Question about graphing the clusters in K means
StandardScaler subtracts the mean from each variable and then divides it by the standard deviation. It's a common preprocessing step, certainly for k-means because this algorithm heavily depends on the scaling of the data. 

If I understand correctly you want to visualize the original data and make use of the labels from k-means by doing so. You could either add the labels to the original data (assuming the order of the records did not change):

original_with_label = numpy.concatenate(original, labels, axis = 1)


Or you could transform the data back to its original scale:

transformed_back_to_original = scalar_fit.inverse_transform(transformed_data)