我想在熊猫条形图中添加95%置信区间误差线,例如here。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
我查看了熊猫documentation上有关如何使用错误栏的信息,并尝试复制其代码示例。我提出了以下建议:
# 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()
但是,这将在下面的图中显示出来,显然没有误差线。我怎么了我以为我误解了将图函数作为yerr参数传递给我的确切含义。
编辑:使用Quang Hoang的答案,我如下更改代码以实现所需的置信区间线:
# 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()