How to visualize 95% confidence interval in matplotlib?

Question

I found out how to find the 95% confidence interval with scipy.stats.t this way

 In [1]: from scipy.stats import t In [2]: t.interval(0.95, 10, loc=1, scale=2) # 95% confidence interval Out[2]: (-3.4562777039298762, 5.4562777039298762) In [3]: t.interval(0.99, 10, loc=1, scale=2) # 99% confidence interval Out[3]: (-5.338545334351676, 7.338545334351676) 

However, visualization is important to me. I am wondering how can I show a confidence interval line on each node of my curve in matplotlib ?

What I expect is something like this

Answer 1

You do not need the .interval method to get the size of the confidence interval, you just need the .ppf method.

 import numpy as np import scipy.stats as ss data_m=np.array([1,2,3,4]) #(Means of your data) data_df=np.array([5,6,7,8]) #(Degree-of-freedoms of your data) data_sd=np.array([11,12,12,14]) #(Standard Deviations of your data) import matplotlib.pyplot as plt plt.errorbar([0,1,2,3], data_m, yerr=ss.t.ppf(0.95, data_df)*data_sd) plt.xlim((-1,4)) 

ss.t.ppf(0.95, data_df)*data_sd is a fully vectorized way to get (half) the size of an interval, taking into account degrees of freedom and standard deviation.

Answer 2

you need to divide by the standard deviation, and secondly, if your data is two-way (as shown in the graph), you need to allow 2.5% of the passes on each side of the Gaussian, that is:

 ss.t.ppf(0.975, data_df)/np.sqrt(data_df) 

Since you miss 2.5% on both sides, you get only 5%.