I have some code that help me to predic tsome missing values.This is the code
from datawig import SimpleImputer from datawig.utils import random_split from sklearn.metrics import f1_score, classification_report df_train, df_test = random_split(df, split_ratios=[0.8, 0.2]) # Initialize a SimpleImputer model imputer = SimpleImputer( input_columns=['SITUACION_DNI_A'], # columns containing information about the column we want to impute output_column='EXTRANJERO_A', # the column we'd like to impute values for output_path='imputer_model' # stores model data and metrics ) # Fit an imputer model on the train data imputer.fit(train_df=df_train, num_epochs=10) # Impute missing values and return original dataframe with predictions predictions = imputer.predict(df_test) After that i get a new dataframe with less rows than the original, how can i insert the values that i get in the prediction into my original dataframe, or there's is a way to run the code with all my dataframe and not the test
