def string_column(name,
non_nullable=False,
unique=False,
ignore_missing_vals=False,
is_required=None):
"""
Simple constructor for PandasColumns that expresses constraints on string dtypes.
Args:
name (str): Name of the column. This must match up with the column name in the dataframe you
expect to receive.
non_nullable (Optional[bool]): If true, this column will enforce a constraint that all values in the column
ought to be non null values.
unique (Optional[bool]): If true, this column will enforce a uniqueness constraint on the column values.
ignore_missing_vals (Optional[bool]): A flag that is passed into most constraints. If true, the constraint will
only evaluate non-null data. Ignore_missing_vals and non_nullable cannot both be True.
is_required (Optional[bool]): Flag indicating the optional/required presence of the column.
If the column exists the validate function will validate the column. Default to True.
"""
return PandasColumn(
name=check.str_param(name, "name"),
constraints=[ColumnDTypeFnConstraint(is_string_dtype)] +
_construct_keyword_constraints(
non_nullable=non_nullable,
unique=unique,
ignore_missing_vals=ignore_missing_vals),
is_required=is_required,
)
def float_column(
name,
min_value=-float("inf"),
max_value=float("inf"),
non_nullable=False,
unique=False,
ignore_missing_vals=False,
is_required=None,
):
"""
Simple constructor for PandasColumns that expresses numeric constraints on float dtypes.
Args:
name (str): Name of the column. This must match up with the column name in the dataframe you
expect to receive.
min_value (Optional[Union[int,float]]): The lower bound for values you expect in this column. Defaults to -float('inf')
max_value (Optional[Union[int,float]]): The upper bound for values you expect in this column. Defaults to float('inf')
non_nullable (Optional[bool]): If true, this column will enforce a constraint that all values in the column
ought to be non null values.
unique (Optional[bool]): If true, this column will enforce a uniqueness constraint on the column values.
ignore_missing_vals (Optional[bool]): A flag that is passed into most constraints. If true, the constraint will
only evaluate non-null data. Ignore_missing_vals and non_nullable cannot both be True.
is_required (Optional[bool]): Flag indicating the optional/required presence of the column.
If the column exists the validate function will validate the column. Default to True.
"""
return PandasColumn(
name=check.str_param(name, "name"),
constraints=[
ColumnDTypeFnConstraint(is_float_dtype),
InRangeColumnConstraint(
check.numeric_param(min_value, "min_value"),
check.numeric_param(max_value, "max_value"),
ignore_missing_vals=ignore_missing_vals,
),
] + _construct_keyword_constraints(
non_nullable=non_nullable,
unique=unique,
ignore_missing_vals=ignore_missing_vals),
is_required=is_required,
)
