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FloatFormatter

Compatibility: numerical data

The FloatFormatter transforms numerical data. By default, it does nothing because numerical data is already ready to use for data science. But it can optionally handle missing values, learn rounding schemes and min/max bounds.

from rdt.transformers.numerical import FloatFormatter
transformer = FloatFormatter()

Parameters

missing_value_replacement: Add this argument to replace missing values during the transform phase

(default) 'mean'

Replace all missing values with the average value.

'random'

Replace missing values with a random value. The value is chosen uniformly at random from the min/max range.

'mode'

Replace all missing values with the most frequently occurring value

<number>

Replace all missing values with the specified number (0, -1, 0.5, etc.)

None

Do not replace missing values. The transformed data will continue to have missing values.

(deprecated) model_missing_values: Use the missing_value_generation parameter instead.

missing_value_generation: Add this argument to determine how to recreate missing values during the reverse transform phase

(default) 'random'

Randomly assign missing values in roughly the same proportion as the original data.

'from_column'

Create a new column to store whether the value should be missing. Use it to recreate missing values. Note: Adding extra columns uses more memory and increases the RDT processing time.

None

Do not recreate missing values.

enforce_min_max_values: Add this argument to allow the transformer to learn the min and max allowed values from the data.

(default) False

Do not learn any min or max values from the dataset. When reverse transforming the data, the values may be above or below what was originally present.

True

Learn the min and max values from the input data. When reverse transforming the data, any out-of-bounds values will be clipped to the min or max value.

learn_rounding_scheme: Add this argument to allow the transformer to learn about rounded values in your dataset.

(default) False

Do not learn or enforce any rounding scheme. When reverse transforming the data, there may be many decimal places present.

True

Learn the rounding rules from the input data. When reverse transforming the data, round the number of digits to match the original.

computer_representation: Add this argument when the original data has a specific representation, even if it's not loaded that way into Python. The transformer will make sure that any reverse transformed data is compatible with this representation.

(default) 'Float'

The data is a float

'Int8', 'Int16', 'Int32', 'Int64'

The data is a signed integer represented as an 8, 16, 32 or 64-bit number

'UInt8', 'UInt16', 'UInt32', 'UInt64'

The data is an unsigned integer represented as an 9, 16, 32 or 64-bit number

Examples

from transformers.numerical import FloatFormatter

ff = FloatFormatter(missing_value_replacement='mean',
                    learn_rounding_scheme=True,
                    missing_value_generation='from_column')
On the forward transform, this transformer uses missing_value_replacement and missing_value_generation strategy. In this case, we create an extra column storing that the value is missing.
On the reverse transform, enforce_min_max_values and learn_rounding_scheme are applied. In this case, the values are rounded to 2 decimal digits like the original data. Also, missing values are added back in.

FAQs

When are the min/max values and rounding scheme enforced?

Using these options will enforce the min/max values or rounding scheme when reverse transforming your data. Use these parameters if you want to recover data in the same format as the original.

Should I replace missing values?

The decision to replace missing values is based on how you plan to use your data. For example, you might be using RDT to clean your data for machine learning (ML). Check to see whether the ML techniques you plan to use allow missing values.

What methods are best for replacing missing values?

The method for replacing missing values is dependent on what they mean in your dataset. For example:

  • If missing values are the equivalent of 0, replace them with a 0.

  • If missing values indicate that you don't know the value at all, you might replace them with the 'mean' or the 'mode'

When is it necessary to model missing values?

When setting the missing_value_generation parameter, consider whether the "missingness" of the data is something important. For example, maybe the user opted out of supplying the info on purpose, or maybe a missing value is highly correlated with another column your dataset. If "missingness" is something you want to account for, you should model missing values.

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