sklearn - how to incorporate missing data when one-hot encoding

If you have pandas, this is pretty simple.

s = pd.Series(['A', 'A', 0, 'B', 0, 'A', np.nan])
s

0      A
1      A
2      0
3      B
4      0
5      A
6    NaN
dtype: object

Use replace to convert 0 to NaN -

s = s.replace({0 : np.nan, '0' : np.nan})
s

0      A
1      A
2    NaN
3      B
4    NaN
5      A
6    NaN
dtype: object

Now, call pd.get_dummies, which ignores NaN values.

pd.get_dummies(s)

   A  B
0  1  0
1  1  0
2  0  0
3  0  1
4  0  0
5  1  0
6  0  0

The solution is the same for a dataframe.

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