How to Resolve 'The truth value of a Series is ambiguous' Error in Pandas Filtering?

Ambiguous Truth Value in Pandas Series: Filtering with OR Condition

We encounter the error "The truth value of a Series is ambiguous" when attempting to filter a DataFrame using an OR condition, as seen below:

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

This error stems from the ambiguous nature of truth values in pandas Series. To resolve it, we should employ "bitwise" operators instead, which are specifically designed for element-wise Boolean operations in pandas.

Bitwise OR and AND Operators

The bitwise OR operator | and the bitwise AND operator & are suitable for our purpose. They operate element-wise on the Series, returning a Boolean Series where each element represents the result of the OR or AND operation on the corresponding elements in the original Series.

In our case, we can rewrite the filter expression using the bitwise OR operator as follows:

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

Alternative Methods

The exception message also suggests alternative methods for obtaining truth values from a Series:

• .empty: Checks if the Series is empty.
• .bool(): Returns a Boolean Series where each element is True if the corresponding element is non-zero or non-False, and False otherwise.
• .item(): Returns the first and only item in the Series.
• .any(): Returns True if any element in the Series is non-zero, non-empty, or non-False; otherwise, it returns False.
• .all(): Returns True if all elements in the Series are non-zero, non-empty, or non-False; otherwise, it returns False.

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