nvtabular.ops.ListSlice

class nvtabular.ops.ListSlice(start, end=None, pad=False, pad_value=0.0)[source]

Slices a list column

This operator provides the ability to slice list column by row. For example, to truncate a list column to only include the first 10 elements per row:

truncated = column_names >> ops.ListSlice(10)

Take the first 10 items, ignoring the first element:

truncated = column_names >> ops.ListSlice(1, 11)

Take the last 10 items from each row:

truncated = column_names >> ops.ListSlice(-10)

Parameters

start (int) – The starting value to slice from if end isn’t given, otherwise the end value to slice to
end (int, optional) – The end value to slice to
pad (bool, default False) – Whether to pad out rows to have the same number of elements. If not set rows may not all have the same number of entries.
pad_value (float) – When pad=True, this is the value used to pad missing entries

Methods

`__init__`(start[, end, pad, pad_value])
`column_mapping`(col_selector)	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.
`create_node`(selector)
`export`(path, input_schema, output_schema, …)	Export the class object as a config and all related files to the user defined path.
`load_artifacts`([artifact_path])	Load artifacts from disk required for operator function.
`output_column_names`(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
`save_artifacts`([artifact_path])	Save artifacts required to be reload operator state from disk
`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.

Attributes

`dependencies`	Defines an optional list of column dependencies for this operator.
`dynamic_dtypes`
`export_name`	Provides a clear common english identifier for this operator.
`is_subgraph`
`label`
`output_dtype`
`output_properties`
`output_tags`
`supported_formats`
`supports`	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

Parameters

col_selector (ColumnSelector) – The columns to apply this operator to
transformable (Transformable) – A pandas or cudf dataframe that this operator will work on

Returns

Returns a transformed dataframe or dictarray for this operator

Return type

Transformable