具有指定置信区间的Seaborn条形图

Question

我想在Seaborn条形图上绘制置信区间，但我已经计算了置信区间。我如何让Seaborn绘制我的置信区间，而不是自己计算它们

例如，假设我有以下数据帧：

   x = pd.DataFrame([
        ['Group 1', 0.5, 0.05],
        ['Group 1', 0.6, 0.07],
    ], columns=['Group', 'Mean', 'SD'])

如何用这些平均值和标准差绘制条形图

Answer 1

您可以使用seaborn绘制没有错误条的条形图。然后使用matplotlib的errorbar添加错误条。下面的代码假定'Group'列包含两个不同的值：

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

x = pd.DataFrame([
    ['Group 1', 0.5, 0.05],
    ['Group 2', 0.6, 0.07],
], columns=['Group', 'Mean', 'SD'])

ax = sns.barplot(data=x, x='Group', y='Mean', color='dodgerblue')
ax.errorbar(data=x, x='Group', y='Mean', yerr='SD', ls='', lw=3, color='black')
plt.show()

下面是一个嵌套条的尝试。首先绘制错误条以获得其标准位置，并提取x坐标。然后移除这些条，并使用新位置再次创建。我不确定它是否在所有情况下都有效

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

x = pd.DataFrame([
    ['Group 1', 'A', 0.4, 0.08],
    ['Group 1', 'B', 0.5, 0.05],
    ['Group 1', 'C', 0.5, 0.02],
    ['Group 2', 'A', 0.6, 0.07],
    ['Group 2', 'B', 0.7, 0.09],
    ['Group 2', 'C', 0.7, 0.02],
    ['Group 3', 'A', 0.6, 0.07],
    ['Group 3', 'B', 0.2, 0.09],
    ['Group 3', 'C', 0.4, 0.11],
], columns=['Group', 'Subgroup', 'Mean', 'SD'])

num_hues = len(np.unique(x['Subgroup']))
ax = sns.barplot(data=x, x='Group', y='Mean', hue='Subgroup')
for (hue, df_hue), dogde_dist in zip(x.groupby('Subgroup'), np.linspace(-0.4, 0.4, 2 * num_hues + 1)[1::2]):
    bars = ax.errorbar(data=df_hue, x='Group', y='Mean', yerr='SD', ls='', lw=3, color='black')
    xys = bars.lines[0].get_xydata()
    bars.remove()
    ax.errorbar(data=df_hue, x=xys[:, 0] + dogde_dist, y='Mean', yerr='SD', ls='', lw=3, color='black')
plt.show()