# Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

The `or` and `and` python statements require `truth`-values. For `pandas` these are considered ambiguous so you should use “bitwise” `|` (or) or `&` (and) operations:

```result = result[(result['var']>0.25) | (result['var']<-0.25)]
```

These are overloaded for these kind of datastructures to yield the element-wise `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 element-wise comparisons):

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 (like `list``tuple`, …) as truth-value if it has no explicit boolean interpretation. So if you want the python-like check, you could do: `if x.size` or `if not x.empty` instead of `if 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 not-zero, not-empty or not-False:`>>> x = pd.Series([0, 1, 2]) >>> x.all() # because one element is zero False >>> x.any() # because one (or more) elements are non-zero True`