This operator standardizes continuous features such that they are between 0 and 1.
# Use NormalizeMinMax to define a NVTabular workflow cont_features = CONTINUOUS_COLUMNS >> ops.NormalizeMinMax() processor = nvtabular.Workflow(cont_features)
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
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 a transformed dataframe for this operator
- Return type
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.
Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’