Seaborn箱线图和线图未正确显示 [英] Seaborn boxplot and lineplot not showing properly

问题描述

我正在尝试将sealine lineplot覆盖在seaborn boxplot上结果某种程度上是震惊"的.:)似乎两个图放在同一图中但分开箱形图在左侧压缩，线形图在右侧压缩

请注意，如果我分别运行两个图形，则它们可以正常工作我无法搞清楚如何使其工作预先感谢您的帮助

 将pandas导入为pd导入matplotlib.pyplot作为plt将seaborn导入为snsmydata = pd.DataFrame({'a':[2012，2012，2012，2012，2013，2013，2013，2013，2014，2014，2014，2014，2015，2015，2015，2015，2015，2016，2016，2016，2016，2016，2017，2017，2017，2017、2018、2018、2018、2018、2019、2019、2019、2019、2020、2020、2020、2020、2020]'v':[383.00，519.00，366.00，436.00，1348.00，211.00，139.00，614.00，365.00，365.00，383.00，602.00，994.00，719.00，589.00，365.00，990.00，1142.00，262.00，1263.00，507.00，222.00，363.00，274.00、195.00、730.00、730.00、592.00、479.00、607.00、292.00、657.00、453.00、691.00、673.00、705]]})意味着= mydata.groupby('a').v.mean().reset_index()无花果，ax = plt.subplots(figsize =(15,8))sns.boxplot(data = mydata，x ='a'，y ='v'，ax = ax，showfliers = False)sns.lineplot(数据=平均值，x ='a'，y ='v'，ax = ax)plt.show() 

解决方案

令人惊讶的是，我没有找到一个很好的答案与此重复的问题，因此我将评论提高为一个.崛起，爵士评论:

您应该使用

Pointplot等效于

区别在于此示例对 lineplot 没有任何歧义.Seaborn lineplot 可以同时使用-分类数据和数字数据.貌似，代码首先尝试将其绘制为数值数据，如果不可能，则将它们用作分类变量(我不知道源代码).这可能是seaborn做出的一个很好的软件决策，因为相比于试图将分类数据和数值数据绘制到同一图中的罕见情况，其他情况(不接受分类数据)将引起更多的问题.不过，seaborn的警告会是一件好事.

I'm trying to overlay a seaborn lineplot over a seaborn boxplot The result is someway "shocking" :) It seems like the two graphs are put in the same figure but separate The box plot is compressed on the left side, the line plot is compressed on the right side

Notice that if I run the two graph separatly they work fine I cannot fugure out how to make it work Thank you in advance for any help

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

mydata = pd.DataFrame({
    'a':[2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020],
    'v':[383.00, 519.00, 366.00, 436.00, 1348.00, 211.00, 139.00, 614.00, 365.00, 365.00, 383.00, 602.00, 994.00, 719.00, 589.00, 365.00, 990.00, 1142.00, 262.00, 1263.00, 507.00, 222.00, 363.00, 274.00, 195.00, 730.00, 730.00, 592.00, 479.00, 607.00, 292.00, 657.00, 453.00, 691.00, 673.00, 705]
})

means =mydata.groupby('a').v.mean().reset_index()

fig, ax = plt.subplots(figsize=(15,8))
sns.boxplot(data=mydata, x='a', y='v', ax=ax, showfliers=False)
sns.lineplot(data=means, x='a', y='v', ax=ax)
plt.show()

解决方案

Surprisingly, I did not find a duplicate for this question with a good answer, so I elevate my comment to one. Arise, Sir Comment:

Instead of lineplot, you should use pointplot

...
sns.boxplot(data=mydata, x='a', y='v', ax=ax, showfliers=False)
sns.pointplot(data=means, x='a', y='v', ax=ax) 
plt.show()

Sample output:

Pointplot is the equivalent to lineplot for categorical variables that are used for boxplot. Please read here more about relational and categorical plotting.

The question came up why there is no problem with lineplot for the following data:

mydata = pd.DataFrame({'a':["m1", "m1", "m1", "m2", "m2", "m2", "m2", "m3", "m3", "m3", "m3", "m4", "m4", "m4", "m4"],     'v':[11.37, 11.31, 10.93, 9.43, 9.62, 6.61, 9.31, 11.27, 8.47, 11.86, 8.77, 8.8, 9.58, 12.26, 10] })  
means =mydata.groupby('a').v.mean().reset_index()  
print(means)
fig, ax = plt.subplots(figsize=(15,8)) 
sns.boxplot(data=mydata, x='a', y='v', ax=ax, showfliers=False) 
sns.lineplot(data=means, x='a', y='v', ax=ax) 
plt.show()

Output:

The difference is that this example does not have any ambiguity for lineplot. Seaborn lineplot can use both - categorical and numerical data. Seemingly, the code tries first to plot it as numerical data, and if this is not possible uses them as categorical variables (I don't know the source code). This is probably a good software decision by seaborn because the other case (not accepting categorical data) would cause way more problems than the rare case that people try to plot both categorical and numerical data into the same figure. A warning by seaborn would be a good thing, though.

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