getml.datasets.load_consumer_expenditures(roles: bool = True, units: bool = True, as_pandas: bool = False, as_dict: bool = False) Union[Tuple[Union[DataFrame, DataFrame], ...], Dict[str, Union[DataFrame, DataFrame]]][source]

Binary classification dataset on consumer expenditures

The Consumer Expenditure Data Set is a public domain data set provided by the American Bureau of Labor Statistics (https://www.bls.gov/cex/pumd.htm). It includes the diary entries, where American consumers are asked to keep diaries of the products they have purchased each month,

We use this dataset to classify wether an item was pruchased as a gift.

roles (bool):

Return data with roles set

units (bool):

Return data with units set

as_pandas (bool):

Return data as pandas.DataFrame s

as_dict (bool):

Return data as dict with df.name s as keys and df s as values.


Tuple containing (sorted alphabetically by df.name`s) the data as :class:`~getml.DataFrame s or pandas.DataFrame s (if as_pandas is True) or


if as_dict is True: Dictionary containing the data as DataFrame s or pandas.DataFrame s (if as_pandas is True). The keys correspond to the name of the DataFrame on the engine.

The following DataFrames are returned:

  • population

  • expd

  • fmld

  • memd

>>> ce = getml.datasets.load_consumer_expenditures(as_dict=True)
>>> type(ce["expd"])
... getml.data.data_frame.DataFrame

For an full analysis of the occupancy dataset including all necessary preprocessing steps please refer to getml-examples.


Roles and units can be set ad-hoc by supplying the respective flag. If roles is False, all columns in the returned DataFrames s have roles unused_string or unused_float.` Before using them in an analysis, a data model needs to be constructed using Placeholder s.