Pandas replace all items in a row with NaN if one value is NaN

Question

I want to get rid of some records with NaNs. This works perfectly:

df.dropna(axis=0, how='any',inplace=True)

However, it changes the shape of my dataframe, and the index is no longer uniformly spaced. Therefore, I'd like to replace all items in these rows with np.nan. Is there a simple way to do this?

I was thinking about resampling the dataframe after dropna, but that only seems to work with a prescribed interval, whereas I would rather use the original index. Another approach would be to loop over the dataframe with iterrows, but that also feels cumbersome.

Answer 1

The command below selects all rows with any value equal to Nan, and assigns NaNs to the rest of those rows.

df.loc[df.isnull().any(axis=1), :] = np.nan

Answer 2

using this code also works without slicingdf = df.replace('nan',np.nan)