pandas Seaborn 因子图自定义误差线
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Seaborn factor plot custom error bars
提问by crackedegg
I'd like to plot a factorplot in seaborn but manually provide the error bars instead of having seaborn calculate them.
我想在 seaborn 中绘制因子图，但手动提供误差线而不是让 seaborn 计算它们。
I have a pandas dataframe that looks roughly like this:
我有一个大致如下所示的 Pandas 数据框：
model output feature mean std
0 first two a 9.00 2.00
1 first one b 0.00 0.00
2 first one c 0.00 0.00
3 first two d 0.60 0.05
...
77 third four a 0.30 0.02
78 third four b 0.30 0.02
79 third four c 0.10 0.01
and I'm outputting a plot that looks roughly like this:
我正在输出一个大致如下所示的图：
I'm using this seaborn commands to generate the plot:
我正在使用这个 seaborn 命令来生成图：
g = sns.factorplot(data=pltdf, x='feature', y='mean', kind='bar',
col='output', col_wrap=2, sharey=False, hue='model')
g.set_xticklabels(rotation=90)
However, I can't figure out how to have seaborn use the 'std' column as the error bars. Unfortunately, it would be quite time consuming to recompute the output for the data frame in question.
但是，我不知道如何让 seaborn 使用“std”列作为误差线。不幸的是，重新计算相关数据帧的输出会非常耗时。
This is a little similar to this q: Plotting errors bars from dataframe using Seaborn FacetGrid
这有点类似于 q: Plotting errors bar from dataframe using Seaborn FacetGrid
Except I can't figure out how to get it to work with the matplotlib.pyplot.bar function.
除了我不知道如何让它与 matplotlib.pyplot.bar 函数一起工作。
Is there a way to do this using seaborn factorplotor FacetGridcombined with matplotlib?
有没有办法使用 seabornfactorplot或FacetGrid结合 matplotlib来做到这一点？
Thanks!
谢谢！
采纳答案by mwaskom
You could do something like
你可以做类似的事情
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import sem
tips = sns.load_dataset("tips")
tip_sumstats = (tips.groupby(["day", "sex", "smoker"])
.total_bill
.agg(["mean", sem])
.reset_index())
def errplot(x, y, yerr, **kwargs):
ax = plt.gca()
data = kwargs.pop("data")
data.plot(x=x, y=y, yerr=yerr, kind="bar", ax=ax, **kwargs)
g = sns.FacetGrid(tip_sumstats, col="sex", row="smoker")
g.map_dataframe(errplot, "day", "mean", "sem")
