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.

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

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
  • 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