Replace negative values in single DataFrame column

Question

If I have the following DataFrame:

>>> df_1 = pd.DataFrame({'A':[-1, 2,  3],
                         'B':[4, -5,  6],
                         'C':[7,  8, -9]}, 
                          index=pd.to_datetime(['2017-01-01 00:01:00', '2017-01-01 00:02:00', '2017-01-02 00:01:00']))

>>> df_1
                      A   B   C
2017-01-01 00:01:00  -1   4   7
2017-01-01 00:02:00   2  -5   8
2017-01-02 00:01:00   3   6  -9

How would I replace all of the negative values in a specific column with something else? For example, if I want to replace all of the negative values in the 'B' column but none of the others with, say 0, the following would be my result.

>>> df_2
                      A   B   C
2017-01-01 00:01:00  -1   4   7
2017-01-01 00:02:00   2   0   8
2017-01-02 00:01:00   3   6  -9

Answer 1

I think you can using mask

df_1.B=df_1.B.mask(df_1.B.lt(0),0)
df_1
Out[1437]: 
                     A  B  C
2017-01-01 00:01:00 -1  4  7
2017-01-01 00:02:00  2  0  8
2017-01-02 00:01:00  3  6 -9

If we combine with fillna ()Assuming different columns should fill will different value)

df_1.mask(df_1.lt(0)).fillna({'A':9999,'B':0,'C':-9999})
Out[1440]: 
                          A    B       C
2017-01-01 00:01:00  9999.0  4.0     7.0
2017-01-01 00:02:00     2.0  0.0     8.0
2017-01-02 00:01:00     3.0  6.0 -9999.0