根据条件用0和1替换数据框中的值

Question

I would like to filter and replace. For the columns with are lower or higher than zero and not NaN's, I would like to set for one and the others, set to zero.

mask = ((ts[x] > 0) 
        | (ts[x] < 0))
ts[mask]=1
ts[ts[x]==1]

I did this and is working but I have to deal with the values that do not attend this condition replacing with zero. Any recommendations? I am quite confusing, and also would be better to use where function in this case?

Thanks all!

Sample Data

    asset.relativeSetpoint.350
0                        -60.0
1                          0.0
2                          NaN
3                        100.0
4                          0.0
5                          NaN
6                       -120.0
7                       -245.0
8                          0.0
9                        123.0
10                         0.0
11                      -876.0

Expected result

    asset.relativeSetpoint.350
0                            1
1                            0
2                            0
3                            1
4                            0
5                            0
6                            1
7                            1
8                            0
9                            1
10                           0
11                           1

Answer 1

如何使用apply ？

df[COLUMN_NAME] = df[COLUMN_NAME].apply(lambda x: 1 if x != 0 else 0)

Answer 2

You can do this by applying a logical AND on the two conditions and converting the resultant mask to integer.

df

    asset.relativeSetpoint.350
0                        -60.0
1                          0.0
2                          NaN
3                        100.0
4                          0.0
5                          NaN
6                       -120.0
7                       -245.0
8                          0.0
9                        123.0
10                         0.0
11                      -876.0

(df['asset.relativeSetpoint.350'].ne(0) 
 & df['asset.relativeSetpoint.350'].notnull()).astype(int)

0     1
1     0
2     0
3     1
4     0
5     0
6     1
7     1
8     0
9     1
10    0
11    1
Name: asset.relativeSetpoint.350, dtype: int64

---

The first condition df['asset.relativeSetpoint.350'].ne(0) gets a boolean mask of all elements that are not equal to 0 (this would include <0, >0, and NaN).

The second condition df['asset.relativeSetpoint.350'].notnull() will get a boolean mask of elements that are not NaNs.

The two masks are ANDed, and converted to integer.