如果其他列中的值相同，则向前或向后填充 NA

Question

Given this example:

import pandas as pd
df = pd.DataFrame({
    "date": ["20180724", "20180725", "20180731", "20180723", "20180731"],
    "identity": [None, "A123456789", None, None, None],
    "hid": [12345, 12345, 12345, 54321, 54321],
    "hospital": ["A", "A", "A", "B", "B"],
    "result": [70, None, 100, 90, 78]
})

Because the first three rows have the same hid and hospital , the values in identity should also be identical. As for the other two rows, they have the same hid and hospital as well, but no known identity was provided, so the values in identity should remain missing. In other words, the desired output is:

       date    identity    hid hospital  result
0  20180724  A123456789  12345        A    70.0
1  20180725  A123456789  12345        A     NaN
2  20180731  A123456789  12345        A   100.0
3  20180723        None  54321        B    90.0
4  20180731        None  54321        B    78.0

I can loop through all combinations of hid s and hospital s like for hid, hospital in df[["hid", "hospital"]].drop_duplicates().itertuples(index=False) , but I don't know how to do next.

Answer 1

Use groupby and apply in combination with ffill and bfill :

df['identity'] = df.groupby(['hid', 'hospital'])['identity'].apply(lambda x: x.ffill().bfill())

This will fill NaNs forward and backwards while separating the values for the specified groups.