How to select specific values in a pandas data frame?

I am working on a pandas data frame, which some of the row has one number and others has more than a number. I need to create a label column, which consists of values copied from the rows with one specific number and those rows with more than one value should be assigned to zero.
This is an example:

I tried the following code, but it does not work here.

df["label"] = df[["Column1", "Column2", "Column3", " Column4", "Column5"]].max(axis=1)

Can anyone suggest a way to solve this?

Reproducible input:

df = pd.DataFrame({'A': [1, 0, 0, 0, 0, 1, 0, 0, 0],
                   'B': [2, 2, 0, 0, 2, 0, 0, 0, 4],
                   'C': [0, 0, 3, 3, 0, 0, 0, 3, 2],
                   'D': [0, 0, 0, 4, 4, 0, 4, 0, 0],
                   'E': [0, 0, 0, 0, 0, 0, 0, 0, 5]})

You can use groupby operations:

g = df.filter(like='Column').replace(0, float('nan')).stack().groupby(level=0)

df['Label'] = g.first().where(g.size().eq(1), 0)

Or a mask and bfill:

tmp = df.filter(like='Column')
m = tmp.ne(0)
df['Label'] = tmp.where(m).bfill(axis=1).iloc[:, 0].where(m.sum(axis=1).eq(1), 0)

Or following your original approach with max:

tmp = df.filter(like='Column')
df['Label'] = tmp.max(axis=1).where(tmp.ne(0).sum(axis=1).eq(1), 0)

Output:

   Column1  Column2  Column3  Column4  Column5  Label
0        1        2        0        0        0      0
1        0        2        0        0        0      2
2        0        0        3        0        0      3
3        0        0        3        4        0      0
4        0        2        0        4        0      0
5        1        0        0        0        0      1
6        0        0        0        4        0      4
7        0        0        3        0        0      3
8        0        4        2        0        5      0