Python pandas remove rows where multiple conditions are not met -

lets have dataframe this:

   id  num 0   1    1 1   2    2 2   3    1 3   4    2 4   1    1 5   2    2 6   3    1 7   4    2 

the above can generated testing purposes:

test = pd.dataframe({'id': np.array([1,2,3,4] * 2,dtype='int32'),                      'num': np.array([1,2] * 4,dtype='int32')                     }) 

now, want keep rows condition met: id not 1 , num not 1. want remove rows index 0 , 4. actual dataset easier remove rows dont want rather specify rows want

i have tried this:

test = test[(test['id'] != 1) & (test['num'] != 1)] 

however, gives me this:

   id  num 1   2    2 3   4    2 5   2    2 7   4    2 

it seems have removed rows id 1 or num 1

i've seen number of other questions answer 1 used above doesn't seem working out in case

if change boolean condition equality , invert combined boolean conditions enclosing both in additional parentheses desired behaviour:

in [14]:     test = test[~((test['id'] == 1) & (test['num'] == 1))] test  out[14]:    id  num 1   2    2 2   3    1 3   4    2 5   2    2 6   3    1 7   4    2 

i think understanding of boolean syntax incorrect want or conditions:

in [22]: test = test[(test['id'] != 1) | (test['num'] != 1)] test  out[22]:    id  num 1   2    2 2   3    1 3   4    2 5   2    2 6   3    1 7   4    2 

if think means first condition excludes row 'id' equal 1 , 'num' column:

in [24]: test[test['id'] != 1]  out[24]:    id  num 1   2    2 2   3    1 3   4    2 5   2    2 6   3    1 7   4    2  in [25]: test[test['num'] != 1]  out[25]:    id  num 1   2    2 3   4    2 5   2    2 7   4    2 

so wanted or (|) above conditions