具有分类轴的3D图[Python/Matplotlib] [英] 3D plot with categorical axis [Python/Matplotlib]

问题描述

尝试绘制具有以下特征的三维图形:

Trying to plot a 3 dimensional graph with:

x 轴 - 值(浮动)y轴-值(浮点)z轴-类别(字符串)

x axis - Values (float) y axis - Values (float) z axis - Category (string)

现在我已经尝试使用 pandas.factorize(table.zcolumn) 来转换 z 向量

Now I've tried to convert the z vector using pandas.factorize(table.zcolumn)

Output: (array([ 0,  0,  0, ..., -1, -1,  1]), Index([u'London', u'National'], dtype='object'))

所以我可以画出数字没问题.

So I can plot the numbers no problem.

您会看到有 NaN 值会转换为 -1，因此当我绘制图形时，有一堆值为 -1 的值.数据包含伦敦、国家和 NaN 类别.

You'll see there are NaN values which convert to -1, so when I plot the graph there are a bunch of values at -1. The data hold London, National and NaN categories.

如何标记坐标轴以适合我的数据?我觉得应该有一个简单的函数可以匹配它.

How can I label the axes to fit my data? I feel like there should be a simple function to match it.

在 z 轴上，我需要重新分配刻度 -1 以变为NA"，将 0 变为伦敦"，将 1 变为全国"

On the z-axis I need to reassign the ticks -1 to become 'NA', 0 to become 'London' and 1 to become 'National'

我也对使用大量类别执行此操作的方法感兴趣，因此无需手动输入每个类别字符串的代码

I'd also be interested in a way for doing this with large numbers of categories, so code that does not need manually inputting each category string

regions = pandas.factorize(dataTable.Region[id_range])
regions_num = regions[0]

fig = plot.figure()
ax = fig.add_subplot(111,projection='3d')
ax.scatter(y, x, zs=regions_num)

ax.axes.set_zticklabels(["London","National","N/A"])

plot.show()

推荐答案

您只需将 zticks 设置为与您的类别相对应的三个z值:

You just need to set the zticks to the three z-values corresponding to your categories:

ax.axes.set_zticks(regions_num)

话虽如此，但我并不是说这实际上是绘制数据的一种很好的方法.当您的X，Y和Z值都是连续变量时，3D图最有用.如果区域"是一个序数变量，则将区域表示为不同的z级别可能会更有意义，但是出于任何原因，为什么'N/A'应该比'code>'更高'国家/地区的?3D图通常也比2D图更难读-例如，由于透视投影，在'National'类别中的一个点可能看起来很像一个更远的点，但在'N/A' 类别.

Having said that, I don't this is actually a very good way of plotting your data. 3D plots are most useful when your X, Y and Z values are all continuous variables. Representing regions as different z-levels might make a bit more sense if 'region' was an ordinal variable, but is there any reason why 'N/A' should be 'higher' than 'National'? 3D plots are also generally harder to read than 2D plots - for example, because of the perspective projection a point that's nearby in the 'National' category might look a lot like a point that's further away but in the 'N/A' category.

一个更合适的选择可能是将这些数据表示为二维坐标轴上的散点图，并用不同的颜色表示不同的类别.

A more appropriate choice might be to represent these data as a scatter plot on 2D axes, with different colours corresponding to the different categories.

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