booleanmask

Boolean mask, often referred to as a boolean mask or boolean indexing, is an array of boolean values (True and False) used to select or modify elements in another array. In programming and data analysis, a boolean mask serves as a filter: positions labeled True indicate the corresponding elements of the data to keep or operate on, while False positions are ignored or left unchanged.

In practice, boolean masks are common in numerical computing libraries such as NumPy. A mask has a shape that is compatible with the target data, typically matching the data’s shape or being broadcastable to it. Boolean masks are created by applying comparisons or logical operations (for example, a > 3 or (a < 0) | (a == 2)). They can be used for indexing, where data[mask] returns the elements where the mask is True. Masks can also be used to assign values (data[mask] = new_value) or to perform in-place updates.

Boolean masking supports combining conditions with logical operators, such as &, |, and ~, or their NumPy equivalents like numpy.logical_and. Care is needed with operator precedence; parentheses are often required. Masks enable vectorized operations, avoiding explicit loops and improving performance.

In addition to NumPy, boolean masks are also utilized in data frame libraries like Pandas, where a boolean Series can filter rows in a DataFrame. Regardless of the library, boolean masks provide a concise, efficient mechanism for conditional data selection and modification.