我会用这个答案来拟合高斯（正态分布）曲线，然后用scipy.stats方法生成分布 .interval(0.95) （这里）给出包含95%cdf的端点。
例子：

import pandas as pd
from scipy.stats import norm
import numpy as np
from matplotlib import pyplot as plt
normal = np.random.normal(size=1000)
noise = np.random.uniform(size=500, low=-2, high=2)
data = np.concatenate([normal, noise])   # some dummy data
# make it a DataFrame
df = pd.DataFrame(data=data, index=range(len(data)), columns=["data"])  
df.plot(kind="density")
########### YOU ARE HERE ###################
data = df.to_numpy()                              # Numpy arrays are easier for 1D data
mu, std = norm.fit(data)                          # Fit a normal distribution
print("Mu and Std: ", mu, std)
CI_95 = norm.interval(0.95, loc=mu, scale=std)    # Find the 95% CI endpoints
print("Confidence Interval: ", CI_95)
plt.vlines(CI_95, ymin=0, ymax=0.4)               # plotting stuff
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, norm.pdf(x, mu, sigma))
plt.show()

输出：

Mu and Std:  -0.014830093874393395 1.0238114937847707
Confidence Interval:  (-2.0214637486506972, 1.9918035609019102)