trjhtr

The goal is to maintain the relationship between two columns by setting to NaN all the values from one column in another column.

Having the following data frame:

df = pd.DataFrame('a': [np.nan, 2, np.nan, 4],'b': [11, 12 , 13, 14])

 a b
0 NaN 11
1 2 12
2 NaN 13
3 4 14

Maintaining the relationship from column a to column b, where all NaN values are updated results in:

 a b
0 NaN NaN
1 2 12
2 NaN NaN
3 4 14

One way that it is possible to achieve the desired behaviour is:

df.b.where(~df.a.isnull(), np.nan)

Is there any other way to maintain such a relationship?

You could use mask on NaN rows.

In [366]: df.mask(df.a.isnull())
Out[366]:
 a b
0 NaN NaN
1 2.0 12.0
2 NaN NaN
3 4.0 14.0

For, presence of any NaN across columns use df.mask(df.isnull().any(1))

Using pd.Series.notnull to avoid having to take the negative of your Boolean series:

df.b.where(df.a.notnull(), np.nan)

But, really, there's nothing wrong with your existing solution.

Using dropna with reindex

df.dropna().reindex(df.index)
Out[151]: 
 a b
0 NaN NaN
1 2.0 12.0
2 NaN NaN
3 4.0 14.0

Another one would be:

df.loc[df.a.isnull(), 'b'] = df.a

Isn't shorter but does the job.

Using np.where(),

df['b'] = np.where(df.a.isnull(), df.a, df.b)

Working - np.where(condition, [a, b])

Return elements, either from a or b, depending on condition.

Output:

>>> df
 a b
0 NaN NaN
1 2.0 12.0
2 NaN NaN
3 4.0 14.0