Why does not log transformation make the data normalized? Having some skewed features as shown in the following figure. I am trying to imply log transformation to the feature called vBMD(mgHA/cm3). I run the following codes




  import numpy as np
  
  import pandas as pd
  
  from sklearn.preprocessing import MinMaxScaler
  
  df=pd.read_csv("Data.csv")
  
  scaler=MinMaxScaler(feature_range=(0,1))
  
  df['vBMD (mgHA/cm3)']=scaler.fit_transform(np.array(df['vBMD
  (mgHA/cm3)']).reshape(-1,1))
  
  df['vBMD (mgHA/cm3)']=np.log(np.array(df['vBMD (mgHA/cm3)']))


After the transfromation, I have got the following result.


While I am waiting that the feature will be normalized, its skewness increased. Thus, what am I doing wrong?
Log transformation leads to a normal distribution only for log-normal distributions. Not all distributions are log-normal, meaning they will not become normal after the log transformation.

EDIT:

As you have commented, if you are trying to convert an arbitrary distribution to normal, methods like QuantileTransformer can be used. But note that these transformations make a distribution normal by changing (destroying) some information from the original data.