Replacing a value in a column with a value from the same column based upon information

Question

I am looking for a way to do Missing value imputation. There is a table of entries over a given time, with an entry per hour done on days. There is a seperate entry per hour, which also has a day. So the table would be:

Day	Hour	entry
01.01.1998	1	78
01.01.1998	2	87
01.01.1998	3	NaN
...	...	...
31.01.1998	23	784
31.01.1998	24	8734

Now I want to detect certain NaN values in my column of entry, and replace that with the entry done the day before. (Or in case its the first day, from the day after.) How would I do that?

Answer 1

You can do

df['entry'] = df['entry'].ffill().bfill()