how to input data for shapiro wilk test using python scipy - numpy

Question

I am trying to do Normality test with my data.
# Method 1
import numpy as np
from scipy.stats import shapiro
data = [1874181.6503, 2428393.05517, 2486600.8183,...] # there are 146 data points
data = np.array(data)
stat, p = shapiro(data)
print('statistics=%.3f, p=%.3f' % (stat, p))
alpha = 0.05
if p > alpha:
print('its Gaussian ')
else:
print('not Gaussian')
----Output----
statistics=0.582, p=0.000
not Gaussian
When i run it i am getting its not gaussian, and when i calculate mean and standard deviation and generate sample using np.random.normal(mu,sigma, 149) (sample shown below )then its showing as Gaussian
# Method 2
import numpy as np
from scipy.stats import shapiro
data = [1874181.6503, 2428393.05517, 2486600.8183,...] # there are 146 data points
data = np.array(data)
d_mu = np.mean(data)
d_sig = np.std(data)
data = np.random.normal(d_mu,d_sig, 146)
stat, p = shapiro(data)
print('statistics=%.3f, p=%.3f' % (stat, p))
alpha = 0.05
if p > alpha:
print('its Gaussian ')
else:
print('not Gaussian')
------ Output ----
statistics=0.987, p=0.212
its Gaussian
I am very new to Data analytics, It will be helpful if someone can help me on the below doubts
Which is the right method to do shapiro test ..? Method 1 or Method 2..?
I have difficulty in understanding the np.random.normal(d_mu,d_sig, 146) function . The definition given in docs is "Draw random samples from a normal (Gaussian) distribution." But what data sample its generating , we already have data(my input data) and we have calculated mean and standard deviation to plot the normal distribution and the function returns some other data sample and my shapiro test works for that ( i know i am completely taking it wrongly, but not able to decide which one is right )
I am trying to do normal distribution for timeseries data . Any docs helpful links any one can suggest ...? to do normality test and to normal distribution . Anything that guide me in the right direction