Pandas Boolean Indexing: Why Use `&` Instead of `and`?

Logical Operators for Boolean Indexing in Pandas

When performing Boolean indexing in Pandas, it's crucial to understand the difference between the logical operators & (bitwise AND) and and (logical AND).

Why Use & over and for Boolean Indexing?

Consider the following example:

a = pd.DataFrame({'x': [1, 1], 'y': [10, 20]})

a[(a['x'] == 1) & (a['y'] == 10)]

This code returns the expected result:

    x   y
0   1  10

However, if you use and instead of &, you'll encounter an error:

a[(a['x'] == 1) and (a['y'] == 10)]

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Understanding the Error

The error occurs because and tries to evaluate the truthiness of each Series individually (a['x'] and a['y']). However, these Series do not have a clear Boolean value, which leads to the ambiguous truth value error.

In contrast, the bitwise & operator performs element-wise logical operations. It returns a boolean array where each element represents the result of the operation between the corresponding elements in a['x'] and a['y']. This allows you to create a Boolean mask for indexing.

Parentheses: A Mandatory Requirement

Note that it's mandatory to use parentheses when using &. Without them, the operation would be evaluated incorrectly due to the higher operator precedence of & over ==.

a['x'] == 1 & a['y'] == 10  # Incorrect: Triggers the error

(a['x'] == 1) & (a['y'] == 10)  # Correct: Boolean indexing works as expected

Conclusion

When performing boolean indexing in Pandas, always use the & operator for element-wise logical operations. This ensures proper evaluation and avoids the ambiguous truth value error.

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