How can I modify apply and lambda function to create new column based on other in Python Pandas?

Question

I have table like below in Python Pandas with float64 data type:

col1
--------
245.121
NaN
44.908

And I try to create new column "col2" using below code:

data["col2"] = data.apply(lambda x: 1 if x.col1== np.nan else 0, axis = 1)

Unfortunately, when I use above code I have 0 everywhere, why ? How can I modyfi my code to achieve something like below:

col1      col2
--------
245.121  | 0
NaN      | 1
44.908   | 0

How can I do that in Python Pandas ?

Use `np.isnan(x.col1)` or `pd.isnull(x.col1)`. https://stackoverflow.com/questions/44367557/why-does-assert-np-nan-np-nan-cause-an-error — Emma, Dec 08 '21 at 00:29

score 1 · Accepted Answer · edited Dec 08 '21 at 00:37

1

Try:

data["col2"] = data.apply(lambda x: 1 if x.col1 in [np.nan] else 0, axis = 1)

This should work, while yours doesn't because it's a feature that np.nan != np.nan

edited Dec 08 '21 at 00:37

richardec

answered Dec 08 '21 at 00:20

Tortar

Unfortunately it still does not work, hmmm I it impossiblem I do not understand why – bony767 Dec 08 '21 at 00:29
sorry I used df while debugging now it should work – Tortar Dec 08 '21 at 00:34
np.where(df.col1.isin([np.nan]), 1, 0) – bony767 Dec 08 '21 at 00:34

1 Answers1