I wrote a function to fill NaN with Values, but instead this function first fills the NaN with values and then deletes every value that was in the list before I did the function

def preprocessing(df):
median_male_3= df[(df["Sex"]=="male") &  (df["Pclass"] ==3 )]["Age"].median()
median_male_2= df[(df["Sex"]=="male") &  (df["Pclass"] ==2 )]["Age"].median()
median_male_1= df[(df["Sex"]=="male") &  (df["Pclass"] ==1 )]["Age"].median()
median_female_3= df[(df["Sex"]=="female") &  (df["Pclass"] ==3 )]["Age"].median()
median_female_2= df[(df["Sex"]=="female") &  (df["Pclass"] ==2 )]["Age"].median()
median_female_1= df[(df["Sex"]=="female") &  (df["Pclass"] ==1 )]["Age"].median()

def agemaking(para):
    Age=para[0]
    bookclass=para[1]
    sex=para[2]
    
    if pd.isnull(Age):
        print(train_titanic["Age"])          #want to check what happens inside
        if bookclass==3 and sex=="male":
            return median_male_3
        elif bookclass==2 and sex=="male":
            return median_male_2
        elif bookclass==1 and sex=="male":
            return median_male_1
        elif bookclass==3 and sex=="female":
            return median_female_3
        elif bookclass==2 and sex=="female":
            return median_female_2
        elif bookclass==1 and sex=="female":
            return median_female_1
        
        else:
            return Age
        
train_titanic['Age']= train_titanic[['Age','Pclass','Sex']].apply(agemaking,axis=1)

thats my function

thats what i looked like before
thats the suprising result

>Solution :

Try this

def agemaking(para):
    Age=para[0]
    bookclass=para[1]
    sex=para[2]
    
    if pd.isnull(Age):
        print(train_titanic["Age"])
        if bookclass==3 and sex=="male":
            return median_male_3
        elif bookclass==2 and sex=="male":
            return median_male_2
        elif bookclass==1 and sex=="male":
            return median_male_1
        elif bookclass==3 and sex=="female":
            return median_female_3
        elif bookclass==2 and sex=="female":
            return median_female_2
        elif bookclass==1 and sex=="female":
            return median_female_1
        
        else:
            return Age
    else: 
        return age