class nvtabular.ops.HashBucket(num_buckets: Union[int, Dict[str, int]])[source]

Bases: nvtabular.ops.operator.Operator

This op maps categorical columns to a contiguous integer range by first hashing the column, then reducing modulo the number of buckets.

Example usage:

cat_names = ["feature_a", "feature_b"]

# this will hash both features a and b to 100 buckets
hash_features = cat_names >> ops.HashBucket({"feature_a": 100, "feature_b": 50})
processor = nvtabular.Workflow(hash_features)

The output of this op would be:

   feature_a  feature_b
0         90         11
1         70         40
2         52          9

If you would like to do frequency capping or frequency hashing, you should use Categorify op instead. See Categorify op for example usage.


num_buckets (int or dictionary:{column: num_hash_buckets}) – Column-wise modulo to apply after hash function. Note that this means that the corresponding value will be the categorical cardinality of the transformed categorical feature. If given as an int, that value will be used as the number of “hash buckets” for every feature. If a dictionary is passed, it will be used to specify explicit mappings from a column name to a number of buckets. In this case, only the columns specified in the keys of num_buckets will be transformed.

__init__(num_buckets: Union[int, Dict[str, int]])[source]




Compute which output columns depend on which input columns

compute_column_schema(col_name, input_schema)

compute_input_schema(root_schema, …)

Given the schemas coming from upstream sources and a column selector for the input columns, returns a set of schemas for the input columns this operator will use

compute_output_schema(input_schema, col_selector)

Given a set of schemas and a column selector for the input columns, returns a set of schemas for the transformed columns this operator will produce

compute_selector(input_schema, selector[, …])

Provides a hook method for sub-classes to override to implement custom column selection logic.



inference_initialize(col_selector, model_config)

Configures this operator for use in inference.


Given a set of columns names returns the names of the transformed columns this operator will produce

transform(col_selector, df)

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

validate_schemas(parents_schema, …[, …])

Provides a hook method that sub-classes can override to implement schema validation logic.



Defines an optional list of column dependencies for this operator.







Returns what kind of data representation this operator supports

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

  • 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


property output_tags
property output_dtype