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`