I have a pd Dataframe and some values are nan.
What I would like to do is assign nan values to all the elements of a row (excluding the element of the first column) if that row has one nan value.
For example, given the following dataframe:
Code 1996 1997 1998 GBA 100 nan 5 JOY 120 10 30 WII 300 nan nan the desired output is:
Code 1996 1997 1998 GBA nan nan nan JOY 120 10 30 WII nan nan nan You can first get a boolean series that says whether a row has any NaNs in it starting from first column. Then you can use boolean indexing and set those rows' columns starting from first to NaN:
has_nan = df.loc[:, "1996":].isna().any(axis=1) df.loc[has_nan, "1996":] = np.nan to get
>>> df Code 1996 1997 1998 0 GBA NaN NaN NaN 1 JOY 120.0 10.0 30.0 2 WII NaN NaN NaN has_nan is
0 True 1 False 2 True dtype: bool i.e., first and last rows are NaNed.
