nvtabular.ops.Filter

class nvtabular.ops.Filter(f: Callable[[pandas.core.frame.DataFrame], Union[pandas.core.frame.DataFrame, pandas.core.series.Series]])[source]

Bases: nvtabular.ops.operator.Operator

Filters rows from the dataset. This works by taking a callable that accepts a dataframe, and returns a dataframe with unwanted rows filtered out.

For example to filter out all rows that have a negative value in the a column:

filtered = cont_names >> ops.Filter(f=lambda df: df["a"] >=0)
processor = nvtabular.Workflow(filtered)

Parameters: f (callable) – Defines a function that takes a dataframe as an argument, and returns a new dataframe with unwanted rows filtered out.

__init__(f: Callable[[pandas.core.frame.DataFrame], Union[pandas.core.frame.DataFrame, pandas.core.series.Series]])[source]

Methods

`__init__`(f)
`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)
`inference_initialize`(col_selector, model_config)	Configures this operator for use in inference.
`output_column_names`(col_selector)	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.

Attributes

`dependencies`	Defines an optional list of column dependencies for this operator.
`dynamic_dtypes`
`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

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