Adding a new column in dataframe conditionally-CodePudding

I have a dataframe as follows: | Time | A | B |C | | ---- |-- | ---- |---- | | 1.0 | 0 | 1 | 0 | | 2.0 | 1 | 1 | 0 | | 3.0 | 1 | 1 | 1 | | 4.0 | 0 | 1 | 1 | | . | . | . | . | | 1000.0| 1 | 1 | 1 |

I want to write to a new column D=1 whenever A==1 & B==1 & C==1 and 0 otherwise

CodePudding user response：

We could also use eq all on axis:

df['D'] = df.drop(columns='Time').eq(1).all(axis=1).astype(int)

Output:

     Time  A  B  C  D
0     1.0  0  1  0  0
1     2.0  1  1  0  0
2     3.0  1  1  1  1
3     4.0  0  1  1  0
4  1000.0  1  1  1  1

CodePudding user response：

This is a possible solution, although it might not be the more efficient:

df["D"] = ((df["A"] == 1) & (df["B"] == 1) & (df["C"] == 1)).astype(int)

This one looks better, and should return the same output as the previous one:

df["D"] = (df["A"] & df["B"] & df["C"]).astype(int)

Another option to check if each column is equal to one and then sum their values (credit to @enke for removing the binary constrain of the old version):

df["D"] = (df[["A", "B", "C"]].eq(1).sum(axis=1) == 3).astype(int)