Python 使用 matplotlib 将点的散点添加到箱线图
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原文地址: http://stackoverflow.com/questions/29779079/
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Adding a scatter of points to a boxplot using matplotlib
提问by Wok
I have seen this wonderful boxplot in this article(Fig.2).
我在这篇文章中看到了这个美妙的箱线图（图 2）。
As you can see, this is a boxplot on which are superimposed a scatter of black points: x indexes the black points (in a random order), y is the variable of interest. I would like to do something similar using Matplotlib, but I have no idea where to start. So far, the boxplots which I have found online are way less cool and look like this:
如您所见，这是一个箱线图，其上叠加了散点的黑点：x 索引黑点（以随机顺序），y 是感兴趣的变量。我想使用 Matplotlib 做类似的事情，但我不知道从哪里开始。到目前为止，我在网上找到的箱线图不太酷，看起来像这样：
Documentation of matplotlib: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot
matplotlib 的文档：http: //matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot
Ways to colorize boxplots: https://github.com/jbmouret/matplotlib_for_papers#colored-boxes
为箱线图着色的方法：https: //github.com/jbmouret/matplotlib_for_papers#colored-boxes
采纳答案by Kyrubas
What you're looking for is a way to add jitter to the x-axis.
您正在寻找的是一种向 x 轴添加抖动的方法。
Something like this taken from here:
像这样的东西取自这里：
bp = titanic.boxplot(column='age', by='pclass', grid=False)
for i in [1,2,3]:
y = titanic.age[titanic.pclass==i].dropna()
# Add some random "jitter" to the x-axis
x = np.random.normal(i, 0.04, size=len(y))
plot(x, y, 'r.', alpha=0.2)
Quoting the link:
引用链接：
One way to add additional information to a boxplot is to overlay the actual data; this is generally most suitable with small- or moderate-sized data series. When data are dense, a couple of tricks used above help the visualization:
- reducing the alpha level to make the points partially transparent
- adding random "jitter" along the x-axis to avoid overstriking
向箱线图中添加附加信息的一种方法是叠加实际数据；这通常最适合小型或中等规模的数据系列。当数据密集时，上面使用的一些技巧有助于可视化：
- 降低 alpha 级别以使点部分透明
- 沿 x 轴添加随机“抖动”以避免过度打击
The code looks like this:
代码如下所示：
import pylab as P
import numpy as np
# Define data
# Define numBoxes
P.figure()
bp = P.boxplot(data)
for i in range(numBoxes):
y = data[i]
x = np.random.normal(1+i, 0.04, size=len(y))
P.plot(x, y, 'r.', alpha=0.2)
P.show()
回答by hwang
Expanding on Kyrubas's solution and using only matplotlib for the plotting part (sometimes I have difficulty formatting pandas plots with matplotlib).
扩展 Kyrubas 的解决方案并仅将 matplotlib 用于绘图部分（有时我很难用 matplotlib 格式化熊猫图）。
from matplotlib import cm
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# initialize dataframe
n = 200
ngroup = 3
df = pd.DataFrame({'data': np.random.rand(n), 'group': map(np.floor, np.random.rand(n) * ngroup)})
group = 'group'
column = 'data'
grouped = df.groupby(group)
names, vals, xs = [], [] ,[]
for i, (name, subdf) in enumerate(grouped):
names.append(name)
vals.append(subdf[column].tolist())
xs.append(np.random.normal(i+1, 0.04, subdf.shape[0]))
plt.boxplot(vals, labels=names)
ngroup = len(vals)
clevels = np.linspace(0., 1., ngroup)
for x, val, clevel in zip(xs, vals, clevels):
plt.scatter(x, val, c=cm.prism(clevel), alpha=0.4)
回答by HS-nebula
As a simpler, possibly newer option, you could use seaborn's swarmplotoption.
作为一个更简单、可能更新的选项，您可以使用seaborn'sswarmplot选项。
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips, showfliers = False)
ax = sns.swarmplot(x="day", y="total_bill", data=tips, color=".25")
plt.show()
Looking at the original question again (and having more experience myself), I think instead of sns.swarmplot, sns.stripplotwould be more accurate.
再次查看原始问题（并且自己有更多经验），我认为而不是sns.swarmplot,sns.stripplot会更准确。
