如何使用 pandas 根据列模式填充缺失值？

Question

I have a data frame like as shown below

import pandas as pd
import numpy as np
df = pd.DataFrame({'source_value':['Male','Female',np.nan,np.nan,np.nan,'M'],
                       'new_id':[1,2,3,4,5,6],
                       'month_of_birth':[11,12,1,3,5,6],
                       'day_of_birth':[11,21,23,26,10,12],
                       'year_of_birth':[1967,1987,1956,1999,2005,1987],
                       'datetime_off':['11/11/1967','21/12/1987','23/01/1956','26/03/1999','10/05/2005','12/06/1987'],
'test_id':[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]})

I would like to fill missing values in the column with keywords id , value and datetime .

I tried the below based on startswith , endswith and contains

col = df.columns.str
c1 = col.endswith('id')
c2 = col.contains('value')
c3 = col.contains('datetime')
missing_value_filled = np.select([c1,c2,c3],[df.fillna(0),df.fillna(np.nan),df.fillna("01/01/2000 00:00:00")])
pd.DataFrame(missing_value_filled, columns=df.columns)

But the problem is it makes month_of_birth , day_of_birth and year_of_birth as zeroes even though they don't match my pattern mentioned above. May I know why does this happen?

How can I retain the original values of month , day and year of birth columns?

I get an output like below which is incorrect

My expected output is given below

Answer 1

Let us redefine the fillna function that takes the arguments as input df , column masks ( col_masks ) along with the corresponding fill values ( fill_values ):

def fillna(df, col_masks, fill_values):
    df = df.copy()
    for m, v in zip(col_masks, fill_values):
        df.loc[:, m] = df.loc[:, m].fillna(v)
    return df

---

>>> fillna(df, [c1, c2, c3], [0, np.nan, '01/01/2000 00:00:00'])

  source_value  new_id  month_of_birth  day_of_birth  year_of_birth datetime_off  test_id
0         Male       1              11            11           1967   11/11/1967      0.0
1       Female       2              12            21           1987   21/12/1987      0.0
2          NaN       3               1            23           1956   23/01/1956      0.0
3          NaN       4               3            26           1999   26/03/1999      0.0
4          NaN       5               5            10           2005   10/05/2005      0.0
5            M       6               6            12           1987   12/06/1987      0.0