将 95% 置信区间作为误差条添加到 Pandas 条形图

Question

我想95％的置信区间误差线添加到大熊猫条形图，喜欢这里。 这是我的数据的样子：

ciRatings.head(20)

                            count   mean        std
condition   envCond         
c01         CSNoisyLvl1     40      4.875000    0.404304
            CSNoisyLvl2     40      4.850000    0.361620
            LabNoisyLvl1    52      4.826923    0.382005
            LabNoisyLvl2    52      4.826923    0.430283
            LabQuiet        92      4.826087    0.408930
c02         CSNoisyLvl1     40      2.825000    0.902631
            CSNoisyLvl2     40      3.000000    0.816497
            LabNoisyLvl1    52      3.250000    1.218726
            LabNoisyLvl2    52      3.096154    1.089335
            LabQuiet        92      2.956522    1.036828
c03         CSNoisyLvl1     40      3.750000    0.669864
            CSNoisyLvl2     40      3.775000    0.659740
            LabNoisyLvl1    52      4.307692    0.728643
            LabNoisyLvl2    52      4.288462    0.723188
            LabQuiet        92      3.967391    0.790758
c06         CSNoisyLvl1     40      4.450000    0.638508
            CSNoisyLvl2     40      4.250000    0.669864
            LabNoisyLvl1    52      4.692308    0.578655
            LabNoisyLvl2    52      4.384615    0.599145
            LabQuiet        92      4.717391    0.452735

我查看了有关如何使用错误栏的 Pandas 文档，并尝试复制他们的代码示例。 我想出了以下内容：

# calculate range of CI around mean (as it is symmetric)
ci95_lower = []

for i in ciRatings.index:
    count, mean, std = ciRatings.loc[i]
    ci95_lower.append(mean - 1.96*std/math.sqrt(count))

ciRatings['CI95_lower'] = ci95_lower
ciRatings['CI95_range'] = ciRatings['mean'] - ciRatings['CI95_lower']

# extract CI range and means
ciRange = ciRatings[['CI95_range']]
ciRange = ciRange.unstack()
ciRatings = ciRatings[['mean']]

# bar plot with CI95 as error lines
ciBarPlot = ciRatings.unstack().plot(kind='bar', yerr=ciRange, capsize=4)

plt.show()

然而，这会导致下图，显然没有误差线。 我的错误是什么？ 我假设我误解了我必须将 plot 函数作为 yerr 参数传递的确切内容。

编辑：使用 Quang Hoang 的答案，我按如下方式更改了代码以实现所需的置信区间条：

# calculate range of CI around mean (as it is symmetric)
ci95_lower = []

for i in ciRatings.index:
    count, mean, std = ciRatings.loc[i]
    ci95_lower.append(mean - 1.96*std/math.sqrt(count))

ciRatings['CI95_lower'] = ci95_lower
ciRatings['CI95_range'] = ciRatings['mean'] - ciRatings['CI95_lower']

# bar plot with CI95 lines
ciBarPlot = ciRatings['mean'].unstack(level=1).plot.bar(
            yerr=ciRatings['CI95_range'].unstack(level=1), capsize=4)

plt.show()

Answer 1

给定的链接表明：

fig, ax = plt.subplots(figsize=(12,8))
(df['mean'].unstack(level=1)
           .plot.bar(yerr=df['std'].unstack(level=1) * 1.96,
                     ax=ax, capsize=4)
)
plt.show()

输出：