我正在从sklearn获得负值作为IterativeImputer的输出

I'm using a Multiple Imputer from sklearn library to impute some missing values from rain datasets, containing the rain stations and the rain data (each station a column, and the index are DateTime). I was able to run the IterativeImputer and get an output with all my missing values filled. The problem is that the output contains negative values. It's possible to change de min_value that he imputes, but it sets a unique value for all the columns. I wanna set a min_value based on the minimal value for each column before the imputation. There is a response here in Stack for that answer, but I've no clue how to do it. I'm a beginner python user, so if you can answer this, it will help me a lot.

我正在使用的代码:

imputer_data = IterativeImputer(random_state = 0,skip_complete=True,sample_posterior=True, max_iter = 10, missing_values = np.nan) 
data = babi_ana1 
imputer_data.fit(data.iloc[:,:].values)
data_imputed = imputer_data.transform(data.iloc[:,:].values)
data_imputed = pd.DataFrame(data_imputed)