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

I want to filter my dataframe with an `or` condition to keep rows with a particular column’s values that are outside the range `[-0.25, 0.25]`. I tried:

``````df = df[(df['col'] < -0.25) or (df['col'] > 0.25)]
``````

But I get the error:

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

## Solution

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 kinds of data structures 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`, if you want element-wise comparisons, you can use:

• ``````  >>> import numpy as np
>>> np.logical_or(x, y)
``````

or simply the `|` operator:

``````  >>> x | y
``````
• ``````  >>> np.logical_and(x, y)
``````

or simply the `&` operator:

``````  >>> x & y
``````

If you’re using the operators, then be sure to set your parentheses correctly because of 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
``````

Source: StackOverflow.com