Normalizing Inputs

So I took the Andrew Ng course 2 of deep learning specialization. In it Andrew was normalizing input by subtracting mean and dividing it by the variance. Variance was calculated after subtracting mean from each example but on google I saw that there is the Z-score normalization where standard deviation is being used to divide the inputs by.
What is the accurate way of normalizing the inputs?

I am currently normalizing using the following python code
Input is a dataset …

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