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###Edit:

###Edit II

Edit:

Edit II

>>> a = np.sort(df['col'].values).tolist()[::10]
[0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1687, 0.1687, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.2005, 0.2216, 0.2216, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.48, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912]

>>> from scipy.stats import normaltest
>>> normaltest(a)
NormaltestResult(statistic=0.05292848970575527, pvalue=0.9738828645381232)

>>> a = np.sort(df['col'].values).tolist()[::10]
[0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1687, 0.1687, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.2005, 0.2216, 0.2216, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.48, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912]

>>> from scipy.stats import normaltest
>>> normaltest(a)
NormaltestResult(statistic=0.05292848970575527, pvalue=0.9738828645381232)

>>> from matplotlib import pyplot as plt
>>> plt.hist(a) # Results in a similar graph to above

a = [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1687, 0.1687, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.2005, 0.2216, 0.2216, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.48, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912]

from scipy.stats import normaltest
normaltest(a)
> NormaltestResult(statistic=0.05292848970575527, pvalue=0.9738828645381232)

>>> a = np.sort(df['col'].values).tolist()[::10]
[0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1622, 0.1687, 0.1687, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.1729, 0.2005, 0.2216, 0.2216, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.2498, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.3143, 0.48, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.4854, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5078, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.5328, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.6496, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.9119, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912, 0.912]

>>> from scipy.stats import normaltest
>>> normaltest(a)
NormaltestResult(statistic=0.05292848970575527, pvalue=0.9738828645381232)