Matplotlib：避免在“散点/点/蜂窝”中的重叠数据点情节 [英] Matplotlib: avoiding overlapping datapoints in a "scatter/dot/beeswarm" plot

问题描述

使用matplotlib绘制点图时，我想偏移重叠的数据点，以使它们全部可见。例如，如果我有

  CategoryA：0,0,3,0,5 
 CategoryB：5,10 ，5,5,10 
  

我想让 CategoryA 0数据点并排设置，而不是直接在上面，而仍然与 CategoryB 不同。

在R（ ggplot2 ）中有一个jitter 。在matplotlib中是否有类似的选项，还是有另一种方法可以产生类似的结果？

strong>澄清， beeswarm R 中的情节本质上就是我的想法， pybeeswarm 是一个早期但有用的开始在matplotlib / Python版本。

编辑 以添加Seaborn的 Swarmplot ，在0.7版本中引入，是我想要的一个很好的实现。

 <$ c> 
 $ c> def rand_jitter（arr）：
 stdev = .01 *（max（arr）-min（arr））
 return arr + np.random.randn（len（arr））* stdev 
 
 def jitter（x，y，s = 20，c ='b'，marker ='o'，cmap = None，norm = None，vmin = None，vmax = None，alpha = None， linewidths = None，verts = None，hold = None，** kwargs）：
 return scatter（rand_jitter（x），rand_jitter（y），s = s，c = c，marker = marker，cmap = cmap， norm = norm，vmin = vmin，vmax = vmax，alpha = alpha，linewidths = linewidths，verts = verts，hold = hold，** kwargs）
   
 
  stdev 变量确保抖动足以在不同的比例上被看到，但它假设轴的极限<0>           / code>。
 
When drawing a dot plot using matplotlib, I would like to offset overlapping datapoints to keep them all visible.  For examples, if I have  
CategoryA: 0,0,3,0,5  
CategoryB: 5,10,5,5,10  
I want each of the CategoryA "0" datapoints to be set side by side, rather than right on top of each other, while still remaining distinct from CategoryB.

In R (ggplot2) there is a "jitter" option that does this. Is there a similar option in matplotlib, or is there another approach that would lead to a similar result?

Edit: to clarify, the "beeswarm" plot in R is essentially what I have in mind, and pybeeswarm is an early but useful start at a matplotlib/Python version.  

Edit: to add that Seaborn's Swarmplot, introduced in version 0.7, is an excellent implementation of what I wanted.
解决方案
Extending the answer by @user2467675, here's how I did it:
def rand_jitter(arr):
    stdev = .01*(max(arr)-min(arr))
    return arr + np.random.randn(len(arr)) * stdev

def jitter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs):
    return scatter(rand_jitter(x), rand_jitter(y), s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, verts=verts, hold=hold, **kwargs)
The stdev variable makes sure that the jitter is enough to be seen on different scales, but it assumes that the limits of the axes are 0 and the max value.

You can then call jitter instead of scatter.

                        
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