Numerical

Numerical columns contain numbers. The defining aspect of numerical data is that the numbers have an order and you can apply a variety of mathematical computations to them (average, sum, etc.) The actual values may follow a specific format, such as being rounded to 2 decimal digits and remaining between min/max bounds.

For example, you might be storing product purchase amounts (USD) with 2 decimal digits. You might be storing the ages of your customers as whole numbers that must be 18 or above.

If the data is stored as integers, it does not mean it's necessarily numerical. For example, HTTP response codes such as 200, 404, etc. are stored as integers but they are distinct categories. They cannot be combined or averaged.

General Numerical Transformers

These transformers can format, analyze, and reshape your numerical data into simpler shapes for data science.

FloatFormatter

A general transformer that imputes missing values.

ClusterBasedNormalizer

Use Gaussian Mixture Models to cluster and normalize the data.

GaussianNormalizer

Use a probability integral transform to normalize the data. Choose from several distributions.

＊OutlierEncoder

Detect and encode outliers for more accurate data science.

❖ XGaussianNormalizer

Use a probability integral transform to normalize data. Choose from 100s of distributions.

❖ ECDFNormalizer

Normalize the data directly instead of using a predefined shape.

＊SDV Enterprise Feature. This feature is available to our licensed users and is not currently in our public library. For more information, visit our page to Explore SDV.

❖ SDV Enterprise bundle. This feature is available for purchase as an SDV Enterprise bundle. For more information, visit our page to Explore SDV.

Differential Privacy Transformers

These transformers use differential privacy techniques to add noise or reshape your column of numerical data. As a result, your column — and any statistics about it — can be shared with differential privacy guarantees.

❖ DPECDFNormalizer

Normalize the data by computing CDF function and adding noise.

❖ DPLaplaceNoiser

Privatize the data by adding Laplacian noise

❖ 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 page. Coming soon!