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How to use null_dataframe_masks method in pandera

Best Python code snippet using pandera_python
strategies.py
Source:strategies.py
...78        val = _mask(val, null_mask)79        return pd.Index(val)80    return _mask(val, null_mask)81@composite82def null_dataframe_masks(83    draw,84    strategy: Optional[SearchStrategy],85    nullable_columns: Dict[str, bool],86):87    """Strategy for masking a values in a pandas DataFrame.88    :param strategy: an optional hypothesis strategy. If specified, the89        pandas dtype strategy will be chained onto this strategy.90    :param nullable_columns: dictionary where keys are column names and91        values indicate whether that column is nullable.92    """93    val = draw(strategy)94    size = val.shape[0]95    columns_strat = []96    for name, nullable in nullable_columns.items():97        element_st = st.booleans() if nullable else st.just(False)98        columns_strat.append(99            pdst.column(100                name=name,101                elements=element_st,102                dtype=bool,103                fill=st.just(False),104            )105        )106    mask_st = pdst.data_frames(107        columns=columns_strat,108        index=pdst.range_indexes(min_size=size, max_size=size),109    )110    null_mask = draw(mask_st)111    for column in val:112        val[column] = _mask(val[column], null_mask[column])113    return val114@composite115def set_pandas_index(116    draw,117    df_or_series_strat: SearchStrategy,118    index: IndexComponent,119):120    """Sets Index or MultiIndex object to pandas Series or DataFrame."""121    df_or_series = draw(df_or_series_strat)122    df_or_series.index = draw(index.strategy(size=df_or_series.shape[0]))123    return df_or_series124def verify_dtype(125    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],126    schema_type: str,127    name: Optional[str],128):129    """Verify that pandera_dtype argument is not None."""130    if pandera_dtype is None:131        raise SchemaDefinitionError(132            f"'{schema_type}' schema with name '{name}' has no specified "133            "dtype. You need to specify one in order to synthesize "134            "data from a strategy."135        )136def strategy_import_error(fn: F) -> F:137    """Decorator to generate input error if dependency is missing."""138    @wraps(fn)139    def _wrapper(*args, **kwargs):140        if not HAS_HYPOTHESIS:  # pragma: no cover141            raise ImportError(142                'Strategies for generating data requires "hypothesis" to be \n'143                "installed. You can install pandera together with the IO \n"144                "dependencies with:\n"145                "pip install pandera[strategies]"146            )147        return fn(*args, **kwargs)148    return cast(F, _wrapper)149def register_check_strategy(strategy_fn: StrategyFn):150    """Decorate a Check method with a strategy.151    This should be applied to a built-in :class:`~pandera.checks.Check` method.152    :param strategy_fn: add strategy to a check, using check statistics to153        generate a ``hypothesis`` strategy.154    """155    def register_check_strategy_decorator(class_method):156        """Decorator that wraps Check class method."""157        @wraps(class_method)158        def _wrapper(cls, *args, **kwargs):159            check = class_method(cls, *args, **kwargs)160            if check.statistics is None:161                raise AttributeError(162                    "check object doesn't have a defined statistics property. "163                    "Use the checks.register_check_statistics decorator to "164                    f"specify the statistics for the {class_method.__name__} "165                    "method."166                )167            strategy_kwargs = {168                arg: stat169                for arg, stat in check.statistics.items()170                if stat is not None171            }172            check.strategy = partial(strategy_fn, **strategy_kwargs)173            return check174        return _wrapper175    return register_check_strategy_decorator176# pylint: disable=line-too-long177# Values taken from178# https://hypothesis.readthedocs.io/en/latest/_modules/hypothesis/extra/numpy.html#from_dtype  # noqa179MIN_DT_VALUE = -(2**63)180MAX_DT_VALUE = 2**63 - 1181def _is_datetime_tz(pandera_dtype: DataType) -> bool:182    native_type = getattr(pandera_dtype, "type", None)183    return isinstance(native_type, pd.DatetimeTZDtype)184def _datetime_strategy(185    dtype: Union[np.dtype, pd.DatetimeTZDtype], strategy186) -> SearchStrategy:187    if isinstance(dtype, pd.DatetimeTZDtype):188        def _to_datetime(value) -> pd.DatetimeTZDtype:189            if isinstance(value, pd.Timestamp):190                return value.tz_convert(tz=dtype.tz)  # type: ignore[union-attr]191            return pd.Timestamp(value, unit=dtype.unit, tz=dtype.tz)  # type: ignore[union-attr]192        return st.builds(_to_datetime, strategy)193    else:194        res = (195            st.just(dtype.str.split("[")[-1][:-1])196            if "[" in dtype.str197            else st.sampled_from(npst.TIME_RESOLUTIONS)198        )199        return st.builds(dtype.type, strategy, res)200def numpy_time_dtypes(201    dtype: Union[np.dtype, pd.DatetimeTZDtype], min_value=None, max_value=None202):203    """Create numpy strategy for datetime and timedelta data types.204    :param dtype: numpy datetime or timedelta datatype205    :param min_value: minimum value of the datatype to create206    :param max_value: maximum value of the datatype to create207    :returns: ``hypothesis`` strategy208    """209    def _to_unix(value: Any) -> int:210        if dtype.type is np.timedelta64:211            return pd.Timedelta(value).value212        return pd.Timestamp(value).value213    min_value = MIN_DT_VALUE if min_value is None else _to_unix(min_value)214    max_value = MAX_DT_VALUE if max_value is None else _to_unix(max_value)215    return _datetime_strategy(dtype, st.integers(min_value, max_value))216def numpy_complex_dtypes(217    dtype,218    min_value: complex = complex(0, 0),219    max_value: Optional[complex] = None,220    allow_infinity: bool = None,221    allow_nan: bool = None,222):223    """Create numpy strategy for complex numbers.224    :param dtype: numpy complex number datatype225    :param min_value: minimum value, must be complex number226    :param max_value: maximum value, must be complex number227    :returns: ``hypothesis`` strategy228    """229    max_real: Optional[float]230    max_imag: Optional[float]231    if max_value:232        max_real = max_value.real233        max_imag = max_value.imag234    else:235        max_real = max_imag = None236    if dtype.itemsize == 8:237        width = 32238    else:239        width = 64240    # switch min and max values for imaginary if min value > max value241    if max_imag is not None and min_value.imag > max_imag:242        min_imag = max_imag243        max_imag = min_value.imag244    else:245        min_imag = min_value.imag246    strategy = st.builds(247        complex,248        st.floats(249            min_value=min_value.real,250            max_value=max_real,251            width=width,252            allow_infinity=allow_infinity,253            allow_nan=allow_nan,254        ),255        st.floats(256            min_value=min_imag,257            max_value=max_imag,258            width=width,259            allow_infinity=allow_infinity,260            allow_nan=allow_nan,261        ),262    ).map(dtype.type)263    @st.composite264    def build_complex(draw):265        value = draw(strategy)266        hypothesis.assume(min_value <= value)267        if max_value is not None:268            hypothesis.assume(max_value >= value)269        return value270    return build_complex()271def to_numpy_dtype(pandera_dtype: DataType):272    """Convert a :class:`~pandera.dtypes.DataType` to numpy dtype compatible273    with hypothesis."""274    try:275        np_dtype = pandas_engine.Engine.numpy_dtype(pandera_dtype)276    except TypeError as err:277        if is_datetime(pandera_dtype):278            return np.dtype("datetime64[ns]")279        raise TypeError(280            f"Data generation for the '{pandera_dtype}' data type is "281            "currently unsupported."282        ) from err283    if np_dtype == np.dtype("object") or str(pandera_dtype) == "str":284        np_dtype = np.dtype(str)285    return np_dtype286def pandas_dtype_strategy(287    pandera_dtype: DataType,288    strategy: Optional[SearchStrategy] = None,289    **kwargs,290) -> SearchStrategy:291    # pylint: disable=line-too-long,no-else-raise292    """Strategy to generate data from a :class:`pandera.dtypes.DataType`.293    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.294    :param strategy: an optional hypothesis strategy. If specified, the295        pandas dtype strategy will be chained onto this strategy.296    :kwargs: key-word arguments passed into297        `hypothesis.extra.numpy.from_dtype <https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.numpy.from_dtype>`_ .298        For datetime, timedelta, and complex number datatypes, these arguments299        are passed into :func:`~pandera.strategies.numpy_time_dtypes` and300        :func:`~pandera.strategies.numpy_complex_dtypes`.301    :returns: ``hypothesis`` strategy302    """303    def compat_kwargs(*args):304        return {k: v for k, v in kwargs.items() if k in args}305    # hypothesis doesn't support categoricals or objects, so we'll will need to306    # build a pandera-specific solution.307    if is_category(pandera_dtype):308        raise TypeError(309            "data generation for the Category dtype is currently "310            "unsupported. Consider using a string or int dtype and "311            "Check.isin(values) to ensure a finite set of values."312        )313    np_dtype = to_numpy_dtype(pandera_dtype)314    if strategy:315        if _is_datetime_tz(pandera_dtype):316            return _datetime_strategy(pandera_dtype.type, strategy)  # type: ignore317        return strategy.map(np_dtype.type)318    elif is_datetime(pandera_dtype) or is_timedelta(pandera_dtype):319        return numpy_time_dtypes(320            pandera_dtype.type if _is_datetime_tz(pandera_dtype) else np_dtype,  # type: ignore321            **compat_kwargs("min_value", "max_value"),322        )323    elif is_complex(pandera_dtype):324        return numpy_complex_dtypes(325            np_dtype,326            **compat_kwargs(327                "min_value", "max_value", "allow_infinity", "allow_nan"328            ),329        )330    return npst.from_dtype(331        np_dtype,332        **{  # type: ignore333            "allow_nan": False,334            "allow_infinity": False,335            **kwargs,336        },337    )338def eq_strategy(339    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],340    strategy: Optional[SearchStrategy] = None,341    *,342    value: Any,343) -> SearchStrategy:344    """Strategy to generate a single value.345    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.346    :param strategy: an optional hypothesis strategy. If specified, the347        pandas dtype strategy will be chained onto this strategy.348    :param value: value to generate.349    :returns: ``hypothesis`` strategy350    """351    # override strategy preceding this one and generate value of the same type352    # pylint: disable=unused-argument353    return pandas_dtype_strategy(pandera_dtype, st.just(value))354def ne_strategy(355    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],356    strategy: Optional[SearchStrategy] = None,357    *,358    value: Any,359) -> SearchStrategy:360    """Strategy to generate anything except for a particular value.361    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.362    :param strategy: an optional hypothesis strategy. If specified, the363        pandas dtype strategy will be chained onto this strategy.364    :param value: value to avoid.365    :returns: ``hypothesis`` strategy366    """367    if strategy is None:368        strategy = pandas_dtype_strategy(pandera_dtype)369    return strategy.filter(lambda x: x != value)370def gt_strategy(371    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],372    strategy: Optional[SearchStrategy] = None,373    *,374    min_value: Union[int, float],375) -> SearchStrategy:376    """Strategy to generate values greater than a minimum value.377    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.378    :param strategy: an optional hypothesis strategy. If specified, the379        pandas dtype strategy will be chained onto this strategy.380    :param min_value: generate values larger than this.381    :returns: ``hypothesis`` strategy382    """383    if strategy is None:384        strategy = pandas_dtype_strategy(385            pandera_dtype,386            min_value=min_value,387            exclude_min=True if is_float(pandera_dtype) else None,388        )389    return strategy.filter(lambda x: x > min_value)390def ge_strategy(391    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],392    strategy: Optional[SearchStrategy] = None,393    *,394    min_value: Union[int, float],395) -> SearchStrategy:396    """Strategy to generate values greater than or equal to a minimum value.397    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.398    :param strategy: an optional hypothesis strategy. If specified, the399        pandas dtype strategy will be chained onto this strategy.400    :param min_value: generate values greater than or equal to this.401    :returns: ``hypothesis`` strategy402    """403    if strategy is None:404        return pandas_dtype_strategy(405            pandera_dtype,406            min_value=min_value,407            exclude_min=False if is_float(pandera_dtype) else None,408        )409    return strategy.filter(lambda x: x >= min_value)410def lt_strategy(411    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],412    strategy: Optional[SearchStrategy] = None,413    *,414    max_value: Union[int, float],415) -> SearchStrategy:416    """Strategy to generate values less than a maximum value.417    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.418    :param strategy: an optional hypothesis strategy. If specified, the419        pandas dtype strategy will be chained onto this strategy.420    :param max_value: generate values less than this.421    :returns: ``hypothesis`` strategy422    """423    if strategy is None:424        strategy = pandas_dtype_strategy(425            pandera_dtype,426            max_value=max_value,427            exclude_max=True if is_float(pandera_dtype) else None,428        )429    return strategy.filter(lambda x: x < max_value)430def le_strategy(431    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],432    strategy: Optional[SearchStrategy] = None,433    *,434    max_value: Union[int, float],435) -> SearchStrategy:436    """Strategy to generate values less than or equal to a maximum value.437    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.438    :param strategy: an optional hypothesis strategy. If specified, the439        pandas dtype strategy will be chained onto this strategy.440    :param max_value: generate values less than or equal to this.441    :returns: ``hypothesis`` strategy442    """443    if strategy is None:444        return pandas_dtype_strategy(445            pandera_dtype,446            max_value=max_value,447            exclude_max=False if is_float(pandera_dtype) else None,448        )449    return strategy.filter(lambda x: x <= max_value)450def in_range_strategy(451    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],452    strategy: Optional[SearchStrategy] = None,453    *,454    min_value: Union[int, float],455    max_value: Union[int, float],456    include_min: bool = True,457    include_max: bool = True,458) -> SearchStrategy:459    """Strategy to generate values within a particular range.460    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.461    :param strategy: an optional hypothesis strategy. If specified, the462        pandas dtype strategy will be chained onto this strategy.463    :param min_value: generate values greater than this.464    :param max_value: generate values less than this.465    :param include_min: include min_value in generated data.466    :param include_max: include max_value in generated data.467    :returns: ``hypothesis`` strategy468    """469    if strategy is None:470        return pandas_dtype_strategy(471            pandera_dtype,472            min_value=min_value,473            max_value=max_value,474            exclude_min=not include_min,475            exclude_max=not include_max,476        )477    min_op = operator.ge if include_min else operator.gt478    max_op = operator.le if include_max else operator.lt479    return strategy.filter(480        lambda x: min_op(x, min_value) and max_op(x, max_value)481    )482def isin_strategy(483    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],484    strategy: Optional[SearchStrategy] = None,485    *,486    allowed_values: Sequence[Any],487) -> SearchStrategy:488    """Strategy to generate values within a finite set.489    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.490    :param strategy: an optional hypothesis strategy. If specified, the491        pandas dtype strategy will be chained onto this strategy.492    :param allowed_values: set of allowable values.493    :returns: ``hypothesis`` strategy494    """495    if strategy is None:496        return pandas_dtype_strategy(497            pandera_dtype, st.sampled_from(allowed_values)498        )499    return strategy.filter(lambda x: x in allowed_values)500def notin_strategy(501    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],502    strategy: Optional[SearchStrategy] = None,503    *,504    forbidden_values: Sequence[Any],505) -> SearchStrategy:506    """Strategy to generate values excluding a set of forbidden values507    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.508    :param strategy: an optional hypothesis strategy. If specified, the509        pandas dtype strategy will be chained onto this strategy.510    :param forbidden_values: set of forbidden values.511    :returns: ``hypothesis`` strategy512    """513    if strategy is None:514        strategy = pandas_dtype_strategy(pandera_dtype)515    return strategy.filter(lambda x: x not in forbidden_values)516def str_matches_strategy(517    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],518    strategy: Optional[SearchStrategy] = None,519    *,520    pattern: str,521) -> SearchStrategy:522    """Strategy to generate strings that patch a regex pattern.523    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.524    :param strategy: an optional hypothesis strategy. If specified, the525        pandas dtype strategy will be chained onto this strategy.526    :param pattern: regex pattern.527    :returns: ``hypothesis`` strategy528    """529    if strategy is None:530        return st.from_regex(pattern, fullmatch=True).map(531            to_numpy_dtype(pandera_dtype).type532        )533    def matches(x):534        return re.match(pattern, x)535    return strategy.filter(matches)536def str_contains_strategy(537    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],538    strategy: Optional[SearchStrategy] = None,539    *,540    pattern: str,541) -> SearchStrategy:542    """Strategy to generate strings that contain a particular pattern.543    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.544    :param strategy: an optional hypothesis strategy. If specified, the545        pandas dtype strategy will be chained onto this strategy.546    :param pattern: regex pattern.547    :returns: ``hypothesis`` strategy548    """549    if strategy is None:550        return st.from_regex(pattern, fullmatch=False).map(551            to_numpy_dtype(pandera_dtype).type552        )553    def contains(x):554        return re.search(pattern, x)555    return strategy.filter(contains)556def str_startswith_strategy(557    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],558    strategy: Optional[SearchStrategy] = None,559    *,560    string: str,561) -> SearchStrategy:562    """Strategy to generate strings that start with a specific string pattern.563    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.564    :param strategy: an optional hypothesis strategy. If specified, the565        pandas dtype strategy will be chained onto this strategy.566    :param string: string pattern.567    :returns: ``hypothesis`` strategy568    """569    if strategy is None:570        return st.from_regex(f"\\A{string}", fullmatch=False).map(571            to_numpy_dtype(pandera_dtype).type572        )573    return strategy.filter(lambda x: x.startswith(string))574def str_endswith_strategy(575    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],576    strategy: Optional[SearchStrategy] = None,577    *,578    string: str,579) -> SearchStrategy:580    """Strategy to generate strings that end with a specific string pattern.581    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.582    :param strategy: an optional hypothesis strategy. If specified, the583        pandas dtype strategy will be chained onto this strategy.584    :param string: string pattern.585    :returns: ``hypothesis`` strategy586    """587    if strategy is None:588        return st.from_regex(f"{string}\\Z", fullmatch=False).map(589            to_numpy_dtype(pandera_dtype).type590        )591    return strategy.filter(lambda x: x.endswith(string))592def str_length_strategy(593    pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],594    strategy: Optional[SearchStrategy] = None,595    *,596    min_value: int,597    max_value: int,598) -> SearchStrategy:599    """Strategy to generate strings of a particular length600    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.601    :param strategy: an optional hypothesis strategy. If specified, the602        pandas dtype strategy will be chained onto this strategy.603    :param min_value: minimum string length.604    :param max_value: maximum string length.605    :returns: ``hypothesis`` strategy606    """607    if strategy is None:608        return st.text(min_size=min_value, max_size=max_value).map(609            to_numpy_dtype(pandera_dtype).type610        )611    return strategy.filter(lambda x: min_value <= len(x) <= max_value)612def _timestamp_to_datetime64_strategy(613    strategy: SearchStrategy,614) -> SearchStrategy:615    """Convert timestamp to numpy.datetime64616    Hypothesis only supports pure numpy dtypes but numpy.datetime64() truncates617    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))786    if nullable:787        strategy = null_field_masks(strategy)788    return strategy789def dataframe_strategy(790    pandera_dtype: Optional[DataType] = None,791    strategy: Optional[SearchStrategy] = None,792    *,793    columns: Optional[Dict] = None,794    checks: Optional[Sequence] = None,795    unique: Optional[List[str]] = None,796    index: Optional[IndexComponent] = None,797    size: Optional[int] = None,798    n_regex_columns: int = 1,799):800    """Strategy to generate a pandas DataFrame.801    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.802    :param strategy: if specified, this will raise a BaseStrategyOnlyError,803        since it cannot be chained to a prior strategy.804    :param columns: a dictionary where keys are column names and values805        are :class:`~pandera.schema_components.Column` objects.806    :param checks: sequence of :class:`~pandera.checks.Check` s to constrain807        the values of the data at the dataframe level.808    :param unique: a list of column names that should be jointly unique.809    :param index: Index or MultiIndex schema component.810    :param size: number of elements in the Series.811    :param n_regex_columns: number of regex columns to generate.812    :returns: ``hypothesis`` strategy.813    """814    # pylint: disable=too-many-locals,too-many-branches,too-many-statements815    if n_regex_columns < 1:816        raise ValueError(817            "`n_regex_columns` must be a positive integer, found: "818            f"{n_regex_columns}"819        )820    if strategy:821        raise BaseStrategyOnlyError(822            "The dataframe strategy is a base strategy. You cannot specify "823            "the strategy argument to chain it to a parent strategy."824        )825    columns = {} if columns is None else columns826    checks = [] if checks is None else checks827    def undefined_check_strategy(strategy, check, column=None):828        """Strategy for checks with undefined strategies."""829        def _element_wise_check_fn(element):830            return check._check_fn(element)831        def _column_check_fn(dataframe):832            return check(dataframe[column]).check_passed833        def _dataframe_check_fn(dataframe):834            return check(dataframe).check_passed835        if check.element_wise:836            check_fn = _element_wise_check_fn837            warning_type = "Element-wise"838        elif column is None:839            check_fn = _dataframe_check_fn840            warning_type = "Dataframe"841        else:842            check_fn = _column_check_fn843            warning_type = "Column"844        warnings.warn(845            f"{warning_type} check doesn't have a defined strategy. "846            "Falling back to filtering drawn values based on the check "847            "definition. This can considerably slow down data-generation."848        )849        return strategy.filter(check_fn)850    def make_row_strategy(col, checks):851        strategy = None852        for check in checks:853            if hasattr(check, "strategy"):854                strategy = check.strategy(col.dtype, strategy)855            else:856                strategy = undefined_check_strategy(857                    strategy=(858                        pandas_dtype_strategy(col.dtype)859                        if strategy is None860                        else strategy861                    ),862                    check=check,863                )864        if strategy is None:865            strategy = pandas_dtype_strategy(col.dtype)866        return strategy867    @composite868    def _dataframe_strategy(draw):869        row_strategy_checks = []870        undefined_strat_df_checks = []871        for check in checks:872            if hasattr(check, "strategy") or check.element_wise:873                # we can apply element-wise checks defined at the dataframe874                # level to the row strategy875                row_strategy_checks.append(check)876            else:877                undefined_strat_df_checks.append(check)878        # expand column set to generate column names for columns where879        # regex=True.880        expanded_columns = {}881        for col_name, column in columns.items():882            if unique and col_name in unique:883                # if the column is in the set of columns specified in `unique`,884                # make the column strategy independently unique. This is885                # technically stricter than it should be, since the list of886                # columns in `unique` are required to be jointly unique, but887                # this is a simple solution that produces synthetic data that888                # fulfills the uniqueness constraints of the dataframe.889                column = deepcopy(column)890                column.unique = True891            if not column.regex:892                expanded_columns[col_name] = column893            else:894                regex_columns = draw(895                    st.lists(896                        st.from_regex(column.name, fullmatch=True),897                        min_size=n_regex_columns,898                        max_size=n_regex_columns,899                        unique=True,900                    )901                )902                for regex_col in regex_columns:903                    expanded_columns[regex_col] = deepcopy(column).set_name(904                        regex_col905                    )906        # collect all non-element-wise column checks with undefined strategies907        undefined_strat_column_checks: Dict[str, list] = defaultdict(list)908        for col_name, column in expanded_columns.items():909            undefined_strat_column_checks[col_name].extend(910                check911                for check in column.checks912                if not hasattr(check, "strategy") and not check.element_wise913            )914        # override the column datatype with dataframe-level datatype if915        # specified916        col_dtypes = {917            col_name: str(col.dtype)918            if pandera_dtype is None919            else str(pandera_dtype)920            for col_name, col in expanded_columns.items()921        }922        nullable_columns = {923            col_name: col.nullable924            for col_name, col in expanded_columns.items()925        }926        row_strategy = None927        if row_strategy_checks:928            row_strategy = st.fixed_dictionaries(929                {930                    col_name: make_row_strategy(col, row_strategy_checks)931                    for col_name, col in expanded_columns.items()932                }933            )934        strategy = pdst.data_frames(935            columns=[936                column.strategy_component()937                for column in expanded_columns.values()938            ],939            rows=row_strategy,940            index=pdst.range_indexes(941                min_size=0 if size is None else size, max_size=size942            ),943        )944        # this is a hack to convert np.str_ data values into native python str.945        for col_name, col_dtype in col_dtypes.items():946            if col_dtype in {"object", "str"} or col_dtype.startswith(947                "string"948            ):949                # pylint: disable=cell-var-from-loop,undefined-loop-variable950                strategy = strategy.map(951                    lambda df: df.assign(**{col_name: df[col_name].map(str)})952                )953        strategy = strategy.map(954            lambda df: df if df.empty else df.astype(col_dtypes)955        )956        if size is not None and size > 0 and any(nullable_columns.values()):957            strategy = null_dataframe_masks(strategy, nullable_columns)958        if index is not None:959            strategy = set_pandas_index(strategy, index)960        for check in undefined_strat_df_checks:961            strategy = undefined_check_strategy(strategy, check)962        for col_name, column_checks in undefined_strat_column_checks.items():963            for check in column_checks:  # type: ignore964                strategy = undefined_check_strategy(965                    strategy, check, column=col_name966                )967        return draw(strategy)968    return _dataframe_strategy()969# pylint: disable=unused-argument970def multiindex_strategy(971    pandera_dtype: Optional[DataType] = None,972    strategy: Optional[SearchStrategy] = None,973    *,974    indexes: Optional[List] = None,975    size: Optional[int] = None,976):977    """Strategy to generate a pandas MultiIndex object.978    :param pandera_dtype: :class:`pandera.dtypes.DataType` instance.979    :param strategy: an optional hypothesis strategy. If specified, the980        pandas dtype strategy will be chained onto this strategy.981    :param indexes: a list of :class:`~pandera.schema_components.Index`982        objects.983    :param size: number of elements in the Series.984    :returns: ``hypothesis`` strategy.985    """986    # pylint: disable=unnecessary-lambda987    if strategy:988        raise BaseStrategyOnlyError(989            "The dataframe strategy is a base strategy. You cannot specify "990            "the strategy argument to chain it to a parent strategy."991        )992    indexes = [] if indexes is None else indexes993    index_dtypes = {994        index.name if index.name is not None else i: str(index.dtype)995        for i, index in enumerate(indexes)996    }997    nullable_index = {998        index.name if index.name is not None else i: index.nullable999        for i, index in enumerate(indexes)1000    }1001    strategy = pdst.data_frames(1002        [index.strategy_component() for index in indexes],1003        index=pdst.range_indexes(1004            min_size=0 if size is None else size, max_size=size1005        ),1006    ).map(lambda x: x.astype(index_dtypes))1007    # this is a hack to convert np.str_ data values into native python str.1008    for name, dtype in index_dtypes.items():1009        if dtype in {"object", "str"} or dtype.startswith("string"):1010            # pylint: disable=cell-var-from-loop,undefined-loop-variable1011            strategy = strategy.map(1012                lambda df: df.assign(**{name: df[name].map(str)})1013            )1014    if any(nullable_index.values()):1015        strategy = null_dataframe_masks(strategy, nullable_index)...
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