mip_dmp.process.mapping module

Module that provides functions to support the mapping of datasets to a specific CDEs metadata schema.

mip_dmp.process.mapping.apply_transform_map(dataset_column, transform)[source]

Apply the transform map for binomial and multinominal variables.

Parameters

dataset_columnpandas.DataFrame: Dataset column to be transformed.
transformstr: Transformation to be applied to the dataset column. Can be a JSON string for the “map” transformation type or a scaling factor.

mip_dmp.process.mapping.apply_transform_scale(dataset_column, cde_code, cde_type, scaling_factor)[source]

Apply the transform scale for real and integer variables.

dataset_columnpandas.DataFrame: Dataset column to be transformed.
cde_codestr: CDE code of the dataset column.
cde_typestr: CDE type of the dataset column. Can be “binomial”, “multinomial”, “integer” or “real”.
scaling_factorfloat: Scaling factor to be applied to the dataset column.

mip_dmp.process.mapping.map_dataset(dataset, mappings, cde_codes)[source]

Map the dataset to the schema.

mip_dmp.process.mapping.transform_dataset_column(dataset_column, cde_code, cde_type, transform_type, transform)[source]

Transform the dataset column.

dataset_columnpandas.DataFrame: Dataset column to be transformed.
cde_codestr: CDE code of the dataset column.
cde_typestr: CDE type of the dataset column. Can be “binomial”, “multinomial”, “integer” or “real”.
transform_typestr: Type of transformation to be applied to the dataset column. Can be “map” or “scale”.
transformstr: Transformation to be applied to the dataset column. Can be a JSON string for the “map” transformation type or a scaling factor.