woodwork_utils¶
Woodwork utility methods.
Module Contents¶
Functions¶
Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns. If a column’s type is not specified, it will be inferred by Woodwork. |
Attributes Summary¶
Contents¶
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evalml.utils.woodwork_utils.infer_feature_types(data, feature_types=None, ignore_nullable_types=False)[source]¶ Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns. If a column’s type is not specified, it will be inferred by Woodwork.
- Parameters
data (pd.DataFrame, pd.Series) – Input data to convert to a Woodwork data structure.
feature_types (string, ww.logical_type obj, dict, optional) – If data is a 2D structure, feature_types must be a dictionary mapping column names to the type of data represented in the column. If data is a 1D structure, then feature_types must be a Woodwork logical type or a string representing a Woodwork logical type (“Double”, “Integer”, “Boolean”, “Categorical”, “Datetime”, “NaturalLanguage”)
ignore_nullable_types (bool) – Whether to ignore raising an error upon detection of Nullable types. Defaults to False.
- Returns
A Woodwork data structure where the data type of each column was either specified or inferred.
- Raises
ValueError – If there is a mismatch between the dataframe and the woodwork schema.
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evalml.utils.woodwork_utils.numeric_and_boolean_ww¶