normality_checks.py

#%%
data = ...

#%%
import numpy
import scipy
values = []
for rand_state in range(1000):
    mask = numpy.random.rand(len(data)) < 0.5
    test = scipy.stats.ttest_ind(data[mask], data[~mask], equal_var=False)
    values.append(test.pvalue)

#%%
# https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/
from matplotlib import pyplot as plt

plt.hist(values); plt.show()

plt.hist(data, bins=1000); plt.show()

from statsmodels.graphics.gofplots import qqplot
qqplot(data, line='s'); plt.show()

#%%
from scipy.stats import norm
from scipy.stats import shapiro
test = shapiro(data)
print("Shapiro", test)

from scipy.stats import normaltest # D’Agostino’s K^2 Test
test = normaltest(data)
print(test)