> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/rdt/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/rdt/transformers-glossary/categorical.md). # Categorical Categorical columns contain distinct categories. The defining aspect of a categorical column is that there is **a set number of predefined categories** and that **they do not have an order**. ![For example, you may be recording the credit card company of your users. This can only take on specific values like "VISA", "AMEX" or "DISCOVER". This is nominal because the categories don't have any order.](https://2225246359-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVGX92M819eIp0rMg5elc%2Fuploads%2F0bThYSM3yPAxeJJT3CME%2Frdt_transformers-glossary-categorical_June%2002%202025.png?alt=media\&token=f02aeef6-ff07-4232-9078-9be7b5795a40) ## General Categorical Transformers These transformers encode your categorical values as numerical values, ready for data science and machine learning.


LabelEncoder	Encode categories as numerical labels.	LabelEncoder
UniformEncoder	Encode categories as a smooth distribution. Useful for imbalanced values.	UniformEncoder
OneHotEncoder	Encode categories into multiple, binary columns using one hot encoding.	OneHotEncoder
BinaryEncoder	Encode boolean data as binary `0/1` labels.	BinaryEncoder

## Differential Privacy Transformers These transformers use differential privacy techniques to add noise or reshape your column of categorical data. As a result, your column — and any statistics about it — can be shared with differential privacy guarantees. {% hint style="info" %} ❖ **SDV Enterprise Bundle**. This feature is available as part of the **Differential Privacy Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [Differential Privacy Bundle](https://docs.sdv.dev/sdv/reference/explore-sdv/differential-privacy-bundle) page. *Coming soon!* {% endhint %}


❖ DPDiscreteECDFNormalizer	Normalize the data by computing CDF function and adding noise.	❖ DPDiscreteECDFNormalizer
❖ DPResponseRandomizer	Privatize the data by adding Laplacian noise randomly to each category.	❖ DPResponseRandomizer
❖ DPWeightedResponseRandomizer	Privatize the data by adding Laplacian noise in a weighted way, based on category frequencies.	❖ DPWeightedResponseRandomizer

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