我正在尝试使用Seaborn创建箱形图,该箱形图具有来自一个数据集的数据,但是根据不同的数据集进行着色。以下是一个较小的数据集作为示例。我实际使用的数据集要大得多。
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
caa0b94c1d98 qnib/plain-kafka:1.1.0 "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) broker.2.3fij6pt90qt9sb9aco0i2dpys
b888cb6f783a qnib/plain-kafka:1.1.0 "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) broker.3.xqmjnfnfg7ha46lf6drlrq4ki
dcdda2d778c2 qnib/plain-kafka:1.1.0 "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) broker.1.gtgluxt6q58z2irzgfmu969ba
843def0b24fb qnib/plain-zkui "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) zkui.1.7zks618eae8sp4woc7araydix
d7ced19be88c qnib/plain-kafka-manager:2018-04-25 "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) manager.1.jdu5gnprhr4d982vz50511rhg
a67ac962e682 qnib/plain-zookeeper:2018-04-25 "/usr/local/bin/entr…" 3 minutes ago Up 3 minutes (healthy) 2181/tcp, 2888/tcp, 3888/tcp zookeeper.1.xar7cmdgozdj79orow0bmj3ev
880121f2fee5 qnib/golang-kafka-producer:2018-05-01.5 "kafka-producer" 3 minutes ago Up 3 minutes (healthy) producer.2.hety8za590v1twdgj2byvrmse
b6487d29812e qnib/golang-kafka-producer:2018-05-01.5 "kafka-producer" 3 minutes ago Up 3 minutes (healthy) producer.1.5oz02c8cw5oefc97xbarq5qoa
8b3a81905e90 qnib/golang-kafka-producer:2018-05-01.5 "kafka-producer" 3 minutes ago Up 3 minutes (healthy) producer.3.p8uh3hzr22fgm7u4gl1p3fiyw
import numpy as np
import pandas as pd
import seaborn as sns
data = ([[0.038095,0.259664,-0.016144],
[0.070850,0.533989,0.221025],
[0.010452,0.108146,0.007267],
[0.033338,0.006664,0.160160],
[0.005897,0.060313,-0.001070],
[0.089018,0.002074,0.409608],
[-0.010612,0.006957,0.331146],
[-0.002889,0.005181,0.928332]])
dataset = pd.DataFrame(data,columns=['A','B','C'])
dataset
sns.boxplot(data=dataset)
那么,如何根据类型数据集为箱形图着色?我需要合并数据集吗?任何/所有帮助表示赞赏!谢谢。
答案 0 :(得分:0)
在palette
中使用boxplot
关键字参数:
sns.boxplot(data=dataset, palette=['r', 'r', 'g'])
或从type_dataset
对象中获取元数据:
cmap = {'Yes': 'green', 'No': 'red'}
sns.boxplot(data=dataset, palette=[cmap[v] for v in type_dataset[1]])