NormalizeMinMax

class nvtabular.ops.NormalizeMinMax(out_dtype=None)[source]

Bases: nvtabular.ops.stat_operator.StatOperator

This operator standardizes continuous features such that they are between 0 and 1.

Example usage:

# Use NormalizeMinMax to define a NVTabular workflow
cont_features = CONTINUOUS_COLUMNS >> ops.NormalizeMinMax()
processor = nvtabular.Workflow(cont_features)

Parameters: out_dtype (str, default is float64) – dtype of output columns.

transform(col_selector: merlin.dag.selector.ColumnSelector, df: pandas.core.frame.DataFrame)[source]

Transform the dataframe by applying this operator to the set of input columns

Parameters

columns (list of str or list of list of str) – The columns to apply this operator to
df (Dataframe) – A pandas or cudf dataframe that this operator will work on

Returns

Returns a transformed dataframe for this operator

Return type

DataFrame

fit(col_selector: merlin.dag.selector.ColumnSelector, ddf)[source]: Calculate statistics for this operator, and return a dask future to these statistics, which will be computed by the workflow.

fit_finalize(dask_stats)[source]: Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’

clear()[source]

property supports

property output_tags

property output_dtype