Normalize

class nvtabular.ops.Normalize[source]

Bases: nvtabular.ops.stat_operator.StatOperator

Standardizing the features around 0 with a standard deviation of 1 is a common technique to compare measurements that have different units. This operation can be added to the workflow to standardize the features.

It performs Normalization using the mean std method.

Example usage:

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

fit(col_selector: merlin.dag.selector.ColumnSelector, ddf: dask.dataframe.core.DataFrame)[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’

transform(col_selector: merlin.dag.selector.ColumnSelector, df: pandas.core.frame.DataFrame) → 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

property supports

clear()[source]

property output_tags

property output_dtype