nvtabular.ops.FillMissing

class nvtabular.ops.FillMissing(fill_val=0, add_binary_cols=False)[source]

Bases: nvtabular.ops.operator.Operator

This operation replaces missing values with a constant pre-defined value

Example usage:

# Use FillMissing to define a workflow for continuous columns and specify the fill value
# Default is 0
cont_features = ['cont1', 'cont2', 'cont3'] >> ops.FillMissing() >> ...
processor = nvtabular.Workflow(cont_features)

Parameters

fill_val (float, default 0) – The constant value to replace missing values with.
add_binary_cols (boolean, default False) – When True, adds binary columns that indicate whether cells in each column were filled

__init__(fill_val=0, add_binary_cols=False)[source]

Methods

`__init__`([fill_val, add_binary_cols])
`column_mapping`(col_selector)
`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, …)	load up extra configuration about this op.
`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

inference_initialize(col_selector, inference_config)[source]: load up extra configuration about this op.

column_mapping(col_selector)[source]