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How to use _dataframe_strategy method in pandera

Best Python code snippet using pandera_python

strategies.py

Source:strategies.py

...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....

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