这是一个带有置信区间的seaborn图示例:
import plotly.express as px
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(1)
df = pd.DataFrame({'x': np.tile(np.arange(5), 6), 'y': np.random.randn(30), 'hue': np.repeat(['foo', 'bar'], [15, 15])})
sns.lineplot(data=df, x='x', y='y', hue='hue')
输出
这里尝试在 plotly
中执行相同的操作:
group = ['hue', 'x']
err = df.groupby(group)['y'].std() / np.sqrt(df.groupby(group)['y'].size())
pdf = df.groupby(group)['y'].mean().reset_index()
pdf['2'] = pdf['y'] + 1.96*pdf.set_index(group).index.map(err)
pdf['1'] = pdf['y'] - 1.96*pdf.set_index(group).index.map(err)
pdf['0'] = pdf['y']
pdf = pdf.drop('y', axis=1)
pdf = pd.melt(pdf, id_vars=['x', 'hue'])
pdf = pdf.sort_values(['x', 'variable', 'hue'], ascending = True)
fig = px.line(
pdf[pdf['hue']=='foo'],
line_group='hue',
x = 'x',
y = 'value',
color='variable',
color_discrete_map = {'0': 'blue', '1': 'blue', '2': 'blue'}
)
fig.update_traces(name = 'interval', selector = dict(name = '2'), showlegend=False)
fig.update_traces(fill = 'tonexty')
fig.update_traces(fillcolor = 'rgba(0,0,0,0)', selector = dict(name = '0'))
fig.update_traces(fillcolor = 'rgba(0,0,0,0)', line_color = 'rgba(0, 0, 255, 0.5)',
showlegend = False, selector = dict(name = '1'))
fig
输出:
所以,这与seaborn 的 plotly 相同,但仅适用于其中一种色调。我怎样才能将另一种色调的相同图绘制到同一图上,使其看起来像seaborn?
最佳答案
我花了相当多的时间试图解决这个问题,所以我最终整理了一个小包来做到这一点,使用与seaborn相同的API:https://github.com/MarcoGorelli/bornly
示例:
import bornly as bns
fmri = bns.load_dataset("fmri")
bns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")
关于python - plotly.express - 像 sns.lineplot 中的置信区间,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/70234198/