Pandas Series: Log Normalize
If your data is in the range (-1;+1) (assuming you lost the minus in your question) then log transform is probably not what you need. At least from a theoretical point of view, it's obviously the wrong thing to do.
Maybe your data has already been preprocessed (inadequately)? Can you get the raw data? Why do you think log transform will help?
If you don't care about what is the meaningful thing to do, you can call log1p, which is the same as log(1+x) and which will thus work on (-1;∞).
I have a Pandas Series, that needs to be log-transformed to be normal distributed. But I can´t log transform yet, because there are values =0 and values below 1 (0-4000). Therefore I want to normalize the Series first. I heard of StandardScaler(scikit-learn), Z-score standardization and Min-Max scaling(normalization).
I want to cluster the data later, which would be the best method?
StandardScaler and Z-score standardization use mean, variance etc. Can I use them on "not yet normal distibuted" data?