polars.testing.parametric.column#

class polars.testing.parametric.column(
name: str,
dtype: PolarsDataType | None = None,
strategy: SearchStrategy[Any] | None = None,
null_probability: float | None = None,
unique: bool = False,
)[source]#

Define a column for use with the @dataframes strategy.

Parameters:
namestr

string column name.

dtypePolarsDataType

a recognised polars dtype.

strategystrategy, optional

supports overriding the default strategy for the given dtype.

null_probabilityfloat, optional

percentage chance (expressed between 0.0 => 1.0) that a generated value is None. this is applied independently of any None values generated by the underlying strategy.

uniquebool, optional

flag indicating that all values generated for the column should be unique.

Examples

>>> from hypothesis.strategies import sampled_from
>>> from polars.testing.parametric import column
>>>
>>> column(name="unique_small_ints", dtype=pl.UInt8, unique=True)
column(name='unique_small_ints', dtype=UInt8, strategy=None, null_probability=None, unique=True)
>>> column(name="ccy", strategy=sampled_from(["GBP", "EUR", "JPY"]))
column(name='ccy', dtype=String, strategy=sampled_from(['GBP', 'EUR', 'JPY']), null_probability=None, unique=False)
__init__(
name: str,
dtype: PolarsDataType | None = None,
strategy: SearchStrategy[Any] | None = None,
null_probability: float | None = None,
unique: bool = False,
) None[source]#

Methods

__init__(name[, dtype, strategy, ...])

Attributes

dtype

null_probability

strategy

unique

name