如何修改 Python 中的代码以便仅对 Pandas 中的 NOT NaN 行进行计算？

Question

I have Pandas Data Frame in Python like below:

NR
--------
910517196
921122192
NaN

And by using below code I try to calculate age based on column NR in above Data Frame (it does not matter how below code works, I know that it is correct - briefly I take 6 first values to calculate age, because for example 910517 is 1991-05-17 :)):

df["age"] = (ABT_DATE - pd.to_datetime(df.NR.str[:6], format = '%y%m%d')) / np.timedelta64(1, 'Y')

My problem is: I can modify above code to calculate age only using NOT NaN values in column "NR" in Data Frame, nevertheless some values are NaN.

My question is: How can I modify my code so as to take to calculations only these rows from column "NR" where is not NaN ??

As a result I need something like below, so simply I need to temporarily disregard NaN rows and, where there is a NaN in column NR, insert also a NaN in the calculated age column:

NR         age
------------------
910517196 | 30
921122192 | 29
NaN       | NaN

How can I do that in Python Pandas ?

Answer 1

df['age']=np.where(df['NR'].notnull(),'your_calculation',np.nan)