在pandas的条形图中添加95%置信区间作为误差线

Question

我想在pandas条形图中添加95%置信区间误差线，就像这里一样。这是我的数据：

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Pandas关于如何使用误差条的文档，并尝试复制他们的代码示例。我得出了以下代码：

# 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，这导致了下图的结果，显然没有误差条。我的错误是什么？我认为我误解了作为yerr参数传递给plot函数的确切内容。

编辑：使用Quang Hoang的答案，我更改了我的代码如下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()

- cactus

Answer 1

给定的链接建议：

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：

- Quang Hoang

---

谢谢。我稍微修改了您的代码，并将最终结果编辑到问题中。 - cactus