Modifying a subset of rows in a pandas dataframe


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



without success.

9/6/2012 7:32:25 PM

Accepted 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.

12/3/2018 6:52:51 AM

Here is from pandas docs on advanced indexing:

The section will explain exactly what you need! Turns out df.loc (as .ix has been deprecated -- as many have pointed out below) can be used for cool slicing/dicing of a dataframe. And. It can also be used to set things.

df.loc[selection criteria, columns I want] = value

So Bren's answer is saying 'find me all the places where df.A == 0, select column B and set it to np.nan'

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