整个列表(Identity)作为集群项返回。|980761|clusters+=+FindClusters[Rule+@@+Through[{Most,+Identity}@#]+&+/@+data,+Method+->+"MeanShift"];|980762|clusters的外壳与ConvexHullMesh。|980763|hulls+=+ConvexHullMesh+/@+Map[Most]+/@+clusters;|980764|然后ListPlot+clusters与每个点Labeled，同时使用Prolog绘制点下的船体。|980765|ListPlot[
+Map[Labeled[Most@#,+Last@#]+&]+/@+clusters,
+PlotStyle+->+ColorData[110],
+PlotTheme+->+"Detailed",
+FrameLabel+->+{"HomeOwnershipRate",+"HealthInsuranceCoverageRate"},
+Prolog+->+{
+++Opacity[.1],
+++MapIndexed[
++++{ColorData[110]+@@+#2,+
++++++EdgeForm[{Thin,+ColorData[110]+@@+#2}],+#1}+&,
++++hulls
++++]}
+]|980766|980767|📷|atomic|980768|默认行为是在标签过多的地方自动使用Tooltip。|980769|希望这能有所帮助。|entityMap|0|LINK|mutability|MUTABLE|url|https://reference.wolfram.com/language/|1|http://reference.wolfram.com/language/ref/FindClusters.html|2|http://reference.wolfram.com/language/ref/ConvexHullMesh.html|3|http://reference.wolfram.com/language/ref/ListPlot.html|4|http://reference.wolfram.com/language/ref/Prolog.html|5|http://reference.wolfram.com/language/ref/Labeled.html|6|http://reference.wolfram.com/language/ref/entity/AdministrativeDivision.html|7|http://reference.wolfram.com/language/ref/StringDelete.html|8|http://reference.wolfram.com/language/ref/Quantity.html|9|http://reference.wolfram.com/language/ref/Identity.html|10|IMAGE|IMMUTABLE|imageUrl|https://i.stack.imgur.com/6RoeE.gif|imageAlt|11|http://reference.wolfram.com/language/ref/Tooltip.html^0|H|C|10|E|1O|8|1Y|6|29|7|1|9|0|H|C|1|10|E|2|1O|8|3|1Y|6|4|29|7|5|0|C|O|19|6|1O|C|C|O|6|1O|C|7|0|0|0|D|9|N|8|N|8|8|0|W|C|1U|8|1U|8|9|0|0|0|8|C|E|0|0|2|8|B|8|N|7|Z|6|0|0|0|0|1|A|0|H|7|H|7|B|0^^$0|@$1|2|3|4|5|6|7|28|8|@$9|29|A|2A|B|C]|$9|2B|A|2C|B|C]|$9|2D|A|2E|B|C]|$9|2F|A|2G|B|C]|$9|2H|A|2I|B|C]]|D|@$9|2J|A|2K|1|2L]|$9|2M|A|2N|1|2O]|$9|2P|A|2Q|1|2R]|$9|2S|A|2T|1|2U]|$9|2V|A|2W|1|2X]|$9|2Y|A|2Z|1|30]]|E|$]]|$1|F|3|G|5|6|7|31|8|@$9|32|A|33|B|C]|$9|34|A|35|B|C]|$9|36|A|37|B|C]]|D|@$9|38|A|39|1|3A]|$9|3B|A|3C|1|3D]]|E|$]]|$1|H|3|I|5|J|7|3E|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|O|7|3F|8|@]|D|@]|E|$]]|$1|P|3|Q|5|6|7|3G|8|@$9|3H|A|3I|B|C]|$9|3J|A|3K|B|C]]|D|@$9|3L|A|3M|1|3N]]|E|$]]|$1|R|3|S|5|6|7|3O|8|@$9|3P|A|3Q|B|C]|$9|3R|A|3S|B|C]]|D|@$9|3T|A|3U|1|3V]]|E|$]]|$1|T|3|U|5|J|7|3W|8|@]|D|@]|E|$K|L]]|$1|V|3|W|5|6|7|3X|8|@$9|3Y|A|3Z|B|C]|$9|40|A|41|B|C]]|D|@]|E|$]]|$1|X|3|Y|5|J|7|42|8|@]|D|@]|E|$K|L]]|$1|Z|3|10|5|6|7|43|8|@$9|44|A|45|B|C]|$9|46|A|47|B|C]|$9|48|A|49|B|C]|$9|4A|A|4B|B|C]]|D|@]|E|$]]|$1|11|3|12|5|J|7|4C|8|@]|D|@]|E|$K|L]]|$1|13|3|-4|5|O|7|4D|8|@]|D|@]|E|$]]|$1|14|3|15|5|16|7|4E|8|@]|D|@$9|4F|A|4G|1|4H]]|E|$]]|$1|17|3|18|5|6|7|4I|8|@$9|4J|A|4K|B|C]]|D|@$9|4L|A|4M|1|4N]]|E|$]]|$1|19|3|1A|5|6|7|4O|8|@]|D|@]|E|$]]]|1B|$1C|$5|1D|1E|1F|E|$1G|1H]]|1I|$5|1D|1E|1F|E|$1G|1J]]|1K|$5|1D|1E|1F|E|$1G|1L]]|1M|$5|1D|1E|1F|E|$1G|1N]]|1O|$5|1D|1E|1F|E|$1G|1P]]|1Q|$5|1D|1E|1F|E|$1G|1R]]|1S|$5|1D|1E|1F|E|$1G|1T]]|1U|$5|1D|1E|1F|E|$1G|1V]]|1W|$5|1D|1E|1F|E|$1G|1X]]|1Y|$5|1D|1E|1F|E|$1G|1Z]]|20|$5|21|1E|22|E|$23|24|25|-4]]|26|$5|1D|1E|1F|E|$1G|27]]]]

In <a href="https://reference.wolfram.com/language/" rel="nofollow noreferrer">Wolfram Language</a> you may use <a href="http://reference.wolfram.com/language/ref/FindClusters.html" rel="nofollow noreferrer"><code>FindClusters</code></a> to identify the clusters, <a href="http://reference.wolfram.com/language/ref/ConvexHullMesh.html" rel="nofollow noreferrer"><code>ConvexHullMesh</code></a> to calculate the convex hull of the clusters, and <a href="http://reference.wolfram.com/language/ref/ListPlot.html" rel="nofollow noreferrer"><code>ListPlot</code></a> with the <a href="http://reference.wolfram.com/language/ref/Prolog.html" rel="nofollow noreferrer"><code>Prolog</code></a> option and <a href="http://reference.wolfram.com/language/ref/Labeled.html" rel="nofollow noreferrer"><code>Labeled</code></a> to display.

First we will collect some example data from the USA <a href="http://reference.wolfram.com/language/ref/entity/AdministrativeDivision.html" rel="nofollow noreferrer"><code>"AdministrativeDivision"</code></a> states. The <code>"Name"</code> property includes the county so this is removed with <a href="http://reference.wolfram.com/language/ref/StringDelete.html" rel="nofollow noreferrer"><code>StringDelete</code></a>.

<pre><code>data = Select[FreeQ[_Missing]]@EntityValue[
 EntityClass["AdministrativeDivision", {"ParentRegion" -&gt; Entity["Country", "UnitedStates"]}],
 {"HomeOwnershipRate", "HealthInsuranceCoverageRate", "Name"}];
data = MapAt[StringDelete[", United States"], {All, -1}]@data;
First@data
</code></pre>

<blockquote>
<pre><code>{70.7% , 85.2%, "Alabama"}
</code></pre>
</blockquote>

For each state we get two <code>"Percent"</code> <a href="http://reference.wolfram.com/language/ref/Quantity.html" rel="nofollow noreferrer"><code>Quantity</code></a> objects and the state name.

We can <code>FindClusters</code> on this list by using all but the last item in each list (<a href="http://reference.wolfram.com/language/ref/Most.html" rel="nofollow noreferrer"><code>Most</code></a>) for the cluster algorithm but returning the entire list (<a href="http://reference.wolfram.com/language/ref/Identity.html" rel="nofollow noreferrer"><code>Identity</code></a>), which includes the name, as the cluster item.

<pre><code>clusters = FindClusters[Rule @@ Through[{Most, Identity}@#] &amp; /@ data, Method -&gt; "MeanShift"];
</code></pre>

The hulls of <code>clusters</code> with <code>ConvexHullMesh</code>.

<pre><code>hulls = ConvexHullMesh /@ Map[Most] /@ clusters;
</code></pre>

Then <code>ListPlot</code> <code>clusters</code> with each point <code>Labeled</code> while using <code>Prolog</code> to plot the hulls beneath the points.

<pre><code>ListPlot[
 Map[Labeled[Most@#, Last@#] &amp;] /@ clusters,
 PlotStyle -&gt; ColorData[110],
 PlotTheme -&gt; "Detailed",
 FrameLabel -&gt; {"HomeOwnershipRate", "HealthInsuranceCoverageRate"},
 Prolog -&gt; {
 Opacity[.1],
 MapIndexed[
 {ColorData[110] @@ #2, 
 EdgeForm[{Thin, ColorData[110] @@ #2}], #1} &amp;,
 hulls
 ]}
 ]
</code></pre>

<blockquote>
 <a href="https://i.stack.imgur.com/6RoeE.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/6RoeE.gif" alt="enter image description here"></a>
</blockquote>

The default behaviour is to automatically use <a href="http://reference.wolfram.com/language/ref/Tooltip.html" rel="nofollow noreferrer"><code>Tooltip</code></a> in places where labels would overcrowd.

Hope this helps.

It looks like R has some cool visualization function for clusters that gives output like this:

<a href="https://i.stack.imgur.com/Cpyr9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Cpyr9.png" alt="enter image description here"></a>

The input is 2D Points and labels for them.
How can i get same visualization in Python?

How to get similar visualization to R fviz_cluster function in Python?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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看起来R为集群提供了一些很酷的可视化功能，提供了如下的输出：📷输入是2D点和它们的标签。如何在Python中实现相同的可视化？

问如何在Python中实现与R fviz_cluster函数类似的可视化？
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问如何在Python中实现与R fviz_cluster函数类似的可视化？EN