4种绘制带误差线的柱形图

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Bar(
    name='Control',
    x=['Trial 1', 'Trial 2', 'Trial 3'], y=[3, 6, 4],
    error_y=dict(type='data', array=[1, 0.5, 1.5])
))
fig.add_trace(go.Bar(
    name='Experimental',
    x=['Trial 1', 'Trial 2', 'Trial 3'], y=[4, 7, 3],
    error_y=dict(type='data', array=[0.5, 1, 2])
))
fig.update_layout(barmode='group')
fig.show()

import numpy as np
import matplotlib.pyplot as plt

size = 3
x = np.arange(size)
a = [3, 6, 4]
a_SD = [1, 0.5, 1.5]
b = [4, 7, 3]
b_SD = [0.5, 1, 2]

total_width, n = 0.8, 2
width = total_width / n
x = x - (total_width - width) / 2
labels = ['Trial 1', 'Trial 2', 'Trial 3']

plt.bar(x, a,  width=width, yerr = a_SD, tick_label=labels, label='Control')
plt.bar(x + width, b, width=width, yerr = b_SD, tick_label=labels ,label='Experimental')

plt.legend()
plt.show()

import numpy as np 
arr = [3, 6, 4]

#求标准差
arr_std = np.std(arr,ddof=1)
print("标准差为:%f" % arr_std)

# 测试数据
tg <- ToothGrowth
# 转换为因子类型
tgc2 <- tgc
tgc2$dose <- factor(tgc2$dose)


# （左）误差线1：Error bars represent standard error of the mean
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity") +
    geom_errorbar(aes(ymin=len-se, ymax=len+se),
                  width=.2, # 设置误差线的宽度 


# （右）误差线2：使用95%置信区间
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity") +
    geom_errorbar(aes(ymin=len-ci, ymax=len+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))