Having the following distributions (actual and predicted), Hist 1 to 3 (left to right).
[![enter image description here](https://i.stack.imgur.com/aa16T.png)](https://i.stack.imgur.com/aa16T.png)

I would like to get a score ranging from 0-1 of how close the actual distribution is to be normal. I've found a couple of statistical normality tests:

* Shapiro-Wilk Test
* D’Agostino’s K^2 Test

My DataSet is large therefore I've decided to check the skew and kurtosis statistics and got the following results:

```
hist-1 Skewness is 0.028386209063816035 and Kurtosis is 2.4224694251429764 <-- Most normal
hist-2 Skewness is 3.7702212103585246 and Kurtosis is 15.214567975037294
hist-3 Skewness is -0.40471550878367296 and Kurtosis is 1.4106438684701157

```

How can I calculate a score ranging between 0-1 using those params? Or is there a better approach to calculate the score?

*Update:*
As suggested, I've tried `stats.kstest(data,"norm")`, however the results does not differentiate the differences between the distributions, or maybe I'm missing something?

```
Hist-1 - KstestResult(statistic=0.9274310194094191, pvalue=0.0)
Hist-2 - KstestResult(statistic=0.9999966401777812, pvalue=0.0)
Hist-3 - KstestResult(statistic=0.9911610021388533, pvalue=0.0)

```