How do I remove rows in a Pandas dataframe that have the same values in different columns?

I have a dataframe that looks like this:

I want to delete rows that have multiple 1 in different columns, so it become like this:

Using a numpy mask:

df[np.sum(df.values[:,1:]) < 2]

should be faster than a pandas based computation.

You can use boolean indexing with the sum of matches or values (if only 0/1) as reference:

out = df[df.drop(columns='Items').sum(axis=1).lt(2)]

Or:

out = df[df.eq(1).sum(axis=1).lt(2)]

Output:

    Items  notebook  ballpoint  pencil  eraser  pencil.1  sharpener  stapler  paper  scissors  glue
2  image3         0          0       0       0         1          0        0      0         0   NaN
3  image4         0          0       0       0         0          1        0      0         0   NaN
4  image5         0          0       0       0         0          0        0      1         0   NaN

Intermediate indexing Series:

df.drop(columns='Items').sum(axis=1).lt(2)
# or
# df.eq(1).sum(axis=1).lt(2)

0    False
1    False
2     True
3     True
4     True
dtype: bool