Fool the entire DataFrame (all columns) with Scikit-learn (sklearn) without iterating over the columns

Question

I want to use all the columns in a pandas DataFrame ... The only way I can do this is column by column as shown below ...

Is there any operation in which I can impose an entire DataFrame without iterating through the columns?

#!/usr/bin/python from sklearn.preprocessing import Imputer import numpy as np import pandas as pd #Imputer fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1) #Model 1 DF = pd.DataFrame([[0,1,np.nan],[2,np.nan,3],[np.nan,2,5]]) DF.columns = "c1.c2.c3".split(".") DF.index = "i1.i2.i3".split(".") #Impute Series imputed_DF = DF for col in DF.columns: imputed_column = fill_NaN.fit_transform(DF[col]).T #Fill in Series on DataFrame imputed_DF[col] = imputed_column #DF #c1 c2 c3 #i1 0 1 NaN #i2 2 NaN 3 #i3 NaN 2 5 #imputed_DF #c1 c2 c3 #i1 0 1.0 4 #i2 2 1.5 3 #i3 1 2.0 5 

Answer 1

If you want mean or median , you can do something like:

 fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1) imputed_DF = pd.DataFrame(fill_NaN.fit_transform(DF)) imputed_DF.columns = DF.columns imputed_DF.index = DF.index 

If you want to fill them with 0 or something you can always do:

 DF[DF.isnull()] = 0 

Answer 2

If for some reason you do not need to specifically use Imple Imputer , it seems to me that a simpler option is to simply do:

 df = df.fillna(df.mean()) 

Answer 3

Can I impute multiple columns?