First replace() infs with NaN:

df.replace([np.inf, -np.inf], np.nan, inplace=True)

and then drop NaNs via dropna():

df.dropna(subset=["col1", "col2"], how="all", inplace=True)

For example:

>>> df = pd.DataFrame({"col1": [1, np.inf, -np.inf], "col2": [2, 3, np.nan]})
>>> df
   col1  col2
0   1.0   2.0
1   inf   3.0
2  -inf   NaN

>>> df.replace([np.inf, -np.inf], np.nan, inplace=True)
>>> df
   col1  col2
0   1.0   2.0
1   NaN   3.0
2   NaN   NaN

>>> df.dropna(subset=["col1", "col2"], how="all", inplace=True)
>>> df
   col1  col2
0   1.0   2.0
1   NaN   3.0

The same method also works for Series.

All you have to do is wrap pd.isnull in a way that in case it gets an iterable it will be forced to check it element-wise. This way you will always get a scalar boolean as output.

from collections import Iterable

def is_scalar_null(value):
    if isinstance(value, Iterable):
        return all(not pd.isnull(v) for v in value)
    return not pd.isnull(value)

assert is_scalar_null(3)
assert is_scalar_null([1, 2])
assert is_scalar_null(pd.Series([1]))
assert not is_scalar_null(None)
assert not is_scalar_null(np.nan)
assert not is_scalar_null([np.nan, 1])
assert not is_scalar_null(pd.Series([np.nan, 1]))

You can then patch the actual pd.isnull, but I can not say that I suggest doing so.

from collections import Iterable

orig_pd_is_null = pd.isnull

def is_scalar_null(value):
    if isinstance(value, Iterable):
        return all(not orig_pd_is_null(v) for v in value)
    return not orig_pd_is_null(value)

pd.isnull = is_scalar_null

assert pd.isnull(3)
assert pd.isnull([1, 2])
assert pd.isnull(pd.Series([1]))
assert not pd.isnull(None)
assert not pd.isnull(np.nan)
assert not pd.isnull([np.nan, 1])
assert not pd.isnull(pd.Series([np.nan, 1]))

This approach will probably break in case of nested iterables, but that can be fixed by using recursion in is_scalar_null.

This worked for me:

number_inf = df[df == np.inf].count()