FloatFormatter
Compatibility:
numerical
dataThe
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
ff = FloatFormatter()
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 |
missing_value_replacement
: Add this argument to replace missing values during the transform phase(default) 'mean' | Replace all missing values with the average value. |
'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.) |
model_missing_values
: Add this argument to create another column describing whether the values are missing(default) False | Do not create a new column. During the reverse transform, missing values are added in again randomly. |
True | Create a new column (if there are missing values). This allows you to keep track of the missing values so you can recreate them on the reverse transform. |
Setting this value to
True
may add another column to your dataset. Adding extra columns uses more memory and increases the RDT processing time.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. |
from transformers.numerical import FloatFormatter
ff = FloatFormatter(missing_value_replacement='mean',
learn_rounding_scheme=True,
model_missing_values=True)

On the forward transform, this transformer uses
missing_value_replacement
and model_missing_values
. 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.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 a0
. - If missing values indicate that you don't know the value at all, you might replace them with the
'mean'
or the'mode'
When setting the
model_missing_values
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.Last modified 2mo ago