Numerical
Last updated
Last updated
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.
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.
These transformers can format, analyze, and reshape your numerical data into simpler shapes for data science.
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.
Use Gaussian Mixture Models to cluster and normalize the data.
Use a probability integral transform to normalize the data. Choose from several distributions.
A general transformer that imputes missing values.
Detect and encode outliers for more accurate data science.
Use a probability integral transform to normalize data. Choose from 100s of distributions.
Normalize the data directly instead of using a predefined shape.
Normalize the data by computing CDF function and adding noise.
Privatize the data by adding Laplacian noise
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