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How to use field_element_strategy method in pandera

Best Python code snippet using pandera_python

strategies.py

Source:strategies.py

...617    nanoseconds if given a pandas.Timestamp. We need to pass the unix epoch via618    the pandas.Timestamp.value attribute.619    """620    return st.builds(lambda x: np.datetime64(x.value, "ns"), strategy)621def field_element_strategy(622    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],623    strategy: Optional[SearchStrategy] = None,624    *,625    checks: Optional[Sequence] = None,626) -> SearchStrategy:627    """Strategy to generate elements of a column or index.628    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.629    :param strategy: an optional hypothesis strategy. If specified, the630        pandas dtype strategy will be chained onto this strategy.631    :param checks: sequence of :class:`~pandera.checks.Check` s to constrain632        the values of the data in the column/index.633    :returns: ``hypothesis`` strategy634    """635    if strategy:636        raise BaseStrategyOnlyError(637            "The series strategy is a base strategy. You cannot specify the "638            "strategy argument to chain it to a parent strategy."639        )640    checks = [] if checks is None else checks641    elements = None642    def undefined_check_strategy(elements, check):643        """Strategy for checks with undefined strategies."""644        warnings.warn(645            "Element-wise check doesn't have a defined strategy."646            "Falling back to filtering drawn values based on the check "647            "definition. This can considerably slow down data-generation."648        )649        return (650            pandas_dtype_strategy(pandera_dtype)651            if elements is None652            else elements653        ).filter(check._check_fn)654    for check in checks:655        if hasattr(check, "strategy"):656            elements = check.strategy(pandera_dtype, elements)657        elif check.element_wise:658            elements = undefined_check_strategy(elements, check)659        # NOTE: vectorized checks with undefined strategies should be handled660        # by the series/dataframe strategy.661    if elements is None:662        elements = pandas_dtype_strategy(pandera_dtype)663    # Hypothesis only supports pure numpy datetime64 (i.e. timezone naive).664    # We cast to datetime64 after applying the check strategy so that checks665    # can see timezone-aware values.666    if _is_datetime_tz(pandera_dtype):667        elements = _timestamp_to_datetime64_strategy(elements)668    return elements669def series_strategy(670    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],671    strategy: Optional[SearchStrategy] = None,672    *,673    checks: Optional[Sequence] = None,674    nullable: bool = False,675    unique: bool = False,676    name: Optional[str] = None,677    size: Optional[int] = None,678):679    """Strategy to generate a pandas Series.680    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.681    :param strategy: an optional hypothesis strategy. If specified, the682        pandas dtype strategy will be chained onto this strategy.683    :param checks: sequence of :class:`~pandera.checks.Check` s to constrain684        the values of the data in the column/index.685    :param nullable: whether or not generated Series contains null values.686    :param unique: whether or not generated Series contains unique values.687    :param name: name of the Series.688    :param size: number of elements in the Series.689    :returns: ``hypothesis`` strategy.690    """691    elements = field_element_strategy(pandera_dtype, strategy, checks=checks)692    strategy = (693        pdst.series(694            elements=elements,695            dtype=to_numpy_dtype(pandera_dtype),696            index=pdst.range_indexes(697                min_size=0 if size is None else size, max_size=size698            ),699            unique=unique,700        )701        .filter(lambda x: x.shape[0] > 0)702        .map(lambda x: x.rename(name))703        .map(lambda x: x.astype(pandera_dtype.type))704    )705    if nullable:706        strategy = null_field_masks(strategy)707    def undefined_check_strategy(strategy, check):708        """Strategy for checks with undefined strategies."""709        warnings.warn(710            "Vectorized check doesn't have a defined strategy."711            "Falling back to filtering drawn values based on the check "712            "definition. This can considerably slow down data-generation."713        )714        def _check_fn(series):715            return check(series).check_passed716        return strategy.filter(_check_fn)717    for check in checks if checks is not None else []:718        if not hasattr(check, "strategy") and not check.element_wise:719            strategy = undefined_check_strategy(strategy, check)720    return strategy721def column_strategy(722    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],723    strategy: Optional[SearchStrategy] = None,724    *,725    checks: Optional[Sequence] = None,726    unique: bool = False,727    name: Optional[str] = None,728):729    # pylint: disable=line-too-long730    """Create a data object describing a column in a DataFrame.731    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.732    :param strategy: an optional hypothesis strategy. If specified, the733        pandas dtype strategy will be chained onto this strategy.734    :param checks: sequence of :class:`~pandera.checks.Check` s to constrain735        the values of the data in the column/index.736    :param unique: whether or not generated Series contains unique values.737    :param name: name of the Series.738    :returns: a `column <https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.pandas.column>`_ object.739    """740    verify_dtype(pandera_dtype, schema_type="column", name=name)741    elements = field_element_strategy(pandera_dtype, strategy, checks=checks)742    return pdst.column(743        name=name,744        elements=elements,745        dtype=to_numpy_dtype(pandera_dtype),746        unique=unique,747    )748def index_strategy(749    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],750    strategy: Optional[SearchStrategy] = None,751    *,752    checks: Optional[Sequence] = None,753    nullable: bool = False,754    unique: bool = False,755    name: Optional[str] = None,756    size: Optional[int] = None,757):758    """Strategy to generate a pandas Index.759    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.760    :param strategy: an optional hypothesis strategy. If specified, the761        pandas dtype strategy will be chained onto this strategy.762    :param checks: sequence of :class:`~pandera.checks.Check` s to constrain763        the values of the data in the column/index.764    :param nullable: whether or not generated Series contains null values.765    :param unique: whether or not generated Series contains unique values.766    :param name: name of the Series.767    :param size: number of elements in the Series.768    :returns: ``hypothesis`` strategy.769    """770    verify_dtype(pandera_dtype, schema_type="index", name=name)771    elements = field_element_strategy(pandera_dtype, strategy, checks=checks)772    strategy = pdst.indexes(773        elements=elements,774        dtype=to_numpy_dtype(pandera_dtype),775        min_size=0 if size is None else size,776        max_size=size,777        unique=unique,778    ).map(lambda x: x.astype(pandera_dtype.type))779    # this is a hack to convert np.str_ data values into native python str.780    col_dtype = str(pandera_dtype)781    if col_dtype in {"object", "str"} or col_dtype.startswith("string"):782        # pylint: disable=cell-var-from-loop,undefined-loop-variable783        strategy = strategy.map(lambda index: index.map(str))784    if name is not None:785        strategy = strategy.map(lambda index: index.rename(name))...

test_strategies.py

Source:test_strategies.py

...584        example = multiindex.example()585        multiindex(pd.DataFrame(index=example))586@pytest.mark.parametrize("data_type", NULLABLE_DTYPES)587@hypothesis.given(st.data())588def test_field_element_strategy(data_type, data):589    """Test strategy for generating elements in columns/indexes."""590    strategy = strategies.field_element_strategy(data_type)591    element = data.draw(strategy)592    expected_type = strategies.to_numpy_dtype(data_type).type593    assert element.dtype.type == expected_type594    with pytest.raises(pa.errors.BaseStrategyOnlyError):595        strategies.field_element_strategy(596            data_type, strategies.pandas_dtype_strategy(data_type)597        )598@pytest.mark.parametrize("data_type", NULLABLE_DTYPES)599@pytest.mark.parametrize(600    "field_strategy",601    [strategies.index_strategy, strategies.series_strategy],602)603@pytest.mark.parametrize("nullable", [True, False])604@hypothesis.given(st.data())605@hypothesis.settings(606    suppress_health_check=[hypothesis.HealthCheck.too_slow],607)608def test_check_nullable_field_strategy(609    data_type, field_strategy, nullable, data...

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