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How to use in_range_strategy method in pandera

Best Python code snippet using pandera_python

checks.py

Source:checks.py

1"""Data validation checks."""2import inspect3import operator4import re5from collections import ChainMap, namedtuple6from functools import partial, wraps7from itertools import chain8from typing import (9    Any,10    Callable,11    Dict,12    Iterable,13    List,14    Optional,15    Type,16    TypeVar,17    Union,18    no_type_check,19)20import pandas as pd21from . import check_utils, constants, errors22from . import strategies as st23CheckResult = namedtuple(24    "CheckResult",25    ["check_output", "check_passed", "checked_object", "failure_cases"],26)27GroupbyObject = Union[28    pd.core.groupby.SeriesGroupBy, pd.core.groupby.DataFrameGroupBy29]30SeriesCheckObj = Union[pd.Series, Dict[str, pd.Series]]31DataFrameCheckObj = Union[pd.DataFrame, Dict[str, pd.DataFrame]]32def register_check_statistics(statistics_args):33    """Decorator to set statistics based on Check method."""34    def register_check_statistics_decorator(class_method):35        @wraps(class_method)36        def _wrapper(cls, *args, **kwargs):37            args = list(args)38            arg_names = inspect.getfullargspec(class_method).args[1:]39            if not arg_names:40                arg_names = statistics_args41            args_dict = {**dict(zip(arg_names, args)), **kwargs}42            check = class_method(cls, *args, **kwargs)43            check.statistics = {44                stat: args_dict.get(stat) for stat in statistics_args45            }46            check.statistics_args = statistics_args47            return check48        return _wrapper49    return register_check_statistics_decorator50_T = TypeVar("_T", bound="_CheckBase")51class _CheckMeta(type):  # pragma: no cover52    """Check metaclass."""53    REGISTERED_CUSTOM_CHECKS: Dict[str, Callable] = {}  # noqa54    def __getattr__(cls, name: str) -> Any:55        """Prevent attribute errors for registered checks."""56        attr = ChainMap(cls.__dict__, cls.REGISTERED_CUSTOM_CHECKS).get(name)57        if attr is None:58            raise AttributeError(59                f"'{cls}' object has no attribute '{name}'. "60                "Make sure any custom checks have been registered "61                "using the extensions api."62            )63        return attr64    def __dir__(cls) -> Iterable[str]:65        """Allow custom checks to show up as attributes when autocompleting."""66        return chain(super().__dir__(), cls.REGISTERED_CUSTOM_CHECKS.keys())67    # pylint: disable=line-too-long68    # mypy has limited metaclass support so this doesn't pass typecheck69    # see https://mypy.readthedocs.io/en/stable/metaclasses.html#gotchas-and-limitations-of-metaclass-support70    # pylint: enable=line-too-long71    @no_type_check72    def __contains__(cls: Type[_T], item: Union[_T, str]) -> bool:73        """Allow lookups for registered checks."""74        if isinstance(item, cls):75            name = item.name76            return hasattr(cls, name)77        # assume item is str78        return hasattr(cls, item)79class _CheckBase(metaclass=_CheckMeta):80    """Check base class."""81    def __init__(82        self,83        check_fn: Union[84            Callable[[pd.Series], Union[pd.Series, bool]],85            Callable[[pd.DataFrame], Union[pd.DataFrame, pd.Series, bool]],86        ],87        groups: Optional[Union[str, List[str]]] = None,88        groupby: Optional[Union[str, List[str], Callable]] = None,89        ignore_na: bool = True,90        element_wise: bool = False,91        name: str = None,92        error: Optional[str] = None,93        raise_warning: bool = False,94        n_failure_cases: Union[int, None] = constants.N_FAILURE_CASES,95        title: Optional[str] = None,96        description: Optional[str] = None,97        **check_kwargs,98    ) -> None:99        """Apply a validation function to each element, Series, or DataFrame.100        :param check_fn: A function to check pandas data structure. For Column101            or SeriesSchema checks, if element_wise is True, this function102            should have the signature: ``Callable[[pd.Series],103            Union[pd.Series, bool]]``, where the output series is a boolean104            vector.105            If element_wise is False, this function should have the signature:106            ``Callable[[Any], bool]``, where ``Any`` is an element in the107            column.108            For DataFrameSchema checks, if element_wise=True, fn109            should have the signature: ``Callable[[pd.DataFrame],110            Union[pd.DataFrame, pd.Series, bool]]``, where the output dataframe111            or series contains booleans.112            If element_wise is True, fn is applied to each row in113            the dataframe with the signature ``Callable[[pd.Series], bool]``114            where the series input is a row in the dataframe.115        :param groups: The dict input to the `fn` callable will be constrained116            to the groups specified by `groups`.117        :param groupby: If a string or list of strings is provided, these118            columns are used to group the Column series. If a119            callable is passed, the expected signature is: ``Callable[120            [pd.DataFrame], pd.core.groupby.DataFrameGroupBy]``121            The the case of ``Column`` checks, this function has access to the122            entire dataframe, but ``Column.name`` is selected from this123            DataFrameGroupby object so that a SeriesGroupBy object is passed124            into ``check_fn``.125            Specifying the groupby argument changes the ``check_fn`` signature126            to:127            ``Callable[[Dict[Union[str, Tuple[str]], pd.Series]], Union[bool, pd.Series]]``  # noqa128            where the input is a dictionary mapping129            keys to subsets of the column/dataframe.130        :param ignore_na: If True, null values will be ignored when determining131            if a check passed or failed. For dataframes, ignores rows with any132            null value. *New in version 0.4.0*133        :param element_wise: Whether or not to apply validator in an134            element-wise fashion. If bool, assumes that all checks should be135            applied to the column element-wise. If list, should be the same136            number of elements as checks.137        :param name: optional name for the check.138        :param error: custom error message if series fails validation139            check.140        :param raise_warning: if True, raise a UserWarning and do not throw141            exception instead of raising a SchemaError for a specific check.142            This option should be used carefully in cases where a failing143            check is informational and shouldn't stop execution of the program.144        :param n_failure_cases: report the first n unique failure cases. If145            None, report all failure cases.146        :param title: A human-readable label for the check.147        :param description: An arbitrary textual description of the check.148        :param check_kwargs: key-word arguments to pass into ``check_fn``149        :example:150        >>> import pandas as pd151        >>> import pandera as pa152        >>>153        >>>154        >>> # column checks are vectorized by default155        >>> check_positive = pa.Check(lambda s: s > 0)156        >>>157        >>> # define an element-wise check158        >>> check_even = pa.Check(lambda x: x % 2 == 0, element_wise=True)159        >>>160        >>> # checks can be given human-readable metadata161        >>> check_with_metadata = pa.Check(162        ...     lambda x: True,163        ...     title="Always passes",164        ...     description="This check always passes."165        ... )166        >>>167        >>> # specify assertions across categorical variables using `groupby`,168        >>> # for example, make sure the mean measure for group "A" is always169        >>> # larger than the mean measure for group "B"170        >>> check_by_group = pa.Check(171        ...     lambda measures: measures["A"].mean() > measures["B"].mean(),172        ...     groupby=["group"],173        ... )174        >>>175        >>> # define a wide DataFrame-level check176        >>> check_dataframe = pa.Check(177        ...     lambda df: df["measure_1"] > df["measure_2"])178        >>>179        >>> measure_checks = [check_positive, check_even, check_by_group]180        >>>181        >>> schema = pa.DataFrameSchema(182        ...     columns={183        ...         "measure_1": pa.Column(int, checks=measure_checks),184        ...         "measure_2": pa.Column(int, checks=measure_checks),185        ...         "group": pa.Column(str),186        ...     },187        ...     checks=check_dataframe188        ... )189        >>>190        >>> df = pd.DataFrame({191        ...     "measure_1": [10, 12, 14, 16],192        ...     "measure_2": [2, 4, 6, 8],193        ...     "group": ["B", "B", "A", "A"]194        ... })195        >>>196        >>> schema.validate(df)[["measure_1", "measure_2", "group"]]197            measure_1  measure_2 group198        0         10          2     B199        1         12          4     B200        2         14          6     A201        3         16          8     A202        See :ref:`here<checks>` for more usage details.203        """204        if element_wise and groupby is not None:205            raise errors.SchemaInitError(206                "Cannot use groupby when element_wise=True."207            )208        self._check_fn = check_fn209        self._check_kwargs = check_kwargs210        self.element_wise = element_wise211        self.error = error212        self.name = name or getattr(213            self._check_fn, "__name__", self._check_fn.__class__.__name__214        )215        self.ignore_na = ignore_na216        self.raise_warning = raise_warning217        self.n_failure_cases = n_failure_cases218        self.title = title219        self.description = description220        if groupby is None and groups is not None:221            raise ValueError(222                "`groupby` argument needs to be provided when `groups` "223                "argument is defined"224            )225        if isinstance(groupby, str):226            groupby = [groupby]227        self.groupby = groupby228        if isinstance(groups, str):229            groups = [groups]230        self.groups = groups231        self.failure_cases = None232        self._statistics = None233    @property234    def statistics(self) -> Dict[str, Any]:235        """Get check statistics."""236        return getattr(self, "_statistics")237    @statistics.setter238    def statistics(self, statistics):239        """Set check statistics."""240        self._statistics = statistics241    @staticmethod242    def _format_groupby_input(243        groupby_obj: GroupbyObject,244        groups: Optional[List[str]],245    ) -> Union[Dict[str, Union[pd.Series, pd.DataFrame]]]:246        """Format groupby object into dict of groups to Series or DataFrame.247        :param groupby_obj: a pandas groupby object.248        :param groups: only include these groups in the output.249        :returns: dictionary mapping group names to Series or DataFrame.250        """251        if groups is None:252            return dict(list(groupby_obj))253        group_keys = set(group_key for group_key, _ in groupby_obj)254        invalid_groups = [g for g in groups if g not in group_keys]255        if invalid_groups:256            raise KeyError(257                f"groups {invalid_groups} provided in `groups` argument not a valid group "258                f"key. Valid group keys: {group_keys}"259            )260        return {261            group_key: group262            for group_key, group in groupby_obj263            if group_key in groups264        }265    def _prepare_series_input(266        self,267        df_or_series: Union[pd.Series, pd.DataFrame],268        column: Optional[str] = None,269    ) -> SeriesCheckObj:270        """Prepare input for Column check.271        :param pd.Series series: one-dimensional ndarray with axis labels272            (including time series).273        :param pd.DataFrame dataframe_context: optional dataframe to supply274            when checking a Column in a DataFrameSchema.275        :returns: a Series, or a dictionary mapping groups to Series276            to be used by `_check_fn` and `_vectorized_check`277        """278        if check_utils.is_field(df_or_series):279            return df_or_series280        elif self.groupby is None:281            return df_or_series[column]282        elif isinstance(self.groupby, list):283            return self._format_groupby_input(284                df_or_series.groupby(self.groupby)[column],285                self.groups,286            )287        elif callable(self.groupby):288            return self._format_groupby_input(289                self.groupby(df_or_series)[column],290                self.groups,291            )292        raise TypeError("Type %s not recognized for `groupby` argument.")293    def _prepare_dataframe_input(294        self, dataframe: pd.DataFrame295    ) -> DataFrameCheckObj:296        """Prepare input for DataFrameSchema check.297        :param dataframe: dataframe to validate.298        :returns: a DataFrame, or a dictionary mapping groups to pd.DataFrame299            to be used by `_check_fn` and `_vectorized_check`300        """301        if self.groupby is None:302            return dataframe303        groupby_obj = dataframe.groupby(self.groupby)304        return self._format_groupby_input(groupby_obj, self.groups)305    def __call__(306        self,307        df_or_series: Union[pd.DataFrame, pd.Series],308        column: Optional[str] = None,309    ) -> CheckResult:310        # pylint: disable=too-many-branches311        """Validate pandas DataFrame or Series.312        :param df_or_series: pandas DataFrame of Series to validate.313        :param column: for dataframe checks, apply the check function to this314            column.315        :returns: CheckResult tuple containing:316            ``check_output``: boolean scalar, ``Series`` or ``DataFrame``317            indicating which elements passed the check.318            ``check_passed``: boolean scalar that indicating whether the check319            passed overall.320            ``checked_object``: the checked object itself. Depending on the321            options provided to the ``Check``, this will be a pandas Series,322            DataFrame, or if the ``groupby`` option is specified, a323            ``Dict[str, Series]`` or ``Dict[str, DataFrame]`` where the keys324            are distinct groups.325            ``failure_cases``: subset of the check_object that failed.326        """327        # prepare check object328        if check_utils.is_field(df_or_series) or (329            column is not None and check_utils.is_table(df_or_series)330        ):331            check_obj = self._prepare_series_input(df_or_series, column)332        elif check_utils.is_table(df_or_series):333            check_obj = self._prepare_dataframe_input(df_or_series)334        else:335            raise ValueError(336                f"object of type {type(df_or_series)} not supported. Must be "337                "a Series, a dictionary of Series, or DataFrame"338            )339        # apply check function to check object340        check_fn = partial(self._check_fn, **self._check_kwargs)341        if self.element_wise:342            check_output = (343                check_obj.apply(check_fn, axis=1)  # type: ignore344                if check_utils.is_table(check_obj)345                else check_obj.map(check_fn)  # type: ignore346                if check_utils.is_field(check_obj)347                else check_fn(check_obj)348            )349        else:350            # vectorized check function case351            check_output = check_fn(check_obj)352        # failure cases only apply when the check function returns a boolean353        # series that matches the shape and index of the check_obj354        if (355            isinstance(check_obj, dict)356            or isinstance(check_output, bool)357            or not check_utils.is_supported_check_obj(check_output)358            or check_obj.shape[0] != check_output.shape[0]359            or (check_obj.index != check_output.index).all()360        ):361            failure_cases = None362        elif check_utils.is_field(check_output):363            (364                check_output,365                failure_cases,366            ) = check_utils.prepare_series_check_output(367                check_obj,368                check_output,369                ignore_na=self.ignore_na,370                n_failure_cases=self.n_failure_cases,371            )372        elif check_utils.is_table(check_output):373            (374                check_output,375                failure_cases,376            ) = check_utils.prepare_dataframe_check_output(377                check_obj,378                check_output,379                df_orig=df_or_series,380                ignore_na=self.ignore_na,381                n_failure_cases=self.n_failure_cases,382            )383        else:384            raise TypeError(385                f"output type of check_fn not recognized: {type(check_output)}"386            )387        check_passed = (388            check_output.all()389            if check_utils.is_field(check_output)390            else check_output.all(axis=None)391            if check_utils.is_table(check_output)392            else check_output393        )394        return CheckResult(395            check_output, check_passed, check_obj, failure_cases396        )397    def __eq__(self, other: object) -> bool:398        if not isinstance(other, type(self)):399            return NotImplemented400        are_check_fn_objects_equal = (401            self._get_check_fn_code() == other._get_check_fn_code()402        )403        try:404            are_strategy_fn_objects_equal = all(405                getattr(self.__dict__.get("strategy"), attr)406                == getattr(other.__dict__.get("strategy"), attr)407                for attr in ["func", "args", "keywords"]408            )409        except AttributeError:410            are_strategy_fn_objects_equal = True411        are_all_other_check_attributes_equal = {412            k: v413            for k, v in self.__dict__.items()414            if k not in ["_check_fn", "strategy"]415        } == {416            k: v417            for k, v in other.__dict__.items()418            if k not in ["_check_fn", "strategy"]419        }420        return (421            are_check_fn_objects_equal422            and are_strategy_fn_objects_equal423            and are_all_other_check_attributes_equal424        )425    def _get_check_fn_code(self):426        check_fn = self.__dict__["_check_fn"]427        try:428            code = check_fn.__code__.co_code429        except AttributeError:430            # try accessing the functools.partial wrapper431            code = check_fn.func.__code__.co_code432        return code433    def __hash__(self) -> int:434        return hash(self._get_check_fn_code())435    def __repr__(self) -> str:436        return (437            f"<Check {self.name}: {self.error}>"438            if self.error is not None439            else f"<Check {self.name}>"440        )441class Check(_CheckBase):442    """Check a pandas Series or DataFrame for certain properties."""443    @classmethod444    @st.register_check_strategy(st.eq_strategy)445    @register_check_statistics(["value"])446    def equal_to(cls, value, **kwargs) -> "Check":447        """Ensure all elements of a series equal a certain value.448        *New in version 0.4.5*449        Alias: ``eq``450        :param value: All elements of a given :class:`pandas.Series` must have451            this value452        :param kwargs: key-word arguments passed into the `Check` initializer.453        :returns: :class:`Check` object454        """455        def _equal(series: pd.Series) -> pd.Series:456            """Comparison function for check"""457            return series == value458        return cls(459            _equal,460            name=cls.equal_to.__name__,461            error=f"equal_to({value})",462            **kwargs,463        )464    eq = equal_to465    @classmethod466    @st.register_check_strategy(st.ne_strategy)467    @register_check_statistics(["value"])468    def not_equal_to(cls, value, **kwargs) -> "Check":469        """Ensure no elements of a series equals a certain value.470        *New in version 0.4.5*471        Alias: ``ne``472        :param value: This value must not occur in the checked473            :class:`pandas.Series`.474        :param kwargs: key-word arguments passed into the `Check` initializer.475        :returns: :class:`Check` object476        """477        def _not_equal(series: pd.Series) -> pd.Series:478            """Comparison function for check"""479            return series != value480        return cls(481            _not_equal,482            name=cls.not_equal_to.__name__,483            error=f"not_equal_to({value})",484            **kwargs,485        )486    ne = not_equal_to487    @classmethod488    @st.register_check_strategy(st.gt_strategy)489    @register_check_statistics(["min_value"])490    def greater_than(cls, min_value, **kwargs) -> "Check":491        """Ensure values of a series are strictly greater than a minimum value.492        *New in version 0.4.5*493        Alias: ``gt``494        :param min_value: Lower bound to be exceeded. Must be a type comparable495            to the dtype of the :class:`pandas.Series` to be validated (e.g. a496            numerical type for float or int and a datetime for datetime).497        :param kwargs: key-word arguments passed into the `Check` initializer.498        :returns: :class:`Check` object499        """500        if min_value is None:501            raise ValueError("min_value must not be None")502        def _greater_than(series: pd.Series) -> pd.Series:503            """Comparison function for check"""504            return series > min_value505        return cls(506            _greater_than,507            name=cls.greater_than.__name__,508            error=f"greater_than({min_value})",509            **kwargs,510        )511    gt = greater_than512    @classmethod513    @st.register_check_strategy(st.ge_strategy)514    @register_check_statistics(["min_value"])515    def greater_than_or_equal_to(cls, min_value, **kwargs) -> "Check":516        """Ensure all values are greater or equal a certain value.517        *New in version 0.4.5*518        Alias: ``ge``519        :param min_value: Allowed minimum value for values of a series. Must be520            a type comparable to the dtype of the :class:`pandas.Series` to be521            validated.522        :param kwargs: key-word arguments passed into the `Check` initializer.523        :returns: :class:`Check` object524        """525        if min_value is None:526            raise ValueError("min_value must not be None")527        def _greater_or_equal(series: pd.Series) -> pd.Series:528            """Comparison function for check"""529            return series >= min_value530        return cls(531            _greater_or_equal,532            name=cls.greater_than_or_equal_to.__name__,533            error=f"greater_than_or_equal_to({min_value})",534            **kwargs,535        )536    ge = greater_than_or_equal_to537    @classmethod538    @st.register_check_strategy(st.lt_strategy)539    @register_check_statistics(["max_value"])540    def less_than(cls, max_value, **kwargs) -> "Check":541        """Ensure values of a series are strictly below a maximum value.542        *New in version 0.4.5*543        Alias: ``lt``544        :param max_value: All elements of a series must be strictly smaller545            than this. Must be a type comparable to the dtype of the546            :class:`pandas.Series` to be validated.547        :param kwargs: key-word arguments passed into the `Check` initializer.548        :returns: :class:`Check` object549        """550        if max_value is None:551            raise ValueError("max_value must not be None")552        def _less_than(series: pd.Series) -> pd.Series:553            """Comparison function for check"""554            return series < max_value555        return cls(556            _less_than,557            name=cls.less_than.__name__,558            error=f"less_than({max_value})",559            **kwargs,560        )561    lt = less_than562    @classmethod563    @st.register_check_strategy(st.le_strategy)564    @register_check_statistics(["max_value"])565    def less_than_or_equal_to(cls, max_value, **kwargs) -> "Check":566        """Ensure values are less than or equal to a maximum value.567        *New in version 0.4.5*568        Alias: ``le``569        :param max_value: Upper bound not to be exceeded. Must be a type570            comparable to the dtype of the :class:`pandas.Series` to be571            validated.572        :param kwargs: key-word arguments passed into the `Check` initializer.573        :returns: :class:`Check` object574        """575        if max_value is None:576            raise ValueError("max_value must not be None")577        def _less_or_equal(series: pd.Series) -> pd.Series:578            """Comparison function for check"""579            return series <= max_value580        return cls(581            _less_or_equal,582            name=cls.less_than_or_equal_to.__name__,583            error=f"less_than_or_equal_to({max_value})",584            **kwargs,585        )586    le = less_than_or_equal_to587    @classmethod588    @st.register_check_strategy(st.in_range_strategy)589    @register_check_statistics(590        ["min_value", "max_value", "include_min", "include_max"]591    )592    def in_range(593        cls, min_value, max_value, include_min=True, include_max=True, **kwargs594    ) -> "Check":595        """Ensure all values of a series are within an interval.596        :param min_value: Left / lower endpoint of the interval.597        :param max_value: Right / upper endpoint of the interval. Must not be598            smaller than min_value.599        :param include_min: Defines whether min_value is also an allowed value600            (the default) or whether all values must be strictly greater than601            min_value.602        :param include_max: Defines whether min_value is also an allowed value603            (the default) or whether all values must be strictly smaller than604            max_value.605        :param kwargs: key-word arguments passed into the `Check` initializer.606        Both endpoints must be a type comparable to the dtype of the607        :class:`pandas.Series` to be validated.608        :returns: :class:`Check` object609        """610        if min_value is None:611            raise ValueError("min_value must not be None")612        if max_value is None:613            raise ValueError("max_value must not be None")614        if max_value < min_value or (615            min_value == max_value and (not include_min or not include_max)616        ):617            raise ValueError(618                f"The combination of min_value = {min_value} and max_value = {max_value} "619                "defines an empty interval!"620            )621        # Using functions from operator module to keep conditions out of the622        # closure623        left_op = operator.le if include_min else operator.lt624        right_op = operator.ge if include_max else operator.gt625        def _in_range(series: pd.Series) -> pd.Series:626            """Comparison function for check"""627            return left_op(min_value, series) & right_op(max_value, series)628        return cls(629            _in_range,630            name=cls.in_range.__name__,631            error=f"in_range({min_value}, {max_value})",632            **kwargs,633        )634    @classmethod635    @st.register_check_strategy(st.isin_strategy)636    @register_check_statistics(["allowed_values"])637    def isin(cls, allowed_values: Iterable, **kwargs) -> "Check":638        """Ensure only allowed values occur within a series.639        :param allowed_values: The set of allowed values. May be any iterable.640        :param kwargs: key-word arguments passed into the `Check` initializer.641        :returns: :class:`Check` object642        .. note::643            It is checked whether all elements of a :class:`pandas.Series`644            are part of the set of elements of allowed values. If allowed645            values is a string, the set of elements consists of all distinct646            characters of the string. Thus only single characters which occur647            in allowed_values at least once can meet this condition. If you648            want to check for substrings use :func:`Check.str_is_substring`.649        """650        # Turn allowed_values into a set. Not only for performance but also651        # avoid issues with a mutable argument passed by reference which may be652        # changed from outside.653        try:654            allowed_values = frozenset(allowed_values)655        except TypeError as exc:656            raise ValueError(657                f"Argument allowed_values must be iterable. Got {allowed_values}"658            ) from exc659        def _isin(series: pd.Series) -> pd.Series:660            """Comparison function for check"""661            return series.isin(allowed_values)662        return cls(663            _isin,664            name=cls.isin.__name__,665            error=f"isin({set(allowed_values)})",666            **kwargs,667        )668    @classmethod669    @st.register_check_strategy(st.notin_strategy)670    @register_check_statistics(["forbidden_values"])671    def notin(cls, forbidden_values: Iterable, **kwargs) -> "Check":672        """Ensure some defined values don't occur within a series.673        :param forbidden_values: The set of values which should not occur. May674            be any iterable.675        :param raise_warning: if True, check raises UserWarning instead of676            SchemaError on validation.677        :returns: :class:`Check` object678        .. note::679            Like :func:`Check.isin` this check operates on single characters if680            it is applied on strings. A string as paraforbidden_valuesmeter681            forbidden_values is understood as set of prohibited characters. Any682            string of length > 1 can't be in it by design.683        """684        # Turn forbidden_values into a set. Not only for performance but also685        # avoid issues with a mutable argument passed by reference which may be686        # changed from outside.687        try:688            forbidden_values = frozenset(forbidden_values)689        except TypeError as exc:690            raise ValueError(691                f"Argument forbidden_values must be iterable. Got {forbidden_values}"692            ) from exc693        def _notin(series: pd.Series) -> pd.Series:694            """Comparison function for check"""695            return ~series.isin(forbidden_values)696        return cls(697            _notin,698            name=cls.notin.__name__,699            error=f"notin({set(forbidden_values)})",700            **kwargs,701        )702    @classmethod703    @st.register_check_strategy(st.str_matches_strategy)704    @register_check_statistics(["pattern"])705    def str_matches(cls, pattern: str, **kwargs) -> "Check":706        """Ensure that string values match a regular expression.707        :param pattern: Regular expression pattern to use for matching708        :param kwargs: key-word arguments passed into the `Check` initializer.709        :returns: :class:`Check` object710        The behaviour is as of :func:`pandas.Series.str.match`.711        """712        # By compiling the regex we get the benefit of an early argument check713        try:714            regex = re.compile(pattern)715        except TypeError as exc:716            raise ValueError(717                f'pattern="{pattern}" cannot be compiled as regular expression'718            ) from exc719        def _match(series: pd.Series) -> pd.Series:720            """721            Check if all strings in the series match the regular expression.722            """723            return series.str.match(regex, na=False)724        return cls(725            _match,726            name=cls.str_matches.__name__,727            error=f"str_matches({regex})",728            **kwargs,729        )730    @classmethod731    @st.register_check_strategy(st.str_contains_strategy)732    @register_check_statistics(["pattern"])733    def str_contains(cls, pattern: str, **kwargs) -> "Check":734        """Ensure that a pattern can be found within each row.735        :param pattern: Regular expression pattern to use for searching736        :param kwargs: key-word arguments passed into the `Check` initializer.737        :returns: :class:`Check` object738        The behaviour is as of :func:`pandas.Series.str.contains`.739        """740        # By compiling the regex we get the benefit of an early argument check741        try:742            regex = re.compile(pattern)743        except TypeError as exc:744            raise ValueError(745                f'pattern="{pattern}" cannot be compiled as regular expression'746            ) from exc747        def _contains(series: pd.Series) -> pd.Series:748            """Check if a regex search is successful within each value"""749            return series.str.contains(regex, na=False)750        return cls(751            _contains,752            name=cls.str_contains.__name__,753            error=f"str_contains({regex})",754            **kwargs,755        )756    @classmethod757    @st.register_check_strategy(st.str_startswith_strategy)758    @register_check_statistics(["string"])759    def str_startswith(cls, string: str, **kwargs) -> "Check":760        """Ensure that all values start with a certain string.761        :param string: String all values should start with762        :param kwargs: key-word arguments passed into the `Check` initializer.763        :returns: :class:`Check` object764        """765        def _startswith(series: pd.Series) -> pd.Series:766            """Returns true only for strings starting with string"""767            return series.str.startswith(string, na=False)768        return cls(769            _startswith,770            name=cls.str_startswith.__name__,771            error=f"str_startswith({string})",772            **kwargs,773        )774    @classmethod775    @st.register_check_strategy(st.str_endswith_strategy)776    @register_check_statistics(["string"])777    def str_endswith(cls, string: str, **kwargs) -> "Check":778        """Ensure that all values end with a certain string.779        :param string: String all values should end with780        :param kwargs: key-word arguments passed into the `Check` initializer.781        :returns: :class:`Check` object782        """783        def _endswith(series: pd.Series) -> pd.Series:784            """Returns true only for strings ending with string"""785            return series.str.endswith(string, na=False)786        return cls(787            _endswith,788            name=cls.str_endswith.__name__,789            error=f"str_endswith({string})",790            **kwargs,791        )792    @classmethod793    @st.register_check_strategy(st.str_length_strategy)794    @register_check_statistics(["min_value", "max_value"])795    def str_length(796        cls, min_value: int = None, max_value: int = None, **kwargs797    ) -> "Check":798        """Ensure that the length of strings is within a specified range.799        :param min_value: Minimum length of strings (default: no minimum)800        :param max_value: Maximum length of strings (default: no maximum)801        :param kwargs: key-word arguments passed into the `Check` initializer.802        :returns: :class:`Check` object803        """804        if min_value is None and max_value is None:805            raise ValueError(806                "At least a minimum or a maximum need to be specified. Got "807                "None."808            )809        if max_value is None:810            def _str_length(series: pd.Series) -> pd.Series:811                """Check for the minimum string length"""812                return series.str.len() >= min_value813        elif min_value is None:814            def _str_length(series: pd.Series) -> pd.Series:815                """Check for the maximum string length"""816                return series.str.len() <= max_value817        else:818            def _str_length(series: pd.Series) -> pd.Series:819                """Check for both, minimum and maximum string length"""820                return (series.str.len() <= max_value) & (821                    series.str.len() >= min_value822                )823        return cls(824            _str_length,825            name=cls.str_length.__name__,826            error=f"str_length({min_value}, {max_value})",827            **kwargs,...

test_strategies.py

Source:test_strategies.py

...199@hypothesis.given(st.data())200@hypothesis.settings(201    suppress_health_check=[hypothesis.HealthCheck.too_slow],202)203def test_in_range_strategy(data_type, chained, data):204    """Test the built-in in-range strategy can correctly generate data."""205    min_value, max_value = data.draw(value_ranges(data_type))206    hypothesis.assume(min_value < max_value)207    base_st_in_range = None208    if chained:209        if is_float(data_type):210            base_st_kwargs = {211                "exclude_min": False,212                "exclude_max": False,213            }214        else:215            base_st_kwargs = {}216        # constraining the strategy this way makes testing more efficient217        base_st_in_range = strategies.pandas_dtype_strategy(218            data_type,219            min_value=min_value,220            max_value=max_value,221            **base_st_kwargs,  # type: ignore[arg-type]222        )223    strat = strategies.in_range_strategy(224        data_type,225        base_st_in_range,226        min_value=min_value,227        max_value=max_value,228    )229    assert min_value <= data.draw(strat) <= max_value230@pytest.mark.parametrize(231    "data_type",232    [data_type for data_type in SUPPORTED_DTYPES if data_type.continuous],233)234@pytest.mark.parametrize("chained", [True, False])235@hypothesis.given(st.data())236@hypothesis.settings(237    suppress_health_check=[hypothesis.HealthCheck.too_slow],...

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