Error: float object has no attribute notnull

Question

I have a dataframe:

  a     b     c
0 nan   Y     nan
1  23   N      3
2 nan   N      2
3  44   Y     nan

I wish to have this output:

Answer 1

Since you just want Nans to be propagated, multiplying the columns takes care of that for you:

>>> df = pd.read_clipboard()
>>> df
      a  b    c
0   NaN  Y  NaN
1  23.0  N  3.0
2   NaN  N  2.0
3  44.0  Y  NaN
>>> df.a * df.c
0     NaN
1    69.0
2     NaN
3     NaN
dtype: float64
>>>

If you want to do it on a condition, you can use np.where here instead of .apply. all you need is the following:

>>> df
      a  b    c
0   NaN  Y  NaN
1  23.0  N  3.0
2   NaN  N  2.0
3  44.0  Y  NaN
>>> np.where(df.b == 'N', df.a*df.c, df.a)
array([ nan,  69.,  nan,  44.])

This is the default behavior for most operations involving Nan. So, you can simply assign the result of the above:

>>> df['d'] = np.where(df.b == 'N', df.a*df.c, df.a)
>>> df
      a  b    c     d
0   NaN  Y  NaN   NaN
1  23.0  N  3.0  69.0
2   NaN  N  2.0   NaN
3  44.0  Y  NaN  44.0
>>>

Just to elaborate on what this:

np.where(df.b == 'N', df.a*df.c, df.a)

Is doing, you can think of it as "where df.b == 'N', give me the result of df.a * df.c, else, give me just df.a:

>>> np.where(df.b == 'N', df.a*df.c, df.a)
array([ nan,  69.,  nan,  44.])

Also note, if your dataframe were a little different:

>>> df
      a  b    c
0   NaN  Y  NaN
1  23.0  Y  3.0
2   NaN  N  2.0
3  44.0  Y  NaN
>>> df.loc[0,'a'] = 99
>>> df.loc[0, 'b']= 'N'
>>> df
      a  b    c
0  99.0  N  NaN
1  23.0  N  3.0
2   NaN  N  2.0
3  44.0  Y  NaN

Then the following would not be equivalent:

>>> np.where(df.b == 'N', df.a*df.c, df.a)
array([ nan,  69.,  nan,  44.])
>>> np.where((df.b == 'N') & (~df.c.isnull()), df.a*df.c, df.a)
array([ 99.,  69.,  nan,  44.])

So you might want to use the slightly more verbose:

>>> df['d'] = np.where((df.b == 'N') & (~df.c.isnull()), df.a*df.c, df.a)
>>> df
      a  b    c     d
0  99.0  N  NaN  99.0
1  23.0  N  3.0  69.0
2   NaN  N  2.0   NaN
3  44.0  Y  NaN  44.0
>>>