Question
Asked By – Arthur B.
Assume I have a pandas DataFrame with two columns, A and B. I’d like to modify this DataFrame (or create a copy) so that B is always NaN whenever A is 0. How would I achieve that?
I tried the following
df['A'==0]['B'] = np.nan
and
df['A'==0]['B'].values.fill(np.nan)
without success.
Now we will see solution for issue: Modifying a subset of rows in a pandas dataframe
Answer
Use .loc for label based indexing:
df.loc[df.A==0, 'B'] = np.nan
The df.A==0 expression creates a boolean series that indexes the rows, 'B' selects the column. You can also use this to transform a subset of a column, e.g.:
df.loc[df.A==0, 'B'] = df.loc[df.A==0, 'B'] / 2
I don’t know enough about pandas internals to know exactly why that works, but the basic issue is that sometimes indexing into a DataFrame returns a copy of the result, and sometimes it returns a view on the original object. According to documentation here, this behavior depends on the underlying numpy behavior. I’ve found that accessing everything in one operation (rather than [one][two]) is more likely to work for setting.
This question is answered By – BrenBarn
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