根据另一列的值填充一列中的缺失值

Question

I have a df that has 3 columns: recnum , state , and zip_code . The state column is missing values and I want to fill them in based on matching zip_code . I have tried using .ffill with no luck. Below is a sample of how the df currently looks and what I want the output to look like. Any assistance would be greatly appreciated.

recnum: int64 state: string zip: float64

Current df

recnum	state	zip_code
1	AL	11111
2	CO	22222
3	TX	33333
4	NaN	11111
5	AL	11111
6	CO	22222
7	TX	33333
8	NaN	22222

Desired Output:

recnum	state	zip_code
1	AL	11111
2	CO	22222
3	TX	33333
4	AL	11111
5	AL	11111
6	CO	22222
7	TX	33333
8	CO	22222

Answer 1

Try grouping by zip_code then using ffill, bfill to fill out the nans:

import numpy as np
import pandas as pd

df = pd.DataFrame({'recnum': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8},
                   'state': {0: 'AL', 1: 'CO', 2: 'TX', 3: np.nan, 4: 'AL',
                             5: 'CO', 6: 'TX', 7: np.nan},
                   'zip_code': {0: 11111, 1: 22222, 2: 33333, 3: 11111,
                                4: 11111, 5: 22222, 6: 33333, 7: 22222}})

df['state'] = df.groupby('zip_code')['state'].ffill().bfill()
print(df)

df :

   recnum state  zip_code
0       1    AL     11111
1       2    CO     22222
2       3    TX     33333
3       4    AL     11111
4       5    AL     11111
5       6    CO     22222
6       7    TX     33333
7       8    CO     22222