Project_question

#Checking count of how many values have age as zero
print(data.loc[data['dob_years'] == 0]['dob_years'].count())


#Calculating mean ages for respective family status and storing them
civil_partnership_mean_age = data.groupby('family_status')['dob_years'].mean()[0]
divorced_mean_age = data.groupby('family_status')['dob_years'].mean()[1]
married_mean_age = data.groupby('family_status')['dob_years'].mean()[2]
unmarried_mean_age = data.groupby('family_status')['dob_years'].mean()[3]
widow_mean_age = data.groupby('family_status')['dob_years'].mean()[4]

print(civil_partnership_mean_age)
print(divorced_mean_age)
print(married_mean_age)
print(unmarried_mean_age)
print(widow_mean_age)

def replace_0 (row):
    family= row['family_status']
    dob = row['dob_years']

    if dob == 0:
        if family =='civil partnership':
            return civil_partnership_mean_age
        elif family =='divorced':
            return divorced_mean_age
        elif family =='married':
            return married_mean_age
        elif family =='unmarried':
            return unmarried_mean_age
        elif family == 'widow / widower':
            return widow_mean_age
    return

data['dob_years'] = data['dob_years'].apply(replace_0)