> 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/uniformencoder.md).

# UniformEncoder

**Compatibility:** `categorical` or `boolean` data

The `UniformEncoder` transforms data that represents categorical values into a uniform distribution in the `[0,1]` interval. It is highly accurate at preserving the overall frequencies of each category.

<figure><img src="https://2225246359-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVGX92M819eIp0rMg5elc%2Fuploads%2FTBFs3heAY2fY0Q5Yo6v5%2Frdt_transformers-glossary-categorical-uniform-encoder_June%2002%202025.png?alt=media&amp;token=1fc2c7f3-3445-438e-a29c-9bc7e3af0ab6" alt=""><figcaption></figcaption></figure>

```python
from rdt.transformers.categorical import UniformEncoder

transformer = UniformEncoder()
```

## Parameters

**`missing_value_encoding`**: Control how to encode missing values present in the categorical column.

<table data-header-hidden><thead><tr><th width="232.5"></th><th></th></tr></thead><tbody><tr><td>(default) <code>'new_category'</code></td><td>Treat missing values as a new category type, just like any non-missing values. <em>Any type of missing value (None, NaN, etc.) is treated as a single category of "missing".</em></td></tr><tr><td><code>None</code></td><td>Do not encode missing values. In this case, missing values will be kept as-is during the forward and reverse transforms.</td></tr></tbody></table>

### Examples

```python
from rdt.transformers.categorical import UniformEncoder

transformer = UniformEncoder(
    order_by='alphabetical'
)
```

The transformer assigns each category to a unique, non-overlapping subset of the `[0,1]` interval. The length of the interval is based on the category's frequency. For example if category `'CASH'` occurs with 60% frequency, the subset will have the length `0.6` such as `[0.2, 0.8]`.

## Attributes

After fitting the transformer, you can access the learned values through the attributes.

**`frequencies`**: A dictionary that maps each category value to the observed frequency, as a float between 0 and 1

```python
>>> transformer.frequencies
{
  'CREDIT': 0.2, 
  'CASH': 0.6,
  'DEBIT': 0.2
}
```

**`intervals`**: A dictionary that maps each category value to an interval between `[0,1]`. This allows you to determine the exact rules used for transforming and reverse transforming.

```python
>>> transformer.intervals
{
  'CREDIT': [0, 0.2],
  'CASH': [0.2, 0.8],
  'DEBIT': [0.8, 1.0]
}
```

## FAQs

<details>

<summary>When should I use this transformer?</summary>

The UniformEncoder is shown to preserve the frequency of each category value with high accuracy. This is especially useful if you have a data imbalance, for example if `True` occurs only 1% of the time while `False` occurs 99% of the time.&#x20;

</details>

<details>

<summary>What if I'd like to sort the values by a custom order?</summary>

If the values in the column represent ordered categories, the column should be marked as *ordinal*, not *categorical*. Please see the transformers available for the [ordinal sdtype](/rdt/transformers-glossary/ordinal.md).

</details>


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