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strategies.py
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strategies.py
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# pylint: disable=no-value-for-parameter,too-many-lines
"""Generate synthetic data from a schema definition.
*new in 0.6.0*
This module is responsible for generating data based on the type and check
constraints specified in a ``pandera`` schema. It's built on top of the
`hypothesis <https://hypothesis.readthedocs.io/en/latest/index.html>`_ package
to compose strategies given multiple checks specified in a schema.
See the :ref:`user guide<data synthesis strategies>` for more details.
"""
import operator
import re
import warnings
from collections import defaultdict
from copy import deepcopy
from functools import partial, wraps
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Sequence,
TypeVar,
Union,
cast,
)
import numpy as np
import pandas as pd
from .dtypes import (
DataType,
is_category,
is_complex,
is_datetime,
is_float,
is_timedelta,
)
from .engines import numpy_engine, pandas_engine
from .errors import BaseStrategyOnlyError, SchemaDefinitionError
try:
import hypothesis
import hypothesis.extra.numpy as npst
import hypothesis.extra.pandas as pdst
import hypothesis.strategies as st
from hypothesis.strategies import SearchStrategy, composite
except ImportError: # pragma: no cover
# pylint: disable=too-few-public-methods
class SearchStrategy: # type: ignore
"""placeholder type."""
def composite(fn):
"""placeholder composite strategy."""
return fn
HAS_HYPOTHESIS = False
else:
HAS_HYPOTHESIS = True
StrategyFn = Callable[..., SearchStrategy]
# Fix this when modules have been re-organized to avoid circular imports
IndexComponent = Any
F = TypeVar("F", bound=Callable)
def _mask(
val: Union[pd.Series, pd.Index], null_mask: List[bool]
) -> Union[pd.Series, pd.Index]:
if pd.api.types.is_timedelta64_dtype(val):
return val.mask(null_mask, pd.NaT)
return val.mask(null_mask)
@composite
def null_field_masks(draw, strategy: Optional[SearchStrategy]):
"""Strategy for masking a column/index with null values.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
"""
val = draw(strategy)
size = val.shape[0]
null_mask = draw(st.lists(st.booleans(), min_size=size, max_size=size))
# assume that there is at least one masked value
hypothesis.assume(any(null_mask))
if isinstance(val, pd.Index):
val = val.to_series()
val = _mask(val, null_mask)
return pd.Index(val)
return _mask(val, null_mask)
@composite
def null_dataframe_masks(
draw,
strategy: Optional[SearchStrategy],
nullable_columns: Dict[str, bool],
):
"""Strategy for masking a values in a pandas DataFrame.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param nullable_columns: dictionary where keys are column names and
values indicate whether that column is nullable.
"""
val = draw(strategy)
size = val.shape[0]
columns_strat = []
for name, nullable in nullable_columns.items():
element_st = st.booleans() if nullable else st.just(False)
columns_strat.append(
pdst.column(
name=name,
elements=element_st,
dtype=bool,
fill=st.just(False),
)
)
mask_st = pdst.data_frames(
columns=columns_strat,
index=pdst.range_indexes(min_size=size, max_size=size),
)
null_mask = draw(mask_st)
# assume that there is at least one masked value
hypothesis.assume(null_mask.any(axis=None))
for column in val:
val[column] = _mask(val[column], null_mask[column])
return val
@composite
def set_pandas_index(
draw,
df_or_series_strat: SearchStrategy,
index: IndexComponent,
):
"""Sets Index or MultiIndex object to pandas Series or DataFrame."""
df_or_series = draw(df_or_series_strat)
df_or_series.index = draw(index.strategy(size=df_or_series.shape[0]))
return df_or_series
def verify_dtype(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
schema_type: str,
name: Optional[str],
):
"""Verify that pandera_dtype argument is not None."""
if pandera_dtype is None:
raise SchemaDefinitionError(
f"'{schema_type}' schema with name '{name}' has no specified "
"dtype. You need to specify one in order to synthesize "
"data from a strategy."
)
def strategy_import_error(fn: F) -> F:
"""Decorator to generate input error if dependency is missing."""
@wraps(fn)
def _wrapper(*args, **kwargs):
if not HAS_HYPOTHESIS: # pragma: no cover
raise ImportError(
'Strategies for generating data requires "hypothesis" to be \n'
"installed. You can install pandera together with the IO \n"
"dependencies with:\n"
"pip install pandera[strategies]"
)
return fn(*args, **kwargs)
return cast(F, _wrapper)
def register_check_strategy(strategy_fn: StrategyFn):
"""Decorate a Check method with a strategy.
This should be applied to a built-in :class:`~pandera.checks.Check` method.
:param strategy_fn: add strategy to a check, using check statistics to
generate a ``hypothesis`` strategy.
"""
def register_check_strategy_decorator(class_method):
"""Decorator that wraps Check class method."""
@wraps(class_method)
def _wrapper(cls, *args, **kwargs):
check = class_method(cls, *args, **kwargs)
if check.statistics is None:
raise AttributeError(
"check object doesn't have a defined statistics property. "
"Use the checks.register_check_statistics decorator to "
f"specify the statistics for the {class_method.__name__} "
"method."
)
strategy_kwargs = {
arg: stat
for arg, stat in check.statistics.items()
if stat is not None
}
check.strategy = partial(strategy_fn, **strategy_kwargs)
return check
return _wrapper
return register_check_strategy_decorator
# pylint: disable=line-too-long
# Values taken from
# https://hypothesis.readthedocs.io/en/latest/_modules/hypothesis/extra/numpy.html#from_dtype # noqa
MIN_DT_VALUE = -(2 ** 63)
MAX_DT_VALUE = 2 ** 63 - 1
def _is_datetime_tz(pandera_dtype: DataType) -> bool:
native_type = getattr(pandera_dtype, "type", None)
return isinstance(native_type, pd.DatetimeTZDtype)
def _datetime_strategy(
dtype: Union[np.dtype, pd.DatetimeTZDtype], strategy
) -> SearchStrategy:
if isinstance(dtype, pd.DatetimeTZDtype):
def _to_datetime(value) -> pd.DatetimeTZDtype:
if isinstance(value, pd.Timestamp):
return value.tz_convert(tz=dtype.tz) # type: ignore[union-attr]
return pd.Timestamp(value, unit=dtype.unit, tz=dtype.tz) # type: ignore[union-attr]
return st.builds(_to_datetime, strategy)
else:
res = (
st.just(dtype.str.split("[")[-1][:-1])
if "[" in dtype.str
else st.sampled_from(npst.TIME_RESOLUTIONS)
)
return st.builds(dtype.type, strategy, res)
def _to_unix_timestamp(value: Any) -> int:
return pd.Timestamp(value).value
def numpy_time_dtypes(
dtype: Union[np.dtype, pd.DatetimeTZDtype], min_value=None, max_value=None
):
"""Create numpy strategy for datetime and timedelta data types.
:param dtype: numpy datetime or timedelta datatype
:param min_value: minimum value of the datatype to create
:param max_value: maximum value of the datatype to create
:returns: ``hypothesis`` strategy
"""
min_value = (
MIN_DT_VALUE if min_value is None else _to_unix_timestamp(min_value)
)
max_value = (
MAX_DT_VALUE if max_value is None else _to_unix_timestamp(max_value)
)
return _datetime_strategy(dtype, st.integers(min_value, max_value))
def numpy_complex_dtypes(
dtype,
min_value: complex = complex(0, 0),
max_value: Optional[complex] = None,
allow_infinity: bool = None,
allow_nan: bool = None,
):
"""Create numpy strategy for complex numbers.
:param dtype: numpy complex number datatype
:param min_value: minimum value, must be complex number
:param max_value: maximum value, must be complex number
:returns: ``hypothesis`` strategy
"""
max_real: Optional[float]
max_imag: Optional[float]
if max_value:
max_real = max_value.real
max_imag = max_value.imag
else:
max_real = max_imag = None
if dtype.itemsize == 8:
width = 32
else:
width = 64
# switch min and max values for imaginary if min value > max value
if max_imag is not None and min_value.imag > max_imag:
min_imag = max_imag
max_imag = min_value.imag
else:
min_imag = min_value.imag
strategy = st.builds(
complex,
st.floats(
min_value=min_value.real,
max_value=max_real,
width=width,
allow_infinity=allow_infinity,
allow_nan=allow_nan,
),
st.floats(
min_value=min_imag,
max_value=max_imag,
width=width,
allow_infinity=allow_infinity,
allow_nan=allow_nan,
),
).map(dtype.type)
@st.composite
def build_complex(draw):
value = draw(strategy)
hypothesis.assume(min_value <= value)
if max_value is not None:
hypothesis.assume(max_value >= value)
return value
return build_complex()
def to_numpy_dtype(pandera_dtype: DataType):
"""Convert a :class:`~pandera.dtypes.DataType` to numpy dtype compatible
with hypothesis."""
try:
np_dtype = pandas_engine.Engine.numpy_dtype(pandera_dtype)
except TypeError as err:
if is_datetime(pandera_dtype):
return np.dtype("datetime64[ns]")
raise TypeError(
f"Data generation for the '{pandera_dtype}' data type is currently "
"unsupported."
) from err
if np_dtype == np.dtype("object"):
np_dtype = np.dtype(str)
return np_dtype
def pandas_dtype_strategy(
pandera_dtype: DataType,
strategy: Optional[SearchStrategy] = None,
**kwargs,
) -> SearchStrategy:
# pylint: disable=line-too-long,no-else-raise
"""Strategy to generate data from a :class:`pandera.dtypes.DataType`.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:kwargs: key-word arguments passed into
`hypothesis.extra.numpy.from_dtype <https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.numpy.from_dtype>`_ .
For datetime, timedelta, and complex number datatypes, these arguments
are passed into :func:`~pandera.strategies.numpy_time_dtypes` and
:func:`~pandera.strategies.numpy_complex_dtypes`.
:returns: ``hypothesis`` strategy
"""
def compat_kwargs(*args):
return {k: v for k, v in kwargs.items() if k in args}
# hypothesis doesn't support categoricals or objects, so we'll will need to
# build a pandera-specific solution.
if is_category(pandera_dtype):
raise TypeError(
"data generation for the Category dtype is currently "
"unsupported. Consider using a string or int dtype and "
"Check.isin(values) to ensure a finite set of values."
)
np_dtype = to_numpy_dtype(pandera_dtype)
if strategy:
if _is_datetime_tz(pandera_dtype):
return _datetime_strategy(pandera_dtype.type, strategy) # type: ignore
return strategy.map(np_dtype.type)
elif is_datetime(pandera_dtype) or is_timedelta(pandera_dtype):
return numpy_time_dtypes(
pandera_dtype.type if _is_datetime_tz(pandera_dtype) else np_dtype, # type: ignore
**compat_kwargs("min_value", "max_value"),
)
elif is_complex(pandera_dtype):
return numpy_complex_dtypes(
np_dtype,
**compat_kwargs(
"min_value", "max_value", "allow_infinity", "allow_nan"
),
)
return npst.from_dtype(
np_dtype,
**{ # type: ignore
"allow_nan": False,
"allow_infinity": False,
**kwargs,
},
)
def eq_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
value: Any,
) -> SearchStrategy:
"""Strategy to generate a single value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param value: value to generate.
:returns: ``hypothesis`` strategy
"""
# override strategy preceding this one and generate value of the same type
# pylint: disable=unused-argument
return pandas_dtype_strategy(pandera_dtype, st.just(value))
def ne_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
value: Any,
) -> SearchStrategy:
"""Strategy to generate anything except for a particular value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param value: value to avoid.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
strategy = pandas_dtype_strategy(pandera_dtype)
return strategy.filter(lambda x: x != value)
def gt_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
min_value: Union[int, float],
) -> SearchStrategy:
"""Strategy to generate values greater than a minimum value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param min_value: generate values larger than this.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
strategy = pandas_dtype_strategy(
pandera_dtype,
min_value=min_value,
exclude_min=True if is_float(pandera_dtype) else None,
)
return strategy.filter(lambda x: x > min_value)
def ge_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
min_value: Union[int, float],
) -> SearchStrategy:
"""Strategy to generate values greater than or equal to a minimum value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param min_value: generate values greater than or equal to this.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return pandas_dtype_strategy(
pandera_dtype,
min_value=min_value,
exclude_min=False if is_float(pandera_dtype) else None,
)
return strategy.filter(lambda x: x >= min_value)
def lt_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
max_value: Union[int, float],
) -> SearchStrategy:
"""Strategy to generate values less than a maximum value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param max_value: generate values less than this.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
strategy = pandas_dtype_strategy(
pandera_dtype,
max_value=max_value,
exclude_max=True if is_float(pandera_dtype) else None,
)
return strategy.filter(lambda x: x < max_value)
def le_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
max_value: Union[int, float],
) -> SearchStrategy:
"""Strategy to generate values less than or equal to a maximum value.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param max_value: generate values less than or equal to this.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return pandas_dtype_strategy(
pandera_dtype,
max_value=max_value,
exclude_max=False if is_float(pandera_dtype) else None,
)
return strategy.filter(lambda x: x <= max_value)
def in_range_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
min_value: Union[int, float],
max_value: Union[int, float],
include_min: bool = True,
include_max: bool = True,
) -> SearchStrategy:
"""Strategy to generate values within a particular range.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param min_value: generate values greater than this.
:param max_value: generate values less than this.
:param include_min: include min_value in generated data.
:param include_max: include max_value in generated data.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return pandas_dtype_strategy(
pandera_dtype,
min_value=min_value,
max_value=max_value,
exclude_min=not include_min,
exclude_max=not include_max,
)
min_op = operator.ge if include_min else operator.gt
max_op = operator.le if include_max else operator.lt
return strategy.filter(
lambda x: min_op(x, min_value) and max_op(x, max_value)
)
def isin_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
allowed_values: Sequence[Any],
) -> SearchStrategy:
"""Strategy to generate values within a finite set.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param allowed_values: set of allowable values.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return pandas_dtype_strategy(
pandera_dtype, st.sampled_from(allowed_values)
)
return strategy.filter(lambda x: x in allowed_values)
def notin_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
forbidden_values: Sequence[Any],
) -> SearchStrategy:
"""Strategy to generate values excluding a set of forbidden values
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param forbidden_values: set of forbidden values.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
strategy = pandas_dtype_strategy(pandera_dtype)
return strategy.filter(lambda x: x not in forbidden_values)
def str_matches_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
pattern: str,
) -> SearchStrategy:
"""Strategy to generate strings that patch a regex pattern.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param pattern: regex pattern.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return st.from_regex(pattern, fullmatch=True).map(
to_numpy_dtype(pandera_dtype).type
)
def matches(x):
return re.match(pattern, x)
return strategy.filter(matches)
def str_contains_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
pattern: str,
) -> SearchStrategy:
"""Strategy to generate strings that contain a particular pattern.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param pattern: regex pattern.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return st.from_regex(pattern, fullmatch=False).map(
to_numpy_dtype(pandera_dtype).type
)
def contains(x):
return re.search(pattern, x)
return strategy.filter(contains)
def str_startswith_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
string: str,
) -> SearchStrategy:
"""Strategy to generate strings that start with a specific string pattern.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param string: string pattern.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return st.from_regex(f"\\A{string}", fullmatch=False).map(
to_numpy_dtype(pandera_dtype).type
)
return strategy.filter(lambda x: x.startswith(string))
def str_endswith_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
string: str,
) -> SearchStrategy:
"""Strategy to generate strings that end with a specific string pattern.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param string: string pattern.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return st.from_regex(f"{string}\\Z", fullmatch=False).map(
to_numpy_dtype(pandera_dtype).type
)
return strategy.filter(lambda x: x.endswith(string))
def str_length_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
min_value: int,
max_value: int,
) -> SearchStrategy:
"""Strategy to generate strings of a particular length
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param min_value: minimum string length.
:param max_value: maximum string length.
:returns: ``hypothesis`` strategy
"""
if strategy is None:
return st.text(min_size=min_value, max_size=max_value).map(
to_numpy_dtype(pandera_dtype).type
)
return strategy.filter(lambda x: min_value <= len(x) <= max_value)
def _timestamp_to_datetime64_strategy(
strategy: SearchStrategy,
) -> SearchStrategy:
"""Convert timestamp to numpy.datetime64
Hypothesis only supports pure numpy dtypes but numpy.datetime64() truncates
nanoseconds if given a pandas.Timestamp. We need to pass the unix epoch via
the pandas.Timestamp.value attribute.
"""
return st.builds(lambda x: np.datetime64(x.value, "ns"), strategy)
def field_element_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
checks: Optional[Sequence] = None,
) -> SearchStrategy:
"""Strategy to generate elements of a column or index.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param checks: sequence of :class:`~pandera.checks.Check` s to constrain
the values of the data in the column/index.
:returns: ``hypothesis`` strategy
"""
if strategy:
raise BaseStrategyOnlyError(
"The series strategy is a base strategy. You cannot specify the "
"strategy argument to chain it to a parent strategy."
)
checks = [] if checks is None else checks
elements = None
def undefined_check_strategy(elements, check):
"""Strategy for checks with undefined strategies."""
warnings.warn(
"Element-wise check doesn't have a defined strategy."
"Falling back to filtering drawn values based on the check "
"definition. This can considerably slow down data-generation."
)
return (
pandas_dtype_strategy(pandera_dtype)
if elements is None
else elements
).filter(check._check_fn)
for check in checks:
if hasattr(check, "strategy"):
elements = check.strategy(pandera_dtype, elements)
elif check.element_wise:
elements = undefined_check_strategy(elements, check)
# NOTE: vectorized checks with undefined strategies should be handled
# by the series/dataframe strategy.
if elements is None:
elements = pandas_dtype_strategy(pandera_dtype)
# Hypothesis only supports pure numpy datetime64 (i.e. timezone naive).
# We cast to datetime64 after applying the check strategy so that checks
# can see timezone-aware values.
if _is_datetime_tz(pandera_dtype):
elements = _timestamp_to_datetime64_strategy(elements)
return elements
def series_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
checks: Optional[Sequence] = None,
nullable: bool = False,
unique: bool = False,
name: Optional[str] = None,
size: Optional[int] = None,
):
"""Strategy to generate a pandas Series.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param checks: sequence of :class:`~pandera.checks.Check` s to constrain
the values of the data in the column/index.
:param nullable: whether or not generated Series contains null values.
:param unique: whether or not generated Series contains unique values.
:param name: name of the Series.
:param size: number of elements in the Series.
:returns: ``hypothesis`` strategy.
"""
elements = field_element_strategy(pandera_dtype, strategy, checks=checks)
strategy = (
pdst.series(
elements=elements,
dtype=to_numpy_dtype(pandera_dtype),
index=pdst.range_indexes(
min_size=0 if size is None else size, max_size=size
),
unique=unique,
)
.filter(lambda x: x.shape[0] > 0)
.map(lambda x: x.rename(name))
.map(lambda x: x.astype(str(pandera_dtype)))
)
if nullable:
strategy = null_field_masks(strategy)
def undefined_check_strategy(strategy, check):
"""Strategy for checks with undefined strategies."""
warnings.warn(
"Vectorized check doesn't have a defined strategy."
"Falling back to filtering drawn values based on the check "
"definition. This can considerably slow down data-generation."
)
def _check_fn(series):
return check(series).check_passed
return strategy.filter(_check_fn)
for check in checks if checks is not None else []:
if not hasattr(check, "strategy") and not check.element_wise:
strategy = undefined_check_strategy(strategy, check)
return strategy
def column_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
checks: Optional[Sequence] = None,
unique: bool = False,
name: Optional[str] = None,
):
# pylint: disable=line-too-long
"""Create a data object describing a column in a DataFrame.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param checks: sequence of :class:`~pandera.checks.Check` s to constrain
the values of the data in the column/index.
:param unique: whether or not generated Series contains unique values.
:param name: name of the Series.
:returns: a `column <https://hypothesis.readthedocs.io/en/latest/numpy.html#hypothesis.extra.pandas.column>`_ object.
"""
verify_dtype(pandera_dtype, schema_type="column", name=name)
elements = field_element_strategy(pandera_dtype, strategy, checks=checks)
return pdst.column(
name=name,
elements=elements,
dtype=to_numpy_dtype(pandera_dtype),
unique=unique,
)
def index_strategy(
pandera_dtype: Union[numpy_engine.DataType, pandas_engine.DataType],
strategy: Optional[SearchStrategy] = None,
*,
checks: Optional[Sequence] = None,
nullable: bool = False,
unique: bool = False,
name: Optional[str] = None,
size: Optional[int] = None,
):
"""Strategy to generate a pandas Index.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: an optional hypothesis strategy. If specified, the
pandas dtype strategy will be chained onto this strategy.
:param checks: sequence of :class:`~pandera.checks.Check` s to constrain
the values of the data in the column/index.
:param nullable: whether or not generated Series contains null values.
:param unique: whether or not generated Series contains unique values.
:param name: name of the Series.
:param size: number of elements in the Series.
:returns: ``hypothesis`` strategy.
"""
verify_dtype(pandera_dtype, schema_type="index", name=name)
elements = field_element_strategy(pandera_dtype, strategy, checks=checks)
strategy = pdst.indexes(
elements=elements,
dtype=to_numpy_dtype(pandera_dtype),
min_size=0 if size is None else size,
max_size=size,
unique=unique,
).map(lambda x: x.astype(str(pandera_dtype)))
if name is not None:
strategy = strategy.map(lambda index: index.rename(name))
if nullable:
strategy = null_field_masks(strategy)
return strategy
def dataframe_strategy(
pandera_dtype: Optional[DataType] = None,
strategy: Optional[SearchStrategy] = None,
*,
columns: Optional[Dict] = None,
checks: Optional[Sequence] = None,
unique: Optional[List[str]] = None,
index: Optional[IndexComponent] = None,
size: Optional[int] = None,
n_regex_columns: int = 1,
):
"""Strategy to generate a pandas DataFrame.
:param pandera_dtype: :class:`pandera.dtypes.DataType` instance.
:param strategy: if specified, this will raise a BaseStrategyOnlyError,
since it cannot be chained to a prior strategy.
:param columns: a dictionary where keys are column names and values
are :class:`~pandera.schema_components.Column` objects.
:param checks: sequence of :class:`~pandera.checks.Check` s to constrain
the values of the data at the dataframe level.
:param unique: a list of column names that should be jointly unique.
:param index: Index or MultiIndex schema component.
:param size: number of elements in the Series.
:param n_regex_columns: number of regex columns to generate.
:returns: ``hypothesis`` strategy.
"""
# pylint: disable=too-many-locals,too-many-branches,too-many-statements
if n_regex_columns < 1:
raise ValueError(
"`n_regex_columns` must be a positive integer, found: "
f"{n_regex_columns}"
)
if strategy:
raise BaseStrategyOnlyError(
"The dataframe strategy is a base strategy. You cannot specify "
"the strategy argument to chain it to a parent strategy."
)
columns = {} if columns is None else columns
checks = [] if checks is None else checks
def undefined_check_strategy(strategy, check, column=None):
"""Strategy for checks with undefined strategies."""
def _element_wise_check_fn(element):
return check._check_fn(element)
def _column_check_fn(dataframe):
return check(dataframe[column]).check_passed
def _dataframe_check_fn(dataframe):
return check(dataframe).check_passed
if check.element_wise:
check_fn = _element_wise_check_fn
warning_type = "Element-wise"
elif column is None:
check_fn = _dataframe_check_fn
warning_type = "Dataframe"
else:
check_fn = _column_check_fn
warning_type = "Column"
warnings.warn(
f"{warning_type} check doesn't have a defined strategy. "
"Falling back to filtering drawn values based on the check "
"definition. This can considerably slow down data-generation."
)
return strategy.filter(check_fn)
def make_row_strategy(col, checks):