A common imputation heuristic for missing categorical fields is to treat the absence of data itself as a distinct category. For example, if a column contains both valid string categories and NaN entries, it can be transformed into multiple binary dummy variables—where one specific column serves to indicate whether the original value was missing by using $$1$$ for missing and $$0$$ otherwise.

Categorical Imputation via Dummy Variables

The `pandas.get_dummies()` function is used to convert categorical variables into binary indicator columns. To apply categorical imputation, the `dummy_na=True` argument can be passed, which instructs the function to treat `NaN` as a valid category and generate a dedicated dummy column to explicitly flag missing entries.

pandas.get_dummies

A dummy variable, or indicator variable, is created to numerically represent categorical data. When a categorical feature has multiple possible discrete values, it is converted into a series of separate binary columns. For a given record, the column corresponding to its correct category is assigned a value of $$1$$, while all other dummy columns are set to $$0$$.

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A categorical variable is a type of variable that represents a characteristic or quality, typically assessed by assigning a descriptive category label to each individual rather than a number. Examples include a person's chosen major, nationality, occupation, or whether they are receiving psychotherapy.

Categorical Variable

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In survey design, mutually exclusive categories refer to response options that do not overlap with one another. When assessing categorical variables, it is crucial that the provided categories are mutually exclusive so that a respondent's answer unambiguously falls into only one category.

Mutually Exclusive Categories

Exhaustive categories in a survey item provide enough options to cover all possible responses a participant might make. Since it is often impossible to list every potential answer for a categorical variable, researchers frequently include an 'Other' option with a blank space to ensure the categories remain exhaustive.

Exhaustive Categories

Categorical variables represent qualities that are measured by assigning a descriptive label or category to each individual rather than a numerical amount. Common illustrations of categorical variables include a student's chosen university major (such as Psychology, English, or Nursing), a person's nationality, their current occupation, and whether or not they are receiving psychotherapy.

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