fill missing value based on one column to another

January 18, 2022

I have two columns like this:

what i want to do is suppose for ‘age’ columns value between 30-39,i want to fill the missing value of age_band = 30. Like that suppose for ‘age’ columns value between 80-89,i want to fill the missing value of age_band = 80. How can i do this in pandas dataframe?

I tried like this but the loop is running like forever

for ages in data['age']:
if 0<=ages<=9:
    data['age_band']= data['age_band'].fillna(0)
elif 10<=ages<=19:
    data['age_band']= data['age_band'].fillna(10)
elif 20<=ages<=29:
    data['age_band']= data['age_band'].fillna(20)
elif 30<=ages<=39:
    data['age_band']= data['age_band'].fillna(30)
elif 40<=ages<=49:
    data['age_band']= data['age_band'].fillna(40)
elif 50<=ages<=59:
    data['age_band']= data['age_band'].fillna(50)
elif 60<=ages<=69:
    data['age_band']= data['age_band'].fillna(60)
elif 70<=ages<=79:
    data['age_band']= data['age_band'].fillna(70)
elif 80<=ages<=89:
    data['age_band']= data['age_band'].fillna(80)
elif 90<=ages<=99:
    data['age_band']= data['age_band'].fillna(90)
elif 100<=ages<=109:
    data['age_band']= data['age_band'].fillna(100)

please help me

>Solution :

Try this shortcut:

data['age_band'] = data['age_band'].fillna(data['age'] // 10 * 10).astype(int)
print(data)

# Output
   age  age_band
0   93        90
1   46        40
2   50        50
3   56        50
4   89        80
5   19        10
6   25        20
7   17        10
8   54        50
9   42        40

Setup:

import pandas as pd
import numpy as np

np.random.seed(2022)
data = pd.DataFrame({'age': np.random.randint(1, 111, 10), 'age_band': np.nan})
print(data)

# Output
   age  age_band
0   93       NaN
1   46       NaN
2   50       NaN
3   56       NaN
4   89       NaN
5   19       NaN
6   25       NaN
7   17       NaN
8   54       NaN
9   42       NaN