How to replace values in a column if another column is a NaN?

Question

So this should be the easiest thing on earth. Pseudocode:

Replace column C with NaN if column E is NaN

I know I can do this by pulling out all dataframe rows where column E is NaN, replacing all of Column C, and then merging that on the original dataset, but that seems like a lot of work for a simple operation. Why doesn't this work:

Sample data:

dfz = pd.DataFrame({'A' : [1,0,0,1,0,0],
               'B' : [1,0,0,1,0,1],
               'C' : [1,0,0,1,3,1],
               'D' : [1,0,0,1,0,0],
               'E' : [22.0,15.0,None,10.,None,557.0]})

Replace Function:

def NaNfunc(dfz):
  if dfz['E'] == None:
    return None
  else:
    return dfz['C']

dfz['C'] = dfz.apply(NaNfunc, axis=1)

And how to do this in one line?

EdChum · Accepted Answer · 2016-03-18 22:38:45Z

7

Use np.where:

In [34]:
dfz['C'] = np.where(dfz['E'].isnull(), dfz['E'], dfz['C'])
dfz

Out[34]:
   A  B   C  D    E
0  1  1   1  1   22
1  0  0   0  0   15
2  0  0 NaN  0  NaN
3  1  1   1  1   10
4  0  0 NaN  0  NaN
5  0  1   1  0  557

Or simply mask the df:

In [38]:
dfz.loc[dfz['E'].isnull(), 'C'] = dfz['E']
dfz

Out[38]:
   A  B   C  D    E
0  1  1   1  1   22
1  0  0   0  0   15
2  0  0 NaN  0  NaN
3  1  1   1  1   10
4  0  0 NaN  0  NaN
5  0  1   1  0  557

edited Mar 18, 2016 at 22:38

answered Mar 18, 2016 at 22:36

EdChum

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1 Comment

EdChum Over a year ago

Your solution worked for me but it's a little confusing naming the func param the same as your df, better to name it row or x to avoid ambiguity

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