熊猫和matplotlib的置信区间为95％

Question

I don't know why this is taking me so long to figure out, but I cannot find a way to plot an error bar of the confidence interval of my data.

I have some data in a Python list.

I found this code from another question to calculate the 95% confidence interval of some data.

def mean_confidence_interval(data, confidence=0.95):
    a = 1.0 * np.array(data)
    n = len(a)
    m, se = np.mean(a), scipy.stats.sem(a)
    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
    return m, m-h, m+h

I am using this to get the confidence interval of one bar of my bar chart. The question is how do I plot the error bar since I have a triple here? Do I just plot the max out of these values per bar?

Edit

I tried to implement what was suggested in the comments. Let's say I have 3 bars in my chart, then I created a 2X3 list containing in the first row mh values of each bar and in the second row m+h values of each bar. Giving this to the chart however produces some strange error bars (for example one bar spans beyond 500 although I don't have such value in my errors).

[[200.0446804785922, 109.31657288869792, 93.43052190866868], 
[200.0957195214078, 222.0113671113021, 217.6619980913313]]

Answer 1

Using Seaborn and Pandas this is really easy:

import pandas as pd
import seaborn as sns

pd_df = pd.DataFrame(your_list, columns=['x_data', 'y_data', 'group_categories'])
sns.lineplot(data=pd_df, 
             x='x_data', y='y_data', hue='group_categories', ci=95,
             legend="full", palette="Set1")