Create a function iterating in Panda's Dataframe rows to replace null values-CodePudding

It's days that i'm struggling on this piece of code, i thought i should give it a try here.

I have a DataFrame with some null values that I want to substitute with mean values that I have in other DataFrame. I've created a function that it should later be implemented with a lambda but I keep getting an error.

I Have a DataFrame like this:

CustomerType	Category	Satisfaction	Age
Not Premium	Electronics	Not Satisfied	NaN
Not Premium	Beauty	Satisfied	NaN
Premium	Sports	Satisfied	38.0
Not Premium	Sports	Not Satisfied	NaN

That i need to fill with this data:

CustomerType	Satisfaction	Age
Not Premium	Not Satisfied	32.440740
Not Premium	Satisfied	28.896348
Premium	Not Satisfied	43.767723
Premium	Satisfied	44.075901

So I've created a function:

def fill_age(x):
if x.isnull()== True:
    return[(grp.CustomerType==x.CustomerType) | (grp.Satisfaction==x.Satisfaction)]['Age'].values[0]

That I would like to apply to my dataframe using a lambda function to iterate through all the rows:

df['Age'] = [df.apply(lambda x: fill_age(x) if np.isnan(x['Age']) else 
                                            x['Age'], axis=1) for x in df]

But i keep getting this error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Can anyone of you help me? Thank you so much!!

CodePudding user response：

Supposing that you are calling incorrectly apply in your DataFrame and that fill_age() are working correctly on df["Age"] values, you need to replace this statement, just to evaluate x and asign a determined value (current Age or to be replace with external data) then checking by else-if conditional, this code shouldn't return errors

df["Age"] = df["Age"].apply(lambda x: fill_age(x) if np.isnan(x) else x)

CodePudding user response：

We should try avoid use apply, so we could use instead:

df['Age'] = df['Age'].fillna(
    df.groupby(['CustomerType', 'Satisfaction'])['Age'].transform('first')
)