The or
and and
python statements require truth
values. For pandas
these are considered ambiguous so you should use “bitwise” 
(or) or &
(and) operations:
df = df[(df['col'] < 0.25)  (df['col'] > 0.25)]
These are overloaded for these kind of data structures to yield the elementwise or
(or and
).
Just to add some more explanation to this statement:
The exception is thrown when you want to get the bool
of a pandas.Series
:
>>> import pandas as pd
>>> x = pd.Series([1])
>>> bool(x)
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
What you hit was a place where the operator implicitly converted the operands to bool
(you used or
but it also happens for and
, if
and while
):
>>> x or x
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
>>> x and x
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
>>> if x:
... print('fun')
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
>>> while x:
... print('fun')
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Besides these 4 statements there are several python functions that hide some bool
calls (like any
, all
, filter
, …) these are normally not problematic with pandas.Series
but for completeness I wanted to mention these.
In your case the exception isn’t really helpful, because it doesn’t mention the right alternatives. For and
and or
you can use (if you want elementwise comparisons):

numpy.logical_or
:>>> import numpy as np >>> np.logical_or(x, y)
or simply the

operator:>>> x  y

numpy.logical_and
:>>> np.logical_and(x, y)
or simply the
&
operator:>>> x & y
If you’re using the operators then make sure you set your parenthesis correctly because of the operator precedence.
There are several logical numpy functions which should work on pandas.Series
.
The alternatives mentioned in the Exception are more suited if you encountered it when doing if
or while
. I’ll shortly explain each of these:

If you want to check if your Series is empty:
>>> x = pd.Series([]) >>> x.empty True >>> x = pd.Series([1]) >>> x.empty False
Python normally interprets the
len
gth of containers (likelist
,tuple
, …) as truthvalue if it has no explicit boolean interpretation. So if you want the pythonlike check, you could do:if x.size
orif not x.empty
instead ofif x
. 
If your
Series
contains one and only one boolean value:>>> x = pd.Series([100]) >>> (x > 50).bool() True >>> (x < 50).bool() False

If you want to check the first and only item of your Series (like
.bool()
but works even for not boolean contents):>>> x = pd.Series([100]) >>> x.item() 100

If you want to check if all or any item is notzero, notempty or notFalse:
>>> x = pd.Series([0, 1, 2]) >>> x.all() # because one element is zero False >>> x.any() # because one (or more) elements are nonzero True