How to convert non-standard lognormal data to normal (scipy)? I want to transform some data points, which I assume follow an unknown non-standard lognormal distribution, to follow a normal distribution.
When I fit the data with a lognormal distribution using scipy, I get a non-zero value for the loc parameter (shift).
So I use the following procedure:

*

*I fit a lognormal distribution to my data to find the value of the shift parameter (loc)

*I shift the data: data - loc

*I compute the natural logarithm of the data

In code the procedure is the following:
data = ... # pandas series
shape, loc, scale = lognorm.fit(data) # fit the data
data = data.apply(lambda x: np.log(x - loc)) # shift and apply logarithm

In some answers, such as this,they just say to take the logarithm of the data, and do not address a possible shift.
In my case this would be possible because the data is shifted to the right, so values are all positive, but would I obtain normally distributed data? I think that, depending from the shift, the transformed data would deviate from normality. Am I wrong?
To summarize these are my questions:

*

*is my procedure correct?

*If I don't shift the data, to save on the computational complexity of fitting the distribution (in my application this is important), do I introduce an error (deviation from normality)? If yes, can I quantify it in some way?


The definition of a log-normal distribution is that taking the log gives a normal distribution.
$$
X\sim\text{log-normal}$$$$
\Bigg\Updownarrow$$$$
\log\left(
X
\right)\sim\text{Normal}
$$
If you believe the first distribution to be log-normal, just take the logarithm to get a normal distribution.
If there is a shift $k$ from the standard location of a log-normal distribution, then subtracting $k$ would happen before the logarithm, with the resulting distribution being normal. If you do not know this shift, however, then its estimation is subject to error, and $\log\left(X-\hat k\right)$ might not be quite the right transformation to give a normal distribution. For instance, if you have $k=2$ but $\hat k = 2.1$, then you subtract the wrong location shift. This will be particularly problematic if $\hat k\ge X_{(1)}$ (that is, if the estimated location shift is greater than or equal to the smallest observed value (first order statistics)), as that will result in taking the logarithm of a value $\le 0$.
