pyplot.scatter 中基于值的随机颜色

发布于 2025-01-14 11:53:45 字数 849 浏览 2 评论 0原文

我有一个大的 pandas 数据框，我对其进行了聚类，并且集群 ID 存储在数据框的一列中。我想以每个簇都有不同颜色的方式显示簇。我尝试使用颜色图执行此操作，但问题是我有太多的点和簇，因此附近的簇仅分配略有不同的颜色，因此当我绘制所有这些时，我只会得到一个如下所示的大混搭：

注意该图像包含大约 4000 个簇，但由于簇的颜色只是从上到下分配的，因此附近的簇混合在一起。

我希望附近的簇被涂成不同的颜色，所以我尝试为每个簇制作随机颜色，然后根据每个点的簇标签为每个点分配一个颜色，如下所示：

# creating a color for each distinct cluster label
colors = [(random.random(), random.random(), random.random())
          for _ in range(len(set(data['labels'])))]
# assigning color to a point based on its cluster label
for index, row in data.iterrows():
    plt.scatter(row['x'], row['y'], color=colors[int(row['labels'])])

现在这个程序可以工作，但是很多上面的矢量化版本较慢。

有没有一种方法可以在不编写 for 循环的情况下为每个簇着色明显不同的颜色？

原文

I have a large pandas dataframe that I clustered and the cluster id is stored in a column of a dataframe.
I would like to display clusters in such a way that each cluster has a different color.
I tried doing this with a colormap but the problem is that I have too many points and clusters so nearby clusters get assign only slightly different colors, so when I plot all of them I just get a big mashup that looks like this:

Note that this is image contains about 4000 clusters, but because colors of clusters are just assigned top to bottom, nearby clusters blend together.

I would like nearby clusters to be painted in different colors so I tried making a random color for each cluster and then assign each point a color based on its cluster label like this:

# creating a color for each distinct cluster label
colors = [(random.random(), random.random(), random.random())
          for _ in range(len(set(data['labels'])))]
# assigning color to a point based on its cluster label
for index, row in data.iterrows():
    plt.scatter(row['x'], row['y'], color=colors[int(row['labels'])])

Now this program works but it is much slower that vectorized version above.

Is there a way to do color each cluster in clearly different colors without writing a for loop?

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坠似风落 2025-01-21 11:53:45

这会创建一个 256 种颜色的随机颜色图，然后您可以将其传递给 scatter ：

def segmentation_cmap():
    vals = np.linspace(0, 1, 256)
    np.random.shuffle(vals)
    return plt.cm.colors.ListedColormap(plt.cm.CMRmap(vals))


ax.scatter(row['x'],row['y'],c=row['labels'],s=1,cmap=segmentation_cmap())

您可以添加颜色，但在某些时候无论如何您都很难看到差异！

This creates a random colormap of 256 colors that you can then pass to scatter :

def segmentation_cmap():
    vals = np.linspace(0, 1, 256)
    np.random.shuffle(vals)
    return plt.cm.colors.ListedColormap(plt.cm.CMRmap(vals))


ax.scatter(row['x'],row['y'],c=row['labels'],s=1,cmap=segmentation_cmap())

You may add colors, but you would have trouble seeing the differences anyways at some point !

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